diff --git a/.circleci/config.yml b/.circleci/config.yml
index 5957a5e7e3..ffb051468a 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -90,8 +90,9 @@ commands:
             # Install
             . /opt/conda/etc/profile.d/conda.sh
             mkdir /logs
-            mamba env create >> /logs/conda.txt 2>&1
+            mamba env create
             conda activate esmvaltool
+            mamba list >> /logs/conda.txt
             pip install << parameters.flags >> ".[<<parameters.extra>>]"> /logs/install.txt 2>&1
             esmvaltool install Julia > /logs/install_julia.txt 2>&1
             if [[ "<<parameters.flags>>" != *'--editable'* ]]
@@ -158,7 +159,7 @@ jobs:
   test_installation_from_source_test_mode:
     # Test installation from source
     docker:
-      - image: condaforge/mambaforge:latest
+      - image: condaforge/miniforge3:latest
     resource_class: large
     steps:
       - test_installation_from_source:
@@ -167,7 +168,7 @@ jobs:
   test_installation_from_source_develop_mode:
     # Test development installation
     docker:
-      - image: condaforge/mambaforge:latest
+      - image: condaforge/miniforge3:latest
     resource_class: large
     steps:
       - test_installation_from_source:
@@ -179,7 +180,7 @@ jobs:
     # purpose of this test to discover backward-incompatible changes early on in
     # the development cycle.
     docker:
-      - image: condaforge/mambaforge:latest
+      - image: condaforge/miniforge3:latest
     resource_class: large
     steps:
       - run:
@@ -201,8 +202,9 @@ jobs:
             # https://docs.esmvaltool.org/en/latest/quickstart/installation.html#install-from-source
             . /opt/conda/etc/profile.d/conda.sh
             mkdir /logs
-            mamba env create >> /logs/conda.txt 2>&1
+            mamba env create
             conda activate esmvaltool
+            mamba list >> /logs/conda.txt
             pip install --editable .[develop]
             esmvaltool install Julia > /logs/install_julia.txt 2>&1
             git clone https://github.com/ESMValGroup/ESMValCore $HOME/ESMValCore
@@ -216,8 +218,8 @@ jobs:
             conda activate esmvaltool
             mkdir -p ~/climate_data
             esmvaltool config get_config_user
-            echo "search_esgf: when_missing" >> ~/.esmvaltool/config-user.yml
-            cat ~/.esmvaltool/config-user.yml
+            echo "search_esgf: when_missing" >> ~/.config/esmvaltool/config-user.yml
+            cat ~/.config/esmvaltool/config-user.yml
             for recipe in esmvaltool/recipes/testing/recipe_*.yml; do
               esmvaltool run "$recipe"
             done
@@ -233,7 +235,7 @@ jobs:
   build_documentation:
     # Test building documentation
     docker:
-      - image: condaforge/mambaforge:latest
+      - image: condaforge/miniforge3:latest
     resource_class: medium
     steps:
       - checkout
@@ -257,7 +259,7 @@ jobs:
   test_installation_from_conda:
     # Test conda package installation
     docker:
-      - image: condaforge/mambaforge:latest
+      - image: condaforge/miniforge3:latest
     resource_class: large
     steps:
       - run:
diff --git a/.codacy.yml b/.codacy.yml
index 06a0ea342f..afe979f5c7 100644
--- a/.codacy.yml
+++ b/.codacy.yml
@@ -21,5 +21,6 @@ engines:
 exclude_paths: [
     'doc/sphinx/**',
     'esmvaltool/cmor/tables/**',
-    'tests/**'
+    'tests/**',
+    'esmvaltool/utils/recipe_test_workflow/app/configure/bin/test_configure.py'
 ]
diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS
index 2086d60173..3478d469b4 100644
--- a/.github/CODEOWNERS
+++ b/.github/CODEOWNERS
@@ -1,2 +1,3 @@
 esmvaltool/cmorizers @ESMValGroup/obs-maintainers
 .github/workflows @valeriupredoi
+esmvaltool/utils/recipe_test_workflow/ @alistairsellar @ehogan
diff --git a/.github/workflows/check-rtw.yml b/.github/workflows/check-rtw.yml
new file mode 100644
index 0000000000..b5c6a0f32e
--- /dev/null
+++ b/.github/workflows/check-rtw.yml
@@ -0,0 +1,78 @@
+# This workflow performs various validation steps for Cylc and Rose.
+name: Check Recipe Test Workflow (RTW)
+
+# Controls when the action will run
+on:
+  # Triggers the workflow on push events
+  push:
+    paths:
+      - esmvaltool/utils/recipe_test_workflow/**
+
+  # Schedule this workflow to run at 04:00 every 10 days
+  schedule:
+    - cron: '0 4 */10 * *'
+
+# Common variables are defined here
+env:
+  RTW_ROOT_DIR: esmvaltool/utils/recipe_test_workflow
+
+# Required shell entrypoint to have properly configured bash shell
+defaults:
+  run:
+    shell: bash -l {0}
+
+# A workflow run is made up of one or more jobs that can run
+# sequentially or in parallel
+jobs:
+  # This workflow contains a single job called "check-rtw"
+  check-rtw:
+    # The type of runner that the job will run on
+    runs-on: ubuntu-latest
+
+    # Steps represent a sequence of tasks that will be executed as part
+    # of the job
+    steps:
+      # Checks-out your repository under $GITHUB_WORKSPACE, so your job
+      # can access it
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - name: Setup Cylc
+        uses: cylc/setup-cylc@v1
+        with:
+          cylc_rose: true
+
+      - name: Validate Cylc workflow
+        run: |
+          cd ${RTW_ROOT_DIR}
+          cylc validate . -O metoffice
+
+      - name: Run Cylc configuration linter
+        run: |
+          cd ${RTW_ROOT_DIR}
+          cylc lint
+
+      - name: Validate format of Rose configuration files
+        run: |
+          cd ${RTW_ROOT_DIR}
+          output="$(rose config-dump)"
+          msg="Run 'rose config-dump' to re-dump the Rose configuration files"
+          msg="${msg} in the common format, then commit the changes."
+          # The '-z' option returns true if 'output' is empty.
+          if [[ -z "${output}" ]]; then true; else echo "${msg}" && exit 1; fi
+
+      - name: Validate Rose configuration metadata
+        run: |
+          cd ${RTW_ROOT_DIR}
+          rose metadata-check -C meta/
+
+      - name: Run Rose configuration validation macros
+        run: |
+          cd ${RTW_ROOT_DIR}
+          rose macro -V
+
+      - name: Lint shell scripts
+        run: |
+          cd ${RTW_ROOT_DIR}
+          output=$(find . -name "*.sh" -exec shellcheck {} \;)
+          if [ "$output" ]; then echo "${output}" && exit 1; fi
diff --git a/.github/workflows/create-condalock-file.yml b/.github/workflows/create-condalock-file.yml
index a88f919c17..7babd2a456 100644
--- a/.github/workflows/create-condalock-file.yml
+++ b/.github/workflows/create-condalock-file.yml
@@ -27,9 +27,8 @@ jobs:
         with:
           auto-update-conda: true
           activate-environment: esmvaltool-fromlock
-          python-version: "3.11"
+          python-version: "3.12"
           miniforge-version: "latest"
-          miniforge-variant: Mambaforge
           use-mamba: true
       - name: Show conda config
         run: |
@@ -37,7 +36,9 @@ jobs:
           conda --version
           # setup-miniconda@v3 installs an old conda and mamba
           # forcing a modern mamba updates both mamba and conda
-          conda install -c conda-forge "mamba>=1.4.8"
+          # unpin mamba after conda-lock=3 release
+          # see github.com/ESMValGroup/ESMValTool/issues/3782
+          conda install -c conda-forge "mamba>=1.4.8,<2"
           conda config --show-sources
           conda config --show
           conda --version
diff --git a/.github/workflows/cron_esmvalbot_test.yml b/.github/workflows/cron_esmvalbot_test.yml
new file mode 100644
index 0000000000..23d4c390e2
--- /dev/null
+++ b/.github/workflows/cron_esmvalbot_test.yml
@@ -0,0 +1,50 @@
+name: Run Esmvalbot Test
+
+on:
+  # push:
+  #   branches:
+  #     - cron_esmvalbot_test
+  # scheduled once every 2 weeks
+  schedule:
+    - cron: '0 4 */14 * *'
+
+# Required shell entrypoint to have properly configured bash shell
+defaults:
+  run:
+    shell: bash -l {0}
+
+jobs:
+  run-esmvalbot:
+    runs-on: 'ubuntu-latest'
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+      - name: Create empty commit on branch
+        run: |
+          git config user.name 'Valeriu Predoi'
+          git config user.email 'valeriu.predoi@gmail.com'
+          git commit --allow-empty -m "empty commit"
+      # Automated PR where we run "@esmvalbot please run examples/recipe_python.yml"
+      # as comment in the PR
+      # see https://github.com/marketplace/actions/create-pull-request
+      - name: Create Auto PR
+        uses: peter-evans/create-pull-request@v7
+        with:
+          token: ${{ secrets.GITHUB_TOKEN }}
+          commit-message: empty message
+          # defaults are GH bot: # ${{ github.actor }} <${{ github.actor }}@users.noreply.github.com>
+          committer: Valeriu Predoi <valeriu.predoi@gmail.com>
+          author: Valeriu Predoi <valeriu.predoi@gmail.com>
+          signoff: false
+          branch: run-esmvalbot
+          delete-branch: true
+          title: '[EsmvalbotTest] Periodic reminder to run an esmvalbot test'
+          body: 'Automatic PR; please DO NOT merge! This is for testing Esmvalbot only. @valeriupredoi @bouweandela @schlunma please run an ESMValBot test here; if the bot runs fine, please close the auto PR, if it has issues, please open a Github issue and tag @valeriupredoi. Many thanks :beers:'
+          labels: |
+            testing
+            esmvalbot
+            automatedPR
+          assignees: valeriupredoi
+          reviewers: valeriupredoi
+          draft: true
diff --git a/.github/workflows/install-from-conda.yml b/.github/workflows/install-from-conda.yml
index 862fd0aad6..185add02a8 100644
--- a/.github/workflows/install-from-conda.yml
+++ b/.github/workflows/install-from-conda.yml
@@ -20,14 +20,13 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.9", "3.10", "3.11"]
+        python-version: ["3.10", "3.11", "3.12"]
     name: Linux Python ${{ matrix.python-version }}
     steps:
       - uses: conda-incubator/setup-miniconda@v3
         with:
           python-version: ${{ matrix.python-version }}
           miniforge-version: "latest"
-          miniforge-variant: Mambaforge
           use-mamba: true
       - run: mkdir -p conda_install_linux_artifacts_python_${{ matrix.python-version }}
       - name: Record versions
@@ -56,7 +55,7 @@ jobs:
 #    runs-on: "macos-latest"
 #    strategy:
 #      matrix:
-#        python-version: ["3.9", "3.10", "3.11"]
+#        python-version: ["3.10", "3.11"]
 #      fail-fast: false
 #    name: OSX Python ${{ matrix.python-version }}
 #    steps:
diff --git a/.github/workflows/install-from-condalock-file.yml b/.github/workflows/install-from-condalock-file.yml
index ef19a4cb15..0f11cddc6e 100644
--- a/.github/workflows/install-from-condalock-file.yml
+++ b/.github/workflows/install-from-condalock-file.yml
@@ -30,7 +30,7 @@ jobs:
     runs-on: "ubuntu-latest"
     strategy:
       matrix:
-        python-version: ["3.9", "3.10", "3.11"]
+        python-version: ["3.10", "3.11", "3.12"]
       fail-fast: false
     name: Linux Python ${{ matrix.python-version }}
     steps:
diff --git a/.github/workflows/install-from-source.yml b/.github/workflows/install-from-source.yml
index 6e9c1de19a..018fcb2a0a 100644
--- a/.github/workflows/install-from-source.yml
+++ b/.github/workflows/install-from-source.yml
@@ -19,7 +19,7 @@ jobs:
     runs-on: "ubuntu-latest"
     strategy:
       matrix:
-        python-version: ["3.9", "3.10", "3.11"]
+        python-version: ["3.10", "3.11", "3.12"]
       fail-fast: false
     name: Linux Python ${{ matrix.python-version }}
     steps:
@@ -32,7 +32,6 @@ jobs:
           environment-file: environment.yml
           python-version: ${{ matrix.python-version }}
           miniforge-version: "latest"
-          miniforge-variant: Mambaforge
           use-mamba: true
       - run: mkdir -p source_install_linux_artifacts_python_${{ matrix.python-version }}
       - name: Record versions
@@ -59,7 +58,7 @@ jobs:
 #    runs-on: "macos-latest"
 #    strategy:
 #      matrix:
-#        python-version: ["3.9", "3.10", "3.11"]
+#        python-version: ["3.10", "3.11"]
 #      fail-fast: false
 #    name: OSX Python ${{ matrix.python-version }}
 #    steps:
diff --git a/.github/workflows/pypi-build-and-deploy.yml b/.github/workflows/pypi-build-and-deploy.yml
index 4dff1e4d69..d6df3626e6 100644
--- a/.github/workflows/pypi-build-and-deploy.yml
+++ b/.github/workflows/pypi-build-and-deploy.yml
@@ -17,10 +17,10 @@ jobs:
       - uses: actions/checkout@v4
         with:
           fetch-depth: 0
-      - name: Set up Python 3.11
+      - name: Set up Python 3.12
         uses: actions/setup-python@v1
         with:
-          python-version: "3.11"
+          python-version: "3.12"
       - name: Install pep517
         run: >-
           python -m
diff --git a/.github/workflows/run-tests-monitor.yml b/.github/workflows/run-tests-monitor.yml
index 52cc282235..1fc657e387 100644
--- a/.github/workflows/run-tests-monitor.yml
+++ b/.github/workflows/run-tests-monitor.yml
@@ -23,7 +23,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.9", "3.10", "3.11"]
+        python-version: ["3.10", "3.11", "3.12"]
     name: Linux Python ${{ matrix.python-version }}
     steps:
       - uses: actions/checkout@v4
@@ -35,7 +35,6 @@ jobs:
           environment-file: environment.yml
           python-version: ${{ matrix.python-version }}
           miniforge-version: "latest"
-          miniforge-variant: Mambaforge
           use-mamba: true
       - run: mkdir -p test_linux_artifacts_python_${{ matrix.python-version }}
       - name: Record versions
@@ -67,7 +66,7 @@ jobs:
     runs-on: "macos-latest"
     strategy:
       matrix:
-        python-version: ["3.9", "3.10", "3.11"]
+        python-version: ["3.10", "3.11", "3.12"]
         architecture: ["x64"]  # need to force Intel, arm64 builds have issues
       fail-fast: false
     name: OSX Python ${{ matrix.python-version }}
@@ -82,7 +81,6 @@ jobs:
           environment-file: environment_osx.yml
           python-version: ${{ matrix.python-version }}
           miniforge-version: "latest"
-          miniforge-variant: Mambaforge
           use-mamba: true
       # - name: Install libomp with homebrew
       #   run: brew install libomp
diff --git a/.github/workflows/test-development.yml b/.github/workflows/test-development.yml
index c75cd23cb8..f6718a866e 100644
--- a/.github/workflows/test-development.yml
+++ b/.github/workflows/test-development.yml
@@ -26,7 +26,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.9", "3.10", "3.11"]
+        python-version: ["3.10", "3.11", "3.12"]
     name: Linux Python ${{ matrix.python-version }}
     steps:
       - uses: actions/checkout@v4
@@ -38,7 +38,6 @@ jobs:
           environment-file: environment.yml
           python-version: ${{ matrix.python-version }}
           miniforge-version: "latest"
-          miniforge-variant: Mambaforge
           use-mamba: true
       - run: mkdir -p develop_test_linux_artifacts_python_${{ matrix.python-version }}
       - name: Record versions
diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
index 49f1a14003..8b3c9ceb39 100644
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -20,7 +20,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.9", "3.10", "3.11"]
+        python-version: ["3.10", "3.11", "3.12"]
     name: Linux Python ${{ matrix.python-version }}
     steps:
       - uses: actions/checkout@v4
@@ -32,7 +32,6 @@ jobs:
           environment-file: environment.yml
           python-version: ${{ matrix.python-version }}
           miniforge-version: "latest"
-          miniforge-variant: Mambaforge
           use-mamba: true
       - run: mkdir -p test_linux_artifacts_python_${{ matrix.python-version }}
       - name: Record versions
@@ -45,6 +44,8 @@ jobs:
         run: conda list
       - name: Install ESMValTool
         run: pip install -e .[develop] 2>&1 | tee test_linux_artifacts_python_${{ matrix.python-version }}/install.txt
+      - name: Examine conda environment
+        run: conda list
       - name: Install Julia dependencies
         run: esmvaltool install Julia
       - name: Export Python minor version
@@ -72,7 +73,7 @@ jobs:
     runs-on: "macos-latest"
     strategy:
       matrix:
-        python-version: ["3.9", "3.10", "3.11"]
+        python-version: ["3.10", "3.11", "3.12"]
         architecture: ["x64"]  # need to force Intel, arm64 builds have issues
       fail-fast: false
     name: OSX Python ${{ matrix.python-version }}
@@ -87,7 +88,6 @@ jobs:
           environment-file: environment_osx.yml
           python-version: ${{ matrix.python-version }}
           miniforge-version: "latest"
-          miniforge-variant: Mambaforge
           use-mamba: true
       # - name: Install libomp with homebrew
       #   run: brew install libomp
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index f13cea8c72..f3ac440f05 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -8,7 +8,7 @@ exclude: |
   ^esmvaltool/diag_scripts/cvdp/
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.4.0
+    rev: v4.6.0
     hooks:
       - id: check-added-large-files
       - id: check-ast
@@ -19,7 +19,7 @@ repos:
       - id: trailing-whitespace
         args: [--markdown-linebreak-ext=md]
   - repo: https://github.com/adrienverge/yamllint
-    rev: 'v1.31.0'
+    rev: 'v1.35.1'
     hooks:
       - id: yamllint
   - repo: local  # nclcodestyle is installed alongside ESMValTool
@@ -30,16 +30,16 @@ repos:
         language: system
         files: '\.(ncl|NCL)$'
   - repo: https://github.com/lorenzwalthert/precommit/  # Checks for R
-    rev: 'v0.3.2.9007'
+    rev: 'v0.4.2'
     hooks:
       - id: style-files  # styler
       - id: lintr
   - repo: https://github.com/codespell-project/codespell
-    rev: 'v2.2.4'
+    rev: 'v2.3.0'
     hooks:
       - id: codespell
   - repo: https://github.com/PyCQA/isort
-    rev: '5.12.0'
+    rev: '5.13.2'
     hooks:
       - id: isort
   - repo: https://github.com/pre-commit/mirrors-yapf
@@ -49,10 +49,10 @@ repos:
         additional_dependencies:
           - 'toml'
   - repo: https://github.com/myint/docformatter
-    rev: 'v1.6.5'
+    rev: 'v1.7.5'
     hooks:
       - id: docformatter
   - repo: https://github.com/pycqa/flake8
-    rev: '6.0.0'
+    rev: '5.0.4'
     hooks:
       - id: flake8
diff --git a/.readthedocs.yaml b/.readthedocs.yaml
index 071686d373..3b66ab14aa 100644
--- a/.readthedocs.yaml
+++ b/.readthedocs.yaml
@@ -7,21 +7,22 @@ version: 2
 
 # Set the version of Python and other tools you might need
 build:
-  os: ubuntu-22.04
+  os: ubuntu-lts-latest
   tools:
-    # updated and deployed from Aug 1, 2023
-    python: "mambaforge-22.9"
+    # try miniforge3 when available? see github.com/ESMValGroup/ESMValTool/issues/3779
+    # DO NOT use mambaforge-*; that is currently sunsetted
+    python: "miniconda-latest"
   jobs:
-    pre_create_environment:
-      # update mamba just in case
-      - mamba update --yes --quiet --name=base mamba 'zstd=1.5.2'
-      - mamba --version
-      - mamba list --name=base
-    post_create_environment:
-      - conda run -n ${CONDA_DEFAULT_ENV} mamba list
-      # use conda run executable wrapper to have all env variables
-      - conda run -n ${CONDA_DEFAULT_ENV} mamba --version
-      - conda run -n ${CONDA_DEFAULT_ENV} pip install . --no-deps
+    post_checkout:
+      # The ESMValTool repository is shallow i.e., has a .git/shallow,
+      # therefore complete the repository with a full history in order
+      # to allow setuptools-scm to correctly auto-discover the version.
+      - git fetch --unshallow
+      - git fetch --all
+    pre_install:
+      - git stash
+    post_install:
+      - git stash pop
 
 # Declare the requirements required to build your docs
 conda:
@@ -33,6 +34,11 @@ sphinx:
   configuration: doc/sphinx/source/conf.py
   fail_on_warning: true
 
+python:
+  install:
+    - method: pip
+      path: .
+
 # If using Sphinx, optionally build your docs in additional formats such as PDF
 formats:
   - pdf
diff --git a/.zenodo.json b/.zenodo.json
index c6a731981f..be799a9dc1 100644
--- a/.zenodo.json
+++ b/.zenodo.json
@@ -81,13 +81,17 @@
       "name": "Berg, Peter",
       "orcid": "0000-0002-1469-2568"
     },
+    {
+      "affiliation": "Met Office, UK",
+      "name": "Billows, Chris"
+    },
     {
       "affiliation": "DLR, Germany",
       "name": "Bock, Lisa",
       "orcid": "0000-0001-7058-5938"
     },
     {
-      "affiliation": "MetOffice, UK",
+      "affiliation": "Met Office, UK",
       "name": "Bodas-Salcedo, Alejandro",
       "orcid": "0000-0002-7890-2536"
     },
@@ -142,7 +146,7 @@
       "name": "Docquier, David"
     },
     {
-      "affiliation": "MetOffice, UK",
+      "affiliation": "Met Office, UK",
       "name": "Dreyer, Laura"
     },
     {
@@ -150,13 +154,21 @@
       "name": "Ehbrecht, Carsten"
     },
     {
-      "affiliation": "MetOffice, UK",
+      "affiliation": "Met Office, UK",
       "name": "Earnshaw, Paul"
     },
+    {
+      "affiliation": "Met Office, UK",
+      "name": "Geddes, Theo"
+    },
     {
       "affiliation": "University of Bremen, Germany",
       "name": "Gier, Bettina"
     },
+    {
+      "affiliation": "Met Office, UK",
+      "name": "Gillett, Ed"
+    },
     {
       "affiliation": "BSC, Spain",
       "name": "Gonzalez-Reviriego, Nube",
@@ -171,6 +183,11 @@
       "name": "Hagemann, Stefan",
       "orcid": "0000-0001-5444-2945"
     },
+    {
+      "affiliation": "University of Canterbury, New Zealand",
+      "name": "Hardacre, Catherine",
+      "orcid": "0000-0001-9093-4656"
+    },
     {
       "affiliation": "ISAC-CNR, Italy",
       "name": "von Hardenberg, Jost",
@@ -186,6 +203,10 @@
       "name": "Heuer, Helge",
       "orcid": "0000-0003-2411-7150"
     },
+    {
+      "affiliation": "Met Office, UK",
+      "name": "Hogan, Emma"
+    },
     {
       "affiliation": "BSC, Spain",
       "name": "Hunter, Alasdair",
@@ -222,7 +243,7 @@
       "orcid": "0000-0001-6085-5914"
     },
     {
-      "affiliation": "MetOffice, UK",
+      "affiliation": "Met Office, UK",
       "name": "Little, Bill"
     },
     {
@@ -274,7 +295,7 @@
       "name": "Sandstad, Marit"
     },
     {
-      "affiliation": "MetOffice, UK",
+      "affiliation": "Met Office, UK",
       "name": "Sellar, Alistair"
     },
     {
@@ -300,6 +321,10 @@
       "name": "Swaminathan, Ranjini",
       "orcid": "0000-0001-5853-2673"
     },
+    {
+      "affiliation": "Met Office, UK",
+      "name": "Tomkins, Katherine"
+    },
     {
       "affiliation": "BSC, Spain",
       "name": "Torralba, Verónica"
@@ -380,15 +405,20 @@
       "affiliation": "DLR, Germany",
       "name": "Bonnet, Pauline",
       "orcid": "0000-0003-3780-0784"
+    },
+    {
+      "affiliation": "Met Office, UK",
+      "name": "Munday, Gregory",
+      "orcid": "0000-0003-4750-9923"
     }
   ],
   "description": "ESMValTool: A community diagnostic and performance metrics tool for routine evaluation of Earth system models in CMIP.",
   "license": {
     "id": "Apache-2.0"
   },
-  "publication_date": "2023-07-06",
+  "publication_date": "2024-07-04",
   "title": "ESMValTool",
-  "version": "v2.9.0",
+  "version": "v2.11.0",
   "communities": [ 
       { 
           "identifier": "is-enes3" 
diff --git a/CITATION.cff b/CITATION.cff
index cd621538b7..ab158d2436 100644
--- a/CITATION.cff
+++ b/CITATION.cff
@@ -85,13 +85,17 @@ authors:
     family-names: Berg
     given-names: Peter
     orcid: "https://orcid.org/0000-0002-1469-2568"
+  -
+    affiliation: "Met Office, UK"
+    family-names: Billows
+    given-names: Chris
   -
     affiliation: "DLR, Germany"
     family-names: Bock
     given-names: Lisa
     orcid: "https://orcid.org/0000-0001-7058-5938"
   -
-    affiliation: "MetOffice, UK"
+    affiliation: "Met Office, UK"
     family-names: Bodas-Salcedo
     given-names: Alejandro
     orcid: "https://orcid.org/0000-0002-7890-2536"
@@ -146,7 +150,7 @@ authors:
     family-names: Docquier
     given-names: David
   -
-    affiliation: "MetOffice, UK"
+    affiliation: "Met Office, UK"
     family-names: Dreyer
     given-names: Laura
   -
@@ -154,13 +158,21 @@ authors:
     family-names: Ehbrecht
     given-names: Carsten
   -
-    affiliation: "MetOffice, UK"
+    affiliation: "Met Office, UK"
     family-names: Earnshaw
     given-names: Paul
+  -
+    affiliation: "Met Office, UK"
+    family-names: Geddes
+    given-names: Theo
   -
     affiliation: "University of Bremen, Germany"
     family-names: Gier
     given-names: Bettina
+  -
+    affiliation: "Met Office, UK"
+    family-names: Gillett
+    given-names: Ed
   -
     affiliation: "BSC, Spain"
     family-names: Gonzalez-Reviriego
@@ -196,6 +208,10 @@ authors:
     family-names: Heuer
     given-names: Helge
     orcid: "https://orcid.org/0000-0003-2411-7150"
+  -
+    affiliation: "Met Office, UK"
+    family-names: Hogan
+    given-names: Emma
   -
     affiliation: "BSC, Spain"
     family-names: Hunter
@@ -232,7 +248,7 @@ authors:
     given-names: Valerio
     orcid: "https://orcid.org/0000-0001-6085-5914"
   -
-    affiliation: "MetOffice, UK"
+    affiliation: "Met Office, UK"
     family-names: Little
     given-names: Bill
   -
@@ -275,6 +291,11 @@ authors:
     family-names: Phillips
     given-names: Adam
     orcid: "https://orcid.org/0000-0003-4859-8585"
+  -
+    affiliation: "ACCESS-NRI, Australia"
+    family-names: Proft
+    given-names: Max
+    orcid: "https://orcid.org/0009-0003-1611-9516"
   -
     affiliation: "University of Arizona, USA"
     family-names: Russell
@@ -284,7 +305,7 @@ authors:
     family-names: Sandstad
     given-names: Marit
   -
-    affiliation: "MetOffice, UK"
+    affiliation: "Met Office, UK"
     family-names: Sellar
     given-names: Alistair
   -
@@ -310,6 +331,10 @@ authors:
     family-names: Swaminathan
     given-names: Ranjini
     orcid: "https://orcid.org/0000-0001-5853-2673"
+  -
+    affiliation: "Met Office, UK"
+    family-names: Tomkins
+    given-names: Katherine
   -
     affiliation: "BSC, Spain"
     family-names: Torralba
@@ -391,13 +416,18 @@ authors:
     family-names: Bonnet
     given-names: Pauline
     orcid: "https://orcid.org/0000-0003-3780-0784"
+  -
+    affiliation: "Met Office, UK"
+    family-names: Munday
+    given-names: Gregory
+    orcid: "https://orcid.org/0000-0003-4750-9923"
 
 cff-version: 1.2.0
-date-released: 2023-12-20
+date-released: 2024-07-04
 doi: "10.5281/zenodo.3401363"
 license: "Apache-2.0"
 message: "If you use this software, please cite it using these metadata."
 repository-code: "https://github.com/ESMValGroup/ESMValTool/"
 title: ESMValTool
-version: "v2.10.0"
+version: "v2.11.0"
 ...
diff --git a/README.md b/README.md
index b196f7fbb8..4ac7d694ee 100644
--- a/README.md
+++ b/README.md
@@ -6,11 +6,11 @@
 [![CircleCI](https://circleci.com/gh/ESMValGroup/ESMValTool/tree/main.svg?style=svg)](https://circleci.com/gh/ESMValGroup/ESMValTool/tree/main)
 [![Test in Full Development Mode](https://github.com/ESMValGroup/ESMValTool/actions/workflows/test-development.yml/badge.svg)](https://github.com/ESMValGroup/ESMValTool/actions/workflows/test-development.yml)
 [![Codacy Badge](https://app.codacy.com/project/badge/Grade/79bf6932c2e844eea15d0fb1ed7e415c)](https://app.codacy.com/gh/ESMValGroup/ESMValTool/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_grade)
-[![Docker Build Status](https://img.shields.io/docker/cloud/build/esmvalgroup/esmvaltool.svg)](https://hub.docker.com/r/esmvalgroup/esmvaltool/)
+[![Docker Build Status](https://img.shields.io/docker/automated/esmvalgroup/esmvaltool)](https://hub.docker.com/r/esmvalgroup/esmvaltool/)
 [![Anaconda-Server Badge](https://img.shields.io/conda/vn/conda-forge/ESMValTool?color=blue&label=conda-forge&logo=conda-forge&logoColor=white)](https://anaconda.org/conda-forge/esmvaltool)
 ![stand with Ukraine](https://badgen.net/badge/stand%20with/UKRAINE/?color=0057B8&labelColor=FFD700)
 
-![esmvaltoollogo](https://raw.githubusercontent.com/ESMValGroup/ESMValTool/main/doc/sphinx/source/figures/ESMValTool-logo-2.png)
+![esmvaltoollogo](https://raw.githubusercontent.com/ESMValGroup/ESMValTool/main/doc/sphinx/source/figures/ESMValTool-logo-2-glow.png)
 
