diff --git a/.circleci/config.yml b/.circleci/config.yml
index 82492e724f..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'* ]]
@@ -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
diff --git a/.github/workflows/check-rtw.yml b/.github/workflows/check-rtw.yml
index 611601dfd7..b5c6a0f32e 100644
--- a/.github/workflows/check-rtw.yml
+++ b/.github/workflows/check-rtw.yml
@@ -6,10 +6,11 @@ on:
   # Triggers the workflow on push events
   push:
     paths:
-#      - esmvaltool/utils/recipe_test_workflow/**
+      - esmvaltool/utils/recipe_test_workflow/**
 
-  # Allows you to run this workflow manually from the Actions tab
-  workflow_dispatch:
+  # Schedule this workflow to run at 04:00 every 10 days
+  schedule:
+    - cron: '0 4 */10 * *'
 
 # Common variables are defined here
 env:
@@ -33,19 +34,13 @@ jobs:
     steps:
       # Checks-out your repository under $GITHUB_WORKSPACE, so your job
       # can access it
-      - uses: actions/checkout@v4
-      - uses: conda-incubator/setup-miniconda@v3
-        with:
-          miniforge-version: "latest"
-          miniforge-variant: Miniforge3
-          use-mamba: true
-          conda-remove-defaults: "true"
-
-      - name: Install Cylc and Rose
-        run: conda install cylc-flow>=8.2 cylc-rose metomi-rose
+      - name: Checkout repository
+        uses: actions/checkout@v4
 
-      - name: Check current environment
-        run: conda list
+      - name: Setup Cylc
+        uses: cylc/setup-cylc@v1
+        with:
+          cylc_rose: true
 
       - name: Validate Cylc workflow
         run: |
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/.readthedocs.yaml b/.readthedocs.yaml
index 974ac2ee78..3b66ab14aa 100644
--- a/.readthedocs.yaml
+++ b/.readthedocs.yaml
@@ -13,8 +13,16 @@ build:
     # DO NOT use mambaforge-*; that is currently sunsetted
     python: "miniconda-latest"
   jobs:
-    post_create_environment:
-      - 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:
@@ -26,6 +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/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 a3ad9b680c..c475b3c9a2 100644
--- a/conda-linux-64.lock
+++ b/conda-linux-64.lock
@@ -1,48 +1,53 @@
 # Generated by conda-lock.
 # platform: linux-64
-# input_hash: fafc256cb40a5d6ebcbc180cb08e91d1bd9ca77a04c258188faad5c05c60f1b9
+# input_hash: f5c4487c952927f123c46e72b510f59759905df49bd2ea87696869038fe11a8f
 @EXPLICIT
 https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81
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-https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.8.30-hbcca054_0.conda#c27d1c142233b5bc9ca570c6e2e0c244
+https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.12.14-hbcca054_0.conda#720523eb0d6a9b0f6120c16b2aa4e7de
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-https://conda.anaconda.org/conda-forge/linux-64/pandoc-3.5-ha770c72_0.conda#2889e6b9c666c3a564ab90cedc5832fd
+https://conda.anaconda.org/conda-forge/linux-64/pandoc-3.6.2-ha770c72_0.conda#4ded4ab71d9fd3764d796a23ca3e722b
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-https://conda.anaconda.org/conda-forge/noarch/tzdata-2024b-hc8b5060_0.conda#8ac3367aafb1cc0a068483c580af8015
+https://conda.anaconda.org/conda-forge/noarch/tzdata-2025a-h78e105d_0.conda#dbcace4706afdfb7eb891f7b37d07c04
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+https://conda.anaconda.org/conda-forge/linux-64/libglvnd-1.7.0-ha4b6fd6_2.conda#434ca7e50e40f4918ab701e3facd59a0
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-https://conda.anaconda.org/conda-forge/noarch/sysroot_linux-64-2.17-h4a8ded7_18.conda#0ea96f90a10838f58412aa84fdd9df09
+https://conda.anaconda.org/conda-forge/noarch/sysroot_linux-64-2.17-h0157908_18.conda#460eba7851277ec1fd80a1a24080787a
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+https://conda.anaconda.org/conda-forge/linux-64/libegl-1.7.0-ha4b6fd6_2.conda#c151d5eb730e9b7480e6d48c0fc44048
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-https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.3-heb4867d_0.conda#09a6c610d002e54e18353c06ef61a253
+https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.4-hb9d3cd8_0.conda#e2775acf57efd5af15b8e3d1d74d72d3
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+https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.6.3-hb9d3cd8_1.conda#2ecf2f1c7e4e21fcfe6423a51a992d84
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+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
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-https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.2-hb9d3cd8_0.conda#4d638782050ab6faa27275bed57e9b4e
+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
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-https://conda.anaconda.org/conda-forge/linux-64/tzcode-2024b-hb9d3cd8_0.conda#db124840386e1f842f93372897d1b857
-https://conda.anaconda.org/conda-forge/linux-64/xorg-libice-1.1.1-hb9d3cd8_1.conda#19608a9656912805b2b9a2f6bd257b04
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+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
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-https://conda.anaconda.org/conda-forge/linux-64/xorg-xextproto-7.3.0-hb9d3cd8_1004.conda#bc4cd53a083b6720d61a1519a1900878
-https://conda.anaconda.org/conda-forge/linux-64/xorg-xorgproto-2024.1-hb9d3cd8_1.conda#7c21106b851ec72c037b162c216d8f05
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@@ -62,6 +67,7 @@ https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.1-h166bdaf_0.tar.bz
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+https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20240808-pl5321h7949ede_0.conda#8247f80f3dc464d9322e85007e307fe8
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@@ -69,42 +75,45 @@ https://conda.anaconda.org/conda-forge/linux-64/libgettextpo-0.22.5-he02047a_3.c
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+https://conda.anaconda.org/conda-forge/linux-64/liblzma-devel-5.6.3-hb9d3cd8_1.conda#cc4687e1814ed459f3bd6d8e05251ab2
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-https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-qthelp-2.0.0-pyhd8ed1ab_0.conda#d6e5ea5fe00164ac6c2dcc5d76a42192
-https://conda.anaconda.org/conda-forge/noarch/sphinx-8.1.3-pyhd8ed1ab_0.conda#05706dd5a145a9c91861495cd435409a
-https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-serializinghtml-1.1.10-pyhd8ed1ab_0.conda#e507335cb4ca9cff4c3d0fa9cdab255e
+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/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/index.rst b/doc/sphinx/source/recipes/index.rst
index 94777d7744..33fbb13126 100644
--- a/doc/sphinx/source/recipes/index.rst
+++ b/doc/sphinx/source/recipes/index.rst
@@ -95,6 +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/utils/RTW/user_guide/quick_start.rst b/doc/sphinx/source/utils/RTW/user_guide/quick_start.rst
index f15082ae84..6574aa24ee 100644
--- a/doc/sphinx/source/utils/RTW/user_guide/quick_start.rst
+++ b/doc/sphinx/source/utils/RTW/user_guide/quick_start.rst
@@ -38,5 +38,17 @@ Quick Start Guide
         export CYLC_VERSION=8
         cylc vip -O jasmin
 
