diff --git a/.circleci/config.yml b/.circleci/config.yml
index ad43c7f495..5be43d58c6 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -27,6 +27,7 @@ commands:
           name: Log versions
           command: |
             . /opt/conda/etc/profile.d/conda.sh
+            conda env export --name base > /logs/base_environment.yml
             conda activate esmvaltool
             esmvaltool version
             dpkg -l > /logs/versions.txt
@@ -40,6 +41,7 @@ commands:
           command: |
             . /opt/conda/etc/profile.d/conda.sh
             conda activate esmvaltool
+            mamba --version
             pytest -n 4 --junitxml=test-reports/report.xml
             esmvaltool version
             esmvaltool -- --help
@@ -214,7 +216,7 @@ jobs:
             conda activate esmvaltool
             mkdir -p ~/climate_data
             esmvaltool config get_config_user
-            echo "offline: false" >> ~/.esmvaltool/config-user.yml
+            echo "search_esgf: when_missing" >> ~/.esmvaltool/config-user.yml
             cat ~/.esmvaltool/config-user.yml
             for recipe in esmvaltool/recipes/testing/recipe_*.yml; do
               esmvaltool run "$recipe"
@@ -232,7 +234,7 @@ jobs:
     # Test building documentation
     docker:
       - image: condaforge/mambaforge:latest
-    resource_class: small
+    resource_class: medium
     steps:
       - checkout
       - run:
@@ -265,7 +267,7 @@ jobs:
             # Install prerequisites
             mkdir /logs
             # Install ESMValTool in a new conda environment
-            mamba create -y --name esmvaltool -c conda-forge esmvaltool julia 'python=3.10' >> /logs/conda.txt 2>&1
+            mamba create -y --name esmvaltool -c conda-forge esmvaltool julia 'python=3.11' >> /logs/conda.txt 2>&1
             # Activate the environment
             set +x; conda activate esmvaltool; set -x
             # install the Julia dependencies
diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS
index e1aaa42272..2086d60173 100644
--- a/.github/CODEOWNERS
+++ b/.github/CODEOWNERS
@@ -1,2 +1,2 @@
-esmvaltool/cmorizers @remi-kazeroni
+esmvaltool/cmorizers @ESMValGroup/obs-maintainers
 .github/workflows @valeriupredoi
diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md
index 83cf97eb4c..5041b22420 100644
--- a/.github/pull_request_template.md
+++ b/.github/pull_request_template.md
@@ -62,6 +62,7 @@ It is the responsibility of the author to make sure the pull request is ready to
 
 - [ ] [🛠][1] [Documentation](https://docs.esmvaltool.org/en/latest/community/dataset.html#dataset-documentation) is available
 - [ ] [🛠][1] The dataset has been [added to the CMOR check recipe](https://docs.esmvaltool.org/en/latest/community/dataset.html#testing)
+- [ ] [🛠][1] The dataset has been added to the shared [data pools](https://docs.esmvaltool.org/en/latest/community/dataset.html#cmorized-data) of DKRZ and Jasmin by the @ESMValGroup/OBS-maintainers team
 - [ ] [🧪][2] Numbers and units of the data look [physically meaningful](https://docs.esmvaltool.org/en/latest/community/dataset.html#scientific-sanity-check)
 
 ***
diff --git a/.github/workflows/test-development.yml b/.github/workflows/test-development.yml
index dfede54280..cab6489548 100644
--- a/.github/workflows/test-development.yml
+++ b/.github/workflows/test-development.yml
@@ -12,6 +12,7 @@ on:
   push:
     branches:
       - main
+      - fix_recipe_filler_bkwds_incompatibility
   schedule:
     - cron: '0 0 * * *'
 
diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
index 4e2698454f..9eec648279 100644
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -6,6 +6,7 @@ on:
   push:
     branches:
       - main
+      - fix_recipe_filler_bkwds_incompatibility
   schedule:
     - cron: '0 0 * * *'
 
diff --git a/.readthedocs.yaml b/.readthedocs.yaml
index 07a53170b7..071686d373 100644
--- a/.readthedocs.yaml
+++ b/.readthedocs.yaml
@@ -14,9 +14,11 @@ build:
   jobs:
     pre_create_environment:
       # update mamba just in case
-      - mamba update --yes --quiet --name=base mamba
+      - mamba update --yes --quiet --name=base mamba 'zstd=1.5.2'
       - mamba --version
+      - mamba list --name=base
     post_create_environment:
+      - conda run -n ${CONDA_DEFAULT_ENV} mamba list
       # use conda run executable wrapper to have all env variables
       - conda run -n ${CONDA_DEFAULT_ENV} mamba --version
       - conda run -n ${CONDA_DEFAULT_ENV} pip install . --no-deps
diff --git a/.zenodo.json b/.zenodo.json
index 083fa51356..c6a731981f 100644
--- a/.zenodo.json
+++ b/.zenodo.json
@@ -1,382 +1,426 @@
 {
-    "creators": [
-        {
-            "affiliation": "NLeSC, Netherlands",
-            "name": "Andela, Bouwe",
-            "orcid": "0000-0001-9005-8940"
-        },
-        {
-            "affiliation": "DLR, Germany",
-            "name": "Broetz, Bjoern"
-        },
-        {
-            "affiliation": "PML, UK",
-            "name": "de Mora, Lee",
-            "orcid": "0000-0002-5080-3149"
-        },
-        {
-            "affiliation": "NLeSC, Netherlands",
-            "name": "Drost, Niels",
-            "orcid": "0000-0001-9795-7981"
-        },
-        {
-            "affiliation": "DLR, Germany",
-            "name": "Eyring, Veronika",
-            "orcid": "0000-0002-6887-4885"
-        },
-        {
-            "affiliation": "AWI, Germany",
-            "name": "Koldunov, Nikolay",
-            "orcid": "0000-0002-3365-8146"
-        },
-        {
-            "affiliation": "DLR, Germany",
-            "name": "Lauer, Axel",
-            "orcid": "0000-0002-9270-1044"
-        },
-        {
-            "affiliation": "LMU, Germany",
-            "name": "Mueller, Benjamin"
-        },
-        {
-            "affiliation": "URead, UK",
-            "name": "Predoi, Valeriu",
-            "orcid": "0000-0002-9729-6578"
-        },
-        {
-            "affiliation": "DLR, Germany",
-            "name": "Righi, Mattia",
-            "orcid": "0000-0003-3827-5950"
-        },
-        {
-            "affiliation": "DLR, Germany",
-            "name": "Schlund, Manuel",
-            "orcid": "0000-0001-5251-0158"
-        },
-        {
-            "affiliation": "BSC, Spain",
-            "name": "Vegas-Regidor, Javier",
-            "orcid": "0000-0003-0096-4291"
-        },
-        {
-            "affiliation": "SMHI, Sweden",
-            "name": "Zimmermann, Klaus"
-        },
-        {
-            "affiliation": "University of Bremen, Germany",
-            "name": "Adeniyi, Kemisola"
-        },
-        {
-            "affiliation": "ISAC-CNR, Italy",
-            "name": "Arnone, Enrico",
-            "orcid": "0000-0001-6740-5051"
-        },
-        {
-            "affiliation": "BSC, Spain",
-            "name": "Bellprat, Omar",
-            "orcid": "0000-0001-6434-1793"
-        },
-        {
-            "affiliation": "SMHI, Sweden",
-            "name": "Berg, Peter",
-            "orcid": "0000-0002-1469-2568"
-        },
-        {
-            "affiliation": "DLR, Germany",
-            "name": "Bock, Lisa",
-            "orcid": "0000-0001-7058-5938"
-        },
-        {
-            "affiliation": "BSC, Spain",
-            "name": "Caron, Louis-Philippe",
-            "orcid": "0000-0001-5221-0147"
-        },
-        {
-            "affiliation": "MPI for Biogeochemistry, Germany",
-            "name": "Carvalhais, Nuno"
-        },
-        {
-            "affiliation": "ENEA, Italy",
-            "name": "Cionni, Irene",
-            "orcid": "0000-0002-0591-9193"
-        },
-        {
-            "affiliation": "BSC, Spain",
-            "name": "Cortesi, Nicola",
-            "orcid": "0000-0002-1442-9225"
-        },
-        {
-            "affiliation": "ISAC-CNR, Italy",
-            "name": "Corti, Susanna"
-        },
-        {
-            "affiliation": "ETH Zurich, Switzerland",
-            "name": "Crezee, Bas",
-            "orcid": "0000-0002-1774-1126"
-        },
-        {
-            "affiliation": "ETH Zurich, Switzerland",
-            "name": "Davin, Edouard Leopold",
-            "orcid": "0000-0003-3322-9330"
-        },
-        {
-            "affiliation": "ISAC-CNR, Italy",
-            "name": "Davini, Paolo",
-            "orcid": "0000-0003-3389-7849"
-        },
-        {
-            "affiliation": "NCAR, USA",
-            "name": "Deser, Clara"
-        },
-        {
-            "affiliation": "NLeSC, Netherlands",
-            "name": "Diblen, Faruk"
-        },
-        {
-            "affiliation": "UCLouvain, Belgium",
-            "name": "Docquier, David"
-        },
-        {
-            "affiliation": "MetOffice, UK",
-            "name": "Dreyer, Laura"
-        },
-        {
-            "affiliation": "DKRZ, Germany",
-            "name": "Ehbrecht, Carsten"
-        },
-        {
-            "affiliation": "MetOffice, UK",
-            "name": "Earnshaw, Paul"
-        },
-        {
-            "affiliation": "University of Bremen, Germany",
-            "name": "Gier, Bettina"
-        },
-        {
-            "affiliation": "BSC, Spain",
-            "name": "Gonzalez-Reviriego, Nube",
-            "orcid": "0000-0002-5919-6701"
-        },
-        {
-            "affiliation": "University of Arizona, USA",
-            "name": "Goodman, Paul"
-        },
-        {
-            "affiliation": "HZG, Germany",
-            "name": "Hagemann, Stefan",
-            "orcid": "0000-0001-5444-2945"
-        },
-        {
-            "affiliation": "ISAC-CNR, Italy",
-            "name": "von Hardenberg, Jost",
-            "orcid": "0000-0002-5312-8070"
-        },
-        {
-            "affiliation": "DLR, Germany",
-            "name": "Hassler, Birgit",
-            "orcid": "0000-0003-2724-709X"
-        },
-        {
-            "affiliation": "BSC, Spain",
-            "name": "Hunter, Alasdair",
-            "orcid": "0000-0001-8365-3709"
-        },
-        {
-            "affiliation": "FUB, Germany",
-            "name": "Kadow, Christopher"
-        },
-        {
-            "affiliation": "DKRZ, Germany",
-            "name": "Kindermann, Stephan",
-            "orcid": "0000-0001-9335-1093"
-        },
-        {
-            "affiliation": "MPI for Biogeochemistry, Germany",
-            "name": "Koirala, Sujan"
-        },
-        {
-            "affiliation": "BSC, Spain",
-            "name": "Lledó, Llorenç"
-        },
-        {
-            "affiliation": "ETH Zurich, Switzerland",
-            "name": "Lejeune, Quentin"
-        },
-        {
-            "affiliation": "University of Hamburg, German",
-            "name": "Lembo, Valerio",
-            "orcid": "0000-0001-6085-5914"
-        },
-        {
-            "affiliation": "MetOffice, UK",
-            "name": "Little, Bill"
-        },
-        {
-            "affiliation": "BSC, Spain",
-            "name": "Loosveldt-Tomas, Saskia"
-        },
-        {
-            "affiliation": "ETH Zurich, Switzerland",
-            "name": "Lorenz, Ruth",
-            "orcid": "0000-0002-3986-1268"
-        },
-        {
-            "affiliation": "CMCC, Italy",
-            "name": "Lovato, Tomas",
-            "orcid": "0000-0002-5188-6767"
-        },
-        {
-            "affiliation": "University of Hamburg, German",
-            "name": "Lucarini, Valerio"
-        },
-        {
-            "affiliation": "UCLouvain, Belgium",
-            "name": "Massonnet, François"
-        },
-        {
-            "affiliation": "NIBIO, Norway",
-            "name": "Mohr, Christian Wilhelm",
-            "orcid": "0000-0003-2656-1802"
-        },
-        {
-            "affiliation": "BSC, Spain",
-            "name": "Moreno-Chamarro, Eduardo"
-        },
-        {
-            "affiliation": "University of Arizona, USA",
-            "name": "Amarjiit, Pandde"
-        },
-        {
-            "affiliation": "BSC, Spain",
-            "name": "Pérez-Zanón, Núria"
-        },
-        {
-            "affiliation": "NCAR, USA",
-            "name": "Phillips, Adam",
-            "orcid": "0000-0003-4859-8585"
-        },
-        {
-            "affiliation": "University of Arizona, USA",
-            "name": "Russell, Joellen"
-        },
-        {
-            "affiliation": "CICERO, Norway",
-            "name": "Sandstad, Marit"
-        },
-        {
-            "affiliation": "MetOffice, UK",
-            "name": "Sellar, Alistair"
-        },
-        {
-            "affiliation": "DLR, Germany",
-            "name": "Senftleben, Daniel"
-        },
-        {
-            "affiliation": "ISMAR-CNR, Italy",
-            "name": "Serva, Federico",
-            "orcid": "0000-0002-7118-0817"
-        },
-        {
-            "affiliation": "CICERO, Norway",
-            "name": "Sillmann, Jana"
-        },
-        {
-            "affiliation": "MPI-M, Germany",
-            "name": "Stacke, Tobias",
-            "orcid": "0000-0003-4637-5337"
-        },
-        {
-            "affiliation": "URead, UK",
-            "name": "Swaminathan, Ranjini",
-            "orcid": "0000-0001-5853-2673"
-        },
-        {
-            "affiliation": "BSC, Spain",
-            "name": "Torralba, Verónica"
-        },
-        {
-            "affiliation": "University of Bremen, Germany",
-            "name": "Weigel, Katja",
-            "orcid": "0000-0001-6133-7801"
-        },
-        {
-            "affiliation": "University of Reading, UK",
-            "name": "Roberts, Charles",
-            "orcid": "0000-0002-1147-8961"
-        },
-        {
-            "affiliation": "Netherlands eScience Center",
-            "name": "Kalverla, Peter",
-            "orcid": "0000-0002-5025-7862"
-        },
-        {
-            "affiliation": "Netherlands eScience Center",
-            "name": "Alidoost, Sarah",
-            "orcid": "0000-0001-8407-6472"
-        },
-        {
-            "affiliation": "Netherlands eScience Center",
-            "name": "Verhoeven, Stefan",
-            "orcid": "0000-0002-5821-2060"
-        },
-        {
-            "affiliation": "Netherlands eScience Center",
-            "name": "Vreede, Barbara",
-            "orcid": "0000-0002-5023-4601"
-        },
-        {
-            "affiliation": "Netherlands eScience Center",
-            "name": "Smeets, Stef",
-            "orcid": "0000-0002-5413-9038"
-        },
-        {
-            "affiliation": "Netherlands eScience Center",
-            "name": "Soares Siqueira, Abel",
-            "orcid": "0000-0003-4451-281X"
-        },
-        {
-            "affiliation": "DLR, Germany",
-            "name": "Kazeroni, Rémi",
-            "orcid": "0000-0001-7205-9528"
-        }
-    ],
-    "description": "ESMValTool: A community diagnostic and performance metrics tool for routine evaluation of Earth system models in CMIP.",
-    "license": {
-        "id": "Apache-2.0"
-    },
-    "title": "ESMValTool",
-    "communities": [
-        {
-            "identifier": "is-enes3"
-        },
-        {
