diff --git a/.github/workflows/citation_file_validator.yml b/.github/workflows/citation_file_validator.yml
index 43a4d5c444..e957d40f86 100644
--- a/.github/workflows/citation_file_validator.yml
+++ b/.github/workflows/citation_file_validator.yml
@@ -16,7 +16,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Check out a copy of the repository
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
 
       - name: Check whether the citation metadata from CITATION.cff is valid
         uses: citation-file-format/cffconvert-github-action@2.0.0
diff --git a/.github/workflows/create-condalock-file.yml b/.github/workflows/create-condalock-file.yml
index 9aefad7498..a88f919c17 100644
--- a/.github/workflows/create-condalock-file.yml
+++ b/.github/workflows/create-condalock-file.yml
@@ -20,10 +20,10 @@ jobs:
     name: Create conda lock file for latest Python
     runs-on: 'ubuntu-latest'
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
         with:
           fetch-depth: 0
-      - uses: conda-incubator/setup-miniconda@v2
+      - uses: conda-incubator/setup-miniconda@v3
         with:
           auto-update-conda: true
           activate-environment: esmvaltool-fromlock
@@ -35,7 +35,7 @@ jobs:
         run: |
           conda update -n base -c conda-forge conda
           conda --version
-          # setup-miniconda@v2 installs an old conda and mamba
+          # setup-miniconda@v3 installs an old conda and mamba
           # forcing a modern mamba updates both mamba and conda
           conda install -c conda-forge "mamba>=1.4.8"
           conda config --show-sources
diff --git a/.github/workflows/install-from-conda.yml b/.github/workflows/install-from-conda.yml
index 55897e7fe4..862fd0aad6 100644
--- a/.github/workflows/install-from-conda.yml
+++ b/.github/workflows/install-from-conda.yml
@@ -23,7 +23,7 @@ jobs:
         python-version: ["3.9", "3.10", "3.11"]
     name: Linux Python ${{ matrix.python-version }}
     steps:
-      - uses: conda-incubator/setup-miniconda@v2
+      - uses: conda-incubator/setup-miniconda@v3
         with:
           python-version: ${{ matrix.python-version }}
           miniforge-version: "latest"
@@ -44,7 +44,7 @@ jobs:
           esmvaltool version 2>&1 | tee conda_install_linux_artifacts_python_${{ matrix.python-version }}/version.txt
       - name: Upload artifacts
         if: ${{ always() }}  # upload artifacts even if fail
-        uses: actions/upload-artifact@v2
+        uses: actions/upload-artifact@v4
         with:
           name: Conda_Install_Linux_python_${{ matrix.python-version }}
           path: conda_install_linux_artifacts_python_${{ matrix.python-version }}
@@ -61,7 +61,7 @@ jobs:
 #    name: OSX Python ${{ matrix.python-version }}
 #    steps:
 #      - uses: actions/checkout@v2
-#      - uses: conda-incubator/setup-miniconda@v2
+#      - uses: conda-incubator/setup-miniconda@v3
 #        with:
 #          python-version: ${{ matrix.python-version }}
 #          miniconda-version: "latest"
@@ -82,7 +82,7 @@ jobs:
 #      - run: esmvaltool version 2>&1 | tee conda_install_osx_artifacts_python_${{ matrix.python-version }}/version.txt
 #      - name: Upload artifacts
 #        if: ${{ always() }}  # upload artifacts even if fail
-#        uses: actions/upload-artifact@v2
+#        uses: actions/upload-artifact@v4
 #        with:
 #          name: Conda_Install_OSX_python_${{ matrix.python-version }}
 #          path: conda_install_osx_artifacts_python_${{ matrix.python-version }}
diff --git a/.github/workflows/install-from-condalock-file.yml b/.github/workflows/install-from-condalock-file.yml
index a209c06f32..ef19a4cb15 100644
--- a/.github/workflows/install-from-condalock-file.yml
+++ b/.github/workflows/install-from-condalock-file.yml
@@ -34,10 +34,10 @@ jobs:
       fail-fast: false
     name: Linux Python ${{ matrix.python-version }}
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
         with:
           fetch-depth: 0
-      - uses: conda-incubator/setup-miniconda@v2
+      - uses: conda-incubator/setup-miniconda@v3
         with:
           activate-environment: esmvaltool-fromlock
           python-version: ${{ matrix.python-version }}
@@ -57,7 +57,7 @@ jobs:
       - run: pytest -n 2 -m "not installation"
       - name: Upload artifacts
         if: ${{ always() }}  # upload artifacts even if fail
-        uses: actions/upload-artifact@v2
+        uses: actions/upload-artifact@v4
         with:
           name: Source_Install_Linux_python_${{ matrix.python-version }}
           path: source_install_linux_artifacts_python_${{ matrix.python-version }}
diff --git a/.github/workflows/install-from-source.yml b/.github/workflows/install-from-source.yml
index 2e24b8f049..6e9c1de19a 100644
--- a/.github/workflows/install-from-source.yml
+++ b/.github/workflows/install-from-source.yml
@@ -23,10 +23,10 @@ jobs:
       fail-fast: false
     name: Linux Python ${{ matrix.python-version }}
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
         with:
           fetch-depth: 0
-      - uses: conda-incubator/setup-miniconda@v2
+      - uses: conda-incubator/setup-miniconda@v3
         with:
           activate-environment: esmvaltool
           environment-file: environment.yml
@@ -47,7 +47,7 @@ jobs:
           esmvaltool version 2>&1 | tee source_install_linux_artifacts_python_${{ matrix.python-version }}/version.txt
       - name: Upload artifacts
         if: ${{ always() }}  # upload artifacts even if fail
-        uses: actions/upload-artifact@v2
+        uses: actions/upload-artifact@v4
         with:
           name: Source_Install_Linux_python_${{ matrix.python-version }}
           path: source_install_linux_artifacts_python_${{ matrix.python-version }}
@@ -64,7 +64,7 @@ jobs:
 #    name: OSX Python ${{ matrix.python-version }}
 #    steps:
 #      - uses: actions/checkout@v2
-#      - uses: conda-incubator/setup-miniconda@v2
+#      - uses: conda-incubator/setup-miniconda@v3
 #        with:
 #          activate-environment: esmvaltool
 #          environment-file: environment.yml
@@ -79,7 +79,7 @@ jobs:
 #      - run: esmvaltool version 2>&1 | tee source_install_osx_artifacts_python_${{ matrix.python-version }}/version.txt
 #      - name: Upload artifacts
 #        if: ${{ always() }}  # upload artifacts even if fail
-#        uses: actions/upload-artifact@v2
+#        uses: actions/upload-artifact@v4
 #        with:
 #          name: Source_Install_OSX_python_${{ matrix.python-version }}
 #          path: source_install_osx_artifacts_python_${{ matrix.python-version }}
diff --git a/.github/workflows/pypi-build-and-deploy.yml b/.github/workflows/pypi-build-and-deploy.yml
index f1ed214e12..4dff1e4d69 100644
--- a/.github/workflows/pypi-build-and-deploy.yml
+++ b/.github/workflows/pypi-build-and-deploy.yml
@@ -14,7 +14,7 @@ jobs:
     name: Build and publish ESMValTool on PyPi
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
         with:
           fetch-depth: 0
       - name: Set up Python 3.11
diff --git a/.github/workflows/run-tests-monitor.yml b/.github/workflows/run-tests-monitor.yml
index 1efe54a66a..52cc282235 100644
--- a/.github/workflows/run-tests-monitor.yml
+++ b/.github/workflows/run-tests-monitor.yml
@@ -26,10 +26,10 @@ jobs:
         python-version: ["3.9", "3.10", "3.11"]
     name: Linux Python ${{ matrix.python-version }}
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
         with:
           fetch-depth: 0
-      - uses: conda-incubator/setup-miniconda@v2
+      - uses: conda-incubator/setup-miniconda@v3
         with:
           activate-environment: esmvaltool
           environment-file: environment.yml
@@ -42,6 +42,8 @@ jobs:
         run: |
           mamba --version 2>&1 | tee test_linux_artifacts_python_${{ matrix.python-version }}/conda_version.txt
           python -V 2>&1 | tee test_linux_artifacts_python_${{ matrix.python-version }}/python_version.txt
+      - name: Inspect environment
+        run: conda list
       - name: Install pytest-monitor
         run: pip install pytest-monitor
       - name: Install ESMValTool
@@ -56,7 +58,7 @@ jobs:
         run: python tests/parse_pymon.py
       - name: Upload artifacts
         if: ${{ always() }}  # upload artifacts even if fail
-        uses: actions/upload-artifact@v2
+        uses: actions/upload-artifact@v4
         with:
           name: Test_Linux_python_${{ matrix.python-version }}
           path: test_linux_artifacts_python_${{ matrix.python-version }}
@@ -66,14 +68,16 @@ jobs:
     strategy:
       matrix:
         python-version: ["3.9", "3.10", "3.11"]
+        architecture: ["x64"]  # need to force Intel, arm64 builds have issues
       fail-fast: false
     name: OSX Python ${{ matrix.python-version }}
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
         with:
           fetch-depth: 0
-      - uses: conda-incubator/setup-miniconda@v2
+      - uses: conda-incubator/setup-miniconda@v3
         with:
+          architecture: ${{ matrix.architecture }}
           activate-environment: esmvaltool
           environment-file: environment_osx.yml
           python-version: ${{ matrix.python-version }}
@@ -87,6 +91,10 @@ jobs:
         run: |
           mamba --version 2>&1 | tee test_osx_artifacts_python_${{ matrix.python-version }}/conda_version.txt
           python -V 2>&1 | tee test_osx_artifacts_python_${{ matrix.python-version }}/python_version.txt
+      - name: Inspect environment
+        run: conda list
+      - name: Install git
+        run: mamba install -c conda-forge git
       - name: Install pytest-monitor
         run: pip install pytest-monitor
       - name: Install ESMValTool
@@ -101,7 +109,7 @@ jobs:
         run: python tests/parse_pymon.py
       - name: Upload artifacts
         if: ${{ always() }}  # upload artifacts even if fail
-        uses: actions/upload-artifact@v2
+        uses: actions/upload-artifact@v4
         with:
           name: Test_OSX_python_${{ matrix.python-version }}
           path: test_osx_artifacts_python_${{ matrix.python-version }}
diff --git a/.github/workflows/test-development.yml b/.github/workflows/test-development.yml
index cab6489548..7ff8c93680 100644
--- a/.github/workflows/test-development.yml
+++ b/.github/workflows/test-development.yml
@@ -30,10 +30,10 @@ jobs:
         python-version: ["3.9", "3.10", "3.11"]
     name: Linux Python ${{ matrix.python-version }}
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
         with:
           fetch-depth: 0
-      - uses: conda-incubator/setup-miniconda@v2
+      - uses: conda-incubator/setup-miniconda@v3
         with:
           activate-environment: esmvaltool
           environment-file: environment.yml
@@ -62,7 +62,7 @@ jobs:
         run: pytest -n 2 -m "not installation" 2>&1 | tee develop_test_linux_artifacts_python_${{ matrix.python-version }}/test_report.txt
       - name: Upload artifacts
         if: ${{ always() }}  # upload artifacts even if fail
-        uses: actions/upload-artifact@v2
+        uses: actions/upload-artifact@v4
         with:
           name: Develop_Test_Linux_python_${{ matrix.python-version }}
           path: develop_test_linux_artifacts_python_${{ matrix.python-version }}
diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
index 9eec648279..49f1a14003 100644
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -6,7 +6,6 @@ on:
   push:
     branches:
       - main
-      - fix_recipe_filler_bkwds_incompatibility
   schedule:
     - cron: '0 0 * * *'
 