-* Browse the logs using `Cylc Review`_, a web service for browsing logs via an
-  HTTP interface.
+  * 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
index 5871df7b78..f571e1e148 100644
--- a/doc/sphinx/source/utils/RTW/user_guide/workflow.rst
+++ b/doc/sphinx/source/utils/RTW/user_guide/workflow.rst
@@ -25,9 +25,7 @@ The |RTW| performs the following steps:
      GitHub, or gets the latest container image from DockerHub and converts to
      a singularity image, depending on ``SITE``
   :Runs on:
-     Localhost (if cloning), or ``COMPUTE`` (if getting container), which
-     depends on the ``SITE``; on JASMIN, the ``get_esmval`` jobs will run on
-     LOTUS
+     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
@@ -48,8 +46,7 @@ The |RTW| performs the following steps:
   :Description:
      Runs the requested recipes using |ESMValTool|
   :Runs on:
-     ``COMPUTE``, which depends on the ``SITE``; at the Met Office, the
-     ``process`` jobs will run on SPICE
+     ``COMPUTE``, which depends on the ``SITE``
   :Executes:
      The |ESMValTool| command line script from the |Rose| app
   :Details:
@@ -60,8 +57,7 @@ The |RTW| performs the following steps:
   :Description:
      Compares the output from the ``process`` job with |KGOs|
   :Runs on:
-     ``COMPUTE``, which depends on the ``SITE``; at the Met Office, the
-     ``compare`` jobs will run on SPICE
+     ``COMPUTE``, which depends on the ``SITE``
   :Executes:
      The :ref:`compare.py <compare_recipe_runs>` script from |ESMValTool|
      from the |Rose| app
diff --git a/esmvaltool/config-references.yml b/esmvaltool/config-references.yml
index 6eda568bac..701b16e5b3 100644
--- a/esmvaltool/config-references.yml
+++ b/esmvaltool/config-references.yml
@@ -228,6 +228,11 @@ 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
@@ -237,6 +242,11 @@ 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
@@ -374,7 +384,7 @@ 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:
@@ -405,6 +415,11 @@ 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
@@ -514,6 +529,11 @@ 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
@@ -555,6 +575,11 @@ 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
@@ -712,7 +737,7 @@ authors:
     orcid:
   reader_cathy:
     name: Reader, Cathy
-    institute:
+    institute: CCCma, ECCC, Canada
     orcid:
     github: mcreader97
   rumbold_heather:
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/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/ensclus/__init__.py b/esmvaltool/diag_scripts/ensclus/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/esmvaltool/diag_scripts/monitor/multi_datasets.py b/esmvaltool/diag_scripts/monitor/multi_datasets.py
index 41f238a64e..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``,
@@ -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
@@ -296,9 +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')
@@ -448,9 +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')
@@ -535,9 +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
@@ -654,6 +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,
@@ -729,7 +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', {})
@@ -763,7 +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', {})
@@ -809,7 +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', {})
@@ -838,7 +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', {})
@@ -873,7 +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)
@@ -1007,13 +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):
@@ -1171,7 +1190,10 @@ 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],
@@ -1319,7 +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),
@@ -1363,7 +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])
@@ -1472,7 +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),
@@ -1529,7 +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))
@@ -1592,7 +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])
@@ -1710,7 +1747,10 @@ 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])
@@ -1837,7 +1877,10 @@ def _plot_hovmoeller_time_vs_lat_or_lon_without_ref(self, plot_func,
             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),
@@ -2592,16 +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/zmnam/__init__.py b/esmvaltool/diag_scripts/zmnam/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
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/ipccwg1ar6ch3/recipe_ipccwg1ar6ch3_fig_3_42_a.yml b/esmvaltool/recipes/ipccwg1ar6ch3/recipe_ipccwg1ar6ch3_fig_3_42_a.yml
index 55c53147ec..35954b47a1 100644
--- a/esmvaltool/recipes/ipccwg1ar6ch3/recipe_ipccwg1ar6ch3_fig_3_42_a.yml
+++ b/esmvaltool/recipes/ipccwg1ar6ch3/recipe_ipccwg1ar6ch3_fig_3_42_a.yml
@@ -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 4277313428..f5eea141dc 100644
--- a/esmvaltool/recipes/monitor/recipe_monitor_with_refs.yml
+++ b/esmvaltool/recipes/monitor/recipe_monitor_with_refs.yml
@@ -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]
 