 - [**Documentation**](https://docs.esmvaltool.org/en/latest/)
 - [**ESMValTool Website**](https://www.esmvaltool.org/)
diff --git a/SECURITY.md b/SECURITY.md
new file mode 100644
index 0000000000..17c90fb2a6
--- /dev/null
+++ b/SECURITY.md
@@ -0,0 +1,14 @@
+# Security Policy
+
+## Supported Versions
+
+Only the [latest version][latest] of ESMValTool is currently being supported
+with security updates.
+
+## Reporting a Vulnerability
+
+If you find a vulnerability, please contact the
+[ESMValTool Tech Lead Team][TLT].
+
+[latest]: https://github.com/ESMValGroup/ESMValTool/releases
+[TLT]: mailto:esmvaltool_tech_lead_team@listserv.dfn.de
diff --git a/conda-linux-64.lock b/conda-linux-64.lock
index c7670dd5cd..c475b3c9a2 100644
--- a/conda-linux-64.lock
+++ b/conda-linux-64.lock
@@ -1,679 +1,718 @@
 # Generated by conda-lock.
 # platform: linux-64
-# input_hash: 6b13b0874631d4e45248b978f87b5b87d49cf73206e43bd1989bedfb09b60743
+# input_hash: f5c4487c952927f123c46e72b510f59759905df49bd2ea87696869038fe11a8f
 @EXPLICIT
 https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81
 https://conda.anaconda.org/conda-forge/linux-64/_py-xgboost-mutex-2.0-gpu_0.tar.bz2#7702188077361f43a4d61e64c694f850
 https://conda.anaconda.org/conda-forge/noarch/_r-mutex-1.0.1-anacondar_1.tar.bz2#19f9db5f4f1b7f5ef5f6d67207f25f38
-https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.6.2-hbcca054_0.conda#847c3c2905cc467cea52c24f9cfa8080
+https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.12.14-hbcca054_0.conda#720523eb0d6a9b0f6120c16b2aa4e7de
 https://conda.anaconda.org/conda-forge/noarch/cuda-version-11.8-h70ddcb2_3.conda#670f0e1593b8c1d84f57ad5fe5256799
 https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2#0c96522c6bdaed4b1566d11387caaf45
 https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2#34893075a5c9e55cdafac56607368fc6
 https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2#4d59c254e01d9cde7957100457e2d5fb
-https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_2.conda#cbbe59391138ea5ad3658c76912e147f
-https://conda.anaconda.org/conda-forge/noarch/kernel-headers_linux-64-2.6.32-he073ed8_17.conda#d731b543793afc0433c4fd593e693fce
-https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.40-hf3520f5_3.conda#7c1062eaa78dec4ea8a9a988dbda6045
-https://conda.anaconda.org/conda-forge/linux-64/libboost-headers-1.85.0-ha770c72_1.conda#012455a6eddcbf487ef0ddd1715f0b80
-https://conda.anaconda.org/conda-forge/noarch/libgcc-devel_linux-64-13.2.0-hceb6213_107.conda#2cc37ba482c6321237ce72329e1aaea2
-https://conda.anaconda.org/conda-forge/noarch/libstdcxx-devel_linux-64-13.2.0-hceb6213_107.conda#2b409e9645fb3d69115d04496d1219cc
-https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-13.2.0-hc0a3c3a_7.conda#53ebd4c833fa01cb2c6353e99f905406
-https://conda.anaconda.org/conda-forge/linux-64/pandoc-3.2-ha770c72_0.conda#8c924f0b7f3e064b1c954a08e7c32fba
+https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_3.conda#49023d73832ef61042f6a237cb2687e7
+https://conda.anaconda.org/conda-forge/noarch/kernel-headers_linux-64-3.10.0-he073ed8_18.conda#ad8527bf134a90e1c9ed35fa0b64318c
+https://conda.anaconda.org/conda-forge/linux-64/pandoc-3.6.2-ha770c72_0.conda#4ded4ab71d9fd3764d796a23ca3e722b
 https://conda.anaconda.org/conda-forge/noarch/poppler-data-0.4.12-hd8ed1ab_0.conda#d8d7293c5b37f39b2ac32940621c6592
-https://conda.anaconda.org/conda-forge/linux-64/python_abi-3.11-4_cp311.conda#d786502c97404c94d7d58d258a445a65
-https://conda.anaconda.org/conda-forge/noarch/tzdata-2024a-h0c530f3_0.conda#161081fc7cec0bfda0d86d7cb595f8d8
-https://conda.anaconda.org/conda-forge/linux-64/xorg-imake-1.0.7-0.tar.bz2#23acfc5a339a6a34cc2241f64e4111be
+https://conda.anaconda.org/conda-forge/linux-64/python_abi-3.12-5_cp312.conda#0424ae29b104430108f5218a66db7260
+https://conda.anaconda.org/conda-forge/noarch/tzdata-2025a-h78e105d_0.conda#dbcace4706afdfb7eb891f7b37d07c04
 https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-0.tar.bz2#f766549260d6815b0c52253f1fb1bb29
-https://conda.anaconda.org/conda-forge/linux-64/libgomp-13.2.0-h77fa898_7.conda#abf3fec87c2563697defa759dec3d639
-https://conda.anaconda.org/conda-forge/noarch/sysroot_linux-64-2.12-he073ed8_17.conda#595db67e32b276298ff3d94d07d47fbf
+https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.43-h712a8e2_2.conda#048b02e3962f066da18efe3a21b77672
+https://conda.anaconda.org/conda-forge/noarch/libgcc-devel_linux-64-14.2.0-h41c2201_101.conda#fb126e22f5350c15fec6ddbd062f4871
+https://conda.anaconda.org/conda-forge/linux-64/libglvnd-1.7.0-ha4b6fd6_2.conda#434ca7e50e40f4918ab701e3facd59a0
+https://conda.anaconda.org/conda-forge/linux-64/libgomp-14.2.0-h77fa898_1.conda#cc3573974587f12dda90d96e3e55a702
+https://conda.anaconda.org/conda-forge/noarch/libstdcxx-devel_linux-64-14.2.0-h41c2201_101.conda#60b9a16fd147f7184b5a964aa08f3b0f
+https://conda.anaconda.org/conda-forge/noarch/sysroot_linux-64-2.17-h0157908_18.conda#460eba7851277ec1fd80a1a24080787a
 https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2#73aaf86a425cc6e73fcf236a5a46396d
-https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.40-ha1999f0_3.conda#2b0c0d451353cde73295d799ea8886b1
+https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.43-h4bf12b8_2.conda#cf0c5521ac2a20dfa6c662a4009eeef6
 https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2#fee5683a3f04bd15cbd8318b096a27ab
-https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-13.2.0-h77fa898_7.conda#72ec1b1b04c4d15d4204ece1ecea5978
-https://conda.anaconda.org/conda-forge/linux-64/aom-3.9.0-hac33072_0.conda#93a3bf248e5bc729807db198a9c89f07
-https://conda.anaconda.org/conda-forge/linux-64/aws-c-common-0.9.15-hd590300_0.conda#ad8955a300fd09e97e76c38638ac7157
-https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hd590300_5.conda#69b8b6202a07720f448be700e300ccf4
-https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.28.1-hd590300_0.conda#dcde58ff9a1f30b0037a2315d1846d1f
-https://conda.anaconda.org/conda-forge/linux-64/charls-2.4.2-h59595ed_0.conda#4336bd67920dd504cd8c6761d6a99645
+https://conda.anaconda.org/conda-forge/linux-64/libegl-1.7.0-ha4b6fd6_2.conda#c151d5eb730e9b7480e6d48c0fc44048
+https://conda.anaconda.org/conda-forge/linux-64/libgcc-14.2.0-h77fa898_1.conda#3cb76c3f10d3bc7f1105b2fc9db984df
+https://conda.anaconda.org/conda-forge/linux-64/aws-c-common-0.9.28-hb9d3cd8_0.conda#1b53af320b24547ce0fb8196d2604542
+https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.4-hb9d3cd8_0.conda#e2775acf57efd5af15b8e3d1d74d72d3
+https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.1.0-hb9d3cd8_2.conda#41b599ed2b02abcfdd84302bff174b23
+https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.6.4-h5888daf_0.conda#db833e03127376d461e1e13e76f09b6c
+https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-14.2.0-h69a702a_1.conda#e39480b9ca41323497b05492a63bc35b
+https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-14.2.0-hd5240d6_1.conda#9822b874ea29af082e5d36098d25427d
+https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.6.3-hb9d3cd8_1.conda#2ecf2f1c7e4e21fcfe6423a51a992d84
+https://conda.anaconda.org/conda-forge/linux-64/libntlm-1.8-hb9d3cd8_0.conda#7c7927b404672409d9917d49bff5f2d6
+https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-14.2.0-hc0a3c3a_1.conda#234a5554c53625688d51062645337328
+https://conda.anaconda.org/conda-forge/linux-64/libutf8proc-2.8.0-hf23e847_1.conda#b1aa0faa95017bca11369bd080487ec4
+https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.5.0-h851e524_0.conda#63f790534398730f59e1b899c3644d4a
+https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda#edb0dca6bc32e4f4789199455a1dbeb8
+https://conda.anaconda.org/conda-forge/linux-64/make-4.4.1-hb9d3cd8_2.conda#33405d2a66b1411db9f7242c8b97c9e7
+https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h2d0b736_2.conda#04b34b9a40cdc48cfdab261ab176ff74
+https://conda.anaconda.org/conda-forge/linux-64/openssl-3.4.0-h7b32b05_1.conda#4ce6875f75469b2757a65e10a5d05e31
+https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-hb9d3cd8_1002.conda#b3c17d95b5a10c6e64a21fa17573e70e
+https://conda.anaconda.org/conda-forge/linux-64/tzcode-2025a-hb9d3cd8_0.conda#e18bdeebf29176790089b654cfedaaec
+https://conda.anaconda.org/conda-forge/linux-64/xorg-libice-1.1.2-hb9d3cd8_0.conda#fb901ff28063514abb6046c9ec2c4a45
+https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.12-hb9d3cd8_0.conda#f6ebe2cb3f82ba6c057dde5d9debe4f7
+https://conda.anaconda.org/conda-forge/linux-64/xorg-libxdmcp-1.1.5-hb9d3cd8_0.conda#8035c64cb77ed555e3f150b7b3972480
+https://conda.anaconda.org/conda-forge/linux-64/aws-c-cal-0.7.4-hfd43aa1_1.conda#f301eb944d297fc879c441fffe461d8a
+https://conda.anaconda.org/conda-forge/linux-64/aws-c-compression-0.2.19-h756ea98_1.conda#5e08c385a1b8a79b52012b74653bbb99
+https://conda.anaconda.org/conda-forge/linux-64/aws-c-sdkutils-0.1.19-h756ea98_3.conda#bfe6623096906d2502c78ccdbfc3bc7a
+https://conda.anaconda.org/conda-forge/linux-64/aws-checksums-0.1.18-h756ea98_11.conda#eadcc12bedac44f13223a2909c0e5bcc
+https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda#62ee74e96c5ebb0af99386de58cf9553
 https://conda.anaconda.org/conda-forge/linux-64/dav1d-1.2.1-hd590300_0.conda#418c6ca5929a611cbd69204907a83995
+https://conda.anaconda.org/conda-forge/linux-64/expat-2.6.4-h5888daf_0.conda#1d6afef758879ef5ee78127eb4cd2c4a
 https://conda.anaconda.org/conda-forge/linux-64/fribidi-1.0.10-h36c2ea0_0.tar.bz2#ac7bc6a654f8f41b352b38f4051135f8
-https://conda.anaconda.org/conda-forge/linux-64/geos-3.12.0-h59595ed_0.conda#3fdf79ef322c8379ae83be491d805369
-https://conda.anaconda.org/conda-forge/linux-64/gettext-tools-0.22.5-h59595ed_2.conda#985f2f453fb72408d6b6f1be0f324033
-https://conda.anaconda.org/conda-forge/linux-64/gflags-2.2.2-he1b5a44_1004.tar.bz2#cddaf2c63ea4a5901cf09524c490ecdc
-https://conda.anaconda.org/conda-forge/linux-64/ghostscript-10.03.1-h59595ed_0.conda#be973b4541601270b77232bc46249a3a
+https://conda.anaconda.org/conda-forge/linux-64/gettext-tools-0.22.5-he02047a_3.conda#fcd2016d1d299f654f81021e27496818
+https://conda.anaconda.org/conda-forge/linux-64/gflags-2.2.2-h5888daf_1005.conda#d411fc29e338efb48c5fd4576d71d881
+https://conda.anaconda.org/conda-forge/linux-64/ghostscript-10.04.0-h5888daf_0.conda#3b8d7a2df810ad5109a51472b23dbd8e
 https://conda.anaconda.org/conda-forge/linux-64/giflib-5.2.2-hd590300_0.conda#3bf7b9fd5a7136126e0234db4b87c8b6
-https://conda.anaconda.org/conda-forge/linux-64/gmp-6.3.0-h59595ed_1.conda#e358c7c5f6824c272b5034b3816438a7
-https://conda.anaconda.org/conda-forge/linux-64/graphite2-1.3.13-h59595ed_1003.conda#f87c7b7c2cb45f323ffbce941c78ab7c
-https://conda.anaconda.org/conda-forge/linux-64/gtest-1.14.0-h00ab1b0_1.conda#d362a81b815334cc921b9362782881f3
-https://conda.anaconda.org/conda-forge/linux-64/icu-73.2-h59595ed_0.conda#cc47e1facc155f91abd89b11e48e72ff
 https://conda.anaconda.org/conda-forge/linux-64/jbig-2.1-h7f98852_2003.tar.bz2#1aa0cee79792fa97b7ff4545110b60bf
-https://conda.anaconda.org/conda-forge/linux-64/json-c-0.17-h7ab15ed_0.conda#9961b1f100c3b6852bd97c9233d06979
+https://conda.anaconda.org/conda-forge/linux-64/json-c-0.17-h1220068_1.conda#f8f0f0c4338bad5c34a4e9e11460481d
 https://conda.anaconda.org/conda-forge/linux-64/jxrlib-1.1-hd590300_3.conda#5aeabe88534ea4169d4c49998f293d6c
 https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.1-h166bdaf_0.tar.bz2#30186d27e2c9fa62b45fb1476b7200e3
-https://conda.anaconda.org/conda-forge/linux-64/lerc-4.0.0-h27087fc_0.tar.bz2#76bbff344f0134279f225174e9064c8f
-https://conda.anaconda.org/conda-forge/linux-64/libabseil-20240116.2-cxx17_h59595ed_0.conda#682bdbe046a68f749769b492f3625c5c
-https://conda.anaconda.org/conda-forge/linux-64/libaec-1.1.3-h59595ed_0.conda#5e97e271911b8b2001a8b71860c32faa
-https://conda.anaconda.org/conda-forge/linux-64/libasprintf-0.22.5-h661eb56_2.conda#dd197c968bf9760bba0031888d431ede
-https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.1.0-hd590300_1.conda#aec6c91c7371c26392a06708a73c70e5
-https://conda.anaconda.org/conda-forge/linux-64/libcrc32c-1.1.2-h9c3ff4c_0.tar.bz2#c965a5aa0d5c1c37ffc62dff36e28400
-https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.19-hd590300_0.conda#1635570038840ee3f9c71d22aa5b8b6d
+https://conda.anaconda.org/conda-forge/linux-64/libbrotlidec-1.1.0-hb9d3cd8_2.conda#9566f0bd264fbd463002e759b8a82401
+https://conda.anaconda.org/conda-forge/linux-64/libbrotlienc-1.1.0-hb9d3cd8_2.conda#06f70867945ea6a84d35836af780f1de
+https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.21-h4bc722e_0.conda#36ce76665bf67f5aac36be7a0d21b7f3
+https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20240808-pl5321h7949ede_0.conda#8247f80f3dc464d9322e85007e307fe8
 https://conda.anaconda.org/conda-forge/linux-64/libev-4.33-hd590300_2.conda#172bf1cd1ff8629f2b1179945ed45055
-https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.6.2-h59595ed_0.conda#e7ba12deb7020dd080c6c70e7b6f6a3d
+https://conda.anaconda.org/conda-forge/linux-64/libevent-2.1.12-hf998b51_1.conda#a1cfcc585f0c42bf8d5546bb1dfb668d
 https://conda.anaconda.org/conda-forge/linux-64/libffi-3.4.2-h7f98852_5.tar.bz2#d645c6d2ac96843a2bfaccd2d62b3ac3
-https://conda.anaconda.org/conda-forge/linux-64/libgettextpo-0.22.5-h59595ed_2.conda#172bcc51059416e7ce99e7b528cede83
-https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-13.2.0-hca663fb_7.conda#c0bd771f09a326fdcd95a60b617795bf
+https://conda.anaconda.org/conda-forge/linux-64/libgettextpo-0.22.5-he02047a_3.conda#efab66b82ec976930b96d62a976de8e7
+https://conda.anaconda.org/conda-forge/linux-64/libgfortran-14.2.0-h69a702a_1.conda#f1fd30127802683586f768875127a987
 https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.17-hd590300_2.conda#d66573916ffcf376178462f1b61c941e
-https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-2.1.5.1-hd590300_1.conda#323e90742f0f48fc22bea908735f55e6
-https://conda.anaconda.org/conda-forge/linux-64/libnl-3.9.0-hd590300_0.conda#d27c451db4f1d3c983c78167d2fdabc2
+https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-3.0.0-hd590300_1.conda#ea25936bb4080d843790b586850f82b8
+https://conda.anaconda.org/conda-forge/linux-64/liblzma-devel-5.6.3-hb9d3cd8_1.conda#cc4687e1814ed459f3bd6d8e05251ab2
 https://conda.anaconda.org/conda-forge/linux-64/libnsl-2.0.1-hd590300_0.conda#30fd6e37fe21f86f4bd26d6ee73eeec7
 https://conda.anaconda.org/conda-forge/linux-64/libopenlibm4-0.8.1-hd590300_1.conda#e6af610e01d04927a5060c95ce4e0875
-https://conda.anaconda.org/conda-forge/linux-64/libsanitizer-13.2.0-h6ddb7a1_7.conda#ecba88d2296bf40186a9dc65bdf7b621
-https://conda.anaconda.org/conda-forge/linux-64/libsodium-1.0.18-h36c2ea0_1.tar.bz2#c3788462a6fbddafdb413a9f9053e58d
-https://conda.anaconda.org/conda-forge/linux-64/libtool-2.4.7-h27087fc_0.conda#f204c8ba400ec475452737094fb81d52
-https://conda.anaconda.org/conda-forge/linux-64/libunwind-1.6.2-h9c3ff4c_0.tar.bz2#a730b2badd586580c5752cc73842e068
-https://conda.anaconda.org/conda-forge/linux-64/libutf8proc-2.8.0-h166bdaf_0.tar.bz2#ede4266dc02e875fe1ea77b25dd43747
+https://conda.anaconda.org/conda-forge/linux-64/libpciaccess-0.18-hd590300_0.conda#48f4330bfcd959c3cfb704d424903c82
+https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.45-h943b412_0.conda#85cbdaacad93808395ac295b5667d25b
+https://conda.anaconda.org/conda-forge/linux-64/libsanitizer-14.2.0-h2a3dede_1.conda#160623b9425f5c04941586da43bd1a9c
+https://conda.anaconda.org/conda-forge/linux-64/libsodium-1.0.20-h4ab18f5_0.conda#a587892d3c13b6621a6091be690dbca2
+https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.48.0-hee588c1_0.conda#84bd1c9a82b455e7a2f390375fb38f90
+https://conda.anaconda.org/conda-forge/linux-64/libssh2-1.11.1-hf672d98_0.conda#be2de152d8073ef1c01b7728475f2fe7
+https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-14.2.0-h4852527_1.conda#8371ac6457591af2cf6159439c1fd051
+https://conda.anaconda.org/conda-forge/linux-64/libudunits2-2.2.28-h40f5838_3.conda#4bdace082e911a3e1f1f0b721bed5b56
 https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.38.1-h0b41bf4_0.conda#40b61aab5c7ba9ff276c41cfffe6b80b
-https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.3.2-hd590300_1.conda#049b7df8bae5e184d1de42cdf64855f8
+https://conda.anaconda.org/conda-forge/linux-64/libxcb-1.17.0-h8a09558_0.conda#92ed62436b625154323d40d5f2f11dd7
 https://conda.anaconda.org/conda-forge/linux-64/libxcrypt-4.4.36-hd590300_1.conda#5aa797f8787fe7a17d1b0821485b5adc
-https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-h4ab18f5_1.conda#57d7dc60e9325e3de37ff8dffd18e814
-https://conda.anaconda.org/conda-forge/linux-64/libzopfli-1.0.3-h9c3ff4c_0.tar.bz2#c66fe2d123249af7651ebde8984c51c2
-https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.9.4-hcb278e6_0.conda#318b08df404f9c9be5712aaa5a6f0bb0
 https://conda.anaconda.org/conda-forge/linux-64/lzo-2.10-hd590300_1001.conda#ec7398d21e2651e0dcb0044d03b9a339
-https://conda.anaconda.org/conda-forge/linux-64/make-4.3-hd18ef5c_1.tar.bz2#4049ebfd3190b580dffe76daed26155a
-https://conda.anaconda.org/conda-forge/linux-64/mbedtls-3.5.1-h59595ed_0.conda#a7b444a6e008b804b35521895e3440e2
-https://conda.anaconda.org/conda-forge/linux-64/metis-5.1.0-h59595ed_1007.conda#40ccb8318df2500f83bd868dd8fcd201
-https://conda.anaconda.org/conda-forge/linux-64/nccl-2.21.5.1-h6103f9b_0.conda#05381b62b2faed9609fb68b27cd575aa
-https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h59595ed_0.conda#fcea371545eda051b6deafb24889fc69
-https://conda.anaconda.org/conda-forge/linux-64/nspr-4.35-h27087fc_0.conda#da0ec11a6454ae19bff5b02ed881a2b1
-https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.1-h4ab18f5_0.conda#a41fa0e391cc9e0d6b78ac69ca047a6c
-https://conda.anaconda.org/conda-forge/linux-64/p7zip-16.02-h9c3ff4c_1001.tar.bz2#941066943c0cac69d5aa52189451aa5f
-https://conda.anaconda.org/conda-forge/linux-64/pixman-0.43.2-h59595ed_0.conda#71004cbf7924e19c02746ccde9fd7123
-https://conda.anaconda.org/conda-forge/linux-64/pkg-config-0.29.2-h36c2ea0_1008.tar.bz2#fbef41ff6a4c8140c30057466a1cdd47
-https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-h36c2ea0_1001.tar.bz2#22dad4df6e8630e8dff2428f6f6a7036
+https://conda.anaconda.org/conda-forge/linux-64/mbedtls-3.5.1-h5888daf_1.conda#9fc429740cfb418a3b3ca107ba7b5e7d
+https://conda.anaconda.org/conda-forge/linux-64/metis-5.1.0-hd0bcaf9_1007.conda#28eb714416de4eb83e2cbc47e99a1b45
+https://conda.anaconda.org/conda-forge/linux-64/nspr-4.36-h5888daf_0.conda#de9cd5bca9e4918527b9b72b6e2e1409
+https://conda.anaconda.org/conda-forge/linux-64/pixman-0.44.2-h29eaf8c_0.conda#5e2a7acfa2c24188af39e7944e1b3604
+https://conda.anaconda.org/conda-forge/linux-64/pkg-config-0.29.2-h4bc722e_1009.conda#1bee70681f504ea424fb07cdb090c001
 https://conda.anaconda.org/conda-forge/linux-64/rav1e-0.6.6-he8a937b_2.conda#77d9955b4abddb811cb8ab1aa7d743e4
+https://conda.anaconda.org/conda-forge/linux-64/readline-8.2-h8228510_1.conda#47d31b792659ce70f470b5c82fdfb7a4
+https://conda.anaconda.org/conda-forge/linux-64/s2n-1.5.5-h3931f03_0.conda#334dba9982ab9f5d62033c61698a8683
 https://conda.anaconda.org/conda-forge/linux-64/sed-4.8-he412f7d_0.tar.bz2#7362f0042e95681f5d371c46c83ebd08
-https://conda.anaconda.org/conda-forge/linux-64/snappy-1.1.10-hdb0a2a9_1.conda#78b8b85bdf1f42b8a2b3cb577d8742d1
-https://conda.anaconda.org/conda-forge/linux-64/svt-av1-2.1.0-hac33072_0.conda#2a08edb7cd75e56623f2712292a97325
-https://conda.anaconda.org/conda-forge/linux-64/tzcode-2024a-h3f72095_0.conda#32146e34aaec3745a08b6f49af3f41b0
-https://conda.anaconda.org/conda-forge/linux-64/uriparser-0.9.8-hac33072_0.conda#d71d3a66528853c0a1ac2c02d79a0284
-https://conda.anaconda.org/conda-forge/linux-64/xorg-inputproto-2.3.2-h7f98852_1002.tar.bz2#bcd1b3396ec6960cbc1d2855a9e60b2b
-https://conda.anaconda.org/conda-forge/linux-64/xorg-kbproto-1.0.7-h7f98852_1002.tar.bz2#4b230e8381279d76131116660f5a241a
-https://conda.anaconda.org/conda-forge/linux-64/xorg-libice-1.1.1-hd590300_0.conda#b462a33c0be1421532f28bfe8f4a7514
-https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.11-hd590300_0.conda#2c80dc38fface310c9bd81b17037fee5
-https://conda.anaconda.org/conda-forge/linux-64/xorg-libxdmcp-1.1.3-h7f98852_0.tar.bz2#be93aabceefa2fac576e971aef407908
-https://conda.anaconda.org/conda-forge/linux-64/xorg-makedepend-1.0.9-h59595ed_0.conda#71c756cfcc6649ed7614eb07712bfce0
-https://conda.anaconda.org/conda-forge/linux-64/xorg-renderproto-0.11.1-h7f98852_1002.tar.bz2#06feff3d2634e3097ce2fe681474b534
-https://conda.anaconda.org/conda-forge/linux-64/xorg-xextproto-7.3.0-h0b41bf4_1003.conda#bce9f945da8ad2ae9b1d7165a64d0f87
-https://conda.anaconda.org/conda-forge/linux-64/xorg-xproto-7.0.31-h7f98852_1007.tar.bz2#b4a4381d54784606820704f7b5f05a15
+https://conda.anaconda.org/conda-forge/linux-64/snappy-1.2.1-h8bd8927_1.conda#3b3e64af585eadfb52bb90b553db5edf
+https://conda.anaconda.org/conda-forge/linux-64/svt-av1-2.3.0-h5888daf_0.conda#355898d24394b2af353eb96358db9fdd
+https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_h4845f30_101.conda#d453b98d9c83e71da0741bb0ff4d76bc
+https://conda.anaconda.org/conda-forge/linux-64/xorg-imake-1.0.10-h5888daf_0.conda#040f0ca9f518151897759ad09ea98b2d
 https://conda.anaconda.org/conda-forge/linux-64/xxhash-0.8.2-hd590300_0.conda#f08fb5c89edfc4aadee1c81d4cfb1fa1
-https://conda.anaconda.org/conda-forge/linux-64/xz-5.2.6-h166bdaf_0.tar.bz2#2161070d867d1b1204ea749c8eec4ef0
+https://conda.anaconda.org/conda-forge/linux-64/xz-gpl-tools-5.6.3-hbcc6ac9_1.conda#f529917bab7862aaad6867bf2ea47a99
+https://conda.anaconda.org/conda-forge/linux-64/xz-tools-5.6.3-hb9d3cd8_1.conda#de3f31a6eed01bc2b8c7dcad07ad9034
 https://conda.anaconda.org/conda-forge/linux-64/yaml-0.2.5-h7f98852_2.tar.bz2#4cb3ad778ec2d5a7acbdf254eb1c42ae
-https://conda.anaconda.org/conda-forge/linux-64/zfp-1.0.1-h59595ed_0.conda#fd486bffbf0d6841cf1456a8f2e3a995
-https://conda.anaconda.org/conda-forge/linux-64/zlib-ng-2.0.7-h0b41bf4_0.conda#49e8329110001f04923fe7e864990b0c
-https://conda.anaconda.org/conda-forge/linux-64/aws-c-cal-0.6.11-heb1d5e4_0.conda#98784bb35b316e2ba8698f4a75326e9a
-https://conda.anaconda.org/conda-forge/linux-64/aws-c-compression-0.2.18-hce8ee76_3.conda#b19224a5179ecb512c4aac9f8a6d57a7
-https://conda.anaconda.org/conda-forge/linux-64/aws-c-sdkutils-0.1.15-hce8ee76_3.conda#0c4f0205a1ae4ca6c89af922ec54271c
-https://conda.anaconda.org/conda-forge/linux-64/aws-checksums-0.1.18-hce8ee76_3.conda#9aa734a17b9b0b793c7696435fe7789a
-https://conda.anaconda.org/conda-forge/linux-64/expat-2.6.2-h59595ed_0.conda#53fb86322bdb89496d7579fe3f02fd61
-https://conda.anaconda.org/conda-forge/linux-64/gcc_impl_linux-64-13.2.0-h9eb54c0_7.conda#57f0fcb5d432d5f98be5705e2bf65352
-https://conda.anaconda.org/conda-forge/linux-64/glog-0.7.0-hed5481d_0.conda#a9ea19c48e11754899299f8123070f4e
-https://conda.anaconda.org/conda-forge/linux-64/hdf4-4.2.15-h501b40f_6.conda#c3e9338e15d90106f467377017352b97
-https://conda.anaconda.org/conda-forge/linux-64/libasprintf-devel-0.22.5-h661eb56_2.conda#02e41ab5834dcdcc8590cf29d9526f50
-https://conda.anaconda.org/conda-forge/linux-64/libavif16-1.0.4-hd2f8ffe_4.conda#cb911b3e0d863ca9caafd767525f7cac
-https://conda.anaconda.org/conda-forge/linux-64/libbrotlidec-1.1.0-hd590300_1.conda#f07002e225d7a60a694d42a7bf5ff53f
-https://conda.anaconda.org/conda-forge/linux-64/libbrotlienc-1.1.0-hd590300_1.conda#5fc11c6020d421960607d821310fcd4d
-https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20191231-he28a2e2_2.tar.bz2#4d331e44109e3f0e19b4cb8f9b82f3e1
-https://conda.anaconda.org/conda-forge/linux-64/libevent-2.1.12-hf998b51_1.conda#a1cfcc585f0c42bf8d5546bb1dfb668d
-https://conda.anaconda.org/conda-forge/linux-64/libgettextpo-devel-0.22.5-h59595ed_2.conda#b63d9b6da3653179a278077f0de20014
-https://conda.anaconda.org/conda-forge/linux-64/libgfortran-ng-13.2.0-h69a702a_7.conda#1b84f26d9f4f6026e179e7805d5a15cd
-https://conda.anaconda.org/conda-forge/linux-64/libkml-1.3.0-h01aab08_1018.conda#3eb5f16bcc8a02892199aa63555c731f
+https://conda.anaconda.org/conda-forge/linux-64/zfp-1.0.1-h5888daf_2.conda#e0409515c467b87176b070bff5d9442e
+https://conda.anaconda.org/conda-forge/linux-64/zlib-1.3.1-hb9d3cd8_2.conda#c9f075ab2f33b3bbee9e62d4ad0a6cd8
+https://conda.anaconda.org/conda-forge/linux-64/zlib-ng-2.2.3-h7955e40_0.conda#01cf93c645fa03d44ffe603f51f3d27f
+https://conda.anaconda.org/conda-forge/linux-64/aom-3.9.1-hac33072_0.conda#346722a0be40f6edc53f12640d301338
+https://conda.anaconda.org/conda-forge/linux-64/aws-c-io-0.14.18-h2af50b2_12.conda#700f1883f5a0a28c30fd98c43d4d946f
+https://conda.anaconda.org/conda-forge/linux-64/brotli-bin-1.1.0-hb9d3cd8_2.conda#c63b5e52939e795ba8d26e35d767a843
+https://conda.anaconda.org/conda-forge/linux-64/bwidget-1.10.1-ha770c72_0.conda#bfe52ddc809cff2e046781928d195400
+https://conda.anaconda.org/conda-forge/linux-64/charls-2.4.2-h59595ed_0.conda#4336bd67920dd504cd8c6761d6a99645
+https://conda.anaconda.org/conda-forge/linux-64/fmt-11.0.2-h434a139_0.conda#995f7e13598497691c1dc476d889bc04
+https://conda.anaconda.org/conda-forge/linux-64/freetype-2.12.1-h267a509_2.conda#9ae35c3d96db2c94ce0cef86efdfa2cb
+https://conda.anaconda.org/conda-forge/linux-64/gcc_impl_linux-64-14.2.0-h6b349bd_1.conda#0551d01d65027359bf011c049f9c6401
+https://conda.anaconda.org/conda-forge/linux-64/geos-3.12.2-he02047a_1.conda#aab9195bc018b82dc77a84584b36cce9
+https://conda.anaconda.org/conda-forge/linux-64/glog-0.7.1-hbabe93e_0.conda#ff862eebdfeb2fd048ae9dc92510baca
+https://conda.anaconda.org/conda-forge/linux-64/gmp-6.3.0-hac33072_2.conda#c94a5994ef49749880a8139cf9afcbe1
+https://conda.anaconda.org/conda-forge/linux-64/graphite2-1.3.13-h59595ed_1003.conda#f87c7b7c2cb45f323ffbce941c78ab7c
+https://conda.anaconda.org/conda-forge/linux-64/gtest-1.14.0-h434a139_2.conda#89971b339bb4dfbf3759f1f2528d81b1
+https://conda.anaconda.org/conda-forge/linux-64/hdf4-4.2.15-h2a13503_7.conda#bd77f8da987968ec3927990495dc22e4
+https://conda.anaconda.org/conda-forge/linux-64/icu-75.1-he02047a_0.conda#8b189310083baabfb622af68fd9d3ae3
+https://conda.anaconda.org/conda-forge/linux-64/krb5-1.21.3-h659f571_0.conda#3f43953b7d3fb3aaa1d0d0723d91e368
+https://conda.anaconda.org/conda-forge/linux-64/lerc-4.0.0-h27087fc_0.tar.bz2#76bbff344f0134279f225174e9064c8f
+https://conda.anaconda.org/conda-forge/linux-64/libabseil-20240116.2-cxx17_he02047a_1.conda#c48fc56ec03229f294176923c3265c05
+https://conda.anaconda.org/conda-forge/linux-64/libaec-1.1.3-h59595ed_0.conda#5e97e271911b8b2001a8b71860c32faa
+https://conda.anaconda.org/conda-forge/linux-64/libasprintf-0.22.5-he8f35ee_3.conda#4fab9799da9571266d05ca5503330655
+https://conda.anaconda.org/conda-forge/linux-64/libcrc32c-1.1.2-h9c3ff4c_0.tar.bz2#c965a5aa0d5c1c37ffc62dff36e28400
+https://conda.anaconda.org/conda-forge/linux-64/libde265-1.0.15-h00ab1b0_0.conda#407fee7a5d7ab2dca12c9ca7f62310ad
+https://conda.anaconda.org/conda-forge/linux-64/libdrm-2.4.124-hb9d3cd8_0.conda#8bc89311041d7fcb510238cf0848ccae
+https://conda.anaconda.org/conda-forge/linux-64/libgettextpo-devel-0.22.5-he02047a_3.conda#9aba7960731e6b4547b3a52f812ed801
+https://conda.anaconda.org/conda-forge/linux-64/libgfortran-ng-14.2.0-h69a702a_1.conda#0a7f4cd238267c88e5d69f7826a407eb
+https://conda.anaconda.org/conda-forge/linux-64/libhwy-1.1.0-h00ab1b0_0.conda#88928158ccfe797eac29ef5e03f7d23d
 https://conda.anaconda.org/conda-forge/linux-64/libllvm14-14.0.6-hcd5def8_4.conda#73301c133ded2bf71906aa2104edae8b
-https://conda.anaconda.org/conda-forge/linux-64/libnghttp2-1.58.0-h47da74e_1.conda#700ac6ea6d53d5510591c4344d5c989a
-https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.43-h2797004_0.conda#009981dd9cfcaa4dbfa25ffaed86bcae
-https://conda.anaconda.org/conda-forge/linux-64/libprotobuf-4.25.3-h08a7969_0.conda#6945825cebd2aeb16af4c69d97c32c13
-https://conda.anaconda.org/conda-forge/linux-64/libre2-11-2023.09.01-h5a48ba9_2.conda#41c69fba59d495e8cf5ffda48a607e35
-https://conda.anaconda.org/conda-forge/linux-64/librttopo-1.1.0-hb58d41b_14.conda#264f9a3a4ea52c8f4d3e8ae1213a3335
-https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.46.0-hde9e2c9_0.conda#18aa975d2094c34aef978060ae7da7d8
-https://conda.anaconda.org/conda-forge/linux-64/libssh2-1.11.0-h0841786_0.conda#1f5a58e686b13bcfde88b93f547d23fe
-https://conda.anaconda.org/conda-forge/linux-64/libudunits2-2.2.28-h40f5838_3.conda#4bdace082e911a3e1f1f0b721bed5b56
-https://conda.anaconda.org/conda-forge/linux-64/libxcb-1.15-h0b41bf4_0.conda#33277193f5b92bad9fdd230eb700929c
-https://conda.anaconda.org/conda-forge/linux-64/libxgboost-2.0.3-cuda118_h09a87be_4.conda#bfaf927bc7665d327ff5b8e5b8dbf2df
-https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.12.7-hc051c1a_1.conda#340278ded8b0dc3a73f3660bbb0adbc6
-https://conda.anaconda.org/conda-forge/linux-64/libzip-1.10.1-h2629f0a_3.conda#ac79812548e7e8cf61f7b0abdef01d3b
-https://conda.anaconda.org/conda-forge/linux-64/mpfr-4.2.1-h9458935_1.conda#8083b20f566639c22f78bcd6ca35b276
+https://conda.anaconda.org/conda-forge/linux-64/libnghttp2-1.64.0-h161d5f1_0.conda#19e57602824042dfd0446292ef90488b
+https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.28-pthreads_h94d23a6_1.conda#62857b389e42b36b686331bec0922050
+https://conda.anaconda.org/conda-forge/linux-64/libopenblas-ilp64-0.3.28-pthreads_h3e26593_1.conda#9d5c316d93ee4c5effd9afda8e8af823
+https://conda.anaconda.org/conda-forge/linux-64/libsuitesparseconfig-7.8.3-ss783_h83006af.conda#1dbec7fae0400c4f83cd400a01d187d6
+https://conda.anaconda.org/conda-forge/linux-64/libthrift-0.20.0-h0e7cc3e_1.conda#d0ed81c4591775b70384f4cc78e05cd1
+https://conda.anaconda.org/conda-forge/linux-64/libunwind-1.6.2-h9c3ff4c_0.tar.bz2#a730b2badd586580c5752cc73842e068
+https://conda.anaconda.org/conda-forge/linux-64/libzip-1.11.2-h6991a6a_0.conda#a7b27c075c9b7f459f1c022090697cba
+https://conda.anaconda.org/conda-forge/linux-64/libzopfli-1.0.3-h9c3ff4c_0.tar.bz2#c66fe2d123249af7651ebde8984c51c2
+https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.9.4-hcb278e6_0.conda#318b08df404f9c9be5712aaa5a6f0bb0
+https://conda.anaconda.org/conda-forge/linux-64/nccl-2.24.3.1-h03a54cd_0.conda#7aca64af9bbbf8e681ac68d839973162
+https://conda.anaconda.org/conda-forge/linux-64/nss-3.107-hdf54f9c_0.conda#294b7009fe9010b35c25bb683f663bc3
 https://conda.anaconda.org/conda-forge/linux-64/openlibm-0.8.1-hd590300_1.conda#6eba22eb06d69e53d0ca01eef42bc675
-https://conda.anaconda.org/conda-forge/linux-64/pcre2-10.40-hc3806b6_0.tar.bz2#69e2c796349cd9b273890bee0febfe1b
+https://conda.anaconda.org/conda-forge/linux-64/p7zip-16.02-h9c3ff4c_1001.tar.bz2#941066943c0cac69d5aa52189451aa5f
+https://conda.anaconda.org/conda-forge/linux-64/pcre2-10.44-hba22ea6_2.conda#df359c09c41cd186fffb93a2d87aa6f5
 https://conda.anaconda.org/conda-forge/linux-64/perl-5.32.1-7_hd590300_perl5.conda#f2cfec9406850991f4e3d960cc9e3321
-https://conda.anaconda.org/conda-forge/linux-64/rdma-core-51.1-he02047a_0.conda#d3729d689f9febadb17462936a90e2e9
-https://conda.anaconda.org/conda-forge/linux-64/readline-8.2-h8228510_1.conda#47d31b792659ce70f470b5c82fdfb7a4
-https://conda.anaconda.org/conda-forge/linux-64/s2geometry-0.10.0-h8413349_4.conda#d19f88cf8812836e6a4a2a7902ed0e77
-https://conda.anaconda.org/conda-forge/linux-64/s2n-1.4.12-h06160fa_0.conda#bf1899cfd6dea061a220fa7e96a1f4bd
-https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_h4845f30_101.conda#d453b98d9c83e71da0741bb0ff4d76bc
-https://conda.anaconda.org/conda-forge/linux-64/xorg-fixesproto-5.0-h7f98852_1002.tar.bz2#65ad6e1eb4aed2b0611855aff05e04f6
-https://conda.anaconda.org/conda-forge/linux-64/xorg-libsm-1.2.4-h7391055_0.conda#93ee23f12bc2e684548181256edd2cf6
-https://conda.anaconda.org/conda-forge/linux-64/zlib-1.3.1-h4ab18f5_1.conda#9653f1bf3766164d0e65fa723cabbc54
-https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.6-ha6fb4c9_0.conda#4d056880988120e29d75bfff282e0f45
-https://conda.anaconda.org/conda-forge/linux-64/aws-c-io-0.14.7-hbfbeace_6.conda#d6382461de9a91a2665e964f92d8da0a
-https://conda.anaconda.org/conda-forge/linux-64/blosc-1.21.5-h0f2a231_0.conda#009521b7ed97cca25f8f997f9e745976
-https://conda.anaconda.org/conda-forge/linux-64/brotli-bin-1.1.0-hd590300_1.conda#39f910d205726805a958da408ca194ba
-https://conda.anaconda.org/conda-forge/linux-64/bwidget-1.9.14-ha770c72_1.tar.bz2#5746d6202ba2abad4a4707f2a2462795
-https://conda.anaconda.org/conda-forge/linux-64/c-blosc2-2.12.0-hb4ffafa_0.conda#1a9b16afb84d734a1bb2d196c308d477
-https://conda.anaconda.org/conda-forge/linux-64/fftw-3.3.10-nompi_hf1063bd_110.conda#ee3e687b78b778db7b304e5b00a4dca6
-https://conda.anaconda.org/conda-forge/linux-64/freetype-2.12.1-h267a509_2.conda#9ae35c3d96db2c94ce0cef86efdfa2cb
-https://conda.anaconda.org/conda-forge/linux-64/gettext-0.22.5-h59595ed_2.conda#219ba82e95d7614cf7140d2a4afc0926
-https://conda.anaconda.org/conda-forge/linux-64/gfortran_impl_linux-64-13.2.0-h738fc78_7.conda#0a9c20316a67fbfee9d5778ed0aff24d
-https://conda.anaconda.org/conda-forge/linux-64/gxx_impl_linux-64-13.2.0-h2a599c4_7.conda#2ac5f82e4fd21f968b370e54bb839a4e
-https://conda.anaconda.org/conda-forge/linux-64/hdfeos2-2.20-hebf79cf_1003.conda#23bb57b64a629bc3b33379beece7f0d7
-https://conda.anaconda.org/conda-forge/linux-64/krb5-1.21.2-h659d440_0.conda#cd95826dbd331ed1be26bdf401432844
-https://conda.anaconda.org/conda-forge/linux-64/libarchive-3.7.4-hfca40fe_0.conda#32ddb97f897740641d8d46a829ce1704
-https://conda.anaconda.org/conda-forge/linux-64/libgit2-1.7.1-hca3a8ce_0.conda#6af97ac284ffaf76d8f63cc1f9d64f7a
-https://conda.anaconda.org/conda-forge/linux-64/libhwloc-2.10.0-default_h5622ce7_1001.conda#fc2d5b79c2d3f8568fbab31db7ae02f3
-https://conda.anaconda.org/conda-forge/linux-64/libllvm16-16.0.6-hb3ce162_3.conda#a4d48c40dd5c60edbab7fd69c9a88967
-https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.27-pthreads_h413a1c8_0.conda#a356024784da6dfd4683dc5ecf45b155
-https://conda.anaconda.org/conda-forge/linux-64/libopenblas-ilp64-0.3.27-pthreads_h384dd9e_0.conda#025629e15edccee84df4713ce69192e5
-https://conda.anaconda.org/conda-forge/linux-64/libthrift-0.19.0-hb90f79a_1.conda#8cdb7d41faa0260875ba92414c487e2d
-https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.6.0-h29866fb_1.conda#4e9afd30f4ccb2f98645e51005f82236
-https://conda.anaconda.org/conda-forge/linux-64/libxslt-1.1.39-h76b75d6_0.conda#e71f31f8cfb0a91439f2086fc8aa0461
-https://conda.anaconda.org/conda-forge/linux-64/minizip-4.0.6-h9d307f2_0.conda#857b62ff5fc3b6282189798bf06aa2ca
-https://conda.anaconda.org/conda-forge/linux-64/nss-3.100-hca3bf56_0.conda#949c4a82290ee58b3c970cef4bcfd4ad
-https://conda.anaconda.org/conda-forge/linux-64/orc-2.0.0-h1e5e2c1_0.conda#53e8f030579d34e1a36a735d527c021f
-https://conda.anaconda.org/conda-forge/linux-64/python-3.11.9-hb806964_0_cpython.conda#ac68acfa8b558ed406c75e98d3428d7b
-https://conda.anaconda.org/conda-forge/linux-64/re2-2023.09.01-h7f4b329_2.conda#8f70e36268dea8eb666ef14c29bd3cda
-https://conda.anaconda.org/conda-forge/linux-64/sqlite-3.46.0-h6d4b2fc_0.conda#77ea8dff5cf8550cc8f5629a6af56323
-https://conda.anaconda.org/conda-forge/linux-64/tiledb-2.16.3-hf0b6e87_3.conda#1e28da846782f91a696af3952a2472f9
-https://conda.anaconda.org/conda-forge/linux-64/tktable-2.10-h0c5db8f_5.conda#9464044754ea25557a9c93f0327d90a6
-https://conda.anaconda.org/conda-forge/linux-64/ucx-1.15.0-ha691c75_8.conda#3f9bc6137b240642504a6c9b07a10c25
+https://conda.anaconda.org/conda-forge/linux-64/python-3.12.8-h9e4cc4f_1_cpython.conda#7fd2fd79436d9b473812f14e86746844
+https://conda.anaconda.org/conda-forge/linux-64/qhull-2020.2-h434a139_5.conda#353823361b1d27eb3960efb076dfcaf6
+https://conda.anaconda.org/conda-forge/linux-64/sqlite-3.48.0-h9eae976_0.conda#2b3a22991c20ed6ea2ed65d3407a91f4
+https://conda.anaconda.org/conda-forge/linux-64/tktable-2.10-h8bc8fbc_6.conda#dff3627fec2c0584ded391205295abf0
 https://conda.anaconda.org/conda-forge/linux-64/udunits2-2.2.28-h40f5838_3.conda#6bb8deb138f87c9d48320ac21b87e7a1
-https://conda.anaconda.org/conda-forge/linux-64/xorg-libx11-1.8.9-h8ee46fc_0.conda#077b6e8ad6a3ddb741fce2496dd01bec
-https://conda.anaconda.org/conda-forge/noarch/affine-2.4.0-pyhd8ed1ab_0.conda#ae5f4ad87126c55ba3f690ef07f81d64
-https://conda.anaconda.org/conda-forge/noarch/alabaster-0.7.16-pyhd8ed1ab_0.conda#def531a3ac77b7fb8c21d17bb5d0badb
-https://conda.anaconda.org/conda-forge/noarch/antlr-python-runtime-4.11.1-pyhd8ed1ab_0.tar.bz2#15109c4977d39ad7aa3423f57243e286
-https://conda.anaconda.org/conda-forge/noarch/asciitree-0.3.3-py_2.tar.bz2#c0481c9de49f040272556e2cedf42816
-https://conda.anaconda.org/conda-forge/noarch/attrs-23.2.0-pyh71513ae_0.conda#5e4c0743c70186509d1412e03c2d8dfa
-https://conda.anaconda.org/conda-forge/linux-64/aws-c-event-stream-0.4.2-h01f5eca_8.conda#afb85fc0f01032d115c57c961950e7d8
-https://conda.anaconda.org/conda-forge/linux-64/aws-c-http-0.8.1-hdb68c23_10.conda#cb6065938167da2d2f078c2f08473b84
-https://conda.anaconda.org/conda-forge/linux-64/backports.zoneinfo-0.2.1-py311h38be061_8.conda#5384590f14dfe6ccd02811236afc9f8e
-https://conda.anaconda.org/conda-forge/linux-64/brotli-1.1.0-hd590300_1.conda#f27a24d46e3ea7b70a1f98e50c62508f
-https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.1.0-py311hb755f60_1.conda#cce9e7c3f1c307f2a5fb08a2922d6164
-https://conda.anaconda.org/conda-forge/noarch/certifi-2024.6.2-pyhd8ed1ab_0.conda#8821ec1c8fcdc9e1d291d7b9f6e9968a
-https://conda.anaconda.org/conda-forge/noarch/cfgv-3.3.1-pyhd8ed1ab_0.tar.bz2#ebb5f5f7dc4f1a3780ef7ea7738db08c
-https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.3.2-pyhd8ed1ab_0.conda#7f4a9e3fcff3f6356ae99244a014da6a
-https://conda.anaconda.org/conda-forge/noarch/click-8.1.7-unix_pyh707e725_0.conda#f3ad426304898027fc619827ff428eca
-https://conda.anaconda.org/conda-forge/noarch/cloudpickle-3.0.0-pyhd8ed1ab_0.conda#753d29fe41bb881e4b9c004f0abf973f
-https://conda.anaconda.org/conda-forge/noarch/codespell-2.3.0-pyhd8ed1ab_0.conda#6e67fa19bedafa7eb7d6ea91de53e03d
-https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_0.tar.bz2#3faab06a954c2a04039983f2c4a50d99
+https://conda.anaconda.org/conda-forge/linux-64/uriparser-0.9.8-hac33072_0.conda#d71d3a66528853c0a1ac2c02d79a0284
+https://conda.anaconda.org/conda-forge/linux-64/x265-3.5-h924138e_3.tar.bz2#e7f6ed84d4623d52ee581325c1587a6b
+https://conda.anaconda.org/conda-forge/linux-64/xorg-libsm-1.2.5-he73a12e_0.conda#4c3e9fab69804ec6077697922d70c6e2
+https://conda.anaconda.org/conda-forge/linux-64/xorg-libx11-1.8.10-h4f16b4b_1.conda#125f34a17d7b4bea418a83904ea82ea6
+https://conda.anaconda.org/conda-forge/linux-64/xorg-makedepend-1.0.9-h59595ed_0.conda#71c756cfcc6649ed7614eb07712bfce0
+https://conda.anaconda.org/conda-forge/linux-64/xz-5.6.3-hbcc6ac9_1.conda#62aae173382a8aae284726353c6a6a24
+https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.6-ha6fb4c9_0.conda#4d056880988120e29d75bfff282e0f45
+https://conda.anaconda.org/conda-forge/noarch/affine-2.4.0-pyhd8ed1ab_1.conda#8c4061f499edec6b8ac7000f6d586829
+https://conda.anaconda.org/conda-forge/noarch/aiohappyeyeballs-2.4.4-pyhd8ed1ab_1.conda#296b403617bafa89df4971567af79013
+https://conda.anaconda.org/conda-forge/noarch/alabaster-1.0.0-pyhd8ed1ab_1.conda#1fd9696649f65fd6611fcdb4ffec738a
+https://conda.anaconda.org/conda-forge/linux-64/astroid-3.3.8-py312h7900ff3_0.conda#9b0b26c5846fdbe76a146e1f8715a1ac
+https://conda.anaconda.org/conda-forge/noarch/attrs-24.3.0-pyh71513ae_0.conda#356927ace43302bf6f5926e2a58dae6a
+https://conda.anaconda.org/conda-forge/linux-64/aws-c-event-stream-0.4.3-h235a6dd_1.conda#c05358e3a231195f7f0b3f592078bb0c
+https://conda.anaconda.org/conda-forge/linux-64/aws-c-http-0.8.9-h5e77a74_0.conda#d7714013c40363f45850a25113e2cb05
+https://conda.anaconda.org/conda-forge/linux-64/backports.zoneinfo-0.2.1-py312h7900ff3_9.conda#6df4f61b215587c40ec93810734778ca
+https://conda.anaconda.org/conda-forge/linux-64/blosc-1.21.6-hef167b5_0.conda#54fe76ab3d0189acaef95156874db7f9
+https://conda.anaconda.org/conda-forge/linux-64/brotli-1.1.0-hb9d3cd8_2.conda#98514fe74548d768907ce7a13f680e8f
+https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.1.0-py312h2ec8cdc_2.conda#b0b867af6fc74b2a0aa206da29c0f3cf
+https://conda.anaconda.org/conda-forge/linux-64/c-blosc2-2.15.2-h68e2383_0.conda#e7b11b508252ddc35c4b51dedef17b01
+https://conda.anaconda.org/conda-forge/noarch/certifi-2024.12.14-pyhd8ed1ab_0.conda#6feb87357ecd66733be3279f16a8c400
+https://conda.anaconda.org/conda-forge/noarch/cfgv-3.3.1-pyhd8ed1ab_1.conda#57df494053e17dce2ac3a0b33e1b2a2e
+https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.4.1-pyhd8ed1ab_0.conda#e83a31202d1c0a000fce3e9cf3825875
+https://conda.anaconda.org/conda-forge/noarch/click-8.1.8-pyh707e725_0.conda#f22f4d4970e09d68a10b922cbb0408d3
+https://conda.anaconda.org/conda-forge/noarch/cloudpickle-3.1.1-pyhd8ed1ab_0.conda#364ba6c9fb03886ac979b482f39ebb92
+https://conda.anaconda.org/conda-forge/noarch/codespell-2.3.0-pyhd8ed1ab_1.conda#840293a20343a601ca2738d359e403eb
+https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda#962b9857ee8e7018c22f2776ffa0b2d7
 https://conda.anaconda.org/conda-forge/noarch/config-0.5.1-pyhd8ed1ab_0.tar.bz2#97275d4898af65967b1ad57923cef770
-https://conda.anaconda.org/conda-forge/noarch/configargparse-1.7-pyhd8ed1ab_0.conda#0d07dc29b1c1cc973f76b74beb44915f
-https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhd8ed1ab_0.conda#5cd86562580f274031ede6aa6aa24441
-https://conda.anaconda.org/conda-forge/linux-64/cython-3.0.10-py311hb755f60_0.conda#f3a8a500a2e743ff92f418f0eaf9bf71
+https://conda.anaconda.org/conda-forge/noarch/configargparse-1.7-pyhd8ed1ab_1.conda#c5f4eec949e6514ca49e606b1fb7c043
+https://conda.anaconda.org/conda-forge/linux-64/crc32c-2.7.1-py312h66e93f0_0.conda#85a6111a6fb1b1e569e68484df606963
+https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhd8ed1ab_1.conda#44600c4667a319d67dbe0681fc0bc833
+https://conda.anaconda.org/conda-forge/linux-64/cyrus-sasl-2.1.27-h54b06d7_7.conda#dce22f70b4e5a407ce88f2be046f4ceb
+https://conda.anaconda.org/conda-forge/linux-64/cython-3.0.11-py312h8fd2918_3.conda#21e433caf1bb1e4c95832f8bb731d64c
 https://conda.anaconda.org/conda-forge/noarch/defusedxml-0.7.1-pyhd8ed1ab_0.tar.bz2#961b3a227b437d82ad7054484cfa71b2
-https://conda.anaconda.org/conda-forge/noarch/dill-0.3.8-pyhd8ed1ab_0.conda#78745f157d56877a2c6e7b386f66f3e2
-https://conda.anaconda.org/conda-forge/noarch/distlib-0.3.8-pyhd8ed1ab_0.conda#db16c66b759a64dc5183d69cc3745a52
-https://conda.anaconda.org/conda-forge/noarch/docutils-0.21.2-pyhd8ed1ab_0.conda#e8cd5d629f65bdf0f3bb312cde14659e
+https://conda.anaconda.org/conda-forge/noarch/dill-0.3.9-pyhd8ed1ab_1.conda#5e11310fca410e9f31381157079dee55
+https://conda.anaconda.org/conda-forge/noarch/distlib-0.3.9-pyhd8ed1ab_1.conda#8d88f4a2242e6b96f9ecff9a6a05b2f1
+https://conda.anaconda.org/conda-forge/noarch/docutils-0.21.2-pyhd8ed1ab_1.conda#24c1ca34138ee57de72a943237cde4cc
 https://conda.anaconda.org/conda-forge/noarch/dodgy-0.2.1-py_0.tar.bz2#62a69d073f7446c90f417b0787122f5b
-https://conda.anaconda.org/conda-forge/noarch/ecmwf-api-client-1.6.3-pyhd8ed1ab_0.tar.bz2#15621abf59053e184114d3e1d4f9d01e
-https://conda.anaconda.org/conda-forge/noarch/entrypoints-0.4-pyhd8ed1ab_0.tar.bz2#3cf04868fee0a029769bd41f4b2fbf2d
-https://conda.anaconda.org/conda-forge/noarch/et_xmlfile-1.1.0-pyhd8ed1ab_0.conda#a2f2138597905eaa72e561d8efb42cf3
-https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.2.0-pyhd8ed1ab_2.conda#8d652ea2ee8eaee02ed8dc820bc794aa
-https://conda.anaconda.org/conda-forge/noarch/execnet-2.1.1-pyhd8ed1ab_0.conda#15dda3cdbf330abfe9f555d22f66db46
-https://conda.anaconda.org/conda-forge/noarch/fasteners-0.17.3-pyhd8ed1ab_0.tar.bz2#348e27e78a5e39090031448c72f66d5e
-https://conda.anaconda.org/conda-forge/noarch/filelock-3.14.0-pyhd8ed1ab_0.conda#831d85ae0acfba31b8efd0f0d07da736
+https://conda.anaconda.org/conda-forge/noarch/ecmwf-api-client-1.6.3-pyhd8ed1ab_1.conda#375691f2607aa31feaf360c58f342b43
+https://conda.anaconda.org/conda-forge/noarch/entrypoints-0.4-pyhd8ed1ab_1.conda#3366592d3c219f2731721f11bc93755c
+https://conda.anaconda.org/conda-forge/noarch/et_xmlfile-2.0.0-pyhd8ed1ab_1.conda#71bf9646cbfabf3022c8da4b6b4da737
+https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.2.2-pyhd8ed1ab_1.conda#a16662747cdeb9abbac74d0057cc976e
+https://conda.anaconda.org/conda-forge/noarch/execnet-2.1.1-pyhd8ed1ab_1.conda#a71efeae2c160f6789900ba2631a2c90
+https://conda.anaconda.org/conda-forge/linux-64/fftw-3.3.10-nompi_hf1063bd_110.conda#ee3e687b78b778db7b304e5b00a4dca6
+https://conda.anaconda.org/conda-forge/noarch/filelock-3.16.1-pyhd8ed1ab_1.conda#d692e9ba6f92dc51484bf3477e36ce7c
 https://conda.anaconda.org/conda-forge/noarch/findlibs-0.0.5-pyhd8ed1ab_0.conda#8f325f63020af6f7acbe2c4cb4c920db
-https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.14.2-h14ed4e7_0.conda#0f69b688f52ff6da70bccb7ff7001d1d
-https://conda.anaconda.org/conda-forge/linux-64/freexl-2.0.0-h743c826_0.conda#12e6988845706b2cfbc3bc35c9a61a95
-https://conda.anaconda.org/conda-forge/linux-64/frozenlist-1.4.1-py311h459d7ec_0.conda#b267e553a337e1878512621e374845c5
-https://conda.anaconda.org/conda-forge/noarch/fsspec-2024.6.0-pyhff2d567_0.conda#ad6af3f92e71b1579ac2362b6cf29105
-https://conda.anaconda.org/conda-forge/noarch/geographiclib-2.0-pyhd8ed1ab_0.tar.bz2#6b1f32359fc5d2ab7b491d0029bfffeb
-https://conda.anaconda.org/conda-forge/noarch/humanfriendly-10.0-pyhd8ed1ab_6.conda#2ed1fe4b9079da97c44cfe9c2e5078fd
-https://conda.anaconda.org/conda-forge/noarch/idna-3.7-pyhd8ed1ab_0.conda#c0cc1420498b17414d8617d0b9f506ca
+https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.15.0-h7e30c49_1.conda#8f5b0b297b59e1ac160ad4beec99dbee
+https://conda.anaconda.org/conda-forge/linux-64/frozenlist-1.5.0-py312h66e93f0_0.conda#f98e36c96b2c66d9043187179ddb04f4
+https://conda.anaconda.org/conda-forge/noarch/fsspec-2024.12.0-pyhd8ed1ab_0.conda#e041ad4c43ab5e10c74587f95378ebc7
+https://conda.anaconda.org/conda-forge/noarch/geographiclib-2.0-pyhd8ed1ab_1.conda#8b9328ab4aafb8fde493ab32c5eba731
+https://conda.anaconda.org/conda-forge/linux-64/gfortran_impl_linux-64-14.2.0-hc73f493_1.conda#131a59b3bb1dbbfc63ec0f21eb0e8c65
+https://conda.anaconda.org/conda-forge/linux-64/gxx_impl_linux-64-14.2.0-h2c03514_1.conda#41664acd4c99ef4d192e12950ff68ca6
+https://conda.anaconda.org/conda-forge/linux-64/hdfeos2-2.20-h3e53b52_1004.conda#c21dc684e0e8efa507aba61a030f65e7
+https://conda.anaconda.org/conda-forge/noarch/hpack-4.0.0-pyhd8ed1ab_1.conda#2aa5ff7fa34a81b9196532c84c10d865
+https://conda.anaconda.org/conda-forge/noarch/humanfriendly-10.0-pyh707e725_8.conda#7fe569c10905402ed47024fc481bb371
+https://conda.anaconda.org/conda-forge/noarch/hyperframe-6.0.1-pyhd8ed1ab_1.conda#566e75c90c1d0c8c459eb0ad9833dc7a
+https://conda.anaconda.org/conda-forge/noarch/idna-3.10-pyhd8ed1ab_1.conda#39a4f67be3286c86d696df570b1201b7
 https://conda.anaconda.org/conda-forge/noarch/imagesize-1.4.1-pyhd8ed1ab_0.tar.bz2#7de5386c8fea29e76b303f37dde4c352
-https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.0.0-pyhd8ed1ab_0.conda#f800d2da156d08e289b14e87e43c1ae5
-https://conda.anaconda.org/conda-forge/noarch/itsdangerous-2.2.0-pyhd8ed1ab_0.conda#ff7ca04134ee8dde1d7cf491a78ef7c7
-https://conda.anaconda.org/conda-forge/linux-64/kiwisolver-1.4.5-py311h9547e67_1.conda#2c65bdf442b0d37aad080c8a4e0d452f
-https://conda.anaconda.org/conda-forge/linux-64/lazy-object-proxy-1.10.0-py311h459d7ec_0.conda#d39020c78fd00ed774ff9c876e8aba07
-https://conda.anaconda.org/conda-forge/linux-64/lcms2-2.15-h7f713cb_2.conda#9ab79924a3760f85a799f21bc99bd655
-https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-22_linux64_openblas.conda#1a2a0cd3153464fee6646f3dd6dad9b8
-https://conda.anaconda.org/conda-forge/linux-64/libcurl-8.8.0-hca28451_0.conda#f21c27f076a07907e70c49bb57bd0f20
-https://conda.anaconda.org/conda-forge/linux-64/libglib-2.78.1-hebfc3b9_0.conda#ddd09e8904fde46b85f41896621803e6
-https://conda.anaconda.org/conda-forge/linux-64/libgrpc-1.62.2-h15f2491_0.conda#8dabe607748cb3d7002ad73cd06f1325
-https://conda.anaconda.org/conda-forge/linux-64/libpq-16.3-ha72fbe1_0.conda#bac737ae28b79cfbafd515258d97d29e
-https://conda.anaconda.org/conda-forge/linux-64/libwebp-1.3.2-hdffd6e0_0.conda#a8661c87c873d8c8f90479318ebf0a17
-https://conda.anaconda.org/conda-forge/linux-64/llvmlite-0.42.0-py311ha6695c7_1.conda#d6e13a53b4f0cc38f4a348f47bfd5b97
+https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.0.0-pyhd8ed1ab_1.conda#6837f3eff7dcea42ecd714ce1ac2b108
+https://conda.anaconda.org/conda-forge/noarch/isodate-0.7.2-pyhd8ed1ab_1.conda#14c42a6334f38c412449f5a5e4043a5a
+https://conda.anaconda.org/conda-forge/noarch/itsdangerous-2.2.0-pyhd8ed1ab_1.conda#7ac5f795c15f288984e32add616cdc59
+https://conda.anaconda.org/conda-forge/linux-64/kiwisolver-1.4.8-py312h84d6215_0.conda#6713467dc95509683bfa3aca08524e8a
+https://conda.anaconda.org/conda-forge/noarch/legacy-cgi-2.6.1-pyh5b84bb0_3.conda#f258b7f54b5d9ddd02441f10c4dca2ac
+https://conda.anaconda.org/conda-forge/linux-64/libamd-3.3.3-ss783_h889e182.conda#3f5acd96dfc9ff4c3b02c38120d7dc8b
+https://conda.anaconda.org/conda-forge/linux-64/libasprintf-devel-0.22.5-he8f35ee_3.conda#1091193789bb830127ed067a9e01ac57
+https://conda.anaconda.org/conda-forge/linux-64/libavif16-1.1.1-h1909e37_2.conda#21e468ed3786ebcb2124b123aa2484b7
+https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-26_linux64_openblas.conda#ac52800af2e0c0e7dac770b435ce768a
+https://conda.anaconda.org/conda-forge/linux-64/libbtf-2.3.2-ss783_h2377355.conda#c60157e1d22e52ccc5aabc7b2f61c94d
+https://conda.anaconda.org/conda-forge/linux-64/libcamd-3.3.3-ss783_h2377355.conda#0178bc68cbcdb89d02b46594acace3d6
+https://conda.anaconda.org/conda-forge/linux-64/libccolamd-3.3.4-ss783_h2377355.conda#69a12c4c23969027c00a5ccca335df74
+https://conda.anaconda.org/conda-forge/linux-64/libcolamd-3.3.4-ss783_h2377355.conda#15fbaba693872fecc4f557922e41859d
+https://conda.anaconda.org/conda-forge/linux-64/libcurl-8.9.1-hdb1bdb2_0.conda#7da1d242ca3591e174a3c7d82230d3c0
+https://conda.anaconda.org/conda-forge/linux-64/libcxsparse-4.4.1-ss783_h2377355.conda#1728fa3e6b6e0852e06418cba06e2596
+https://conda.anaconda.org/conda-forge/linux-64/libgit2-1.8.4-hd24f944_1.conda#81d00656b41bc42266a999f613dd0fc9
+https://conda.anaconda.org/conda-forge/linux-64/libglib-2.82.2-h2ff4ddf_1.conda#37d1af619d999ee8f1f73cf5a06f4e2f
+https://conda.anaconda.org/conda-forge/linux-64/libglx-1.7.0-ha4b6fd6_2.conda#c8013e438185f33b13814c5c488acd5c
+https://conda.anaconda.org/conda-forge/linux-64/libjxl-0.11.1-hdb8da77_0.conda#32b23f3487beae7e81495fbc1099ae9e
+https://conda.anaconda.org/conda-forge/linux-64/libkml-1.3.0-hf539b9f_1021.conda#e8c7620cc49de0c6a2349b6dd6e39beb
+https://conda.anaconda.org/conda-forge/linux-64/libldl-3.3.2-ss783_h2377355.conda#22292db3a270520d9a63ac2625931603
+https://conda.anaconda.org/conda-forge/linux-64/libprotobuf-4.25.3-hd5b35b9_1.conda#06def97690ef90781a91b786cb48a0a9
+https://conda.anaconda.org/conda-forge/linux-64/librbio-4.3.4-ss783_h2377355.conda#02b58b64883d9d888252c6a485e003e6
+https://conda.anaconda.org/conda-forge/linux-64/libre2-11-2023.09.01-h5a48ba9_2.conda#41c69fba59d495e8cf5ffda48a607e35
+https://conda.anaconda.org/conda-forge/linux-64/librttopo-1.1.0-hc670b87_16.conda#3d9f3a2e5d7213c34997e4464d2f938c
+https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.7.0-h6565414_0.conda#80eaf80d84668fa5620ac9ec1b4bf56f
+https://conda.anaconda.org/conda-forge/linux-64/libxgboost-2.1.3-cuda118_h09a87be_0.conda#4aeecb7576dd57f1f45e9725383f8f36
+https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.13.5-h8d12d68_1.conda#1a21e49e190d1ffe58531a81b6e400e1
+https://conda.anaconda.org/conda-forge/linux-64/llvmlite-0.43.0-py312h374181b_1.conda#ed6ead7e9ab9469629c6cfb363b5c6e2
 https://conda.anaconda.org/conda-forge/noarch/locket-1.0.0-pyhd8ed1ab_0.tar.bz2#91e27ef3d05cc772ce627e51cff111c4
-https://conda.anaconda.org/conda-forge/linux-64/lxml-5.2.2-py311hc0a218f_0.conda#5a9c71f5cbdf3c5b1ad2504e13792629
-https://conda.anaconda.org/conda-forge/linux-64/lz4-4.3.3-py311h38e4bf4_0.conda#3910c815fc788621f88b2bdc0fa9f0a6
-https://conda.anaconda.org/conda-forge/linux-64/markupsafe-2.1.5-py311h459d7ec_0.conda#a322b4185121935c871d201ae00ac143
-https://conda.anaconda.org/conda-forge/noarch/mccabe-0.7.0-pyhd8ed1ab_0.tar.bz2#34fc335fc50eef0b5ea708f2b5f54e0c
-https://conda.anaconda.org/conda-forge/noarch/mistune-3.0.2-pyhd8ed1ab_0.conda#5cbee699846772cc939bef23a0d524ed
-https://conda.anaconda.org/conda-forge/linux-64/msgpack-python-1.0.8-py311h52f7536_0.conda#f33f59b8130753174992f409a41e112e
-https://conda.anaconda.org/conda-forge/linux-64/multidict-6.0.5-py311h459d7ec_0.conda#4288ea5cbe686d1b18fc3efb36c009a5
-https://conda.anaconda.org/conda-forge/noarch/munch-4.0.0-pyhd8ed1ab_0.conda#376b32e8f9d3eacbd625f37d39bd507d
+https://conda.anaconda.org/conda-forge/linux-64/lz4-4.3.3-py312hb3f7f12_1.conda#b99d90ef4e77acdab74828f79705a919
+https://conda.anaconda.org/conda-forge/linux-64/markupsafe-3.0.2-py312h178313f_1.conda#eb227c3e0bf58f5bd69c0532b157975b
+https://conda.anaconda.org/conda-forge/noarch/mccabe-0.7.0-pyhd8ed1ab_1.conda#827064ddfe0de2917fb29f1da4f8f533
+https://conda.anaconda.org/conda-forge/linux-64/minizip-4.0.7-h05a5f5f_3.conda#eec77634ccdb2ba6c231290c399b1dae
+https://conda.anaconda.org/conda-forge/linux-64/mpfr-4.2.1-h90cbb55_3.conda#2eeb50cab6652538eee8fc0bc3340c81
+https://conda.anaconda.org/conda-forge/linux-64/msgpack-python-1.1.0-py312h68727a3_0.conda#5c9b020a3f86799cdc6115e55df06146
+https://conda.anaconda.org/conda-forge/linux-64/multidict-6.1.0-py312h178313f_2.conda#5b5e3267d915a107eca793d52e1b780a
 https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyh9f0ad1d_0.tar.bz2#2ba8498c1018c1e9c61eb99b973dfe19
-https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.0.0-pyha770c72_0.conda#4eccaeba205f0aed9ac3a9ea58568ca3
-https://conda.anaconda.org/conda-forge/noarch/natsort-8.4.0-pyhd8ed1ab_0.conda#70959cd1db3cf77b2a27a0836cfd08a7
-https://conda.anaconda.org/conda-forge/noarch/networkx-3.3-pyhd8ed1ab_1.conda#d335fd5704b46f4efb89a6774e81aef0
-https://conda.anaconda.org/conda-forge/linux-64/openblas-ilp64-0.3.27-pthreads_h3d04fff_0.conda#15e92dc6a755173a10d993b92d700e70
-https://conda.anaconda.org/conda-forge/linux-64/openjpeg-2.5.2-h488ebb8_0.conda#7f2e286780f072ed750df46dc2631138
-https://conda.anaconda.org/conda-forge/noarch/packaging-24.0-pyhd8ed1ab_0.conda#248f521b64ce055e7feae3105e7abeb8
+https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.0.0-pyha770c72_1.conda#29097e7ea634a45cc5386b95cac6568f
+https://conda.anaconda.org/conda-forge/noarch/natsort-8.4.0-pyh29332c3_1.conda#0aa03903d33997f3886be58abc890aef
+https://conda.anaconda.org/conda-forge/noarch/networkx-3.4.2-pyh267e887_2.conda#fd40bf7f7f4bc4b647dc8512053d9873
+https://conda.anaconda.org/conda-forge/linux-64/openblas-ilp64-0.3.28-pthreads_h3d04fff_1.conda#fdaa89df7b34f5c904f8f1348e5a62a5
+https://conda.anaconda.org/conda-forge/noarch/packaging-24.2-pyhd8ed1ab_2.conda#3bfed7e6228ebf2f7b9eaa47f1b4e2aa
 https://conda.anaconda.org/conda-forge/noarch/pandocfilters-1.5.0-pyhd8ed1ab_0.tar.bz2#457c2c8c08e54905d6954e79cb5b5db9
-https://conda.anaconda.org/conda-forge/noarch/pathspec-0.12.1-pyhd8ed1ab_0.conda#17064acba08d3686f1135b5ec1b32b12
-https://conda.anaconda.org/conda-forge/noarch/pkgutil-resolve-name-1.3.10-pyhd8ed1ab_1.conda#405678b942f2481cecdb3e010f4925d9
-https://conda.anaconda.org/conda-forge/noarch/platformdirs-4.2.2-pyhd8ed1ab_0.conda#6f6cf28bf8e021933869bae3f84b8fc9
-https://conda.anaconda.org/conda-forge/noarch/pluggy-1.5.0-pyhd8ed1ab_0.conda#d3483c8fc2dc2cc3f5cf43e26d60cabf
-https://conda.anaconda.org/conda-forge/linux-64/psutil-5.9.8-py311h459d7ec_0.conda#9bc62d25dcf64eec484974a3123c9d57
-https://conda.anaconda.org/conda-forge/noarch/pycodestyle-2.9.1-pyhd8ed1ab_0.tar.bz2#0191dd7efe1a94262812770183b68892
-https://conda.anaconda.org/conda-forge/noarch/pycparser-2.22-pyhd8ed1ab_0.conda#844d9eb3b43095b031874477f7d70088
-https://conda.anaconda.org/conda-forge/noarch/pyflakes-2.5.0-pyhd8ed1ab_0.tar.bz2#1b3bef4313288ae8d35b1dfba4cd84a3
-https://conda.anaconda.org/conda-forge/noarch/pygments-2.18.0-pyhd8ed1ab_0.conda#b7f5c092b8f9800150d998a71b76d5a1
-https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.1.2-pyhd8ed1ab_0.conda#b9a4dacf97241704529131a0dfc0494f
-https://conda.anaconda.org/conda-forge/noarch/pyshp-2.3.1-pyhd8ed1ab_0.tar.bz2#92a889dc236a5197612bc85bee6d7174
-https://conda.anaconda.org/conda-forge/noarch/pysocks-1.7.1-pyha2e5f31_6.tar.bz2#2a7de29fb590ca14b5243c4c812c8025
-https://conda.anaconda.org/conda-forge/noarch/python-fastjsonschema-2.19.1-pyhd8ed1ab_0.conda#4d3ceee3af4b0f9a1f48f57176bf8625
-https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2024.1-pyhd8ed1ab_0.conda#98206ea9954216ee7540f0c773f2104d
-https://conda.anaconda.org/conda-forge/linux-64/python-xxhash-3.4.1-py311h459d7ec_0.conda#60b5332b3989fda37884b92c7afd6a91
-https://conda.anaconda.org/conda-forge/noarch/pytz-2024.1-pyhd8ed1ab_0.conda#3eeeeb9e4827ace8c0c1419c85d590ad
-https://conda.anaconda.org/conda-forge/linux-64/pyyaml-6.0.1-py311h459d7ec_1.conda#52719a74ad130de8fb5d047dc91f247a
-https://conda.anaconda.org/conda-forge/linux-64/rpds-py-0.18.1-py311h5ecf98a_0.conda#9ce82e95681cb5c5e4bd872ed0a7aceb
-https://conda.anaconda.org/conda-forge/linux-64/ruamel.yaml.clib-0.2.8-py311h459d7ec_0.conda#7865c897d89a39abc0056d89e37bd9e9
-https://conda.anaconda.org/conda-forge/noarch/semver-3.0.2-pyhd8ed1ab_0.conda#5efb3fccda53974aed800b6d575f72ed
+https://conda.anaconda.org/conda-forge/noarch/pathspec-0.12.1-pyhd8ed1ab_1.conda#617f15191456cc6a13db418a275435e5
+https://conda.anaconda.org/conda-forge/noarch/pkgutil-resolve-name-1.3.10-pyhd8ed1ab_2.conda#5a5870a74432aa332f7d32180633ad05
+https://conda.anaconda.org/conda-forge/noarch/platformdirs-4.3.6-pyhd8ed1ab_1.conda#577852c7e53901ddccc7e6a9959ddebe
+https://conda.anaconda.org/conda-forge/noarch/pluggy-1.5.0-pyhd8ed1ab_1.conda#e9dcbce5f45f9ee500e728ae58b605b6
+https://conda.anaconda.org/conda-forge/linux-64/propcache-0.2.1-py312h66e93f0_0.conda#55d5742a696d7da1c1262e99b6217ceb
+https://conda.anaconda.org/conda-forge/linux-64/psutil-6.1.1-py312h66e93f0_0.conda#add2c79595fa8a9b6d653d7e4e2cf05f
+https://conda.anaconda.org/conda-forge/noarch/pycodestyle-2.12.1-pyhd8ed1ab_1.conda#e895db5e6cee923018cbb1656c8ca7fa
+https://conda.anaconda.org/conda-forge/noarch/pycparser-2.22-pyh29332c3_1.conda#12c566707c80111f9799308d9e265aef
+https://conda.anaconda.org/conda-forge/noarch/pyflakes-3.2.0-pyhd8ed1ab_1.conda#4731450b2c059fc567696242bcb7fc05
+https://conda.anaconda.org/conda-forge/noarch/pygments-2.19.1-pyhd8ed1ab_0.conda#232fb4577b6687b2d503ef8e254270c9
+https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.2.1-pyhd8ed1ab_0.conda#285e237b8f351e85e7574a2c7bfa6d46
+https://conda.anaconda.org/conda-forge/noarch/pyshp-2.3.1-pyhd8ed1ab_1.conda#856b387c270e9eaf6e41e978057a2b62
+https://conda.anaconda.org/conda-forge/noarch/pysocks-1.7.1-pyha55dd90_7.conda#461219d1a5bd61342293efa2c0c90eac
+https://conda.anaconda.org/conda-forge/noarch/python-fastjsonschema-2.21.1-pyhd8ed1ab_0.conda#38e34d2d1d9dca4fb2b9a0a04f604e2c
+https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2024.2-pyhd8ed1ab_1.conda#c0def296b2f6d2dd7b030c2a7f66bb1f
+https://conda.anaconda.org/conda-forge/linux-64/python-xxhash-3.5.0-py312h66e93f0_1.conda#39aed2afe4d0cf76ab3d6b09eecdbea7
+https://conda.anaconda.org/conda-forge/noarch/pytz-2024.2-pyhd8ed1ab_1.conda#f26ec986456c30f6dff154b670ae140f
+https://conda.anaconda.org/conda-forge/linux-64/pyyaml-6.0.2-py312h66e93f0_1.conda#549e5930e768548a89c23f595dac5a95
+https://conda.anaconda.org/conda-forge/linux-64/rpds-py-0.22.3-py312h12e396e_0.conda#bfb49da0cc9098597d527def04d66f8b
+https://conda.anaconda.org/conda-forge/linux-64/ruamel.yaml.clib-0.2.8-py312h66e93f0_1.conda#532c3e5d0280be4fea52396ec1fa7d5d
+https://conda.anaconda.org/conda-forge/linux-64/s2geometry-0.10.0-h8413349_4.conda#d19f88cf8812836e6a4a2a7902ed0e77
+https://conda.anaconda.org/conda-forge/noarch/semver-3.0.3-pyhd8ed1ab_0.conda#6f0d1772b767a39d70631dce48421f3d
 https://conda.anaconda.org/conda-forge/noarch/setoptconf-tmp-0.3.1-pyhd8ed1ab_0.tar.bz2#af3e36d4effb85b9b9f93cd1db0963df
-https://conda.anaconda.org/conda-forge/noarch/setuptools-70.0.0-pyhd8ed1ab_0.conda#c8ddb4f34a208df4dd42509a0f6a1c89
-https://conda.anaconda.org/conda-forge/linux-64/simplejson-3.19.2-py311h459d7ec_0.conda#d6478cbce002db6303f0d749860f3e22
-https://conda.anaconda.org/conda-forge/noarch/six-1.16.0-pyh6c4a22f_0.tar.bz2#e5f25f8dbc060e9a8d912e432202afc2
+https://conda.anaconda.org/conda-forge/noarch/setuptools-75.8.0-pyhff2d567_0.conda#8f28e299c11afdd79e0ec1e279dcdc52
+https://conda.anaconda.org/conda-forge/linux-64/simplejson-3.19.3-py312h66e93f0_1.conda#c8d1a609d5f3358d715c2273011d9f4d
+https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhd8ed1ab_0.conda#a451d576819089b0d672f18768be0f65
 https://conda.anaconda.org/conda-forge/noarch/smmap-5.0.0-pyhd8ed1ab_0.tar.bz2#62f26a3d1387acee31322208f0cfa3e0
 https://conda.anaconda.org/conda-forge/noarch/snowballstemmer-2.2.0-pyhd8ed1ab_0.tar.bz2#4d22a9315e78c6827f806065957d566e
 https://conda.anaconda.org/conda-forge/noarch/sortedcontainers-2.4.0-pyhd8ed1ab_0.tar.bz2#6d6552722448103793743dabfbda532d
 https://conda.anaconda.org/conda-forge/noarch/soupsieve-2.5-pyhd8ed1ab_1.conda#3f144b2c34f8cb5a9abd9ed23a39c561
-https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-jsmath-1.0.1-pyhd8ed1ab_0.conda#da1d979339e2714c30a8e806a33ec087
-https://conda.anaconda.org/conda-forge/noarch/sqlparse-0.5.0-pyhd8ed1ab_0.conda#4dd428bd295ba44babd13050f2bcc622
-https://conda.anaconda.org/conda-forge/linux-64/tbb-2021.12.0-h297d8ca_1.conda#3ff978d8994f591818a506640c6a7071
-https://conda.anaconda.org/conda-forge/noarch/tblib-3.0.0-pyhd8ed1ab_0.conda#04eedddeb68ad39871c8127dd1c21f4f
-https://conda.anaconda.org/conda-forge/noarch/tenacity-8.3.0-pyhd8ed1ab_0.conda#216cfa8e32bcd1447646768351df6059
-https://conda.anaconda.org/conda-forge/noarch/termcolor-2.4.0-pyhd8ed1ab_0.conda#a5033708ad9283907c3b1bc1f90d0d0d
+https://conda.anaconda.org/conda-forge/linux-64/spdlog-1.14.1-hed91bc2_1.conda#909188c8979846bac8e586908cf1ca6a
+https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-jsmath-1.0.1-pyhd8ed1ab_1.conda#fa839b5ff59e192f411ccc7dae6588bb
+https://conda.anaconda.org/conda-forge/noarch/tblib-3.0.0-pyhd8ed1ab_1.conda#60ce69f73f3e75b21f1c27b1b471320c
+https://conda.anaconda.org/conda-forge/noarch/tenacity-9.0.0-pyhd8ed1ab_1.conda#a09f66fe95a54a92172e56a4a97ba271
+https://conda.anaconda.org/conda-forge/noarch/termcolor-2.5.0-pyhd8ed1ab_1.conda#1ce02d60767af357e864ce61895268d2
 https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.5.0-pyhc1e730c_0.conda#df68d78237980a159bd7149f33c0e8fd
-https://conda.anaconda.org/conda-forge/noarch/toml-0.10.2-pyhd8ed1ab_0.tar.bz2#f832c45a477c78bebd107098db465095
-https://conda.anaconda.org/conda-forge/noarch/tomli-2.0.1-pyhd8ed1ab_0.tar.bz2#5844808ffab9ebdb694585b50ba02a96
-https://conda.anaconda.org/conda-forge/noarch/tomlkit-0.12.5-pyha770c72_0.conda#e5dde5caf905e9d95895e05f94967e14
-https://conda.anaconda.org/conda-forge/noarch/toolz-0.12.1-pyhd8ed1ab_0.conda#2fcb582444635e2c402e8569bb94e039
-https://conda.anaconda.org/conda-forge/linux-64/tornado-6.4.1-py311h331c9d8_0.conda#e29e451c96bf8e81a5760b7565c6ed2c
-https://conda.anaconda.org/conda-forge/noarch/traitlets-5.14.3-pyhd8ed1ab_0.conda#3df84416a021220d8b5700c613af2dc5
-https://conda.anaconda.org/conda-forge/noarch/trove-classifiers-2024.5.22-pyhd8ed1ab_0.conda#a887538e7f6697ed52a487dbaa0ebff5
-https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.12.2-pyha770c72_0.conda#ebe6952715e1d5eb567eeebf25250fa7
-https://conda.anaconda.org/conda-forge/linux-64/ujson-5.10.0-py311h4332511_0.conda#442a260df22ffad7f666c7e3f119b5ab
-https://conda.anaconda.org/conda-forge/noarch/untokenize-0.1.1-py_0.tar.bz2#1447ead40f2a01733a9c8dfc32988375
-https://conda.anaconda.org/conda-forge/noarch/webencodings-0.5.1-pyhd8ed1ab_2.conda#daf5160ff9cde3a468556965329085b9
-https://conda.anaconda.org/conda-forge/noarch/webob-1.8.7-pyhd8ed1ab_0.tar.bz2#a8192f3585f341ea66c60c189580ac67
-https://conda.anaconda.org/conda-forge/noarch/wheel-0.43.0-pyhd8ed1ab_1.conda#0b5293a157c2b5cd513dd1b03d8d3aae
-https://conda.anaconda.org/conda-forge/linux-64/wrapt-1.16.0-py311h459d7ec_0.conda#6669b5529d206c1f880b642cdd17ae05
-https://conda.anaconda.org/conda-forge/noarch/xlsxwriter-3.1.9-pyhd8ed1ab_0.conda#70e533db62a710ae216fdaccc4a983c8
-https://conda.anaconda.org/conda-forge/linux-64/xorg-libxext-1.3.4-h0b41bf4_2.conda#82b6df12252e6f32402b96dacc656fec
-https://conda.anaconda.org/conda-forge/linux-64/xorg-libxfixes-5.0.3-h7f98852_1004.tar.bz2#e9a21aa4d5e3e5f1aed71e8cefd46b6a
-https://conda.anaconda.org/conda-forge/linux-64/xorg-libxrender-0.9.11-hd590300_0.conda#ed67c36f215b310412b2af935bf3e530
-https://conda.anaconda.org/conda-forge/linux-64/xorg-libxt-1.3.0-hd590300_1.conda#ae92aab42726eb29d16488924f7312cb
-https://conda.anaconda.org/conda-forge/noarch/xyzservices-2024.6.0-pyhd8ed1ab_0.conda#de631703d59e40af41c56c4b4e2928ab
-https://conda.anaconda.org/conda-forge/linux-64/zeromq-4.3.5-h75354e8_4.conda#03cc8d9838ad9dd0060ab532e81ccb21
-https://conda.anaconda.org/conda-forge/noarch/zict-3.0.0-pyhd8ed1ab_0.conda#cf30c2c15b82aacb07f9c09e28ff2275
-https://conda.anaconda.org/conda-forge/noarch/zipp-3.19.2-pyhd8ed1ab_0.conda#49808e59df5535116f6878b2a820d6f4
-https://conda.anaconda.org/conda-forge/noarch/accessible-pygments-0.0.4-pyhd8ed1ab_0.conda#46a2e6e3dfa718ce3492018d5a110dd6
-https://conda.anaconda.org/conda-forge/noarch/aiosignal-1.3.1-pyhd8ed1ab_0.tar.bz2#d1e1eb7e21a9e2c74279d87dafb68156
-https://conda.anaconda.org/conda-forge/noarch/asgiref-3.8.1-pyhd8ed1ab_0.conda#b5c2e1034ccc76fb14031637924880eb
-https://conda.anaconda.org/conda-forge/linux-64/astroid-2.15.8-py311h38be061_0.conda#46d70fcb74472aab178991f0231ee3c6
-https://conda.anaconda.org/conda-forge/linux-64/atk-1.0-2.38.0-hd4edc92_1.tar.bz2#6c72ec3e660a51736913ef6ea68c454b
-https://conda.anaconda.org/conda-forge/linux-64/aws-c-auth-0.7.17-he0b1f16_2.conda#ea6d998135d5f8932cffc91381104690
-https://conda.anaconda.org/conda-forge/linux-64/aws-c-mqtt-0.10.3-h50844eb_4.conda#e72fdd8942f266ea79c70ec085661d6c
-https://conda.anaconda.org/conda-forge/noarch/babel-2.14.0-pyhd8ed1ab_0.conda#9669586875baeced8fc30c0826c3270e
-https://conda.anaconda.org/conda-forge/noarch/beautifulsoup4-4.12.3-pyha770c72_0.conda#332493000404d8411859539a5a630865
-https://conda.anaconda.org/conda-forge/noarch/bleach-6.1.0-pyhd8ed1ab_0.conda#0ed9d7c0e9afa7c025807a9a8136ea3e
+https://conda.anaconda.org/conda-forge/noarch/toml-0.10.2-pyhd8ed1ab_1.conda#b0dd904de08b7db706167240bf37b164
+https://conda.anaconda.org/conda-forge/noarch/tomli-2.2.1-pyhd8ed1ab_1.conda#ac944244f1fed2eb49bae07193ae8215
+https://conda.anaconda.org/conda-forge/noarch/tomlkit-0.13.2-pyha770c72_1.conda#1d9ab4fc875c52db83f9c9b40af4e2c8
+https://conda.anaconda.org/conda-forge/noarch/toolz-1.0.0-pyhd8ed1ab_1.conda#40d0ed782a8aaa16ef248e68c06c168d
+https://conda.anaconda.org/conda-forge/linux-64/tornado-6.4.2-py312h66e93f0_0.conda#e417822cb989e80a0d2b1b576fdd1657
+https://conda.anaconda.org/conda-forge/noarch/traitlets-5.14.3-pyhd8ed1ab_1.conda#019a7385be9af33791c989871317e1ed
+https://conda.anaconda.org/conda-forge/noarch/trove-classifiers-2025.1.15.22-pyhd8ed1ab_0.conda#246846b15ba47e8684a7c88ce1de3e82
+https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.12.2-pyha770c72_1.conda#d17f13df8b65464ca316cbc000a3cb64
+https://conda.anaconda.org/conda-forge/linux-64/ujson-5.10.0-py312h2ec8cdc_1.conda#96226f62dddc63226472b7477d783967
+https://conda.anaconda.org/conda-forge/linux-64/unicodedata2-16.0.0-py312h66e93f0_0.conda#617f5d608ff8c28ad546e5d9671cbb95
+https://conda.anaconda.org/conda-forge/noarch/untokenize-0.1.1-pyhd8ed1ab_2.conda#a7bafc627d25a939bb68206bd7abd483
+https://conda.anaconda.org/conda-forge/noarch/webencodings-0.5.1-pyhd8ed1ab_3.conda#2841eb5bfc75ce15e9a0054b98dcd64d
+https://conda.anaconda.org/conda-forge/noarch/wheel-0.45.1-pyhd8ed1ab_1.conda#75cb7132eb58d97896e173ef12ac9986
+https://conda.anaconda.org/conda-forge/linux-64/wrapt-1.17.2-py312h66e93f0_0.conda#669e63af87710f8d52fdec9d4d63b404
+https://conda.anaconda.org/conda-forge/linux-64/xerces-c-3.2.5-h988505b_2.conda#9dda9667feba914e0e80b95b82f7402b
+https://conda.anaconda.org/conda-forge/noarch/xlsxwriter-3.2.0-pyhd8ed1ab_1.conda#c57589177253e911e293345226c7eca1
+https://conda.anaconda.org/conda-forge/linux-64/xorg-libxext-1.3.6-hb9d3cd8_0.conda#febbab7d15033c913d53c7a2c102309d
+https://conda.anaconda.org/conda-forge/linux-64/xorg-libxfixes-6.0.1-hb9d3cd8_0.conda#4bdb303603e9821baf5fe5fdff1dc8f8
+https://conda.anaconda.org/conda-forge/linux-64/xorg-libxrender-0.9.12-hb9d3cd8_0.conda#96d57aba173e878a2089d5638016dc5e
+https://conda.anaconda.org/conda-forge/linux-64/xorg-libxt-1.3.1-hb9d3cd8_0.conda#279b0de5f6ba95457190a1c459a64e31
+https://conda.anaconda.org/conda-forge/noarch/xyzservices-2025.1.0-pyhd8ed1ab_0.conda#fdf07e281a9e5e10fc75b2dd444136e9
+https://conda.anaconda.org/conda-forge/noarch/yapf-0.32.0-pyhd8ed1ab_0.tar.bz2#177cba0b4bdfacad5c5fbb0ed31504c4
+https://conda.anaconda.org/conda-forge/linux-64/zeromq-4.3.5-h3b0a872_7.conda#3947a35e916fcc6b9825449affbf4214
+https://conda.anaconda.org/conda-forge/noarch/zict-3.0.0-pyhd8ed1ab_1.conda#e52c2ef711ccf31bb7f70ca87d144b9e
+https://conda.anaconda.org/conda-forge/noarch/zipp-3.21.0-pyhd8ed1ab_1.conda#0c3cc595284c5e8f0f9900a9b228a332
+https://conda.anaconda.org/conda-forge/noarch/accessible-pygments-0.0.5-pyhd8ed1ab_1.conda#74ac5069774cdbc53910ec4d631a3999
+https://conda.anaconda.org/conda-forge/noarch/aiosignal-1.3.2-pyhd8ed1ab_0.conda#1a3981115a398535dbe3f6d5faae3d36
+https://conda.anaconda.org/conda-forge/linux-64/atk-1.0-2.38.0-h04ea711_2.conda#f730d54ba9cd543666d7220c9f7ed563
+https://conda.anaconda.org/conda-forge/linux-64/aws-c-auth-0.7.30-hec5e740_0.conda#bc1b9f70ea7fa533aefa6a8b6fbe8da7
+https://conda.anaconda.org/conda-forge/linux-64/aws-c-mqtt-0.10.5-h0009854_0.conda#d393d0a6c9b993771fbc67a998fccf6c
+https://conda.anaconda.org/conda-forge/linux-64/azure-core-cpp-1.13.0-h935415a_0.conda#debd1677c2fea41eb2233a260f48a298
+https://conda.anaconda.org/conda-forge/noarch/babel-2.16.0-pyhd8ed1ab_1.conda#3e23f7db93ec14c80525257d8affac28
+https://conda.anaconda.org/conda-forge/noarch/beautifulsoup4-4.12.3-pyha770c72_1.conda#d48f7e9fdec44baf6d1da416fe402b04
+https://conda.anaconda.org/conda-forge/noarch/bleach-6.2.0-pyh29332c3_4.conda#f0b4c8e370446ef89797608d60a564b3
 https://conda.anaconda.org/conda-forge/linux-64/brunsli-0.1-h9c3ff4c_0.tar.bz2#c1ac6229d0bfd14f8354ff9ad2a26cad
-https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.0-h3faef2a_0.conda#f907bb958910dc404647326ca80c263e
-https://conda.anaconda.org/conda-forge/linux-64/cffi-1.16.0-py311hb3a22ac_0.conda#b3469563ac5e808b0cd92810d0697043
-https://conda.anaconda.org/conda-forge/linux-64/cfitsio-4.3.0-hbdc6101_0.conda#797554b8b7603011e8677884381fbcc5
-https://conda.anaconda.org/conda-forge/noarch/click-plugins-1.1.1-py_0.tar.bz2#4fd2c6b53934bd7d96d1f3fdaf99b79f
-https://conda.anaconda.org/conda-forge/noarch/cligj-0.7.2-pyhd8ed1ab_1.tar.bz2#a29b7c141d6b2de4bb67788a5f107734
-https://conda.anaconda.org/conda-forge/linux-64/coverage-7.5.3-py311h331c9d8_0.conda#543dd05fd661e4e9c9deb3b37093d6a2
-https://conda.anaconda.org/conda-forge/linux-64/curl-8.8.0-he654da7_0.conda#042341d8b9ba4ee7f2722b81fae9f0ad
-https://conda.anaconda.org/conda-forge/linux-64/cytoolz-0.12.3-py311h459d7ec_0.conda#13d385f635d7fbe9acc93600f67a6cb4
-https://conda.anaconda.org/conda-forge/noarch/docformatter-1.7.5-pyhd8ed1ab_0.conda#3a941b6083e945aa87e739a9b85c82e9
+https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.2-h3394656_1.conda#b34c2833a1f56db610aeb27f206d800d
+https://conda.anaconda.org/conda-forge/linux-64/cffi-1.17.1-py312h06ac9bb_0.conda#a861504bbea4161a9170b85d4d2be840
+https://conda.anaconda.org/conda-forge/linux-64/cfitsio-4.4.1-hf8ad068_0.conda#1b7a01fd02d11efe0eb5a676842a7b7d
+https://conda.anaconda.org/conda-forge/noarch/click-plugins-1.1.1-pyhd8ed1ab_1.conda#82bea35e4dac4678ba623cf10e95e375
+https://conda.anaconda.org/conda-forge/noarch/cligj-0.7.2-pyhd8ed1ab_2.conda#55c7804f428719241a90b152016085a1
+https://conda.anaconda.org/conda-forge/linux-64/coverage-7.6.10-py312h178313f_0.conda#df113f58bdfc79c98f5e07b6bd3eb4c2
+https://conda.anaconda.org/conda-forge/linux-64/curl-8.9.1-h18eb788_0.conda#2e7dedf73dfbfcee662e2a0f6175e4bb
+https://conda.anaconda.org/conda-forge/linux-64/cytoolz-1.0.1-py312h66e93f0_0.conda#6198b134b1c08173f33653896974d477
+https://conda.anaconda.org/conda-forge/noarch/deprecated-1.2.15-pyhd8ed1ab_1.conda#eaef2e94d5bd76f758545d172c1fda67
+https://conda.anaconda.org/conda-forge/noarch/docformatter-1.7.5-pyhd8ed1ab_1.conda#af53e838cab8872e7e014a8336aaaffb
 https://conda.anaconda.org/conda-forge/noarch/docrep-0.3.2-pyh44b312d_0.tar.bz2#235523955bc1bfb019d7ec8a2bb58f9a
-https://conda.anaconda.org/conda-forge/noarch/fire-0.6.0-pyhd8ed1ab_0.conda#e9ed10aa8fa1dd6782940b95c942a6ae
-https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.53.0-py311h331c9d8_0.conda#2daef6c4ce74840c8d7a431498be83e9
-https://conda.anaconda.org/conda-forge/linux-64/gdk-pixbuf-2.42.10-h6c15284_3.conda#06f97c8b69157d91993af0c4f2e16bdc
-https://conda.anaconda.org/conda-forge/noarch/geopy-2.4.1-pyhd8ed1ab_1.conda#358c17429c97883b2cb9ab5f64bc161b
-https://conda.anaconda.org/conda-forge/noarch/gitdb-4.0.11-pyhd8ed1ab_0.conda#623b19f616f2ca0c261441067e18ae40
+https://conda.anaconda.org/conda-forge/noarch/donfig-0.8.1.post1-pyhd8ed1ab_1.conda#c56a7fa5597ad78b62e1f5d21f7f8b8f
+https://conda.anaconda.org/conda-forge/noarch/fire-0.7.0-pyhd8ed1ab_0.conda#c8eefdf1e822c56a6034602e67bc92a5
+https://conda.anaconda.org/conda-forge/noarch/flake8-7.1.1-pyhd8ed1ab_1.conda#c43926857c0c7ba1a29c8c4a221a5e78
+https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.55.3-py312h178313f_1.conda#bc18c46eda4c2b29431981998507e723
+https://conda.anaconda.org/conda-forge/linux-64/freexl-2.0.0-h9dce30a_2.conda#ecb5d11305b8ba1801543002e69d2f2f
+https://conda.anaconda.org/conda-forge/linux-64/gdk-pixbuf-2.42.12-hb9ae30d_0.conda#201db6c2d9a3c5e46573ac4cb2e92f4f
+https://conda.anaconda.org/conda-forge/noarch/geopy-2.4.1-pyhd8ed1ab_2.conda#40182a8d62a61d147ec7d3e4c5c36ac2
+https://conda.anaconda.org/conda-forge/linux-64/gettext-0.22.5-he02047a_3.conda#c7f243bbaea97cd6ea1edd693270100e
+https://conda.anaconda.org/conda-forge/linux-64/git-2.46.0-pl5321hb5640b7_0.conda#825d146359bc8b85083d92259d0a0e1b
+https://conda.anaconda.org/conda-forge/noarch/gitdb-4.0.12-pyhd8ed1ab_0.conda#7c14f3706e099f8fcd47af2d494616cc
 https://conda.anaconda.org/conda-forge/linux-64/gts-0.7.6-h977cf35_4.conda#4d8df0b0db060d33c9a702ada998a8fe
+https://conda.anaconda.org/conda-forge/noarch/h2-4.1.0-pyhd8ed1ab_1.conda#825927dc7b0f287ef8d4d0011bb113b1
 https://conda.anaconda.org/conda-forge/linux-64/hdf5-1.14.3-nompi_hdf9ad27_105.conda#7e1729554e209627636a0f6fabcdd115
-https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-7.1.0-pyha770c72_0.conda#0896606848b2dc5cebdf111b6543aa04
-https://conda.anaconda.org/conda-forge/noarch/importlib_resources-6.4.0-pyhd8ed1ab_0.conda#c5d3907ad8bd7bf557521a1833cf7e6d
-https://conda.anaconda.org/conda-forge/noarch/isodate-0.6.1-pyhd8ed1ab_0.tar.bz2#4a62c93c1b5c0b920508ae3fd285eaf5
-https://conda.anaconda.org/conda-forge/noarch/isort-5.13.2-pyhd8ed1ab_0.conda#1d25ed2b95b92b026aaa795eabec8d91
-https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.4-pyhd8ed1ab_0.conda#7b86ecb7d3557821c649b3c31e3eb9f2
-https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.2-pyhd8ed1ab_0.conda#25df261d4523d9f9783bcdb7208d872f
-https://conda.anaconda.org/conda-forge/linux-64/jupyter_core-5.7.2-py311h38be061_0.conda#f85e78497dfed6f6a4b865191f42de2e
-https://conda.anaconda.org/conda-forge/noarch/jupyterlab_pygments-0.3.0-pyhd8ed1ab_1.conda#afcd1b53bcac8844540358e33f33d28f
+https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-7.2.1-pyha770c72_0.conda#b9f5330c0853ccabc39a9878c6f1a2ab
+https://conda.anaconda.org/conda-forge/noarch/importlib_resources-6.5.2-pyhd8ed1ab_0.conda#c85c76dc67d75619a92f51dfbce06992
+https://conda.anaconda.org/conda-forge/noarch/isort-5.13.2-pyhd8ed1ab_1.conda#ef7dc847f19fe4859d5aaa33385bf509
+https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.5-pyhd8ed1ab_0.conda#2752a6ed44105bfb18c9bef1177d9dcd
+https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.2-pyhd8ed1ab_1.conda#bf8243ee348f3a10a14ed0cae323e0c1
+https://conda.anaconda.org/conda-forge/noarch/jupyter_core-5.7.2-pyh31011fe_1.conda#0a2980dada0dd7fd0998f0342308b1b1
+https://conda.anaconda.org/conda-forge/noarch/jupyterlab_pygments-0.3.0-pyhd8ed1ab_2.conda#fd312693df06da3578383232528c468d
 https://conda.anaconda.org/conda-forge/noarch/latexcodec-2.0.1-pyh9f0ad1d_0.tar.bz2#8d67904973263afd2985ba56aa2d6bb4
-https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-22_linux64_openblas.conda#4b31699e0ec5de64d5896e580389c9a1
-https://conda.anaconda.org/conda-forge/linux-64/libgd-2.3.3-he9388d3_8.conda#f3abc6e6ab60fa404c23094f5a03ec9b
-https://conda.anaconda.org/conda-forge/linux-64/libglu-9.0.0-hac7e632_1003.conda#50c389a09b6b7babaef531eb7cb5e0ca
-https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-2.22.0-h9be4e54_1.conda#4b4e36a91e7dabf7345b82d85767a7c3
-https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-22_linux64_openblas.conda#b083767b6c877e24ee597d93b87ab838
+https://conda.anaconda.org/conda-forge/linux-64/lcms2-2.16-hb7c19ff_0.conda#51bb7010fc86f70eee639b4bb7a894f5
+https://conda.anaconda.org/conda-forge/linux-64/libarchive-3.7.7-hadbb8c3_0.conda#4a099677417658748239616b6ca96bb6
+https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-26_linux64_openblas.conda#ebcc5f37a435aa3c19640533c82f8d76
+https://conda.anaconda.org/conda-forge/linux-64/libgd-2.3.3-hd3e95f3_10.conda#30ee3a29c84cf7b842a8c5828c4b7c13
+https://conda.anaconda.org/conda-forge/linux-64/libgl-1.7.0-ha4b6fd6_2.conda#928b8be80851f5d8ffb016f9c81dae7a
+https://conda.anaconda.org/conda-forge/linux-64/libheif-1.19.5-gpl_hc21c24c_100.conda#3b57852666eaacc13414ac811dde3f8a
+https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-26_linux64_openblas.conda#3792604c43695d6a273bc5faaac47d48
+https://conda.anaconda.org/conda-forge/linux-64/libspex-3.2.1-ss783_h5a7e440.conda#16f6f74255e7dead91a9809386ad531c
+https://conda.anaconda.org/conda-forge/linux-64/libwebp-1.5.0-hae8dbeb_0.conda#bb895ca27e7e33ab7a7c2c63529ce1e0
+https://conda.anaconda.org/conda-forge/linux-64/libxslt-1.1.39-h76b75d6_0.conda#e71f31f8cfb0a91439f2086fc8aa0461
 https://conda.anaconda.org/conda-forge/noarch/logilab-common-1.7.3-py_0.tar.bz2#6eafcdf39a7eb90b6d951cfff59e8d3b
-https://conda.anaconda.org/conda-forge/noarch/nested-lookup-0.2.25-pyhd8ed1ab_1.tar.bz2#2f59daeb14581d41b1e2dda0895933b2
-https://conda.anaconda.org/conda-forge/noarch/nodeenv-1.9.1-pyhd8ed1ab_0.conda#dfe0528d0f1c16c1f7c528ea5536ab30
-https://conda.anaconda.org/conda-forge/linux-64/openpyxl-3.1.2-py311h459d7ec_1.conda#5c809fb753f06a04c2f114394404769e
+https://conda.anaconda.org/conda-forge/noarch/mistune-3.1.0-pyhd8ed1ab_0.conda#d10024c163a52eeecbb166fdeaef8b12
+https://conda.anaconda.org/conda-forge/noarch/nested-lookup-0.2.25-pyhd8ed1ab_2.conda#cc2da171723d50bc1a7f8a53a8d0319f
+https://conda.anaconda.org/conda-forge/noarch/nodeenv-1.9.1-pyhd8ed1ab_1.conda#7ba3f09fceae6a120d664217e58fe686
+https://conda.anaconda.org/conda-forge/linux-64/openjpeg-2.5.3-h5fbd93e_0.conda#9e5816bc95d285c115a3ebc2f8563564
+https://conda.anaconda.org/conda-forge/linux-64/openldap-2.6.9-he970967_0.conda#ca2de8bbdc871bce41dbf59e51324165
+https://conda.anaconda.org/conda-forge/linux-64/openpyxl-3.1.5-py312h710cb58_1.conda#69a8838436435f59d72ddcb8dfd24a28
+https://conda.anaconda.org/conda-forge/linux-64/orc-2.0.2-h669347b_0.conda#1e6c10f7d749a490612404efeb179eb8
 https://conda.anaconda.org/conda-forge/noarch/partd-1.4.2-pyhd8ed1ab_0.conda#0badf9c54e24cecfb0ad2f99d680c163
-https://conda.anaconda.org/conda-forge/linux-64/pillow-10.0.1-py311h8aef010_1.conda#4d66ee2081a7cd444ff6f30d95873eef
-https://conda.anaconda.org/conda-forge/noarch/pip-24.0-pyhd8ed1ab_0.conda#f586ac1e56c8638b64f9c8122a7b8a67
-https://conda.anaconda.org/conda-forge/noarch/plotly-5.22.0-pyhd8ed1ab_0.conda#5b409a5f738e7d76c2b426eddb7e9956
-https://conda.anaconda.org/conda-forge/linux-64/postgresql-16.3-h8e811e2_0.conda#e4d52462da124ed3792472f95a36fc2a
-https://conda.anaconda.org/conda-forge/linux-64/proj-9.3.0-h1d62c97_2.conda#b5e57a0c643da391bef850922963eece
-https://conda.anaconda.org/conda-forge/noarch/pydocstyle-6.3.0-pyhd8ed1ab_0.conda#7e23a61a7fbaedfef6eb0e1ac775c8e5
-https://conda.anaconda.org/conda-forge/noarch/pyproject_hooks-1.1.0-pyhd8ed1ab_0.conda#03736d8ced74deece64e54be348ddd3e
-https://conda.anaconda.org/conda-forge/noarch/pytest-8.2.2-pyhd8ed1ab_0.conda#0f3f49c22c7ef3a1195fa61dad3c43be
-https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0-pyhd8ed1ab_0.conda#2cf4264fffb9e6eff6031c5b6884d61c
-https://conda.anaconda.org/conda-forge/noarch/python-utils-3.8.2-pyhd8ed1ab_0.conda#89703b4f38bd1c0353881f085bc8fdaa
-https://conda.anaconda.org/conda-forge/linux-64/pyzmq-26.0.3-py311h08a0b41_0.conda#8bef21c0a0160e7369fc2f494acf85d0
-https://conda.anaconda.org/conda-forge/noarch/referencing-0.35.1-pyhd8ed1ab_0.conda#0fc8b52192a8898627c3efae1003e9f6
-https://conda.anaconda.org/conda-forge/noarch/retrying-1.3.3-py_2.tar.bz2#a11f356d6f93b74b4a84e9501afd48b4
-https://conda.anaconda.org/conda-forge/linux-64/ruamel.yaml-0.18.6-py311h459d7ec_0.conda#4dccc0bc3bb4d6e5c30bccbd053c4f90
-https://conda.anaconda.org/conda-forge/noarch/tinycss2-1.3.0-pyhd8ed1ab_0.conda#8662629d9a05f9cff364e31ca106c1ac
-https://conda.anaconda.org/conda-forge/noarch/tqdm-4.66.4-pyhd8ed1ab_0.conda#e74cd796e70a4261f86699ee0a3a7a24
-https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.12.2-hd8ed1ab_0.conda#52d648bd608f5737b123f510bb5514b5
-https://conda.anaconda.org/conda-forge/noarch/url-normalize-1.4.3-pyhd8ed1ab_0.tar.bz2#7c4076e494f0efe76705154ac9302ba6
-https://conda.anaconda.org/conda-forge/noarch/urllib3-2.2.1-pyhd8ed1ab_0.conda#08807a87fa7af10754d46f63b368e016
-https://conda.anaconda.org/conda-forge/noarch/virtualenv-20.26.2-pyhd8ed1ab_0.conda#7d36e7a485ea2f5829408813bdbbfb38
-https://conda.anaconda.org/conda-forge/linux-64/xerces-c-3.2.5-hac6953d_0.conda#63b80ca78d29380fe69e69412dcbe4ac
-https://conda.anaconda.org/conda-forge/linux-64/xorg-libxi-1.7.10-h7f98852_0.tar.bz2#e77615e5141cad5a2acaa043d1cf0ca5
-https://conda.anaconda.org/conda-forge/linux-64/xorg-libxmu-1.1.3-h4ab18f5_1.conda#4d6c9925cdcda27e9d022e40eb3eac05
-https://conda.anaconda.org/conda-forge/linux-64/xorg-libxpm-3.5.17-hd590300_0.conda#12bf78e12f71405775e1c092902959d3
-https://conda.anaconda.org/conda-forge/noarch/yamale-5.2.1-pyhca7485f_0.conda#c089f90a086b6214c5606368d0d3bad0
-https://conda.anaconda.org/conda-forge/noarch/yamllint-1.35.1-pyhd8ed1ab_0.conda#a1240b99a7ccd953879dc63111823986
-https://conda.anaconda.org/conda-forge/linux-64/yarl-1.9.4-py311h459d7ec_0.conda#fff0f2058e9d86c8bf5848ee93917a8d
-https://conda.anaconda.org/conda-forge/linux-64/aiohttp-3.9.5-py311h459d7ec_0.conda#0175d2636cc41dc019b51462c13ce225
-https://conda.anaconda.org/conda-forge/linux-64/arpack-3.7.0-hdefa2d7_2.tar.bz2#8763fe86163198ef1778d1d8d22bb078
-https://conda.anaconda.org/conda-forge/linux-64/aws-c-s3-0.5.7-hb7bd14b_1.conda#82bd3d7da86d969c62ff541bab19526a
-https://conda.anaconda.org/conda-forge/noarch/cattrs-23.2.3-pyhd8ed1ab_0.conda#91fc4700dcce4a46d439900a132fe4e5
-https://conda.anaconda.org/conda-forge/linux-64/cryptography-42.0.8-py311h4a61cc7_0.conda#962bcc96f59a31b62c43ac2b306812af
-https://conda.anaconda.org/conda-forge/noarch/django-5.0.6-pyhd8ed1ab_0.conda#8451875d7b41dbab4d282f02719f3d6d
-https://conda.anaconda.org/conda-forge/noarch/flake8-5.0.4-pyhd8ed1ab_0.tar.bz2#8079ea7dec0a917dd0cb6c257f7ea9ea
-https://conda.anaconda.org/conda-forge/linux-64/freeglut-3.2.2-hac7e632_2.conda#6e553df297f6e64668efb54302e0f139
+https://conda.anaconda.org/conda-forge/noarch/pip-24.3.1-pyh8b19718_2.conda#04e691b9fadd93a8a9fad87a81d4fd8f
+https://conda.anaconda.org/conda-forge/noarch/plotly-5.24.1-pyhd8ed1ab_1.conda#71ac632876630091c81c50a05ec5e030
+https://conda.anaconda.org/conda-forge/linux-64/proj-9.4.1-h54d7996_1.conda#e479d1991c725e1a355f33c0e40dbc66
+https://conda.anaconda.org/conda-forge/noarch/pydocstyle-6.3.0-pyhd8ed1ab_1.conda#a36aa6e0119331d3280f4bba043314c7
+https://conda.anaconda.org/conda-forge/noarch/pyproject_hooks-1.2.0-pyhd8ed1ab_1.conda#d4582021af437c931d7d77ec39007845
+https://conda.anaconda.org/conda-forge/noarch/pytest-8.3.4-pyhd8ed1ab_1.conda#799ed216dc6af62520f32aa39bc1c2bb
+https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhff2d567_1.conda#5ba79d7c71f03c678c8ead841f347d6e
+https://conda.anaconda.org/conda-forge/noarch/python-utils-3.9.1-pyhff2d567_1.conda#24ed1dc544b101075fa7462be5c3a5c5
+https://conda.anaconda.org/conda-forge/linux-64/pyzmq-26.2.0-py312hbf22597_3.conda#746ce19f0829ec3e19c93007b1a224d3
+https://conda.anaconda.org/conda-forge/noarch/rdflib-7.1.3-pyhd8ed1ab_0.conda#72ec20859c97d33c82edcc8bdb70d536
+https://conda.anaconda.org/conda-forge/linux-64/re2-2023.09.01-h7f4b329_2.conda#8f70e36268dea8eb666ef14c29bd3cda
+https://conda.anaconda.org/conda-forge/noarch/referencing-0.36.1-pyhd8ed1ab_0.conda#dbb48421efd666ea133c6d5e67291766
+https://conda.anaconda.org/conda-forge/noarch/requirements-detector-1.3.2-pyhd8ed1ab_1.conda#cc0f2fcfec5e6b67a20cb24f8b9f3c93
+https://conda.anaconda.org/conda-forge/noarch/retrying-1.3.3-pyhd8ed1ab_3.conda#1f7482562f2082f1b2abf8a3e2a41b63
+https://conda.anaconda.org/conda-forge/linux-64/ruamel.yaml-0.18.10-py312h66e93f0_0.conda#5260b7fb19694ee5bc4ed0ee7a2a769f
+https://conda.anaconda.org/conda-forge/noarch/tinycss2-1.4.0-pyhd8ed1ab_0.conda#f1acf5fdefa8300de697982bcb1761c9
+https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.1-pyhd8ed1ab_1.conda#9efbfdc37242619130ea42b1cc4ed861
+https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.12.2-hd8ed1ab_1.conda#b6a408c64b78ec7b779a3e5c7a902433
+https://conda.anaconda.org/conda-forge/noarch/url-normalize-1.4.3-pyhd8ed1ab_1.conda#c7b1961b139c21381764de4704b6bbfb
+https://conda.anaconda.org/conda-forge/noarch/virtualenv-20.29.1-pyhd8ed1ab_0.conda#de06336c9833cffd2a4bd6f27c4cf8ea
+https://conda.anaconda.org/conda-forge/noarch/webob-1.8.9-pyhd8ed1ab_1.conda#293718ddac83a0fbc0f2193ff77d1e1c
+https://conda.anaconda.org/conda-forge/linux-64/xorg-libxdamage-1.1.6-hb9d3cd8_0.conda#b5fcc7172d22516e1f965490e65e33a4
+https://conda.anaconda.org/conda-forge/linux-64/xorg-libxi-1.8.2-hb9d3cd8_0.conda#17dcc85db3c7886650b8908b183d6876
+https://conda.anaconda.org/conda-forge/linux-64/xorg-libxmu-1.2.1-hb9d3cd8_1.conda#f35a9a2da717ade815ffa70c0e8bdfbd
+https://conda.anaconda.org/conda-forge/linux-64/xorg-libxxf86vm-1.1.6-hb9d3cd8_0.conda#5efa5fa6243a622445fdfd72aee15efa
+https://conda.anaconda.org/conda-forge/noarch/yamale-5.3.0-pyhd8ed1ab_0.conda#d4b5f3a50decd28cd747f4b5f7aea33f
+https://conda.anaconda.org/conda-forge/noarch/yamllint-1.35.1-pyhd8ed1ab_1.conda#fe91647002aab05979fee9392d745f96
+https://conda.anaconda.org/conda-forge/linux-64/yarl-1.18.3-py312h66e93f0_0.conda#91df2efaa08730416bec2a4502309275
+https://conda.anaconda.org/conda-forge/linux-64/aiohttp-3.11.11-py312h178313f_0.conda#8219afa093757bbe07b9825eb1973ed9
+https://conda.anaconda.org/conda-forge/linux-64/arpack-3.9.1-nompi_hf03ea27_102.conda#e09af397232ef1070e0b6cbf4c64aacb
+https://conda.anaconda.org/conda-forge/linux-64/aws-c-s3-0.6.5-hbaf354b_4.conda#2cefeb144de7712995d1b52cc6a3864c
+https://conda.anaconda.org/conda-forge/linux-64/azure-identity-cpp-1.8.0-hd126650_2.conda#36df3cf05459de5d0a41c77c4329634b
+https://conda.anaconda.org/conda-forge/linux-64/azure-storage-common-cpp-12.7.0-h10ac4d7_1.conda#ab6d507ad16dbe2157920451d662e4a1
+https://conda.anaconda.org/conda-forge/noarch/bleach-with-css-6.2.0-h82add2a_4.conda#a30e9406c873940383555af4c873220d
+https://conda.anaconda.org/conda-forge/noarch/cattrs-24.1.2-pyhd8ed1ab_1.conda#53eca64665361194ca4bbaf87c0ded99
+https://conda.anaconda.org/conda-forge/linux-64/cryptography-44.0.0-py312hda17c39_0.conda#50052304026b6f33fdd34563ee4b47b8
+https://conda.anaconda.org/conda-forge/noarch/dask-core-2025.1.0-pyhd8ed1ab_0.conda#0abebcf57fa0d8f2f0d92f49c47d3f06
+https://conda.anaconda.org/conda-forge/noarch/flake8-polyfill-1.0.2-pyhd8ed1ab_1.conda#4f282d2b2904441afca6367764fa718e
+https://conda.anaconda.org/conda-forge/linux-64/freeglut-3.2.2-ha6d2627_3.conda#84ec3f5b46f3076be49f2cf3f1cfbf02
 https://conda.anaconda.org/conda-forge/noarch/funcargparse-0.2.5-pyhd8ed1ab_0.tar.bz2#e557b70d736251fa0bbb7c4497852a92
-https://conda.anaconda.org/conda-forge/linux-64/geotiff-1.7.1-hee599c5_13.conda#8c55dacddd589be64b2bd6a5d4264be6
-https://conda.anaconda.org/conda-forge/linux-64/git-2.42.0-pl5321h86e50cf_0.conda#96ad24c67e0056d171385859c43218a2
-https://conda.anaconda.org/conda-forge/noarch/gitpython-3.1.43-pyhd8ed1ab_0.conda#0b2154c1818111e17381b1df5b4b0176
+https://conda.anaconda.org/conda-forge/linux-64/geotiff-1.7.3-hf7fa9e8_2.conda#1d6bdc6b2c62c8cc90c67b50142d7b7f
+https://conda.anaconda.org/conda-forge/noarch/gitpython-3.1.44-pyhff2d567_0.conda#140a4e944f7488467872e562a2a52789
 https://conda.anaconda.org/conda-forge/linux-64/gsl-2.7-he838d99_0.tar.bz2#fec079ba39c9cca093bf4c00001825de
-https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-8.3.0-h3d44ed6_0.conda#5a6f6c00ef982a9bc83558d9ac8f64a0
-https://conda.anaconda.org/conda-forge/linux-64/hdfeos5-5.1.16-hf1a501a_15.conda#d2e16a32f41d67c7d280da11b2846328
-https://conda.anaconda.org/conda-forge/noarch/importlib_metadata-7.1.0-hd8ed1ab_0.conda#6ef2b72d291b39e479d7694efa2b2b98
-https://conda.anaconda.org/conda-forge/noarch/jsonschema-specifications-2023.12.1-pyhd8ed1ab_0.conda#a0e4efb5f35786a05af4809a2fb1f855
-https://conda.anaconda.org/conda-forge/linux-64/kealib-1.5.3-hee9dde6_1.conda#c5b7b29e2b66107553d0366538257a51
-https://conda.anaconda.org/conda-forge/noarch/lazy_loader-0.4-pyhd8ed1ab_0.conda#a284ff318fbdb0dd83928275b4b6087c
-https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-storage-2.22.0-hc7a4891_1.conda#7811f043944e010e54640918ea82cecd
+https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-10.2.0-h4bba637_0.conda#9e38e86167e8b1ea0094747d12944ce4
+https://conda.anaconda.org/conda-forge/linux-64/hdfeos5-5.1.16-h51d0b48_17.conda#21ce981dc10b2d7ec8da29872b6fa96c
+https://conda.anaconda.org/conda-forge/noarch/importlib_metadata-7.2.1-hd8ed1ab_0.conda#d6c936d009aa63e5f82d216c95cdcaee
+https://conda.anaconda.org/conda-forge/noarch/jsonschema-specifications-2024.10.1-pyhd8ed1ab_1.conda#3b519bc21bc80e60b456f1e62962a766
+https://conda.anaconda.org/conda-forge/noarch/jupyter_client-8.6.3-pyhd8ed1ab_1.conda#4ebae00eae9705b0c3d6d1018a81d047
+https://conda.anaconda.org/conda-forge/linux-64/kealib-1.5.3-hf8d3e68_2.conda#ffe68c611ae0ccfda4e7a605195e22b3
+https://conda.anaconda.org/conda-forge/noarch/lazy-loader-0.4-pyhd8ed1ab_2.conda#d10d9393680734a8febc4b362a4c94f2
+https://conda.anaconda.org/conda-forge/linux-64/libcholmod-5.3.0-ss783_h3fa60b6.conda#c832eba46245682fabd96afb3f561601
+https://conda.anaconda.org/conda-forge/linux-64/libglu-9.0.3-h03adeef_0.conda#b1df5affe904efe82ef890826b68881d
+https://conda.anaconda.org/conda-forge/linux-64/libgrpc-1.62.2-h15f2491_0.conda#8dabe607748cb3d7002ad73cd06f1325
 https://conda.anaconda.org/conda-forge/linux-64/libnetcdf-4.9.2-nompi_h135f659_114.conda#a908e463c710bd6b10a9eaa89fdf003c
-https://conda.anaconda.org/conda-forge/linux-64/libspatialite-5.1.0-h090f1da_1.conda#9a2d6acaa8ce6d53a150248e7b11165e
-https://conda.anaconda.org/conda-forge/linux-64/numpy-1.26.4-py311h64a7726_0.conda#a502d7aad449a1206efb366d6a12c52d
-https://conda.anaconda.org/conda-forge/linux-64/poppler-23.08.0-hf2349cb_2.conda#fb75401ae7e2e3f354dff72e9da95cae
-https://conda.anaconda.org/conda-forge/noarch/progressbar2-4.4.2-pyhd8ed1ab_0.conda#aca82be28a1c676a3e0365e83892f412
-https://conda.anaconda.org/conda-forge/noarch/pybtex-0.24.0-pyhd8ed1ab_2.tar.bz2#2099b86a7399c44c0c61cdb6de6915ba
-https://conda.anaconda.org/conda-forge/noarch/pylint-2.17.7-pyhd8ed1ab_0.conda#3cab6aee60038b3f621bce3e50f52bed
-https://conda.anaconda.org/conda-forge/linux-64/pyproj-3.6.1-py311h1facc83_4.conda#75d504c6787edc377ebdba087a26a61b
-https://conda.anaconda.org/conda-forge/noarch/pytest-cov-5.0.0-pyhd8ed1ab_0.conda#c54c0107057d67ddf077751339ec2c63
-https://conda.anaconda.org/conda-forge/noarch/pytest-env-1.1.3-pyhd8ed1ab_0.conda#1dbdf019d740419852c4a7803fff49d9
-https://conda.anaconda.org/conda-forge/noarch/pytest-metadata-3.1.1-pyhd8ed1ab_0.conda#52b91ecba854d55b28ad916a8b10da24
-https://conda.anaconda.org/conda-forge/noarch/pytest-mock-3.14.0-pyhd8ed1ab_0.conda#4b9b5e086812283c052a9105ab1e254e
-https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.5.0-pyhd8ed1ab_0.conda#d5f595da2daead898ca958ac62f0307b
-https://conda.anaconda.org/conda-forge/noarch/python-build-1.2.1-pyhd8ed1ab_0.conda#d657cde3b3943fcedf6038138eea84de
-https://conda.anaconda.org/conda-forge/noarch/rdflib-7.0.0-pyhd8ed1ab_0.conda#44d14ef95495b3d4438f28998e0296a9
-https://conda.anaconda.org/conda-forge/noarch/requests-2.32.3-pyhd8ed1ab_0.conda#5ede4753180c7a550a443c430dc8ab52
-https://conda.anaconda.org/conda-forge/noarch/requirements-detector-1.2.2-pyhd8ed1ab_0.conda#6626918380d99292df110f3c91b6e5ec
-https://conda.anaconda.org/conda-forge/linux-64/suitesparse-5.10.1-h5a4f163_3.conda#f363554b9084fb9d5e3366fbbc0d18e0
-https://conda.anaconda.org/conda-forge/linux-64/ukkonen-1.0.1-py311h9547e67_4.conda#586da7df03b68640de14dc3e8bcbf76f
-https://conda.anaconda.org/conda-forge/linux-64/xorg-libxaw-1.0.14-h7f98852_1.tar.bz2#45b68dc2fc7549c16044d533ceaf340e
-https://conda.anaconda.org/conda-forge/noarch/yapf-0.40.1-pyhd8ed1ab_0.conda#f269942e802d5e148632143d4c37acc9
-https://conda.anaconda.org/conda-forge/linux-64/aws-crt-cpp-0.26.6-hf567797_4.conda#ffb662b31aef333e68a00dd17fda2027
-https://conda.anaconda.org/conda-forge/linux-64/cftime-1.6.3-py311h18e1886_1.conda#f1beb063aad4446eb146d8b88420a4ea
-https://conda.anaconda.org/conda-forge/noarch/chart-studio-1.1.0-pyh9f0ad1d_0.tar.bz2#acd9a12a35e5a0221bdf39eb6e4811dc
-https://conda.anaconda.org/conda-forge/noarch/colorspacious-1.1.2-pyh24bf2e0_0.tar.bz2#b73afa0d009a51cabd3ec99c4d2ef4f3
-https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.2.1-py311h9547e67_0.conda#74ad0ae64f1ef565e27eda87fa749e84
-https://conda.anaconda.org/conda-forge/noarch/dask-core-2024.5.2-pyhd8ed1ab_0.conda#1a57a819915e1c169b74933720b138f2
-https://conda.anaconda.org/conda-forge/noarch/eofs-1.4.1-pyhd8ed1ab_1.conda#5fc43108dee4106f23050acc7a101233
-https://conda.anaconda.org/conda-forge/noarch/flake8-polyfill-1.0.2-py_0.tar.bz2#a53db35e3d07f0af2eccd59c2a00bffe
-https://conda.anaconda.org/conda-forge/noarch/identify-2.5.36-pyhd8ed1ab_0.conda#ba68cb5105760379432cebc82b45af40
-https://conda.anaconda.org/conda-forge/linux-64/imagecodecs-2023.9.18-py311h9b38416_0.conda#67bed2bd92ffa76b20506d83427706ae
-https://conda.anaconda.org/conda-forge/noarch/imageio-2.34.1-pyh4b66e23_0.conda#bcf6a6f4c6889ca083e8d33afbafb8d5
-https://conda.anaconda.org/conda-forge/linux-64/jasper-4.0.0-h32699f2_1.conda#fdde5424ecef5f7ad310b4242229291c
-https://conda.anaconda.org/conda-forge/noarch/jsonschema-4.22.0-pyhd8ed1ab_0.conda#b9661a4b1200d6bc7d8a4cdafdc91468
-https://conda.anaconda.org/conda-forge/linux-64/julia-1.9.3-h06b7c97_0.conda#6214d0563598ae0cc9b954344b9f9c10
-https://conda.anaconda.org/conda-forge/noarch/jupyter_client-8.6.2-pyhd8ed1ab_0.conda#3cdbb2fa84490e5fd44c9f9806c0d292
-https://conda.anaconda.org/conda-forge/linux-64/libgdal-3.7.2-h6238fc3_5.conda#2fef4283b2bb45a66f8b81099d36721e
+https://conda.anaconda.org/conda-forge/linux-64/libpq-17.2-h04577a9_0.conda#52dd46162c6fb2765b49e6fd06adf8d5
+https://conda.anaconda.org/conda-forge/linux-64/libspatialite-5.1.0-h15fa968_9.conda#4957a903bd6a68cc2e53e47476f9c6f4
+https://conda.anaconda.org/conda-forge/linux-64/lxml-5.3.0-py312he28fd5a_2.conda#3acf38086326f49afed094df4ba7c9d9
+https://conda.anaconda.org/conda-forge/linux-64/numpy-1.26.4-py312heda63a1_0.conda#d8285bea2a350f63fab23bf460221f3f
+https://conda.anaconda.org/conda-forge/linux-64/pillow-11.1.0-py312h80c1187_0.conda#d3894405f05b2c0f351d5de3ae26fa9c
+https://conda.anaconda.org/conda-forge/linux-64/poppler-24.08.0-h47131b8_1.conda#0854b9ff0cc10a1f6f67b0f352b8e75a
+https://conda.anaconda.org/conda-forge/noarch/progressbar2-4.5.0-pyhd8ed1ab_1.conda#e557abf678a0bf100fe7cf9d2b4f4a72
+https://conda.anaconda.org/conda-forge/noarch/pybtex-0.24.0-pyhd8ed1ab_3.conda#556a52a96313364aa79990ed1337b9a5
+https://conda.anaconda.org/conda-forge/noarch/pylint-3.3.3-pyhd8ed1ab_0.conda#5842a1fa3b9b4f9fe7069b9ca5ed068d
+https://conda.anaconda.org/conda-forge/linux-64/pyproj-3.6.1-py312h9211aeb_9.conda#173afeb0d112c854fd1a9fcac4b5cce3
+https://conda.anaconda.org/conda-forge/noarch/pytest-cov-6.0.0-pyhd8ed1ab_1.conda#79963c319d1be62c8fd3e34555816e01
+https://conda.anaconda.org/conda-forge/noarch/pytest-env-1.1.5-pyhd8ed1ab_1.conda#b92ee0cf3b26087455244248b1a04e0a
+https://conda.anaconda.org/conda-forge/noarch/pytest-metadata-3.1.1-pyhd8ed1ab_1.conda#c4b7295798eff80144dc4ca4551efa80
+https://conda.anaconda.org/conda-forge/noarch/pytest-mock-3.14.0-pyhd8ed1ab_1.conda#1ca25f3fdf32ebd8a51ee9efa97c9a45
+https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.6.1-pyhd8ed1ab_1.conda#59aad4fb37cabc0bacc73cf344612ddd
+https://conda.anaconda.org/conda-forge/noarch/python-build-1.2.2.post1-pyhff2d567_1.conda#0903621fe8a9f37286596529528f4f74
+https://conda.anaconda.org/conda-forge/linux-64/ukkonen-1.0.1-py312h68727a3_5.conda#f9664ee31aed96c85b7319ab0a693341
+https://conda.anaconda.org/conda-forge/linux-64/xorg-libxpm-3.5.17-hb9d3cd8_1.conda#f35bec7fface97f67f44ca952fc740b7
+https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.23.0-py312hef9b889_1.conda#8b7069e9792ee4e5b4919a7a306d2e67
+https://conda.anaconda.org/conda-forge/linux-64/aws-crt-cpp-0.28.2-h6c0439f_6.conda#4e472c316d08af60faeb71f86d7563e1
+https://conda.anaconda.org/conda-forge/linux-64/azure-storage-blobs-cpp-12.12.0-hd2e3451_0.conda#61f1c193452f0daa582f39634627ea33
+https://conda.anaconda.org/conda-forge/linux-64/cftime-1.6.4-py312hc0a28a1_1.conda#990033147b0a998e756eaaed6b28f48d
+https://conda.anaconda.org/conda-forge/noarch/colorspacious-1.1.2-pyhecae5ae_1.conda#04151bb8e351c6209caad045e4b1f4bd
+https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.3.1-py312h68727a3_0.conda#f5fbba0394ee45e9a64a73c2a994126a
+https://conda.anaconda.org/conda-forge/noarch/eofs-2.0.0-pyhff2d567_0.conda#a3cce45423d73c3d4420b71351e71fef
+https://conda.anaconda.org/conda-forge/noarch/identify-2.6.6-pyhd8ed1ab_0.conda#d751c3b4a973ed15b57be90d68c716d1
+https://conda.anaconda.org/conda-forge/linux-64/imagecodecs-2024.6.1-py312h6d9a048_4.conda#a810fadedc4edc06b4282d1222467837
+https://conda.anaconda.org/conda-forge/noarch/imageio-2.36.1-pyh12aca89_1.conda#84d5a2f075c861a8f98afd2842f7eb6e
+https://conda.anaconda.org/conda-forge/linux-64/jasper-4.2.4-h536e39c_0.conda#9518ab7016cf4564778aef08b6bd8792
+https://conda.anaconda.org/conda-forge/noarch/jsonschema-4.23.0-pyhd8ed1ab_1.conda#a3cead9264b331b32fe8f0aabc967522
+https://conda.anaconda.org/conda-forge/noarch/lazy_loader-0.4-pyhd8ed1ab_2.conda#bb0230917e2473c77d615104dbe8a49d
+https://conda.anaconda.org/conda-forge/linux-64/libgdal-core-3.9.2-h353785f_1.conda#c363d0b330b4b21b4c1b10e0981d3a99
+https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-2.28.0-h26d7fe4_0.conda#2c51703b4d775f8943c08a361788131b
+https://conda.anaconda.org/conda-forge/linux-64/libklu-2.3.5-ss783_hfbdfdfc.conda#9ef3af7a987541ae75e8d3765c6f31a2
+https://conda.anaconda.org/conda-forge/linux-64/libspqr-4.3.4-ss783_hae1ff0d.conda#04b223bbfa980f40315c3699a0e0ad33
+https://conda.anaconda.org/conda-forge/linux-64/libumfpack-6.3.5-ss783_hd4f9ce1.conda#7196f225e50e2582ea887e12146ddb5f
 https://conda.anaconda.org/conda-forge/noarch/magics-python-1.5.8-pyhd8ed1ab_1.conda#3fd7e3db129f12362642108f23fde521
-https://conda.anaconda.org/conda-forge/noarch/multiurl-0.3.1-pyhd8ed1ab_0.conda#4dff4abb5728f7662ecaaa8bee3a0260
-https://conda.anaconda.org/conda-forge/linux-64/netcdf-fortran-4.6.1-nompi_h228c76a_104.conda#91bc3ac73308181d55a09d9e4aeb4496
-https://conda.anaconda.org/conda-forge/linux-64/numba-0.59.1-py311h96b013e_0.conda#cd581f19ea0c298ec6ef612fdf7d041c
-https://conda.anaconda.org/conda-forge/linux-64/numcodecs-0.12.1-py311h4332511_1.conda#887aa6096851eab5c34fe95ed1641591
-https://conda.anaconda.org/conda-forge/linux-64/pandas-2.1.4-py311h320fe9a_0.conda#e44ccb61b6621bf3f8053ae66eba7397
-https://conda.anaconda.org/conda-forge/linux-64/pango-1.50.14-ha41ecd1_2.conda#1a66c10f6a0da3dbd2f3a68127e7f6a0
-https://conda.anaconda.org/conda-forge/noarch/patsy-0.5.6-pyhd8ed1ab_0.conda#a5b55d1cb110cdcedc748b5c3e16e687
-https://conda.anaconda.org/conda-forge/noarch/pooch-1.8.2-pyhd8ed1ab_0.conda#8dab97d8a9616e07d779782995710aed
-https://conda.anaconda.org/conda-forge/noarch/pylint-plugin-utils-0.7-pyhd8ed1ab_0.tar.bz2#1657976383aee04dbb3ae3bdf654bb58
-https://conda.anaconda.org/conda-forge/noarch/pyopenssl-24.0.0-pyhd8ed1ab_0.conda#b50aec2c744a5c493c09cce9e2e7533e
-https://conda.anaconda.org/conda-forge/noarch/pyroma-4.2-pyhd8ed1ab_0.conda#fe2aca9a5d4cb08105aefc451ef96950
-https://conda.anaconda.org/conda-forge/linux-64/pys2index-0.1.5-py311h92ebd52_0.conda#ee757dff4cdb96bb972200c85b37f9e8
-https://conda.anaconda.org/conda-forge/noarch/pytest-html-4.1.1-pyhd8ed1ab_0.conda#4d2040212307d18392a2687772b3a96d
-https://conda.anaconda.org/conda-forge/linux-64/pywavelets-1.4.1-py311h1f0f07a_1.conda#86b71ff85f3e4c8a98b5bace6d9c4565
-https://conda.anaconda.org/conda-forge/noarch/requests-cache-1.2.0-pyhd8ed1ab_1.conda#f351ee57bb9c3e4b2f4952980461ba1d
-https://conda.anaconda.org/conda-forge/linux-64/scipy-1.13.1-py311h517d4fd_0.conda#764b0e055f59dbd7d114d32b8c6e55e6
-https://conda.anaconda.org/conda-forge/linux-64/shapely-2.0.2-py311he06c224_0.conda#c90e2469d7512f3bba893533a82d7a02
-https://conda.anaconda.org/conda-forge/noarch/snuggs-1.4.7-py_0.tar.bz2#cb83a3d6ecf73f50117635192414426a
-https://conda.anaconda.org/conda-forge/linux-64/tempest-remap-2.2.0-h13910d2_3.conda#7f10762cd62c8ad03323c4dc3ee544b1
-https://conda.anaconda.org/conda-forge/linux-64/aws-sdk-cpp-1.11.267-hbf3e495_6.conda#a6caf5a0d9ca940d95f21d40afe8f857
-https://conda.anaconda.org/conda-forge/noarch/bokeh-3.4.1-pyhd8ed1ab_0.conda#0f8e0831bbf38d83973438ce9af9af9a
-https://conda.anaconda.org/conda-forge/noarch/cads-api-client-1.0.3-pyhd8ed1ab_0.conda#46b7eb9d2f3ca144f3195dc6aa7bdf93
-https://conda.anaconda.org/conda-forge/linux-64/cf-units-3.2.0-py311h18e1886_5.conda#6cd3facab7a79de14abb1a86a2d830fa
-https://conda.anaconda.org/conda-forge/noarch/distributed-2024.5.2-pyhd8ed1ab_0.conda#2fa6807bd19e5cdc77fe1b6a42c86228
-https://conda.anaconda.org/conda-forge/linux-64/eccodes-2.32.1-h35c6de3_0.conda#09d044f9206700e021916675a16d1e4d
-https://conda.anaconda.org/conda-forge/linux-64/esmf-8.6.1-nompi_h4869a97_1.conda#308799e5ffa83b7b520a9c2c8c0319c1
-https://conda.anaconda.org/conda-forge/linux-64/gdal-3.7.2-py311h815a124_5.conda#84a14fd830b72b09ef886a23de557a16
-https://conda.anaconda.org/conda-forge/linux-64/gtk2-2.24.33-h90689f9_2.tar.bz2#957a0255ab58aaf394a91725d73ab422
-https://conda.anaconda.org/conda-forge/noarch/imagehash-4.3.1-pyhd8ed1ab_0.tar.bz2#132ad832787a2156be1f1b309835001a
-https://conda.anaconda.org/conda-forge/linux-64/librsvg-2.56.3-he3f83f7_1.conda#03bd1ddcc942867a19528877143b9852
-https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.8.4-py311ha4ca890_2.conda#0848e2084cbb57014f232f48568561af
-https://conda.anaconda.org/conda-forge/noarch/myproxyclient-2.1.1-pyhd8ed1ab_0.conda#bcdbeb2b693eba886583a907840c6421
-https://conda.anaconda.org/conda-forge/noarch/nbformat-5.10.4-pyhd8ed1ab_0.conda#0b57b5368ab7fc7cdc9e3511fa867214
-https://conda.anaconda.org/conda-forge/linux-64/netcdf4-1.6.5-nompi_py311h74118c1_102.conda#5f98c1d56c5853270c7876bb1000a631
+https://conda.anaconda.org/conda-forge/linux-64/netcdf-fortran-4.6.1-nompi_h22f9119_108.conda#0967d692b1dd33e7d809cfa355090e4b
+https://conda.anaconda.org/conda-forge/linux-64/numba-0.60.0-py312h83e6fd3_0.conda#e064ca33edf91ac117236c4b5dee207a
+https://conda.anaconda.org/conda-forge/linux-64/numcodecs-0.15.0-py312hf9745cd_0.conda#639af8e91d06907903b8686cc7d6a509
+https://conda.anaconda.org/conda-forge/linux-64/pandas-2.1.4-py312hfb8ada1_0.conda#d0745ae74c2b26571b692ddde112eebb
+https://conda.anaconda.org/conda-forge/linux-64/pango-1.56.0-h861ebed_0.conda#8779ee58be1c8b35e7af464a73674957
+https://conda.anaconda.org/conda-forge/noarch/patsy-1.0.1-pyhd8ed1ab_1.conda#ee23fabfd0a8c6b8d6f3729b47b2859d
 https://conda.anaconda.org/conda-forge/noarch/pep8-naming-0.10.0-pyh9f0ad1d_0.tar.bz2#b3c5536e4f9f58a4b16adb6f1e11732d
-https://conda.anaconda.org/conda-forge/noarch/pre-commit-3.7.1-pyha770c72_0.conda#724bc4489c1174fc8e3233b0624fa51f
+https://conda.anaconda.org/conda-forge/linux-64/postgresql-17.2-h1122569_0.conda#848402b976b31bfecb3e476ea85cb285
+https://conda.anaconda.org/conda-forge/noarch/pylint-plugin-utils-0.8.2-pyhd8ed1ab_1.conda#7bfe485a0e86e9e4b7111357765c73dc
+https://conda.anaconda.org/conda-forge/noarch/pyopenssl-25.0.0-pyhd8ed1ab_0.conda#195fbabc5cc805f2cc10cb881a19cf8b
+https://conda.anaconda.org/conda-forge/linux-64/pys2index-0.1.5-py312hfb10629_0.conda#325cc5f0e0dc36562f3de2a4dbded572
+https://conda.anaconda.org/conda-forge/noarch/pytest-html-4.1.1-pyhd8ed1ab_1.conda#010e50e74c467db278f1398a74106a04
+https://conda.anaconda.org/conda-forge/linux-64/python-stratify-0.3.0-py312hc0a28a1_3.conda#81bbcb20ea4a53b05a8cf51f31496038
+https://conda.anaconda.org/conda-forge/linux-64/pywavelets-1.8.0-py312hc0a28a1_0.conda#3f62987017ad18e9e7dadce9899de9ef
+https://conda.anaconda.org/conda-forge/linux-64/scipy-1.15.1-py312h180e4f1_0.conda#355bcf0f629159c9bd10a406cd8b6c3a
+https://conda.anaconda.org/conda-forge/linux-64/shapely-2.0.6-py312h6cab151_1.conda#5be02e05e1adaa42826cc6800ce399bc
+https://conda.anaconda.org/conda-forge/noarch/snuggs-1.4.7-pyhd8ed1ab_2.conda#9aa358575bbd4be126eaa5e0039f835c
+https://conda.anaconda.org/conda-forge/linux-64/tempest-remap-2.2.0-heeae502_5.conda#2977d5268a6050196c42f03fd2a70020
+https://conda.anaconda.org/conda-forge/noarch/urllib3-2.3.0-pyhd8ed1ab_0.conda#32674f8dbfb7b26410ed580dd3c10a29
+https://conda.anaconda.org/conda-forge/linux-64/xorg-libxaw-1.0.16-hb9d3cd8_0.conda#7c0a9bf62d573409d12ad14b362a96e5
+https://conda.anaconda.org/conda-forge/linux-64/aws-sdk-cpp-1.11.379-h5a9005d_9.conda#5dc18b385893b7991a3bbeb135ad7c3e
+https://conda.anaconda.org/conda-forge/linux-64/azure-storage-files-datalake-cpp-12.11.0-h325d260_1.conda#11d926d1f4a75a1b03d1c053ca20424b
+https://conda.anaconda.org/conda-forge/noarch/bokeh-3.6.2-pyhd8ed1ab_1.conda#976ff24762f1f991b08f7a7a41875086
+https://conda.anaconda.org/conda-forge/linux-64/cf-units-3.3.0-py312hc0a28a1_0.conda#8b5b812d4c18cb37bda7a7c8d3a6acb3
+https://conda.anaconda.org/conda-forge/noarch/distributed-2025.1.0-pyhd8ed1ab_0.conda#5ec97e707606eaa891eedb406eba507b
+https://conda.anaconda.org/conda-forge/linux-64/eccodes-2.39.0-h8bb6dbc_1.conda#87c13b15a9f3ec25cd3c9d8f2e33fe0b
+https://conda.anaconda.org/conda-forge/linux-64/esmf-8.4.2-nompi_h9e768e6_3.conda#c330e87e698bae8e7381c0315cf25dd0
+https://conda.anaconda.org/conda-forge/linux-64/gdal-3.9.2-py312h1299960_7.conda#9cf27e3f9d97ea13f250db9253a25dc8
+https://conda.anaconda.org/conda-forge/linux-64/gtk2-2.24.33-h8ee276e_7.conda#28a9681054948a7d7e96a7b8fe9b604e
+https://conda.anaconda.org/conda-forge/noarch/imagehash-4.3.1-pyhd8ed1ab_1.conda#d733874844f9808ed46a93362f89bc2d
+https://conda.anaconda.org/conda-forge/linux-64/libgdal-fits-3.9.2-h2db6552_7.conda#524e64f1aa0ebc87230109e684f392f4
+https://conda.anaconda.org/conda-forge/linux-64/libgdal-grib-3.9.2-hc3b29a1_7.conda#56a7436a66a1a4636001ce4b621a3a33
+https://conda.anaconda.org/conda-forge/linux-64/libgdal-hdf4-3.9.2-hd5ecb85_7.conda#9c8431dc0b83d5fe9c12a2c0b6861a72
+https://conda.anaconda.org/conda-forge/linux-64/libgdal-hdf5-3.9.2-h6283f77_7.conda#c8c82df3aece4e23804d178a8a8b308a
+https://conda.anaconda.org/conda-forge/linux-64/libgdal-jp2openjpeg-3.9.2-h1b2c38e_7.conda#f0f86f8cb8835bb91acb8c7fa2c350b0
+https://conda.anaconda.org/conda-forge/linux-64/libgdal-pdf-3.9.2-h600f43f_7.conda#567066db0820f4983a6741e429c651d1
+https://conda.anaconda.org/conda-forge/linux-64/libgdal-pg-3.9.2-h5e77dd0_7.conda#e86b26f53ae868565e95fde5b10753d3
+https://conda.anaconda.org/conda-forge/linux-64/libgdal-postgisraster-3.9.2-h5e77dd0_7.conda#3392965ffc4e8b7c66a532750ce0e91f
+https://conda.anaconda.org/conda-forge/linux-64/libgdal-xls-3.9.2-h03c987c_7.conda#165f12373452e8d17889e9c877431acf
+https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-storage-2.28.0-ha262f82_0.conda#9e7960f0b9ab3895ef73d92477c47dae
+https://conda.anaconda.org/conda-forge/linux-64/libparu-1.0.0-ss783_h8814b27.conda#707eb61a0c3b223702ac326c04546b50
+https://conda.anaconda.org/conda-forge/linux-64/librsvg-2.58.4-h49af25d_2.conda#b9846db0abffb09847e2cb0fec4b4db6
+https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.10.0-py312hd3ec401_0.conda#c27a17a8c54c0d35cf83bbc0de8f7f77
+https://conda.anaconda.org/conda-forge/noarch/myproxyclient-2.1.1-pyhd8ed1ab_1.conda#1efb1227abaf20324ceb7ac9c06bb86d
+https://conda.anaconda.org/conda-forge/noarch/nbformat-5.10.4-pyhd8ed1ab_1.conda#bbe1963f1e47f594070ffe87cdf612ea
+https://conda.anaconda.org/conda-forge/linux-64/netcdf4-1.7.2-nompi_py312h21d6d8e_101.conda#0cabc6745882859ec7be35433c7b8f24
+https://conda.anaconda.org/conda-forge/noarch/pre-commit-4.1.0-pyha770c72_0.conda#5353f5eb201a9415b12385e35ed1148d
 https://conda.anaconda.org/conda-forge/noarch/pylint-celery-0.3-py_1.tar.bz2#e29456a611a62d3f26105a2f9c68f759
-https://conda.anaconda.org/conda-forge/noarch/pylint-django-2.5.3-pyhd8ed1ab_0.tar.bz2#00d8853fb1f87195722ea6a582cc9b56
+https://conda.anaconda.org/conda-forge/noarch/pylint-django-2.6.1-pyhd8ed1ab_1.conda#8f854db1ce53e64891f718db849adbb0
 https://conda.anaconda.org/conda-forge/noarch/pylint-flask-0.6-py_0.tar.bz2#5a9afd3d0a61b08d59eed70fab859c1b
-https://conda.anaconda.org/conda-forge/linux-64/python-stratify-0.3.0-py311h1f0f07a_1.conda#cd36a89a048ad2bcc6d8b43f648fb1d0
-https://conda.anaconda.org/conda-forge/linux-64/r-base-4.3.1-h639d9d3_5.conda#f320a6b4aba89a080d757a365138b642
-https://conda.anaconda.org/conda-forge/linux-64/rasterio-1.3.9-py311h40fbdff_0.conda#dcee6ba4d1ac6af18827d0941b6a1b42
-https://conda.anaconda.org/conda-forge/linux-64/scikit-learn-1.5.0-py311he08f58d_1.conda#d55e4dde3b30272090f33ddd367c580b
-https://conda.anaconda.org/conda-forge/noarch/seawater-3.3.5-pyhd8ed1ab_0.conda#8e1b01f05e8f97b0fcc284f957175903
-https://conda.anaconda.org/conda-forge/noarch/sparse-0.15.4-pyhd8ed1ab_0.conda#846d12530687ba836791dd54db1f45c5
-https://conda.anaconda.org/conda-forge/linux-64/statsmodels-0.14.2-py311h18e1886_0.conda#82c29bf38b3fb66da09736106609b5fe
-https://conda.anaconda.org/conda-forge/noarch/tifffile-2024.5.22-pyhd8ed1ab_0.conda#3930cabe8ca8c8594026fa8768cae75c
-https://conda.anaconda.org/conda-forge/noarch/xarray-2024.5.0-pyhd8ed1ab_0.conda#e839fd0ae78a368c930f0b1feafa6736
-https://conda.anaconda.org/conda-forge/noarch/zarr-2.18.2-pyhd8ed1ab_0.conda#02f53038910b6fbc9d36bd5f663318e8
-https://conda.anaconda.org/conda-forge/linux-64/cartopy-0.23.0-py311h14de704_1.conda#27e5956e552c6e71f56cb1ec042617a8
-https://conda.anaconda.org/conda-forge/noarch/cdsapi-0.7.0-pyhd8ed1ab_0.conda#f7433e3bd2749b934ddf81451a45967d
-https://conda.anaconda.org/conda-forge/noarch/cf_xarray-0.9.1-pyhd8ed1ab_0.conda#9b1ddfcc35856fbd69e193b945b7d7f2
-https://conda.anaconda.org/conda-forge/noarch/cmocean-4.0.3-pyhd8ed1ab_0.conda#53df00540de0348ed1b2a62684dd912b
-https://conda.anaconda.org/conda-forge/noarch/dask-jobqueue-0.8.5-pyhd8ed1ab_0.conda#abfb434fb6654f83d740428863ec85a8
-https://conda.anaconda.org/conda-forge/noarch/esgf-pyclient-0.3.1-pyhca7485f_3.conda#1d43833138d38ad8324700ce45a7099a
-https://conda.anaconda.org/conda-forge/noarch/esmpy-8.6.1-pyhc1e730c_0.conda#25a9661177fd68bfdb4314fd658e5c3b
-https://conda.anaconda.org/conda-forge/linux-64/fiona-1.9.5-py311hbac4ec9_0.conda#786d3808394b1bdfd3f41f2e2c67279e
-https://conda.anaconda.org/conda-forge/linux-64/graphviz-8.1.0-h28d9a01_0.conda#33628e0e3de7afd2c8172f76439894cb
-https://conda.anaconda.org/conda-forge/linux-64/libarrow-15.0.2-h176673d_2_cpu.conda#c130ba0c765437749dbc37fa9de85ce5
-https://conda.anaconda.org/conda-forge/linux-64/magics-4.14.2-haee2765_1.conda#0c46d548472ee1b043c65d4ab4ad6a83
-https://conda.anaconda.org/conda-forge/noarch/nbclient-0.10.0-pyhd8ed1ab_0.conda#15b51397e0fe8ea7d7da60d83eb76ebc
-https://conda.anaconda.org/conda-forge/noarch/nc-time-axis-1.4.1-pyhd8ed1ab_0.tar.bz2#281b58948bf60a2582de9e548bcc5369
-https://conda.anaconda.org/conda-forge/linux-64/ncl-6.6.2-he3b17a9_50.conda#a37fcb5a2da31cfebe6734b0fda20bd5
-https://conda.anaconda.org/conda-forge/linux-64/nco-5.2.4-he646072_0.conda#ccf011f3b8edd84d938edd0848ab56d4
-https://conda.anaconda.org/conda-forge/noarch/prospector-1.10.3-pyhd8ed1ab_0.conda#f551d4d859a1d70c6abff8310a655481
-https://conda.anaconda.org/conda-forge/linux-64/psyplot-1.5.0-py311h38be061_1.conda#e172dce6d5f3dbf0c8dfc537c8146be3
-https://conda.anaconda.org/conda-forge/noarch/py-xgboost-2.0.3-cuda120_pyh3ef1b53_4.conda#101b6519015db5451632163bc6fed36a
-https://conda.anaconda.org/conda-forge/noarch/r-abind-1.4_5-r43hc72bb7e_1005.conda#dd7719a32c342948c6fbafb13bd2ee03
-https://conda.anaconda.org/conda-forge/linux-64/r-backports-1.5.0-r43hb1dbf0f_0.conda#9f3376d466dc88a5a8f2b8cf3e052772
-https://conda.anaconda.org/conda-forge/noarch/r-bigmemory.sri-0.1.8-r43hc72bb7e_0.conda#be78955142329022e2927b57b11e98fa
-https://conda.anaconda.org/conda-forge/linux-64/r-cli-3.6.2-r43ha503ecb_0.conda#7a325f68b1354c1a5261688b5e54d008
-https://conda.anaconda.org/conda-forge/noarch/r-codetools-0.2_20-r43hc72bb7e_0.conda#6fd216ce15e662d68b5fa1c7e6c1a75d
-https://conda.anaconda.org/conda-forge/linux-64/r-colorspace-2.1_0-r43h57805ef_1.conda#22117ffc4b380dba0732166eec62d8cb
-https://conda.anaconda.org/conda-forge/linux-64/r-contfrac-1.1_12-r43h57805ef_1004.conda#b4dca65264e43e9a3ef2e2f39f687349
-https://conda.anaconda.org/conda-forge/noarch/r-cpp11-0.4.7-r43hc72bb7e_0.conda#c9ec548d90ba0c4de83a14dab499d015
-https://conda.anaconda.org/conda-forge/noarch/r-crayon-1.5.2-r43hc72bb7e_2.conda#f13cc355add93ff41ef3d7aeefee247a
-https://conda.anaconda.org/conda-forge/noarch/r-dbi-1.2.3-r43hc72bb7e_0.conda#b5c79fe477c107fba33e9700d1d7e2a1
-https://conda.anaconda.org/conda-forge/linux-64/r-desolve-1.40-r43hd9ac46e_0.conda#a85bc66bb89726a8fb5310f2def5d8ac
-https://conda.anaconda.org/conda-forge/linux-64/r-digest-0.6.35-r43ha503ecb_0.conda#d28a7db9b060ceaf683f0b036780b962
-https://conda.anaconda.org/conda-forge/noarch/r-docopt-0.7.1-r43hc72bb7e_3.conda#d1a51c4e48b1184be5bb47ff13298472
-https://conda.anaconda.org/conda-forge/linux-64/r-dotcall64-1.1_1-r43h61816a4_0.conda#54040b9373933275c211ccbb1d5de023
-https://conda.anaconda.org/conda-forge/noarch/r-evaluate-0.24.0-r43hc72bb7e_0.conda#1c3aa7ca297fae40ea8d6a8fe78317f0
-https://conda.anaconda.org/conda-forge/linux-64/r-fansi-1.0.6-r43h57805ef_0.conda#c94b12c76504df3548707218409b678f
-https://conda.anaconda.org/conda-forge/linux-64/r-farver-2.1.2-r43ha18555a_0.conda#b56c791245428e7b08601e3bca812b0a
-https://conda.anaconda.org/conda-forge/noarch/r-functional-0.6-r43ha770c72_1004.conda#2a0d8d39d328f570af24f6a2d10a47ed
-https://conda.anaconda.org/conda-forge/noarch/r-generics-0.1.3-r43hc72bb7e_2.conda#bf424d7238a8a02bd3970346112de34f
-https://conda.anaconda.org/conda-forge/noarch/r-geomapdata-2.0_2-r43hc72bb7e_0.conda#a865cc1e66d1370e8545baa03d2901fe
-https://conda.anaconda.org/conda-forge/linux-64/r-git2r-0.33.0-r43hbae1c7c_0.conda#74577e5aad177b12c066b268a7cd864b
-https://conda.anaconda.org/conda-forge/linux-64/r-glue-1.7.0-r43h57805ef_0.conda#20e2cb7c0d27bccf8fe21ec5c3897751
-https://conda.anaconda.org/conda-forge/linux-64/r-goftest-1.2_3-r43h57805ef_2.conda#14d5dcb272fd5df57248893446a4948f
-https://conda.anaconda.org/conda-forge/linux-64/r-isoband-0.2.7-r43ha503ecb_2.conda#6db3b5f97166c8b0fd8c995fbe32c89d
-https://conda.anaconda.org/conda-forge/noarch/r-iterators-1.0.14-r43hc72bb7e_2.conda#5f0c56c25fafdfde7acaf0f332f24195
-https://conda.anaconda.org/conda-forge/linux-64/r-jsonlite-1.8.8-r43h57805ef_0.conda#fb01953d78a87de6211b1ebccafed273
-https://conda.anaconda.org/conda-forge/linux-64/r-kernsmooth-2.23_24-r43hc2011d3_0.conda#9fbf29bd0ded7844a2a493a6190ef9d9
-https://conda.anaconda.org/conda-forge/noarch/r-labeling-0.4.3-r43hc72bb7e_0.conda#8d5f7d07433437c48c1c497ec8c77aec
-https://conda.anaconda.org/conda-forge/linux-64/r-lattice-0.22_6-r43h57805ef_0.conda#748c702a5c6c89f9794d148bdcbbdfbc
-https://conda.anaconda.org/conda-forge/linux-64/r-lazyeval-0.2.2-r43h57805ef_4.conda#412e7e2defa8af842c9f4d7844d7b949
-https://conda.anaconda.org/conda-forge/linux-64/r-lmom-3.0-r43h61816a4_0.conda#e2ee8254c7baa313122de7a08330c296
-https://conda.anaconda.org/conda-forge/noarch/r-logging-0.10_108-r43ha770c72_4.conda#e32f157d485d2fbd076abf006156d757
-https://conda.anaconda.org/conda-forge/linux-64/r-magrittr-2.0.3-r43h57805ef_2.conda#7b04cb810f47eb12efc2e9be5f8ca1f1
-https://conda.anaconda.org/conda-forge/linux-64/r-maps-3.4.2-r43h57805ef_0.conda#a8a9f2a4fc6692046eebb1d89860b94a
-https://conda.anaconda.org/conda-forge/linux-64/r-mass-7.3_60-r43h57805ef_1.conda#be049620c8ceefdb69a24db0c1919c85
-https://conda.anaconda.org/conda-forge/linux-64/r-mba-0.1_0-r43ha503ecb_1.conda#71752b2b57c41eb2e03653bea2a44d94
-https://conda.anaconda.org/conda-forge/noarch/r-nbclust-3.0.1-r43hc72bb7e_2.conda#1a7497d490b8f19d192c26040b6b35e5
-https://conda.anaconda.org/conda-forge/linux-64/r-ncdf4-1.22-r43h5647f33_0.conda#8ff19fc3195d7742c704a957c55cc87a
-https://conda.anaconda.org/conda-forge/linux-64/r-pcict-0.5_4.4-r43h57805ef_1.conda#238a8a2e93424fa8cb9942925fc515a2
-https://conda.anaconda.org/conda-forge/noarch/r-pkgconfig-2.0.3-r43hc72bb7e_3.conda#8978eef741fffaa48be070693a17f42f
-https://conda.anaconda.org/conda-forge/linux-64/r-proxy-0.4_27-r43h57805ef_2.conda#742133e20563c7321e9d0cbd7e7102c7
-https://conda.anaconda.org/conda-forge/linux-64/r-ps-1.7.6-r43h57805ef_0.conda#48f9e73215aa6de4545ec3ec36e5e534
-https://conda.anaconda.org/conda-forge/noarch/r-r.methodss3-1.8.2-r43hc72bb7e_2.conda#e6976d24b97b9cdcb9a74ac55495f404
-https://conda.anaconda.org/conda-forge/noarch/r-r6-2.5.1-r43hc72bb7e_2.conda#79f167972554dc6b65aa099cd85c22a1
-https://conda.anaconda.org/conda-forge/noarch/r-rcolorbrewer-1.1_3-r43h785f33e_2.conda#d998f26d6a55b83dab1c25b583e1463a
-https://conda.anaconda.org/conda-forge/linux-64/r-rcpp-1.0.12-r43h7df8631_0.conda#a8aac8be0868617b28f27484ea548d6c
-https://conda.anaconda.org/conda-forge/noarch/r-remotes-2.5.0-r43hc72bb7e_0.conda#a3f5e53761f368633e54c76917940eb6
-https://conda.anaconda.org/conda-forge/linux-64/r-rlang-1.1.4-r43ha18555a_0.conda#74c4cb6644843dccabc350cba4b56ec6
-https://conda.anaconda.org/conda-forge/noarch/r-rpmg-2.2_7-r43hc72bb7e_0.conda#9fceb6466208ae034b8847cdd27bf6db
-https://conda.anaconda.org/conda-forge/noarch/r-rprojroot-2.0.4-r43hc72bb7e_0.conda#279a906f3d37ee80ee94950754eb9ded
-https://conda.anaconda.org/conda-forge/noarch/r-snow-0.4_4-r43hc72bb7e_2.conda#ceedb5263134a3100813b84c32747c78
-https://conda.anaconda.org/conda-forge/linux-64/r-udunits2-0.13.2.1-r43h57805ef_3.conda#f80ce6fa5f07c33a4fac0bcfc151b161
-https://conda.anaconda.org/conda-forge/linux-64/r-utf8-1.2.4-r43h57805ef_0.conda#d228498392dce22d3fe6266554498493
-https://conda.anaconda.org/conda-forge/linux-64/r-uuid-1.2_0-r43h57805ef_0.conda#88a02431c8ab8be38eed2b01f9d44ab4
-https://conda.anaconda.org/conda-forge/noarch/r-viridislite-0.4.2-r43hc72bb7e_1.conda#307f6c8262afed91d2999568e2268ed6
-https://conda.anaconda.org/conda-forge/noarch/r-withr-3.0.0-r43hc72bb7e_0.conda#7380a50d9daae38b40777f7630b8c3ba
-https://conda.anaconda.org/conda-forge/linux-64/r-xfun-0.44-r43ha18555a_0.conda#8fbbfbc83f3f8572c8d5bb066a1eb8a4
-https://conda.anaconda.org/conda-forge/noarch/r-xmlparsedata-1.0.5-r43hc72bb7e_2.conda#a973f1f52947adda7b4cd1ea3c2f9941
-https://conda.anaconda.org/conda-forge/linux-64/r-yaml-2.3.8-r43h57805ef_0.conda#41db1ee9d0073339e681379b92e7dfb6
-https://conda.anaconda.org/conda-forge/linux-64/scikit-image-0.23.2-py311hd037940_0.conda#a27a1dc2d447c48b3a38125c70d2dc89
-https://conda.anaconda.org/conda-forge/noarch/seaborn-base-0.13.2-pyhd8ed1ab_2.conda#b713b116feaf98acdba93ad4d7f90ca1
-https://conda.anaconda.org/conda-forge/linux-64/cdo-2.3.0-h24bcfa3_0.conda#238311a432a8e49943d3348e279af714
-https://conda.anaconda.org/conda-forge/linux-64/imagemagick-7.1.1_19-pl5321h7e74ff9_0.conda#a4a0ce7caba20cae61aac9aeacbd76c2
-https://conda.anaconda.org/conda-forge/noarch/iris-3.9.0-pyha770c72_0.conda#efaf150eb009f04efa58f1401c767192
-https://conda.anaconda.org/conda-forge/linux-64/libarrow-acero-15.0.2-hac33072_2_cpu.conda#12951edff85582aedcd2db0b79393102
-https://conda.anaconda.org/conda-forge/linux-64/libarrow-flight-15.0.2-hd42f311_2_cpu.conda#dcc3a1e12157bbbbae96029d9d34fd0e
-https://conda.anaconda.org/conda-forge/linux-64/libarrow-gandiva-15.0.2-hd4ab825_2_cpu.conda#a4aa5cd69e0d1959f7c965437e7ac93d
-https://conda.anaconda.org/conda-forge/linux-64/libparquet-15.0.2-h6a7eafb_2_cpu.conda#b06caaa4ef20db071dc832323701e5e3
-https://conda.anaconda.org/conda-forge/noarch/lime-0.2.0.1-pyhd8ed1ab_1.tar.bz2#789ce01416721a5533fb74aa4361fd13
+https://conda.anaconda.org/conda-forge/linux-64/r-base-4.2.3-h32f4cee_16.conda#feee98a221344be7a447b80b410df867
+https://conda.anaconda.org/conda-forge/noarch/requests-2.32.3-pyhd8ed1ab_1.conda#a9b9368f3701a417eac9edbcae7cb737
+https://conda.anaconda.org/conda-forge/linux-64/scikit-learn-1.6.1-py312h7a48858_0.conda#102727f71df02a51e9e173f2e6f87d57
+https://conda.anaconda.org/conda-forge/noarch/seawater-3.3.5-pyhd8ed1ab_1.conda#3917cd49c30278aad07536ee77084eb0
+https://conda.anaconda.org/conda-forge/noarch/sparse-0.15.5-pyh72ffeb9_0.conda#e640762c293807bbd75c003e18d8ff0c
+https://conda.anaconda.org/conda-forge/linux-64/statsmodels-0.14.4-py312hc0a28a1_0.conda#97dc960f3d9911964d73c2cf240baea5
+https://conda.anaconda.org/conda-forge/noarch/tifffile-2024.12.12-pyhd8ed1ab_0.conda#59d646aa704b6024cb965be43493ed45
+https://conda.anaconda.org/conda-forge/noarch/xarray-2025.1.1-pyhd8ed1ab_0.conda#81db80ba986122da460800a67bf8ac7f
+https://conda.anaconda.org/conda-forge/noarch/zarr-3.0.1-pyhd8ed1ab_0.conda#66820cb25ad3c48f9ee46a48289b3454
+https://conda.anaconda.org/conda-forge/linux-64/cartopy-0.24.0-py312hf9745cd_0.conda#ea213e31805199cb7d0da457b879ceed
+https://conda.anaconda.org/conda-forge/noarch/cf_xarray-0.10.0-pyhd8ed1ab_2.conda#ed15dcf944706ae6ea54968dfa4a06a5
+https://conda.anaconda.org/conda-forge/noarch/chart-studio-1.1.0-pyhd8ed1ab_1.conda#a8dc13f40343dd9706cab9b9cbc49099
+https://conda.anaconda.org/conda-forge/noarch/cmocean-4.0.3-pyhd8ed1ab_1.conda#dd71e4ec2fbffe38c0359976505f816e
+https://conda.anaconda.org/conda-forge/noarch/dask-jobqueue-0.9.0-pyhd8ed1ab_0.conda#a201de7d36907f2355426e019168d337
+https://conda.anaconda.org/conda-forge/noarch/esmpy-8.4.2-pyhc1e730c_4.conda#ddcf387719b2e44df0cc4dd467643951
+https://conda.anaconda.org/conda-forge/linux-64/graphviz-12.0.0-hba01fac_0.conda#953e31ea00d46beb7e64a79fc291ec44
+https://conda.anaconda.org/conda-forge/linux-64/libarrow-17.0.0-h8d2e343_13_cpu.conda#dc379f362829d5df5ce6722565110029
+https://conda.anaconda.org/conda-forge/linux-64/libgdal-kea-3.9.2-h1df15e4_7.conda#c693e703649051ee9db0fabd4fcd0483
+https://conda.anaconda.org/conda-forge/linux-64/libgdal-netcdf-3.9.2-hf2d2f32_7.conda#4015ef020928219acc0b5c9edbce8d30
+https://conda.anaconda.org/conda-forge/linux-64/magics-4.15.4-h24e9adf_1.conda#9731bb0d2a3917cab718fd7c90dea857
+https://conda.anaconda.org/conda-forge/noarch/multiurl-0.3.3-pyhd8ed1ab_1.conda#b7ad70c100bced5b6bc2d479c473d2a7
+https://conda.anaconda.org/conda-forge/noarch/nbclient-0.10.2-pyhd8ed1ab_0.conda#6bb0d77277061742744176ab555b723c
+https://conda.anaconda.org/conda-forge/noarch/nc-time-axis-1.4.1-pyhd8ed1ab_1.conda#9a2be7d0089f5934b550933ca0d9fe85
+https://conda.anaconda.org/conda-forge/linux-64/nco-5.3.1-hf7c1f58_0.conda#3f7887fed8b12d3e29dd15eb82cbc5b0
+https://conda.anaconda.org/conda-forge/noarch/pooch-1.8.2-pyhd8ed1ab_1.conda#b3e783e8e8ed7577cf0b6dee37d1fbac
+https://conda.anaconda.org/conda-forge/noarch/prospector-1.13.3-pyhd8ed1ab_1.conda#0d6d32575a276ff0a1e612220d435b4a
+https://conda.anaconda.org/conda-forge/linux-64/psyplot-1.5.1-py312h7900ff3_1.conda#f110e71421e5c86e50232cc027c6d85c
+https://conda.anaconda.org/conda-forge/noarch/py-xgboost-2.1.3-cuda118_pyh0cc23a4_0.conda#44e85f4f035efbdb185400a3c35d11c8
+https://conda.anaconda.org/conda-forge/noarch/pyroma-4.2-pyhd8ed1ab_0.conda#fe2aca9a5d4cb08105aefc451ef96950
+https://conda.anaconda.org/conda-forge/linux-64/python-eccodes-2.37.0-py312hc0a28a1_0.conda#476b0357e207e10d2b7b13ed82156e6d
+https://conda.anaconda.org/conda-forge/noarch/r-abind-1.4_5-r42hc72bb7e_1005.conda#f2744985b083b1bbffd4df19437cf1e8
+https://conda.anaconda.org/conda-forge/linux-64/r-backports-1.5.0-r42hb1dbf0f_0.conda#d879e1fbd80113312364a5db3682c789
+https://conda.anaconda.org/conda-forge/noarch/r-bigmemory.sri-0.1.8-r42hc72bb7e_0.conda#383f36b5a0b7dd7c467aa1b6b5fe7307
+https://conda.anaconda.org/conda-forge/linux-64/r-cli-3.6.3-r42ha18555a_0.conda#93fc8055b8aee751e201604a02d7d06f
+https://conda.anaconda.org/conda-forge/noarch/r-codetools-0.2_20-r42hc72bb7e_0.conda#a9e9276ab95d053b9db56159cfeda2c9
+https://conda.anaconda.org/conda-forge/linux-64/r-colorspace-2.1_0-r42h57805ef_1.conda#68ec691b072953b496ca1a4d83b2bc3d
+https://conda.anaconda.org/conda-forge/linux-64/r-contfrac-1.1_12-r42h57805ef_1004.conda#bc308888aa4b4fb4e37a7a17fdc911c9
+https://conda.anaconda.org/conda-forge/noarch/r-cpp11-0.4.7-r42hc72bb7e_0.conda#941d7bcf2b94a682419ea1fbf6789d1f
+https://conda.anaconda.org/conda-forge/noarch/r-crayon-1.5.3-r42hc72bb7e_0.conda#4a74a6114bbea1ad8d488e99b83df3da
+https://conda.anaconda.org/conda-forge/linux-64/r-desolve-1.40-r42hd9ac46e_0.conda#7232f8b5707fc9739cb2f8fdc5b4b64d
+https://conda.anaconda.org/conda-forge/linux-64/r-digest-0.6.36-r42ha18555a_0.conda#332551d9a37018826d528cf16701bd2b
+https://conda.anaconda.org/conda-forge/noarch/r-docopt-0.7.1-r42hc72bb7e_3.conda#99be998b67c40ef6eb1a5af90e307c1d
+https://conda.anaconda.org/conda-forge/linux-64/r-dotcall64-1.1_1-r42h61816a4_0.conda#d83332ff8d9912151d9a4b4972fd1da0
+https://conda.anaconda.org/conda-forge/noarch/r-evaluate-0.24.0-r42hc72bb7e_0.conda#c2a50c427d0febc367122a875239e771
+https://conda.anaconda.org/conda-forge/linux-64/r-fansi-1.0.6-r42h57805ef_0.conda#c12524190662098e2e8a245a3d1bc7dc
+https://conda.anaconda.org/conda-forge/linux-64/r-farver-2.1.2-r42ha18555a_0.conda#475d7bcc6de3c5851b112675eb55f497
+https://conda.anaconda.org/conda-forge/noarch/r-functional-0.6-r42ha770c72_1004.conda#9e27c34589b883accd340d651bdeaa02
+https://conda.anaconda.org/conda-forge/noarch/r-generics-0.1.3-r42hc72bb7e_2.conda#c492355d73e184353c82b62f5087a601
+https://conda.anaconda.org/conda-forge/noarch/r-geomapdata-2.0_2-r42hc72bb7e_0.conda#799a671bad7a89ac1d9da5cb98f75367
+https://conda.anaconda.org/conda-forge/linux-64/r-git2r-0.30.1-r42hf72769b_1.tar.bz2#f64adeea481006f0cb22bdcc066680df
+https://conda.anaconda.org/conda-forge/linux-64/r-glue-1.7.0-r42h57805ef_0.conda#eab803a28d66337ae3732b04c5f5604f
+https://conda.anaconda.org/conda-forge/linux-64/r-goftest-1.2_3-r42h57805ef_2.conda#4210e40893bbac7533714429ac4d0fe9
+https://conda.anaconda.org/conda-forge/linux-64/r-isoband-0.2.7-r42ha503ecb_2.conda#44979df954a15195470f336cd18b5eb6
+https://conda.anaconda.org/conda-forge/noarch/r-iterators-1.0.14-r42hc72bb7e_2.conda#616ab7b008326d3d76d59ba35b3fb592
+https://conda.anaconda.org/conda-forge/linux-64/r-jsonlite-1.8.8-r42h57805ef_0.conda#d0b27ba963de139270a7b53f897afdf6
+https://conda.anaconda.org/conda-forge/noarch/r-labeling-0.4.3-r42hc72bb7e_0.conda#b9b940011dd81d8b60859fcd0d9775f4
+https://conda.anaconda.org/conda-forge/linux-64/r-lattice-0.22_6-r42h57805ef_0.conda#93cee3961cc5277443a3e437f6991010
+https://conda.anaconda.org/conda-forge/linux-64/r-lazyeval-0.2.2-r42h57805ef_4.conda#109112b1c26d932414daa139a45d3a69
+https://conda.anaconda.org/conda-forge/linux-64/r-lmom-3.0-r42h61816a4_0.conda#0cffcf07f72a3be278b236e3b2f451c9
+https://conda.anaconda.org/conda-forge/noarch/r-logging-0.10_108-r42ha770c72_4.conda#d9980750f18496909aa8327037a43f8b
+https://conda.anaconda.org/conda-forge/linux-64/r-magrittr-2.0.3-r42h57805ef_2.conda#ea3b13247660dd534a745a26f8d02365
+https://conda.anaconda.org/conda-forge/linux-64/r-maps-3.4.2-r42h57805ef_0.conda#a0367e4720045d5d17cb841a415ada1e
+https://conda.anaconda.org/conda-forge/linux-64/r-mass-7.3_60.0.1-r42h57805ef_0.conda#0427fa6c4da6a4b2e43d8dfd022e933b
+https://conda.anaconda.org/conda-forge/linux-64/r-mba-0.1_0-r42ha503ecb_1.conda#ab0ffee07ebd556b0e0119017439218a
+https://conda.anaconda.org/conda-forge/noarch/r-nbclust-3.0.1-r42hc72bb7e_2.conda#fffd3a5ced3a6949fe7a20af1ff4b2c6
+https://conda.anaconda.org/conda-forge/linux-64/r-ncdf4-1.22-r42h5647f33_0.conda#d23e6cd8fe41079eb1421b6a6d1f1c67
+https://conda.anaconda.org/conda-forge/linux-64/r-pcict-0.5_4.4-r42h57805ef_1.conda#6e5770da5c174a2617096cbc2b8d96f4
+https://conda.anaconda.org/conda-forge/noarch/r-pkgconfig-2.0.3-r42hc72bb7e_3.conda#469b66f84a5d234689b423c9821b188c
+https://conda.anaconda.org/conda-forge/linux-64/r-ps-1.7.6-r42h57805ef_0.conda#3a592c79e0fade3a0c3574696fa143a3
+https://conda.anaconda.org/conda-forge/noarch/r-r.methodss3-1.8.2-r42hc72bb7e_2.conda#305fe9f97f7598d9722c76d6be7bf794
+https://conda.anaconda.org/conda-forge/noarch/r-r6-2.5.1-r42hc72bb7e_2.conda#1473a12b55128f8ac776ae5595a4d0cb
+https://conda.anaconda.org/conda-forge/noarch/r-rcolorbrewer-1.1_3-r42h785f33e_2.conda#b7b475c73493f70cbbb9d7213b94aed1
+https://conda.anaconda.org/conda-forge/linux-64/r-rcpp-1.0.12-r42h7df8631_0.conda#096448d673973c0e45b9d803da251971
+https://conda.anaconda.org/conda-forge/noarch/r-remotes-2.5.0-r42hc72bb7e_0.conda#c595028f27588c6ff242fcb0dab79363
+https://conda.anaconda.org/conda-forge/linux-64/r-rlang-1.1.4-r42ha18555a_0.conda#ab6364a17b32268b82c46f09695a9cc9
+https://conda.anaconda.org/conda-forge/noarch/r-rpmg-2.2_7-r42hc72bb7e_0.conda#9e34ca8c73b895781e13b1d399105f35
+https://conda.anaconda.org/conda-forge/noarch/r-rprojroot-2.0.4-r42hc72bb7e_0.conda#c2bb0aa15018f8d9a4bc7b9e459dc94f
+https://conda.anaconda.org/conda-forge/noarch/r-snow-0.4_4-r42hc72bb7e_2.conda#97cc50b630391cbc89ea70425ebb6ade
+https://conda.anaconda.org/conda-forge/linux-64/r-udunits2-0.13.2.1-r42h57805ef_3.conda#56d551dc25582293fed533026356a79e
+https://conda.anaconda.org/conda-forge/linux-64/r-utf8-1.2.4-r42h57805ef_0.conda#1da2e3bcbf75c6ddc3466941d88ff93f
+https://conda.anaconda.org/conda-forge/linux-64/r-uuid-1.2_0-r42h57805ef_0.conda#f7585e68687b274880bbd68f34c0524d
+https://conda.anaconda.org/conda-forge/noarch/r-viridislite-0.4.2-r42hc72bb7e_1.conda#e7a6483f639fb958747100bd17550ed6
+https://conda.anaconda.org/conda-forge/noarch/r-withr-3.0.0-r42hc72bb7e_0.conda#972eaab581c25fff9ea6986aa6ab281a
+https://conda.anaconda.org/conda-forge/linux-64/r-xfun-0.45-r42ha18555a_0.conda#9e13c392bfcee4a261e4b513d6d862e7
+https://conda.anaconda.org/conda-forge/noarch/r-xmlparsedata-1.0.5-r42hc72bb7e_2.conda#2f3614450b54f222c1eff786ec2a45ec
+https://conda.anaconda.org/conda-forge/linux-64/r-yaml-2.3.8-r42h57805ef_0.conda#97f60a93ca12f4fdd5f44049dcee4345
+https://conda.anaconda.org/conda-forge/noarch/requests-cache-1.2.1-pyhd8ed1ab_1.conda#584e6aab3a5cffde537c575ad6a673ff
+https://conda.anaconda.org/conda-forge/linux-64/scikit-image-0.25.0-py312hf9745cd_1.conda#7494c06b01edc857a4f7910946c408ab
+https://conda.anaconda.org/conda-forge/noarch/seaborn-base-0.13.2-pyhd8ed1ab_3.conda#fd96da444e81f9e6fcaac38590f3dd42
+https://conda.anaconda.org/conda-forge/linux-64/suitesparse-7.8.3-ss783_h36e971b.conda#64c7f927c297e2e3b94c4330e3d9db18
+https://conda.anaconda.org/conda-forge/linux-64/tiledb-2.26.0-h86fa3b2_0.conda#061175d9d4c046a1cf8bffe95a359fab
+https://conda.anaconda.org/conda-forge/linux-64/cdo-2.4.1-h9fe33b1_1.conda#a326dab3d2a1a8e32c2a6f792fac3161
+https://conda.anaconda.org/conda-forge/noarch/cfgrib-0.9.15.0-pyhd8ed1ab_0.conda#211303621409d703235598be8c378b3c
+https://conda.anaconda.org/conda-forge/noarch/datapi-0.1.2-pyhd8ed1ab_0.conda#9f2f0d8106777cdcd4f2eac665065091
+https://conda.anaconda.org/conda-forge/noarch/esgf-pyclient-0.3.1-pyhd8ed1ab_5.conda#6779887899e0b0b6fb316253eb0f5c64
+https://conda.anaconda.org/conda-forge/linux-64/imagemagick-7.1.1_43-imagemagick_hf2058d9_2.conda#d656f8b4405fb5f1821f3ebba98e5e10
+https://conda.anaconda.org/conda-forge/noarch/iris-3.11.0-pyha770c72_0.conda#a5e36260789ce92074c3736533ecdd61
+https://conda.anaconda.org/conda-forge/linux-64/julia-1.10.4-hf18f99d_1.conda#cc0ef9c191bab16211970a29b6787d69
+https://conda.anaconda.org/conda-forge/linux-64/libarrow-acero-17.0.0-h5888daf_13_cpu.conda#b654d072b8d5da807495e49b28a0b884
+https://conda.anaconda.org/conda-forge/linux-64/libgdal-tiledb-3.9.2-h4a3bace_2.conda#c3fac34ecba2fcf9d5d31a03b975d5a1
+https://conda.anaconda.org/conda-forge/linux-64/libparquet-17.0.0-h39682fd_13_cpu.conda#49c60a8dc089d8127b9368e9eb6c1a77
+https://conda.anaconda.org/conda-forge/noarch/lime-0.2.0.1-pyhd8ed1ab_2.conda#7807e1b976aaab2b457002abffdf5e1c
 https://conda.anaconda.org/conda-forge/noarch/mapgenerator-1.0.7-pyhd8ed1ab_0.conda#d18db96ef2a920b0ecefe30282b0aecf
-https://conda.anaconda.org/conda-forge/noarch/nbconvert-core-7.16.4-pyhd8ed1ab_0.conda#43d9cd74e3950ab09cbddf36f1706b9f
-https://conda.anaconda.org/conda-forge/linux-64/psy-simple-1.5.0-py311h38be061_1.conda#0c795bac4990aec7adabb34caa9d3873
-https://conda.anaconda.org/conda-forge/noarch/py-cordex-0.7.1-pyhd8ed1ab_0.conda#447d61cb83a9c3a3916fba1bc3cff0bf
-https://conda.anaconda.org/conda-forge/linux-64/pydot-2.0.0-py311h38be061_0.conda#cdfd23a54a18f3c8d5320d7717f4ed52
-https://conda.anaconda.org/conda-forge/linux-64/r-bigmemory-4.6.4-r43ha503ecb_0.conda#8737c1d2787e754db2e88ef1610c737a
-https://conda.anaconda.org/conda-forge/linux-64/r-checkmate-2.3.1-r43h57805ef_0.conda#b3e470863d9e21349b1a6c075b681901
-https://conda.anaconda.org/conda-forge/linux-64/r-class-7.3_22-r43h57805ef_1.conda#edcd749bf55569ea5c2f419c10e6ee59
-https://conda.anaconda.org/conda-forge/linux-64/r-climdex.pcic-1.1_11-r43ha503ecb_2.conda#59f6775bc30ccc52a2ec7aa8f353dd9b
-https://conda.anaconda.org/conda-forge/noarch/r-desc-1.4.3-r43hc72bb7e_0.conda#3bd51d5d0cc81f943e93eabb982b318b
-https://conda.anaconda.org/conda-forge/linux-64/r-ellipsis-0.3.2-r43h57805ef_2.conda#10c5892991072cfa3a61489d2ac6ad66
-https://conda.anaconda.org/conda-forge/noarch/r-elliptic-1.4_0-r43hc72bb7e_4.conda#16e4352829cf433be8884e27e1b8e4a1
-https://conda.anaconda.org/conda-forge/noarch/r-foreach-1.5.2-r43hc72bb7e_2.conda#cf8d448c534caecc11d3999a041d4769
-https://conda.anaconda.org/conda-forge/noarch/r-highr-0.11-r43hc72bb7e_0.conda#ecd3b95a01caa7bdcf63714427b5b88c
-https://conda.anaconda.org/conda-forge/noarch/r-lifecycle-1.0.4-r43hc72bb7e_0.conda#291d2fb68ea1b181644b40d6743ae000
-https://conda.anaconda.org/conda-forge/linux-64/r-mapproj-1.2.11-r43h57805ef_1.conda#cb9973326cda79323ab1127d0006c3fd
-https://conda.anaconda.org/conda-forge/linux-64/r-matrix-1.6_5-r43h316c678_0.conda#8146ef1b3c54e1a484822085d39357b0
-https://conda.anaconda.org/conda-forge/noarch/r-munsell-0.5.1-r43hc72bb7e_0.conda#2f189c59532c130f4b83e4a50626c08b
-https://conda.anaconda.org/conda-forge/noarch/r-ncdf4.helpers-0.3_6-r43hc72bb7e_2.conda#23938872f695db31c28a0207c51c0f09
-https://conda.anaconda.org/conda-forge/linux-64/r-nlme-3.1_165-r43hbcb9c34_0.conda#261b441154346a173a7efd53925729dc
-https://conda.anaconda.org/conda-forge/linux-64/r-plyr-1.8.9-r43ha503ecb_0.conda#47bf330a88b5a59bc6e110624f1b8e7a
-https://conda.anaconda.org/conda-forge/linux-64/r-processx-3.8.4-r43h57805ef_0.conda#660d4e9ff8beef35dbda645484d17a25
-https://conda.anaconda.org/conda-forge/noarch/r-r.oo-1.26.0-r43hc72bb7e_0.conda#e467767d55b77c81b3167f00f2ecac65
-https://conda.anaconda.org/conda-forge/linux-64/r-rcpparmadillo-0.12.8.4.0-r43h58a4165_0.conda#57e177923b3c89382db34962ff5320e2
-https://conda.anaconda.org/conda-forge/noarch/r-rex-1.2.1-r43hc72bb7e_2.conda#0a2c2b272177122311ca46834b8ca6ab
-https://conda.anaconda.org/conda-forge/linux-64/r-sp-2.1_4-r43hb1dbf0f_0.conda#01d55e9a8ae9cb6d222b0154316346a4
-https://conda.anaconda.org/conda-forge/linux-64/r-spam-2.10_0-r43h9f9f741_0.conda#b1f50ff35422ee8bc5e3d6c0565b3e62
-https://conda.anaconda.org/conda-forge/linux-64/r-timechange-0.3.0-r43ha503ecb_0.conda#9502bf10997943c1f3d6abeced14b792
-https://conda.anaconda.org/conda-forge/linux-64/r-units-0.8_5-r43ha503ecb_0.conda#001680df8c5745b7dc3b799a9e1ac2b0
-https://conda.anaconda.org/conda-forge/linux-64/r-wk-0.9.1-r43ha503ecb_0.conda#4a0dad8564d2b623e91d9d9f38e5abb5
-https://conda.anaconda.org/conda-forge/linux-64/r-xml2-1.3.6-r43hbfba7a4_1.conda#545f26a602ff99102e7fd70f425ead32
-https://conda.anaconda.org/conda-forge/linux-64/r-zoo-1.8_12-r43h57805ef_1.conda#5e4c7ad3824c359f05601463ec7ba9c5
-https://conda.anaconda.org/conda-forge/noarch/seaborn-0.13.2-hd8ed1ab_2.conda#a79d8797f62715255308d92d3a91ef2e
-https://conda.anaconda.org/conda-forge/noarch/xesmf-0.8.5-pyhd8ed1ab_0.conda#0f74ff434e03fa89315ec45ec1bc4284
-https://conda.anaconda.org/conda-forge/noarch/xgboost-2.0.3-cuda120_pyh68bd8d9_4.conda#aaaadc3a408067943ebc10299393a7c3
-https://conda.anaconda.org/conda-forge/linux-64/libarrow-dataset-15.0.2-hac33072_2_cpu.conda#48c711b4e07664ec7b245a9664be60a1
-https://conda.anaconda.org/conda-forge/linux-64/libarrow-flight-sql-15.0.2-h9241762_2_cpu.conda#97e46f0f20157e19487ca3e65100247a
-https://conda.anaconda.org/conda-forge/noarch/nbconvert-pandoc-7.16.4-hd8ed1ab_0.conda#391934bd1a79990c23df1d1809ddc821
+https://conda.anaconda.org/conda-forge/noarch/nbconvert-core-7.16.5-pyhd8ed1ab_1.conda#dd50a122c5b9782b1e9b2695473bfd95
+https://conda.anaconda.org/conda-forge/linux-64/psy-simple-1.5.1-py312h7900ff3_0.conda#683ec8787a523de54b02c885e2c2aefa
+https://conda.anaconda.org/conda-forge/noarch/py-cordex-0.9.0-pyhd8ed1ab_1.conda#53912b9ade4f2ea4dd1d5d6d3de7df70
+https://conda.anaconda.org/conda-forge/linux-64/pyarrow-core-17.0.0-py312h01725c0_2_cpu.conda#add603bfa43d9bf3f06783f780e1a817
+https://conda.anaconda.org/conda-forge/linux-64/pydot-3.0.4-py312h7900ff3_0.conda#331fc9a56a16f02592f193c8109e4543
+https://conda.anaconda.org/conda-forge/linux-64/r-bigmemory-4.6.4-r42ha503ecb_0.conda#12b6fa8fe80a6494a948c6ea2f34340d
+https://conda.anaconda.org/conda-forge/linux-64/r-checkmate-2.3.1-r42h57805ef_0.conda#9febce7369c72d991e2399d7d28f3390
+https://conda.anaconda.org/conda-forge/linux-64/r-climdex.pcic-1.1_11-r42ha503ecb_2.conda#cff1d95fe315f109a1f01a7ef112fdd6
+https://conda.anaconda.org/conda-forge/noarch/r-desc-1.4.3-r42hc72bb7e_0.conda#8c535581a9a3a1e2a0f5ef6d7e4d6a7f
+https://conda.anaconda.org/conda-forge/linux-64/r-ellipsis-0.3.2-r42h57805ef_2.conda#1673236a1895ca5cce15c888435ad2f9
+https://conda.anaconda.org/conda-forge/noarch/r-elliptic-1.4_0-r42hc72bb7e_4.conda#8388c500125813b91332f9d3720f3471
+https://conda.anaconda.org/conda-forge/noarch/r-foreach-1.5.2-r42hc72bb7e_2.conda#16f5453742f10816f2964a2b05bc20d3
+https://conda.anaconda.org/conda-forge/noarch/r-highr-0.11-r42hc72bb7e_0.conda#2edda9394885683f7cad76673eeb2025
+https://conda.anaconda.org/conda-forge/noarch/r-lifecycle-1.0.4-r42hc72bb7e_0.conda#7d00a412d44005a8714c192589143b81
+https://conda.anaconda.org/conda-forge/linux-64/r-mapproj-1.2.11-r42h57805ef_1.conda#af943f7f8db88fddf340980fa53ed3e5
+https://conda.anaconda.org/conda-forge/linux-64/r-matrix-1.6_5-r42h316c678_0.conda#4f52f9c56146c8692503a7d2057ba7ba
+https://conda.anaconda.org/conda-forge/noarch/r-munsell-0.5.1-r42hc72bb7e_0.conda#e7036a0b18f2ee6b108fe12b908b18f3
+https://conda.anaconda.org/conda-forge/noarch/r-ncdf4.helpers-0.3_6-r42hc72bb7e_2.conda#c7180e87be344175853f1ebfcdce04d0
+https://conda.anaconda.org/conda-forge/linux-64/r-nlme-3.1_165-r42hbcb9c34_0.conda#a83fa459c53c4674ee394b80b7b2fbd5
+https://conda.anaconda.org/conda-forge/linux-64/r-plyr-1.8.9-r42ha503ecb_0.conda#9b63113ec0c6f1a69c53f77d8f8fa4dc
+https://conda.anaconda.org/conda-forge/linux-64/r-processx-3.8.4-r42h57805ef_0.conda#97f10f23ade292cb369d5635b119befa
+https://conda.anaconda.org/conda-forge/noarch/r-r.oo-1.26.0-r42hc72bb7e_0.conda#8d5929eebbe7d431fa3f989874b090eb
+https://conda.anaconda.org/conda-forge/linux-64/r-rcpparmadillo-0.12.8.4.0-r42h58a4165_0.conda#49973fea110c814e316d8277bb08e516
+https://conda.anaconda.org/conda-forge/noarch/r-rex-1.2.1-r42hc72bb7e_2.conda#b45f1b94fd106c19eb79303b24dc9a7c
+https://conda.anaconda.org/conda-forge/linux-64/r-sp-2.1_4-r42hb1dbf0f_0.conda#681bb0a7290d86f9f8bf8dc816f114c0
+https://conda.anaconda.org/conda-forge/linux-64/r-spam-2.10_0-r42h9f9f741_0.conda#159d8ab59a2777a26a739f8090b5a80c
+https://conda.anaconda.org/conda-forge/linux-64/r-timechange-0.3.0-r42ha503ecb_0.conda#3d62906e9c1fecf61370a3ad6e808e5e
+https://conda.anaconda.org/conda-forge/linux-64/r-xml2-1.3.6-r42hbfba7a4_1.conda#5c3d7a89a2d5e1c0885f92d1aa6fde30
+https://conda.anaconda.org/conda-forge/linux-64/r-zoo-1.8_12-r42h57805ef_1.conda#5367d265c0c9c151dea85f1ccb515ec1
+https://conda.anaconda.org/conda-forge/noarch/seaborn-0.13.2-hd8ed1ab_3.conda#62afb877ca2c2b4b6f9ecb37320085b6
+https://conda.anaconda.org/conda-forge/noarch/xesmf-0.8.7-pyhd8ed1ab_0.conda#42301f78a4c6d2500f891b9723160d5c
+https://conda.anaconda.org/conda-forge/noarch/xgboost-2.1.3-cuda118_pyh256f914_0.conda#cfb6634184cb3d62af351ce8e5be72c3
+https://conda.anaconda.org/conda-forge/noarch/cdsapi-0.7.5-pyhd8ed1ab_1.conda#b53d59d2abbc0b383d8a1bc0f06ed2d6
+https://conda.anaconda.org/conda-forge/linux-64/libarrow-dataset-17.0.0-h5888daf_13_cpu.conda#cd2c36e8865b158b82f61c6aac28b7e1
+https://conda.anaconda.org/conda-forge/linux-64/libgdal-3.9.2-ha770c72_7.conda#63779711c7afd4fcf9cea67538baa67a
+https://conda.anaconda.org/conda-forge/noarch/nbconvert-pandoc-7.16.5-hd8ed1ab_1.conda#593a8fd80968f14f8a7b3a685ddc455e
 https://conda.anaconda.org/conda-forge/noarch/prov-2.0.0-pyhd3deb0d_0.tar.bz2#aa9b3ad140f6c0668c646f32e20ccf82
-https://conda.anaconda.org/conda-forge/linux-64/psy-maps-1.5.0-py311h38be061_1.conda#d7901c26884613539e958c10e9973413
-https://conda.anaconda.org/conda-forge/linux-64/psy-reg-1.4.0-py311h38be061_3.conda#6f7871722c07922028043144e8873b37
-https://conda.anaconda.org/conda-forge/noarch/python-cdo-1.6.0-pyhd8ed1ab_0.conda#3fd1a0b063c1fbbe4b7bd5a5a7601e84
-https://conda.anaconda.org/conda-forge/linux-64/r-akima-0.6_3.4-r43h61816a4_2.conda#3864b228566ce025f29d1ef672139c07
-https://conda.anaconda.org/conda-forge/noarch/r-callr-3.7.6-r43hc72bb7e_0.conda#5f01d28492d5ca80b54fdba4510a653f
-https://conda.anaconda.org/conda-forge/noarch/r-doparallel-1.0.17-r43hc72bb7e_2.conda#5eb327ee913c177e3c02278e5103f9a1
-https://conda.anaconda.org/conda-forge/linux-64/r-e1071-1.7_14-r43ha503ecb_0.conda#99f29679579f01d7ffdf0a6d47495eb2
-https://conda.anaconda.org/conda-forge/noarch/r-gtable-0.3.5-r43hc72bb7e_0.conda#4afa6b3bc18ef585bac26420528ed1de
-https://conda.anaconda.org/conda-forge/noarch/r-hypergeo-1.2_13-r43hc72bb7e_1004.conda#960f0770e69b4d8e154e9b751763b672
-https://conda.anaconda.org/conda-forge/noarch/r-knitr-1.47-r43hc72bb7e_0.conda#0310660d998a5933bb4545a312ed7385
-https://conda.anaconda.org/conda-forge/linux-64/r-lmoments-1.3_1-r43h7ce84a7_5.conda#24bcd1ee177f4e977657f5560fdf6616
-https://conda.anaconda.org/conda-forge/linux-64/r-lubridate-1.9.3-r43h57805ef_0.conda#48f220862d7b0ac7d1397f8c6601fb70
-https://conda.anaconda.org/conda-forge/linux-64/r-mgcv-1.9_1-r43h316c678_0.conda#c73d9ed0dc98182d712cbea33a3e5d59
-https://conda.anaconda.org/conda-forge/noarch/r-r.utils-2.12.3-r43hc72bb7e_0.conda#5ea010c037dd64b6d6a4de2c06059231
-https://conda.anaconda.org/conda-forge/linux-64/r-reshape-0.8.9-r43hc72bb7e_2.conda#e24945cba9c156cf4e7154b3857f416d
-https://conda.anaconda.org/conda-forge/linux-64/r-s2-1.1.6-r43h5eac2b3_0.conda#8295dd173068e7ca92a742ebcc72d75a
-https://conda.anaconda.org/conda-forge/noarch/r-scales-1.3.0-r43hc72bb7e_0.conda#508360956e18c2b0cc18968cdb786c78
-https://conda.anaconda.org/conda-forge/linux-64/r-specsverification-0.5_3-r43ha503ecb_3.conda#e9e632dc89a5235a6a44b42f23b497d8
-https://conda.anaconda.org/conda-forge/linux-64/r-vctrs-0.6.5-r43ha503ecb_0.conda#e398bd0451e6350a876a8561f8e90682
-https://conda.anaconda.org/conda-forge/linux-64/libarrow-substrait-15.0.2-h9241762_2_cpu.conda#c18bbb60ed10774dbf9ea86484728a74
-https://conda.anaconda.org/conda-forge/noarch/nbconvert-7.16.4-hd8ed1ab_0.conda#c9d64b8a7ee8e6bdbf0e7d8aa7f39601
-https://conda.anaconda.org/conda-forge/linux-64/r-classint-0.4_10-r43h61816a4_0.conda#3cd17c77cac1801072c599c7cddff7f2
-https://conda.anaconda.org/conda-forge/noarch/r-cyclocomp-1.1.1-r43hc72bb7e_0.conda#5287430003206a614ae64a91c8490e3c
-https://conda.anaconda.org/conda-forge/noarch/r-gridextra-2.3-r43hc72bb7e_1005.conda#15f718c5ad9c479dc80f1812cc787c27
-https://conda.anaconda.org/conda-forge/noarch/r-lmomco-2.5.1-r43hc72bb7e_0.conda#3eff921d171972770deabc2cfff9fcd2
-https://conda.anaconda.org/conda-forge/noarch/r-multiapply-2.1.4-r43hc72bb7e_1.conda#72783cf9e09eeb47114fc506de126613
-https://conda.anaconda.org/conda-forge/noarch/r-pillar-1.9.0-r43hc72bb7e_1.conda#7cff01456566a69381d3907d520c10b2
-https://conda.anaconda.org/conda-forge/linux-64/r-purrr-1.0.2-r43h57805ef_0.conda#713053b11419075641f09df086ef58d9
-https://conda.anaconda.org/conda-forge/noarch/r-r.cache-0.16.0-r43hc72bb7e_2.conda#fa01685a9c07191e186357c001b19ece
-https://conda.anaconda.org/conda-forge/linux-64/pyarrow-15.0.2-py311h78dcc79_2_cpu.conda#6f20003320c613f2505cf248bfce48f6
-https://conda.anaconda.org/conda-forge/noarch/r-climprojdiags-0.3.3-r43hc72bb7e_0.conda#5b6f07861439a584c942ec0c3967660f
-https://conda.anaconda.org/conda-forge/noarch/r-lintr-3.1.2-r43hc72bb7e_0.conda#d2b855cb2d8c0c5c870fe61d0f7e0d0c
-https://conda.anaconda.org/conda-forge/linux-64/r-sf-1.0_14-r43h85a8d9e_1.conda#a2ea960cebc252c3e10c5fb9fb4dfefc
-https://conda.anaconda.org/conda-forge/linux-64/r-tibble-3.2.1-r43h57805ef_2.conda#afa7d3f21fbc5a2fbaa48cb9bacb7bce
-https://conda.anaconda.org/conda-forge/noarch/dask-expr-1.1.2-pyhd8ed1ab_0.conda#34db694d2afc672094f1a74af51cb44e
-https://conda.anaconda.org/conda-forge/noarch/pyarrow-hotfix-0.6-pyhd8ed1ab_0.conda#ccc06e6ef2064ae129fab3286299abda
-https://conda.anaconda.org/conda-forge/noarch/r-ggplot2-3.5.1-r43hc72bb7e_0.conda#efb6b27dba202d74e5f182faae28794a
-https://conda.anaconda.org/conda-forge/noarch/r-rematch2-2.1.2-r43hc72bb7e_3.conda#d1f849bc6b24f93c392f4177f65578b4
-https://conda.anaconda.org/conda-forge/noarch/dask-2024.5.2-pyhd8ed1ab_0.conda#6bb2c18b838161e550a30de0fdff6993
-https://conda.anaconda.org/conda-forge/noarch/r-styler-1.10.3-r43hc72bb7e_0.conda#de41a01071ff652806dd79cd7ddaf239
-https://conda.anaconda.org/conda-forge/linux-64/r-tlmoments-0.7.5.3-r43ha503ecb_1.conda#cc23eacc44f680ecc36222de5a1c6694
-https://conda.anaconda.org/conda-forge/noarch/r-viridis-0.6.5-r43hc72bb7e_0.conda#327e96357f5ba37795f2e292e29091c7
-https://conda.anaconda.org/conda-forge/noarch/iris-esmf-regrid-0.10.0-pyhd8ed1ab_0.conda#a5ccce1a87da81d6c690cd11ae0687a2
-https://conda.anaconda.org/conda-forge/linux-64/r-fields-15.2-r43h61816a4_0.conda#9e320d2aa158a698f8ba09b3176607ea
-https://conda.anaconda.org/conda-forge/noarch/r-spei-1.8.1-r43hc72bb7e_1.conda#3be1d0c7f8edcd4e7e8a64596020b90f
-https://conda.anaconda.org/conda-forge/noarch/esmvalcore-2.10.0-pyhd8ed1ab_0.conda#18bc5ed0e0583cb0b212927795debea7
-https://conda.anaconda.org/conda-forge/linux-64/r-geomap-2.5_5-r43h57805ef_0.conda#dc942c6f2062894f7baf00fc6b907a79
-https://conda.anaconda.org/conda-forge/noarch/r-s2dverification-2.10.3-r43hc72bb7e_2.conda#13f4b1126272c8f195fc6ef38cc19d31
-https://conda.anaconda.org/conda-forge/noarch/autodocsumm-0.2.6-pyhd8ed1ab_0.tar.bz2#4409dd7e06a62c3b2aa9e96782c49c6d
-https://conda.anaconda.org/conda-forge/noarch/nbsphinx-0.9.4-pyhd8ed1ab_0.conda#9dc80eaeff56fb67dbf4f871b81bc13a
-https://conda.anaconda.org/conda-forge/noarch/pydata-sphinx-theme-0.15.3-pyhd8ed1ab_0.conda#55e445f4fcb07f2471fb0e1102d36488
-https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-applehelp-1.0.8-pyhd8ed1ab_0.conda#611a35a27914fac3aa37611a6fe40bb5
-https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-devhelp-1.0.6-pyhd8ed1ab_0.conda#d7e4954df0d3aea2eacc7835ad12671d
-https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-htmlhelp-2.0.5-pyhd8ed1ab_0.conda#7e1e7437273682ada2ed5e9e9714b140
-https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-qthelp-1.0.7-pyhd8ed1ab_0.conda#26acae54b06f178681bfb551760f5dd1
-https://conda.anaconda.org/conda-forge/noarch/sphinx-7.3.7-pyhd8ed1ab_0.conda#7b1465205e28d75d2c0e1a868ee00a67
-https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-serializinghtml-1.1.10-pyhd8ed1ab_0.conda#e507335cb4ca9cff4c3d0fa9cdab255e
+https://conda.anaconda.org/conda-forge/linux-64/psy-maps-1.5.0-py312h7900ff3_1.conda#080bc8f34a9cb0ab81ae0369fd43b7ab
+https://conda.anaconda.org/conda-forge/linux-64/psy-reg-1.5.0-py312h7900ff3_1.conda#ea719cfcc2e5b815b137b7082ece8aeb
+https://conda.anaconda.org/conda-forge/noarch/python-cdo-1.6.0-pyhff2d567_1.conda#61f71e3d4df9dbe55340c129f96cb8ee
+https://conda.anaconda.org/conda-forge/linux-64/r-akima-0.6_3.4-r42h61816a4_2.conda#8536251313f441c4d70ff11ad976d294
+https://conda.anaconda.org/conda-forge/noarch/r-callr-3.7.6-r42hc72bb7e_0.conda#4fb1765d6dc531936db81af3f6be316a
+https://conda.anaconda.org/conda-forge/noarch/r-doparallel-1.0.17-r42hc72bb7e_2.conda#1cddfbaade4416f0234670391bb31ba2
+https://conda.anaconda.org/conda-forge/noarch/r-gtable-0.3.5-r42hc72bb7e_0.conda#b5cff9c0564c9fcd8b62632430a0cee5
+https://conda.anaconda.org/conda-forge/noarch/r-hypergeo-1.2_13-r42hc72bb7e_1004.conda#7a207a992c606168044d13dcffd80ad4
+https://conda.anaconda.org/conda-forge/noarch/r-knitr-1.47-r42hc72bb7e_0.conda#0a20a2f6546bc0cde246c53a92a7964d
+https://conda.anaconda.org/conda-forge/linux-64/r-lmoments-1.3_1-r42h7ce84a7_5.conda#e727f948785d9aad6426e912e135f935
+https://conda.anaconda.org/conda-forge/linux-64/r-lubridate-1.9.3-r42h57805ef_0.conda#01fd816e4231ae7cf2833e5661a92611
+https://conda.anaconda.org/conda-forge/linux-64/r-mgcv-1.9_1-r42h316c678_0.conda#5c3d738118f5948f6cc29ccb63d6e2ff
+https://conda.anaconda.org/conda-forge/noarch/r-r.utils-2.12.3-r42hc72bb7e_0.conda#81f505dec8850e227d9b2a7e88fa505f
+https://conda.anaconda.org/conda-forge/linux-64/r-reshape-0.8.9-r42hc72bb7e_2.conda#17e75917161bf824248cc54a412b4394
+https://conda.anaconda.org/conda-forge/noarch/r-scales-1.3.0-r42hc72bb7e_0.conda#0af4021fe6d0047bbf7a34bf21c50bdd
+https://conda.anaconda.org/conda-forge/linux-64/r-specsverification-0.5_3-r42h7525677_2.tar.bz2#1521b8a303852af0496245e368d3c61c
+https://conda.anaconda.org/conda-forge/linux-64/r-splancs-2.01_45-r42hbcb9c34_0.conda#bcd96dc088f54514a54d57e6b8ed51b6
+https://conda.anaconda.org/conda-forge/linux-64/r-vctrs-0.6.5-r42ha503ecb_0.conda#5689030c60302fb5bb7a48b54c11dbe8
+https://conda.anaconda.org/conda-forge/linux-64/fiona-1.10.1-py312h5aa26c2_1.conda#4a30f4277a1894928a7057d0e14c1c95
+https://conda.anaconda.org/conda-forge/linux-64/libarrow-substrait-17.0.0-hf54134d_13_cpu.conda#46f41533959eee8826c09e55976b8c06
+https://conda.anaconda.org/conda-forge/noarch/nbconvert-7.16.5-hd8ed1ab_1.conda#82ffc2974cd09b45182f018b5af731c8
+https://conda.anaconda.org/conda-forge/linux-64/ncl-6.6.2-h7cb714c_54.conda#7363202c15302898deb49e82ca3e5f58
+https://conda.anaconda.org/conda-forge/noarch/r-cyclocomp-1.1.1-r42hc72bb7e_0.conda#6bd41a85dc43541400311eca03d4e2d4
+https://conda.anaconda.org/conda-forge/noarch/r-gridextra-2.3-r42hc72bb7e_1005.conda#da116b29105a8d48571975a185e9bb94
+https://conda.anaconda.org/conda-forge/noarch/r-lmomco-2.5.1-r42hc72bb7e_0.conda#6efbdfe5d41b3ef5652be1ea2e0a6e3c
+https://conda.anaconda.org/conda-forge/noarch/r-multiapply-2.1.4-r42hc72bb7e_1.conda#7aa5a8ca336904418caeb7395fd867e6
+https://conda.anaconda.org/conda-forge/noarch/r-pillar-1.9.0-r42hc72bb7e_1.conda#07d5ce8e710897745f14c951ff947cdd
+https://conda.anaconda.org/conda-forge/linux-64/r-purrr-1.0.2-r42h57805ef_0.conda#7985dada48799b7814ca069794d0b1a3
+https://conda.anaconda.org/conda-forge/noarch/r-r.cache-0.16.0-r42hc72bb7e_2.conda#34daac4e8faee056f15abdee858fc721
+https://conda.anaconda.org/conda-forge/linux-64/rasterio-1.3.11-py312hd177ed6_1.conda#246c5f31c607ecfe1ece1e8cc6ecc9c5
+https://conda.anaconda.org/conda-forge/linux-64/pyarrow-17.0.0-py312h9cebb41_2.conda#5f7d505626cb057e1320bbd46dd02ef2
+https://conda.anaconda.org/conda-forge/noarch/r-climprojdiags-0.3.3-r42hc72bb7e_0.conda#f34d40a3f0f9160fdd2bccaae8e185d1
+https://conda.anaconda.org/conda-forge/noarch/r-lintr-3.1.2-r42hc72bb7e_0.conda#ef49cc606b94a9d5f30b9c48f5f68848
+https://conda.anaconda.org/conda-forge/linux-64/r-tibble-3.2.1-r42h57805ef_2.conda#b1278a5148c9e52679bb72112770cdc3
+https://conda.anaconda.org/conda-forge/noarch/dask-2025.1.0-pyhd8ed1ab_0.conda#a5f91379331b61157c203ca69da6331b
+https://conda.anaconda.org/conda-forge/noarch/r-ggplot2-3.5.1-r42hc72bb7e_0.conda#77cc0254e0dc92e5e7791ce20a170f74
+https://conda.anaconda.org/conda-forge/noarch/r-rematch2-2.1.2-r42hc72bb7e_3.conda#5ccfee6f3b94e6b247c7e1929b24f1cc
+https://conda.anaconda.org/conda-forge/noarch/iris-esmf-regrid-0.11.0-pyhd8ed1ab_1.conda#86286b197e33e3b034416c18ba0f574c
+https://conda.anaconda.org/conda-forge/noarch/r-styler-1.10.3-r42hc72bb7e_0.conda#1b2b8fa85a9d0556773abac4763d8ef9
+https://conda.anaconda.org/conda-forge/linux-64/r-tlmoments-0.7.5.3-r42ha503ecb_1.conda#6aa1414e06dfffc39d3b5ca78b60b377
+https://conda.anaconda.org/conda-forge/noarch/r-viridis-0.6.5-r42hc72bb7e_0.conda#959f69b6dfd4b620a15489975fa27670
+https://conda.anaconda.org/conda-forge/noarch/esmvalcore-2.11.1-pyhd8ed1ab_0.conda#54cad67b1fb303d452019c45e4fea1bc
+https://conda.anaconda.org/conda-forge/linux-64/r-fields-15.2-r42h61816a4_0.conda#d84fe2f9e893e92089370b195e2263a0
+https://conda.anaconda.org/conda-forge/noarch/r-spei-1.8.1-r42hc72bb7e_1.conda#7fe060235dac0fc0b3d387f98e79d128
+https://conda.anaconda.org/conda-forge/linux-64/r-geomap-2.5_0-r42h57805ef_2.conda#020534c6abdee4f1253c221e926a5341
+https://conda.anaconda.org/conda-forge/noarch/r-s2dverification-2.10.3-r42hc72bb7e_2.conda#8079a86a913155fe2589ec0b76dc9f5e
+https://conda.anaconda.org/conda-forge/noarch/autodocsumm-0.2.14-pyhd8ed1ab_0.conda#351a11ac1215eb4f6c5b82e30070277a
+https://conda.anaconda.org/conda-forge/noarch/nbsphinx-0.9.6-pyhd8ed1ab_0.conda#2e4c30e09d50d025836279d80140d0a4
+https://conda.anaconda.org/conda-forge/noarch/pydata-sphinx-theme-0.16.1-pyhd8ed1ab_0.conda#837aaf71ddf3b27acae0e7e9015eebc6
+https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-applehelp-2.0.0-pyhd8ed1ab_1.conda#16e3f039c0aa6446513e94ab18a8784b
+https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-devhelp-2.0.0-pyhd8ed1ab_1.conda#910f28a05c178feba832f842155cbfff
+https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-htmlhelp-2.1.0-pyhd8ed1ab_1.conda#e9fb3fe8a5b758b4aff187d434f94f03
+https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-qthelp-2.0.0-pyhd8ed1ab_1.conda#00534ebcc0375929b45c3039b5ba7636
+https://conda.anaconda.org/conda-forge/noarch/sphinx-8.1.3-pyhd8ed1ab_1.conda#1a3281a0dc355c02b5506d87db2d78ac
+https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-serializinghtml-1.1.10-pyhd8ed1ab_1.conda#3bc61f7161d28137797e038263c04c54
diff --git a/config-user-example.yml b/config-user-example.yml
deleted file mode 100644
index c102928db9..0000000000
--- a/config-user-example.yml
+++ /dev/null
@@ -1,274 +0,0 @@
-###############################################################################
-# Example user configuration file for ESMValTool
-###############################################################################
-#
-# Note for users:
-# --------------
-# Site-specific entries for different HPC centers are given at the bottom of
-# this file. Comment out/replace as needed. This default version of the file
-# can be used in combination with the command line argument
-# ``search_esgf=when_missing``. If only certain values are allowed for an
-# option, these are listed after ``---``. The option in square brackets is the
-# default value, i.e., the one that is used if this option is omitted in the
-# file.
-#
-###############################################################################
-#
-# Note for developers:
-# -------------------
-# Two identical copies of this file (``ESMValTool/config-user-example.yml`` and
-# ``ESMValCore/esmvalcore/config-user.yml``) exist. If you change one of it,
-# make sure to apply the changes to the other.
-#
-###############################################################################
----
-
-# Destination directory where all output will be written
-# Includes log files and performance stats.
-output_dir: ~/esmvaltool_output
-
-# Auxiliary data directory
-# Used by some recipes to look for additional datasets.
-auxiliary_data_dir: ~/auxiliary_data
-
-# Automatic data download from ESGF --- [never]/when_missing/always
-# Use automatic download of missing CMIP3, CMIP5, CMIP6, CORDEX, and obs4MIPs
-# data from ESGF. ``never`` disables this feature, which is useful if you are
-# working on a computer without an internet connection, or if you have limited
-# disk space. ``when_missing`` enables the automatic download for files that
-# are not available locally. ``always`` will always check ESGF for the latest
-# version of a file, and will only use local files if they correspond to that
-# latest version.
-search_esgf: never
-
-# Directory for storing downloaded climate data
-# Make sure to use a directory where you can store multiple GBs of data. Your
-# home directory on a HPC is usually not suited for this purpose, so please
-# change the default value in this case!
-download_dir: ~/climate_data
-
-# Run at most this many tasks in parallel --- [null]/1/2/3/4/...
-# Set to ``null`` to use the number of available CPUs. If you run out of
-# memory, try setting max_parallel_tasks to ``1`` and check the amount of
-# memory you need for that by inspecting the file ``run/resource_usage.txt`` in
-# the output directory. Using the number there you can increase the number of
-# parallel tasks again to a reasonable number for the amount of memory
-# available in your system.
-max_parallel_tasks: null
-
-# Log level of the console --- debug/[info]/warning/error
-# For much more information printed to screen set log_level to ``debug``.
-log_level: info
-
-# Exit on warning --- true/[false]
-# # Only used in NCL diagnostic scripts.
-exit_on_warning: false
-
-# Plot file format --- [png]/pdf/ps/eps/epsi
-output_file_type: png
-
-# Remove the ``preproc`` directory if the run was successful --- [true]/false
-# By default this option is set to ``true``, so all preprocessor output files
-# will be removed after a successful run. Set to ``false`` if you need those
-# files.
-remove_preproc_dir: true
-
-# Use netCDF compression --- true/[false]
-compress_netcdf: false
-
-# Save intermediary cubes in the preprocessor --- true/[false]
-# Setting this to ``true`` will save the output cube from each preprocessing
-# step. These files are numbered according to the preprocessing order.
-save_intermediary_cubes: false
-
-# Path to custom ``config-developer.yml`` file
-# This can be used to customise project configurations. See
-# ``config-developer.yml`` for an example. Set to ``null`` to use the default.
-config_developer_file: null
-
-# Use a profiling tool for the diagnostic run --- [false]/true
-# A profiler tells you which functions in your code take most time to run.
-# Only available for Python diagnostics.
-profile_diagnostic: false
-
-# Rootpaths to the data from different projects
-# This default setting will work if files have been downloaded by ESMValTool
-# via ``search_esgf``. Lists are also possible. For site-specific entries and
-# more examples, see below. Comment out these when using a site-specific path.
-rootpath:
-  default: ~/climate_data
-
-# Directory structure for input data --- [default]/ESGF/BADC/DKRZ/ETHZ/etc.
-# This default setting will work if files have been downloaded by ESMValTool
-# via ``search_esgf``. See ``config-developer.yml`` for definitions.  Comment
-# out/replace as per needed.
-drs:
-  CMIP3: ESGF
-  CMIP5: ESGF
-  CMIP6: ESGF
-  CORDEX: ESGF
-  obs4MIPs: ESGF
-
-# Example rootpaths and directory structure that showcases the different
-# projects and also the use of lists
-# For site-specific entries, see below.
-#rootpath:
-#  CMIP3: [~/cmip3_inputpath1, ~/cmip3_inputpath2]
-#  CMIP5: [~/cmip5_inputpath1, ~/cmip5_inputpath2]
-#  CMIP6: [~/cmip6_inputpath1, ~/cmip6_inputpath2]
-#  OBS: ~/obs_inputpath
-#  OBS6: ~/obs6_inputpath
-#  obs4MIPs: ~/obs4mips_inputpath
-#  ana4mips: ~/ana4mips_inputpath
-#  native6: ~/native6_inputpath
-#  RAWOBS: ~/rawobs_inputpath
-#  default: ~/default_inputpath
-#drs:
-#  CMIP3: default
-#  CMIP5: default
-#  CMIP6: default
-#  CORDEX: default
-#  obs4MIPs: default
-
-# Directory tree created by automatically downloading from ESGF
-# Uncomment the lines below to locate data that has been automatically
-# downloaded from ESGF (using ``search_esgf``).
-#rootpath:
-#  CMIP3: ~/climate_data
-#  CMIP5: ~/climate_data
-#  CMIP6: ~/climate_data
-#  CORDEX: ~/climate_data
-#  obs4MIPs: ~/climate_data
-#drs:
-#  CMIP3: ESGF
-#  CMIP5: ESGF
-#  CMIP6: ESGF
-#  CORDEX: ESGF
-#  obs4MIPs: ESGF
-
-# Site-specific entries: JASMIN
-# Uncomment the lines below to locate data on JASMIN.
-#auxiliary_data_dir: /gws/nopw/j04/esmeval/aux_data/AUX
-#rootpath:
-#  CMIP6: /badc/cmip6/data/CMIP6
-#  CMIP5: /badc/cmip5/data/cmip5/output1
-#  CMIP3: /badc/cmip3_drs/data/cmip3/output
-#  OBS: /gws/nopw/j04/esmeval/obsdata-v2
-#  OBS6: /gws/nopw/j04/esmeval/obsdata-v2
-#  obs4MIPs: /gws/nopw/j04/esmeval/obsdata-v2
-#  ana4mips: /gws/nopw/j04/esmeval/obsdata-v2
-#  CORDEX: /badc/cordex/data/CORDEX/output
-#drs:
-#  CMIP6: BADC
-#  CMIP5: BADC
-#  CMIP3: BADC
-#  CORDEX: BADC
-#  OBS: default
-#  OBS6: default
-#  obs4MIPs: default
-#  ana4mips: default
-
-# Site-specific entries: DKRZ-Levante
-# For bd0854 members a shared download directory is available
-#search_esgf: when_missing
-#download_dir: /work/bd0854/DATA/ESMValTool2/download
-# Uncomment the lines below to locate data on Levante at DKRZ.
-#auxiliary_data_dir: /work/bd0854/DATA/ESMValTool2/AUX
-#rootpath:
-#  CMIP6: /work/bd0854/DATA/ESMValTool2/CMIP6_DKRZ
-#  CMIP5: /work/bd0854/DATA/ESMValTool2/CMIP5_DKRZ
-#  CMIP3: /work/bd0854/DATA/ESMValTool2/CMIP3
-#  CORDEX: /work/ik1017/C3SCORDEX/data/c3s-cordex/output
-#  OBS: /work/bd0854/DATA/ESMValTool2/OBS
-#  OBS6: /work/bd0854/DATA/ESMValTool2/OBS
-#  obs4MIPs: /work/bd0854/DATA/ESMValTool2/OBS
-#  ana4mips: /work/bd0854/DATA/ESMValTool2/OBS
-#  native6: /work/bd0854/DATA/ESMValTool2/RAWOBS
-#  RAWOBS: /work/bd0854/DATA/ESMValTool2/RAWOBS
-#drs:
-#  CMIP6: DKRZ
-#  CMIP5: DKRZ
-#  CMIP3: DKRZ
-#  CORDEX: BADC
-#  obs4MIPs: default
-#  ana4mips: default
-#  OBS: default
-#  OBS6: default
-#  native6: default
-
-# Site-specific entries: ETHZ
-# Uncomment the lines below to locate data at ETHZ.
-#rootpath:
-#  CMIP6: /net/atmos/data/cmip6
-#  CMIP5: /net/atmos/data/cmip5
-#  CMIP3: /net/atmos/data/cmip3
-#  OBS: /net/exo/landclim/PROJECTS/C3S/datadir/obsdir/
-#drs:
-#  CMIP6: ETHZ
-#  CMIP5: ETHZ
-#  CMIP3: ETHZ
-
-# Site-specific entries: IPSL
-# Uncomment the lines below to locate data on Ciclad at IPSL.
-#rootpath:
-#  IPSLCM: /
-#  CMIP5: /bdd/CMIP5/output
-#  CMIP6: /bdd/CMIP6
-#  CMIP3: /bdd/CMIP3
-#  CORDEX: /bdd/CORDEX/output
-#  obs4MIPs: /bdd/obs4MIPS/obs-CFMIP/observations
-#  ana4mips: /not_yet
-#  OBS: /not_yet
-#  OBS6: /not_yet
-#  RAWOBS: /not_yet
-#drs:
-#  CMIP6: DKRZ
-#  CMIP5: DKRZ
-#  CMIP3: IPSL
-#  CORDEX: BADC
-#  obs4MIPs: IPSL
-#  ana4mips: default
-#  OBS: not_yet
-#  OBS6: not_yet
-
-# Site-specific entries: Met Office
-# Uncomment the lines below to locate data at the Met Office.
-#rootpath:
-#  CMIP5: /project/champ/data/cmip5/output1
-#  CMIP6: /project/champ/data/CMIP6
-#  CORDEX: /project/champ/data/cordex/output
-#  OBS: /data/users/esmval/ESMValTool/obs
-#  OBS6: /data/users/esmval/ESMValTool/obs
-#  obs4MIPs: /data/users/esmval/ESMValTool/obs
-#  ana4mips: /project/champ/data/ana4MIPs
-#  native6: /data/users/esmval/ESMValTool/rawobs
-#  RAWOBS: /data/users/esmval/ESMValTool/rawobs
-#drs:
-#  CMIP5: BADC
-#  CMIP6: BADC
-#  CORDEX: BADC
-#  OBS: default
-#  OBS6: default
-#  obs4MIPs: default
-#  ana4mips: BADC
-#  native6: default
-
-# Site-specific entries: NCI
-# Uncomment the lines below to locate data at NCI.
-#rootpath:
-#  CMIP6: [/g/data/oi10/replicas/CMIP6, /g/data/fs38/publications/CMIP6, /g/data/xp65/public/apps/esmvaltool/replicas/CMIP6]
-#  CMIP5: [/g/data/r87/DRSv3/CMIP5, /g/data/al33/replicas/CMIP5/combined, /g/data/rr3/publications/CMIP5/output1, /g/data/xp65/public/apps/esmvaltool/replicas/cmip5/output1]
-#  CMIP3: /g/data/r87/DRSv3/CMIP3
-#  OBS: /g/data/ct11/access-nri/replicas/esmvaltool/obsdata-v2
-#  OBS6: /g/data/ct11/access-nri/replicas/esmvaltool/obsdata-v2
-#  obs4MIPs: /g/data/ct11/access-nri/replicas/esmvaltool/obsdata-v2
-#  ana4mips: /g/data/ct11/access-nri/replicas/esmvaltool/obsdata-v2
-#  native6: /g/data/xp65/public/apps/esmvaltool/native6
-#
-#drs:
-#  CMIP6: NCI
-#  CMIP5: NCI
-#  CMIP3: NCI
-#  CORDEX: ESGF
-#  obs4MIPs: default
-#  ana4mips: default
diff --git a/doc/sphinx/source/api/esmvaltool.diag_scripts.portrait_plot.rst b/doc/sphinx/source/api/esmvaltool.diag_scripts.portrait_plot.rst
new file mode 100644
index 0000000000..68e18613db
--- /dev/null
+++ b/doc/sphinx/source/api/esmvaltool.diag_scripts.portrait_plot.rst
@@ -0,0 +1,11 @@
+
+.. _api.esmvaltool.diag_scripts.portrait_plot:
+
+Portrait Plot
+=============
+
+
+.. automodule:: esmvaltool.diag_scripts.portrait_plot
+  :no-members:
+  :no-inherited-members:
+  :no-show-inheritance:
diff --git a/doc/sphinx/source/api/esmvaltool.rst b/doc/sphinx/source/api/esmvaltool.rst
index b080b81ac8..6eda5f5912 100644
--- a/doc/sphinx/source/api/esmvaltool.rst
+++ b/doc/sphinx/source/api/esmvaltool.rst
@@ -29,3 +29,4 @@ Diagnostic Scripts
    esmvaltool.diag_scripts.ocean
    esmvaltool.diag_scripts.psyplot_diag
    esmvaltool.diag_scripts.seaborn_diag
+   esmvaltool.diag_scripts.portrait_plot
diff --git a/doc/sphinx/source/changelog.rst b/doc/sphinx/source/changelog.rst
index d1180d3b8b..76c0a86da5 100644
--- a/doc/sphinx/source/changelog.rst
+++ b/doc/sphinx/source/changelog.rst
@@ -3,6 +3,135 @@
 Changelog
 =========
 