-            "identifier": "dlr_de"
-        },
-        {
-            "identifier": "ecfunded"
-        },
-        {
-            "identifier": "nlesc"
-        }
-    ],
-    "grants": [
-        {
-            "id": "10.13039/501100000780::282672"
-        },
-        {
-            "id": "10.13039/501100000780::641727"
-        },
-        {
-            "id": "10.13039/501100000780::641816"
-        },
-        {
-            "id": "10.13039/501100000780::727862"
-        },
-        {
-            "id": "10.13039/501100000780::776613"
-        },
-        {
-            "id": "10.13039/501100000780::824084"
-        }
-    ]
+  "creators": [
+    {
+      "affiliation": "NLeSC, Netherlands",
+      "name": "Andela, Bouwe",
+      "orcid": "0000-0001-9005-8940"
+    },
+    {
+      "affiliation": "DLR, Germany",
+      "name": "Broetz, Bjoern"
+    },
+    {
+      "affiliation": "PML, UK",
+      "name": "de Mora, Lee",
+      "orcid": "0000-0002-5080-3149"
+    },
+    {
+      "affiliation": "NLeSC, Netherlands",
+      "name": "Drost, Niels",
+      "orcid": "0000-0001-9795-7981"
+    },
+    {
+      "affiliation": "DLR, Germany",
+      "name": "Eyring, Veronika",
+      "orcid": "0000-0002-6887-4885"
+    },
+    {
+      "affiliation": "AWI, Germany",
+      "name": "Koldunov, Nikolay",
+      "orcid": "0000-0002-3365-8146"
+    },
+    {
+      "affiliation": "DLR, Germany",
+      "name": "Lauer, Axel",
+      "orcid": "0000-0002-9270-1044"
+    },
+    {
+      "affiliation": "LMU, Germany",
+      "name": "Mueller, Benjamin"
+    },
+    {
+      "affiliation": "URead, UK",
+      "name": "Predoi, Valeriu",
+      "orcid": "0000-0002-9729-6578"
+    },
+    {
+      "affiliation": "DLR, Germany",
+      "name": "Righi, Mattia",
+      "orcid": "0000-0003-3827-5950"
+    },
+    {
+      "affiliation": "DLR, Germany",
+      "name": "Schlund, Manuel",
+      "orcid": "0000-0001-5251-0158"
+    },
+    {
+      "affiliation": "BSC, Spain",
+      "name": "Vegas-Regidor, Javier",
+      "orcid": "0000-0003-0096-4291"
+    },
+    {
+      "affiliation": "SMHI, Sweden",
+      "name": "Zimmermann, Klaus"
+    },
+    {
+      "affiliation": "University of Bremen, Germany",
+      "name": "Adeniyi, Kemisola"
+    },
+    {
+      "affiliation": "ISAC-CNR, Italy",
+      "name": "Arnone, Enrico",
+      "orcid": "0000-0001-6740-5051"
+    },
+    {
+      "affiliation": "BSC, Spain",
+      "name": "Bellprat, Omar",
+      "orcid": "0000-0001-6434-1793"
+    },
+    {
+      "affiliation": "SMHI, Sweden",
+      "name": "Berg, Peter",
+      "orcid": "0000-0002-1469-2568"
+    },
+    {
+      "affiliation": "DLR, Germany",
+      "name": "Bock, Lisa",
+      "orcid": "0000-0001-7058-5938"
+    },
+    {
+      "affiliation": "MetOffice, UK",
+      "name": "Bodas-Salcedo, Alejandro",
+      "orcid": "0000-0002-7890-2536"
+    },
+    {
+      "affiliation": "BSC, Spain",
+      "name": "Caron, Louis-Philippe",
+      "orcid": "0000-0001-5221-0147"
+    },
+    {
+      "affiliation": "MPI for Biogeochemistry, Germany",
+      "name": "Carvalhais, Nuno"
+    },
+    {
+      "affiliation": "ENEA, Italy",
+      "name": "Cionni, Irene",
+      "orcid": "0000-0002-0591-9193"
+    },
+    {
+      "affiliation": "BSC, Spain",
+      "name": "Cortesi, Nicola",
+      "orcid": "0000-0002-1442-9225"
+    },
+    {
+      "affiliation": "ISAC-CNR, Italy",
+      "name": "Corti, Susanna"
+    },
+    {
+      "affiliation": "ETH Zurich, Switzerland",
+      "name": "Crezee, Bas",
+      "orcid": "0000-0002-1774-1126"
+    },
+    {
+      "affiliation": "ETH Zurich, Switzerland",
+      "name": "Davin, Edouard Leopold",
+      "orcid": "0000-0003-3322-9330"
+    },
+    {
+      "affiliation": "ISAC-CNR, Italy",
+      "name": "Davini, Paolo",
+      "orcid": "0000-0003-3389-7849"
+    },
+    {
+      "affiliation": "NCAR, USA",
+      "name": "Deser, Clara"
+    },
+    {
+      "affiliation": "NLeSC, Netherlands",
+      "name": "Diblen, Faruk"
+    },
+    {
+      "affiliation": "UCLouvain, Belgium",
+      "name": "Docquier, David"
+    },
+    {
+      "affiliation": "MetOffice, UK",
+      "name": "Dreyer, Laura"
+    },
+    {
+      "affiliation": "DKRZ, Germany",
+      "name": "Ehbrecht, Carsten"
+    },
+    {
+      "affiliation": "MetOffice, UK",
+      "name": "Earnshaw, Paul"
+    },
+    {
+      "affiliation": "University of Bremen, Germany",
+      "name": "Gier, Bettina"
+    },
+    {
+      "affiliation": "BSC, Spain",
+      "name": "Gonzalez-Reviriego, Nube",
+      "orcid": "0000-0002-5919-6701"
+    },
+    {
+      "affiliation": "University of Arizona, USA",
+      "name": "Goodman, Paul"
+    },
+    {
+      "affiliation": "HZG, Germany",
+      "name": "Hagemann, Stefan",
+      "orcid": "0000-0001-5444-2945"
+    },
+    {
+      "affiliation": "ISAC-CNR, Italy",
+      "name": "von Hardenberg, Jost",
+      "orcid": "0000-0002-5312-8070"
+    },
+    {
+      "affiliation": "DLR, Germany",
+      "name": "Hassler, Birgit",
+      "orcid": "0000-0003-2724-709X"
+    },
+    {
+      "affiliation": "DLR, Germany",
+      "name": "Heuer, Helge",
+      "orcid": "0000-0003-2411-7150"
+    },
+    {
+      "affiliation": "BSC, Spain",
+      "name": "Hunter, Alasdair",
+      "orcid": "0000-0001-8365-3709"
+    },
+    {
+      "affiliation": "FUB, Germany",
+      "name": "Kadow, Christopher"
+    },
+    {
+      "affiliation": "DKRZ, Germany",
+      "name": "Kindermann, Stephan",
+      "orcid": "0000-0001-9335-1093"
+    },
+    {
+      "affiliation": "MPI for Biogeochemistry, Germany",
+      "name": "Koirala, Sujan"
+    },
+    {
+      "affiliation": "DLR, Germany",
+      "name": "Kuehbacher, Birgit"
+    },
+    {
+      "affiliation": "BSC, Spain",
+      "name": "Lledó, Llorenç"
+    },
+    {
+      "affiliation": "ETH Zurich, Switzerland",
+      "name": "Lejeune, Quentin"
+    },
+    {
+      "affiliation": "University of Hamburg, German",
+      "name": "Lembo, Valerio",
+      "orcid": "0000-0001-6085-5914"
+    },
+    {
+      "affiliation": "MetOffice, UK",
+      "name": "Little, Bill"
+    },
+    {
+      "affiliation": "BSC, Spain",
+      "name": "Loosveldt-Tomas, Saskia"
+    },
+    {
+      "affiliation": "ETH Zurich, Switzerland",
+      "name": "Lorenz, Ruth",
+      "orcid": "0000-0002-3986-1268"
+    },
+    {
+      "affiliation": "CMCC, Italy",
+      "name": "Lovato, Tomas",
+      "orcid": "0000-0002-5188-6767"
+    },
+    {
+      "affiliation": "University of Hamburg, German",
+      "name": "Lucarini, Valerio"
+    },
+    {
+      "affiliation": "UCLouvain, Belgium",
+      "name": "Massonnet, François"
+    },
+    {
+      "affiliation": "NIBIO, Norway",
+      "name": "Mohr, Christian Wilhelm",
+      "orcid": "0000-0003-2656-1802"
+    },
+    {
+      "affiliation": "University of Arizona, USA",
+      "name": "Amarjiit, Pandde"
+    },
+    {
+      "affiliation": "BSC, Spain",
+      "name": "Pérez-Zanón, Núria"
+    },
+    {
+      "affiliation": "NCAR, USA",
+      "name": "Phillips, Adam",
+      "orcid": "0000-0003-4859-8585"
+    },
+    {
+      "affiliation": "University of Arizona, USA",
+      "name": "Russell, Joellen"
+    },
+    {
+      "affiliation": "CICERO, Norway",
+      "name": "Sandstad, Marit"
+    },
+    {
+      "affiliation": "MetOffice, UK",
+      "name": "Sellar, Alistair"
+    },
+    {
+      "affiliation": "DLR, Germany",
+      "name": "Senftleben, Daniel"
+    },
+    {
+      "affiliation": "ISMAR-CNR, Italy",
+      "name": "Serva, Federico",
+      "orcid": "0000-0002-7118-0817"
+    },
+    {
+      "affiliation": "CICERO, Norway",
+      "name": "Sillmann, Jana"
+    },
+    {
+      "affiliation": "MPI-M, Germany",
+      "name": "Stacke, Tobias",
+      "orcid": "0000-0003-4637-5337"
+    },
+    {
+      "affiliation": "URead, UK",
+      "name": "Swaminathan, Ranjini",
+      "orcid": "0000-0001-5853-2673"
+    },
+    {
+      "affiliation": "BSC, Spain",
+      "name": "Torralba, Verónica"
+    },
+    {
+      "affiliation": "University of Bremen, Germany",
+      "name": "Weigel, Katja",
+      "orcid": "0000-0001-6133-7801"
+    },
+    {
+      "affiliation": "DLR, Germany",
+      "name": "Sarauer, Ellen"
+    },
+    {
+      "affiliation": "University of Reading, UK",
+      "name": "Roberts, Charles",
+      "orcid": "0000-0002-1147-8961"
+    },
+    {
+      "affiliation": "Netherlands eScience Center",
+      "name": "Kalverla, Peter",
+      "orcid": "0000-0002-5025-7862"
+    },
+    {
+      "affiliation": "Netherlands eScience Center",
+      "name": "Alidoost, Sarah",
+      "orcid": "0000-0001-8407-6472"
+    },
+    {
+      "affiliation": "Netherlands eScience Center",
+      "name": "Verhoeven, Stefan",
+      "orcid": "0000-0002-5821-2060"
+    },
+    {
+      "affiliation": "Netherlands eScience Center",
+      "name": "Vreede, Barbara",
+      "orcid": "0000-0002-5023-4601"
+    },
+    {
+      "affiliation": "Netherlands eScience Center",
+      "name": "Smeets, Stef",
+      "orcid": "0000-0002-5413-9038"
+    },
+    {
+      "affiliation": "Netherlands eScience Center",
+      "name": "Soares Siqueira, Abel",
+      "orcid": "0000-0003-4451-281X"
+    },
+    {
+      "affiliation": "DLR, Germany",
+      "name": "Kazeroni, Rémi",
+      "orcid": "0000-0001-7205-9528"
+    },
+    {
+      "affiliation": "NASA, USA",
+      "name": "Potter, Jerry"
+    },
+    {
+      "affiliation": "DLR, Germany",
+      "name": "Winterstein, Franziska",
+      "orcid": "0000-0002-2406-4936"
+    },
+    {
+      "affiliation": "ACCESS-NRI, Australia",
+      "name": "Beucher, Romain",
+      "orcid": "0000-0003-3891-5444"
+    },
+    {
+      "affiliation": "DLR, Germany",
+      "name": "Kraft, Jeremy"
+    },
+    {
+      "affiliation": "University of Bremen, Germany",
+      "name": "Ruhe, Lukas",
+      "orcid": "0000-0001-6349-9118"
+    },
+    {
+      "affiliation": "DLR, Germany",
+      "name": "Bonnet, Pauline",
+      "orcid": "0000-0003-3780-0784"
+    }
+  ],
+  "description": "ESMValTool: A community diagnostic and performance metrics tool for routine evaluation of Earth system models in CMIP.",
+  "license": {
+    "id": "Apache-2.0"
+  },
+  "publication_date": "2023-07-06",
+  "title": "ESMValTool",
+  "version": "v2.9.0",
+  "communities": [ 
+      { 
+          "identifier": "is-enes3" 
+      }, 
+      { 
+          "identifier": "dlr_de" 
+      }, 
+      { 
+          "identifier": "ecfunded" 
+      }, 
+      { 
+          "identifier": "nlesc" 
+      } 
+  ], 
+  "grants": [ 
+      { 
+          "id": "10.13039/501100000780::282672" 
+      }, 
+      { 
+          "id": "10.13039/501100000780::641727" 
+      }, 
+      { 
+          "id": "10.13039/501100000780::641816" 
+      }, 
+      { 
+          "id": "10.13039/501100000780::727862" 
+      }, 
+      { 
+          "id": "10.13039/501100000780::776613" 
+      }, 
+      { 
+          "id": "10.13039/501100000780::824084" 
+      } 
+  ] 
 }
diff --git a/CITATION.cff b/CITATION.cff
index 49226a7fda..147e3acd17 100644
--- a/CITATION.cff
+++ b/CITATION.cff
@@ -90,6 +90,11 @@ authors:
     family-names: Bock
     given-names: Lisa
     orcid: "https://orcid.org/0000-0001-7058-5938"
+  -
+    affiliation: "MetOffice, UK"
+    family-names: Bodas-Salcedo
+    given-names: Alejandro
+    orcid: "https://orcid.org/0000-0002-7890-2536"
   -
     affiliation: "BSC, Spain"
     family-names: Caron
@@ -181,6 +186,11 @@ authors:
     family-names: Hassler
     given-names: Birgit
     orcid: "https://orcid.org/0000-0003-2724-709X"
+  -
+    affiliation: "DLR, Germany"
+    family-names: Heuer
+    given-names: Helge
+    orcid: "https://orcid.org/0000-0003-2411-7150"
   -
     affiliation: "BSC, Spain"
     family-names: Hunter
@@ -199,6 +209,10 @@ authors:
     affiliation: "MPI for Biogeochemistry, Germany"
     family-names: Koirala
     given-names: Sujan
+  -
+    affiliation: "DLR, Germany"
+    family-names: Kuehbacher
+    given-names: Birgit
   -
     affiliation: "BSC, Spain"
     family-names: Lledó
@@ -300,6 +314,10 @@ authors:
     family-names: Weigel
     given-names: Katja
     orcid: "https://orcid.org/0000-0001-6133-7801"
+  -
+    affiliation: "DLR, Germany"
+    family-names: Sarauer
+    given-names: Ellen
   -
     affiliation: "University of Reading, UK"
     family-names: Roberts
@@ -354,6 +372,20 @@ authors:
     family-names: Beucher
     given-names: Romain
     orcid: "https://orcid.org/0000-0003-3891-5444"
+  -
+    affiliation: "DLR, Germany"
+    family-names: Kraft
+    given-names: Jeremy
+  -
+    affiliation: "University of Bremen, Germany"
+    family-names: Ruhe
+    given-names: Lukas
+    orcid: "https://orcid.org/0000-0001-6349-9118"
+  -
+    affiliation: "DLR, Germany"
+    family-names: Bonnet
+    given-names: Pauline
+    orcid: "https://orcid.org/0000-0003-3780-0784"
 
 cff-version: 1.2.0
 date-released: 2023-07-06
diff --git a/README.md b/README.md
index 069ce0a0c6..4fbe8aa84e 100644
--- a/README.md
+++ b/README.md
@@ -8,7 +8,7 @@
 [![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)
 [![Docker Build Status](https://img.shields.io/docker/cloud/build/esmvalgroup/esmvaltool.svg)](https://hub.docker.com/r/esmvalgroup/esmvaltool/)
-[![Anaconda-Server Badge](https://anaconda.org/conda-forge/esmvaltool/badges/version.svg)](https://anaconda.org/conda-forge/esmvaltool)
+[![Anaconda-Server Badge](https://img.shields.io/conda/vn/conda-forge/ESMValTool?color=blue&label=conda-forge&logo=conda-forge&logoColor=white)](https://anaconda.org/conda-forge/esmvaltool)
 ![stand with Ukraine](https://badgen.net/badge/stand%20with/UKRAINE/?color=0057B8&labelColor=FFD700)
 
 ![esmvaltoollogo](https://raw.githubusercontent.com/ESMValGroup/ESMValTool/main/doc/sphinx/source/figures/ESMValTool-logo-2.png)
@@ -53,11 +53,11 @@ ESMValTool can run with the following types of [data as input](https://docs.esmv
 
 # Getting started
 
-Please see [getting started](https://docs.esmvaltool.org/en/latest/quickstart/index.html) on readthedocs as well as [ESMValTool tutorial](https://tutorial.esmvaltool.org/index.html). The tutorial is a set of lessons that together teach skills needed to work with ESMValTool in climate-related domains.
+Please see [getting started](https://docs.esmvaltool.org/en/latest/quickstart/index.html) on our instance of Read the Docs as well as [ESMValTool tutorial](https://tutorial.esmvaltool.org/index.html). The tutorial is a set of lessons that together teach skills needed to work with ESMValTool in climate-related domains.
 
 ## Getting help
 
-The easiest way to get help if you cannot find the answer in the documentation on [readthedocs](https://docs.esmvaltool.org), is to open an [issue on GitHub](https://github.com/ESMValGroup/ESMValTool/issues).
+The easiest way to get help, if you cannot find the answer in the documentation in our [docs](https://docs.esmvaltool.org), is to open an [issue on GitHub](https://github.com/ESMValGroup/ESMValTool/issues).