@@ -230,6 +232,11 @@ diagnostics:
       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
@@ -255,5 +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_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/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/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/flow.cylc b/esmvaltool/utils/recipe_test_workflow/flow.cylc
index 0dcd7e2528..0338dce5a0 100644
--- a/esmvaltool/utils/recipe_test_workflow/flow.cylc
+++ b/esmvaltool/utils/recipe_test_workflow/flow.cylc
@@ -53,7 +53,6 @@
             ROSE_APP_OPT_CONF_KEYS = {{ SITE }}
 
     [[configure]]
-        pre-script = "mkdir -p ${USER_CONFIG_DIR}"
         [[[environment]]]
             ROSE_TASK_APP = configure
             DRS_CMIP3 = {{ DRS_CMIP3 }}
diff --git a/esmvaltool/utils/recipe_test_workflow/opt/rose-suite-dkrz.conf b/esmvaltool/utils/recipe_test_workflow/opt/rose-suite-dkrz.conf
new file mode 100644
index 0000000000..dca068f8c2
--- /dev/null
+++ b/esmvaltool/utils/recipe_test_workflow/opt/rose-suite-dkrz.conf
@@ -0,0 +1,10 @@
+[template variables]
+DRS_CMIP5="DKRZ"
+DRS_CMIP6="DKRZ"
+ENV_NAME="experimental"
+KGO_ROOT_PATH="/work/bd0854/KGO/esmvaltool_output/"
+ROOTPATH_CMIP5="/work/bd0854/DATA/ESMValTool2/CMIP5_DKRZ"
+ROOTPATH_CMIP6="/work/bd0854/DATA/ESMValTool2/CMIP6_DKRZ"
+ROOTPATH_OBS="/work/bd0854/DATA/ESMValTool2/OBS"
+ROOTPATH_OBS4MIPS="/work/bd0854/DATA/ESMValTool2/OBS"
+SITE="dkrz"
diff --git a/esmvaltool/utils/recipe_test_workflow/site/dkrz-env b/esmvaltool/utils/recipe_test_workflow/site/dkrz-env
new file mode 100755
index 0000000000..0821e59be2
--- /dev/null
+++ b/esmvaltool/utils/recipe_test_workflow/site/dkrz-env
@@ -0,0 +1,57 @@
+#!/bin/bash
+#
+# USAGE dkrz-env COMMAND
+#
+# ENVIRONMENT
+#   ENV_NAME            The name of the container being used
+#   QUIET_MODE          Don't print confirmation messages
+#   PYTHONPATH_PREPEND  The path to prepend to PYTHONPATH
+#
+# OPTIONS
+#   COMMAND             The command to execute with options
+set -eu
+
+# Must be run before importing numpy, see
+# https://docs.dask.org/en/stable/array-best-practices.html#avoid-oversubscribing-threads
+export OMP_NUM_THREADS=1
+export OPENBLAS_NUM_THREADS=1
+export MKL_NUM_THREADS=1
+export VECLIB_MAXIMUM_THREADS=1
+export NUMEXPR_NUM_THREADS=1
+
+# Ensure '~/.local' isn't added to 'sys.path'.
+export PYTHONNOUSERSITE=True
+
+WORKFLOW_RUN_BIN_DIR="${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.cylc b/esmvaltool/utils/recipe_test_workflow/site/jasmin.cylc
index 5eb1b083ad..56c70f9df7 100644
--- a/esmvaltool/utils/recipe_test_workflow/site/jasmin.cylc
+++ b/esmvaltool/utils/recipe_test_workflow/site/jasmin.cylc
@@ -2,7 +2,8 @@
 [runtime]
     [[root]]
         [[[environment]]]
-            # Warning: fragile assumption of location of esmvaltool code, see #3437
+            # 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.
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