@@ -24,10 +23,10 @@ jobs:
         python-version: ["3.9", "3.10", "3.11"]
     name: Linux Python ${{ matrix.python-version }}
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
         with:
           fetch-depth: 0
-      - uses: conda-incubator/setup-miniconda@v2
+      - uses: conda-incubator/setup-miniconda@v3
         with:
           activate-environment: esmvaltool
           environment-file: environment.yml
@@ -42,6 +41,8 @@ jobs:
           python -V 2>&1 | tee test_linux_artifacts_python_${{ matrix.python-version }}/python_version.txt
           # this is how to export variables to the GITHUB var environment
           echo "pver0=$(python -V)" >> $GITHUB_ENV
+      - name: Inspect environment
+        run: conda list
       - name: Install ESMValTool
         run: pip install -e .[develop] 2>&1 | tee test_linux_artifacts_python_${{ matrix.python-version }}/install.txt
       - name: Install Julia dependencies
@@ -54,13 +55,15 @@ jobs:
           echo "Python minor version changed after Julia install"
           python -V
           exit 1
+      - name: Inspect environment
+        run: conda list
       - name: Run flake8
         run: flake8
       - name: Run tests
         run: pytest -n 2 -m "not installation" 2>&1 | tee test_linux_artifacts_python_${{ matrix.python-version }}/test_report.txt
       - name: Upload artifacts
         if: ${{ always() }}  # upload artifacts even if fail
-        uses: actions/upload-artifact@v2
+        uses: actions/upload-artifact@v4
         with:
           name: Test_Linux_python_${{ matrix.python-version }}
           path: test_linux_artifacts_python_${{ matrix.python-version }}
@@ -70,14 +73,16 @@ jobs:
     strategy:
       matrix:
         python-version: ["3.9", "3.10", "3.11"]
+        architecture: ["x64"]  # need to force Intel, arm64 builds have issues
       fail-fast: false
     name: OSX Python ${{ matrix.python-version }}
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
         with:
           fetch-depth: 0
-      - uses: conda-incubator/setup-miniconda@v2
+      - uses: conda-incubator/setup-miniconda@v3
         with:
+          architecture: ${{ matrix.architecture }}
           activate-environment: esmvaltool
           environment-file: environment_osx.yml
           python-version: ${{ matrix.python-version }}
@@ -91,15 +96,25 @@ jobs:
         run: |
           mamba --version 2>&1 | tee test_osx_artifacts_python_${{ matrix.python-version }}/conda_version.txt
           python -V 2>&1 | tee test_osx_artifacts_python_${{ matrix.python-version }}/python_version.txt
+      - name: Inspect environment
+        run: conda list
+      - name: Determine if git
+        run: |
+          which git
+          git --version
+      - name: Install git
+        run: mamba install -c conda-forge git
       - name: Install ESMValTool
         run: pip install -e .[develop] 2>&1 | tee test_osx_artifacts_python_${{ matrix.python-version }}/install.txt
+      - name: Inspect environment
+        run: conda list
       - name: Run flake8
         run: flake8
       - name: Run tests
         run: pytest -n 2 -m "not installation" 2>&1 | tee test_osx_artifacts_python_${{ matrix.python-version }}/test_report.txt
       - name: Upload artifacts
         if: ${{ always() }}  # upload artifacts even if fail
-        uses: actions/upload-artifact@v2
+        uses: actions/upload-artifact@v4
         with:
           name: Test_OSX_python_${{ matrix.python-version }}
           path: test_osx_artifacts_python_${{ matrix.python-version }}
diff --git a/CITATION.cff b/CITATION.cff
index 5c253e3bb5..cd621538b7 100644
--- a/CITATION.cff
+++ b/CITATION.cff
@@ -175,6 +175,11 @@ authors:
     family-names: Hagemann
     given-names: Stefan
     orcid: "https://orcid.org/0000-0001-5444-2945"
+  -
+    affiliation: "University of Canterbury, New Zealand"
+    family-names: Hardacre
+    given-names: Catherine
+    orcid: "https://orcid.org/0000-0001-9093-4656"
   -
     affiliation: "ISAC-CNR, Italy"
     name-particle: von
diff --git a/README.md b/README.md
index 4fbe8aa84e..b196f7fbb8 100644
--- a/README.md
+++ b/README.md
@@ -5,8 +5,7 @@
 [![Chat on Matrix](https://matrix.to/img/matrix-badge.svg)](https://matrix.to/#/#ESMValGroup_Lobby:gitter.im)
 [![CircleCI](https://circleci.com/gh/ESMValGroup/ESMValTool/tree/main.svg?style=svg)](https://circleci.com/gh/ESMValGroup/ESMValTool/tree/main)
 [![Test in Full Development Mode](https://github.com/ESMValGroup/ESMValTool/actions/workflows/test-development.yml/badge.svg)](https://github.com/ESMValGroup/ESMValTool/actions/workflows/test-development.yml)
-[![Codacy Badge](https://app.codacy.com/project/badge/Coverage/79bf6932c2e844eea15d0fb1ed7e415c)](https://www.codacy.com/gh/ESMValGroup/ESMValTool?utm_source=github.com&utm_medium=referral&utm_content=ESMValGroup/ESMValTool&utm_campaign=Badge_Coverage)
-[![Codacy Badge](https://app.codacy.com/project/badge/Grade/79bf6932c2e844eea15d0fb1ed7e415c)](https://www.codacy.com/gh/ESMValGroup/ESMValTool?utm_source=github.com&utm_medium=referral&utm_content=ESMValGroup/ESMValTool&utm_campaign=Badge_Grade)
+[![Codacy Badge](https://app.codacy.com/project/badge/Grade/79bf6932c2e844eea15d0fb1ed7e415c)](https://app.codacy.com/gh/ESMValGroup/ESMValTool/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_grade)
 [![Docker Build Status](https://img.shields.io/docker/cloud/build/esmvalgroup/esmvaltool.svg)](https://hub.docker.com/r/esmvalgroup/esmvaltool/)
 [![Anaconda-Server Badge](https://img.shields.io/conda/vn/conda-forge/ESMValTool?color=blue&label=conda-forge&logo=conda-forge&logoColor=white)](https://anaconda.org/conda-forge/esmvaltool)
 ![stand with Ukraine](https://badgen.net/badge/stand%20with/UKRAINE/?color=0057B8&labelColor=FFD700)
diff --git a/conda-linux-64.lock b/conda-linux-64.lock
index b3ef76dd7a..f37c2e310d 100644
--- a/conda-linux-64.lock
+++ b/conda-linux-64.lock
@@ -1,6 +1,6 @@
 # Generated by conda-lock.
 # platform: linux-64
-# input_hash: e484ece0f2a2f8e9f3009df8b9de1be258c70868b9df17114bb859f15f903ccf
+# input_hash: be61247fa188c644bffb3a402ab7a5b10c4294187450b4a075a088bde1255a2e
 @EXPLICIT
 https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81
 https://conda.anaconda.org/conda-forge/linux-64/_py-xgboost-mutex-2.0-gpu_0.tar.bz2#7702188077361f43a4d61e64c694f850
@@ -10,88 +10,91 @@ https://conda.anaconda.org/conda-forge/noarch/cuda-version-11.8-h70ddcb2_3.conda
 https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2#0c96522c6bdaed4b1566d11387caaf45
 https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2#34893075a5c9e55cdafac56607368fc6
 https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2#4d59c254e01d9cde7957100457e2d5fb
-https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_1.conda#6185f640c43843e5ad6fd1c5372c3f80
+https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_2.conda#cbbe59391138ea5ad3658c76912e147f
 https://conda.anaconda.org/conda-forge/noarch/kernel-headers_linux-64-2.6.32-he073ed8_17.conda#d731b543793afc0433c4fd593e693fce
-https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.40-h41732ed_0.conda#7aca3059a1729aa76c597603f10b0dd3
-https://conda.anaconda.org/conda-forge/noarch/libgcc-devel_linux-64-13.2.0-ha9c7c90_105.conda#3bc29a967fee57e193ce51f51c598bca
-https://conda.anaconda.org/conda-forge/noarch/libstdcxx-devel_linux-64-13.2.0-ha9c7c90_105.conda#66383205c2e1bdf013df52fa9e3e6763
-https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-13.2.0-h7e041cc_5.conda#f6f6600d18a4047b54f803cf708b868a
-https://conda.anaconda.org/conda-forge/linux-64/pandoc-3.1.12.2-ha770c72_0.conda#4f04ceee8d953367da7de726af233303
+https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.40-h55db66e_0.conda#10569984e7db886e4f1abc2b47ad79a1
+https://conda.anaconda.org/conda-forge/linux-64/libboost-headers-1.85.0-ha770c72_1.conda#012455a6eddcbf487ef0ddd1715f0b80
+https://conda.anaconda.org/conda-forge/noarch/libgcc-devel_linux-64-13.2.0-hceb6213_107.conda#2cc37ba482c6321237ce72329e1aaea2
+https://conda.anaconda.org/conda-forge/noarch/libstdcxx-devel_linux-64-13.2.0-hceb6213_107.conda#2b409e9645fb3d69115d04496d1219cc
+https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-13.2.0-hc0a3c3a_7.conda#53ebd4c833fa01cb2c6353e99f905406
+https://conda.anaconda.org/conda-forge/linux-64/pandoc-3.2-ha770c72_0.conda#8c924f0b7f3e064b1c954a08e7c32fba
 https://conda.anaconda.org/conda-forge/noarch/poppler-data-0.4.12-hd8ed1ab_0.conda#d8d7293c5b37f39b2ac32940621c6592
 https://conda.anaconda.org/conda-forge/linux-64/python_abi-3.11-4_cp311.conda#d786502c97404c94d7d58d258a445a65
 https://conda.anaconda.org/conda-forge/noarch/tzdata-2024a-h0c530f3_0.conda#161081fc7cec0bfda0d86d7cb595f8d8
 https://conda.anaconda.org/conda-forge/linux-64/xorg-imake-1.0.7-0.tar.bz2#23acfc5a339a6a34cc2241f64e4111be
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-https://conda.anaconda.org/conda-forge/noarch/r-styler-1.10.1-r41hc72bb7e_0.conda#c12b81cff8bb8745ffbe7aeb9dfd795f
-https://conda.anaconda.org/conda-forge/linux-64/r-tlmoments-0.7.5.3-r41h38f115c_0.conda#d6992015dff73f17ee76a9380b367b9f
-https://conda.anaconda.org/conda-forge/noarch/r-viridis-0.6.3-r41hc72bb7e_0.conda#8502ff3a06bb92a4bba056127965f9d9
-https://conda.anaconda.org/conda-forge/noarch/r-waldo-0.5.1-r41hc72bb7e_0.conda#70a78bfe3a730e73308eb0858a78476f
-https://conda.anaconda.org/conda-forge/linux-64/r-fields-14.1-r41hac0b197_1.conda#3178d2a57796f6462ea7457ba4b6447c
-https://conda.anaconda.org/conda-forge/noarch/r-spei-1.8.1-r41hc72bb7e_0.conda#c928901be40f7694f1237570a148416c
-https://conda.anaconda.org/conda-forge/linux-64/r-testthat-3.1.8-r41ha503ecb_0.conda#d0c8f9f62ce32fad40fa9ec17dd21ee0
-https://conda.anaconda.org/conda-forge/linux-64/r-geomap-2.5_0-r41h06615bd_1.tar.bz2#dabe8f942d619075cbd13c1481c8b538
-https://conda.anaconda.org/conda-forge/noarch/r-lintr-3.0.2-r41hc72bb7e_0.tar.bz2#769c2305486b74fd61d85bfef2296f27
-https://conda.anaconda.org/conda-forge/noarch/r-s2dverification-2.10.3-r41hc72bb7e_1.tar.bz2#2253f130c8dab435824d6ddb10a41c73
+https://conda.anaconda.org/conda-forge/linux-64/r-geomap-2.5_5-r43h57805ef_0.conda#dc942c6f2062894f7baf00fc6b907a79
+https://conda.anaconda.org/conda-forge/noarch/r-s2dverification-2.10.3-r43hc72bb7e_2.conda#13f4b1126272c8f195fc6ef38cc19d31
 https://conda.anaconda.org/conda-forge/noarch/autodocsumm-0.2.6-pyhd8ed1ab_0.tar.bz2#4409dd7e06a62c3b2aa9e96782c49c6d
-https://conda.anaconda.org/conda-forge/noarch/nbsphinx-0.9.3-pyhd8ed1ab_0.conda#0dbaa7d08d3d79b2a1a4dd6a02cc4581
+https://conda.anaconda.org/conda-forge/noarch/nbsphinx-0.9.4-pyhd8ed1ab_0.conda#9dc80eaeff56fb67dbf4f871b81bc13a
 https://conda.anaconda.org/conda-forge/noarch/pydata-sphinx-theme-0.15.2-pyhd8ed1ab_0.conda#ce99859070b0e17ccc63234ca58f3ed8
 https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-applehelp-1.0.8-pyhd8ed1ab_0.conda#611a35a27914fac3aa37611a6fe40bb5
 https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-devhelp-1.0.6-pyhd8ed1ab_0.conda#d7e4954df0d3aea2eacc7835ad12671d
 https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-htmlhelp-2.0.5-pyhd8ed1ab_0.conda#7e1e7437273682ada2ed5e9e9714b140
 https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-qthelp-1.0.7-pyhd8ed1ab_0.conda#26acae54b06f178681bfb551760f5dd1
-https://conda.anaconda.org/conda-forge/noarch/sphinx-7.2.6-pyhd8ed1ab_0.conda#bbfd1120d1824d2d073bc65935f0e4c0
+https://conda.anaconda.org/conda-forge/noarch/sphinx-7.3.7-pyhd8ed1ab_0.conda#7b1465205e28d75d2c0e1a868ee00a67
 https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-serializinghtml-1.1.10-pyhd8ed1ab_0.conda#e507335cb4ca9cff4c3d0fa9cdab255e
diff --git a/config-user-example.yml b/config-user-example.yml
index ab9c8f3b4d..c102928db9 100644
--- a/config-user-example.yml
+++ b/config-user-example.yml
@@ -237,13 +237,38 @@ drs:
 #  CMIP5: /project/champ/data/cmip5/output1
 #  CMIP6: /project/champ/data/CMIP6
 #  CORDEX: /project/champ/data/cordex/output
-#  OBS: /data/users/esmval/ESMValTool/temporary/obs/
-#  obs4MIPs: /data/users/esmval/ESMValTool/temporary/obs/
+#  OBS: /data/users/esmval/ESMValTool/obs
+#  OBS6: /data/users/esmval/ESMValTool/obs
+#  obs4MIPs: /data/users/esmval/ESMValTool/obs
 #  ana4mips: /project/champ/data/ana4MIPs
+#  native6: /data/users/esmval/ESMValTool/rawobs
+#  RAWOBS: /data/users/esmval/ESMValTool/rawobs
 #drs:
 #  CMIP5: BADC
 #  CMIP6: BADC
 #  CORDEX: BADC
 #  OBS: default
+#  OBS6: default
 #  obs4MIPs: default
 #  ana4mips: BADC
+#  native6: default
+
+# Site-specific entries: NCI
+# Uncomment the lines below to locate data at NCI.
+#rootpath:
+#  CMIP6: [/g/data/oi10/replicas/CMIP6, /g/data/fs38/publications/CMIP6, /g/data/xp65/public/apps/esmvaltool/replicas/CMIP6]
+#  CMIP5: [/g/data/r87/DRSv3/CMIP5, /g/data/al33/replicas/CMIP5/combined, /g/data/rr3/publications/CMIP5/output1, /g/data/xp65/public/apps/esmvaltool/replicas/cmip5/output1]
+#  CMIP3: /g/data/r87/DRSv3/CMIP3
+#  OBS: /g/data/ct11/access-nri/replicas/esmvaltool/obsdata-v2
+#  OBS6: /g/data/ct11/access-nri/replicas/esmvaltool/obsdata-v2
+#  obs4MIPs: /g/data/ct11/access-nri/replicas/esmvaltool/obsdata-v2
+#  ana4mips: /g/data/ct11/access-nri/replicas/esmvaltool/obsdata-v2
+#  native6: /g/data/xp65/public/apps/esmvaltool/native6
+#
+#drs:
+#  CMIP6: NCI
+#  CMIP5: NCI
+#  CMIP3: NCI
+#  CORDEX: ESGF
+#  obs4MIPs: default
+#  ana4mips: default
diff --git a/doc/sphinx/source/community/release_strategy/release_strategy.rst b/doc/sphinx/source/community/release_strategy/release_strategy.rst
index b358fdb7f3..cae1c43807 100644
--- a/doc/sphinx/source/community/release_strategy/release_strategy.rst
+++ b/doc/sphinx/source/community/release_strategy/release_strategy.rst
@@ -54,9 +54,19 @@ With the following release schedule, we strive to have three releases per year a
 Upcoming releases
 ^^^^^^^^^^^^^^^^^
 
-- 2.11.0 (Release Manager: TBD)
+- 2.11.0 (Release Manager: Met Office: `Emma Hogan`_, `Chris Billows`_, `Ed Gillett`_)
 
-Planned for February or March 2024
++------------+------------+----------------------------------------+-------------------------------------+
+|  Planned   |    Done    |            Event                       |             Changelog               |
++============+============+========================================+=====================================+
+| 2024-04-22 |            | ESMValCore `Feature Freeze`_           |                                     |
++------------+------------+----------------------------------------+-------------------------------------+
+| 2023-05-03 |            | ESMValCore released                    |                                     |
++------------+------------+----------------------------------------+-------------------------------------+
+| 2023-05-06 |            | ESMValTool `Feature Freeze`_           |                                     |
++------------+------------+----------------------------------------+-------------------------------------+
+| 2023-05-17 |            | ESMValTool released                    |                                     |
++------------+------------+----------------------------------------+-------------------------------------+
 
 Past releases
 ^^^^^^^^^^^^^
@@ -647,3 +657,6 @@ Changelog
 .. _Manuel Schlund: https://github.com/schlunma
 .. _Javier Vegas-Regidor: https://github.com/jvegasbsc
 .. _Klaus Zimmermann: https://github.com/zklaus
+.. _Emma Hogan: https://github.com/ehogan
+.. _Chris Billows: https://github.com/chrisbillowsMO
+.. _Ed Gillett: https://github.com/mo-gill
diff --git a/doc/sphinx/source/develop/dataset.rst b/doc/sphinx/source/develop/dataset.rst
index 4a87a2dd93..f3c168a17c 100644
--- a/doc/sphinx/source/develop/dataset.rst
+++ b/doc/sphinx/source/develop/dataset.rst
@@ -74,7 +74,7 @@ The folder ``RAWOBS`` needs the subdirectories ``Tier1``, ``Tier2`` and
 ``Tier3``. The different tiers describe the different levels of restrictions
 for downloading (e.g. providing contact information, licence agreements)
 and using the observations. The unformatted (raw) observations
-should then be stored then in the appropriate of these three folders.
+should then be stored in the appropriate of these three folders.
 