+.. _changelog-v2-11-0:
+
+v2.11.0
+-------
+Highlights
+
+- Two new recipes have been added:
+
+  - Recipe :ref:`recipe_aod_aeronet_assess.yml <recipe_aod_aeronet_assess>`
+    evaluates model aerosol optical depth (AOD) climatologies against ground
+    based observations from the AeroNET measurement network.
+  - Recipe :ref:`recipe_climate_patterns.yml <recipes_climate_patterns>`
+    generates climate patterns from CMIP6 model datasets.
+
+- The ESACCI-WATERVAPOUR CMORizer now includes daily data and uses the
+  officially released CDR2 data.
+- Support for 5 new datasets have been added:
+
+  - AeroNET
+  - ANU Climate 2.0 Australian data
+  - Australian Gridded Climate Data(AGCD) precipitation
+  - NOAA-ERSST
+  - NSIDC-G02202-sh sea ice fraction
+
+- NEW TREND: First time release manager shout-outs!
+
+  - This is the first ESMValTool release managed by the Met Office! We want to
+    shout this out - and for all future first time release managers to
+    shout-out - to celebrate the growing, thriving ESMValTool community.
+
+This release includes
+
+Bug fixes
+~~~~~~~~~
+
+-  Recipe_ocean_quadmap: Update ATSR to match ESGF name (:pull:`3443`) by :user:`rbeucher`
+-  Fix recipe_bock20jgr_fig_8-10.yml (:pull:`3665`) by :user:`LisaBock`
+-  Update the list of datasets used in ``recipe_easy_ipcc.yml`` (:pull:`3710`) by :user:`bouweandela`
+
+Documentation
+~~~~~~~~~~~~~
+
+-  Improve release tools and documentation (:pull:`3462`) by :user:`bouweandela`
+-  Fix a typo in the references file (:pull:`3499`) by :user:`bouweandela`
+-  Fix recipe path in ``recipe_perfmetrics.rst`` (:pull:`3532`) by :user:`TomasTorsvik`
+-  Improved description of model evaluation recipes (:pull:`3541`) by :user:`schlunma`
+-  Remove double word in cmorizer documentation (:pull:`3553`) by :user:`bettina-gier`
+-  Fix Codacy badge (:pull:`3558`) by :user:`bouweandela`
+-  Update the release schedule for v2.11.0 (:pull:`3573`) by :user:`ehogan`
+-  Improve the formatting of the recipe documentation template (:pull:`3652`) by :user:`mo-gill`
+-  Add introduction material on the main documentation page (:pull:`3628`) by :user:`bouweandela`
+-  Avoid warning in documentation build (:pull:`3675`) by :user:`bouweandela`
+-  Update the list of broken recipes for ``v2.11.0`` (:pull:`3706`) by :user:`ehogan`
+
+Diagnostics
+~~~~~~~~~~~
+
+-  ``monitor/multi_dataset.py`` improvements: allow data w/o ``timerange`` and improve text formatting (:pull:`3528`) by :user:`schlunma`
+-  Allow datasets without ``project`` in multi_datasets.py (:pull:`3552`) by :user:`schlunma`
+-  Prevent overlapping time axis tick labels in monitoring recipe (:pull:`3682`) by :user:`schlunma`
+
+New recipe
+~~~~~~~~~~
+
+-  Add support for aerosol optical depth climatology metrics to the AutoAssess replacement (:pull:`3048`) by :user:`catherinehardacre`
+-  CMIP6 climate patterns  (:pull:`2785`) by :user:`mo-gregmunday`
+
+Observational and re-analysis dataset support
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+-  Add cmorizer scripts for NOAA-ERSST. (:pull:`1799`) by :user:`bjoernbroetz`
+-  Update OceanSODA-ETHZ CMORizer with new source file (:pull:`3535`) by :user:`TomasTorsvik`
+-  Add CMORizer script for NSIDC-G02202-sh sea ice fraction (:pull:`3512`) by :user:`flicj191`
+-  CMORizer Australian Gridded Climate Data(AGCD) precipitation (:pull:`3445`) by :user:`flicj191`
+-  Extend CMORizer NCEP-DOE-R2 (:pull:`3469`) by :user:`axel-lauer`
+-  Add comment to recipe_lauer13jclim regarding UWisc being superseded by MAC-LWP (:pull:`3537`) by :user:`rbeucher`
+-  Recipe_autoassess_landsurface_surfrad: Remove CERES-EBAF version to fix ESGF search (:pull:`3438`) by :user:`rbeucher`
+-  Updating ESACCI-WATERVAPOUR cmorizer (:pull:`3282`) by :user:`malininae`
+-  CMORiser for ANU Climate 2.0 Australian data (:pull:`3511`) by :user:`flicj191`
+-  Add AERONET cmorizer (:pull:`3227`) by :user:`zklaus`
+-  Update CRU CMORizer (:pull:`3381`) by :user:`lukruh`
+-  Fix recipe_check_obs to be aligned with DKRZ (:pull:`3673`) by :user:`LisaBock`
+-  Update AERONET data version (:pull:`3692`) by :user:`ehogan`
+
+Automatic testing
+~~~~~~~~~~~~~~~~~
+
+-  Move code into function in batch job generation script (:pull:`3491`) by :user:`bouweandela`
+-  Fix sklearn tests (:pull:`3506`) by :user:`schlunma`
+-  Pinned sklearn>=1.4.0 (:pull:`3508`) by :user:`schlunma`
+-  Update sklearn tests to be compatible with current pytest version (pytest >=8.0.0) (:pull:`3517`) by :user:`schlunma`
+-  Update sklearn tests to be compatible with current pytest version (pytest >=8.0.0) Part 2 (:pull:`3518`) by :user:`schlunma`
+-  [Circle/CI]Fix `test_installation_from_conda` Circle CI tests (:pull:`3538`) by :user:`valeriupredoi`
+-  [Github Actions] install git in OSX and add environment inspection (:pull:`3581`) by :user:`valeriupredoi`
+-  [CI Github Actions] Update (outdated) actions versions that produce Node.js warnings (:pull:`3586`) by :user:`valeriupredoi`
+-  Fix ``flake8==7`` linting issues (:pull:`3634`) by :user:`valeriupredoi`
+-  Use ``importlib`` as the import mode for ``pytest`` (:pull:`3672`) by :user:`ehogan`
+
+Installation
+~~~~~~~~~~~~
+
+-  Update dependencies (:pull:`3487`) by :user:`bouweandela`
+-  Merge v2.10.x into main (:pull:`3489`) by :user:`schlunma`
+-  Add imagehash package as an ESMValTool dependency (:pull:`3557`) by :user:`alistairsellar`
+-  Unpin ``r-akima`` (:pull:`3564`) by :user:`valeriupredoi`
+-  Adding pys2index dependency (:pull:`3577`) by :user:`ljoakim`
+-  Pin esmpy <8.6.0 (:pull:`3585`) by :user:`valeriupredoi`
+-  Pin R <4.3.0 (:pull:`3689`) by :user:`ehogan`
+-  Pin importlib_metadata <8 (:pull:`3700`) by :user:`ehogan`
+-  Pin matplotlib <3.9.0 on ESMValTool release branch (:pull:`3712`) by :user:`ehogan`
+
+Dependency updates
+~~~~~~~~~~~~~~~~~~
+
+-  Fix for ``recipe_seaice_drift.yml``: fix CRS transformer for "North Pole Stereographic" (:pull:`3531`) by :user:`flicj191`
+-  Fixed attribute handling in austral_jet/main.ncl for iris>=3.8 (:pull:`3603`) by :user:`schlunma`
+-  Fixed attribute handling in emergent constraint diagnostic for iris>=3.8 (:pull:`3605`) by :user:`schlunma`
+-  Update the name of the remapcon2 operator in R recipes (:pull:`3611`) by :user:`ehogan`
+-  Use ``iris.FUTURE.save_split_attrs = True`` to remove iris warning in many diagnostics (:pull:`3651`) by :user:`schlunma`
+-  Avoid concatenation error in recipe_pcrglobwb.yml (:pull:`3645`) by :user:`bouweandela`
+-  Update `scipy.integrate.simps` import (:pull:`3704`) by :user:`ehogan`
+
+Improvements
+~~~~~~~~~~~~
+
+-  Add native6, OBS6 and RAWOBS rootpaths to metoffice template in config-user-example.yml and remove temporary dir (:pull:`3613`) by :user:`alistairsellar`
+
+.. _changelog-v2-10-0:
+
 v2.10.0
 -------
 Highlights
diff --git a/doc/sphinx/source/community/dataset.rst b/doc/sphinx/source/community/dataset.rst
index 424d4d4694..7a24e7c923 100644
--- a/doc/sphinx/source/community/dataset.rst
+++ b/doc/sphinx/source/community/dataset.rst
@@ -42,14 +42,15 @@ and run the recipe, to make sure the CMOR checks pass without warnings or errors
 
 To test a pull request for a new CMORizer script:
 
-#. Download the data following the instructions included in the script and place
-   it in the ``RAWOBS`` path specified in your ``config-user.yml``
+#. Download the data following the instructions included in the script and
+   place it in the ``RAWOBS`` ``rootpath`` specified in your
+   :ref:`configuration <esmvalcore:config_options>`
 #. If available, use the downloading script by running
    ``esmvaltool data download --config_file <config-file>  <dataset>``
 #. Run the cmorization by running ``esmvaltool data format <config-file> <dataset>``
 #. Copy the resulting data to the ``OBS`` (for CMIP5 compliant data) or ``OBS6``
-   (for CMIP6 compliant data) path specified in your
-   ``config-user.yml``
+   (for CMIP6 compliant data) ``rootpath`` specified in your
+   :ref:`configuration <esmvalcore:config_options>`
 #. Run ``recipes/examples/recipe_check_obs.yml`` with the new dataset to check that
    the data can be used
 
diff --git a/doc/sphinx/source/community/diagnostic.rst b/doc/sphinx/source/community/diagnostic.rst
index 285815f7cf..1be820f7b8 100644
--- a/doc/sphinx/source/community/diagnostic.rst
+++ b/doc/sphinx/source/community/diagnostic.rst
@@ -64,7 +64,7 @@ If it is just a few simple scripts or packaging is not possible (i.e. for NCL) y
 and paste the source code into the ``esmvaltool/diag_scripts`` directory.
 
 If you have existing code in a compiled language like
-C, C++, or Fortran that you want to re-use, the recommended way to proceed is to add Python bindings and publish
+C, C++, or Fortran that you want to reuse, the recommended way to proceed is to add Python bindings and publish
 the package on PyPI so it can be installed as a Python dependency. You can then call the functions it provides
 using a Python diagnostic.
 
@@ -134,9 +134,8 @@ Diagnostic output
 Typically, diagnostic scripts create plots, but any other output such as e.g.
 text files or tables is also possible.
 Figures should be saved in the ``plot_dir``, either in both ``.pdf`` and
-``.png`` format (preferred), or
-respect the ``output_file_type`` specified in the
-:ref:`esmvalcore:user configuration file`.
+``.png`` format (preferred), or respect the :ref:`configuration option
+<esmvalcore:config_options>` ``output_file_type`` .
 Data should be saved in the ``work_dir``, preferably as a ``.nc``
 (`NetCDF <https://www.unidata.ucar.edu/software/netcdf/>`__) file, following the
 `CF-Conventions <https://cfconventions.org/>`__ as much as possible.
@@ -181,7 +180,7 @@ human inspection.
 In addition to provenance information, a caption is also added to the plots.
 
 Provenance information from the recipe is automatically recorded by ESMValCore, whereas
-diagnostic scripts must include code specifically to record provenance. See below for 
+diagnostic scripts must include code specifically to record provenance. See below for
 documentation of provenance attributes that can be included in a recipe.
 When contributing a diagnostic, please make sure it records the provenance,
 and that no warnings related to provenance are generated when running the recipe.
@@ -252,7 +251,7 @@ for example
   plot_types:
     errorbar: error bar plot
 
-To use these items, include them in the provenance record dictionary in the form 
+To use these items, include them in the provenance record dictionary in the form
 :code:`key: [value]`
 i.e. for the example above as
 :code:`'plot_types': ['errorbar']`.
@@ -275,8 +274,8 @@ Always use :func:`esmvaltool.diag_scripts.shared.run_diagnostic` at the end of y
     with run_diagnostic() as config:
         main(config)
 
-Create a ``provenance_record`` for each diagnostic file (i.e. image or data 
-file) that the diagnostic script outputs. The ``provenance_record`` is a 
+Create a ``provenance_record`` for each diagnostic file (i.e. image or data
+file) that the diagnostic script outputs. The ``provenance_record`` is a
 dictionary of provenance items, for example:
 
 .. code-block:: python
@@ -296,15 +295,15 @@ dictionary of provenance items, for example:
         'statistics': ['mean'],
       }
 
-To save a matplotlib figure, use the convenience function 
-:func:`esmvaltool.diag_scripts.shared.save_figure`. Similarly, to save Iris cubes use 
+To save a matplotlib figure, use the convenience function
+:func:`esmvaltool.diag_scripts.shared.save_figure`. Similarly, to save Iris cubes use
 :func:`esmvaltool.diag_scripts.shared.save_data`. Both of these functions take
 ``provenance_record`` as an argument and log the provenance accordingly.
 Have a look at the example Python diagnostic in
 `esmvaltool/diag_scripts/examples/diagnostic.py <https://github.com/ESMValGroup/ESMValTool/blob/main/esmvaltool/diag_scripts/examples/diagnostic.py>`_
 for a complete example.
 
-For any other files created, you will need to make use of a 
+For any other files created, you will need to make use of a
 :class:`esmvaltool.diag_scripts.shared.ProvenanceLogger` to log provenance. Include the
 following code directly after the file is saved:
 
@@ -489,7 +488,7 @@ This includes the following items:
 * In-code documentation (comments, docstrings)
 * Code quality (e.g. no hardcoded pathnames)
 * No Codacy errors reported
-* Re-use of existing functions whenever possible
+* Reuse of existing functions whenever possible
 * Provenance implemented
 
 Run recipe
diff --git a/doc/sphinx/source/community/release_strategy/detailed_release_procedure.rst b/doc/sphinx/source/community/release_strategy/detailed_release_procedure.rst
index a73643f454..d0d7f74672 100644
--- a/doc/sphinx/source/community/release_strategy/detailed_release_procedure.rst
+++ b/doc/sphinx/source/community/release_strategy/detailed_release_procedure.rst
@@ -49,7 +49,7 @@ and attach it in the release testing issue; to record the environment in a yaml
 
 Modifications to configuration files need to be documented as well.
 To test recipes, it is recommended to only use the default options and DKRZ data directories, simply by uncommenting
-the DKRZ-Levante block of a newly generated ``config-user.yml`` file.
+the DKRZ-Levante block of a :ref:`newly generated configuration file <esmvalcore:config_yaml_files>`.
 
 Submit run scripts - test recipe runs
 -------------------------------------
@@ -61,7 +61,7 @@ You will have to set the name of your environment, your email address (if you wa
 More information on running jobs with SLURM on DKRZ/Levante can be found in the DKRZ `documentation
 <https://docs.dkrz.de/doc/levante/running-jobs/index.html>`_.
 
-You can also specify the path to your ``config-user.yml`` file where ``max_parallel_tasks`` can be set. The script was found to work well with ``max_parallel_tasks=8``. Some recipes need to be run with ``max_parallel_tasks=1`` (large memory requirements, CMIP3 data, diagnostic issues, ...). These recipes are listed in `ONE_TASK_RECIPES`.
+You can also specify the path to your configuration directory where ``max_parallel_tasks`` can be set in a YAML file. The script was found to work well with ``max_parallel_tasks=8``. Some recipes need to be run with ``max_parallel_tasks=1`` (large memory requirements, CMIP3 data, diagnostic issues, ...). These recipes are listed in `ONE_TASK_RECIPES`.
 
 Some recipes need other job requirements, you can add their headers in the `SPECIAL_RECIPES` dictionary. Otherwise the header will be written following the template that is written in the lines below. If you want to exclude recipes, you can do so by uncommenting the `exclude` lines.
 
diff --git a/doc/sphinx/source/community/release_strategy/release_strategy.rst b/doc/sphinx/source/community/release_strategy/release_strategy.rst
index cae1c43807..72c55266dd 100644
--- a/doc/sphinx/source/community/release_strategy/release_strategy.rst
+++ b/doc/sphinx/source/community/release_strategy/release_strategy.rst
@@ -53,6 +53,23 @@ With the following release schedule, we strive to have three releases per year a
 
 Upcoming releases
 ^^^^^^^^^^^^^^^^^
+- 2.12.0 (Release Manager: `Saskia Loosveldt Tomas`_)
+
++------------+------------+----------------------------------------+-------------------------------------+
+|  Planned   |    Done    |            Event                       |             Changelog               |
++============+============+========================================+=====================================+
+| 2025-01-13 |            | ESMValCore `Feature Freeze`_           |                                     |
++------------+------------+----------------------------------------+-------------------------------------+
+| 2025-01-20 |            | ESMValCore Release 2.12.0              |                                     |
++------------+------------+----------------------------------------+-------------------------------------+
+| 2025-01-27 |            | ESMValTool `Feature Freeze`_           |                                     |
++------------+------------+----------------------------------------+-------------------------------------+
+| 2025-02-03 |            | ESMValTool Release 2.12.0              |                                     |
++------------+------------+----------------------------------------+-------------------------------------+
+
+
+Past releases
+^^^^^^^^^^^^^
 
 - 2.11.0 (Release Manager: Met Office: `Emma Hogan`_, `Chris Billows`_, `Ed Gillett`_)
 
@@ -61,16 +78,13 @@ Upcoming releases
 +============+============+========================================+=====================================+
 | 2024-04-22 |            | ESMValCore `Feature Freeze`_           |                                     |
 +------------+------------+----------------------------------------+-------------------------------------+
-| 2023-05-03 |            | ESMValCore released                    |                                     |
+| 2023-05-03 | 2024-07-03 | :esmvalcore-release:`v2.11.0` released | :ref:`esmvalcore:changelog-v2-11-0` |
 +------------+------------+----------------------------------------+-------------------------------------+
 | 2023-05-06 |            | ESMValTool `Feature Freeze`_           |                                     |
 +------------+------------+----------------------------------------+-------------------------------------+
-| 2023-05-17 |            | ESMValTool released                    |                                     |
+| 2023-05-17 | 2024-07-04 | :release:`v2.11.0` released            | :ref:`changelog-v2-11-0`            |
 +------------+------------+----------------------------------------+-------------------------------------+
 
-Past releases
-^^^^^^^^^^^^^
-
 - 2.10.0 (Release Manager: `Klaus Zimmermann`_)
 
 +------------+------------+----------------------------------------+-------------------------------------+
diff --git a/doc/sphinx/source/community/upgrading.rst b/doc/sphinx/source/community/upgrading.rst
index 9ed7f8b5b1..9a9b37f178 100644
--- a/doc/sphinx/source/community/upgrading.rst
+++ b/doc/sphinx/source/community/upgrading.rst
@@ -145,7 +145,7 @@ Many operations previously performed by the diagnostic scripts, are now included
 
 The backend operations are fully controlled by the ``preprocessors`` section in the recipe. Here, a number of preprocessor sets can be defined, with different options for each of the operations. The sets defined in this section are applied in the ``diagnostics`` section to preprocess a given variable.
 
-It is recommended to proceed step by step, porting and testing each operation separately before proceeding with the next one. A useful setting in the user configuration file (``config-private.yml``) called ``write_intermediary_cube`` allows writing out the variable field after each preprocessing step, thus facilitating the comparison with the old version (e.g., after CMORization, level selection, after regridding, etc.). The CMORization step of the new backend exactly corresponds to the operation performed by the old backend (and stored in the ``climo`` directory, now called ``preprec``): this is the very first step to be checked, by simply comparing the intermediary file produced by the new backend after CMORization with the output of the old backend in the ``climo`` directorsy (see "Testing" below for instructions).
+It is recommended to proceed step by step, porting and testing each operation separately before proceeding with the next one. A useful setting in the configuration called ``write_intermediary_cube`` allows writing out the variable field after each preprocessing step, thus facilitating the comparison with the old version (e.g., after CMORization, level selection, after regridding, etc.). The CMORization step of the new backend exactly corresponds to the operation performed by the old backend (and stored in the ``climo`` directory, now called ``preprec``): this is the very first step to be checked, by simply comparing the intermediary file produced by the new backend after CMORization with the output of the old backend in the ``climo`` directorsy (see "Testing" below for instructions).
 
 The new backend also performs variable derivation, replacing the ``calculate`` function in the ``variable_defs`` scripts. If the recipe which is being ported makes use of derived variables, the corresponding calculation must be ported from the ``./variable_defs/<variable>.ncl`` file to ``./esmvaltool/preprocessor/_derive.py``.
 
@@ -159,7 +159,7 @@ In the new version, all settings are centralized in the recipe, completely repla
 Make sure the diagnostic script writes NetCDF output
 ======================================================
 
-Each diagnostic script is required to write the output of the anaylsis in one or more NetCDF files. This is to give the user the possibility to further look into the results, besides the plots, but (most importantly) for tagging purposes when publishing the data in a report and/or on a website.
+Each diagnostic script is required to write the output of the analysis in one or more NetCDF files. This is to give the user the possibility to further look into the results, besides the plots, but (most importantly) for tagging purposes when publishing the data in a report and/or on a website.
 
 For each of the plot produced by the diagnostic script a single NetCDF file has to be generated. The variable saved in this file should also contain all the necessary metadata that documents the plot (dataset names, units, statistical methods, etc.).
 The files have to be saved in the work directory (defined in `cfg['work_dir']` and `config_user_info@work_dir`, for the python and NCL diagnostics, respectively).
@@ -209,7 +209,7 @@ Before submitting a pull request, the code should be cleaned to adhere to the co
 Update the documentation
 ========================
 
-If necessary, add or update the documentation for your recipes in the corrsponding rst file, which is now in ``doc\sphinx\source\recipes``. Do not forget to also add the documentation file to the list in ``doc\sphinx\source\annex_c`` to make sure it actually appears in the documentation.
+If necessary, add or update the documentation for your recipes in the corresponding rst file, which is now in ``doc\sphinx\source\recipes``. Do not forget to also add the documentation file to the list in ``doc\sphinx\source\annex_c`` to make sure it actually appears in the documentation.
 
 Open a pull request
 ===================
diff --git a/doc/sphinx/source/conf.py b/doc/sphinx/source/conf.py
index 600eaa8253..de7feb4775 100644
--- a/doc/sphinx/source/conf.py
+++ b/doc/sphinx/source/conf.py
@@ -82,6 +82,11 @@
     'autosummary': True,
 }
 
+# See https://github.com/sphinx-doc/sphinx/issues/12589
+suppress_warnings = [
+    'autosummary.import_cycle',
+]
+
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ['_templates']
 
@@ -163,8 +168,13 @@
 # `conf.py` file.Be aware that `navigation_with_keys = True` has negative
 # accessibility implications:
 # https://github.com/pydata/pydata-sphinx-theme/issues/1492"
-html_theme_options = {"navigation_with_keys": False}
-
+html_theme_options = {
+    "navigation_with_keys": False,
+    "logo": {
+        "image_light": "figures/ESMValTool-logo-2.png",
+        "image_dark": "figures/ESMValTool-logo-2-dark.png",
+    },
+}
 # Add any paths that contain custom themes here, relative to this directory.
 # html_theme_path = []
 
@@ -187,7 +197,7 @@
 # Add any paths that contain custom static files (such as style sheets) here,
 # relative to this directory. They are copied after the builtin static files,
 # so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = []
+html_static_path = ["figures/ESMValTool-logo-2-dark.png"]
 
 # Add any extra paths that contain custom files (such as robots.txt or
 # .htaccess) here, relative to this directory. These files are copied
diff --git a/doc/sphinx/source/develop/dataset.rst b/doc/sphinx/source/develop/dataset.rst
index f3c168a17c..f624a44feb 100644
--- a/doc/sphinx/source/develop/dataset.rst
+++ b/doc/sphinx/source/develop/dataset.rst
@@ -76,7 +76,7 @@ for downloading (e.g. providing contact information, licence agreements)
 and using the observations. The unformatted (raw) observations
 should then be stored in the appropriate of these three folders.
 
-For each additional dataset, an entry needs to be made to the file 
+For each additional dataset, an entry needs to be made to the file
 `datasets.yml
 <https://github.com/ESMValGroup/ESMValTool/blob/main/esmvaltool/cmorizers/data/datasets.yml>`_.
 The dataset entry should contain:
@@ -92,10 +92,10 @@ of the cmorizing script (see Section `4. Create a cmorizer for the dataset`_).
 3.1 Downloader script (optional)
 --------------------------------
 
-A Python script can be written to download raw observations 
+A Python script can be written to download raw observations
 from source and store the data in the appropriate tier subdirectory of the
 folder ``RAWOBS`` automatically.
-There are many downloading scripts available in 
+There are many downloading scripts available in
 `/esmvaltool/cmorizers/data/downloaders/datasets/
 <https://github.com/ESMValGroup/ESMValTool/blob/main/esmvaltool/data/downloaders/datasets/>`_
 where several data download mechanisms are provided:
@@ -108,18 +108,18 @@ Note that the name of this downloading script has to be identical to the
 name of the dataset.
 
 Depending on the source server, the downloading script needs to contain paths to
-raw observations, filename patterns and various necessary fields to retrieve 
+raw observations, filename patterns and various necessary fields to retrieve
 the data.
-Default ``start_date`` and ``end_date`` can be provided in cases where raw data 
+Default ``start_date`` and ``end_date`` can be provided in cases where raw data
 are stored in daily, monthly, and yearly files.
 
 The downloading script for the given dataset can be run with:
 
 .. code-block:: console
 
- esmvaltool data download --config_file <config-user.yml>  <dataset-name>
+ esmvaltool data download --config_dir </path/to/config/dir/>  <dataset-name>
 
-The options ``--start`` and ``--end`` can be added to the command above to 
+The options ``--start`` and ``--end`` can be added to the command above to
 restrict the download of raw data to a time range. They will be ignored if a specific dataset
 does not support it (i.e. because it is provided as a single file). Valid formats are
 ``YYYY``, ``YYYYMM`` and ``YYYYMMDD``. By default, already downloaded data are not overwritten
@@ -128,7 +128,7 @@ unless the option ``--overwrite=True`` is used.
 4. Create a cmorizer for the dataset
 ====================================
 
-There are many cmorizing scripts available in 
+There are many cmorizing scripts available in
 `/esmvaltool/cmorizers/data/formatters/datasets/
 <https://github.com/ESMValGroup/ESMValTool/blob/main/esmvaltool/cmorizers/data/formatters/datasets/>`_
 where solutions to many kinds of format issues with observational data are
@@ -158,7 +158,7 @@ configuration file: `MTE.yml
 <https://github.com/ESMValGroup/ESMValTool/blob/main/esmvaltool/cmorizers/data/cmor_config/MTE.yml>`_
 in the directory ``ESMValTool/esmvaltool/cmorizers/data/cmor_config/``. Note
 that both the name of this configuration file and the cmorizing script have to be
-identical to the name of your dataset. 
+identical to the name of your dataset.
 It is recommended that you set ``project`` to ``OBS6`` in the
 configuration file. That way, the variables defined in the CMIP6 CMOR table,
 augmented with the custom variables described above, are available to your script.
@@ -188,7 +188,8 @@ The main body of the CMORizer script must contain a function called
 with this exact call signature. Here, ``in_dir`` corresponds to the input
 directory of the raw files, ``out_dir`` to the output directory of final
 reformatted data set, ``cfg`` to the dataset-specific configuration file,
-``cfg_user`` to the user configuration file, ``start_date`` to the start
+``cfg_user`` to the configuration object (which behaves basically like a
+dictionary), ``start_date`` to the start
 of the period to format, and ``end_date`` to the end of the period to format.
 If not needed, the last three arguments can be ignored using underscores.
 The return value of this function is ignored. All
@@ -256,9 +257,9 @@ The cmorizing script for the given dataset can be run with:
 
 .. code-block:: console
 
- esmvaltool data format --config_file <config-user.yml> <dataset-name>
+ esmvaltool data format --config_dir </path/to/config/dir/> <dataset-name>
 
-The options ``--start`` and ``--end`` can be added to the command above to 
+The options ``--start`` and ``--end`` can be added to the command above to
 restrict the formatting of raw data to a time range. They will be ignored if a specific dataset
 does not support it (i.e. because it is provided as a single file). Valid formats are
 ``YYYY``, ``YYYYMM`` and ``YYYYMMDD``.
@@ -267,12 +268,12 @@ does not support it (i.e. because it is provided as a single file). Valid format
 
    The output path given in the configuration file is the path where
    your cmorized dataset will be stored. The ESMValTool will create a folder
-   with the correct tier information 
+   with the correct tier information
    (see Section `2. Edit your configuration file`_) if that tier folder is not
-   already available, and then a folder named after the dataset. 
+   already available, and then a folder named after the dataset.
    In this folder the cmorized data set will be stored as a NetCDF file.
    The cmorized dataset will be automatically moved to the correct tier
-   subfolder of your OBS or OBS6 directory if the option 
+   subfolder of your OBS or OBS6 directory if the option
    ``--install=True`` is used in the command above and no such directory
    was already created.
 
@@ -284,9 +285,9 @@ the cmorizing scripts can be run in a single command with:
 
 .. code-block:: console
 
- esmvaltool data prepare --config_file <config-user.yml> <dataset-name>
+ esmvaltool data prepare --config_dir </path/to/config/dir/> <dataset-name>
 
-Note that options from the ```esmvaltool data download`` and 
+Note that options from the ```esmvaltool data download`` and
 ``esmvaltool data format`` commands can be passed to the above command.
 
 6. Naming convention of the observational data files
diff --git a/doc/sphinx/source/faq.rst b/doc/sphinx/source/faq.rst
index 10c72bd2cb..43251a801b 100644
--- a/doc/sphinx/source/faq.rst
+++ b/doc/sphinx/source/faq.rst
@@ -59,12 +59,17 @@ This is a useful functionality because it allows the user to `fix` things on-the
 quitting the Ipython console, code execution continues as per normal.
 
 
-Use multiple config-user.yml files
-==================================
+Using multiple configuration directories
+========================================
+
+By default, ESMValTool will read YAML configuration files from the user
+configuration directory ``~/.config/esmvaltool``, which can be changed with the
+``ESMVALTOOL_CONFIG_DIR`` environment variable.
+If required, users can specify the command line option ``--config_dir`` to
+select another configuration directory, which is read **in addition** to the
+user configuration directory
+See the section on configuration :ref:`config_yaml_files` for details on this.
 
-The user selects the configuration yaml file at run time. It's possible to
-have several configurations files. For instance, it may be practical to have one
-config file for debugging runs and another for production runs.
 
 Create a symbolic link to the latest output directory
 =====================================================
diff --git a/doc/sphinx/source/figures/ESMValTool-logo-2-dark.png b/doc/sphinx/source/figures/ESMValTool-logo-2-dark.png
new file mode 100644
index 0000000000..e120b2e731
Binary files /dev/null and b/doc/sphinx/source/figures/ESMValTool-logo-2-dark.png differ
diff --git a/doc/sphinx/source/figures/ESMValTool-logo-2-glow.png b/doc/sphinx/source/figures/ESMValTool-logo-2-glow.png
new file mode 100644
index 0000000000..14aef201ee
Binary files /dev/null and b/doc/sphinx/source/figures/ESMValTool-logo-2-glow.png differ
diff --git a/doc/sphinx/source/figures/ESMValTool-logo-2.png b/doc/sphinx/source/figures/ESMValTool-logo-2.png
index e876219038..aaaa3578a5 100644
Binary files a/doc/sphinx/source/figures/ESMValTool-logo-2.png and b/doc/sphinx/source/figures/ESMValTool-logo-2.png differ
diff --git a/doc/sphinx/source/functionalities.rst b/doc/sphinx/source/functionalities.rst
index 5b49c118a2..0098d95ded 100644
--- a/doc/sphinx/source/functionalities.rst
+++ b/doc/sphinx/source/functionalities.rst
@@ -12,9 +12,9 @@ that it can:
 -  execute the workflow; and
 -  output the desired collective data and media.
 
-To facilitate these four steps, the user has control over the tool via
-two main input files: the :ref:`user configuration file <config-user>`
-and the :ref:`recipe <esmvalcore:recipe>`. The configuration file sets
+To facilitate these four steps, the user has control over the tool via the
+:ref:`configuration <esmvalcore:config_overview>` and the :ref:`recipe
+<esmvalcore:recipe>`. The configuration sets
 user and site-specific parameters (like input and output paths, desired
 output graphical formats, logging level, etc.), whereas the recipe file
 sets data, preprocessing and diagnostic-specific parameters (data
@@ -27,7 +27,7 @@ recyclable; the recipe file can be used for a large number of
 applications, since it may include as many datasets, preprocessors and
 diagnostics sections as the user deems useful.
 
-Once the user configuration files and the recipe are at hand, the user
+Once the configuration files and the recipe are at hand, the user
 can start the tool. A schematic overview of the ESMValTool workflow is
 depicted in the figure below.
 
diff --git a/doc/sphinx/source/gensidebar.py b/doc/sphinx/source/gensidebar.py
index 970722ff0a..f8b766ab7d 100644
--- a/doc/sphinx/source/gensidebar.py
+++ b/doc/sphinx/source/gensidebar.py
@@ -65,7 +65,7 @@ def _header(project, text):
     _write("esmvaltool", "Obtaining input data", "input")
     _write("esmvaltool", "Making a recipe or diagnostic", "develop/index")
     _write("esmvaltool", "Contributing to the community", "community/index")
-    _write("esmvaltool", "Utilities", "utils")
+    _write("esmvaltool", "Utilities", "utils/utils")
     _write("esmvaltool", "Diagnostics API Reference", "api/esmvaltool")
     _write("esmvaltool", "Frequently Asked Questions", "faq")
     _write("esmvaltool", "Changelog", "changelog")
diff --git a/doc/sphinx/source/index.rst b/doc/sphinx/source/index.rst
index 9975172bc4..136c2eba08 100644
--- a/doc/sphinx/source/index.rst
+++ b/doc/sphinx/source/index.rst
@@ -6,6 +6,27 @@
 Welcome to ESMValTool's documentation!
 ======================================
 
+To get a first impression of what ESMValTool and ESMValCore can do for you,
+have a look at our blog posts
+`Analysis-ready climate data with ESMValCore <https://blog.esciencecenter.nl/easy-ipcc-powered-by-esmvalcore-19a0b6366ea7>`_
+and
+`ESMValTool: Recipes for solid climate science <https://blog.esciencecenter.nl/esmvaltool-recipes-for-solid-climate-science-da5b33814f69>`_.
+
+A tutorial is available on https://tutorial.esmvaltool.org.
+
+A series of video lectures has been created by `ACCESS-NRI <https://access-nri.org.au>`_.
+While these are tailored for ACCESS users, they are still very informative.
+
+.. raw:: html
+
+   <iframe width="560" height="315" src="https://www.youtube.com/embed/videoseries?si=pUXrXB8C8bLRfQHY&amp;list=PLFjfi2xLaFpJp59LvDc1upQsj_xzFlFLc" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
+
+|
+
+For more detailed information, the documentation is available below.
+
+Get in touch! Contact information is available :ref:`here <Support-and-Contact>`.
+
 .. include:: _sidebar.rst.inc
 
 Indices and tables
@@ -13,4 +34,3 @@ Indices and tables
 
 * :ref:`genindex`
 * :ref:`search`
-
diff --git a/doc/sphinx/source/input.rst b/doc/sphinx/source/input.rst
index b0085d857a..a6ba17851e 100644
--- a/doc/sphinx/source/input.rst
+++ b/doc/sphinx/source/input.rst
@@ -76,7 +76,7 @@ For example, run
 
 to run the default example recipe and automatically download the required data
 to the directory ``~/climate_data``.
-The data only needs to be downloaded once, every following run will re-use
+The data only needs to be downloaded once, every following run will reuse
 previously downloaded data stored in this directory.
 See :ref:`esmvalcore:config-esgf` for a more in depth explanation and the
 available configuration options.
@@ -112,12 +112,27 @@ ESMValTool currently supports two ways to perform this reformatting (aka
    checks and fixes').  Details on this second method are given at the
    :ref:`end of this chapter <inputdata_native_datasets>`.
 
+Tiers
+-----
+
+All observational datasets are grouped into in three tiers:
+
+* **Tier 1**: obs4mips and ana4mips datasets. These datasets are publicly and freely available without any license restrictions. These datasets do not need any reformatting and can be used as is with ESMValTool.
+* **Tier 2** other freely available datasets that are not obs4mips. There are no license restrictions. These datasets need to be reformatted to be used with ESMValTool ('CMORization', see above). 
+* **Tier 3** restricted datasets. Datasets which require registration to be downloaded or that can only be obtained upon request from the respective authors. License restrictions do not allow us to redistribute Tier 3 datasets. The data have to be obtained and reformatted by the user ('CMORization', see above).
+
+[!NOTE]
+.. _tier3_note:
+For some of the Tier 3 datasets, we obtained permission from the dataset providers to share the data among ESMValTool users on HPC systems. These Tier 3 datasets are marked with an asterisk in the table in section :ref:`supported datasets below<supported_datasets>`.
+
+An overview of the Tier 2 and Tier 3 datasets for which a CMORizing script is available in ESMValTool v2.0 is given in section :ref:`supported datasets below<supported_datasets>`.
+
 A collection of readily CMORized OBS and OBS6 datasets can be accessed directly on CEDA/JASMIN and DKRZ. At CEDA/JASMIN
 OBS and OBS6 data is stored in the `esmeval` Group Workspace (GWS), and to be granted read (and execute) permissions to the
 GWS, one must apply at https://accounts.jasmin.ac.uk/services/group_workspaces/esmeval/ ; after permission has been granted, the user
 is encouraged to use the data locally, and not move it elsewhere, to minimize both data transfers and
 stale disk usage; to note that Tier 3 data is subject to data protection restrictions; for further inquiries,
-the GWS is adminstered by [Valeriu Predoi](mailto:valeriu.predoi@ncas.ac.uk).
+the GWS is administered by [Valeriu Predoi](mailto:valeriu.predoi@ncas.ac.uk).
 
 Using a CMORizer script
 -----------------------
@@ -193,8 +208,8 @@ To CMORize one or more datasets, run:
 
     esmvaltool data format --config_file [CONFIG_FILE] [DATASET_LIST]
 
-The path to the raw data to be CMORized must be specified in the :ref:`user
-configuration file<config-user>` as RAWOBS.
+The ``rootpath`` to the raw data to be CMORized must be specified in the
+:ref:`configuration <esmvalcore:config_options>` as ``RAWOBS``.
 Within this path, the data are expected to be organized in subdirectories
 corresponding to the data tier: Tier2 for freely-available datasets (other than
 obs4MIPs and ana4mips) and Tier3 for restricted datasets (i.e., dataset which
@@ -246,7 +261,7 @@ A list of the datasets for which a CMORizers is available is provided in the fol
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
 | CALIPSO-GOCCP                | clcalipso (cfMon)                                                                                    |   2  | NCL             |
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
-| CALIPSO-ICECLOUD             | cli (AMon)                                                                                           |   3  | NCL             |
+| CALIPSO-ICECLOUD* [#t3]_     | cli (AMon)                                                                                           |   3  | NCL             |
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
 | CDS-SATELLITE-ALBEDO         | bdalb (Lmon), bhalb (Lmon)                                                                           |   3  | Python          |
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
@@ -269,6 +284,8 @@ A list of the datasets for which a CMORizers is available is provided in the fol
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
 | CLOUDSAT-L2                  | clw, clivi, clwvi, lwp (Amon)                                                                        |   3  | NCL             |
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
+| CMAP                         | pr (Amon)                                                                                            |   2  | Python          |
++------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
 | CowtanWay                    | tasa (Amon)                                                                                          |   2  | Python          |
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
 | CRU                          | tas, tasmin, tasmax,  pr, clt (Amon), evspsblpot (Emon)                                              |   2  | Python          |
@@ -298,7 +315,17 @@ A list of the datasets for which a CMORizers is available is provided in the fol
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
 | ESACCI-FIRE                  | burntArea (Lmon)                                                                                     |   2  | NCL             |
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
-| ESACCI-LANDCOVER             | baresoilFrac, cropFrac, grassFrac, shrubFrac, treeFrac (Lmon)                                        |   2  | NCL             |
+| ESACCI-LANDCOVER v1.6.1      | baresoilFrac, cropFrac, grassFrac, shrubFrac, treeFrac (Lmon)                                        |   2  | NCL             |
+|                              |                                                                                                      |      | (CMORizer       |
+|                              |                                                                                                      |      | available until |
+|                              |                                                                                                      |      | ESMValTool      |
+|                              |                                                                                                      |      | v2.11.0)        |
++------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
+| ESACCI-LANDCOVER v2.0.8      | baresoilFrac, cropFrac, grassFrac, shrubFrac, treeFrac (Lmon, frequency=yr)                          |   2  | Python          |
+|                              |                                                                                                      |      | (CMORizer       |
+|                              |                                                                                                      |      | available since |
+|                              |                                                                                                      |      | ESMValTool      |
+|                              |                                                                                                      |      | v2.12.0)        |
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
 | ESACCI-LST                   | ts (Amon)                                                                                            |   2  | Python          |
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
@@ -308,7 +335,7 @@ A list of the datasets for which a CMORizers is available is provided in the fol
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
 | ESACCI-SEA-SURFACE-SALINITY  | sos (Omon)                                                                                           |   2  | Python          |
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
-| ESACCI-SOILMOISTURE          | dos, dosStderr, sm, smStderr (Lmon)                                                                  |   2  | NCL             |
+| ESACCI-SOILMOISTURE          | sm (Eday, Lmon), smStderr (Eday)                                                                     |   2  | Python          |
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
 | ESACCI-SST                   | ts, tsStderr (Amon)                                                                                  |   2  | NCL             |
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
@@ -318,7 +345,7 @@ A list of the datasets for which a CMORizers is available is provided in the fol
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
 | ESRL                         | co2s (Amon)                                                                                          |   2  | NCL             |
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
-| FLUXCOM                      | gpp (Lmon)                                                                                           |   3  | Python          |
+| FLUXCOM* [#t3]_              | gpp (Lmon)                                                                                           |   3  | Python          |
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
 | GCP2018                      | fgco2 (Omon [#note3]_), nbp (Lmon [#note3]_)                                                         |   2  | Python          |
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
@@ -358,6 +385,8 @@ A list of the datasets for which a CMORizers is available is provided in the fol
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
 | JRA-25                       | clt, hus, prw, rlut, rlutcs, rsut, rsutcs (Amon)                                                     |   2  | Python          |
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
+| JRA-55                       | cli, clivi, clw, clwvi, clt, prw, rlus, rlut, rlutcs, rsus, rsuscs, rsut, rsutcs, ta, tas, wap (Amon)|   2  | Python          |
++------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
 | Kadow2020                    | tasa (Amon)                                                                                          |   2  | Python          |
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
 | LAI3g                        | lai (Lmon)                                                                                           |   3  | Python          |
@@ -368,17 +397,17 @@ A list of the datasets for which a CMORizers is available is provided in the fol
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
 | Landschuetzer2020            | spco2 (Omon)                                                                                         |   2  | Python          |
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
-| MAC-LWP                      | lwp, lwpStderr (Amon)                                                                                |   3  | NCL             |
+| MAC-LWP* [#t3]_              | lwp, lwpStderr (Amon)                                                                                |   3  | NCL             |
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
 | MERRA                        | cli, clivi, clt, clw, clwvi, hur, hus, lwp, pr, prw, ps, psl, rlut, rlutcs, rsdt, rsut, rsutcs, ta,  |   3  | NCL             |
 |                              | tas, ts, ua, va, wap, zg (Amon)                                                                      |      |                 |
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
-| MERRA2                       | sm (Lmon)                                                                                            |   3  | Python          |
+| MERRA2* [#t3]_               | sm (Lmon)                                                                                            |   3  | Python          |
 |                              | clt, pr, evspsbl, hfss, hfls, huss, prc, prsn, prw, ps, psl, rlds, rldscs, rlus, rlut, rlutcs, rsds, |      |                 |
 |                              | rsdscs, rsdt, tas, tasmin, tasmax, tauu, tauv, ts, uas, vas, rsus, rsuscs, rsut, rsutcs, ta, ua, va, |      |                 |
 |                              | tro3, zg, hus, wap, hur, cl, clw, cli, clwvi, clivi (Amon)                                           |      |                 |
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
-| MLS-AURA                     | hur, hurStderr (day)                                                                                 |   3  | Python          |
+| MLS-AURA* [#t3]_             | hur, hurStderr (day)                                                                                 |   3  | Python          |
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
 | MOBO-DIC_MPIM                | dissic (Omon)                                                                                        |   2  | Python          |
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
@@ -388,19 +417,19 @@ A list of the datasets for which a CMORizers is available is provided in the fol
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
 | MSWEP [#note1]_              | pr                                                                                                   |   3  | n/a             |
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
-| MTE                          | gpp, gppStderr (Lmon)                                                                                |   3  | Python          |
+| MTE* [#t3]_                  | gpp, gppStderr (Lmon)                                                                                |   3  | Python          |
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
 | NCEP-NCAR-R1                 | clt, hur, hurs, hus, pr, prw, psl, rlut, rlutcs, rsut, rsutcs, sfcWind, ta, tas,                     |   2  | Python          |
 |                              | tasmax, tasmin, ts, ua, va, wap, zg (Amon)                                                           |      |                 |
 |                              | pr, rlut, ua, va (day)                                                                               |      |                 |
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
-| NCEP-DOE-R2                  | clt, hur, prw, ta, wap (Amon)                                                                        |   2  | Python          |
+| NCEP-DOE-R2                  | clt, hur, prw, ta, wap, pr, tauu, tauv, tos (Amon)                                                   |   2  | Python          |
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
 | NDP                          | cVeg (Lmon)                                                                                          |   3  | Python          |
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
-| NIWA-BS                      | toz, tozStderr (Amon)                                                                                |   3  | NCL             |
+| NIWA-BS* [#t3]_              | toz, tozStderr (Amon)                                                                                |   3  | NCL             |
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
-| NOAA-CIRES-20CR-V2           | clt, clwvi, hus, prw, rlut, rsut (Amon)                                                              |   2  | Python          |
+| NOAA-CIRES-20CR-V2           | clt, clwvi, hus, prw, rlut, rsut, pr, tauu, tauv (Amon)                                              |   2  | Python          |
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
 | NOAA-CIRES-20CR-V3           | clt, clwvi, hus, prw, rlut, rlutcs, rsut, rsutcs (Amon)                                              |   2  | Python          |
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
@@ -436,7 +465,7 @@ A list of the datasets for which a CMORizers is available is provided in the fol
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
 | TCOM-N2O                     | n2o (Amon [#note3]_)                                                                                 |   2  | Python          |
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
-| UWisc                        | clwvi, lwpStderr (Amon)                                                                              |   3  | NCL             |
+| UWisc* [#t3]_                | clwvi, lwpStderr (Amon)                                                                              |   3  | NCL             |
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
 | WFDE5                        | tas, pr (Amon, day)                                                                                  |   2  | Python          |
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
@@ -444,6 +473,9 @@ A list of the datasets for which a CMORizers is available is provided in the fol
 |                              | no3, o2, po4, si (Oyr)                                                                               |      |                 |
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
 
+.. [#t3] We obtained permission from the dataset provider to share this dataset
+         among ESMValTool users on HPC systems.
+
 .. [#note1] CMORization is built into ESMValTool through the native6 project,
             so there is no separate CMORizer script.
 
@@ -482,8 +514,8 @@ A list of all currently supported native datasets is :ref:`provided here
 A detailed description of how to include new native datasets is given
 :ref:`here <esmvalcore:add_new_fix_native_datasets>`.
 
-To use this functionality, users need to provide a path in the
-:ref:`esmvalcore:user configuration file` for the ``native6`` project data
+To use this functionality, users need to provide a ``rootpath`` in the
+:ref:`configuration <config_option_rootpath>` for the ``native6`` project data
 and/or the dedicated project used for the native dataset, e.g., ``ICON``.
 Then, in the recipe, they can refer to those projects.
 For example:
diff --git a/doc/sphinx/source/quickstart/configuration.rst b/doc/sphinx/source/quickstart/configuration.rst
index 34c29aac5c..9cea6413b6 100644
--- a/doc/sphinx/source/quickstart/configuration.rst
+++ b/doc/sphinx/source/quickstart/configuration.rst
@@ -1,4 +1,4 @@
-.. _config-user:
+.. _config:
 
 *************
 Configuration
@@ -7,22 +7,23 @@ Configuration
 The ``esmvaltool`` command is provided by the ESMValCore package, the
 documentation on configuring ESMValCore can be found
 :ref:`here <esmvalcore:config>`.
-In particular, it is recommended to read the section on the
-:ref:`User configuration file <esmvalcore:user configuration file>`
-and the section on
+An overview of all configuration options can be found
+:ref:`here <esmvalcore:config_options>`.
+In particular, it is recommended to read the section on how to :ref:`specify
+configuration options  <esmvalcore:config_for_cli>` and the section on
 :ref:`Finding data <esmvalcore:findingdata>`.
 
-To install the default configuration file in the default location, run
+To install the default configuration in the default location, run
 
  .. code:: bash
 
 	  esmvaltool config get_config_user
 
-Note that this file needs to be customized using the instructions above, so
+Note that this needs to be customized using the instructions above, so
 the ``esmvaltool`` command can find the data on your system, before it can run
 a recipe.
 
 There is a lesson available in the
 `ESMValTool tutorial <https://tutorial.esmvaltool.org/>`_
-that describes how to personalize the configuration file. It can be found
+that describes how to personalize the configuration. It can be found
 `at this site <https://tutorial.esmvaltool.org/03-configuration/index.html>`_.
diff --git a/doc/sphinx/source/quickstart/installation.rst b/doc/sphinx/source/quickstart/installation.rst
index a4f9f2a64c..9f66c1f670 100644
--- a/doc/sphinx/source/quickstart/installation.rst
+++ b/doc/sphinx/source/quickstart/installation.rst
@@ -8,7 +8,7 @@ Installation
    ESMValTool now uses `mamba` instead of `conda` for the recommended installation.
    For more information about the change, have a look at :ref:`Move to Mamba<move-to-mamba>`.
 
-ESMValTool supports Python 3.9 and later and requires Linux or MacOS.
+ESMValTool supports Python 3.10 and later and requires Linux or MacOS.
 Successful usage on Windows has been reported by following the Linux
 installation instructions with
 `WSL <https://learn.microsoft.com/en-us/windows/wsl/install>`__.
@@ -72,15 +72,15 @@ https://mamba.readthedocs.io/en/latest/installation.html.
     installation.
 
 First download the installation file for
-`Linux <https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-Linux-x86_64.sh>`_
+`Linux <https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh>`_
 or
-`MacOSX <https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-MacOSX-x86_64.sh>`_.
+`MacOSX <https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-MacOSX-x86_64.sh>`_.
 After downloading the installation file from one of the links above, execute it
 by running (Linux example):
 
 .. code-block:: bash
 
-    bash Mambaforge-Linux-x86_64.sh
+    bash Miniforge3-Linux-x86_64.sh
 
 and follow the instructions on your screen.
 
@@ -99,7 +99,7 @@ later by running:
     source <prefix>/etc/profile.d/conda.sh
 
 where ``<prefix>`` is the installation location of mamba (e.g.
-``/home/$USER/mambaforge`` if you chose the default installation path).
+``/home/$USER/miniforge3`` if you chose the default installation path).
 
 If you use another shell than Bash, have a look at the available configurations
 in the ``<prefix>/etc/profile.d`` directory.
@@ -111,7 +111,7 @@ You can check that mamba installed correctly by running
     which mamba
 
 this should show the path to your mamba executable, e.g.
-``~/mambaforge/bin/mamba``.
+``~/miniforge3/bin/mamba``.
 
 It is recommended to update both mamba and conda after installing:
 
@@ -489,7 +489,7 @@ To check that the installation was successful, run
 this should show the directory of the source code that you just downloaded.
 
 If the command above shows a directory inside your conda environment instead,
-e.g. ``~/mambaforge/envs/esmvaltool/lib/python3.9/site-packages/esmvalcore``,
+e.g. ``~/miniforge3/envs/esmvaltool/lib/python3.11/site-packages/esmvalcore``,
 you may need to manually remove that directory and run
 ``pip install --editable '.[develop]'`` again.
 
@@ -684,40 +684,6 @@ repository, a direct download link can be found `here <https://raw.githubusercon
 Common installation problems and their solutions
 ================================================
 
-Mamba fails to solve the environment
-------------------------------------
-If you see the text ``Solving environment:`` with the characters ``-\|/`` rotating
-behind it for more than 10 minutes, mamba may be having problems finding a
-working combination of versions of the packages that the ESMValTool depends on.
-Because the ESMValTool is a community tool, there is no strict selection of
-which tools can be used and installing the ESMValTool requires installing almost
-any package that is available for processing climate data.
-To help mamba solve the environment, you can try the following.
-
-Always use the latest version of mamba, as problems have been reported by people
-using older versions, to update, run:
-
-.. code-block:: bash
-
-    mamba update --name base mamba
-
-Usually mamba is much better at solving new environments than updating older
-environments, so it is often a good idea to create a new environment if updating
-does not work.
-
-It can help mamba if you let it know what version of certain packages you want,
-for example by running
-
-.. code-block:: bash
-
-    mamba create -n esmvaltool esmvaltool 'python=3.11'
-
-you ask for Python 3.11 specifically and that makes it much easier for mamba to
-solve the environment, because now it can ignore any packages that were built
-for other Python versions. Note that, since the esmvaltool package is built
-with Python>=3.9, asking for an older Python version, e.g. `python=3.7`, in
-this way, it will result in installation failure.
-
 Problems with proxies
 ---------------------
 If you are installing ESMValTool from source from behind a proxy that does not
@@ -778,7 +744,7 @@ Problems when updating the conda environment
 --------------------------------------------
 Usually mamba is much better at solving new environments than updating older
 environments, so it is often a good idea to create a new environment if updating
-does not work. See also `Mamba fails to solve the environment`_.
+does not work.
 
 Do not run ``mamba update --update-all`` in the ``esmvaltool``
 environment since that will update some packages that are pinned to
diff --git a/doc/sphinx/source/quickstart/output.rst b/doc/sphinx/source/quickstart/output.rst
index 4a33e8ca42..33836f1c9a 100644
--- a/doc/sphinx/source/quickstart/output.rst
+++ b/doc/sphinx/source/quickstart/output.rst
@@ -5,8 +5,9 @@ Output
 ******
 
 ESMValTool automatically generates a new output directory with every run. The
-location is determined by the output_dir option  in the config-user.yml file,
-the recipe name, and the date and time, using the the format: YYYYMMDD_HHMMSS.
+location is determined by the :ref:`configuration option
+<esmvalcore:config_options>` ``output_dir``, the recipe name, and the date and
+time, using the the format: YYYYMMDD_HHMMSS.
 
 For instance, a typical output location would be:
 output_directory/recipe_ocean_amoc_20190118_1027/
@@ -33,13 +34,15 @@ The preprocessed datasets will be stored to the preproc/ directory.
 Each variable in each diagnostic will have its own the `metadata.yml`_
 interface files saved in the preproc directory.
 
-If the option ``save_intermediary_cubes`` is set to ``true`` in the
-config-user.yml file, then the intermediary cubes will also be saved here.
-This option is set to false in the default ``config-user.yml`` file.
+If the :ref:`configuration option <esmvalcore:config_options>`
+``save_intermediary_cubes`` is set to ``true`` , then the intermediary cubes
+will also be saved here.
+This option is set to ``false`` by default.
 
-If the option ``remove_preproc_dir`` is set to ``true`` in the config-user.yml
-file, then the preproc directory will be deleted after the run completes. This
-option is set to true in the default  ``config-user.yml`` file.
+If the :ref:`configuration option <esmvalcore:config_options>`
+``remove_preproc_dir`` is set to ``true`` , then the preproc directory will be
+deleted after the run completes.
+This option is set to ``true`` by default.
 
 
 Run
@@ -70,8 +73,8 @@ Plots
 =====
 
 The plots directory is where diagnostics save their output figures. These
-plots are saved in the format requested by the option `output_file_type` in the
-config-user.yml file.
+plots are saved in the format requested by the :ref:`configuration option
+<esmvalcore:config_options>` ``output_file_type``.
 
 
 Settings.yml
@@ -81,10 +84,10 @@ The settings.yml file is automatically generated by ESMValCore. For each diagnos
 a unique settings.yml file will be produced.
 
 The settings.yml file passes several global level keys to diagnostic scripts.
-This includes several flags from the config-user.yml file (such as
-'write_netcdf', 'write_plots', etc...), several paths which are specific to the
-diagnostic being run (such as 'plot_dir' and 'run_dir') and the location on
-disk of the metadata.yml file (described below).
+This includes several flags from the configuration (such as
+``write_netcdf``, ``write_plots``, etc...), several paths which are specific to
+the diagnostic being run (such as ``plot_dir`` and ``run_dir``) and the
+location on disk of the metadata.yml file (described below).
 
 .. code-block:: yaml
 
@@ -147,5 +150,5 @@ As you can see, this is effectively a dictionary with several items including
 data paths, metadata and other information.
 
 There are  several tools available in python which are built to read and parse
-these files. The tools are avaialbe in the shared directory in the diagnostics
+these files. The tools are available in the shared directory in the diagnostics
 directory.
diff --git a/doc/sphinx/source/quickstart/running.rst b/doc/sphinx/source/quickstart/running.rst
index 7f9cadbaa1..20cb8620b0 100644
--- a/doc/sphinx/source/quickstart/running.rst
+++ b/doc/sphinx/source/quickstart/running.rst
@@ -39,20 +39,20 @@ from ESGF to the local directory ``~/climate_data``, run
 
 The ``--search_esgf=when_missing`` option tells ESMValTool to search for and
 download the necessary climate data files, if they cannot be found locally.
-The data only needs to be downloaded once, every following run will re-use
+The data only needs to be downloaded once, every following run will reuse
 previously downloaded data.
 If you have all required data available locally, you can run the tool with
 ``--search_esgf=never`` argument (the default).
 Note that in that case the required data should be located in the directories
-specified in your user configuration file.
+specified in the configuration (see :ref:`esmvalcore:config_option_rootpath`).
 A third option ``--search_esgf=always`` is available.
 With this option, the tool will first check the ESGF for the needed data,
 regardless of any local data availability; if the data found on ESGF is newer
 than the local data (if any) or the user specifies a version of the data that
 is available only from the ESGF, then that data will be downloaded; otherwise,
 local data will be used.
-Recall that the chapter :ref:`Configuring ESMValTool <config-user>`
-provides an explanation of how to create your own config-user.yml file.
+Recall that the chapter on :ref:`configuring ESMValTool <config>`
+provides an explanation of how to set up the configuration.
 
 See :ref:`running esmvaltool <esmvalcore:running>` in the ESMValCore
 documentation for a more complete introduction to the ``esmvaltool`` command.
diff --git a/doc/sphinx/source/recipes/broken_recipe_list.rst b/doc/sphinx/source/recipes/broken_recipe_list.rst
index e2c5b874d8..f2c25623ac 100644
--- a/doc/sphinx/source/recipes/broken_recipe_list.rst
+++ b/doc/sphinx/source/recipes/broken_recipe_list.rst
@@ -9,26 +9,36 @@ More details can be found in the :ref:`broken recipe policy
 <broken-recipe-policy>`.
 
 .. list-table:: Broken recipes
-   :widths: 25 25 25 25
+   :widths: 25 25 25 25 25
    :header-rows: 1
 
    * - Broken recipe
      - Affected diagnostics
+     - Broken since release
      - Problem
      - GitHub issue
-   * - `recipe_check_obs.yml`
-     - `ERA5_native6`
-     - Derivation of custom variables `rlus` and `rsus`
-     - `#1388 <https://github.com/ESMValGroup/ESMValCore/issues/1388>`_
    * - :ref:`recipe_julia.yml <recipe_examples>`
      - `example`
-     - fill values are not interpreted, resulting in an unusable plot
+     - v2.5.0
+     - Fill values are not interpreted, resulting in an unusable plot
      - `#2595 <https://github.com/ESMValGroup/ESMValTool/issues/2595>`_
-   * - :ref:`recipe_seaice_drift.yml <recipes_seaice_drift>`
-     - `sea_ice_drift_SCICEX`
-     - ``shapely 2`` issue
-     - `#3243 <https://github.com/ESMValGroup/ESMValTool/issues/3243>`_
-   * - :ref:`recipe_pysplot.yml <recipes_psyplot_diag>`
-     - `plot_map`
-     - ``shapely 2`` issue
-     - `#3483 <https://github.com/ESMValGroup/ESMValTool/issues/3483>`_
+   * - :ref:`recipe_climwip_brunner2019_med.yml <recipe_climwip>`
+     - All (preprocessor issue)
+     - v2.11.0
+     - Failed to run preprocessor function ``fix_metadata`` on the data: Unable to convert units
+     - `#3694 <https://github.com/ESMValGroup/ESMValTool/issues/3694>`_
+   * - :ref:`recipe_ocean_amoc.yml <XML_oceans>`
+     - ``diag_timeseries_amoc``, ``diag_transects``
+     - v2.11.0
+     - CESM1 CMIP5 Omon data no longer available
+     - `#3693 <https://github.com/ESMValGroup/ESMValTool/issues/3693>`_
+   * - :ref:`recipe_russell18jgr.yml <nml_oceanmetrics>`
+     - ``Figure_4``
+     - v2.11.0
+     - CESM1 CMIP5 Omon data no longer available
+     - `#3693 <https://github.com/ESMValGroup/ESMValTool/issues/3693>`_
+   * - :ref:`recipe_wenzel14jgr.yml <recipe_wenzel14jgr>`
+     - ``diag_tsline_Fig2d``
+     - v2.11.0
+     - CESM1 CMIP5 Omon data no longer available
+     - `#3693 <https://github.com/ESMValGroup/ESMValTool/issues/3693>`_
diff --git a/doc/sphinx/source/recipes/figures/bock24acp/boxplot_ssp585_south_oc.png b/doc/sphinx/source/recipes/figures/bock24acp/boxplot_ssp585_south_oc.png
new file mode 100644
index 0000000000..65f242fac2
Binary files /dev/null and b/doc/sphinx/source/recipes/figures/bock24acp/boxplot_ssp585_south_oc.png differ
diff --git a/doc/sphinx/source/recipes/figures/bock24acp/map_netcre.png b/doc/sphinx/source/recipes/figures/bock24acp/map_netcre.png
new file mode 100644
index 0000000000..d34e1e963a
Binary files /dev/null and b/doc/sphinx/source/recipes/figures/bock24acp/map_netcre.png differ
diff --git a/doc/sphinx/source/recipes/figures/bock24acp/zonal_diff_clt_ssp585.png b/doc/sphinx/source/recipes/figures/bock24acp/zonal_diff_clt_ssp585.png
new file mode 100644
index 0000000000..683ac90956
Binary files /dev/null and b/doc/sphinx/source/recipes/figures/bock24acp/zonal_diff_clt_ssp585.png differ
diff --git a/doc/sphinx/source/recipes/figures/climate_patterns/patterns.png b/doc/sphinx/source/recipes/figures/climate_patterns/patterns.png
new file mode 100644
index 0000000000..396fd78830
Binary files /dev/null and b/doc/sphinx/source/recipes/figures/climate_patterns/patterns.png differ
diff --git a/doc/sphinx/source/recipes/figures/portrait/portrait_plot.png b/doc/sphinx/source/recipes/figures/portrait/portrait_plot.png
new file mode 100644
index 0000000000..a6020c2d37
Binary files /dev/null and b/doc/sphinx/source/recipes/figures/portrait/portrait_plot.png differ
diff --git a/doc/sphinx/source/recipes/index.rst b/doc/sphinx/source/recipes/index.rst
index 0f0ce7667d..33fbb13126 100644
--- a/doc/sphinx/source/recipes/index.rst
+++ b/doc/sphinx/source/recipes/index.rst
@@ -23,6 +23,7 @@ large variety of input data.
 
    recipe_model_evaluation
    recipe_monitor
+   recipe_portrait
    recipe_psyplot
    recipe_seaborn
 
@@ -32,6 +33,7 @@ Atmosphere
    :maxdepth: 1
 
    recipe_miles
+   recipe_climate_patterns
    recipe_clouds
    recipe_cmug_h2o
    recipe_crem
@@ -93,6 +95,7 @@ Future projections
    recipe_tcr
    recipe_tebaldi21esd
    recipe_climate_change_hotspot
+   recipe_bock24acp
 
 IPCC
 ^^^^
diff --git a/doc/sphinx/source/recipes/recipe_bock24acp.rst b/doc/sphinx/source/recipes/recipe_bock24acp.rst
new file mode 100644
index 0000000000..bc1d4d3717
--- /dev/null
+++ b/doc/sphinx/source/recipes/recipe_bock24acp.rst
@@ -0,0 +1,143 @@
+.. _recipes_bock24acp:
+
+Cloud properties and their projected changes in CMIP models with low to high climate sensitivity
+================================================================================================
+
+Overview
+--------
+
+The recipes recipe_bock24acp_*.yml reproduce figures (Fig. 3, 4, 6 and 7) from the publication `Bock and Lauer, 2024`_ investigating cloud properties and their projected changes in CMIP models with low to high climate sensitivity.
+
+.. _`Bock and Lauer, 2024`: https://doi.org/10.5194/acp-24-1587-2024
+
+Available recipes and diagnostics
+---------------------------------
+
+Recipes are stored in recipes/clouds
+
+    * recipe_bock24acp_fig3-4_maps.yml
+    * recipe_bock24acp_fig6_zonal.yml
+    * recipe_bock24acp_fig7_boxplots.yml
+
+Diagnostics are stored in diag_scripts/
+
+    Fig. 3 and 4:
+
+    * clouds/clouds_ecs_groups_maps.py: Geographical maps of the multi-year annual means for group means of historical CMIP simulations from all three ECS groups.
+
+    Fig. 6:
+
+    * clouds/clouds_ecs_groups_zonal.py: Zonally averaged group means.
+
+    Fig. 7:
+
+    * clouds/clouds_ecs_groups_boxplots.py: Boxplots of relative changes for all groups.
+
+
+User settings in recipe
+-----------------------
+
+#. Script clouds_ecs_groups_maps.py 
+
+   *Required settings (scripts)*
+
+   reference: if true, a reference dataset is given within 'variable_group' equal 'OBS'
+
+   *Optional settings (scripts)*
+
+   plot_each_model: one figure for each single model
+
+
+#. Script clouds/clouds_ecs_groups_zonal.py
+
+   *Required settings (scripts)*
+
+   group_by: list of 'variable_group's to have the order
+   plot_type: 'zonal' and 'height' plots are available 
+
+   *Optional settings (scripts)*
+
+   filename_attach: attachment to the output files
+
+
+#. Script clouds/clouds_ecs_groups_boxplots.py
+
+   *Required settings (scripts)*
+
+   exclude_datasets: list of datasets which are not used for the statistics, default is ['MultiModelMean', 'MultiModelP5', 'MultiModelP95']
+   group_by: list of 'variable_group's to have the order
+   plot_type: 'zonal' and 'height' plots are available 
+
+   *Optional settings (scripts)*
+
+   filename_attach: attachment to the output files
+   title: set title of figure
+   y_range: set range of the y-axes
+
+
+Variables
+---------
+
+* clt (atmos, monthly, longitude latitude time)
+* clivi (atmos, monthly, longitude latitude time)
+* clwvi (atmos, monthly, longitude latitude time)
+* rlut (atmos, monthly, longitude latitude time)
+* rsut (atmos, monthly, longitude latitude time)
+* rlutcs (atmos, monthly, longitude latitude time)
+* rsutcs (atmos, monthly, longitude latitude time)
+* tas (atmos, monthly, longitude latitude time)
+
+
+Observations and reformat scripts
+---------------------------------
+
+* CERES-EBAF (Ed4.2) - TOA radiation fluxes (used for calculation of
+  the cloud radiative effects)
+
+  *Reformat script:* cmorizers/data/formatters/datasets/ceres_ebaf.py
+
+
+References
+----------
+
+* Bock, L. and Lauer, A.: Cloud properties and their projected changes in CMIP
+  models with low to high climate sensitivity, Atmos. Chem. Phys., 24, 1587–1605,
+  https://doi.org/10.5194/acp-24-1587-2024, 2024.
+
+
+Example plots
+-------------
+
+.. _fig_bock24acp_1:
+.. figure::  /recipes/figures/bock24acp/map_netcre.png
+   :align:   center
+
+   Geographical map of the multi-year annual mean net cloud radiative effect from 
+   (a) CERES–EBAF Ed4.2 (OBS) and (b–d) group means of historical CMIP simulations
+   from all three ECS groups (Fig. 4).
+
+.. _fig_bock24acp_2:
+.. figure::  /recipes/figures/bock24acp/zonal_diff_clt_ssp585.png
+   :align:   center
+
+   The upper panel show the zonally averaged group means of total cloud
+   fraction from historical simulations (solid lines)
+   and RCP8.5/SSP5-8.5 scenarios (dashed lines) for the three different ECS groups.
+   Lower panels show the corresponding relative differences of all zonally
+   averaged group means between the RCP8.5/SSP5-8.5 scenarios and the corresponding
+   historical simulations. Shading indicates the 5 % and 95 % quantiles of the single
+   model results (Fig. 6a).
+
+.. _fig_bock24acp_3:
+.. figure::  /recipes/figures/bock24acp/boxplot_ssp585_south_oc.png
+   :align:   center
+
+   Relative change (calculated as the difference between the scenario value and the
+   historical value divided by the historical value) of total cloud fraction (clt),
+   ice water path (iwp), liquid water path (lwp), and net cloud radiative effect
+   (netcre) per degree of warming averaged over the Southern Ocean (30–65°S). In the
+   box plot, each box indicates the range from the first
+   quartile to the third quartile, the vertical line shows the median, and the
+   whiskers the minimum and maximum values, excluding the outliers. Outliers are
+   defined as being outside 1.5 times the interquartile range (Fig. 7b).
+
diff --git a/doc/sphinx/source/recipes/recipe_carvalhais14nat.rst b/doc/sphinx/source/recipes/recipe_carvalhais14nat.rst
index dc26a745e2..b551bbbdc5 100644
--- a/doc/sphinx/source/recipes/recipe_carvalhais14nat.rst
+++ b/doc/sphinx/source/recipes/recipe_carvalhais14nat.rst
@@ -73,7 +73,7 @@ The settings needed for loading the observational dataset in all diagnostics
 are provided in the recipe through `obs_info` within `obs_details` section.
 
     * ``obs_data_subdir``: subdirectory of auxiliary_data_dir (set in
-      config-user file) where observation data are stored {e.g.,
+      configuration) where observation data are stored {e.g.,
       data_ESMValTool_Carvalhais2014}.
     * ``source_label``: source data label {'Carvalhais2014'}.
     * ``variant_label``: variant of the observation {'BE'} for best estimate.
@@ -112,7 +112,7 @@ Script land_carbon_cycle/diag_global_turnover.py
     * ``y0``: {``float``, 1.0} Y - coordinate of the upper edge of the figure.
     * ``wp``: {``float``, 1 / number of models} - width of each map.
     * ``hp``: {``float``, = wp} - height of each map.
-    * ``xsp``: {``float``, 0} - spacing betweeen maps in X - direction.
+    * ``xsp``: {``float``, 0} - spacing between maps in X - direction.
     * ``ysp``: {``float``, -0.03} - spacing between maps in Y -direction.
       Negative to reduce the spacing below default.
     * ``aspect_map``: {``float``, 0.5} - aspect of the maps.
@@ -217,10 +217,10 @@ Due to inherent dependence of the diagnostic on uncertainty estimates in
 observation, the data needed for each diagnostic script are processed at
 different spatial resolutions (as in Carvalhais et al., 2014), and provided in
 11 different resolutions (see Table 1). Note that the uncertainties were
-estimated at the resolution of the selected models, and, thus, only the 
-pre-processed observed data can be used with the recipe. 
-It is not possible to use regridding functionalities of ESMValTool to regrid 
-the observational data to other spatial resolutions, as the uncertainty 
+estimated at the resolution of the selected models, and, thus, only the
+pre-processed observed data can be used with the recipe.
+It is not possible to use regridding functionalities of ESMValTool to regrid
+the observational data to other spatial resolutions, as the uncertainty
 estimates cannot be regridded.
 
 Table 1. A summary of the observation datasets at different resolutions.
@@ -309,7 +309,7 @@ Example plots
 
    Comparison of latitudinal (zonal) variations of pearson correlation between
    turnover time and climate: turnover time and precipitation, controlled for
-   temperature (left) and vice-versa (right). Reproduces figures 2c and 2d in 
+   temperature (left) and vice-versa (right). Reproduces figures 2c and 2d in
    `Carvalhais et al. (2014)`_.
 
 .. _fig_carvalhais14nat_2:
@@ -320,7 +320,7 @@ Example plots
 
    Comparison of observation-based and modelled ecosystem carbon turnover time.
    Along the diagnonal, tau_ctotal are plotted, above the bias, and below
-   density plots. The inset text in density plots indicate the correlation. 
+   density plots. The inset text in density plots indicate the correlation.
 
 .. _fig_carvalhais14nat_3:
 
@@ -328,11 +328,11 @@ Example plots
    :align: center
    :width: 80%
 
-   Global distributions of multimodel bias and model agreement. Multimodel bias 
-   is calculated as the ratio of multimodel median turnover time and that from 
-   observation.  Stippling indicates the regions where only less than one 
-   quarter of the models fall within the range of observational uncertainties 
-   (`5^{th}` and `95^{th}` percentiles). Reproduces figure 3 in `Carvalhais et 
+   Global distributions of multimodel bias and model agreement. Multimodel bias
+   is calculated as the ratio of multimodel median turnover time and that from
+   observation.  Stippling indicates the regions where only less than one
+   quarter of the models fall within the range of observational uncertainties
+   (`5^{th}` and `95^{th}` percentiles). Reproduces figure 3 in `Carvalhais et
    al. (2014)`_.
 
 .. _fig_carvalhais14nat_4:
@@ -341,7 +341,7 @@ Example plots
    :align: center
    :width: 80%
 
-   Comparison of latitudinal (zonal) variations of observation-based and 
-   modelled ecosystem carbon turnover time. The zonal turnover time is 
-   calculated as the ratio of zonal `ctotal` and `gpp`. Reproduces figures 2a 
+   Comparison of latitudinal (zonal) variations of observation-based and
+   modelled ecosystem carbon turnover time. The zonal turnover time is
+   calculated as the ratio of zonal `ctotal` and `gpp`. Reproduces figures 2a
    and 2b in `Carvalhais et al. (2014)`_.
diff --git a/doc/sphinx/source/recipes/recipe_climate_patterns.rst b/doc/sphinx/source/recipes/recipe_climate_patterns.rst
new file mode 100644
index 0000000000..f7336c91c4
--- /dev/null
+++ b/doc/sphinx/source/recipes/recipe_climate_patterns.rst
@@ -0,0 +1,107 @@
+.. _recipes_climate_patterns:
+
+Generating Climate Patterns from CMIP6 Models
+=============================================
+
+Overview
+--------
+
+The recipe recipe_climate_patterns generates climate patterns from CMIP6 model
+datasets.
+
+.. note::
+  The regrid setting in the recipe is set to a 2.5x3.75 grid. This is done to
+  match the current resolution in the IMOGEN-JULES model, but can be
+  adjusted with no issues for a finer/coarser patterns grid.
+
+
+Available recipes and diagnostics
+---------------------------------
+
+Recipes are stored in esmvaltool/recipes/
+
+* recipe_climate_patterns.yml
+
+Diagnostics are stored in esmvaltool/diag_scripts/climate_patterns/
+
+* climate_patterns.py: generates climate patterns from input datasets
+* sub_functions.py: set of sub functions to assist with driving scripts
+* plotting.py: contains all plotting functions for driving scripts
+
+
+User settings in recipe
+-----------------------
+
+#. Script climate_patterns.py
+
+   *Required settings for script*
+
+   None
+
+   *Optional settings for script*
+
+   * jules_mode: output jules-specific var names + .nc files
+   * parallelise: parallelise over models or not
+   * area: calculate the patterns globally, or over land only
+
+   *Required settings for variables*
+
+   * short_name
+   * additional_datasets
+
+   *Optional settings for variables*
+
+   None
+
+   *Required settings for preprocessor*
+
+   * monthly_statistics: converts data to mean monthly data
+
+   *Optional settings for preprocessor*
+
+   * regrid: regrids data
+
+
+Variables
+---------
+
+#. Script climate_patterns.py
+
+* tasmax (atmos, monthly, longitude latitude time)
+* tasmin (atmos, monthly, longitude latitude time)
+* tas (atmos, monthly, longitude latitude time)
+* huss (atmos, monthly, longitude latitude time)
+* pr (atmos, monthly, longitude latitude time)
+* sfcWind (atmos, monthly, longitude latitude time)
+* ps (atmos, monthly, longitude latitude time)
+* rsds (atmos, monthly, longitude latitude time)
+* rlds (atmos, monthly, longitude latitude time)
+
+
+Observations and reformat scripts
+---------------------------------
+
+None
+
+References
+----------
+
+* Huntingford, C., Cox, P. An analogue model to derive additional climate
+  change scenarios from existing GCM simulations.
+  Climate Dynamics 16, 575–586 (2000). https://doi.org/10.1007/s003820000067
+
+* Mathison, C. T. et al. A rapid application emissions-to-impacts tool
+  for scenario assessment: Probabilistic Regional Impacts from Model patterns
+  and Emissions (PRIME).
+  EGUsphere [preprint], (2024). https://doi.org/10.5194/egusphere-2023-2932
+
+Example plots
+-------------
+
+.. _fig_climate_patterns_2:
+.. figure::  /recipes/figures/climate_patterns/patterns.png
+   :align:   center
+   :width: 80%
+
+   Patterns generated for CMIP6 models, gridded view. Patterns are shown per
+   variable, for the month of January.
\ No newline at end of file
diff --git a/doc/sphinx/source/recipes/recipe_climwip.rst b/doc/sphinx/source/recipes/recipe_climwip.rst
index 0928ba939f..900698b85a 100644
--- a/doc/sphinx/source/recipes/recipe_climwip.rst
+++ b/doc/sphinx/source/recipes/recipe_climwip.rst
@@ -43,9 +43,9 @@ Using shapefiles for cutting scientific regions
 
 To use shapefiles for selecting SREX or AR6 regions by name it is necessary to download them, e.g.,
 from the sources below and reference the file using the `shapefile` parameter. This can either be a
-absolute or a relative path. In the example recipes they are stored in  a subfolder `shapefiles`
-in the `auxiliary_data_dir` (with is specified in the
-`config-user.yml <https://docs.esmvaltool.org/projects/ESMValCore/en/latest/quickstart/configure.html#user-configuration-file>`_).
+absolute or a relative path. In the example recipes they are stored in a subfolder `shapefiles`
+in the :ref:`configuration option <esmvalcore:config_options>`
+``auxiliary_data_dir``.
 
 SREX regions (AR5 reference regions): http://www.ipcc-data.org/guidelines/pages/ar5_regions.html
 
@@ -249,7 +249,7 @@ Brunner et al. (2020) recipe and example independence weighting
 
 The recipe uses an additional step between pre-processor and weight calculation to calculate anomalies relative to the global mean (e.g., tas_ANOM = tas_CLIM - global_mean(tas_CLIM)). This means we do not use the absolute temperatures of a model as performance criterion but rather the horizontal temperature distribution (see `Brunner et al. 2020 <https://doi.org/10.5194/esd-11-995-2020>`_ for a discussion).
 
-This recipe also implements a somewhat general independence weighting for CMIP6. In contrast to model performance (which should be case specific) model independence can largely be seen as only dependet on the multi-model ensemble in use but not the target variable or region. This means that the configuration used should be valid for similar subsets of CMIP6 as used in this recipe:
+This recipe also implements a somewhat general independence weighting for CMIP6. In contrast to model performance (which should be case specific) model independence can largely be seen as only dependent on the multi-model ensemble in use but not the target variable or region. This means that the configuration used should be valid for similar subsets of CMIP6 as used in this recipe:
 
 
 .. code-block:: yaml
diff --git a/doc/sphinx/source/recipes/recipe_gier20bg.rst b/doc/sphinx/source/recipes/recipe_gier20bg.rst
index bb11770a24..b8f8fb9b8e 100644
--- a/doc/sphinx/source/recipes/recipe_gier20bg.rst
+++ b/doc/sphinx/source/recipes/recipe_gier20bg.rst
@@ -53,7 +53,7 @@ User settings in recipe
 
     * Optional diag_script_info attributes:
         * ``styleset``: styleset for color coding panels
-        * ``output_file_type``: output file type for plots, default: config_user -> png
+        * ``output_file_type``: output file type for plots, default: png
         * ``var_plotname``: NCL string formatting how variable should be named in plots
           defaults to short_name if not assigned.
 
@@ -64,7 +64,7 @@ User settings in recipe
           amplitude contour plot
 
     * Optional diag_script_info attributes:
-        * ``output_file_type``: output file type for plots, default: config_user -> png
+        * ``output_file_type``: output file type for plots, default: png
 
 #. Script xco2_analysis/main.ncl:
 
@@ -77,7 +77,7 @@ User settings in recipe
           accounting for the ensemble member named in "ensemble_refs"
 
     * Optional diag_script_info attributes:
-        * ``output_file_type``: output file type for plots, default: config_user -> png
+        * ``output_file_type``: output file type for plots, default: png
         * ``ensemble_refs``: list of model-ensemble pairs to denote which ensemble
           member to use for calculating multi-model mean. required if
           ensemble_mean = true
@@ -97,17 +97,17 @@ User settings in recipe
         * ``plot_var2_mean``: If True adds mean of seasonal cycle to panel as string.
 
     * Optional diag_script_info attributes:
-        * ``output_file_type``: output file type for plots, default: config_user -> png
+        * ``output_file_type``: output file type for plots, default: png
         * ``var_plotname``: String formatting how variable should be named in plots
           defaults to short_name if not assigned
 
 #. Script xco2_analysis/sat_masks.ncl:
 
     * Optional diag_script_info attributes:
-        * ``output_file_type``: output file type for plots, default: config_user -> png
+        * ``output_file_type``: output file type for plots, default: png
         * ``var_plotname``: String formatting how variable should be named in plots
           defaults to short_name if not assigned
-        * ``c3s_plots``: Missing value plots seperated by timeseries of c3s satellites
+        * ``c3s_plots``: Missing value plots separated by timeseries of c3s satellites
 
 #. Script xco2_analysis/station_comparison.ncl:
 
@@ -116,7 +116,7 @@ User settings in recipe
           first, then 2D variable, followed by surface stations
 
     * Optional diag_script_info attributes:
-        * ``output_file_type``: output file type for plots, default: config_user -> png
+        * ``output_file_type``: output file type for plots, default: png
         * ``var_plotnames``: String formatting how variables should be named in plots
           defaults to short_name if not assigned
         * ``overwrite_altitudes``: Give other altitude values than the ones attached in
diff --git a/doc/sphinx/source/recipes/recipe_hydrology.rst b/doc/sphinx/source/recipes/recipe_hydrology.rst
index d0e2e0bcb3..995a70b3ae 100644
--- a/doc/sphinx/source/recipes/recipe_hydrology.rst
+++ b/doc/sphinx/source/recipes/recipe_hydrology.rst
@@ -62,13 +62,13 @@ Diagnostics are stored in esmvaltool/diag_scripts/hydrology
     * wflow.py
     * lisflood.py
     * hype.py
-    * globwat.py 
+    * globwat.py
 
 
 User settings in recipe
 -----------------------
 
-All hydrological recipes require a shapefile as an input to produce forcing data. This shapefile determines the shape of the basin for which the data will be cut out and processed. All recipes are tested with `the shapefiles <https://github.com/eWaterCycle/recipes_auxiliary_datasets/tree/main/>`_  that are used for the eWaterCycle project. In principle any shapefile can be used, for example, the freely available basin shapefiles from the `HydroSHEDS project <https://www.hydrosheds.org/>`_. 
+All hydrological recipes require a shapefile as an input to produce forcing data. This shapefile determines the shape of the basin for which the data will be cut out and processed. All recipes are tested with `the shapefiles <https://github.com/eWaterCycle/recipes_auxiliary_datasets/tree/main/>`_  that are used for the eWaterCycle project. In principle any shapefile can be used, for example, the freely available basin shapefiles from the `HydroSHEDS project <https://www.hydrosheds.org/>`_.
 
 #. recipe_pcrglobwb.yml
 
@@ -87,7 +87,7 @@ All hydrological recipes require a shapefile as an input to produce forcing data
 
       *extract_shape:*
 
-         * shapefile: Meuse.shp (MARRMoT is a hydrological Lumped model that needs catchment-aggregated forcing data. The catchment is provided as a shapefile, the path can be relative to ``auxiliary_data_dir`` as defined in config-user.yml.).
+         * shapefile: Meuse.shp (MARRMoT is a hydrological Lumped model that needs catchment-aggregated forcing data. The catchment is provided as a shapefile, the path can be relative to :ref:`configuration option <esmvalcore:config_options>` ``auxiliary_data_dir``).
          * method: contains
          * crop: true
 
@@ -107,7 +107,7 @@ All hydrological recipes require a shapefile as an input to produce forcing data
 	    * dem_file: netcdf file containing a digital elevation model with
 	      elevation in meters and coordinates latitude and longitude.
               A wflow example dataset is available at: https://github.com/openstreams/wflow/tree/master/examples/wflow_rhine_sbm
-              The example dem_file can be obtained from https://github.com/openstreams/wflow/blob/master/examples/wflow_rhine_sbm/staticmaps/wflow_dem.map 
+              The example dem_file can be obtained from https://github.com/openstreams/wflow/blob/master/examples/wflow_rhine_sbm/staticmaps/wflow_dem.map
 	    * regrid: the regridding scheme for regridding to the digital elevation model. Choose ``area_weighted`` (slow) or ``linear``.
 
 #. recipe_lisflood.yml
diff --git a/doc/sphinx/source/recipes/recipe_ipccwg1ar6ch3.rst b/doc/sphinx/source/recipes/recipe_ipccwg1ar6ch3.rst
index 42bedcec09..718c345b19 100644
--- a/doc/sphinx/source/recipes/recipe_ipccwg1ar6ch3.rst
+++ b/doc/sphinx/source/recipes/recipe_ipccwg1ar6ch3.rst
@@ -6,7 +6,7 @@ IPCC AR6 Chapter 3 (selected figures)
 Overview
 --------
 
-This recipe collects selected diagnostics used in IPCC AR6 WGI Chapter 3: 
+This recipe collects selected diagnostics used in IPCC AR6 WGI Chapter 3:
 Human influence on the climate system (`Eyring et al., 2021`_). Plots from IPCC
 AR6 can be readily reproduced and compared to previous versions. The aim is to
 be able to start with what was available now the next time allowing us to focus
@@ -15,7 +15,8 @@ on developing more innovative analysis methods rather than constantly having to
 
 Processing of CMIP3 models currently works only in serial mode, due to an issue
 in the input data still under investigation. To run the recipe for Fig 3.42a
-and Fig. 3.43 set "max_parallel_tasks: 1" in the config-user.yml file.
+and Fig. 3.43 set the :ref:`configuration option <esmvalcore:config_options>`
+``max_parallel_tasks: 1``.
 
 The plots are produced collecting the diagnostics from individual recipes. The
 following figures from `Eyring et al. (2021)`_ can currently be reproduced:
@@ -43,10 +44,9 @@ To reproduce Fig. 3.9 you need the shapefile of the `AR6 reference regions
 (`Iturbide et al., 2020 <https://doi.org/10.5194/essd-12-2959-2020>`_).
 Please download the file `IPCC-WGI-reference-regions-v4_shapefile.zip
 <https://github.com/SantanderMetGroup/ATLAS/blob/v1.6/reference-regions/IPCC-WGI-reference-regions-v4_shapefile.zip>`_,
-unzip and store it in `<auxiliary_data_dir>/IPCC-regions/` (the `auxiliary_data_dir`
-is defined in the `config-user.yml
-<https://docs.esmvaltool.org/projects/ESMValCore/en/latest/quickstart/configure.html#user-configuration-file>`_
-file).
+unzip and store it in `<auxiliary_data_dir>/IPCC-regions/` (where
+``auxiliary_data_dir`` is given as :ref:`configuration option
+<esmvalcore:config_options>`).
 
 .. _`Eyring et al., 2021`: https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-3/
 .. _`Eyring et al. (2021)`: https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-3/
@@ -179,7 +179,7 @@ User settings in recipe
 
    * start_year: start year in figure
    * end_year: end year in figure
-   * panels: list of variable blocks for each panel 
+   * panels: list of variable blocks for each panel
 
    *Optional settings for script*
 
@@ -205,7 +205,7 @@ User settings in recipe
    * plot_units: variable unit for plotting
    * y-min: set min of y-axis
    * y-max: set max of y-axis
-   * order: order in which experiments should be plotted 
+   * order: order in which experiments should be plotted
    * stat_shading: if true: shading of statistic range
    * ref_shading: if true: shading of reference period
 
@@ -225,7 +225,7 @@ User settings in recipe
 
    * plot_legend: if true, plot legend will be plotted
    * plot_units: variable unit for plotting
-   * multi_model_mean: if true, multi-model mean and uncertaintiy will be 
+   * multi_model_mean: if true, multi-model mean and uncertainty will be
      plotted
 
    *Optional settings for variables*
@@ -304,7 +304,7 @@ User settings in recipe
    * labels: List of labels for each variable on the x-axis
    * model_spread: if True, model spread is shaded
    * plot_median: if True, median is plotted
-   * project_order: give order of projects 
+   * project_order: give order of projects
 
 
 Variables
@@ -452,7 +452,7 @@ Example plots
    2013). For line colours see the legend of Figure 3.4. Additionally, the
    multi-model mean (red) and standard deviation (grey shading) are shown.
    Observational and model datasets were detrended by removing the
-   least-squares quadratic trend. 
+   least-squares quadratic trend.
 
 .. figure::  /recipes/figures/ipccwg1ar6ch3/tas_anom_damip_global_1850-2020.png
    :align:   center
@@ -467,7 +467,7 @@ Example plots
    anomalies are shown relative to 1950-2010 for Antarctica and relative to
    1850-1900 for other continents. CMIP6 historical simulations are expanded by
    the SSP2-4.5 scenario simulations. All available ensemble members were used.
-   Regions are defined by Iturbide et al. (2020). 
+   Regions are defined by Iturbide et al. (2020).
 
 .. figure::  /recipes/figures/ipccwg1ar6ch3/model_bias_pr_annualclim_CMIP6.png
    :align:   center
@@ -487,7 +487,7 @@ Example plots
    show a change greater than the variability threshold; crossed lines indicate
    regions with conflicting signal, where >=66% of models show change greater
    than the variability threshold and <80% of all models agree on the sign of
-   change. 
+   change.
 
 .. figure::  /recipes/figures/ipccwg1ar6ch3/precip_anom_1950-2014.png
    :align:   center
@@ -511,7 +511,7 @@ Example plots
    forcings (brown) and natural forcings only (blue). Observed trends for each
    observational product are shown as horizontal lines. Panel (b) shows annual
    mean precipitation rate (mm day-1) of GHCN version 2 for the years 1950-2014
-   over land areas used to compute the plots. 
+   over land areas used to compute the plots.
 
 .. figure::  /recipes/figures/ipccwg1ar6ch3/zonal_westerly_winds.png
    :align:   center
diff --git a/doc/sphinx/source/recipes/recipe_kcs.rst b/doc/sphinx/source/recipes/recipe_kcs.rst
index fa07f0a167..1ed117ecb6 100644
--- a/doc/sphinx/source/recipes/recipe_kcs.rst
+++ b/doc/sphinx/source/recipes/recipe_kcs.rst
@@ -30,7 +30,7 @@ In the second diagnostic, for both the control and future periods, the N target
 2. Further constrain the selection by picking samples that represent either high or low changes in summer precipitation and summer and winter temperature, by limiting the remaining samples to certain percentile ranges: relatively wet/cold in the control and dry/warm in the future, or vice versa. The percentile ranges are listed in table 1 of Lenderink 2014's supplement. This should result is approximately 50 remaining samples for each scenario, for both control and future.
 3. Use a Monte-Carlo method to make a final selection of 8 resamples with minimal reuse of the same ensemble member/segment.
 
-Datasets have been split in two parts: the CMIP datasets and the target model datasets. An example use case for this recipe is to compare between CMIP5 and CMIP6, for example. The recipe can work with a target model that is not part of CMIP, provided that the data are CMOR compatible, and using the same data referece syntax as the CMIP data. Note that you can specify :ref:`multiple data paths<config-user-rootpath>` in the user configuration file.
+Datasets have been split in two parts: the CMIP datasets and the target model datasets. An example use case for this recipe is to compare between CMIP5 and CMIP6, for example. The recipe can work with a target model that is not part of CMIP, provided that the data are CMOR compatible, and using the same data reference syntax as the CMIP data. Note that you can specify :ref:`multiple data paths<config_option_rootpath>` in the configuration.
 
 
 Available recipes and diagnostics
@@ -128,7 +128,7 @@ AND highlighting the selected steering parameters and resampling periods:
 .. figure::  /recipes/figures/kcs/global_matching.png
    :align:   center
 
-The diagnostic ``local_resampling`` procudes a number of output files:
+The diagnostic ``local_resampling`` produces a number of output files:
 
 * ``season_means_<scenario>.nc``: intermediate results, containing the season means for each segment of the original target model ensemble.
 * ``top1000_<scenario>.csv``: intermediate results, containing the 1000 combinations that have been selected based on winter mean precipitation.
diff --git a/doc/sphinx/source/recipes/recipe_model_evaluation.rst b/doc/sphinx/source/recipes/recipe_model_evaluation.rst
index 9e199815e0..c61f34aa62 100644
--- a/doc/sphinx/source/recipes/recipe_model_evaluation.rst
+++ b/doc/sphinx/source/recipes/recipe_model_evaluation.rst
@@ -35,9 +35,9 @@ User settings
 -------------
 
 It is recommended to use a vector graphic file type (e.g., SVG) for the output
-format when running this recipe, i.e., run the recipe with the command line
-option ``--output_file_type=svg`` or use ``output_file_type: svg`` in your
-:ref:`esmvalcore:user configuration file`.
+format when running this recipe, i.e., run the recipe with the
+:ref:`configuration options <esmvalcore:config_options>` ``output_file_type:
+svg``.
 Note that map and profile plots are rasterized by default.
 Use ``rasterize: false`` in the recipe to disable
 this.
diff --git a/doc/sphinx/source/recipes/recipe_monitor.rst b/doc/sphinx/source/recipes/recipe_monitor.rst
index ee3b9b44fa..8f4893fc12 100644
--- a/doc/sphinx/source/recipes/recipe_monitor.rst
+++ b/doc/sphinx/source/recipes/recipe_monitor.rst
@@ -36,9 +36,9 @@ User settings
 -------------
 
 It is recommended to use a vector graphic file type (e.g., SVG) for the output
-files when running this recipe, i.e., run the recipe with the command line
-option ``--output_file_type=svg`` or use ``output_file_type: svg`` in your
-:ref:`esmvalcore:user configuration file`.
+format when running this recipe, i.e., run the recipe with the
+:ref:`configuration options <esmvalcore:config_options>` ``output_file_type:
+svg``.
 Note that map and profile plots are rasterized by default.
 Use ``rasterize_maps: false`` or ``rasterize: false`` (see `Recipe settings`_)
 in the recipe to disable this.
diff --git a/doc/sphinx/source/recipes/recipe_oceans.rst b/doc/sphinx/source/recipes/recipe_oceans.rst
index d8bf3143e1..17552b39fa 100644
--- a/doc/sphinx/source/recipes/recipe_oceans.rst
+++ b/doc/sphinx/source/recipes/recipe_oceans.rst
@@ -458,7 +458,7 @@ and a latitude and longitude coordinates.
 
 This diagnostic also includes the optional arguments, `maps_range` and
 `diff_range` to manually define plot ranges. Both arguments are a list of two floats
-to set plot range minimun and maximum values respectively for Model and Observations
+to set plot range minimum and maximum values respectively for Model and Observations
 maps (Top panels) and for the Model minus Observations panel (bottom left).
 Note that if input data have negative values the Model over Observations map
 (bottom right) is not produced.
@@ -491,14 +491,14 @@ diagnostic_maps_multimodel.py
 The diagnostic_maps_multimodel.py_ diagnostic makes model(s) vs observations maps
 and if data are not provided it draws only model field.
 
-It is always nessary to define the overall layout trough the argument `layout_rowcol`,
+It is always necessary to define the overall layout through the argument `layout_rowcol`,
 which is a list of two integers indicating respectively the number of rows and columns
 to organize the plot. Observations has not be accounted in here as they are automatically
 added at the top of the figure.
 
 This diagnostic also includes the optional arguments, `maps_range` and
 `diff_range` to manually define plot ranges. Both arguments are a list of two floats
-to set plot range minimun and maximum values respectively for variable data and
+to set plot range minimum and maximum values respectively for variable data and
 the Model minus Observations range.
 
 Note that this diagnostic assumes that the preprocessors do the bulk of the
@@ -748,7 +748,7 @@ These tools are:
 - bgc_units: converts to sensible units where appropriate (ie Celsius, mmol/m3)
 - timecoord_to_float: Converts time series to decimal time ie: Midnight on January 1st 1970 is 1970.0
 - add_legend_outside_right: a plotting tool, which adds a legend outside the axes.
-- get_image_format: loads the image format, as defined in the global user config.yml.
+- get_image_format: loads the image format, as defined in the global configuration.
 - get_image_path: creates a path for an image output.
 - make_cube_layer_dict: makes a dictionary for several layers of a cube.
 
@@ -762,8 +762,8 @@ A note on the auxiliary data directory
 Some of these diagnostic scripts may not function on machines with no access
 to the internet, as cartopy may try to download the shape files. The solution
 to this issue is the put the relevant cartopy shapefiles in a directory which
-is visible to esmvaltool, then link that path to ESMValTool via
-the `auxiliary_data_dir` variable in your config-user.yml file.
+is visible to esmvaltool, then link that path to ESMValTool via the
+:ref:`configuration option <esmvalcore:config_options>` ``auxiliary_data_dir``.
 
 The cartopy masking files can be downloaded from:
 https://www.naturalearthdata.com/downloads/
diff --git a/doc/sphinx/source/recipes/recipe_perfmetrics.rst b/doc/sphinx/source/recipes/recipe_perfmetrics.rst
index 067b65af85..d2f82dbffc 100644
--- a/doc/sphinx/source/recipes/recipe_perfmetrics.rst
+++ b/doc/sphinx/source/recipes/recipe_perfmetrics.rst
@@ -3,12 +3,27 @@
 Performance metrics for essential climate parameters
 ====================================================
 
+.. note::
+  Some of the results of this diagnostics can also be reproduced utilizing
+  python diagnostics:
+  Portrait plot: :ref:`recipe_portrait`
+  Monitoring: :ref:`recipe_monitor`
+
 Overview
 --------
 
-The goal is to create a standard recipe for the calculation of performance metrics to quantify the ability of the models to reproduce the climatological mean annual cycle for selected "Essential Climate Variables" (ECVs) plus some additional corresponding diagnostics and plots to better understand and interpret the results.
+The goal is to create a standard recipe for the calculation of performance
+metrics to quantify the ability of the models to reproduce the climatological
+mean annual cycle for selected "Essential Climate Variables" (ECVs) plus some
+additional corresponding diagnostics and plots to better understand and
+interpret the results.
+
+The recipe can be used to calculate performance metrics at different vertical
+levels (e.g., 5, 30, 200, 850 hPa as in
+`Gleckler et al. (2008) <http://dx.doi.org/10.1029/2007JD008972>`_) and in
+different regions. As an additional reference, we consider
+`Righi et al. (2015) <https://doi.org/10.5194/gmd-8-733-2015>`_.
 
-The recipe can be used to calculate performance metrics at different vertical levels (e.g., 5, 30, 200, 850 hPa as in `Gleckler et al. (2008) <http://dx.doi.org/10.1029/2007JD008972>`_ and in different regions. As an additional reference, we consider `Righi et al. (2015) <https://doi.org/10.5194/gmd-8-733-2015>`_.
 
 Available recipes and diagnostics
 -----------------------------------
@@ -21,12 +36,19 @@ Recipes are stored in recipes/
 
 Diagnostics are stored in diag_scripts/perfmetrics/
 
-* main.ncl: calculates and (optionally) plots annual/seasonal cycles, zonal means, lat-lon fields and time-lat-lon fields. The calculated fields can also be plotted as difference w.r.t. a given reference dataset. main.ncl also calculates RMSD, bias and taylor metrics. Input data have to be regridded to a common grid in the preprocessor. Each plot type is created by a separated routine, as detailed below.
+* main.ncl: calculates and (optionally) plots annual/seasonal cycles, zonal
+  means, lat-lon fields and time-lat-lon fields. The calculated fields can also
+  be plotted as difference w.r.t. a given reference dataset. main.ncl also
+  calculates RMSD, bias and taylor metrics. Input data have to be regridded to
+  a common grid in the preprocessor. Each plot type is created by a separated
+  routine, as detailed below.
 * cycle.ncl: creates an annual/seasonal cycle plot.
 * zonal.ncl: creates a zonal (lat-pressure) plot.
 * latlon.ncl: creates a lat-lon plot.
-* cycle_latlon.ncl: precalculates the metrics for a time-lat-lon field, with different options for normalization.
-* collect.ncl: collects and plots the metrics previously calculated by cycle_latlon.ncl.
+* cycle_latlon.ncl: precalculates the metrics for a time-lat-lon field, with
+  different options for normalization.
+* collect.ncl: collects and plots the metrics previously calculated by
+  cycle_latlon.ncl.
 
 User settings in recipe
 -----------------------
@@ -37,9 +59,12 @@ User settings in recipe
 
    *Required settings (scripts)*
 
-   * plot_type: cycle (time), zonal (plev, lat), latlon (lat, lon), cycle_latlon (time, lat, lon), cycle_zonal (time, plev, lat)
+   * plot_type: cycle (time), zonal (plev, lat), latlon (lat, lon), cycle_latlon
+     (time, lat, lon), cycle_zonal (time, plev, lat)
    * time_avg: type of time average (monthlyclim, seasonalclim, annualclim)
-   * region: selected region (global, trop, nhext, shext, nhtrop, shtrop, nh, sh, nhmidlat, shmidlat, nhpolar, shpolar, eq)
+   * region: selected region (global, trop, nhext, shext, nhtrop, shtrop, nh,
+     sh, nhmidlat, shmidlat, nhpolar, shpolar, eq)
+
 
    *Optional settings (scripts)*
 
@@ -51,9 +76,12 @@ User settings in recipe
    * projection: map projection for plot_type latlon (default: CylindricalEquidistant)
    * plot_diff: draws difference plots (default: False)
    * calc_grading: calculates grading metrics (default: False)
-   * stippling: uses stippling to mark statistically significant differences (default: False = mask out non-significant differences in gray)
-   * show_global_avg: diplays the global avaerage of the input field as string at the top-right of lat-lon plots (default: False)
-   * annots: choose the annotation style, e.g. ```alias``` which would display the alias of the dataset as title (applies to plot_type zonal and cycle_zonal)
+   * stippling: uses stippling to mark statistically significant differences
+     (default: False = mask out non-significant differences in gray)
+   * show_global_avg: displays the global avaerage of the input field as string
+     at the top-right of lat-lon plots (default: False)
+   * annots: choose the annotation style, e.g. ```alias``` which would display
+     the alias of the dataset as title (applies to plot_type zonal and cycle_zonal)
    * metric: chosen grading metric(s) (if calc_grading is True)
    * normalization: metric normalization (for RMSD and BIAS metrics only)
    * abs_levs: list of contour levels for absolute plot
@@ -114,8 +142,8 @@ User settings in recipe
 
    *Optional settings (scripts)*
 
-   * label_lo: adds lower triange for values outside range
-   * label_hi: adds upper triange for values outside range
+   * label_lo: adds lower triangle for values outside range
+   * label_hi: adds upper triangle for values outside range
    * cm_interval: min and max color of the color table
    * cm_reverse: reverses the color table
    * sort: sorts datasets in alphabetic order (excluding MMM)
@@ -157,14 +185,21 @@ Variables
 Observations and reformat scripts
 ---------------------------------
 
-The following list shows the currently used observational data sets for this recipe with their variable names and the reference to their respective reformat scripts in parentheses. Please note that obs4MIPs data can be used directly without any reformating. For non-obs4MIPs data use `esmvaltool data info DATASET` or see headers of cmorization scripts (in `/esmvaltool/cmorizers/data/formatters/datasets/
-<https://github.com/ESMValGroup/ESMValTool/blob/main/esmvaltool/cmorizers/data/formatters/datasets/>`_) for downloading and processing instructions.
+The following list shows the currently used observational data sets for this
+recipe with their variable names and the reference to their respective reformat
+scripts in parentheses. Please note that obs4MIPs data can be used directly
+without any reformatitng. For non-obs4MIPs data use `esmvaltool data info DATASET`
+or see headers of cmorization scripts (in `/esmvaltool/cmorizers/data/formatters/datasets/
+<https://github.com/ESMValGroup/ESMValTool/blob/main/esmvaltool/cmorizers/data/formatters/datasets/>`_)
+for downloading and processing instructions.
+
 #.  recipe_perfmetrics_CMIP5.yml
 
     * AIRS (hus - obs4MIPs)
     * CERES-EBAF (rlut, rlutcs, rsut, rsutcs - obs4MIPs)
     * ERA-Interim (tas, ta, ua, va, zg, hus - esmvaltool/cmorizers/data/formatters/datasets/era-interim.py)
-    * ESACCI-AEROSOL (od550aer, od870aer, od550abs, od550lt1aer - esmvaltool/cmorizers/data/formatters/datasets/esacci-aerosol.ncl)
+    * ESACCI-AEROSOL (od550aer, od870aer, od550abs, od550lt1aer -
+      esmvaltool/cmorizers/data/formatters/datasets/esacci-aerosol.ncl)
     * ESACCI-CLOUD (clt - esmvaltool/cmorizers/data/formatters/datasets/esacci-cloud.ncl)
     * ESACCI-OZONE (toz - esmvaltool/cmorizers/data/formatters/datasets/esacci-ozone.ncl)
     * ESACCI-SOILMOISTURE (sm - esmvaltool/cmorizers/data/formatters/datasets/esacci_soilmoisture.ncl)
@@ -190,9 +225,13 @@ The following list shows the currently used observational data sets for this rec
 References
 ----------
 
-* Gleckler, P. J., K. E. Taylor, and C. Doutriaux, Performance metrics for climate models, J. Geophys. Res., 113, D06104, doi: 10.1029/2007JD008972 (2008).
+* Gleckler, P. J., K. E. Taylor, and C. Doutriaux, Performance metrics for climate models, J.
+  Geophys. Res., 113, D06104, doi: 10.1029/2007JD008972 (2008).
+
+* Righi, M., Eyring, V., Klinger, C., Frank, F., Gottschaldt, K.-D., Jöckel, P.,
+  and Cionni, I.: Quantitative evaluation of ozone and selected climate parameters in a set of EMAC simulations,
+  Geosci. Model Dev., 8, 733, doi: 10.5194/gmd-8-733-2015 (2015).
 
-* Righi, M., Eyring, V., Klinger, C., Frank, F., Gottschaldt, K.-D., Jöckel, P., and Cionni, I.: Quantitative evaluation of oone and selected climate parameters in a set of EMAC simulations, Geosci. Model Dev., 8, 733, doi: 10.5194/gmd-8-733-2015 (2015).
 
 Example plots
 -------------
@@ -200,17 +239,24 @@ Example plots
 .. figure:: /recipes/figures/perfmetrics/perfmetrics_fig_1.png
    :width: 90%
 
-   Annual cycle of globally averaged temperature at 850 hPa (time period 1980-2005) for different CMIP5 models (historical simulation) (thin colored lines) in comparison to ERA-Interim (thick yellow line) and NCEP-NCAR-R1 (thick black dashed line) reanalysis data.
+   Annual cycle of globally averaged temperature at 850 hPa (time period 1980-2005)
+   for different CMIP5 models (historical simulation) (thin colored lines) in comparison to
+   ERA-Interim (thick yellow line) and NCEP-NCAR-R1 (thick black dashed line) reanalysis data.
 
 .. figure:: /recipes/figures/perfmetrics/perfmetrics_fig_2.png
    :width: 90%
 
-   Taylor diagram of globally averaged temperature at 850 hPa (ta) and longwave cloud radiative effect (lwcre) for different CMIP5 models (historical simulation, 1980-2005). Reference data (REF) are ERA-Interim for temperature (1980-2005) and CERES-EBAF (2001-2012) for longwave cloud radiative effect.
+   Taylor diagram of globally averaged temperature at 850 hPa (ta) and longwave cloud
+   radiative effect (lwcre) for different CMIP5 models (historical simulation, 1980-2005).
+   Reference data (REF) are ERA-Interim for temperature (1980-2005) and CERES-EBAF (2001-2012)
+   for longwave cloud radiative effect.
 
 .. figure:: /recipes/figures/perfmetrics/perfmetrics_fig_3.png
    :width: 90%
 
-   Difference in annual mean of zonally averaged temperature (time period 1980-2005) between the CMIP5 model MPI-ESM-MR (historical simulation) and ERA-Interim. Stippled areas indicdate differences that are statistically significant at a 95% confidence level.
+   Difference in annual mean of zonally averaged temperature (time period 1980-2005) between the
+   CMIP5 model MPI-ESM-MR (historical simulation) and ERA-Interim. Stippled areas indicdate
+   differences that are statistically significant at a 95% confidence level.
 
 .. figure:: /recipes/figures/perfmetrics/perfmetrics_fig_4.png
    :width: 90%
@@ -221,4 +267,9 @@ Example plots
    :width: 90%
    :align: center
 
-   Relative space-time root-mean-square deviation (RMSD) calculated from the climatological seasonal cycle of CMIP5 simulations. A relative performance is displayed, with blue shading indicating better and red shading indicating worse performance than the median of all model results. A diagonal split of a grid square shows the relative error with respect to the reference data set (lower right triangle) and the alternative data set (upper left triangle). White boxes are used when data are not available for a given model and variable.
+   Relative space-time root-mean-square deviation (RMSD) calculated from the climatological
+   seasonal cycle of CMIP5 simulations. A relative performance is displayed, with blue shading
+   indicating better and red shading indicating worse performance than the median of all model results.
+   A diagonal split of a grid square shows the relative error with respect to the reference data set
+   (lower right triangle) and the alternative data set (upper left triangle).
+   White boxes are used when data are not available for a given model and variable.
diff --git a/doc/sphinx/source/recipes/recipe_portrait.rst b/doc/sphinx/source/recipes/recipe_portrait.rst
new file mode 100644
index 0000000000..38fb5b4489
--- /dev/null
+++ b/doc/sphinx/source/recipes/recipe_portrait.rst
@@ -0,0 +1,95 @@
+.. _recipe_portrait:
+
+Portrait plot
+=============
+
+
+Overview
+--------
+Portrait plots are a flexible way to visualize performance metrics for multiple
+datasets and up to four references. In this recipe ``recipe_portrait_CMIP.yml``
+the normalized Root Mean Squared Deviation (RMSD) of global mean seasonal
+climatologies is calculated for a selection of CMIP models.
+In the example recipe, for each variable up to two observation based datasets
+are used as reference.
+See :ref:`variables` for complete list of references.
+The recipe uses preprocessor functions (distance metrics, global mean,
+climate statistics) to calculate a scalar metric for each combination of
+dataset, variable and reference, which is plotted by the ``portrait_plot.py``
+diagnostic script.
+
+
+User settings in recipe
+-----------------------
+
+By default cells are plotted for combinations of ``short_name``,
+``dataset``, ``project`` and ``split``,
+where ``split`` is an optional extra_facet for variables.
+However, this can be customized using the ``x_by``,
+``y_by``, ``group_by`` and ``split_by`` script settings.
+For a complete and detailed list of settings, see the
+:doc:`diagnostic documentation </api/esmvaltool.diag_scripts.portrait_plot>`.
+While this allows very flexible use for any kind of data, there are some
+limitations as well: The grouping (subplots) and normalization is always
+applied along the x-axis.
+With default settings this means normalizing all metrics for each variable
+and grouping all datasets by project.
+
+To plot distance metrics like RMSE, pearson R, bias etc. the
+:func:`distance_metric <esmvalcore.preprocessor.distance_metric>` preprocessor
+or custom diagnostics can be used.
+
+
+
+.. _variables:
+
+Variables and Datasets
+------------------------
+
+.. note::
+
+   The recipe generally works for any variable that is preprocessed correctly.
+   To use different preprocessors or reference datasets it could be useful
+   to create different variable groups and link them with the same extra_facet
+   like ``variable_name``. See recipe for examples. Listed below are the variables
+   used to produce the example figure.
+
+
+The following list shows which observational dataset is used as reference for
+each variable in this recipe. All variables are atmospheric monthly means.
+For 3D variables the selected pressure level is specified in parentheses.
+
+* clt (Ref1: ESACCI-CLOUD, Ref2: PATMOS-x)
+* pr (Ref1: GPCP-V2.2)
+* rlut, rsut (Ref1: CERES-EBAF)
+* tas (Ref1: ERA-Interim, Ref2: NCEP-NCAR-R1)
+* ts (Ref1: ESACCI-SST, Ref2: HadISST)
+* ua (200 hPa, Ref1: ERA-Interim, Ref2: NCEP-NCAR-R1)
+* zg (500 hPa, Ref1: ERA-Interim, Ref2: NCEP-NCAR-R1)
+
+
+References
+----------
+
+* Gleckler, P. J., K. E. Taylor, and C. Doutriaux, Performance metrics for climate models, J.
+  Geophys. Res., 113, D06104, doi: 10.1029/2007JD008972 (2008).
+
+* Righi, M., Eyring, V., Klinger, C., Frank, F., Gottschaldt, K.-D., Jöckel, P.,
+  and Cionni, I.: Quantitative evaluation of ozone and selected climate parameters in a set of EMAC simulations,
+  Geosci. Model Dev., 8, 733, doi: 10.5194/gmd-8-733-2015 (2015).
+
+
+Example plots
+-------------
+
+.. _fig_portrait_plot:
+
+.. figure:: /recipes/figures/portrait/portrait_plot.png
+   :width: 90%
+   :align: center
+
+
+   Relative space-time root-mean-square deviation (RMSD) calculated from the climatological
+   seasonal cycle of CMIP5 and CMIP6 simulations. A relative performance is displayed, with blue shading
+   indicating better and red shading indicating worse performance than the median of all model results.
+   A diagonal split of a grid square shows the relative error with respect to the reference data set.
diff --git a/doc/sphinx/source/recipes/recipe_rainfarm.rst b/doc/sphinx/source/recipes/recipe_rainfarm.rst
index d6c06c6f7a..aeb7cd0638 100644
--- a/doc/sphinx/source/recipes/recipe_rainfarm.rst
+++ b/doc/sphinx/source/recipes/recipe_rainfarm.rst
@@ -32,7 +32,7 @@ User settings
 * nf: number of subdivisions for downscaling (e.g. 8 will produce output fields with linear resolution increased by a factor 8)
 * conserv_glob: logical, if to conserve precipitation over full domain
 * conserv_smooth: logical, if to conserve precipitation using convolution (if neither conserv_glob or conserv_smooth is chosen, box conservation is used)
-* weights_climo: set to false or omit if no orographic weights are to be used, else set it to the path to a fine-scale precipitation climatology file. If a relative file path is used, `auxiliary_data_dir` will be searched for this file. The file is expected to be in NetCDF format and should contain at least one precipitation field. If several fields at different times are provided, a climatology is derived by time averaging. Suitable climatology files could be for example a fine-scale precipitation climatology from a high-resolution regional climate model (see e.g. Terzago et al. 2018), a local high-resolution gridded climatology from observations, or a reconstruction such as those which can be downloaded from the WORLDCLIM (http://www.worldclim.org) or CHELSA (http://chelsa-climate.org) websites. The latter data will need to be converted to NetCDF format before being used (see for example the GDAL tools (https://www.gdal.org).
+* weights_climo: set to false or omit if no orographic weights are to be used, else set it to the path to a fine-scale precipitation climatology file. If a relative file path is used, ``auxiliary_data_dir`` will be searched for this file. The file is expected to be in NetCDF format and should contain at least one precipitation field. If several fields at different times are provided, a climatology is derived by time averaging. Suitable climatology files could be for example a fine-scale precipitation climatology from a high-resolution regional climate model (see e.g. Terzago et al. 2018), a local high-resolution gridded climatology from observations, or a reconstruction such as those which can be downloaded from the WORLDCLIM (http://www.worldclim.org) or CHELSA (http://chelsa-climate.org) websites. The latter data will need to be converted to NetCDF format before being used (see for example the GDAL tools (https://www.gdal.org).
 