 
 ## Contributing
 
diff --git a/conda-linux-64.lock b/conda-linux-64.lock
index c7e91c6a20..b0facf9382 100644
--- a/conda-linux-64.lock
+++ b/conda-linux-64.lock
@@ -1,49 +1,46 @@
 # Generated by conda-lock.
 # platform: linux-64
-# input_hash: f47d35f9d83344217c0911cc8cede368892e578ba25206f3c774436c197445b3
+# input_hash: 05ccb0eb21a22bcda85523e4fc91a3c5d43c69b2fc56e17f5c81159b2e906d6b
 @EXPLICIT
 https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81
 https://conda.anaconda.org/conda-forge/linux-64/_py-xgboost-mutex-2.0-gpu_0.tar.bz2#7702188077361f43a4d61e64c694f850
 https://conda.anaconda.org/conda-forge/noarch/_r-mutex-1.0.1-anacondar_1.tar.bz2#19f9db5f4f1b7f5ef5f6d67207f25f38
-https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2023.7.22-hbcca054_0.conda#a73ecd2988327ad4c8f2c331482917f2
+https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2023.11.17-hbcca054_0.conda#01ffc8d36f9eba0ce0b3c1955fa780ee
 https://conda.anaconda.org/conda-forge/noarch/cuda-version-11.8-h70ddcb2_2.conda#601900ec9ff06f62f76a247148e52c04
 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-hab24e00_0.tar.bz2#19410c3df09dfb12d1206132a1d357c5
+https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_1.conda#6185f640c43843e5ad6fd1c5372c3f80
 https://conda.anaconda.org/conda-forge/noarch/kernel-headers_linux-64-2.6.32-he073ed8_16.conda#7ca122655873935e02c91279c5b03c8c
 https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.40-h41732ed_0.conda#7aca3059a1729aa76c597603f10b0dd3
-https://conda.anaconda.org/conda-forge/linux-64/libgcc-devel_linux-64-13.1.0-he3cc6c4_0.conda#5ec50dcd74ba7461709c4ac9c4cc4190
-https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-13.1.0-h15d22d2_0.conda#afb656a334c409dd9805508af1c89c7a
-https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-devel_linux-64-13.1.0-he3cc6c4_0.conda#e703914ad2288ab24cf5ac94d812fc11
-https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-13.1.0-hfd8a6a1_0.conda#067bcc23164642f4c226da631f2a2e1d
+https://conda.anaconda.org/conda-forge/noarch/libgcc-devel_linux-64-13.2.0-ha9c7c90_103.conda#db8cd1a871a07404d94f7dcc78c21a61
+https://conda.anaconda.org/conda-forge/noarch/libstdcxx-devel_linux-64-13.2.0-ha9c7c90_103.conda#46947f93254fdedc5ae0725b11ca3610
+https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-13.2.0-h7e041cc_3.conda#937eaed008f6bf2191c5fe76f87755e9
 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-3.11-3_cp311.conda#c2e2630ddb68cf52eec74dc7dfab20b5
+https://conda.anaconda.org/conda-forge/linux-64/python_abi-3.11-4_cp311.conda#d786502c97404c94d7d58d258a445a65
 https://conda.anaconda.org/conda-forge/noarch/tzdata-2023c-h71feb2d_0.conda#939e3e74d8be4dac89ce83b20de2492a
 https://conda.anaconda.org/conda-forge/linux-64/xorg-imake-1.0.7-0.tar.bz2#23acfc5a339a6a34cc2241f64e4111be
 https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-0.tar.bz2#f766549260d6815b0c52253f1fb1bb29
-https://conda.anaconda.org/conda-forge/linux-64/libgfortran-ng-13.1.0-h69a702a_0.conda#506dc07710dd5b0ba63cbf134897fc10
-https://conda.anaconda.org/conda-forge/linux-64/libgomp-13.1.0-he5830b7_0.conda#56ca14d57ac29a75d23a39eb3ee0ddeb
+https://conda.anaconda.org/conda-forge/linux-64/libgomp-13.2.0-h807b86a_3.conda#7124cbb46b13d395bdde68f2d215c989
 https://conda.anaconda.org/conda-forge/noarch/sysroot_linux-64-2.12-he073ed8_16.conda#071ea8dceff4d30ac511f4a2f8437cd1
 https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2#73aaf86a425cc6e73fcf236a5a46396d
 https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.40-hf600244_0.conda#33084421a8c0af6aef1b439707f7662a
 https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2#fee5683a3f04bd15cbd8318b096a27ab
-https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-13.1.0-he5830b7_0.conda#cd93f779ff018dd85c7544c015c9db3c
+https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-13.2.0-h807b86a_3.conda#23fdf1fef05baeb7eadc2aed5fb0011f
 https://conda.anaconda.org/conda-forge/linux-64/aom-3.5.0-h27087fc_0.tar.bz2#a08150fd2298460cd1fcccf626305642
 https://conda.anaconda.org/conda-forge/linux-64/aws-c-common-0.8.23-hd590300_0.conda#cc4f06f7eedb1523f3b83fd0fb3942ff
-https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h7f98852_4.tar.bz2#a1fd65c7ccbf10880423d82bca54eb54
-https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.19.1-hd590300_0.conda#e8c18d865be43e2fb3f7a145b6adf1f5
+https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hd590300_5.conda#69b8b6202a07720f448be700e300ccf4
+https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.23.0-hd590300_0.conda#d459949bc10f64dee1595c176c2e6291
 https://conda.anaconda.org/conda-forge/linux-64/charls-2.4.2-h59595ed_0.conda#4336bd67920dd504cd8c6761d6a99645
 https://conda.anaconda.org/conda-forge/linux-64/dav1d-1.2.1-hd590300_0.conda#418c6ca5929a611cbd69204907a83995
-https://conda.anaconda.org/conda-forge/linux-64/fftw-3.3.10-nompi_hc118613_108.conda#6fa90698000b05dfe8ce6515794fe71a
 https://conda.anaconda.org/conda-forge/linux-64/freexl-1.0.6-h166bdaf_1.tar.bz2#897e772a157faf3330d72dd291486f62
 https://conda.anaconda.org/conda-forge/linux-64/fribidi-1.0.10-h36c2ea0_0.tar.bz2#ac7bc6a654f8f41b352b38f4051135f8
 https://conda.anaconda.org/conda-forge/linux-64/geos-3.11.2-hcb278e6_0.conda#3b8e364995e3575e57960d29c1e5ab14
 https://conda.anaconda.org/conda-forge/linux-64/gettext-0.21.1-h27087fc_0.tar.bz2#14947d8770185e5153fdd04d4673ed37
 https://conda.anaconda.org/conda-forge/linux-64/gflags-2.2.2-he1b5a44_1004.tar.bz2#cddaf2c63ea4a5901cf09524c490ecdc
-https://conda.anaconda.org/conda-forge/linux-64/ghostscript-9.54.0-h27087fc_2.tar.bz2#c3b35ac18d09ffc8d46064fb09a696af
+https://conda.anaconda.org/conda-forge/linux-64/ghostscript-10.02.1-h59595ed_0.conda#3750ef83be92ff8de6e44da80d509847
 https://conda.anaconda.org/conda-forge/linux-64/giflib-5.2.1-h0b41bf4_3.conda#96f3b11872ef6fad973eac856cd2624f
-https://conda.anaconda.org/conda-forge/linux-64/gmp-6.2.1-h58526e2_0.tar.bz2#b94cf2db16066b242ebd26db2facbd56
+https://conda.anaconda.org/conda-forge/linux-64/gmp-6.3.0-h59595ed_0.conda#0e33ef437202db431aa5a928248cf2e8
 https://conda.anaconda.org/conda-forge/linux-64/graphite2-1.3.13-h58526e2_1001.tar.bz2#8c54672728e8ec6aa6db90cf2806d220
 https://conda.anaconda.org/conda-forge/linux-64/icu-72.1-hcb278e6_0.conda#7c8d20d847bb45f56bd941578fcfa146
 https://conda.anaconda.org/conda-forge/linux-64/jbig-2.1-h7f98852_2003.tar.bz2#1aa0cee79792fa97b7ff4545110b60bf
@@ -52,20 +49,20 @@ https://conda.anaconda.org/conda-forge/linux-64/jxrlib-1.1-h7f98852_2.tar.bz2#8e
 https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.1-h166bdaf_0.tar.bz2#30186d27e2c9fa62b45fb1476b7200e3
 https://conda.anaconda.org/conda-forge/linux-64/lerc-4.0.0-h27087fc_0.tar.bz2#76bbff344f0134279f225174e9064c8f
 https://conda.anaconda.org/conda-forge/linux-64/libabseil-20230125.3-cxx17_h59595ed_0.conda#d1db1b8be7c3a8983dcbbbfe4f0765de
-https://conda.anaconda.org/conda-forge/linux-64/libaec-1.0.6-hcb278e6_1.conda#0f683578378cddb223e7fd24f785ab2a
+https://conda.anaconda.org/conda-forge/linux-64/libaec-1.1.2-h59595ed_1.conda#127b0be54c1c90760d7fe02ea7a56426
 https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.0.9-h166bdaf_9.conda#61641e239f96eae2b8492dc7e755828c
 https://conda.anaconda.org/conda-forge/linux-64/libcrc32c-1.1.2-h9c3ff4c_0.tar.bz2#c965a5aa0d5c1c37ffc62dff36e28400
 https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.18-h0b41bf4_0.conda#6aa9c9de5542ecb07fdda9ca626252d8
-https://conda.anaconda.org/conda-forge/linux-64/libev-4.33-h516909a_1.tar.bz2#6f8720dff19e17ce5d48cfe7f3d2f0a3
+https://conda.anaconda.org/conda-forge/linux-64/libev-4.33-hd590300_2.conda#172bf1cd1ff8629f2b1179945ed45055
 https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.5.0-hcb278e6_1.conda#6305a3dd2752c76335295da4e581f2fd
 https://conda.anaconda.org/conda-forge/linux-64/libffi-3.4.2-h7f98852_5.tar.bz2#d645c6d2ac96843a2bfaccd2d62b3ac3
+https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-13.2.0-ha4646dd_3.conda#c714d905cdfa0e70200f68b80cc04764
 https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.17-h166bdaf_0.tar.bz2#b62b52da46c39ee2bc3c162ac7f1804d
-https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-2.1.5.1-h0b41bf4_0.conda#1edd9e67bdb90d78cea97733ff6b54e6
-https://conda.anaconda.org/conda-forge/linux-64/libnsl-2.0.0-h7f98852_0.tar.bz2#39b1328babf85c7c3a61636d9cd50206
+https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-2.1.5.1-hd590300_1.conda#323e90742f0f48fc22bea908735f55e6
+https://conda.anaconda.org/conda-forge/linux-64/libnsl-2.0.1-hd590300_0.conda#30fd6e37fe21f86f4bd26d6ee73eeec7
 https://conda.anaconda.org/conda-forge/linux-64/libnuma-2.0.16-h0b41bf4_1.conda#28bfe2cb11357ccc5be21101a6b7ce86
-https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.23-pthreads_h80387f5_0.conda#9c5ea51ccb8ffae7d06c645869d24ce6
-https://conda.anaconda.org/conda-forge/linux-64/libopenblas-ilp64-0.3.23-pthreads_h5c82d6a_0.conda#14e3fb938a49ea46df201eb105bb9068
-https://conda.anaconda.org/conda-forge/linux-64/libsanitizer-13.1.0-hfd8a6a1_0.conda#7594fd17fb4d1b8b0e47a6b306fe01ae
+https://conda.anaconda.org/conda-forge/linux-64/libopenlibm4-0.8.1-hd590300_1.conda#e6af610e01d04927a5060c95ce4e0875
+https://conda.anaconda.org/conda-forge/linux-64/libsanitizer-13.2.0-h7e041cc_3.conda#c63848839569bb82a3eff11f01e5de00
 https://conda.anaconda.org/conda-forge/linux-64/libsodium-1.0.18-h36c2ea0_1.tar.bz2#c3788462a6fbddafdb413a9f9053e58d
 https://conda.anaconda.org/conda-forge/linux-64/libtool-2.4.7-h27087fc_0.conda#f204c8ba400ec475452737094fb81d52
 https://conda.anaconda.org/conda-forge/linux-64/libunwind-1.6.2-h9c3ff4c_0.tar.bz2#a730b2badd586580c5752cc73842e068
@@ -78,22 +75,21 @@ https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.9.4-hcb278e6_0.conda#318
 https://conda.anaconda.org/conda-forge/linux-64/lzo-2.10-h516909a_1000.tar.bz2#bb14fcb13341b81d5eb386423b9d2bac
 https://conda.anaconda.org/conda-forge/linux-64/make-4.3-hd18ef5c_1.tar.bz2#4049ebfd3190b580dffe76daed26155a
 https://conda.anaconda.org/conda-forge/linux-64/mbedtls-3.3.0-hcb278e6_0.conda#cc1213f464c357b647cc5dde5cfca881
-https://conda.anaconda.org/conda-forge/linux-64/metis-5.1.1-h59595ed_0.conda#9c9e667050d3e258d321e63a0989ebb1
-https://conda.anaconda.org/conda-forge/linux-64/nccl-2.18.3.1-h12f7317_0.conda#c0041b39fc72c1d12139b45363c8a510
-https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.4-hcb278e6_0.conda#681105bccc2a3f7f1a837d47d39c9179
+https://conda.anaconda.org/conda-forge/linux-64/metis-5.1.1-h59595ed_2.conda#9ba5910c34210e7ad60736d172bbcd4c
+https://conda.anaconda.org/conda-forge/linux-64/nccl-2.19.4.1-h6103f9b_0.conda#2946f0e841f1f0be90c90bc67877d417
+https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.4-h59595ed_2.conda#7dbaa197d7ba6032caf7ae7f32c1efa0
 https://conda.anaconda.org/conda-forge/linux-64/nspr-4.35-h27087fc_0.conda#da0ec11a6454ae19bff5b02ed881a2b1
-https://conda.anaconda.org/conda-forge/linux-64/openlibm-0.8.1-h7f98852_0.tar.bz2#ba0c56761f2380babaa783b2a37b4d08
-https://conda.anaconda.org/conda-forge/linux-64/openssl-3.1.2-hd590300_0.conda#e5ac5227582d6c83ccf247288c0eb095
+https://conda.anaconda.org/conda-forge/linux-64/openssl-3.1.4-hd590300_0.conda#412ba6938c3e2abaca8b1129ea82e238
 https://conda.anaconda.org/conda-forge/linux-64/p7zip-16.02-h9c3ff4c_1001.tar.bz2#941066943c0cac69d5aa52189451aa5f
-https://conda.anaconda.org/conda-forge/linux-64/pixman-0.40.0-h36c2ea0_0.tar.bz2#660e72c82f2e75a6b3fe6a6e75c79f19
+https://conda.anaconda.org/conda-forge/linux-64/pixman-0.42.2-h59595ed_0.conda#700edd63ccd5fc66b70b1c028cea9a68
 https://conda.anaconda.org/conda-forge/linux-64/pkg-config-0.29.2-h36c2ea0_1008.tar.bz2#fbef41ff6a4c8140c30057466a1cdd47
 https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-h36c2ea0_1001.tar.bz2#22dad4df6e8630e8dff2428f6f6a7036
 https://conda.anaconda.org/conda-forge/linux-64/rdma-core-28.9-h59595ed_1.conda#aeffb7c06b5f65e55e6c637408dc4100
 https://conda.anaconda.org/conda-forge/linux-64/re2-2023.03.02-h8c504da_0.conda#206f8fa808748f6e90599c3368a1114e