 For each additional dataset, an entry needs to be made to the file 
 `datasets.yml
diff --git a/doc/sphinx/source/input.rst b/doc/sphinx/source/input.rst
index a72bce73aa..c6bc77d614 100644
--- a/doc/sphinx/source/input.rst
+++ b/doc/sphinx/source/input.rst
@@ -232,8 +232,12 @@ A list of the datasets for which a CMORizers is available is provided in the fol
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
 | Dataset                      | Variables (MIP)                                                                                      | Tier | Script language |
 +==============================+======================================================================================================+======+=================+
+| AERONET                      | od440aer, od550aer, od870aer (AERmon)                                                                |   3  | Python          |
++------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
 | AGCD                         | pr (Amon)                                                                                            |   2  | Python          |
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
+| ANU Climate                  | pr, tas, tasmin, tasmax (Amon)                                                                       |   3  | Python          |
++------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
 | APHRO-MA                     | pr, tas (day), pr, tas (Amon)                                                                        |   3  | Python          |
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
 | AURA-TES                     | tro3 (Amon)                                                                                          |   3  | NCL             |
diff --git a/doc/sphinx/source/recipes/figures/aod_aeronet_assess/UKESM1-0-LL_CMIP_AERmon_historical_od440aer_gn_1988_2008_DJF.png b/doc/sphinx/source/recipes/figures/aod_aeronet_assess/UKESM1-0-LL_CMIP_AERmon_historical_od440aer_gn_1988_2008_DJF.png
new file mode 100644
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diff --git a/doc/sphinx/source/recipes/figures/aod_aeronet_assess/UKESM1-0-LL_CMIP_AERmon_historical_od440aer_gn_1988_2008_scatter.png b/doc/sphinx/source/recipes/figures/aod_aeronet_assess/UKESM1-0-LL_CMIP_AERmon_historical_od440aer_gn_1988_2008_scatter.png
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diff --git a/doc/sphinx/source/recipes/figures/aod_aeronet_assess/UKESM1-0-LL_CMIP_AERmon_historical_od440aer_gn_1994_2014_scatter.png b/doc/sphinx/source/recipes/figures/aod_aeronet_assess/UKESM1-0-LL_CMIP_AERmon_historical_od440aer_gn_1994_2014_scatter.png
new file mode 100644
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diff --git a/doc/sphinx/source/recipes/index.rst b/doc/sphinx/source/recipes/index.rst
index edcc48977a..0f0ce7667d 100644
--- a/doc/sphinx/source/recipes/index.rst
+++ b/doc/sphinx/source/recipes/index.rst
@@ -62,6 +62,7 @@ Atmosphere
    recipe_thermodyn_diagtool
    recipe_validation
    recipe_radiation_budget
+   recipe_aod_aeronet_assess
 
 Climate metrics
 ^^^^^^^^^^^^^^^
diff --git a/doc/sphinx/source/recipes/recipe_aod_aeronet_assess.rst b/doc/sphinx/source/recipes/recipe_aod_aeronet_assess.rst
new file mode 100644
index 0000000000..fec1bed761
--- /dev/null
+++ b/doc/sphinx/source/recipes/recipe_aod_aeronet_assess.rst
@@ -0,0 +1,161 @@
+.. _recipe_aod_aeronet_assess:
+
+AOD AeroNET Assess
+==================
+
+Overview
+--------
+
+This diagnostic evaluates model aerosol optical depth (AOD) against ground
+based observations from the AeroNET measurement network. Monthly mean AOD
+data is downloaded from the AeroNET website and formatted (CMORized) using the
+AERONET downloader and formatter within ESMValTool.
+
+Multiannual seasonal means are calculated from the model output and compared
+with a multiannual seasonal mean climatology generated from AeroNET
+observational data. At each AeroNET station the data are screened for validity
+according to the following default criteria:
+
+  * 1. Monthly means must be generated from at least one AOD observation in that
+    month.
+
+  * 2. Seasonal means for DJF, MAM, JJA and SON must be calculated from three 
+    monthly means, i.e. a monthly mean from December January and Feburary.
+
+  * 3. For a given year to be valid, there must be a seasonal mean for each climate
+    season i.e. DJF, MAM, JJA and SON.
+
+  * 4. For a multiannual seasonal means there must be at least five seasonaal means
+    over the time range of interest.
+
+NOTE: The code is designed to be flexible and the default criteria can be 
+changed according to the user's requirements (see the user settings below).  
+
+The evaluation is visualised by plotting model output as 2D filled contours and
+overlaying AeroNET observations at model grid cells co-located with the AeroNET
+measurement stations. Statistical data (root mean square error) is generated
+using AeroNET observations at model grid cells co-located with the AeroNET
+measurement stations.
+
+Available recipes and diagnostics
+---------------------------------
+
+Recipes are stored in esmvaltool/recipes/
+
+    * recipe_aod_aeronet_assess.yml
+
+Diagnostics are stored in esmvaltool/diag_scripts/aerosols/
+
+    * aod_aeronet_assess.py: Plot the AOD evaluation.
+    * aero_utils.py: Utility functions commonly used by aerosol assessment routines.
+
+
+User settings in recipe
+-----------------------
+
+#. Script aod_aeronet_assess.py
+
+   *Required settings for script*
+
+   * wavel: The wavelength of interest for the evaluation, currently set up for 440nm
+   * min_days_per_mon: The minimum number of days used to calculate the AOD monthly mean
+   * min_mon_per_seas: The minimum number of seasons used to calculate each
+     seasonal mean. This must be between 1 and 3.
+   * min_seas_per_year: The minimum number of seasonal means in each year. This
+     must be between 1 and 4.
+   * min_seas_per_clim: The minimum number of seasonal means used to calculate
+     the multiannual seasonal mean. This must be btween 1 and the number of years
+     of available AeroNET data.
+
+   *Optional settings for script*
+
+   * None
+
+   *Required settings for variables*
+
+   * None
+
+   *Optional settings for variables*
+
+   * None
+
+   *Required settings for preprocessor*
+
+   * None
+
+   *Optional settings for preprocessor*
+
+   * None
+
+   *Color tables*
+
+   * brewer_Spectral_11
+
+
+Variables
+---------
+
+* od440aer (atmos, monthly mean, longitude latitude time)
+
+
+Observations and reformat scripts
+---------------------------------
+
+* Note: (1) obs4MIPs data can be used directly without any preprocessing; (2) 
+  see headers of reformat scripts for non-obs4MIPs data for download
+  instructions.
+
+* The AeroNET data is downloaded from the AeroNET website using the downloader:
+
+  .. code-block:: yaml
+
+        $ esmvaltool data download AERONET.
+
+* The AeroNET data is formatteed (CMORized) using the formatter:
+
+  .. code-block:: yaml
+
+        $ esmvaltool data format AERONET.
+
+
+
+References
+----------
+* Holben B.N., T.F.Eck, I.Slutsker, D.Tanre, J.P.Buis, A.Setzer, E.Vermote, J.A.Reagan, Y.Kaufman, T.Nakajima, F.Lavenu, I.Jankowiak, and A.Smirnov, 1998: AERONET - A federated instrument network and data archive for aerosol characterization, Rem. Sens. Environ., 66, 1-16.
+
+* Holben, B.N., D.Tanre, A.Smirnov, T.F.Eck, I.Slutsker, N.Abuhassan, W.W.Newcomb, J.Schafer, B.Chatenet, F.Lavenue, Y.J.Kaufman, J.Vande Castle, A.Setzer, B.Markham, D.Clark, R.Frouin, R.Halthore, A.Karnieli, N.T.O'Neill, C.Pietras, R.T.Pinker, K.Voss, and G.Zibordi, 2001: An emerging ground-based aerosol climatology: Aerosol Optical Depth from AERONET, J. Geophys. Res., 106, 12 067-12 097.
+
+* Mulcahy, J. P., Johnson, C., Jones, C. G., Povey, A. C., Scott, C. E., Sellar, A., Turnock, S. T., Woodhouse, M. T., Abraham, N. L., Andrews, M. B., Bellouin, N., Browse, J., Carslaw, K. S., Dalvi, M., Folberth, G. A., Glover, M., Grosvenor, D. P., Hardacre, C., Hill, R., Johnson, B., Jones, A., Kipling, Z., Mann, G., Mollard, J., O’Connor, F. M., Palmiéri, J., Reddington, C., Rumbold, S. T., Richardson, M., Schitgens, N. A. J., Stier, P., Stringer, M., Tang, Y., Walton, J., Woodward, S., and Yool. A.: Description and evaluation of aerosol in UKESM1 and HadGEM3-GC3.1 CMIP6 historical simulations, Geosci. Model Dev., 13, 6383–6423, 2020
+
+Example plots
+-------------
+
+.. _fig_aod_aeronet_assess_1:
+.. figure::  /recipes/figures/aod_aeronet_assess/UKESM1-0-LL_CMIP_AERmon_historical_od440aer_gn_1994_2014_DJF.png
+   :align:   center
+
+   Evaluation of AOD at 440 nm from UKESM1 historical ensemble member r1i1p1f2 against the AeroNET climatology from ground-based observations for Dec-Jan-Feb. The multiannual seasonal mean is calculated for the model data for the period 1994-2014. The model output is overlaid with the observational climatology.
+
+.. _fig_aod_aeronet_assess_2:
+.. figure::  /recipes/figures/aod_aeronet_assess/UKESM1-0-LL_CMIP_AERmon_historical_od440aer_gn_1994_2014_MAM.png
+   :align:   center
+
+   Evaluation of AOD at 440 nm from UKESM1 historical ensemble member r1i1p1f2 against the AeroNET climatology from ground-based observations for Mar_Apr_May. The multiannual seasonal mean is calculated for the model data for the period 1994-2014. The model output is overlaid with the observational climatology.
+
+.. _fig_aod_aeronet_assess_3:
+.. figure::  /recipes/figures/aod_aeronet_assess/UKESM1-0-LL_CMIP_AERmon_historical_od440aer_gn_1994_2014_JJA.png
+   :align:   center
+
+   Evaluation of AOD at 440 nm from UKESM1 historical ensemble member r1i1p1f2 against the AeroNET climatology from ground-based observations for Jun-Jul-Aug. The multiannual seasonal mean is calculated for the model data for the period 1994-2014. The model output is overlaid with the observational climatology.
+
+.. _fig_aod_aeronet_assess_4:
+.. figure::  /recipes/figures/aod_aeronet_assess/UKESM1-0-LL_CMIP_AERmon_historical_od440aer_gn_1994_2014_SON.png
+   :align:   center
+
+   Evaluation of AOD at 440 nm from UKESM1 historical ensemble member r1i1p1f2 against the AeroNET climatology from ground-based observations for Sep-Oct-Nov. The multiannual seasonal mean is calculated for the model data for the period 1994-2014. The model output is overlaid with the observational climatology.
+
+.. _fig_aod_aeronet_assess_5:
+.. figure::  /recipes/figures/aod_aeronet_assess/UKESM1-0-LL_CMIP_AERmon_historical_od440aer_gn_1994_2014_scatter.png
+   :align:   center
+
+   Evaluation of AOD at 440 nm from UKESM1 historical ensemble member r1i1p1f2 against the AeroNET climatology from ground-based observations for Dec-Jan-Feb, Mar_Apr_May, Jun-Jul-Aug and Sep-Oct-Nov. The multiannual seasonal mean is calculated for the model data for the period 1994-2014.
diff --git a/environment.yml b/environment.yml
index 1a1c960942..547aaecec1 100644
--- a/environment.yml
+++ b/environment.yml
@@ -11,7 +11,7 @@ channels:
 dependencies:
   - aiohttp
   - cartopy
-  - cdo >=1.9.7
+  - cdo >=2.3.0
   - cdsapi
   - cf-units
   - cftime
@@ -21,10 +21,11 @@ dependencies:
   - distributed
   - ecmwf-api-client
   - eofs
-  - esmpy
+  - esmpy !=8.1.0,<8.6.0  # https://github.com/SciTools-incubator/iris-esmf-regrid/pull/342#issuecomment-2092921514
   - esmvalcore 2.10.*
   - fiona
   - fire
+  - fsspec
   - gdal
   - iris >=3.6.1
   - iris-esmf-regrid >=0.7.0
@@ -40,7 +41,7 @@ dependencies:
   - numpy !=1.24.3  # severe masking bug
   - openpyxl
   - packaging
-  - pandas
+  - pandas !=2.2.0,!=2.2.1,!=2.2.2 # github.com/ESMValGroup/ESMValCore/pull/2305
   - pip !=21.3
   - progressbar2
   - prov
@@ -49,6 +50,7 @@ dependencies:
   - psy-reg
   - psy-simple
   - pyproj >=2.1
+  - pys2index  # only from conda-forge
   - python >=3.9
   - python-cdo
   - python-dateutil
@@ -67,7 +69,7 @@ dependencies:
   - xgboost >1.6.1  # github.com/ESMValGroup/ESMValTool/issues/2779
   - xlsxwriter
   - zarr
-  # Python packages needed for testing
+  # Python packages needed for unit testing
   - flake8
   - pytest >=3.9,!=6.0.0rc1,!=6.0.0
   - pytest-cov
@@ -84,6 +86,7 @@ dependencies:
   # Python packages needed for development
   - codespell
   - docformatter
+  - imagehash
   - isort
   - pre-commit
   - prospector
@@ -102,7 +105,7 @@ dependencies:
   - cdo
   - r-base >=3.5
   - r-abind
-  - r-akima <=0.6-2.3  # see https://github.com/ESMValGroup/ESMValTool/issues/2695
+  - r-akima
   - r-climdex.pcic
   - r-climprojdiags
   - r-docopt
diff --git a/environment_osx.yml b/environment_osx.yml
index 6d14f65227..809cb09346 100644
--- a/environment_osx.yml
+++ b/environment_osx.yml
@@ -11,7 +11,7 @@ channels:
 dependencies:
   - aiohttp
   - cartopy
-  - cdo >=1.9.7
+  - cdo >=2.3.0
   - cdsapi
   - cf-units
   - cftime
@@ -21,9 +21,10 @@ dependencies:
   - distributed
   - ecmwf-api-client
   - eofs
-  - esmpy
+  - esmpy !=8.1.0,<8.6.0  # https://github.com/SciTools-incubator/iris-esmf-regrid/pull/342#issuecomment-2092921514
   - esmvalcore 2.10.*
   - fiona
+  - fsspec
   - fire
   - gdal
   - iris >=3.6.1
@@ -40,7 +41,7 @@ dependencies:
   - numpy !=1.24.3  # severe masking bug
   - openpyxl
   - packaging
-  - pandas
+  - pandas !=2.2.0,!=2.2.1,!=2.2.2 # github.com/ESMValGroup/ESMValCore/pull/2305
   - pip !=21.3
   - progressbar2
   - prov
@@ -48,7 +49,8 @@ dependencies:
   - psy-maps
   - psy-reg
   - psy-simple
-  - pyproj>=2.1
+  - pyproj >=2.1
+  - pys2index  # only from conda-forge
   - python >=3.9
   - python-cdo
   - python-dateutil
@@ -67,7 +69,7 @@ dependencies:
   - xgboost >1.6.1  # github.com/ESMValGroup/ESMValTool/issues/2779
   - xlsxwriter
   - zarr
-  # Python packages needed for testing
+  # Python packages needed for unit testing
   - flake8
   - pytest >=3.9,!=6.0.0rc1,!=6.0.0
   - pytest-cov
@@ -84,6 +86,7 @@ dependencies:
   # Python packages needed for development
   - codespell
   - docformatter
+  - imagehash
   - isort
   - pre-commit
   - prospector
diff --git a/esmvaltool/cmorizers/data/cmor_config/AERONET.yml b/esmvaltool/cmorizers/data/cmor_config/AERONET.yml
new file mode 100644
index 0000000000..33ae35879d
--- /dev/null
+++ b/esmvaltool/cmorizers/data/cmor_config/AERONET.yml
@@ -0,0 +1,28 @@
+---
+# Filename
+filename: 'AOD_Level20_Monthly_V3.tar.gz'
+
+# Common global attributes for Cmorizer output
+attributes:
+  dataset_id: AERONET
+  version: 20230610
+  tier: 3
+  modeling_realm: atmos
+  project_id: OBS6
+  source: 'https://aeronet.gsfc.nasa.gov/new_web/download_all_v3_aod.html'
+  reference: 'aeronetv3'
+  comment:
+    'Notice to users: this data has recommended guidelines for use and publication,
+    please refer to https://aeronet.gsfc.nasa.gov/new_web/data_usage.html.'
+
+# Variables to cmorize
+variables:
+  od440aer:
+    mip: AERmon
+    wavelength: 440
+  od550aer:
+    mip: AERmon
+    wavelength: 551
+  od870aer:
+    mip: AERmon
+    wavelength: 870
diff --git a/esmvaltool/cmorizers/data/cmor_config/ANUClimate.yml b/esmvaltool/cmorizers/data/cmor_config/ANUClimate.yml
new file mode 100644
index 0000000000..3b5ce35b31
--- /dev/null
+++ b/esmvaltool/cmorizers/data/cmor_config/ANUClimate.yml
@@ -0,0 +1,32 @@
+---
+filename: 'ANUClimate_{version}_{raw}_{freq}_.*.nc' #yyyymm
+
+attributes:
+  project_id: OBS6
+  dataset_id: ANUClimate
+  version: 'v2-0'
+  tier: 3
+  modeling_realm: reanaly
+  # resolution: '0.01 degree'
+  source: 'https://dx.doi.org/10.25914/60a10aa56dd1b'
+  reference: 'anuclimate2'
+  comment: 'hosted on NCI'
+
+variables:
+  pr:
+    mip: Amon
+    freq: monthly  # daily available
+    raw: rain
+
+  tasmax:
+    mip: Amon
+    freq: monthly
+    raw: tmax
+  tasmin:
+    mip: Amon
+    freq: monthly
+    raw: tmin
+  tas:
+    mip: Amon
+    freq: monthly
+    raw: tavg
diff --git a/esmvaltool/cmorizers/data/datasets.yml b/esmvaltool/cmorizers/data/datasets.yml
index 757fce7d67..1d1e90aaaf 100644
--- a/esmvaltool/cmorizers/data/datasets.yml
+++ b/esmvaltool/cmorizers/data/datasets.yml
@@ -1,6 +1,13 @@
 # Dataset information
 ---
 datasets:
+  AERONET:
+    tier: 3
+    source: "https://aeronet.gsfc.nasa.gov/"
+    last_access: 2023-06-13
+    info: |
+      Aerosol Optical Depth information from a worldwide network of stations.
+
   AGCD:
     tier: 2
     source: "http://dx.doi.org/10.25914/6009600786063"
@@ -19,6 +26,17 @@ datasets:
       Data from NCI (National Computing Infrastructure Australia https://nci.org.au/), 
       requires an NCI account and access to Gadi(Supercomputer in Canberra) and the project found in catalogue record.
       Access can be requested through NCI. NCI is an ESGF node (https://esgf.nci.org.au/projects/esgf-nci/)
+  
+  ANUClimate:
+    tier: 3
+    source: "https://dx.doi.org/10.25914/60a10aa56dd1b"
+    last_access: 2023-11-21
+    info: |
+      Data from NCI project requiring an NCI account and access to GADI
+
+      ANUClimate 2.0 consists of gridded daily and monthly climate variables across the terrestrial landmass of Australia
+      from at least 1970 to the present. Rainfall grids are generated from 1900 to the present. The underpinning spatial 
+      models have been developed at the Fenner School of Environment and Society of the Australian National University.
 