 
 Variables
@@ -60,4 +60,4 @@ Example plots
 .. figure:: /recipes/figures/rainfarm/rainfarm.png
    :width: 14cm
 
-   Example of daily cumulated precipitation from the CMIP5 EC-EARTH model on a specific day, downscaled using RainFARM from its original resolution (1.125°) (left panel), increasing spatial resolution by a factor of 8 to 0.14°; Two stochastic realizations are shown (central and right panel). A fixed spectral slope of s=1.7 was used. Notice how the downscaled fields introduce fine scale precipitation structures, while still maintaining on average the original coarse-resolution precipitation. Different stochastic realizations are shown to demonstrate how an ensemble of realizations can be used to reproduce unresolved subgrid variability. (N.B.: this plot was not produced by ESMValTool - the recipe output is netcdf only). 
+   Example of daily cumulated precipitation from the CMIP5 EC-EARTH model on a specific day, downscaled using RainFARM from its original resolution (1.125°) (left panel), increasing spatial resolution by a factor of 8 to 0.14°; Two stochastic realizations are shown (central and right panel). A fixed spectral slope of s=1.7 was used. Notice how the downscaled fields introduce fine scale precipitation structures, while still maintaining on average the original coarse-resolution precipitation. Different stochastic realizations are shown to demonstrate how an ensemble of realizations can be used to reproduce unresolved subgrid variability. (N.B.: this plot was not produced by ESMValTool - the recipe output is netcdf only).
diff --git a/doc/sphinx/source/recipes/recipe_seaborn.rst b/doc/sphinx/source/recipes/recipe_seaborn.rst
index 3c8fa64357..4eb3c6571c 100644
--- a/doc/sphinx/source/recipes/recipe_seaborn.rst
+++ b/doc/sphinx/source/recipes/recipe_seaborn.rst
@@ -16,11 +16,11 @@ Available recipes and diagnostics
 
 Recipes are stored in recipes/
 
-   * recipe_seaborn.yml
+* recipe_seaborn.yml
 
 Diagnostics are stored in diag_scripts/
 
-   * :ref:`seaborn_diag.py <api.esmvaltool.diag_scripts.seaborn_diag>`
+* :ref:`seaborn_diag.py <api.esmvaltool.diag_scripts.seaborn_diag>`
 
 
 Variables
diff --git a/doc/sphinx/source/recipes/recipe_shapeselect.rst b/doc/sphinx/source/recipes/recipe_shapeselect.rst
index 63afbcae6c..12da974c28 100644
--- a/doc/sphinx/source/recipes/recipe_shapeselect.rst
+++ b/doc/sphinx/source/recipes/recipe_shapeselect.rst
@@ -29,7 +29,7 @@ User settings in recipe
 
    *Required settings (scripts)*
 
-   * shapefile: path to the user provided shapefile. A relative path is relative to the auxiliary_data_dir as configured in config-user.yml.
+   * shapefile: path to the user provided shapefile. A relative path is relative to the :ref:`configuration option <esmvalcore:config_options>` ``auxiliary_data_dir``.
 
    * weighting_method: the preferred weighting method 'mean_inside' - mean of all grid points inside polygon; 'representative' - one point inside or close to the polygon is used to represent the complete area.
 
diff --git a/doc/sphinx/source/recipes/recipe_template.rst.template b/doc/sphinx/source/recipes/recipe_template.rst.template
index 55e28ddf7e..6c248ed5d7 100644
--- a/doc/sphinx/source/recipes/recipe_template.rst.template
+++ b/doc/sphinx/source/recipes/recipe_template.rst.template
@@ -14,11 +14,11 @@ Available recipes and diagnostics
 
 Recipes are stored in esmvaltool/recipes/
 
-    * recipe_<mynewrecipe>.yml
+* recipe_<mynewrecipe>.yml
 
 Diagnostics are stored in esmvaltool/diag_scripts/<mynewdiag>/
 
-    * <mynewdiag.py/.ncl/.r>: one line scription
+* <mynewdiag.py/.ncl/.r>: one line scription
 
 
 User settings in recipe
diff --git a/doc/sphinx/source/recipes/recipe_wenzel14jgr.rst b/doc/sphinx/source/recipes/recipe_wenzel14jgr.rst
index 7c07c6e1f5..4faa05c2a9 100644
--- a/doc/sphinx/source/recipes/recipe_wenzel14jgr.rst
+++ b/doc/sphinx/source/recipes/recipe_wenzel14jgr.rst
@@ -1,3 +1,5 @@
+.. _recipe_wenzel14jgr:
+
 Emergent constraints on carbon cycle feedbacks
 ==============================================
 
@@ -26,8 +28,8 @@ User settings
 
 .. note::
 
-   Make sure to run this recipe setting ``max_parallel_tasks: 1`` in the ``config_user.yml``
-   file or using the CLI flag ``--max_parallel_tasks=1``.
+   Make sure to run this recipe with the :ref:`configuration option
+   <esmvalcore:config_options>` ``max_parallel_tasks: 1``.
 
 User setting files (cfg files) are stored in nml/cfg_carbon/
 
diff --git a/doc/sphinx/source/recipes/recipe_wenzel16nat.rst b/doc/sphinx/source/recipes/recipe_wenzel16nat.rst
index 03bb822545..a661844e70 100644
--- a/doc/sphinx/source/recipes/recipe_wenzel16nat.rst
+++ b/doc/sphinx/source/recipes/recipe_wenzel16nat.rst
@@ -35,9 +35,8 @@ User settings
 
 .. note::
 
-   Make sure to run this recipe setting ``output_file_type: pdf`` in the ``config_user.yml``
-   file or using the CLI flag ``--output_file_type=pdf``.
-
+   Make sure to run this recipe with the :ref:`configuration option
+   <esmvalcore:config_options>` ``max_parallel_tasks: 1``.
 
 #. Script carbon_beta.ncl
 
@@ -58,7 +57,7 @@ User settings
 
    none
 
-#. Script carbon_co2_cycle.ncl 
+#. Script carbon_co2_cycle.ncl
 
    *Required Settings (scripts)*
 
@@ -72,7 +71,7 @@ User settings
 
    *Required settings (variables)*
 
-   * reference_dataset: name of reference datatset (observations)
+   * reference_dataset: name of reference dataset (observations)
 
    *Optional settings (variables)*
 
@@ -102,15 +101,15 @@ Example plots
 -------------
 
 .. figure:: /recipes/figures/wenzel16nat/fig_1.png
-   :width: 12 cm 
+   :width: 12 cm
    :align: center
-   
+
    Comparison of CO\ :sub:`2` seasonal amplitudes for CMIP5 historical simulations and observations showing annual mean atmospheric CO\ :sub:`2` versus the amplitudes of the CO\ :sub:`2` seasonal cycle at Pt. Barrow, Alaska (produced with carbon_co2_cycle.ncl, similar to Fig. 1a from Wenzel et al. (2016)).
-      
+
 .. figure:: /recipes/figures/wenzel16nat/fig_2.png
-   :width: 12 cm 
+   :width: 12 cm
    :align: center
-   
+
    Barchart showing the gradient of the linear correlations for the comparison of CO\ :sub:`2` seasonal amplitudes for CMIP5 historical for at Pt. Barrow, Alaska (produced with carbon_co2_cycle.ncl, similar to Fig. 1b from Wenzel et al. (2016)).
 
 .. figure:: /recipes/figures/wenzel16nat/fig_3.png
diff --git a/doc/sphinx/source/utils/RTW/about.rst b/doc/sphinx/source/utils/RTW/about.rst
new file mode 100644
index 0000000000..62883fe2e1
--- /dev/null
+++ b/doc/sphinx/source/utils/RTW/about.rst
@@ -0,0 +1,14 @@
+*****
+About
+*****
+
+.. include:: common.txt
+
+The Recipe Test Workflow (|RTW|) is a workflow that is used to regularly run
+recipes so issues can be discovered during the development process sooner
+rather than later.
+
+|Cylc| v8 and |Rose| v2 are used as the workflow engine and application
+configuration system for the |RTW|, respectively. |Cylc| and |Rose| are not
+included in the ESMValTool environment as they are typically already centrally
+installed at sites e.g. JASMIN and the Met Office.
diff --git a/doc/sphinx/source/utils/RTW/add_a_recipe.rst b/doc/sphinx/source/utils/RTW/add_a_recipe.rst
new file mode 100644
index 0000000000..954b518a36
--- /dev/null
+++ b/doc/sphinx/source/utils/RTW/add_a_recipe.rst
@@ -0,0 +1,143 @@
+How to add a recipe to the |RTW|
+================================
+
+.. include:: common.txt
+
+Overview
+--------
+
+To add a recipe to the |RTW| you will:
+
+* Run the recipe at your site
+* Note the actual duration and memory usage
+* Edit your site's recipe file
+* Create the recipe's KGOs
+* Request a review
+
+The recipe will then run at your site whenever the |RTW| is run.
+
+Preparation
+-----------
+
+#. Open a `new ESMValTool issue`_ on GitHub. Assign yourself to the issue and
+   add the ``Recipe Test Workflow (RTW)`` label. `ESMValTool issue #3663`_
+   provides an example.
+
+#. Create a branch.
+
+#. Run the recipe:
+
+   * with the latest version of ESMValTool
+   * on the compute server you use at your site
+
+   .. hint::
+      Your compute server is defined in the
+      ``esmvaltool/utils/recipe_test_workflow/site/<site>.cylc`` file as
+      follows::
+
+         [[COMPUTE]]
+            platform = <your compute server>
+
+#. Obtain the actual duration and memory usage of the recipe. This can be found
+   either in the message printed to screen, or at the end of the
+   ``run/main_log.txt`` file in the recipe output directory. The relevant lines will
+   look something like::
+
+      YYYY-MM-DD HH:MM:SS:sss UTC [12345] INFO    Time for running the recipe was: 0:02:13.334742
+      YYYY-MM-DD HH:MM:SS:sss UTC [12345] INFO    Maximum memory used (estimate): 2.4 GB
+      [...]
+      YYYY-MM-DD HH:MM:SS:sss UTC [12345] INFO    Run was successful
+
+Adding the recipe
+-----------------
+
+#. Add the recipe in alphabetical order to either ``FAST_RECIPES`` or
+   ``MEDIUM_RECIPES`` in the ``<site>-recipes.jinja`` file. It should look
+   something like::
+
+      {
+         'recipe_path': 'recipe_a_fast_recipe',
+         'actual': '2m13s, 2.4 GB on YYYY-MM-DD',
+         'max_time': 'PT3M',
+         'max_memory': '3G',
+      }
+
+   .. important::
+      Add the recipe to ``FAST_RECIPES`` if it takes *less* than 10 mins to
+      run at your site. Add the recipe to ``MEDIUM_RECIPES`` if it takes *more*
+      than 10 mins.
+
+   .. hint::
+      The :ref:`site/site-recipes.jinja <site_recipes_file>`.
+      file provides more information.
+
+   .. hint::
+      Set the ``max_time`` to 10-20% more than the actual duration. For actual
+      durations of up to ``1m45s``, set ``max_time`` to ``PT2M`` (2 minutes).
+
+   .. hint::
+      Try not to regularly waste more than 500 MiB in memory usage. Typically,
+      rounding the actual memory usage up to the nearest integer is acceptable.
+
+Create the |KGOs|
+-----------------
+
+#. Run the |RTW|, as detailed in the :ref:`quick_start_guide`; it is expected
+   that the ``compare`` task will fail.
+
+   .. important::
+      The ``compare`` task fails because the |KGOs| for the recipe do not yet
+      exist. This run of the |RTW| will generate the outputs that will be
+      used as |KGOs|.
+
+#. Recursively copy the recipe output directory
+   ``recipe_<recipe>_<date>_<time>/`` to your site-specific |KGO| directory::
+
+      cp -r ${HOME}/cylc-run/recipe_test_workflow/runN/share/cycle/<cycle>/recipe_<recipe>_<date>_<time> <KGO_ROOT_PATH>
+
+   .. hint::
+      ``<cycle>`` will look something like: ``20250101T0900Z``. The recipe output
+      directory will look something like: ``recipe_python_20250101_090000``
+
+   .. hint::
+      Find your site specific ``KGO_ROOT_PATH`` in the ``rose-suite-<site>.conf`` file
+      in the ``esmvaltool/utils/recipe_test_workflow/opt/`` directory.
+
+   .. note::
+      Cylc is typically configured such that
+      ``${HOME}/cylc-run/recipe_test_workflow/runN/share`` is a symbolic link
+      to a share directory located on a scratch disk; the recipe output
+      directory is not stored in ``${HOME}``.
+
+#. Enable write permissions for all users on the recipe output directory in
+   your site-specific |KGO| directory::
+
+      chmod -R a+w <KGO_ROOT_PATH>/recipe_<recipe>_<date>_<time>
+
+#. Stop any running ``recipe_test_workflow`` workflows::
+
+      cylc stop recipe_test_workflow/*
+
+#. Run the |RTW| again, as detailed in the :ref:`quick_start_guide`; the
+   ``compare`` task should now succeed.
+
+   .. important::
+      Unless you want the |RTW| to continue running every night, repeat the
+      previous step to stop the workflow.
+
+Request a review
+----------------
+
+#. Commit and push your changes.
+
+#. Create a PR. Assign yourself to the PR, and add the
+   ``Recipe Test Workflow (RTW)`` label to the PR. Refer to
+   `ESMValTool PR #3664`_ for an example.
+
+   .. note::
+      Reviewers will automatically be assigned to your PR.
+
+Congratulations!
+----------------
+
+The recipe will now run at your site whenever the RTW is run.
diff --git a/doc/sphinx/source/utils/RTW/common.txt b/doc/sphinx/source/utils/RTW/common.txt
new file mode 100644
index 0000000000..d82899a53a
--- /dev/null
+++ b/doc/sphinx/source/utils/RTW/common.txt
@@ -0,0 +1,29 @@
+.. Glossary substitutions make it easier to refer to pluralised /
+.. capitalised glossary terms.
+
+.. |RTW| replace:: :term:`RTW`
+.. |CMIP| replace:: :term:`CMIP`
+.. |Cylc| replace:: :term:`Cylc`
+.. |ESM| replace:: :term:`ESM`
+.. |ESMs| replace:: :term:`ESMs <ESM>`
+.. |ESMValCore| replace:: :term:`ESMValCore`
+.. |ESMValTool| replace:: :term:`ESMValTool`
+.. |KGOs| replace:: :term:`KGOs`
+.. |KGO| replace:: :term:`KGOs`
+.. |Rose| replace:: :term:`Rose`
+
+.. Links
+
+.. _CMIP Documentation: https://www.wcrp-climate.org/wgcm-cmip
+.. _Cylc Documentation: https://cylc.github.io/cylc-doc/stable/html/index.html
+.. _Cylc ISO8601 Durations: https://cylc.github.io/cylc-doc/stable/html/tutorial/scheduling/datetime-cycling.html#iso8601-durations
+.. _Cylc Jinja2: https://cylc.github.io/cylc-doc/stable/html/user-guide/writing-workflows/jinja2.html
+.. _Cylc Review: https://cylc.github.io/cylc-doc/7.9.3/html/tutorial.html#viewing-suite-logs-in-a-web-browser-cylc-review
+.. _ESMValTool Documentation: https://esmvaltool.org/
+.. _ESMValTool issue #3663: https://github.com/ESMValGroup/ESMValTool/issues/3663
+.. _ESMValTool PR #3664: https://github.com/ESMValGroup/ESMValTool/pull/3664
+.. _Jinja2: https://jinja.palletsprojects.com/en/stable/
+.. _new ESMValTool issue: https://github.com/ESMValGroup/ESMValTool/issues/new
+.. _Rose Documentation: https://metomi.github.io/rose/doc/html/index.html
+.. _metoffice-recipes.cylc: https://github.com/ESMValGroup/ESMValTool/blob/main/esmvaltool/utils/recipe_test_workflow/site/metoffice-recipes.cylc
+.. _Slurm sbatch --mem: https://slurm.schedmd.com/sbatch.html#OPT_mem
diff --git a/doc/sphinx/source/utils/RTW/glossary.rst b/doc/sphinx/source/utils/RTW/glossary.rst
new file mode 100644
index 0000000000..15675146e1
--- /dev/null
+++ b/doc/sphinx/source/utils/RTW/glossary.rst
@@ -0,0 +1,39 @@
+********
+Glossary
+********
+
+.. include:: common.txt
+
+.. glossary::
+
+   CMIP
+     Coupled Model Intercomparison Project, see the `CMIP Documentation`_.
+
+   Cylc
+     Cylc (“silk”) is a workflow engine for cycling systems, see the
+     `Cylc Documentation`_.
+
+   ESM
+     Earth System Model
+
+   ESMValCore
+     ESMValCore contains the core functionalities for the ESMValTool.
+
+   ESMValTool
+     ESMValTool is a community diagnostic and performance metrics tool for
+     routine evaluation of |ESMs| in |CMIP|, see the
+     `ESMValTool Documentation`_.
+
+   KGOs
+    Known Good Outputs (KGOs) are outputs from an ESMValTool recipe that have
+    been confirmed (typically by a scientist) to be as expected. KGOs are used
+    in the compare task in the |RTW| to check whether any changes have been
+    introduced to the outputs from an ESMValTool recipe after changes are made
+    to either ESMValCore or ESMValTool.
+
+   Rose
+     Rose is a toolkit for writing, editing and running application
+     configurations, see the `Rose Documentation`_.
+
+   RTW
+     Recipe Test Workflow
diff --git a/doc/sphinx/source/utils/RTW/index.rst b/doc/sphinx/source/utils/RTW/index.rst
new file mode 100644
index 0000000000..9b04ea4b3b
--- /dev/null
+++ b/doc/sphinx/source/utils/RTW/index.rst
@@ -0,0 +1,11 @@
+Recipe Test Workflow (RTW)
+==========================
+
+.. toctree::
+   :maxdepth: 1
+
+   about
+   tested_recipes
+   user_guide/index
+   add_a_recipe
+   glossary
diff --git a/doc/sphinx/source/utils/RTW/tested_recipes.rst b/doc/sphinx/source/utils/RTW/tested_recipes.rst
new file mode 100644
index 0000000000..1100d2f99e
--- /dev/null
+++ b/doc/sphinx/source/utils/RTW/tested_recipes.rst
@@ -0,0 +1,18 @@
+.. _currently_tested_recipes:
+
+************************
+Currently tested recipes
+************************
+
+.. include:: common.txt
+
+The recipes tested with the |RTW| at each site are defined in the following
+files:
+
+* `metoffice-recipes.cylc`_
+* dkrz-recipes.cylc **(COMING SOON!)**
+* jasmin-recipes.cylc **(COMING SOON!)**
+
+.. hint::
+   The :ref:`site/site-recipes.jinja <site_recipes_file>`.
+   file provides more information.
diff --git a/doc/sphinx/source/utils/RTW/user_guide/index.rst b/doc/sphinx/source/utils/RTW/user_guide/index.rst
new file mode 100644
index 0000000000..01359728e2
--- /dev/null
+++ b/doc/sphinx/source/utils/RTW/user_guide/index.rst
@@ -0,0 +1,9 @@
+**********
+User Guide
+**********
+
+.. toctree::
+   :maxdepth: 1
+
+   quick_start
+   workflow
diff --git a/doc/sphinx/source/utils/RTW/user_guide/quick_start.rst b/doc/sphinx/source/utils/RTW/user_guide/quick_start.rst
new file mode 100644
index 0000000000..6574aa24ee
--- /dev/null
+++ b/doc/sphinx/source/utils/RTW/user_guide/quick_start.rst
@@ -0,0 +1,54 @@
+.. _quick_start_guide:
+
+Quick Start Guide
+=================
+
+.. include:: ../common.txt
+
+* Clone the ESMValTool repository (which contains the |RTW|)::
+
+    git clone git@github.com:ESMValGroup/ESMValTool.git
+
+* Configure the |RTW|::
+
+    cd ESMValTool/esmvaltool/utils/recipe_test_workflow
+    rose edit
+
+* Run the |RTW|:
+
+  * at the Met Office::
+
+      export CYLC_VERSION=8
+      cylc vip -O metoffice
+
+  * on JASMIN:
+
+    * add the following line to your ``~/.bashrc`` file to ensure the Cylc and
+      Rose executables can be found::
+
+        export PATH=/apps/jasmin/metomi/bin:$PATH
+
+    * SSH to the Rose and Cylc server::
+
+        ssh -X cylc
+
+    * run the RTW on JASMIN::
+
+        cd ESMValTool/esmvaltool/utils/recipe_test_workflow
+        export CYLC_VERSION=8
+        cylc vip -O jasmin
+
+  * on DKRZ:
+
+    * add the following line to your ``~/.bashrc`` file to ensure the Cylc and
+      Rose executables can be found::
+
+        export PATH=/work/bd0854/metomi/bin:$PATH
+
+    * run the RTW on DKRZ::
+
+        cd ESMValTool/esmvaltool/utils/recipe_test_workflow
+        cylc vip -O dkrz
+
+* Optionally browse the logs using `Cylc Review`_,
+  a web service for browsing logs via an HTTP interface.
diff --git a/doc/sphinx/source/utils/RTW/user_guide/workflow.rst b/doc/sphinx/source/utils/RTW/user_guide/workflow.rst
new file mode 100644
index 0000000000..aa017ac6c7
--- /dev/null
+++ b/doc/sphinx/source/utils/RTW/user_guide/workflow.rst
@@ -0,0 +1,112 @@
+.. include:: ../common.txt
+
+The workflow
+============
+
+An overview of the workflow
+---------------------------
+
+The |RTW| performs the following steps:
+
+``install_env_file``
+  :Description:
+     Copies the environment file for |ESMValTool|, based on the ``SITE``
+     provided
+  :Runs on:
+     Localhost
+  :Executes:
+     The ``cp`` command from the |Rose| app
+  :Details:
+     Runs once at the start of the workflow
+
+``get_esmval``
+  :Description:
+     Either clones the latest versions of |ESMValTool| and |ESMValCore| from
+     GitHub, or gets the latest container image from DockerHub and converts to
+     a singularity image, depending on ``SITE``
+  :Runs on:
+     Localhost, or ``COMPUTE`` on JASMIN
+  :Executes:
+     The ``clone_latest_esmval.sh`` script (if cloning), or a
+     ``singularity build`` command (if getting container) from the |Rose| app
+  :Details:
+     Runs at the start of each cycle
+
+``configure``
+  :Description:
+     Creates the |ESMValTool| user configuration file and validates it
+  :Runs on:
+     Localhost
+  :Executes:
+     The ``configure.py`` script from the |Rose| app
+  :Details:
+     Runs each cycle after ``get_esmval`` has completed
+
+``process``
+  :Description:
+     Runs the requested recipes using |ESMValTool|
+  :Runs on:
+     ``COMPUTE``, which depends on the ``SITE``
+  :Executes:
+     The |ESMValTool| command line script from the |Rose| app
+  :Details:
+     Runs each cycle for every recipe defined in the |RTW| after ``configure``
+     has completed
+
+``compare``
+  :Description:
+     Compares the output from the ``process`` job with |KGOs|
+  :Runs on:
+     ``COMPUTE``, which depends on the ``SITE``
+  :Executes:
+     The :ref:`compare.py <compare_recipe_runs>` script from |ESMValTool|
+     from the |Rose| app
+  :Details:
+     Runs each cycle for every recipe defined in the |RTW| after ``process``
+     has completed
+
+Design considerations
+---------------------
+
+Portability
+~~~~~~~~~~~
+
+The |RTW| is portable; site-specific information can be found in the ``site``
+and ``opt`` directories within the |RTW|. The files required are:
+
+.. _site_recipes_file:
+
+``site/<site>-recipes.jinja``
+   Contains all the recipes run at the ``SITE``
+
+.. hint::
+   * The file uses the `Jinja2`_ templating language, which has a similar syntax
+     to Python
+   * Jinja2 gives |Cylc| many powerful features. Refer to `Cylc Jinja2`_ for
+     more information
+
+``site/<site>.cylc``
+  Contains task definitions specific to the ``SITE``, for example, ``COMPUTE``
+
+``site/<site>-env``
+  Contains details on how to set up the environment for ESMValTool at the
+  ``SITE``
+
+``opt/rose-suite-<site>.conf``
+  Contains configuration items specific to the ``SITE``, including ``SITE``
+  and where to find the required datasets
+
+Metadata
+~~~~~~~~
+
+The |RTW| uses Rose metadata. Every item defined in the suite configuration
+file (``rose-suite.conf``) will have an entry in the main metadata
+configuration file (``meta/rose-meta.conf``).
+
+Resources
+~~~~~~~~~
+
+The resources used by the ``process`` jobs are defined in the
+``site/<site>.cylc`` file, allowing the jobs to be configured by ``SITE`` as
+well as by recipe. This ensures only the required resources are requested when
+running each of the ``process`` jobs.
diff --git a/doc/sphinx/source/utils.rst b/doc/sphinx/source/utils/utils.rst
similarity index 74%
rename from doc/sphinx/source/utils.rst
rename to doc/sphinx/source/utils/utils.rst
index 03e2793dca..97916c9edd 100644
--- a/doc/sphinx/source/utils.rst
+++ b/doc/sphinx/source/utils/utils.rst
@@ -135,10 +135,11 @@ This suite is configured to work with versions of cylc older than 8.0.0 .
 To prepare for using this tool:
 
 #. Log in to a system that uses `slurm <https://slurm.schedmd.com/quickstart.html>`_
-#. Make sure the required CMIP and observational datasets are available and configured in config-user.yml
+#. Make sure the required CMIP and observational datasets are available and
+   their ``rootpath`` and ``drs`` is properly set up in the :ref:`configuration
+   <esmvalcore:config_options>`
 #. Make sure the required auxiliary data is available (see :ref:`recipe documentation <recipes>`)
 #. Install ESMValTool
-#. Update config-user.yml so it points to the right data locations
 
 Next, get started with `cylc <https://cylc.github.io/cylc-doc/7.9.3/html/index.html>`_:
 
@@ -152,67 +153,6 @@ Next, get started with `cylc <https://cylc.github.io/cylc-doc/7.9.3/html/index.h
 
 To generate an overview page of the recipe runs, use the ``summarize.py`` :ref:`utility script <overview_page>`.
 
-Using Rose and cylc
--------------------
-It is possible to run more than one recipe in one go: currently this relies on the user
-having access to a HPC that has ``rose`` and ``cylc`` installed since the procedure involves
-installing and submitting a Rose suite. The utility that allows you to do this is
-``esmvaltool/utils/rose-cylc/esmvt_rose_wrapper.py``.
-
-Base suite
-..........
-The base suite to run esmvaltool via rose-cylc is `u-bd684`; you can find
-this suite in the Met Office Rose repository at:
-
-https://code.metoffice.gov.uk/svn/roses-u/b/d/6/8/4/trunk/
-
-When ``rose`` will be working with python3.x, this location will become
-default and the pipeline will aceess it independently of user, unless, of
-course the user will specify ``-s $SUITE_LOCATION``; until then the user needs
-to grab a copy of it in ``$HOME`` or specify the default location via ``-s`` option.
-
-Environment
-...........
-We will move to a unified and centrally-installed esmvaltool environment;
-until then, the user will have to alter the env_setup script:
-
-``u-bd684/app/esmvaltool/env_setup``
-
-with the correct pointers to esmvaltool installation, if desired.
-
-To be able to submit to cylc, you need to have the `/metomi/` suite in path
-AND use a `python2.7` environment. Use the Jasmin-example below for guidance.
-
-Jasmin-example
-..............
-This shows how to interact with rose-cylc and run esmvaltool under cylc
-using this script:
-
-.. code:: bash
-
-   export PATH=/apps/contrib/metomi/bin:$PATH
-   export PATH=/home/users/valeriu/miniconda2/bin:$PATH
-   mkdir esmvaltool_rose
-   cd esmvaltool_rose
-   cp ESMValTool/esmvaltool/utils/rose-cylc/esmvt_rose_wrapper.py .
-   svn checkout https://code.metoffice.gov.uk/svn/roses-u/b/d/6/8/4/trunk/ ~/u-bd684
-   [enter Met Office password]
-   [configure ~/u-bd684/rose_suite.conf]
-   [configure ~/u-bd684/app/esmvaltool/env_setup]
-   python esmvt_rose_wrapper.py -c config-user.yml \
-   -r recipe_autoassess_stratosphere.yml recipe_OceanPhysics.yml \
-   -d $HOME/esmvaltool_rose
-   rose suite-run u-bd684
-
-Note that you need to pass FULL PATHS to cylc, no `.` or `..` because all
-operations are done remotely on different nodes.
-
-A practical actual example of running the tool can be found on JASMIN:
-``/home/users/valeriu/esmvaltool_rose``.
-There you will find the run shell: ``run_example``, as well as an example
-how to set the configuration file. If you don't have Met Office credentials,
-a copy of `u-bd684` is always located in ``/home/users/valeriu/roses/u-bd684`` on Jasmin.
-
 .. _utils_batch_jobs:
 
 Using the scripts in `utils/batch-jobs`
@@ -238,11 +178,11 @@ The following parameters have to be set in the script in order to make it run:
 * ``submit``, *bool*: Whether or not to automatically submit the job after creating the launch script. Default value is ``False``.
 * ``account``, *str*: Name of the DKRZ account in which the job will be billed.
 * ``outputs``, *str*: Name of the directory in which the job outputs (.out and .err files) are going to be saved. The outputs will be saved in `/home/user/<outputs>`.
-* ``conda_path``, *str*: Full path to the `mambaforge/etc/profile.d/conda.sh` executable.
+* ``conda_path``, *str*: Full path to the `miniforge3/etc/profile.d/conda.sh` executable.
 
 Optionally, the following parameters can be edited:
 
-* ``config_file``, *str*: Path to ``config-user.yml`` if default ``~/.esmvaltool/config-user.yml`` not used.
+* ``config_dir``, *str*: Path to :ref:`configuration directory <esmvalcore:config_yaml_files>`, by default ``~/.config/esmvaltool/``.
 * ``partition``, *str*: Name of the DKRZ partition used to run jobs. Default is ``interactive`` to minimize computing cost compared to ``compute`` for which nodes cannot be shared.
 * ``memory``, *str*: Amount of memory requested for each run. Default is ``64G`` to allow to run 4 recipes on the same node in parallel.
 * ``time``, *str*: Time limit. Default is ``04:00:00`` to increase the job priority. Jobs can run for up to 8 hours and 12 hours on the compute and interactive partitions, respectively.
@@ -291,7 +231,7 @@ script as well as a list of all available recipes. To generate the list, run the
 
    for recipe in $(esmvaltool recipes list | grep '\.yml$'); do echo $(basename "$recipe"); done > all_recipes.txt
 
-To keep the script execution fast, it is recommended to use ``log_level: info`` in your config-user.yml file so that SLURM
+To keep the script execution fast, it is recommended to use ``log_level: info`` in the configuration so that SLURM
 output files are rather small.
 
 .. _overview_page:
@@ -443,63 +383,6 @@ klaus.zimmermann@smhi.se
 .. _pygithub: https://pygithub.readthedocs.io/en/latest/introduction.html
 
 
-Recipe filler
-=============
-
-If you need to fill in a blank recipe with additional datasets, you can do that with
-the command `recipe_filler`. This runs a tool to obtain a set of additional datasets when
-given a blank recipe, and you can give an arbitrary number of data parameters. The blank recipe
-should contain, to the very least, a list of diagnostics, each with their variable(s).
-Example of running the tool:
-
-.. code-block:: bash
-
-    recipe_filler recipe.yml
-
-where `recipe.yml` is the recipe that needs to be filled with additional datasets; a minimal
-example of this recipe could be:
-
-.. code-block:: yaml
-
-    diagnostics:
-      diagnostic:
-        variables:
-          ta:
-            mip: Amon  # required
-            start_year: 1850  # required
-            end_year: 1900  # required
-
-
-Key features
-------------
-
-- you can add as many variable parameters as are needed; if not added, the
-  tool will use the ``"*"`` wildcard and find all available combinations;
-- you can restrict the number of datasets to be looked for with the ``dataset:``
-  key for each variable, pass a list of datasets as value, e.g.
-  ``dataset: [MPI-ESM1-2-LR, MPI-ESM-LR]``;
-- you can specify a pair of experiments, e.g. ``exp: [historical, rcp85]``
-  for each variable; this will look for each available dataset per experiment
-  and assemble an aggregated data stretch from each experiment to complete
-  for the total data length specified by ``start_year`` and ``end_year``; equivalent to
-  ESMValTool's syntax on multiple experiments; this option needs an ensemble
-  to be declared explicitly; it will return no entry if there are gaps in data;
-- ``start_year`` and ``end_year`` are required and are used to filter out the
-  datasets that don't have data in the interval; as noted above, the tool will not
-  return datasets with partial coverage from ``start_year`` to ``end_year``;
-  if you want all possible years hence no filtering on years just use ``"*"``
-  for start and end years;
-- ``config-user: rootpath: CMIPX`` may be a list, rootpath lists are supported;
-- all major DRS paths (including ``default``, ``BADC``, ``ETHZ`` etc) are supported;
-- speedup is achieved through CMIP mip tables lookup, so ``mip`` is required in recipe;
-
-Caveats
--------
-
-- the tool doesn't yet work with derived variables; it will not return any available datasets;
-- operation restricted to CMIP data only, OBS lookup is not available yet.
-
-
 Extracting a list of input files from the provenance
 ====================================================
 
@@ -517,3 +400,17 @@ The tool is based on the `prov <https://prov.readthedocs.io/en/latest/readme.htm
 library, a useful library for working with provenance files.
 With minor adaptations, this script could also print out global attributes
 of the input NetCDF files, e.g. the tracking_id.
+
+Recipe Test Workflow (RTW)
+==========================
+
+.. include:: RTW/common.txt
+
+The Recipe Test Workflow (|RTW|) is a workflow that is used to regularly run
+recipes so issues can be discovered during the development process sooner
+rather than later.
+
+.. toctree::
+   :maxdepth: 1
+
+   RTW/index
diff --git a/docker/Dockerfile b/docker/Dockerfile
index 9ee3ddf0f8..9670028c7b 100644
--- a/docker/Dockerfile
+++ b/docker/Dockerfile
@@ -1,6 +1,6 @@
 # To build this container, go to ESMValTool root folder and execute:
 # docker build -t esmvaltool:latest . -f docker/Dockerfile
-FROM condaforge/mambaforge
+FROM condaforge/miniforge3
 
 WORKDIR /src/ESMValTool
 COPY environment.yml .
diff --git a/docker/Dockerfile.dev b/docker/Dockerfile.dev
index 65f1a34ea5..b7204abaa7 100644
--- a/docker/Dockerfile.dev
+++ b/docker/Dockerfile.dev
@@ -1,6 +1,6 @@
 # To build this container, go to ESMValTool root folder and execute:
 # docker build -t esmvaltool:development . -f docker/Dockerfile.dev
-FROM condaforge/mambaforge
+FROM condaforge/miniforge3
 
 WORKDIR /src/ESMValTool
 RUN apt update && DEBIAN_FRONTEND=noninteractive apt install -y curl git ssh && apt clean
diff --git a/docker/Dockerfile.exp b/docker/Dockerfile.exp
index a522995fc4..062a64b8ab 100644
--- a/docker/Dockerfile.exp
+++ b/docker/Dockerfile.exp
@@ -1,6 +1,6 @@
 # To build this container, go to ESMValTool root folder and execute:
 # docker build -t esmvaltool:experimental . -f docker/Dockerfile.exp
-FROM condaforge/mambaforge
+FROM condaforge/miniforge3
 RUN apt update && apt install -y git && apt clean
 
 WORKDIR /src/ESMValTool
diff --git a/environment.yml b/environment.yml
index c3d746f34f..72ccf127f6 100644
--- a/environment.yml
+++ b/environment.yml
@@ -14,20 +14,23 @@ dependencies:
   - cdo >=2.3.0
   - cdsapi
   - cf-units
+  - cfgrib
   - cftime
   - cmocean
+  - curl <8.10  # https://github.com/ESMValGroup/ESMValTool/issues/3758
   - cython
-  - dask
+  - dask !=2024.8.0  # https://github.com/dask/dask/issues/11296
   - distributed
   - ecmwf-api-client
   - eofs
-  - esmpy >=8.6.0  # github.com/SciTools-incubator/iris-esmf-regrid/pull/342
-  - esmvalcore 2.10.*
+  - esmpy
+  - esmvalcore 2.11.*
   - fiona
   - fire
   - fsspec
-  - gdal
-  - iris >=3.6.1
+  - gdal >=3.9.0
+  - importlib_metadata <8  # https://github.com/ESMValGroup/ESMValTool/issues/3699 only for Python 3.10/11 and esmpy<8.6
+  - iris >=3.11
   - iris-esmf-regrid >=0.10.0  # github.com/SciTools-incubator/iris-esmf-regrid/pull/342
   - jinja2
   - joblib
@@ -41,21 +44,21 @@ dependencies:
   - numpy !=1.24.3  # severe masking bug
   - openpyxl
   - packaging
-  - pandas !=2.2.0,!=2.2.1,!=2.2.2 # github.com/ESMValGroup/ESMValCore/pull/2305
+  - pandas==2.1.4  # unpin when ESMValCore released with https://github.com/ESMValGroup/ESMValCore/pull/2529
   - pip !=21.3
   - progressbar2
   - prov
-  - psyplot
-  - psy-maps
-  - psy-reg
-  - psy-simple
+  - psyplot >=1.5.0
+  - psy-maps >=1.5.0
+  - psy-reg >=1.5.0
+  - psy-simple >=1.5.0
   - pyproj >=2.1
   - pys2index  # only from conda-forge
-  - python >=3.9
+  - python >=3.10,<3.13
   - python-cdo
   - python-dateutil
   - pyyaml
-  - rasterio
+  - rasterio >=1.3.10
   - requests
   - ruamel.yaml
   - scikit-image
@@ -63,14 +66,14 @@ dependencies:
   - scipy
   - seaborn
   - seawater
-  - shapely >=2
+  - shapely >=2.0.2
   - xarray >=0.12.0
   - xesmf >=0.7.1
   - xgboost >1.6.1  # github.com/ESMValGroup/ESMValTool/issues/2779
   - xlsxwriter
   - zarr
   # Python packages needed for unit testing
-  - flake8
+  - flake8 >=6
   - pytest >=3.9,!=6.0.0rc1,!=6.0.0
   - pytest-cov
   - pytest-env
@@ -84,26 +87,26 @@ dependencies:
   - sphinx >=6.1.3
   - pydata-sphinx-theme
   # Python packages needed for development
-  - codespell
-  - docformatter
+  - codespell ==2.3.0
+  - docformatter ==1.7.5
   - imagehash
-  - isort
+  - isort ==5.13.2
   - pre-commit
-  - prospector
+  - prospector >=1.12  # earliest support for Python 3.12
   - pyroma
   # - vprof not on conda-forge
-  - yamllint
-  - yapf
+  - yamllint ==1.35.1
+  - yapf ==0.32.0
 
   # NCL and dependencies
-  - ncl
+  - ncl >=6.6.2
   - cdo
   - imagemagick
   - nco
 
   # R and dependencies
   - cdo
-  - r-base >=3.5
+  - r-base >=3.5,<4.3.0
   - r-abind
   - r-akima
   - r-climdex.pcic
@@ -113,7 +116,6 @@ dependencies:
   - r-functional
   - r-ggplot2
   - r-gridextra
-  - r-lintr
   - r-logging
   - r-mapproj
   - r-maps
@@ -127,13 +129,12 @@ dependencies:
   - r-s2dverification
   - r-snow
   - r-spei
-  - r-styler
   - r-udunits2
   - r-yaml
   # R packages needed for development
   - r-git2r  # dependency of lintr
-  - r-lintr
-  - r-styler
+  - r-lintr ==3.1.2
+  - r-styler ==1.10.3
 
   # Julia (dependencies installed by separate script)
   - julia
diff --git a/environment_osx.yml b/environment_osx.yml
index 92eb9fed93..242f0a4f56 100644
--- a/environment_osx.yml
+++ b/environment_osx.yml
@@ -14,20 +14,22 @@ dependencies:
   - cdo >=2.3.0
   - cdsapi
   - cf-units
+  - cfgrib
   - cftime
   - cmocean
   - cython
-  - dask
+  - dask !=2024.8.0  # https://github.com/dask/dask/issues/11296
   - distributed
   - ecmwf-api-client
   - eofs
-  - esmpy >=8.6.0  # github.com/SciTools-incubator/iris-esmf-regrid/pull/342
-  - esmvalcore 2.10.*
+  - esmpy
+  - esmvalcore 2.11.*
   - fiona
-  - fsspec
   - fire
-  - gdal
-  - iris >=3.6.1
+  - fsspec
+  - gdal >=3.9.0
+  - importlib_metadata <8  # https://github.com/ESMValGroup/ESMValTool/issues/3699 only for Python 3.10/11 and esmpy<8.6
+  - iris >=3.11
   - iris-esmf-regrid >=0.10.0  # github.com/SciTools-incubator/iris-esmf-regrid/pull/342
   - jinja2
   - joblib
@@ -41,21 +43,21 @@ dependencies:
   - numpy !=1.24.3  # severe masking bug
   - openpyxl
   - packaging
-  - pandas !=2.2.0,!=2.2.1,!=2.2.2 # github.com/ESMValGroup/ESMValCore/pull/2305
+  - pandas==2.1.4  # unpin when ESMValCore released with https://github.com/ESMValGroup/ESMValCore/pull/2529
   - pip !=21.3
   - progressbar2
   - prov
-  - psyplot
-  - psy-maps
-  - psy-reg
-  - psy-simple
+  - psyplot >=1.5.0
+  - psy-maps >=1.5.0
+  - psy-reg >=1.5.0
+  - psy-simple >=1.5.0
   - pyproj >=2.1
-  - pys2index  # only from conda-forge
-  - python >=3.9
+  - pys2index >=0.1.5  # only from conda-forge; https://github.com/ESMValGroup/ESMValTool/pull/3792
+  - python >=3.10,<3.13
   - python-cdo
   - python-dateutil
   - pyyaml
-  - rasterio
+  - rasterio >=1.3.10
   - requests
   - ruamel.yaml
   - scikit-image
@@ -63,14 +65,14 @@ dependencies:
   - scipy
   - seaborn
   - seawater
-  - shapely >=2
+  - shapely >=2.0.2
   - xarray >=0.12.0
   - xesmf >=0.7.1
   - xgboost >1.6.1  # github.com/ESMValGroup/ESMValTool/issues/2779
   - xlsxwriter
   - zarr
   # Python packages needed for unit testing
-  - flake8
+  - flake8 >=6
   - pytest >=3.9,!=6.0.0rc1,!=6.0.0
   - pytest-cov
   - pytest-env
@@ -84,13 +86,13 @@ dependencies:
   - sphinx >=6.1.3
   - pydata-sphinx-theme
   # Python packages needed for development
-  - codespell
-  - docformatter
+  - codespell ==2.3.0
+  - docformatter ==1.7.5
   - imagehash
-  - isort
+  - isort ==5.13.2
   - pre-commit
-  - prospector
+  - prospector >=1.12  # earliest support for Python 3.12
   - pyroma
   # - vprof not on conda-forge
-  - yamllint
-  - yapf
+  - yamllint ==1.35.1
+  - yapf ==0.32.0
diff --git a/esmvaltool/cmorizers/data/cmor_config/AERONET.yml b/esmvaltool/cmorizers/data/cmor_config/AERONET.yml
index 33ae35879d..0e6ebf2934 100644
--- a/esmvaltool/cmorizers/data/cmor_config/AERONET.yml
+++ b/esmvaltool/cmorizers/data/cmor_config/AERONET.yml
@@ -5,7 +5,7 @@ filename: 'AOD_Level20_Monthly_V3.tar.gz'
 # Common global attributes for Cmorizer output
 attributes:
   dataset_id: AERONET
-  version: 20230610
+  version: 20240406
   tier: 3
   modeling_realm: atmos
   project_id: OBS6
diff --git a/esmvaltool/cmorizers/data/cmor_config/CMAP.yml b/esmvaltool/cmorizers/data/cmor_config/CMAP.yml
new file mode 100644
index 0000000000..eef1861f08
--- /dev/null
+++ b/esmvaltool/cmorizers/data/cmor_config/CMAP.yml
@@ -0,0 +1,21 @@
+---
+# Global attributes of NetCDF file
+attributes:
+  dataset_id: CMAP
+  project_id: OBS6
+  tier: 2
+  version: "v1"
+  modeling_realm: reanaly
+  source: "https://psl.noaa.gov/data/gridded/data.cmap.html"
+  reference: "cmap"
+  comment: |
+    ''
+
+# Variables to CMORize
+variables:
+  # monthly frequency
+  pr_month:
+    short_name: pr
+    mip: Amon
+    raw: precip
+    file: "precip.mon.mean.nc"
diff --git a/esmvaltool/cmorizers/data/cmor_config/ESACCI-LANDCOVER.yml b/esmvaltool/cmorizers/data/cmor_config/ESACCI-LANDCOVER.yml
new file mode 100644
index 0000000000..925057dc12
--- /dev/null
+++ b/esmvaltool/cmorizers/data/cmor_config/ESACCI-LANDCOVER.yml
@@ -0,0 +1,32 @@
+attributes:
+  project_id: 'OBS'
+  dataset_id: 'ESACCI-LANDCOVER'
+  tier: 2
+  modeling_realm: sat
+  institution: 'Universite catholique de Louvain'
+  reference: 'esacci-landcover'
+  source: 'ftp://anon-ftp.ceda.ac.uk/neodc/esacci/land_cover/data/pft'
+  title: 'ESA CCI Land Cover'
+  version: 'v2.0.8'
+  comment: ''
+filename: ESACCI-LC-L4-PFT-Map-300m-P1Y-{year}-v2.0.8.nc
+variables:
+  baresoilFrac:
+    mip: Lmon
+    long_name: 'BARE'
+    frequency: yr
+  cropFrac:
+    mip: Lmon
+    long_name: 'GRASS-MAN'
+    frequency: yr
+  grassFrac:
+    mip: Lmon
+    long_name: 'GRASS-NAT'
+    frequency: yr
+  shrubFrac:
+    mip: Lmon
+    frequency: yr
+  treeFrac:
+    mip: Lmon
+    frequency: yr
+
diff --git a/esmvaltool/cmorizers/data/cmor_config/ESACCI-SOILMOISTURE.yml b/esmvaltool/cmorizers/data/cmor_config/ESACCI-SOILMOISTURE.yml
new file mode 100644
index 0000000000..f2b7a1053d
--- /dev/null
+++ b/esmvaltool/cmorizers/data/cmor_config/ESACCI-SOILMOISTURE.yml
@@ -0,0 +1,21 @@
+attributes:
+  project_id: 'OBS'
+  dataset_id: 'ESACCI-SOILMOISTURE'
+  tier: 2
+  modeling_realm: sat
+  institution: 'TU Wien (AUT); VanderSat B.V. (NL); Planet Labs (NL); CESBIO (FR), EODC Gmbh (AUT)'
+  reference: 'esacci-soilmoisture'
+  source: 'ftp://anon-ftp.ceda.ac.uk/neodc/esacci/soil_moisture/data/'
+  title: 'ESA CCI Soil Moisture'
+  version: 'L3S-SSMV-COMBINED-v08.1'
+  comment: ''
+variables:
+  sm:
+    mip: Eday
+    raw: sm
+    filename: ESACCI-SOILMOISTURE-L3S-SSMV-COMBINED-{year}????000000-fv08.1.nc
+  smStderr:
+    mip: Eday
+    raw: sm_uncertainty
+    filename: ESACCI-SOILMOISTURE-L3S-SSMV-COMBINED-{year}????000000-fv08.1.nc
+     
\ No newline at end of file
diff --git a/esmvaltool/cmorizers/data/cmor_config/JRA-55.yml b/esmvaltool/cmorizers/data/cmor_config/JRA-55.yml
new file mode 100644
index 0000000000..a4f4c8b379
--- /dev/null
+++ b/esmvaltool/cmorizers/data/cmor_config/JRA-55.yml
@@ -0,0 +1,103 @@
+---
+# Common global attributes for Cmorizer output
+attributes:
+  dataset_id: JRA-55
+  version: '1'
+  tier: 2
+  modeling_realm: reanaly
+  project_id: OBS6
+  source: 'https://rda.ucar.edu/datasets/ds628.1/'
+  reference: 'jra_55'
+  comment: |
+    '' 
+
+# Variables to cmorize
+variables:
+  cli:
+    short_name: cli
+    mip: Amon
+    file: fcst_p125.229_ciwc.{year}01_{year}12.grb
+
+  clivi:
+    short_name: clivi
+    mip: Amon
+    file: fcst_column125.058_cice.{year}01_{year}12.grb
+
+  clw:
+    short_name: clw
+    mip: Amon
+    file: fcst_p125.228_clwc.{year}01_{year}12.grb
+
+  clwvi:
+    short_name: clwvi
+    mip: Amon
+    operator: sum
+    files:
+      - 'fcst_column125.058_cice.{year}01_{year}12.grb'
+      - 'fcst_column125.227_cw.{year}01_{year}12.grb'
+
+  clt:
+    short_name: clt
+    mip: Amon
+    file: fcst_surf125.071_tcdc.{year}01_{year}12.grb
+
+  prw:
+    short_name: prw
+    mip: Amon
+    file: fcst_column125.054_pwat.{year}01_{year}12.grb
+
+  rlus:
+    short_name: rlus
+    mip: Amon
+    typeOfLevel: surface
+    file: fcst_phy2m125.212_ulwrf.{year}01_{year}12.grb
+
+  rlut:
+    short_name: rlut
+    mip: Amon
+    typeOfLevel: nominalTop
+    file: fcst_phy2m125.212_ulwrf.{year}01_{year}12.grb
+
+  rlutcs:
+    short_name: rlutcs
+    mip: Amon
+    file: fcst_phy2m125.162_csulf.{year}01_{year}12.grb
+
+  rsus:
+    short_name: rsus
+    mip: Amon
+    typeOfLevel: surface
+    file: fcst_phy2m125.211_uswrf.{year}01_{year}12.grb
+
+  rsuscs:
+    short_name: rsuscs
+    mip: Amon
+    typeOfLevel: surface
+    file: fcst_phy2m125.160_csusf.{year}01_{year}12.grb
+
+  rsut:
+    short_name: rsut
+    mip: Amon
+    typeOfLevel: nominalTop
+    file: fcst_phy2m125.211_uswrf.{year}01_{year}12.grb
+
+  rsutcs:
+    short_name: rsutcs
+    mip: Amon
+    typeOfLevel: nominalTop
+    file: fcst_phy2m125.160_csusf.{year}01_{year}12.grb
+
+  ta:
+    short_name: ta
+    mip: Amon
+    file: anl_p125.011_tmp.{year}01_{year}12.grb
+
+  tas:
+    short_name: tas
+    mip: Amon
+    file: anl_surf125.011_tmp.{year}01_{year}12.grb
+
+  wap:
+    short_name: wap
+    mip: Amon
+    file: anl_p125.039_vvel.{year}01_{year}12.grb
diff --git a/esmvaltool/cmorizers/data/cmor_config/NCEP-DOE-R2.yml b/esmvaltool/cmorizers/data/cmor_config/NCEP-DOE-R2.yml
index e0768cf354..f18f76f5a9 100644
--- a/esmvaltool/cmorizers/data/cmor_config/NCEP-DOE-R2.yml
+++ b/esmvaltool/cmorizers/data/cmor_config/NCEP-DOE-R2.yml
@@ -39,3 +39,25 @@ variables:
     mip: Amon
     raw: omega
     file: 'omega\.mon\.mean\.nc'
+  pr_month:
+    short_name: pr
+    mip: Amon
+    raw: prate
+    file: 'prate.sfc.mon.mean.nc'
+  tauu_month:
+    short_name: tauu
+    mip: Amon
+    raw: uflx
+    file: 'uflx.sfc.mon.mean.nc'
+    make_negative: true
+  tauv_month:
+    short_name: tauv
+    mip: Amon
+    raw: vflx
+    file: 'vflx.sfc.mon.mean.nc'
+    make_negative: true
+  tos_month:
+    short_name: tos
+    mip: Amon
+    raw: skt
+    file: 'skt.sfc.mon.mean.nc'
diff --git a/esmvaltool/cmorizers/data/cmor_config/NOAA-CIRES-20CR-V2.yml b/esmvaltool/cmorizers/data/cmor_config/NOAA-CIRES-20CR-V2.yml
index 7591e99257..faded8f9d6 100644
--- a/esmvaltool/cmorizers/data/cmor_config/NOAA-CIRES-20CR-V2.yml
+++ b/esmvaltool/cmorizers/data/cmor_config/NOAA-CIRES-20CR-V2.yml
@@ -44,3 +44,21 @@ variables:
     mip: Amon
     raw: uswrf
     file: 'uswrf.ntat.mon.mean.nc'
+  pr_month:
+    short_name: pr
+    mip: Amon
+    raw: prate
+    file: 'prate.mon.mean.nc'
+  tauu_month:
+    short_name: tauu
+    mip: Amon
+    raw: uflx
+    file: 'uflx.mon.mean.nc'
+    make_negative: true
+  tauv_month:
+    short_name: tauv
+    mip: Amon
+    raw: vflx
+    file: 'vflx.mon.mean.nc'
+    make_negative: true
+    
diff --git a/esmvaltool/cmorizers/data/cmorizer.py b/esmvaltool/cmorizers/data/cmorizer.py
index 16b7666350..5e66b7a70f 100755
--- a/esmvaltool/cmorizers/data/cmorizer.py
+++ b/esmvaltool/cmorizers/data/cmorizer.py
@@ -10,6 +10,7 @@
 import os
 import shutil
 import subprocess
+import warnings
 from pathlib import Path
 
 import esmvalcore
@@ -18,13 +19,14 @@
 from esmvalcore.config import CFG
 from esmvalcore.config._logging import configure_logging
 
+from esmvaltool import ESMValToolDeprecationWarning
 from esmvaltool.cmorizers.data.utilities import read_cmor_config
 
 logger = logging.getLogger(__name__)
 datasets_file = os.path.join(os.path.dirname(__file__), 'datasets.yml')
 
 
-class Formatter():
+class _Formatter():
     """
     Class to manage the download and formatting of datasets.
 
@@ -39,26 +41,40 @@ def __init__(self, info):
         self.datasets_info = info
         self.config = ''
 
-    def start(self, command, datasets, config_file, options):
+    def start(self, command, datasets, config_file, config_dir, options):
         """Read configuration and set up formatter for data processing.
 
         Parameters
         ----------
         command: str
-            Name of the command to execute
+            Name of the command to execute.
         datasets: str
-            List of datasets to process, comma separated
+            List of datasets to process, comma separated.
         config_file: str
-            Config file to use
+            Config file to use. Option will be removed in v2.14.0.
+        config_dir: str
+            Config directory to use.
         options: dict()
-            Extra options to overwrite config user file
+            Extra options to overwrite configuration.
+
         """
         if isinstance(datasets, str):
             self.datasets = datasets.split(',')
         else:
             self.datasets = datasets
 
-        CFG.load_from_file(config_file)
+        if config_file is not None:  # remove in v2.14.0
+            CFG.load_from_file(config_file)
+        elif config_dir is not None:
+            config_dir = Path(
+                os.path.expandvars(config_dir)
+            ).expanduser().absolute()
+            if not config_dir.is_dir():
+                raise NotADirectoryError(
+                    f"Invalid --config_dir given: {config_dir} is not an "
+                    f"existing directory"
+                )
+            CFG.update_from_dirs([config_dir])
         CFG.update(options)
         self.config = CFG.start_session(f'data_{command}')
 
@@ -199,8 +215,9 @@ def format(self, start, end, install):
                 failed_datasets.append(dataset)
 
         if failed_datasets:
-            raise Exception(
-                f'Format failed for datasets {" ".join(failed_datasets)}')
+            raise RuntimeError(
+                f'Format failed for datasets {" ".join(failed_datasets)}'
+            )
 
     @staticmethod
     def has_downloader(dataset):
@@ -400,7 +417,7 @@ class DataCommand():
     def __init__(self):
         with open(datasets_file, 'r', encoding='utf8') as data:
             self._info = yaml.safe_load(data)
-        self.formatter = Formatter(self._info)
+        self.formatter = _Formatter(self._info)
 
     def _has_downloader(self, dataset):
         return 'Yes' if self.formatter.has_downloader(dataset) else "No"
@@ -441,28 +458,48 @@ def download(self,
                  start=None,
                  end=None,
                  overwrite=False,
+                 config_dir=None,
                  **kwargs):
         """Download datasets.
 
         Parameters
         ----------
-        datasets : list(str)
+        datasets: list(str)
             List of datasets to format
-        config_file : str, optional
-            Path to ESMValTool's config user file, by default None
-        start : str, optional
+        config_file: str, optional
+            Path to ESMValTool's config user file, by default None.
+
+            .. deprecated:: 2.12.0
+                This option has been deprecated in ESMValTool version 2.12.0
+                and is scheduled for removal in version 2.14.0. Please use the
+                option `config_dir` instead.
+        start: str, optional
             Start of the interval to process, by default None. Valid formats
             are YYYY, YYYYMM and YYYYMMDD.
-        end : str, optional
+        end: str, optional
             End of the interval to process, by default None. Valid formats
             are YYYY, YYYYMM and YYYYMMDD.
-        overwrite : bool, optional
+        overwrite: bool, optional
             If true, download already present data again
+        config_dir: str, optional
+            Path to additional ESMValTool configuration directory. See
+            :ref:`esmvalcore:config_yaml_files` for details.
+
         """
+        if config_file is not None:
+            msg = (
+                "The option `config_file` has been deprecated in ESMValTool "
+                "version 2.12.0 and is scheduled for removal in version "
+                "2.14.0. Please use the option ``config_dir`` instead."
+            )
+            warnings.warn(msg, ESMValToolDeprecationWarning)
+
         start = self._parse_date(start)
         end = self._parse_date(end)
 
-        self.formatter.start('download', datasets, config_file, kwargs)
+        self.formatter.start(
+            'download', datasets, config_file, config_dir, kwargs
+        )
         self.formatter.download(start, end, overwrite)
 
     def format(self,
@@ -471,6 +508,7 @@ def format(self,
                start=None,
                end=None,
                install=False,
+               config_dir=None,
                **kwargs):
         """Format datasets.
 
@@ -480,6 +518,11 @@ def format(self,
             List of datasets to format
         config_file : str, optional
             Path to ESMValTool's config user file, by default None
+
+            .. deprecated:: 2.12.0
+                This option has been deprecated in ESMValTool version 2.12.0
+                and is scheduled for removal in version 2.14.0. Please use the
+                option `config_dir` instead.
         start : str, optional
             Start of the interval to process, by default None. Valid formats
             are YYYY, YYYYMM and YYYYMMDD.
@@ -488,11 +531,25 @@ def format(self,
             are YYYY, YYYYMM and YYYYMMDD.
         install : bool, optional
             If true, move processed data to the folder, by default False
+        config_dir: str, optional
+            Path to additional ESMValTool configuration directory. See
+            :ref:`esmvalcore:config_yaml_files` for details.
+
         """
+        if config_file is not None:
+            msg = (
+                "The option `config_file` has been deprecated in ESMValTool "
+                "version 2.12.0 and is scheduled for removal in version "
+                "2.14.0. Please use the option ``config_dir`` instead."
+            )
+            warnings.warn(msg, ESMValToolDeprecationWarning)
+
         start = self._parse_date(start)
         end = self._parse_date(end)
 
-        self.formatter.start('formatting', datasets, config_file, kwargs)
+        self.formatter.start(
+            'formatting', datasets, config_file, config_dir, kwargs
+        )
         self.formatter.format(start, end, install)
 
     def prepare(self,
@@ -502,6 +559,7 @@ def prepare(self,
                 end=None,
                 overwrite=False,
                 install=False,
+                config_dir=None,
                 **kwargs):
         """Download and format a set of datasets.
 
@@ -511,6 +569,11 @@ def prepare(self,
             List of datasets to format
         config_file : str, optional
             Path to ESMValTool's config user file, by default None
+
+            .. deprecated:: 2.12.0
+                This option has been deprecated in ESMValTool version 2.12.0
+                and is scheduled for removal in version 2.14.0. Please use the
+                option `config_dir` instead.
         start : str, optional
             Start of the interval to process, by default None. Valid formats
             are YYYY, YYYYMM and YYYYMMDD.
@@ -521,11 +584,25 @@ def prepare(self,
             If true, move processed data to the folder, by default False
         overwrite : bool, optional
             If true, download already present data again
+        config_dir: str, optional
+            Path to additional ESMValTool configuration directory. See
+            :ref:`esmvalcore:config_yaml_files` for details.
+
         """
+        if config_file is not None:
+            msg = (
+                "The option `config_file` has been deprecated in ESMValTool "
+                "version 2.12.0 and is scheduled for removal in version "
+                "2.14.0. Please use the option ``config_dir`` instead."
+            )
+            warnings.warn(msg, ESMValToolDeprecationWarning)
+
         start = self._parse_date(start)
         end = self._parse_date(end)
 
-        self.formatter.start('preparation', datasets, config_file, kwargs)
+        self.formatter.start(
+            'preparation', datasets, config_file, config_dir, kwargs
+        )
         if self.formatter.download(start, end, overwrite):
             self.formatter.format(start, end, install)
         else:
diff --git a/esmvaltool/cmorizers/data/datasets.yml b/esmvaltool/cmorizers/data/datasets.yml
index 10481704ae..afb98e4925 100644
--- a/esmvaltool/cmorizers/data/datasets.yml
+++ b/esmvaltool/cmorizers/data/datasets.yml
@@ -4,7 +4,7 @@ datasets:
   AERONET:
     tier: 3
     source: "https://aeronet.gsfc.nasa.gov/"
-    last_access: 2023-06-13
+    last_access: 2024-04-06
     info: |
       Aerosol Optical Depth information from a worldwide network of stations.
 
@@ -17,16 +17,16 @@ datasets:
       analyses covering analysis of monthly rainfall. The dataset provides consistent temporal and spatial analyses
       across Australia for each observed data variable. This accounts for spatial and temporal gaps in observations.
       Where possible, the gridded analysis techniques provide useful estimates in data-sparse regions
-      such as central Australia. 
-      
+      such as central Australia.
+
       Time coverage: Site-based data are used to provide gridded climate data at the monthly timescale for rainfall (1900+).
       Reference: Evans, A., Jones, D.A., Smalley, R., and Lellyett, S. 2020. An enhanced gridded rainfall analysis scheme
       for Australia. Bureau of Meteorology Research Report. No. 41.
       National Computational Infrastructure (NCI) - Catalogue Record: http://dx.doi.org/10.25914/6009600786063.
-      Data from NCI (National Computing Infrastructure Australia https://nci.org.au/), 
+      Data from NCI (National Computing Infrastructure Australia https://nci.org.au/),
       requires an NCI account and access to Gadi(Supercomputer in Canberra) and the project found in catalogue record.
       Access can be requested through NCI. NCI is an ESGF node (https://esgf.nci.org.au/projects/esgf-nci/)
-  
+
   ANUClimate:
     tier: 3
     source: "https://dx.doi.org/10.25914/60a10aa56dd1b"
@@ -35,7 +35,7 @@ datasets:
       Data from NCI project requiring an NCI account and access to GADI
 
       ANUClimate 2.0 consists of gridded daily and monthly climate variables across the terrestrial landmass of Australia
-      from at least 1970 to the present. Rainfall grids are generated from 1900 to the present. The underpinning spatial 
+      from at least 1970 to the present. Rainfall grids are generated from 1900 to the present. The underpinning spatial
       models have been developed at the Fenner School of Environment and Society of the Australian National University.
 
   APHRO-MA:
@@ -264,6 +264,15 @@ datasets:
          named like the year (e.g. 2007), no subdirectories with
          days etc.
 
+  CMAP:
+    tier: 2
+    source: https://psl.noaa.gov/data/gridded/data.cmap.html
+    last_access: 2024-09-09
+    info: |
+      To facilitate the download, the links to the https server are provided.
+      https://downloads.psl.noaa.gov/Datasets/cmap/enh/
+        precip.mon.mean.nc
+
   CowtanWay:
     tier: 2
     source: https://www-users.york.ac.uk/~kdc3/papers/coverage2013/series.html
@@ -301,7 +310,7 @@ datasets:
     last_access: 2020-03-23
     info: |
       Create a new empty directory ``$RAWOBSPATH/Tier2/CT2019`` (where
-      ``$RAWOBSPATH`` is given by your user configuration file) where the raw
+      ``$RAWOBSPATH`` is given by your configuration) where the raw
       data will be stored. The download of the data is automatically handled by
       this script. If data is already present in this directory, the download is
       skipped (to force a new download delete your old files).
@@ -473,24 +482,16 @@ datasets:
 
   ESACCI-LANDCOVER:
     tier: 2
-    source: ftp://anon-ftp.ceda.ac.uk/neodc/esacci/land_cover/data/land_cover_maps/
-    last_access: 2019-01-10
+    source: ftp://anon-ftp.ceda.ac.uk/neodc/esacci/land_cover/data/pft/v2.0.8/
+    last_access: 2024-07-11
     info: |
-      Download the 3 NetCDF files for 2000, 2005 and 2010.
-      Download the CCI-LC Tools from:
-        http://maps.elie.ucl.ac.be/CCI/viewer/download/lc-user-tools-3.14.zip
-      Unpack and run the CCI-LC Tools on each of the NetCDF files as follows:
-        bash lc-user-tools-3.14/bin/aggregate-map.sh \
-          -PgridName=GEOGRAPHIC_LAT_LON -PnumMajorityClasses=1 \
-          -PoutputAccuracy=false -PoutputPFTClasses=true \
-          -PoutputLCCSClasses=false -PnumRows=360 <inputfile.nc>
-      Put the resulting processed data in input_dir_path.
-
-      Caveats
-        The CCI-LC Tools must be applied before running this script.
-        The CCI-LC Tools require Java Version 7 or higher.
-        The input data are available for a single year and are copied over to
-        generate a time series over their time range of validity.
+        Download and processing instructions:
+        Use the following CLI to download all the files:
+        esmvaltool data download ESACCI-LANDCOVER
+        The underlying downloader is located here:
+        /ESMValTool/esmvaltool/cmorizers/data/downloaders/datasets/esacci_landcover.py
+        and it will download all the files currently available on CEDA (1992-2020)
+        under a single directory as follow: ${RAWOBS}/Tier2/ESACCI-LANDCOVER
 
   ESACCI-LST:
     tier: 2
@@ -531,11 +532,11 @@ datasets:
   ESACCI-SOILMOISTURE:
     tier: 2
     source: ftp://anon-ftp.ceda.ac.uk/neodc/esacci/soil_moisture/data/
-    last_access: 2019-02-01
+    last_access: 2024-06-19
     info: |
       Download the data from:
-        daily_files/COMBINED/v04.2/
-        ancillary/v04.2/
+        daily_files/COMBINED/v08.1/
+        ancillary/v08.1/
       Put all files under a single directory (no subdirectories with years).
 
   ESACCI-SEA-SURFACE-SALINITY:
@@ -562,7 +563,7 @@ datasets:
     source: https://wui.cmsaf.eu/safira/action/viewDoiDetails?acronym=COMBI_V001
     last_access: 2024-02-21
     info: |
-      CDR2 requires registration at EUMETSAT CM SAF, the information on how to 
+      CDR2 requires registration at EUMETSAT CM SAF, the information on how to
           download the order will be emailed once the order is ready.
       All files need to be in one directory, not in yearly subdirectories.
 
@@ -832,6 +833,15 @@ datasets:
         mon/atmos/rsut/rsut_Amon_reanalysis_JRA-25_197901-201312.nc
         mon/atmos/rsutcs/rsutcs_Amon_reanalysis_JRA-25_197901-201312.nc
 
+  JRA-55:
+    tier: 2
+    source: https://rda.ucar.edu/datasets/ds628.1/
+    last_access: 2023-03-22
+    info: |
+      Create an account on the research data archive (RDA) in order to be able
+      to download the data (1.25 degree, pressure levels). See
+      https://rda.ucar.edu/login/register/ for more details.
+
   Kadow2020:
     tier: 2
     source: http://users.met.fu-berlin.de/~ChristopherKadow/
@@ -918,11 +928,11 @@ datasets:
       Select "Data Access" -> "Subset/Get Data" -> "Get Data" and follow the
       "Instructions for downloading". All *.he5 files need to be saved in the
       $RAWOBS/Tier3/MLS-AURA directory, where $RAWOBS refers to the RAWOBS
-      directory defined in the user configuration file. Apply this procedure to
-      both links provided above. The temperature fields are necessary for quality
+      directory defined in the configuration. Apply this procedure to both
+      links provided above. The temperature fields are necessary for quality
       control of the RHI data (see Data Quality Document for MLS-AURA for more
       information).
-      A registration is required
+      A registration is required.
 
   MOBO-DIC_MPIM:
     tier: 2
@@ -976,9 +986,14 @@ datasets:
       pressure/
         rhum.mon.mean.nc
         air.mon.mean.nc
+        omega.mon.mean.nc
       https://downloads.psl.noaa.gov/Datasets/ncep.reanalysis2/Monthlies/
       gaussian_grid
         tcdc.eatm.mon.mean.nc
+        prate.sfc.mon.mean.nc
+        uflx.sfc.mon.mean.nc
+        vflx.sfc.mon.mean.nc
+        skt.sfc.mon.mean.nc
       https://downloads.psl.noaa.gov/Datasets/ncep.reanalysis2/Monthlies/
       surface
         pr_wtr.eatm.mon.mean.nc
@@ -1069,7 +1084,9 @@ datasets:
         gaussian/monolevel/tcdc.eatm.mon.mean.nc
         gaussian/monolevel/ulwrf.ntat.mon.mean.nc
         gaussian/monolevel/uswrf.ntat.mon.mean.nc
-
+        gaussian/monolevel/prate.mon.mean.nc
+        gaussian/monolevel/uflx.mon.mean.nc
+        gaussian/monolevel/vflx.mon.mean.nc
   NOAA-CIRES-20CR-V3:
     tier: 2
     source: ftp.cdc.noaa.gov/Projects/20thC_ReanV3/Monthlies/
@@ -1091,7 +1108,7 @@ datasets:
     last_access: 2023-12-04
     info: |
       Download the following files:
-        ersst.yyyymm.nc 
+        ersst.yyyymm.nc
         for years 1854 to 2020
 
   NOAA-ERSSTv5:
@@ -1100,7 +1117,7 @@ datasets:
     last_access: 2023-12-04
     info: |
       Download the following files:
-        ersst.v5.yyyymm.nc 
+        ersst.v5.yyyymm.nc
         for years 1854 onwards
 
   NOAAGlobalTemp:
@@ -1127,13 +1144,13 @@ datasets:
       Download daily data from:
       https://nsidc.org/data/NSIDC-0116
       Login required for download, and also requires citation only to use
-   
+
   NSIDC-G02202-sh:
     tier: 3
     source: https://polarwatch.noaa.gov/erddap/griddap/nsidcG02202v4shmday
     last_access: 2023-05-13
     info: |
-      Download monthly data. 
+      Download monthly data.
       Login required for download, and also requires citation only to use
 
   OceanSODA-ETHZ:
diff --git a/esmvaltool/cmorizers/data/download_scripts/download_era_interim.py b/esmvaltool/cmorizers/data/download_scripts/download_era_interim.py
index 72cf8d98af..374c750ef6 100644
--- a/esmvaltool/cmorizers/data/download_scripts/download_era_interim.py
+++ b/esmvaltool/cmorizers/data/download_scripts/download_era_interim.py
@@ -12,8 +12,13 @@
 4. Copy/paste the text in https://api.ecmwf.int/v1/key/ into a blank text file
 and save it as $HOME/.ecmwfapirc
 
-5. Use ESMValCore/esmvalcore/config-user.yml as an template
-and set the rootpath of the output directory in RAWOBS
+5. Copy the default configuration file with
+
+```bash
+esmvaltool config get_config_user --path=config-user.yml
+```
+
+and set the ``rootpath`` for the RAWOBS project.
 
 6. Check the description of the variables at
 https://apps.ecmwf.int/codes/grib/param-db
diff --git a/esmvaltool/cmorizers/data/downloaders/datasets/cmap.py b/esmvaltool/cmorizers/data/downloaders/datasets/cmap.py
new file mode 100644
index 0000000000..5fd58b5ac1
--- /dev/null
+++ b/esmvaltool/cmorizers/data/downloaders/datasets/cmap.py
@@ -0,0 +1,38 @@
+"""Script to download CMAP (CPC Merged Analysis of Precipitation)."""
+
+import logging
+
+from esmvaltool.cmorizers.data.downloaders.ftp import FTPDownloader
+
+logger = logging.getLogger(__name__)
+
+
+def download_dataset(config, dataset, dataset_info, start_date, end_date,
+                     overwrite):
+    """Download dataset.
+
+    Parameters
+    ----------
+    config : dict
+        ESMValTool's user configuration
+    dataset : str
+        Name of the dataset
+    dataset_info : dict
+         Dataset information from the datasets.yml file
+    start_date : datetime
+        Start of the interval to download
+    end_date : datetime
+        End of the interval to download
+    overwrite : bool
+        Overwrite already downloaded files
+    """
+    downloader = FTPDownloader(
+        config=config,
+        server="ftp2.psl.noaa.gov",
+        dataset=dataset,
+        dataset_info=dataset_info,
+        overwrite=overwrite,
+    )
+    downloader.connect()
+
+    downloader.download_file("/Datasets/cmap/enh/precip.mon.mean.nc")
diff --git a/esmvaltool/cmorizers/data/downloaders/datasets/esacci_landcover.py b/esmvaltool/cmorizers/data/downloaders/datasets/esacci_landcover.py
new file mode 100644
index 0000000000..efffa2aaaa
--- /dev/null
+++ b/esmvaltool/cmorizers/data/downloaders/datasets/esacci_landcover.py
@@ -0,0 +1,52 @@
+"""Script to download ESACCI-LANDCOVER pft data from the CEDA."""
+
+from datetime import datetime
+
+from esmvaltool.cmorizers.data.downloaders.ftp import CCIDownloader
+
+
+def download_dataset(config, dataset, dataset_info, start_date, end_date,
+                     overwrite):
+    """Download dataset.
+
+    Parameters
+    ----------
+    config : dict
+        ESMValTool's user configuration
+    dataset : str
+        Name of the dataset
+    dataset_info : dict
+         Dataset information from the datasets.yml file
+    start_date : datetime
+        Start of the interval to download
+    end_date : datetime
+        End of the interval to download
+    overwrite : bool
+        Overwrite already downloaded files
+    """
+    # Default start and end dates if not provided
+    if not start_date:
+        start_date = datetime(1992, 1, 1)
+    if not end_date:
+        end_date = datetime(2020, 12, 31)
+
+    # Initialize the downloader
+    downloader = CCIDownloader(
+        config=config,
+        dataset=dataset,
+        dataset_info=dataset_info,
+        overwrite=overwrite,
+    )
+    downloader.ftp_name = 'land_cover'
+    downloader.connect()
+
+    # Set current working directory to the main directory with the files
+    downloader.set_cwd('/pft/v2.0.8/')
+
+    # Create a regex pattern to match any .nc files
+    year_range = '|'.join(str(year) for year in range(start_date.year,
+                                                      end_date.year + 1))
+    pattern = rf".*-(?:{year_range}).*\.nc$"
+
+    # Download all .nc files in the directory
+    downloader.download_folder('.', filter_files=pattern)
diff --git a/esmvaltool/cmorizers/data/downloaders/datasets/esacci_soilmoisture.py b/esmvaltool/cmorizers/data/downloaders/datasets/esacci_soilmoisture.py
index d31f330497..0d29e96ff9 100644
--- a/esmvaltool/cmorizers/data/downloaders/datasets/esacci_soilmoisture.py
+++ b/esmvaltool/cmorizers/data/downloaders/datasets/esacci_soilmoisture.py
@@ -26,9 +26,9 @@ def download_dataset(config, dataset, dataset_info, start_date, end_date,
         Overwrite already downloaded files
     """
     if start_date is None:
-        start_date = datetime(1979, 1, 1)
+        start_date = datetime(1978, 11, 1)
     if end_date is None:
-        end_date = datetime(2016, 1, 1)
+        end_date = datetime(2022, 12, 31)
 
     loop_date = start_date
 
@@ -40,9 +40,9 @@ def download_dataset(config, dataset, dataset_info, start_date, end_date,
     )
     downloader.ftp_name = 'soil_moisture'
     downloader.connect()
-    downloader.set_cwd('ancillary/v04.2/')
+    downloader.set_cwd('ancillary/v08.1/')
     downloader.download_folder('.')
-    downloader.set_cwd('daily_files/COMBINED/v04.2/')
+    downloader.set_cwd('daily_files/COMBINED/v08.1/')
     while loop_date <= end_date:
         year = loop_date.year
         downloader.download_year(f'{year}')
diff --git a/esmvaltool/cmorizers/data/downloaders/datasets/jra_55.py b/esmvaltool/cmorizers/data/downloaders/datasets/jra_55.py
new file mode 100644
index 0000000000..7a9e374136
--- /dev/null
+++ b/esmvaltool/cmorizers/data/downloaders/datasets/jra_55.py
@@ -0,0 +1,113 @@
+"""Script to download JRA-55 from RDA."""
+import logging
+import os
+from datetime import datetime
+
+from dateutil import relativedelta
+
+from esmvaltool.cmorizers.data.downloaders.wget import WGetDownloader
+
+logger = logging.getLogger(__name__)
+
+
+def download_dataset(config, dataset, dataset_info, start_date, end_date,
+                     overwrite):
+    """Download dataset.
+
+    Parameters
+    ----------
+    config : dict
+        ESMValTool's user configuration
+    dataset : str
+        Name of the dataset
+    dataset_info : dict
+         Dataset information from the datasets.yml file
+    start_date : datetime
+        Start of the interval to download
+    end_date : datetime
+        End of the interval to download
+    overwrite : bool
+        Overwrite already downloaded files
+    """
+    downloader = WGetDownloader(
+        config=config,
+        dataset=dataset,
+        dataset_info=dataset_info,
+        overwrite=overwrite,
+    )
+
+    os.makedirs(downloader.local_folder, exist_ok=True)
+
+    user = os.environ.get("rda-user")
+    if user is None:
+        user = str(input("RDA user name? "))
+        if user == "":
+            errmsg = ("A RDA account is required to download JRA-55 data."
+                      " Please visit https://rda.ucar.edu/login/register/"
+                      " to create an account at the Research Data Archive"
+                      " (RDA) if needed.")
+            logger.error(errmsg)
+            raise ValueError
+
+    passwd = os.environ.get("rda-passwd")
+    if passwd is None:
+        passwd = str(input("RDA password? "))
+
+    if start_date is None:
+        start_date = datetime(1958, 1, 1)
+    if end_date is None:
+        end_date = datetime(2022, 12, 31)
+    loop_date = start_date
+
+    options = ["-O", "Authentication.log", "--save-cookies=auth.rda_ucar_edu",
+               f"--post-data=\"email={user}&passwd={passwd}&action=login\""]
+
+    # login to Research Data Archive (RDA)
+
+    downloader.login("https://rda.ucar.edu/cgi-bin/login", options)
+
+    # download files
+
+    url = "https://data.rda.ucar.edu/ds628.1"
+    download_options = ["--load-cookies=auth.rda_ucar_edu"]
+
+    # define variables to download
+
+    var = [["011_tmp", "anl_p125"],
+           ["011_tmp", "anl_surf125"],
+           ["039_vvel", "anl_p125"],
+           ["071_tcdc", "fcst_surf125"],
+           ["054_pwat", "fcst_column125"],
+           ["058_cice", "fcst_column125"],
+           ["160_csusf", "fcst_phy2m125"],
+           ["162_csulf", "fcst_phy2m125"],
+           ["211_uswrf", "fcst_phy2m125"],
+           ["212_ulwrf", "fcst_phy2m125"],
+           ["227_cw", "fcst_column125"],
+           ["228_clwc", "fcst_p125"],
+           ["229_ciwc", "fcst_p125"]]
+
+    # download data
+
+    while loop_date <= end_date:
+        year = loop_date.year
+
+        for item in var:
+            varname = item[0]
+            channel = item[1]
+            fname = f"{channel}.{varname}.{year}01_{year}12"
+            # download file
+            downloader.download_file(url + f"/{channel}/{year}/" +
+                                     fname, download_options)
+            # add file extension ".grb"
+            os.rename(downloader.local_folder + "/" + fname,
+                      downloader.local_folder + "/" + fname + ".grb")
+
+        loop_date += relativedelta.relativedelta(years=1)
+
+    # clean up temporary files
+
+    if os.path.exists("Authentication.log"):
+        os.remove("Authentication.log")
+    if os.path.exists("auth.rda_ucar_edu"):
+        os.remove("auth.rda_ucar_edu")
diff --git a/esmvaltool/cmorizers/data/downloaders/datasets/ncep_doe_r2.py b/esmvaltool/cmorizers/data/downloaders/datasets/ncep_doe_r2.py
index 704493554f..2d691e710d 100644
--- a/esmvaltool/cmorizers/data/downloaders/datasets/ncep_doe_r2.py
+++ b/esmvaltool/cmorizers/data/downloaders/datasets/ncep_doe_r2.py
@@ -48,3 +48,11 @@ def download_dataset(config, dataset, dataset_info, start_date, end_date,
                              wget_options=[])
     downloader.download_file(url + "surface/pr_wtr.eatm.mon.mean.nc",
                              wget_options=[])
+    downloader.download_file(url + "gaussian_grid/prate.sfc.mon.mean.nc",
+                             wget_options=[])
+    downloader.download_file(url + "gaussian_grid/uflx.sfc.mon.mean.nc",
+                             wget_options=[])
+    downloader.download_file(url + "gaussian_grid/vflx.sfc.mon.mean.nc",
+                             wget_options=[])
+    downloader.download_file(url + "gaussian_grid/skt.sfc.mon.mean.nc",
+                             wget_options=[])
diff --git a/esmvaltool/cmorizers/data/downloaders/datasets/noaa_cires_20cr_v2.py b/esmvaltool/cmorizers/data/downloaders/datasets/noaa_cires_20cr_v2.py
index fb2d733f06..bbbd708293 100644
--- a/esmvaltool/cmorizers/data/downloaders/datasets/noaa_cires_20cr_v2.py
+++ b/esmvaltool/cmorizers/data/downloaders/datasets/noaa_cires_20cr_v2.py
@@ -34,7 +34,7 @@ def download_dataset(config, dataset, dataset_info, start_date, end_date,
     )
     downloader.connect()
 
-    downloader.set_cwd("Projects/20thC_ReanV2/Monthlies/")
+    downloader.set_cwd("/Projects/20thC_ReanV2/Monthlies/")
     downloader.download_file("monolevel/cldwtr.eatm.mon.mean.nc",
                              sub_folder='surface')
     downloader.download_file("monolevel/pr_wtr.eatm.mon.mean.nc",
@@ -47,3 +47,9 @@ def download_dataset(config, dataset, dataset_info, start_date, end_date,
                              sub_folder='surface_gauss')
     downloader.download_file("gaussian/monolevel/uswrf.ntat.mon.mean.nc",
                              sub_folder='surface_gauss')
+    downloader.download_file("gaussian/monolevel/prate.mon.mean.nc",
+                             sub_folder='surface_gauss')
+    downloader.download_file("gaussian/monolevel/uflx.mon.mean.nc",
+                             sub_folder='surface_gauss')
+    downloader.download_file("gaussian/monolevel/vflx.mon.mean.nc",
+                             sub_folder='surface_gauss')
diff --git a/esmvaltool/cmorizers/data/downloaders/datasets/noaa_ersstv3b.py b/esmvaltool/cmorizers/data/downloaders/datasets/noaa_ersstv3b.py
index 0ac6a3e012..5a54080be4 100644
--- a/esmvaltool/cmorizers/data/downloaders/datasets/noaa_ersstv3b.py
+++ b/esmvaltool/cmorizers/data/downloaders/datasets/noaa_ersstv3b.py
@@ -1,6 +1,7 @@
 """Script to download NOAA-ERSST-v3b."""
 import logging
 from datetime import datetime
+
 from dateutil import relativedelta
 
 from esmvaltool.cmorizers.data.downloaders.wget import WGetDownloader
diff --git a/esmvaltool/cmorizers/data/downloaders/datasets/noaa_ersstv5.py b/esmvaltool/cmorizers/data/downloaders/datasets/noaa_ersstv5.py
index f995f9d2c7..7dbeccfe12 100644
--- a/esmvaltool/cmorizers/data/downloaders/datasets/noaa_ersstv5.py
+++ b/esmvaltool/cmorizers/data/downloaders/datasets/noaa_ersstv5.py
@@ -1,6 +1,7 @@
 """Script to download NOAA-ERSST-V5."""
 import logging
 from datetime import datetime
+
 from dateutil import relativedelta
 
 from esmvaltool.cmorizers.data.downloaders.wget import WGetDownloader
diff --git a/esmvaltool/cmorizers/data/downloaders/datasets/nsidc_g02202_sh.py b/esmvaltool/cmorizers/data/downloaders/datasets/nsidc_g02202_sh.py
index 798decda96..8c3c02c410 100644
--- a/esmvaltool/cmorizers/data/downloaders/datasets/nsidc_g02202_sh.py
+++ b/esmvaltool/cmorizers/data/downloaders/datasets/nsidc_g02202_sh.py
@@ -1,6 +1,7 @@
 """Script to download NSIDC-G02202-sh."""
 import logging
 from datetime import datetime
+
 from dateutil import relativedelta
 
 from esmvaltool.cmorizers.data.downloaders.wget import WGetDownloader
diff --git a/esmvaltool/cmorizers/data/downloaders/wget.py b/esmvaltool/cmorizers/data/downloaders/wget.py
index 8544e1d727..2afcca1d5a 100644
--- a/esmvaltool/cmorizers/data/downloaders/wget.py
+++ b/esmvaltool/cmorizers/data/downloaders/wget.py
@@ -54,6 +54,20 @@ def download_file(self, server_path, wget_options):
         logger.debug(command)
         subprocess.check_output(command)
 
+    def login(self, server_path, wget_options):
+        """Login.
+
+        Parameters
+        ----------
+        server_path: str
+            Path to remote file
+        wget_options: list(str)
+            Extra options for wget
+        """
+        command = ['wget'] + wget_options + [server_path]
+        logger.debug(command)
+        subprocess.check_output(command)
+
     @property
     def overwrite_options(self):
         """Get overwrite options as configured in downloader."""
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/aeronet.py b/esmvaltool/cmorizers/data/formatters/datasets/aeronet.py
index 215c67d7a8..e3b5c968d8 100755
--- a/esmvaltool/cmorizers/data/formatters/datasets/aeronet.py
+++ b/esmvaltool/cmorizers/data/formatters/datasets/aeronet.py
@@ -7,7 +7,7 @@
     https://aeronet.gsfc.nasa.gov/
 
 Last access
-    20230613
+    20240406
 
 Download and processing instructions
     Download the following file:
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/agcd.py b/esmvaltool/cmorizers/data/formatters/datasets/agcd.py
index a8b138f7b9..f0d6b290ef 100644
--- a/esmvaltool/cmorizers/data/formatters/datasets/agcd.py
+++ b/esmvaltool/cmorizers/data/formatters/datasets/agcd.py
@@ -80,7 +80,7 @@ def _extract_variable(cmor_info, attrs, filepath, out_dir):
 
         utils.fix_var_metadata(cube, cmor_info)
 
-        utils.fix_coords(cube)
+        cube = utils.fix_coords(cube)
         bounds = get_time_bounds(cube.coords('time')[0], 'mon')
         cube.coords('time')[0].bounds = bounds
         utils.set_global_atts(cube, attrs)
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/anuclimate.py b/esmvaltool/cmorizers/data/formatters/datasets/anuclimate.py
index 0077bd17a4..f82ad295ca 100644
--- a/esmvaltool/cmorizers/data/formatters/datasets/anuclimate.py
+++ b/esmvaltool/cmorizers/data/formatters/datasets/anuclimate.py
@@ -87,7 +87,7 @@ def _extract_variable(cmor_info, attrs, filepaths, out_dir):
     for cbls in [cbls_1, cbls_2]:
         iris.util.equalise_attributes(cbls)
         cubesave = cbls.concatenate_cube()
-        utils.fix_coords(cubesave)
+        cubesave = utils.fix_coords(cubesave)
 
         logger.info("Saving file")
         utils.save_variable(cubesave,
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/aphro_ma.py b/esmvaltool/cmorizers/data/formatters/datasets/aphro_ma.py
index 002c83662d..1e1f9dbc4b 100755
--- a/esmvaltool/cmorizers/data/formatters/datasets/aphro_ma.py
+++ b/esmvaltool/cmorizers/data/formatters/datasets/aphro_ma.py
@@ -94,7 +94,7 @@ def _extract_variable(short_name, var, cfg, filepath, out_dir, version):
     # fix coordinates
     if 'height2m' in cmor_info.dimensions:
         utils.add_height2m(cube)
-    utils.fix_coords(cube)
+    cube = utils.fix_coords(cube)
 
     # Fix metadata
     attrs = cfg['attributes'].copy()
@@ -124,7 +124,7 @@ def _extract_variable(short_name, var, cfg, filepath, out_dir, version):
             attrs['mip'] = 'Amon'
 
             # Fix coordinates
-            utils.fix_coords(cube)
+            cube = utils.fix_coords(cube)
 
             # Save variable
             utils.save_variable(cube,
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/berkeleyearth.py b/esmvaltool/cmorizers/data/formatters/datasets/berkeleyearth.py
index 81e4909584..c2be3dce7e 100644
--- a/esmvaltool/cmorizers/data/formatters/datasets/berkeleyearth.py
+++ b/esmvaltool/cmorizers/data/formatters/datasets/berkeleyearth.py
@@ -172,7 +172,7 @@ def _extr_var_n_calc_abs_tas(short_name, var, cfg, filepath, out_dir):
     for s_name, cube in zip(short_names, [cube_abs, cube_anom]):
         cmor_info = cfg['cmor_table'].get_variable(var['mip'], s_name)
 
-        utils.fix_coords(cube)
+        cube = utils.fix_coords(cube)
         if 'height2m' in cmor_info.dimensions:
             utils.add_height2m(cube)
 
@@ -209,7 +209,7 @@ def _extr_var_n_calc_abs_tas(short_name, var, cfg, filepath, out_dir):
     cube_sftlf = cubes.extract(NameConstraint(var_name=raw_var_sftlf))[0]
 
     # fix coordinates
-    utils.fix_coords(cube_sftlf)
+    cube_sftlf = utils.fix_coords(cube_sftlf)
 
     # cmorize sftlf units
     cmor_info_sftlf = cfg['cmor_table'].get_variable(var['rawsftlf_mip'],
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/ceres_ebaf.py b/esmvaltool/cmorizers/data/formatters/datasets/ceres_ebaf.py
index c63f72170a..e02332130d 100644
--- a/esmvaltool/cmorizers/data/formatters/datasets/ceres_ebaf.py
+++ b/esmvaltool/cmorizers/data/formatters/datasets/ceres_ebaf.py
@@ -50,7 +50,7 @@ def _extract_variable(short_name, var, cfg, filepath, out_dir):
     utils.convert_timeunits(cube, 1950)
 
     # Fix coordinates
-    utils.fix_coords(cube)
+    cube = utils.fix_coords(cube)
 
     # Fix metadata
     attrs = cfg['attributes']
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/cmap.py b/esmvaltool/cmorizers/data/formatters/datasets/cmap.py
new file mode 100644
index 0000000000..fecd2b128e
--- /dev/null
+++ b/esmvaltool/cmorizers/data/formatters/datasets/cmap.py
@@ -0,0 +1,69 @@
+"""ESMValTool CMORizer for CMAP (CPC Merged Analysis of Precipitation) data.
+
+Tier
+    Tier 2: other freely-available dataset.
+
+Source
+    https://psl.noaa.gov/data/gridded/data.cmap.html
+
+Last access
+    20240909
+
+Download and processing instructions
+    To facilitate the download, the links to the ftp server are provided.
+
+    https://downloads.psl.noaa.gov/Datasets/cmap/enh/
+        precip.mon.mean.nc
+
+Caveats
+
+"""
+
+import logging
+import re
+from copy import deepcopy
+from pathlib import Path
+
+import iris
+
+from esmvaltool.cmorizers.data import utilities as utils
+
+logger = logging.getLogger(__name__)
+
+
+def _extract_variable(short_name, var, cfg, raw_filepath, out_dir):
+    cmor_info = cfg["cmor_table"].get_variable(var["mip"], short_name)
+    attributes = deepcopy(cfg["attributes"])
+    attributes["mip"] = var["mip"]
+
+    cubes = iris.load(raw_filepath)
+    for cube in cubes:
+        assert cube.units == "mm/day", f"unknown units:{cube.units}"
+        # convert data from mm/day to kg m-2 s-1
+        # mm/day ~ density_water * mm/day
+        # = 1000 kg m-3 * 1/(1000*86400) m s-1 = 1/86400 kg m-2 s-1
+        cube = cube / 86400
+        cube.units = "kg m-2 s-1"
+
+        utils.fix_var_metadata(cube, cmor_info)
+        cube = utils.fix_coords(cube)
+        utils.set_global_atts(cube, attributes)
+
+        logger.info("Saving file")
+        utils.save_variable(cube, short_name, out_dir, attributes,
+                            unlimited_dimensions=["time"])
+
+
+def cmorization(in_dir, out_dir, cfg, cfg_user, start_date, end_date):
+    """Cmorization func call."""
+    for short_name, var in cfg["variables"].items():
+        logger.info("CMORizing variable '%s'", short_name)
+        short_name = var["short_name"]
+        raw_filenames = Path(in_dir).rglob("*.nc")
+        filenames = []
+        for raw_filename in raw_filenames:
+            if re.search(var["file"], str(raw_filename)) is not None:
+                filenames.append(raw_filename)
+
+        for filename in sorted(filenames):
+            _extract_variable(short_name, var, cfg, filename, out_dir)
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/cowtanway.py b/esmvaltool/cmorizers/data/formatters/datasets/cowtanway.py
index 76c9d525c8..dc2073f825 100644
--- a/esmvaltool/cmorizers/data/formatters/datasets/cowtanway.py
+++ b/esmvaltool/cmorizers/data/formatters/datasets/cowtanway.py
@@ -43,7 +43,7 @@ def _extract_variable(short_name, var, vkey, version, cfg, filepath, out_dir):
     utils.convert_timeunits(cube, 1950)
 
     # Fix coordinates
-    utils.fix_coords(cube)
+    cube = utils.fix_coords(cube)
     if 'height2m' in cmor_info.dimensions:
         utils.add_height2m(cube)
 
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/cru.py b/esmvaltool/cmorizers/data/formatters/datasets/cru.py
index 03d1ac77f4..28d1f9fb7e 100644
--- a/esmvaltool/cmorizers/data/formatters/datasets/cru.py
+++ b/esmvaltool/cmorizers/data/formatters/datasets/cru.py
@@ -72,7 +72,7 @@ def _extract_variable(short_name, var, cfg, filepath, out_dir):
             Unit("days since 1950-1-1 00:00:00", calendar="gregorian"))
 
     # Fix coordinates
-    utils.fix_coords(cube)
+    cube = utils.fix_coords(cube)
     if "height2m" in cmor_info.dimensions:
         utils.add_height2m(cube)
     if version not in ["TS4.02"]:
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/ct2019.py b/esmvaltool/cmorizers/data/formatters/datasets/ct2019.py
index 395d78e25d..64f64f4e82 100644
--- a/esmvaltool/cmorizers/data/formatters/datasets/ct2019.py
+++ b/esmvaltool/cmorizers/data/formatters/datasets/ct2019.py
@@ -11,7 +11,7 @@
 
 Download and processing instructions
     Create a new empty directory ``$RAWOBSPATH/Tier2/CT2019`` (where
-    ``$RAWOBSPATH`` is given by your user configuration file) where the raw
+    ``$RAWOBSPATH`` is given in the configuration) where the raw
     data will be stored. The download of the data is automatically handled by
     this script. If data is already present in this directory, the download is
     skipped (to force a new download delete your old files).
@@ -39,7 +39,7 @@ def _add_aux_coords(cube, input_files, coords_to_add):
         logger.info("Adding auxiliary coordinate '%s' to '%s'", coord_name,
                     cube.var_name)
         coord_cube = _load_cube(input_files, coord_name)
-        utils.fix_coords(coord_cube)
+        coord_cube = utils.fix_coords(coord_cube)
         dim_coords = [c.name() for c in coord_cube.coords(dim_coords=True)]
         if 'boundary' in dim_coords:
             (points, bounds) = _interpolate_center(coord_cube)
@@ -166,7 +166,7 @@ def _extract_variable(short_name, var, cfg, input_files, out_dir):
     utils.convert_timeunits(cube, 1950)
 
     # Fix coordinates
-    utils.fix_coords(cube)
+    cube = utils.fix_coords(cube)
 
     # Fix metadata
     attrs = cfg['attributes']
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/duveiller2018.py b/esmvaltool/cmorizers/data/formatters/datasets/duveiller2018.py
index 8e070a3ae0..a793f8cbb1 100644
--- a/esmvaltool/cmorizers/data/formatters/datasets/duveiller2018.py
+++ b/esmvaltool/cmorizers/data/formatters/datasets/duveiller2018.py
@@ -111,13 +111,9 @@ def extract_variable(var_info, raw_info, out_dir, attrs):
             # Fix metadata
             fix_var_metadata(cube, var_info)
             # Fix coords
-            fix_coords(cube)
+            cube = fix_coords(cube)
             # Now set the time coordinate properly
             fix_time_coord_duveiller2018(cube)
-            # Latitude has to be increasing so flip it
-            # (this is not fixed in fix_coords)
-            logger.info("Flipping dimensional coordinate latitude")
-            cube = cube[:, ::-1, :]
             # Global attributes
             set_global_atts(cube, attrs)
             save_variable(cube, var, out_dir, attrs, local_keys=['positive'])
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/eppley_vgpm_modis.py b/esmvaltool/cmorizers/data/formatters/datasets/eppley_vgpm_modis.py
index 6a6d15d267..6fae2d2d1e 100644
--- a/esmvaltool/cmorizers/data/formatters/datasets/eppley_vgpm_modis.py
+++ b/esmvaltool/cmorizers/data/formatters/datasets/eppley_vgpm_modis.py
@@ -54,7 +54,7 @@ def extract_variable(var_info, raw_info, out_dir, attrs):
     for cube in cubes:
         if cube.var_name == rawvar:
             fix_var_metadata(cube, var_info)
-            fix_coords(cube)
+            cube = fix_coords(cube)
             _fix_data(cube, var)
             set_global_atts(cube, attrs)
             save_variable(
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/esacci_landcover.ncl b/esmvaltool/cmorizers/data/formatters/datasets/esacci_landcover.ncl
deleted file mode 100644
index 8472cef6fb..0000000000
--- a/esmvaltool/cmorizers/data/formatters/datasets/esacci_landcover.ncl
+++ /dev/null
@@ -1,217 +0,0 @@
-; #############################################################################
-; ESMValTool CMORizer for ESACCI-LANDCOVER data
-; #############################################################################
-;
-; Tier
-;    Tier 2: other freely-available dataset.
-;
-; Source
-;    ftp://anon-ftp.ceda.ac.uk/neodc/esacci/land_cover/data/land_cover_maps/
-;
-; Last access
-;    20190110
-;
-; Download and processing instructions
-;    Download the 3 NetCDF files for 2000, 2005 and 2010.
-;    Download the CCI-LC Tools from:
-;      http://maps.elie.ucl.ac.be/CCI/viewer/download/lc-user-tools-3.14.zip
-;    Unpack and run the CCI-LC Tools on each of the NetCDF files as follows:
-;      bash lc-user-tools-3.14/bin/aggregate-map.sh \
-;        -PgridName=GEOGRAPHIC_LAT_LON -PnumMajorityClasses=1 \
-;        -PoutputAccuracy=false -PoutputPFTClasses=true \
-;        -PoutputLCCSClasses=false -PnumRows=360 <inputfile.nc>
-;    Put the resulting processed data in input_dir_path.
-;
-; Caveat
-;    The CCI-LC Tools must be applied before running this script.
-;    The CCI-LC Tools require Java Version 7 or higher.
-;    The input data are available for a single year and are copied over to
-;    generate a time series over their time range of validity.
-;
-; Modification history
-;    20190110-righi_mattia: rewritten in NCL for v2.
-;    20160714-benjamin_mueller: written.
-;
-; #############################################################################
-loadscript(getenv("esmvaltool_root") + \
-           "/data/formatters/interface.ncl")
-
-begin
-
-  ; Script name (for logger)
-  DIAG_SCRIPT = "esacci_landcover.ncl"
-
-  ; Source name
-  OBSNAME = "ESACCI-LANDCOVER"
-
-  ; Tier
-  TIER = 2
-
-  ; Years
-  YEARS = (/2000, 2005, 2010/)
-
-  ; Variable names
-  VAR = \
-    (/"baresoilFrac", "cropFrac", "grassFrac", "shrubFrac", "treeFrac"/)
-
-  ; Corresponding aggregation classes in the raw data
-  CLASSES = [/"Bare_Soil", \
-              "Managed_Grass", \
-              "Natural_Grass", \
-              (/"Shrub_Broadleaf_Deciduous",    \
-                "Shrub_Broadleaf_Evergreen",    \
-                "Shrub_Needleleaf_Evergreen"/), \
-              (/"Tree_Broadleaf_Deciduous",  \
-                "Tree_Broadleaf_Evergreen",  \
-                "Tree_Needleleaf_Deciduous", \
-                "Tree_Needleleaf_Evergreen"/)/]
-
-  ; MIPs
-  MIP = (/"Lmon", "Lmon", "Lmon", "Lmon", "Lmon"/)
-
-  ; Frequency
-  FREQ = (/"mon", "mon", "mon", "mon", "mon"/)
-
-  ; CMOR table
-  CMOR_TABLE = getenv("cmor_tables") + "/cmip5/Tables/CMIP5_Lmon"
-
-  ; Type
-  TYPE = "sat"
-
-  ; Version
-  VERSION = "L4-LCCS-Map-300m-P5Y-aggregated-0.500000Deg"
-
-  ; Global attributes
-  SOURCE = "ftp://anon-ftp.ceda.ac.uk/neodc/esacci/land_cover/data/"
-  REF = "Defourny, P.: ESA Land Cover Climate Change Initiative " + \
-    "(Land_Cover_cci): Global Land Cover Maps, Version 1.6.1. " + \
-    "Centre for Environmental Data Analysis, " + \
-    "http://catalogue.ceda.ac.uk/uuid/4761751d7c844e228ec2f5fe11b2e3b0, 2016."
-  COMMENT = ""
-
-end
-
-begin
-
-  do yy = 0, dimsizes(YEARS) - 1
-
-    fname = \
-      input_dir_path + "ESACCI-LC-" + VERSION + "-" + YEARS(yy) + "-v1.6.1.nc"
-
-    f = addfile(fname, "r")
-
-    ; Create time coordinate
-    YEAR1 = YEARS(yy) - 2
-    YEAR2 = YEARS(yy) + 2
-    time = create_timec(YEAR1, YEAR2)
-
-    do vv = 0, dimsizes(VAR) - 1
-
-      log_info("Processing " + VAR(vv) + " (" + MIP(vv) + ")")
-
-      ; Set classes to be added up
-      class = CLASSES[vv]
-
-      ; Save mask before adding up classes
-      do cc = 0, dimsizes(class) - 1
-        qq = f->$class(cc)$
-        replace_ieeenan(qq, FILL, 0)
-        qq@_FillValue = FILL
-        tmp = ismissing(qq)
-        delete(qq)
-        if (cc.eq.0) then
-          lmask = tmp
-        else
-          lmask := lmask .and. tmp
-        end if
-        delete(tmp)
-      end do
-
-      ; Add up classes
-      do cc = 0, dimsizes(class) - 1
-        log_info("  adding class " + class(cc))
-        tmp = f->$class(cc)$
-        replace_ieeenan(tmp, FILL, 0)
-        tmp@_FillValue = FILL
-        tmp = where(ismissing(tmp), 0., tmp)
-        if (cc.eq.0) then
-          xx = tmp
-        else
-          xx = xx + tmp
-        end if
-        delete(tmp)
-      end do
-      delete(class)
-
-      ; Reapply mask of missing values
-      xx = where(lmask, xx@_FillValue, xx)
-
-      ; Define output array
-      output = \
-        new((/dimsizes(time), dimsizes(xx&lat), dimsizes(xx&lon)/), float)
-      output!0 = "time"
-      output&time = time
-      output!1 = "lat"
-      output&lat = xx&lat
-      output!2 = "lon"
-      output&lon = xx&lon
-      output = conform(output, xx, (/1, 2/))
-      delete(xx)
-
-      ; Set standard fill value
-      output@_FillValue = FILL
-
-      ; Convert units
-      output = output * 100
-      output@units = "%"
-
-      ; Format coordinates
-      output!0 = "time"
-      output!1 = "lat"
-      output!2 = "lon"
-      format_coords(output, YEAR1 + "0101", YEAR2 + "1231", FREQ(vv))
-
-      ; Set variable attributes
-      tmp = format_variable(output, VAR(vv), CMOR_TABLE)
-      delete(output)
-      output = tmp
-      delete(tmp)
-
-      ; Calculate coordinate bounds
-      bounds = guess_coord_bounds(output, FREQ(vv))
-
-      ; Set global attributes
-      gAtt = set_global_atts(OBSNAME, TIER, SOURCE, REF, COMMENT)
-
-      ; Output file
-      DATESTR = YEAR1 + "01-" + YEAR2 + "12"
-      fout = output_dir_path + \
-        str_join((/"OBS", OBSNAME, TYPE, VERSION, \
-                   MIP(vv), VAR(vv), DATESTR/), "_") + ".nc"
-
-      ; Special case for baresoilFrac: add auxiliary coordinate
-      if (VAR(vv).eq."baresoilFrac") then
-        output@coordinates = "type"
-      end if
-
-      ; Write variable
-      write_nc(fout, VAR(vv), output, bounds, gAtt)
-      delete(gAtt)
-      delete(output)
-      delete(bounds)
-
-      ; Special case for baresoilFrac: add auxiliary coordinate
-      if (VAR(vv).eq."baresoilFrac") then
-        type = tochar("bare_ground")
-        type!0 = "strlen"
-        type@long_name = "surface type"
-        type@standard_name = "area_type"
-        w = addfile(fout, "w")
-        w->type = type
-        delete(w)
-      end if
-
-    end do
-  end do
-
-end
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/esacci_landcover.py b/esmvaltool/cmorizers/data/formatters/datasets/esacci_landcover.py
new file mode 100644
index 0000000000..d0e4d9d722
--- /dev/null
+++ b/esmvaltool/cmorizers/data/formatters/datasets/esacci_landcover.py
@@ -0,0 +1,190 @@
+"""ESMValTool CMORizer for ESACCI-LANDCOVER pft data.
+
+Tier
+    Tier 2: other freely-available dataset.
+
+Source
+    ftp://anon-ftp.ceda.ac.uk/neodc/esacci/land_cover/data/pft/
+
+Last access
+    20240626
+
+Download and processing instructions
+    Download the data from:
+      pft/v2.0.8/
+    Put all files under a single directory (no subdirectories with years).
+    in ${RAWOBS}/Tier2/ESACCI-LANDCOVER
+
+"""
+
+import os
+import glob
+import logging
+from datetime import datetime
+import iris
+import numpy as np
+
+from esmvaltool.cmorizers.data.utilities import (
+    fix_coords,
+    fix_var_metadata,
+    set_global_atts,
+    add_typebare,
+    save_variable,
+)
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Enable the new split-attributes handling mode
+iris.FUTURE.save_split_attrs = True
+
+
+def average_block(data, block_size):
+    """Average the data within each block of size block_size.
+
+    Parameters
+    ----------
+    data : numpy.ndarray
+        The input data array to be block averaged.
+    block_size : int
+        The size of the block used for averaging. The data is averaged
+        within non-overlapping blocks of this size along the spatial dimensions
+        (latitude and longitude).
+
+    Returns
+    -------
+    numpy.ndarray
+        The block-averaged data array.
+    """
+    shape = data.shape
+    reshaped_data = data.reshape(shape[0], shape[1] // block_size,
+                                 block_size, shape[2] // block_size,
+                                 block_size)
+    averaged_data = reshaped_data.mean(axis=(2, 4))
+    return averaged_data
+
+
+def regrid_iris(cube):
+    """Regrid the cubes using block averaging.
+
+    Parameters
+    ----------
+    cube : iris.cube.Cube
+        The input data cube to be regridded.
+
+    Returns
+    -------
+    iris.cube.Cube
+        The regridded data cube.
+
+    Notes
+    -----
+    The block size is set to 100, which means the data will be averaged within
+    non-overlapping blocks of 100x100 grid cells along the spatial dimensions.
+    """
+    logger.info("Regridding using block averaging")
+
+    block_size = 100   # Number of grid cells to average in each block
+
+    combined_data = average_block(cube.data, block_size)
+
+    # Define target latitude and longitude ranges
+    target_lats = np.linspace(90 - 0.5 * (180 / combined_data.shape[1]),
+                              -90 + 0.5 * (180 / combined_data.shape[1]),
+                              combined_data.shape[1])
+    target_lons = np.linspace(-180 + 0.5 * (360 / combined_data.shape[2]),
+                              180 - 0.5 * (360 / combined_data.shape[2]),
+                              combined_data.shape[2])
+
+    combined_cube = iris.cube.Cube(combined_data,
+                                   dim_coords_and_dims=[
+                                       (cube.coord('time'), 0),
+                                       (iris.coords.DimCoord(
+                                           target_lats,
+                                           standard_name='latitude',
+                                           units='degrees'), 1),
+                                       (iris.coords.DimCoord(
+                                           target_lons,
+                                           standard_name='longitude',
+                                           units='degrees'), 2)])
+
+    combined_cube.coord('latitude').guess_bounds()
+    combined_cube.coord('longitude').guess_bounds()
+
+    return combined_cube
+
+
+def regrid_fix(cube, glob_attrs, var_name, var_info):
+    """Regrid cube and fixes.
+
+    Regrids the cube, fixes metadata, coordinates and glob_attrs.
+
+    Parameters
+    ----------
+    cube: iris.cube.Cube
+          Data cube to be regridded.
+
+    vals: dict
+          Variable long_name.
+
+    glob_attrs: dict
+          Dictionary holding cube metadata attributes.
+
+    var_name: str
+          Variable name.
+
+    var_info: dict
+          Dictionary holding cube metadata attributes.
+
+    Returns
+    -------
+    cube: iris.cube.Cube
+        data cube regridded and with fixed coordinates.
+    """
+    logger.info("Regridding cube for %s", var_name)
+    regridded_cube = regrid_iris(cube)
+    fix_var_metadata(regridded_cube, var_info)
+    regridded_cube = fix_coords(regridded_cube)
+    set_global_atts(regridded_cube, glob_attrs)
+
+    return regridded_cube
+
+
+def cmorization(in_dir, out_dir, cfg, cfg_user, start_date, end_date):
+    """Cmorize data."""
+    glob_attrs = cfg['attributes']
+    if not start_date:
+        start_date = datetime(1992, 1, 1)
+    if not end_date:
+        end_date = datetime(2020, 12, 31)
+
+    for year in range(start_date.year, end_date.year + 1):
+        inpfile_pattern = os.path.join(in_dir, cfg['filename'])
+        year_inpfile_pattern = inpfile_pattern.format(year=year)
+        inpfiles = sorted(glob.glob(year_inpfile_pattern))
+        for inpfile in inpfiles:
+            cubes = iris.load(inpfile)
+            for var_name, vals in cfg['variables'].items():
+                var_info = cfg['cmor_table'].get_variable(vals['mip'],
+                                                          var_name)
+                glob_attrs['mip'] = vals['mip']
+                glob_attrs['frequency'] = vals['frequency']
+                if var_name == 'shrubFrac':
+                    cube = cubes.extract_cube('SHRUBS-BD') + \
+                        cubes.extract_cube('SHRUBS-BE') + \
+                        cubes.extract_cube('SHRUBS-ND') + \
+                        cubes.extract_cube('SHRUBS-NE')
+                elif var_name == 'treeFrac':
+                    cube = cubes.extract_cube('TREES-BD') + \
+                        cubes.extract_cube('TREES-BE') + \
+                        cubes.extract_cube('TREES-ND') + \
+                        cubes.extract_cube('TREES-NE')
+                else:
+                    cube = cubes.extract_cube(vals['long_name'])
+                regridded_cube = regrid_fix(cube, glob_attrs,
+                                            var_name, var_info)
+                if var_name == 'baresoilFrac':
+                    add_typebare(regridded_cube)
+                save_variable(regridded_cube, var_name, out_dir, glob_attrs,
+                              unlimited_dimensions=['time'])
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/esacci_oc.py b/esmvaltool/cmorizers/data/formatters/datasets/esacci_oc.py
index 9ac8ac1a76..c267222c5c 100644
--- a/esmvaltool/cmorizers/data/formatters/datasets/esacci_oc.py
+++ b/esmvaltool/cmorizers/data/formatters/datasets/esacci_oc.py
@@ -114,7 +114,7 @@ def extract_variable(var_info, raw_info, out_dir, attrs):
         if cube.var_name == rawvar:
             fix_var_metadata(cube, var_info)
             _fix_time(cube, var_info.frequency)
-            fix_coords(cube, overwrite_time_bounds=False)
+            cube = fix_coords(cube, overwrite_time_bounds=False)
             cube = _add_depth_coord(cube)
             _fix_data(cube, var)
             set_global_atts(cube, attrs)
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/esacci_soilmoisture.ncl b/esmvaltool/cmorizers/data/formatters/datasets/esacci_soilmoisture.ncl
deleted file mode 100644
index 96ebe7f648..0000000000
--- a/esmvaltool/cmorizers/data/formatters/datasets/esacci_soilmoisture.ncl
+++ /dev/null
@@ -1,174 +0,0 @@
-; #############################################################################
-; ESMValTool CMORizer for ESACCI-SOILMOISTURE data
-; #############################################################################
-;
-; Tier
-;    Tier 2: other freely-available dataset.
-;
-; Source
-;    ftp://anon-ftp.ceda.ac.uk/neodc/esacci/soil_moisture/data/
-;
-; Last access
-;    20190201
-;
-; Download and processing instructions
-;    Download the data from:
-;      daily_files/COMBINED/v04.2/
-;      ancillary/v04.2/
-;    Put all files under a single directory (no subdirectories with years).
-;
-; Modification history
-;    20190201-righi_mattia: adapted to v2, use new input data version 4.2.
-;    20160824-lauer_axel: added processing of volumetric soil moisture
-;                         content (sm, smStderr).
-;    20160721-lauer_axel: use daily files, added processing of uncertainty.
-;    20150523-righi_mattia: written.
-;
-; #############################################################################
-loadscript(getenv("esmvaltool_root") + \
-           "/data/formatters/interface.ncl")
-
-begin
-
-  ; Script name (for logger)
-  DIAG_SCRIPT = "esacci_soilmoisture.ncl"
-
-  ; Source name
-  OBSNAME = "ESACCI-SOILMOISTURE"
-
-  ; Tier
-  TIER = 2
-
-  ; Period
-  YEAR1 = get_year(start_year, 1979)
-  YEAR2 = get_year(end_year, 2016)
-
-  ; Selected variable (standard name)
-  VAR = (/"sm", "smStderr", "dos", "dosStderr"/)
-
-  ; Name in the raw data
-  NAME = (/"sm", "sm_uncertainty", "sm", "sm_uncertainty"/)
-
-  ; MIP
-  MIP = (/"Lmon", "Lmon", "Lmon", "Lmon"/)
-
-  ; Frequency
-  FREQ = (/"mon", "mon", "mon", "mon"/)
-
-  ; CMOR table
-  CMOR_TABLE = getenv("cmor_tables") + "/custom/CMOR_" + VAR + ".dat"
-
-  ; Type
-  TYPE = "sat"
-
-  ; Version
-  VERSION = "L3S-SSMV-COMBINED-v4.2"
-
-  ; Global attributes
-  SOURCE = "ftp://anon-ftp.ceda.ac.uk/neodc/esacci/soil_moisture/data/"
-  REF = \
-    "Liu et al., Hydrol. Earth Syst. Sci., doi:10.5194/hess-15-425-2011, 2011."
-  COMMENT = ""
-
-end
-
-begin
-
-  do vv = 0, dimsizes(VAR) - 1
-
-    log_info("Processing " + VAR(vv) + " (" + MIP(vv) + ")")
-
-    do yy = YEAR1, YEAR2
-
-      ; Set list of files
-      files = systemfunc("ls " + input_dir_path + \
-                         "ESACCI-SOILMOISTURE-L3S-SSMV-" + \
-                         "COMBINED-" + yy + "????000000-fv04.2.nc")
-      f = addfiles(files, "r")
-      delete(files)
-
-      ; Read data
-      xx = f[:]->$NAME(vv)$
-      if (isatt(xx, "scale_factor")) then
-        tmp = tofloat(xx * xx@scale_factor)
-        copy_VarAtts(xx, tmp)
-        copy_VarCoords(xx, tmp)
-        delete(xx)
-        xx = tmp
-        delete(tmp)
-      end if
-      delete(f)
-
-      ; Derive dos using porosity
-      if (any(VAR(vv).eq.(/"dos", "dosStderr"/))) then
-        g = addfile(input_dir_path + \
-                    "/ESACCI-SOILMOISTURE-POROSITY_V01.1.nc", "r")
-        zz = g->porosity
-        xx = xx * 100. / conform(xx, zz, (/1, 2/))
-        delete(zz)
-      end if
-
-      ; Add a minor time shift for correct extraction of monthly mean below
-      xx&time = xx&time + 0.1
-
-      ; Calculate monthly means
-      if (isStrSubset(VAR(vv), "Stderr")) then
-        xx2 = xx
-        xx2 = xx ^ 2  ; save metadata
-        tmp = calculate_monthly_values(xx2, "avg", 0, False)
-        delete(xx)
-        delete(xx2)
-        xx = sqrt(tmp)
-        copy_VarAtts(tmp, xx)
-        copy_VarCoords(tmp, xx)
-        delete(tmp)
-      else
-        tmp = calculate_monthly_values(xx, "avg", 0, False)
-        delete(xx)
-        xx = tmp
-        delete(tmp)
-      end if
-
-      ; Append to time-series
-      if (.not.isdefined("output")) then
-        output = xx
-      else
-        output := array_append_record(output, xx, 0)
-      end if
-      delete(xx)
-
-    end do
-
-    ; Format coordinates
-    output!0 = "time"
-    output!1 = "lat"
-    output!2 = "lon"
-    format_coords(output, YEAR1 + "0101", YEAR2 + "1231", FREQ(vv))
-
-    ; Set variable attributes
-    tmp = format_variable(output, VAR(vv), CMOR_TABLE(vv))
-    delete(output)
-    output = tmp
-    delete(tmp)
-
-    ; Calculate coordinate bounds
-    bounds = guess_coord_bounds(output, FREQ(vv))
-
-    ; Set global attributes
-    gAtt = set_global_atts(OBSNAME, TIER, SOURCE, REF, COMMENT)
-
-    ; Output file
-    DATESTR = YEAR1 + "01-" + YEAR2 + "12"
-    fout = output_dir_path + \
-      str_join((/"OBS", OBSNAME, TYPE, VERSION, \
-                 MIP(vv), VAR(vv), DATESTR/), "_") + ".nc"
-
-    ; Write variable
-    write_nc(fout, VAR(vv), output, bounds, gAtt)
-    delete(gAtt)
-    delete(output)
-    delete(bounds)
-
-  end do
-
-end
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/esacci_soilmoisture.py b/esmvaltool/cmorizers/data/formatters/datasets/esacci_soilmoisture.py
new file mode 100644
index 0000000000..66859b420b
--- /dev/null
+++ b/esmvaltool/cmorizers/data/formatters/datasets/esacci_soilmoisture.py
@@ -0,0 +1,149 @@
+"""ESMValTool CMORizer for ESACCI-SOILMOISTURE data.
+
+Tier
+    Tier 2: other freely-available dataset.
+
+Source
+    ftp://anon-ftp.ceda.ac.uk/neodc/esacci/soil_moisture/data/
+
+Last access
+    20240626
+
+Download and processing instructions
+    Download the data from:
+      daily_files/COMBINED/v08.1/
+      ancillary/v08.1/
+    Put all files under a single directory (no subdirectories with years).
+    in ${RAWOBS}/Tier2/ESACCI-SOILMOISTURE
+
+"""
+
+import glob
+import logging
+import os
+from datetime import datetime
+import iris
+from esmvalcore.preprocessor import concatenate, monthly_statistics
+from cf_units import Unit
+
+from ...utilities import (
+    fix_var_metadata,
+    fix_dim_coordnames,
+    fix_bounds,
+    save_variable,
+    set_global_atts
+)
+
+logger = logging.getLogger(__name__)
+
+
+def fix_coords(cube):
+    """Fix coordinates to CMOR standards.
+
+    Fixes coordinates eg time to have correct units, bounds etc;
+    longitude to be CMOR-compliant 0-360deg; fixes some attributes
+    and bounds - the user can avert bounds fixing by using supplied
+    arguments; if bounds are None they will be fixed regardless.
+
+    Parameters
+    ----------
+    cube: iris.cube.Cube
+        data cube with coordinates to be fixed.
+
+
+    Returns
+    -------
+    cube: iris.cube.Cube
+        data cube with fixed coordinates.
+    """
+    # First fix any completely missing coord var names
+    fix_dim_coordnames(cube)
+
+    # Convert longitude from -180...180 to 0...360
+    cube = cube.intersection(longitude=(0.0, 360.0))
+
+    # Fix individual coords
+    for cube_coord in cube.coords():
+        # Fix time
+        if cube_coord.var_name == 'time':
+            logger.info("Fixing time...")
+            cube.coord('time').convert_units(
+                Unit('days since 1970-01-01T00:00:00+00:00',
+                     calendar='proleptic_gregorian'))
+
+        # Fix latitude
+        if cube_coord.var_name == 'lat':
+            logger.info("Fixing latitude...")
+            cube = iris.util.reverse(cube, cube_coord)
+
+        # Fix bounds of all coordinates
+        fix_bounds(cube, cube_coord)
+
+    return cube
+
+
+def extract_variable(raw_info):
+    """Extract variables."""
+    rawvar = raw_info['name']
+    constraint = iris.Constraint(name=rawvar)
+    if rawvar == 'sm_uncertainty':
+        sm_cube = iris.load_cube(raw_info['file'],
+                                 iris.NameConstraint(var_name='sm'))
+        ancillary_var = sm_cube.ancillary_variable(
+            'Volumetric Soil Moisture Uncertainty'
+        )
+        cube = sm_cube.copy(ancillary_var.core_data())
+    else:
+        cube = iris.load_cube(raw_info['file'], constraint)
+
+    # Remove dysfunctional ancillary data without standard names
+    for ancillary_variable in cube.ancillary_variables():
+        cube.remove_ancillary_variable(ancillary_variable)
+
+    return cube
+
+
+def cmorization(in_dir, out_dir, cfg, cfg_user, start_date, end_date):
+    """Cmorize data."""
+    glob_attrs = cfg['attributes']
+    if not start_date:
+        start_date = datetime(1978, 1, 1)
+    if not end_date:
+        end_date = datetime(2022, 12, 31)
+
+    # run the cmorization
+    for var_name, vals in cfg['variables'].items():
+        all_data_cubes = []
+        if not isinstance(vals, dict):  # Ensure vals is a dictionary
+            raise ValueError(
+                f"Invalid format for variable {var_name}: {type(vals)}"
+            )
+        var_info = cfg['cmor_table'].get_variable(vals['mip'], var_name)
+        glob_attrs['mip'] = vals['mip']
+        raw_info = {'name': vals['raw']}
+        inpfile_pattern = os.path.join(in_dir, vals['filename'])
+        logger.info("CMORizing var %s from file type %s",
+                    var_name, inpfile_pattern)
+
+        for year in range(start_date.year, end_date.year + 1):
+            year_inpfile_pattern = inpfile_pattern.format(year=year)
+            inpfiles = sorted(glob.glob(year_inpfile_pattern))
+            for inpfile in inpfiles:
+                raw_info['file'] = inpfile
+                cube = extract_variable(raw_info)
+                all_data_cubes.append(cube)
+        final_cube = concatenate(all_data_cubes)
+        fix_var_metadata(final_cube, var_info)
+        final_cube = fix_coords(final_cube)
+        set_global_atts(final_cube, glob_attrs)
+
+        save_variable(final_cube, var_name, out_dir, glob_attrs,
+                      unlimited_dimensions=['time'])
+
+        # For sm, also save monthly means
+        if var_name == 'sm':
+            monthly_mean_cube = monthly_statistics(final_cube, 'mean')
+            glob_attrs['mip'] = 'Lmon'
+            monthly_mean_cube.attributes.update(glob_attrs)
+            save_variable(monthly_mean_cube, var_name, out_dir, glob_attrs,
+                          unlimited_dimensions=['time'])
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/esacci_sst.py b/esmvaltool/cmorizers/data/formatters/datasets/esacci_sst.py
index 8e55296f9e..c009b96ffb 100644
--- a/esmvaltool/cmorizers/data/formatters/datasets/esacci_sst.py
+++ b/esmvaltool/cmorizers/data/formatters/datasets/esacci_sst.py
@@ -62,7 +62,7 @@ def extract_variable(var_info, raw_info, attrs, year):
     # Fix cube
     fix_var_metadata(cube, var_info)
     convert_timeunits(cube, year)
-    fix_coords(cube)
+    cube = fix_coords(cube)
     set_global_atts(cube, attrs)
     return cube
 
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/esacci_watervapour.py b/esmvaltool/cmorizers/data/formatters/datasets/esacci_watervapour.py
index d4901007cc..d662f0c752 100644
--- a/esmvaltool/cmorizers/data/formatters/datasets/esacci_watervapour.py
+++ b/esmvaltool/cmorizers/data/formatters/datasets/esacci_watervapour.py
@@ -53,7 +53,7 @@ def extract_variable(var_info, raw_info, attrs, year):
     # Fix cube
     fix_var_metadata(cube, var_info)
     convert_timeunits(cube, year)
-    fix_coords(cube, overwrite_time_bounds=False)
+    cube = fix_coords(cube, overwrite_time_bounds=False)
     set_global_atts(cube, attrs)
     # Remove dysfunctional ancillary data without sandard name
     for ancillary_variable in cube.ancillary_variables():
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/esdc.py b/esmvaltool/cmorizers/data/formatters/datasets/esdc.py
index bf473f53be..529f497396 100644
--- a/esmvaltool/cmorizers/data/formatters/datasets/esdc.py
+++ b/esmvaltool/cmorizers/data/formatters/datasets/esdc.py
@@ -47,7 +47,7 @@ def _fix_cube(var, cube, cfg):
         logger.info("Converting time units to gregorian")
         cube.coord('time').units = cf_units.Unit(old_unit.origin,
                                                  calendar='gregorian')
-    utils.fix_coords(cube)
+    cube = utils.fix_coords(cube)
     cube.convert_units(cmor_info.units)
     if 'height2m' in cmor_info.dimensions:
         utils.add_height2m(cube)
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/esrl.py b/esmvaltool/cmorizers/data/formatters/datasets/esrl.py
index a0343e3417..ab9e0930e9 100644
--- a/esmvaltool/cmorizers/data/formatters/datasets/esrl.py
+++ b/esmvaltool/cmorizers/data/formatters/datasets/esrl.py
@@ -187,7 +187,7 @@ def _extract_variable(short_name, var, cfg, out_dir, station_dic):
 