-https://conda.anaconda.org/conda-forge/linux-64/rocm-smi-5.6.0-h59595ed_1.conda#2980fb3b7777f495dacd46d5cc5d74bb
 https://conda.anaconda.org/conda-forge/linux-64/sed-4.8-he412f7d_0.tar.bz2#7362f0042e95681f5d371c46c83ebd08
 https://conda.anaconda.org/conda-forge/linux-64/snappy-1.1.10-h9fff704_0.conda#e6d228cd0bb74a51dd18f5bfce0b4115
 https://conda.anaconda.org/conda-forge/linux-64/tzcode-2023c-h0b41bf4_0.conda#0c0533894f21c3d35697cb8378d390e2
+https://conda.anaconda.org/conda-forge/linux-64/uriparser-0.9.7-hcb278e6_1.conda#2c46deb08ba9b10e90d0a6401ad65deb
 https://conda.anaconda.org/conda-forge/linux-64/xorg-inputproto-2.3.2-h7f98852_1002.tar.bz2#bcd1b3396ec6960cbc1d2855a9e60b2b
 https://conda.anaconda.org/conda-forge/linux-64/xorg-kbproto-1.0.7-h7f98852_1002.tar.bz2#4b230e8381279d76131116660f5a241a
 https://conda.anaconda.org/conda-forge/linux-64/xorg-libice-1.1.1-hd590300_0.conda#b462a33c0be1421532f28bfe8f4a7514
@@ -103,396 +99,401 @@ https://conda.anaconda.org/conda-forge/linux-64/xorg-makedepend-1.0.8-h59595ed_0
 https://conda.anaconda.org/conda-forge/linux-64/xorg-renderproto-0.11.1-h7f98852_1002.tar.bz2#06feff3d2634e3097ce2fe681474b534
 https://conda.anaconda.org/conda-forge/linux-64/xorg-xextproto-7.3.0-h0b41bf4_1003.conda#bce9f945da8ad2ae9b1d7165a64d0f87
 https://conda.anaconda.org/conda-forge/linux-64/xorg-xproto-7.0.31-h7f98852_1007.tar.bz2#b4a4381d54784606820704f7b5f05a15
-https://conda.anaconda.org/conda-forge/linux-64/xxhash-0.8.1-h0b41bf4_0.conda#e9c3bcf0e0c719431abec8ca447eee27
+https://conda.anaconda.org/conda-forge/linux-64/xxhash-0.8.2-hd590300_0.conda#f08fb5c89edfc4aadee1c81d4cfb1fa1
 https://conda.anaconda.org/conda-forge/linux-64/xz-5.2.6-h166bdaf_0.tar.bz2#2161070d867d1b1204ea749c8eec4ef0
 https://conda.anaconda.org/conda-forge/linux-64/yaml-0.2.5-h7f98852_2.tar.bz2#4cb3ad778ec2d5a7acbdf254eb1c42ae
-https://conda.anaconda.org/conda-forge/linux-64/zfp-1.0.0-h27087fc_3.tar.bz2#0428af0510c3fafedf1c66b43102a34b
+https://conda.anaconda.org/conda-forge/linux-64/zfp-1.0.0-h59595ed_4.conda#9cfbafab420f42b572f3c032ad59da85
 https://conda.anaconda.org/conda-forge/linux-64/zlib-ng-2.0.7-h0b41bf4_0.conda#49e8329110001f04923fe7e864990b0c
 https://conda.anaconda.org/conda-forge/linux-64/aws-c-cal-0.6.0-h93469e0_0.conda#580a52a05f5be28ce00764149017c6d4
 https://conda.anaconda.org/conda-forge/linux-64/aws-c-compression-0.2.17-h862ab75_1.conda#0013fcee7acb3cfc801c5929824feb3c
 https://conda.anaconda.org/conda-forge/linux-64/aws-c-sdkutils-0.1.11-h862ab75_1.conda#6fbc9bd49434eb36d3a59c5020f4af95
 https://conda.anaconda.org/conda-forge/linux-64/aws-checksums-0.1.16-h862ab75_1.conda#f883d61afbc95c50f7b3f62546da4235
 https://conda.anaconda.org/conda-forge/linux-64/expat-2.5.0-hcb278e6_1.conda#8b9b5aca60558d02ddaa09d599e55920
-https://conda.anaconda.org/conda-forge/linux-64/gcc_impl_linux-64-13.1.0-hc4be1a9_0.conda#99d1a8a8ee1665ee9435f8d160df69fe
+https://conda.anaconda.org/conda-forge/linux-64/gcc_impl_linux-64-13.2.0-h338b0a0_3.conda#79ae2d39f23e568b18be949973e9a025
 https://conda.anaconda.org/conda-forge/linux-64/glog-0.6.0-h6f12383_0.tar.bz2#b31f3565cb84435407594e548a2fb7b2
 https://conda.anaconda.org/conda-forge/linux-64/hdf4-4.2.15-h501b40f_6.conda#c3e9338e15d90106f467377017352b97
 https://conda.anaconda.org/conda-forge/linux-64/libavif-0.11.1-h8182462_2.conda#41c399ed4c439e37b844c24ab5621b5a
-https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-17_linux64_openblas.conda#57fb44770b1bc832fb2dbefa1bd502de
 https://conda.anaconda.org/conda-forge/linux-64/libbrotlidec-1.0.9-h166bdaf_9.conda#081aa22f4581c08e4372b0b6c2f8478e
 https://conda.anaconda.org/conda-forge/linux-64/libbrotlienc-1.0.9-h166bdaf_9.conda#1f0a03af852a9659ed2bf08f2f1704fd
 https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20191231-he28a2e2_2.tar.bz2#4d331e44109e3f0e19b4cb8f9b82f3e1
 https://conda.anaconda.org/conda-forge/linux-64/libevent-2.1.12-hf998b51_1.conda#a1cfcc585f0c42bf8d5546bb1dfb668d
+https://conda.anaconda.org/conda-forge/linux-64/libgfortran-ng-13.2.0-h69a702a_3.conda#73031c79546ad06f1fe62e57fdd021bc
+https://conda.anaconda.org/conda-forge/linux-64/libkml-1.3.0-h01aab08_1016.conda#4d0907546d556ef7f14b1dcfa0e217ce
 https://conda.anaconda.org/conda-forge/linux-64/libllvm14-14.0.6-hcd5def8_4.conda#73301c133ded2bf71906aa2104edae8b
-https://conda.anaconda.org/conda-forge/linux-64/libnghttp2-1.52.0-h61bc06f_0.conda#613955a50485812985c059e7b269f42e
+https://conda.anaconda.org/conda-forge/linux-64/libnghttp2-1.58.0-h47da74e_0.conda#9b13d5ee90fc9f09d54fd403247342b4
 https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.39-h753d276_0.conda#e1c890aebdebbfbf87e2c917187b4416
-https://conda.anaconda.org/conda-forge/linux-64/libprotobuf-4.23.3-hd1fb520_0.conda#c8da7f04073ed0fabcb60885a4c1a722
+https://conda.anaconda.org/conda-forge/linux-64/libprotobuf-4.23.3-hd1fb520_1.conda#78c10e8637a6f8d377f9989327d0267d
 https://conda.anaconda.org/conda-forge/linux-64/librttopo-1.1.0-h0d5128d_13.conda#e1d6139ff0500977a760567a4bec1ce9
-https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.42.0-h2797004_0.conda#fdaae20a1cf7cd62130a0973190a31b7
+https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.44.2-h2797004_0.conda#3b6a9f225c3dbe0d24f4fedd4625c5bf
 https://conda.anaconda.org/conda-forge/linux-64/libssh2-1.11.0-h0841786_0.conda#1f5a58e686b13bcfde88b93f547d23fe
+https://conda.anaconda.org/conda-forge/linux-64/libudunits2-2.2.28-h40f5838_3.conda#4bdace082e911a3e1f1f0b721bed5b56
 https://conda.anaconda.org/conda-forge/linux-64/libxcb-1.15-h0b41bf4_0.conda#33277193f5b92bad9fdd230eb700929c
-https://conda.anaconda.org/conda-forge/linux-64/libxgboost-1.7.6-cuda112h7a395e5_0.conda#8f5e1a6eb68812ed72cb7992f1db537d
+https://conda.anaconda.org/conda-forge/linux-64/libxgboost-1.7.6-cuda118_hd3b444d_6.conda#78509401d0879ef5aac72b8d1e104493
 https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.11.5-h0d562d8_0.conda#558ab736404275d7df61c473c1af35aa
-https://conda.anaconda.org/conda-forge/linux-64/libzip-1.9.2-hc929e4a_1.tar.bz2#5b122b50e738c4be5c3f2899f010d7cf
-https://conda.anaconda.org/conda-forge/linux-64/mpfr-4.2.0-hb012696_0.conda#14d87bdff2cbd3b1179a29fb316ed743
-https://conda.anaconda.org/conda-forge/linux-64/openblas-ilp64-0.3.23-pthreads_h3d04fff_0.conda#9787e8492f0a51a6b0498dc86f3864f9
+https://conda.anaconda.org/conda-forge/linux-64/libzip-1.10.1-h2629f0a_3.conda#ac79812548e7e8cf61f7b0abdef01d3b
+https://conda.anaconda.org/conda-forge/linux-64/mpfr-4.2.1-h9458935_0.conda#4c28f3210b30250037a4a627eeee9e0f
+https://conda.anaconda.org/conda-forge/linux-64/openlibm-0.8.1-hd590300_1.conda#6eba22eb06d69e53d0ca01eef42bc675
 https://conda.anaconda.org/conda-forge/linux-64/pcre2-10.40-hc3806b6_0.tar.bz2#69e2c796349cd9b273890bee0febfe1b
 https://conda.anaconda.org/conda-forge/linux-64/perl-5.32.1-4_hd590300_perl5.conda#3e785bff761095eb7f8676f4694bd1b1
 https://conda.anaconda.org/conda-forge/linux-64/readline-8.2-h8228510_1.conda#47d31b792659ce70f470b5c82fdfb7a4
 https://conda.anaconda.org/conda-forge/linux-64/s2n-1.3.46-h06160fa_0.conda#413d96a0b655c8f8aacc36473a2dbb04
-https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.12-h27826a3_0.tar.bz2#5b8c42eb62e9fc961af70bdd6a26e168
-https://conda.anaconda.org/conda-forge/linux-64/ucx-1.14.1-h4a2ce2d_2.conda#9ad153019bdfd939b235825eddea53bc
+https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_h4845f30_101.conda#d453b98d9c83e71da0741bb0ff4d76bc
+https://conda.anaconda.org/conda-forge/linux-64/ucx-1.14.1-h64cca9d_5.conda#39aa3b356d10d7e5add0c540945a0944
 https://conda.anaconda.org/conda-forge/linux-64/xorg-fixesproto-5.0-h7f98852_1002.tar.bz2#65ad6e1eb4aed2b0611855aff05e04f6
 https://conda.anaconda.org/conda-forge/linux-64/xorg-libsm-1.2.4-h7391055_0.conda#93ee23f12bc2e684548181256edd2cf6
-https://conda.anaconda.org/conda-forge/linux-64/zeromq-4.3.4-h9c3ff4c_1.tar.bz2#21743a8d2ea0c8cfbbf8fe489b0347df
+https://conda.anaconda.org/conda-forge/linux-64/zeromq-4.3.5-h59595ed_0.conda#8851084c192dbc56215ac4e3c9aa30fa
 https://conda.anaconda.org/conda-forge/linux-64/zlib-1.2.13-hd590300_5.conda#68c34ec6149623be41a1933ab996a209
-https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.2-hfc55251_7.conda#32ae18eb2a687912fc9e92a501c0a11b
+https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.5-hfc55251_0.conda#04b88013080254850d6c01ed54810589
 https://conda.anaconda.org/conda-forge/linux-64/aws-c-io-0.13.28-h3870b5a_0.conda#b775667301ab249f94ad2bea91fc4223
-https://conda.anaconda.org/conda-forge/linux-64/blosc-1.21.4-h0f2a231_0.conda#876286b5941933a0f558777e57d883cc
+https://conda.anaconda.org/conda-forge/linux-64/blosc-1.21.5-h0f2a231_0.conda#009521b7ed97cca25f8f997f9e745976
 https://conda.anaconda.org/conda-forge/linux-64/boost-cpp-1.78.0-h6582d0a_3.conda#d3c3c7698d0b878aab1b86db95407c8e
 https://conda.anaconda.org/conda-forge/linux-64/brotli-bin-1.0.9-h166bdaf_9.conda#d47dee1856d9cb955b8076eeff304a5b
 https://conda.anaconda.org/conda-forge/linux-64/bwidget-1.9.14-ha770c72_1.tar.bz2#5746d6202ba2abad4a4707f2a2462795
-https://conda.anaconda.org/conda-forge/linux-64/c-blosc2-2.10.0-hb4ffafa_0.conda#63b397207c1e039b3373dd45af045d8b
-https://conda.anaconda.org/conda-forge/linux-64/freetype-2.12.1-hca18f0e_1.conda#e1232042de76d24539a436d37597eb06
-https://conda.anaconda.org/conda-forge/linux-64/gfortran_impl_linux-64-13.1.0-hd511a9b_0.conda#e0dead77d88f1bcc59c0e0c2f8975af1
-https://conda.anaconda.org/conda-forge/linux-64/gxx_impl_linux-64-13.1.0-hc4be1a9_0.conda#e6591b3c81fc5fb83e342b20a2506e80
+https://conda.anaconda.org/conda-forge/linux-64/c-blosc2-2.11.3-hb4ffafa_0.conda#f394ac64ab0e1fcb0152cc9c16df3d85
+https://conda.anaconda.org/conda-forge/linux-64/fftw-3.3.10-nompi_hc118613_108.conda#6fa90698000b05dfe8ce6515794fe71a
+https://conda.anaconda.org/conda-forge/linux-64/freetype-2.12.1-h267a509_2.conda#9ae35c3d96db2c94ce0cef86efdfa2cb
+https://conda.anaconda.org/conda-forge/linux-64/gfortran_impl_linux-64-13.2.0-h76e1118_3.conda#4a04c8f0a51d525776f267112198684d
+https://conda.anaconda.org/conda-forge/linux-64/gxx_impl_linux-64-13.2.0-h338b0a0_3.conda#a5e463121f06f300e5462f98b82d0709
 https://conda.anaconda.org/conda-forge/linux-64/hdfeos2-2.20-hebf79cf_1003.conda#23bb57b64a629bc3b33379beece7f0d7
 https://conda.anaconda.org/conda-forge/linux-64/krb5-1.20.1-h81ceb04_0.conda#89a41adce7106749573d883b2f657d78
 https://conda.anaconda.org/conda-forge/linux-64/libarchive-3.6.2-h039dbb9_1.conda#29cf970521d30d113f3425b84cb250f6
-https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-17_linux64_openblas.conda#7ef0969b00fe3d6eef56a8151d3afb29
 https://conda.anaconda.org/conda-forge/linux-64/libgit2-1.5.1-h1f77430_0.conda#16802fd0c80290248ea79a570bd83b95
-https://conda.anaconda.org/conda-forge/linux-64/libglib-2.76.4-hebfc3b9_0.conda#c6f951789c888f7bbd2dd6858eab69de
-https://conda.anaconda.org/conda-forge/linux-64/libglu-9.0.0-hac7e632_1002.conda#4c4dce87e96b321308f81ba2c10d2897
-https://conda.anaconda.org/conda-forge/linux-64/libgrpc-1.56.2-h3905398_0.conda#a87e780f3d9cc7cf432e47ced83a67ce
-https://conda.anaconda.org/conda-forge/linux-64/libhwloc-2.9.2-nocuda_h7313eea_1008.conda#3b4cad0666b5a8ec5eaf2de0ef41d6c9
-https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-17_linux64_openblas.conda#a2103882c46492e26500fcb56c03de8b
+https://conda.anaconda.org/conda-forge/linux-64/libglib-2.78.1-hebfc3b9_0.conda#ddd09e8904fde46b85f41896621803e6
+https://conda.anaconda.org/conda-forge/linux-64/libgrpc-1.56.2-h3905398_1.conda#0b01e6ff8002994bd4ddbffcdbec7856
+https://conda.anaconda.org/conda-forge/linux-64/libhwloc-2.9.3-default_h554bfaf_1009.conda#f36ddc11ca46958197a45effdd286e45
+https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.25-pthreads_h413a1c8_0.conda#d172b34a443b95f86089e8229ddc9a17
+https://conda.anaconda.org/conda-forge/linux-64/libopenblas-ilp64-0.3.25-pthreads_h384dd9e_0.conda#637ee73ecb26ad42c38278a6619a20ca
 https://conda.anaconda.org/conda-forge/linux-64/libthrift-0.18.1-h8fd135c_2.conda#bbf65f7688512872f063810623b755dc