   APHRO-MA:
     tier: 3
diff --git a/esmvaltool/cmorizers/data/downloaders/datasets/aeronet.py b/esmvaltool/cmorizers/data/downloaders/datasets/aeronet.py
new file mode 100644
index 0000000000..668a688bb6
--- /dev/null
+++ b/esmvaltool/cmorizers/data/downloaders/datasets/aeronet.py
@@ -0,0 +1,38 @@
+"""Script to download Aeronet from its webpage."""
+import logging
+
+from esmvaltool.cmorizers.data.downloaders.wget import WGetDownloader
+
+logger = logging.getLogger(__name__)
+
+
+def download_dataset(config, dataset, dataset_info, start_date, end_date,
+                     overwrite):
+    """Download dataset.
+
+    Parameters
+    ----------
+    config : dict
+        ESMValTool's user configuration
+    dataset : str
+        Name of the dataset
+    dataset_info : dict
+         Dataset information from the datasets.yml file
+    start_date : datetime
+        Start of the interval to download
+    end_date : datetime
+        End of the interval to download
+    overwrite : bool
+        Overwrite already downloaded files
+    """
+    downloader = WGetDownloader(
+        config=config,
+        dataset=dataset,
+        dataset_info=dataset_info,
+        overwrite=overwrite,
+    )
+    filename = "AOD_Level20_Monthly_V3.tar.gz"
+    downloader.download_file(
+        f"https://aeronet.gsfc.nasa.gov/data_push/V3/AOD/{filename}",
+        wget_options=[],
+    )
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/aeronet.py b/esmvaltool/cmorizers/data/formatters/datasets/aeronet.py
new file mode 100755
index 0000000000..215c67d7a8
--- /dev/null
+++ b/esmvaltool/cmorizers/data/formatters/datasets/aeronet.py
@@ -0,0 +1,410 @@
+"""ESMValTool CMORizer for Aeronet data.
+
+Tier
+    Tier 3: restricted dataset.
+
+Source
+    https://aeronet.gsfc.nasa.gov/
+
+Last access
+    20230613
+
+Download and processing instructions
+    Download the following file:
+    https://aeronet.gsfc.nasa.gov/data_push/V3/AOD/AOD_Level20_Monthly_V3.tar.gz
+"""
+
+import logging
+import os.path
+import re
+from datetime import datetime
+from typing import NamedTuple
+
+import cf_units
+import dask.array as da
+import iris
+import iris.coords
+import iris.cube
+import numpy as np
+import pandas as pd
+from fsspec.implementations.tar import TarFileSystem
+from pys2index import S2PointIndex
+
+from esmvaltool.cmorizers.data import utilities as utils
+
+logger = logging.getLogger(__name__)
+
+AERONET_HEADER = "AERONET Version 3;"
+LEVEL_HEADER = "Version 3: AOD Level 2.0"
+LEVEL_DESCRIPTION = (
+    "The following data are automatically cloud cleared and quality assured "
+    "with pre-field and post-field calibration applied.")
+UNITS_HEADER = (
+    "UNITS can be found at,,, https://aeronet.gsfc.nasa.gov/new_web/units.html"
+)
+DATA_QUALITY_LEVEL = "lev20"
+
+CONTACT_PATTERN = re.compile(
+    "Contact: PI=(?P<names>[^;]*); PI Email=(?P<emails>.*)")
+
+
+def compress_column(data_frame, name):
+    """Assert all values in DataFrame column are equal, and return value."""
+    compressed = data_frame.pop(name).unique()
+    if len(compressed) != 1:
+        raise ValueError(
+            f"Data frame column '{name}' must only contain"
+            f" one unique value, found {len(compressed)}"
+        )
+    return compressed[0]
+
+
+class AeronetStation(NamedTuple):
+    """AERONET station data."""
+
+    station_name: str
+    latitude: float
+    longitude: float
+    elevation: float
+    contacts: str
+    data_frame: pd.DataFrame
+
+
+class AeronetStations(NamedTuple):
+    """AERONET station data lists."""
+
+    station_name: list[str]
+    latitude: list[float]
+    longitude: list[float]
+    elevation: list[float]
+    contacts: list[str]
+    data_frame: list[pd.DataFrame]
+
+
+def parse_contact(contact):
+    """Parse and reformat contact information in AERONET file."""
+    match = CONTACT_PATTERN.fullmatch(contact)
+    if match is None:
+        raise RuntimeError(f"Could not parse contact line {contact}")
+    names = match.group("names").replace("_", " ").split(" and ")
+    emails = match.group("emails").split("_and_")
+    mailboxes = ", ".join([
+        f'"{name}" <{email}>' for name, email in zip(names, emails)
+    ])
+    return mailboxes
+
+
+def load_file(filesystem, path_like):
+    """Load AERONET data from fsspec filesystem instance."""
+    with filesystem.open(path_like, mode="rt", encoding="iso-8859-1") as file:
+        aeronet_header = file.readline().strip()
+        if aeronet_header != AERONET_HEADER:
+            raise ValueError(
+                f"File header identifier is '{aeronet_header}',"
+                f" expected '{AERONET_HEADER}'"
+            )
+        station_name = file.readline().strip()
+        level_header = file.readline().strip()
+        if level_header != LEVEL_HEADER:
+            raise ValueError(
+                f"File level string is '{level_header}',"
+                f" expected '{LEVEL_HEADER}'"
+            )
+        level_description = file.readline().strip()
+        if level_description != LEVEL_DESCRIPTION:
+            raise ValueError(
+                f"File data description string is"
+                f" '{level_description}', expected '{LEVEL_DESCRIPTION}'"
+            )
+        contact_string = file.readline().strip()
+        units_header = file.readline().strip()
+        if units_header != UNITS_HEADER:
+            raise ValueError(
+                f"File units info string is '{units_header}',"
+                f" expected '{UNITS_HEADER}'"
+            )
+        data_frame = pd.read_csv(
+            file,
+            index_col=0,
+            na_values=-999.0,
+            date_format="%Y-%b",
+            parse_dates=[0],
+            usecols=lambda x: "AOD_Empty" not in x,
+        )
+    contacts = parse_contact(contact_string)
+    elevation = compress_column(data_frame, "Elevation(meters)")
+    latitude = compress_column(data_frame, "Latitude(degrees)")
+    longitude = compress_column(data_frame, "Longitude(degrees)")
+    data_quality_level = compress_column(data_frame, "Data_Quality_Level")
+    if data_quality_level != DATA_QUALITY_LEVEL:
+        raise ValueError(
+            f"File data quality level is '{data_quality_level}',"
+            f" expected '{DATA_QUALITY_LEVEL}'"
+        )
+    station = AeronetStation(
+        station_name,
+        latitude,
+        longitude,
+        elevation,
+        contacts,
+        data_frame,
+    )
+    return station
+
+
+def sort_data_columns(columns):
+    """Sort AOD station data columns."""
+    data_columns = [c for c in columns if "NUM_" not in c]
+    if len(columns) != 3 * len(data_columns):
+        raise ValueError(
+            "Station data contains unexpected number of columns."
+        )
+    aod_columns = [c for c in data_columns if c.startswith("AOD_")]
+    precipitable_water_columns = [
+        c for c in data_columns if c == "Precipitable_Water(cm)"
+    ]
+    angstrom_exponent_columns = [
+        c for c in data_columns if "_Angstrom_Exponent" in c
+    ]
+    if len(data_columns) != (len(aod_columns) +
+                             len(precipitable_water_columns) +
+                             len(angstrom_exponent_columns)):
+        raise ValueError(
+            "Station data contains unexpected number of columns."
+        )
+    return (aod_columns, precipitable_water_columns, angstrom_exponent_columns)
+
+
+def merge_stations(stations):
+    """Collect and merge station data into AeronetStations instance."""
+    columns = {}
+    for name, dtype in (
+        ("station_name", str),
+        ("latitude", np.float64),
+        ("longitude", np.float64),
+        ("elevation", np.float64),
+        ("contacts", str),
+        ("data_frame", object),
+    ):
+        columns[name] = np.array(
+            [getattr(station, name) for station in stations],
+            dtype=dtype,
+        )
+    return AeronetStations(**columns)
+
+
+def assemble_cube(stations, idx, wavelengths=None):
+    """Assemble Iris cube with station data.
+
+    Parameters
+    ----------
+    stations : AeronetStations
+        Station data
+    idx : int
+        Unique ids of all stations
+    wavelengths : list, optional
+        Wavelengths to include in data.
+
+    Returns
+    -------
+    Iris cube
+        Iris cube with station data.
+
+    Raises
+    ------
+    ValueError
+        If station data has inconsistent variable names.
+    """
+    min_time = np.array([df.index.min() for df in stations.data_frame]).min()
+    max_time = np.array([df.index.max() for df in stations.data_frame]).max()
+    date_index = pd.date_range(min_time, max_time, freq="MS")
+    data_frames = [df.reindex(index=date_index) for df in stations.data_frame]
+    all_data_columns = np.unique(
+        np.array([df.columns for df in data_frames], dtype=str),
+        axis=0,
+    )
+    if len(all_data_columns) != 1:
+        raise ValueError(
+            "Station data frames has different sets of column names."
+        )
+    aod_columns, _, _ = sort_data_columns(all_data_columns[0])
+    if wavelengths is None:
+        wavelengths = sorted([int(c[4:-2]) for c in aod_columns])
+
+    aod = da.stack([
+        da.stack([df[f"AOD_{wl}nm"].values for wl in wavelengths], axis=-1)
+        for df in data_frames
+    ], axis=-1)[..., idx]
+    num_days = da.stack([
+        da.stack([
+            df[f"NUM_DAYS[AOD_{wl}nm]"].values.astype(np.float32)
+            for wl in wavelengths
+        ], axis=-1) for df in data_frames
+    ], axis=-1)[..., idx]
+    num_points = da.stack([
+        da.stack([
+            df[f"NUM_POINTS[AOD_{wl}nm]"].values.astype(np.float32)
+            for wl in wavelengths
+        ], axis=-1) for df in data_frames
+    ], axis=-1)[..., idx]
+
+    wavelength_points = da.array(wavelengths, dtype=np.float64)
+    wavelength_coord = iris.coords.DimCoord(
+        points=wavelength_points,
+        standard_name="radiation_wavelength",
+        long_name="Wavelength",
+        var_name="wl",
+        units="nm",
+    )
+    times = date_index.to_pydatetime()
+    time_points = np.array(
+        [datetime(year=t.year, month=t.month, day=15) for t in times])
+    time_bounds_lower = times
+    time_bounds_upper = np.array([
+        datetime(year=t.year + (t.month == 12),
+                 month=t.month + 1 - (t.month == 12) * 12,
+                 day=1) for t in times
+    ])
+    time_bounds = np.stack([time_bounds_lower, time_bounds_upper], axis=-1)
+    time_units = cf_units.Unit("days since 1850-01-01", calendar="standard")
+    time_coord = iris.coords.DimCoord(
+        points=time_units.date2num(time_points),
+        standard_name="time",
+        long_name="time",
+        var_name="time",
+        units=time_units,
+        bounds=time_units.date2num(time_bounds),
+    )
+    index_coord = iris.coords.DimCoord(
+        points=da.arange(aod.shape[-1]),
+        standard_name=None,
+        long_name="Station index (arbitrary)",
+        var_name="station_index",
+        units="1",
+    )
+    name_coord = iris.coords.AuxCoord(
+        points=stations.station_name[idx],
+        standard_name="platform_name",
+        long_name="Aeronet Station Name",
+        var_name="station_name",
+    )
+    elevation_coord = iris.coords.AuxCoord(
+        points=stations.elevation[idx],
+        standard_name="height_above_mean_sea_level",
+        long_name="Elevation",
+        var_name="elev",
+        units="m",
+    )
+    latitude_coord = iris.coords.AuxCoord(
+        points=stations.latitude[idx],
+        standard_name="latitude",
+        long_name="Latitude",
+        var_name="lat",
+        units="degrees_north",
+    )
+    longitude_coord = iris.coords.AuxCoord(
+        points=stations.longitude[idx],
+        standard_name="longitude",
+        long_name="Longitude",
+        var_name="lon",
+        units="degrees_east",
+    )
+    num_days_ancillary = iris.coords.AncillaryVariable(
+        data=da.ma.masked_array(num_days, da.isnan(num_days),
+                                fill_value=1.e20),
+        standard_name=None,
+        long_name="Number of days",
+        var_name="num_days",
+        units="1",
+    )
+    num_points_ancillary = iris.coords.AncillaryVariable(
+        data=da.ma.masked_array(num_days,
+                                da.isnan(num_points),
+                                fill_value=1.e20),
+        standard_name="number_of_observations",
+        long_name="Number of observations",
+        var_name="num_points",
+        units="1",
+    )
+    cube = iris.cube.Cube(
+        data=da.ma.masked_array(aod, da.isnan(aod), fill_value=1.e20),
+        standard_name=(
+            "atmosphere_optical_thickness_due_to_ambient_aerosol_particles"),
+        long_name="Aerosol Optical Thickness",
+        var_name="aod",
+        units="1",
+        dim_coords_and_dims=[
+            (time_coord, 0),
+            (wavelength_coord, 1),
+            (index_coord, 2),
+        ],
+        aux_coords_and_dims=[
+            (latitude_coord, 2),
+            (longitude_coord, 2),
+            (elevation_coord, 2),
+            (name_coord, 2),
+        ],
+        ancillary_variables_and_dims=[
+            (num_days_ancillary, (0, 1, 2)),
+            (num_points_ancillary, (0, 1, 2)),
+        ],
+    )
+    return cube
+
+
+def build_cube(filesystem, paths, wavelengths=None):
+    """Build station data cube."""
+    individual_stations = [
+        load_file(filesystem, file_path) for file_path in paths
+    ]
+    stations = merge_stations(individual_stations)
+    latlon_points = np.stack([stations.latitude, stations.longitude], axis=-1)
+    index = S2PointIndex(latlon_points)
+    cell_ids = index.get_cell_ids()
+    idx = np.argsort(cell_ids)
+    cube = assemble_cube(stations, idx, wavelengths)
+    return cube
+
+
+def cmorization(in_dir, out_dir, cfg, cfg_user, start_date, end_date):
+    """Cmorization func call."""
+    raw_filename = cfg['filename']
+
+    tar_file_system = TarFileSystem(f"{in_dir}/{raw_filename}")
+    paths = tar_file_system.glob("AOD/AOD20/MONTHLY/*.lev20")
+    versions = np.unique(
+        np.array([os.path.basename(p).split("_")[1] for p in paths],
+                 dtype=str))
+    if len(versions) != 1:
+        raise ValueError(
+            "All station datasets in tar file must have same version."
+        )
+    version = versions[0]
+    wavelengths = sorted(
+        [var["wavelength"] for var in cfg['variables'].values()])
+    cube = build_cube(tar_file_system, paths, wavelengths)
+
+    attrs = cfg['attributes'].copy()
+    attrs['version'] = version
+    attrs['source'] = attrs['source']
+
+    # Run the cmorization
+    for (short_name, var) in cfg['variables'].items():
+        logger.info("CMORizing variable '%s'", short_name)
+
+        idx = wavelengths.index(var["wavelength"])
+        sub_cube = cube[:, idx]
+
+        attrs['mip'] = var['mip']
+        # attrs['reference'] = var['reference']
+        # Fix metadata
+        utils.set_global_atts(sub_cube, attrs)
+
+        # Save variable
+        utils.save_variable(
+            sub_cube,
+            short_name,
+            out_dir,
+            attrs,
+            unlimited_dimensions=['time'],
+        )
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/anuclimate.py b/esmvaltool/cmorizers/data/formatters/datasets/anuclimate.py
new file mode 100644
index 0000000000..0077bd17a4
--- /dev/null
+++ b/esmvaltool/cmorizers/data/formatters/datasets/anuclimate.py
@@ -0,0 +1,125 @@
+"""ESMValTool CMORizer for ANU Climate data.
+
+Tier
+    Tier 3: restricted dataset.
+
+Source
+    https://dx.doi.org/10.25914/60a10aa56dd1b
+
+Last access
+    20231121
+
+Download and processing instructions
+    Data from NCI project requiring an NCI account and access to GADI
+    Processing is done on GADI
+
+"""
+import logging
+import os
+import re
+import calendar
+
+import iris
+
+from esmvaltool.cmorizers.data import utilities as utils
+
+logger = logging.getLogger(__name__)
+
+
+def _get_filepaths(in_dir, basename):
+    """Find correct name of file (extend basename with timestamp)."""
+    regex = re.compile(basename)
+    return_files = []
+    # Search sub folders of raw data directory
+    for root, _dir, files in os.walk(in_dir, followlinks=True):
+
+        for filename in files:
+            if regex.match(filename):
+
+                return_files.append(os.path.join(root, filename))
+
+    return return_files
+
+
+def fix_data_var(cube, var):
+    """Convert units in cube for the variable."""
+    # get month, year from cube
+    tcoord = cube.coord('time')
+    tdate = tcoord.units.num2date(tcoord.points[0])
+    no_ofdays = calendar.monthrange(tdate.year, tdate.month)[1]
+
+    if var == 'pr':
+
+        cube = cube / (no_ofdays * 86400)  # days in month
+        cube.units = 'kg m-2 s-1'
+
+    elif var in ['tas', 'tasmin', 'tasmax']:  # other variables in v1
+        cube = cube + 273.15
+        cube.units = 'K'
+        utils.add_height2m(cube)
+
+    else:
+        logger.info("Variable %s not converted", var)
+
+    return cube, tdate.year
+
+
+def _extract_variable(cmor_info, attrs, filepaths, out_dir):
+    """Extract variable."""
+    var = cmor_info.short_name
+    logger.info("Var is %s", var)
+    cbls_2 = iris.cube.CubeList()
+    cbls_1 = iris.cube.CubeList()
+    for filepath in filepaths:
+        cubes = iris.load(filepath)
+
+        cube, year = fix_data_var(cubes[0], var)
+
+        utils.fix_var_metadata(cube, cmor_info)
+
+        utils.set_global_atts(cube, attrs)
+
+        if year < 2000:  # split for cube save
+            cbls_1.append(cube)
+        else:
+            cbls_2.append(cube)
+
+    for cbls in [cbls_1, cbls_2]:
+        iris.util.equalise_attributes(cbls)
+        cubesave = cbls.concatenate_cube()
+        utils.fix_coords(cubesave)
+
+        logger.info("Saving file")
+        utils.save_variable(cubesave,
+                            var,
+                            out_dir,
+                            attrs,
+                            unlimited_dimensions=['time'])
+
+
+def cmorization(in_dir, out_dir, cfg, cfg_user, start_date, end_date):
+    """Cmorization func call."""
+    glob_attrs = cfg['attributes']
+    cmor_table = cfg['cmor_table']
+
+    ver = cfg['attributes']['version']
+    logger.info(cfg, cfg_user)
+
+    # Run the cmorization, multiple variables
+    for (var, var_info) in cfg['variables'].items():
+
+        glob_attrs['mip'] = var_info['mip']
+
+        raw_filename = cfg['filename'].format(version=ver,
+                                              raw=var_info['raw'],
+                                              freq=var_info['freq'])
+        filepaths = _get_filepaths(in_dir, raw_filename)
+
+        if len(filepaths) == 0:
+            logger.info("no files for %s pattern: %s", var, raw_filename)
+            logger.info("directory: %s", in_dir)
+        else:
+            logger.info("Found files, count %s", len(filepaths))
+
+            cmor_info = cmor_table.get_variable(var_info['mip'], var)
+            _extract_variable(cmor_info, glob_attrs, filepaths, out_dir)
diff --git a/esmvaltool/cmorizers/data/utilities.py b/esmvaltool/cmorizers/data/utilities.py
index e31add6652..3620cee30e 100644
--- a/esmvaltool/cmorizers/data/utilities.py
+++ b/esmvaltool/cmorizers/data/utilities.py
@@ -495,7 +495,7 @@ def fix_dtype(cube):
                     cube.dtype)
         cube.data = cube.core_data().astype(np.float32, casting='same_kind')
     for coord in cube.coords():
-        if coord.dtype != np.float64:
+        if coord.dtype.kind != "U" and coord.dtype != np.float64:
             logger.info(
                 "Converting data type of coordinate points of '%s' from '%s' "
                 "to 'float64'", coord.name(), coord.dtype)
diff --git a/esmvaltool/config-references.yml b/esmvaltool/config-references.yml
index 285ace740e..b5f43bc911 100644
--- a/esmvaltool/config-references.yml
+++ b/esmvaltool/config-references.yml
@@ -252,6 +252,10 @@ authors:
     name: Hansson, Ulf
     institute: SMHI, Sweden
     orcid:
+  hardacre_catherine:
+    name: Hardacre, Catherine
+    institute: University of Canterbury, New Zealand
+    orcid: https://orcid.org/0000-0001-9093-4656
   hassler_birgit:
     name: Hassler, Birgit
     institute: DLR, Germany
diff --git a/esmvaltool/diag_scripts/aerosols/aero_utils.py b/esmvaltool/diag_scripts/aerosols/aero_utils.py
new file mode 100644
index 0000000000..623a85f2aa
--- /dev/null
+++ b/esmvaltool/diag_scripts/aerosols/aero_utils.py
@@ -0,0 +1,193 @@
+"""Part of the ESMValTool Aerosol diagnostics.
+
+This module contains utility functions commonly used by aerosol
+assessment routines.
+"""
+
+import iris
+import numpy as np
+
+
+class AeroAnsError(Exception):
+
+    """Exception class for errors raised when model data is checked in the
+    extract_pt module.
+    """
+
+
+def add_bounds(cube):
+    """Add bounds to a cube's latitude and longitude coordinates.
+
+    Parameters
+    ----------
+    cube : Iris cube
+        Iris cube with latitude and longitude coordinates.
+
+    Returns
+    -------
+    cube : Iris cube.
+        Iris cube with bounds added to the latitude and longitude coordinates.
+    """
+
+    if not cube.coord('latitude').has_bounds():
+        cube.coord('latitude').guess_bounds()
+    if not cube.coord('longitude').has_bounds():
+        cube.coord('longitude').guess_bounds()
+
+    return cube
+
+
+def extract_pt(icube, pt_lat, pt_lon, height=None, level=None, nearest=False):
+    """Extracts given location(s) (3-D) from a cube.
+
+    Method
+    ------
+    Uses Iris module Analysis.Interpolate to extract values,
+    initially based on horizontal coordinates, and then based on
+    height, if specified.
+
+    If height ('altitude') is requested, checks if cube heights
+    include orography, i.e. HybridHeights have been derived.
+
+    Parameters
+    ----------
+    icube : Iris cube
+    pt_lat, pt_lon : Float or list/array of floats. Latitude and longitude
+                     coordinates of desired points.
+    args:
+        height  : Float or list/array of floats. Altitude (above geoid) of
+                  point. Initialized to None.
+        level   : Integer . Model level or pseudo level or tile number.
+                  Initialized to None, meaning that all available levels in
+                  the cube are used.
+        nearest : Boolean. Specify whether to use 'nearest neighbour', instead
+                  of 'linear' method while extracting data. Default is False.
+
+    Returns