     # Fix metadata
     utils.convert_timeunits(cube, 1950)
-    utils.fix_coords(cube)
+    cube = utils.fix_coords(cube)
     cmor_info = cfg['cmor_table'].get_variable(var['mip'], short_name)
     cube.convert_units(cmor_info.units)
     attrs = cfg['attributes']
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/fluxcom.py b/esmvaltool/cmorizers/data/formatters/datasets/fluxcom.py
index 93a41fffd4..3e25d8a894 100644
--- a/esmvaltool/cmorizers/data/formatters/datasets/fluxcom.py
+++ b/esmvaltool/cmorizers/data/formatters/datasets/fluxcom.py
@@ -29,7 +29,6 @@
 import re
 
 import iris
-import numpy as np
 
 from esmvaltool.cmorizers.data import utilities as utils
 
@@ -66,11 +65,8 @@ def _extract_variable(cmor_info, attrs, filepath, out_dir):
         cube.coord('lon').standard_name = 'longitude'
         utils.fix_var_metadata(cube, cmor_info)
         utils.convert_timeunits(cube, 1950)
-        utils.fix_coords(cube)
+        cube = utils.fix_coords(cube)
         utils.set_global_atts(cube, attrs)
-        utils.flip_dim_coord(cube, 'latitude')
-        coord = cube.coord('latitude')
-        coord.bounds = np.flip(coord.bounds, axis=1)
         logger.info("Saving file")
         utils.save_variable(cube,
                             var,
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/ghcn_cams.py b/esmvaltool/cmorizers/data/formatters/datasets/ghcn_cams.py
index 5b343eed18..2f3eff6bdd 100644
--- a/esmvaltool/cmorizers/data/formatters/datasets/ghcn_cams.py
+++ b/esmvaltool/cmorizers/data/formatters/datasets/ghcn_cams.py
@@ -35,7 +35,7 @@ def _extract_variable(short_name, var, cfg, filepath, out_dir):
     utils.convert_timeunits(cube, 1950)
 
     # Fix coordinates
-    utils.fix_coords(cube)
+    cube = utils.fix_coords(cube)
     if 'height2m' in cmor_info.dimensions:
         utils.add_height2m(cube)
 
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/gistemp.py b/esmvaltool/cmorizers/data/formatters/datasets/gistemp.py
index 81beb56c91..01366a0c06 100644
--- a/esmvaltool/cmorizers/data/formatters/datasets/gistemp.py
+++ b/esmvaltool/cmorizers/data/formatters/datasets/gistemp.py
@@ -33,7 +33,7 @@ def _extract_variable(short_name, var, cfg, filepath, out_dir):
     utils.convert_timeunits(cube, 1950)
 
     # Fix coordinates
-    utils.fix_coords(cube)
+    cube = utils.fix_coords(cube)
     if 'height2m' in cmor_info.dimensions:
         utils.add_height2m(cube)
 
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/glodap.py b/esmvaltool/cmorizers/data/formatters/datasets/glodap.py
index c96f0a1771..0323f8b800 100644
--- a/esmvaltool/cmorizers/data/formatters/datasets/glodap.py
+++ b/esmvaltool/cmorizers/data/formatters/datasets/glodap.py
@@ -118,7 +118,7 @@ def extract_variable(in_files, out_dir, attrs, raw_info, cmor_table):
                              bounds=[0., 12.]), 0)
 
     fix_var_metadata(cube, var_info)
-    fix_coords(cube)
+    cube = fix_coords(cube)
     _fix_data(cube, var)
     set_global_atts(cube, attrs)
     save_variable(cube, var, out_dir, attrs, unlimited_dimensions=['time'])
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/hwsd.py b/esmvaltool/cmorizers/data/formatters/datasets/hwsd.py
index 30e2a8975b..68c894f39b 100644
--- a/esmvaltool/cmorizers/data/formatters/datasets/hwsd.py
+++ b/esmvaltool/cmorizers/data/formatters/datasets/hwsd.py
@@ -50,7 +50,7 @@ def _extract_variable(short_name, var, cfg, filepath, out_dir):
             long_name='time')
         cube.add_dim_coord(time_dim, 0)
         utils.convert_timeunits(cube, 1950)
-    utils.fix_coords(cube)
+    cube = utils.fix_coords(cube)
 
     # Fix units
     if 'kg C' in cube.units.origin:
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/jma_transcom.py b/esmvaltool/cmorizers/data/formatters/datasets/jma_transcom.py
index 6ac33cb8b5..bd41512294 100644
--- a/esmvaltool/cmorizers/data/formatters/datasets/jma_transcom.py
+++ b/esmvaltool/cmorizers/data/formatters/datasets/jma_transcom.py
@@ -66,7 +66,7 @@ def _extract_variable(cmor_info, attrs, in_dir, out_dir, ctl):
     # Fix metadata
     utils.fix_var_metadata(cube, cmor_info)
     utils.convert_timeunits(cube, 1950)
-    utils.fix_coords(cube)
+    cube = utils.fix_coords(cube)
     utils.set_global_atts(cube, attrs)
     utils.save_variable(cube,
                         cmor_info.short_name,
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/jra_55.py b/esmvaltool/cmorizers/data/formatters/datasets/jra_55.py
new file mode 100644
index 0000000000..16125d4c2f
--- /dev/null
+++ b/esmvaltool/cmorizers/data/formatters/datasets/jra_55.py
@@ -0,0 +1,176 @@
+"""
+ESMValTool CMORizer for JRA-55 data.
+
+Tier
+    Tier 2: other freely-available dataset.
+
+Source
+    Research Data Archive (RDA):
+    https://rda.ucar.edu/datasets/ds628.1/
+
+Last access
+    20230322
+
+Download and processing instructions
+    see download script cmorizers/data/downloaders/datasets/jra_55.py
+"""
+
+import copy
+import logging
+import os
+import xarray as xr
+
+from cf_units import Unit
+
+import iris
+
+from esmvaltool.cmorizers.data import utilities as utils
+
+logger = logging.getLogger(__name__)
+
+
+def _load_jra55_grib(filenames, var):
+    """Load data from GRIB file and return list of cubes."""
+    leveltype = var.get('typeOfLevel')
+    cubelist = []
+    if leveltype is not None:
+        dataset = xr.open_mfdataset(filenames, engine="cfgrib",
+                                    filter_by_keys={'typeOfLevel': leveltype})
+    else:
+        dataset = xr.open_mfdataset(filenames, engine="cfgrib")
+    varnames = list(dataset.data_vars)
+    for varname in varnames:
+        da_tmp = dataset[varname]
+        # conversion to Iris cubes requires a valid standard_name
+        da_tmp.attrs['standard_name'] = var['standard_name']
+        cube = da_tmp.to_iris()
+        # remove auxiliary coordinate 'time'
+        cube.remove_coord('time')
+        # rename coordinate from 'forecast_reference_time' to 'time
+        timecoord = cube.dim_coords[0]
+        timecoord.rename("time")
+        # convert unit string to cf_unit object
+        # (calendar (calendar=coord.units.calendar) must be irgnored
+        # or conversion fails
+        timecoord.units = Unit(timecoord.units)
+        # add forecast period to time coordinate to get the actual time
+        # for which the data are valid
+        forecast = cube.coord('forecast_period')  # forecast period in hours
+        timecoord.points = timecoord.points + forecast.points * 3600
+        # remove unneeded scalar variables to prevent warnings
+        auxcoordnames = ['step', 'entireAtmosphere', 'number', 'isobaricLayer',
+                         'surface', 'nominalTop', 'heightAboveGround']
+        for aux_coord in cube.coords(dim_coords=False):
+            if aux_coord.var_name in auxcoordnames:
+                cube.remove_coord(aux_coord)
+        cubelist.append(cube)
+
+    return cubelist
+
+
+def _extract_variable(short_name, var, in_files, cfg, out_dir):
+    """Extract variable."""
+    # load data (returns a list of cubes)
+    cmor_info = cfg['cmor_table'].get_variable(var['mip'], short_name)
+    var['standard_name'] = cmor_info.standard_name
+    cubes = _load_jra55_grib(in_files, var)
+
+    # apply operators (if any)
+    if len(cubes) > 1:
+        if var.get('operator', '') == 'sum':
+            # Multiple variables case using sum operation
+            cube = None
+            for in_cube in cubes:
+                if cube is None:
+                    cube = in_cube
+                else:
+                    cube += in_cube
+        elif var.get('operator', '') == 'diff':
+            # two variables case using diff operation
+            if len(cubes) != 2:
+                errmsg = (f'operator diff selected for variable {short_name} '
+                          f'expects exactly two input variables and two input '
+                          f'files')
+                raise ValueError(errmsg)
+            cube = cubes[0] - cubes[1]
+        else:
+            oper = var.get('operator')
+            raise ValueError(
+                f'multiple input files found for variable {short_name} '
+                f'with unknown operator {oper}')
+    else:
+        cube = cubes[0]
+
+    # Fix metadata
+    attrs = copy.deepcopy(cfg['attributes'])
+    attrs['mip'] = var['mip']
+    utils.fix_var_metadata(cube, cmor_info)
+
+    if cube.var_name in ['hfls', 'hfss', 'rlus', 'rlut', 'rlutcs', 'rsus',
+                         'rsuscs', 'rsut', 'rsutcs']:
+        attrs['positive'] = 'up'
+
+    if cube.var_name in ['rlds', 'rldscs', 'rsds', 'rsdscs', 'rsdt', 'rtmt',
+                         'tauu', 'tauv']:
+        attrs['positive'] = 'down'
+
+    # fix longitudes and z-coordinate (if present)
+    for coord in cube.dim_coords:
+        coord_type = iris.util.guess_coord_axis(coord)
+        if coord_type == 'X':
+            # -> shift longitude coordinate by one grid box
+            # to match obs4mips/CREATE-IP grid
+            coord.points = coord.points + 360 / len(coord.points)
+        if coord_type == 'Z':
+            coord.standard_name = 'air_pressure'
+            coord.long_name = 'pressure'
+            coord.var_name = 'plev'
+            coord.attributes['positive'] = 'down'
+            if coord.units == "hPa":
+                coord.convert_units('Pa')
+            utils.flip_dim_coord(cube, coord.standard_name)
+
+    utils.fix_dim_coordnames(cube)
+    utils.fix_coords(cube)
+    if 'height2m' in cmor_info.dimensions:
+        utils.add_height2m(cube)
+    utils.set_global_atts(cube, attrs)
+
+    # Save variable
+    utils.save_variable(cube,
+                        short_name,
+                        out_dir,
+                        attrs,
+                        unlimited_dimensions=['time'],
+                        local_keys=['positive'])
+
+
+def cmorization(in_dir, out_dir, cfg, cfg_user, start_date, end_date):
+    """Cmorization func call."""
+    # Run the cmorization
+    if start_date is None:
+        start_date = 1958
+    else:
+        start_date = start_date.year
+    if end_date is None:
+        end_date = 2022
+    else:
+        end_date = end_date.year
+    for (short_name, var) in cfg['variables'].items():
+        short_name = var['short_name']
+        filename = []
+        for year in range(start_date, end_date + 1):
+            if 'file' in var:
+                filename.append(os.path.join(in_dir,
+                                var['file'].format(year=year)))
+            elif 'files' in var:
+                for file in var['files']:
+                    filename.append(os.path.join(in_dir,
+                                    file.format(year=year)))
+            else:
+                raise ValueError(f"No input file(s) specified for variable "
+                                 f"{short_name}.")
+
+        logger.info("CMORizing variable '%s' from file '%s'", short_name,
+                    filename)
+        _extract_variable(short_name, var, filename, cfg, out_dir)
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/lai3g.py b/esmvaltool/cmorizers/data/formatters/datasets/lai3g.py
index 218a22a0cf..1db260d13d 100644
--- a/esmvaltool/cmorizers/data/formatters/datasets/lai3g.py
+++ b/esmvaltool/cmorizers/data/formatters/datasets/lai3g.py
@@ -92,9 +92,7 @@ def _extract_variable(cmor_info, attrs, in_dir, out_dir, cfg):
     final_cube = cubes.concatenate_cube()
     utils.fix_var_metadata(final_cube, cmor_info)
     utils.convert_timeunits(final_cube, 1950)
-    utils.fix_coords(final_cube)
-    if not cfg.get('regrid'):
-        utils.flip_dim_coord(final_cube, 'latitude')
+    final_cube = utils.fix_coords(final_cube)
     utils.set_global_atts(final_cube, attrs)
     utils.save_variable(final_cube,
                         cmor_info.short_name,
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/landflux_eval.py b/esmvaltool/cmorizers/data/formatters/datasets/landflux_eval.py
index f1e516a89d..f8b0a3ad7c 100644
--- a/esmvaltool/cmorizers/data/formatters/datasets/landflux_eval.py
+++ b/esmvaltool/cmorizers/data/formatters/datasets/landflux_eval.py
@@ -38,7 +38,7 @@ def _extract_variable(raw_var, cmor_info, attrs, filepath, out_dir):
     _fix_time_coord(cube)
     utils.fix_var_metadata(cube, cmor_info)
     utils.convert_timeunits(cube, 1950)
-    utils.fix_coords(cube)
+    cube = utils.fix_coords(cube)
     utils.set_global_atts(cube, attrs)
     utils.save_variable(cube,
                         var,
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/landschuetzer2016.py b/esmvaltool/cmorizers/data/formatters/datasets/landschuetzer2016.py
index e7984bb23a..306c4f8f27 100644
--- a/esmvaltool/cmorizers/data/formatters/datasets/landschuetzer2016.py
+++ b/esmvaltool/cmorizers/data/formatters/datasets/landschuetzer2016.py
@@ -74,7 +74,7 @@ def extract_variable(var_info, raw_info, out_dir, attrs):
     for cube in cubes:
         if cube.var_name == rawvar:
             fix_var_metadata(cube, var_info)
-            fix_coords(cube)
+            cube = fix_coords(cube)
             _fix_data(cube, var)
             set_global_atts(cube, attrs)
             save_variable(
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/landschuetzer2020.py b/esmvaltool/cmorizers/data/formatters/datasets/landschuetzer2020.py
index d5739cb8f1..e8419b320b 100644
--- a/esmvaltool/cmorizers/data/formatters/datasets/landschuetzer2020.py
+++ b/esmvaltool/cmorizers/data/formatters/datasets/landschuetzer2020.py
@@ -103,7 +103,7 @@ def _extract_variable(var_info, cmor_info, attrs, filepath, out_dir):
 
     # Fix coordinates
     _fix_climatological_time(cube)
-    utils.fix_coords(
+    cube = utils.fix_coords(
         cube,
         overwrite_lat_bounds=False,
         overwrite_lon_bounds=False,
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/merra.ncl b/esmvaltool/cmorizers/data/formatters/datasets/merra.ncl
index b57bca6a09..d9fbf761df 100644
--- a/esmvaltool/cmorizers/data/formatters/datasets/merra.ncl
+++ b/esmvaltool/cmorizers/data/formatters/datasets/merra.ncl
@@ -14,7 +14,7 @@
 ; Download and processing instructions
 ; (requires EarthData login; see https://urs.earthdata.nasa.gov/)
 ;    Use ESMValTool automatic download:
-;       esmvaltool data download --config_file <config-user.yml> MERRA
+;       esmvaltool data download MERRA
 ;
 ; Modification history
 ;    20230818-lauer_axel: added output of clwvi (iwp + lwp)
@@ -209,7 +209,7 @@ begin
 
         delete(tmp)
 
-        ; calcuation of outgoing fluxes: out = in - net
+        ; calculation of outgoing fluxes: out = in - net
         if ((VAR(vv) .eq. "rsut") .or. (VAR(vv) .eq. "rsutcs")) then
           tmp = f->SWTDN
           if (isatt(tmp, "scale_factor") .or. isatt(tmp, "add_offset")) then
@@ -220,7 +220,8 @@ begin
           delete(tmp)
         end if
 
-        ; calcuation of total precipitation flux = large-scale+convective+anvil
+        ; calculation of total precipitation flux =
+        ; large-scale+convective+anvil
         if (VAR(vv) .eq. "pr") then
           tmp = f->PRECCON  ; surface precipitation flux from convection
           if (isatt(tmp, "scale_factor") .or. isatt(tmp, "add_offset")) then
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/mls_aura.py b/esmvaltool/cmorizers/data/formatters/datasets/mls_aura.py
index 1cde246026..0a5031b243 100644
--- a/esmvaltool/cmorizers/data/formatters/datasets/mls_aura.py
+++ b/esmvaltool/cmorizers/data/formatters/datasets/mls_aura.py
@@ -14,7 +14,7 @@
     Select "Data Access" -> "Subset/Get Data" -> "Get Data" and follow the
     "Instructions for downloading". All *.he5 files need to be saved in the
     $RAWOBS/Tier3/MLS-AURA directory, where $RAWOBS refers to the RAWOBS
-    directory defined in the user configuration file. Apply this procedure to
+    directory defined in the configuration. Apply this procedure to
     both links provided above. The temperature fields are necessary for quality
     control of the RHI data (see Data Quality Document for MLS-AURA for more
     information).
@@ -312,7 +312,7 @@ def _save_cube(cube, cmor_info, attrs, out_dir):
     cube.coord('air_pressure').convert_units('Pa')
     utils.fix_var_metadata(cube, cmor_info)
     utils.convert_timeunits(cube, 1950)
-    utils.fix_coords(cube)
+    cube = utils.fix_coords(cube)
     utils.set_global_atts(cube, attrs)
     utils.save_variable(cube,
                         cmor_info.short_name,
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/mobo_dic_mpim.py b/esmvaltool/cmorizers/data/formatters/datasets/mobo_dic_mpim.py
index 9ae096104f..7b10ef0b5e 100644
--- a/esmvaltool/cmorizers/data/formatters/datasets/mobo_dic_mpim.py
+++ b/esmvaltool/cmorizers/data/formatters/datasets/mobo_dic_mpim.py
@@ -171,7 +171,7 @@ def _extract_variable(var_info, cmor_info, attrs, filepath, out_dir):
     elif cube.coords('Julian Day'):  # MOBO-DIC2004-2019
         _fix_time(cube)
     cube.coord('depth').units = 'm'
-    utils.fix_coords(cube, overwrite_time_bounds=False)
+    cube = utils.fix_coords(cube, overwrite_time_bounds=False)
 
     # Fix global metadata
     utils.set_global_atts(cube, attrs)
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/mte.py b/esmvaltool/cmorizers/data/formatters/datasets/mte.py
index 78ee04636b..e82baab967 100644
--- a/esmvaltool/cmorizers/data/formatters/datasets/mte.py
+++ b/esmvaltool/cmorizers/data/formatters/datasets/mte.py
@@ -57,9 +57,8 @@ def _extract_variable(raw_var, cmor_info, attrs, filepath, out_dir):
     _fix_time_coord(cube)
     utils.fix_var_metadata(cube, cmor_info)
     utils.convert_timeunits(cube, 1950)
-    utils.fix_coords(cube)
+    cube = utils.fix_coords(cube)
     utils.set_global_atts(cube, attrs)
-    utils.flip_dim_coord(cube, 'latitude')
     utils.save_variable(cube,
                         var,
                         out_dir,
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/ncep_ncar_r1.py b/esmvaltool/cmorizers/data/formatters/datasets/ncep_ncar_r1.py
index 5e2829af07..c0f33286d5 100644
--- a/esmvaltool/cmorizers/data/formatters/datasets/ncep_ncar_r1.py
+++ b/esmvaltool/cmorizers/data/formatters/datasets/ncep_ncar_r1.py
@@ -84,10 +84,7 @@ def _fix_units(cube, definition):
 
 
 def _fix_coordinates(cube, definition, cmor_info):
-    # fix flipped latitude
-    utils.flip_dim_coord(cube, 'latitude')
-    # fix other coordinates
-    utils.fix_coords(cube)
+    cube = utils.fix_coords(cube)
 
     if 'height2m' in cmor_info.dimensions:
         utils.add_height2m(cube)
@@ -146,6 +143,9 @@ def _extract_variable(short_name, var, cfg, raw_filepath, out_dir):
 
     cube = _fix_coordinates(cube, definition, cmor_info)
 
+    if var.get("make_negative"):
+        cube.data = -1 * cube.data
+
     utils.save_variable(
         cube,
         short_name,
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/ndp.py b/esmvaltool/cmorizers/data/formatters/datasets/ndp.py
index 76d82cdf27..0e393a452b 100644
--- a/esmvaltool/cmorizers/data/formatters/datasets/ndp.py
+++ b/esmvaltool/cmorizers/data/formatters/datasets/ndp.py
@@ -61,7 +61,7 @@ def _extract_variable(cmor_info, attrs, var_file, out_dir, cfg):
     cube.convert_units('kg m-2')
     utils.fix_var_metadata(cube, cmor_info)
     utils.convert_timeunits(cube, 1950)
-    utils.fix_coords(cube)
+    cube = utils.fix_coords(cube)
     utils.set_global_atts(cube, attrs)
     utils.save_variable(cube,
                         cmor_info.short_name,
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/noaa_ersstv5.py b/esmvaltool/cmorizers/data/formatters/datasets/noaa_ersstv5.py
index b9f6421e63..c01783724c 100644
--- a/esmvaltool/cmorizers/data/formatters/datasets/noaa_ersstv5.py
+++ b/esmvaltool/cmorizers/data/formatters/datasets/noaa_ersstv5.py
@@ -69,7 +69,7 @@ def _extract_variable(raw_var, cmor_info, attrs, filepaths, out_dir):
     cube = iris.util.squeeze(cube)
 
     utils.fix_var_metadata(cube, cmor_info)
-    utils.fix_coords(cube)
+    cube = utils.fix_coords(cube)
 
     utils.set_global_atts(cube, attrs)
     utils.save_variable(cube,
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/nsidc_g02202_sh.py b/esmvaltool/cmorizers/data/formatters/datasets/nsidc_g02202_sh.py
index c206f817cb..202e370043 100644
--- a/esmvaltool/cmorizers/data/formatters/datasets/nsidc_g02202_sh.py
+++ b/esmvaltool/cmorizers/data/formatters/datasets/nsidc_g02202_sh.py
@@ -27,13 +27,15 @@
 import re
 
 import numpy as np
-
 import iris
 from cf_units import Unit
 from iris.coords import AuxCoord
 
+from esmvalcore.cmor._fixes.common import OceanFixGrid
+from esmvalcore.cmor.fixes import get_time_bounds
 from esmvaltool.cmorizers.data import utilities as utils
 
+
 logger = logging.getLogger(__name__)
 
 
@@ -71,7 +73,7 @@ def _create_coord(cubes, var_name, standard_name):
         standard_name=standard_name,
         long_name=cube.long_name,
         var_name=var_name,
-        units='degrees'  # cube.units,
+        units='degrees'
     )
     return coord
 
@@ -85,24 +87,27 @@ def _extract_variable(raw_var, cmor_info, attrs, filepath, out_dir, latlon):
     cube = cubes.concatenate_cube()
     iris.util.promote_aux_coord_to_dim_coord(cube, 'projection_y_coordinate')
     iris.util.promote_aux_coord_to_dim_coord(cube, 'projection_x_coordinate')
-    cube.coord('projection_y_coordinate').rename('y')
-    cube.coord('projection_x_coordinate').rename('x')
 
     cube.add_aux_coord(latlon[0], (1, 2))
     cube.add_aux_coord(latlon[1], (1, 2))
+
     # add coord typesi
     area_type = AuxCoord([1.0], standard_name='area_type', var_name='type',
                          long_name='Sea Ice area type')
     cube.add_aux_coord(area_type)
 
-    # cube.convert_units(cmor_info.units)
     cube.units = '%'
     cube.data[cube.data > 100] = np.nan
     cube = cube * 100
 
-    # utils.fix_coords(cube) #latlon multidimensional
     utils.fix_var_metadata(cube, cmor_info)
     utils.set_global_atts(cube, attrs)
+    # latlon are multidimensional, create bounds
+    siconc = OceanFixGrid(cmor_info)
+    cube = siconc.fix_metadata(cubes=[cube])[0]
+    # time bounds
+    cube.coord('time').bounds = get_time_bounds(cube.coord('time'),
+                                                cmor_info.frequency)
 
     utils.save_variable(cube,
                         var,
@@ -133,8 +138,9 @@ def _create_areacello(cfg, in_dir, sample_cube, glob_attrs, out_dir):
                           long_name=var_info.long_name,
                           var_name=var_info.short_name,
                           units='m2',
-                          dim_coords_and_dims=[(sample_cube.coord('y'), 0),
-                                               (sample_cube.coord('x'), 1)])
+                          # time is index 0, add cell index dim
+                          dim_coords_and_dims=[(sample_cube.coords()[1], 0),
+                                               (sample_cube.coords()[2], 1)])
     cube.add_aux_coord(lat_coord, (0, 1))
     cube.add_aux_coord(sample_cube.coord('longitude'), (0, 1))
     utils.fix_var_metadata(cube, var_info)
@@ -152,15 +158,17 @@ def cmorization(in_dir, out_dir, cfg, cfg_user, start_date, end_date):
     cubesaux = iris.load(os.path.join(in_dir, 'G02202-cdr-ancillary-sh.nc'))
     lat_coord = _create_coord(cubesaux, 'lat', 'latitude')
     lon_coord = _create_coord(cubesaux, 'lon', 'longitude')
+
     year = 1978
     # split by year..
     sample_cube = None
-    while year <= 2022:
+    for year in range(1979, 2022, 1):
 
         filepaths = _get_filepaths(in_dir, cfg['filename'], year)
 
         if len(filepaths) > 0:
-            logger.info("Found %d files in '%s'", len(filepaths), in_dir)
+            logger.info("Year %d: Found %d files in '%s'",
+                        year, len(filepaths), in_dir)
 
             for (var, var_info) in cfg['variables'].items():
                 logger.info("CMORizing variable '%s'", var)
@@ -173,10 +181,8 @@ def cmorization(in_dir, out_dir, cfg, cfg_user, start_date, end_date):
                                                           lon_coord])
 
         else:
-            logger.info("No files found ")
-            logger.info("year: %d basename: %s", year, cfg['filename'])
-
-        year += 1
+            logger.info("No files found year: %d basename: %s",
+                        year, cfg['filename'])
 
-        if sample_cube is not None:
-            _create_areacello(cfg, in_dir, sample_cube, glob_attrs, out_dir)
+    if sample_cube is not None:
+        _create_areacello(cfg, in_dir, sample_cube, glob_attrs, out_dir)
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/oceansoda_ethz.py b/esmvaltool/cmorizers/data/formatters/datasets/oceansoda_ethz.py
index a818af0424..2e8baf2c8f 100644
--- a/esmvaltool/cmorizers/data/formatters/datasets/oceansoda_ethz.py
+++ b/esmvaltool/cmorizers/data/formatters/datasets/oceansoda_ethz.py
@@ -42,12 +42,14 @@ def _fix_coords(cube, cmor_info):
         time_coord.points = time_coord.units.date2num(new_dates)
     cube.coord('lat').standard_name = 'latitude'
     cube.coord('lon').standard_name = 'longitude'
-    utils.fix_coords(cube)
+    cube = utils.fix_coords(cube)
 
     # Scalar coordinates
     if cmor_info.short_name in ('fgco2', 'spco2'):
         utils.add_scalar_depth_coord(cube)
 
+    return cube
+
 
 def _fix_data(cube, var):
     """Fix data."""
@@ -109,7 +111,7 @@ def _extract_variable(var_info, cmor_info, attrs, filepath, out_dir):
     _fix_var_metadata(var_info, cmor_info, attrs, cube)
 
     # Fix coordinates
-    _fix_coords(cube, cmor_info)
+    cube = _fix_coords(cube, cmor_info)
 
     # Fix global metadata
     utils.set_global_atts(cube, attrs)
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/persiann_cdr.py b/esmvaltool/cmorizers/data/formatters/datasets/persiann_cdr.py
index 323422b9a5..1b72aaddb5 100644
--- a/esmvaltool/cmorizers/data/formatters/datasets/persiann_cdr.py
+++ b/esmvaltool/cmorizers/data/formatters/datasets/persiann_cdr.py
@@ -113,7 +113,7 @@ def _extract_variable(short_name, var, cfg, input_files, out_dir):
     cube.units = 'kg m-2 s-1'
 
     # Fix coordinates
-    utils.fix_coords(cube)
+    cube = utils.fix_coords(cube)
 
     # Fix metadata
     attrs = cfg['attributes']
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/phc.py b/esmvaltool/cmorizers/data/formatters/datasets/phc.py
index a554ebff7c..84a924d48d 100644
--- a/esmvaltool/cmorizers/data/formatters/datasets/phc.py
+++ b/esmvaltool/cmorizers/data/formatters/datasets/phc.py
@@ -101,7 +101,7 @@ def extract_variable(var_info, raw_info, out_dir, attrs):
 
     cube = _fix_data(xr_time, var)
     fix_var_metadata(cube, var_info)
-    fix_coords(cube)
+    cube = fix_coords(cube)
     set_global_atts(cube, attrs)
     print(out_dir)
     if var != "areacello":
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/regen.py b/esmvaltool/cmorizers/data/formatters/datasets/regen.py
index a26971f8a8..f38424ae20 100644
--- a/esmvaltool/cmorizers/data/formatters/datasets/regen.py
+++ b/esmvaltool/cmorizers/data/formatters/datasets/regen.py
@@ -44,7 +44,7 @@ def _extract_variable(short_name, var, cfg, file_path, out_dir):
     utils.convert_timeunits(cube, 1950)
 
     # Fix coordinates
-    utils.fix_coords(cube)
+    cube = utils.fix_coords(cube)
 
     # Fix metadata
     attrs = cfg['attributes']
@@ -72,7 +72,7 @@ def _extract_variable(short_name, var, cfg, file_path, out_dir):
             attrs['mip'] = 'Amon'
 
             # Fix coordinates
-            utils.fix_coords(cube)
+            cube = utils.fix_coords(cube)
 
             # Save variable
             utils.save_variable(cube,
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/scripps_co2_kum.py b/esmvaltool/cmorizers/data/formatters/datasets/scripps_co2_kum.py
index bae7423e86..6a3ccf6ac0 100644
--- a/esmvaltool/cmorizers/data/formatters/datasets/scripps_co2_kum.py
+++ b/esmvaltool/cmorizers/data/formatters/datasets/scripps_co2_kum.py
@@ -93,7 +93,7 @@ def _extract_variable(short_name, var, cfg, filepath, out_dir):
 
     # Fix metadata
     utils.convert_timeunits(cube, 1950)
-    utils.fix_coords(cube)
+    cube = utils.fix_coords(cube)
     cmor_info = cfg['cmor_table'].get_variable(var['mip'], short_name)
     cube.convert_units(cmor_info.units)
     attrs = cfg['attributes']
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/wfde5.py b/esmvaltool/cmorizers/data/formatters/datasets/wfde5.py
index b61a043f04..0cc467e161 100644
--- a/esmvaltool/cmorizers/data/formatters/datasets/wfde5.py
+++ b/esmvaltool/cmorizers/data/formatters/datasets/wfde5.py
@@ -72,7 +72,7 @@ def _extract_variable(var, cfg, filenames, out_dir):
     _fix_time_coord(cube, var)
 
     # Fix coordinates
-    utils.fix_coords(cube)
+    cube = utils.fix_coords(cube)
     if 'height2m' in cmor_info.dimensions:
         utils.add_height2m(cube)
 
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/woa.py b/esmvaltool/cmorizers/data/formatters/datasets/woa.py
index cac388a0fd..35db6d810d 100644
--- a/esmvaltool/cmorizers/data/formatters/datasets/woa.py
+++ b/esmvaltool/cmorizers/data/formatters/datasets/woa.py
@@ -110,7 +110,7 @@ def extract_variable(in_files, out_dir, attrs, raw_info, cmor_table):
                                     calendar='gregorian')
 
     fix_var_metadata(cube, var_info)
-    fix_coords(cube)
+    cube = fix_coords(cube)
     _fix_data(cube, var, attrs['version'])
     set_global_atts(cube, attrs)
     save_variable(cube, var, out_dir, attrs, unlimited_dimensions=['time'])
diff --git a/esmvaltool/cmorizers/data/utilities.py b/esmvaltool/cmorizers/data/utilities.py
index 3620cee30e..ed8b9a9af9 100644
--- a/esmvaltool/cmorizers/data/utilities.py
+++ b/esmvaltool/cmorizers/data/utilities.py
@@ -94,6 +94,21 @@ def add_scalar_height_coord(cube: Cube, height: float = 2.0) -> None:
     cube.add_aux_coord(height_coord, ())
 
 
+def add_typebare(cube, value='bare_ground'):
+    """Add scalar coordinate 'typebare' with value of `value`."""
+    logger.debug("Adding typebare coordinate (%s)", value)
+    typebare_coord = iris.coords.AuxCoord(value,
+                                          var_name='typebare',
+                                          standard_name='area_type',
+                                          long_name='surface type',
+                                          units=Unit('no unit'))
+    try:
+        cube.coord('area_type')
+    except iris.exceptions.CoordinateNotFoundError:
+        cube.add_aux_coord(typebare_coord, ())
+    return cube
+
+
 @contextmanager
 def constant_metadata(cube):
     """Do cube math without modifying units, attributes etc.
@@ -204,14 +219,7 @@ def fix_coords(cube,
         if cube_coord.var_name == 'lon':
             logger.info("Fixing longitude...")
             if cube_coord.ndim == 1:
-                if cube_coord.points[0] < 0. and \
-                        cube_coord.points[-1] < 181.:
-                    cube_coord.points = \
-                        cube_coord.points + 180.
-                    cube.attributes['geospatial_lon_min'] = 0.
-                    cube.attributes['geospatial_lon_max'] = 360.
-                    nlon = len(cube_coord.points)
-                    roll_cube_data(cube, nlon // 2, -1)
+                cube = cube.intersection(longitude=(0.0, 360.0))
             if overwrite_lon_bounds or not cube_coord.has_bounds():
                 fix_bounds(cube, cube_coord)
 
@@ -220,6 +228,8 @@ def fix_coords(cube,
             logger.info("Fixing latitude...")
             if overwrite_lat_bounds or not cube.coord('latitude').has_bounds():
                 fix_bounds(cube, cube.coord('latitude'))
+            if cube_coord.core_points()[0] > cube_coord.core_points()[-1]:
+                cube = iris.util.reverse(cube, cube_coord)
 
         # fix depth
         if cube_coord.var_name == 'lev':
@@ -326,7 +336,10 @@ def save_variable(cube, var, outdir, attrs, **kwargs):
     except iris.exceptions.CoordinateNotFoundError:
         time_suffix = None
     else:
-        if len(time.points) == 1 and "mon" not in cube.attributes.get('mip'):
+        if (
+                len(time.points) == 1 and
+                "mon" not in cube.attributes.get('mip')
+        ) or cube.attributes.get("frequency") == "yr":
             year = str(time.cell(0).point.year)
             time_suffix = '-'.join([year + '01', year + '12'])
         else:
@@ -359,7 +372,7 @@ def save_variable(cube, var, outdir, attrs, **kwargs):
 def extract_doi_value(tags):
     """Extract doi(s) from a bibtex entry."""
     reference_doi = []
-    pattern = r'doi\ = {(.*?)\},'
+    pattern = r'doi\s*=\s*{([^}]+)}'
 
     if not isinstance(tags, list):
         tags = [tags]
@@ -368,17 +381,18 @@ def extract_doi_value(tags):
         bibtex_file = REFERENCES_PATH / f'{tag}.bibtex'
         if bibtex_file.is_file():
             reference_entry = bibtex_file.read_text()
-            if re.search("doi", reference_entry):
-                reference_doi.append(
-                    f'doi:{re.search(pattern, reference_entry).group(1)}')
+            dois = re.findall(pattern, reference_entry)
+            if dois:
+                for doi in dois:
+                    reference_doi.append(f'doi:{doi}')
             else:
                 reference_doi.append('doi not found')
-                logger.warning('The reference file %s does not have a doi.',
-                               bibtex_file)
+                logger.warning(
+                    'The reference file %s does not have a doi.', bibtex_file)
         else:
             reference_doi.append('doi not found')
-            logger.warning('The reference file %s does not exist.',
-                           bibtex_file)
+            logger.warning(
+                'The reference file %s does not exist.', bibtex_file)
     return ', '.join(reference_doi)
 
 
@@ -424,7 +438,7 @@ def set_global_atts(cube, attrs):
 
     # Additional attributes
     glob_dict.update(attrs)
-    cube.attributes = glob_dict
+    cube.attributes.globals = glob_dict
 
 
 def fix_bounds(cube, dim_coord):
diff --git a/esmvaltool/cmorizers/mip_convert/config-mipconv-user.yml b/esmvaltool/cmorizers/mip_convert/config-mipconv-user.yml
deleted file mode 100644
index 93362f92d7..0000000000
--- a/esmvaltool/cmorizers/mip_convert/config-mipconv-user.yml
+++ /dev/null
@@ -1,22 +0,0 @@
-###############################################################################
-# User's configuration file for the ESMValTool with mip_convert
-# For further details see the README document; current sections are
-# mandatory and should be populated with valid entries.
-# Author: V. Predoi / UREAD / November 2018
-###############################################################################
----
-# root to directory where mip_convert rose suites will be run
-# make this different than your usual /roses/ dir
-ROSES_ROOT: "/home/users/$USER/roses_mipconv"
-# root to directory where mip_convert rose suites will write output
-ROSES_OUTPUT: "/home/users/$USER/roses_mipconv_output"
-# map dataset name to relevant UM suite
-DATASET_TO_SUITE: {"UKESM1-0-LL": "u-ar766a"}
-# map variable standard name to stream definition
-STREAM_MAP: {"ps": "ap4", "ta": "ap4", "va": "ap4", "ua": "ap5", "mrsos": "ap5", "toz":"apm"}
-# root directory where PP data lives
-# this directory is in Jasmin/Archer structure; this one here
-# is an actual directory with data
-INPUT_DIR: "/group_workspaces/jasmin4/ncas_cms/valeriu/MASS_DATA"
-# map streams to realm components
-STREAM_COMPONENTS: {"ap4": ["atmos-physics", "land"], "apm": ["atmos-physics"], "ap5": ["land"]}
diff --git a/esmvaltool/cmorizers/mip_convert/esmvt_mipconv_setup.py b/esmvaltool/cmorizers/mip_convert/esmvt_mipconv_setup.py
deleted file mode 100644
index 8868827d5d..0000000000
--- a/esmvaltool/cmorizers/mip_convert/esmvt_mipconv_setup.py
+++ /dev/null
@@ -1,527 +0,0 @@
-"""
-Run the first communication between esmvaltool's recipe and mip_convert.
-
-Description:
-------------
-
-This script sets up the correct rose suite directories to run mip_convert
-on different UM suite data. You can run this tool in three different ways:
- - (with -m --mode option) setup-only: will set up the mip convert rose
-   directories only; it will use the -c configuration file for user options;
- - (with -m --mode option) setup-run-suites: will set up the mip convert rose
-   suites and will go ahead and submit them to cylc via rose suite-run;
- - (with -m --mode option) postproc: will symlink newly created netCDF data
-   into a directory per esmvaltool recipe; note that for now, there is no
-   DRS-like path set up in that directory;
-
-Usage:
-------
--c --config-file:  [REQUIRED]  user specific configuration file;
--r --recipe-file:  [REQUIRED]  single or multiple (space-sep) recipe files;
--m --mode:         [OPTIONAL]  running mode (setup-only, setup-run-suites,
-                               postproc), default=setup-only
--l --log-level:    [OPTIONAL]  log level, default=info
-
-Environment
------------
-current JASMIN rose/cyclc need python2.7; esmvaltool needs python3.x
-So it is impossible at the moment to run this script as executable from an
-esmvaltool environment. Instead, you can run it as a stand-alone tool in a
-python 2.7 environment, intwo stages:
-
-[set up mip_convert suites and run them]
-python esmvt_mipconv_setup.py -c config.yml -r recipe.yml -m setup-run-suites
-[check succesful completion of mip_convert suites]
-[run the symlinking]
-python esmvt_mipconv_setup.py -c config.yml -r recipe.yml -m postproc
-
-A practical example of running the tool can be found on JASMIN:
-/home/users/valeriu/esmvaltool_mip_convert
-There you will find the two component shells: run_conversion
-and run_symlink, as well as an example how to set the configuration file.
-
-The suite used is now on MOSRS (as of 3 December 2018): u-bd681
-You can use the default location on Jasmin:
-DEFAULT_SUITE_LOCATION = "/home/users/valeriu/roses/u-bd681"
-alternatively this can be turned off, should you want to check out the suite
-off MOSRS and use it locally.
-
-Contact:
---------
-author: Valeriu Predoi (UREAD, valeriu.predoi@ncas.ac.uk)
-"""
-import argparse
-import configparser
-import datetime
-import logging
-import os
-import shutil
-import subprocess
-import socket
-
-import yaml
-
-####################
-# global variables #
-####################
-
-# the tool uses a specially tailored mip_convert Rose suite
-# locations of the suite depends on the host
-host_name = socket.gethostname().split('.')
-if len(host_name) > 1:
-    if host_name[1] == 'ceda':
-        # default location for mip_convert suite on JASMIN:
-        # previous suite: u-ak283_esmvt; new one u-bd681
-        # DEFAULT_SUITE_LOCATION = "/home/users/valeriu/roses/u-ak283_esmvt"
-        DEFAULT_SUITE_LOCATION = "/home/users/valeriu/roses/u-bd681"
-        # note that you can fcm checkout it straight from the MOSRS
-
-# stream mapping; taken from hadsdk.streams
-# these are used to set defaults if not overrides
-STREAM_MAP = {
-    'CMIP5': {
-        '3hr': 'apk',
-        '6hrPlev': 'apc',
-        '6hrlev': 'apg',
-        'Amon': 'apm',
-        'Lmon': 'apm',
-        'LImon': 'apm',
-        'Oday': 'opa',
-        'Omon': 'opm',
-        'Oyr': 'opy',
-        'CF3hr': 'apk',
-        'CFday': 'apa',
-        'CFmon': 'apm',
-        'CFsubhr': 'ape',
-        'day': 'apa'
-    },
-    'CMIP6': {
-        '3hr': 'ap8',
-        '6hrLev': 'ap7',
-        '6hrPlev': 'ap7',
-        '6hrPlevPt': 'ap7',
-        'AERday': 'ap6',
-        'AERhr': 'ap9',
-        'AERmon': 'ap4',
-        'AERmonZ': 'ap4',
-        'Amon': 'ap5',
-        'CF3hr': 'ap8',
-        'CFday': 'ap6',
-        'CFmon': 'ap5',
-        'E1hr': 'ap9',
-        'E1hrClimMon': 'ap9',
-        'E3hr': 'ap8',
-        'E3hrPt': 'ap8',
-        'E6hrZ': 'ap7',
-        'Eday': 'ap6',
-        'EdayZ': 'ap6',
-        'Efx': 'ancil',
-        'Emon': 'ap5',
-        'EmonZ': 'ap5',
-        'Esubhr': 'ap8',
-        'Eyr': 'ap5',
-        'LImon': 'ap5',
-        'Lmon': 'ap5',
-        'Oday': 'ond',
-        'Ofx': 'ancil',
-        'Omon': 'onm',
-        'SIday': 'ind',
-        'SImon': 'inm',
-        'day': 'ap6',
-        'fx': 'ancil',
-        'prim1hrpt': 'ap9',
-        'prim3hr': 'ap8',
-        'prim3hrpt': 'ap8',
-        'prim6hr': 'ap7',
-        'prim6hrpt': 'ap7',
-        'primDay': 'ap6',
-        'primMon': 'ap5',
-        'primSIday': 'ap6'
-    }
-}
-
-# set up logging
-logger = logging.getLogger(__name__)
-
-# print the header
-HEADER = r"""
-______________________________________________________________________
-
-     ESMValTool + mip_convert: linking mip_convert to ESMValTool
-______________________________________________________________________
-
-""" + __doc__
-
-
-def get_args():
-    """Define the `esmvaltool` command line."""
-    # parse command line args
-    parser = argparse.ArgumentParser(
-        description=HEADER,
-        formatter_class=argparse.RawDescriptionHelpFormatter)
-    parser.add_argument(
-        '-c',
-        '--config-file',
-        default=os.path.join(os.path.dirname(__file__), 'config-user.yml'),
-        help='Configuration file')
-    parser.add_argument(
-        '-r',
-        '--recipe-files',
-        type=str,
-        nargs='+',
-        help='Recipe files (list or single file)')
-    parser.add_argument(
-        '-m',
-        '--mode',
-        default='setup-only',
-        choices=['setup-only', 'setup-run-suites', 'postproc'],
-        help='How to run: setup: sets up mipconvert suites only;\n' +
-        'or setup-run-suites: sets up suites and runs them as well;\n' +
-        'or postproc: grab the output from mip_convert and use it.')
-    parser.add_argument(
-        '-l',
-        '--log-level',
-        default='info',
-        choices=['debug', 'info', 'warning', 'error'])
-    args = parser.parse_args()
-    return args
-
-
-def _set_logger(logging, out_dir, log_file, log_level):
-    # set logging for screen and file output
-    root_logger = logging.getLogger()
-    out_fmt = "%(asctime)s %(levelname)-8s %(name)s,%(lineno)s\t%(message)s"
-    logging.basicConfig(
-        filename=os.path.join(out_dir, log_file),
-        filemode='a',
-        format=out_fmt,
-        datefmt='%H:%M:%S',
-        level=logging.DEBUG)
-    root_logger.setLevel(log_level.upper())
-    logfmt = logging.Formatter(out_fmt)
-    console_handler = logging.StreamHandler()
-    console_handler.setFormatter(logfmt)
-    root_logger.addHandler(console_handler)
-
-
-def read_yaml_file(yaml_file):
-    """Read recipe into a dictionary."""
-    with open(yaml_file, 'r') as yfile:
-        loaded_file = yaml.safe_load(yfile)
-    return loaded_file
-
-
-def map_var_to_stream(diagnostics, stream_map):
-    """Map variable standard name to stream string."""
-    stream_list = []
-    for _, diag in diagnostics.items():
-        for var in diag['variables']:
-            stream = stream_map[var]
-            stream_list.append(stream)
-    stream_list = list(set(stream_list))
-    return stream_list
-
-
-def write_rose_conf(rose_config_template, recipe_file, config_file, log_level):
-    """Write the new rose conf file per suite."""
-    # Build the ConfigParser object
-    config = configparser.ConfigParser()
-    config.optionxform = str
-    config.read(rose_config_template)
-    recipe_object = read_yaml_file(recipe_file)
-    conf_file = read_yaml_file(config_file)
-    datasets = recipe_object['datasets']
-
-    # check if dataset needs analysis
-    datasets_to_analyze = []
-    for dataset in datasets:
-        if dataset['dataset'] not in conf_file['DATASET_TO_SUITE']:
-            logger.warning("Dataset %s has no mapping to suite",
-                           dataset['dataset'])
-            logger.warning("Assuming data retrival from elsewhere.")
-        else:
-            datasets_to_analyze.append(dataset)
-    diagnostics = recipe_object['diagnostics']
-    active_streams = map_var_to_stream(diagnostics, conf_file['STREAM_MAP'])
-
-    # set stream overrides to None and set components
-    # also set CYCLING_FREQUENCIES to P1Y overall
-    stream_overrides = {}
-    stream_components = {}
-    cycling_frequencies = {}
-    for stream in active_streams:
-        stream_overrides[stream] = 'None'
-        stream_components[stream] = conf_file['STREAM_COMPONENTS'][stream]
-        cycling_frequencies[stream] = 'P1Y'
-
-    # set the logger to start outputting
-    if not os.path.exists(conf_file['ROSES_OUTPUT']):
-        os.makedirs(conf_file['ROSES_OUTPUT'])
-    _set_logger(logging, conf_file['ROSES_OUTPUT'], 'rose_suites_setup.log',
-                log_level)
-    logger.info(HEADER)
-
-    # store the rose suite locations
-    rose_suite_locations = []
-
-    # loop through datasets (different suites for different datasets)
-    for dataset in datasets_to_analyze:
-
-        # set correct paths
-        rose_suite = os.path.join(
-            conf_file['ROSES_ROOT'],
-            conf_file['DATASET_TO_SUITE'][dataset['dataset']])
-        rose_suite_locations.append(rose_suite)
-        rose_output = os.path.join(
-            conf_file['ROSES_OUTPUT'],
-            conf_file['DATASET_TO_SUITE'][dataset['dataset']])
-        if os.path.exists(rose_suite):
-            shutil.rmtree(rose_suite)
-        if os.path.exists(DEFAULT_SUITE_LOCATION):
-            shutil.copytree(DEFAULT_SUITE_LOCATION, rose_suite)
-        else:
-            logger.error("Default Suite Location not found: %s",
-                         DEFAULT_SUITE_LOCATION)
-            break
-        if not os.path.exists(rose_output):
-            os.makedirs(rose_output)
-        new_mipconv_config = os.path.join(rose_suite, 'mip_convert_config')
-
-        # start logging
-        logger.info("Working on dataset: %s", dataset)
-        logger.info("Mapping dataset to suite: %s", rose_suite)
-        logger.info("Output and logs written to: %s", rose_output)
-        logger.info("Creating rose suite directories...")
-        logger.info("Use rose-suite.conf template %s", rose_config_template)
-        logger.info("Use user config file %s", config_file)
-
-        # write the file
-        config.set('jinja2:suite.rc', 'INPUT_DIR',
-                   '"' + conf_file['INPUT_DIR'] + '"')
-        config.set('jinja2:suite.rc', 'OUTPUT_DIR', '"' + rose_output + '"')
-        config.set('jinja2:suite.rc', 'CDDS_DIR',
-                   '"' + DEFAULT_SUITE_LOCATION + '"')
-        config.set('jinja2:suite.rc', 'MIP_CONVERT_CONFIG_DIR',
-                   '"' + new_mipconv_config + '"')
-        config.set('jinja2:suite.rc', 'ACTIVE_STREAMS', str(active_streams))
-        config.set('jinja2:suite.rc', 'STREAM_TIME_OVERRIDES',
-                   str(stream_overrides))
-        config.set('jinja2:suite.rc', 'FIRST_YEAR', str(dataset['start_year']))
-        config.set('jinja2:suite.rc', 'REF_YEAR', str(dataset['start_year']))
-        config.set('jinja2:suite.rc', 'FINAL_YEAR', str(dataset['end_year']))
-        config.set('jinja2:suite.rc', 'STREAM_COMPONENTS',
-                   str(stream_components))
-        config.set('jinja2:suite.rc', 'CYCLING_FREQUENCIES',
-                   str(cycling_frequencies))
-        config.set(
-            'jinja2:suite.rc', 'TARGET_SUITE_NAME',
-            '"' + conf_file['DATASET_TO_SUITE'][dataset['dataset']] + '"')
-        with open(os.path.join(rose_suite, 'rose-suite.conf'), 'w') as r_c:
-            logger.info("Writing rose-suite.conf file %s",
-                        os.path.join(rose_suite, 'rose-suite.conf'))
-            config.write(r_c)
-
-        # now that we have to conf file set up we need to
-        # edit the mip_convert configuration file with the correct data
-        for key, values in conf_file['STREAM_COMPONENTS'].items():
-            for comp in values:
-                mipconv_config = os.path.join(new_mipconv_config,
-                                              'mip_convert.cfg.' + comp)
-                _edit_mip_convert_config(mipconv_config, conf_file, dataset,
-                                         key)
-
-    return rose_suite_locations
-
-
-def _edit_mip_convert_config(mipconv_config, conf_file, dataset, stream):
-    """Edit the mip_convert file for correct runs."""
-    # set the correct variables
-    base_date = str(dataset['start_year']) + '-01-01-00-00-00'
-    suite_id = conf_file['DATASET_TO_SUITE'][dataset['dataset']]
-    cdds_dir = os.path.join(DEFAULT_SUITE_LOCATION, 'mip_convert_aux')
-
-    # Build the ConfigParser object
-    config = configparser.ConfigParser()
-    config.optionxform = str
-    config.read(mipconv_config)
-
-    # set the correct fields
-    config.set('COMMON', 'cdds_dir', cdds_dir)
-    config.set('request', 'base_date', base_date)
-    config.set('request', 'suite_id', suite_id)
-    stream_section = '_'.join(['stream', stream])
-    # add the section if not there already
-    if not config.has_section(stream_section):
-        config.add_section(stream_section)
-    if 'mip' not in dataset:
-        # can work without any mip in dataset
-        # will not take it from diagnostic (will assemble
-        # all possible mappings instead)
-        logger.warning("No mip in the recipe dataset section.")
-        logger.warning("Assigning mapping from default dictionary.")
-        stream_map_default = STREAM_MAP[dataset['project']]
-        variables = []
-        cmip_types = []
-        for key, val in conf_file['STREAM_MAP'].items():
-            for key_def, val_def in stream_map_default.items():
-                if val == val_def:
-                    cmip_types.append('_'.join([dataset['project'], key_def]))
-                    variables.append(key)
-        str_variables = ' '.join(list(set([v for v in variables])))
-        if variables:
-            for cmip_type in cmip_types:
-                config.set(stream_section, cmip_type, str_variables)
-    else:
-        cmip_type = '_'.join([dataset['project'], dataset['mip']])
-        all_vars = conf_file['STREAM_MAP'].keys()
-        str_variables = ' '.join(
-            [v for v in all_vars if conf_file['STREAM_MAP'][v] == stream])
-        config.set(stream_section, cmip_type, str_variables)
-
-    # write to file
-    with open(mipconv_config, 'w') as r_c:
-        logger.info("Writing mip_convert config file %s", mipconv_config)
-        config.write(r_c)
-
-
-def _put_in_env(env_script):
-    """Put new system vars in environment."""
-    logger.info("Setting environment for suite submission...")
-
-    # First make it executable.
-    chmod_command = ["chmod", "+x", env_script]
-    proc = subprocess.Popen(chmod_command, stdout=subprocess.PIPE)
-    proc.communicate()
-    logger.info("Script %s is now executable.", env_script)
-
-    # set the environment
-    for line in open(env_script, 'r'):
-        if line.split("=")[0] == 'export PATH':
-            logger.info("Appending %s to path...",
-                        line.split("=")[1].strip("\n"))
-            add_path = line.split("=")[1].strip("\n").strip(":$PATH")
-            os.environ["PATH"] += os.pathsep + add_path
-        elif line.split("=")[0] == 'export PYTHONPATH':
-            logger.info("Exporting %s as PYTHONPATH...",
-                        line.split("=")[1].strip("\n"))
-            os.environ["PYTHONPATH"] = line.split("=")[1].strip("\n")
-
-    # print and check
-    logger.info("New path: %s", str(os.environ["PATH"]))
-    logger.info("mip_convert PYTHONPATH: %s", str(os.environ["PYTHONPATH"]))
-    proc = subprocess.Popen(["which", "rose"], stdout=subprocess.PIPE)
-    out, err = proc.communicate()
-    logger.info("rose: %s %s", out, err)
-    proc = subprocess.Popen(["which", "mip_convert"], stdout=subprocess.PIPE)
-    out, err = proc.communicate()
-    logger.info("mip_convert: %s %s", out, err)
-
-
-def _source_envs(suite):
-    """Source relevant environments."""
-    # source the Met Office rose/cylc environment
-    # and the suite specific environment
-    suite_env = os.path.join(suite, 'env_setup_command_line.sh')  # suite env
-    env_file_mo = os.path.join(suite, 'sourcepaths.sh')  # metomi env
-    _put_in_env(suite_env)
-    _put_in_env(env_file_mo)
-
-
-def _run_suite(suite):
-    """Run the mip_convert suite."""
-    os.chdir(suite)
-    logger.info("Submitting suite from %s", suite)
-    proc = subprocess.Popen(["rose", "suite-run"], stdout=subprocess.PIPE)
-    out, err = proc.communicate()
-    logger.info("Rose communications: %s %s", str(out), str(err))
-
-
-def symlink_data(recipe_file, config_file, log_level):
-    """Grab the mip_converted output and manage it for ESMValTool."""
-    # get configuration and recipe
-    recipe_object = read_yaml_file(recipe_file)
-    conf_file = read_yaml_file(config_file)
-    datasets = recipe_object['datasets']
-
-    # create directory that stores all the output netCDF files
-    now = datetime.datetime.utcnow().strftime("%Y%m%d_%H%M%S")
-    new_subdir = '_'.join((recipe_file.strip('.yml'), now))
-    sym_output_dir = os.path.join(conf_file['ROSES_OUTPUT'],
-                                  'mip_convert_symlinks', new_subdir)
-    if not os.path.exists(sym_output_dir):
-        os.makedirs(sym_output_dir)
-
-    # set the logger to start outputting
-    _set_logger(logging, conf_file['ROSES_OUTPUT'], 'file_simlink.log',
-                log_level)
-    logger.info(HEADER)
-
-    # loop through all datasets to symlink output
-    for dataset in datasets:
-        rose_output = os.path.join(
-            conf_file['ROSES_OUTPUT'],
-            conf_file['DATASET_TO_SUITE'][dataset['dataset']])
-        logger.info("Working on dataset: %s", dataset)
-        logger.info("Output and logs written to: %s", rose_output)
-
-        # create the dataset dir
-        dataset_output = os.path.join(sym_output_dir, dataset['dataset'])
-        if os.path.exists(dataset_output):
-            shutil.rmtree(dataset_output)
-        os.makedirs(dataset_output)
-
-        # loop through files
-        for root, _, files in os.walk(rose_output):
-            for xfile in files:
-                real_file = os.path.join(root, xfile)
-                imag_file = os.path.join(dataset_output, xfile)
-
-                # symlink it if nc file
-                if real_file.endswith('.nc') and \
-                        xfile.split('_')[2] == dataset['dataset']:
-                    if not os.path.islink(imag_file):
-                        logger.info("File to symlink: %s", real_file)
-                        logger.info("Symlinked file: %s", imag_file)
-                        os.symlink(real_file, imag_file)
-                    else:
-                        logger.info("Symlinked file exists...")
-                        logger.info("Original file: %s", real_file)
-                        logger.info("Symlinked file: %s", imag_file)
-
-
-def main():
-    """Run the the meat of the code."""
-    logger.info("Running main function...")
-    args = get_args()
-    rose_config_template = os.path.join(
-        os.path.dirname(__file__), "rose-suite-template.conf")
-
-    # make sure the file is retrieved nonetheless
-    if not os.path.isfile(rose_config_template):
-        logger.info("Fetching rose template config from suite %s",
-                    DEFAULT_SUITE_LOCATION)
-        rose_config_template = os.path.join(DEFAULT_SUITE_LOCATION,
-                                            "rose-suite-template.conf")
-
-    recipe_files = args.recipe_files
-    config_file = args.config_file
-    log_level = args.log_level
-    for recipe_file in recipe_files:
-        if args.mode == 'setup-only':
-            # set up the rose suites
-            write_rose_conf(rose_config_template, recipe_file, config_file,
-                            log_level)
-        elif args.mode == 'setup-run-suites':
-            # setup roses
-            roses = write_rose_conf(rose_config_template, recipe_file,
-                                    config_file, log_level)
-            # set up the environment and submit
-            for rose in roses:
-                _source_envs(rose)
-                _run_suite(rose)
-        elif args.mode == 'postproc':
-            symlink_data(recipe_file, config_file, log_level)
-
-
-if __name__ == '__main__':
-    main()
diff --git a/esmvaltool/cmorizers/mip_convert/recipe_mip_convert.yml b/esmvaltool/cmorizers/mip_convert/recipe_mip_convert.yml
deleted file mode 100644
index 8d5168a975..0000000000
--- a/esmvaltool/cmorizers/mip_convert/recipe_mip_convert.yml
+++ /dev/null
@@ -1,51 +0,0 @@
-#### summary
-# Example of ESMValTool recipe that can be used with the mip_convert capability
-# Data for this recipe exists in pp format on JASMIN, ready for mip_convert-ion
-# The recipe is no different than any typical ESMValTool recipes, but can be used
-# for a test run of mip_convert capability; see the README document and the included
-# config-mipconv-user.yml configuration file.
-# Author: V. Predoi (Uni Reading, valeriu.predoi@ncas.ac.uk)
-# Date: first draft/November 2018
-###########################################################################################################
----
-
-datasets:
-  - {dataset: UKESM1-0-LL,  project: CMIP6,  mip: Amon,  exp: piControl-spinup,  ensemble: r1i1p1f1_gn,  start_year: 1850,  end_year: 1860}
-
-preprocessors:
-  pp_rad:
-    regrid:
-      target_grid: 1x1
-      scheme: linear
-
-diagnostics:
-  validation_mip_convert:
-    description: "Test with mip convert" 
-    variables:
-      # mapping of standard_name to stream for CMIP6
-      # see the associated config file for input
-      # "ps": "ap4", "ta": "ap4", "va": "ap4", "ua": "ap5", "mrsos": "ap5", "toz":"apm"
-      ps:
-        preprocessor: pp_rad
-        field: T2Ms
-      ta:
-        preprocessor: pp_rad
-        field: T2Ms
-      va:
-        preprocessor: pp_rad
-        field: T2Ms
-      ua:
-        preprocessor: pp_rad
-        field: T2Ms
-      toz:
-        preprocessor: pp_rad
-        field: T2Ms
-    scripts:
-      meridional_mean:
-        script: validation.py
-        title: ""
-        control_model: UKESM1-0-LL
-        exper_model: UKESM1-0-LL
-        analysis_type: meridional_mean
-        seasonal_analysis: True
-
diff --git a/esmvaltool/cmorizers/mip_convert/rose-suite-template.conf b/esmvaltool/cmorizers/mip_convert/rose-suite-template.conf
deleted file mode 100644
index 5562333fed..0000000000
--- a/esmvaltool/cmorizers/mip_convert/rose-suite-template.conf
+++ /dev/null
@@ -1,20 +0,0 @@
-[jinja2:suite.rc]
-ACTIVE_STREAMS =
-CONCATENATE = "FALSE"
-CYCLING_FREQUENCIES =
-DUMMY_RUN = "FALSE"
-FINAL_YEAR =
-FIRST_YEAR =
-REF_YEAR =
-INPUT_DIR =
-LOCATION = "LOTUS"
-MEMORY = "70000"
-MIP_CONVERT_CONFIG_DIR =
-OUTPUT_DIR =
-PARALLEL_TASKS = "20"
-NTHREADS_CONCATENATE = "6"
-CDDS_DIR =
-STREAM_COMPONENTS =
-STREAM_TIME_OVERRIDES =
-TARGET_SUITE_NAME =
-WALL_TIME = "6:00:00"
diff --git a/esmvaltool/config-references.yml b/esmvaltool/config-references.yml
index b5f43bc911..1ece453343 100644
--- a/esmvaltool/config-references.yml
+++ b/esmvaltool/config-references.yml
@@ -24,6 +24,12 @@ authors:
     institute: DLR, Germany
     email: bjoern.broetz@dlr.de
     orcid:
+  cammarano_diego:
+    name: Cammarano, Diego
+    institute: DLR, Germany
+    email: diego.cammarano@dlr.de
+    github: diegokam
+    orcid:
   debeire_kevin:
     name: Debeire, Kevin
     institute: DLR, Germany
@@ -223,6 +229,11 @@ authors:
     name: Galytska, Evgenia
     institute: IUP, Bremen
     orcid: https://orcid.org/0000-0001-6575-1559
+  garcia_perdomo_karen:
+    name: Garcia Perdomo, Karen
+    institute: CCCma, ECCC, Canada
+    orcid: https://orcid.org/0009-0004-2333-3358
+    github: Karen-A-Garcia
   gettelman_andrew:
     name: Gettelman, Andrew
     institute: NCAR, USA
@@ -232,6 +243,11 @@ authors:
     institute: University of Bremen, Germany
     orcid: https://orcid.org/0000-0002-2928-8664
     github: bettina-gier
+  gillett_nathan:
+    name: Gillett, Nathan
+    institute: CCCma, ECCC, Canada
+    orcid: https://orcid.org/0000-0002-2957-0002
+    github: npgillett
   gonzalez-reviriego_nube:
     name: Gonzalez-Reviriego, Nube
     institute: BSC, Spain
@@ -336,6 +352,11 @@ authors:
     name: Lillis, Jon
     institute: MetOffice, UK
     orcid:
+  lindenlaub_lukas:
+    name: Lindenlaub, Lukas
+    institute: University of Bremen, Germany
+    orcid: https://orcid.org/0000-0001-6349-9118
+    github: lukruh
   little_bill:
     name: Little, Bill
     institute: MetOffice, UK
@@ -364,7 +385,7 @@ authors:
     orcid:
   malinina_elizaveta:
     name: Malinina, Elizaveta
-    institute: CCCma, Canada
+    institute: CCCma, ECCC, Canada
     orcid: https://orcid.org/0000-0002-4102-2877
     github: malininae
   maloney_eric:
@@ -395,6 +416,11 @@ authors:
     name: Mello, Felipe
     institute: INPE, Brazil
     orcid: https://orcid.org/0000-0002-8832-2869
+  menelaou_konstantinos:
+    name: Menelaou, Konstantinos
+    institute: CCCma, ECCC, Canada
+    orcid: https://orcid.org/0000-0002-9676-7657
+    github: konmenelaou
   mohr_christianwilhelm:
     name: Mohr, Christian Wilhelm
     institute: Cicero, Norway
@@ -404,6 +430,11 @@ authors:
     institute: BSC, Spain
     orcid:
     github: emchamarro
+  munday_gregory:
+    name: Munday, Gregory
+    institute: MetOffice, UK
+    orcid: https://orcid.org/0000-0003-4750-9923
+    github: mo-gregmunday
   nikulin_grigory:
     name: Nikulin, Grigory
     institute: SMHI, Sweden
@@ -461,11 +492,6 @@ authors:
   rol_evert:
     name: Rol, Evert
     orcid: https://orcid.org/0000-0001-8357-4453
-  ruhe_lukas:
-    name: Ruhe, Lukas
-    institute: University of Bremen, Germany
-    orcid: https://orcid.org/0000-0001-6349-9118
-    github: lukruh
   russell_joellen:
     name: Russell, Joellen
     institute: Univ. of Arizona, USA
@@ -504,6 +530,11 @@ authors:
     name: Sterl, Andreas
     institute: KNMI, Netherlands
     orcid: https://orcid.org/0000-0003-3457-0434
+  stevens_robin:
+    name: Stevens, Robin
+    institute: CCCma, ECCC, Canada
+    orcid: https://orcid.org/0000-0002-8737-6988
+    github: Row-Bean
   swaminathan_ranjini:
     name: Swaminathan, Ranjini
     institute: University of Reading, UK
@@ -545,6 +576,11 @@ authors:
     institute: University of Bremen and DLR, Germany
     orcid: https://orcid.org/0000-0001-6133-7801
     github: katjaweigel
+  webb_kristi:
+    name: Webb, Kristi
+    institute: CCCma, ECCC, Canada
+    orcid: https://orcid.org/0000-0002-8610-0672
+    github: k-a-webb
   wenzel_sabrina:
     name: Wenzel, Sabrina
     institute: DLR, Germany
@@ -702,7 +738,7 @@ authors:
     orcid:
   reader_cathy:
     name: Reader, Cathy
-    institute:
+    institute: CCCma, ECCC, Canada
     orcid:
     github: mcreader97
   rumbold_heather:
diff --git a/esmvaltool/diag_scripts/aerosols/aod_aeronet_assess.py b/esmvaltool/diag_scripts/aerosols/aod_aeronet_assess.py
index 27ab6b2714..3866e3c51a 100644
--- a/esmvaltool/diag_scripts/aerosols/aod_aeronet_assess.py
+++ b/esmvaltool/diag_scripts/aerosols/aod_aeronet_assess.py
@@ -11,10 +11,10 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import scipy
-from aero_utils import add_bounds, extract_pt
 from matplotlib import colors, gridspec
 from numpy import ma
 
+from esmvaltool.diag_scripts.aerosols.aero_utils import add_bounds, extract_pt
 from esmvaltool.diag_scripts.shared import group_metadata, run_diagnostic
 from esmvaltool.diag_scripts.shared._base import get_plot_filename
 
diff --git a/esmvaltool/diag_scripts/arctic_ocean/__init__.py b/esmvaltool/diag_scripts/arctic_ocean/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/esmvaltool/diag_scripts/climate_patterns/__init__.py b/esmvaltool/diag_scripts/climate_patterns/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/esmvaltool/diag_scripts/climate_patterns/climate_patterns.py b/esmvaltool/diag_scripts/climate_patterns/climate_patterns.py
new file mode 100644
index 0000000000..bc265cda00
--- /dev/null
+++ b/esmvaltool/diag_scripts/climate_patterns/climate_patterns.py
@@ -0,0 +1,658 @@
+# (C) Crown Copyright 2022-2024, Met Office.
+"""Diagnostic script to build climate patterns from CMIP6 models.
+
+Description
+-----------
+Builds patterns, anomaly and climatology cubes from CMIP6 models.
+This diagnostic needs preprocessed mean monthly cubes, with no
+gridding requirements. Default re-grid specification exists to
+decrease CPU-load and run-time.
+
+Author
+------
+Gregory Munday (Met Office, UK)
+
+
+Configuration options in recipe
+-------------------------------
+jules_mode: bool, optional (default: false)
+    options: true, false
+    def: outputs extra data (anomaly, climatology) per variable
+         to drive JULES-IMOGEN configuration
+parallelise: bool, optional (default: false)
+    options: true, false
+    def: parallelises code to run N models at once
+area: str, optional (default: global)
+    options: global, land
+    def: area over which to calculate climate patterns
+"""
+
+import logging
+import os
+from pathlib import Path
+
+import iris
+import iris.coord_categorisation
+import iris.cube
+import numpy as np
+import sklearn.linear_model
+from esmvalcore.preprocessor import (
+    area_statistics,
+    climate_statistics,
+    extract_time,
+)
+
+import esmvaltool.diag_scripts.climate_patterns.sub_functions as sf
+from esmvaltool.diag_scripts.climate_patterns.plotting import (
+    plot_patterns,
+    plot_timeseries,
+)
+from esmvaltool.diag_scripts.shared import run_diagnostic
+
+logger = logging.getLogger(Path(__file__).stem)
+
+
+def calculate_climatology(cube, syr=1850, eyr=1889):
+    """Handle aggregation to make climatology.
+
+    Parameters
+    ----------
+    cube : cube
+        cube loaded from config dictionary
+    syr : int
+        set climatology start year
+    eyr : int
+        set climatology end year
+
+    Returns
+    -------
+    cube_aggregated : cube
+        40 year climatology cube from syr-eyr (default 1850-1889)
+    """
+    cube_40yr = extract_time(
+        cube,
+        start_year=syr,
+        start_month=1,
+        start_day=1,
+        end_year=eyr,
+        end_month=12,
+        end_day=31
+    )
+    cube_aggregated = climate_statistics(cube_40yr, 'mean', 'month')
+
+    return cube_aggregated
+
+
+def diurnal_temp_range(cubelist):
+    """Calculate diurnal range from monthly max and min temperatures.
+
+    Parameters
+    ----------
+    cubelist : cubelist
+        cubelist of tasmin and tasmax
+
+    Returns
+    -------
+    range_cube : cube
+        cube of calculated diurnal range
+    """
+    range_cube = cubelist[0] - cubelist[1]
+
+    # check in case cubes are wrong way around
+    if np.mean(range_cube.data) < 0:
+        range_cube = -range_cube
+
+    range_cube.rename("Diurnal Range")
+    range_cube.var_name = "range_tl1"
+
+    return range_cube
+
+
+def calculate_diurnal_range(clim_list, ts_list):
+    """Facilitate diurnal range calculation and appending.
+
+    Parameters
+    ----------
+    clim_list : cubelist
+        cubelist of climatology cubes
+    ts_list : cubelist
+        cubelist of standard timeseries cubes
+
+    Returns
+    -------
+    clim_list_final : cubelist
+        cubelist of climatology cubes including diurnal range
+    ts_list_final : cubelist
+        cubelist of standard timeseries cubes including diurnal range
+    """
+    temp_range_list_clim = iris.cube.CubeList([])
+    temp_range_list_ts = iris.cube.CubeList([])
+    comb_list = [clim_list, ts_list]
+
+    for cube_list in comb_list:
+        for cube in cube_list:
+            if (cube.var_name in ("tasmax", "tasmin")) and cube in clim_list:
+                temp_range_list_clim.append(cube)
+            elif (cube.var_name in ("tasmax", "tasmin")) and cube in ts_list:
+                temp_range_list_ts.append(cube)
+            else:
+                pass
+
+    derived_diurnal_clim = diurnal_temp_range(temp_range_list_clim)
+    derived_diurnal_ts = diurnal_temp_range(temp_range_list_ts)
+
+    # append diurnal range to lists
+    clim_list_final, ts_list_final = append_diurnal_range(
+        derived_diurnal_clim, derived_diurnal_ts, clim_list, ts_list
+    )
+
+    return clim_list_final, ts_list_final
+
+
+def append_diurnal_range(derived_diurnal_clim,
+                         derived_diurnal_ts,
+                         clim_list,
+                         ts_list):
+    """Append diurnal range to cubelists.
+
+    Parameters
+    ----------
+    derived_diurnal_clim : cube
+        derived diurnal climatology cube
+    derived_diurnal_ts : cube
+        derived diurnal timeseries cube
+    clim_list : cubelist
+        existing climatology cubelist, no range
+    ts_list : cubelist
+        existing timeseries cubelist, no range
+
+    Returns
+    -------
+    clim_list_final : cubelist
+        cubelist of climatology cubes including diurnal range
+    ts_list_final : cubelist
+        cubelist of standard timeseries cubes including diurnal range
+    """
+    # creating cube list without tasmax or tasmin
+    # (since we just wanted the diurnal range)
+    clim_list_final = iris.cube.CubeList([])
+    ts_list_final = iris.cube.CubeList([])
+
+    for cube in clim_list:
+        if cube.var_name not in ("tasmax", "tasmin"):
+            clim_list_final.append(cube)
+
+    for cube in ts_list:
+        if cube.var_name not in ("tasmax", "tasmin"):
+            ts_list_final.append(cube)
+
+    clim_list_final.append(derived_diurnal_clim)
+    ts_list_final.append(derived_diurnal_ts)
+
+    return clim_list_final, ts_list_final
+
+
+def calculate_anomaly(clim_list, ts_list):
+    """Calculate variables as anomalies, and adds diurnal range as variable.
+
+    Parameters
+    ----------
+    clim_list : cubelist
+        cubelist of climatology variables
+    ts_list : cubelist
+        cubelist of standard variable timeseries
+
+    Returns
+    -------
+    clim_list_final : cubelist
+        cubelist of clim. vars, inc. diurnal range
+    anom_list_final : cubelist
+        cubelist of anomaly vars, inc. diurnal range
+    """
+    # calculate diurnal temperature range cube
+    clim_list_final, ts_list_final = calculate_diurnal_range(
+        clim_list,
+        ts_list
+    )
+
+    anom_list_final = ts_list_final.copy()
+
+    # calc the anom by subtracting the monthly climatology from
+    # the time series
+    for i, _ in enumerate(ts_list_final):
+        i_months = (
+            anom_list_final[i].coord("month_number").points - 1
+        )  # -1 because months are numbered 1..12
+        anom_list_final[i].data -= clim_list_final[i][i_months].data
+
+    return clim_list_final, anom_list_final
+
+
+def regression(tas, cube_data, area, ocean_frac=None, land_frac=None):
+    """Calculate coeffs of regression between global surf temp and variable.
+
+    Parameters
+    ----------
+    tas : cube
+        near-surface air temperature
+    cube_data : arr
+        cube.data array of a variable
+    area: str
+        area over which to calculate patterns
+    ocean_frac: cube
+        gridded ocean fraction
+    land_frac: cube
+        gridded land fraction
+
+    Returns
+    -------
+    slope_array : arr
+        array of grid cells with same shape as initial cube,
+        containing the regression slope
+    """
+    if area == "land":
+        # calculate average warming over land
+        tas_data = sf.area_avg_landsea(
+            tas, ocean_frac, land_frac, land=True, return_cube=False
+        )
+    else:
+        # calculate global average warming
+        tas_data = area_statistics(tas, 'mean').data
+
+    # Reshape cube for regression
+    cube_reshaped = cube_data.reshape(cube_data.shape[0], -1)
+
+    # Perform linear regression on valid values
+    model = sklearn.linear_model.LinearRegression(
+        fit_intercept=False, copy_X=True
+    )
+    model.fit(tas_data.reshape(-1, 1), cube_reshaped)
+
+    # Extract regression coefficients
+    slopes = model.coef_
+
+    # Reshape the regression coefficients back to the shape of the grid cells
+    slope_array = slopes.reshape(cube_data.shape[1:])
+
+    return slope_array
+
+
+def create_cube(tas_cube, ssp_cube, array, month_number, units=None):
+    """Create a new cube from existing metadata, and new aray data.
+
+    Parameters
+    ----------
+    tas_cube: cube
+        near-surface air temperature
+    ssp_cube: cube
+        cube of a given variable
+    array: array
+        output array from regression
+    month_number: int
+        month related to the regression array
+    units: str
+        units related to the regression variable
+
+    Returns
+    -------
+    cube: cube
+        cube filled with regression array and metadata
+
+    """
+    # assigning dim_coords
+    coord1 = tas_cube.coord(contains_dimension=1)
+    coord2 = tas_cube.coord(contains_dimension=2)
+    dim_coords_and_dims = [(coord1, 0), (coord2, 1)]
+
+    # assigning aux_coord
+    coord_month = iris.coords.AuxCoord(month_number, var_name="imogen_drive")
+    aux_coords_and_dims = [(coord_month, ())]
+
+    cube = sf.rename_variables(ssp_cube, has_orig_vars=False)
+
+    # creating cube
+    cube = iris.cube.Cube(
+        array,
+        units=units,
+        dim_coords_and_dims=dim_coords_and_dims,
+        aux_coords_and_dims=aux_coords_and_dims,
+        var_name=cube.var_name,
+        standard_name=cube.standard_name,
+    )
+
+    return cube
+
+
+def calculate_regressions(
+    anom_list,
+    area,
+    ocean_frac=None,
+    land_frac=None,
+    yrs=86
+):
+    """Facilitate the calculation of regression coeffs (climate patterns).
+
+    Also creates of a new cube of patterns per variable.
+
+    Parameters
+    ----------
+    anom_list : cubelist
+        cube list of variables as anomalies
+    area: str
+        area over which to calculate patterns
+    ocean_frac: cube
+        gridded ocean fraction
+    land_frac: cube
+        gridded land fraction
+    yrs : int
+        int to specify length of scenario
+
+    Returns
+    -------
+    regr_var_list : cubelist
+        cube list of newly created regression slope value cubes, for each var
+    """
+    regr_var_list = iris.cube.CubeList([])
+
+    for cube in anom_list:
+        if cube.var_name == "tl1_anom":
+            # convert years to months when selecting
+            tas = cube[-yrs * 12:]
+
+    for cube in anom_list:
+        cube = cube[-yrs * 12:]
+        month_list = iris.cube.CubeList([])
+
+        # extracting months, regressing, and merging
+        for i in range(1, 13):
+            month_cube = cube.extract(iris.Constraint(imogen_drive=i))
+            month_tas = tas.extract(iris.Constraint(imogen_drive=i))
+
+            if area == 'land':
+                regr_array = regression(
+                    month_tas,
+                    month_cube.data,
+                    area=area,
+                    ocean_frac=ocean_frac,
+                    land_frac=land_frac,
+                )
+            else:
+                regr_array = regression(
+                    month_tas,
+                    month_cube.data,
+                    area=area,
+                )
+
+            if cube.var_name in ("swdown_anom", "lwdown_anom"):
+                units = "W m-2 K-1"
+            else:
+                units = cube.units / tas.units
+
+            # create, and append cube of regression values
+            month_list.append(
+                create_cube(tas, cube.copy(), regr_array, i, units=units)
+            )
+
+        month_list = month_list.merge_cube()
+        regr_var_list.append(month_list)
+
+    return regr_var_list
+
+
+def cube_saver(list_of_cubelists, work_path, name_list, jules_mode):
+    """Save desired cubelists to work_dir, depending on switch settings.
+
+    Parameters
+    ----------
+    list_of_cubelists : list
+        list containing desired cubelists
+    work_path : path
+        path to work_dir, to save cubelists
+    name_list : list
+        list of filename strings for saving
+    jules_mode : str
+        switch option passed through by ESMValTool config dict
+
+    Returns
+    -------
+    None
+    """
+    if jules_mode:
+        for i in range(0, 3):
+            iris.save(
+                list_of_cubelists[i],
+                os.path.join(work_path, name_list[i])
+            )
+    else:
+        for i, cube in enumerate(list_of_cubelists[2]):
+            list_of_cubelists[2][i] = sf.rename_variables(
+                cube, has_orig_vars=False
+            )
+        iris.save(
+            list_of_cubelists[2],
+            os.path.join(work_path, name_list[2])
+        )
+
+
+def save_outputs(
+    cfg,
+    list_of_cubelists,
+    model
+):
+    """Save data and plots to relevant directories.
+
+    Parameters
+    ----------
+    cfg: dict
+        Dictionary passed in by ESMValTool preprocessors
+    list_of_cubelists: list
+        List of cubelists to save
+    model : str
+        model name
+
+    Returns
+    -------
+    None
+    """
+    work_path, plot_path = sf.make_model_dirs(
+        cfg, model
+    )
+
+    name_list = [
+        "climatology_variables.nc",
+        "anomaly_variables.nc",
+        "patterns.nc",
+    ]
+
+    # saving data + plotting
+    if cfg["jules_mode"] is True:
+        plot_timeseries(
+            list_of_cubelists[0],
+            plot_path,
+            "40 Year Climatologies, 1850-1889",
+            "Climatologies"
+        )
+        plot_timeseries(
+            list_of_cubelists[1],
+            plot_path,
+            "Anomaly Timeseries, 1850-2100",
+            "Anomalies"
+        )
+        plot_patterns(list_of_cubelists[2], plot_path)
+        cube_saver(
+            list_of_cubelists,
+            work_path,
+            name_list,
+            jules_mode=cfg["jules_mode"]
+        )
+
+    else:
+        plot_patterns(list_of_cubelists[2], plot_path)
+        cube_saver(
+            list_of_cubelists,
+            work_path,
+            name_list,
+            jules_mode=cfg["jules_mode"]
+        )
+
+
+def get_provenance_record():
+    """Create a provenance record describing the diagnostic data and plot.
+
+    Parameters
+    ----------
+    None
+
+    Returns
+    -------
+    record : dict
+        provenance record
+    """
+    record = {
+        "caption": ["Generating Climate Patterns from CMIP6 Models"],
+        "statistics": ["mean", "other"],
+        "domains": ["global"],
+        "themes": ["carbon"],
+        "realms": ["atmos"],
+        "authors": ["munday_gregory"],
+    }
+
+    return record
+
+
+def extract_data_from_cfg(cfg, model):
+    """Extract model data from the cfg.
+
+    Parameters
+    ----------
+    cfg: dict
+        Dictionary passed in by ESMValTool preprocessors
+    model : str
+        model name
+
+    Returns
+    -------
+    clim_list: cubelist
+        cubelist of climatologies
+    ts_list: cubelist
+        cubelist of spatial timeseries
+    sftlf: cube
+        land fraction cube
+    """
+    clim_list = iris.cube.CubeList([])
+    ts_list = iris.cube.CubeList([])
+
+    for dataset in cfg["input_data"].values():
+        if dataset["dataset"] == model:
+            input_file = dataset["filename"]
+
+            # preparing single cube
+            cube = sf.load_cube(input_file)
+
+            if dataset["exp"] != "historical-ssp585":
+                sftlf = cube
+            else:
+                # appending to timeseries list
+                ts_list.append(cube)
+
+                # making climatology
+                clim_cube = calculate_climatology(cube)
+                clim_list.append(clim_cube)
+
+    if cfg["area"] == 'land':
+        return clim_list, ts_list, sftlf
+
+    return clim_list, ts_list, None
+
+
+def patterns(model, cfg):
+    """Driving function for script, taking in model data and saving parameters.
+
+    Parameters
+    ----------
+    model : str
+        model name
+    cfg: dict
+        Dictionary passed in by ESMValTool preprocessors
+
+    Returns
+    -------
+    None
+    """
+    clim_list, ts_list, sftlf = extract_data_from_cfg(cfg, model)
+
+    if cfg["area"] == 'land':
+        # calculate land/ocean_fracs
+        ocean_frac, land_frac = sf.ocean_fraction_calc(sftlf)
+
+    # calculate anomaly over historical + ssp timeseries
+    clim_list_final, anom_list_final = calculate_anomaly(clim_list, ts_list)
+
+    for i, cube in enumerate(clim_list_final):
+        clim_list_final[i] = sf.rename_variables(
+            cube, has_orig_vars=True, new_extension="_clim"
+        )
+        anom_list_final[i] = sf.rename_variables(
+            anom_list_final[i], has_orig_vars=True, new_extension="_anom"
+        )
+
+    if cfg["area"] == 'land':
+        regressions = calculate_regressions(
+            anom_list_final,
+            cfg["area"],
+            ocean_frac=ocean_frac,
+            land_frac=land_frac
+        )
+    else:
+        regressions = calculate_regressions(
+            anom_list_final, cfg["area"]
+        )
+
+    list_of_cubelists = [clim_list_final, anom_list_final, regressions]
+
+    save_outputs(cfg, list_of_cubelists, model)
+
+    # Provenance Logging, removed due to sporadic errors. Fix later.
+
+    # model_work_dir, _ = sf.make_model_dirs(
+    #     cfg,
+    #     model
+    # )
+
+    # provenance_record = get_provenance_record()
+    # path = os.path.join(model_work_dir, "patterns.nc")
+    # with ProvenanceLogger(cfg) as provenance_logger:
+    #     provenance_logger.log(path, provenance_record)
+
+
+def main(cfg):
+    """Take in driving data with parallelisation options.
+
+    Parameters
+    ----------
+    cfg : dict
+        the global config dictionary, passed by ESMValTool.
+
+    Returns
+    -------
+    None
+    """
+    input_data = cfg["input_data"].values()
+    parallelise = cfg["parallelise"]
+
+    models = []
+    for mod in input_data:
+        model = mod["dataset"]
+        if model not in models:
+            models.append(model)
+
+    if parallelise is True:
+        sf.parallelise(patterns)(models, cfg)
+    else:
+        for model in models:
+            patterns(model, cfg)
+
+
+if __name__ == "__main__":
+    with run_diagnostic() as config:
+        main(config)
diff --git a/esmvaltool/diag_scripts/climate_patterns/plotting.py b/esmvaltool/diag_scripts/climate_patterns/plotting.py
new file mode 100644
index 0000000000..447055a392
--- /dev/null
+++ b/esmvaltool/diag_scripts/climate_patterns/plotting.py
@@ -0,0 +1,128 @@
+# (C) Crown Copyright 2022-2024, Met Office.
+"""Script containing plotting functions for driving scripts.
+
+Author
+------
+Gregory Munday (Met Office, UK)
+"""
+import os
+
+import iris.quickplot as qplt
+import matplotlib.pyplot as plt
+import numpy as np
+
+from esmvalcore.preprocessor import area_statistics
+
+
+def subplot_positions(j):
+    """Determine sub-plot positions in a 3x3 figure.
+
+    Parameters
+    ----------
+    j : int
+        index of cube position in cubelist
+
+    Returns
+    -------
+    x_pos : int
+        x subplot position
+    y_pos : int
+        y subplot position
+    """
+    if j <= 2:
+        y_pos = j
+        x_pos = 0
+    elif 2 < j <= 5:
+        y_pos = j - 3
+        x_pos = 1
+    else:
+        y_pos = j - 6
+        x_pos = 2
+
+    return x_pos, y_pos
+
+
+def plot_patterns(cube_list, plot_path):
+    """Plot climate patterns for jules_mode: off.
+
+    Parameters
+    ----------
+    cube_list : cubelist
+        input cubelist for plotting patterns per variable
+    plot_path : path
+        path to plot_dir
+
+    Returns
+    -------
+    None
+    """
+    fig, axis = plt.subplots(3, 3, figsize=(14, 12), sharex=True)
+    fig.suptitle("Patterns from a random grid-cell", fontsize=18, y=0.98)
+
+    plt.figure(figsize=(14, 12))
+    plt.subplots_adjust(hspace=0.5)
+    plt.suptitle("Global Patterns, January", fontsize=18, y=0.95)
+
+    for j, cube in enumerate(cube_list):
+        # determining plot positions
+        x_pos, y_pos = subplot_positions(j)
+        months = np.arange(1, 13)
+        # plots patterns for an arbitrary grid cell
+        axis[x_pos, y_pos].plot(months, cube[:, 50, 50].data)
+        axis[x_pos,
+             y_pos].set_ylabel(str(cube.var_name) + " / " + str(cube.units))
+        if j > 5:
+            axis[x_pos, y_pos].set_xlabel("Time")
+
+        # January patterns
+        plt.subplot(3, 3, j + 1)
+        qplt.pcolormesh(cube[0])
+
+    plt.tight_layout()
+    plt.savefig(os.path.join(plot_path, "Patterns"), dpi=300)
+    plt.close()
+
+    fig.tight_layout()
+    fig.savefig(os.path.join(plot_path, "Patterns Timeseries"), dpi=300)
+
+
+def plot_timeseries(cubelist, plot_path, title, save_name):
+    """Plot timeseries and maps of climatologies, anomalies and patterns.
+
+    Parameters
+    ----------
+    cubelist : cubelist
+        input cubelist for plotting per variable
+    plot_path : path
+        path to plot_dir
+    title: str
+        title for the figure
+    save_name: str
+        name for the saved figure
+
+    Returns
+    -------
+    None
+    """
+    fig, axs = plt.subplots(3, 3, figsize=(14, 12), sharex=True)
+    fig.suptitle(f"{title}", fontsize=18, y=0.98)
+
+    for j, cube in enumerate(cubelist):
+        # determining plot positions
+        x_pos, y_pos = subplot_positions(j)
+        yrs = (1850 + np.arange(cube.shape[0])).astype("float")
+        months = np.arange(1, 13)
+
+        # anomaly timeseries
+        avg_cube = area_statistics(cube, 'mean').data
+        if save_name == "Climatologies":
+            axs[x_pos, y_pos].plot(months, avg_cube)
+        else:
+            axs[x_pos, y_pos].plot(yrs, avg_cube)
+        axs[x_pos,
+            y_pos].set_ylabel(cube.long_name + " / " + str(cube.units))
+        if j > 5:
+            axs[x_pos, y_pos].set_xlabel("Time")
+
+    fig.tight_layout()
+    fig.savefig(os.path.join(plot_path, f"{save_name}"), dpi=300)
diff --git a/esmvaltool/diag_scripts/climate_patterns/sub_functions.py b/esmvaltool/diag_scripts/climate_patterns/sub_functions.py
new file mode 100644
index 0000000000..4b3fe00141
--- /dev/null
+++ b/esmvaltool/diag_scripts/climate_patterns/sub_functions.py
@@ -0,0 +1,267 @@
+# (C) Crown Copyright 2022-2024, Met Office.
+"""Script containing relevant sub-functions for driving scripts.
+
+Author
+------
+Gregory Munday (Met Office, UK)
+"""
+
+import logging
+import multiprocessing as mp
+import os
+from functools import partial
+from pathlib import Path
+
+import iris
+import iris.analysis.cartography
+import iris.coord_categorisation
+import dask as da
+
+logger = logging.getLogger(Path(__file__).stem)
+
+
+def load_cube(filename):
+    """Load cube, remove any dimensions of length: 1.
+
+    Parameters
+    ----------
+    filename : path
+        path to load cube file
+
+    Returns
+    -------
+    cube : cube
+        a cube
+    """
+    logger.debug("Loading %s", filename)
+    cube = iris.load_cube(filename)
+    cube = iris.util.squeeze(cube)
+
+    return cube
+
+
+def ocean_fraction_calc(sftlf):
+    """Calculate gridded land and ocean fractions.
+
+    Parameters
+    ----------
+    sftlf: cube
+        land-fraction cube from piControl experiment
+
+    Returns
+    -------
+    ocean_frac: cube
+        ocean_fraction cube for area-weights
+    land_frac: cube
+        land_fraction cube for area-weights
+    """
+    sftlf.coord("latitude").coord_system = iris.coord_systems.GeogCS(
+        6371229.0
+    )
+    sftlf.coord("longitude").coord_system = iris.coord_systems.GeogCS(
+        6371229.0
+    )
+    sftof = 100 - sftlf
+
+    ocean_frac = sftof / 100
+    land_frac = sftlf / 100
+
+    return ocean_frac, land_frac
+
+
+def area_avg_landsea(cube,
+                     ocean_frac,
+                     land_frac,
+                     land=True,
+                     return_cube=False):
+    """Calculate the global mean of a variable in a cube.
+
+    Parameters
+    ----------
+    cube : cube
+        input cube
+    ocean_frac : cube
+        ocean fraction cube, found from sftlf
+    land_frac : cube
+        land fraction cube, sftlf
+    land : bool
+        option to weight be land or ocean
+    return_cube : bool
+        option to return a cube or array
+
+    Returns
+    -------
+    cube2 : cube
+        cube with collapsed lat-lons, global mean over time
+    cube2.data : arr
+        array with collapsed lat-lons, global mean over time
+    """
+    if not cube.coord("latitude").has_bounds():
+        cube.coord("latitude").guess_bounds()
+    if not cube.coord("longitude").has_bounds():
+        cube.coord("longitude").guess_bounds()
+
+    global_weights = iris.analysis.cartography.area_weights(
+        cube,
+        normalize=False
+    )
+
+    if land is False:
+        ocean_frac.data = da.array.ma.masked_less(ocean_frac.core_data(), 0.01)
+        weights = iris.analysis.cartography.area_weights(
+            ocean_frac,
+            normalize=False
+        )
+        ocean_area = (
+            ocean_frac.collapsed(
+                ["latitude", "longitude"], iris.analysis.SUM, weights=weights
+            )
+            / 1e12
+        )
+        cube2 = cube * global_weights * ocean_frac
+
+        cube2 = (
+            cube2.collapsed(["latitude", "longitude"], iris.analysis.SUM)
+            / 1e12
+            / ocean_area
+        )
+
+    if land:
+        land_frac.data = da.array.ma.masked_less(land_frac.core_data(), 0.01)
+        weights = iris.analysis.cartography.area_weights(
+            land_frac,
+            normalize=False
+        )
+        land_area = (
+            land_frac.collapsed(
+                ["latitude", "longitude"], iris.analysis.SUM, weights=weights
+            )
+            / 1e12
+        )
+
+        # Iris is too strict so we need to use core_data in this calculation
+        cube2 = cube * global_weights * land_frac.core_data()
+        cube2 = (
+            cube2.collapsed(["latitude", "longitude"], iris.analysis.SUM)
+            / 1e12
+            / land_area
+        )
+
+    if return_cube:
+        return cube2
+
+    return cube2.data
+
+
+def make_model_dirs(cfg, model):
+    """Create directories for each input model for saving.
+
+    Parameters
+    ----------
+    cfg: dict
+        Dictionary passed in by ESMValTool preprocessors
+    model : str
+        model name
+
+    Returns
+    -------
+    model_work_dir : path
+        path to specific model directory in work_dir
+    model_plot_dir : path
+        path to specific plot directory in plot_dir
+    """
+    work_path = cfg["work_dir"]
+    plot_path = cfg["plot_dir"]
+    model_work_dir = os.path.join(work_path, model)
+    model_plot_dir = os.path.join(plot_path, model)
+
+    if not os.path.exists(model_work_dir):
+        os.mkdir(model_work_dir)
+    if not os.path.exists(model_plot_dir):
+        os.mkdir(model_plot_dir)
+
+    return model_work_dir, model_plot_dir
+
+
+def rename_variables(cube, has_orig_vars=True, new_extension=""):
+    """Rename variables and a coord to fit in JULES framework.
+
+    Parameters
+    ----------
+    cube : cube
+        input cube
+    has_orig_vars : bool
+        if True, rename to new var names with correct extension
+    new_extension : str
+        extension to add to variable names
+
+    Returns
+    -------
+    cube : cube
+        cube with renamed variables
+    """
+    original_var_names = ["tas", "range_tl1", "huss", "pr",
+                          "sfcWind", "ps", "rsds", "rlds"]
+    new_var_names = ["tl1", "range_tl1", "ql1", "precip",
+                     "wind", "pstar", "swdown", "lwdown"]
+    long_var_names = [
+        "Air Temperature",
+        "Diurnal Range",
+        "Specific Humidity",
+        "Precipitation",
+        "Wind Speed",
+        "Surface Pressure",
+        "Surface Downwelling Shortwave Radiation",
+        "Surface Downwelling Longwave Radiation"
+    ]
+    for orig_var, new_var, long_var in zip(
+        original_var_names, new_var_names, long_var_names
+    ):
+        if has_orig_vars:
+            if cube.var_name == orig_var:
+                cube.var_name = f"{new_var}{new_extension}"
+                cube.coord("month_number").rename("imogen_drive")
+                return cube
+        else:
+            if cube.var_name == f"{new_var}_anom":
+                cube.rename(long_var)
+                cube.var_name = f"{new_var}_patt"
+                return cube
+            if cube.var_name == f"{new_var}_patt":
+                cube.rename(long_var)
+                cube.var_name = orig_var
+                cube.coord("imogen_drive").rename("month_number")
+                return cube
+
+    return None
+
+
+def parallelise(function, processes=None):
+    """Parallelise any function, by George Ford, Met Office.
+
+    Parameters
+    ----------
+    function : function
+        function to be parallelised
+    processes : int
+        number of threads to be used in parallelisation
+
+    Returns
+    -------
+    result : any
+        results of parallelised elements
+    """
+    if processes is None:
+        processes = max(1, mp.cpu_count() - 1)
+    if processes <= 0:
+        processes = 1
+
+    def easy_parallise(func, sequence, cfg):
+        with mp.Pool(processes=processes) as pool:
+            config_wrapper = partial(func, cfg=cfg)
+            result = pool.map_async(config_wrapper, sequence).get()
+            pool.close()
+            pool.join()
+            return result
+
+    return partial(easy_parallise, function)
diff --git a/esmvaltool/diag_scripts/clouds/clouds_ecs_groups_boxplots.py b/esmvaltool/diag_scripts/clouds/clouds_ecs_groups_boxplots.py
new file mode 100644
index 0000000000..2f24436fcc
--- /dev/null
+++ b/esmvaltool/diag_scripts/clouds/clouds_ecs_groups_boxplots.py
@@ -0,0 +1,227 @@
+"""Python diagnostic for plotting boxplots."""
+import logging
+from pathlib import Path
+
+import iris
+import matplotlib.pyplot as plt
+import pandas as pd
+import seaborn as sns
+
+from esmvaltool.diag_scripts.shared import (
+    ProvenanceLogger,
+    get_diagnostic_filename,
+    get_plot_filename,
+    group_metadata,
+    run_diagnostic,
+    select_metadata,
+)
+
+logger = logging.getLogger(Path(__file__).stem)
+
+VAR_NAMES = {
+    'cl': 'cloud_fraction',
+    'cli': 'ice_water_content',
+    'clw': 'liquid_water_content',
+}
+PALETTE = {
+    'high ECS': 'royalblue',
+    'med ECS': 'green',
+    'low ECS': 'orange',
+}
+
+
+def get_provenance_record(ancestor_files):
+    """Create a provenance record describing the diagnostic data and plot."""
+    caption = ("Relative change per degree of warming averaged over the"
+               "chosen region.")
+
+    record = {
+        'caption': caption,
+        'statistics': ['mean'],
+        'domains': ['global'],
+        'plot_types': ['zonal'],
+        'authors': [
+            'bock_lisa',
+        ],
+        'references': [
+            'bock24acp',
+        ],
+        'ancestors': ancestor_files,
+    }
+    return record
+
+
+def read_data(filename):
+    """Compute an example diagnostic."""
+    logger.debug("Loading %s", filename)
+    cube = iris.load_cube(filename)
+
+    if cube.var_name == 'cli':
+        cube.convert_units('g/kg')
+    elif cube.var_name == 'clw':
+        cube.convert_units('g/kg')
+
+    cube = iris.util.squeeze(cube)
+    return cube
+
+
+def compute_diff(filename1, filename2):
+    """Compute difference between two cubes."""
+    logger.debug("Loading %s", filename1)
+    cube1 = iris.load_cube(filename1)
+    cube2 = iris.load_cube(filename2)
+
+    if cube1.var_name == 'cli':
+        cube1.convert_units('g/kg')
+        cube2.convert_units('g/kg')
+    elif cube1.var_name == 'clw':
+        cube1.convert_units('g/kg')
+        cube2.convert_units('g/kg')
+
+    cube = cube2 - cube1
+    cube.metadata = cube1.metadata
+    return cube
+
+
+def compute_diff_temp(input_data, group, var, dataset):
+    """Compute relative change per temperture change."""
+    dataset_name = dataset['dataset']
+    var = dataset['short_name']
+
+    input_file_1 = dataset['filename']
+
+    var_data_2 = select_metadata(input_data,
+                                 short_name=var,
+                                 dataset=dataset_name,
+                                 variable_group=var + "_" + group[1])
+    if not var_data_2:
+        raise ValueError(
+            f"No '{var}' data for '{dataset_name}' in '{group[1]}' available")
+
+    input_file_2 = var_data_2[0]['filename']
+
+    tas_data_1 = select_metadata(input_data,
+                                 short_name='tas',
+                                 dataset=dataset_name,
+                                 variable_group='tas_' + group[0])
+    tas_data_2 = select_metadata(input_data,
+                                 short_name='tas',
+                                 dataset=dataset_name,
+                                 variable_group='tas_' + group[1])
+    if not tas_data_1:
+        raise ValueError(
+            f"No 'tas' data for '{dataset_name}' in '{group[0]}' available")
+    if not tas_data_2:
+        raise ValueError(
+            f"No 'tas' data for '{dataset_name}' in '{group[1]}' available")
+    input_file_tas_1 = tas_data_1[0]['filename']
+    input_file_tas_2 = tas_data_2[0]['filename']
+
+    cube = read_data(input_file_1)
+
+    cube_diff = compute_diff(input_file_1, input_file_2)
+    cube_tas_diff = compute_diff(input_file_tas_1, input_file_tas_2)
+
+    cube_diff = (100. * (cube_diff / iris.analysis.maths.abs(cube)) /
+                 cube_tas_diff)
+
+    return cube_diff
+
+
+def create_data_frame(input_data, cfg):
+    """Create data frame."""
+    data_frame = pd.DataFrame(columns=['Variable', 'Group', 'Dataset', 'Data'])
+
+    ifile = 0
+
+    all_vars = group_metadata(input_data, 'short_name')
+    groups = group_metadata(input_data, 'variable_group', sort='dataset')
+
+    for var in all_vars:
+        if var != 'tas':
+            logger.info("Processing variable %s", var)
+
+            if var == 'clivi':
+                varname = 'iwp'
+            else:
+                varname = var
+
+            for group_names in cfg['group_by']:
+                logger.info("Processing group %s of variable %s",
+                            group_names[0], var)
+
+                for dataset in groups[var + "_" + group_names[0]]:
+                    dataset_name = dataset['dataset']
+
+                    if dataset_name not in cfg['exclude_datasets']:
+                        cube_diff = compute_diff_temp(input_data, group_names,
+                                                      var, dataset)
+
+                        group_name = group_names[0].split('_')[1] + " ECS"
+
+                        data_frame.loc[ifile] = [
+                            varname, group_name, dataset_name, cube_diff.data
+                        ]
+                        ifile = ifile + 1
+
+    data_frame['Data'] = data_frame['Data'].astype(str).astype(float)
+
+    return data_frame
+
+
+def plot_boxplot(data_frame, input_data, cfg):
+    """Create boxplot."""
+    sns.set_style('darkgrid')
+    sns.set(font_scale=2)
+    sns.boxplot(data=data_frame,
+                x='Variable',
+                y='Data',
+                hue='Group',
+                palette=PALETTE)
+    plt.ylabel('Relative change (%/K)')
+    if 'y_range' in cfg:
+        plt.ylim(cfg.get('y_range'))
+    plt.title(cfg['title'])
+
+    provenance_record = get_provenance_record(
+        ancestor_files=[d['filename'] for d in input_data])
+
+    # Save plot
+    plot_path = get_plot_filename('boxplot' + '_' + cfg['filename_attach'],
+                                  cfg)
+    plt.savefig(plot_path)
+    logger.info("Wrote %s", plot_path)
+    plt.close()
+
+    with ProvenanceLogger(cfg) as provenance_logger:
+        provenance_logger.log(plot_path, provenance_record)
+
+
+def main(cfg):
+    """Run diagnostic."""
+    cfg.setdefault('exclude_datasets',
+                   ['MultiModelMean', 'MultiModelP5', 'MultiModelP95'])
+    cfg.setdefault('title', 'Test')
+
+    plt.figure(constrained_layout=True, figsize=(12, 8))
+
+    # Get input data
+    input_data = list(cfg['input_data'].values())
+
+    # Create data frame
+    data_frame = create_data_frame(input_data, cfg)
+
+    # Create plot
+    plot_boxplot(data_frame, input_data, cfg)
+
+    # Save file
+    basename = "boxplot_region_" + cfg['filename_attach']
+    csv_path = get_diagnostic_filename(basename, cfg).replace('.nc', '.csv')
+    data_frame.to_csv(csv_path)
+    logger.info("Wrote %s", csv_path)
+
+
+if __name__ == '__main__':
+
+    with run_diagnostic() as config:
+        main(config)
diff --git a/esmvaltool/diag_scripts/clouds/clouds_ecs_groups_maps.py b/esmvaltool/diag_scripts/clouds/clouds_ecs_groups_maps.py
new file mode 100644
index 0000000000..fe9a0946e5
--- /dev/null
+++ b/esmvaltool/diag_scripts/clouds/clouds_ecs_groups_maps.py
@@ -0,0 +1,516 @@
+"""Python diagnostic for plotting geographical maps."""
+import logging
+import secrets
+from copy import deepcopy
+from pathlib import Path
+
+import cartopy.crs as ccrs
+import iris
+import iris.plot as iplt
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+from iris.analysis.stats import pearsonr
+
+from esmvaltool.diag_scripts.shared import (
+    extract_variables,
+    get_diagnostic_filename,
+    get_plot_filename,
+    group_metadata,
+    run_diagnostic,
+    save_data,
+    save_figure,
+)
+
+logger = logging.getLogger(Path(__file__).stem)
+
+VAR_NAMES = {
+    'clt': 'total_cloud_fraction',
+    'lwp': 'liquid_water_path',
+    'clivi': 'ice_water_path',
+    'netcre': 'net_cre',
+    'swcre': 'sw_cre',
+    'lwcre': 'lw_cre',
+}
+PANEL = {'ECS_high': 222, 'ECS_med': 223, 'ECS_low': 224, 'OBS': 221}
+PANEL_woOBS = {
+    'ECS_high': 131,
+    'ECS_med': 132,
+    'ECS_low': 133,
+}
+PANEL_LABELS = {
+    'ECS_high': 'b)',
+    'ECS_med': 'c)',
+    'ECS_low': 'd)',
+    'OBS': 'a)'
+}
+PANEL_LABELS_woOBS = {
+    'ECS_high': 'a)',
+    'ECS_med': 'b)',
+    'ECS_low': 'c)',
+}
+PANDAS_PRINT_OPTIONS = ['display.max_rows', None, 'display.max_colwidth', -1]
+
+
+def get_provenance_record(attributes, ancestor_files):
+    """Create a provenance record describing the diagnostic data and plot."""
+    caption = f"Climatology of {attributes['short_name']}."
+
+    record = {
+        'caption': caption,
+        'statistics': ['mean'],
+        'domains': ['global'],
+        'plot_types': ['map'],
+        'authors': [
+            'bock_lisa',
+        ],
+        'references': [
+            'bock24acp',
+        ],
+        'ancestors': ancestor_files,
+    }
+    return record
+
+
+def area_weighted_mean(cube):
+    """Calculate area weighted mean over the globe."""
+    logger.debug("Computing field mean")
+    grid_areas = iris.analysis.cartography.area_weights(cube)
+    mean = cube.collapsed(['longitude', 'latitude'],
+                          iris.analysis.MEAN,
+                          weights=grid_areas)
+    return mean
+
+
+def calculate_bias(model_cube, obs_cube):
+    """Calculate area weighted mean over the globe."""
+    logger.debug("Computing bias")
+    diff = model_cube - obs_cube
+    bias = area_weighted_mean(diff)
+    bias.attributes = model_cube.attributes
+    return bias
+
+
+def calculate_rmsd(model_cube, obs_cube):
+    """Calculate global RMSD."""
+    logger.debug("Computing RMSD")
+    diff = model_cube - obs_cube
+    rmsd = area_weighted_mean(diff**2)**0.5
+    rmsd.attributes = model_cube.attributes
+    return rmsd
+
+
+def calculate_corr(model_cube, obs_cube):
+    """Calculate pattern correlation."""
+    logger.debug("Computing Correlation")
+    grid_areas = iris.analysis.cartography.area_weights(model_cube)
+    corr = pearsonr(model_cube, obs_cube, weights=grid_areas)
+    return corr
+
+
+def compute_diagnostic(filename):
+    """Load cube."""
+    logger.debug("Loading %s", filename)
+    cube = iris.load_cube(filename)
+
+    cube = iris.util.squeeze(cube)
+    return cube
+
+
+def plot_model(cube, attributes, cfg):
+    """Plot each model."""
+    levels = [10, 20, 30, 40, 50, 60, 70, 80, 90]
+    if attributes['short_name'] == 'clt':
+        levels = [10, 20, 30, 40, 50, 60, 70, 80, 90]
+        cmap = 'viridis'
+    elif attributes['short_name'] == 'clivi':
+        levels = [0.02, 0.04, 0.06, 0.08, 0.1, 0.12, 0.14, 0.16, 0.18, 0.2]
+        cmap = 'viridis'
+    elif attributes['short_name'] == 'lwp':
+        levels = [0.02, 0.04, 0.06, 0.08, 0.1, 0.12, 0.14, 0.16, 0.18, 0.2]
+        cmap = 'viridis'
+    elif attributes['short_name'] == 'netcre':
+        levels = [-50, -40, -30, -20, -10, 0, 10, 20, 30, 40, 50]
+        cmap = 'bwr'
+    elif attributes['short_name'] == 'lwcre':
+        levels = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90]
+        cmap = 'Reds'
+    elif attributes['short_name'] == 'swcre':
+        levels = [-90, -80, -70, -60, -50, -40, -30, -20, -10, 0]
+        cmap = 'Blues_r'
+    else:  # default
+        cmap = 'viridis'
+    plt.axes(projection=ccrs.Robinson())
+    iplt.contourf(cube, levels=levels, cmap=cmap, extend='both')
+    plt.gca().coastlines()
+    colorbar = plt.colorbar(orientation='horizontal')
+    colorbar.set_label(cube.var_name + '/' + cube.units.origin)
+    if attributes['short_name'] == 'clt':
+        ticks = [10, 20, 30, 40, 50, 60, 70, 80, 90]
+    elif attributes['short_name'] == 'clivi':
+        ticks = [0.02, 0.04, 0.06, 0.08, 0.1, 0.12, 0.14, 0.16, 0.18, 0.2]
+    elif attributes['short_name'] == 'lwp':
+        ticks = [0.02, 0.04, 0.06, 0.08, 0.1, 0.12, 0.14, 0.16, 0.18, 0.2]
+    elif attributes['short_name'] == 'netcre':
+        ticks = [-50, -40, -30, -20, -10, 0, 10, 20, 30, 40, 50]
+    elif attributes['short_name'] == 'lwcre':
+        ticks = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90]
+    elif attributes['short_name'] == 'swcre':
+        ticks = [-90, -80, -70, -60, -50, -40, -30, -20, -10, 0]
+    else:
+        ticks = levels
+    colorbar.set_ticks(ticks)
+    colorbar.set_ticklabels([str(tick) for tick in ticks])
+
+    # Appearance
+    dataset_name = attributes['dataset']
+    exp_name = attributes['exp']
+    title = f'{VAR_NAMES.get(cube.var_name, cube.var_name)} for {dataset_name}'
+    filename = (f'{VAR_NAMES.get(cube.var_name, cube.var_name)}_'
+                f'{exp_name}_{dataset_name}')
+
+    plt.title(title)
+    plot_path = get_plot_filename(filename, cfg)
+    plt.savefig(plot_path, bbox_inches='tight', orientation='landscape')
+    logger.info("Wrote %s", plot_path)
+    plt.close()
+
+
+def read_data(groups, cfg):
+    """Collect cubes."""
+    logger.debug("Read data")
+    cubes = iris.cube.CubeList()
+    cubes_out = iris.cube.CubeList()
+
+    for group_name in groups:
+        logger.info("Processing variable %s", group_name)
+
+        for attributes in groups[group_name]:
+            logger.info("Processing dataset %s", attributes['dataset'])
+            input_file = attributes['filename']
+            cube = compute_diagnostic(input_file)
+            cube.attributes['variable_group'] = group_name
+            cube.attributes['dataset'] = attributes['dataset']
+
+            cubes.append(cube)
+
+            if (attributes['dataset'] == 'MultiModelMean'
+                    or group_name == 'OBS'):
+                cubes_out.append(cube)
+            else:
+                if cfg['plot_each_model']:
+                    plot_model(cube, attributes, cfg)
+
+    return cubes, cubes_out
+
+
+def plot_diagnostic(cubes, attributes, input_data, cfg):
+    """Create diagnostic data and plot it."""
+    if cfg['reference']:
+        fig = plt.figure(figsize=(14, 9))
+        title = attributes['long_name']
+        fig.suptitle(title, fontsize=22)
+        plt.subplots_adjust(left=0.05,
+                            bottom=0.15,
+                            right=0.95,
+                            top=0.90,
+                            wspace=0.2,
+                            hspace=0.05)
+    else:
+        fig = plt.figure(figsize=(10, 3))
+        title = attributes['long_name']
+        fig.suptitle(title, fontsize=16)
+        plt.subplots_adjust(left=0.02,
+                            bottom=0.10,
+                            right=0.98,
+                            top=0.95,
+                            wspace=0.01,
+                            hspace=0.01)
+
+    cmap = 'bwr'
+    if attributes['short_name'] == 'clt':
+        levels = [10, 20, 30, 40, 50, 60, 70, 80, 90]
+        cmap = 'viridis'
+    elif attributes['short_name'] == 'clivi':
+        levels = [0.02, 0.04, 0.06, 0.08, 0.1, 0.12, 0.14, 0.16, 0.18, 0.2]
+        cmap = 'viridis'
+    elif attributes['short_name'] == 'lwp':
+        levels = [0.02, 0.04, 0.06, 0.08, 0.1, 0.12, 0.14, 0.16, 0.18, 0.2]
+        cmap = 'viridis'
+    elif attributes['short_name'] == 'netcre':
+        levels = [-50, -40, -30, -20, -10, 0, 10, 20, 30, 40, 50]
+        cmap = 'bwr'
+    elif attributes['short_name'] == 'lwcre':
+        levels = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90]
+        cmap = 'Reds'
+    elif attributes['short_name'] == 'swcre':
+        levels = [-90, -80, -70, -60, -50, -40, -30, -20, -10, 0]
+        cmap = 'Blues_r'
+    elif attributes['short_name'] == 'clt_diff':
+        levels = list(np.arange(-30, 31, 2.5))
+    elif attributes['short_name'] == 'clivi_diff':
+        levels = list(np.arange(-0.1, 0.105, 0.01))
+    elif attributes['short_name'] == 'lwp_diff':
+        levels = list(np.arange(-0.1, 0.105, 0.01))
+    elif attributes['short_name'] in [
+            'netcre_diff', 'lwcre_diff', 'swcre_diff'
+    ]:
+        levels = list(np.arange(-30, 31, 2.5))
+    else:
+        levels = list(np.linspace(min(cubes), max(cubes), 10))
+
+    for cube in cubes:
+        logger.info("Plotting %s %s of group %s", cube.attributes['dataset'],
+                    attributes['short_name'],
+                    cube.attributes['variable_group'])
+        mean = area_weighted_mean(cube)
+
+        legend = cube.attributes['variable_group']
+
+        if cfg['reference']:
+            ipanel = PANEL.get(legend, None)
+        else:
+            ipanel = PANEL_woOBS.get(legend, None)
+
+        plt.subplot(ipanel, projection=ccrs.Robinson())
+
+        im = iplt.contourf(cube, levels=levels, cmap=cmap, extend='both')
+
+        plt.gca().coastlines()
+
+        if cfg['reference']:
+            plt.title(legend, fontsize=18)
+            ipanel_label = PANEL_LABELS.get(legend, None)
+            plt.title(ipanel_label, fontsize=22, loc='left')
+            fsize = 14
+        else:
+            plt.title(legend, fontsize=9)
+            ipanel_label = PANEL_LABELS_woOBS.get(legend, None)
+            plt.title(ipanel_label, fontsize=12, loc='left')
+            fsize = 8
+        if attributes['short_name'] in ['clt', 'netcre']:
+            plt.title(f'mean = {mean.data:.1f}      ',
+                      fontsize=fsize,
+                      loc='right')
+        elif attributes['short_name'] in ['clivi', 'lwp']:
+            plt.title(f'mean = {mean.data:.3f}      ',
+                      fontsize=fsize,
+                      loc='right')
+        elif attributes['short_name'] in ['clivi_diff', 'lwp_diff']:
+            plt.title(f'bias = {mean.data:.3f}      ',
+                      fontsize=fsize,
+                      loc='right')
+        elif attributes['short_name'] in ['clt_diff', 'netcre_diff']:
+            plt.title(f'bias = {mean.data:.1f}      ',
+                      fontsize=fsize,
+                      loc='right')
+        else:
+            plt.title(f'{mean.data:.1f}      ', fontsize=fsize, loc='right')
+
+    if cfg['reference']:
+        cbar_ax = fig.add_axes([0.2, 0.08, 0.6, 0.03])
+        colorbar = fig.colorbar(im, cax=cbar_ax, orientation='horizontal')
+    else:
+        cbar_ax = fig.add_axes([0.2, 0.18, 0.6, 0.03])
+        colorbar = fig.colorbar(im, cax=cbar_ax, orientation='horizontal')
+
+    if cubes[0].var_name == "clivi":
+        colorbar.set_label('iwp / ' + cubes[0].units.origin)
+    else:
+        colorbar.set_label(cubes[0].var_name + ' / ' + cubes[0].units.origin)
+    if attributes['short_name'] == 'clt':
+        ticks = [10, 20, 30, 40, 50, 60, 70, 80, 90]
+    elif attributes['short_name'] == 'clivi':
+        ticks = [0.02, 0.04, 0.06, 0.08, 0.1, 0.12, 0.14, 0.16, 0.18, 0.2]
+    elif attributes['short_name'] == 'lwp':
+        ticks = [0.02, 0.04, 0.06, 0.08, 0.1, 0.12, 0.14, 0.16, 0.18, 0.2]
+    elif attributes['short_name'] == 'netcre':
+        ticks = [-50, -40, -30, -20, -10, 0, 10, 20, 30, 40, 50]
+    elif attributes['short_name'] == 'lwcre':
+        ticks = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90]
+    elif attributes['short_name'] == 'swcre':
+        ticks = [-90, -80, -70, -60, -50, -40, -30, -20, -10, 0]
+
+    elif attributes['short_name'] == 'clt_diff':
+        ticks = list(np.arange(-30, 31, 5))
+    elif attributes['short_name'] == 'clivi_diff':
+        ticks = [
+            -0.1, -0.08, -0.06, -0.04, -0.02, 0., 0.02, 0.04, 0.06, 0.08, 0.1
+        ]
+    elif attributes['short_name'] == 'lwp_diff':
+        ticks = [
+            -0.1, -0.08, -0.06, -0.04, -0.02, 0., 0.02, 0.04, 0.06, 0.08, 0.1
+        ]
+    elif attributes['short_name'] in [
+            'netcre_diff', 'lwcre_diff', 'swcre_diff'
+    ]:
+        ticks = list(np.arange(-30, 31, 5))
+    else:
+        ticks = levels
+
+    colorbar.set_ticks(ticks)
+    colorbar.set_ticklabels([str(tick) for tick in ticks])
+
+    # Save the data and the plot
+    provenance_record = get_provenance_record(
+        attributes, ancestor_files=[d['filename'] for d in input_data])
+    basename = 'map_' + attributes['short_name']
+
+    save_data(basename, provenance_record, cfg, cubes)
+    save_figure(basename, provenance_record, cfg)
+
+
+def get_dataframe(cubes, cube_obs):
+    """Create dataframe."""
+    df = pd.DataFrame(columns=['Dataset', 'Group', 'Statistic', 'Value'])
+    idf = 0
+
+    for cube in cubes:
+        dataset = cube.attributes['dataset']
+        group = cube.attributes['variable_group']
+        logger.info("Computing statistics of dataset %s", dataset)
+
+        mean = area_weighted_mean(cube)
+        bias = calculate_bias(cube, cube_obs)
+        rmsd = calculate_rmsd(cube, cube_obs)
+        corr = calculate_corr(cube, cube_obs)
+
+        df.loc[idf] = [dataset, group, 'Mean', mean.data]
+        idf = idf + 1
+        df.loc[idf] = [dataset, group, 'Bias', bias.data]
+        idf = idf + 1
+        df.loc[idf] = [dataset, group, 'RMSD', rmsd.data]
+        idf = idf + 1
+        df.loc[idf] = [dataset, group, 'Corr', corr.data]
+        idf = idf + 1
+
+    return df
+
+
+def write_statistics(df, attributes, cfg):
+    """Write statistics in csv file."""
+    df['Value'] = df['Value'].astype(str).astype(float)
+
+    basename = "statistic_all_" + attributes['short_name']
+    csv_path = get_diagnostic_filename(basename, cfg).replace('.nc', '.csv')
+    df.to_csv(csv_path)
+    logger.info("Wrote %s", csv_path)
+
+    stat = df.groupby(['Statistic', 'Group'])['Value'].describe()
+    basename = "statistic_" + attributes['short_name']
+    csv_path = get_diagnostic_filename(basename, cfg).replace('.nc', '.csv')
+    stat.to_csv(csv_path)
+    logger.info("Wrote %s", csv_path)
+
+
+def bootstrapping(cubes, cube_obs, all_groups, attributes, cfg):
+    """Calculate bootstrapping."""
+    logger.info("Bootstrapping")
+
+    for group in all_groups:
+        if group != 'OBS':
+            logger.info("Processing group %s", group)
+            cubes_part = {}
+            datasets = []
+            for cube in cubes:
+                if cube.attributes['variable_group'] == group:
+                    dataset = cube.attributes['dataset']
+                    cubes_part[dataset] = cube
+                    datasets.append(dataset)
+
+            nsample = 1000
+            sample_stat = pd.DataFrame(
+                columns=['Mean', 'Bias', 'RMSD', 'Corr'])
+
+            ncubes = len(cubes_part)
+            array = list(np.arange(0, ncubes))
+            for iboot in range(0, nsample):
+                cube = cubes_part[datasets[0]].copy()
+                ires = [secrets.choice(array) for _ in range(len(array))]
+                for i, icube in enumerate(ires):
+                    if i == 0:
+                        cube = cubes_part[datasets[icube]].copy()
+                    else:
+                        cube += cubes_part[datasets[icube]]
+                cube.data = cube.data / ncubes
+                sample_stat.loc[iboot] = [
+                    area_weighted_mean(cube).data,
+                    calculate_bias(cube, cube_obs).data,
+                    calculate_rmsd(cube, cube_obs).data,
+                    calculate_corr(cube, cube_obs).data
+                ]
+
+            sample_stat = sample_stat.astype(float)
+            stat = sample_stat.describe()
+            basename = f"bootstrapping_{attributes['short_name']}_{group}"
+            csv_path = (get_diagnostic_filename(basename,
+                                                cfg).replace('.nc', '.csv'))
+            stat.to_csv(csv_path)
+            logger.info("Wrote %s", csv_path)
+
+
+def main(cfg):
+    """Run diagnostic."""
+    cfg = deepcopy(cfg)
+    cfg.setdefault('plot_each_model', False)
+    cfg.setdefault('plot_bias', False)
+
+    input_data = list(cfg['input_data'].values())
+
+    groups = group_metadata(input_data, 'variable_group', sort='dataset')
+    attributes = next(iter(extract_variables(cfg).values()))
+    all_groups = list(group_metadata(input_data, 'variable_group'))
+
+    # Read data
+    cubes, cubes_out = read_data(groups, cfg)
+
+    # Plotting climatologies
+    plot_diagnostic(cubes_out, attributes, input_data, cfg)
+
+    if cfg['reference']:
+        # Compute bias plots
+        cube_obs = cubes_out.extract_cube(
+            iris.Constraint(cube_func=lambda cube: cube.attributes[
+                'variable_group'] == 'OBS'))
+
+        # Bootstrapping
+        bootstrapping(cubes, cube_obs, all_groups, attributes, cfg)
+
+        # Compute statistics
+        df = get_dataframe(cubes, cube_obs)
+
+        # write statistics
+        write_statistics(df, attributes, cfg)
+
+        # compute bias
+        cubes_diff = iris.cube.CubeList()
+        attributes['short_name'] = attributes['short_name'] + "_diff"
+
+        if cfg['plot_bias']:
+            for cube in cubes_out:
+                if (cube.attributes['variable_group'] != 'OBS'
+                        or cube.attributes['dataset'] != 'MultiModelMean'):
+                    logger.info("Processing %s of group %s",
+                                cube.attributes['dataset'],
+                                cube.attributes['variable_group'])
+                    bias = calculate_bias(cube, cube_obs)
+                    rmsd = calculate_rmsd(cube, cube_obs)
+                    corr = calculate_corr(cube, cube_obs)
+                    cube_diff = cube - cube_obs
+                    cube_diff.attributes = cube.attributes
+                    cube_diff.var_name = cube.var_name
+                    cube_diff.attributes['short_name'] = attributes[
+                        'short_name']
+                    cubes_diff.append(cube_diff)
+                    logger.info('%s : bias = %f, rmsd = %f, corr = %f',
+                                cube.attributes['variable_group'], bias.data,
+                                rmsd.data, corr.data)
+
+            # Plotting biases
+            plot_diagnostic(cubes_diff, attributes, input_data, cfg)
+
+
+if __name__ == '__main__':
+
+    with run_diagnostic() as config:
+        main(config)
diff --git a/esmvaltool/diag_scripts/clouds/clouds_ecs_groups_zonal.py b/esmvaltool/diag_scripts/clouds/clouds_ecs_groups_zonal.py
new file mode 100644
index 0000000000..7bb71eb54f
--- /dev/null
+++ b/esmvaltool/diag_scripts/clouds/clouds_ecs_groups_zonal.py
@@ -0,0 +1,486 @@
+"""Python diagnostic for plotting zonal averages."""
+import logging
+from copy import deepcopy
+from pathlib import Path
+
+import iris
+import iris.quickplot as qplt
+import matplotlib.pyplot as plt
+import numpy as np
+
+from esmvaltool.diag_scripts.shared import (
+    group_metadata,
+    run_diagnostic,
+    save_data,
+    save_figure,
+    select_metadata,
+)
+
+logger = logging.getLogger(Path(__file__).stem)
+
+VAR_NAMES = {
+    'clt': 'total_cloud_fraction',
+    'clivi': 'ice_water_path',
+    'lwp': 'liquid_water_path',
+    'swcre': 'shortwave_cloud_radiative_effect',
+    'lwcre': 'longwave_cloud_radiative_effect',
+    'netcre': 'net_cloud_radiative_effect',
+}
+LINE_LEGEND = {
+    'ECS_high_hist': 'ECS_high',
+    'ECS_med_hist': 'ECS_med',
+    'ECS_low_hist': 'ECS_low',
+}
+LINE_COLOR = {
+    'ECS_high_hist': 'royalblue',
+    'ECS_high_scen': 'royalblue',
+    'ECS_med_hist': 'green',
+    'ECS_med_scen': 'green',
+    'ECS_low_hist': 'orange',
+    'ECS_low_scen': 'orange',
+    'CMIP6': 'firebrick',
+    'CMIP5': 'royalblue',
+    'CMIP3': 'darkcyan',
+    'OBS': 'black'
+}
+LINE_DASH = {
+    'ECS_high_hist': 'solid',
+    'ECS_high_scen': 'dashed',
+    'ECS_med_hist': 'solid',
+    'ECS_med_scen': 'dashed',
+    'ECS_low_hist': 'solid',
+    'ECS_low_scen': 'dashed',
+    'CMIP6': 'solid',
+    'CMIP5': 'solid',
+    'CMIP3': 'solid',
+    'OBS': 'solid'
+}
+
+
+def get_provenance_record(short_name, ancestor_files):
+    """Create a provenance record describing the diagnostic data and plot."""
+    caption = (f"Zonally averaged group means of {short_name} in the upper"
+               "panel and the corresponding relative differences in lower"
+               "panel.")
+
+    record = {
+        'caption': caption,
+        'statistics': ['mean'],
+        'domains': ['global'],
+        'plot_types': ['zonal'],
+        'authors': [
+            'bock_lisa',
+        ],
+        'references': [
+            'bock24acp',
+        ],
+        'ancestors': ancestor_files,
+    }
+    return record
+
+
+def _get_multi_model_mean(cubes, var):
+    """Compute multi-model mean."""
+    logger.debug("Calculating multi-model mean")
+    datasets = []
+    mmm = []
+    for (dataset_name, cube) in cubes.items():
+        datasets.append(dataset_name)
+        mmm.append(cube.data)
+    mmm = np.ma.array(mmm)
+    dataset_0 = list(cubes.keys())[0]
+    mmm_cube = cubes[dataset_0].copy(data=np.ma.mean(mmm, axis=0))
+    attributes = {
+        'dataset': 'MultiModelMean',
+        'short_name': var,
+        'datasets': '|'.join(datasets),
+    }
+    mmm_cube.attributes = attributes
+    return mmm_cube
+
+
+def _get_multi_model_quantile(cubes, var, quantile):
+    """Compute multi-model quantile."""
+    logger.debug("Calculating multi-model %s quantile", quantile)
+    datasets = []
+    mmq = []
+    for (dataset_name, cube) in cubes.items():
+        datasets.append(dataset_name)
+        mmq.append(cube.data)
+    mmq = np.ma.array(mmq)
+    dataset_0 = list(cubes.keys())[0]
+    mmq_cube = cubes[dataset_0].copy(data=np.quantile(mmq, quantile, axis=0))
+    attributes = {
+        'dataset': 'MultiModel' + str(quantile),
+        'short_name': var,
+        'datasets': '|'.join(datasets),
+    }
+    mmq_cube.attributes = attributes
+    return mmq_cube
+
+
+def compute_diagnostic(filename):
+    """Compute an example diagnostic."""
+    logger.debug("Loading %s", filename)
+    cube = iris.load_cube(filename)
+
+    if cube.var_name == 'cli':
+        cube.convert_units('g/kg')
+    elif cube.var_name == 'clw':
+        cube.convert_units('g/kg')
+
+    logger.debug("Reading %s", filename)
+    cube = iris.util.squeeze(cube)
+    return cube
+
+
+def compute_diff(filename1, filename2):
+    """Compute difference between two cubes."""
+    logger.debug("Loading %s", filename1)
+    cube1 = iris.load_cube(filename1)
+    cube2 = iris.load_cube(filename2)
+
+    if cube1.var_name == 'cli':
+        cube1.convert_units('g/kg')
+        cube2.convert_units('g/kg')
+    elif cube1.var_name == 'clw':
+        cube1.convert_units('g/kg')
+        cube2.convert_units('g/kg')
+
+    cube = cube2 - cube1
+    cube.metadata = cube1.metadata
+    cube = iris.util.squeeze(cube)
+    return cube
+
+
+def compute_diff_temp(input_data, group, dataset, plot_type):
+    """Compute relative change per temperture change."""
+    dataset_name = dataset['dataset']
+    var = dataset['short_name']
+
+    input_file_1 = dataset['filename']
+
+    var_data_2 = select_metadata(input_data,
+                                 short_name=var,
+                                 dataset=dataset_name,
+                                 variable_group=group[1])
+    if not var_data_2:
+        raise ValueError(
+            f"No '{var}' data for '{dataset_name}' in '{group[1]}' available")
+
+    input_file_2 = var_data_2[0]['filename']
+
+    if plot_type == 'zonal':
+        ta_data_1 = select_metadata(input_data,
+                                    short_name='tas',
+                                    dataset=dataset_name,
+                                    variable_group='tas_' + group[0])
+        ta_data_2 = select_metadata(input_data,
+                                    short_name='tas',
+                                    dataset=dataset_name,
+                                    variable_group='tas_' + group[1])
+    elif plot_type == 'height':
+        ta_data_1 = select_metadata(input_data,
+                                    short_name='ta',
+                                    dataset=dataset_name,
+                                    variable_group='ta_' + group[0])
+        ta_data_2 = select_metadata(input_data,
+                                    short_name='ta',
+                                    dataset=dataset_name,
+                                    variable_group='ta_' + group[1])
+    else:
+        raise ValueError(f"The plot_type '{var}' is not implemented.")
+
+    if not ta_data_1:
+        raise ValueError(f"No temperature data for '{dataset_name}' "
+                         f"in '{group[0]}' available")
+    if not ta_data_2:
+        raise ValueError(f"No temperature data for '{dataset_name}' "
+                         f"in '{group[1]}' available")
+    input_file_ta_1 = ta_data_1[0]['filename']
+    input_file_ta_2 = ta_data_2[0]['filename']
+
+    cube = compute_diagnostic(input_file_1)
+    if var in ['lwp', 'clivi', 'clw', 'cli']:
+        cube.data[cube.data < 0.001] = np.nan
+    elif var in ['cl']:
+        cube.data[cube.data < 0.1] = np.nan
+    elif var in ['netcre', 'swcre', 'lwcre']:
+        cube.data[abs(cube.data) < 1.] = np.nan
+
+    cube_diff = compute_diff(input_file_1, input_file_2)
+    cube_ta_diff = compute_diff(input_file_ta_1, input_file_ta_2)
+
+    cube_ta_diff.data[cube_ta_diff.data < 1.] = np.nan
+
+    cube_diff = (100. * (cube_diff / iris.analysis.maths.abs(cube)) /
+                 cube_ta_diff)
+
+    cube_diff.metadata = cube.metadata
+
+    if plot_type == 'zonal':
+        logger.debug("Computing zonal mean")
+        cube_diff = cube_diff.collapsed('longitude', iris.analysis.MEAN)
+    elif plot_type == 'height':
+        logger.debug("Computing field mean")
+        grid_areas = iris.analysis.cartography.area_weights(cube_diff)
+        cube_diff = cube_diff.collapsed(['longitude', 'latitude'],
+                                        iris.analysis.MEAN,
+                                        weights=grid_areas)
+    else:
+        raise ValueError(f"Plot type {plot_type} is not implemented.")
+
+    cube_diff.units = '%/K'
+
+    return cube_diff
+
+
+def plot_diagnostic(cube, legend, plot_type):
+    """Create diagnostic data and plot it."""
+    cube_label = legend
+    line_color = LINE_COLOR.get(legend, legend)
+    line_dash = LINE_DASH.get(legend, legend)
+
+    plt.subplot(211)
+
+    if plot_type == 'height':
+        cube.coord('air_pressure').convert_units('hPa')
+        y_axis = cube.coord('air_pressure')
+        qplt.plot(cube,
+                  y_axis,
+                  label=cube_label,
+                  color=line_color,
+                  linestyle=line_dash)
+    else:
+        lat = cube.coord('latitude')
+        qplt.plot(lat,
+                  cube,
+                  label=cube_label,
+                  color=line_color,
+                  linestyle=line_dash)
+
+    logger.info("Plotting %s", legend)
+
+
+def plot_diagnostic_diff(cube, legend, plot_type):
+    """Create diagnostic data and plot it."""
+    cube_label = LINE_LEGEND.get(legend, legend)
+    line_color = LINE_COLOR.get(legend, legend)
+    line_dash = LINE_DASH.get(legend, legend)
+
+    plt.subplot(212)
+
+    if cube.var_name == 'pr':
+        cube.units = cube.units / 'kg m-3'
+        cube.data = cube.core_data() / 1000.
+        cube.convert_units('mm day-1')
+    elif cube.var_name == 'cli':
+        cube.convert_units('g/kg')
+    elif cube.var_name == 'clw':
+        cube.convert_units('g/kg')
+
+    if plot_type == 'height':
+        cube.coord('air_pressure').convert_units('hPa')
+        y_axis = cube.coord('air_pressure')
+        qplt.plot(cube,
+                  y_axis,
+                  label=cube_label,
+                  color=line_color,
+                  linestyle=line_dash)
+    else:
+        lat = cube.coord('latitude')
+        qplt.plot(lat,
+                  cube,
+                  label=cube_label,
+                  color=line_color,
+                  linestyle=line_dash)
+
+    logger.info("Plotting %s", legend)
+
+
+def plot_errorband(cube1, cube2, legend, plot_type):
+    """Create diagnostic data and plot it."""
+    line_color = LINE_COLOR.get(legend, legend)
+    line_dash = LINE_DASH.get(legend, legend)
+
+    plt.subplot(211)
+
+    if cube1.var_name == 'pr':
+        cube1.units = cube1.units / 'kg m-3'
+        cube1.data = cube1.core_data() / 1000.
+        cube1.convert_units('mm day-1')
+        cube2.units = cube2.units / 'kg m-3'
+        cube2.data = cube2.core_data() / 1000.
+        cube2.convert_units('mm day-1')
+    elif cube1.var_name == 'cli':
+        cube1.convert_units('g/kg')
+        cube2.convert_units('g/kg')
+    elif cube1.var_name == 'clw':
+        cube1.convert_units('g/kg')
+        cube2.convert_units('g/kg')
+
+    if plot_type == 'height':
+        cube1.coord('air_pressure').convert_units('hPa')
+        cube2.coord('air_pressure').convert_units('hPa')
+        y_axis = cube1.coord('air_pressure').points
+        plt.fill_betweenx(y_axis,
+                          cube1.data,
+                          cube2.data,
+                          color=line_color,
+                          linestyle=line_dash,
+                          alpha=.1)
+    else:
+        lat = cube1.coord('latitude').points
+        plt.fill_between(lat,
+                         cube1.data,
+                         cube2.data,
+                         color=line_color,
+                         linestyle=line_dash,
+                         alpha=.1)
+    logger.info("Plotting %s", legend)
+
+
+def main(cfg):
+    """Run diagnostic."""
+    cfg = deepcopy(cfg)
+    cfg.setdefault('filename_attach', 'base')
+
+    plot_type = cfg['plot_type']
+
+    input_data = list(cfg['input_data'].values())
+
+    groups = group_metadata(input_data, 'variable_group', sort='dataset')
+
+    plt.figure(figsize=(8, 12))
+
+    for group_name in groups:
+        if ('tas_' not in group_name) and ('ta_' not in group_name):
+            logger.info("Processing variable %s", group_name)
+
+            dataset_names = []
+            cubes = {}
+
+            for dataset in groups[group_name]:
+
+                dataset_name = dataset['dataset']
+                var = dataset['short_name']
+
+                if dataset_name not in [
+                        'MultiModelMean', 'MultiModelP5', 'MultiModelP95'
+                ]:
+
+                    logger.info("Loop dataset %s", dataset_name)
+
+                    input_file = dataset['filename']
+                    cube = compute_diagnostic(input_file)
+                    logger.debug("Computing zonal mean")
+                    if plot_type == 'zonal':
+                        cube = cube.collapsed('longitude', iris.analysis.MEAN)
+                    elif plot_type == 'height':
+                        grid_areas = (
+                            iris.analysis.cartography.area_weights(cube))
+                        cube = cube.collapsed(['longitude', 'latitude'],
+                                              iris.analysis.MEAN,
+                                              weights=grid_areas)
+                    else:
+                        raise ValueError(
+                            f"Plot type {plot_type} is not implemented.")
+
+                    cubes[dataset_name] = cube
+
+            cube_mmm = _get_multi_model_mean(cubes, var)
+
+            plot_diagnostic(cube_mmm, group_name, plot_type)
+
+            cube_p5 = _get_multi_model_quantile(cubes, var, 0.05)
+            cube_p95 = _get_multi_model_quantile(cubes, var, 0.95)
+
+            plot_errorband(cube_p5, cube_p95, group_name, plot_type)
+
+    if plot_type == 'height':
+        plt.ylim(1000., 100.)
+        plt.yscale('log')
+        plt.yticks([1000., 800., 600., 400., 300., 200., 100.],
+                   [1000, 800, 600, 400, 300, 200, 100])
+
+    long_name = input_data[0]['long_name']
+    if plot_type == 'height':
+        title = 'Vertical mean of ' + long_name
+    elif plot_type == 'zonal':
+        if long_name == 'Total Cloud Cover Percentage':
+            title = 'Zonal mean of Total Cloud Fraction'
+        else:
+            title = 'Zonal mean of ' + long_name
+    else:
+        title = long_name
+
+    plt.title(title)
+    plt.legend(ncol=1)
+    plt.grid(True)
+
+    for group_name in cfg['group_by']:
+
+        logger.info("Processing group %s", group_name[0])
+
+        dataset_names = []
+        cubes_diff = {}
+
+        for dataset in groups[group_name[0]]:
+            dataset_name = dataset['dataset']
+            var = dataset['short_name']
+
+            if dataset_name not in [
+                    'MultiModelMean', 'MultiModelP5', 'MultiModelP95'
+            ]:
+                logger.info("Loop dataset %s", dataset_name)
+                dataset_names.append(dataset_name)
+
+                cube_diff = compute_diff_temp(input_data, group_name, dataset,
+                                              plot_type)
+
+                cubes_diff[dataset_name] = cube_diff
+
+        cube_mmm = _get_multi_model_mean(cubes_diff, var)
+
+        plot_diagnostic_diff(cube_mmm, group_name[0], plot_type)
+
+    if plot_type == 'height':
+        plt.xlim(0., 1.)
+        plt.ylim(1000., 100.)
+        plt.yscale('log')
+        plt.yticks([1000., 800., 600., 400., 300., 200., 100.],
+                   [1000, 800, 600, 400, 300, 200, 100])
+        plt.axvline(x=0, ymin=0., ymax=1., color='black', linewidth=3)
+        title = 'Difference of vertical mean of ' + long_name
+    elif plot_type == 'zonal':
+        plt.axhline(y=0, xmin=-90., xmax=90., color='black', linewidth=3)
+        title = 'Difference of zonal mean of ' + long_name
+    else:
+        title = long_name
+
+    plt.title(title)
+    plt.legend(ncol=1)
+    plt.grid(True)
+
+    short_name = input_data[0]['short_name']
+    provenance_record = get_provenance_record(
+        short_name, ancestor_files=[d['filename'] for d in input_data])
+
+    if plot_type == 'height':
+        basename = ('level_diff_' + short_name + '_' +
+                    cfg['filename_attach'])
+    else:
+        basename = ('zonal_diff_' + short_name + '_' +
+                    cfg['filename_attach'])
+
+    # Save the data used for the plot
+    save_data(basename, provenance_record, cfg, cube_mmm)
+
+    # And save the plot
+    save_figure(basename, provenance_record, cfg)
+
+
+if __name__ == '__main__':
+
+    with run_diagnostic() as config:
+        main(config)
diff --git a/esmvaltool/diag_scripts/cmorizers/era5.py b/esmvaltool/diag_scripts/cmorizers/era5.py
index 28c3d4ca3a..97f410d54c 100644
--- a/esmvaltool/diag_scripts/cmorizers/era5.py
+++ b/esmvaltool/diag_scripts/cmorizers/era5.py
@@ -26,6 +26,11 @@ def main(cfg):
                 if CMOR_TABLES['CMIP6'].get_variable(mip, info['short_name']):
                     basename = basename.replace('E1hr', mip)
             basename = basename.replace('E1hr', 'day')
+        elif info['diagnostic'] == '3hourly':
+            for mip in ['3hr', 'E3hr', 'CF3hr']:
+                if CMOR_TABLES['CMIP6'].get_variable(mip, info['short_name']):
+                    basename = basename.replace('E1hr', mip)
+            basename = basename.replace('E1hr', '3hr')
 