-https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.5.1-h8b53f26_0.conda#8ad377fb60abab446a9f02c62b3c2190
+https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.5.1-h8b53f26_1.conda#5b09e13d732dda1a2bc9adc711164f4d
 https://conda.anaconda.org/conda-forge/linux-64/libxslt-1.1.37-h0054252_1.conda#f27960e8873abb5476e96ef33bdbdccd
-https://conda.anaconda.org/conda-forge/linux-64/nss-3.89-he45b914_0.conda#2745719a58eeaab6657256a3f142f099
+https://conda.anaconda.org/conda-forge/linux-64/nss-3.95-h1d7d5a4_0.conda#d3a8067adcc45a923f4b1987c91d69da
 https://conda.anaconda.org/conda-forge/linux-64/orc-1.9.0-h385abfd_1.conda#2cd5aac7ef1b4c6ac51bf521251a89b3
 https://conda.anaconda.org/conda-forge/linux-64/pandoc-3.1.3-h32600fe_0.conda#8287aeb8462e2d4b235eff788e75919d
-https://conda.anaconda.org/conda-forge/linux-64/python-3.11.4-hab00c5b_0_cpython.conda#1c628861a2a126b9fc9363ca1b7d014e
-https://conda.anaconda.org/conda-forge/linux-64/sqlite-3.42.0-h2c6b66d_0.conda#1192f6ec654a5bc4ee1d64bdc4a3e5cc
-https://conda.anaconda.org/conda-forge/linux-64/tktable-2.10-hb7b940f_3.tar.bz2#ea4d0879e40211fa26f38d8986db1bbe
-https://conda.anaconda.org/conda-forge/linux-64/udunits2-2.2.28-hc3e0081_0.tar.bz2#d4c341e0379c31e9e781d4f204726867
-https://conda.anaconda.org/conda-forge/linux-64/xorg-libx11-1.8.6-h8ee46fc_0.conda#7590b76c3d11d21caa44f3fc38ac584a
+https://conda.anaconda.org/conda-forge/linux-64/python-3.11.6-hab00c5b_0_cpython.conda#b0dfbe2fcbfdb097d321bfd50ecddab1
+https://conda.anaconda.org/conda-forge/linux-64/sqlite-3.44.2-h2c6b66d_0.conda#4f2892c672829693fd978d065db4e8be
+https://conda.anaconda.org/conda-forge/linux-64/tktable-2.10-h0c5db8f_5.conda#9464044754ea25557a9c93f0327d90a6
+https://conda.anaconda.org/conda-forge/linux-64/udunits2-2.2.28-h40f5838_3.conda#6bb8deb138f87c9d48320ac21b87e7a1
+https://conda.anaconda.org/conda-forge/linux-64/xorg-libx11-1.8.7-h8ee46fc_0.conda#49e482d882669206653b095f5206c05b
 https://conda.anaconda.org/conda-forge/noarch/affine-2.4.0-pyhd8ed1ab_0.conda#ae5f4ad87126c55ba3f690ef07f81d64
 https://conda.anaconda.org/conda-forge/noarch/alabaster-0.7.13-pyhd8ed1ab_0.conda#06006184e203b61d3525f90de394471e
-https://conda.anaconda.org/conda-forge/linux-64/antlr-python-runtime-4.7.2-py311h38be061_1003.tar.bz2#0ab8f8f0cae99343907fe68cda11baea
-https://conda.anaconda.org/conda-forge/linux-64/arpack-3.7.0-hdefa2d7_2.tar.bz2#8763fe86163198ef1778d1d8d22bb078
+https://conda.anaconda.org/conda-forge/noarch/antlr-python-runtime-4.11.1-pyhd8ed1ab_0.tar.bz2#15109c4977d39ad7aa3423f57243e286
 https://conda.anaconda.org/conda-forge/noarch/asciitree-0.3.3-py_2.tar.bz2#c0481c9de49f040272556e2cedf42816
 https://conda.anaconda.org/conda-forge/linux-64/atk-1.0-2.38.0-hd4edc92_1.tar.bz2#6c72ec3e660a51736913ef6ea68c454b
 https://conda.anaconda.org/conda-forge/noarch/attrs-23.1.0-pyh71513ae_1.conda#3edfead7cedd1ab4400a6c588f3e75f8
 https://conda.anaconda.org/conda-forge/linux-64/aws-c-event-stream-0.3.1-h9599702_1.conda#a8820ce2dbe6f7d54f6540d9a3a0028a
 https://conda.anaconda.org/conda-forge/linux-64/aws-c-http-0.7.11-hbe98c3e_0.conda#067641478d8f706b80a5a434a22b82be
-https://conda.anaconda.org/conda-forge/linux-64/backports.zoneinfo-0.2.1-py311h38be061_7.tar.bz2#ec62b3c5b953cb610f5e2b09cd776caf
+https://conda.anaconda.org/conda-forge/linux-64/backports.zoneinfo-0.2.1-py311h38be061_8.conda#5384590f14dfe6ccd02811236afc9f8e
 https://conda.anaconda.org/conda-forge/linux-64/brotli-1.0.9-h166bdaf_9.conda#4601544b4982ba1861fa9b9c607b2c06
 https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.0.9-py311ha362b79_9.conda#ced5340f5dc6cff43a80deac8d0e398f
-https://conda.anaconda.org/conda-forge/noarch/certifi-2023.7.22-pyhd8ed1ab_0.conda#7f3dbc9179b4dde7da98dfb151d0ad22
+https://conda.anaconda.org/conda-forge/noarch/certifi-2023.11.17-pyhd8ed1ab_0.conda#2011bcf45376341dd1d690263fdbc789
 https://conda.anaconda.org/conda-forge/noarch/cfgv-3.3.1-pyhd8ed1ab_0.tar.bz2#ebb5f5f7dc4f1a3780ef7ea7738db08c
-https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.2.0-pyhd8ed1ab_0.conda#313516e9a4b08b12dfb1e1cd390a96e3
-https://conda.anaconda.org/conda-forge/noarch/click-8.1.6-unix_pyh707e725_0.conda#64dbb3b205546691a61204d1cfb208e3
-https://conda.anaconda.org/conda-forge/noarch/cloudpickle-2.2.1-pyhd8ed1ab_0.conda#b325bfc4cff7d7f8a868f1f7ecc4ed16
-https://conda.anaconda.org/conda-forge/noarch/codespell-2.2.5-pyhd8ed1ab_0.conda#c73551c990f6e7e9c83cdb8bdbafdeb8
+https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.3.2-pyhd8ed1ab_0.conda#7f4a9e3fcff3f6356ae99244a014da6a
+https://conda.anaconda.org/conda-forge/noarch/click-8.1.7-unix_pyh707e725_0.conda#f3ad426304898027fc619827ff428eca
+https://conda.anaconda.org/conda-forge/noarch/cloudpickle-3.0.0-pyhd8ed1ab_0.conda#753d29fe41bb881e4b9c004f0abf973f
+https://conda.anaconda.org/conda-forge/noarch/codespell-2.2.6-pyhd8ed1ab_0.conda#a206349b7bb7475ae580f987cb425bdd
 https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_0.tar.bz2#3faab06a954c2a04039983f2c4a50d99
 https://conda.anaconda.org/conda-forge/noarch/config-0.5.1-pyhd8ed1ab_0.tar.bz2#97275d4898af65967b1ad57923cef770
 https://conda.anaconda.org/conda-forge/noarch/configargparse-1.7-pyhd8ed1ab_0.conda#0d07dc29b1c1cc973f76b74beb44915f
-https://conda.anaconda.org/conda-forge/noarch/cycler-0.11.0-pyhd8ed1ab_0.tar.bz2#a50559fad0affdbb33729a68669ca1cb
-https://conda.anaconda.org/conda-forge/linux-64/cython-3.0.0-py311hb755f60_0.conda#257dfede48699e2e6372528d08399e5a
+https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhd8ed1ab_0.conda#5cd86562580f274031ede6aa6aa24441
+https://conda.anaconda.org/conda-forge/linux-64/cython-3.0.6-py311hb755f60_0.conda#88cc84238dda72e11285d9cfcbe43e51
 https://conda.anaconda.org/conda-forge/noarch/defusedxml-0.7.1-pyhd8ed1ab_0.tar.bz2#961b3a227b437d82ad7054484cfa71b2
 https://conda.anaconda.org/conda-forge/noarch/dill-0.3.7-pyhd8ed1ab_0.conda#5e4f3466526c52bc9af2d2353a1460bd
 https://conda.anaconda.org/conda-forge/noarch/distlib-0.3.7-pyhd8ed1ab_0.conda#12d8aae6994f342618443a8f05c652a0
-https://conda.anaconda.org/conda-forge/linux-64/docutils-0.20.1-py311h38be061_0.conda#207175b7d514d42f977ec505800d6824
+https://conda.anaconda.org/conda-forge/linux-64/docutils-0.20.1-py311h38be061_3.conda#1c33f55e5cdcc2a2b973c432b5225bfe
 https://conda.anaconda.org/conda-forge/noarch/dodgy-0.2.1-py_0.tar.bz2#62a69d073f7446c90f417b0787122f5b
 https://conda.anaconda.org/conda-forge/noarch/ecmwf-api-client-1.6.3-pyhd8ed1ab_0.tar.bz2#15621abf59053e184114d3e1d4f9d01e
 https://conda.anaconda.org/conda-forge/noarch/entrypoints-0.4-pyhd8ed1ab_0.tar.bz2#3cf04868fee0a029769bd41f4b2fbf2d
 https://conda.anaconda.org/conda-forge/noarch/et_xmlfile-1.1.0-pyhd8ed1ab_0.conda#a2f2138597905eaa72e561d8efb42cf3
-https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.1.2-pyhd8ed1ab_0.conda#de4cb3384374e1411f0454edcf546cdb
+https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.2.0-pyhd8ed1ab_0.conda#f6c211fee3c98229652b60a9a42ef363
 https://conda.anaconda.org/conda-forge/noarch/execnet-2.0.2-pyhd8ed1ab_0.conda#67de0d8241e1060a479e3c37793e26f9
 https://conda.anaconda.org/conda-forge/noarch/fasteners-0.17.3-pyhd8ed1ab_0.tar.bz2#348e27e78a5e39090031448c72f66d5e
-https://conda.anaconda.org/conda-forge/noarch/filelock-3.12.2-pyhd8ed1ab_0.conda#53522ec72e6adae42bd373ef58357230
+https://conda.anaconda.org/conda-forge/noarch/filelock-3.13.1-pyhd8ed1ab_0.conda#0c1729b74a8152fde6a38ba0a2ab9f45
 https://conda.anaconda.org/conda-forge/noarch/findlibs-0.0.5-pyhd8ed1ab_0.conda#8f325f63020af6f7acbe2c4cb4c920db
 https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.14.2-h14ed4e7_0.conda#0f69b688f52ff6da70bccb7ff7001d1d
-https://conda.anaconda.org/conda-forge/linux-64/frozenlist-1.4.0-py311h459d7ec_0.conda#54bd8f0776418b17a8eccd6f10181f95
-https://conda.anaconda.org/conda-forge/noarch/fsspec-2023.6.0-pyh1a96a4e_0.conda#50ea2067ec92dfcc38b4f07992d7e235
+https://conda.anaconda.org/conda-forge/linux-64/frozenlist-1.4.0-py311h459d7ec_1.conda#23d0b2d02252b32ee14e5063ccfb41e2
+https://conda.anaconda.org/conda-forge/noarch/fsspec-2023.12.1-pyhca7485f_0.conda#b38946846cdf39f9bce93f75f571d913
 https://conda.anaconda.org/conda-forge/linux-64/gdk-pixbuf-2.42.10-h6b639ba_2.conda#ee8220db21db8094998005990418fe5b
 https://conda.anaconda.org/conda-forge/noarch/geographiclib-1.52-pyhd8ed1ab_0.tar.bz2#6880e7100ebae550a33ce26663316d85
-https://conda.anaconda.org/conda-forge/linux-64/gsl-2.7-he838d99_0.tar.bz2#fec079ba39c9cca093bf4c00001825de
 https://conda.anaconda.org/conda-forge/linux-64/gts-0.7.6-h977cf35_4.conda#4d8df0b0db060d33c9a702ada998a8fe
-https://conda.anaconda.org/conda-forge/linux-64/humanfriendly-10.0-py311h38be061_4.tar.bz2#5c4f38a9e482f00a7bf23fe479c8ca29
-https://conda.anaconda.org/conda-forge/noarch/idna-3.4-pyhd8ed1ab_0.tar.bz2#34272b248891bddccc64479f9a7fffed
+https://conda.anaconda.org/conda-forge/noarch/humanfriendly-10.0-pyhd8ed1ab_6.conda#2ed1fe4b9079da97c44cfe9c2e5078fd
+https://conda.anaconda.org/conda-forge/noarch/idna-3.6-pyhd8ed1ab_0.conda#1a76f09108576397c41c0b0c5bd84134
 https://conda.anaconda.org/conda-forge/noarch/imagesize-1.4.1-pyhd8ed1ab_0.tar.bz2#7de5386c8fea29e76b303f37dde4c352
 https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.0.0-pyhd8ed1ab_0.conda#f800d2da156d08e289b14e87e43c1ae5
 https://conda.anaconda.org/conda-forge/noarch/itsdangerous-2.1.2-pyhd8ed1ab_0.tar.bz2#3c3de74912f11d2b590184f03c7cd09b
-https://conda.anaconda.org/conda-forge/linux-64/kiwisolver-1.4.4-py311h4dd048b_1.tar.bz2#46d451f575392c01dc193069bd89766d
-https://conda.anaconda.org/conda-forge/linux-64/lazy-object-proxy-1.9.0-py311h2582759_0.conda#07745544b144855ed4514a4cf0aadd74
-https://conda.anaconda.org/conda-forge/noarch/lazy_loader-0.2-pyhd8ed1ab_0.conda#d060d017720c9882c4eca0544a4a0592
+https://conda.anaconda.org/conda-forge/linux-64/kiwisolver-1.4.5-py311h9547e67_1.conda#2c65bdf442b0d37aad080c8a4e0d452f
+https://conda.anaconda.org/conda-forge/linux-64/lazy-object-proxy-1.9.0-py311h459d7ec_1.conda#7cc99d87755a9e64586a6004c5f0f534
+https://conda.anaconda.org/conda-forge/noarch/lazy_loader-0.3-pyhd8ed1ab_0.conda#69ea1d0fa7ab33b48c88394ad1dead65
 https://conda.anaconda.org/conda-forge/linux-64/lcms2-2.15-haa2dc70_1.conda#980d8aca0bc23ca73fa8caa3e7c84c28
+https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-20_linux64_openblas.conda#2b7bb4f7562c8cf334fc2e20c2d28abc
 https://conda.anaconda.org/conda-forge/linux-64/libcurl-8.1.2-h409715c_0.conda#50c873c9660ed116707ae15b663928d8
-https://conda.anaconda.org/conda-forge/linux-64/libkml-1.3.0-h37653c0_1015.tar.bz2#37d3747dd24d604f63d2610910576e63
 https://conda.anaconda.org/conda-forge/linux-64/libpq-15.3-hbcd7760_1.conda#8afb2a97d256ffde95b91a6283bc598c
 https://conda.anaconda.org/conda-forge/linux-64/libwebp-1.3.1-hbf2b3c1_0.conda#4963f3f12db45a576f2b8fbe9a0b8569
-https://conda.anaconda.org/conda-forge/linux-64/llvmlite-0.40.1-py311ha6695c7_0.conda#7a2b62d839516ba0cf56717e902229f4
+https://conda.anaconda.org/conda-forge/linux-64/llvmlite-0.41.1-py311ha6695c7_0.conda#60fa8c1f3fb0d99dd10a9af2aff9c400
 https://conda.anaconda.org/conda-forge/noarch/locket-1.0.0-pyhd8ed1ab_0.tar.bz2#91e27ef3d05cc772ce627e51cff111c4
-https://conda.anaconda.org/conda-forge/linux-64/lxml-4.9.3-py311h1a07684_0.conda#59a580306d62ef144c9dd592b5120f36
-https://conda.anaconda.org/conda-forge/linux-64/lz4-4.3.2-py311h9f220a4_0.conda#b8aad2507303e04037e8d02d8ac54217
-https://conda.anaconda.org/conda-forge/linux-64/markupsafe-2.1.3-py311h459d7ec_0.conda#9904dc4adb5d547cb21e136f98cb24b0
+https://conda.anaconda.org/conda-forge/linux-64/lxml-4.9.3-py311h1a07684_1.conda#aab51e50d994e58efdfa5382139b0468
+https://conda.anaconda.org/conda-forge/linux-64/lz4-4.3.2-py311h38e4bf4_1.conda#f8e0b648d77bbe44d1fe8af8cc56a590
+https://conda.anaconda.org/conda-forge/linux-64/markupsafe-2.1.3-py311h459d7ec_1.conda#71120b5155a0c500826cf81536721a15
 https://conda.anaconda.org/conda-forge/noarch/mccabe-0.7.0-pyhd8ed1ab_0.tar.bz2#34fc335fc50eef0b5ea708f2b5f54e0c