+    -------
+    data_out : List
+        List of single point values, corresponding to each point specified.
+
+    Raises
+    ------
+    AeroAnsError : If the number of latitude and longitude points are
+        mismatched. OR if both and level and height are passed as args.
+        OR if the cube contains a time coordinate. OR if a pseudo level
+        coordinate is requested, but not present in the cube. OR if the numbers
+        of latitude/longitude and height points are mismatched. OR if height
+        is requested but the cube does not contain an altitude coordinate.
+    """
+
+    # Check that input data is a (single) cube
+    if not isinstance(icube, iris.cube.Cube):
+        raise AeroAnsError('Extract_pt:First argument must be a single cube')
+
+    # Check if the cube contains a time dimension, which is
+    # currently unsupported.
+    if icube.coords()[0].name() == 'time':
+        raise AeroAnsError(
+            'Extract_pt:Cannot handle time dimension at present')
+
+    # Check that equal number of lat/lon pairs are passed in point coordinates.
+    # Convert arguments to lists for easier processing if necessary.
+    pt_lat1 = []
+    pt_lon1 = []
+
+    if not isinstance(pt_lat, list):
+        pt_lat1.append(pt_lat)
+        pt_lon1.append(pt_lon)
+
+    else:
+        for n_lat in np.arange(len(pt_lat)):
+            pt_lat1.append(pt_lat[n_lat])
+            pt_lon1.append(pt_lon[n_lat])
+
+    if len(pt_lat1) != len(pt_lon1):
+        raise AeroAnsError('Extract_pt:Mismatch in number of lat/long values')
+
+    # Check that both level and height haven't been requested.
+    if level is not None and height is not None:
+        raise AeroAnsError('Extract_pt: Both Level and Height requested')
+
+    # Check that the cube has a level coordinate if level has been requested.
+    if level is not None and not icube.coord(
+            'model_level_number') and not icube.coord('pseudo_level'):
+        raise AeroAnsError('Extract_pt:Level requested, but not found in cube')
+
+    # Check that the number of height points is equal to the number of
+    # lat/lon pairs. Convert the argument to a list for easier
+    # processing if necessary.
+    if height is not None:
+        pt_hgt = []
+
+#        if isinstance(height, list):
+#            pt_hgt.extend(height)
+#        else:
+#            pt_hgt.append(height)
+        pt_hgt.extend(height) if \
+            isinstance(height, list) else \
+            pt_hgt.append(height)
+
+        if len(pt_lat1) != len(pt_hgt):
+            raise AeroAnsError(
+                'Extract_pt:Mismatch in number of points for lat/long/height')
+
+        # Check that heights have been merged with orography.
+        if not icube.coords('altitude'):
+            raise AeroAnsError(
+                'Extract_pt:Height requested but input data does not contain \
+                "Altitude" coordinate')
+
+        # Store the min and max altitudes from cube data so that user
+        # cannot request points located below/ above that.
+        # Will extract =min/max if beyond limits.
+        hgt_min = icube.coord('altitude').points.min()
+        hgt_max = icube.coord('altitude').points.max()
+
+    # ---------- Finished checks -- begin processing -------------------------
+
+    # If level specified, extract slice first
+    if level is not None:
+
+        try:
+            icube = icube.extract(
+                iris.Constraint(model_level_number=level))
+
+        except Exception:
+            print('Model level number not available. Use pseudo level')
+
+        else:
+            icube = icube.extract(
+                iris.Constraint(pseudo_level=level))
+
+    # Extract values for specified points lat/lon
+    # NOTE: Does not seem to handle multiple points if 3-D
+    data_out = []
+
+    # Set lat/lon coordinates for model grid cell interpolation
+    for n_lat1 in np.arange(len(pt_lat1)):
+        latlon_coords = [('latitude', pt_lat1[n_lat1]),
+                         ('longitude', pt_lon1[n_lat1])]
+
+        if nearest:
+            tcube = icube.interpolate(latlon_coords, iris.analysis.Nearest())
+        else:
+            tcube = icube.interpolate(latlon_coords, iris.analysis.Linear())
+
+        # If height specified, interpolate to requested height
+        if height is not None:
+
+            # Set vertical coordinates for model grid cell interpolation
+            point = max(pt_hgt[n_lat1], hgt_min)
+            point = min(pt_hgt[n_lat1], hgt_max)
+            hgt_coords = [('altitude', point)]
+
+            if nearest:
+                tcube = tcube.interpolate(hgt_coords, iris.analysis.Nearest())
+            else:
+                tcube = tcube.interpolate(hgt_coords, iris.analysis.Linear())
+
+        # Append processed data point
+        data_out.append(tcube.data)
+
+    return data_out
diff --git a/esmvaltool/diag_scripts/aerosols/aod_aeronet_assess.py b/esmvaltool/diag_scripts/aerosols/aod_aeronet_assess.py
new file mode 100644
index 0000000000..27ab6b2714
--- /dev/null
+++ b/esmvaltool/diag_scripts/aerosols/aod_aeronet_assess.py
@@ -0,0 +1,448 @@
+"""Implement the AOD climatology metric from ground-based
+   AeroNet observations.
+"""
+import logging
+import os
+
+import iris
+import iris.plot as iplt
+import matplotlib.cm as mpl_cm
+import matplotlib.lines as mlines
+import matplotlib.pyplot as plt
+import numpy as np
+import scipy
+from aero_utils import add_bounds, extract_pt
+from matplotlib import colors, gridspec
+from numpy import ma
+
+from esmvaltool.diag_scripts.shared import group_metadata, run_diagnostic
+from esmvaltool.diag_scripts.shared._base import get_plot_filename
+
+logger = logging.getLogger(os.path.basename(__file__))
+fontsizedict = {"title": 25, "axis": 20, "legend": 18, "ticklabel": 18}
+
+
+def get_provenance_record(filenames):
+    """Return a provenance record describing the metric.
+
+    Parameters
+    ----------
+    filenames : List of strings
+        The filenames containing the data used to create the metric.
+
+    Returns
+    -------
+    dictionary
+        The provenance record describing the metric.
+    """
+    record = {
+        "ancestors": filenames,
+    }
+
+    return record
+
+
+def plot_aod_mod_obs(md_data, obs_data, aeronet_obs_cube, plot_dict):
+    """Plot AOD contour overlaid with Aeronet climatology.
+
+    Parameters
+    ----------
+    md_data : Iris cube
+        Model AOD as a cube with latitude and longitude coordinates.
+    obs_data : List.
+        Observations of AOD from each AeroNET station.
+    aeronet_obs_cube : Iris cube.
+        Holds information about Aeronet measurement stations including
+        station names, station latitude and station longitude.
+    plot_dict : Dictionary.
+        Holds plotting settings.
+    """
+    # Plot model data
+    cf_plot = iplt.contourf(md_data,
+                            plot_dict["Levels"],
+                            colors=plot_dict["Colours"],
+                            extend="max")
+
+    # Latitude and longitude of stations.
+    anet_aod_lats = aeronet_obs_cube.coord("latitude").points
+    anet_aod_lons = ((aeronet_obs_cube.coord("longitude").points + 180) % 360 -
+                     180)
+
+    # Loop over stations
+    for istn, stn_data in enumerate(obs_data):
+        if ma.is_masked(stn_data):
+            continue
+
+        # Find position of the observed AOD on the colorscale.
+        # np.searchsorted returns index at which inserting new value will
+        # maintain a sorted array. We use the color to the left of index.
+        cid = np.searchsorted(plot_dict["Levels"], stn_data)
+        cid = max(0, cid - 1)  # filter out zero and max when seeking 'left'
+        cid = min(len(plot_dict["Colours"]) - 1, cid)
+        pcol = plot_dict["Colours"][cid]
+
+        # Overlay contourf with observations
+        plt.plot(
+            anet_aod_lons[istn],
+            anet_aod_lats[istn],
+            color=pcol,
+            marker="o",
+            markeredgecolor="k",
+            markeredgewidth=2,
+            markersize=9,
+        )
+
+    # Decorate the plot
+    plt.title(plot_dict["Title"], size=24)
+    colbar = plt.colorbar(cf_plot, orientation="horizontal")
+    colbar.set_ticks(plot_dict["Levels"])
+    colbar.set_ticklabels(plot_dict["tick_labels"])
+    plt.gca().coastlines(color="#525252")
+
+    # Statistics on plot
+    plt.figtext(
+        0.12,
+        0.27,
+        (f'''Global mean AOD={plot_dict["Mean_aod"]:.3f}; RMSE='''
+         f'''{plot_dict["RMS_aod"]:.3f}; Stn mean: md='''
+         f'''{plot_dict["Stn_mn_md"]:.3f}; obs='''
+         f'''{plot_dict["Stn_mn_obs"]:.3f}'''),
+        size=16,
+    )
+
+
+def aod_analyse(model_data, aeronet_obs_cube, clim_seas, wavel):
+    """Evaluates AOD vs Aeronet, generates plots and returns evaluation
+    metrics.
+
+    Parameters
+    ----------
+    model_data : Iris Cube.
+        Contains model output of AOD with coordinates; time, latitude and
+        longitude.
+    aeronet_obs_cube : Iris Cube.
+        Contains information about Aeronet measurement stations including
+        station names, station latitude and station longitude.
+    clim_seas : List.
+       Strings to denote climate seasons ["DJF", "MAM", "JJA", "SON"]
+    wavel : String.
+        AOD wavelength, default = 440nm - translates to pseudo-level.
+
+    Returns
+    -------
+    figures : List.
+        Contains figure instances for the seasonal contour plots overlaid with
+        observations of AOD from AeroNET.
+    fig_scatter : Figure object.
+        The scatter plot comparing modelled and observed AOD at 440nm.
+    """
+    # Convert wave length nm -> um
+    wv_mi = str(float(wavel) / 1000.0)
+
+    # Get model run id
+    if "parent_source_id" in model_data.attributes:
+        model_id = model_data.attributes["parent_source_id"]
+    else:
+        model_id = "Multi-Model-Mean"
+
+    # Add bounds for lat and lon if not present
+    model_data = add_bounds(model_data)
+
+    # Co-locate model grid points with measurement sites --func from aero_utils
+    anet_aod_lats = aeronet_obs_cube.coord("latitude").points.tolist()
+    anet_aod_lons = aeronet_obs_cube.coord("longitude").points.tolist()
+    aod_at_anet = extract_pt(model_data, anet_aod_lats, anet_aod_lons)
+
+    # Set up seasonal contour plots
+    figures = []
+
+    clevs = [0.0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 2.0]
+    clabs = [
+        "0.0", "", "0.1", "", "0.2", "", "0.3", "", "0.4", "", "0.5", "2.0"
+    ]
+    cmapr = mpl_cm.get_cmap("brewer_Spectral_11")
+    cmap = colors.ListedColormap(cmapr(range(cmapr.N))[-1::-1])
+    colours = cmap.colors
+
+    # Set up the figure for scatter plotting
+    fig_scatter = plt.figure(figsize=(10, 10))
+    gs_scatter = gridspec.GridSpec(ncols=1, nrows=1)
+    ax_scatter = fig_scatter.add_subplot(gs_scatter[0, 0])
+    col_scatter = ["#081d58", "#41ab5d", "#fe9929", "#7f0000"]
+    leg_scatter = []
+
+    # Loop over seasons
+    for season in aeronet_obs_cube.slices_over("clim_season"):
+
+        # Match Aeronet obs season with model season number
+        model_sn = [c.lower() for c in clim_seas
+                    ].index(season.coord("clim_season").points[0])
+        model_season = model_data[model_sn]
+
+        logger.info('Analysing AOD for %s: %s', {model_id},
+                    {clim_seas[model_sn]})
+
+        # Generate statistics required - area-weighted mean
+        grid_areas = iris.analysis.cartography.area_weights(model_season)
+        global_mean = model_season.collapsed(
+            ["latitude", "longitude"],
+            iris.analysis.MEAN,
+            weights=grid_areas,
+        )
+
+        # Extract model and obs data for season number (model_sn)
+        seas_anet_obs = season.data
+        seas_anet_md = np.array([x[model_sn] for x in aod_at_anet])
+
+        # Match model data with valid obs data
+        valid_indices = ma.where(seas_anet_obs)
+        valid_obs = seas_anet_obs[valid_indices]
+        valid_md = seas_anet_md[valid_indices]
+
+        # Model - obs statistics (diff, model mean and RMS, r2)
+        diff = valid_md - valid_obs
+        stn_mn_obs = np.mean(valid_obs)
+        stn_mn_md = np.mean(valid_md)
+        rms_aod = np.sqrt(np.mean(diff**2))
+        linreg = scipy.stats.linregress(valid_obs, valid_md)
+
+        # Plot scatter of co-located model and obs data
+        ax_scatter.scatter(valid_obs, valid_md, color=col_scatter[model_sn])
+
+        # Legend
+        label = f"{clim_seas[model_sn]} = {linreg.rvalue**2:.2f}"
+        leg_scatter.append(
+            mlines.Line2D(
+                [0],
+                [0],
+                marker="o",
+                color="w",
+                label=label,
+                markersize=15,
+                markerfacecolor=col_scatter[model_sn],
+            ))
+
+        # Plot contours overlaid with obs for this run and season
+        fig_cf = plt.figure(figsize=(11, 8), dpi=300)
+
+        n_stn = str(len(valid_obs))
+        title = ("\nTotal Aerosol Optical Depth at " + wv_mi + " microns" +
+                 "\n" + model_id + ", " + clim_seas[model_sn] +
+                 ", N stations=" + n_stn)
+
+        # Plot dictionary
+        plot_dict = {
+            "Mean_aod": global_mean.data,
+            "Stn_mn_obs": stn_mn_obs,
+            "Stn_mn_md": stn_mn_md,
+            "RMS_aod": rms_aod,
+            "Levels": clevs,
+            "Colours": colours,
+            "tick_labels": clabs,
+            "Title": title,
+            "Season": clim_seas[model_sn],
+        }
+        plot_aod_mod_obs(model_season, seas_anet_obs, aeronet_obs_cube,
+                         plot_dict)
+
+        figures.append(fig_cf)
+
+    # Decorate the scatter plot
+    line = mlines.Line2D([0, 1], [0, 1], color="#696969")
+    transform = ax_scatter.transAxes
+    line.set_transform(transform)
+    ax_scatter.add_line(line)
+
+    ax_scatter.set(
+        xlim=(0, 1),
+        xticks=np.linspace(0.0, 1.0, num=6),
+        ylim=(0, 1),
+        yticks=np.linspace(0.0, 1.0, num=6),
+    )
+    ax_scatter.set_xlabel("AeroNET AOD", fontsize=fontsizedict["axis"])
+    ax_scatter.set_ylabel(model_id + " AOD", fontsize=fontsizedict["axis"])
+
+    ax_scatter.tick_params(axis="both",
+                           which="major",
+                           labelsize=fontsizedict["ticklabel"])
+
+    ax_scatter.set_title(
+        "Model vs obs: Total Aerosol Optical Depth \n at " + wv_mi +
+        " microns",
+        fontsize=fontsizedict["title"],
+    )
+
+    ax_scatter.legend(
+        handles=leg_scatter,
+        loc="lower right",
+        title="Seasonal R2",
+        title_fontsize=fontsizedict["legend"],
+        fontsize=fontsizedict["legend"],
+    )
+
+    return figures, fig_scatter
+
+
+def preprocess_aod_obs_dataset(obs_dataset):
+    """Calculate a multiannual seasonal mean AOD climatology.
+
+    Observational AOD timeseries data from AeroNET are used to generate a
+    multiannual seasonal mean climatology for each AeroNET station. The
+    user sets thresholds (or uses the default settings) to specify the
+    amount of valid data required for the climatology. At this stage
+    ESMValTool preprocessors are unsuitable for pre-processing the AeroNET
+    AOD observations because of the bespoke nature and application of the
+    filtering thresholds.
+
+    Parameters
+    ----------
+    obs_dataset : ESMValTool dictionary. Holds meta data for the observational
+        AOD dataset.
+
+    Returns
+    -------
+     multiannual_seaonal_mean : Iris cube. Preprocessed observational
+         AOD climatology.
+    """
+    obs_cube = iris.load_cube(obs_dataset[0]["filename"])
+
+    # Set up thresholds for generating the multi annual seasonal mean
+    min_days_per_mon = 1
+    min_mon_per_seas = 3
+    min_seas_per_year = 4
+    min_seas_per_clim = 5
+
+    # Add the clim_season and season_year coordinates.
+    iris.coord_categorisation.add_year(obs_cube, 'time', name='year')
+
+    iris.coord_categorisation.add_season(obs_cube, 'time', name='clim_season')
+
+    iris.coord_categorisation.add_season_year(obs_cube,
+                                              'time',
+                                              name='season_year')
+
+    # Copy obs cube and mask all months with fewer
+    # "Number of days" than given threshold.
+    num_days_var = obs_cube.ancillary_variable("Number of days")
+    masked_months_obs_cube = obs_cube.copy(data=ma.masked_where(
+        num_days_var.data < min_days_per_mon, obs_cube.data))
+
+    # Aggregate (mean) by season.
+    # The number of unmasked months per season is counted,
+    # and where there are fewer unmasked months than the
+    # given threshold, the computed mean is masked.
+    annual_seasonal_mean = masked_months_obs_cube.aggregated_by(
+        ['clim_season', 'season_year'],
+        iris.analysis.MEAN,
+    )
+    annual_seasonal_count = masked_months_obs_cube.aggregated_by(
+        ['clim_season', 'season_year'],
+        iris.analysis.COUNT,
+        function=lambda values: ~ma.getmask(values),
+    )
+    annual_seasonal_mean.data = ma.masked_where(
+        annual_seasonal_count.data < min_mon_per_seas,
+        annual_seasonal_mean.data,
+    )
+
+    # Aggregate (mean) by multi-annual season.
+    # The number of unmasked seasons per multi-annual season
+    # is counted, and where there are fewer unmasked seasons
+    # than the given threshold, the computed multi-annual
+    # season is masked.
+    multi_annual_seasonal_mean = annual_seasonal_mean.aggregated_by(
+        'clim_season',
+        iris.analysis.MEAN,
+    )
+    clim_season_agg_count = annual_seasonal_mean.aggregated_by(
+        'clim_season',
+        iris.analysis.COUNT,
+        function=lambda values: ~ma.getmask(values),
+    )
+    multi_annual_seasonal_mean.data = ma.masked_where(
+        clim_season_agg_count.data < min_seas_per_clim,
+        multi_annual_seasonal_mean.data,
+    )
+    year_agg_count = multi_annual_seasonal_mean.aggregated_by(
+        'year',
+        iris.analysis.COUNT,
+        function=lambda values: ~ma.getmask(values),
+    )
+
+    counter = range(len(
+        multi_annual_seasonal_mean.coord('clim_season').points))
+    for iseas in counter:
+        multi_annual_seasonal_mean.data[iseas, :] = ma.masked_where(
+            year_agg_count.data[0, :] < min_seas_per_year,
+            multi_annual_seasonal_mean.data[iseas, :],
+        )
+
+    return multi_annual_seasonal_mean
+
+
+def main(config):
+    """Produce the AOD climatology metric from ground-based AeroNet
+    observations.
+
+    Parameters
+    ----------
+    wavel : String.
+        User defined. Default is "440".
+    config : dict
+        The ESMValTool configuration.
+    """
+    input_data = config["input_data"]
+    datasets = group_metadata(input_data.values(), "dataset")
+
+    # Default wavelength
+    wavel = "440"
+
+    # Produce climatology for observational dataset
+    obs_dataset = datasets.pop(config["observational_dataset"])
+    obs_cube = preprocess_aod_obs_dataset(obs_dataset)
+
+    for model_dataset, group in datasets.items():
+        # 'model_dataset' is the name of the model dataset.
+        # 'group' is a list of dictionaries containing metadata.
+        logger.info("Processing data for %s", model_dataset)
+        logger.info(group)
+
+        for attributes in group:
+            logger.info(attributes["filename"])
+
+            input_file = attributes["filename"]
+            provenance_record = get_provenance_record(input_file)
+            logger.info(provenance_record)
+            cube = iris.load_cube(input_file)
+
+            # Set up for analysis and plotting
+            seasons = ["DJF", "MAM", "JJA", "SON"]
+
+            plot_file_prefix = (model_dataset + "_" + attributes["activity"] +
+                                "_" + attributes["mip"] + "_" +
+                                attributes["exp"] + "_" +
+                                attributes["short_name"] + "_" +
+                                str(attributes["start_year"]) + "_" +
+                                str(attributes["end_year"]) + "_")
+
+            # Analysis and plotting for model-obs comparison
+            figures, fig_scatter = aod_analyse(cube,
+                                               obs_cube,
+                                               seasons,
+                                               wavel=wavel)
+
+        # Save the scatter plot
+        output_file = plot_file_prefix + "scatter"
+        output_path = get_plot_filename(output_file, config)
+        fig_scatter.savefig(output_path)
+
+        # Save the contour plots
+        for ifig, seas_fig in enumerate(figures):
+            output_file = plot_file_prefix + seasons[ifig]
+            output_path = get_plot_filename(output_file, config)
+            seas_fig.savefig(output_path)
+
+
+if __name__ == "__main__":
+    with run_diagnostic() as CONFIG:
+        main(CONFIG)
diff --git a/esmvaltool/diag_scripts/austral_jet/main.ncl b/esmvaltool/diag_scripts/austral_jet/main.ncl
index c078830687..1b7604d4f5 100644
--- a/esmvaltool/diag_scripts/austral_jet/main.ncl
+++ b/esmvaltool/diag_scripts/austral_jet/main.ncl
@@ -342,14 +342,22 @@ begin
         lat_range = f->lat_bnds
       end if
     else
-      if (isatt(f, "plev") .and. \
+      f_var = f->$var0$
+      if (isatt(f, "plev") .and. \  ; ESMValCore < 2.11.0
           isatt(f, "lat_range_0") .and. \
           isatt(f, "lat_range_1")) then
         lev = f@plev
         lat_range = (/f@lat_range_0, f@lat_range_1/)
+      else if (isatt(f_var, "plev") .and. \  ; ESMValCore >= 2.11.0
+               isatt(f_var, "lat_range_0") .and. \
+               isatt(f_var, "lat_range_1")) then
+        lev = f_var@plev
+        lat_range = (/f_var@lat_range_0, f_var@lat_range_1/)
       else
         error_msg("f", DIAG_SCRIPT, "", "cannot read plev and latrange")
       end if
+      end if
+      delete(f_var)
     end if
     log_info("Retrieved pressure level " + lev + " and latitude range " + \
              lat_range(0) + " to " + lat_range(1) + " from preprocessed data")
diff --git a/esmvaltool/diag_scripts/emergent_constraints/__init__.py b/esmvaltool/diag_scripts/emergent_constraints/__init__.py
index 1949719acb..545a07d9b5 100644
--- a/esmvaltool/diag_scripts/emergent_constraints/__init__.py
+++ b/esmvaltool/diag_scripts/emergent_constraints/__init__.py
@@ -297,12 +297,16 @@ def _get_data_frame(var_type, cubes, label_all_data, group_by=None):
 