         cube = iris.load_cube(file)
         try:
@@ -36,6 +41,9 @@ def main(cfg):
             if info['diagnostic'] == "monthly":
                 start = time.cell(0).point.strftime("%Y%m")
                 end = time.cell(-1).point.strftime("%Y%m")
+            elif "hourly" in info['diagnostic']:
+                start = time.cell(0).point.strftime("%Y%m%d%H%M")
+                end = time.cell(-1).point.strftime("%Y%m%d%H%M")
             else:
                 start = time.cell(0).point.strftime("%Y%m%d")
                 end = time.cell(-1).point.strftime("%Y%m%d")
diff --git a/esmvaltool/diag_scripts/emergent_constraints/__init__.py b/esmvaltool/diag_scripts/emergent_constraints/__init__.py
index 545a07d9b5..5a03750bbe 100644
--- a/esmvaltool/diag_scripts/emergent_constraints/__init__.py
+++ b/esmvaltool/diag_scripts/emergent_constraints/__init__.py
@@ -9,10 +9,16 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
+import scipy
 import seaborn as sns
 import yaml
+from packaging.version import Version
 from scipy import integrate
 from scipy.stats import linregress
+if Version(scipy.version.version) < Version('1.14.0'):
+    from scipy.integrate import simps as simpson
+else:
+    from scipy.integrate import simpson
 
 from esmvaltool.diag_scripts.shared import (
     ProvenanceLogger,
@@ -1673,7 +1679,7 @@ def cdf(data, pdf):
 
     """
     idx_range = range(1, len(data) + 1)
-    cum_dens = [integrate.simps(pdf[:idx], data[:idx]) for idx in idx_range]
+    cum_dens = [simpson(pdf[:idx], x=data[:idx]) for idx in idx_range]
     return np.array(cum_dens)
 
 
diff --git a/esmvaltool/diag_scripts/ensclus/__init__.py b/esmvaltool/diag_scripts/ensclus/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/esmvaltool/diag_scripts/ensclus/ens_anom.py b/esmvaltool/diag_scripts/ensclus/ens_anom.py
index a3b664d11a..ef9c303786 100644
--- a/esmvaltool/diag_scripts/ensclus/ens_anom.py
+++ b/esmvaltool/diag_scripts/ensclus/ens_anom.py
@@ -1,12 +1,19 @@
 """Computation of ensemble anomalies based on a desired value."""
 
 import os
+
 import numpy as np
 from scipy import stats
 
 # User-defined packages
-from read_netcdf import read_iris, save_n_2d_fields
-from sel_season_area import sel_area, sel_season
+from esmvaltool.diag_scripts.ensclus.read_netcdf import (
+    read_iris,
+    save_n_2d_fields,
+)
+from esmvaltool.diag_scripts.ensclus.sel_season_area import (
+    sel_area,
+    sel_season,
+)
 
 
 def ens_anom(filenames, dir_output, name_outputs, varname, numens, season,
diff --git a/esmvaltool/diag_scripts/ensclus/ens_eof_kmeans.py b/esmvaltool/diag_scripts/ensclus/ens_eof_kmeans.py
index 2be17195b0..940af71278 100644
--- a/esmvaltool/diag_scripts/ensclus/ens_eof_kmeans.py
+++ b/esmvaltool/diag_scripts/ensclus/ens_eof_kmeans.py
@@ -10,8 +10,8 @@
 from sklearn.cluster import KMeans
 
 # User-defined libraries
-from eof_tool import eof_computation
-from read_netcdf import read_n_2d_fields
+from esmvaltool.diag_scripts.ensclus.eof_tool import eof_computation
+from esmvaltool.diag_scripts.ensclus.read_netcdf import read_n_2d_fields
 
 
 def ens_eof_kmeans(dir_output, name_outputs, numens, numpcs, perc, numclus):
diff --git a/esmvaltool/diag_scripts/ensclus/ens_plots.py b/esmvaltool/diag_scripts/ensclus/ens_plots.py
index 55ad420b08..1b06acd671 100644
--- a/esmvaltool/diag_scripts/ensclus/ens_plots.py
+++ b/esmvaltool/diag_scripts/ensclus/ens_plots.py
@@ -3,12 +3,12 @@
 import math
 import os
 
+import cartopy.crs as ccrs
 import matplotlib.pyplot as plt
 import numpy as np
-import cartopy.crs as ccrs
 
 # User-defined libraries
-from read_netcdf import read_n_2d_fields
+from esmvaltool.diag_scripts.ensclus.read_netcdf import read_n_2d_fields
 
 
 def ens_plots(dir_output, dir_plot, name_outputs, numclus,
diff --git a/esmvaltool/diag_scripts/ensclus/ensclus.py b/esmvaltool/diag_scripts/ensclus/ensclus.py
index a669a9a02d..df06ea411c 100644
--- a/esmvaltool/diag_scripts/ensclus/ensclus.py
+++ b/esmvaltool/diag_scripts/ensclus/ensclus.py
@@ -13,16 +13,21 @@
     20170710-mavilia_irene: routines written.
 """
 
-import os
 import logging
+import os
+
 import numpy as np
-from esmvaltool.diag_scripts.shared import group_metadata, run_diagnostic
-from esmvaltool.diag_scripts.shared import ProvenanceLogger, sorted_metadata
 
 # Import user diagnostic routines
-from ens_anom import ens_anom
-from ens_eof_kmeans import ens_eof_kmeans
-from ens_plots import ens_plots
+from esmvaltool.diag_scripts.ensclus.ens_anom import ens_anom
+from esmvaltool.diag_scripts.ensclus.ens_eof_kmeans import ens_eof_kmeans
+from esmvaltool.diag_scripts.ensclus.ens_plots import ens_plots
+from esmvaltool.diag_scripts.shared import (
+    ProvenanceLogger,
+    group_metadata,
+    run_diagnostic,
+    sorted_metadata,
+)
 
 logger = logging.getLogger(os.path.basename(__file__))
 
diff --git a/esmvaltool/diag_scripts/examples/correlate.py b/esmvaltool/diag_scripts/examples/correlate.py
index 171a24a51f..052f3d2bdc 100644
--- a/esmvaltool/diag_scripts/examples/correlate.py
+++ b/esmvaltool/diag_scripts/examples/correlate.py
@@ -6,7 +6,7 @@
 from iris.analysis import MEAN
 from iris.analysis.stats import pearsonr
 
-from diagnostic import plot_diagnostic
+from esmvaltool.diag_scripts.examples.diagnostic import plot_diagnostic
 from esmvaltool.diag_scripts.shared import group_metadata, run_diagnostic
 
 logger = logging.getLogger(os.path.basename(__file__))
diff --git a/esmvaltool/diag_scripts/iht_toa/single_model_diagnostics.py b/esmvaltool/diag_scripts/iht_toa/single_model_diagnostics.py
index fc917a8aa5..e56240c67a 100644
--- a/esmvaltool/diag_scripts/iht_toa/single_model_diagnostics.py
+++ b/esmvaltool/diag_scripts/iht_toa/single_model_diagnostics.py
@@ -16,8 +16,8 @@
 import numpy as np
 from iris import NameConstraint
 from matplotlib import gridspec, rcParams
-from poisson_solver import SphericalPoisson
 
+from esmvaltool.diag_scripts.iht_toa.poisson_solver import SphericalPoisson
 from esmvaltool.diag_scripts.shared import (
     group_metadata,
     run_diagnostic,
diff --git a/esmvaltool/diag_scripts/kcs/local_resampling.py b/esmvaltool/diag_scripts/kcs/local_resampling.py
index 9eb2ea28ed..0bf6260d65 100644
--- a/esmvaltool/diag_scripts/kcs/local_resampling.py
+++ b/esmvaltool/diag_scripts/kcs/local_resampling.py
@@ -292,7 +292,7 @@ def select_final_subset(cfg, subsets, prov=None):
 
     Final set of eight samples should have with minimal reuse of the
     same ensemble member for the same period. From 10.000 randomly
-    selected sets of 8 samples, count and penalize re-used segments (1
+    selected sets of 8 samples, count and penalize reused segments (1
     for 3*reuse, 5 for 4*reuse). Choose the set with the lowest penalty.
     """
     n_samples = cfg['n_samples']
@@ -387,7 +387,7 @@ def _get_climatology(cfg, scenario_name, table, prov=None):
 
     resampled_control = _recombine(segments_control, table['control'])
     resampled_future = _recombine(segments_future, table['future'])
-    # Store the resampled contol climates
+    # Store the resampled control climates
     filename = get_diagnostic_filename(f'resampled_control_{scenario_name}',
                                        cfg,
                                        extension='nc')
diff --git a/esmvaltool/diag_scripts/monitor/compute_eofs.py b/esmvaltool/diag_scripts/monitor/compute_eofs.py
index dea5d63b9a..a07ca835c0 100644
--- a/esmvaltool/diag_scripts/monitor/compute_eofs.py
+++ b/esmvaltool/diag_scripts/monitor/compute_eofs.py
@@ -24,10 +24,10 @@
     Path to the folder to store figures. Defaults to
     ``{plot_dir}/../../{dataset}/{exp}/{modeling_realm}/{real_name}``.  All
     tags (i.e., the entries in curly brackets, e.g., ``{dataset}``, are
-    replaced with the corresponding tags).  ``{plot_dir}`` is replaced with the
+    replaced with the corresponding tags). ``{plot_dir}`` is replaced with the
     default ESMValTool plot directory (i.e.,
     ``output_dir/plots/diagnostic_name/script_name/``, see
-    :ref:`esmvalcore:user configuration file`).
+    :ref:`esmvalcore:outputdata`).
 rasterize_maps: bool, optional (default: True)
     If ``True``, use `rasterization
     <https://matplotlib.org/stable/gallery/misc/rasterization_demo.html>`_ for
diff --git a/esmvaltool/diag_scripts/monitor/monitor.py b/esmvaltool/diag_scripts/monitor/monitor.py
index 59e37b9842..dda5aa4f3d 100644
--- a/esmvaltool/diag_scripts/monitor/monitor.py
+++ b/esmvaltool/diag_scripts/monitor/monitor.py
@@ -52,10 +52,10 @@
     Path to the folder to store figures. Defaults to
     ``{plot_dir}/../../{dataset}/{exp}/{modeling_realm}/{real_name}``.  All
     tags (i.e., the entries in curly brackets, e.g., ``{dataset}``, are
-    replaced with the corresponding tags).  ``{plot_dir}`` is replaced with the
+    replaced with the corresponding tags). ``{plot_dir}`` is replaced with the
     default ESMValTool plot directory (i.e.,
     ``output_dir/plots/diagnostic_name/script_name/``, see
-    :ref:`esmvalcore:user configuration file`).
+    :ref:`esmvalcore:outputdata`).
 rasterize_maps: bool, optional (default: True)
     If ``True``, use `rasterization
     <https://matplotlib.org/stable/gallery/misc/rasterization_demo.html>`_ for
diff --git a/esmvaltool/diag_scripts/monitor/multi_datasets.py b/esmvaltool/diag_scripts/monitor/multi_datasets.py
index a760a312f6..a5b3afca2d 100644
--- a/esmvaltool/diag_scripts/monitor/multi_datasets.py
+++ b/esmvaltool/diag_scripts/monitor/multi_datasets.py
@@ -84,6 +84,12 @@
 group_variables_by: str, optional (default: 'short_name')
     Facet which is used to create variable groups. For each variable group, an
     individual plot is created.
+matplotlib_rc_params: dict, optional (default: {})
+    Optional :class:`matplotlib.RcParams` used to customize matplotlib plots.
+    Options given here will be passed to :func:`matplotlib.rc_context` and used
+    for all plots produced with this diagnostic. Note: fontsizes specified here
+    might be overwritten by the plot-type-specific option ``fontsize`` (see
+    below).
 plots: dict, optional
     Plot types plotted by this diagnostic (see list above). Dictionary keys
     must be ``timeseries``, ``annual_cycle``, ``map``, ``zonal_mean_profile``,
@@ -100,10 +106,10 @@
     Path to the folder to store figures. Defaults to
     ``{plot_dir}/../../{dataset}/{exp}/{modeling_realm}/{real_name}``.  All
     tags (i.e., the entries in curly brackets, e.g., ``{dataset}``, are
-    replaced with the corresponding tags).  ``{plot_dir}`` is replaced with the
+    replaced with the corresponding tags). ``{plot_dir}`` is replaced with the
     default ESMValTool plot directory (i.e.,
     ``output_dir/plots/diagnostic_name/script_name/``, see
-    :ref:`esmvalcore:user configuration file`).
+    :ref:`esmvalcore:outputdata`).
 savefig_kwargs: dict, optional
     Optional keyword arguments for :func:`matplotlib.pyplot.savefig`. By
     default, uses ``bbox_inches: tight, dpi: 300, orientation: landscape``.
@@ -138,12 +144,12 @@
 pyplot_kwargs: dict, optional
     Optional calls to functions of :mod:`matplotlib.pyplot`. Dictionary keys
     are functions of :mod:`matplotlib.pyplot`. Dictionary values are used as
-    single argument for these functions. String arguments can include facets in
-    curly brackets which will be derived from the datasets plotted in the
-    corresponding plot, e.g., ``{short_name}``, ``{exp}``. Facets like
-    ``{project}`` that vary between the different datasets will be transformed
-    to something like  ``ambiguous_project``. Examples: ``title: 'Awesome Plot
-    of {long_name}'``, ``xlabel: '{short_name}'``, ``xlim: [0, 5]``.
+    argument(s) for these functions (if values are dictionaries, these are
+    interpreted as keyword arguments; otherwise a single argument is assumed).
+    String arguments can include facets in curly brackets which will be derived
+    from the corresponding dataset, e.g., ``{project}``, ``{short_name}``,
+    ``{exp}``. Examples: ``title: 'Awesome Plot of {long_name}'``, ``xlabel:
+    '{short_name}'``, ``xlim: [0, 5]``.
 time_format: str, optional (default: None)
     :func:`~datetime.datetime.strftime` format string that is used to format
     the time axis using :class:`matplotlib.dates.DateFormatter`. If ``None``,
@@ -171,12 +177,12 @@
 pyplot_kwargs: dict, optional
     Optional calls to functions of :mod:`matplotlib.pyplot`. Dictionary keys
     are functions of :mod:`matplotlib.pyplot`. Dictionary values are used as
-    single argument for these functions. String arguments can include facets in
-    curly brackets which will be derived from the datasets plotted in the
-    corresponding plot, e.g., ``{short_name}``, ``{exp}``. Facets like
-    ``{project}`` that vary between the different datasets will be transformed
-    to something like  ``ambiguous_project``. Examples: ``title: 'Awesome Plot
-    of {long_name}'``, ``xlabel: '{short_name}'``, ``xlim: [0, 5]``.
+    argument(s) for these functions (if values are dictionaries, these are
+    interpreted as keyword arguments; otherwise a single argument is assumed).
+    String arguments can include facets in curly brackets which will be derived
+    from the corresponding dataset, e.g., ``{project}``, ``{short_name}``,
+    ``{exp}``. Examples: ``title: 'Awesome Plot of {long_name}'``, ``xlabel:
+    '{short_name}'``, ``xlim: [0, 5]``.
 
 Configuration options for plot type ``map``
 -------------------------------------------
@@ -206,9 +212,11 @@
     (top right panel). Thus, the use of the ``plot_kwargs`` ``vmin`` and
     ``vmax`` or ``levels`` is highly recommend when using this ``common_cbar:
     true``. This option has no effect if no reference dataset is given.
-fontsize: int, optional (default: 10)
+fontsize: int, optional (default: None)
     Fontsize used for ticks, labels and titles. For the latter, use the given
-    fontsize plus 2. Does not affect suptitles.
+    fontsize plus 2. Does not affect suptitles. If not given, use default
+    matplotlib values. For a more fine-grained definition of fontsizes, use the
+    option ``matplotlib_rc_params`` (see above).
 gridline_kwargs: dict, optional
     Optional keyword arguments for grid lines. By default, ``color: lightgrey,
     alpha: 0.5`` are used. Use ``gridline_kwargs: false`` to not show grid
@@ -247,10 +255,12 @@
 pyplot_kwargs: dict, optional
     Optional calls to functions of :mod:`matplotlib.pyplot`. Dictionary keys
     are functions of :mod:`matplotlib.pyplot`. Dictionary values are used as
-    single argument for these functions. String arguments can include facets in
-    curly brackets which will be derived from the corresponding dataset, e.g.,
-    ``{project}``, ``{short_name}``, ``{exp}``.  Examples: ``title: 'Awesome
-    Plot of {long_name}'``, ``xlabel: '{short_name}'``, ``xlim: [0, 5]``.
+    argument(s) for these functions (if values are dictionaries, these are
+    interpreted as keyword arguments; otherwise a single argument is assumed).
+    String arguments can include facets in curly brackets which will be derived
+    from the corresponding dataset, e.g., ``{project}``, ``{short_name}``,
+    ``{exp}``. Examples: ``title: 'Awesome Plot of {long_name}'``, ``xlabel:
+    '{short_name}'``, ``xlim: [0, 5]``.
 rasterize: bool, optional (default: True)
     If ``True``, use rasterization_ for map plots to produce smaller files.
     This is only relevant for vector graphics (e.g., ``output_file_type:
@@ -294,9 +304,11 @@
     (top right panel). Thus, the use of the ``plot_kwargs`` ``vmin`` and
     ``vmax`` or ``levels`` is highly recommend when using this ``common_cbar:
     true``. This option has no effect if no reference dataset is given.
-fontsize: int, optional (default: 10)
+fontsize: int, optional (default: None)
     Fontsize used for ticks, labels and titles. For the latter, use the given
-    fontsize plus 2. Does not affect suptitles.
+    fontsize plus 2. Does not affect suptitles. If not given, use default
+    matplotlib values. For a more fine-grained definition of fontsizes, use the
+    option ``matplotlib_rc_params`` (see above).
 log_y: bool, optional (default: True)
     Use logarithmic Y-axis.
 plot_func: str, optional (default: 'contourf')
@@ -326,10 +338,12 @@
 pyplot_kwargs: dict, optional
     Optional calls to functions of :mod:`matplotlib.pyplot`. Dictionary keys
     are functions of :mod:`matplotlib.pyplot`. Dictionary values are used as
-    single argument for these functions. String arguments can include facets in
-    curly brackets which will be derived from the corresponding dataset, e.g.,
-    ``{project}``, ``{short_name}``, ``{exp}``.  Examples: ``title: 'Awesome
-    Plot of {long_name}'``, ``xlabel: '{short_name}'``, ``xlim: [0, 5]``.
+    argument(s) for these functions (if values are dictionaries, these are
+    interpreted as keyword arguments; otherwise a single argument is assumed).
+    String arguments can include facets in curly brackets which will be derived
+    from the corresponding dataset, e.g., ``{project}``, ``{short_name}``,
+    ``{exp}``. Examples: ``title: 'Awesome Plot of {long_name}'``, ``xlabel:
+    '{short_name}'``, ``xlim: [0, 5]``.
 rasterize: bool, optional (default: True)
     If ``True``, use rasterization_ for profile plots to produce smaller files.
     This is only relevant for vector graphics (e.g., ``output_file_type:
@@ -378,12 +392,12 @@
 pyplot_kwargs: dict, optional
     Optional calls to functions of :mod:`matplotlib.pyplot`. Dictionary keys
     are functions of :mod:`matplotlib.pyplot`. Dictionary values are used as
-    single argument for these functions. String arguments can include facets in
-    curly brackets which will be derived from the datasets plotted in the
-    corresponding plot, e.g., ``{short_name}``, ``{exp}``. Facets like
-    ``{project}`` that vary between the different datasets will be transformed
-    to something like  ``ambiguous_project``. Examples: ``title: 'Awesome Plot
-    of {long_name}'``, ``xlabel: '{short_name}'``, ``xlim: [0, 5]``.
+    argument(s) for these functions (if values are dictionaries, these are
+    interpreted as keyword arguments; otherwise a single argument is assumed).
+    String arguments can include facets in curly brackets which will be derived
+    from the corresponding dataset, e.g., ``{project}``, ``{short_name}``,
+    ``{exp}``. Examples: ``title: 'Awesome Plot of {long_name}'``, ``xlabel:
+    '{short_name}'``, ``xlim: [0, 5]``.
 show_y_minor_ticklabels: bool, optional (default: False)
     Show tick labels for the minor ticks on the Y axis.
 
@@ -409,12 +423,12 @@
 pyplot_kwargs: dict, optional
     Optional calls to functions of :mod:`matplotlib.pyplot`. Dictionary keys
     are functions of :mod:`matplotlib.pyplot`. Dictionary values are used as
-    single argument for these functions. String arguments can include facets in
-    curly brackets which will be derived from the datasets plotted in the
-    corresponding plot, e.g., ``{short_name}``, ``{exp}``. Facets like
-    ``{project}`` that vary between the different datasets will be transformed
-    to something like  ``ambiguous_project``. Examples: ``title: 'Awesome Plot
-    of {long_name}'``, ``xlabel: '{short_name}'``, ``xlim: [0, 5]``.
+    argument(s) for these functions (if values are dictionaries, these are
+    interpreted as keyword arguments; otherwise a single argument is assumed).
+    String arguments can include facets in curly brackets which will be derived
+    from the corresponding dataset, e.g., ``{project}``, ``{short_name}``,
+    ``{exp}``. Examples: ``title: 'Awesome Plot of {long_name}'``, ``xlabel:
+    '{short_name}'``, ``xlim: [0, 5]``.
 
 Configuration options for plot type ``hovmoeller_z_vs_time``
 ------------------------------------------------------------
@@ -444,9 +458,11 @@
     (top right panel). Thus, the use of the ``plot_kwargs`` ``vmin`` and
     ``vmax`` or ``levels`` is highly recommend when using this ``common_cbar:
     true``. This option has no effect if no reference dataset is given.
-fontsize: int, optional (default: 10)
+fontsize: int, optional (default: None)
     Fontsize used for ticks, labels and titles. For the latter, use the given
-    fontsize plus 2. Does not affect suptitles.
+    fontsize plus 2. Does not affect suptitles. If not given, use default
+    matplotlib values. For a more fine-grained definition of fontsizes, use the
+    option ``matplotlib_rc_params`` (see above).
 log_y: bool, optional (default: True)
     Use logarithmic Y-axis.
 plot_func: str, optional (default: 'contourf')
@@ -476,10 +492,12 @@
 pyplot_kwargs: dict, optional
     Optional calls to functions of :mod:`matplotlib.pyplot`. Dictionary keys
     are functions of :mod:`matplotlib.pyplot`. Dictionary values are used as
-    single argument for these functions. String arguments can include facets in
-    curly brackets which will be derived from the corresponding dataset, e.g.,
-    ``{project}``, ``{short_name}``, ``{exp}``.  Examples: ``title: 'Awesome
-    Plot of {long_name}'``, ``xlabel: '{short_name}'``, ``xlim: [0, 5]``.
+    argument(s) for these functions (if values are dictionaries, these are
+    interpreted as keyword arguments; otherwise a single argument is assumed).
+    String arguments can include facets in curly brackets which will be derived
+    from the corresponding dataset, e.g., ``{project}``, ``{short_name}``,
+    ``{exp}``. Examples: ``title: 'Awesome Plot of {long_name}'``, ``xlabel:
+    '{short_name}'``, ``xlim: [0, 5]``.
 rasterize: bool, optional (default: True)
     If ``True``, use rasterization_ for profile plots to produce smaller files.
     This is only relevant for vector graphics (e.g., ``output_file_type:
@@ -529,9 +547,11 @@
     (top right panel). Thus, the use of the ``plot_kwargs`` ``vmin`` and
     ``vmax`` or ``levels`` is highly recommend when using this ``common_cbar:
     true``. This option has no effect if no reference dataset is given.
-fontsize: int, optional (default: 10)
+fontsize: int, optional (default: None)
     Fontsize used for ticks, labels and titles. For the latter, use the given
-    fontsize plus 2. Does not affect suptitles.
+    fontsize plus 2. Does not affect suptitles. If not given, use default
+    matplotlib values. For a more fine-grained definition of fontsizes, use the
+    option ``matplotlib_rc_params`` (see above).
 plot_func: str, optional (default: 'contourf')
     Plot function used to plot the profiles. Must be a function of
     :mod:`iris.plot` that supports plotting of 2D cubes with coordinates
@@ -559,10 +579,12 @@
 pyplot_kwargs: dict, optional
     Optional calls to functions of :mod:`matplotlib.pyplot`. Dictionary keys
     are functions of :mod:`matplotlib.pyplot`. Dictionary values are used as
-    single argument for these functions. String arguments can include facets in
-    curly brackets which will be derived from the corresponding dataset, e.g.,
-    ``{project}``, ``{short_name}``, ``{exp}``.  Examples: ``title: 'Awesome
-    Plot of {long_name}'``, ``xlabel: '{short_name}'``, ``xlim: [0, 5]``.
+    argument(s) for these functions (if values are dictionaries, these are
+    interpreted as keyword arguments; otherwise a single argument is assumed).
+    String arguments can include facets in curly brackets which will be derived
+    from the corresponding dataset, e.g., ``{project}``, ``{short_name}``,
+    ``{exp}``. Examples: ``title: 'Awesome Plot of {long_name}'``, ``xlabel:
+    '{short_name}'``, ``xlim: [0, 5]``.
 rasterize: bool, optional (default: True)
     If ``True``, use rasterization_ for profile plots to produce smaller files.
     This is only relevant for vector graphics (e.g., ``output_file_type:
@@ -575,6 +597,11 @@
     :func:`~datetime.datetime.strftime` format string that is used to format
     the time axis using :class:`matplotlib.dates.DateFormatter`. If ``None``,
     use the default formatting imposed by the iris plotting function.
+time_on: str, optional (default: y-axis)
+    Optional switch to change the orientation of the plot so that time is on
+    the x-axis ``time_on: x-axis``. Default orientation is time on y-axis and
+    lat/lon on x-axis.
+
 
 .. hint::
 
@@ -595,6 +622,7 @@
 from pprint import pformat
 
 import cartopy.crs as ccrs
+import dask.array as da
 import iris
 import matplotlib as mpl
 import matplotlib.dates as mdates
@@ -604,6 +632,7 @@
 from iris.analysis.cartography import area_weights
 from iris.coord_categorisation import add_year
 from iris.coords import AuxCoord
+from iris.exceptions import ConstraintMismatchError
 from matplotlib.colors import CenteredNorm
 from matplotlib.gridspec import GridSpec
 from matplotlib.ticker import (
@@ -639,6 +668,7 @@ def __init__(self, config):
         self.cfg.setdefault('facet_used_for_labels', 'dataset')
         self.cfg.setdefault('figure_kwargs', {'constrained_layout': True})
         self.cfg.setdefault('group_variables_by', 'short_name')
+        self.cfg.setdefault('matplotlib_rc_params', {})
         self.cfg.setdefault('savefig_kwargs', {
             'bbox_inches': 'tight',
             'dpi': 300,
@@ -714,7 +744,7 @@ def __init__(self, config):
                 )
                 self.plots[plot_type].setdefault('cbar_kwargs_bias', {})
                 self.plots[plot_type].setdefault('common_cbar', False)
-                self.plots[plot_type].setdefault('fontsize', 10)
+                self.plots[plot_type].setdefault('fontsize', None)
                 self.plots[plot_type].setdefault('gridline_kwargs', {})
                 self.plots[plot_type].setdefault('plot_func', 'contourf')
                 self.plots[plot_type].setdefault('plot_kwargs', {})
@@ -748,7 +778,7 @@ def __init__(self, config):
                 )
                 self.plots[plot_type].setdefault('cbar_kwargs_bias', {})
                 self.plots[plot_type].setdefault('common_cbar', False)
-                self.plots[plot_type].setdefault('fontsize', 10)
+                self.plots[plot_type].setdefault('fontsize', None)
                 self.plots[plot_type].setdefault('log_y', True)
                 self.plots[plot_type].setdefault('plot_func', 'contourf')
                 self.plots[plot_type].setdefault('plot_kwargs', {})
@@ -794,7 +824,7 @@ def __init__(self, config):
                                                  {'orientation': 'vertical'})
                 self.plots[plot_type].setdefault('cbar_kwargs_bias', {})
                 self.plots[plot_type].setdefault('common_cbar', False)
-                self.plots[plot_type].setdefault('fontsize', 10)
+                self.plots[plot_type].setdefault('fontsize', None)
                 self.plots[plot_type].setdefault('log_y', True)
                 self.plots[plot_type].setdefault('plot_func', 'contourf')
                 self.plots[plot_type].setdefault('plot_kwargs', {})
@@ -823,7 +853,7 @@ def __init__(self, config):
                 )
                 self.plots[plot_type].setdefault('cbar_kwargs_bias', {})
                 self.plots[plot_type].setdefault('common_cbar', False)
-                self.plots[plot_type].setdefault('fontsize', 10)
+                self.plots[plot_type].setdefault('fontsize', None)
                 self.plots[plot_type].setdefault('plot_func', 'contourf')
                 self.plots[plot_type].setdefault('plot_kwargs', {})
                 self.plots[plot_type].setdefault('plot_kwargs_bias', {})
@@ -842,6 +872,7 @@ def __init__(self, config):
                     'show_x_minor_ticks', True
                 )
                 self.plots[plot_type].setdefault('time_format', None)
+                self.plots[plot_type].setdefault('time_on', 'y-axis')
 
         # Check that facet_used_for_labels is present for every dataset
         for dataset in self.input_data:
@@ -857,7 +888,9 @@ def __init__(self, config):
     def _add_colorbar(self, plot_type, plot_left, plot_right, axes_left,
                       axes_right, dataset_left, dataset_right):
         """Add colorbar(s) for plots."""
-        fontsize = self.plots[plot_type]['fontsize']
+        fontsize = (
+            self.plots[plot_type]['fontsize'] or mpl.rcParams['axes.labelsize']
+        )
         cbar_kwargs = self._get_cbar_kwargs(plot_type)
         cbar_label_left = self._get_cbar_label(plot_type, dataset_left)
         cbar_label_right = self._get_cbar_label(plot_type, dataset_right)
@@ -991,13 +1024,15 @@ def _add_stats(self, plot_type, axes, dim_coords, dataset,
 
     def _get_custom_mpl_rc_params(self, plot_type):
         """Get custom matplotlib rcParams."""
+        custom_rc_params = {}
         fontsize = self.plots[plot_type]['fontsize']
-        custom_rc_params = {
-            'axes.titlesize': fontsize + 2.0,
-            'axes.labelsize': fontsize,
-            'xtick.labelsize': fontsize,
-            'ytick.labelsize': fontsize,
-        }
+        if fontsize is not None:
+            custom_rc_params.update({
+                'axes.titlesize': fontsize + 2.0,
+                'axes.labelsize': fontsize,
+                'xtick.labelsize': fontsize,
+                'ytick.labelsize': fontsize,
+            })
         return custom_rc_params
 
     def _get_label(self, dataset):
@@ -1099,7 +1134,22 @@ def _load_and_preprocess_data(self):
         for dataset in input_data:
             filename = dataset['filename']
             logger.info("Loading %s", filename)
-            cube = iris.load_cube(filename)
+            cubes = iris.load(filename)
+            if len(cubes) == 1:
+                cube = cubes[0]
+            else:
+                var_name = dataset['short_name']
+                try:
+                    cube = cubes.extract_cube(iris.NameConstraint(
+                        var_name=var_name
+                    ))
+                except ConstraintMismatchError as exc:
+                    var_names = [c.var_name for c in cubes]
+                    raise ValueError(
+                        f"Cannot load data: multiple variables ({var_names}) "
+                        f"are available in file {filename}, but not the "
+                        f"requested '{var_name}'"
+                    ) from exc
 
             # Fix time coordinate if present
             if cube.coords('time', dim_coords=True):
@@ -1140,13 +1190,26 @@ def _plot_map_with_ref(self, plot_func, dataset, ref_dataset):
             projection = self._get_map_projection()
             plot_kwargs = self._get_plot_kwargs(plot_type, dataset)
             gridline_kwargs = self._get_gridline_kwargs(plot_type)
-            fontsize = self.plots[plot_type]['fontsize']
+            fontsize = (
+                self.plots[plot_type]['fontsize'] or
+                mpl.rcParams['axes.labelsize']
+            )
 
             # Plot dataset (top left)
             axes_data = fig.add_subplot(gridspec[0:2, 0:2],
                                         projection=projection)
             plot_kwargs['axes'] = axes_data
-            plot_data = plot_func(cube, **plot_kwargs)
+            if plot_func is iris.plot.contourf:
+                # see https://github.com/SciTools/cartopy/issues/2457
+                # and https://github.com/SciTools/cartopy/issues/2468
+                plot_kwargs['transform_first'] = True
+                npx = da if cube.has_lazy_data() else np
+                cube_to_plot = cube.copy(
+                    npx.ma.filled(cube.core_data(), np.nan)
+                )
+            else:
+                cube_to_plot = cube
+            plot_data = plot_func(cube_to_plot, **plot_kwargs)
             axes_data.coastlines()
             if gridline_kwargs is not False:
                 axes_data.gridlines(**gridline_kwargs)
@@ -1163,7 +1226,17 @@ def _plot_map_with_ref(self, plot_func, dataset, ref_dataset):
             if self.plots[plot_type]['common_cbar']:
                 plot_kwargs.setdefault('vmin', plot_data.get_clim()[0])
                 plot_kwargs.setdefault('vmax', plot_data.get_clim()[1])
-            plot_ref = plot_func(ref_cube, **plot_kwargs)
+            if plot_func is iris.plot.contourf:
+                # see https://github.com/SciTools/cartopy/issues/2457
+                # and https://github.com/SciTools/cartopy/issues/2468
+                plot_kwargs['transform_first'] = True
+                npx = da if ref_cube.has_lazy_data() else np
+                ref_cube_to_plot = ref_cube.copy(
+                    npx.ma.filled(ref_cube.core_data(), np.nan)
+                )
+            else:
+                ref_cube_to_plot = ref_cube
+            plot_ref = plot_func(ref_cube_to_plot, **plot_kwargs)
             axes_ref.coastlines()
             if gridline_kwargs is not False:
                 axes_ref.gridlines(**gridline_kwargs)
@@ -1182,7 +1255,17 @@ def _plot_map_with_ref(self, plot_func, dataset, ref_dataset):
             plot_kwargs_bias = self._get_plot_kwargs(plot_type, dataset,
                                                      bias=True)
             plot_kwargs_bias['axes'] = axes_bias
-            plot_bias = plot_func(bias_cube, **plot_kwargs_bias)
+            if plot_func is iris.plot.contourf:
+                # see https://github.com/SciTools/cartopy/issues/2457
+                # and https://github.com/SciTools/cartopy/issues/2468
+                plot_kwargs_bias['transform_first'] = True
+                npx = da if bias_cube.has_lazy_data() else np
+                bias_cube_to_plot = bias_cube.copy(
+                    npx.ma.filled(bias_cube.core_data(), np.nan)
+                )
+            else:
+                bias_cube_to_plot = bias_cube
+            plot_bias = plot_func(bias_cube_to_plot, **plot_kwargs_bias)
             axes_bias.coastlines()
             if gridline_kwargs is not False:
                 axes_bias.gridlines(**gridline_kwargs)
@@ -1238,7 +1321,17 @@ def _plot_map_without_ref(self, plot_func, dataset):
             axes = fig.add_subplot(projection=self._get_map_projection())
             plot_kwargs = self._get_plot_kwargs(plot_type, dataset)
             plot_kwargs['axes'] = axes
-            plot_map = plot_func(cube, **plot_kwargs)
+            if plot_func is iris.plot.contourf:
+                # see https://github.com/SciTools/cartopy/issues/2457
+                # and https://github.com/SciTools/cartopy/issues/2468
+                plot_kwargs['transform_first'] = True
+                npx = da if cube.has_lazy_data() else np
+                cube_to_plot = cube.copy(
+                    npx.ma.filled(cube.core_data(), np.nan)
+                )
+            else:
+                cube_to_plot = cube
+            plot_map = plot_func(cube_to_plot, **plot_kwargs)
             axes.coastlines()
             gridline_kwargs = self._get_gridline_kwargs(plot_type)
             if gridline_kwargs is not False:
@@ -1248,7 +1341,10 @@ def _plot_map_without_ref(self, plot_func, dataset):
             self._add_stats(plot_type, axes, dim_coords_dat, dataset)
 
             # Setup colorbar
-            fontsize = self.plots[plot_type]['fontsize']
+            fontsize = (
+                self.plots[plot_type]['fontsize'] or
+                mpl.rcParams['axes.labelsize']
+            )
             colorbar = fig.colorbar(plot_map, ax=axes,
                                     **self._get_cbar_kwargs(plot_type))
             colorbar.set_label(self._get_cbar_label(plot_type, dataset),
@@ -1292,7 +1388,10 @@ def _plot_zonal_mean_profile_with_ref(self, plot_func, dataset,
 
             # Options used for all subplots
             plot_kwargs = self._get_plot_kwargs(plot_type, dataset)
-            fontsize = self.plots[plot_type]['fontsize']
+            fontsize = (
+                self.plots[plot_type]['fontsize'] or
+                mpl.rcParams['axes.labelsize']
+            )
 
             # Plot dataset (top left)
             axes_data = fig.add_subplot(gridspec[0:2, 0:2])
@@ -1401,7 +1500,10 @@ def _plot_zonal_mean_profile_without_ref(self, plot_func, dataset):
             self._add_stats(plot_type, axes, dim_coords_dat, dataset)
 
             # Setup colorbar
-            fontsize = self.plots[plot_type]['fontsize']
+            fontsize = (
+                self.plots[plot_type]['fontsize'] or
+                mpl.rcParams['axes.labelsize']
+            )
             colorbar = fig.colorbar(plot_zonal_mean_profile, ax=axes,
                                     **self._get_cbar_kwargs(plot_type))
             colorbar.set_label(self._get_cbar_label(plot_type, dataset),
@@ -1458,7 +1560,10 @@ def _plot_hovmoeller_z_vs_time_without_ref(self, plot_func, dataset):
             self._add_stats(plot_type, axes, dim_coords_dat, dataset)
 
             # Setup colorbar
-            fontsize = self.plots[plot_type]['fontsize']
+            fontsize = (
+                self.plots[plot_type]['fontsize'] or
+                mpl.rcParams['axes.labelsize']
+            )
             colorbar = fig.colorbar(plot_hovmoeller,
                                     ax=axes,
                                     **self._get_cbar_kwargs(plot_type))
@@ -1521,7 +1626,10 @@ def _plot_hovmoeller_z_vs_time_with_ref(self, plot_func, dataset,
 
             # Options used for all subplots
             plot_kwargs = self._get_plot_kwargs(plot_type, dataset)
-            fontsize = self.plots[plot_type]['fontsize']
+            fontsize = (
+                self.plots[plot_type]['fontsize'] or
+                mpl.rcParams['axes.labelsize']
+            )
 
             # Plot dataset (top left)
             axes_data = fig.add_subplot(gridspec[0:2, 0:2])
@@ -1626,6 +1734,10 @@ def _plot_hovmoeller_time_vs_lat_or_lon_with_ref(self, plot_func, dataset,
         ref_cube = ref_dataset['cube']
         dim_coords_dat = self._check_cube_dimensions(cube, plot_type)
         self._check_cube_dimensions(ref_cube, plot_type)
+        if 'latitude' in dim_coords_dat:
+            non_time_label = 'latitude [°N]'
+        else:
+            non_time_label = 'longitude [°E]'
 
         # Create single figure with multiple axes
         with mpl.rc_context(self._get_custom_mpl_rc_params(plot_type)):
@@ -1635,21 +1747,31 @@ def _plot_hovmoeller_time_vs_lat_or_lon_with_ref(self, plot_func, dataset,
 
             # Options used for all subplots
             plot_kwargs = self._get_plot_kwargs(plot_type, dataset)
-            fontsize = self.plots[plot_type]['fontsize']
+            fontsize = (
+                self.plots[plot_type]['fontsize'] or
+                mpl.rcParams['axes.labelsize']
+            )
 
             # Plot dataset (top left)
             axes_data = fig.add_subplot(gridspec[0:2, 0:2])
             plot_kwargs['axes'] = axes_data
-            coord_names = [coord[0].name() for coord in cube.dim_coords]
-            if coord_names[0] == "time":
-                coord_names.reverse()
-            plot_kwargs['coords'] = coord_names
+            if self.plots[plot_type]['time_on'] == 'x-axis':
+                plot_kwargs['coords'] = list(dim_coords_dat)
+                x_label = 'time'
+                y_label = non_time_label
+                time_axis = axes_data.get_xaxis()
+            else:
+                plot_kwargs['coords'] = list(reversed(dim_coords_dat))
+                x_label = non_time_label
+                y_label = 'time'
+                time_axis = axes_data.get_yaxis()
             plot_data = plot_func(cube, **plot_kwargs)
             axes_data.set_title(self._get_label(dataset), pad=3.0)
-            axes_data.set_ylabel('time')
+            axes_data.set_ylabel(y_label)
             if self.plots[plot_type]['time_format'] is not None:
-                axes_data.get_yaxis().set_major_formatter(mdates.DateFormatter(
-                    self.plots[plot_type]['time_format']))
+                time_axis.set_major_formatter(mdates.DateFormatter(
+                    self.plots[plot_type]['time_format']
+                ))
             if self.plots[plot_type]['show_y_minor_ticks']:
                 axes_data.get_yaxis().set_minor_locator(AutoMinorLocator())
             if self.plots[plot_type]['show_x_minor_ticks']:
@@ -1681,17 +1803,14 @@ def _plot_hovmoeller_time_vs_lat_or_lon_with_ref(self, plot_func, dataset,
             plot_kwargs_bias = self._get_plot_kwargs(plot_type, dataset,
                                                      bias=True)
             plot_kwargs_bias['axes'] = axes_bias
-            plot_kwargs_bias['coords'] = coord_names
+            plot_kwargs_bias['coords'] = plot_kwargs['coords']
             plot_bias = plot_func(bias_cube, **plot_kwargs_bias)
             axes_bias.set_title(
                 f"{self._get_label(dataset)} - {self._get_label(ref_dataset)}",
                 pad=3.0,
             )
-            axes_bias.set_ylabel('time')
-            if 'latitude' in dim_coords_dat:
-                axes_bias.set_xlabel('latitude [°N]')
-            elif 'longitude' in dim_coords_dat:
-                axes_bias.set_xlabel('longitude [°E]')
+            axes_bias.set_xlabel(x_label)
+            axes_bias.set_ylabel(y_label)
             cbar_kwargs_bias = self._get_cbar_kwargs(plot_type, bias=True)
             cbar_bias = fig.colorbar(plot_bias, ax=axes_bias,
                                      **cbar_kwargs_bias)
@@ -1732,6 +1851,10 @@ def _plot_hovmoeller_time_vs_lat_or_lon_without_ref(self, plot_func,
         # Make sure that the data has the correct dimensions
         cube = dataset['cube']
         dim_coords_dat = self._check_cube_dimensions(cube, plot_type)
+        if 'latitude' in dim_coords_dat:
+            non_time_label = 'latitude [°N]'
+        else:
+            non_time_label = 'longitude [°E]'
 
         # Create plot with desired settings
         with mpl.rc_context(self._get_custom_mpl_rc_params(plot_type)):
@@ -1740,12 +1863,24 @@ def _plot_hovmoeller_time_vs_lat_or_lon_without_ref(self, plot_func,
             plot_kwargs = self._get_plot_kwargs(plot_type, dataset)
             plot_kwargs['axes'] = axes
 
-            # Make sure time is on y-axis
-            plot_kwargs['coords'] = list(reversed(dim_coords_dat))
+            # Put time on desired axis
+            if self.plots[plot_type]['time_on'] == 'x-axis':
+                plot_kwargs['coords'] = list(dim_coords_dat)
+                x_label = 'time'
+                y_label = non_time_label
+                time_axis = axes.get_xaxis()
+            else:
+                plot_kwargs['coords'] = list(reversed(dim_coords_dat))
+                x_label = non_time_label
+                y_label = 'time'
+                time_axis = axes.get_yaxis()
             plot_hovmoeller = plot_func(cube, **plot_kwargs)
 
             # Setup colorbar
-            fontsize = self.plots[plot_type]['fontsize']
+            fontsize = (
+                self.plots[plot_type]['fontsize'] or
+                mpl.rcParams['axes.labelsize']
+            )
             colorbar = fig.colorbar(plot_hovmoeller, ax=axes,
                                     **self._get_cbar_kwargs(plot_type))
             colorbar.set_label(self._get_cbar_label(plot_type, dataset),
@@ -1755,15 +1890,12 @@ def _plot_hovmoeller_time_vs_lat_or_lon_without_ref(self, plot_func,
             # Customize plot
             axes.set_title(self._get_label(dataset))
             fig.suptitle(dataset['long_name'])
-            if 'latitude' in dim_coords_dat:
-                axes.set_xlabel('latitude [°N]')
-            elif 'longitude' in dim_coords_dat:
-                axes.set_xlabel('longitude [°E]')
-            axes.set_ylabel('time')
+            axes.set_xlabel(x_label)
+            axes.set_ylabel(y_label)
             if self.plots[plot_type]['time_format'] is not None:
-                axes.get_yaxis().set_major_formatter(mdates.DateFormatter(
-                    self.plots[plot_type]['time_format'])
-                )
+                time_axis.set_major_formatter(mdates.DateFormatter(
+                    self.plots[plot_type]['time_format']
+                ))
             if self.plots[plot_type]['show_y_minor_ticks']:
                 axes.get_yaxis().set_minor_locator(AutoMinorLocator())
             if self.plots[plot_type]['show_x_minor_ticks']:
@@ -1791,6 +1923,8 @@ def _process_pyplot_kwargs(self, plot_type, dataset):
                 )
             if arg is None:
                 getattr(plt, func)()
+            elif isinstance(arg, dict):
+                getattr(plt, func)(**arg)
             else:
                 getattr(plt, func)(arg)
 
@@ -2485,7 +2619,11 @@ def create_hovmoeller_time_vs_lat_or_lon_plot(self, datasets):
             # Provenance tracking
             provenance_record = {
                 'ancestors': ancestors,
-                'authors': ['schlund_manuel', 'kraft_jeremy', 'ruhe_lukas'],
+                'authors': [
+                    'schlund_manuel',
+                    'kraft_jeremy',
+                    'lindenlaub_lukas'
+                ],
                 'caption': caption,
                 'plot_types': ['zonal'],
                 'long_names': [dataset['long_name']],
@@ -2497,16 +2635,17 @@ def create_hovmoeller_time_vs_lat_or_lon_plot(self, datasets):
 
     def compute(self):
         """Plot preprocessed data."""
-        for (var_key, datasets) in self.grouped_input_data.items():
-            logger.info("Processing variable %s", var_key)
-            self.create_timeseries_plot(datasets)
-            self.create_annual_cycle_plot(datasets)
-            self.create_map_plot(datasets)
-            self.create_zonal_mean_profile_plot(datasets)
-            self.create_1d_profile_plot(datasets)
-            self.create_variable_vs_lat_plot(datasets)
-            self.create_hovmoeller_z_vs_time_plot(datasets)
-            self.create_hovmoeller_time_vs_lat_or_lon_plot(datasets)
+        with mpl.rc_context(self.cfg['matplotlib_rc_params']):
+            for (var_key, datasets) in self.grouped_input_data.items():
+                logger.info("Processing variable %s", var_key)
+                self.create_timeseries_plot(datasets)
+                self.create_annual_cycle_plot(datasets)
+                self.create_map_plot(datasets)
+                self.create_zonal_mean_profile_plot(datasets)
+                self.create_1d_profile_plot(datasets)
+                self.create_variable_vs_lat_plot(datasets)
+                self.create_hovmoeller_z_vs_time_plot(datasets)
+                self.create_hovmoeller_time_vs_lat_or_lon_plot(datasets)
 
 
 def main():
diff --git a/esmvaltool/diag_scripts/mpqb/mpqb_lineplot.py b/esmvaltool/diag_scripts/mpqb/mpqb_lineplot.py
index fe4cf2deb0..b08a2cd012 100644
--- a/esmvaltool/diag_scripts/mpqb/mpqb_lineplot.py
+++ b/esmvaltool/diag_scripts/mpqb/mpqb_lineplot.py
@@ -7,9 +7,9 @@
 import iris
 import matplotlib.dates as mdates
 import matplotlib.pyplot as plt
-from mpqb_utils import get_mpqb_cfg
 
 import esmvaltool.diag_scripts.shared.iris_helpers as ih
+from esmvaltool.diag_scripts.mpqb.mpqb_utils import get_mpqb_cfg
 from esmvaltool.diag_scripts.shared import group_metadata, run_diagnostic
 from esmvaltool.diag_scripts.shared._base import (
     ProvenanceLogger,
diff --git a/esmvaltool/diag_scripts/mpqb/mpqb_lineplot_anncyc.py b/esmvaltool/diag_scripts/mpqb/mpqb_lineplot_anncyc.py
index e2a5a662b2..d087499d60 100644
--- a/esmvaltool/diag_scripts/mpqb/mpqb_lineplot_anncyc.py
+++ b/esmvaltool/diag_scripts/mpqb/mpqb_lineplot_anncyc.py
@@ -6,8 +6,8 @@
 
 import iris
 import matplotlib.pyplot as plt
-from mpqb_utils import get_mpqb_cfg
 
+from esmvaltool.diag_scripts.mpqb.mpqb_utils import get_mpqb_cfg
 from esmvaltool.diag_scripts.shared import group_metadata, run_diagnostic
 from esmvaltool.diag_scripts.shared._base import (
     ProvenanceLogger,
diff --git a/esmvaltool/diag_scripts/mpqb/mpqb_lineplot_growthrate.py b/esmvaltool/diag_scripts/mpqb/mpqb_lineplot_growthrate.py
index a6ec136966..90662be72b 100644
--- a/esmvaltool/diag_scripts/mpqb/mpqb_lineplot_growthrate.py
+++ b/esmvaltool/diag_scripts/mpqb/mpqb_lineplot_growthrate.py
@@ -8,9 +8,9 @@
 import matplotlib.dates as mdates
 import matplotlib.pyplot as plt
 import numpy as np
-from mpqb_utils import get_mpqb_cfg
 
 import esmvaltool.diag_scripts.shared.iris_helpers as ih
+from esmvaltool.diag_scripts.mpqb.mpqb_utils import get_mpqb_cfg
 from esmvaltool.diag_scripts.shared import group_metadata, run_diagnostic
 from esmvaltool.diag_scripts.shared._base import (
     ProvenanceLogger,
diff --git a/esmvaltool/diag_scripts/portrait_plot.py b/esmvaltool/diag_scripts/portrait_plot.py
new file mode 100644
index 0000000000..d8a19b52a9
--- /dev/null
+++ b/esmvaltool/diag_scripts/portrait_plot.py
@@ -0,0 +1,519 @@
+"""Portrait Plot Diagnostic.
+
+Plot performance metrics of multiple datasets vs up to four references
+A :doc:`documented example recipe </recipes/recipe_portrait>` to use this
+diagnostic is provided as ``recipes/recipe_portrait_CMIP.yml``.
+
+Description
+-----------
+This diagnostic provides plot functionalities for performance metrics,
+and is written to be as flexible as possible to be adaptable to further use
+cases. X and Y axis, grouping parameter and splits for each rectangle can be
+configured in the recipe. All ``*_by`` parameters can be set to any metadata
+key. To split by 'reference' this key needs to be set as extra_facet in recipe.
+
+Authors
+-------
+- Lukas Lindenlaub (Universität Bremen, Germany)
+- Diego Cammarano
+
+Configuration parameters through recipe:
+----------------------------------------
+axes_properties: dict, optional
+    Dictionary that gets passed to :meth:`matplotlib.axes.Axes.set`.
+    Subplots can be widely customized.
+    E.g. xlabel, ylabel, yticklabels, xmargin...
+    By default {}.
+cbar_kwargs: dict, optional
+    Dictionary that gets passed to :meth:`matplotlib.pyplot.colorbar`.
+    E.g. label, ticks...
+    By default {}.
+default_split: str, optional
+    Data labeled with this string, will be used as main rectangles. All other
+    splits will be plotted as overlays. This can be used to choose the base
+    reference, while all references are labeled for the legend. If None, the
+    first split will be used as default.
+    By default None.
+dpi: int, optional
+    Dots per inch for the figure. By default 300.
+domain: str, optional
+    Domain for provenance. By default 'global'.
+figsize: tuple of float, optional
+    [width, height] of the figure in inches. The final figure will be saved
+    with bbox_inches="tight", which can change the resulting aspect ratio.
+    By default [7.5, 3.5].
+group_by: str, optional
+    Split portrait groups into multiple groups (one matrix per group).
+    By default 'project'.
+legend: dict, optional
+    Customize, if, how and where the legend is plotted. The 'best' position
+    and size of the legend depends on multiple parameters of the figure
+    (i.e. lengths of labels, aspect ratio of the plots...). Might require
+    manual adjustment of ``x``, ``y`` and ``size`` to fit the figure layout.
+    Keys (each optional) that will be handled are:
+
+    position: str or None, optional
+        Position of the legend. Can be 'right' or 'left'.
+        Or set to None to disable plotting the legend. By default 'right'.
+    size: float, optional
+        Size of the legend in Inches. By default 0.3.
+    x_offset: float, optional
+        Manually adjust horizontal position to save space or fix overlap.
+        Number given in Inches. By default 0.
+    y_offset: float, optional
+        Manually adjust vertical position to save space or fix overlap.
+        Number given in Inches. By default 0.
+
+matplotlib_rc_params: dict, optional
+    Optional :class:`matplotlib.RcParams` used to customize matplotlib plots.
+    Options given here will be passed to :func:`matplotlib.rc_context` and used
+    for all plots produced with this diagnostic. Note: fontsizes specified here
+    might be overwritten by the plot-type-specific option ``fontsize`` (see
+    below).
+nan_color: str or None, optional
+    Matplotlib named color or hexcode for NaN values. If set to None,
+    no triangles are plotted for NaN values.
+    By default 'white'.
+normalize: str or None, optional
+    ('mean', 'median', 'centered_mean', 'centered_median', None).
+    Divide by median or mean if not None. Subtract median/mean afterwards if
+    centered.
+    By default 'centered_median'.
+plot_kwargs: dict, optional
+    Dictionary that gets passed as kwargs to
+    :meth:`matplotlib.axes.Axes.imshow`. Colormaps will be converted to 11
+    discrete steps automatically.
+    Default colormap is ``cmap='RdYlBu_r'`` with limits ``vmin=-0.5`` and
+    ``vmax=0.5``.
+plot_legend: bool, optional
+    If True, a legend is plotted, when multiple splits are given.
+    By default True.
+split_by: str, optional
+    The rectangles can be split into 2-4 triangles. This is used to show
+    metrics for different references. For this case there is no need to change
+    this parameter. Multiple variables can be set in the recipe with ``split``
+    assigned as extra_facet to label the different references. Data without
+    a split assigned will be plotted as main rectangles, this can be changed
+    by setting default_split parameter.
+    By default 'split'.
+x_by: str, optional
+    Metadata key for x coordinate.
+    By default 'alias'.
+y_by: str, optional
+    Metadata key for y coordinate.
+    By default 'variable_group'.
+
+"""
+
+import itertools
+import logging
+
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+import numpy as np
+import xarray as xr
+from matplotlib import patches
+from mpl_toolkits.axes_grid1 import ImageGrid
+
+from esmvaltool.diag_scripts.shared import (
+    ProvenanceLogger,
+    get_diagnostic_filename,
+    get_plot_filename,
+    group_metadata,
+    run_diagnostic,
+    select_metadata,
+)
+
+log = logging.getLogger(__name__)
+
+
+def get_provenance(cfg):
+    """Return provenance for this diagnostic."""
+    return {
+        'ancestors': list(cfg["input_data"].keys()),
+        'authors': ["lindenlaub_lukas", "cammarano_diego"],
+        'caption': 'RMSE performance metric',
+        'domains': [cfg["domain"]],
+        'plot_types': ['portrait'],
+        'references': [
+            'gleckler08jgr',
+        ],
+        'statistics': ['rmsd'],
+    }
+
+
+def unify_limits(grid):
+    """Ensure same limits for all subplots."""
+    vmin, vmax = np.inf, -np.inf
+    images = [ax.get_images()[0] for ax in grid]
+    for img in images:
+        vmin = min(vmin, img.get_clim()[0])
+        vmax = max(vmax, img.get_clim()[1])
+    for img in images:
+        img.set_clim(vmin, vmax)
+
+
+def plot_matrix(data, row_labels, col_labels, axe, plot_kwargs):
+    """Create an image for given data."""
+    img = axe.imshow(data, **plot_kwargs)
+    # Show all ticks and label them with the respective list entries.
+    axe.set_xticks(np.arange(data.shape[1]), labels=col_labels)
+    axe.set_yticks(np.arange(data.shape[0]), labels=row_labels)
+    # Rotate the tick labels and set their alignment.
+    plt.setp(
+        axe.get_xticklabels(),
+        rotation=90,
+        ha="right",
+        va="center",
+        rotation_mode="anchor",
+    )
+    axe.set_xticks(np.arange(data.shape[1] + 1) - 0.5, minor=True)
+    axe.set_yticks(np.arange(data.shape[0] + 1) - 0.5, minor=True)
+    axe.grid(which="minor", color="black", linestyle="-", linewidth=0.8)
+    axe.tick_params(which="both", bottom=False, left=False)
+    return img
+
+
+def remove_reference(metas):
+    """Remove reference for metric from list of metadata."""
+    for meta in list(metas):  # list() creates a copy to allow remove in place
+        if meta.get("reference_for_metric", False):
+            metas.remove(meta)
+
+
+def add_missing_facets(cfg, metas):
+    """Ensure that all facets are present in metadata."""
+    for meta in metas:
+        facet_config = ["x_by", "y_by", "group_by", "split_by"]
+        facets = [cfg[key] for key in facet_config]
+        for facet in facets:
+            meta.setdefault(facet, "unknown")
+
+
+def open_file(metadata, **selection):
+    """Try to find a single file for selection and return data.
+
+    If multiple files are found, raise an error. If no file is found,
+    return np.nan.
+    """
+    metas = select_metadata(metadata, **selection)
+    if len(metas) > 1:
+        raise ValueError(f"Multiple files found for {selection}")
+    if len(metas) < 1:
+        log.debug("No files found for %s", selection)
+        return np.nan
+    log.debug("File found for %s", selection)
+    das = xr.open_dataset(metas[0]["filename"])
+    varname = list(das.data_vars.keys())[0]
+    try:
+        return das[varname].values.item()
+    except ValueError as exc:
+        msg = f"Expected scalar in input file {metas[0]['filename']}."
+        raise ValueError(msg) from exc
+
+
+def load_data(cfg, metas):
+    """Load all netcdf files from metadata into xarray dataset.
+
+    The dataset contains all relevant information for the plot. Coord
+    names are metadata keys, ordered as x, y, group, split. The default
+    reference is None, or if all references are named the first from the
+    list.
+    """
+    coords = {  # order matters: x, y, group, split
+        cfg["x_by"]: list(group_metadata(metas, cfg["x_by"]).keys()),
+        cfg["y_by"]: list(group_metadata(metas, cfg["y_by"]).keys()),
+        cfg["group_by"]: list(group_metadata(metas, cfg["group_by"]).keys()),
+        cfg["split_by"]: list(group_metadata(metas, cfg["split_by"]).keys()),
+    }
+    shape = [len(coord) for coord in coords.values()]
+    var_data = xr.DataArray(np.full(shape, np.nan), dims=list(coords.keys()))
+    data = xr.Dataset({"var": var_data}, coords=coords)
+    # loop over each cell (coord combination) and load data if existing
+    for coord_tuple in itertools.product(*coords.values()):
+        selection = dict(zip(coords.keys(), coord_tuple))
+        data['var'].loc[selection] = open_file(metas, **selection)
+    if cfg["default_split"] is None:
+        cfg["default_split"] = data.coords[cfg["split_by"]].values[0]
+    log.debug("Using %s as default split", cfg["default_split"])
+    log.debug("Loaded Data: %s", data)
+    return data
+
+
+def split_legend(cfg, grid, data):
+    """Create legend for references, based on split coordinate in the dataset.
+
+    Mpl handles axes positions in relative figure coordinates. To anchor the
+    legend to the origin of the first graph (bottom left) with fixed size,
+    without messing up the layout for changing figure sizes, a few extra steps
+    are required.
+    NOTE: maybe ``mpl_toolkits.axes_grid1.axes_divider.AxesDivider`` simplifies
+    this a bit by using ``append_axes``.
+    """
+    grid[0].get_figure().canvas.draw()  # set axes position in figure
+    size = cfg["legend"]["size"]  # rect width in physical size (inch)
+    fig_size = grid[0].get_figure().get_size_inches()  # physical figure size
+    ax_size = (size / fig_size[0], size / fig_size[1])  # legend (fig coords)
+    gaps = [0.3 / fig_size[0], 0.3 / fig_size[1]]  # margins (fig coords)
+    # anchor legend on origin of first plot or colorbar
+    anchor = grid[0].get_position().bounds  # relative figure coordinates
+    if cfg["legend"]["position"] == "right":
+        cbar_x = grid.cbar_axes[0].get_position().bounds[0]
+        gaps[0] *= 0.8  # compensate colorbar padding
+        anchor = (cbar_x + gaps[0] + cfg["legend"]["x_offset"],
+                  anchor[1] - gaps[1] - ax_size[1] + cfg["legend"]["y_offset"])
+    else:
+        anchor = (anchor[0] - gaps[0] - ax_size[0] + cfg["legend"]["x_offset"],
+                  anchor[1] - gaps[1] - ax_size[1] + cfg["legend"]["y_offset"])
+    # create legend as empty imshow like axes in figure coordinates
+    axes = {"main": grid[0].get_figure().add_axes([*anchor, *ax_size])}
+    axes["main"].imshow(np.zeros((1, 1)))  # same axes properties as main plot
+    axes["main"].set_xticks([])
+    axes["main"].set_yticks([])
+    axes["twiny"], axes["twinx"] = [axes["main"].twiny(), axes["main"].twinx()]
+    axes["twinx"].set_yticks([])
+    axes["twiny"].set_xticks([])
+    label_at = [  # order matches get_triangle_nodes (halves and quarters)
+        axes["main"].set_ylabel,  # left
+        axes["twinx"].set_ylabel,  # right
+        axes["main"].set_xlabel,  # bottom
+        axes["twiny"].set_xlabel,  # top
+    ]
+    for i, label in enumerate(data.coords[cfg["split_by"]].values):
+        nodes = get_triangle_nodes(i, len(data.coords[cfg["split_by"]].values))
+        axes["main"].add_patch(
+            patches.Polygon(nodes,
+                            closed=True,
+                            facecolor=["#bbb", "#ccc", "#ddd", "#eee"][i],
+                            edgecolor="black",
+                            linewidth=0.5,
+                            fill=True))
+        label_at[i](label)
+
+
+def overlay_reference(cfg, axe, data, triangle):
+    """Create triangular overlays for given data and axes."""
+    # use same colors as in main plot
+    cmap = axe.get_images()[0].get_cmap()
+    norm = axe.get_images()[0].norm
+    if cfg["nan_color"] is not None:
+        cmap.set_bad(cfg["nan_color"])
+    for i, j in itertools.product(*map(range, data.shape)):
+        if np.isnan(data[i, j]) and cfg["nan_color"] is None:
+            continue
+        color = cmap(norm(data[i, j]))
+        edges = [(e[0] + j, e[1] + i) for e in triangle]
+        patch = patches.Polygon(
+            edges,
+            closed=True,
+            facecolor=color,
+            edgecolor="black",
+            linewidth=0.5,
+            fill=True,
+        )
+        axe.add_patch(patch)
+
+
+def plot_group(cfg, axe, data, title=None):
+    """Create matrix for one subplot in ax using plt.imshow.
+
+    By default split None is used, if all splits are named the first is
+    used. Other splits will be added by overlaying triangles.
+    """
+    split = data.sel({cfg["split_by"]: cfg["default_split"]})
+    plot_matrix(
+        split.values.T,  # 2d numpy array
+        split.coords[cfg["y_by"]].values,  # y_labels
+        split.coords[cfg["x_by"]].values,  # x_labels
+        axe,
+        cfg["plot_kwargs"],
+    )
+    if title is not None:
+        axe.set_title(title)
+    axe.set(**cfg["axes_properties"])
+
+
+def get_triangle_nodes(position, total_count=2):
+    """Return list of nodes with relative x, y coordinates.
+
+    The nodes of the triangle are given as list of three tuples. Each tuple
+    contains relative coordinates (-0.5 to +0.5). For total of <= 2 a top left
+    (position=0) and bottom right (position=1) rectangle is returned.
+    For higher counts (3 or 4) one quartile is returned for each position.
+    NOTE: Order matters. Ensure axis labels for the legend match when changing.
+    """
+    if total_count < 3:
+        halves = [
+            [(0.5, -0.5), (-0.5, -0.5), (-0.5, 0.5)],  # top left
+            [(0.5, -0.5), (0.5, 0.5), (-0.5, 0.5)],  # bottom right
+        ]
+        return halves[position]
+    quarters = [
+        [(-0.5, -0.5), (0, 0), (-0.5, 0.5)],  # left
+        [(0.5, -0.5), (0, 0), (0.5, 0.5)],  # right
+        [(-0.5, 0.5), (0, 0), (0.5, 0.5)],  # bottom
+        [(-0.5, -0.5), (0, 0), (0.5, -0.5)],  # top
+    ]
+    return quarters[position]
+
+
+def plot_overlays(cfg, grid, data):
+    """Call overlay_reference for each split in data and each group in grid."""
+    split_count = data.shape[3]
+    group_count = data.shape[2]
+    for i in range(group_count):
+        if split_count < 2:
+            log.debug("No additional splits for overlay.")
+            break
+        if split_count > 4:
+            log.warning("Too many splits for overlay, only 3 will be plotted.")
+        group_data = data.isel({cfg["group_by"]: i})
+        group_data = group_data.dropna(cfg["x_by"], how="all")
+        for sss in range(split_count):
+            split = group_data.isel({cfg["split_by"]: sss})
+            split_label = split.coords[cfg["split_by"]].values.item()
+            if split_label == cfg["default_split"]:
+                log.debug("Skipping default split for overlay.")
+                continue
+            nodes = get_triangle_nodes(sss, split_count)
+            overlay_reference(cfg, grid[i], split.values.T, nodes)
+
+
+def plot(cfg, data):
+    """Create figure with subplots for each group and save to NetCDF.
+
+    Sets same color range and overlays additional references based on
+    the content of data (xr.DataArray).
+    """
+    fig = plt.figure(1, cfg["figsize"])
+    group_count = len(data.coords[cfg["group_by"]])
+    grid = ImageGrid(
+        fig,
+        111,  # similar to subplot(111)
+        cbar_mode="single",
+        cbar_location="right",
+        cbar_pad=0.1,
+        cbar_size=0.2,
+        nrows_ncols=(1, group_count),
+        axes_pad=0.1,
+    )
+    # remap colorbar to 10 discrete steps
+    cmap = mpl.cm.get_cmap(cfg["plot_kwargs"]["cmap"], 10)
+    cfg["plot_kwargs"]["cmap"] = cmap
+    for i in range(group_count):
+        group = data.isel({cfg["group_by"]: i})
+        group = group.dropna(cfg["x_by"], how="all")
+        title = None
+        if group_count > 1:
+            title = group.coords[cfg["group_by"]].values.item()
+        plot_group(cfg, grid[i], group, title=title)
+    # use same colorrange and colorbar for all subplots:
+    unify_limits(grid)
+    # set cb of first image as single cb for the figure
+    grid.cbar_axes[0].colorbar(grid[0].get_images()[0], **cfg["cbar_kwargs"])
+    if data.shape[3] > 1:
+        plot_overlays(cfg, grid, data)
+    if cfg["plot_legend"] and data.shape[3] > 1:
+        split_legend(cfg, grid, data)
+    basename = "portrait_plot"
+    fname = get_plot_filename(basename, cfg)
+    plt.savefig(fname, bbox_inches="tight", dpi=cfg["dpi"])
+    with ProvenanceLogger(cfg) as prov_logger:
+        prov_logger.log(fname, get_provenance(cfg))
+    log.info("Figure saved:")
+    log.info(fname)
+
+
+def normalize(array, method, dims):
+    """Divide and shift values along dims depending on method."""
+    shift = 0
+    norm = 1
+    if "mean" in method:
+        norm = array.mean(dim=dims)
+    elif "median" in method:
+        norm = array.median(dim=dims)
+    if "centered" in method:
+        shift = norm
+    normalized = (array - shift) / norm
+    return normalized
+
+
+def set_defaults(cfg):
+    """Set default values for most important config parameters."""
+    cfg.setdefault("axes_properties", {})
+    cfg.setdefault("cbar_kwargs", {})
+    cfg.setdefault("default_split", None)
+    cfg.setdefault("dpi", 300)
+    cfg.setdefault("domain", "global")
+    cfg.setdefault("figsize", (7.5, 3.5))
+    cfg.setdefault("group_by", "project")
+    cfg.setdefault("legend", {})
+    cfg["legend"].setdefault("position", "right")
+    cfg["legend"].setdefault("size", 0.3)
+    cfg["legend"].setdefault("x_offset", 0)
+    cfg["legend"].setdefault("y_offset", 0)
+    cfg.setdefault("matplotlib_rc_params", {})
+    cfg.setdefault("nan_color", 'white')
+    cfg.setdefault("normalize", "centered_median")
+    cfg.setdefault("plot_kwargs", {})
+    cfg["plot_kwargs"].setdefault("cmap", "RdYlBu_r")
+    cfg["plot_kwargs"].setdefault("vmin", -0.5)
+    cfg["plot_kwargs"].setdefault("vmax", 0.5)
+    cfg.setdefault("plot_legend", True)
+    cfg.setdefault("split_by", "split")  # extra facet
+    cfg.setdefault("x_by", "alias")
+    cfg.setdefault("y_by", "variable_group")
+
+
+def sort_data(cfg, dataset):
+    """Sort the dataset along by custom or alphabetical order."""
+    dataset = dataset.sortby([
+        dataset[cfg["x_by"]].str.lower(), dataset[cfg["y_by"]].str.lower(),
+        dataset[cfg["group_by"]].str.lower(),
+        dataset[cfg["split_by"]].str.lower()
+    ])
+    if cfg["x_by"] in ["alias", "dataset"]:
+        # NOTE: not clean, but it works for many cases
+        mm_stats = [
+            v for v in dataset[cfg["x_by"]].values
+            if "Mean" in v or "Median" in v or "Percentile" in v
+        ]
+        others = [
+            v for v in dataset[cfg["x_by"]].values
+            if "Mean" not in v and "Median" not in v and "Percentile" not in v
+        ]
+        new_order = mm_stats + others
+        dataset = dataset.reindex({cfg["x_by"]: new_order})
+    return dataset
+
+
+def save_to_netcdf(cfg, data):
+    """Save the final dataset to a NetCDF file."""
+    basename = "portrait"
+    fname = get_diagnostic_filename(basename, cfg, extension='nc')
+    data.to_netcdf(fname)
+    log.info("NetCDF file saved:")
+    log.info(fname)
+    with ProvenanceLogger(cfg) as prov_logger:
+        prov_logger.log(fname, get_provenance(cfg))
+
+
+def main(cfg):
+    """Run the diagnostic."""
+    set_defaults(cfg)
+    metas = list(cfg["input_data"].values())
+    remove_reference(metas)
+    add_missing_facets(cfg, metas)
+    dataset = load_data(cfg, metas)
+    dataset = sort_data(cfg, dataset)
+    if cfg["normalize"] is not None:
+        dataset["var"] = normalize(dataset["var"], cfg["normalize"],
+                                   [cfg["x_by"], cfg["group_by"]])
+    with mpl.rc_context(cfg['matplotlib_rc_params']):
+        plot(cfg, dataset["var"])
+    save_to_netcdf(cfg, dataset["var"])
+
+
+if __name__ == '__main__':
+    with run_diagnostic() as config:
+        main(config)
diff --git a/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig6a.ncl b/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig6a.ncl
index bd672ed3cf..0f1b49c224 100644
--- a/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig6a.ncl
+++ b/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig6a.ncl
@@ -151,10 +151,8 @@ begin
       fx_variable = "volcello"
       error_msg("f", "russell18jgr-fig6.ncl", " ", "volcello file for "  \
                 + vo_datasets(iii) \
-                + " not found in the metadata file, please add "\
-                + "'fx_files: [volcello]' to the variable dictionary in the " \
-                + "recipe or add the location of file to input directory " \
-                + "in config-user.yml ")
+                + " not found in the metadata file, please specify " \
+                + "'volcello' as supplementary variable in the recipe.")
     end if
 
     dataset_so_time = read_data(so_items[iii])
diff --git a/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig6b.ncl b/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig6b.ncl
index 6b019625f0..71323f411d 100644
--- a/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig6b.ncl
+++ b/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig6b.ncl
@@ -45,10 +45,10 @@
 ;
 ; Caveats
 ;
-;  - MIROC-ESM and BNU-ESM doesnot work as depth variable is not called lev.
-;  - MRI_ESM1 doesnot work as the data is ofset by 80 degrees in longitude
+;  - MIROC-ESM and BNU-ESM does not work as depth variable is not called lev.
+;  - MRI_ESM1 does not work as the data is offset by 80 degrees in longitude
 ;        and causes problem in interpolation.
-;  - CCSM4 ans CESM1-CAM5 dont work as the units for so is 1, not accepted
+;  - CCSM4 and CESM1-CAM5 dont work as the units for so is 1, not accepted
 ;        by ESMValTool.
 ;  - Transport is very small in case of NorESM1-M and ME as volcello
 ;        values look incorrect(very small).
@@ -153,11 +153,10 @@ begin
 
     if (all(ismissing(fx_var))) then
       fx_variable = "volcello"
-      error_msg("f", "russell_fig-7i.ncl", " ", "areacello file for " + \
+      error_msg("f", "russell_fig-7i.ncl", " ", "volcello file for " + \
                 vo_datasets(iii) \
-                + " not found in the metadata file, please " + \
-                "add 'fx_files: [volcello]' to the variable dictionary in" + \
-                " the recipe or add the location of file to config-user.yml")
+                + " not found in the metadata file, please specify " \
+                + "'volcello' as supplementary variable in the recipe.")
     end if
 
     dataset_so_time = read_data(so_items[iii])
diff --git a/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig7i.ncl b/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig7i.ncl
index 86ce4bee70..cf14857a7b 100644
--- a/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig7i.ncl
+++ b/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig7i.ncl
@@ -156,9 +156,8 @@ begin
         fx_variable = "areacello"
         error_msg("f", "russell_fig-7i.ncl", " ", "areacello file for " + \
                   datasetnames(iii) + " not found in the metadata file," + \
-                  " please add 'fx_files: [areacello]' to the variable " + \
-                  "dictionary in the recipe or add the location of " + \
-                  " file to config-user.yml")
+                  + " not found in the metadata file, please specify " \
+                  + "'areacello' as supplementary variable in the recipe.")
       end if
       areacello_2d = fx_var
       delete(fx_var)
@@ -212,9 +211,9 @@ begin
   "lgPerimOn"                : False                  ; no perimeter
   "lgItemCount"              : dimsizes(annots)       ; how many
   "lgLineLabelStrings"       : annots                 ; labels
-  "lgLabelsOn"               : False                  ; no default lables
+  "lgLabelsOn"               : False                  ; no default labsels
   "lgLineLabelFontHeightF"   : 0.0085                 ; font height
-  "lgDashIndexes"            : dashes                 ; line paterns
+  "lgDashIndexes"            : dashes                 ; line patterns
   "lgLineColors"             : colors
   "lgMonoLineLabelFontColor" : True                   ; one label color
   end create
diff --git a/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig9c.ncl b/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig9c.ncl
index 2fe0cc3e4a..017b70103a 100644
--- a/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig9c.ncl
+++ b/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig9c.ncl
@@ -227,9 +227,8 @@ begin
     if (all(ismissing(fx_var))) then
       error_msg("f", "russell18jgr-fig9c.ncl", " ", "areacello file for " + \
                 datasetnames(iii) + " not found in the metadata file, " + \
-                "please add 'fx_files: [areacello]' to the variable " + \
-                "dictionary in the recipe or add the location of " + \
-                " file to config-user.yml ")
+                + " not found in the metadata file, please specify " \
+                + "'areacello' as supplementary variable in the recipe.")
     end if
     areacello_2d = fx_var
 
@@ -304,9 +303,9 @@ begin
   "lgPerimOn"                : False                ; no perimeter
   "lgItemCount"              : dimsizes(annots)     ; how many
   "lgLabelStrings"           : annots               ; labels
-  "lgLabelsOn"               : True                 ; no default lables
+  "lgLabelsOn"               : True                 ; no default labels
   "lgLabelFontHeightF"       : 0.001                ; font height
-  "lgItemType"               : "markers"            ; line paterns
+  "lgItemType"               : "markers"            ; line patterns
   "lgMarkerColors"           : colors
   "lgMarkerIndexes"          : markers              ; one label color
   end create
diff --git a/esmvaltool/diag_scripts/shared/_base.py b/esmvaltool/diag_scripts/shared/_base.py
index 1789909130..cbc826ae2e 100644
--- a/esmvaltool/diag_scripts/shared/_base.py
+++ b/esmvaltool/diag_scripts/shared/_base.py
@@ -181,7 +181,7 @@ def __init__(self, cfg):
         if not os.path.exists(self._log_file):
             self.table = {}
         else:
-            with open(self._log_file, 'r') as file:
+            with open(self._log_file, 'r', encoding='utf-8') as file:
                 self.table = yaml.safe_load(file)
 
     def log(self, filename, record):
@@ -212,8 +212,8 @@ def log(self, filename, record):
         if isinstance(filename, Path):
             filename = str(filename)
         if filename in self.table:
-            raise KeyError(
-                "Provenance record for {} already exists.".format(filename))
+            msg = f"Provenance record for {filename} already exists."
+            raise KeyError(msg)
 
         self.table[filename] = record
 
@@ -222,7 +222,7 @@ def _save(self):
         dirname = os.path.dirname(self._log_file)
         if not os.path.exists(dirname):
             os.makedirs(dirname)
-        with open(self._log_file, 'w') as file:
+        with open(self._log_file, 'w', encoding='utf-8') as file:
             yaml.safe_dump(self.table, file)
 
     def __enter__(self):
@@ -253,9 +253,8 @@ def select_metadata(metadata, **attributes):
     """
     selection = []
     for attribs in metadata:
-        if all(a in attribs and (
-                attribs[a] == attributes[a] or attributes[a] == '*')
-               for a in attributes):
+        if all(a in attribs and v in (attribs[a], '*')
+               for a, v in attributes.items()):
             selection.append(attribs)
     return selection
 
@@ -424,7 +423,7 @@ def get_cfg(filename=None):
     """Read diagnostic script configuration from settings.yml."""
     if filename is None:
         filename = sys.argv[1]
-    with open(filename) as file:
+    with open(filename, encoding='utf-8') as file:
         cfg = yaml.safe_load(file)
     return cfg
 
@@ -441,7 +440,7 @@ def _get_input_data_files(cfg):
 
     input_files = {}
     for filename in metadata_files:
-        with open(filename) as file:
+        with open(filename, encoding='utf-8') as file:
             metadata = yaml.safe_load(file)
             input_files.update(metadata)
 
@@ -469,6 +468,10 @@ def main(cfg):
             with run_diagnostic() as cfg:
                 main(cfg)
 
+    To prevent the diagnostic script from using the Dask Distributed scheduler,
+    set ``no_distributed: true`` in the diagnostic script definition in the
+    recipe or in the resulting settings.yml file.
+
     The `cfg` dict passed to `main` contains the script configuration that
     can be used with the other functions in this module.
     """
@@ -568,7 +571,9 @@ def main(cfg):
         logger.info("Removing %s from previous run.", provenance_file)
         os.remove(provenance_file)
 
-    if not args.no_distributed and 'scheduler_address' in cfg:
+    use_distributed = not (args.no_distributed
+                           or cfg.get('no_distributed', False))
+    if use_distributed and 'scheduler_address' in cfg:
         try:
             client = distributed.Client(cfg['scheduler_address'])
         except OSError as exc:
diff --git a/esmvaltool/diag_scripts/shared/_supermeans.py b/esmvaltool/diag_scripts/shared/_supermeans.py
index 7099ba4725..8543ca99cf 100644
--- a/esmvaltool/diag_scripts/shared/_supermeans.py
+++ b/esmvaltool/diag_scripts/shared/_supermeans.py
@@ -13,7 +13,6 @@
 import cf_units
 import iris
 import iris.coord_categorisation
-from iris.coord_categorisation import _pt_date
 import numpy as np
 
 
@@ -206,6 +205,28 @@ def add_start_hour(cube, coord, name='diurnal_sampling_hour'):
     _add_categorised_coord(cube, name, coord, start_hour_from_bounds)
 
 
+# lifted from iris==3.10 last iris to have it in iris.coord_categorisation
+# Private "helper" function
+def _pt_date(coord, time):
+    """Return the datetime of a time-coordinate point.
+
+    Parameters
+    ----------
+    coord : Coord
+        Coordinate (must be Time-type).
+    time : float
+        Value of a coordinate point.
+
+    Returns
+    -------
+    cftime.datetime
+
+    """
+    # NOTE: All of the currently defined categorisation functions are
+    # calendar operations on Time coordinates.
+    return coord.units.num2date(time, only_use_cftime_datetimes=True)
+
+
 def start_hour_from_bounds(coord, _, bounds):
     """Add hour from bounds."""
     return np.array([_pt_date(coord, _bounds[0]).hour for _bounds in bounds])
diff --git a/esmvaltool/diag_scripts/shared/plot/_plot.py b/esmvaltool/diag_scripts/shared/plot/_plot.py
index d7db4e1b14..092479a999 100644
--- a/esmvaltool/diag_scripts/shared/plot/_plot.py
+++ b/esmvaltool/diag_scripts/shared/plot/_plot.py
@@ -4,6 +4,7 @@
 from copy import deepcopy
 
 import cartopy.crs as ccrs
+import dask.array as da
 import iris.quickplot
 import matplotlib.colors as colors
 import matplotlib.pyplot as plt
@@ -230,7 +231,15 @@ def global_contourf(cube,
         kwargs['levels'] = levels
     axes = plt.axes(projection=ccrs.Robinson(central_longitude=10))
     plt.sca(axes)
-    map_plot = iris.plot.contourf(cube, **kwargs)
+
+    # see https://github.com/SciTools/cartopy/issues/2457
+    # and https://github.com/SciTools/cartopy/issues/2468
+    kwargs['transform_first'] = True
+    npx = da if cube.has_lazy_data() else np
+    map_plot = iris.plot.contourf(
+        cube.copy(npx.ma.filled(cube.core_data(), np.nan)),
+        **kwargs,
+    )
 
     # Appearance
     axes.gridlines(color='lightgrey', alpha=0.5)
diff --git a/esmvaltool/diag_scripts/weighting/climwip/calibrate_sigmas.py b/esmvaltool/diag_scripts/weighting/climwip/calibrate_sigmas.py
index 0998346b22..1d85e1f95a 100644
--- a/esmvaltool/diag_scripts/weighting/climwip/calibrate_sigmas.py
+++ b/esmvaltool/diag_scripts/weighting/climwip/calibrate_sigmas.py
@@ -6,7 +6,13 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import xarray as xr
-from core_functions import (
+from scipy.optimize import brute
+
+from esmvaltool.diag_scripts.shared import (
+    get_diagnostic_filename,
+    get_plot_filename,
+)
+from esmvaltool.diag_scripts.weighting.climwip.core_functions import (
     area_weighted_mean,
     calculate_model_distances,
     calculate_weights,
@@ -14,17 +20,11 @@
     compute_overall_mean,
     weighted_quantile,
 )
-from io_functions import (
+from esmvaltool.diag_scripts.weighting.climwip.io_functions import (
     read_metadata,
     read_model_data,
     read_model_data_ancestor,
 )
-from scipy.optimize import brute
-
-from esmvaltool.diag_scripts.shared import (
-    get_diagnostic_filename,
-    get_plot_filename,
-)
 
 logger = logging.getLogger(os.path.basename(__file__))
 
diff --git a/esmvaltool/diag_scripts/weighting/climwip/main.py b/esmvaltool/diag_scripts/weighting/climwip/main.py
index 6b22399547..f46db62090 100644
--- a/esmvaltool/diag_scripts/weighting/climwip/main.py
+++ b/esmvaltool/diag_scripts/weighting/climwip/main.py
@@ -10,15 +10,22 @@
 import numpy as np
 import seaborn as sns
 import xarray as xr
-from calibrate_sigmas import calibrate_performance_sigma
-from core_functions import (
+
+from esmvaltool.diag_scripts.shared import (
+    get_diagnostic_filename,
+    get_plot_filename,
+    run_diagnostic,
+)
+from esmvaltool.diag_scripts.weighting.climwip.calibrate_sigmas import (
+    calibrate_performance_sigma, )
+from esmvaltool.diag_scripts.weighting.climwip.core_functions import (
     area_weighted_mean,
     calculate_model_distances,
     calculate_weights,
     combine_ensemble_members,
     compute_overall_mean,
 )
-from io_functions import (
+from esmvaltool.diag_scripts.weighting.climwip.io_functions import (
     log_provenance,
     read_metadata,
     read_model_data,
@@ -27,11 +34,6 @@
     read_observation_data_ancestor,
 )
 
-from esmvaltool.diag_scripts.shared import (
-    get_diagnostic_filename,
-    get_plot_filename,
-    run_diagnostic,
-)
 
 logger = logging.getLogger(os.path.basename(__file__))
 
diff --git a/esmvaltool/diag_scripts/weighting/plot_utilities.py b/esmvaltool/diag_scripts/weighting/plot_utilities.py
index 706dfd64c9..a071a3c7f0 100644
--- a/esmvaltool/diag_scripts/weighting/plot_utilities.py
+++ b/esmvaltool/diag_scripts/weighting/plot_utilities.py
@@ -3,7 +3,8 @@
 
 import xarray as xr
 
-from climwip.core_functions import weighted_quantile
+from esmvaltool.diag_scripts.weighting.climwip.core_functions import (
+    weighted_quantile, )
 
 
 def read_weights(filename: str) -> dict:
diff --git a/esmvaltool/diag_scripts/weighting/weighted_temperature_graph.py b/esmvaltool/diag_scripts/weighting/weighted_temperature_graph.py
index 5b871283d7..f5f1086e1e 100644
--- a/esmvaltool/diag_scripts/weighting/weighted_temperature_graph.py
+++ b/esmvaltool/diag_scripts/weighting/weighted_temperature_graph.py
@@ -10,13 +10,16 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import xarray as xr
-from climwip.io_functions import log_provenance, read_model_data
 
 from esmvaltool.diag_scripts.shared import (
     get_diagnostic_filename,
     get_plot_filename,
     run_diagnostic,
 )
+from esmvaltool.diag_scripts.weighting.climwip.io_functions import (
+    log_provenance,
+    read_model_data,
+)
 from esmvaltool.diag_scripts.weighting.plot_utilities import (
     calculate_percentiles,
     read_metadata,
diff --git a/esmvaltool/diag_scripts/weighting/weighted_temperature_map.py b/esmvaltool/diag_scripts/weighting/weighted_temperature_map.py
index 7a161d2535..667a382d94 100644
--- a/esmvaltool/diag_scripts/weighting/weighted_temperature_map.py
+++ b/esmvaltool/diag_scripts/weighting/weighted_temperature_map.py
@@ -11,13 +11,16 @@
 import matplotlib.pyplot as plt
 import numpy as np
 from cartopy.mpl.ticker import LatitudeFormatter, LongitudeFormatter
-from climwip.io_functions import log_provenance, read_model_data
 
 from esmvaltool.diag_scripts.shared import (
     get_diagnostic_filename,
     get_plot_filename,
     run_diagnostic,
 )
+from esmvaltool.diag_scripts.weighting.climwip.io_functions import (
+    log_provenance,
+    read_model_data,
+)
 from esmvaltool.diag_scripts.weighting.plot_utilities import (
     calculate_percentiles,
     read_metadata,
diff --git a/esmvaltool/diag_scripts/zmnam/__init__.py b/esmvaltool/diag_scripts/zmnam/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/esmvaltool/diag_scripts/zmnam/zmnam.py b/esmvaltool/diag_scripts/zmnam/zmnam.py
index d86ee48458..c0450c117a 100644
--- a/esmvaltool/diag_scripts/zmnam/zmnam.py
+++ b/esmvaltool/diag_scripts/zmnam/zmnam.py
@@ -20,9 +20,12 @@
 
 from esmvaltool.diag_scripts.shared import ProvenanceLogger, run_diagnostic
 # Import zmnam diagnostic routines
-from zmnam_calc import zmnam_calc
-from zmnam_plot import zmnam_plot
-from zmnam_preproc import (zmnam_preproc, zmnam_preproc_clean)
+from esmvaltool.diag_scripts.zmnam.zmnam_calc import zmnam_calc
+from esmvaltool.diag_scripts.zmnam.zmnam_plot import zmnam_plot
+from esmvaltool.diag_scripts.zmnam.zmnam_preproc import (
+    zmnam_preproc,
+    zmnam_preproc_clean,
+)
 
 logger = logging.getLogger(__name__)
 
diff --git a/esmvaltool/interface_scripts/logging.ncl b/esmvaltool/interface_scripts/logging.ncl
index 6333479f96..35c3167341 100644
--- a/esmvaltool/interface_scripts/logging.ncl
+++ b/esmvaltool/interface_scripts/logging.ncl
@@ -61,9 +61,9 @@ procedure log_debug(output_string[*]:string)
 ;    output_string: the text to be output as message on screen
 ;
 ; Description
-;    Write a debug message to the log file (only if log_level = debug in
-;    config-user.yml). If the input is an array, each element will be
-;    written on different lines.
+;    Write a debug message to the log file (only if log_level = debug in the
+;    configuration). If the input is an array, each element will be written on
+;    different lines.
 ;
 ; Caveats
 ;
diff --git a/esmvaltool/recipes/bock20jgr/recipe_bock20jgr_fig_8-10.yml b/esmvaltool/recipes/bock20jgr/recipe_bock20jgr_fig_8-10.yml
index a01020e709..bc277209d7 100644
--- a/esmvaltool/recipes/bock20jgr/recipe_bock20jgr_fig_8-10.yml
+++ b/esmvaltool/recipes/bock20jgr/recipe_bock20jgr_fig_8-10.yml
@@ -434,6 +434,8 @@ diagnostics:
         exp: historical
         ensemble: r1i1p1
         mip: Amon
+        modeling_realm: atmos
+        frequency: mon
         additional_datasets: *cmip5_all
 
   fig_9_cmip6:
@@ -446,6 +448,8 @@ diagnostics:
         exp: historical
         ensemble: r1i1p1f1
         mip: Amon
+        modeling_realm: atmos
+        frequency: mon
         grid: gr
         additional_datasets: *cmip6_all
 