-https://conda.anaconda.org/conda-forge/noarch/mistune-3.0.0-pyhd8ed1ab_0.conda#c7d0ea64c37752ecbe6da458aee662d2
-https://conda.anaconda.org/conda-forge/linux-64/msgpack-python-1.0.5-py311ha3edf6b_0.conda#7415f24f8c44e44152623d93c5015000
-https://conda.anaconda.org/conda-forge/linux-64/multidict-6.0.4-py311h2582759_0.conda#8f581c14b50f2df47a2c6bd8d230a579
+https://conda.anaconda.org/conda-forge/noarch/mistune-3.0.2-pyhd8ed1ab_0.conda#5cbee699846772cc939bef23a0d524ed
+https://conda.anaconda.org/conda-forge/linux-64/msgpack-python-1.0.7-py311h9547e67_0.conda#3ac85c6c226e2a2e4b17864fc2ca88ff
+https://conda.anaconda.org/conda-forge/linux-64/multidict-6.0.4-py311h459d7ec_1.conda#3dc76316237c8f7e7231d61b76c62b7c
 https://conda.anaconda.org/conda-forge/noarch/munch-4.0.0-pyhd8ed1ab_0.conda#376b32e8f9d3eacbd625f37d39bd507d
 https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyh9f0ad1d_0.tar.bz2#2ba8498c1018c1e9c61eb99b973dfe19
 https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.0.0-pyha770c72_0.conda#4eccaeba205f0aed9ac3a9ea58568ca3
 https://conda.anaconda.org/conda-forge/noarch/natsort-8.4.0-pyhd8ed1ab_0.conda#70959cd1db3cf77b2a27a0836cfd08a7
-https://conda.anaconda.org/conda-forge/noarch/networkx-3.1-pyhd8ed1ab_0.conda#254f787d5068bc89f578bf63893ce8b4
-https://conda.anaconda.org/conda-forge/linux-64/numpy-1.24.4-py311h64a7726_0.conda#5a03d7c75dd4a9ae9a58850860eca468
+https://conda.anaconda.org/conda-forge/noarch/networkx-3.2.1-pyhd8ed1ab_0.conda#425fce3b531bed6ec3c74fab3e5f0a1c
+https://conda.anaconda.org/conda-forge/linux-64/openblas-ilp64-0.3.25-pthreads_h3d04fff_0.conda#29bd5f6106f71fbaa5c557b4c22c9e0c
 https://conda.anaconda.org/conda-forge/linux-64/openjpeg-2.5.0-hfec8fc6_2.conda#5ce6a42505c6e9e6151c54c3ec8d68ea
-https://conda.anaconda.org/conda-forge/noarch/packaging-23.1-pyhd8ed1ab_0.conda#91cda59e66e1e4afe9476f8ef98f5c30
+https://conda.anaconda.org/conda-forge/noarch/packaging-23.2-pyhd8ed1ab_0.conda#79002079284aa895f883c6b7f3f88fd6
 https://conda.anaconda.org/conda-forge/noarch/pandocfilters-1.5.0-pyhd8ed1ab_0.tar.bz2#457c2c8c08e54905d6954e79cb5b5db9
-https://conda.anaconda.org/conda-forge/noarch/pathspec-0.11.2-pyhd8ed1ab_0.conda#e41debb259e68490e3ab81e46b639ab6
-https://conda.anaconda.org/conda-forge/noarch/pkgutil-resolve-name-1.3.10-pyhd8ed1ab_0.tar.bz2#89e3c7cdde7d3aaa2aee933b604dd07f
-https://conda.anaconda.org/conda-forge/noarch/pluggy-1.2.0-pyhd8ed1ab_0.conda#7263924c642d22e311d9e59b839f1b33
-https://conda.anaconda.org/conda-forge/linux-64/psutil-5.9.5-py311h2582759_0.conda#a90f8e278c1cd7064b2713e6b7db87e6
-https://conda.anaconda.org/conda-forge/noarch/py-1.11.0-pyh6c4a22f_0.tar.bz2#b4613d7e7a493916d867842a6a148054
+https://conda.anaconda.org/conda-forge/noarch/pathspec-0.12.1-pyhd8ed1ab_0.conda#17064acba08d3686f1135b5ec1b32b12
+https://conda.anaconda.org/conda-forge/noarch/pkgutil-resolve-name-1.3.10-pyhd8ed1ab_1.conda#405678b942f2481cecdb3e010f4925d9
+https://conda.anaconda.org/conda-forge/noarch/platformdirs-4.1.0-pyhd8ed1ab_0.conda#45a5065664da0d1dfa8f8cd2eaf05ab9
+https://conda.anaconda.org/conda-forge/noarch/pluggy-1.3.0-pyhd8ed1ab_0.conda#2390bd10bed1f3fdc7a537fb5a447d8d
+https://conda.anaconda.org/conda-forge/linux-64/psutil-5.9.5-py311h459d7ec_1.conda#490d7fa8675afd1aa6f1b2332d156a45
 https://conda.anaconda.org/conda-forge/noarch/pycodestyle-2.9.1-pyhd8ed1ab_0.tar.bz2#0191dd7efe1a94262812770183b68892
 https://conda.anaconda.org/conda-forge/noarch/pycparser-2.21-pyhd8ed1ab_0.tar.bz2#076becd9e05608f8dc72757d5f3a91ff
 https://conda.anaconda.org/conda-forge/noarch/pyflakes-2.5.0-pyhd8ed1ab_0.tar.bz2#1b3bef4313288ae8d35b1dfba4cd84a3
-https://conda.anaconda.org/conda-forge/noarch/pygments-2.16.1-pyhd8ed1ab_0.conda#40e5cb18165466773619e5c963f00a7b
-https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.0.9-pyhd8ed1ab_0.tar.bz2#e8fbc1b54b25f4b08281467bc13b70cc
+https://conda.anaconda.org/conda-forge/noarch/pygments-2.17.2-pyhd8ed1ab_0.conda#140a7f159396547e9799aa98f9f0742e
+https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.1.1-pyhd8ed1ab_0.conda#176f7d56f0cfe9008bdf1bccd7de02fb
 https://conda.anaconda.org/conda-forge/noarch/pyshp-2.3.1-pyhd8ed1ab_0.tar.bz2#92a889dc236a5197612bc85bee6d7174
 https://conda.anaconda.org/conda-forge/noarch/pysocks-1.7.1-pyha2e5f31_6.tar.bz2#2a7de29fb590ca14b5243c4c812c8025
-https://conda.anaconda.org/conda-forge/noarch/python-fastjsonschema-2.18.0-pyhd8ed1ab_0.conda#3be9466311564f80f8056c0851fc5bb7
+https://conda.anaconda.org/conda-forge/noarch/python-fastjsonschema-2.19.0-pyhd8ed1ab_0.conda#e4dbdb3585c0266b4710467fe7b75cf4
 https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2023.3-pyhd8ed1ab_0.conda#2590495f608a63625e165915fb4e2e34
-https://conda.anaconda.org/conda-forge/linux-64/python-xxhash-3.2.0-py311h2582759_0.conda#dfcc3e6e30d6ec2b2bb416fcd8ff4dc1
-https://conda.anaconda.org/conda-forge/noarch/pytz-2023.3-pyhd8ed1ab_0.conda#d3076b483092a435832603243567bc31
-https://conda.anaconda.org/conda-forge/linux-64/pyyaml-6.0-py311hd4cff14_5.tar.bz2#da8769492e423103c59f469f4f17f8d9
-https://conda.anaconda.org/conda-forge/linux-64/pyzmq-25.1.1-py311h75c88c4_0.conda#af6d43afe0d179ac83b7e0c16b2caaad
-https://conda.anaconda.org/conda-forge/linux-64/rpds-py-0.9.2-py311h46250e7_0.conda#bcf66b5abaec47198b42cdd0bb968540
-https://conda.anaconda.org/conda-forge/linux-64/ruamel.yaml.clib-0.2.7-py311h2582759_1.conda#5e997292429a22ad50c11af0a2cb0f08
-https://conda.anaconda.org/conda-forge/noarch/semver-3.0.1-pyhd8ed1ab_0.conda#ed90854ae56fb6edae1f13b4663b21b0
+https://conda.anaconda.org/conda-forge/linux-64/python-xxhash-3.4.1-py311h459d7ec_0.conda#60b5332b3989fda37884b92c7afd6a91
+https://conda.anaconda.org/conda-forge/noarch/pytz-2023.3.post1-pyhd8ed1ab_0.conda#c93346b446cd08c169d843ae5fc0da97
+https://conda.anaconda.org/conda-forge/linux-64/pyyaml-6.0.1-py311h459d7ec_1.conda#52719a74ad130de8fb5d047dc91f247a
+https://conda.anaconda.org/conda-forge/linux-64/pyzmq-25.1.2-py311h34ded2d_0.conda#819aa640a0493d4b52faf938e94d129e
+https://conda.anaconda.org/conda-forge/linux-64/rpds-py-0.13.2-py311h46250e7_0.conda#c5f5089dd1fe0000fecaf0d12eca50b9
+https://conda.anaconda.org/conda-forge/linux-64/ruamel.yaml.clib-0.2.7-py311h459d7ec_2.conda#56bc3fe5180c0b23e05c7a5708153ac7
+https://conda.anaconda.org/conda-forge/noarch/semver-3.0.2-pyhd8ed1ab_0.conda#5efb3fccda53974aed800b6d575f72ed
 https://conda.anaconda.org/conda-forge/noarch/setoptconf-tmp-0.3.1-pyhd8ed1ab_0.tar.bz2#af3e36d4effb85b9b9f93cd1db0963df
-https://conda.anaconda.org/conda-forge/noarch/setuptools-68.0.0-pyhd8ed1ab_0.conda#5a7739d0f57ee64133c9d32e6507c46d
-https://conda.anaconda.org/conda-forge/linux-64/simplejson-3.19.1-py311h2582759_0.conda#c58e325a8500b8755e95cf0622665840
+https://conda.anaconda.org/conda-forge/noarch/setuptools-68.2.2-pyhd8ed1ab_0.conda#fc2166155db840c634a1291a5c35a709
+https://conda.anaconda.org/conda-forge/linux-64/simplejson-3.19.2-py311h459d7ec_0.conda#d6478cbce002db6303f0d749860f3e22
 https://conda.anaconda.org/conda-forge/noarch/six-1.16.0-pyh6c4a22f_0.tar.bz2#e5f25f8dbc060e9a8d912e432202afc2
-https://conda.anaconda.org/conda-forge/noarch/smmap-3.0.5-pyh44b312d_0.tar.bz2#3a8dc70789709aa315325d5df06fb7e4
+https://conda.anaconda.org/conda-forge/noarch/smmap-5.0.0-pyhd8ed1ab_0.tar.bz2#62f26a3d1387acee31322208f0cfa3e0
 https://conda.anaconda.org/conda-forge/noarch/snowballstemmer-2.2.0-pyhd8ed1ab_0.tar.bz2#4d22a9315e78c6827f806065957d566e
 https://conda.anaconda.org/conda-forge/noarch/sortedcontainers-2.4.0-pyhd8ed1ab_0.tar.bz2#6d6552722448103793743dabfbda532d
-https://conda.anaconda.org/conda-forge/noarch/soupsieve-2.3.2.post1-pyhd8ed1ab_0.tar.bz2#146f4541d643d48fc8a75cacf69f03ae
+https://conda.anaconda.org/conda-forge/noarch/soupsieve-2.5-pyhd8ed1ab_1.conda#3f144b2c34f8cb5a9abd9ed23a39c561
 https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-jsmath-1.0.1-pyhd8ed1ab_0.conda#da1d979339e2714c30a8e806a33ec087
 https://conda.anaconda.org/conda-forge/noarch/sqlparse-0.4.4-pyhd8ed1ab_0.conda#2e2f31b3b1c866c29636377e14f8c4c6
-https://conda.anaconda.org/conda-forge/linux-64/tbb-2021.10.0-h00ab1b0_0.conda#9c82b1b389e46b64ec685ec487043e70
-https://conda.anaconda.org/conda-forge/noarch/tblib-1.7.0-pyhd8ed1ab_0.tar.bz2#3d4afc31302aa7be471feb6be048ed76
-https://conda.anaconda.org/conda-forge/noarch/tenacity-8.2.2-pyhd8ed1ab_0.conda#7b39e842b52966a99e229739cd4dc36e
+https://conda.anaconda.org/conda-forge/linux-64/tbb-2021.11.0-h00ab1b0_0.conda#fde515afbbe6e36eb4564965c20b1058
+https://conda.anaconda.org/conda-forge/noarch/tblib-3.0.0-pyhd8ed1ab_0.conda#04eedddeb68ad39871c8127dd1c21f4f
+https://conda.anaconda.org/conda-forge/noarch/tenacity-8.2.3-pyhd8ed1ab_0.conda#1482e77f87c6a702a7e05ef22c9b197b
 https://conda.anaconda.org/conda-forge/noarch/termcolor-2.3.0-pyhd8ed1ab_0.conda#440d508f025b1692168caaf436504af3
 https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.2.0-pyha21a80b_0.conda#978d03388b62173b8e6f79162cf52b86
 https://conda.anaconda.org/conda-forge/noarch/toml-0.10.2-pyhd8ed1ab_0.tar.bz2#f832c45a477c78bebd107098db465095
 https://conda.anaconda.org/conda-forge/noarch/tomli-2.0.1-pyhd8ed1ab_0.tar.bz2#5844808ffab9ebdb694585b50ba02a96
-https://conda.anaconda.org/conda-forge/noarch/tomlkit-0.12.1-pyha770c72_0.conda#62f5b331c53d73e2f6c4c130b53518a0
+https://conda.anaconda.org/conda-forge/noarch/tomlkit-0.12.3-pyha770c72_0.conda#074d0ce7a6261ab8b497c3518796ef3e
 https://conda.anaconda.org/conda-forge/noarch/toolz-0.12.0-pyhd8ed1ab_0.tar.bz2#92facfec94bc02d6ccf42e7173831a36
-https://conda.anaconda.org/conda-forge/linux-64/tornado-6.3.2-py311h459d7ec_0.conda#12b1c374ee90a1aa11ea921858394dc8
-https://conda.anaconda.org/conda-forge/noarch/traitlets-5.9.0-pyhd8ed1ab_0.conda#d0b4f5c87cd35ac3fb3d47b223263a64
-https://conda.anaconda.org/conda-forge/noarch/trove-classifiers-2023.8.7-pyhd8ed1ab_0.conda#347293fe36843c704818dcec8364409a
-https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.7.1-pyha770c72_0.conda#c39d6a09fe819de4951c2642629d9115
-https://conda.anaconda.org/conda-forge/linux-64/ujson-5.7.0-py311hcafe171_0.conda#ec3960b6d13bb60aad9c67f42a801720
+https://conda.anaconda.org/conda-forge/linux-64/tornado-6.3.3-py311h459d7ec_1.conda#a700fcb5cedd3e72d0c75d095c7a6eda
+https://conda.anaconda.org/conda-forge/noarch/traitlets-5.14.0-pyhd8ed1ab_0.conda#886f4a84ddb49b943b1697ac314e85b3
+https://conda.anaconda.org/conda-forge/noarch/trove-classifiers-2023.11.29-pyhd8ed1ab_0.conda#b355907cec4e6a516f2909396add77c8
+https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.9.0-pyha770c72_0.conda#a92a6440c3fe7052d63244f3aba2a4a7
+https://conda.anaconda.org/conda-forge/linux-64/ujson-5.9.0-py311hb755f60_0.conda#36dda52dc99a4fb9cadd3b738ec24848
 https://conda.anaconda.org/conda-forge/noarch/untokenize-0.1.1-py_0.tar.bz2#1447ead40f2a01733a9c8dfc32988375
-https://conda.anaconda.org/conda-forge/noarch/webencodings-0.5.1-py_1.tar.bz2#3563be4c5611a44210d9ba0c16113136
+https://conda.anaconda.org/conda-forge/noarch/webencodings-0.5.1-pyhd8ed1ab_2.conda#daf5160ff9cde3a468556965329085b9
 https://conda.anaconda.org/conda-forge/noarch/webob-1.8.7-pyhd8ed1ab_0.tar.bz2#a8192f3585f341ea66c60c189580ac67
-https://conda.anaconda.org/conda-forge/noarch/wheel-0.41.1-pyhd8ed1ab_0.conda#8f467ba2db2b5470d297953d9c1f9c7d
-https://conda.anaconda.org/conda-forge/linux-64/wrapt-1.15.0-py311h2582759_0.conda#15565d8602a78c6a994e4d9fcb391920
-https://conda.anaconda.org/conda-forge/noarch/xlsxwriter-3.1.2-pyhd8ed1ab_0.conda#e0593431fd5e9c12824b9bfa989c9ed0
+https://conda.anaconda.org/conda-forge/noarch/wheel-0.42.0-pyhd8ed1ab_0.conda#1cdea58981c5cbc17b51973bcaddcea7
+https://conda.anaconda.org/conda-forge/linux-64/wrapt-1.16.0-py311h459d7ec_0.conda#6669b5529d206c1f880b642cdd17ae05
+https://conda.anaconda.org/conda-forge/noarch/xlsxwriter-3.1.9-pyhd8ed1ab_0.conda#70e533db62a710ae216fdaccc4a983c8
 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxext-1.3.4-h0b41bf4_2.conda#82b6df12252e6f32402b96dacc656fec