 def _metadata_to_dict(metadata):
     """Convert :class:`iris.cube.CubeMetadata` to :obj:`dict`."""
-    new_dict = {}
-    for (key, val) in metadata._asdict().items():
-        if isinstance(val, dict):
-            new_dict.update(val)
-        else:
-            new_dict[key] = val
+    new_dict = dict(metadata.attributes)
+    other_keys = [
+        'standard_name',
+        'long_name',
+        'var_name',
+        'units',
+        'cell_methods',
+    ]
+    for key in other_keys:
+        new_dict[key] = getattr(metadata, key)
     return new_dict
 
 
diff --git a/esmvaltool/diag_scripts/hyint/hyint_etccdi_preproc.R b/esmvaltool/diag_scripts/hyint/hyint_etccdi_preproc.R
index bb14906453..36c09f952d 100644
--- a/esmvaltool/diag_scripts/hyint/hyint_etccdi_preproc.R
+++ b/esmvaltool/diag_scripts/hyint/hyint_etccdi_preproc.R
@@ -29,7 +29,7 @@ hyint_etccdi_preproc <-
       sfile_tmp0 <- cdo("delvar", args = "time_bnds", input = sfile)
       gridf <- tempfile()
       cdo("griddes", input = hyint_file, stdout = gridf)
-      sfile_tmp1 <- cdo("remapcon2",
+      sfile_tmp1 <- cdo("remapscon2",
         args = gridf,
         input = sfile_tmp0
       )
diff --git a/esmvaltool/diag_scripts/hyint/hyint_functions.R b/esmvaltool/diag_scripts/hyint/hyint_functions.R
index 5cc2b9d07e..a1595c2a72 100644
--- a/esmvaltool/diag_scripts/hyint/hyint_functions.R
+++ b/esmvaltool/diag_scripts/hyint/hyint_functions.R
@@ -626,7 +626,7 @@ create_landseamask <-
 