diff --git a/esmvaltool/recipes/clouds/recipe_bock24acp_fig3-4_maps.yml b/esmvaltool/recipes/clouds/recipe_bock24acp_fig3-4_maps.yml
new file mode 100644
index 0000000000..88076fafed
--- /dev/null
+++ b/esmvaltool/recipes/clouds/recipe_bock24acp_fig3-4_maps.yml
@@ -0,0 +1,218 @@
+# ESMValTool
+# recipe_bock24acp_fig3-4_maps.yml
+# Note: The variables LWP and IWP are commented out at the moment as a different
+#       standard name for this variables in CMIP5 and CMIP6 gives an error. Iris
+#       is working on a solution for this problem.
+---
+documentation:
+  title: Cloud properties regarding ECS (geographical maps).
+
+  description: |
+    Geographical maps of cloud properties,  models are grouped in
+    three groups regarding their ECS.
+
+  authors:
+    - bock_lisa
+
+  maintainer:
+    - bock_lisa
+
+  references:
+    - bock24acp
+
+  projects:
+    - cmug
+    - esm2025
+
+
+YEARS: &years_hist
+  start_year: 1985
+  end_year: 2004
+
+YEARS_scen: &years_scen
+  start_year: 2081
+  end_year: 2100
+
+
+DATASETS_ECS_HIGH: &datasets_ecs_high
+  # CMIP6
+  - {dataset: CanESM5, grid: gn}
+  - {dataset: CESM2, grid: gn, ensemble: r4i1p1f1}
+  - {dataset: CESM2-WACCM, grid: gn, institute: NCAR}
+  - {dataset: CNRM-CM6-1, ensemble: r1i1p1f2}
+  - {dataset: CNRM-ESM2-1, ensemble: r1i1p1f2}
+  - {dataset: HadGEM3-GC31-LL, grid: gn, ensemble: r1i1p1f3}
+  - {dataset: HadGEM3-GC31-MM, grid: gn, ensemble: r1i1p1f3}
+  - {dataset: IPSL-CM6A-LR}
+  - {dataset: KACE-1-0-G}
+  - {dataset: NESM3, grid: gn}
+  - {dataset: TaiESM1, grid: gn}
+  - {dataset: UKESM1-0-LL, ensemble: r1i1p1f2, grid: gn}
+  # CMIP5
+  - {dataset: CSIRO-Mk3-6-0, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: HadGEM2-ES, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: IPSL-CM5A-LR, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: IPSL-CM5A-MR, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: MIROC-ESM, ensemble: r1i1p1, project: CMIP5}
+
+DATASETS_ECS_MED: &datasets_ecs_med
+  # CMIP6
+  - {dataset: BCC-CSM2-MR, grid: gn}
+  - {dataset: CMCC-CM2-SR5, grid: gn}
+  - {dataset: CMCC-ESM2, grid: gn}
+  - {dataset: FGOALS-f3-L}
+  - {dataset: FGOALS-g3, grid: gn}
+  - {dataset: GISS-E2-1-H, grid: gn}
+  - {dataset: MPI-ESM1-2-HR, grid: gn}
+  - {dataset: MPI-ESM1-2-LR, grid: gn}
+  - {dataset: MRI-ESM2-0, grid: gn}
+  # CMIP5
+  - {dataset: ACCESS1-0, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: ACCESS1-3, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: BNU-ESM, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: CanESM2, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: CCSM4, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: FGOALS-g2, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: GFDL-CM3, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: MPI-ESM-LR, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: MPI-ESM-MR, ensemble: r1i1p1, project: CMIP5}
+
+DATASETS_ECS_LOW: &datasets_ecs_low
+  # CMIP6
+  - {dataset: CAMS-CSM1-0, grid: gn}
+  - {dataset: GISS-E2-1-G, grid: gn}
+  - {dataset: MIROC6, grid: gn}
+  - {dataset: MIROC-ES2L, ensemble: r1i1p1f2, grid: gn}
+  - {dataset: NorESM2-LM, grid: gn, institute: NCC}
+  - {dataset: NorESM2-MM, grid: gn, institute: NCC}
+  # CMIP5
+  - {dataset: bcc-csm1-1, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: bcc-csm1-1-m, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: GFDL-ESM2G, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: GFDL-ESM2M, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: GISS-E2-H, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: GISS-E2-R, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: inmcm4, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: IPSL-CM5B-LR, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: MIROC5, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: MRI-CGCM3, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: NorESM1-M, ensemble: r1i1p1, project: CMIP5}
+
+
+preprocessors:
+
+  lat_lon_mmm:
+    custom_order: true
+    regrid:
+      target_grid: 2x2
+      scheme: linear
+    multi_model_statistics:
+      span: full
+      statistics: [mean]
+    climate_statistics:
+      operator: mean
+
+  lat_lon:
+    regrid:
+      target_grid: 2x2
+      scheme: linear
+    climate_statistics:
+      operator: mean
+
+
+diagnostics:
+
+  # Figure 3abc
+  clt_lat_lon: &lat_lon_diag
+    description: comparison of geographical maps
+    variables:
+      ECS_high: &var_clt
+        short_name: clt
+        preprocessor: lat_lon_mmm
+        mip: Amon
+        project: CMIP6
+        exp: historical
+        ensemble: r1i1p1f1
+        grid: gr
+        <<: *years_hist
+        additional_datasets: *datasets_ecs_high
+      ECS_med:
+        <<: *var_clt
+        additional_datasets: *datasets_ecs_med
+      ECS_low:
+        <<: *var_clt
+        additional_datasets: *datasets_ecs_low
+    scripts:
+      lat_lon:
+        script: clouds/clouds_ecs_groups_maps.py
+        group_by: variable_group
+        plot_each_model: false
+        reference: false
+
+
+  # Figure 3def
+        #  lwp_lat_lon:
+        #    <<: *lat_lon_diag
+        #    variables:
+        #      ECS_high:
+        #        <<: *var_clt
+        #        short_name: lwp
+        #        derive: true
+        #        additional_datasets: *datasets_ecs_high
+        #      ECS_med:
+        #        <<: *var_clt
+        #        short_name: lwp
+        #        derive: true
+        #        additional_datasets: *datasets_ecs_med
+        #      ECS_low:
+        #        <<: *var_clt
+        #        short_name: lwp
+        #        derive: true
+        #        additional_datasets: *datasets_ecs_low
+
+
+  # Figure 3ghi
+        #  iwp_lat_lon:
+        #    <<: *lat_lon_diag
+        #    variables:
+        #      ECS_high:
+        #        <<: *var_clt
+        #        short_name: clivi
+        #        additional_datasets: *datasets_ecs_high
+        #      ECS_med:
+        #        <<: *var_clt
+        #        short_name: clivi
+        #        additional_datasets: *datasets_ecs_med
+        #      ECS_low:
+        #        <<: *var_clt
+        #        short_name: clivi
+        #        additional_datasets: *datasets_ecs_low
+
+
+  # Figure 4
+  netcre_lat_lon: &lat_lon_cre
+    description: comparison of geographical maps
+    variables:
+      ECS_high: &var_cre
+        <<: *var_clt
+        short_name: netcre
+        derive: true
+        additional_datasets: *datasets_ecs_high
+      ECS_med:
+        <<: *var_cre
+        additional_datasets: *datasets_ecs_med
+      ECS_low:
+        <<: *var_cre
+        additional_datasets: *datasets_ecs_low
+      OBS:
+        <<: *var_cre
+        preprocessor: lat_lon
+        additional_datasets:
+          - {dataset: CERES-EBAF, project: OBS, type: sat, version: Ed4.2,
+             tier: 2, start_year: 2001, end_year: 2022}
+    scripts:
+      lat_lon:
+        script: clouds/clouds_ecs_groups_maps.py
+        group_by: variable_group
+        plot_each_model: false
+        reference: true
diff --git a/esmvaltool/recipes/clouds/recipe_bock24acp_fig6_zonal.yml b/esmvaltool/recipes/clouds/recipe_bock24acp_fig6_zonal.yml
new file mode 100644
index 0000000000..69426ffb54
--- /dev/null
+++ b/esmvaltool/recipes/clouds/recipe_bock24acp_fig6_zonal.yml
@@ -0,0 +1,566 @@
+# ESMValTool
+# recipe_bock24acp_fig6_zonal.yml
+---
+documentation:
+  title: Cloud properties regarding ECS (zonal plots).
+
+  description: |
+    Zonal plots of cloud properties and their projected changes,
+    models are grouped in three groups regarding their ECS.
+
+  authors:
+    - bock_lisa
+
+  maintainer:
+    - lauer_axel
+
+  references:
+    - bock24acp
+
+  project:
+    - cmug
+    - esm2025
+
+
+YEARS_hist: &years_hist
+  start_year: 1985
+  end_year: 2004
+
+YEARS_scen: &years_scen
+  start_year: 2081
+  end_year: 2100
+
+
+DATASETS_ECS_HIGH: &datasets_ecs_high
+  # CMIP6
+  - {dataset: CanESM5, grid: gn}
+  - {dataset: CESM2, grid: gn, ensemble: r4i1p1f1}
+  - {dataset: CESM2-WACCM, grid: gn, institute: NCAR}
+  - {dataset: CNRM-CM6-1, ensemble: r1i1p1f2}
+  - {dataset: CNRM-ESM2-1, ensemble: r1i1p1f2}
+  - {dataset: HadGEM3-GC31-LL, grid: gn, ensemble: r1i1p1f3}
+  - {dataset: HadGEM3-GC31-MM, grid: gn, ensemble: r1i1p1f3}
+  - {dataset: IPSL-CM6A-LR}
+  - {dataset: KACE-1-0-G}
+  - {dataset: NESM3, grid: gn}
+  - {dataset: TaiESM1, grid: gn}
+  - {dataset: UKESM1-0-LL, ensemble: r1i1p1f2, grid: gn}
+  # CMIP5
+  - {dataset: CSIRO-Mk3-6-0, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: HadGEM2-ES, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: IPSL-CM5A-LR, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: IPSL-CM5A-MR, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: MIROC-ESM, ensemble: r1i1p1, project: CMIP5}
+
+DATASETS_ECS_HIGH_scenario: &datasets_ecs_high_scenario
+  # CMIP6
+  - {dataset: CanESM5, grid: gn, exp: ssp585}
+  - {dataset: CESM2, grid: gn, ensemble: r4i1p1f1, exp: ssp585}
+  - {dataset: CESM2-WACCM, grid: gn, institute: NCAR, exp: ssp585}
+  - {dataset: CNRM-CM6-1, ensemble: r1i1p1f2, exp: ssp585}
+  - {dataset: CNRM-ESM2-1, ensemble: r1i1p1f2, exp: ssp585}
+  - {dataset: HadGEM3-GC31-LL, grid: gn, ensemble: r1i1p1f3, exp: ssp585}
+  - {dataset: HadGEM3-GC31-MM, grid: gn, ensemble: r1i1p1f3, exp: ssp585}
+  - {dataset: IPSL-CM6A-LR, exp: ssp585}
+  - {dataset: KACE-1-0-G, exp: ssp585}
+  - {dataset: NESM3, grid: gn, exp: ssp585}
+  - {dataset: TaiESM1, grid: gn, exp: ssp585}
+  - {dataset: UKESM1-0-LL, ensemble: r1i1p1f2, grid: gn, exp: ssp585}
+  # CMIP5
+  - {dataset: CSIRO-Mk3-6-0, ensemble: r1i1p1, project: CMIP5, exp: rcp85}
+  - {dataset: HadGEM2-ES, ensemble: r1i1p1, project: CMIP5, exp: rcp85}
+  - {dataset: IPSL-CM5A-LR, ensemble: r1i1p1, project: CMIP5, exp: rcp85}
+  - {dataset: IPSL-CM5A-MR, ensemble: r1i1p1, project: CMIP5, exp: rcp85}
+  - {dataset: MIROC-ESM, ensemble: r1i1p1, project: CMIP5, exp: rcp85}
+
+DATASETS_ECS_MED: &datasets_ecs_med
+  # CMIP6
+  - {dataset: BCC-CSM2-MR, grid: gn}
+  - {dataset: CMCC-CM2-SR5, grid: gn}
+  - {dataset: CMCC-ESM2, grid: gn}
+  - {dataset: FGOALS-f3-L}
+  - {dataset: FGOALS-g3, grid: gn}
+  - {dataset: GISS-E2-1-H, grid: gn}
+  - {dataset: MPI-ESM1-2-HR, grid: gn}
+  - {dataset: MPI-ESM1-2-LR, grid: gn}
+  - {dataset: MRI-ESM2-0, grid: gn}
+  # CMIP5
+  - {dataset: ACCESS1-0, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: ACCESS1-3, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: BNU-ESM, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: CanESM2, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: CCSM4, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: FGOALS-g2, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: GFDL-CM3, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: MPI-ESM-LR, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: MPI-ESM-MR, ensemble: r1i1p1, project: CMIP5}
+
+DATASETS_ECS_MED_scenario: &datasets_ecs_med_scenario
+  # CMIP6
+  - {dataset: BCC-CSM2-MR, grid: gn, exp: ssp585}
+  - {dataset: CMCC-CM2-SR5, grid: gn, exp: ssp585}
+  - {dataset: CMCC-ESM2, grid: gn, exp: ssp585}
+  - {dataset: FGOALS-f3-L, exp: ssp585}
+  - {dataset: FGOALS-g3, grid: gn, exp: ssp585}
+  - {dataset: GISS-E2-1-H, grid: gn, ensemble: r1i1p1f2, exp: ssp585}
+  - {dataset: MPI-ESM1-2-HR, grid: gn, exp: ssp585}
+  - {dataset: MPI-ESM1-2-LR, grid: gn, exp: ssp585}
+  - {dataset: MRI-ESM2-0, grid: gn, exp: ssp585}
+  # CMIP5
+  - {dataset: ACCESS1-0, ensemble: r1i1p1, project: CMIP5, exp: rcp85}
+  - {dataset: ACCESS1-3, ensemble: r1i1p1, project: CMIP5, exp: rcp85}
+  - {dataset: BNU-ESM, ensemble: r1i1p1, project: CMIP5, exp: rcp85}
+  - {dataset: CanESM2, ensemble: r1i1p1, project: CMIP5, exp: rcp85}
+  - {dataset: CCSM4, ensemble: r1i1p1, project: CMIP5, exp: rcp85}
+  - {dataset: FGOALS-g2, ensemble: r1i1p1, project: CMIP5, exp: rcp85}
+  - {dataset: GFDL-CM3, ensemble: r1i1p1, project: CMIP5, exp: rcp85}
+  - {dataset: MPI-ESM-LR, ensemble: r1i1p1, project: CMIP5, exp: rcp85}
+  - {dataset: MPI-ESM-MR, ensemble: r1i1p1, project: CMIP5, exp: rcp85}
+
+DATASETS_ECS_LOW: &datasets_ecs_low
+  # CMIP6
+  - {dataset: CAMS-CSM1-0, grid: gn}
+  - {dataset: GISS-E2-1-G, grid: gn}
+  - {dataset: MIROC6, grid: gn}
+  - {dataset: MIROC-ES2L, ensemble: r1i1p1f2, grid: gn}
+  - {dataset: NorESM2-LM, grid: gn, institute: NCC}
+  - {dataset: NorESM2-MM, grid: gn, institute: NCC}
+  # CMIP5
+  - {dataset: bcc-csm1-1, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: bcc-csm1-1-m, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: GFDL-ESM2G, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: GFDL-ESM2M, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: GISS-E2-H, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: GISS-E2-R, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: inmcm4, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: IPSL-CM5B-LR, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: MIROC5, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: MRI-CGCM3, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: NorESM1-M, ensemble: r1i1p1, project: CMIP5}
+
+DATASETS_ECS_LOW_scenario: &datasets_ecs_low_scenario
+  # CMIP6
+  - {dataset: CAMS-CSM1-0, grid: gn, exp: ssp585, end_year: 2099}
+  - {dataset: GISS-E2-1-G, grid: gn, ensemble: r1i1p1f2, exp: ssp585}
+  - {dataset: MIROC6, grid: gn, exp: ssp585}
+  - {dataset: MIROC-ES2L, ensemble: r1i1p1f2, grid: gn, exp: ssp585}
+  - {dataset: NorESM2-LM, grid: gn, institute: NCC, exp: ssp585}
+  - {dataset: NorESM2-MM, grid: gn, institute: NCC, exp: ssp585}
+  # CMIP5
+  - {dataset: bcc-csm1-1, ensemble: r1i1p1, project: CMIP5, exp: rcp85}
+  - {dataset: bcc-csm1-1-m, ensemble: r1i1p1, project: CMIP5, exp: rcp85}
+  - {dataset: GFDL-ESM2G, ensemble: r1i1p1, project: CMIP5, exp: rcp85}
+  - {dataset: GFDL-ESM2M, ensemble: r1i1p1, project: CMIP5, exp: rcp85}
+  - {dataset: GISS-E2-H, ensemble: r1i1p1, project: CMIP5, exp: rcp85}
+  - {dataset: GISS-E2-R, ensemble: r1i1p1, project: CMIP5, exp: rcp85}
+  - {dataset: inmcm4, ensemble: r1i1p1, project: CMIP5, exp: rcp85}
+  - {dataset: IPSL-CM5B-LR, ensemble: r1i1p1, project: CMIP5, exp: rcp85}
+  - {dataset: MIROC5, ensemble: r1i1p1, project: CMIP5, exp: rcp85}
+  - {dataset: MRI-CGCM3, ensemble: r1i1p1, project: CMIP5, exp: rcp85}
+  - {dataset: NorESM1-M, ensemble: r1i1p1, project: CMIP5, exp: rcp85}
+
+
+preprocessors:
+
+  zonal:
+    regrid:
+      target_grid: 2x2
+      scheme: linear
+    climate_statistics:
+      operator: mean
+
+
+diagnostics:
+
+  # Figure 6a
+  clt_zonal: &zonal_diag
+    description: comparison of zonal mean
+    variables:
+      ECS_high_hist: &var_clt
+        short_name: clt
+        preprocessor: zonal
+        mip: Amon
+        project: CMIP6
+        exp: historical
+        ensemble: r1i1p1f1
+        grid: gr
+        <<: *years_hist
+        additional_datasets: *datasets_ecs_high
+      ECS_med_hist:
+        <<: *var_clt
+        additional_datasets: *datasets_ecs_med
+      ECS_low_hist:
+        <<: *var_clt
+        additional_datasets: *datasets_ecs_low
+      ECS_high_scen:
+        <<: *var_clt
+        <<: *years_scen
+        additional_datasets: *datasets_ecs_high_scenario
+      ECS_med_scen:
+        <<: *var_clt
+        <<: *years_scen
+        additional_datasets: *datasets_ecs_med_scenario
+      ECS_low_scen:
+        <<: *var_clt
+        <<: *years_scen
+        additional_datasets: *datasets_ecs_low_scenario
+      tas_ECS_high_hist: &var_tas
+        <<: *var_clt
+        short_name: tas
+      tas_ECS_med_hist:
+        <<: *var_tas
+        additional_datasets: *datasets_ecs_med
+      tas_ECS_low_hist:
+        <<: *var_tas
+        additional_datasets: *datasets_ecs_low
+      tas_ECS_high_scen:
+        <<: *var_tas
+        <<: *years_scen
+        exp: ssp585
+        additional_datasets: *datasets_ecs_high_scenario
+      tas_ECS_med_scen:
+        <<: *var_tas
+        <<: *years_scen
+        exp: ssp585
+        additional_datasets: *datasets_ecs_med_scenario
+      tas_ECS_low_scen:
+        <<: *var_tas
+        <<: *years_scen
+        exp: ssp585
+        additional_datasets: *datasets_ecs_low_scenario
+    scripts:
+      zonal_diff:
+        script: clouds/clouds_ecs_groups_zonal.py
+        group_by: [['ECS_low_hist', 'ECS_low_scen'],
+                   ['ECS_med_hist', 'ECS_med_scen'],
+                   ['ECS_high_hist', 'ECS_high_scen']]
+        plot_type: zonal
+        filename_attach: 'ssp585'
+
+
+  # Figure 6b
+  lwp_zonal:
+    <<: *zonal_diag
+    variables:
+      ECS_high_hist:
+        <<: *var_clt
+        short_name: lwp
+        derive: true
+        additional_datasets: *datasets_ecs_high
+      ECS_med_hist:
+        <<: *var_clt
+        short_name: lwp
+        derive: true
+        additional_datasets: *datasets_ecs_med
+      ECS_low_hist:
+        <<: *var_clt
+        short_name: lwp
+        derive: true
+        additional_datasets: *datasets_ecs_low
+      ECS_high_scen:
+        <<: *var_clt
+        <<: *years_scen
+        short_name: lwp
+        derive: true
+        additional_datasets: *datasets_ecs_high_scenario
+      ECS_med_scen:
+        <<: *var_clt
+        <<: *years_scen
+        short_name: lwp
+        derive: true
+        additional_datasets: *datasets_ecs_med_scenario
+      ECS_low_scen:
+        <<: *var_clt
+        <<: *years_scen
+        short_name: lwp
+        derive: true
+        additional_datasets: *datasets_ecs_low_scenario
+      tas_ECS_high_hist:
+        <<: *var_tas
+      tas_ECS_med_hist:
+        <<: *var_tas
+        additional_datasets: *datasets_ecs_med
+      tas_ECS_low_hist:
+        <<: *var_tas
+        additional_datasets: *datasets_ecs_low
+      tas_ECS_high_scen:
+        <<: *var_tas
+        <<: *years_scen
+        exp: ssp585
+        additional_datasets: *datasets_ecs_high_scenario
+      tas_ECS_med_scen:
+        <<: *var_tas
+        <<: *years_scen
+        exp: ssp585
+        additional_datasets: *datasets_ecs_med_scenario
+      tas_ECS_low_scen:
+        <<: *var_tas
+        <<: *years_scen
+        exp: ssp585
+        additional_datasets: *datasets_ecs_low_scenario
+
+
+  # Figure 6c
+  iwp_zonal:
+    <<: *zonal_diag
+    variables:
+      ECS_high_hist:
+        <<: *var_clt
+        short_name: clivi
+        additional_datasets: *datasets_ecs_high
+      ECS_med_hist:
+        <<: *var_clt
+        short_name: clivi
+        additional_datasets: *datasets_ecs_med
+      ECS_low_hist:
+        <<: *var_clt
+        short_name: clivi
+        additional_datasets: *datasets_ecs_low
+      ECS_high_scen:
+        <<: *var_clt
+        <<: *years_scen
+        short_name: clivi
+        additional_datasets: *datasets_ecs_high_scenario
+      ECS_med_scen:
+        <<: *var_clt
+        <<: *years_scen
+        short_name: clivi
+        additional_datasets: *datasets_ecs_med_scenario
+      ECS_low_scen:
+        <<: *var_clt
+        <<: *years_scen
+        short_name: clivi
+        additional_datasets: *datasets_ecs_low_scenario
+      tas_ECS_high_hist:
+        <<: *var_tas
+      tas_ECS_med_hist:
+        <<: *var_tas
+        additional_datasets: *datasets_ecs_med
+      tas_ECS_low_hist:
+        <<: *var_tas
+        additional_datasets: *datasets_ecs_low
+      tas_ECS_high_scen:
+        <<: *var_tas
+        <<: *years_scen
+        exp: ssp585
+        additional_datasets: *datasets_ecs_high_scenario
+      tas_ECS_med_scen:
+        <<: *var_tas
+        <<: *years_scen
+        exp: ssp585
+        additional_datasets: *datasets_ecs_med_scenario
+      tas_ECS_low_scen:
+        <<: *var_tas
+        <<: *years_scen
+        exp: ssp585
+        additional_datasets: *datasets_ecs_low_scenario
+
+
+  # Figure 6d
+  netcre_zonal:
+    <<: *zonal_diag
+    variables:
+      ECS_high_hist:
+        <<: *var_clt
+        short_name: netcre
+        derive: true
+        additional_datasets: *datasets_ecs_high
+      ECS_med_hist:
+        <<: *var_clt
+        short_name: netcre
+        derive: true
+        additional_datasets: *datasets_ecs_med
+      ECS_low_hist:
+        <<: *var_clt
+        short_name: netcre
+        derive: true
+        additional_datasets: *datasets_ecs_low
+      ECS_high_scen:
+        <<: *var_clt
+        <<: *years_scen
+        short_name: netcre
+        derive: true
+        additional_datasets: *datasets_ecs_high_scenario
+      ECS_med_scen:
+        <<: *var_clt
+        <<: *years_scen
+        short_name: netcre
+        derive: true
+        additional_datasets: *datasets_ecs_med_scenario
+      ECS_low_scen:
+        <<: *var_clt
+        <<: *years_scen
+        short_name: netcre
+        derive: true
+        additional_datasets: *datasets_ecs_low_scenario
+      tas_ECS_high_hist:
+        <<: *var_tas
+      tas_ECS_med_hist:
+        <<: *var_tas
+        additional_datasets: *datasets_ecs_med
+      tas_ECS_low_hist:
+        <<: *var_tas
+        additional_datasets: *datasets_ecs_low
+      tas_ECS_high_scen:
+        <<: *var_tas
+        <<: *years_scen
+        exp: ssp585
+        additional_datasets: *datasets_ecs_high_scenario
+      tas_ECS_med_scen:
+        <<: *var_tas
+        <<: *years_scen
+        exp: ssp585
+        additional_datasets: *datasets_ecs_med_scenario
+      tas_ECS_low_scen:
+        <<: *var_tas
+        <<: *years_scen
+        exp: ssp585
+        additional_datasets: *datasets_ecs_low_scenario
+      OBS:
+        <<: *var_clt
+        short_name: netcre
+        derive: true
+        preprocessor: zonal
+        additional_datasets:
+          - {dataset: CERES-EBAF, project: OBS, type: sat, version: Ed4.2,
+             tier: 2, start_year: 2001, end_year: 2022}
+
+
+  # Figure 6e
+  swcre_zonal:
+    <<: *zonal_diag
+    variables:
+      ECS_high_hist:
+        <<: *var_clt
+        short_name: swcre
+        derive: true
+        additional_datasets: *datasets_ecs_high
+      ECS_med_hist:
+        <<: *var_clt
+        short_name: swcre
+        derive: true
+        additional_datasets: *datasets_ecs_med
+      ECS_low_hist:
+        <<: *var_clt
+        short_name: swcre
+        derive: true
+        additional_datasets: *datasets_ecs_low
+      ECS_high_scen:
+        <<: *var_clt
+        <<: *years_scen
+        short_name: swcre
+        derive: true
+        additional_datasets: *datasets_ecs_high_scenario
+      ECS_med_scen:
+        <<: *var_clt
+        <<: *years_scen
+        short_name: swcre
+        derive: true
+        additional_datasets: *datasets_ecs_med_scenario
+      ECS_low_scen:
+        <<: *var_clt
+        <<: *years_scen
+        short_name: swcre
+        derive: true
+        additional_datasets: *datasets_ecs_low_scenario
+      tas_ECS_high_hist:
+        <<: *var_tas
+      tas_ECS_med_hist:
+        <<: *var_tas
+        additional_datasets: *datasets_ecs_med
+      tas_ECS_low_hist:
+        <<: *var_tas
+        additional_datasets: *datasets_ecs_low
+      tas_ECS_high_scen:
+        <<: *var_tas
+        <<: *years_scen
+        exp: ssp585
+        additional_datasets: *datasets_ecs_high_scenario
+      tas_ECS_med_scen:
+        <<: *var_tas
+        <<: *years_scen
+        exp: ssp585
+        additional_datasets: *datasets_ecs_med_scenario
+      tas_ECS_low_scen:
+        <<: *var_tas
+        <<: *years_scen
+        exp: ssp585
+        additional_datasets: *datasets_ecs_low_scenario
+      OBS:
+        <<: *var_clt
+        short_name: swcre
+        derive: true
+        preprocessor: zonal
+        additional_datasets:
+          - {dataset: CERES-EBAF, project: OBS, type: sat, version: Ed4.2,
+             tier: 2, start_year: 2001, end_year: 2022}
+
+
+  # Figure 6f
+  lwcre_zonal:
+    <<: *zonal_diag
+    variables:
+      ECS_high_hist:
+        <<: *var_clt
+        short_name: lwcre
+        derive: true
+        additional_datasets: *datasets_ecs_high
+      ECS_med_hist:
+        <<: *var_clt
+        short_name: lwcre
+        derive: true
+        additional_datasets: *datasets_ecs_med
+      ECS_low_hist:
+        <<: *var_clt
+        short_name: lwcre
+        derive: true
+        additional_datasets: *datasets_ecs_low
+      ECS_high_scen:
+        <<: *var_clt
+        <<: *years_scen
+        short_name: lwcre
+        derive: true
+        additional_datasets: *datasets_ecs_high_scenario
+      ECS_med_scen:
+        <<: *var_clt
+        <<: *years_scen
+        short_name: lwcre
+        derive: true
+        additional_datasets: *datasets_ecs_med_scenario
+      ECS_low_scen:
+        <<: *var_clt
+        <<: *years_scen
+        short_name: lwcre
+        derive: true
+        additional_datasets: *datasets_ecs_low_scenario
+      tas_ECS_high_hist:
+        <<: *var_tas
+      tas_ECS_med_hist:
+        <<: *var_tas
+        additional_datasets: *datasets_ecs_med
+      tas_ECS_low_hist:
+        <<: *var_tas
+        additional_datasets: *datasets_ecs_low
+      tas_ECS_high_scen:
+        <<: *var_tas
+        <<: *years_scen
+        exp: ssp585
+        additional_datasets: *datasets_ecs_high_scenario
+      tas_ECS_med_scen:
+        <<: *var_tas
+        <<: *years_scen
+        exp: ssp585
+        additional_datasets: *datasets_ecs_med_scenario
+      tas_ECS_low_scen:
+        <<: *var_tas
+        <<: *years_scen
+        exp: ssp585
+        additional_datasets: *datasets_ecs_low_scenario
+      OBS:
+        <<: *var_clt
+        short_name: lwcre
+        derive: true
+        preprocessor: zonal
+        additional_datasets:
+          - {dataset: CERES-EBAF, project: OBS, type: sat, version: Ed4.2,
+             tier: 2, start_year: 2001, end_year: 2022}
diff --git a/esmvaltool/recipes/clouds/recipe_bock24acp_fig7_boxplots.yml b/esmvaltool/recipes/clouds/recipe_bock24acp_fig7_boxplots.yml
new file mode 100644
index 0000000000..ef4b507be2
--- /dev/null
+++ b/esmvaltool/recipes/clouds/recipe_bock24acp_fig7_boxplots.yml
@@ -0,0 +1,1004 @@
+# ESMValTool
+# recipe_bock24acp_fig7_boxplots.yml
+---
+documentation:
+  title: Cloud properties regarding ECS (boxplots).
+
+  description: |
+    Boxplots fo projected changes of cloud properties
+    for different regions.
+
+  authors:
+    - bock_lisa
+
+  maintainer:
+    - bock_lisa
+
+  references:
+    - bock24acp
+
+  project:
+    - cmug
+    - esm2025
+
+
+YEARS_hist: &years_hist
+  start_year: 1985
+  end_year: 2004
+
+YEARS_scen: &years_scen
+  start_year: 2081
+  end_year: 2100
+
+VARIABLE_SETTINGS: &var_settings
+  short_name: clt
+  mip: Amon
+  project: CMIP6
+  exp: historical
+  ensemble: r1i1p1f1
+  grid: gr
+  <<: *years_hist
+
+BOXPLOT_SETTINGS: &boxplot_settings
+  script: clouds/clouds_ecs_groups_boxplots.py
+  group_by: [['ECS_low_hist', 'ECS_low_scen'],
+             ['ECS_med_hist', 'ECS_med_scen'],
+             ['ECS_high_hist', 'ECS_high_scen']]
+  y_range: [-25., 22.]
+
+
+DATASETS_ECS_HIGH: &datasets_ecs_high
+  # CMIP6
+  - {dataset: CanESM5, grid: gn}
+  - {dataset: CNRM-CM6-1, ensemble: r1i1p1f2}
+  - {dataset: CNRM-ESM2-1, ensemble: r1i1p1f2}
+  - {dataset: HadGEM3-GC31-LL, grid: gn, ensemble: r1i1p1f3}
+  - {dataset: HadGEM3-GC31-MM, grid: gn, ensemble: r1i1p1f3}
+  - {dataset: IPSL-CM6A-LR}
+  - {dataset: KACE-1-0-G}
+  - {dataset: NESM3, grid: gn}
+  - {dataset: TaiESM1, grid: gn}
+  - {dataset: UKESM1-0-LL, ensemble: r1i1p1f2, grid: gn}
+  # CMIP5
+  - {dataset: CSIRO-Mk3-6-0, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: HadGEM2-ES, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: IPSL-CM5A-LR, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: IPSL-CM5A-MR, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: MIROC-ESM, ensemble: r1i1p1, project: CMIP5}
+
+DATASETS_ECS_HIGH_scenario: &datasets_ecs_high_scenario
+  # CMIP6
+  - {dataset: CanESM5, grid: gn, exp: ssp585}
+  - {dataset: CNRM-CM6-1, ensemble: r1i1p1f2, exp: ssp585}
+  - {dataset: CNRM-ESM2-1, ensemble: r1i1p1f2, exp: ssp585}
+  - {dataset: HadGEM3-GC31-LL, grid: gn, ensemble: r1i1p1f3, exp: ssp585}
+  - {dataset: HadGEM3-GC31-MM, grid: gn, ensemble: r1i1p1f3, exp: ssp585}
+  - {dataset: IPSL-CM6A-LR, exp: ssp585}
+  - {dataset: KACE-1-0-G, exp: ssp585}
+  - {dataset: NESM3, grid: gn, exp: ssp585}
+  - {dataset: TaiESM1, grid: gn, exp: ssp585}
+  - {dataset: UKESM1-0-LL, ensemble: r1i1p1f2, grid: gn, exp: ssp585}
+  # CMIP5
+  - {dataset: CSIRO-Mk3-6-0, ensemble: r1i1p1, project: CMIP5, exp: rcp85}
+  - {dataset: HadGEM2-ES, ensemble: r1i1p1, project: CMIP5, exp: rcp85}
+  - {dataset: IPSL-CM5A-LR, ensemble: r1i1p1, project: CMIP5, exp: rcp85}
+  - {dataset: IPSL-CM5A-MR, ensemble: r1i1p1, project: CMIP5, exp: rcp85}
+  - {dataset: MIROC-ESM, ensemble: r1i1p1, project: CMIP5, exp: rcp85}
+
+DATASETS_ECS_MED: &datasets_ecs_med
+  # CMIP6
+  - {dataset: BCC-CSM2-MR, grid: gn}
+  - {dataset: CMCC-CM2-SR5, grid: gn}
+  - {dataset: CMCC-ESM2, grid: gn}
+  - {dataset: FGOALS-f3-L}
+  - {dataset: FGOALS-g3, grid: gn}
+  - {dataset: GISS-E2-1-H, grid: gn}
+  - {dataset: MPI-ESM1-2-HR, grid: gn}
+  - {dataset: MPI-ESM1-2-LR, grid: gn}
+  - {dataset: MRI-ESM2-0, grid: gn}
+  # CMIP5
+  - {dataset: ACCESS1-0, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: ACCESS1-3, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: BNU-ESM, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: CanESM2, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: CCSM4, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: FGOALS-g2, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: GFDL-CM3, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: MPI-ESM-LR, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: MPI-ESM-MR, ensemble: r1i1p1, project: CMIP5}
+
+DATASETS_ECS_MED_scenario: &datasets_ecs_med_scenario
+  # CMIP6
+  - {dataset: BCC-CSM2-MR, grid: gn, exp: ssp585}
+  - {dataset: CMCC-CM2-SR5, grid: gn, exp: ssp585}
+  - {dataset: CMCC-ESM2, grid: gn, exp: ssp585}
+  - {dataset: FGOALS-f3-L, exp: ssp585}
+  - {dataset: FGOALS-g3, grid: gn, exp: ssp585}
+  - {dataset: GISS-E2-1-H, grid: gn, ensemble: r1i1p1f2, exp: ssp585}
+  - {dataset: MPI-ESM1-2-HR, grid: gn, exp: ssp585}
+  - {dataset: MPI-ESM1-2-LR, grid: gn, exp: ssp585}
+  - {dataset: MRI-ESM2-0, grid: gn, exp: ssp585}
+  # CMIP5
+  - {dataset: ACCESS1-0, ensemble: r1i1p1, project: CMIP5, exp: rcp85}
+  - {dataset: ACCESS1-3, ensemble: r1i1p1, project: CMIP5, exp: rcp85}
+  - {dataset: BNU-ESM, ensemble: r1i1p1, project: CMIP5, exp: rcp85}
+  - {dataset: CanESM2, ensemble: r1i1p1, project: CMIP5, exp: rcp85}
+  - {dataset: CCSM4, ensemble: r1i1p1, project: CMIP5, exp: rcp85}
+  - {dataset: FGOALS-g2, ensemble: r1i1p1, project: CMIP5, exp: rcp85}
+  - {dataset: GFDL-CM3, ensemble: r1i1p1, project: CMIP5, exp: rcp85}
+  - {dataset: MPI-ESM-LR, ensemble: r1i1p1, project: CMIP5, exp: rcp85}
+  - {dataset: MPI-ESM-MR, ensemble: r1i1p1, project: CMIP5, exp: rcp85}
+
+DATASETS_ECS_LOW: &datasets_ecs_low
+  # CMIP6
+  - {dataset: CAMS-CSM1-0, grid: gn}
+  - {dataset: GISS-E2-1-G, grid: gn}
+  - {dataset: MIROC6, grid: gn}
+  - {dataset: MIROC-ES2L, ensemble: r1i1p1f2, grid: gn}
+  - {dataset: NorESM2-LM, grid: gn, institute: NCC}
+  - {dataset: NorESM2-MM, grid: gn, institute: NCC}
+  # CMIP5
+  - {dataset: bcc-csm1-1, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: bcc-csm1-1-m, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: GFDL-ESM2G, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: GFDL-ESM2M, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: GISS-E2-H, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: GISS-E2-R, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: inmcm4, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: IPSL-CM5B-LR, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: MIROC5, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: MRI-CGCM3, ensemble: r1i1p1, project: CMIP5}
+  - {dataset: NorESM1-M, ensemble: r1i1p1, project: CMIP5}
+
+DATASETS_ECS_LOW_scenario: &datasets_ecs_low_scenario
+  # CMIP6
+  - {dataset: CAMS-CSM1-0, grid: gn, exp: ssp585, end_year: 2099}
+  - {dataset: GISS-E2-1-G, grid: gn, ensemble: r1i1p1f2, exp: ssp585}
+  - {dataset: MIROC6, grid: gn, exp: ssp585}
+  - {dataset: MIROC-ES2L, ensemble: r1i1p1f2, grid: gn, exp: ssp585}
+  - {dataset: NorESM2-LM, grid: gn, institute: NCC, exp: ssp585}
+  - {dataset: NorESM2-MM, grid: gn, institute: NCC, exp: ssp585}
+  # CMIP5
+  - {dataset: bcc-csm1-1, ensemble: r1i1p1, project: CMIP5, exp: rcp85}
+  - {dataset: bcc-csm1-1-m, ensemble: r1i1p1, project: CMIP5, exp: rcp85}
+  - {dataset: GFDL-ESM2G, ensemble: r1i1p1, project: CMIP5, exp: rcp85}
+  - {dataset: GFDL-ESM2M, ensemble: r1i1p1, project: CMIP5, exp: rcp85}
+  - {dataset: GISS-E2-H, ensemble: r1i1p1, project: CMIP5, exp: rcp85}
+  - {dataset: GISS-E2-R, ensemble: r1i1p1, project: CMIP5, exp: rcp85}
+  - {dataset: inmcm4, ensemble: r1i1p1, project: CMIP5, exp: rcp85}
+  - {dataset: IPSL-CM5B-LR, ensemble: r1i1p1, project: CMIP5, exp: rcp85}
+  - {dataset: MIROC5, ensemble: r1i1p1, project: CMIP5, exp: rcp85}
+  - {dataset: MRI-CGCM3, ensemble: r1i1p1, project: CMIP5, exp: rcp85}
+  - {dataset: NorESM1-M, ensemble: r1i1p1, project: CMIP5, exp: rcp85}
+
+
+preprocessors:
+
+  tropical_ocean:
+    custom_order: true
+    extract_region:
+      start_latitude: -30.
+      end_latitude: 30.
+      start_longitude: 0.
+      end_longitude: 360.
+    mask_landsea: &mask_land
+      mask_out: land
+    area_statistics: &area_mean
+      operator: mean
+    climate_statistics: &clim_mean
+      operator: mean
+
+  southern_ocean:
+    custom_order: true
+    extract_region:
+      start_latitude: -65.
+      end_latitude: -30.
+      start_longitude: 0.
+      end_longitude: 360.
+    mask_landsea: *mask_land
+    area_statistics: *area_mean
+    climate_statistics: *clim_mean
+
+  pacific_itcz:
+    custom_order: true
+    extract_region:
+      start_latitude: 0.
+      end_latitude: 12.
+      start_longitude: 135.
+      end_longitude: 275.
+    mask_landsea: *mask_land
+    area_statistics: *area_mean
+    climate_statistics: *clim_mean
+
+  stratocumulus:
+    custom_order: true
+    extract_shape:
+      shapefile: shapefiles/sc_regions.shp
+      crop: true
+      decomposed: false
+      ids:
+        sc:
+          - SEP
+          - NEP
+          - SEA
+    mask_landsea: *mask_land
+    area_statistics: *area_mean
+    climate_statistics: *clim_mean
+
+  arctic:
+    custom_order: true
+    extract_region:
+      start_latitude: 70.
+      end_latitude: 90.
+      start_longitude: 0.
+      end_longitude: 360.
+    mask_landsea: *mask_land
+    area_statistics: *area_mean
+    climate_statistics: *clim_mean
+
+
+diagnostics:
+
+  # Figure 7c
+  diag_tropical_ocean:
+    description: compare field means
+    variables:
+      clt_ECS_high_hist: &var_clt_1
+        <<: *var_settings
+        preprocessor: tropical_ocean
+        additional_datasets: *datasets_ecs_high
+      clt_ECS_med_hist:
+        <<: *var_clt_1
+        additional_datasets: *datasets_ecs_med
+      clt_ECS_low_hist:
+        <<: *var_clt_1
+        additional_datasets: *datasets_ecs_low
+      clt_ECS_high_scen:
+        <<: *var_clt_1
+        <<: *years_scen
+        additional_datasets: *datasets_ecs_high_scenario
+      clt_ECS_med_scen:
+        <<: *var_clt_1
+        <<: *years_scen
+        additional_datasets: *datasets_ecs_med_scenario
+      clt_ECS_low_scen:
+        <<: *var_clt_1
+        <<: *years_scen
+        additional_datasets: *datasets_ecs_low_scenario
+      clivi_ECS_high_hist:
+        <<: *var_clt_1
+        short_name: clivi
+        additional_datasets: *datasets_ecs_high
+      clivi_ECS_med_hist:
+        <<: *var_clt_1
+        short_name: clivi
+        additional_datasets: *datasets_ecs_med
+      clivi_ECS_low_hist:
+        <<: *var_clt_1
+        short_name: clivi
+        additional_datasets: *datasets_ecs_low
+      clivi_ECS_high_scen:
+        <<: *var_clt_1
+        <<: *years_scen
+        short_name: clivi
+        exp: ssp585
+        additional_datasets: *datasets_ecs_high_scenario
+      clivi_ECS_med_scen:
+        <<: *var_clt_1
+        <<: *years_scen
+        short_name: clivi
+        additional_datasets: *datasets_ecs_med_scenario
+      clivi_ECS_low_scen:
+        <<: *var_clt_1
+        <<: *years_scen
+        short_name: clivi
+        additional_datasets: *datasets_ecs_low_scenario
+      lwp_ECS_high_hist:
+        <<: *var_clt_1
+        short_name: lwp
+        derive: true
+        additional_datasets: *datasets_ecs_high
+      lwp_ECS_med_hist:
+        <<: *var_clt_1
+        short_name: lwp
+        derive: true
+        additional_datasets: *datasets_ecs_med
+      lwp_ECS_low_hist:
+        <<: *var_clt_1
+        short_name: lwp
+        derive: true
+        additional_datasets: *datasets_ecs_low
+      lwp_ECS_high_scen:
+        <<: *var_clt_1
+        <<: *years_scen
+        short_name: lwp
+        derive: true
+        exp: ssp585
+        additional_datasets: *datasets_ecs_high_scenario
+      lwp_ECS_med_scen:
+        <<: *var_clt_1
+        <<: *years_scen
+        short_name: lwp
+        derive: true
+        additional_datasets: *datasets_ecs_med_scenario
+      lwp_ECS_low_scen:
+        <<: *var_clt_1
+        <<: *years_scen
+        short_name: lwp
+        derive: true
+        additional_datasets: *datasets_ecs_low_scenario
+      netcre_ECS_high_hist:
+        <<: *var_clt_1
+        short_name: netcre
+        derive: true
+        additional_datasets: *datasets_ecs_high
+      netcre_ECS_med_hist:
+        <<: *var_clt_1
+        short_name: netcre
+        derive: true
+        additional_datasets: *datasets_ecs_med
+      netcre_ECS_low_hist:
+        <<: *var_clt_1
+        short_name: netcre
+        derive: true
+        additional_datasets: *datasets_ecs_low
+      netcre_ECS_high_scen:
+        <<: *var_clt_1
+        <<: *years_scen
+        short_name: netcre
+        derive: true
+        exp: ssp585
+        additional_datasets: *datasets_ecs_high_scenario
+      netcre_ECS_med_scen:
+        <<: *var_clt_1
+        <<: *years_scen
+        short_name: netcre
+        derive: true
+        additional_datasets: *datasets_ecs_med_scenario
+      netcre_ECS_low_scen:
+        <<: *var_clt_1
+        <<: *years_scen
+        short_name: netcre
+        derive: true
+        additional_datasets: *datasets_ecs_low_scenario
+      tas_ECS_high_hist: &var_tas_1
+        <<: *var_clt_1
+        short_name: tas
+      tas_ECS_med_hist:
+        <<: *var_tas_1
+        additional_datasets: *datasets_ecs_med
+      tas_ECS_low_hist:
+        <<: *var_tas_1
+        additional_datasets: *datasets_ecs_low
+      tas_ECS_high_scen:
+        <<: *var_tas_1
+        <<: *years_scen
+        exp: ssp585
+        additional_datasets: *datasets_ecs_high_scenario
+      tas_ECS_med_scen:
+        <<: *var_tas_1
+        <<: *years_scen
+        exp: ssp585
+        additional_datasets: *datasets_ecs_med_scenario
+      tas_ECS_low_scen:
+        <<: *var_tas_1
+        <<: *years_scen
+        exp: ssp585
+        additional_datasets: *datasets_ecs_low_scenario
+    scripts:
+      barplot:
+        <<: *boxplot_settings
+        filename_attach: 'ssp585_tropoc'
+        title: 'Tropical Ocean'
+
+
+  # Figure 7b
+  diag_southern_ocean:
+    description: compare field means
+    variables:
+      clt_ECS_high_hist: &var_clt_2
+        <<: *var_settings
+        preprocessor: southern_ocean
+        additional_datasets: *datasets_ecs_high
+      clt_ECS_med_hist:
+        <<: *var_clt_2
+        additional_datasets: *datasets_ecs_med
+      clt_ECS_low_hist:
+        <<: *var_clt_2
+        additional_datasets: *datasets_ecs_low
+      clt_ECS_high_scen:
+        <<: *var_clt_2
+        <<: *years_scen
+        additional_datasets: *datasets_ecs_high_scenario
+      clt_ECS_med_scen:
+        <<: *var_clt_2
+        <<: *years_scen
+        additional_datasets: *datasets_ecs_med_scenario
+      clt_ECS_low_scen:
+        <<: *var_clt_2
+        <<: *years_scen
+        additional_datasets: *datasets_ecs_low_scenario
+      clivi_ECS_high_hist:
+        <<: *var_clt_2
+        short_name: clivi
+        additional_datasets: *datasets_ecs_high
+      clivi_ECS_med_hist:
+        <<: *var_clt_2
+        short_name: clivi
+        additional_datasets: *datasets_ecs_med
+      clivi_ECS_low_hist:
+        <<: *var_clt_2
+        short_name: clivi
+        additional_datasets: *datasets_ecs_low
+      clivi_ECS_high_scen:
+        <<: *var_clt_2
+        <<: *years_scen
+        short_name: clivi
+        exp: ssp585
+        additional_datasets: *datasets_ecs_high_scenario
+      clivi_ECS_med_scen:
+        <<: *var_clt_2
+        <<: *years_scen
+        short_name: clivi
+        additional_datasets: *datasets_ecs_med_scenario
+      clivi_ECS_low_scen:
+        <<: *var_clt_2
+        <<: *years_scen
+        short_name: clivi
+        additional_datasets: *datasets_ecs_low_scenario
+      lwp_ECS_high_hist:
+        <<: *var_clt_2
+        short_name: lwp
+        derive: true
+        additional_datasets: *datasets_ecs_high
+      lwp_ECS_med_hist:
+        <<: *var_clt_2
+        short_name: lwp
+        derive: true
+        additional_datasets: *datasets_ecs_med
+      lwp_ECS_low_hist:
+        <<: *var_clt_2
+        short_name: lwp
+        derive: true
+        additional_datasets: *datasets_ecs_low
+      lwp_ECS_high_scen:
+        <<: *var_clt_2
+        <<: *years_scen
+        short_name: lwp
+        derive: true
+        exp: ssp585
+        additional_datasets: *datasets_ecs_high_scenario
+      lwp_ECS_med_scen:
+        <<: *var_clt_2
+        <<: *years_scen
+        short_name: lwp
+        derive: true
+        additional_datasets: *datasets_ecs_med_scenario
+      lwp_ECS_low_scen:
+        <<: *var_clt_2
+        <<: *years_scen
+        short_name: lwp
+        derive: true
+        additional_datasets: *datasets_ecs_low_scenario
+      netcre_ECS_high_hist:
+        <<: *var_clt_2
+        short_name: netcre
+        derive: true
+        additional_datasets: *datasets_ecs_high
+      netcre_ECS_med_hist:
+        <<: *var_clt_2
+        short_name: netcre
+        derive: true
+        additional_datasets: *datasets_ecs_med
+      netcre_ECS_low_hist:
+        <<: *var_clt_2
+        short_name: netcre
+        derive: true
+        additional_datasets: *datasets_ecs_low
+      netcre_ECS_high_scen:
+        <<: *var_clt_2
+        <<: *years_scen
+        short_name: netcre
+        derive: true
+        exp: ssp585
+        additional_datasets: *datasets_ecs_high_scenario
+      netcre_ECS_med_scen:
+        <<: *var_clt_2
+        <<: *years_scen
+        short_name: netcre
+        derive: true
+        additional_datasets: *datasets_ecs_med_scenario
+      netcre_ECS_low_scen:
+        <<: *var_clt_2
+        <<: *years_scen
+        short_name: netcre
+        derive: true
+        additional_datasets: *datasets_ecs_low_scenario
+      tas_ECS_high_hist: &var_tas_2
+        <<: *var_clt_2
+        short_name: tas
+      tas_ECS_med_hist:
+        <<: *var_tas_2
+        additional_datasets: *datasets_ecs_med
+      tas_ECS_low_hist:
+        <<: *var_tas_2
+        additional_datasets: *datasets_ecs_low
+      tas_ECS_high_scen:
+        <<: *var_tas_2
+        <<: *years_scen
+        exp: ssp585
+        additional_datasets: *datasets_ecs_high_scenario
+      tas_ECS_med_scen:
+        <<: *var_tas_2
+        <<: *years_scen
+        exp: ssp585
+        additional_datasets: *datasets_ecs_med_scenario
+      tas_ECS_low_scen:
+        <<: *var_tas_2
+        <<: *years_scen
+        exp: ssp585
+        additional_datasets: *datasets_ecs_low_scenario
+    scripts:
+      barplot:
+        <<: *boxplot_settings
+        filename_attach: 'ssp585_south_oc'
+        title: 'Southern Ocean'
+
+
+  # Figure 7d
+  diag_pacific_itcz:
+    description: compare field means
+    variables:
+      clt_ECS_high_hist: &var_clt_3
+        <<: *var_settings
+        preprocessor: pacific_itcz
+        additional_datasets: *datasets_ecs_high
+      clt_ECS_med_hist:
+        <<: *var_clt_3
+        additional_datasets: *datasets_ecs_med
+      clt_ECS_low_hist:
+        <<: *var_clt_3
+        additional_datasets: *datasets_ecs_low
+      clt_ECS_high_scen:
+        <<: *var_clt_3
+        <<: *years_scen
+        additional_datasets: *datasets_ecs_high_scenario
+      clt_ECS_med_scen:
+        <<: *var_clt_3
+        <<: *years_scen
+        additional_datasets: *datasets_ecs_med_scenario
+      clt_ECS_low_scen:
+        <<: *var_clt_3
+        <<: *years_scen
+        additional_datasets: *datasets_ecs_low_scenario
+      clivi_ECS_high_hist:
+        <<: *var_clt_3
+        short_name: clivi
+        additional_datasets: *datasets_ecs_high
+      clivi_ECS_med_hist:
+        <<: *var_clt_3
+        short_name: clivi
+        additional_datasets: *datasets_ecs_med
+      clivi_ECS_low_hist:
+        <<: *var_clt_3
+        short_name: clivi
+        additional_datasets: *datasets_ecs_low
+      clivi_ECS_high_scen:
+        <<: *var_clt_3
+        <<: *years_scen
+        short_name: clivi
+        exp: ssp585
+        additional_datasets: *datasets_ecs_high_scenario
+      clivi_ECS_med_scen:
+        <<: *var_clt_3
+        <<: *years_scen
+        short_name: clivi
+        additional_datasets: *datasets_ecs_med_scenario
+      clivi_ECS_low_scen:
+        <<: *var_clt_3
+        <<: *years_scen
+        short_name: clivi
+        additional_datasets: *datasets_ecs_low_scenario
+      lwp_ECS_high_hist:
+        <<: *var_clt_3
+        short_name: lwp
+        derive: true
+        additional_datasets: *datasets_ecs_high
+      lwp_ECS_med_hist:
+        <<: *var_clt_3
+        short_name: lwp
+        derive: true
+        additional_datasets: *datasets_ecs_med
+      lwp_ECS_low_hist:
+        <<: *var_clt_3
+        short_name: lwp
+        derive: true
+        additional_datasets: *datasets_ecs_low
+      lwp_ECS_high_scen:
+        <<: *var_clt_3
+        <<: *years_scen
+        short_name: lwp
+        derive: true
+        exp: ssp585
+        additional_datasets: *datasets_ecs_high_scenario
+      lwp_ECS_med_scen:
+        <<: *var_clt_3
+        <<: *years_scen
+        short_name: lwp
+        derive: true
+        additional_datasets: *datasets_ecs_med_scenario
+      lwp_ECS_low_scen:
+        <<: *var_clt_3
+        <<: *years_scen
+        short_name: lwp
+        derive: true
+        additional_datasets: *datasets_ecs_low_scenario
+      netcre_ECS_high_hist:
+        <<: *var_clt_3
+        short_name: netcre
+        derive: true
+        additional_datasets: *datasets_ecs_high
+      netcre_ECS_med_hist:
+        <<: *var_clt_3
+        short_name: netcre
+        derive: true
+        additional_datasets: *datasets_ecs_med
+      netcre_ECS_low_hist:
+        <<: *var_clt_3
+        short_name: netcre
+        derive: true
+        additional_datasets: *datasets_ecs_low
+      netcre_ECS_high_scen:
+        <<: *var_clt_3
+        <<: *years_scen
+        short_name: netcre
+        derive: true
+        exp: ssp585
+        additional_datasets: *datasets_ecs_high_scenario
+      netcre_ECS_med_scen:
+        <<: *var_clt_3
+        <<: *years_scen
+        short_name: netcre
+        derive: true
+        additional_datasets: *datasets_ecs_med_scenario
+      netcre_ECS_low_scen:
+        <<: *var_clt_3
+        <<: *years_scen
+        short_name: netcre
+        derive: true
+        additional_datasets: *datasets_ecs_low_scenario
+      tas_ECS_high_hist: &var_tas_3
+        <<: *var_clt_3
+        short_name: tas
+      tas_ECS_med_hist:
+        <<: *var_tas_3
+        additional_datasets: *datasets_ecs_med
+      tas_ECS_low_hist:
+        <<: *var_tas_3
+        additional_datasets: *datasets_ecs_low
+      tas_ECS_high_scen:
+        <<: *var_tas_3
+        <<: *years_scen
+        exp: ssp585
+        additional_datasets: *datasets_ecs_high_scenario
+      tas_ECS_med_scen:
+        <<: *var_tas_3
+        <<: *years_scen
+        exp: ssp585
+        additional_datasets: *datasets_ecs_med_scenario
+      tas_ECS_low_scen:
+        <<: *var_tas_3
+        <<: *years_scen
+        exp: ssp585
+        additional_datasets: *datasets_ecs_low_scenario
+    scripts:
+      barplot:
+        <<: *boxplot_settings
+        filename_attach: 'ssp585_paitcz'
+        title: 'Pacific ITCZ'
+
+
+  # Figure 7e
+  diag_stratocumulus:
+    description: compare field means
+    variables:
+      clt_ECS_high_hist: &var_clt_str
+        <<: *var_settings
+        preprocessor: stratocumulus
+        additional_datasets: *datasets_ecs_high
+      clt_ECS_med_hist:
+        <<: *var_clt_str
+        additional_datasets: *datasets_ecs_med
+      clt_ECS_low_hist:
+        <<: *var_clt_str
+        additional_datasets: *datasets_ecs_low
+      clt_ECS_high_scen:
+        <<: *var_clt_str
+        <<: *years_scen
+        additional_datasets: *datasets_ecs_high_scenario
+      clt_ECS_med_scen:
+        <<: *var_clt_str
+        <<: *years_scen
+        additional_datasets: *datasets_ecs_med_scenario
+      clt_ECS_low_scen:
+        <<: *var_clt_str
+        <<: *years_scen
+        additional_datasets: *datasets_ecs_low_scenario
+      clivi_ECS_high_hist:
+        <<: *var_clt_str
+        short_name: clivi
+        additional_datasets: *datasets_ecs_high
+      clivi_ECS_med_hist:
+        <<: *var_clt_str
+        short_name: clivi
+        additional_datasets: *datasets_ecs_med
+      clivi_ECS_low_hist:
+        <<: *var_clt_str
+        short_name: clivi
+        additional_datasets: *datasets_ecs_low
+      clivi_ECS_high_scen:
+        <<: *var_clt_str
+        <<: *years_scen
+        short_name: clivi
+        exp: ssp585
+        additional_datasets: *datasets_ecs_high_scenario
+      clivi_ECS_med_scen:
+        <<: *var_clt_str
+        <<: *years_scen
+        short_name: clivi
+        additional_datasets: *datasets_ecs_med_scenario
+      clivi_ECS_low_scen:
+        <<: *var_clt_str
+        <<: *years_scen
+        short_name: clivi
+        additional_datasets: *datasets_ecs_low_scenario
+      lwp_ECS_high_hist:
+        <<: *var_clt_str
+        short_name: lwp
+        derive: true
+        additional_datasets: *datasets_ecs_high
+      lwp_ECS_med_hist:
+        <<: *var_clt_str
+        short_name: lwp
+        derive: true
+        additional_datasets: *datasets_ecs_med
+      lwp_ECS_low_hist:
+        <<: *var_clt_str
+        short_name: lwp
+        derive: true
+        additional_datasets: *datasets_ecs_low
+      lwp_ECS_high_scen:
+        <<: *var_clt_str
+        <<: *years_scen
+        short_name: lwp
+        derive: true
+        exp: ssp585
+        additional_datasets: *datasets_ecs_high_scenario
+      lwp_ECS_med_scen:
+        <<: *var_clt_str
+        <<: *years_scen
+        short_name: lwp
+        derive: true
+        additional_datasets: *datasets_ecs_med_scenario
+      lwp_ECS_low_scen:
+        <<: *var_clt_str
+        <<: *years_scen
+        short_name: lwp
+        derive: true
+        additional_datasets: *datasets_ecs_low_scenario
+      netcre_ECS_high_hist:
+        <<: *var_clt_str
+        short_name: netcre
+        derive: true
+        additional_datasets: *datasets_ecs_high
+      netcre_ECS_med_hist:
+        <<: *var_clt_str
+        short_name: netcre
+        derive: true
+        additional_datasets: *datasets_ecs_med
+      netcre_ECS_low_hist:
+        <<: *var_clt_str
+        short_name: netcre
+        derive: true
+        additional_datasets: *datasets_ecs_low
+      netcre_ECS_high_scen:
+        <<: *var_clt_str
+        <<: *years_scen
+        short_name: netcre
+        derive: true
+        exp: ssp585
+        additional_datasets: *datasets_ecs_high_scenario
+      netcre_ECS_med_scen:
+        <<: *var_clt_str
+        <<: *years_scen
+        short_name: netcre
+        derive: true
+        additional_datasets: *datasets_ecs_med_scenario
+      netcre_ECS_low_scen:
+        <<: *var_clt_str
+        <<: *years_scen
+        short_name: netcre
+        derive: true
+        additional_datasets: *datasets_ecs_low_scenario
+      tas_ECS_high_hist: &var_tas_str
+        <<: *var_clt_str
+        short_name: tas
+      tas_ECS_med_hist:
+        <<: *var_tas_str
+        additional_datasets: *datasets_ecs_med
+      tas_ECS_low_hist:
+        <<: *var_tas_str
+        additional_datasets: *datasets_ecs_low
+      tas_ECS_high_scen:
+        <<: *var_tas_str
+        <<: *years_scen
+        exp: ssp585
+        additional_datasets: *datasets_ecs_high_scenario
+      tas_ECS_med_scen:
+        <<: *var_tas_str
+        <<: *years_scen
+        exp: ssp585
+        additional_datasets: *datasets_ecs_med_scenario
+      tas_ECS_low_scen:
+        <<: *var_tas_str
+        <<: *years_scen
+        exp: ssp585
+        additional_datasets: *datasets_ecs_low_scenario
+    scripts:
+      barplot:
+        <<: *boxplot_settings
+        filename_attach: 'ssp585_stratocumulus'
+        title: 'Stratocumulus region'
+
+
+  # Figure 7a
+  diag_arctic:
+    description: compare field means
+    variables:
+      clt_ECS_high_hist: &var_clt_5
+        <<: *var_settings
+        preprocessor: arctic
+        additional_datasets: *datasets_ecs_high
+      clt_ECS_med_hist:
+        <<: *var_clt_5
+        additional_datasets: *datasets_ecs_med
+      clt_ECS_low_hist:
+        <<: *var_clt_5
+        additional_datasets: *datasets_ecs_low
+      clt_ECS_high_scen:
+        <<: *var_clt_5
+        <<: *years_scen
+        additional_datasets: *datasets_ecs_high_scenario
+      clt_ECS_med_scen:
+        <<: *var_clt_5
+        <<: *years_scen
+        additional_datasets: *datasets_ecs_med_scenario
+      clt_ECS_low_scen:
+        <<: *var_clt_5
+        <<: *years_scen
+        additional_datasets: *datasets_ecs_low_scenario
+      clivi_ECS_high_hist:
+        <<: *var_clt_5
+        short_name: clivi
+        additional_datasets: *datasets_ecs_high
+      clivi_ECS_med_hist:
+        <<: *var_clt_5
+        short_name: clivi
+        additional_datasets: *datasets_ecs_med
+      clivi_ECS_low_hist:
+        <<: *var_clt_5
+        short_name: clivi
+        additional_datasets: *datasets_ecs_low
+      clivi_ECS_high_scen:
+        <<: *var_clt_5
+        <<: *years_scen
+        short_name: clivi
+        exp: ssp585
+        additional_datasets: *datasets_ecs_high_scenario
+      clivi_ECS_med_scen:
+        <<: *var_clt_5
+        <<: *years_scen
+        short_name: clivi
+        additional_datasets: *datasets_ecs_med_scenario
+      clivi_ECS_low_scen:
+        <<: *var_clt_5
+        <<: *years_scen
+        short_name: clivi
+        additional_datasets: *datasets_ecs_low_scenario
+      lwp_ECS_high_hist:
+        <<: *var_clt_5
+        short_name: lwp
+        derive: true
+        additional_datasets: *datasets_ecs_high
+      lwp_ECS_med_hist:
+        <<: *var_clt_5
+        short_name: lwp
+        derive: true
+        additional_datasets: *datasets_ecs_med
+      lwp_ECS_low_hist:
+        <<: *var_clt_5
+        short_name: lwp
+        derive: true
+        additional_datasets: *datasets_ecs_low
+      lwp_ECS_high_scen:
+        <<: *var_clt_5
+        <<: *years_scen
+        short_name: lwp
+        derive: true
+        exp: ssp585
+        additional_datasets: *datasets_ecs_high_scenario
+      lwp_ECS_med_scen:
+        <<: *var_clt_5
+        <<: *years_scen
+        short_name: lwp
+        derive: true
+        additional_datasets: *datasets_ecs_med_scenario
+      lwp_ECS_low_scen:
+        <<: *var_clt_5
+        <<: *years_scen
+        short_name: lwp
+        derive: true
+        additional_datasets: *datasets_ecs_low_scenario
+      netcre_ECS_high_hist:
+        <<: *var_clt_5
+        short_name: netcre
+        derive: true
+        additional_datasets: *datasets_ecs_high
+      netcre_ECS_med_hist:
+        <<: *var_clt_5
+        short_name: netcre
+        derive: true
+        additional_datasets: *datasets_ecs_med
+      netcre_ECS_low_hist:
+        <<: *var_clt_5
+        short_name: netcre
+        derive: true
+        additional_datasets: *datasets_ecs_low
+      netcre_ECS_high_scen:
+        <<: *var_clt_5
+        <<: *years_scen
+        short_name: netcre
+        derive: true
+        exp: ssp585
+        additional_datasets: *datasets_ecs_high_scenario
+      netcre_ECS_med_scen:
+        <<: *var_clt_5
+        <<: *years_scen
+        short_name: netcre
+        derive: true
+        additional_datasets: *datasets_ecs_med_scenario
+      netcre_ECS_low_scen:
+        <<: *var_clt_5
+        <<: *years_scen
+        short_name: netcre
+        derive: true
+        additional_datasets: *datasets_ecs_low_scenario
+      tas_ECS_high_hist: &var_tas_5
+        <<: *var_clt_5
+        short_name: tas
+      tas_ECS_med_hist:
+        <<: *var_tas_5
+        additional_datasets: *datasets_ecs_med
+      tas_ECS_low_hist:
+        <<: *var_tas_5
+        additional_datasets: *datasets_ecs_low
+      tas_ECS_high_scen:
+        <<: *var_tas_5
+        <<: *years_scen
+        exp: ssp585
+        additional_datasets: *datasets_ecs_high_scenario
+      tas_ECS_med_scen:
+        <<: *var_tas_5
+        <<: *years_scen
+        exp: ssp585
+        additional_datasets: *datasets_ecs_med_scenario
+      tas_ECS_low_scen:
+        <<: *var_tas_5
+        <<: *years_scen
+        exp: ssp585
+        additional_datasets: *datasets_ecs_low_scenario
+    scripts:
+      barplot:
+        <<: *boxplot_settings
+        filename_attach: 'ssp585_pol'
+        title: 'Arctic'
diff --git a/esmvaltool/recipes/examples/recipe_check_obs.yml b/esmvaltool/recipes/examples/recipe_check_obs.yml
index 2496679ad3..90c71bfe5d 100644
--- a/esmvaltool/recipes/examples/recipe_check_obs.yml
+++ b/esmvaltool/recipes/examples/recipe_check_obs.yml
@@ -23,7 +23,7 @@ diagnostics:
     variables:
       pr:
     additional_datasets:
-      - {project: OBS6, dataset: AGCD, mip: Amon, tier: 3, 
+      - {project: OBS6, dataset: AGCD, mip: Amon, tier: 2, 
         type: ground, version: v2-0-1}
     scripts: null
 
@@ -61,6 +61,16 @@ diagnostics:
     scripts: null
 
 
+  CMAP:
+    description: CMAP check
+    variables:
+      pr:
+    additional_datasets:
+      - {project: OBS6, dataset: CMAP, mip: Amon, tier: 2,
+        type: reanaly, version: v1}
+    scripts: null
+
+
   CRU:
     description: CRU check
     variables:
@@ -235,8 +245,8 @@ diagnostics:
       treeFrac:
     additional_datasets:
       - {dataset: ESACCI-LANDCOVER, project: OBS, mip: Lmon, tier: 2,
-         type: sat, version: L4-LCCS-Map-300m-P5Y-aggregated-0.500000Deg,
-         start_year: 1998, end_year: 2012}
+         type: sat, version: v2.0.8, frequency: yr,
+         start_year: 1992, end_year: 2020}
     scripts: null
 
   ESACCI-LST:
@@ -297,14 +307,20 @@ diagnostics:
   ESACCI-SOILMOISTURE:
     description: ESACCI-SOILMOISTURE check
     variables:
-      dos:
-      dosStderr:
-        sm:
+      sm_daily:
+        short_name: sm
+        mip: Eday
+        frequency: day
+      sm_monthly:
+        short_name: sm
+        mip: Lmon
+        frequency: mon
       smStderr:
+        mip: Eday
+        frequency: day
     additional_datasets:
-      - {dataset: ESACCI-SOILMOISTURE, project: OBS, mip: Lmon, tier: 2,
-         type: sat, version: L3S-SSMV-COMBINED-v4.2,
-         start_year: 2005, end_year: 2011}
+      - {dataset: ESACCI-SOILMOISTURE, project: OBS, tier: 2,
+         type: sat, version: L3S-SSMV-COMBINED-v08.1, start_year: 1978, end_year: 2022}
     scripts: null
 
 
@@ -549,7 +565,31 @@ diagnostics:
       rsutcs:
     additional_datasets:
       - {dataset: JRA-25, project: OBS6, mip: Amon, tier: 2,
-         type: reanaly, version: 1, start_year: 1979, end_year: 2013}
+         type: reanaly, version: 1, start_year: 1979, end_year: 2007}
+    scripts: null
+
+  JRA-55:
+    description: JRA-55 check
+    variables:
+      cli:
+      clivi:
+      clw:
+      clwvi:
+      clt:
+      prw:
+      rlus:
+      rlut:
+      rlutcs:
+      rsus:
+      rsuscs:
+      rsut:
+      rsutcs:
+      ta:
+      tas:
+      wap:
+    additional_datasets:
+      - {dataset: JRA-55, project: OBS6, mip: Amon, tier: 2,
+         type: reanaly, version: 1, start_year: 1958, end_year: 2022} 
     scripts: null
 
   Kadow2020:
@@ -679,6 +719,10 @@ diagnostics:
       prw:
       ta:
       wap:
+      pr:
+      tauu:
+      tauv:
+      tos:
     additional_datasets:
       - {dataset: NCEP-DOE-R2, project: OBS6, mip: Amon, tier: 2,
          type: reanaly, version: 2, start_year: 1979, end_year: 2022}
@@ -694,6 +738,9 @@ diagnostics:
       prw:
       rlut:
       rsut:
+      pr:
+      tauu:
+      tauv:
     additional_datasets:
       - {dataset: NOAA-CIRES-20CR-V2, project: OBS6, mip: Amon, tier: 2,
          type: reanaly, version: v2, start_year: 1871, end_year: 2012}
@@ -942,7 +989,7 @@ diagnostics:
     variables:
       od440aer:
     additional_datasets:
-      - {dataset: AERONET, project: OBS6, mip: AERmon, tier: 3, type: atmos, version: 20231021}
+      - {dataset: AERONET, project: OBS6, mip: AERmon, tier: 3, type: atmos, version: 20240406}
     scripts: null
 
   ANUClimate:
@@ -1491,10 +1538,10 @@ diagnostics:
         short_name: rlns
         mip: E1hr
         frequency: 1hr
-      rlus_E1hr:
-        short_name: rlus
-        mip: E1hr
-        frequency: 1hr
+        #rlus_E1hr:
+        #  short_name: rlus
+        #  mip: E1hr
+        #  frequency: 1hr
       rsds_E1hr:
         short_name: rsds
         mip: E1hr
@@ -1505,10 +1552,10 @@ diagnostics:
         short_name: rsns
         mip: E1hr
         frequency: 1hr
-      rsus_E1hr:
-        short_name: rsus
-        mip: E1hr
-        frequency: 1hr
+        #rsus_E1hr:
+        #  short_name: rsus
+        #  mip: E1hr
+        #  frequency: 1hr
       rss_E1hr:
         short_name: rss
         mip: E1hr
@@ -1575,9 +1622,9 @@ diagnostics:
       rlns_Amon:
         short_name: rlns
         mip: Amon
-      rlus_Amon:
-        short_name: rlus
-        mip: Amon
+        #rlus_Amon:
+        #  short_name: rlus
+        #  mip: Amon
       rsds_Amon:
         short_name: rsds
         mip: Amon
@@ -1587,9 +1634,9 @@ diagnostics:
       rsns_Amon:
         short_name: rsns
         mip: Amon
-      rsus_Amon:
-        short_name: rsus
-        mip: Amon
+        #rsus_Amon:
+        #  short_name: rsus
+        #  mip: Amon
       rss_Amon:
         short_name: rss
         mip: Amon
diff --git a/esmvaltool/recipes/examples/recipe_easy_ipcc.yml b/esmvaltool/recipes/examples/recipe_easy_ipcc.yml
index 40040e2835..af5d711fa8 100644
--- a/esmvaltool/recipes/examples/recipe_easy_ipcc.yml
+++ b/esmvaltool/recipes/examples/recipe_easy_ipcc.yml
@@ -63,7 +63,7 @@ diagnostics:
         script: examples/make_plot.py
 
 datasets:
-  - {dataset: ACCESS-CM2, ensemble: 'r(1:5)i1p1f1', grid: gn}
+  - {dataset: ACCESS-CM2, ensemble: 'r(1:10)i1p1f1', grid: gn}
   - {dataset: ACCESS-ESM1-5, ensemble: 'r(1:40)i1p1f1', grid: gn}
   - {dataset: AWI-CM-1-1-MR, ensemble: r1i1p1f1, grid: gn}
   - {dataset: BCC-CSM2-MR, ensemble: r1i1p1f1, grid: gn}
@@ -118,11 +118,11 @@ datasets:
   - {dataset: MIROC6, ensemble: 'r(1:50)i1p1f1', grid: gn}
   - {dataset: MPI-ESM1-2-HR, ensemble: 'r1i1p1f1', grid: gn}
   # - {dataset: MPI-ESM1-2-HR, ensemble: 'r(1:2)i1p1f1', grid: gn}  # second ensemble member causes warnings about large graphs in `concatenate` preprocessor step
-  - {dataset: MPI-ESM1-2-LR, ensemble: 'r(1:30)i1p1f1', grid: gn}
+  - {dataset: MPI-ESM1-2-LR, ensemble: 'r(1:50)i1p1f1', grid: gn}
   - {dataset: MRI-ESM2-0, ensemble: 'r(1:5)i1p1f1', grid: gn}
   # - {dataset: NESM3, ensemble: 'r(1:2)i1p1f1', grid: gn}  # cannot be used due to https://github.com/ESMValGroup/ESMValCore/issues/2101
   # - {dataset: NorESM2-LM, ensemble: r1i1p1f1, grid: gn}  # duplicated areacello file with wrong name
   - {dataset: NorESM2-MM, ensemble: r1i1p1f1, grid: gn}
-  - {dataset: TaiESM1, ensemble: r1i1p1f1, grid: gn}
+  # - {dataset: TaiESM1, ensemble: r1i1p1f1, grid: gn}  # download failure of ssp585
   - {dataset: UKESM1-0-LL, ensemble: 'r(1:4)i1p1f2', grid: gn}
   - {dataset: UKESM1-0-LL, ensemble: r8i1p1f2, grid: gn}
diff --git a/esmvaltool/recipes/examples/recipe_extract_shape.yml b/esmvaltool/recipes/examples/recipe_extract_shape.yml
index 79f04371b5..08d1bab490 100644
--- a/esmvaltool/recipes/examples/recipe_extract_shape.yml
+++ b/esmvaltool/recipes/examples/recipe_extract_shape.yml
@@ -7,7 +7,7 @@ documentation:
 
     The example shapefile(s) can be copied from
     esmvaltool/diag_scripts/shapeselect/testdata/Elbe.* and
-    placed in the auxiliary_data_dir defined in config-user.yml.
+    placed in the auxiliary_data_dir defined in the configuration.
 
   title: Example recipe extracting precipitation in the Elbe catchment.
 
diff --git a/esmvaltool/recipes/hydrology/recipe_hydro_forcing.yml b/esmvaltool/recipes/hydrology/recipe_hydro_forcing.yml
index f68a597733..925d9bd420 100644
--- a/esmvaltool/recipes/hydrology/recipe_hydro_forcing.yml
+++ b/esmvaltool/recipes/hydrology/recipe_hydro_forcing.yml
@@ -9,7 +9,7 @@ documentation:
     used to:
 
     1. Plot a timeseries of the raw daily data
-    2. Plot monthly aggregrated data over a certain period
+    2. Plot monthly aggregated data over a certain period
     3. Plot the monthly climate statistics over a certain period
 
   authors:
@@ -33,7 +33,7 @@ datasets:
 preprocessors:
   daily:
     extract_shape: &extract_shape
-      # In aux (config-user.yml)
+      # Relative to auxiliary_data_dir defined in configuration
       shapefile: Lorentz_Basin_Shapefiles/Meuse/Meuse.shp
       method: contains
       crop: true
diff --git a/esmvaltool/recipes/hydrology/recipe_lisflood.yml b/esmvaltool/recipes/hydrology/recipe_lisflood.yml
index ffecbc37be..3acb4be481 100644
--- a/esmvaltool/recipes/hydrology/recipe_lisflood.yml
+++ b/esmvaltool/recipes/hydrology/recipe_lisflood.yml
@@ -37,7 +37,8 @@ preprocessors:
       scheme: linear
     extract_shape:
       # Perhaps a single shapefile needs to be created covering multiple basins
-      shapefile: Lorentz_Basin_Shapefiles/Meuse/Meuse.shp  # (config-user, aux)
+      # Relative to auxiliary_data_dir defined in configuration
+      shapefile: Lorentz_Basin_Shapefiles/Meuse/Meuse.shp
       method: contains
       crop: true  # set to false to keep the entire globe (memory intensive!)
   daily_water:
diff --git a/esmvaltool/recipes/hydrology/recipe_marrmot.yml b/esmvaltool/recipes/hydrology/recipe_marrmot.yml
index dd6eef0a49..e85a66d9b9 100644
--- a/esmvaltool/recipes/hydrology/recipe_marrmot.yml
+++ b/esmvaltool/recipes/hydrology/recipe_marrmot.yml
@@ -28,7 +28,8 @@ preprocessors:
   daily: &daily
     extract_shape:
       # Lumped model: needs catchment-aggregated input data
-      shapefile: Meuse/Meuse.shp  # In aux (config-user.yml)
+      # Relative to auxiliary_data_dir defined in configuration
+      shapefile: Meuse/Meuse.shp
       method: contains
       crop: true
 
diff --git a/esmvaltool/recipes/ipccwg1ar6ch3/recipe_ipccwg1ar6ch3_fig_3_42_a.yml b/esmvaltool/recipes/ipccwg1ar6ch3/recipe_ipccwg1ar6ch3_fig_3_42_a.yml
index 20b0402a23..35954b47a1 100644
--- a/esmvaltool/recipes/ipccwg1ar6ch3/recipe_ipccwg1ar6ch3_fig_3_42_a.yml
+++ b/esmvaltool/recipes/ipccwg1ar6ch3/recipe_ipccwg1ar6ch3_fig_3_42_a.yml
@@ -10,7 +10,7 @@ documentation:
     Contribution to the Sixth Assessment Report: Chapter 3
     Processing of CMIP3 models currently works only in serial mode, due to
     an issue in the input data still under investigation. To run the recipe
-    set: max_parallel_tasks: 1 in the config-user.yml file.
+    set the configuration option ``max_parallel_tasks: 1``.
 
   authors:
     - bock_lisa
@@ -313,7 +313,7 @@ diagnostics:
       pr:
         <<: *perf_var
         preprocessor: ppNOLEV1
-        reference_dataset: GPCP-SG
+        reference_dataset: GPCP-V2.3
         alternative_dataset: GHCN
     additional_datasets:
       - {<<: *cmip3, dataset: bccr_bcm2_0, institute: BCCR}
@@ -344,7 +344,7 @@ diagnostics:
       - {<<: *cmip6, dataset: IPSL-CM5A2-INCA, grid: gr}
       - {<<: *cmip6, dataset: KACE-1-0-G, grid: gr}
       - {<<: *cmip6, dataset: MCM-UA-1-0}
-      - {dataset: GPCP-SG, project: obs4MIPs, level: L3, version: v2.3, tier: 1}
+      - {dataset: GPCP-V2.3, project: obs4MIPs, level: L3, tier: 1}
       - {dataset: GHCN, project: OBS, type: ground, version: 1, tier: 2}
 
 
@@ -462,7 +462,7 @@ diagnostics:
       - {<<: *cmip6, dataset: IPSL-CM5A2-INCA, grid: gr}
       - {<<: *cmip6, dataset: KACE-1-0-G, grid: gr}
       - {<<: *cmip6, dataset: KIOST-ESM, grid: gr1}
-      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, version: Ed2-8,
+      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B,
          tier: 1, start_year: 2001, end_year: 2015}
 
 
@@ -503,7 +503,7 @@ diagnostics:
       - {<<: *cmip6, dataset: IPSL-CM5A2-INCA, grid: gr}
       - {<<: *cmip6, dataset: KACE-1-0-G, grid: gr}
       - {<<: *cmip6, dataset: KIOST-ESM, grid: gr1}
-      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, version: Ed2-8,
+      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B,
          tier: 1, start_year: 2001, end_year: 2015}
 
 
@@ -547,7 +547,7 @@ diagnostics:
       - {<<: *cmip6, dataset: KACE-1-0-G, grid: gr}
       - {<<: *cmip6, dataset: KIOST-ESM, grid: gr1}
       - {<<: *cmip6, dataset: MCM-UA-1-0}
-      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, version: Ed2-8,
+      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B,
          tier: 1, start_year: 2001, end_year: 2015}
 
 
@@ -620,7 +620,7 @@ diagnostics:
       - {<<: *cmip6, dataset: IPSL-CM5A2-INCA, grid: gr}
       - {<<: *cmip6, dataset: KACE-1-0-G, grid: gr}
       - {<<: *cmip6, dataset: KIOST-ESM, grid: gr1}
-      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, version: Ed2-8,
+      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B,
          tier: 1, start_year: 2001, end_year: 2015}
 
 
diff --git a/esmvaltool/recipes/ipccwg1ar6ch3/recipe_ipccwg1ar6ch3_fig_3_43.yml b/esmvaltool/recipes/ipccwg1ar6ch3/recipe_ipccwg1ar6ch3_fig_3_43.yml
index fd7a60efe8..767662167c 100644
--- a/esmvaltool/recipes/ipccwg1ar6ch3/recipe_ipccwg1ar6ch3_fig_3_43.yml
+++ b/esmvaltool/recipes/ipccwg1ar6ch3/recipe_ipccwg1ar6ch3_fig_3_43.yml
@@ -231,7 +231,7 @@ diagnostics:
     variables:
       pr:
         <<: *var_settings
-        reference_dataset: GPCP-SG
+        reference_dataset: GPCP-V2.2
         alternative_dataset: GHCN
     additional_datasets:
       - {<<: *cmip3, dataset: bccr_bcm2_0, institute: BCCR}
@@ -260,7 +260,7 @@ diagnostics:
       - {<<: *cmip6, dataset: GISS-E2-1-G-CC}
       - {<<: *cmip6, dataset: IPSL-CM5A2-INCA, grid: gr}
       - {<<: *cmip6, dataset: MCM-UA-1-0}
-      - {dataset: GPCP-SG, project: obs4mips, level: L3, version: v2.2, tier: 1}
+      - {dataset: GPCP-V2.2, project: obs4mips, level: L3, tier: 1}
       - {dataset: GHCN, project: OBS, type: ground, version: 1, tier: 2}
 
 
@@ -333,7 +333,7 @@ diagnostics:
       - {<<: *cmip6, dataset: IPSL-CM5A2-INCA, grid: gr}
       - {<<: *cmip6, dataset: KIOST-ESM, grid: gr1}
       - {<<: *cmip6, dataset: MCM-UA-1-0}
-      - {dataset: CERES-EBAF, project: obs4mips, level: L3B, version: Ed2-8,
+      - {dataset: CERES-EBAF, project: obs4mips, level: L3B,
          tier: 1, start_year: 2001, end_year: 2015}
 
 
@@ -367,7 +367,7 @@ diagnostics:
       - {<<: *cmip6, dataset: GISS-E2-1-G-CC}
       - {<<: *cmip6, dataset: IPSL-CM5A2-INCA, grid: gr}
       - {<<: *cmip6, dataset: KIOST-ESM, grid: gr1}
-      - {dataset: CERES-EBAF, project: obs4mips, level: L3B, version: Ed2-8,
+      - {dataset: CERES-EBAF, project: obs4mips, level: L3B,
          tier: 1, start_year: 2001, end_year: 2015}
 
 
@@ -401,7 +401,7 @@ diagnostics:
       - {<<: *cmip6, dataset: GISS-E2-1-G-CC}
       - {<<: *cmip6, dataset: IPSL-CM5A2-INCA, grid: gr}
       - {<<: *cmip6, dataset: KIOST-ESM, grid: gr1}
-      - {dataset: CERES-EBAF, project: obs4mips, level: L3B, version: Ed2-8,
+      - {dataset: CERES-EBAF, project: obs4mips, level: L3B,
          tier: 1, start_year: 2001, end_year: 2015}
 
 
@@ -434,7 +434,7 @@ diagnostics:
       - {<<: *cmip6, dataset: GISS-E2-1-G-CC}
       - {<<: *cmip6, dataset: IPSL-CM5A2-INCA, grid: gr}
       - {<<: *cmip6, dataset: KIOST-ESM, grid: gr1}
-      - {dataset: CERES-EBAF, project: obs4mips, level: L3B, version: Ed2-8,
+      - {dataset: CERES-EBAF, project: obs4mips, level: L3B,
          tier: 1, start_year: 2001, end_year: 2015}
 
 
diff --git a/esmvaltool/recipes/monitor/recipe_monitor_with_refs.yml b/esmvaltool/recipes/monitor/recipe_monitor_with_refs.yml
index 681277310c..f5eea141dc 100644
--- a/esmvaltool/recipes/monitor/recipe_monitor_with_refs.yml
+++ b/esmvaltool/recipes/monitor/recipe_monitor_with_refs.yml
@@ -10,7 +10,7 @@ documentation:
     - heuer_helge
     - kraft_jeremy
     - kuehbacher_birgit
-    - ruhe_lukas
+    - lindenlaub_lukas
     - sarauer_ellen
     - winterstein_franziska
   maintainer:
@@ -165,6 +165,7 @@ diagnostics:
         plots:
           map:
             common_cbar: true
+            fontsize: 10
             plot_kwargs_bias:
               levels: [-10.0, -7.5, -5.0, -2.5, 0.0, 2.5, 5.0, 7.5, 10.0]
 
@@ -182,6 +183,7 @@ diagnostics:
         plots:
           zonal_mean_profile:
             common_cbar: true
+            fontsize: 10
             plot_kwargs_bias:
               levels: [-10.0, -7.5, -5.0, -2.5, 0.0, 2.5, 5.0, 7.5, 10.0]
 
@@ -225,11 +227,16 @@ diagnostics:
       ta:
         preprocessor: global_mean_extract_levels
         mip: Amon
-        timerange: '2000/2004'
+        timerange: '2000/2005'
     scripts:
       plot:
         <<: *plot_multi_dataset_default
         script: monitor/multi_datasets.py
+        matplotlib_rc_params:
+          axes.labelsize: small
+          axes.titlesize: small
+          xtick.labelsize: x-small
+          ytick.labelsize: x-small
         plots:
           hovmoeller_z_vs_time:
             plot_func: contourf
@@ -238,6 +245,8 @@ diagnostics:
             log_y: false
             pyplot_kwargs:
               ylim: [0, 20000]
+              xticks:
+                rotation: 25
 
   plot_time_vs_lat_with_references:
     description: Plot Hovmoeller time vs. latitude including reference datasets.
@@ -245,7 +254,7 @@ diagnostics:
       tas:
         mip: Amon
         preprocessor: zonal_mean_2d
-        timerange: '2000/2004'
+        timerange: '2000/2005'
     scripts:
       plot:
         <<: *plot_multi_dataset_default
@@ -253,5 +262,6 @@ diagnostics:
         plots:
           hovmoeller_time_vs_lat_or_lon:
             common_cbar: true
+            fontsize: 10
             show_x_minor_ticks: false
             time_format: '%Y'
diff --git a/esmvaltool/recipes/recipe_aod_aeronet_assess.yml b/esmvaltool/recipes/recipe_aod_aeronet_assess.yml
index 0fc82ec864..51cb14759b 100644
--- a/esmvaltool/recipes/recipe_aod_aeronet_assess.yml
+++ b/esmvaltool/recipes/recipe_aod_aeronet_assess.yml
@@ -44,7 +44,7 @@ diagnostics:
         start_year: 1994
         end_year: 2014
         additional_datasets:
-          - {dataset: AERONET, project: OBS6, mip: AERmon, tier: 3, type: atmos, version: 20231021}
+          - {dataset: AERONET, project: OBS6, mip: AERmon, tier: 3, type: atmos, version: 20240406}
 
       od440aer_season:
         <<: *var_od440aer
diff --git a/esmvaltool/recipes/recipe_carvalhais14nat.yml b/esmvaltool/recipes/recipe_carvalhais14nat.yml
index 9ec0811c00..63bfbb1edd 100644
--- a/esmvaltool/recipes/recipe_carvalhais14nat.yml
+++ b/esmvaltool/recipes/recipe_carvalhais14nat.yml
@@ -8,7 +8,7 @@ documentation:
     Carvalhais et al., 2014, Nature.
     The data required in the obs_details section can be obtained at
     http://www.bgc-jena.mpg.de/geodb/BGI/tau4ESMValTool.php
-    and have to be stored in the auxiliary_data_dir defined i config-user.yml,
+    and have to be stored in the auxiliary_data_dir defined in the configuration
     in a subdirectory obs_data_subdir specified in the obs_details section
     below.
 
diff --git a/esmvaltool/recipes/recipe_climate_patterns.yml b/esmvaltool/recipes/recipe_climate_patterns.yml
new file mode 100644
index 0000000000..08e0c51779
--- /dev/null
+++ b/esmvaltool/recipes/recipe_climate_patterns.yml
@@ -0,0 +1,249 @@
+# ESMValTool
+# recipe_climate_patterns.yml
+---
+documentation:
+  description: Generating climate patterns from CMIP6 models.
+  title: Generating Climate Patterns
+
+  authors:
+    - munday_gregory
+
+  maintainer:
+    - munday_gregory
+
+  references:
+    - mathison2024gmd
+    - huntingford2000climdyn
+
+preprocessors:
+  global_mean_monthly:
+    monthly_statistics:
+      operator: mean
+
+    regrid:
+      target_grid: {start_longitude: -180, end_longitude: 176.25, step_longitude: 3.75,
+                    start_latitude: -55, end_latitude: 82.5, step_latitude: 2.5}
+      scheme: linear
+
+  downscale_sftlf:
+    regrid:
+      target_grid: {start_longitude: -180, end_longitude: 176.25, step_longitude: 3.75,
+                    start_latitude: -55, end_latitude: 82.5, step_latitude: 2.5}
+      scheme: linear
+
+monthly_global_settings: &monthly_global_settings
+  mip: Amon
+  project: CMIP6
+  preprocessor: global_mean_monthly
+
+monthly_global_settings_day: &monthly_global_settings_day
+  mip: day
+  project: CMIP6
+  preprocessor: global_mean_monthly
+
+
+CMIP6_landfrac: &cmip6_landfrac
+  - {dataset: ACCESS-CM2,      exp: piControl,  ensemble: r1i1p1f1, grid: gn, institute: CSIRO-ARCCSS}
+  - {dataset: ACCESS-ESM1-5,   exp: piControl,  ensemble: r1i1p1f1, grid: gn}
+  - {dataset: AWI-CM-1-1-MR,   exp: piControl,  ensemble: r1i1p1f1, grid: gn}
+  - {dataset: BCC-CSM2-MR,     exp: hist-resIPO,ensemble: r1i1p1f1, grid: gn}
+  - {dataset: CanESM5,         exp: piControl,  ensemble: r1i1p1f1, grid: gn}
+  - {dataset: CanESM5-CanOE,   exp: piControl,  ensemble: r1i1p2f1, grid: gn}
+  - {dataset: CanESM5-1,       exp: piControl,  ensemble: r1i1p1f1, grid: gn, institute: CCCma}
+  # - {dataset: CAS-ESM2-0,      exp: piControl,  ensemble: r1i1p1f1, grid: gn} # Global only
+  - {dataset: CMCC-ESM2,       exp: piControl,  ensemble: r1i1p1f1, grid: gn}
+  # - {dataset: CMCC-CM2-SR5,    exp: piControl,  ensemble: r1i1p1f1, grid: gn} # No tasmin/tasmax
+  - {dataset: CNRM-CM6-1,      exp: piControl,  ensemble: r1i1p1f2, grid: gr}
+  - {dataset: CNRM-CM6-1-HR,   exp: piControl,  ensemble: r1i1p1f2, grid: gr}
+  - {dataset: CNRM-ESM2-1,     exp: piControl,  ensemble: r1i1p1f2, grid: gr}
+  # - {dataset: E3SM-1-0,        exp: piControl,  ensemble: r1i1p1f1, grid: gr} # Tasmax == tasmin
+  - {dataset: EC-Earth3,       exp: piControl,  ensemble: r1i1p1f1, grid: gr}
+  # - {dataset: EC-Earth3-CC,    exp: piControl,  ensemble: r1i1p1f1, grid: gr} # Global only
+  - {dataset: EC-Earth3-Veg,   exp: piControl,  ensemble: r1i1p1f1, grid: gr}
+  # - {dataset: FGOALS-f3-L,     exp: historical, ensemble: r1i1p1f1, grid: gr} # No tasmin/tasmax
+  - {dataset: FGOALS-g3,       exp: piControl,  ensemble: r1i1p1f1, grid: gn}
+  # - {dataset: FIO-ESM-2-0,     exp: piControl,  ensemble: r1i1p1f1, grid: gn} # Global only
+  - {dataset: GFDL-CM4,        exp: piControl,  ensemble: r1i1p1f1, grid: gr1}
+  - {dataset: GFDL-ESM4,       exp: ssp370,     ensemble: r1i1p1f1, grid: gr1}
+  - {dataset: GISS-E2-1-H,     exp: piControl,  ensemble: r1i1p1f1, grid: gn}
+  - {dataset: GISS-E2-1-G,     exp: piControl,  ensemble: r1i1p5f1, grid: gn}
+  - {dataset: GISS-E2-2-G,     exp: piControl,  ensemble: r1i1p1f1, grid: gn}
+  - {dataset: HadGEM3-GC31-LL, exp: piControl,  ensemble: r1i1p1f1, grid: gn}
+  - {dataset: HadGEM3-GC31-MM, exp: piControl,  ensemble: r1i1p1f1, grid: gn}
+  - {dataset: INM-CM4-8,       exp: piControl,  ensemble: r1i1p1f1, grid: gr1}
+  - {dataset: INM-CM5-0,       exp: abrupt-4xCO2,  ensemble: r1i1p1f1, grid: gr1}
+  - {dataset: IPSL-CM6A-LR,    exp: piControl,  ensemble: r1i1p1f1, grid: gr}
+  # - {dataset: KACE-1-0-G,      exp: piControl,  ensemble: r1i1p1f1, grid: gr} # Global only, weird tasmin/tasmax
+  # - {dataset: KIOST-ESM,       exp: piControl,  ensemble: r1i1p1f1, grid: gr} # Global only
+  - {dataset: MIROC6,          exp: piControl,  ensemble: r1i1p1f1, grid: gn}
+  - {dataset: MIROC-ES2L,      exp: piControl,  ensemble: r1i1p1f2, grid: gn}
+  - {dataset: MIROC-ES2H,      exp: piControl,  ensemble: r1i1p4f2, grid: gn}
+  - {dataset: MPI-ESM1-2-HR,   exp: piControl,  ensemble: r1i1p1f1, grid: gn}
+  - {dataset: MPI-ESM1-2-LR,   exp: piControl,  ensemble: r1i1p1f1, grid: gn}
+  - {dataset: MRI-ESM2-0,      exp: piControl,  ensemble: r1i1p1f1, grid: gn}
+  # - {dataset: NorESM2-LM,      exp: piControl,  ensemble: r1i1p1f1, grid: gn} # Global only, tasmax == tasmin
+  - {dataset: NorESM2-MM,      exp: piControl,  ensemble: r1i1p1f1, grid: gn}
+  - {dataset: TaiESM1,         exp: piControl,  ensemble: r1i1p1f1, grid: gn}
+  - {dataset: UKESM1-0-LL,     exp: piControl,  ensemble: r1i1p1f2, grid: gn}
+
+CMIP6_no_tasmax: &cmip6_no_tasmax
+  # - {dataset: E3SM-1-0,        exp: [historical, ssp585], ensemble: r1i1p1f1, grid: gr, start_year: 1850, end_year: 2099} # bad tasmin/tasmax
+  # - {dataset: NorESM2-LM,      exp: [historical, ssp585], ensemble: r1i1p1f1, grid: gn, start_year: 1850, end_year: 2100} # bad tasmin/tasmax
+  - {dataset: NorESM2-MM,      exp: [historical, ssp585], ensemble: r1i1p1f1, grid: gn, start_year: 1850, end_year: 2100}
+  - {dataset: TaiESM1,         exp: [historical, ssp585], ensemble: r1i1p1f1, grid: gn, start_year: 1850, end_year: 2100}
+
+CMIP6_DAY: &cmip6_day 
+  # - {dataset: E3SM-1-0,        exp: [historical, ssp585], ensemble: r1i1p1f1, grid: gr, start_year: 1850, end_year: 2099} # bad tasmin/tasmax
+  # - {dataset: NorESM2-LM,      exp: [historical, ssp585], ensemble: r1i1p1f1, grid: gn, start_year: 1850, end_year: 2100} # bad tasmin/tasmax
+  - {dataset: NorESM2-MM,      exp: [historical, ssp585], ensemble: r1i1p1f1, grid: gn, start_year: 1850, end_year: 2100}
+  - {dataset: TaiESM1,         exp: [historical, ssp585], ensemble: r1i1p1f1, grid: gn, start_year: 1850, end_year: 2100}
+
+CMIP6_FULL: &cmip6_full
+  - {dataset: ACCESS-CM2,      exp: [historical, ssp585], ensemble: r1i1p1f1, grid: gn, start_year: 1850, end_year: 2100, institute: CSIRO-ARCCSS}
+  - {dataset: ACCESS-ESM1-5,   exp: [historical, ssp585], ensemble: r3i1p1f1, grid: gn, start_year: 1850, end_year: 2100}
+  - {dataset: AWI-CM-1-1-MR,   exp: [historical, ssp585], ensemble: r1i1p1f1, grid: gn, start_year: 1850, end_year: 2100}
+  - {dataset: BCC-CSM2-MR,     exp: [historical, ssp585], ensemble: r1i1p1f1, grid: gn, start_year: 1850, end_year: 2100}
+  - {dataset: CanESM5,         exp: [historical, ssp585], ensemble: r1i1p1f1, grid: gn, start_year: 1850, end_year: 2100}
+  - {dataset: CanESM5-1,       exp: [historical, ssp585], ensemble: r1i1p1f1, grid: gn, start_year: 1850, end_year: 2100, institute: CCCma} # optional extra
+  - {dataset: CanESM5-CanOE,   exp: [historical, ssp585], ensemble: r1i1p2f1, grid: gn, start_year: 1850, end_year: 2100}
+  # - {dataset: CAS-ESM2-0,      exp: [historical, ssp585], ensemble: r1i1p1f1, grid: gn, start_year: 1850, end_year: 2100} # Global only
+  - {dataset: CMCC-ESM2,       exp: [historical, ssp585], ensemble: r1i1p1f1, grid: gn, start_year: 1850, end_year: 2100}
+  # - {dataset: CMCC-CM2-SR5,    exp: [historical, ssp585], ensemble: r1i1p1f1, grid: gn, start_year: 1850, end_year: 2100} # No tasmin/tasmax
+  - {dataset: CNRM-CM6-1,      exp: [historical, ssp585], ensemble: r1i1p1f2, grid: gr, start_year: 1850, end_year: 2100}
+  - {dataset: CNRM-CM6-1-HR,   exp: [historical, ssp585], ensemble: r1i1p1f2, grid: gr, start_year: 1850, end_year: 2100}
+  - {dataset: CNRM-ESM2-1,     exp: [historical, ssp585], ensemble: r1i1p1f2, grid: gr, start_year: 1850, end_year: 2100}
+  - {dataset: EC-Earth3,       exp: [historical, ssp585], ensemble: r11i1p1f1, grid: gr, start_year: 1850, end_year: 2100}
+  # - {dataset: EC-Earth3-CC,    exp: [historical, ssp585], ensemble: r1i1p1f1, grid: gr, start_year: 1850, end_year: 2100} # Global only
+  - {dataset: EC-Earth3-Veg,   exp: [historical, ssp585], ensemble: r1i1p1f1, grid: gr, start_year: 1850, end_year: 2100}
+  # - {dataset: FGOALS-f3-L,     exp: [historical, ssp585], ensemble: r1i1p1f1, grid: gr, start_year: 1850, end_year: 2100} # No tasmin/tasmax
+  - {dataset: FGOALS-g3,       exp: [historical, ssp585], ensemble: r1i1p1f1, grid: gn, start_year: 1850, end_year: 2100}
+  # - {dataset: FIO-ESM-2-0,     exp: [historical, ssp585], ensemble: r1i1p1f1, grid: gn, start_year: 1850, end_year: 2100} # Global only
+  - {dataset: GFDL-CM4,        exp: [historical, ssp585], ensemble: r1i1p1f1, grid: gr1, start_year: 1850, end_year: 2100}
+  - {dataset: GFDL-ESM4,       exp: [historical, ssp585], ensemble: r1i1p1f1, grid: gr1, start_year: 1850, end_year: 2100}
+  - {dataset: GISS-E2-1-H,     exp: [historical, ssp585], ensemble: r3i1p1f2, grid: gn, start_year: 1850, end_year: 2100}
+  - {dataset: GISS-E2-1-G,     exp: [historical, ssp585], ensemble: r1i1p5f1, grid: gn, start_year: 1850, end_year: 2100}
+  - {dataset: GISS-E2-2-G,     exp: [historical, ssp585], ensemble: r1i1p3f1, grid: gn, start_year: 1850, end_year: 2100}
+  - {dataset: HadGEM3-GC31-LL, exp: [historical, ssp585], ensemble: r1i1p1f3, grid: gn, start_year: 1850, end_year: 2100}
+  - {dataset: HadGEM3-GC31-MM, exp: [historical, ssp585], ensemble: r1i1p1f3, grid: gn, start_year: 1850, end_year: 2100}
+  - {dataset: INM-CM4-8,       exp: [historical, ssp585], ensemble: r1i1p1f1, grid: gr1, start_year: 1850, end_year: 2100}
+  - {dataset: INM-CM5-0,       exp: [historical, ssp585], ensemble: r1i1p1f1, grid: gr1, start_year: 1850, end_year: 2100}
+  - {dataset: IPSL-CM6A-LR,    exp: [historical, ssp585], ensemble: r1i1p1f1, grid: gr, start_year: 1850, end_year: 2100}
+  # - {dataset: KACE-1-0-G,      exp: [historical, ssp585],  ensemble: r1i1p1f1, grid: gr, start_year: 1850, end_year: 2100} # bad tasmin/tasmax
+  # - {dataset: KIOST-ESM,       exp: [historical, ssp585],  ensemble: r1i1p1f1, grid: gr, start_year: 1850, end_year: 2100} # optional extra
+  - {dataset: MIROC6,          exp: [historical, ssp585], ensemble: r1i1p1f1, grid: gn, start_year: 1850, end_year: 2100}
+  - {dataset: MIROC-ES2L,      exp: [historical, ssp585], ensemble: r1i1p1f2, grid: gn, start_year: 1850, end_year: 2100}
+  - {dataset: MIROC-ES2H,      exp: [historical, ssp585], ensemble: r1i1p4f2, grid: gn, start_year: 1850, end_year: 2100} # optional extra
+  - {dataset: MPI-ESM1-2-HR,   exp: [historical, ssp585], ensemble: r1i1p1f1, grid: gn, start_year: 1850, end_year: 2100}
+  - {dataset: MPI-ESM1-2-LR,   exp: [historical, ssp585], ensemble: r1i1p1f1, grid: gn, start_year: 1850, end_year: 2100}
+  - {dataset: MRI-ESM2-0,      exp: [historical, ssp585], ensemble: r1i1p1f1, grid: gn, start_year: 1850, end_year: 2100}
+  - {dataset: UKESM1-0-LL,     exp: [historical, ssp585], ensemble: r1i1p1f2, grid: gn, start_year: 1850, end_year: 2100}
+
+diagnostics:
+  monthly_timeseries:
+    description: Mean monthly variables
+
+    variables:
+
+      # sftlf:
+      #   short_name: sftlf
+      #   mip: fx
+      #   project: CMIP6
+      #   preprocessor: downscale_sftlf
+      #   additional_datasets: *cmip6_landfrac
+
+      tasmax_585:
+        short_name: tasmax
+        <<: *monthly_global_settings
+        additional_datasets: *cmip6_full
+
+      tasmin_585:
+        short_name: tasmin
+        <<: *monthly_global_settings
+        additional_datasets: *cmip6_full
+
+      tas_585:
+        short_name: tas
+        <<: *monthly_global_settings
+        additional_datasets: *cmip6_full
+
+      huss_585:
+        short_name: huss
+        <<: *monthly_global_settings
+        additional_datasets: *cmip6_full
+
+      pr_585:
+        short_name: pr
+        <<: *monthly_global_settings
+        additional_datasets: *cmip6_full
+
+      sfcWind_585:
+        short_name: sfcWind
+        <<: *monthly_global_settings
+        additional_datasets: *cmip6_full
+
+      ps_585:
+        short_name: ps
+        <<: *monthly_global_settings
+        additional_datasets: *cmip6_full
+
+      rsds_585:
+        short_name: rsds
+        <<: *monthly_global_settings
+        additional_datasets: *cmip6_full
+
+      rlds_585:
+        short_name: rlds
+        <<: *monthly_global_settings
+        additional_datasets: *cmip6_full
+
+      tasmax_585_day:
+        short_name: tasmax
+        <<: *monthly_global_settings_day
+        additional_datasets: *cmip6_day
+
+      tasmin_585_day:
+        short_name: tasmin
+        <<: *monthly_global_settings_day
+        additional_datasets: *cmip6_day
+
+      tas_585_no_tasmax:
+        short_name: tas
+        <<: *monthly_global_settings
+        additional_datasets: *cmip6_no_tasmax
+
+      huss_585_no_tasmax:
+        short_name: huss
+        <<: *monthly_global_settings
+        additional_datasets: *cmip6_no_tasmax
+
+      pr_585_no_tasmax:
+        short_name: pr
+        <<: *monthly_global_settings
+        additional_datasets: *cmip6_no_tasmax
+
+      sfcWind_585_no_tasmax:
+        short_name: sfcWind
+        <<: *monthly_global_settings
+        additional_datasets: *cmip6_no_tasmax
+
+      ps_585_no_tasmax:
+        short_name: ps
+        <<: *monthly_global_settings
+        additional_datasets: *cmip6_no_tasmax
+
+      rsds_585_no_tasmax:
+        short_name: rsds
+        <<: *monthly_global_settings
+        additional_datasets: *cmip6_no_tasmax
+
+      rlds_585_no_tasmax:
+        short_name: rlds
+        <<: *monthly_global_settings
+        additional_datasets: *cmip6_no_tasmax
+
+    scripts:
+      climate_patterns_script:
+        script: climate_patterns/climate_patterns.py
+        jules_mode: false  # options: true, false
+        parallelise: false  # options: true, false
+        area: global  # options global, land. If land, uncomment landfrac recipe settings
diff --git a/esmvaltool/recipes/recipe_eady_growth_rate.yml b/esmvaltool/recipes/recipe_eady_growth_rate.yml
index b0eea7f708..d05020d06d 100644
--- a/esmvaltool/recipes/recipe_eady_growth_rate.yml
+++ b/esmvaltool/recipes/recipe_eady_growth_rate.yml
@@ -49,6 +49,7 @@ diagnostics:
     scripts:
       annual_eady_growth_rate:
         script: primavera/eady_growth_rate/eady_growth_rate.py
+        no_distributed: true
         time_statistic: 'annual_mean'
 
 
@@ -63,6 +64,7 @@ diagnostics:
     scripts:
       summer_eady_growth_rate:
         script: primavera/eady_growth_rate/eady_growth_rate.py
+        no_distributed: true
         time_statistic: 'seasonal_mean'
 
   winter_egr:
@@ -76,5 +78,6 @@ diagnostics:
     scripts:
       winter_eady_growth_rate:
         script: primavera/eady_growth_rate/eady_growth_rate.py
+        no_distributed: true
         time_statistic: 'seasonal_mean'
         plot_levels: [70000]
diff --git a/esmvaltool/recipes/recipe_portrait_CMIP.yml b/esmvaltool/recipes/recipe_portrait_CMIP.yml
new file mode 100644
index 0000000000..b09eb1ec72
--- /dev/null
+++ b/esmvaltool/recipes/recipe_portrait_CMIP.yml
@@ -0,0 +1,213 @@
+# ESMValTool
+---
+documentation:
+  title: Performance metrics plots.
+  description: >
+    Compare performance of CMIP simulations to a reference dataset.
+  authors:
+    - cammarano_diego
+    - lindenlaub_lukas
+  maintainer:
+    - lindenlaub_lukas
+  references:
+    - eyring21ipcc
+    - gleckler08jgr
+
+cmip5: &CMIP5
+  project: CMIP5
+  ensemble: r1i1p1
+
+datasets:
+  # cmip5
+  - {<<: *CMIP5, dataset: ACCESS1-0}
+  - {<<: *CMIP5, dataset: CESM1-BGC}
+  - {<<: *CMIP5, dataset: CNRM-CM5}
+  - {<<: *CMIP5, dataset: GFDL-ESM2M}
+  - {<<: *CMIP5, dataset: HadGEM2-CC}
+  - {<<: *CMIP5, dataset: IPSL-CM5B-LR}
+  - {<<: *CMIP5, dataset: MIROC-ESM}
+  - {<<: *CMIP5, dataset: MPI-ESM-LR}
+  - {<<: *CMIP5, dataset: MRI-CGCM3}
+  # cmip6
+  - {dataset: ACCESS-ESM1-5, institute: CSIRO}
+  - {dataset: CESM2, institute: NCAR}
+  - {dataset: CNRM-CM6-1, ensemble: r1i1p1f2, grid: gr}
+  - {dataset: GFDL-CM4, grid: gr1}
+  - {dataset: MIROC-ES2L, ensemble: r1i1p1f2}
+  - {dataset: MPI-ESM1-2-LR}
+  - {dataset: MRI-ESM2-0}
+  - {dataset: UKESM1-0-LL, ensemble: r1i1p1f2}
+
+preprocessors:
+  default: &default  # common preprocessor settings
+    regrid:
+      target_grid: 3x3
+      scheme: linear
+    distance_metric:
+      metric: weighted_rmse
+    climate_statistics:
+      operator: mean
+      period: month
+    mask_fillvalues:
+      threshold_fraction: 0.95
+    multi_model_statistics:
+      span: overlap
+      statistics:
+        - operator: mean
+        - operator: percentile
+          percent: 50
+      groupby: ['project']
+      # exclude all possible reference datasets
+      exclude: [
+        AIRS-2-1,
+        CERES-EBAF,
+        ERA-Interim,
+        ESACCI-AEROSOL,
+        ESACCI-CLOUD,
+        ESACCI-OZONE,
+        ESACCI-SOILMOISTURE,
+        ESACCI-SST,
+        GPCP-SG,
+        HadISST,
+        NCEP-NCAR-R1,
+        MODIS,
+        NIWA-BS,
+        PATMOS-x]
+  pp200:  # only add/overwrite var specific settings
+    <<: *default
+    extract_levels:
+      levels: 20000
+      scheme: linear
+      coordinate: air_pressure
+  pp500:  # only add/overwrite var specific settings
+    <<: *default
+    extract_levels:
+      levels: 50000
+      scheme: linear
+      coordinate: air_pressure
+  thr10:  # only add/overwrite var specific settings
+    <<: *default
+    mask_fillvalues:
+      threshold_fraction: 0.10
+
+var_default: &var_default
+  mip: Amon
+  project: CMIP6
+  exp: historical
+  ensemble: r1i1p1f1
+  preprocessor: default
+  grid: gn
+  start_year: 2000
+  end_year: 2002
+  split: Ref1  # first triangle
+
+diagnostics:
+  portrait_rmse:
+    themes: [aerosols, phys, clouds, atmDyn, chem, ghg]
+    realms: [atmos, land, atmosChem, ocean]
+    variables:
+      zg: &zg
+        <<: *var_default
+        short_name: zg
+        variable: zg500
+        preprocessor: pp500
+        additional_datasets:
+          - {dataset: ERA-Interim, project: OBS6, type: reanaly,
+             version: 1, tier: 3, reference_for_metric: true}
+      zg_2:
+        <<: *zg
+        split: Ref2
+        additional_datasets:
+          - {dataset: NCEP-NCAR-R1, project: OBS6, type: reanaly,
+             version: 1, tier: 2, reference_for_metric: true}
+      clt: &clt
+        <<: *var_default
+        short_name: clt
+        variable: clt
+        additional_datasets:
+          - {dataset: ESACCI-CLOUD, project: OBS, type: sat,
+             version: AVHRR-AMPM-fv3.0, tier: 2, reference_for_metric: true}
+      clt_2:
+        <<: *clt
+        split: Ref2
+        additional_datasets:
+          - {dataset: PATMOS-x, project: OBS, type: sat, version: NOAA, tier: 2, reference_for_metric: true}
+      ts: &ts
+        <<: *var_default
+        short_name: ts
+        variable: ts
+        preprocessor: default
+        additional_datasets:
+          - {dataset: ESACCI-SST, project: OBS, type: sat, version: 2.2, tier: 2, reference_for_metric: true}
+      ts_2:
+        <<: *ts
+        split: Ref2
+        additional_datasets:
+          - {dataset: HadISST, project: OBS, type: reanaly, version: 1, tier: 2, reference_for_metric: true}
+      tas: &tas
+        <<: *var_default
+        short_name: tas
+        variable: tas
+        additional_datasets:
+          - {dataset: ERA-Interim, project: OBS6, type: reanaly,
+             version: 1, tier: 3, reference_for_metric: true}
+      tas_2:
+        <<: *tas
+        split: Ref2
+        additional_datasets:
+          - {dataset: NCEP-NCAR-R1, project: OBS6, type: reanaly,
+             version: 1, tier: 2, reference_for_metric: true}
+      pr:
+        <<: *var_default
+        variable: pr
+        split: Ref1
+        additional_datasets:
+          - {dataset: GPCP-SG, project: OBS, type: atmos,
+             version: 2.3, tier: 2, reference_for_metric: true}
+      rlut:
+        <<: *var_default
+        variable: rlut
+        start_year: 2001
+        end_year: 2003
+        additional_datasets:
+          - {dataset: CERES-EBAF, project: OBS, type: sat, version: Ed4.2,
+             tier: 2, reference_for_metric: true}
+      rsut:
+        <<: *var_default
+        variable: rsut
+        start_year: 2001
+        end_year: 2003
+        additional_datasets:
+          - {dataset: CERES-EBAF, project: OBS, type: sat, version: Ed4.2,
+             tier: 2, reference_for_metric: true}
+      ua200: &ua200
+        <<: *var_default
+        variable: ua200
+        short_name: ua
+        preprocessor: pp200
+        split: Ref1
+        additional_datasets:
+          - {dataset: ERA-Interim, project: OBS6, type: reanaly,
+             version: 1, tier: 3, reference_for_metric: true}
+      ua200_2:
+        <<: *ua200
+        split: Ref2
+        additional_datasets:
+          - {dataset: NCEP-NCAR-R1, project: OBS6, type: reanaly,
+             version: 1, tier: 2, reference_for_metric: true}
+
+    scripts:
+      portrait:
+        script: portrait_plot.py
+        x_by: dataset
+        y_by: variable  # extra_facet
+        group_by: project
+        normalize: "centered_median"
+        default_split: Ref1
+        nan_color: null
+        plot_kwargs:
+          vmin: -0.5
+          vmax: +0.5
+        cbar_kwargs:
+          label: Relative RMSE
+          extend: both
diff --git a/esmvaltool/recipes/recipe_runoff_et.yml b/esmvaltool/recipes/recipe_runoff_et.yml
index 6924321c7c..0a83213caa 100644
--- a/esmvaltool/recipes/recipe_runoff_et.yml
+++ b/esmvaltool/recipes/recipe_runoff_et.yml
@@ -8,7 +8,7 @@ documentation:
     water balance components for different catchments and compares the results
     against observations. Currently, the required catchment mask needs to be
     downloaded manually at https://doi.org/10.5281/zenodo.2025776 and saved in
-    the auxiliary_data_dir defined in config-user.yml.
+    the auxiliary_data_dir defined in configuration.
 
   authors:
     - hagemann_stefan
diff --git a/esmvaltool/recipes/recipe_sea_surface_salinity.yml b/esmvaltool/recipes/recipe_sea_surface_salinity.yml
index 4e670eec7f..43ec0e6b5e 100644
--- a/esmvaltool/recipes/recipe_sea_surface_salinity.yml
+++ b/esmvaltool/recipes/recipe_sea_surface_salinity.yml
@@ -20,8 +20,7 @@ documentation:
 preprocessors:
   timeseries:
     extract_shape:
-      # Relative paths are relative to 'auxiliary_data_dir' as configured in
-      # the config-user.yml file.
+      # Relative paths are relative to the configuration option 'auxiliary_data_dir'.
       # The example shapefile can be downloaded from
       # https://marineregions.org/download_file.php?name=World_Seas_IHO_v3.zip
       # but any shapefile can be used
@@ -50,7 +49,7 @@ datasets:
   - {<<: *cmip6, dataset: MPI-ESM1-2-HR, alias: MPI-ESM1-2-HR}
   - {<<: *cmip6, dataset: NorESM2-MM, alias: NorESM2-MM}
   - {<<: *cmip6, dataset: GISS-E2-2-H, alias: GISS-E2-2-H, institute: NASA-GISS}
-          
+
 
 diagnostics:
   compare_salinity:
diff --git a/esmvaltool/recipes/recipe_shapeselect.yml b/esmvaltool/recipes/recipe_shapeselect.yml
index 0fb22c0d5d..b463f09df8 100644
--- a/esmvaltool/recipes/recipe_shapeselect.yml
+++ b/esmvaltool/recipes/recipe_shapeselect.yml
@@ -11,7 +11,7 @@ documentation:
     - berg_peter
 
   maintainer:
-    - ruhe_lukas
+    - lindenlaub_lukas
 
   projects:
     - c3s-magic
@@ -36,8 +36,7 @@ diagnostics:
         script: shapeselect/diag_shapeselect.py
         # Example shapefiles can be found in:
         # esmvaltool/diag_scripts/shapeselect/testdata/
-        # Relative paths are relative to 'auxiliary_data_dir' as configured in
-        # the config-user.yml file.
+        # Relative paths are relative to configuration option 'auxiliary_data_dir'.
         shapefile: 'Thames.shp'
         weighting_method: 'mean_inside'
         write_xlsx: true
diff --git a/esmvaltool/recipes/recipe_wenzel16jclim.yml b/esmvaltool/recipes/recipe_wenzel16jclim.yml
index 6caed04a93..ea7a583a3e 100644
--- a/esmvaltool/recipes/recipe_wenzel16jclim.yml
+++ b/esmvaltool/recipes/recipe_wenzel16jclim.yml
@@ -303,7 +303,7 @@ diagnostics:
         end_year: 2005
         derive: true
         additional_datasets:
-          - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, version: Ed2-7, tier: 1, start_year: 2001}
+          - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, tier: 1, start_year: 2001}
     additional_datasets: *datasets
     scripts:
       asr_sh: &asr_sh
diff --git a/esmvaltool/references/bock24acp.bibtex b/esmvaltool/references/bock24acp.bibtex
new file mode 100644
index 0000000000..303b019050
--- /dev/null
+++ b/esmvaltool/references/bock24acp.bibtex
@@ -0,0 +1,12 @@
+@article{https://doi.org/10.5194/acp-24-1587-2024,
+author = {Bock, L. and Lauer, A.},
+title = {Cloud properties and their projected changes in CMIP models with low to high climate sensitivity},
+journal = {Atmospheric Chemistry and Physics},
+volume = {24},
+number = {3},
+pages = {1587--1605},
+doi = {10.5194/acp-24-1587-2024},
+url = {https://doi.org/10.5194/acp-24-1587-2024},
+year = {2024}
+}
+
diff --git a/esmvaltool/references/esacci-landcover.bibtex b/esmvaltool/references/esacci-landcover.bibtex
index ca6380e61b..44757b1d04 100644
--- a/esmvaltool/references/esacci-landcover.bibtex
+++ b/esmvaltool/references/esacci-landcover.bibtex
@@ -1,7 +1,8 @@
 @misc{esacci-landcover,
-      url = {http://catalogue.ceda.ac.uk/uuid/4761751d7c844e228ec2f5fe11b2e3b0},
-      title = {IPSL IPSL-CM6A-LR model output prepared for CMIP6 CMIP abrupt-4xCO2},
-      publisher = {ESA Land Cover Climate Change Initiative (Land_Cover_cci): Global Land Cover Maps, Version 1.6.1.},
-      year = {2016},
-      author = {P. Defourny}
+  doi = {10.5194/essd-15-1465-2023},
+  url = {https://catalogue.ceda.ac.uk/uuid/26a0f46c95ee4c29b5c650b129aab788/},
+  title = {A 29-year time series of annual 300 m resolution plant-functional-type maps for climate models},
+  publisher = {Earth System Science Data},
+  year = {2023},
+  author = { Kandice L. Harper, Céline Lamarche, Andrew Hartley, Philippe Peylin, Catherine Ottlé, Vladislav Bastrikov, Rodrigo San Martín, Sylvia I. Bohnenstengel, Grit Kirches, Martin Boettcher, Roman Shevchuk, Carsten Brockmann, and Pierre Defourny }
 }
diff --git a/esmvaltool/references/esacci-soilmoisture.bibtex b/esmvaltool/references/esacci-soilmoisture.bibtex
index 59e275d6c4..7e4404a8f4 100644
--- a/esmvaltool/references/esacci-soilmoisture.bibtex
+++ b/esmvaltool/references/esacci-soilmoisture.bibtex
@@ -1,13 +1,115 @@
 @article{esacci-soilmoisture,
-	doi = {10.5194/hess-15-425-2011},
-	url = {https://doi.org/10.5194%2Fhess-15-425-2011},
-	year = 2011,
-	month = {feb},
-	publisher = {Copernicus {GmbH}},
-	volume = {15},
-	number = {2},
-	pages = {425--436},
-	author = {Y. Y. Liu and R. M. Parinussa and W. A. Dorigo and R. A. M. De Jeu and W. Wagner and A. I. J. M. van Dijk and M. F. McCabe and J. P. Evans},
-	title = {Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals},
-	journal = {Hydrology and Earth System Sciences}
+    doi = {10.5194/essd-11-717-2019},
+    title = {Evolution of the {ESA} {CCI} Soil Moisture climate data records
+               and their underlying merging methodology},
+    author = {Gruber, Alexander and Scanlon, Tracy and van der Schalie, Robin
+               and Wagner, Wolfgang and Dorigo, Wouter},
+    abstract = {The European Space Agency's Climate Change Initiative
+               for Soil Moisture (ESA CCI SM) merging algorithm generates
+               consistent quality-controlled long-term (1978--2018) climate
+               data records for soil moisture, which serves thousands of
+               scientists and data users worldwide. It harmonises and merges
+               soil moisture retrievals from multiple satellites into (i) an
+               active-microwave-based-only product, (ii) a
+               passive-microwave-based-only product and (iii) a combined
+               active--passive product, which are sampled to daily global
+               images on a 0.25∘ regular grid. Since its first release in 2012
+               the algorithm has undergone substantial improvements which have
+               so far not been thoroughly reported in the scientific
+               literature. This paper fills this gap by reviewing and
+               discussing the science behind the three major ESA CCI SM merging
+               algorithms, versions 2
+               (https://doi.org/10.5285/3729b3fbbb434930bf65d82f9b00111c;
+               Wagner et al., 2018), 3
+               (https://doi.org/10.5285/b810601740bd4848b0d7965e6d83d26c;
+               Dorigo et al., 2018) and 4
+               (https://doi.org/10.5285/dce27a397eaf47e797050c220972ca0e;
+               Dorigo et al., 2019), and provides an outlook on the expected
+               improvements planned for the next algorithm, version 5.},
+    journal   = {Earth Syst. Sci. Data},
+    publisher = {Copernicus GmbH},
+    volume    =  {11},
+    number    =  {2},
+    pages     = {717--739},
+    month     =  {may},
+    year      =  2019
 }
+
+@article{esacci-soilmoisture,
+    doi = {10.1016/j.rse.2017.07.001},
+    title = {{ESA} {CCI} Soil Moisture for improved Earth system
+               understanding: State-of-the art and future directions},
+    author = {Dorigo, Wouter and Wagner, Wolfgang and Albergel, Clement and
+               Albrecht, Franziska and Balsamo, Gianpaolo and Brocca, Luca and
+               Chung, Daniel and Ertl, Martin and Forkel, Matthias and Gruber,
+               Alexander and Haas, Eva and Hamer, Paul D and Hirschi, Martin
+               and Ikonen, Jaakko and de Jeu, Richard and Kidd, Richard and
+               Lahoz, William and Liu, Yi Y and Miralles, Diego and
+               Mistelbauer, Thomas and Nicolai-Shaw, Nadine and Parinussa,
+               Robert and Pratola, Chiara and Reimer, Christoph and van der
+               Schalie, Robin and Seneviratne, Sonia I and Smolander, Tuomo and
+               Lecomte, Pascal},
+    abstract  = {Climate Data Records of soil moisture are fundamental for
+               improving our understanding of long-term dynamics in the coupled
+               water, energy, and carbon cycles over land. To respond to this
+               need, in 2012 the European Space Agency (ESA) released the first
+               multi-decadal, global satellite-observed soil moisture (SM)
+               dataset as part of its Climate Change Initiative (CCI) program.
+               This product, named ESA CCI SM, combines various single-sensor
+               active and passive microwave soil moisture products into three
+               harmonised products: a merged ACTIVE, a merged PASSIVE, and a
+               COMBINED active + passive microwave product. Compared to the
+               first product release, the latest version of ESA CCI SM includes
+               a large number of enhancements, incorporates various new
+               satellite sensors, and extends its temporal coverage to the
+               period 1978--2015. In this study, we first provide a
+               comprehensive overview of the characteristics, evolution, and
+               performance of the ESA CCI SM products. Based on original
+               research and a review of existing literature we show that the
+               product quality has steadily increased with each successive
+               release and that the merged products generally outperform the
+               single-sensor input products. Although ESA CCI SM generally
+               agrees well with the spatial and temporal patterns estimated by
+               land surface models and observed in-situ, we identify surface
+               conditions (e.g., dense vegetation, organic soils) for which it
+               still has large uncertainties. Second, capitalising on the
+               results of > 100 research studies that made use of the ESA CCI
+               SM data we provide a synopsis of how it has contributed to
+               improved process understanding in the following Earth system
+               domains: climate variability and change, land-atmosphere
+               interactions, global biogeochemical cycles and ecology,
+               hydrological and land surface modelling, drought applications,
+               and meteorology. While in some disciplines the use of ESA CCI SM
+               is already widespread (e.g. in the evaluation of model soil
+               moisture states) in others (e.g. in numerical weather prediction
+               or flood forecasting) it is still in its infancy. The latter is
+               partly related to current shortcomings of the product, e.g., the
+               lack of near-real-time availability and data gaps in time and
+               space. This study discloses the discrepancies between current
+               ESA CCI SM product characteristics and the preferred
+               characteristics of long-term satellite soil moisture products as
+               outlined by the Global Climate Observing System (GCOS), and
+               provides important directions for future ESA CCI SM product
+               improvements to bridge these gaps.},
+    journal   = {Remote Sens. Environ.},
+    publisher = {Elsevier BV",
+    volume    =  {203},
+    pages     = {185--215},
+    month     = {dec},
+    year      =  2017
+}
+
+@article{esacci-soilmoisture,
+    doi = {10.1109/TGRS.2020.3012896},
+    title = {Homogenization of structural breaks in the global {ESA} {CCI}
+               soil moisture multisatellite climate data record},
+    author = {Preimesberger, Wolfgang and Scanlon, Tracy and Su, Chun-Hsu and
+               Gruber, Alexander and Dorigo, Wouter},
+    journal = {IEEE Trans. Geosci. Remote Sens.},
+    publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
+    volume = {159},
+    number = {14},
+    pages = {12845--2862},
+    month = {apr},
+    year =  2021
+}
\ No newline at end of file
diff --git a/esmvaltool/references/huntingford2000climdyn.bibtex b/esmvaltool/references/huntingford2000climdyn.bibtex
new file mode 100644
index 0000000000..69bc072d49
--- /dev/null
+++ b/esmvaltool/references/huntingford2000climdyn.bibtex
@@ -0,0 +1,14 @@
+@article{huntingford2000,
+	title = {An analogue model to derive additional climate change scenarios from existing {GCM} simulations},
+	volume = {16},
+	issn = {1432-0894},
+	url = {https://doi.org/10.1007/s003820000067},
+	doi = {10.1007/s003820000067},
+	abstract = {Changes in land surface driving variables, predicted by GCM transient climate change experiments, are confirmed to exhibit linearity in the global mean land temperature anomaly, ΔTl. The associated constants of proportionality retain spatial and seasonal characteristics of the GCM output, whilst ΔTlis related to radiative forcing anomalies. The resultant analogue model is shown to be robust between GCM runs and as such provides a computationally efficient technique of extending existing GCM experiments to a large range of climate change scenarios. As an example impacts study, the analogue model is used to drive a terrestrial ecosystem model, and predicted changes in terrestrial carbon are found to be similar to those when using GCM anomalies directly.},
+	number = {8},
+	journal = {Climate Dynamics},
+	author = {Huntingford, C. and Cox, P. M.},
+	month = aug,
+	year = {2000},
+	pages = {575--586},
+}
diff --git a/esmvaltool/references/jra_55.bibtex b/esmvaltool/references/jra_55.bibtex
new file mode 100644
index 0000000000..d979a6c9cc
--- /dev/null
+++ b/esmvaltool/references/jra_55.bibtex
@@ -0,0 +1,10 @@
+@article{jra_55,
+  doi = {https://doi.org/10.5065/D60G3H5B},
+  title={The JRA-55 Reanalysis: General Specifications and Basic Characteristics},
+  author={Kobayashi, S. and Y. Ota and Y. Harada and A. Ebita and M. Moriya and H. Onoda and K. Onogi and H. Kamahori and C. Kobayashi and H. Endo and K. Miyaoka and K. Takahashi},
+  journal={J. Met. Soc. Jap.},
+  volume={93},
+  number={1},
+  pages={5-48},
+  year={2015}
+}
diff --git a/esmvaltool/references/mathison2024gmd.bibtex b/esmvaltool/references/mathison2024gmd.bibtex
new file mode 100644
index 0000000000..a6090db6c7
--- /dev/null
+++ b/esmvaltool/references/mathison2024gmd.bibtex
@@ -0,0 +1,10 @@
+@Article{mathison2024,
+    AUTHOR = {Mathison, C. T. and Burke, E. and Kovacs, E. and Munday, G. and Huntingford, C. and Jones, C. and Smith, C. and Steinert, N. and Wiltshire, A. and Gohar, L. and Varney, R.},
+    TITLE = {A rapid application emissions-to-impacts tool for scenario assessment: Probabilistic Regional Impacts from Model patterns and Emissions (PRIME)},
+    JOURNAL = {EGUsphere},
+    VOLUME = {2024},
+    YEAR = {2024},
+    PAGES = {1--28},
+    URL = {https://egusphere.copernicus.org/preprints/2024/egusphere-2023-2932/},
+    DOI = {10.5194/egusphere-2023-2932}
+}
diff --git a/esmvaltool/utils/batch-jobs/generate.py b/esmvaltool/utils/batch-jobs/generate.py
index afba37906f..428229b6eb 100644
--- a/esmvaltool/utils/batch-jobs/generate.py
+++ b/esmvaltool/utils/batch-jobs/generate.py
@@ -9,7 +9,7 @@
 - conda_path
 2) If needed, edit optional parameters:
 - outputs
-- config_file
+- config_dir
 3) SLURM settings
 This script is configured to optimize the computing
 footprint of the recipe testing. It is not necessary to edit
@@ -46,14 +46,14 @@
 memory = '64G'
 # Default walltime
 time = '04:00:00'
-# Full path to the mambaforge/etc/profile.d/conda.sh executable
+# Full path to the miniforge3/etc/profile.d/conda.sh executable
 # Set the path to conda
-conda_path = 'PATH_TO/mambaforge/etc/profile.d/conda.sh'
-# Full path to config_file
-# If none, ~/.esmvaltool/config-user.yml is used
-config_file = ''
+conda_path = 'PATH_TO/miniforge3/etc/profile.d/conda.sh'
+# Full path to configuration directory
+# If none, ~/.config/esmvaltool/
+config_dir = ''
 # Set max_parallel_tasks
-# If none, read from config_file
+# If none, read from configuration
 default_max_parallel_tasks = 8
 