 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxfixes-5.0.3-h7f98852_1004.tar.bz2#e9a21aa4d5e3e5f1aed71e8cefd46b6a
 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxrender-0.9.11-hd590300_0.conda#ed67c36f215b310412b2af935bf3e530
 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxt-1.3.0-hd590300_1.conda#ae92aab42726eb29d16488924f7312cb
-https://conda.anaconda.org/conda-forge/noarch/xyzservices-2023.7.0-pyhd8ed1ab_0.conda#aacae3c0eaba0204dc6c5497c93c7992
+https://conda.anaconda.org/conda-forge/noarch/xyzservices-2023.10.1-pyhd8ed1ab_0.conda#1e0d85c0e2fef9539218da185b285f54
 https://conda.anaconda.org/conda-forge/noarch/zict-3.0.0-pyhd8ed1ab_0.conda#cf30c2c15b82aacb07f9c09e28ff2275
-https://conda.anaconda.org/conda-forge/noarch/zipp-3.16.2-pyhd8ed1ab_0.conda#2da0451b54c4563c32490cb1b7cf68a1
+https://conda.anaconda.org/conda-forge/noarch/zipp-3.17.0-pyhd8ed1ab_0.conda#2e4d6bc0b14e10f895fc6791a7d9b26a
 https://conda.anaconda.org/conda-forge/noarch/accessible-pygments-0.0.4-pyhd8ed1ab_0.conda#46a2e6e3dfa718ce3492018d5a110dd6
 https://conda.anaconda.org/conda-forge/noarch/aiosignal-1.3.1-pyhd8ed1ab_0.tar.bz2#d1e1eb7e21a9e2c74279d87dafb68156
 https://conda.anaconda.org/conda-forge/noarch/asgiref-3.7.2-pyhd8ed1ab_0.conda#596932155bf88bb6837141550cb721b0
-https://conda.anaconda.org/conda-forge/linux-64/astroid-2.15.6-py311h38be061_0.conda#28b1d4a493fb6acd24cc299d77fed871
+https://conda.anaconda.org/conda-forge/linux-64/astroid-2.15.8-py311h38be061_0.conda#46d70fcb74472aab178991f0231ee3c6
 https://conda.anaconda.org/conda-forge/linux-64/aws-c-auth-0.7.0-hf8751d9_2.conda#deb12196f0c64c441bb3d083d06d0cf8
 https://conda.anaconda.org/conda-forge/linux-64/aws-c-mqtt-0.8.14-h2e270ba_2.conda#58bbee5fd6cf2d4fffbead1bc33a5d3b
-https://conda.anaconda.org/conda-forge/noarch/babel-2.12.1-pyhd8ed1ab_1.conda#ac432e732804a81ddcf29c92ead57cde
+https://conda.anaconda.org/conda-forge/noarch/babel-2.13.1-pyhd8ed1ab_0.conda#3ccff479c246692468f604df9c85ef26
 https://conda.anaconda.org/conda-forge/noarch/beautifulsoup4-4.12.2-pyha770c72_0.conda#a362ff7d976217f8fa78c0f1c4f59717
-https://conda.anaconda.org/conda-forge/noarch/bleach-6.0.0-pyhd8ed1ab_0.conda#d48b143d01385872a88ef8417e96c30e
+https://conda.anaconda.org/conda-forge/noarch/bleach-6.1.0-pyhd8ed1ab_0.conda#0ed9d7c0e9afa7c025807a9a8136ea3e
 https://conda.anaconda.org/conda-forge/linux-64/brunsli-0.1-h9c3ff4c_0.tar.bz2#c1ac6229d0bfd14f8354ff9ad2a26cad
 https://conda.anaconda.org/conda-forge/linux-64/cairo-1.16.0-hbbf8b49_1016.conda#c1dd96500b9b1a75e9e511931f415cbc
-https://conda.anaconda.org/conda-forge/noarch/cattrs-23.1.2-pyhd8ed1ab_0.conda#e554f60477143949704bf470f66a81e7
-https://conda.anaconda.org/conda-forge/linux-64/cffi-1.15.1-py311h409f033_3.conda#9025d0786dbbe4bc91fd8e85502decce
+https://conda.anaconda.org/conda-forge/linux-64/cffi-1.16.0-py311hb3a22ac_0.conda#b3469563ac5e808b0cd92810d0697043
 https://conda.anaconda.org/conda-forge/linux-64/cfitsio-4.2.0-hd9d235c_0.conda#8c57a9adbafd87f5eff842abde599cb4
-https://conda.anaconda.org/conda-forge/linux-64/cftime-1.6.2-py311h4c7f6c3_1.tar.bz2#c7e54004ffd03f8db0a58ab949f2a00b
 https://conda.anaconda.org/conda-forge/noarch/click-plugins-1.1.1-py_0.tar.bz2#4fd2c6b53934bd7d96d1f3fdaf99b79f
 https://conda.anaconda.org/conda-forge/noarch/cligj-0.7.2-pyhd8ed1ab_1.tar.bz2#a29b7c141d6b2de4bb67788a5f107734
-https://conda.anaconda.org/conda-forge/noarch/colorspacious-1.1.2-pyh24bf2e0_0.tar.bz2#b73afa0d009a51cabd3ec99c4d2ef4f3
-https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.1.0-py311h9547e67_0.conda#daf3f23397ab2265d0cdfa339f3627ba
-https://conda.anaconda.org/conda-forge/linux-64/coverage-7.2.7-py311h459d7ec_0.conda#3c2c65575c28b23afc5e4ff721a2fc9f
+https://conda.anaconda.org/conda-forge/linux-64/coverage-7.3.2-py311h459d7ec_0.conda#7b3145fed7adc7c63a0e08f6f29f5480
 https://conda.anaconda.org/conda-forge/linux-64/curl-8.1.2-h409715c_0.conda#9f88cfb15b7d08b25880b138f91e0eb4
-https://conda.anaconda.org/conda-forge/linux-64/cytoolz-0.12.2-py311h459d7ec_0.conda#5c416db47b7816e437eaf0d46e5c3a3d
+https://conda.anaconda.org/conda-forge/linux-64/cytoolz-0.12.2-py311h459d7ec_1.conda#afe341dbe834ae76d2c23157ff00e633
 https://conda.anaconda.org/conda-forge/noarch/docformatter-1.7.5-pyhd8ed1ab_0.conda#3a941b6083e945aa87e739a9b85c82e9
 https://conda.anaconda.org/conda-forge/noarch/docrep-0.3.2-pyh44b312d_0.tar.bz2#235523955bc1bfb019d7ec8a2bb58f9a
-https://conda.anaconda.org/conda-forge/noarch/eofs-1.4.0-py_0.tar.bz2#6e166cd37cfeadefcfca1ffe00f222bb
 https://conda.anaconda.org/conda-forge/noarch/fire-0.5.0-pyhd8ed1ab_0.conda#9fd22aae8d2f319e80f68b295ab91d64
-https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.42.0-py311h459d7ec_0.conda#8c1ac2c00995248898220c4c1a9d81ab
-https://conda.anaconda.org/conda-forge/noarch/geopy-2.3.0-pyhd8ed1ab_0.tar.bz2#529faeecd6eee3a3b782566ddf05ce92
-https://conda.anaconda.org/conda-forge/noarch/gitdb-4.0.10-pyhd8ed1ab_0.conda#3706d2f3d7cb5dae600c833345a76132
+https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.46.0-py311h459d7ec_0.conda#a14114f70e23f7fd5ab9941fec45b095
+https://conda.anaconda.org/conda-forge/noarch/geopy-2.4.1-pyhd8ed1ab_0.conda#c75621ce68f6570fff9a6734cf21c9a7
+https://conda.anaconda.org/conda-forge/noarch/gitdb-4.0.11-pyhd8ed1ab_0.conda#623b19f616f2ca0c261441067e18ae40
 https://conda.anaconda.org/conda-forge/linux-64/hdf5-1.14.0-nompi_hb72d44e_103.conda#975973a4350ab45ff1981fe535a12af5
-https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-6.8.0-pyha770c72_0.conda#4e9f59a060c3be52bc4ddc46ee9b6946
-https://conda.anaconda.org/conda-forge/noarch/importlib_resources-6.0.1-pyhd8ed1ab_0.conda#d978c61aa5fc2c69380d53ad56b5ae86
+https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-7.0.0-pyha770c72_0.conda#a941237cd06538837b25cd245fcd25d8
+https://conda.anaconda.org/conda-forge/noarch/importlib_resources-6.1.1-pyhd8ed1ab_0.conda#3d5fa25cf42f3f32a12b2d874ace8574
 https://conda.anaconda.org/conda-forge/noarch/isodate-0.6.1-pyhd8ed1ab_0.tar.bz2#4a62c93c1b5c0b920508ae3fd285eaf5
 https://conda.anaconda.org/conda-forge/noarch/isort-5.12.0-pyhd8ed1ab_1.conda#07ed3421bad60867234c7a9282ea39d4
 https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.2-pyhd8ed1ab_1.tar.bz2#c8490ed5c70966d232fdd389d0dbed37
 https://conda.anaconda.org/conda-forge/noarch/joblib-1.3.2-pyhd8ed1ab_0.conda#4da50d410f553db77e62ab62ffaa1abc
-https://conda.anaconda.org/conda-forge/noarch/jupyterlab_pygments-0.2.2-pyhd8ed1ab_0.tar.bz2#243f63592c8e449f40cd42eb5cf32f40
+https://conda.anaconda.org/conda-forge/linux-64/jupyter_core-5.5.0-py311h38be061_0.conda#cee83be29258275f75029125e186ab6d
+https://conda.anaconda.org/conda-forge/noarch/jupyterlab_pygments-0.3.0-pyhd8ed1ab_0.conda#3f0915b1fb2252ab73686a533c5f9d3f
 https://conda.anaconda.org/conda-forge/noarch/latexcodec-2.0.1-pyh9f0ad1d_0.tar.bz2#8d67904973263afd2985ba56aa2d6bb4
+https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-20_linux64_openblas.conda#36d486d72ab64ffea932329a1d3729a3
 https://conda.anaconda.org/conda-forge/linux-64/libgd-2.3.3-hfa28ad5_6.conda#ef06bee47510a7f5db3c2297a51d6ce2
+https://conda.anaconda.org/conda-forge/linux-64/libglu-9.0.0-hac7e632_1003.conda#50c389a09b6b7babaef531eb7cb5e0ca
 https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-2.12.0-h840a212_1.conda#03c225a73835f5aa68c13e62eb360406
+https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-20_linux64_openblas.conda#6fabc51f5e647d09cc010c40061557e0
 https://conda.anaconda.org/conda-forge/noarch/logilab-common-1.7.3-py_0.tar.bz2#6eafcdf39a7eb90b6d951cfff59e8d3b
-https://conda.anaconda.org/conda-forge/noarch/magics-python-1.5.8-pyhd8ed1ab_1.conda#3fd7e3db129f12362642108f23fde521
 https://conda.anaconda.org/conda-forge/noarch/nested-lookup-0.2.25-pyhd8ed1ab_1.tar.bz2#2f59daeb14581d41b1e2dda0895933b2
 https://conda.anaconda.org/conda-forge/noarch/nodeenv-1.8.0-pyhd8ed1ab_0.conda#2a75b296096adabbabadd5e9782e5fcc
-https://conda.anaconda.org/conda-forge/linux-64/numba-0.57.1-py311h96b013e_0.conda#618010d18c4a38073a7f51d9dd3fd8a8
-https://conda.anaconda.org/conda-forge/linux-64/numcodecs-0.11.0-py311hcafe171_1.conda#ecdaf0772e524ed51218f6d52ef74424
-https://conda.anaconda.org/conda-forge/linux-64/openpyxl-3.1.2-py311h459d7ec_0.conda#3fd48307e8596409a2a55d516fa3ad1f
-https://conda.anaconda.org/conda-forge/noarch/partd-1.4.0-pyhd8ed1ab_0.conda#721dab5803ea92ce02ddc4ee50aa0c48
+https://conda.anaconda.org/conda-forge/linux-64/openpyxl-3.1.2-py311h459d7ec_1.conda#5c809fb753f06a04c2f114394404769e
+https://conda.anaconda.org/conda-forge/noarch/partd-1.4.1-pyhd8ed1ab_0.conda#acf4b7c0bcd5fa3b0e05801c4d2accd6
 https://conda.anaconda.org/conda-forge/linux-64/pillow-10.0.0-py311h0b84326_0.conda#4b24acdc1fbbae9da03147e7d2cf8c8a
-https://conda.anaconda.org/conda-forge/noarch/pip-23.2.1-pyhd8ed1ab_0.conda#e2783aa3f9235225eec92f9081c5b801
-https://conda.anaconda.org/conda-forge/noarch/plotly-5.15.0-pyhd8ed1ab_0.conda#48573e7cca7860509648522a3b8507d7
+https://conda.anaconda.org/conda-forge/noarch/pip-23.3.1-pyhd8ed1ab_0.conda#2400c0b86889f43aa52067161e1fb108
+https://conda.anaconda.org/conda-forge/noarch/plotly-5.18.0-pyhd8ed1ab_0.conda#9f6a8664f1fe752f79473eeb9bf33a60
 https://conda.anaconda.org/conda-forge/linux-64/postgresql-15.3-hd458b1d_1.conda#4a4b5dede4d2e075e9aa5a44a9fd9f20
 https://conda.anaconda.org/conda-forge/linux-64/proj-9.2.1-ha643af7_0.conda#e992387307f4403ba0ec07d009032550
 https://conda.anaconda.org/conda-forge/noarch/pydocstyle-6.3.0-pyhd8ed1ab_0.conda#7e23a61a7fbaedfef6eb0e1ac775c8e5
 https://conda.anaconda.org/conda-forge/noarch/pyproject_hooks-1.0.0-pyhd8ed1ab_0.conda#21de50391d584eb7f4441b9de1ad773f
-https://conda.anaconda.org/conda-forge/noarch/pytest-7.4.0-pyhd8ed1ab_0.conda#3cfe9b9e958e7238a386933c75d190db
+https://conda.anaconda.org/conda-forge/noarch/pytest-7.4.3-pyhd8ed1ab_0.conda#5bdca0aca30b0ee62bb84854e027eae0
 https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.8.2-pyhd8ed1ab_0.tar.bz2#dd999d1cc9f79e67dbb855c8924c7984
-https://conda.anaconda.org/conda-forge/noarch/python-utils-3.7.0-pyhd8ed1ab_1.conda#d3e9c0f7bf300ddc51a363a8183b3abd
-https://conda.anaconda.org/conda-forge/linux-64/pywavelets-1.4.1-py311hcb2cf0a_0.conda#272ca0c28df344037ba2c4982d4e4791
-https://conda.anaconda.org/conda-forge/noarch/referencing-0.30.2-pyhd8ed1ab_0.conda#a33161b983172ba6ef69d5fc850650cd
+https://conda.anaconda.org/conda-forge/noarch/python-utils-3.8.1-pyhd8ed1ab_0.conda#4dc77041fea14c63ecea50ce0eb92873
+https://conda.anaconda.org/conda-forge/noarch/referencing-0.32.0-pyhd8ed1ab_0.conda#a7b5a535cd614e384594530aee7e6061
 https://conda.anaconda.org/conda-forge/noarch/retrying-1.3.3-py_2.tar.bz2#a11f356d6f93b74b4a84e9501afd48b4
-https://conda.anaconda.org/conda-forge/linux-64/ruamel.yaml-0.17.32-py311h459d7ec_0.conda#628868dc17f9bd39a2eb77846e35980c
-https://conda.anaconda.org/conda-forge/noarch/seawater-3.3.4-py_1.tar.bz2#a9e101e1601faf5e5a119ab2bd7617a4
-https://conda.anaconda.org/conda-forge/linux-64/shapely-2.0.1-py311h54d622a_1.conda#a894c65b48676c4973e9ee8b59bceb9e
-https://conda.anaconda.org/conda-forge/noarch/snuggs-1.4.7-py_0.tar.bz2#cb83a3d6ecf73f50117635192414426a
-https://conda.anaconda.org/conda-forge/linux-64/suitesparse-5.10.1-h9e50725_1.tar.bz2#a3a685b5f9aeb890ed874502fe56accf
+https://conda.anaconda.org/conda-forge/linux-64/ruamel.yaml-0.18.5-py311h459d7ec_0.conda#1101ec27377f8e45d8431a5f21d744f1
 https://conda.anaconda.org/conda-forge/noarch/tinycss2-1.2.1-pyhd8ed1ab_0.tar.bz2#7234c9eefff659501cd2fe0d2ede4d48
 https://conda.anaconda.org/conda-forge/noarch/tqdm-4.66.1-pyhd8ed1ab_0.conda#03c97908b976498dcae97eb4e4f3149c
-https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.7.1-hd8ed1ab_0.conda#f96688577f1faa58096d06a45136afa2
+https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.9.0-hd8ed1ab_0.conda#c16524c1b7227dc80b36b4fa6f77cc86
 https://conda.anaconda.org/conda-forge/noarch/url-normalize-1.4.3-pyhd8ed1ab_0.tar.bz2#7c4076e494f0efe76705154ac9302ba6