     ## Regridding the topographic map to chosen grid
     cdo(
-      "remapcon2",
+      "remapscon2",
       args = paste0("'", regrid, "'"),
       input = ftopo,
       output = regridded_topo
@@ -827,7 +827,7 @@ ncdf_opener_universal <- # nolint
              rotate = "full",
              interp2grid = F,
              grid = "r144x73",
-             remap_method = "remapcon2",
+             remap_method = "remapscon2",
              exportlonlat = TRUE,
              verbose = F) {
     # load package
@@ -1090,7 +1090,7 @@ ncdf_opener <- function(namefile,
                         rotate = "full",
                         interp2grid = F,
                         grid = "r144x73",
-                        remap_method = "remapcon2",
+                        remap_method = "remapscon2",
                         exportlonlat = T) {
   field <-
     ncdf_opener_universal(
@@ -1123,7 +1123,7 @@ ncdf_opener_time <- # nolint
              rotate = "full",
              interp2grid = F,
              grid = "r144x73",
-             remap_method = "remapcon2") {
+             remap_method = "remapscon2") {
     # function to open netcdf files. It uses ncdf4 library
     # time selection of month and years needed automatically rotate matrix
     # to place greenwich at the center (flag "rotate")
diff --git a/esmvaltool/diag_scripts/hyint/hyint_preproc.R b/esmvaltool/diag_scripts/hyint/hyint_preproc.R
index ec0807cf70..c8350a061d 100644
--- a/esmvaltool/diag_scripts/hyint/hyint_preproc.R
+++ b/esmvaltool/diag_scripts/hyint/hyint_preproc.R
@@ -22,7 +22,7 @@ hyint_preproc <- function(work_dir,
     } else {
       gridf <- rgrid
     }
-    tempf <- cdo("remapcon2", args = gridf, input = climofile)
+    tempf <- cdo("remapscon2", args = gridf, input = climofile)
     unlink(gridf)
   } else {
     tempf <- cdo("addc", args = "0", input = climofile)
diff --git a/esmvaltool/diag_scripts/miles/basis_functions.R b/esmvaltool/diag_scripts/miles/basis_functions.R
index 2eb65b0789..adab0c0d6a 100644
--- a/esmvaltool/diag_scripts/miles/basis_functions.R
+++ b/esmvaltool/diag_scripts/miles/basis_functions.R
@@ -336,7 +336,7 @@ ncdf_opener_universal <- # nolint
              interp2grid = FALSE,
              fillmiss = FALSE,
              grid = "r144x73",
-             remap_method = "remapcon2",
+             remap_method = "remapscon2",
              exportlonlat = TRUE,
              verbose = TRUE) {
     # load package
@@ -645,7 +645,7 @@ ncdf_opener <- function(namefile,
                         interp2grid = FALSE,
                         fillmiss = FALSE,
                         grid = "r144x73",
-                        remap_method = "remapcon2",
+                        remap_method = "remapscon2",
                         exportlonlat = TRUE,
                         verbose = FALSE) {
   field <- ncdf_opener_universal(
diff --git a/esmvaltool/diag_scripts/monitor/multi_datasets.py b/esmvaltool/diag_scripts/monitor/multi_datasets.py
index 9d1834651e..a760a312f6 100644
--- a/esmvaltool/diag_scripts/monitor/multi_datasets.py
+++ b/esmvaltool/diag_scripts/monitor/multi_datasets.py
@@ -2059,7 +2059,7 @@ def create_map_plot(self, datasets):
                 )
                 caption = (
                     f"Map plot of {dataset['long_name']} of dataset "
-                    f"{dataset['dataset']} (project {dataset['project']})."
+                    f"{dataset['alias']}."
                 )
             else:
                 (plot_path, netcdf_paths) = (
@@ -2067,9 +2067,8 @@ def create_map_plot(self, datasets):
                 )
                 caption = (
                     f"Map plot of {dataset['long_name']} of dataset "
-                    f"{dataset['dataset']} (project {dataset['project']}) "
-                    f"including bias relative to {ref_dataset['dataset']} "
-                    f"(project {ref_dataset['project']})."
+                    f"{dataset['alias']} including bias relative to "
+                    f"{ref_dataset['alias']}."
                 )
                 ancestors.append(ref_dataset['filename'])
 