 # List of recipes that require non-default SLURM options set above
@@ -315,11 +315,11 @@ def generate_submit():
             file.write(f'. {conda_path}\n')
             file.write(f'conda activate {env}\n')
             file.write('\n')
-            if not config_file:
+            if not config_dir:
                 file.write(f'esmvaltool run {str(recipe)}')
             else:
-                file.write(f'esmvaltool run --config_file '
-                           f'{str(config_file)} {str(recipe)}')
+                file.write(f'esmvaltool run --config_dir '
+                           f'{str(config_dir)} {str(recipe)}')
             # set max_parallel_tasks
             max_parallel_tasks = MAX_PARALLEL_TASKS.get(
                 recipe.stem,
diff --git a/esmvaltool/utils/recipe_filler.py b/esmvaltool/utils/recipe_filler.py
deleted file mode 100755
index 40f637c6d5..0000000000
--- a/esmvaltool/utils/recipe_filler.py
+++ /dev/null
@@ -1,914 +0,0 @@
-"""
-Fill in a blank recipe with additional datasets.
-
-Tool to obtain a set of additional datasets when given a blank recipe.
-The blank recipe should contain, to the very least, a list of diagnostics
-each with their variable(s). Example of minimum settings:
-
-diagnostics:
-  diagnostic:
-    variables:
-      ta:
-        mip: Amon
-        start_year: 1850
-        end_year: 1900
-
-Note that the tool will exit if any of these minimum settings are missing!
-
-Key features:
-
-- you can add as many variable parameters as are needed; if not added, the
-  tool will use the "*" wildcard and find all available combinations;
-- you can restrict the number of datasets to be looked for with the `dataset:`
-  key for each variable, pass a list of datasets as value, e.g.
-  `dataset: [MPI-ESM1-2-LR, MPI-ESM-LR]`;
-- you can specify a pair of experiments eg `exp: [rcp26, rcp85]`
-  for each variable; this will look for each available dataset per experiment
-  and assemble an aggregated data stretch from each experiment; equivalent to
-  esmvaltool's syntax of multiple experiments; this option needs an ensemble
-  to be declared explicitly; it will return no entry if there are gaps in data
-- `start_year` and `end_year` are mandatory and are used to filter out the
-  datasets that don't have data in the interval; if you want all possible years
-  hence no filtering on years just use "*" for start and end years;
-- `config-user: rootpath: CMIPX` may be a list, rootpath lists are supported;
-
-Caveats:
-
-- the tool doesn't yet work for derived variables;
-- operation restricted to CMIP data.
-
-Have fun!
-"""
-import argparse
-import datetime
-import itertools
-import logging
-import logging.config
-import os
-import shutil
-import time
-from glob import glob
-from pathlib import Path
-
-import esmvalcore
-import yaml
-
-from esmvalcore import __version__ as core_ver
-from esmvalcore.cmor.table import CMOR_TABLES, read_cmor_tables
-from packaging import version as pkg_version
-from ruamel.yaml import YAML
-
-logger = logging.getLogger(__name__)
-
-CFG = {}
-
-
-def _purge_file_handlers(cfg: dict) -> None:
-    """Remove handlers with filename set.
-
-    This is used to remove file handlers which require an output
-    directory to be set.
-    """
-    cfg['handlers'] = {
-        name: handler
-        for name, handler in cfg['handlers'].items()
-        if 'filename' not in handler
-    }
-    prev_root = cfg['root']['handlers']
-    cfg['root']['handlers'] = [
-        name for name in prev_root if name in cfg['handlers']
-    ]
-
-
-def _update_stream_level(cfg: dict, level=None):
-    """Update the log level for the stream handlers."""
-    handlers = cfg['handlers']
-
-    for handler in handlers.values():
-        if level is not None and 'stream' in handler:
-            if handler['stream'] in ('ext://sys.stdout', 'ext://sys.stderr'):
-                handler['level'] = level.upper()
-
-
-def _get_log_files(cfg: dict, output_dir: str = None) -> list:
-    """Initialize log files for the file handlers."""
-    log_files = []
-
-    handlers = cfg['handlers']
-
-    for handler in handlers.values():
-        filename = handler.get('filename', None)
-
-        if filename:
-            if not os.path.isabs(filename):
-                handler['filename'] = os.path.join(output_dir, filename)
-            log_files.append(handler['filename'])
-
-    return log_files
-
-
-def configure_logging(cfg_file: str = None,
-                      output_dir: str = None,
-                      console_log_level: str = None) -> list:
-    """Configure logging.
-
-    Parameters
-    ----------
-    cfg_file : str, optional
-        Logging config file. If `None`, defaults to `configure-logging.yml`
-    output_dir : str, optional
-        Output directory for the log files. If `None`, log only to the console.
-    console_log_level : str, optional
-        If `None`, use the default (INFO).
-
-    Returns
-    -------
-    log_files : list
-        Filenames that will be logged to.
-    """
-    if cfg_file is None:
-        cfg_loc = Path(esmvalcore.__file__ + "esmvalcore")
-        if pkg_version.parse(core_ver) < pkg_version.parse('2.8.0'):
-            cfg_file = cfg_loc.parents[0] / '_config' / 'config-logging.yml'
-        else:
-            cfg_file = cfg_loc.parents[0] / 'config' / 'config-logging.yml'
-
-    cfg_file = Path(cfg_file).absolute()
-
-    with open(cfg_file) as file_handler:
-        cfg = yaml.safe_load(file_handler)
-
-    if output_dir is None:
-        _purge_file_handlers(cfg)
-
-    log_files = _get_log_files(cfg, output_dir=output_dir)
-    _update_stream_level(cfg, level=console_log_level)
-
-    logging.config.dictConfig(cfg)
-    logging.Formatter.converter = time.gmtime
-    logging.captureWarnings(True)
-
-    return log_files
-
-
-def read_config_developer_file(cfg_file=None):
-    """Read the developer's configuration file."""
-    if cfg_file is None:
-        cfg_loc = Path(esmvalcore.__file__ + "esmvalcore")
-        cfg_file = cfg_loc.parents[0] / 'config-developer.yml'
-
-    with open(cfg_file, 'r') as file:
-        cfg = yaml.safe_load(file)
-
-    return cfg
-
-
-def _normalize_path(path):
-    """Normalize paths.
-
-    Expand ~ character and environment variables and convert path to absolute.
-
-    Parameters
-    ----------
-    path: str
-        Original path
-
-    Returns
-    -------
-    str:
-        Normalized path
-    """
-    if path is None:
-        return None
-    return os.path.abspath(os.path.expanduser(os.path.expandvars(path)))
-
-
-def read_config_user_file(config_file, folder_name, options=None):
-    """Read config user file and store settings in a dictionary."""
-    if not config_file:
-        config_file = '~/.esmvaltool/config-user.yml'
-    config_file = os.path.abspath(
-        os.path.expandvars(os.path.expanduser(config_file)))
-    # Read user config file
-    if not os.path.exists(config_file):
-        print(f"ERROR: Config file {config_file} does not exist")
-
-    with open(config_file, 'r') as file:
-        cfg = yaml.safe_load(file)
-
-    if options is None:
-        options = dict()
-    for key, value in options.items():
-        cfg[key] = value
-
-    # set defaults
-    defaults = {
-        'compress_netcdf': False,
-        'exit_on_warning': False,
-        'output_file_type': 'png',
-        'output_dir': 'esmvaltool_output',
-        'auxiliary_data_dir': 'auxiliary_data',
-        'save_intermediary_cubes': False,
-        'remove_preproc_dir': True,
-        'max_parallel_tasks': None,
-        'run_diagnostic': True,
-        'profile_diagnostic': False,
-        'config_developer_file': None,
-        'drs': {},
-    }
-
-    for key in defaults:
-        if key not in cfg:
-            logger.info(
-                "No %s specification in config file, "
-                "defaulting to %s", key, defaults[key])
-            cfg[key] = defaults[key]
-
-    cfg['output_dir'] = _normalize_path(cfg['output_dir'])
-    cfg['auxiliary_data_dir'] = _normalize_path(cfg['auxiliary_data_dir'])
-
-    cfg['config_developer_file'] = _normalize_path(
-        cfg['config_developer_file'])
-
-    for key in cfg['rootpath']:
-        root = cfg['rootpath'][key]
-        if isinstance(root, str):
-            cfg['rootpath'][key] = [_normalize_path(root)]
-        else:
-            cfg['rootpath'][key] = [_normalize_path(path) for path in root]
-
-    # insert a directory date_time_recipe_usertag in the output paths
-    now = datetime.datetime.utcnow().strftime("%Y%m%d_%H%M%S")
-    new_subdir = '_'.join((folder_name, now))
-    cfg['output_dir'] = os.path.join(cfg['output_dir'], new_subdir)
-
-    # create subdirectories
-    cfg['preproc_dir'] = os.path.join(cfg['output_dir'], 'preproc')
-    cfg['work_dir'] = os.path.join(cfg['output_dir'], 'work')
-    cfg['plot_dir'] = os.path.join(cfg['output_dir'], 'plots')
-    cfg['run_dir'] = os.path.join(cfg['output_dir'], 'run')
-
-    # Read developer configuration file
-    read_cmor_tables(cfg['config_developer_file'])
-
-    return cfg
-
-
-HEADER = r"""
-______________________________________________________________________
-          _____ ____  __  ____     __    _ _____           _
-         | ____/ ___||  \/  \ \   / /_ _| |_   _|__   ___ | |
-         |  _| \___ \| |\/| |\ \ / / _` | | | |/ _ \ / _ \| |
-         | |___ ___) | |  | | \ V / (_| | | | | (_) | (_) | |
-         |_____|____/|_|  |_|  \_/ \__,_|_| |_|\___/ \___/|_|
-______________________________________________________________________
-
-""" + __doc__
-
-dataset_order = [
-    'dataset', 'project', 'exp', 'mip', 'ensemble', 'grid', 'start_year',
-    'end_year'
-]
-
-# cmip eras
-cmip_eras = ["CMIP5", "CMIP6"]
-
-# The base dictionairy (all wildcards):
-base_dict = {
-    'institute': '*',
-    'dataset': '*',
-    'project': '*',
-    'exp': '*',
-    'frequency': '*',
-    'ensemble': '*',
-    'mip': '*',
-    'modeling_realm': '*',
-    'short_name': '*',
-    'grid': '*',
-    'start_year': '*',
-    'end_year': '*',
-    'activity': '*',
-}
-
-
-def _get_download_dir(yamlconf, cmip_era):
-    """Get the Download Directory from user config file."""
-    if 'download_dir' in yamlconf:
-        return os.path.join(yamlconf['download_dir'], cmip_era)
-    return False
-
-
-def _get_site_rootpath(cmip_era):
-    """Get site (drs) from config-user.yml."""
-    config_yml = get_args().config_file
-    with open(config_yml, 'r') as yamf:
-        yamlconf = yaml.safe_load(yamf)
-    drs = yamlconf['drs'][cmip_era]
-
-    download_dir = _get_download_dir(yamlconf, cmip_era)
-    rootdir = [yamlconf['rootpath'][cmip_era], ]
-
-    if download_dir:
-        rootdir.append(download_dir)
-    logger.debug("%s root directory %s", cmip_era, rootdir)
-    if drs == 'default' and 'default' in yamlconf['rootpath']:
-        rootdir = [yamlconf['rootpath']['default'], ]
-        if download_dir:
-            rootdir.append(download_dir)
-
-        logger.debug("Using drs default and "
-                     "default: %s data directory", rootdir)
-
-    return drs, rootdir
-
-
-def _get_input_dir(cmip_era):
-    """Get input_dir from config-developer.yml."""
-    site = _get_site_rootpath(cmip_era)[0]
-    yamlconf = read_config_developer_file()
-
-    return yamlconf[cmip_era]['input_dir'][site]
-
-
-def _get_input_file(cmip_era):
-    """Get input_file from config-developer.yml."""
-    yamlconf = read_config_developer_file()
-    return yamlconf[cmip_era]['input_file']
-
-
-def _determine_basepath(cmip_era):
-    """Determine a basepath."""
-    if isinstance(_get_site_rootpath(cmip_era)[1], list):
-        rootpaths = _get_site_rootpath(cmip_era)[1]
-    else:
-        rootpaths = [_get_site_rootpath(cmip_era)[1]]
-
-    basepaths = []
-    for rootpath in rootpaths:
-        if _get_input_dir(cmip_era) != os.path.sep:
-            basepath = os.path.join(rootpath, _get_input_dir(cmip_era),
-                                    _get_input_file(cmip_era))
-        else:
-            basepath = os.path.join(rootpath, _get_input_file(cmip_era))
-        basepath = basepath.replace('//', '/')
-        basepaths.append(basepath)
-    logger.debug("We will look for files of patterns %s", basepaths)
-
-    return basepaths
-
-
-def _overlapping_datasets(files, all_years, start_year, end_year):
-    """Process overlapping datasets and check for avail data in time range."""
-    valid_files = []
-    ay_sorted = sorted(all_years)
-    if ay_sorted[0] <= start_year and ay_sorted[-1] >= end_year:
-        yr_pairs = sorted(
-            [all_years[i:i + 2] for i in range(0, len(all_years), 2)])
-        yr_pairs = list(k for k, _ in itertools.groupby(yr_pairs))
-        d_y = [
-            yr_pairs[j][1] - yr_pairs[j + 1][0]
-            for j in range(len(yr_pairs) - 1)
-        ]
-        gaps = [c for c in d_y if c < -1]
-        if not gaps:
-            valid_files = files
-            logger.info("Contiguous data from multiple experiments.")
-        else:
-            logger.warning("Data from multiple exps has >1 year gaps! ")
-            logger.debug("Start %s/end %s requested - "
-                         "files covering %s found.",
-                         start_year, end_year, yr_pairs)
-
-    return valid_files
-
-
-def filter_years(files, start_year, end_year, overlap=False):
-    """
-    Filter out files that are outside requested time range.
-
-    Nifty function that takes a list of files and two years
-    as arguments; it will build a series of filter dictionaries
-    and check if data is available for the entire interval;
-    it will return a single file per dataset, the first file
-    in the list of files that cover the specified interval;
-    optional argument `overlap` used if multiple experiments are
-    used and overlap between datasets is present.
-
-    Parameters
-    ----------
-    files: list
-        A list of files that need filtering by requested time range.
-
-    start_year: int
-        Integer start year of requested range.
-
-    end_year: int
-        Integer end year of requested range.
-
-    overlap: bool
-        Flag if datasets overlap; defaults to False.
-
-    Returns
-    -------
-    list
-        List of files which have been identified as falling in
-        the requested time range; if multiple files within time range
-        per dataset, the first file will be returned.
-
-    """
-    valid_files = []
-    available_years = {}
-
-    if start_year == "*" and end_year == "*":
-        return files
-
-    if not files:
-        return valid_files
-
-    all_files_roots = [("").join(fil.split("_")[0:-1]) for fil in files]
-    for fil in files:
-        available_years[("").join(fil.split("_")[0:-1])] = []
-    for fil in files:
-        available_years[("").join(fil.split("_")[0:-1])].append(
-            fil.split("_")[-1].strip(".nc").split("-"))
-
-    all_years = []
-    for root, yr_list in available_years.items():
-        actual_years = []
-        yr_list = list(itertools.chain.from_iterable(yr_list))
-        for year in yr_list:
-            if len(year) == 4:
-                actual_years.append(int(year))
-            else:
-                actual_years.append(int(year[0:4]))
-        actual_years = sorted(actual_years)
-        all_years.extend(actual_years)
-        if not overlap:
-            actual_years = sorted(list(set(actual_years)))
-            if actual_years[0] <= start_year and actual_years[-1] >= end_year:
-                idx = all_files_roots.index(root)
-                valid_files.append(files[idx])
-
-    # multiple experiments to complete each other
-    if overlap:
-        valid_files = _overlapping_datasets(files, all_years, start_year,
-                                            end_year)
-
-    if not valid_files:
-        logger.warning("No data found to fully cover start "
-                       "%s / end %s as requested!", start_year, end_year)
-
-    return valid_files
-
-
-def _resolve_latestversion(dirname_template):
-    """Resolve the 'latestversion' tag."""
-    for version_separator in ['{latestversion}', '{version}']:
-        if version_separator in dirname_template:
-            break
-    else:
-        return dirname_template
-
-    # Find latest version
-    part1, part2 = dirname_template.split(version_separator)
-    part2 = part2.lstrip(os.sep)
-    part1_contents = glob(part1)
-    if part1_contents:
-        versions = os.listdir(part1_contents[0])
-        versions.sort(reverse=True)
-        for version in ['latest'] + versions:
-            dirname = os.path.join(part1, version, part2)
-            if glob(dirname):
-                return dirname
-
-    return dirname_template
-
-
-def list_all_files(file_dict, cmip_era):
-    """
-    List all files that match the dataset dictionary.
-
-    Function that returns all files that are determined by a
-    file_dict dictionary; file_dict is keyed on usual parameters
-    like `dataset`, `project`, `mip` etc; glob.glob is used
-    to find files; speedup is achieved by replacing wildcards
-    with values from CMOR tables.
-
-    Parameters
-    ----------
-    file_dict: dict
-        Dictionary to hold dataset specifications.
-
-    cmip_era: str
-        Either CMIP5 or CMIP6.
-
-    Returns
-    -------
-    list:
-        List of found files.
-
-    """
-    mip = file_dict['mip']
-    short_name = file_dict['short_name']
-    try:
-        frequency = CMOR_TABLES[cmip_era].get_variable(mip,
-                                                       short_name).frequency
-        realms = CMOR_TABLES[cmip_era].get_variable(mip,
-                                                    short_name).modeling_realm
-    except AttributeError:
-        logger.warning("Could not find %s CMOR table "
-                       "for variable %s with mip %s",
-                       cmip_era, short_name, mip)
-        return []
-    file_dict['frequency'] = frequency
-
-    basepaths = _determine_basepath(cmip_era)
-    all_files = []
-
-    for basepath in basepaths:
-        new_path = basepath[:]
-
-        # could have multiple realms
-        for realm in realms:
-            file_dict['modeling_realm'] = realm
-
-            # load all the files in the custom dict
-            for key, value in file_dict.items():
-                new_path = new_path.replace('{' + key + '}', str(value))
-            new_path = _resolve_latestversion(new_path)
-            if new_path.startswith("~"):
-                new_path = os.path.expanduser(new_path)
-                if not new_path.startswith(os.sep):
-                    raise ValueError(
-                        "Could not expand ~ to user home dir "
-                        "please expand it in the config user file!")
-                logger.info("Expanding path to %s", new_path)
-
-            # Globs all the wildcards into a list of files.
-            files = glob(new_path)
-            all_files.extend(files)
-    if not all_files:
-        logger.warning("Could not find any file for data specifications.")
-
-    return all_files
-
-
-def _file_to_recipe_dataset(fn_path, cmip_era, file_dict):
-    """Convert a filename to an recipe ready dataset."""
-    # Add the obvious ones - ie the one you requested!
-    output_dataset = {}
-    output_dataset['project'] = cmip_era
-    for key, value in file_dict.items():
-        if value == '*':
-            continue
-        if key in dataset_order:
-            output_dataset[key] = value
-
-    # Split file name and base path into directory structure and filenames.
-    basefiles = _determine_basepath(cmip_era)
-    _, fnfile = os.path.split(fn_path)
-
-    for basefile in basefiles:
-        _, basefile = os.path.split(basefile)
-        # Some of the key words include the splitting character '_' !
-        basefile = basefile.replace('short_name', 'shortname')
-        basefile = basefile.replace('start_year', 'startyear')
-        basefile = basefile.replace('end_year', 'endyear')
-
-        # Assume filename is separated by '_'
-        basefile_split = [key.replace("{", "") for key in basefile.split('_')]
-        basefile_split = [key.replace("}", "") for key in basefile_split]
-        fnfile_split = fnfile.split('_')
-
-        # iterate through directory structure looking for useful bits.
-        for base_key, fn_key in zip(basefile_split, fnfile_split):
-            if base_key == '*.nc':
-                fn_key = fn_key.replace('.nc', '')
-                start_year, end_year = fn_key.split('-')
-                output_dataset['start_year'] = start_year
-                output_dataset['end_year'] = end_year
-            elif base_key == "ensemble*.nc":
-                output_dataset['ensemble'] = fn_key
-            elif base_key == "grid*.nc":
-                output_dataset['grid'] = fn_key
-            elif base_key == "shortname":
-                pass
-            else:
-                output_dataset[base_key] = fn_key
-    if "exp" in file_dict:
-        if isinstance(file_dict["exp"], list):
-            output_dataset["exp"] = file_dict["exp"]
-
-    return output_dataset
-
-
-def _remove_duplicates(add_datasets):
-    """
-    Remove accidental duplicates.
-
-    Close to 0% chances this will ever be used.
-    May be used when there are actual duplicates in data
-    storage, we've seen these before, but seldom.
-    """
-    datasets = []
-    seen = set()
-
-    for dataset in add_datasets:
-        orig_exp = dataset["exp"]
-        dataset["exp"] = str(dataset["exp"])
-        tup_dat = tuple(dataset.items())
-        if tup_dat not in seen:
-            seen.add(tup_dat)
-            dataset["exp"] = orig_exp
-            datasets.append(dataset)
-
-    return datasets
-
-
-def _check_recipe(recipe_dict):
-    """Perform a quick recipe check for mandatory fields."""
-    do_exit = False
-    if "diagnostics" not in recipe_dict:
-        logger.error("Recipe missing diagnostics section.")
-        do_exit = True
-    for diag_name, diag in recipe_dict["diagnostics"].items():
-        if "variables" not in diag:
-            logger.error("Diagnostic %s missing variables.", diag_name)
-            do_exit = True
-        for var_name, var_pars in diag["variables"].items():
-            if "mip" not in var_pars:
-                logger.error("Variable %s missing mip.", var_name)
-                do_exit = True
-            if "start_year" not in var_pars:
-                logger.error("Variable %s missing start_year.", var_name)
-                do_exit = True
-            if "end_year" not in var_pars:
-                logger.error("Variable %s missing end_year.", var_name)
-                do_exit = True
-            if "exp" in var_pars:
-                if isinstance(var_pars["exp"],
-                              list) and "ensemble" not in var_pars:
-                    logger.error("Asking for experiments list for ")
-                    logger.error("variable %s - you need to ", var_name)
-                    logger.error("define an ensemble for this case.")
-                    do_exit = True
-    if do_exit:
-        raise ValueError("Please fix the issues in recipe and rerun")
-
-
-def _check_config_file(user_config_file):
-    """Perform a quick recipe check for mandatory fields."""
-    do_exit = False
-    if "rootpath" not in user_config_file:
-        logger.error("Config file missing rootpath section.")
-        do_exit = True
-    if "drs" not in user_config_file:
-        logger.error("Config file missing drs section.")
-        do_exit = True
-    for proj in cmip_eras:
-        if proj not in user_config_file["rootpath"].keys():
-            logger.error("Config file missing rootpath for %s", proj)
-            do_exit = True
-        if proj not in user_config_file["drs"].keys():
-            logger.error("Config file missing drs for %s", proj)
-            do_exit = True
-    if do_exit:
-        raise ValueError("Please fix issues in config file and rerun")
-
-
-def _parse_recipe_to_dicts(yamlrecipe):
-    """Parse a recipe's variables into a dictionary of dictionairies."""
-    output_dicts = {}
-    for diag in yamlrecipe['diagnostics']:
-        for variable, var_dict in yamlrecipe['diagnostics'][diag][
-                'variables'].items():
-            new_dict = base_dict.copy()
-            for var_key, var_value in var_dict.items():
-                if var_key in new_dict:
-                    new_dict[var_key] = var_value
-            output_dicts[(diag, variable)] = new_dict
-
-    return output_dicts
-
-
-def _add_datasets_into_recipe(additional_datasets, output_recipe):
-    """Add the datasets into a new recipe."""
-    yaml = YAML()
-    yaml.default_flow_style = False
-    with open(output_recipe, 'r') as yamlfile:
-        cur_yaml = yaml.load(yamlfile)
-        for diag_var, add_dat in additional_datasets.items():
-            if add_dat:
-                if 'additional_datasets' in cur_yaml['diagnostics']:
-                    cur_yaml['diagnostics'][diag_var[0]]['variables'][
-                        diag_var[1]]['additional_datasets'].extend(add_dat)
-                else:
-                    cur_yaml['diagnostics'][diag_var[0]]['variables'][
-                        diag_var[1]]['additional_datasets'] = add_dat
-    if cur_yaml:
-        with open(output_recipe, 'w') as yamlfile:
-            yaml.dump(cur_yaml, yamlfile)
-
-
-def _find_all_datasets(recipe_dict, cmip_eras):
-    """Find all datasets explicitly."""
-    datasets = []
-    for cmip_era in cmip_eras:
-        if cmip_era == "CMIP6":
-            activity = "CMIP"
-        else:
-            activity = ""
-        drs, site_path = _get_site_rootpath(cmip_era)
-        if drs in ["default", "SMHI"]:
-            logger.info("DRS is %s; filter on dataset disabled.", drs)
-            datasets = ["*"]
-        else:
-            if not isinstance(site_path, list):
-                site_path = [site_path]
-            for site_pth in site_path:
-                if drs in ["BADC", "DKRZ", "CP4CDS"]:
-                    institutes_path = os.path.join(site_pth, activity)
-                elif drs in ["ETHZ", "RCAST"]:
-                    exp = recipe_dict["exp"][0]
-                    if exp == "*":
-                        exp = "piControl"  # all institutes have piControl
-                    mip = recipe_dict["mip"]
-                    var = recipe_dict["short_name"]
-                    institutes_path = os.path.join(site_pth, exp, mip, var)
-
-                if not os.path.isdir(institutes_path):
-                    logger.warning("Path to data %s "
-                                   "does not exist; will look everywhere.",
-                                   institutes_path)
-                    datasets = ["*"]
-                    return datasets
-
-                institutes = os.listdir(institutes_path)
-                if drs in ["BADC", "DKRZ", "CP4CDS"]:
-                    for institute in institutes:
-                        datasets.extend(
-                            os.listdir(os.path.join(institutes_path,
-                                                    institute)))
-                else:
-                    datasets.extend(institutes)
-
-    return datasets
-
-
-def _get_exp(recipe_dict):
-    """Get the correct exp as list of single or multiple exps."""
-    if isinstance(recipe_dict["exp"], list):
-        exps_list = recipe_dict["exp"]
-        logger.info("Multiple %s experiments requested", exps_list)
-    else:
-        exps_list = [recipe_dict["exp"]]
-        logger.info("Single %s experiment requested", exps_list)
-
-    return exps_list
-
-
-def _get_datasets(recipe_dict, cmip_eras):
-    """Get the correct datasets as list if needed."""
-    if recipe_dict["dataset"] == "*":
-        datasets = _find_all_datasets(recipe_dict, cmip_eras)
-        return datasets
-    if isinstance(recipe_dict['dataset'], list):
-        datasets = recipe_dict['dataset']
-        logger.info("Multiple %s datasets requested", datasets)
-    else:
-        datasets = [recipe_dict['dataset']]
-        logger.info("Single %s dataset requested", datasets)
-
-    return datasets
-
-
-def get_args():
-    """Parse command line arguments."""
-    parser = argparse.ArgumentParser(
-        description=__doc__,
-        formatter_class=argparse.RawDescriptionHelpFormatter)
-    parser.add_argument('recipe', help='Path/name of yaml pilot recipe file')
-    parser.add_argument('-c',
-                        '--config-file',
-                        default=os.path.join(os.environ["HOME"], '.esmvaltool',
-                                             'config-user.yml'),
-                        help='User configuration file')
-
-    parser.add_argument('-o',
-                        '--output',
-                        default=os.path.join(os.getcwd(),
-                                             'recipe_autofilled.yml'),
-                        help='Output recipe, default recipe_autofilled.yml')
-
-    args = parser.parse_args()
-    return args
-
-
-def _get_timefiltered_files(recipe_dict, exps_list, cmip_era):
-    """Obtain all files that correspond to requested time range."""
-    # multiple experiments allowed, complement data from each exp
-    if len(exps_list) > 1:
-        files = []
-        for exp in exps_list:
-            recipe_dict["exp"] = exp
-            files.extend(list_all_files(recipe_dict, cmip_era))
-        files = filter_years(files,
-                             recipe_dict["start_year"],
-                             recipe_dict["end_year"],
-                             overlap=True)
-        recipe_dict["exp"] = exps_list
-
-    else:
-        files = list_all_files(recipe_dict, cmip_era)
-        files = filter_years(files, recipe_dict["start_year"],
-                             recipe_dict["end_year"])
-
-    return files
-
-
-def run():
-    """Run the `recipe_filler` tool. Help in __doc__ and via --help."""
-    # Get arguments
-    args = get_args()
-    input_recipe = args.recipe
-    output_recipe = args.output
-    cmip_eras = ["CMIP5", "CMIP6"]
-
-    # read the config file
-    config_user = read_config_user_file(args.config_file,
-                                        'recipe_filler',
-                                        options={})
-
-    # configure logger
-    run_dir = os.path.join(config_user['output_dir'], 'recipe_filler')
-    if not os.path.isdir(run_dir):
-        os.makedirs(run_dir)
-    log_files = configure_logging(output_dir=run_dir,
-                                  console_log_level=config_user['log_level'])
-    logger.info(HEADER)
-    logger.info("Using user configuration file: %s", args.config_file)
-    logger.info("Using pilot recipe file: %s", input_recipe)
-    logger.info("Writing filled out recipe to: %s", output_recipe)
-    log_files = "\n".join(log_files)
-    logger.info("Writing program log files to:\n%s", log_files)
-
-    # check config user file
-    _check_config_file(config_user)
-
-    # parse recipe
-    with open(input_recipe, 'r') as yamlfile:
-        yamlrecipe = yaml.safe_load(yamlfile)
-        _check_recipe(yamlrecipe)
-        recipe_dicts = _parse_recipe_to_dicts(yamlrecipe)
-
-    # Create a list of additional_datasets for each diagnostic/variable.
-    additional_datasets = {}
-    for (diag, variable), recipe_dict in recipe_dicts.items():
-        logger.info("Looking for data for "
-                    "variable %s in diagnostic %s", variable, diag)
-        new_datasets = []
-        if "short_name" not in recipe_dict:
-            recipe_dict['short_name'] = variable
-        elif recipe_dict['short_name'] == "*":
-            recipe_dict['short_name'] = variable
-
-        # adjust cmip era if needed
-        if recipe_dict['project'] != "*":
-            cmip_eras = [recipe_dict['project']]
-
-        # get datasets depending on user request; always a list
-        datasets = _get_datasets(recipe_dict, cmip_eras)
-
-        # get experiments depending on user request; always a list
-        exps_list = _get_exp(recipe_dict)
-
-        # loop through datasets
-        for dataset in datasets:
-            recipe_dict['dataset'] = dataset
-            logger.info("Seeking data for dataset: %s", dataset)
-            for cmip_era in cmip_eras:
-                files = _get_timefiltered_files(recipe_dict, exps_list,
-                                                cmip_era)
-
-                # assemble in new recipe
-                add_datasets = []
-                for fn in sorted(files):
-                    fn_dir = os.path.dirname(fn)
-                    logger.info("Data directory: %s", fn_dir)
-                    out = _file_to_recipe_dataset(fn, cmip_era, recipe_dict)
-                    logger.info("New recipe entry: %s", out)
-                    if out is None:
-                        continue
-                    add_datasets.append(out)
-                new_datasets.extend(add_datasets)
-        additional_datasets[(diag, variable, cmip_era)] = \
-            _remove_duplicates(new_datasets)
-
-    # add datasets to recipe as additional_datasets
-    shutil.copyfile(input_recipe, output_recipe, follow_symlinks=True)
-    _add_datasets_into_recipe(additional_datasets, output_recipe)
-    logger.info("Finished recipe filler. Go get some science done now!")
-
-
-if __name__ == "__main__":
-    run()
diff --git a/esmvaltool/utils/recipe_test_workflow/Jinja2Tests/file_exists.py b/esmvaltool/utils/recipe_test_workflow/Jinja2Tests/file_exists.py
new file mode 100644
index 0000000000..46b1d90ce1
--- /dev/null
+++ b/esmvaltool/utils/recipe_test_workflow/Jinja2Tests/file_exists.py
@@ -0,0 +1,37 @@
+"""
+Module for custom Jinja2 Tests.
+
+In Jinja2 "tests" are used to test a variable against an expression.
+Refer to Jinja2 tests:
+https://jinja.palletsprojects.com/en/stable/templates/#tests
+
+Jinja2 has a number of Builtin tests:
+https://jinja.palletsprojects.com/en/stable/templates/#list-of-builtin-tests
+
+Jinja2 also allows for writing custom tests in Python:
+https://jinja.palletsprojects.com/en/stable/api/#custom-tests
+
+Cylc supports custom filters, tests and globals as described here:
+https://cylc.github.io/cylc-doc/stable/html/user-guide/writing-workflows/jinja2.html#custom-jinja2-filters-tests-and-globals
+
+"""
+from pathlib import Path
+
+
+def file_exists(file_path):
+    """
+    Test if a file exists.
+
+    Parameters
+    ----------
+    file_path : str
+        A file path.
+
+    Returns
+    -------
+    bool
+        Returns True if the file exists.
+    """
+    run_directory = Path.cwd()
+    site_recipes_file_path = run_directory / file_path
+    return site_recipes_file_path.is_file()
diff --git a/esmvaltool/utils/recipe_test_workflow/app/compare/rose-app.conf b/esmvaltool/utils/recipe_test_workflow/app/compare/rose-app.conf
new file mode 100644
index 0000000000..9bcfa28fd5
--- /dev/null
+++ b/esmvaltool/utils/recipe_test_workflow/app/compare/rose-app.conf
@@ -0,0 +1,4 @@
+[command]
+default=BASH_XTRACEFD=1  # Send the output from 'set -x' to 'stdout' rather than 'stderr'.
+       =set -euxo pipefail
+       =rtw-env python ${COMPARE_SCRIPT} ${KGO_DIR} ${COMPARISON_OUTPUT_DIR} --verbose
diff --git a/esmvaltool/utils/recipe_test_workflow/app/configure/bin/__init__.py b/esmvaltool/utils/recipe_test_workflow/app/configure/bin/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/esmvaltool/utils/recipe_test_workflow/app/configure/bin/configure.py b/esmvaltool/utils/recipe_test_workflow/app/configure/bin/configure.py
new file mode 100755
index 0000000000..a625ec24ec
--- /dev/null
+++ b/esmvaltool/utils/recipe_test_workflow/app/configure/bin/configure.py
@@ -0,0 +1,145 @@
+#!/usr/bin/env python
+"""Generate the required user configuration file for ESMValTool."""
+import os
+import pprint
+
+import yaml
+from esmvalcore.config._config_validators import ValidationError, _validators
+
+
+def main():
+    """Write the required user configuration file for ESMValTool.
+
+    The configuration values defined in the environment for the
+    ``configure`` task are written to the configuration file defined
+    by ``USER_CONFIG_PATH`` in the ``flow.cylc`` file.
+    """
+    # Get the configuration values defined in the environment for the
+    # 'configure' task.
+    config_values = get_config_values_from_task_env()
+
+    # Validate the user config file content.
+    validate_user_config_file(config_values)
+
+    # Update the configuration from OS environment.
+    user_config_path = os.environ["USER_CONFIG_PATH"]
+    config_values["config_file"] = user_config_path
+
+    # Write the updated configuration values to the file defined by
+    # 'user_config_path'.
+    print(f"Writing the user configuration file to '{user_config_path}' with "
+          "values: ")
+    pprint.PrettyPrinter().pprint(config_values)
+    write_yaml(user_config_path, config_values)
+
+
+def get_config_values_from_task_env():
+    """Get configuration values from environment for configure task."""
+    # Note that 'auxiliary_data_dir' and 'download_dir' are set to empty
+    # values and cannot currently be configured. 'auxiliary_data_dir' is
+    # used by some recipes to look for additional datasets, so may need
+    # to be configured in the future. 'download_dir' is used only when
+    # using the automatic download feature via ESMValTool, which is not
+    # the intention here, so should not be configurable.
+    #
+    # In addition, 'check_level' and 'extra_facets_dir' are added to the
+    # config object in ESMValTool after loading the user configuration
+    # file, but they need updating to avoid issues when writing the YAML
+    # file.
+    config_values_from_task_env = {
+        "auxiliary_data_dir": "",
+        "check_level": "DEFAULT",
+        "download_dir": "",
+        "drs": {
+            "ana4mips": os.environ["DRS_ANA4MIPS"],
+            "CMIP3": os.environ["DRS_CMIP3"],
+            "CMIP5": os.environ["DRS_CMIP5"],
+            "CMIP6": os.environ["DRS_CMIP6"],
+            "CORDEX": os.environ["DRS_CORDEX"],
+            "native6": os.environ["DRS_NATIVE6"],
+            "OBS": os.environ["DRS_OBS"],
+            "obs4MIPs": os.environ["DRS_OBS4MIPS"],
+            "OBS6": os.environ["DRS_OBS6"],
+        },
+        "extra_facets_dir": [],
+        "max_parallel_tasks": int(os.environ["MAX_PARALLEL_TASKS"]),
+        "output_dir": os.environ["OUTPUT_DIR"],
+        "remove_preproc_dir": False,
+        "rootpath": {
+            "ana4mips": os.environ["ROOTPATH_ANA4MIPS"],
+            "CMIP3": os.environ["ROOTPATH_CMIP3"],
+            "CMIP5": os.environ["ROOTPATH_CMIP5"],
+            "CMIP6": os.environ["ROOTPATH_CMIP6"],
+            "CORDEX": os.environ["ROOTPATH_CORDEX"],
+            "native6": os.environ["ROOTPATH_NATIVE6"],
+            "OBS": os.environ["ROOTPATH_OBS"],
+            "obs4MIPs": os.environ["ROOTPATH_OBS4MIPS"],
+            "OBS6": os.environ["ROOTPATH_OBS6"],
+            "RAWOBS": os.environ["ROOTPATH_RAWOBS"],
+        },
+    }
+    print("The configuration values defined in the environment for the "
+          "'configure' task: ")
+    pprint.PrettyPrinter().pprint(config_values_from_task_env)
+    return config_values_from_task_env
+
+
+def validate_user_config_file(user_config_file_content):
+    """Validate a user config with ``ESMValCore.config._validators`` functions.
+
+    Parameters
+    ----------
+    user_config_file_content: dict
+        An ESMValTool user configuration file loaded in memory as a Python
+        dictionary.
+
+    Raises
+    ------
+    KeyError
+        If ``user_config_file_content`` includes a key for which there is no
+        validator listed in ``_validators``,
+    ValidationError
+        If any of the called validation functions raise a ValidationError.
+    """
+    errors = [
+        "There were validation errors in your user configuration file. See "
+        "details below.\n"
+    ]
+    for user_config_key, usr_config_value in user_config_file_content.items():
+        try:
+            validation_function = _validators[user_config_key]
+        except KeyError as err:
+            errors.append(
+                f'Key Error for {user_config_key.upper()}. May not be a valid '
+                f'ESMValTool user configuration key\nERROR: {err}\n')
+        else:
+            try:
+                print(f'Validating {user_config_key.upper()} with value '
+                      f'"{usr_config_value}" using function '
+                      f'{validation_function.__name__.upper()}.')
+                validation_function(usr_config_value)
+            except ValidationError as err:
+                errors.append(
+                    f'Validation error for {user_config_key.upper()} with '
+                    f'value "{usr_config_value}"\nERROR: {err}\n')
+    if len(errors) > 1:
+        raise ValidationError("\n".join(errors))
+    print("All validation checks passed.")
+
+
+def write_yaml(file_path, contents):
+    """Write ``contents`` to the YAML file ``file_path``.
+
+    Parameters
+    ----------
+    file_path: string
+        The full path to the YAML file to write the contents to.
+    contents: dictionary
+        The contents to write to the YAML file.
+    """
+    with open(file_path, "w", encoding="utf-8") as file_handle:
+        yaml.dump(contents, file_handle, default_flow_style=False)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/esmvaltool/utils/recipe_test_workflow/app/configure/bin/test_configure.py b/esmvaltool/utils/recipe_test_workflow/app/configure/bin/test_configure.py
new file mode 100644
index 0000000000..68decf7f7d
--- /dev/null
+++ b/esmvaltool/utils/recipe_test_workflow/app/configure/bin/test_configure.py
@@ -0,0 +1,76 @@
+import pytest
+from bin.configure import validate_user_config_file
+from esmvalcore.config._config_validators import ValidationError
+
+
+def test_validate_user_config_file():
+    mock_valid_config = {
+        "output_dir": "~/esmvaltool_output",
+        "auxiliary_data_dir": "~/auxiliary_data",
+        "search_esgf": "never",
+        "download_dir": "~/climate_data",
+        "max_parallel_tasks": None,
+        "log_level": "info",
+        "exit_on_warning": True,
+        "output_file_type": "png",
+    }
+    # No assert statement is needed. If the function call errors Pytest
+    # considers the test failed.
+    validate_user_config_file(mock_valid_config)
+
+
+def test_validate_user_config_file_one_validation_error():
+    mock_one_invalid_config = {
+        "output_dir": "~/esmvaltool_output",
+        "auxiliary_data_dir": "~/auxiliary_data",
+        "search_esgf": "never",
+        "download_dir": "~/climate_data",
+        "max_parallel_tasks": None,
+        "log_level": "info",
+        "exit_on_warning": 100,
+        "output_file_type": "png",
+    }
+    with pytest.raises(
+            ValidationError,
+            match='Validation error for EXIT_ON_WARNING with value "100"\n'
+            'ERROR: Could not convert `100` to `bool`\n'):
+        validate_user_config_file(mock_one_invalid_config)
+
+
+def test_validate_user_config_file_two_validation_errors():
+    mock_two_invalids_config = {
+        "output_dir": 111,
+        "auxiliary_data_dir": "~/auxiliary_data",
+        "search_esgf": "never",
+        "download_dir": "~/climate_data",
+        "max_parallel_tasks": None,
+        "log_level": "info",
+        "exit_on_warning": 100,
+        "output_file_type": "png",
+    }
+    with pytest.raises(
+            ValidationError,
+            match='Validation error for OUTPUT_DIR with value "111"\nERROR: '
+            'Expected a path, but got 111\n\nValidation error for '
+            'EXIT_ON_WARNING with value "100"\nERROR: Could not convert `100` '
+            'to `bool`\n'):
+        validate_user_config_file(mock_two_invalids_config)
+
+
+def test_validate_user_config_file_key_error():
+    mock_one_key_error = {
+        "output_dir": "~/esmvaltool_output",
+        "auxiliary_data_dir": "~/auxiliary_data",
+        "search_esgf": "never",
+        "download_dir": "~/climate_data",
+        "one_rogue_field": 90210,
+        "max_parallel_tasks": None,
+        "log_level": "info",
+        "exit_on_warning": True,
+        "output_file_type": "png",
+    }
+    with pytest.raises(
+            ValidationError,
+            match="Key Error for ONE_ROGUE_FIELD. May not be a valid "
+            "ESMValTool user configuration key\nERROR: 'one_rogue_field'\n"):
+        validate_user_config_file(mock_one_key_error)
diff --git a/esmvaltool/utils/recipe_test_workflow/app/configure/rose-app.conf b/esmvaltool/utils/recipe_test_workflow/app/configure/rose-app.conf
new file mode 100644
index 0000000000..d110992bf1
--- /dev/null
+++ b/esmvaltool/utils/recipe_test_workflow/app/configure/rose-app.conf
@@ -0,0 +1,2 @@
+[command]
+default=rtw-env configure.py
diff --git a/esmvaltool/utils/recipe_test_workflow/app/get_esmval/bin/clone_latest_esmval.sh b/esmvaltool/utils/recipe_test_workflow/app/get_esmval/bin/clone_latest_esmval.sh
new file mode 100755
index 0000000000..331eada6c3
--- /dev/null
+++ b/esmvaltool/utils/recipe_test_workflow/app/get_esmval/bin/clone_latest_esmval.sh
@@ -0,0 +1,19 @@
+#!/bin/bash
+# Send the output from 'set -x' to 'stdout' rather than 'stderr'.
+BASH_XTRACEFD=1
+set -eux
+
+# Remove the ESMValTool and ESMValCore directories, if they exist.
+if [[ -d ${ESMVALTOOL_DIR} ]]; then
+    rm -rf "${ESMVALTOOL_DIR}"
+fi
+
+if [[ -d ${ESMVALCORE_DIR} ]]; then
+    rm -rf "${ESMVALCORE_DIR}"
+fi
+
+# Checkout the specified branch for ESMValCore and ESMValTool. Use the
+# quiet ('-q') option to prevent the progress status from being reported
+# (which is done via done via 'stderr').
+git clone -q -b "${BRANCH}" "${ESMVALTOOL_URL}" "${ESMVALTOOL_DIR}"
+git clone -q -b "${BRANCH}" "${ESMVALCORE_URL}" "${ESMVALCORE_DIR}"
diff --git a/esmvaltool/utils/recipe_test_workflow/app/get_esmval/opt/rose-app-dkrz.conf b/esmvaltool/utils/recipe_test_workflow/app/get_esmval/opt/rose-app-dkrz.conf
new file mode 100644
index 0000000000..7656f549bc
--- /dev/null
+++ b/esmvaltool/utils/recipe_test_workflow/app/get_esmval/opt/rose-app-dkrz.conf
@@ -0,0 +1,8 @@
+[command]
+default=singularity-env singularity build ${CONTAINER_PATH} ${DOCKER_SOURCE}
+
+[env]
+DOCKER_SOURCE=docker://esmvalgroup/esmvaltool:${ENV_NAME}
+
+[file:${CONTAINER_DIR}]
+mode=mkdir
diff --git a/esmvaltool/utils/recipe_test_workflow/app/get_esmval/opt/rose-app-jasmin.conf b/esmvaltool/utils/recipe_test_workflow/app/get_esmval/opt/rose-app-jasmin.conf
new file mode 100644
index 0000000000..21a8d26669
--- /dev/null
+++ b/esmvaltool/utils/recipe_test_workflow/app/get_esmval/opt/rose-app-jasmin.conf
@@ -0,0 +1,10 @@
+[command]
+default=singularity build ${CONTAINER_DIR}/${CONTAINER_FILE} ${DOCKER_SOURCE}
+
+[env]
+CONTAINER_DIR=${ROSE_DATAC}/container
+CONTAINER_FILE=esmvaltool.sif
+DOCKER_SOURCE=docker://esmvalgroup/esmvaltool:${ENV_NAME}
+
+[file:${CONTAINER_DIR}]
+mode=mkdir
diff --git a/esmvaltool/utils/recipe_test_workflow/app/get_esmval/opt/rose-app-metoffice.conf b/esmvaltool/utils/recipe_test_workflow/app/get_esmval/opt/rose-app-metoffice.conf
new file mode 100644
index 0000000000..8911ca1cfd
--- /dev/null
+++ b/esmvaltool/utils/recipe_test_workflow/app/get_esmval/opt/rose-app-metoffice.conf
@@ -0,0 +1,7 @@
+[command]
+default=clone_latest_esmval.sh
+
+[env]
+BRANCH=main
+ESMVALCORE_URL=git@github.com:ESMValGroup/ESMValCore.git
+ESMVALTOOL_URL=git@github.com:ESMValGroup/ESMValTool.git
diff --git a/esmvaltool/utils/recipe_test_workflow/app/get_esmval/rose-app.conf b/esmvaltool/utils/recipe_test_workflow/app/get_esmval/rose-app.conf
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/esmvaltool/utils/recipe_test_workflow/app/install_env_file/opt/rose-app-dkrz.conf b/esmvaltool/utils/recipe_test_workflow/app/install_env_file/opt/rose-app-dkrz.conf
new file mode 100644
index 0000000000..8b7e9dd4f3
--- /dev/null
+++ b/esmvaltool/utils/recipe_test_workflow/app/install_env_file/opt/rose-app-dkrz.conf
@@ -0,0 +1,12 @@
+# Copy the site specific environment files to the 'share/bin' directory in the
+# installed Cylc workflow (this directory is automatically added to the
+# ${PATH} by Cylc).
+
+[command]
+default=BASH_XTRACEFD=1  # Send the output from 'set -x' to 'stdout' rather than 'stderr'.
+       =set -euxo pipefail
+       =cp ${ENV_FILE_SITE_PATH} ${SHARE_BIN_DIR}/${ENV_FILE}
+       =cp ${SINGULARITY_ENV_FILE_SITE_PATH} ${SHARE_BIN_DIR}/${SINGULARITY_ENV_FILE}
+
+[file:${SHARE_BIN_DIR}]
+mode=mkdir
diff --git a/esmvaltool/utils/recipe_test_workflow/app/install_env_file/rose-app.conf b/esmvaltool/utils/recipe_test_workflow/app/install_env_file/rose-app.conf
new file mode 100644
index 0000000000..6c3b4fdb84
--- /dev/null
+++ b/esmvaltool/utils/recipe_test_workflow/app/install_env_file/rose-app.conf
@@ -0,0 +1,11 @@
+# Copy the site specific environment file to the 'share/bin' directory in the
+# installed Cylc workflow (this directory is automatically added to the
+# ${PATH} by Cylc).
+
+[command]
+default=BASH_XTRACEFD=1  # Send the output from 'set -x' to 'stdout' rather than 'stderr'.
+       =set -euxo pipefail
+       =cp ${ENV_FILE_SITE_PATH} ${SHARE_BIN_DIR}/${ENV_FILE}
+
+[file:${SHARE_BIN_DIR}]
+mode=mkdir
diff --git a/esmvaltool/utils/recipe_test_workflow/app/process/rose-app.conf b/esmvaltool/utils/recipe_test_workflow/app/process/rose-app.conf
new file mode 100644
index 0000000000..ae4561e614
--- /dev/null
+++ b/esmvaltool/utils/recipe_test_workflow/app/process/rose-app.conf
@@ -0,0 +1,5 @@
+[command]
+default=rtw-env esmvaltool run ${RECIPE_PATH} --config_file=${USER_CONFIG_PATH}
+
+[env]
+USER_CONFIG_PATH=${USER_CONFIG_PATH}
diff --git a/esmvaltool/utils/recipe_test_workflow/flow.cylc b/esmvaltool/utils/recipe_test_workflow/flow.cylc
new file mode 100644
index 0000000000..1785c98d69
--- /dev/null
+++ b/esmvaltool/utils/recipe_test_workflow/flow.cylc
@@ -0,0 +1,135 @@
+#!jinja2
+{{ assert(SITE != "", "SITE must be set to something other than an empty string") }}
+
+{% set SITE_RECIPES_FILE = "site/" ~ SITE ~ "-recipes.jinja" %}
+{% set MISSING_FILE_MSG =
+    "'" ~ SITE_RECIPES_FILE ~ "' is required for the RTW and was not "
+    "found - the 'How to add a recipe to the RTW' documentation provides more "
+    "information"%}
+{% set UNDEFINED_VARIABLE_MSG =
+    "variable must be set in your '" ~ SITE_RECIPES_FILE ~ "' file - the 'How "
+    "to add a recipe to the RTW' documentation provides more information."%}
+
+{{ assert(SITE_RECIPES_FILE is file_exists, MISSING_FILE_MSG) }}
+{% from SITE_RECIPES_FILE import FAST_RECIPES, MEDIUM_RECIPES %}
+
+{{ assert(FAST_RECIPES is defined, "The FAST_RECIPES " ~ UNDEFINED_VARIABLE_MSG) }}
+{{ assert(MEDIUM_RECIPES is defined, "The MEDIUM_RECIPES " ~ UNDEFINED_VARIABLE_MSG) }}
+
+[scheduler]
+    UTC mode = True
+
+[task parameters]
+    fast = {{ FAST_RECIPES | map(attribute="recipe_path") | join(", ") }}
+    medium = {{ MEDIUM_RECIPES | map(attribute="recipe_path") | join(", ") }}
+
+[scheduling]
+    initial cycle point = now
+    [[graph]]
+        R1 = """install_env_file => get_esmval => configure
+            configure => process<fast> => compare<fast>
+            configure => process<medium> => compare<medium>"""
+        T01 = """@wall_clock => get_esmval => configure
+            configure => process<fast> => compare<fast>
+            configure => process<medium> => compare<medium>"""
+
+[runtime]
+    [[root]]
+        script = rose task-run
+        env-script = "eval $(rose task-env)"
+        [[[environment]]]
+            ENV_NAME = {{ ENV_NAME }}
+            USER_CONFIG_DIR = ${ROSE_DATAC}
+            USER_CONFIG_PATH = ${USER_CONFIG_DIR}/config-user.yml
+            OUTPUT_DIR = ${ROSE_DATAC}
+            ESMVALCORE_DIR = ""
+            ESMVALTOOL_DIR = ""
+            PYTHONPATH_PREPEND = ""
+
+    [[install_env_file]]
+        platform = localhost
+        [[[environment]]]
+            SITE = {{ SITE }}
+            ENV_FILE=rtw-env
+            ENV_FILE_SITE_PATH=${CYLC_WORKFLOW_RUN_DIR}/site/${SITE}-env
+            SHARE_BIN_DIR=${CYLC_WORKFLOW_SHARE_DIR}/bin
+
+    [[get_esmval]]
+        [[[environment]]]
+            ROSE_APP_OPT_CONF_KEYS = {{ SITE }}
+
+    [[configure]]
+        [[[environment]]]
+            ROSE_TASK_APP = configure
+            DRS_CMIP3 = {{ DRS_CMIP3 }}
+            DRS_CMIP5 = {{ DRS_CMIP5 }}
+            DRS_CMIP6 = {{ DRS_CMIP6 }}
+            DRS_CORDEX = {{ DRS_CORDEX }}
+            DRS_OBS = {{ DRS_OBS }}
+            DRS_OBS6 = {{ DRS_OBS6 }}
+            DRS_ANA4MIPS = {{ DRS_ANA4MIPS }}
+            DRS_NATIVE6 = {{ DRS_NATIVE6 }}
+            DRS_OBS4MIPS = {{ DRS_OBS4MIPS }}
+            MAX_PARALLEL_TASKS = {{ MAX_PARALLEL_TASKS }}
+            ROOTPATH_CMIP3 = {{ ROOTPATH_CMIP3 }}
+            ROOTPATH_CMIP5 = {{ ROOTPATH_CMIP5 }}
+            ROOTPATH_CMIP6 = {{ ROOTPATH_CMIP6 }}
+            ROOTPATH_CORDEX = {{ ROOTPATH_CORDEX }}
+            ROOTPATH_OBS = {{ ROOTPATH_OBS }}
+            ROOTPATH_OBS6 = {{ ROOTPATH_OBS6 }}
+            ROOTPATH_ANA4MIPS = {{ ROOTPATH_ANA4MIPS }}
+            ROOTPATH_NATIVE6 = {{ ROOTPATH_NATIVE6 }}
+            ROOTPATH_OBS4MIPS = {{ ROOTPATH_OBS4MIPS }}
+            ROOTPATH_RAWOBS = {{ ROOTPATH_RAWOBS }}
+
+    # Bash variable substitution below replaces `--` with `/` to obtain path to
+    # recipe.
+{% for RECIPE in FAST_RECIPES %}
+    [[process<fast={{ RECIPE["recipe_path"] }}>]]
+        inherit = None, COMPUTE
+        execution time limit = {{ RECIPE["max_time"] }}
+        [[[environment]]]
+            ROSE_TASK_APP = process
+            RECIPE_PATH = "${CYLC_TASK_PARAM_fast//--//}.yml"
+        [[[directives]]]
+            --mem = {{ RECIPE["max_memory"] }}
+{% endfor %}
+
+{% for RECIPE in MEDIUM_RECIPES %}
+    [[process<medium={{ RECIPE["recipe_path"] }}>]]
+        inherit = None, COMPUTE
+        execution time limit = {{ RECIPE["max_time"] }}
+        [[[environment]]]
+            ROSE_TASK_APP = process
+            RECIPE_PATH = "${CYLC_TASK_PARAM_medium//--//}.yml"
+        [[[directives]]]
+            --mem = {{ RECIPE["max_memory"] }}
+{% endfor %}
+
+    [[COMPARE]]
+        inherit = None, COMPUTE
+        [[[environment]]]
+            ROSE_TASK_APP = compare
+            KGO_ROOT_PATH = {{ KGO_ROOT_PATH }}
+            COMPARE_SCRIPT=${ESMVALTOOL_DIR}/esmvaltool/utils/testing/regression/compare.py
+
+    # Bash variable manipulation `##*--` below strips leading path from recipe
+    # parameter to obtain RECIPE_NAME.
+    # RECIPE_NAME is the recipe file basename without yml extension, as used by
+    # ESMValTool for naming output directories. E.g. for the recipe
+    # examples/recipe_python.yml, RECIPE_NAME=recipe_python
+    [[compare<fast>]]
+        inherit = COMPARE
+        [[[environment]]]
+            RECIPE_NAME = "${CYLC_TASK_PARAM_fast##*--}"
+            COMPARISON_OUTPUT_DIR=${OUTPUT_DIR}/${RECIPE_NAME}*
+            KGO_DIR=${KGO_ROOT_PATH}/${RECIPE_NAME}*
+
+    [[compare<medium>]]
+        inherit = COMPARE
+        [[[environment]]]
+            RECIPE_NAME = "${CYLC_TASK_PARAM_medium##*--}"
+            COMPARISON_OUTPUT_DIR=${OUTPUT_DIR}/${RECIPE_NAME}*
+            KGO_DIR=${KGO_ROOT_PATH}/${RECIPE_NAME}*
+
+{% include "site/" ~ SITE ~ ".cylc" %}
diff --git a/esmvaltool/utils/recipe_test_workflow/meta/rose-meta.conf b/esmvaltool/utils/recipe_test_workflow/meta/rose-meta.conf
new file mode 100644
index 0000000000..cc827acca9
--- /dev/null
+++ b/esmvaltool/utils/recipe_test_workflow/meta/rose-meta.conf
@@ -0,0 +1,185 @@
+[template variables=DRS_ANA4MIPS]
+compulsory=false
+description=The Data Reference Syntax (DRS) to use when specifying the
+           =directory structure for ana4mips data.
+help=To override the default value for a specific site, define the override
+    =value in a site-specific configuration file located in the 'opt/'
+    =directory.
+type=quoted
+
+[template variables=DRS_CMIP3]
+compulsory=false
+description=The Data Reference Syntax (DRS) to use when specifying the
+           =directory structure for CMIP3 data.
+help=To override the default value for a specific site, define the override
+    =value in a site-specific configuration file located in the 'opt/'
+    =directory.
+type=quoted
+
+[template variables=DRS_CMIP5]
+compulsory=false
+description=The Data Reference Syntax (DRS) to use when specifying the
+           =directory structure for CMIP5 data.
+help=To override the default value for a specific site, define the override
+    =value in a site-specific configuration file located in the 'opt/'
+    =directory.
+type=quoted
+
+[template variables=DRS_CMIP6]
+compulsory=false
+description=The Data Reference Syntax (DRS) to use when specifying the
+           =directory structure for CMIP6 data.
+help=To override the default value for a specific site, define the override
+    =value in a site-specific configuration file located in the 'opt/'
+    =directory.
+type=quoted
+
+[template variables=DRS_CORDEX]
+compulsory=false
+description=The Data Reference Syntax (DRS) to use when specifying the
+           =directory structure for CORDEX data.
+help=To override the default value for a specific site, define the override
+    =value in a site-specific configuration file located in the 'opt/'
+    =directory.
+type=quoted
+
+[template variables=DRS_NATIVE6]
+compulsory=false
+description=The Data Reference Syntax (DRS) to use when specifying the
+           =directory structure for native6 data.
+help=To override the default value for a specific site, define the override
+    =value in a site-specific configuration file located in the 'opt/'
+    =directory.
+type=quoted
+
+[template variables=DRS_OBS]
+compulsory=false
+description=The Data Reference Syntax (DRS) to use when specifying the
+           =directory structure for OBS data.
+help=To override the default value for a specific site, define the override
+    =value in a site-specific configuration file located in the 'opt/'
+    =directory.
+type=quoted
+
+[template variables=DRS_OBS4MIPS]
+compulsory=true
+description=The Data Reference Syntax (DRS) to use when specifying the
+           =directory structure for obs4MIPs data.
+help=To override the default value for a specific site, define the override
+    =value in a site-specific configuration file located in the 'opt/'
+    =directory.
+type=quoted
+
+[template variables=DRS_OBS6]
+compulsory=false
+description=The Data Reference Syntax (DRS) to use when specifying the
+           =directory structure for OBS6 data.
+help=To override the default value for a specific site, define the override
+    =value in a site-specific configuration file located in the 'opt/'
+    =directory.
+type=quoted
+
+[template variables=ENV_NAME]
+compulsory=false
+description=The name of the environment (module or container label) to load for
+           =the RTW.
+help=If required, this value must be set in a site-specific configuration file
+    =located in the 'opt/' directory. Otherwise, set the value equal to an
+    =empty string.
+type=quoted
+
+[template variables=MAX_PARALLEL_TASKS]
+compulsory=true
+description=The number of tasks (within a single call to ESMValTool) to run in
+           =parallel when running ESMValTool.
+help=To override the default value for a specific site, define the override
+    =value in a site-specific configuration file located in the 'opt/'
+    =directory.
+type=integer
+
+[template variables=ROOTPATH_ANA4MIPS]
+compulsory=false
+description=The root path to the input ana4mips data.
+help=If required, this value must be set in a site-specific configuration file
+    =located in the 'opt/' directory. Otherwise, set the value equal to an
+    =empty string.
+type=quoted
+
+[template variables=ROOTPATH_CMIP3]
+compulsory=false
+description=The root path to the input CMIP3 data.
+help=If required, this value must be set in a site-specific configuration file
+    =located in the 'opt/' directory. Otherwise, set the value equal to an
+    =empty string.
+type=quoted
+
+[template variables=ROOTPATH_CMIP5]
+compulsory=false
+description=The root path to the input CMIP5 data.
+help=If required, this value must be set in a site-specific configuration file
+    =located in the 'opt/' directory. Otherwise, set the value equal to an
+    =empty string.
+type=quoted
+
+[template variables=ROOTPATH_CMIP6]
+compulsory=false
+description=The root path to the input CMIP6 data.
+help=If required, this value must be set in a site-specific configuration file
+    =located in the 'opt/' directory. Otherwise, set the value equal to an
+    =empty string.
+type=quoted
+
+[template variables=ROOTPATH_CORDEX]
+compulsory=false
+description=The root path to the input CORDEX data.
+help=If required, this value must be set in a site-specific configuration file
+    =located in the 'opt/' directory. Otherwise, set the value equal to an
+    =empty string.
+type=quoted
+
+[template variables=ROOTPATH_NATIVE6]
+compulsory=false
+description=The root path to the input native6 data.
+help=If required, this value must be set in a site-specific configuration file
+    =located in the 'opt/' directory. Otherwise, set the value equal to an
+    =empty string.
+type=quoted
+
+[template variables=ROOTPATH_OBS]
+compulsory=false
+description=The root path to the input OBS data.
+help=If required, this value must be set in a site-specific configuration file
+    =located in the 'opt/' directory. Otherwise, set the value equal to an
+    =empty string.
+type=quoted
+
+[template variables=ROOTPATH_OBS4MIPS]
+compulsory=false
+description=The root path to the input obs4MIPs data.
+help=If required, this value must be set in a site-specific configuration file
+    =located in the 'opt/' directory. Otherwise, set the value equal to an
+    =empty string.
+type=quoted
+
+[template variables=ROOTPATH_OBS6]
+compulsory=false
+description=The root path to the input OBS6 data.
+help=If required, this value must be set in a site-specific configuration file
+    =located in the 'opt/' directory. Otherwise, set the value equal to an
+    =empty string.
+type=quoted
+
+[template variables=ROOTPATH_RAWOBS]
+compulsory=false
+description=The root path to the input RAWOBS data.
+help=If required, this value must be set in a site-specific configuration file
+    =located in the 'opt/' directory. Otherwise, set the value equal to an
+    =empty string.
+type=quoted
+
+[template variables=SITE]
+compulsory=true
+description=The site at which the Rose suite will run.
+help=This value must be set in a site-specific configuration file located in
+    =the 'opt/' directory.
+type=quoted
diff --git a/esmvaltool/utils/recipe_test_workflow/opt/rose-suite-dkrz.conf b/esmvaltool/utils/recipe_test_workflow/opt/rose-suite-dkrz.conf
new file mode 100644
index 0000000000..dca068f8c2
--- /dev/null
+++ b/esmvaltool/utils/recipe_test_workflow/opt/rose-suite-dkrz.conf
@@ -0,0 +1,10 @@
+[template variables]
+DRS_CMIP5="DKRZ"
+DRS_CMIP6="DKRZ"
+ENV_NAME="experimental"
+KGO_ROOT_PATH="/work/bd0854/KGO/esmvaltool_output/"
+ROOTPATH_CMIP5="/work/bd0854/DATA/ESMValTool2/CMIP5_DKRZ"
+ROOTPATH_CMIP6="/work/bd0854/DATA/ESMValTool2/CMIP6_DKRZ"
+ROOTPATH_OBS="/work/bd0854/DATA/ESMValTool2/OBS"
+ROOTPATH_OBS4MIPS="/work/bd0854/DATA/ESMValTool2/OBS"
+SITE="dkrz"
diff --git a/esmvaltool/utils/recipe_test_workflow/opt/rose-suite-jasmin.conf b/esmvaltool/utils/recipe_test_workflow/opt/rose-suite-jasmin.conf
new file mode 100644
index 0000000000..4a69f339d0
--- /dev/null
+++ b/esmvaltool/utils/recipe_test_workflow/opt/rose-suite-jasmin.conf
@@ -0,0 +1,10 @@
+[template variables]
+DRS_CMIP5="BADC"
+DRS_CMIP6="BADC"
+ENV_NAME="latest"
+KGO_ROOT_PATH="/gws/nopw/j04/esmeval/KGO"
+ROOTPATH_CMIP5="/badc/cmip5/data/cmip5/output1"
+ROOTPATH_CMIP6="/badc/cmip6/data/CMIP6"
+ROOTPATH_OBS="/gws/nopw/j04/esmeval/obsdata-v2"
+ROOTPATH_OBS4MIPS="/gws/nopw/j04/esmeval/obsdata-v2"
+SITE="jasmin"
diff --git a/esmvaltool/utils/recipe_test_workflow/opt/rose-suite-metoffice.conf b/esmvaltool/utils/recipe_test_workflow/opt/rose-suite-metoffice.conf
new file mode 100644
index 0000000000..5a9287cb7a
--- /dev/null
+++ b/esmvaltool/utils/recipe_test_workflow/opt/rose-suite-metoffice.conf
@@ -0,0 +1,10 @@
+[template variables]
+DRS_CMIP5="BADC"
+DRS_CMIP6="BADC"
+ENV_NAME="scitools/community/esmvaltool/2.11.0"
+KGO_ROOT_PATH="/data/users/esmval/KGO"
+ROOTPATH_CMIP5="/project/champ/data/cmip5/output1"
+ROOTPATH_CMIP6="/project/champ/data/CMIP6"
+ROOTPATH_OBS="/data/users/esmval/ESMValTool/temporary/obs/"
+ROOTPATH_OBS4MIPS="/data/users/esmval/ESMValTool/temporary/obs/"
+SITE="metoffice"
diff --git a/esmvaltool/utils/recipe_test_workflow/rose-suite.conf b/esmvaltool/utils/recipe_test_workflow/rose-suite.conf
new file mode 100644
index 0000000000..0786ead352
--- /dev/null
+++ b/esmvaltool/utils/recipe_test_workflow/rose-suite.conf
@@ -0,0 +1,24 @@
+[template variables]
+DRS_ANA4MIPS="default"
+DRS_CMIP3="default"
+DRS_CMIP5="default"
+DRS_CMIP6="default"
+DRS_CORDEX="default"
+DRS_NATIVE6="default"
+DRS_OBS="default"
+DRS_OBS4MIPS="default"
+DRS_OBS6="default"
+ENV_NAME=""
+KGO_ROOT_PATH=""
+MAX_PARALLEL_TASKS=4
+ROOTPATH_ANA4MIPS=""
+ROOTPATH_CMIP3=""
+ROOTPATH_CMIP5=""
+ROOTPATH_CMIP6=""
+ROOTPATH_CORDEX=""
+ROOTPATH_NATIVE6=""
+ROOTPATH_OBS=""
+ROOTPATH_OBS4MIPS=""
+ROOTPATH_OBS6=""
+ROOTPATH_RAWOBS=""
+SITE=""
diff --git a/esmvaltool/utils/recipe_test_workflow/rose-suite.info b/esmvaltool/utils/recipe_test_workflow/rose-suite.info
new file mode 100644
index 0000000000..a39d8a9e9c
--- /dev/null
+++ b/esmvaltool/utils/recipe_test_workflow/rose-suite.info
@@ -0,0 +1,6 @@
+owner=ESMValTool community
+project=RTW
+title=The Recipe Test Workflow (RTW)
+description=The RTW is a Cylc 8 workflow that is used to regularly run recipes so
+           =issues can be discovered during the development process sooner
+           =rather than later
diff --git a/esmvaltool/utils/recipe_test_workflow/site/dkrz-env b/esmvaltool/utils/recipe_test_workflow/site/dkrz-env
new file mode 100755
index 0000000000..0821e59be2
--- /dev/null
+++ b/esmvaltool/utils/recipe_test_workflow/site/dkrz-env
@@ -0,0 +1,57 @@
+#!/bin/bash
+#
+# USAGE dkrz-env COMMAND
+#
+# ENVIRONMENT
+#   ENV_NAME            The name of the container being used
+#   QUIET_MODE          Don't print confirmation messages
+#   PYTHONPATH_PREPEND  The path to prepend to PYTHONPATH
+#
+# OPTIONS
+#   COMMAND             The command to execute with options
+set -eu
+
+# Must be run before importing numpy, see
+# https://docs.dask.org/en/stable/array-best-practices.html#avoid-oversubscribing-threads
+export OMP_NUM_THREADS=1
+export OPENBLAS_NUM_THREADS=1
+export MKL_NUM_THREADS=1
+export VECLIB_MAXIMUM_THREADS=1
+export NUMEXPR_NUM_THREADS=1
+
+# Ensure '~/.local' isn't added to 'sys.path'.
+export PYTHONNOUSERSITE=True
+
+WORKFLOW_RUN_BIN_DIR="${CYLC_WORKFLOW_RUN_DIR}/bin"
+WORKFLOW_SHARE_BIN_DIR="${CYLC_WORKFLOW_SHARE_DIR}/cycle/bin"
+ROSE_APP_BIN_DIR="${CYLC_WORKFLOW_RUN_DIR}/app/${ROSE_TASK_APP:-$CYLC_TASK_NAME}/bin"
+
+# Bind paths for container.
+export SINGULARITY_BIND="/home/b,/work,/scratch/b"
+
+# Suppress an ESMValTool "file not found" warning.
+export SINGULARITYENV_PROJ_DATA="/opt/conda/envs/esmvaltool/share/proj"
+
+# Provide mkfile needed to build esmfpy package.
+export SINGULARITYENV_ESMFMKFILE="/opt/conda/envs/esmvaltool/lib/esmf.mk"
+
+# Ensure that `singularity exec` finds the right version of python.
+export SINGULARITYENV_PREPEND_PATH="/opt/conda/envs/esmvaltool/bin"
+
+# Include Rose/Cylc workflow directories in container PATH.
+export SINGULARITYENV_APPEND_PATH="${WORKFLOW_RUN_BIN_DIR}:${WORKFLOW_SHARE_BIN_DIR}:${ROSE_APP_BIN_DIR}"
+
+# If PYTHONPATH_PREPEND has been set, prepend it to PYTHONPATH to extend the
+# Python environment.
+if [[ ! -z ${PYTHONPATH_PREPEND:-} ]]; then
+    echo "[INFO] Prepending the following to PYTHONPATH: ${PYTHONPATH_PREPEND}"
+    export PYTHONPATH=${PYTHONPATH_PREPEND}:${PYTHONPATH:-}
+fi
+
+if [[ -z ${QUIET_MODE:-} ]]; then
+    echo "[INFO] Using the ${ENV_NAME} container"
+fi
+
+singularity_command="singularity-env singularity -q exec ${CONTAINER_PATH} $@"
+command="/usr/bin/time -v -o ${CYLC_TASK_LOG_ROOT}.time ${singularity_command}"
+exec ${command}
diff --git a/esmvaltool/utils/recipe_test_workflow/site/dkrz-singularity-env b/esmvaltool/utils/recipe_test_workflow/site/dkrz-singularity-env
new file mode 100755
index 0000000000..3e74cd9b4e
--- /dev/null
+++ b/esmvaltool/utils/recipe_test_workflow/site/dkrz-singularity-env
@@ -0,0 +1,34 @@
+#!/bin/bash
+#
+# USAGE dkrz-singularity-env COMMAND
+#
+# OPTIONS
+#   COMMAND             The command to execute with options
+#
+# Since DKRZ uses the module environment to dynamically modify a user's
+# environment via modulefiles (more details are available at
+# https://docs.dkrz.de/doc/levante/access-and-environment.html#module-environment)
+# this additional environment file is required to enable access to singularity
+# on DKRZ. This file is used directly in the 'get_esmval' app for DKRZ, as well
+# as in the 'dkrz-env' environment file.
+set -eu
+
+module_count(){
+    module list -t 2>&1 | wc -l
+}
+
+safe_load(){
+    PRE_LOAD_COUNT=$(module_count)
+
+    module load "${1}"
+    # Check module count to determine whether module load was successful.
+    
+    if (( PRE_LOAD_COUNT == $(module_count) )); then
+        echo "[ERROR] Failed to load: ${1}"
+        exit 1
+    fi
+}
+safe_load "singularity"
+
+command="/usr/bin/time -v -o ${CYLC_TASK_LOG_ROOT}.time $@"
+exec ${command}
diff --git a/esmvaltool/utils/recipe_test_workflow/site/dkrz.cylc b/esmvaltool/utils/recipe_test_workflow/site/dkrz.cylc
new file mode 100644
index 0000000000..f04252d071
--- /dev/null
+++ b/esmvaltool/utils/recipe_test_workflow/site/dkrz.cylc
@@ -0,0 +1,102 @@
+#!jinja2
+[runtime]
+    [[root]]
+        [[[environment]]]
+            CONTAINER_DIR = ${ROSE_DATAC}/container
+            CONTAINER_FILE = esmvaltool.sif
+            CONTAINER_PATH = ${CONTAINER_DIR}/${CONTAINER_FILE}
+            # Warning: fragile assumption of location of esmvaltool code,
+            # issue #3437 contains more details.
+            ESMVALTOOL_DIR = /opt/conda/envs/esmvaltool/lib/python3.12/site-packages
+
+    # COMPUTE provides defaults for computation-heavy tasks.
+    # Specific tasks below override some defaults, e.g. time & memory.
+    [[COMPUTE]]
+        platform = levante
+        execution time limit = PT3M
+        [[[directives]]]
+            --wckey = RTW
+            --account = bk1088
+            --partition = interactive
+            --mem = 15G
+
+    [[install_env_file]]
+        [[[environment]]]
+            ROSE_APP_OPT_CONF_KEYS = {{ SITE }}
+            SINGULARITY_ENV_FILE=singularity-env
+            SINGULARITY_ENV_FILE_SITE_PATH=${CYLC_WORKFLOW_RUN_DIR}/site/${SITE}-singularity-env
+
+    [[get_esmval]]
+        platform = localhost
+        execution time limit = PT10M  # Actual: 3m06s on 2024-12-18.
+        [[[environment]]]
+            # Move the location of the singularity cache from the user's
+            # home area to the directory where the container is saved.
+            # This variable must be specified only for the 'get_esmval'
+            # task, otherwise, for some reason, subsequent steps take much
+            # longer to run than the resources specified.
+            SINGULARITY_CACHEDIR = ${CONTAINER_DIR}
+
+    [[configure]]
+        platform = localhost
+        execution time limit = PT2M  # Actual: 0m14s on 2024-12-18.
+        [[[directives]]]
+            --mem = 2G
+
+    # Resources for recipes that need more than the default. Both time and
+    # memory should be specified, in case the default changes.
+    # Variable (fast, medium) must be consistent with flow.cylc.
+    # Comment indicates example recorded usage on DKRZ.
+    [[process_examples--recipe_python]]
+        # Actual: 0m07s 2.1 GB on 2024-12-18.
+        execution time limit = PT2M
+        [[[directives]]]
+            --mem = 15G
+
+    [[process_recipe_albedolandcover]]
+        # Actual: 0m10s 3.2 GB on 2024-12-18.
+        execution time limit = PT2M
+        [[[directives]]]
+            --mem = 15G
+
+    [[process_recipe_autoassess_landsurface_soilmoisture]]
+        # Actual: 0m13s 3.8 GB on 2024-12-18.
+        execution time limit = PT2M
+        [[[directives]]]
+            --mem = 15G
+
+    [[process_recipe_consecdrydays]]
+        # Actual: 0m08s 2.3 GB on 2024-12-18.
+        execution time limit = PT2M
+        [[[directives]]]
+            --mem = 15G
+
+    [[process_recipe_ensclus]]
+        # Actual: 0m35s 2.0 GB on 2024-12-18.
+        execution time limit = PT2M
+        [[[directives]]]
+            --mem = 15G
+
+    [[process_recipe_heatwaves_coldwaves]]
+        # Actual: 0m30s 1.6 GB on 2024-12-18.
+        execution time limit = PT2M
+        [[[directives]]]
+            --mem = 15G
+
+    [[process_recipe_ocean_amoc]]
+        # Actual: Currently broken: No input files found for Dataset
+        execution time limit = PT2M
+        [[[directives]]]
+            --mem = 15G
+
+    [[process_recipe_ocean_multimap]]
+        # Actual: Currently broken: No input files found for Dataset
+        execution time limit = PT2M
+        [[[directives]]]
+            --mem = 15G
+
+    [[process_recipe_radiation_budget]]
+        # Actual: Currently broken: No input files found for Dataset
+        execution time limit = PT2M
+        [[[directives]]]
+            --mem =15G
diff --git a/esmvaltool/utils/recipe_test_workflow/site/jasmin-env b/esmvaltool/utils/recipe_test_workflow/site/jasmin-env
new file mode 100755
index 0000000000..8266e081cb
--- /dev/null
+++ b/esmvaltool/utils/recipe_test_workflow/site/jasmin-env
@@ -0,0 +1,59 @@
+#!/bin/bash
+#
+# USAGE jasmin-env COMMAND
+#
+# ENVIRONMENT
+#   ENV_NAME            The name of the container being used
+#   QUIET_MODE          Don't print confirmation messages
+#   PYTHONPATH_PREPEND  The path to prepend to PYTHONPATH
+#
+# OPTIONS
+#   COMMAND             The command to execute with options
+set -eu
+
+# Must be run before importing numpy, see
+# https://docs.dask.org/en/stable/array-best-practices.html#avoid-oversubscribing-threads
+export OMP_NUM_THREADS=1
+export OPENBLAS_NUM_THREADS=1
+export MKL_NUM_THREADS=1
+export VECLIB_MAXIMUM_THREADS=1
+export NUMEXPR_NUM_THREADS=1
+
+# Ensure '~/.local' isn't added to 'sys.path'.
+export PYTHONNOUSERSITE=True
+
+WORKFLOW_RUN_BIN_DIR="${CYLC_WORKFLOW_RUN_DIR}/bin"
+WORKFLOW_SHARE_BIN_DIR="${CYLC_WORKFLOW_SHARE_DIR}/cycle/bin"
+ROSE_APP_BIN_DIR="${CYLC_WORKFLOW_RUN_DIR}/app/${ROSE_TASK_APP:-$CYLC_TASK_NAME}/bin"
+CONTAINER=${ROSE_DATAC}/container/esmvaltool.sif
+
+# Bind paths for container. Where symbolic links are used in file paths (e.g.
+# under /badc/cmip6 and /home/users) need to bind the root dirs of both the
+# source and target files or directories.
+export SINGULARITY_BIND="/badc,/datacentre,/home/users,/work,/gws"
+
+# Suppress an ESMValTool "file not found" warning
+export SINGULARITYENV_PROJ_DATA="/opt/conda/envs/esmvaltool/share/proj"
+
+# Provide mkfile needed to build esmfpy package
+export SINGULARITYENV_ESMFMKFILE="/opt/conda/envs/esmvaltool/lib/esmf.mk"
+
+# Ensure that `singularity exec` finds the right version of python
+export SINGULARITYENV_PREPEND_PATH="/opt/conda/envs/esmvaltool/bin"
+
+# Include Rose/Cylc workflow directories in container PATH
+export SINGULARITYENV_APPEND_PATH="${WORKFLOW_RUN_BIN_DIR}:${WORKFLOW_SHARE_BIN_DIR}:${ROSE_APP_BIN_DIR}"
+
+# If PYTHONPATH_PREPEND has been set, prepend it to PYTHONPATH to extend the
+# Python environment.
+if [[ ! -z ${PYTHONPATH_PREPEND:-} ]]; then
+    echo "[INFO] Prepending the following to PYTHONPATH: ${PYTHONPATH_PREPEND}"
+    export PYTHONPATH=${PYTHONPATH_PREPEND}:${PYTHONPATH:-}
+fi
+
+
+if [[ -z ${QUIET_MODE:-} ]]; then
+    echo "[INFO] Using the ${ENV_NAME} container"
+fi
+
+/usr/bin/time -v -o "${CYLC_TASK_LOG_ROOT}.time" singularity -q exec "${CONTAINER}" "$@"
diff --git a/esmvaltool/utils/recipe_test_workflow/site/jasmin.cylc b/esmvaltool/utils/recipe_test_workflow/site/jasmin.cylc
new file mode 100644
index 0000000000..56c70f9df7
--- /dev/null
+++ b/esmvaltool/utils/recipe_test_workflow/site/jasmin.cylc
@@ -0,0 +1,45 @@
+#!jinja2
+[runtime]
+    [[root]]
+        [[[environment]]]
+            # Warning: fragile assumption of location of esmvaltool code,
+            # issue #3437 contains more details.
+            ESMVALTOOL_DIR = /opt/conda/envs/esmvaltool/lib/python3.11/site-packages
+
+    # COMPUTE provides defaults for computation-heavy tasks.
+    # Specific tasks below override some defaults, e.g. time & memory.
+    [[COMPUTE]]
+        platform = lotus
+        execution time limit = PT3M
+        [[[directives]]]
+            --wckey = RTW
+            --ntasks = 1
+            --mem = 2G
+
+    [[get_esmval]]
+        inherit = None, COMPUTE
+        execution time limit = PT60M  # Actual: 29m on 2024-03-29.
+        [[[directives]]]
+            --mem = 2G
+
+    [[configure]]
+        inherit = None, COMPUTE
+        execution time limit = PT2M
+        [[[directives]]]
+            --mem = 2G
+
+    # Resources for recipes that need more than the default. Both time and
+    # memory should be specified, in case the default changes.
+    # Variable (fast, medium) must be consistent with flow.cylc.
+    # Comment indicates example recorded usage on JASMIN.
+    [[process<fast=recipe_autoassess_landsurface_soilmoisture>]]
+        # Actual: 1m35s, 2.1 GB on 2024-03-29.
+        execution time limit = PT3M
+        [[[directives]]]
+            --mem = 3G
+
+    [[process<medium=recipe_ensclus>]]
+        # Actual: 4m23s, 1.5 GB on 2024-03-29.
+        execution time limit = PT6M
+        [[[directives]]]
+            --mem = 2G
diff --git a/esmvaltool/utils/recipe_test_workflow/site/metoffice-env b/esmvaltool/utils/recipe_test_workflow/site/metoffice-env
new file mode 100755
index 0000000000..eda2a52a60
--- /dev/null
+++ b/esmvaltool/utils/recipe_test_workflow/site/metoffice-env
@@ -0,0 +1,55 @@
+#!/bin/bash -l
+#
+# USAGE metoffice-env COMMAND
+#
+# ENVIRONMENT
+#   ENV_NAME            The name of the module to load
+#   PYTHONPATH_PREPEND  The path to prepend to PYTHONPATH after loading the
+#                       module
+#   QUIET_MODE          Don't print confirmation messages
+#
+# OPTIONS
+#   COMMAND             The command to execute with options
+set -eu
+
+module_count(){
+    module list -t 2>&1 | wc -l
+}
+
+safe_load(){
+    PRE_LOAD_COUNT=$(module_count)
+
+    module load "${1}"
+    # Check module count to determine whether module load was successful.
+    if (( PRE_LOAD_COUNT == $(module_count) )); then
+        echo "[ERROR] Failed to load: ${1}"
+        exit 1
+    fi
+}
+
+# Must be run before importing numpy, see
+# https://docs.dask.org/en/stable/array-best-practices.html#avoid-oversubscribing-threads
+export OMP_NUM_THREADS=1
+export OPENBLAS_NUM_THREADS=1
+export MKL_NUM_THREADS=1
+export VECLIB_MAXIMUM_THREADS=1
+export NUMEXPR_NUM_THREADS=1
+
+# Ensure '~/.local' isn't added to 'sys.path'.
+export PYTHONNOUSERSITE=True
+
+safe_load "${ENV_NAME}"
+
+# If PYTHONPATH_PREPEND has been set, prepend it to PYTHONPATH to extend the
+# Python environment.
+if [[ ! -z ${PYTHONPATH_PREPEND:-} ]]; then
+    echo "[INFO] Prepending the following to PYTHONPATH: ${PYTHONPATH_PREPEND}"
+    export PYTHONPATH=${PYTHONPATH_PREPEND}:${PYTHONPATH:-}
+fi
+
+if [[ -z ${QUIET_MODE:-} ]]; then
+    echo "[OK] Modules loaded."
+fi
+
+command="/usr/bin/time -v -o ${CYLC_TASK_LOG_ROOT}.time $@"
+exec ${command}
diff --git a/esmvaltool/utils/recipe_test_workflow/site/metoffice-recipes.jinja b/esmvaltool/utils/recipe_test_workflow/site/metoffice-recipes.jinja
new file mode 100644
index 0000000000..f49cdd1937
--- /dev/null
+++ b/esmvaltool/utils/recipe_test_workflow/site/metoffice-recipes.jinja
@@ -0,0 +1,81 @@
+# This file contains the recipes that are run by the RTW at the Met Office.
+# The "How to add a recipe to the RTW" documentation provides more information.
+#
+# KEYS         VALUES
+# ----         ------
+# recipe_path  The path to the recipe. Recipe paths are specified relative to
+#              esmvaltool/recipes. For recipes in subdirectories, `--` stands
+#              for `/` since the latter is an illegal char.
+# actual       A note of the recipe's actual resource usage. From the
+#              successful run of the recipe on the compute server at the Met Office.
+# max_time     The maximum amount of time the recipe has to complete. Use the
+#              ISO8601 duration format (`Cylc ISO8601 Durations`_
+#              provides more information).
+# max_memory   The memory allocated to running the recipe. Default units are
+#              megabytes. Different units can be specified using the suffix
+#              [K|M|G|T] (`Slurm sbatch --mem`_ provides more information).
+
+{% set FAST_RECIPES =
+    [
+        {
+            "recipe_path": "examples--recipe_python",
+            "actual": "0m17s, 1.9 GB on 2025-01-08",
+            "max_time": "PT2M",
+            "max_memory": "3G",
+        },
+        {
+            "recipe_path": "recipe_albedolandcover",
+            "actual": "0m31s, 2.5 GB on 2024-04-08",
+            "max_time": "PT2M",
+            "max_memory": "3G",
+        },
+        {
+            "recipe_path": "recipe_autoassess_landsurface_soilmoisture",
+            "actual": "0m36s, 2.8 GB on 2024-03-29",
+            "max_time": "PT2M",
+            "max_memory": "3G",
+        },
+        {
+            "recipe_path": "recipe_consecdrydays",
+            "actual": "0m19s, 2.1 GB on 2025-01-08",
+            "max_time": "PT2M",
+            "max_memory": "3G",
+        },
+        {
+            "recipe_path": "recipe_heatwaves_coldwaves",
+            "actual": "1m21s, 1.4 GB on 2025-01-08",
+            "max_time": "PT2M",
+            "max_memory": "2G",
+        },
+        {
+            "recipe_path": "recipe_ocean_amoc",
+            "actual": "1m47s, 3.4 GB on 2024-06-17",
+            "max_time": "PT3M",
+            "max_memory": "4G",
+        },
+        {
+            "recipe_path": "recipe_ocean_multimap",
+            "actual": "6m52s, 2.5 GB on 2024-05-28",
+            "max_time": "PT8M",
+            "max_memory": "3G",
+        },
+        {
+            "recipe_path": "recipe_radiation_budget",
+            "actual": "0m29s, 2.1 GB on 2025-01-08",
+            "max_time": "PT2M",
+            "max_memory": "2G",
+        },
+    ]
+%}
+
+# `recipe_ensclus` should be in fast: is in medium temporarily to avoid empty variable.
+{% set MEDIUM_RECIPES =
+    [
+        {
+            "recipe_path": "recipe_ensclus",
+            "actual": "2m10s, 2.4 GB on 2024-03-29",
+            "max_time": "PT4M",
+            "max_memory": "3G",
+        },
+    ]
+%}
diff --git a/esmvaltool/utils/recipe_test_workflow/site/metoffice.cylc b/esmvaltool/utils/recipe_test_workflow/site/metoffice.cylc
new file mode 100644
index 0000000000..c1589093d6
--- /dev/null
+++ b/esmvaltool/utils/recipe_test_workflow/site/metoffice.cylc
@@ -0,0 +1,23 @@
+#!jinja2
+[runtime]
+    [[root]]
+        [[[environment]]]
+            ESMVALCORE_DIR = ${CYLC_WORKFLOW_RUN_DIR}/share/lib/python/ESMValCore
+            ESMVALTOOL_DIR = ${CYLC_WORKFLOW_RUN_DIR}/share/lib/python/ESMValTool
+            PYTHONPATH_PREPEND = ${ESMVALCORE_DIR}:${ESMVALTOOL_DIR}
+
+    # COMPUTE provides defaults for computation-heavy tasks.
+    [[COMPUTE]]
+        platform = spice
+        [[[directives]]]
+            --wckey = RTW
+            --ntasks = {{ MAX_PARALLEL_TASKS }}
+
+    [[configure]]
+        platform = localhost
+
+    [[get_esmval]]
+        platform = localhost
+        execution time limit = PT2M
+        [[[directives]]]
+            --mem = 2G
diff --git a/esmvaltool/utils/rose-cylc/esmvt_rose_wrapper.py b/esmvaltool/utils/rose-cylc/esmvt_rose_wrapper.py
deleted file mode 100644
index 5965877717..0000000000
--- a/esmvaltool/utils/rose-cylc/esmvt_rose_wrapper.py
+++ /dev/null
@@ -1,258 +0,0 @@
-r"""
-Install and run u-bd684 - the esmvaltool rose-cylc suite.
-
-Usage:
-------
--c --config-file:  [REQUIRED]  user specific configuration file;
--r --recipe-file:  [REQUIRED]  single or multiple (space-sep) recipe files;
--d --main-dir:     [OPTIONAL]  main run dir name (full path);
-                               defaults to $HOME/ESMVALTOOL_ROSE;
--s --suite-dir     [OPTIONAL]  u-bd684 dir full path; can be set by user;
-                               defaults to $HOME/u-bd684;
--n --no-submit     [OPTIONAL]  if specified, will not submit suite to cylc;
--l --log-level:    [OPTIONAL]  log level, default=info
-
-Example:
---------
-python esmvt_rose_wrapper.py -c /home/users/valeriu/input/config-user.yml \
-                             -r /home/users/valeriu/recipes/recipe1.yml \
-                                /home/users/valeriu/recipes/recipe2.yml \
-                             -d /home/users/valeriu/esmvat_WRAPPER \
-                             -s /home/users/valeriu/u-bd684/ \
-                             -n
-
-Base suite:
------------
-The base suite to run esmvaltool via rose-cylc is u-bd684; you can find
-this suite in the Met Office Rose repository at:
-
-https://code.metoffice.gov.uk/svn/roses-u/b/d/6/8/4/trunk/
-
-When rose (exec.) will be working with python3.x, this location will become
-default and the pipeline will aceess it independently of user, unless, of
-course the user will specify -s $SUITE_LOCATION; until then the user needs
-to grab a copy of it in $HOME or specify the default location via -s option.
-
-Environment:
-------------
-We will move to a unified and centrally-installed esmvaltool environment;
-until then, the user will have to alter the env_setup script:
-
-u-bd684/app/esmvaltool/env_setup
-
-with the correct pointers to esmvaltool installation, if desired;
-NOTE that the defaults are working pointers for an install on CEDA-Jasmin.
-
-To be able to submit to cylc, you need to have the /metomi/ suite in path
-AND use a python2.7 environment. Use the Jasmin-example below for guidance.
-
-Jasmin-example:
----------------
-This shows how to interact with rose-cylc and run esmvaltool under cylc
-using this script:
-
-export PATH=/apps/contrib/metomi/bin:$PATH
-export PATH=/home/users/valeriu/miniconda2/bin:$PATH
-mkdir esmvaltool_rose
-cd esmvaltool_rose
-cp $esmvaltool/utils/rose-cylc/esmvt_rose_wrapper.py .
-[get u-bd684 in $HOME, get your recipes and the config]
-python esmvt_rose_wrapper.py -c config-user.yml \
--r recipe_autoassess_stratosphere.yml recipe_OceanPhysics.yml \
--d $HOME/esmvaltool_rose
-
-Note that you need to pass FULL PATHS to cylc, no . or .. because all
-operations are done remotely on different nodes.
-
-A practical actual example of running the tool can be found on JASMIN:
-/home/users/valeriu/esmvaltool_rose
-There you will find the run shell: run_example, as well as an example
-how to set the configuration file. A copy of u-bd684 is always located
-in /home/users/valeriu/roses/u-bd684.
-
-Contact:
---------
-author: Valeriu Predoi (UREAD, valeriu.predoi@ncas.ac.uk)
-"""
-import argparse
-import configparser
-import logging
-import os
-import subprocess
-import shutil
-
-import yaml
-
-
-# set up logging
-logger = logging.getLogger(__name__)
-
-# print the header
-HEADER = r"""
-______________________________________________________________________
-
-                 ESMValTool Rose-Cylc Wrapper
-______________________________________________________________________
-
-""" + __doc__
-
-
-def get_args():
-    """Define the `esmvaltool` command line."""
-    # parse command line args
-    parser = argparse.ArgumentParser(
-        description=HEADER,
-        formatter_class=argparse.RawDescriptionHelpFormatter)
-    parser.add_argument(
-        '-c',
-        '--config-file',
-        default=os.path.join(os.path.dirname(__file__), 'config-user.yml'),
-        help='Configuration file')
-    parser.add_argument(
-        '-r',
-        '--recipe-files',
-        type=str,
-        nargs='+',
-        help='Recipe files (list or single file)')
-    parser.add_argument(
-        '-d',
-        '--main-dir',
-        default=os.path.join(os.environ['HOME'], 'ESMVALTOOL_ROSE'),
-        help='Main analysis directory; default to $HOME/ESMVALTOOL_ROSE')
-    parser.add_argument(
-        '-s',
-        '--suite-dir',
-        default=os.path.join(os.environ['HOME'], 'u-bd684'),
-        help='u-bd684 suite directory; default to $HOME/u-bd684')
-    parser.add_argument(
-        '-n',
-        '--no-submit',
-        action='store_true',
-        help="Flag to NOT submit the Rose suite.")
-    parser.add_argument(
-        '-l',
-        '--log-level',
-        default='info',
-        choices=['debug', 'info', 'warning', 'error'])
-    args = parser.parse_args()
-    return args
-
-
-def _set_logger(logging, out_dir, log_file, log_level):
-    # set logging for screen and file output
-    root_logger = logging.getLogger()
-    out_fmt = "%(asctime)s %(levelname)-8s %(name)s,%(lineno)s\t%(message)s"
-    logging.basicConfig(
-        filename=os.path.join(out_dir, log_file),
-        filemode='a',
-        format=out_fmt,
-        datefmt='%H:%M:%S',
-        level=logging.DEBUG)
-    root_logger.setLevel(log_level.upper())
-    logfmt = logging.Formatter(out_fmt)
-    console_handler = logging.StreamHandler()
-    console_handler.setFormatter(logfmt)
-    root_logger.addHandler(console_handler)
-
-
-def read_yaml_file(yaml_file):
-    """Read recipe into a dictionary."""
-    with open(yaml_file, 'r') as yfile:
-        loaded_file = yaml.safe_load(yfile)
-    return loaded_file
-
-
-def _setup_work(rose_config_template, recipe_files,
-                config_file, main_dir, default_suite, log_level):
-    """Write the new rose conf file per suite."""
-    # Build the ConfigParser object
-    config = configparser.ConfigParser()
-    config.optionxform = str
-    config.read(rose_config_template)
-
-    # set the main work dir
-    if not os.path.exists(main_dir):
-        os.makedirs(main_dir)
-
-    # assemble work tree
-    if not os.path.isfile(os.path.join(main_dir, config_file)):
-        shutil.copy2(config_file, main_dir)
-    if not os.path.exists(os.path.join(main_dir, 'recipes')):
-        os.makedirs(os.path.join(main_dir, 'recipes'))
-    if not os.path.exists(os.path.join(main_dir,
-                                       os.path.basename(config_file))):
-        shutil.copy2(config_file, main_dir)
-    recipes_field = []
-    for recipe in recipe_files:
-        if not os.path.exists(os.path.join(main_dir, 'recipes',
-                                           os.path.basename(recipe))):
-            shutil.copy2(recipe, os.path.join(main_dir, 'recipes'))
-        recipes_field.append(os.path.basename(recipe).strip('.yml'))
-    rose_suite = os.path.join(main_dir, 'u-bd684')
-    if os.path.exists(rose_suite):
-        shutil.rmtree(rose_suite)
-    shutil.copytree(default_suite, rose_suite)
-    out_dir = os.path.join(main_dir, 'output')
-    if not os.path.exists(out_dir):
-        os.makedirs(out_dir)
-
-    # set logging
-    _set_logger(logging, out_dir, 'setup.log', log_level)
-    logger.info(HEADER)
-
-    # start logging
-    logger.info("Main working directory: %s", main_dir)
-    logger.info("Using Rose-Cylc suite base: %s", default_suite)
-    logger.info("Output and logs written to: %s", out_dir)
-    logger.info("Creating rose suite directories...")
-    logger.info("Use rose-suite.conf template %s", rose_config_template)
-    logger.info("Use user config file %s", config_file)
-
-    # write the file
-    config.set('jinja2:suite.rc', 'INPUT_DIR',
-               '"' + main_dir + '"')
-    config.set('jinja2:suite.rc', 'OUTPUT_DIR', '"' + out_dir + '"')
-    config.set('jinja2:suite.rc', 'RECIPES', str(recipes_field))
-    with open(os.path.join(rose_suite, 'rose-suite.conf'), 'w') as r_c:
-        logger.info("Writing rose-suite.conf file %s",
-                    os.path.join(rose_suite, 'rose-suite.conf'))
-        config.write(r_c)
-
-    return rose_suite
-
-
-def _run_suite(suite):
-    """Run the mip_convert suite."""
-    os.chdir(suite)
-    logger.info("Submitting suite from %s", suite)
-    proc = subprocess.Popen(["rose", "suite-run"], stdout=subprocess.PIPE)
-    out, err = proc.communicate()
-    logger.info("Rose communications: %s %s", str(out), str(err))
-
-
-def main():
-    """Run the the meat of the code."""
-    logger.info("Running main function...")
-    args = get_args()
-    # rose suite default location
-    if args.suite_dir:
-        default_suite = args.suite_dir
-    rose_config_template = os.path.join(default_suite, "rose-suite.conf")
-
-    # get command line arguments
-    recipe_files = args.recipe_files
-    config_file = args.config_file
-    main_dir = args.main_dir
-    log_level = args.log_level
-
-    # setup rose suite
-    run_rose = _setup_work(rose_config_template, recipe_files,
-                           config_file, main_dir, default_suite, log_level)
-
-    # submit to cylc
-    if not args.no_submit:
-        _run_suite(run_rose)
-
-
-if __name__ == '__main__':
-    main()
diff --git a/setup.cfg b/setup.cfg
index c738c5d716..da8be4a5ec 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -1,7 +1,9 @@
 [tool:pytest]
 addopts =
+    --import-mode=importlib
     --doctest-modules
     --ignore=doc/sphinx/source/conf.py
+    --ignore=esmvaltool/utils/recipe_test_workflow
     --cov=esmvaltool
     --cov-report=xml:test-reports/coverage.xml
     --cov-report=html:test-reports/coverage_html
@@ -29,4 +31,4 @@ multi_line_output = 3
 include_trailing_comma = true
 
 [codespell]
-ignore-words-list = vas
+ignore-words-list = vas,runn
diff --git a/setup.py b/setup.py
index 5cb030b8a4..cdadaca2d2 100755
--- a/setup.py
+++ b/setup.py
@@ -25,9 +25,10 @@
         'cdo',
         'cdsapi',
         'cf-units',
+        'cfgrib',
         'cftime',
         'cmocean',
-        'dask',
+        'dask!=2024.8.0',  # https://github.com/dask/dask/issues/11296
         'distributed',
         'ecmwf-api-client',
         'eofs',
@@ -50,23 +51,23 @@
         'numpy!=1.24.3',  # severe masking bug
         'openpyxl',
         'packaging',
-        'pandas!=2.2.0,!=2.2.1,!=2.2.2',  # ESMValCore PR2305
+        'pandas==2.1.4',  # see note in environment.yml
         'progressbar2',
-        'psyplot',
-        'psy-maps',
-        'psy-reg',
-        'psy-simple',
+        'psyplot>=1.5.0',  # psy*<1.5.0 are not py312 compat
+        'psy-maps>=1.5.0',
+        'psy-reg>=1.5.0',
+        'psy-simple>=1.5.0',
         'pyproj>=2.1',
         'pys2index',
         'python-dateutil',
         'pyyaml',
-        'rasterio',
+        'rasterio>=1.3.10',
         'requests',
         'ruamel.yaml',
         'scikit-image',
         'scikit-learn>=1.4.0',  # github.com/ESMValGroup/ESMValTool/issues/3504
         'scipy',
-        'scitools-iris>=3.6.1',
+        'scitools-iris>=3.11',
         'seaborn',
         'seawater',
         'shapely>=2',
@@ -103,7 +104,7 @@
         'imagehash',
         'isort',
         'pre-commit',
-        'prospector[with_pyroma]!=1.1.6.3,!=1.1.6.4',
+        'prospector[with_pyroma]>=1.12',
         'vprof',
         'yamllint',
         'yapf',
@@ -221,9 +222,9 @@ def read_description(filename):
         'Natural Language :: English',
         'Operating System :: POSIX :: Linux',
         'Programming Language :: Python :: 3',
-        'Programming Language :: Python :: 3.9',
         'Programming Language :: Python :: 3.10',
         'Programming Language :: Python :: 3.11',
+        'Programming Language :: Python :: 3.12',
         'Topic :: Scientific/Engineering',
         'Topic :: Scientific/Engineering :: Atmospheric Science',
         'Topic :: Scientific/Engineering :: GIS',
@@ -246,13 +247,9 @@ def read_description(filename):
     },
     entry_points={
         'console_scripts': [
-            'mip_convert_setup = '
-            'esmvaltool.cmorizers.mip_convert.esmvt_mipconv_setup:main',
             'nclcodestyle = esmvaltool.utils.nclcodestyle.nclcodestyle:_main',
             'test_recipe = '
             'esmvaltool.utils.testing.recipe_settings.install_expand_run:main',
-            'recipe_filler = '
-            'esmvaltool.utils.recipe_filler:run'
         ],
         'esmvaltool_commands': [
             'colortables = '
diff --git a/tests/integration/diag_scripts/mlr/test_custom_sklearn_functions.py b/tests/integration/diag_scripts/mlr/test_custom_sklearn_functions.py
index c37069f9ec..122afd3b32 100644
--- a/tests/integration/diag_scripts/mlr/test_custom_sklearn_functions.py
+++ b/tests/integration/diag_scripts/mlr/test_custom_sklearn_functions.py
@@ -42,7 +42,6 @@
 # pylint: disable=too-few-public-methods
 # pylint: disable=too-many-arguments
 
-import warnings
 from copy import copy, deepcopy
 
 import numpy as np
@@ -505,9 +504,7 @@ def test_is_pairwise():
     """Test ``_is_pairwise``."""
     # Simple checks for _is_pairwise
     pca = KernelPCA(kernel='precomputed')
-    with warnings.catch_warnings():
-        warnings.simplefilter("error")  # make sure that no warning is raised
-        assert _is_pairwise(pca)
+    assert _is_pairwise(pca)
 
     # Pairwise attribute that is not consistent with the pairwise tag
     class IncorrectTagPCA(KernelPCA):
@@ -533,9 +530,7 @@ class TruePairwise(BaseEstimator):
 
     # Pairwise attribute is not defined thus tag is used
     est = BaseEstimator()
-    with warnings.catch_warnings():
-        warnings.simplefilter("error")  # make sure that no warning is raised
-        assert not _is_pairwise(est)
+    assert not _is_pairwise(est)
 
 
 # _safe_split
diff --git a/tests/integration/test_cmorizer.py b/tests/integration/test_cmorizer.py
index 11bade4190..48f75b951a 100644
--- a/tests/integration/test_cmorizer.py
+++ b/tests/integration/test_cmorizer.py
@@ -4,6 +4,7 @@
 import os
 import sys
 
+import esmvalcore
 import iris
 import iris.coord_systems
 import iris.coords
@@ -13,7 +14,9 @@
 import pytest
 import yaml
 from cf_units import Unit
+from packaging import version
 
+from esmvaltool import ESMValToolDeprecationWarning
 from esmvaltool.cmorizers.data.cmorizer import DataCommand
 
 
@@ -28,8 +31,8 @@ def keep_cwd():
         os.chdir(curr_path)
 
 
-def write_config_user_file(dirname):
-    """Replace config_user file values for testing."""
+def write_config_file(dirname):
+    """Replace configuration values for testing."""
     config_file = dirname / 'config-user.yml'
     cfg = {
         'output_dir': str(dirname / 'output_dir'),
@@ -143,14 +146,59 @@ def arguments(*args):
     sys.argv = backup
 
 
-def test_cmorize_obs_woa_no_data(tmp_path):
+@pytest.mark.skipif(
+    version.parse(esmvalcore.__version__) >= version.parse("2.14.0"),
+    reason='ESMValCore >= v2.14.0',
+)
+def test_cmorize_obs_woa_no_data_config_file(tmp_path):
     """Test for example run of cmorize_obs command."""
+    config_file = write_config_file(tmp_path)
+    os.makedirs(os.path.join(tmp_path, 'raw_stuff', 'Tier2'))
+    os.makedirs(os.path.join(tmp_path, 'output_dir'))
+    with keep_cwd():
+        with pytest.raises(RuntimeError):
+            with pytest.warns(ESMValToolDeprecationWarning):
+                DataCommand().format('WOA', config_file=config_file)
+
+    log_dir = os.path.join(tmp_path, 'output_dir')
+    log_file = os.path.join(log_dir,
+                            os.listdir(log_dir)[0], 'run', 'main_log.txt')
+    check_log_file(log_file, no_data=True)
+
+
+@pytest.mark.skipif(
+    version.parse(esmvalcore.__version__) >= version.parse("2.14.0"),
+    reason='ESMValCore >= v2.14.0',
+)
+def test_cmorize_obs_woa_data_config_file(tmp_path):
+    """Test for example run of cmorize_obs command."""
+    config_file = write_config_file(tmp_path)
+    data_path = os.path.join(tmp_path, 'raw_stuff', 'Tier2', 'WOA')
+    put_dummy_data(data_path)
+    with keep_cwd():
+        with pytest.warns(ESMValToolDeprecationWarning):
+            DataCommand().format('WOA', config_file=config_file)
 
-    config_user_file = write_config_user_file(tmp_path)
+    log_dir = os.path.join(tmp_path, 'output_dir')
+    log_file = os.path.join(log_dir,
+                            os.listdir(log_dir)[0], 'run', 'main_log.txt')
+    check_log_file(log_file, no_data=False)
+    output_path = os.path.join(log_dir, os.listdir(log_dir)[0], 'Tier2', 'WOA')
+    check_output_exists(output_path)
+    check_conversion(output_path)
+
+
+@pytest.mark.skipif(
+    version.parse(esmvalcore.__version__) < version.parse("2.12.0"),
+    reason='ESMValCore < v2.12.0',
+)
+def test_cmorize_obs_woa_no_data(tmp_path):
+    """Test for example run of cmorize_obs command."""
+    write_config_file(tmp_path)
     os.makedirs(os.path.join(tmp_path, 'raw_stuff', 'Tier2'))
     with keep_cwd():
-        with pytest.raises(Exception):
-            DataCommand().format('WOA', config_user_file)
+        with pytest.raises(RuntimeError):
+            DataCommand().format('WOA', config_dir=str(tmp_path))
 
     log_dir = os.path.join(tmp_path, 'output_dir')
     log_file = os.path.join(log_dir,
@@ -158,14 +206,17 @@ def test_cmorize_obs_woa_no_data(tmp_path):
     check_log_file(log_file, no_data=True)
 
 
+@pytest.mark.skipif(
+    version.parse(esmvalcore.__version__) < version.parse("2.12.0"),
+    reason='ESMValCore < v2.12.0',
+)
 def test_cmorize_obs_woa_data(tmp_path):
     """Test for example run of cmorize_obs command."""
-
-    config_user_file = write_config_user_file(tmp_path)
+    write_config_file(tmp_path)
     data_path = os.path.join(tmp_path, 'raw_stuff', 'Tier2', 'WOA')
     put_dummy_data(data_path)
     with keep_cwd():
-        DataCommand().format('WOA', config_user_file)
+        DataCommand().format('WOA', config_dir=str(tmp_path))
 
     log_dir = os.path.join(tmp_path, 'output_dir')
     log_file = os.path.join(log_dir,
diff --git a/tests/integration/test_diagnostic_run.py b/tests/integration/test_diagnostic_run.py
index b0c606f4ee..670f7088dd 100644
--- a/tests/integration/test_diagnostic_run.py
+++ b/tests/integration/test_diagnostic_run.py
@@ -5,12 +5,14 @@
 from pathlib import Path
 from textwrap import dedent
 
+import esmvalcore
 import pytest
 import yaml
 from esmvalcore._main import run
+from packaging import version
 
 
-def write_config_user_file(dirname):
+def write_config_file(dirname):
     config_file = dirname / 'config-user.yml'
     cfg = {
         'output_dir': str(dirname / 'output_dir'),
@@ -68,10 +70,13 @@ def check(result_file):
 ]
 
 
+@pytest.mark.skipif(
+    version.parse(esmvalcore.__version__) >= version.parse("2.14.0"),
+    reason='ESMValCore >= v2.14.0',
+)
 @pytest.mark.installation
 @pytest.mark.parametrize('script_file', SCRIPTS)
-def test_diagnostic_run(tmp_path, script_file):
-
+def test_diagnostic_run_config_file(tmp_path, script_file):
     local_script_file = Path(__file__).parent / script_file
 
     recipe_file = tmp_path / 'recipe_test.yml'
@@ -96,12 +101,58 @@ def test_diagnostic_run(tmp_path, script_file):
         """.format(script_file, result_file))
     recipe_file.write_text(str(recipe))
 
-    config_user_file = write_config_user_file(tmp_path)
+    config_file = write_config_file(tmp_path)
     with arguments(
             'esmvaltool',
             'run',
             '--config_file',
-            config_user_file,
+            config_file,
+            str(recipe_file),
+    ):
+        run()
+
+    check(result_file)
+
+
+@pytest.mark.skipif(
+    version.parse(esmvalcore.__version__) < version.parse("2.12.0"),
+    reason='ESMValCore < v2.12.0',
+)
+@pytest.mark.installation
+@pytest.mark.parametrize('script_file', SCRIPTS)
+def test_diagnostic_run(tmp_path, script_file):
+    local_script_file = Path(__file__).parent / script_file
+
+    recipe_file = tmp_path / 'recipe_test.yml'
+    script_file = tmp_path / script_file
+    result_file = tmp_path / 'result.yml'
+    config_dir = tmp_path / 'config'
+    config_dir.mkdir(exist_ok=True, parents=True)
+
+    shutil.copy(local_script_file, script_file)
+
+    # Create recipe
+    recipe = dedent("""
+        documentation:
+          title: Test recipe
+          description: Recipe with no data.
+          authors: [andela_bouwe]
+
+        diagnostics:
+          diagnostic_name:
+            scripts:
+              script_name:
+                script: {}
+                setting_name: {}
+        """.format(script_file, result_file))
+    recipe_file.write_text(str(recipe))
+
+    write_config_file(config_dir)
+    with arguments(
+            'esmvaltool',
+            'run',
+            '--config_dir',
+            str(config_dir),
             str(recipe_file),
     ):
         run()
diff --git a/tests/integration/test_recipe_filler.py b/tests/integration/test_recipe_filler.py
deleted file mode 100644
index b78ac8c5f8..0000000000
--- a/tests/integration/test_recipe_filler.py
+++ /dev/null
@@ -1,211 +0,0 @@
-"""Tests for _data_finder.py."""
-import contextlib
-import os
-import shutil
-import sys
-import tempfile
-
-import pytest
-import yaml
-
-from esmvaltool.utils.recipe_filler import run
-
-
-# Load test configuration
-with open(os.path.join(os.path.dirname(__file__),
-                       'recipe_filler.yml')) as file:
-    CONFIG = yaml.safe_load(file)
-
-
-@contextlib.contextmanager
-def arguments(*args):
-    backup = sys.argv
-    sys.argv = list(args)
-    yield
-    sys.argv = backup
-
-
-def print_path(path):
-    """Print path."""
-    txt = path
-    if os.path.isdir(path):
-        txt += '/'
-    if os.path.islink(path):
-        txt += ' -> ' + os.readlink(path)
-    print(txt)
-
-
-def tree(path):
-    """Print path, similar to the the `tree` command."""
-    print_path(path)
-    for dirpath, dirnames, filenames in os.walk(path):
-        for dirname in dirnames:
-            print_path(os.path.join(dirpath, dirname))
-        for filename in filenames:
-            print_path(os.path.join(dirpath, filename))
-
-
-def create_file(filename):
-    """Create an empty file."""
-    dirname = os.path.dirname(filename)
-    if not os.path.exists(dirname):
-        os.makedirs(dirname)
-
-    with open(filename, 'a'):
-        pass
-
-
-def create_tree(path, filenames=None, symlinks=None):
-    """Create directory structure and files."""
-    for filename in filenames or []:
-        create_file(os.path.join(path, filename))
-
-    for symlink in symlinks or []:
-        link_name = os.path.join(path, symlink['link_name'])
-        os.symlink(symlink['target'], link_name)
-
-
-def write_config_user_file(dirname, file_path, drs):
-    config_file = dirname / 'config-user.yml'
-    cfg = {
-        'log_level': 'info',
-        'output_dir': str(dirname / 'recipe_filler_output'),
-        'rootpath': {
-            'CMIP5': str(dirname / file_path),
-            'CMIP6': str(dirname / file_path),
-        },
-        'drs': {
-            'CMIP5': drs,
-            'CMIP6': drs,
-        },
-    }
-    config_file.write_text(yaml.safe_dump(cfg, encoding=None))
-    return str(config_file)
-
-
-def write_recipe(dirname, recipe_dict):
-    recipe_file = dirname / 'recipe.yml'
-    diags = {'diagnostics': recipe_dict}
-    recipe_file.write_text(yaml.safe_dump(diags, encoding=None))
-    return str(recipe_file)
-
-
-@pytest.fixture
-def root():
-    """Root function for tests."""
-    dirname = tempfile.mkdtemp()
-    yield os.path.join(dirname, 'output1')
-    print("Directory structure was:")
-    tree(dirname)
-    shutil.rmtree(dirname)
-
-
-def setup_files(tmp_path, root, cfg):
-    """Create config, recipe ,output recipe etc."""
-    user_config_file = write_config_user_file(tmp_path, root, cfg['drs'])
-    diagnostics = {}
-    diagnostics["test_diagnostic"] = {}
-    diagnostics["test_diagnostic"]["variables"] = {}
-    diagnostics["test_diagnostic"]["variables"]["test_var"] = cfg["variable"]
-    recipe = write_recipe(tmp_path, diagnostics)
-    output_recipe = str(tmp_path / "recipe_auto.yml")
-
-    return user_config_file, recipe, output_recipe
-
-
-@pytest.mark.parametrize('cfg', CONFIG['has_additional_datasets'])
-def test_adding_datasets(tmp_path, root, cfg):
-    """Test retrieving additional datasets."""
-    create_tree(root, cfg.get('available_files'),
-                cfg.get('available_symlinks'))
-
-    user_config_file, recipe, output_recipe = setup_files(tmp_path, root, cfg)
-
-    with arguments(
-            'recipe_filler',
-            recipe,
-            '-c',
-            user_config_file,
-            '-o',
-            output_recipe,
-    ):
-        run()
-
-    with open(output_recipe, 'r') as file:
-        autofilled_recipe = yaml.safe_load(file)
-        diag = autofilled_recipe["diagnostics"]["test_diagnostic"]
-        var = diag["variables"]["test_var"]
-        assert "additional_datasets" in var
-
-
-@pytest.mark.parametrize('cfg', CONFIG['no_additional_datasets'])
-def test_not_adding_datasets(tmp_path, root, cfg):
-    """Test retrieving no additional datasets."""
-    create_tree(root, cfg.get('available_files'),
-                cfg.get('available_symlinks'))
-
-    user_config_file, recipe, output_recipe = setup_files(tmp_path, root, cfg)
-
-    with arguments(
-            'recipe_filler',
-            recipe,
-            '-c',
-            user_config_file,
-            '-o',
-            output_recipe,
-    ):
-        run()
-
-    with open(output_recipe, 'r') as file:
-        autofilled_recipe = yaml.safe_load(file)
-        diag = autofilled_recipe["diagnostics"]["test_diagnostic"]
-        var = diag["variables"]["test_var"]
-        assert "additional_datasets" not in var
-
-
-def test_bad_var(tmp_path, root):
-    """Test a bad variable in the works."""
-    cfg = CONFIG['bad_variable'][0]
-    user_config_file, recipe, output_recipe = setup_files(tmp_path, root, cfg)
-
-    # this doesn't fail and it shouldn't since it can go on
-    # and look for data for other valid variables
-    with arguments(
-            'recipe_filler',
-            recipe,
-            '-c',
-            user_config_file,
-            '-o',
-            output_recipe,
-    ):
-        run()
-
-    with open(output_recipe, 'r') as file:
-        autofilled_recipe = yaml.safe_load(file)
-        diag = autofilled_recipe["diagnostics"]["test_diagnostic"]
-        var = diag["variables"]["test_var"]
-        assert "additional_datasets" not in var
-
-
-def test_no_short_name(tmp_path, root):
-    """Test a bad variable in the works."""
-    cfg = CONFIG['no_short_name'][0]
-    user_config_file, recipe, output_recipe = setup_files(tmp_path, root, cfg)
-
-    # this doesn't fail and it shouldn't since it can go on
-    # and look for data for other valid variables
-    with arguments(
-            'recipe_filler',
-            recipe,
-            '-c',
-            user_config_file,
-            '-o',
-            output_recipe,
-    ):
-        run()
-
-    with open(output_recipe, 'r') as file:
-        autofilled_recipe = yaml.safe_load(file)
-        diag = autofilled_recipe["diagnostics"]["test_diagnostic"]
-        var = diag["variables"]["test_var"]
-        assert "additional_datasets" not in var
diff --git a/tests/system/__init__.py b/tests/system/__init__.py
deleted file mode 100644
index 5f7877c08d..0000000000
--- a/tests/system/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-"""Test running esmvaltool"""
diff --git a/tests/system/config-test.yml b/tests/system/config-test.yml
deleted file mode 100644
index ec25dec23d..0000000000
--- a/tests/system/config-test.yml
+++ /dev/null
@@ -1,44 +0,0 @@
-###############################################################################
-# Diagnostic test configuration file for the ESMValTool
-#
-# './setup.py test' will look for this file in the following locations
-# and use the first config-test.yml file found:
-#   - current working directory
-#   - ~/.esmvaltool/
-#   - ESMValTool/tests/test_diagnostics/
-#
-###############################################################################
----
-
-test:
-  # Execute system/diagnostic tests [false]/true
-  run: false
-  # Simulate input data using the dummydata module [true]/false
-  simulate_input: true
-  # Limit testing/generating reference data to the following recipes
-  # An empty list means any recipe in esmvaltool/nml
-  recipes: [
-    recipe_MyVar.yml,
-  ]
-
-# Reference data configuration
-reference:
-  # Directory containing reference output
-  output: ~/esmvaltool_reference_output
-  # Generate reference data instead of checking [false]/true
-  generate: false
-
-# Template for the user configuration file
-user:
-  log_level: warning
-  exit_on_warning: false
-  output_file_type: pdf
-  save_intermediary_cubes: true
-
-  rootpath:
-    CMIP5: ~/esmvaltool_simulated_input
-    OBS: ~/esmvaltool_simulated_input
-    default: ~/esmvaltool_simulated_input
-
-  drs:
-    CMIP5: default
diff --git a/tests/system/data_simulator.py b/tests/system/data_simulator.py
deleted file mode 100644
index 203816ca0e..0000000000
--- a/tests/system/data_simulator.py
+++ /dev/null
@@ -1,114 +0,0 @@
-"""Simulate test data for `esmvaltool`."""
-import os
-import sys
-import tempfile
-import time
-
-import numpy as np
-
-from esmvalcore import __version__ as core_ver
-from packaging import version
-if version.parse(core_ver) < version.parse('2.8.0'):
-    from esmvalcore._config import read_config_user_file
-else:
-    from esmvalcore.config import CFG
-if version.parse(core_ver) <= version.parse('2.7.1'):
-    from esmvalcore._recipe import read_recipe_file
-else:
-    from esmvalcore._recipe.recipe import read_recipe_file
-
-
-def get_input_filename(variable, rootpath, drs):
-    """Get a valid input filename."""
-    # TODO: implement this according to esmvalcore._data_finder.py
-    # or patch get_input_filelist there.
-    return tempfile.NamedTemporaryFile().name + '.nc'
-
-
-def write_data_file(short_name, filename, field, start_year, end_year):
-    """Write a file containing simulated data."""
-    from dummydata.model2 import Model2
-    from dummydata.model3 import Model3
-
-    if 'T2M' in field:
-        writer = Model2
-    elif 'T3M' in field:
-        writer = Model3
-    else:
-        raise NotImplementedError(
-            "Cannot create a model from field {}".format(field))
-
-    # TODO: Maybe this should be made configurable per diagnostic or model
-    cfg = {
-        'ta': {
-            'method': 'gaussian_blobs',
-            'low': 223,
-            'high': 303,
-        },
-        'pr': {
-            'method': 'gaussian_blobs',
-            'low': 1e-7,
-            'high': 2e-4,
-        }
-    }
-
-    kwargs = cfg[short_name] if short_name in cfg else {}
-
-    writer(
-        var=short_name,
-        oname=filename,
-        start_year=start_year,
-        stop_year=end_year,
-        **kwargs)
-
-
-def simulate_input_data(recipe_file, config_user_file=None):
-    """Simulate data for variables defined in recipe"""
-    if config_user_file:
-        if version.parse(core_ver) <= version.parse('2.8.0'):
-            user_config = read_config_user_file(
-                config_file=config_user_file, recipe_name='')
-        else:
-            user_config = CFG.load_from_file(
-                config_file=config_user_file, recipe_name='')
-    else:
-        user_config = {
-            'rootpath': {
-                'default': '.',
-            },
-            'drs': {},
-        }
-
-    recipe = read_recipe_file(recipe_file, user_config, initialize_tasks=False)
-
-    start_time = time.time()
-
-    for diagnostic in recipe.diagnostics.values():
-        np.random.seed(0)
-        for variables in diagnostic['variables'].values():
-            for variable in variables:
-                filename = get_input_filename(
-                    variable=variable,
-                    rootpath=user_config['rootpath'],
-                    drs=user_config['drs'])
-                dirname = os.path.dirname(filename)
-                if not os.path.exists(dirname):
-                    print("Creating {}".format(dirname))
-                    os.makedirs(dirname)
-
-                print("Writing {}".format(filename))
-                write_data_file(
-                    short_name=variable['short_name'],
-                    filename=filename,
-                    field=variable['field'],
-                    start_year=variable['start_year'],
-                    end_year=variable['end_year'],
-                )
-
-    print(
-        "Simulating data took {:.0f} seconds".format(time.time() - start_time))
-
-
-if __name__ == '__main__':
-    for path in sys.argv[1:]:
-        simulate_input_data(recipe_file=path, config_user_file=None)
diff --git a/tests/system/esmvaltool_testlib.py b/tests/system/esmvaltool_testlib.py
deleted file mode 100644
index f73c639a89..0000000000
--- a/tests/system/esmvaltool_testlib.py
+++ /dev/null
@@ -1,227 +0,0 @@
-"""Provide a class for testing esmvaltool."""
-
-import glob
-import os
-import shutil
-import sys
-from unittest import SkipTest
-
-import numpy as np
-import yaml
-# from easytest import EasyTest
-
-import esmvaltool
-
-
-def _load_config(filename=None):
-    """Load test configuration"""
-    if filename is None:
-        # look in default locations for config-test.yml
-        config_file = 'config-test.yml'
-        default_locations = [
-            '.',
-            '~/.esmvaltool',
-            os.path.dirname(__file__),
-        ]
-        for path in default_locations:
-            filepath = os.path.join(os.path.expanduser(path), config_file)
-            if os.path.exists(filepath):
-                filename = os.path.abspath(filepath)
-                break
-
-    with open(filename, 'r') as file:
-        cfg = yaml.safe_load(file)
-
-    cfg['configfile'] = filename
-    cfg['reference']['output'] = os.path.abspath(
-        os.path.expanduser(cfg['reference']['output']))
-
-    if cfg['test'].get('recipes', []) == []:
-        script_root = esmvaltool.get_script_root()
-        recipe_glob = os.path.join(script_root, 'nml', 'recipe_*.yml')
-        cfg['test']['recipes'] = glob.glob(recipe_glob)
-
-    return cfg
-
-
-_CFG = _load_config()
-
-RECIPES = _CFG['test']['recipes']
-
-
-def _create_config_user_file(output_directory):
-    """Write a config-user.yml file.
-
-    Write a configuration file for running ESMValTool
-    such that it writes all output to `output_directory`.
-    """
-    cfg = _CFG['user']
-
-    cfg['output_dir'] = output_directory
-
-    # write to file
-    filename = os.path.join(output_directory, 'config-user.yml')
-    with open(filename, 'w') as file:
-        yaml.safe_dump(cfg, file)
-
-    return filename
-
-
-class ESMValToolTest:  # was ESMValToolTest(EasyTest)
-    """Main class for ESMValTool test runs."""
-
-    def __init__(self, recipe, output_directory, ignore='', **kwargs):
-        """
-        Create ESMValToolTest instance
-
-        recipe: str
-            The filename of the recipe that should be tested.
-        output_directory : str
-            The name of a directory where results can be stored.
-        ignore: str or iterable of str
-            Glob patterns of files to be ignored when testing.
-        """
-        if not _CFG['test']['run']:
-            raise SkipTest("System tests disabled in {}".format(
-                _CFG['configfile']))
-
-        self.ignore = (ignore, ) if isinstance(ignore, str) else ignore
-
-        script_root = esmvaltool.get_script_root()
-
-        # Set recipe path
-        if not os.path.exists(recipe):
-            recipe = os.path.join(
-                os.path.dirname(script_root), 'recipes', recipe)
-        self.recipe_file = os.path.abspath(recipe)
-
-        # Simulate input data?
-        self.simulate_input = _CFG['test']['simulate_input']
-
-        # Create reference output?
-        self.create_reference_output = _CFG['reference']['generate']
-
-        # Define reference output path
-        reference_dir = os.path.join(
-            _CFG['reference']['output'],
-            os.path.splitext(os.path.basename(self.recipe_file))[0])
-
-        # If reference data is neither available nor should be generated, skip
-        if not (os.path.exists(reference_dir) or self.create_reference_output):
-            raise SkipTest(
-                "No reference data available for recipe {} in {}".format(
-                    recipe, _CFG['reference']['output']))
-
-        # Write ESMValTool configuration file
-        self.config_user_file = _create_config_user_file(output_directory)
-
-        super(ESMValToolTest, self).__init__(
-            exe='esmvaltool',
-            args=['-n', self.recipe_file, '-c', self.config_user_file],
-            output_directory=output_directory,
-            refdirectory=reference_dir,
-            **kwargs)
-
-    def run(self, **kwargs):
-        """Run tests or generate reference data."""
-        if self.simulate_input:
-            from .data_simulator import simulate_input_data
-            simulate_input_data(
-                recipe_file=self.recipe_file,
-                config_user_file=self.config_user_file)
-
-        if self.create_reference_output:
-            self.generate_reference_output()
-            raise SkipTest("Generated reference data instead of running test")
-        else:
-            super(ESMValToolTest, self).run_tests(**kwargs)
-
-    def generate_reference_output(self):
-        """Generate reference output.
-
-        Generate reference data by executing the recipe and then moving
-        results to the output directory.
-        """
-        if not os.path.exists(self.refdirectory):
-            self._execute()
-            shutil.move(self.output_directory,
-                        os.path.dirname(self.refdirectory))
-        else:
-            print("Warning: not generating reference data, reference "
-                  "directory {} already exists.".format(self.refdirectory))
-
-    def _execute(self):
-        """Execute ESMValTool
-
-        Override the _execute method because we want to run in our own
-        Python instance to get coverage reporting and we want to update
-        the location of `self.output_directory` afterwards.
-        """
-        # run ESMValTool
-        sys.argv[1:] = self.args
-        esmvaltool.main.run()
-
-        # Update the output directory to point to the output of the run
-        output_directory = self.output_directory  # noqa
-
-        output = []
-        for path in os.listdir(output_directory):
-            path = os.path.join(output_directory, path)
-            if os.path.isdir(path):
-                output.append(path)
-
-        if not output:
-            raise OSError(
-                "Output directory not found in location {}. "
-                "Probably ESMValTool failed to create any output.".format(
-                    output_directory))
-
-        if len(output) > 1:
-            print("Warning: found multiple output directories:\n{}\nin output "
-                  "location {}\nusing the first one.".format(
-                      output, output_directory))
-
-        self.output_directory = output[0] + os.sep  # noqa
-
-    def _get_files_from_refdir(self):
-        """Get a list of files from reference directory.
-
-        Ignore files that match patterns in self.ignore.
-
-        Override this method of easytest.EasyTest to be able to ignore certain
-        files.
-        """
-        from fnmatch import fnmatchcase
-
-        matches = []
-        for root, _, filenames in os.walk(self.refdirectory):
-            for filename in filenames:
-                path = os.path.join(root, filename)
-                relpath = os.path.relpath(path, start=self.refdirectory)
-                for pattern in self.ignore:
-                    if fnmatchcase(relpath, pattern):
-                        break
-                else:
-                    matches.append(path)
-
-        return matches
-
-    def _compare_netcdf_values(self, f1, f2, allow_subset=False):
-        """Compare two netCDF4 Dataset instances.
-
-        Check if dataset2 contains the same variable values as dataset1.
-
-        Override this method of easytest.EasyTest because it is broken
-        for the case where value1 and value2 have no length.
-        """
-        if allow_subset:  # allow that only a subset of data is compared
-            raise NotImplementedError
-
-        for key in f1.variables:
-            values1 = f1.variables[key][:]
-            values2 = f2.variables[key][:]
-
-            if not np.array_equal(values1, values2):
-                return False
-
-        return True
diff --git a/tests/system/test_recipes.py b/tests/system/test_recipes.py
deleted file mode 100644
index 0825707bd4..0000000000
--- a/tests/system/test_recipes.py
+++ /dev/null
@@ -1,35 +0,0 @@
-"""Test script to compare the output of ESMValTool against previous runs."""
-
-import shutil
-import tempfile
-
-import pytest
-
-from .esmvaltool_testlib import RECIPES, ESMValToolTest
-
-
-@pytest.fixture
-def output_directory():
-    """Create a directory for storing ESMValTool output."""
-    tmp = tempfile.mkdtemp()
-    yield tmp
-    shutil.rmtree(tmp, ignore_errors=True)
-
-
-@pytest.mark.parametrize("recipe", RECIPES)
-def test_recipe(output_directory, recipe):  # noqa
-    """Create a test for each recipe in RECIPES and run those."""
-    test = ESMValToolTest(
-        recipe=recipe,
-        output_directory=output_directory,
-        ignore=['tmp/*/*', '*log*.txt', '*.log'],
-        checksum_exclude=['pdf', 'ps', 'png', 'eps', 'epsi', 'nc'])
-
-    test.run(
-        graphics=None,
-        files='all',
-        check_size_gt_zero=True,
-        checksum_files='all',
-        check_file_content=['nc'])
-
-    assert test.sucess
diff --git a/tests/unit/cmorizers/test_utilities.py b/tests/unit/cmorizers/test_utilities.py
index 156a3507c0..f5823aa734 100644
--- a/tests/unit/cmorizers/test_utilities.py
+++ b/tests/unit/cmorizers/test_utilities.py
@@ -8,6 +8,7 @@
 import iris.coords
 import iris.cube
 import iris.fileformats
+import iris.util
 import numpy as np
 import pytest
 from cf_units import Unit
@@ -194,8 +195,9 @@ def test_fix_coords():
     cube.coord("longitude").units = "m"
     cube.coord("latitude").units = "K"
     cube_2 = cube.copy()
-    cube_2.coord("depth").bounds = [[0., 2.5], [2.5, 25.], [25., 250.]]
-    utils.fix_coords(cube)
+
+    cube = utils.fix_coords(cube)
+
     assert cube.coord("time").var_name == "time"
     assert cube.coord("longitude").var_name == "lon"
     assert cube.coord("latitude").var_name == "lat"
@@ -217,24 +219,31 @@ def test_fix_coords():
     # both cf-units <= 3.1.0 and later versions, we list both variants in the
     # following assertion.
     assert cube.coord("time").units.calendar in ("standard", "gregorian")
-    assert cube.coord("longitude").points[0] == 178.5
-    assert cube.coord("longitude").points[1] == 179.5
+    assert cube.coord("longitude").points[0] == 358.5
+    assert cube.coord("longitude").points[1] == 359.5
     assert cube.coord("longitude").has_bounds()
-    assert cube.coord("longitude").bounds[1][1] == 180.
-    assert cube.data[1, 1, 1, 0] == 22.
+    assert cube.coord("longitude").bounds[1][1] == 360.0
+    assert cube.data[1, 1, 1, 1] == 22.
     assert cube.coord("latitude").has_bounds()
     assert cube.coord("depth").has_bounds()
     assert cube.coord('latitude').coord_system is None
     assert cube.coord('longitude').coord_system is None
-    utils.fix_coords(cube_2,
-                     overwrite_time_bounds=False,
-                     overwrite_lon_bounds=False,
-                     overwrite_lat_bounds=False,
-                     overwrite_lev_bounds=False)
+
+    cube_2.coord("depth").bounds = [[0., 2.5], [2.5, 25.], [25., 250.]]
+    cube_2 = iris.util.reverse(cube_2, "latitude")
+    np.testing.assert_allclose(cube_2.coord('latitude').points, [2.5, 1.5])
+    cube_2 = utils.fix_coords(
+        cube_2,
+        overwrite_time_bounds=False,
+        overwrite_lon_bounds=False,
+        overwrite_lat_bounds=False,
+        overwrite_lev_bounds=False,
+    )
     assert cube_2.coord("time").bounds[0][1] == 30.
-    assert cube_2.coord("longitude").bounds[1][1] == 180.
+    assert cube_2.coord("longitude").bounds[1][1] == 360.0
     assert cube_2.coord("latitude").bounds[1][1] == 3.
     assert cube_2.coord("depth").bounds[1][1] == 25.
+    np.testing.assert_allclose(cube_2.coord('latitude').points, [1.5, 2.5])
 
 
 def test_fix_var_metadata():
diff --git a/tests/unit/diag_scripts/shared/test_base.py b/tests/unit/diag_scripts/shared/test_base.py
index ac03435a8c..d59dff9379 100644
--- a/tests/unit/diag_scripts/shared/test_base.py
+++ b/tests/unit/diag_scripts/shared/test_base.py
@@ -367,6 +367,40 @@ def test_run_diagnostic(tmp_path, monkeypatch):
         assert 'example_setting' in cfg
 
 
+@pytest.mark.parametrize("no_distributed", [False, True])
+def test_run_diagnostic_configures_dask(
+    tmp_path,
+    monkeypatch,
+    mocker,
+    no_distributed,
+):
+
+    settings = create_settings(tmp_path)
+    scheduler_address = "tcp://127.0.0.1:38789"
+    settings["scheduler_address"] = scheduler_address
+    if no_distributed:
+        settings["no_distributed"] = True
+    settings_file = write_settings(settings)
+
+    monkeypatch.setattr(sys, 'argv', ['', settings_file])
+
+    # Create files created by ESMValCore
+    for filename in ('log.txt', 'profile.bin', 'resource_usage.txt'):
+        file = Path(settings['run_dir']) / filename
+        file.touch()
+
+    mocker.patch.object(shared._base.distributed, "Client")
+
+    with shared.run_diagnostic() as cfg:
+        assert 'example_setting' in cfg
+
+    if no_distributed:
+        shared._base.distributed.Client.assert_not_called()
+    else:
+        shared._base.distributed.Client.assert_called_once_with(
+            scheduler_address)
+
+
 @pytest.mark.parametrize('flag', ['-l', '--log-level'])
 def test_run_diagnostic_log_level(tmp_path, monkeypatch, flag):
     """Test if setting the log level from the command line works."""