@@ -2134,7 +2133,7 @@ def create_zonal_mean_profile_plot(self, datasets):
                 )
                 caption = (
                     f"Zonal mean profile of {dataset['long_name']} of dataset "
-                    f"{dataset['dataset']} (project {dataset['project']})."
+                    f"{dataset['alias']}."
                 )
             else:
                 (plot_path, netcdf_paths) = (
@@ -2143,9 +2142,8 @@ def create_zonal_mean_profile_plot(self, datasets):
                 )
                 caption = (
                     f"Zonal mean profile of {dataset['long_name']} of dataset "
-                    f"{dataset['dataset']} (project {dataset['project']}) "
-                    f"including bias relative to {ref_dataset['dataset']} "
-                    f"(project {ref_dataset['project']})."
+                    f"{dataset['alias']} including bias relative to "
+                    f"{ref_dataset['alias']}."
                 )
                 ancestors.append(ref_dataset['filename'])
 
@@ -2386,8 +2384,7 @@ def create_hovmoeller_z_vs_time_plot(self, datasets):
                      plot_func, dataset))
                 caption = (
                     f"Hovmoeller Z vs. time plot of {dataset['long_name']} "
-                    f"of dataset "
-                    f"{dataset['dataset']} (project {dataset['project']})."
+                    f"of dataset {dataset['alias']}."
                 )
             else:
                 (plot_path,
@@ -2395,10 +2392,8 @@ def create_hovmoeller_z_vs_time_plot(self, datasets):
                      plot_func, dataset, ref_dataset))
                 caption = (
                     f"Hovmoeller Z vs. time plot of {dataset['long_name']} "
-                    f"of dataset "
-                    f"{dataset['dataset']} (project {dataset['project']}) "
-                    f"including bias relative to {ref_dataset['dataset']} "
-                    f"(project {ref_dataset['project']})."
+                    f"of dataset {dataset['alias']} including bias relative "
+                    f"to {ref_dataset['alias']}."
                 )
                 ancestors.append(ref_dataset['filename'])
 
@@ -2464,7 +2459,7 @@ def create_hovmoeller_time_vs_lat_or_lon_plot(self, datasets):
                 )
                 caption = (
                     f"Hovmoeller plot of {dataset['long_name']} of dataset "
-                    f"{dataset['dataset']} (project {dataset['project']})."
+                    f"{dataset['alias']}."
                 )
             else:
                 (plot_path, netcdf_paths) = (
@@ -2473,9 +2468,8 @@ def create_hovmoeller_time_vs_lat_or_lon_plot(self, datasets):
                 )
                 caption = (
                     f"Hovmoeller plot of {dataset['long_name']} of dataset "
-                    f"{dataset['dataset']} (project {dataset['project']}) "
-                    f"including bias relative to {ref_dataset['dataset']} "
-                    f"(project {ref_dataset['project']})."
+                    f"{dataset['alias']} including bias relative to "
+                    f"{ref_dataset['alias']}."
                 )
                 ancestors.append(ref_dataset['filename'])
 
diff --git a/esmvaltool/diag_scripts/quantilebias/quantilebias_functions.R b/esmvaltool/diag_scripts/quantilebias/quantilebias_functions.R
index 8d520cf545..cbd9e72acf 100644
--- a/esmvaltool/diag_scripts/quantilebias/quantilebias_functions.R
+++ b/esmvaltool/diag_scripts/quantilebias/quantilebias_functions.R
@@ -62,7 +62,7 @@ ncdf_opener_universal <- # nolint
            rotate = "full",
            interp2grid = F,
            grid = "r144x73",
-           remap_method = "remapcon2",
+           remap_method = "remapscon2",
            exportlonlat = TRUE,
            verbose = F) {
     # load package
@@ -326,7 +326,7 @@ ncdf_opener <- function(namefile,
                         rotate = "full",
                         interp2grid = F,
                         grid = "r144x73",
-                        remap_method = "remapcon2",
+                        remap_method = "remapscon2",
                         exportlonlat = T) {
   field <-
     ncdf_opener_universal(
diff --git a/esmvaltool/recipes/examples/recipe_check_obs.yml b/esmvaltool/recipes/examples/recipe_check_obs.yml
index 0fd39be94f..fc5c6ba1e2 100644
--- a/esmvaltool/recipes/examples/recipe_check_obs.yml
+++ b/esmvaltool/recipes/examples/recipe_check_obs.yml
@@ -27,7 +27,6 @@ diagnostics:
         type: ground, version: v2-0-1}
     scripts: null
 
-
   BerkeleyEarth:
     description: BerkeleyEarth check
     variables:
@@ -437,7 +436,7 @@ diagnostics:
       pr:
     additional_datasets:
       - {dataset: GPCP-SG, project: OBS, mip: Amon, tier: 2, type: atmos,
-         version: 2.3,  start_year: 1979,  end_year: 2022}
+         version: 2.3, start_year: 1979, end_year: 2022}
     scripts: null
 
   HadCRUT3:
@@ -910,6 +909,25 @@ diagnostics:
 
   ### TIER 3 ##################################################################
 
+  AERONET:
+    description: Aeronet check
+    variables:
+      od440aer:
+    additional_datasets:
+      - {dataset: AERONET, project: OBS6, mip: AERmon, tier: 3, type: atmos, version: 20231021}
+    scripts: null
+
+  ANUClimate:
+    description: ANUClimate check
+    variables:
+      pr:
+      tasmax:
+      tasmin:
+      tas:
+    additional_datasets:
+      - {dataset: ANUClimate, project: OBS6, mip: Amon, tier: 3, type: reanaly}
+    scripts: null
+
   APHRO-MA:
     description: APHRO-MA check
     variables:
diff --git a/esmvaltool/recipes/recipe_aod_aeronet_assess.yml b/esmvaltool/recipes/recipe_aod_aeronet_assess.yml
new file mode 100644
index 0000000000..0fc82ec864
--- /dev/null
+++ b/esmvaltool/recipes/recipe_aod_aeronet_assess.yml
@@ -0,0 +1,65 @@
+# ESMValTool
+# recipe_aod_aeronet_assess.yml
+---
+documentation:
+  description: |
+    Recipe to plot seasonal maps of global aerosol optical depth (AOD) at 440nm.
+
+  title: Recipe that runs an AOD diagnostic
+
+  authors:
+    - hogan_emma
+    - lillis_jon
+    - hardacre_catherine
+
+  maintainer:
+    - hogan_emma
+    - lillis_jon
+    - hardacre_catherine
+
+  projects:
+    - esmval
+
+preprocessors:
+  ma_season_mean:
+    regrid:
+      target_grid: 2.5x2.5
+      scheme: nearest
+    climate_statistics:
+      operator: mean
+      period: season
+      seasons: ['DJF', 'MAM', 'JJA', 'SON']
+    multi_model_statistics:
+      span: overlap
+      statistics: [mean]
+
+diagnostics:
+  od440aer_climatologies:
+    description: Visualise spatial multi-annual seasonal means AOD at 440nm.
+    variables:
+
+      od440aer: &var_od440aer
+        mip: AERmon
+        short_name: od440aer
+        start_year: 1994
+        end_year: 2014
+        additional_datasets:
+          - {dataset: AERONET, project: OBS6, mip: AERmon, tier: 3, type: atmos, version: 20231021}
+
+      od440aer_season:
+        <<: *var_od440aer
+        preprocessor: ma_season_mean
+        additional_datasets:
+          - {dataset: UKESM1-0-LL, project: CMIP6, mip: AERmon, exp: historical, ensemble: r1i1p1f2, grid: gn}
+          - {dataset: HadGEM3-GC31-LL, project: CMIP6, mip: AERmon, exp: historical, ensemble: r1i1p1f3, grid: gn}
+          - {dataset: EC-Earth3-AerChem, project: CMIP6, mip: AERmon, exp: historical, ensemble: r1i1p1f1, grid: gn}
+#          - {dataset: NorESM2-LM, project: CMIP6, mip: AERmon, exp: historical, ensemble: r1i1p1f1, grid: gn}
+          - {dataset: GFDL-ESM4, project: CMIP6, mip: AERmon, exp: historical, ensemble: r1i1p1f1, grid: gr1}
+          - {dataset: MPI-ESM-1-2-HAM, project: CMIP6, mip: AERmon, exp: historical, ensemble: r1i1p1f1, grid: gn}
+
+    scripts:
+      aeronet:
+        script: aerosols/aod_aeronet_assess.py
+        observational_dataset: AERONET
+        quickplot:
+          plot_type: plot
diff --git a/esmvaltool/recipes/recipe_ocean_quadmap.yml b/esmvaltool/recipes/recipe_ocean_quadmap.yml
index 4afc2c7bb5..2f20a50015 100644
--- a/esmvaltool/recipes/recipe_ocean_quadmap.yml
+++ b/esmvaltool/recipes/recipe_ocean_quadmap.yml
@@ -54,12 +54,10 @@ diagnostics:
          preprocessor: prep_quad_map
          mip: Omon
      additional_datasets:
-#        filename: tos_ATSR_L3_ARC-v1.1.1_199701-201112.nc
-#        download from: https://datashare.is.ed.ac.uk/handle/10283/536
-       - {dataset: ATSR,  project: obs4MIPs,  level: L3,  version: ARC-v1.1.1,  start_year: 2001,  end_year: 2003, tier: 1}
+       - {dataset: ARC-SST-1-1,  project: obs4MIPs,  level: L3,  start_year: 2001,  end_year: 2003, tier: 1}
      scripts:
        Global_Ocean_map: &Global_Ocean_map
          script: ocean/diagnostic_maps_quad.py
          control_model: {dataset: HadGEM2-CC, project: CMIP5, mip: Omon, exp: historical, ensemble: r1i1p1}
          exper_model: {dataset: HadGEM2-ES, project: CMIP5, mip: Omon, exp: historical, ensemble: r1i1p1}
-         observational_dataset: {dataset: ATSR, project: obs4MIPs,}
+         observational_dataset: {dataset: ARC-SST-1-1, project: obs4MIPs,}
diff --git a/esmvaltool/references/aeronetv3.bibtex b/esmvaltool/references/aeronetv3.bibtex
new file mode 100644
index 0000000000..ac05ed0f31
--- /dev/null
+++ b/esmvaltool/references/aeronetv3.bibtex
@@ -0,0 +1,6 @@
+@misc{aeronetv3,
+  author = {},
+  title = {},
+  url = {https://aeronet.gsfc.nasa.gov/new_web/download_all_v3_aod.html},
+  year = 2023
+}
diff --git a/esmvaltool/references/anuclimate2.bibtex b/esmvaltool/references/anuclimate2.bibtex
new file mode 100644
index 0000000000..0c183cd181
--- /dev/null
+++ b/esmvaltool/references/anuclimate2.bibtex
@@ -0,0 +1,11 @@
+@misc{https://doi.org/10.25914/60a10aa56dd1b,
+  doi = {10.25914/60A10AA56DD1B},
+  url = {https://pid.nci.org.au/doi/f2576_7854_4065_1457},
+  author = {Hutchinson,  Michael and Xu,  Tingbao and Kesteven,  Jennifer and Marang,  Ian and Evans,  Bradley},
+  keywords = {Climatology (excl. Climate Change Processes)},
+  language = {en},
+  title = {ANUClimate 2.0},
+  publisher = {NCI Australia},
+  year = {2021},
+  copyright = {Creative Commons Attribution Share Alike 4.0 International}
+}
diff --git a/setup.py b/setup.py
index 16a76cec75..84bd2a7c82 100755
--- a/setup.py
+++ b/setup.py
@@ -31,11 +31,12 @@
         'distributed',
         'ecmwf-api-client',
         'eofs',
-        'ESMPy',
+        'ESMPy',  # not on PyPI
         'esmvalcore',
         'esmf-regrid>=0.7.0',
         'fiona',
         'fire',
+        'fsspec',
         'GDAL',
         'jinja2',
         'joblib',
@@ -49,13 +50,14 @@
         'numpy!=1.24.3',  # severe masking bug
         'openpyxl',
         'packaging',
-        'pandas',
+        'pandas!=2.2.0,!=2.2.1,!=2.2.2',  # ESMValCore PR2305
         'progressbar2',
         'psyplot',
         'psy-maps',
         'psy-reg',
         'psy-simple',
         'pyproj>=2.1',
+        'pys2index',
         'python-dateutil',
         'pyyaml',
         'rasterio',
@@ -74,8 +76,8 @@
         'xlsxwriter',
         'zarr',
     ],
-    # Test dependencies
-    # Execute `pip install .[test]` once and the use `pytest` to run tests
+    # Test dependencies (unit tests)
+    # Execute `pip install .[test]` once and then use `pytest` to run tests
     'test': [
         'flake8',
         'pytest>=3.9,!=6.0.0rc1,!=6.0.0',
@@ -98,6 +100,7 @@
     'develop': [
         'codespell',
         'docformatter',
+        'imagehash',
         'isort',
         'pre-commit',
         'prospector[with_pyroma]!=1.1.6.3,!=1.1.6.4',