-https://conda.anaconda.org/conda-forge/noarch/urllib3-2.0.4-pyhd8ed1ab_0.conda#18badd8fa3648d1beb1fcc7f2e0f756e
+https://conda.anaconda.org/conda-forge/noarch/urllib3-2.1.0-pyhd8ed1ab_0.conda#f8ced8ee63830dec7ecc1be048d1470a
+https://conda.anaconda.org/conda-forge/noarch/virtualenv-20.25.0-pyhd8ed1ab_0.conda#c119653cba436d8183c27bf6d190e587
 https://conda.anaconda.org/conda-forge/linux-64/xerces-c-3.2.4-h8d71039_2.conda#6d5edbe22b07abae2ea0a9065ef6be12
 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxi-1.7.10-h7f98852_0.tar.bz2#e77615e5141cad5a2acaa043d1cf0ca5
 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxmu-1.1.3-h7f98852_0.tar.bz2#3cdb89236358326adfce12be820a8af3
-https://conda.anaconda.org/conda-forge/linux-64/xorg-libxpm-3.5.16-hd590300_0.conda#7a2672267d49208afe2df6cbef8a6a79
+https://conda.anaconda.org/conda-forge/linux-64/xorg-libxpm-3.5.17-hd590300_0.conda#12bf78e12f71405775e1c092902959d3
 https://conda.anaconda.org/conda-forge/noarch/yamale-4.0.4-pyh6c4a22f_0.tar.bz2#cc9f59f147740d88679bf1bd94dbe588
-https://conda.anaconda.org/conda-forge/noarch/yamllint-1.32.0-pyhd8ed1ab_0.conda#6d2425548b0293a225ca4febd80feaa3
-https://conda.anaconda.org/conda-forge/linux-64/yarl-1.9.2-py311h459d7ec_0.conda#4d738187d20e0a3be66973860f134e0a
-https://conda.anaconda.org/conda-forge/noarch/async-timeout-4.0.2-pyhd8ed1ab_0.tar.bz2#25e79f9a1133556671becbd65a170c78
+https://conda.anaconda.org/conda-forge/noarch/yamllint-1.33.0-pyhd8ed1ab_0.conda#57d32eb2c4b76ef288f9dd789f8fe5af
+https://conda.anaconda.org/conda-forge/linux-64/yarl-1.9.3-py311h459d7ec_0.conda#96f995652440b0a9266d66a691d9eff9
+https://conda.anaconda.org/conda-forge/linux-64/aiohttp-3.9.1-py311h459d7ec_0.conda#a51ceb9a9219e3c11af56b2b77794839
+https://conda.anaconda.org/conda-forge/linux-64/arpack-3.7.0-hdefa2d7_2.tar.bz2#8763fe86163198ef1778d1d8d22bb078
 https://conda.anaconda.org/conda-forge/linux-64/aws-c-s3-0.3.13-heb0bb06_2.conda#c0866da05d5e7bb3a3f6b68bcbf7537b
-https://conda.anaconda.org/conda-forge/linux-64/cf-units-3.2.0-py311h1f0f07a_0.conda#43a71a823583d75308eaf3a06c8f150b
-https://conda.anaconda.org/conda-forge/linux-64/cryptography-41.0.3-py311h63ff55d_0.conda#cc8ad641cab65dfe59caddbc23a1aeca
-https://conda.anaconda.org/conda-forge/noarch/django-4.2.3-pyhd8ed1ab_0.conda#00a369ccfec9deaa325eabd749bfae28
+https://conda.anaconda.org/conda-forge/noarch/cattrs-23.2.3-pyhd8ed1ab_0.conda#91fc4700dcce4a46d439900a132fe4e5
+https://conda.anaconda.org/conda-forge/linux-64/cryptography-41.0.7-py311hcb13ee4_1.conda#ca6e04ac7262ecaec846e483d6fdc6c8
+https://conda.anaconda.org/conda-forge/noarch/django-5.0-pyhd8ed1ab_0.conda#9af5d8131de6eb7b0f2f167fba3bdff7
 https://conda.anaconda.org/conda-forge/noarch/flake8-5.0.4-pyhd8ed1ab_0.tar.bz2#8079ea7dec0a917dd0cb6c257f7ea9ea
 https://conda.anaconda.org/conda-forge/linux-64/freeglut-3.2.2-hac7e632_2.conda#6e553df297f6e64668efb54302e0f139
 https://conda.anaconda.org/conda-forge/noarch/funcargparse-0.2.5-pyhd8ed1ab_0.tar.bz2#e557b70d736251fa0bbb7c4497852a92
 https://conda.anaconda.org/conda-forge/linux-64/geotiff-1.7.1-h22adcc9_11.conda#514167b60f598eaed3f7a60e1dceb9ee
-https://conda.anaconda.org/conda-forge/linux-64/git-2.41.0-pl5321h86e50cf_0.conda#14f8341e26b274362b026bbdc72b14fb
-https://conda.anaconda.org/conda-forge/noarch/gitpython-3.1.32-pyhd8ed1ab_0.conda#5809a12901d57388444c3293c975d0bb
+https://conda.anaconda.org/conda-forge/linux-64/git-2.42.0-pl5321h86e50cf_0.conda#96ad24c67e0056d171385859c43218a2
+https://conda.anaconda.org/conda-forge/noarch/gitpython-3.1.40-pyhd8ed1ab_0.conda#6bf74c3b7c13079a91d4bd3da51cefcf
+https://conda.anaconda.org/conda-forge/linux-64/gsl-2.7-he838d99_0.tar.bz2#fec079ba39c9cca093bf4c00001825de
 https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-7.3.0-hdb3a94d_0.conda#765bc76c0dfaf24ff9d8a2935b2510df
 https://conda.anaconda.org/conda-forge/linux-64/hdfeos5-5.1.16-h8b5b2df_13.conda#29a96d50cb53638a5b4806b5ca6e4b1d
-https://conda.anaconda.org/conda-forge/linux-64/imagecodecs-2023.7.10-py311h67b54e4_2.conda#ac734963735ededab90c6991d1d4edff
-https://conda.anaconda.org/conda-forge/noarch/imageio-2.31.1-pyh24c5eb1_0.conda#1051cc0376612ba101d4f59e954a1ff4
-https://conda.anaconda.org/conda-forge/noarch/importlib_metadata-6.8.0-hd8ed1ab_0.conda#b279b07ce18058034e5b3606ba103a8b
-https://conda.anaconda.org/conda-forge/noarch/jsonschema-specifications-2023.7.1-pyhd8ed1ab_0.conda#7c27ea1bdbe520bb830dcadd59f55cbf
+https://conda.anaconda.org/conda-forge/noarch/importlib_metadata-7.0.0-hd8ed1ab_0.conda#12aff14f84c337be5e5636bf612f4140
+https://conda.anaconda.org/conda-forge/noarch/jsonschema-specifications-2023.11.2-pyhd8ed1ab_0.conda#73884ca36d6d96cbce498cde99fab40f
 https://conda.anaconda.org/conda-forge/linux-64/kealib-1.5.1-h3845be2_3.conda#f38e5e47f62d6633166040192ad420a1
 https://conda.anaconda.org/conda-forge/linux-64/libnetcdf-4.9.2-nompi_h0f3d0bb_105.conda#b5d412441b84305460e9df8a016a3392
 https://conda.anaconda.org/conda-forge/linux-64/libspatialite-5.0.1-hca56755_27.conda#918a735059cab21b96fc13a8d04fbcd8
-https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.7.2-py311h54ef318_0.conda#2631a9e423855fb586c05f8a5ee8b177
-https://conda.anaconda.org/conda-forge/linux-64/pandas-2.0.3-py311h320fe9a_1.conda#5f92f46bd33917832a99d1660b4075ac
-https://conda.anaconda.org/conda-forge/noarch/platformdirs-3.10.0-pyhd8ed1ab_0.conda#0809187ef9b89a3d94a5c24d13936236
+https://conda.anaconda.org/conda-forge/linux-64/numpy-1.26.2-py311h64a7726_0.conda#fd2f142dcd680413b5ede5d0fb799205
 https://conda.anaconda.org/conda-forge/linux-64/poppler-23.05.0-hd18248d_1.conda#09e0de1aa7330fe697eed76eaeef666d
 https://conda.anaconda.org/conda-forge/noarch/progressbar2-4.2.0-pyhd8ed1ab_0.tar.bz2#d883564cf1e9ba190f6b285036c5f949
 https://conda.anaconda.org/conda-forge/noarch/pybtex-0.24.0-pyhd8ed1ab_2.tar.bz2#2099b86a7399c44c0c61cdb6de6915ba
-https://conda.anaconda.org/conda-forge/linux-64/pyproj-3.6.0-py311ha169711_1.conda#92633556d37e88ce45193374d408072c
+https://conda.anaconda.org/conda-forge/noarch/pylint-2.17.7-pyhd8ed1ab_0.conda#3cab6aee60038b3f621bce3e50f52bed
+https://conda.anaconda.org/conda-forge/linux-64/pyproj-3.6.1-py311ha169711_0.conda#ad4b6e9be79a89959bb6d7d308027ff2
 https://conda.anaconda.org/conda-forge/noarch/pytest-cov-4.1.0-pyhd8ed1ab_0.conda#06eb685a3a0b146347a58dda979485da
-https://conda.anaconda.org/conda-forge/noarch/pytest-env-0.8.2-pyhd8ed1ab_0.conda#cd20b56ff31042aad74d45e3f47eb5b2
+https://conda.anaconda.org/conda-forge/noarch/pytest-env-1.1.3-pyhd8ed1ab_0.conda#1dbdf019d740419852c4a7803fff49d9
 https://conda.anaconda.org/conda-forge/noarch/pytest-metadata-3.0.0-pyhd8ed1ab_1.conda#8bdcc0f401561213821bf67513abeeff
-https://conda.anaconda.org/conda-forge/noarch/pytest-mock-3.11.1-pyhd8ed1ab_0.conda#fcd2531bc3e492657aeb042349aeaf8a
-https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.3.1-pyhd8ed1ab_0.conda#816073bb54ef59f33f0f26c14f88311b
-https://conda.anaconda.org/conda-forge/noarch/python-build-0.10.0-pyhd8ed1ab_1.conda#0ab47ce574f6a8bcb9f2076436e7fedb
+https://conda.anaconda.org/conda-forge/noarch/pytest-mock-3.12.0-pyhd8ed1ab_0.conda#ac9fedc9a0c397f2318e82525491dd83
+https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.5.0-pyhd8ed1ab_0.conda#d5f595da2daead898ca958ac62f0307b
+https://conda.anaconda.org/conda-forge/noarch/python-build-1.0.3-pyhd8ed1ab_0.conda#d9ccabf228cb98419ca3d5694b25e1a2
 https://conda.anaconda.org/conda-forge/noarch/rdflib-7.0.0-pyhd8ed1ab_0.conda#44d14ef95495b3d4438f28998e0296a9
 https://conda.anaconda.org/conda-forge/noarch/requests-2.31.0-pyhd8ed1ab_0.conda#a30144e4156cdbb236f99ebb49828f8b
 https://conda.anaconda.org/conda-forge/noarch/requirements-detector-1.2.2-pyhd8ed1ab_0.conda#6626918380d99292df110f3c91b6e5ec
+https://conda.anaconda.org/conda-forge/linux-64/suitesparse-5.10.1-h3ec001c_2.conda#9b37d27528c5f86ee09bf1fc6834da8e
 https://conda.anaconda.org/conda-forge/linux-64/tiledb-2.13.2-hd532e3d_0.conda#6d97164f19dbd27575ef1899b02dc1e0
-https://conda.anaconda.org/conda-forge/linux-64/ukkonen-1.0.1-py311h4dd048b_3.tar.bz2#dbfea4376856bf7bd2121e719cf816e5
+https://conda.anaconda.org/conda-forge/linux-64/ukkonen-1.0.1-py311h9547e67_4.conda#586da7df03b68640de14dc3e8bcbf76f
 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxaw-1.0.14-h7f98852_1.tar.bz2#45b68dc2fc7549c16044d533ceaf340e
-https://conda.anaconda.org/conda-forge/noarch/zarr-2.16.0-pyhd8ed1ab_0.conda#40a116bf021c83da8b1cf226de0dabe4
-https://conda.anaconda.org/conda-forge/linux-64/aiohttp-3.8.5-py311h459d7ec_0.conda#82a817c46ac339c0166f043f56492f33
+https://conda.anaconda.org/conda-forge/noarch/yapf-0.40.1-pyhd8ed1ab_0.conda#f269942e802d5e148632143d4c37acc9
 https://conda.anaconda.org/conda-forge/linux-64/aws-crt-cpp-0.20.3-he9c0e7f_4.conda#7695770e1d722ce9029a2ea30c060a3d
-https://conda.anaconda.org/conda-forge/noarch/bokeh-3.2.1-pyhd8ed1ab_0.conda#1f6a50747c4e69d181f3a2ca739d1fe3
-https://conda.anaconda.org/conda-forge/linux-64/cartopy-0.22.0-py311h320fe9a_0.conda#1271b2375735e2aaa6d6770dbe2ad087
 https://conda.anaconda.org/conda-forge/noarch/cdsapi-0.6.1-pyhd8ed1ab_0.conda#454ed214cec806066097ae245a409171
+https://conda.anaconda.org/conda-forge/linux-64/cftime-1.6.3-py311h1f0f07a_0.conda#b7e6d52b39e199238c3400cafaabafb3
 https://conda.anaconda.org/conda-forge/noarch/chart-studio-1.1.0-pyh9f0ad1d_0.tar.bz2#acd9a12a35e5a0221bdf39eb6e4811dc
-https://conda.anaconda.org/conda-forge/noarch/cmocean-3.0.3-pyhd8ed1ab_0.conda#eec7df83d725696d32c7bf99aff21d82
-https://conda.anaconda.org/conda-forge/noarch/dask-core-2023.8.0-pyhd8ed1ab_0.conda#160a92928fc4a0ca40a64b586a2cf671
+https://conda.anaconda.org/conda-forge/noarch/colorspacious-1.1.2-pyh24bf2e0_0.tar.bz2#b73afa0d009a51cabd3ec99c4d2ef4f3
+https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.2.0-py311h9547e67_0.conda#40828c5b36ef52433e21f89943e09f33
+https://conda.anaconda.org/conda-forge/noarch/dask-core-2023.12.0-pyhd8ed1ab_0.conda#95eae0785aed72998493140dc0115382
+https://conda.anaconda.org/conda-forge/noarch/eofs-1.4.0-py_0.tar.bz2#6e166cd37cfeadefcfca1ffe00f222bb
 https://conda.anaconda.org/conda-forge/noarch/flake8-polyfill-1.0.2-py_0.tar.bz2#a53db35e3d07f0af2eccd59c2a00bffe
-https://conda.anaconda.org/conda-forge/noarch/identify-2.5.26-pyhd8ed1ab_0.conda#1ca86f154e13f4aa20b48e20d6bbf924
+https://conda.anaconda.org/conda-forge/noarch/identify-2.5.33-pyhd8ed1ab_0.conda#93c8f8ceb83827d88deeba796f07fba7
+https://conda.anaconda.org/conda-forge/linux-64/imagecodecs-2023.8.12-py311h67b54e4_0.conda#363e5c2f2c67ff69d717aba54422b03d
+https://conda.anaconda.org/conda-forge/noarch/imageio-2.31.5-pyh8c1a49c_0.conda#6820ccf6a3a27df348f18c85dd89014a
 https://conda.anaconda.org/conda-forge/linux-64/jasper-4.0.0-h32699f2_1.conda#fdde5424ecef5f7ad310b4242229291c
-https://conda.anaconda.org/conda-forge/noarch/jsonschema-4.19.0-pyhd8ed1ab_0.conda#989d1df249e1fce06fa1435548eef68a
+https://conda.anaconda.org/conda-forge/noarch/jsonschema-4.20.0-pyhd8ed1ab_0.conda#1116d79def5268414fb0917520b2bbf1
 https://conda.anaconda.org/conda-forge/linux-64/julia-1.8.5-h783901f_0.conda#98c05ba7ca9c15d22216f730499e167a
-https://conda.anaconda.org/conda-forge/linux-64/jupyter_core-5.3.1-py311h38be061_0.conda#0cf8259b01ede82c76007996f73f89ed
+https://conda.anaconda.org/conda-forge/noarch/jupyter_client-8.6.0-pyhd8ed1ab_0.conda#6bd3f1069cdebb44c7ae9efb900e312d
 https://conda.anaconda.org/conda-forge/linux-64/libgdal-3.7.0-h5418a03_2.conda#30ddbe080c260fb36da8509e3fd6c45f
-https://conda.anaconda.org/conda-forge/noarch/nc-time-axis-1.4.1-pyhd8ed1ab_0.tar.bz2#281b58948bf60a2582de9e548bcc5369
+https://conda.anaconda.org/conda-forge/noarch/magics-python-1.5.8-pyhd8ed1ab_1.conda#3fd7e3db129f12362642108f23fde521
 https://conda.anaconda.org/conda-forge/linux-64/netcdf-fortran-4.6.1-nompi_h4f3791c_100.conda#405c5b3ad4ef53eb0d93043b54206dd7
-https://conda.anaconda.org/conda-forge/linux-64/netcdf4-1.6.4-nompi_py311h4d7c953_100.conda#c03492d0342e512e58aa2d6c5fdaaa91
+https://conda.anaconda.org/conda-forge/linux-64/numba-0.58.1-py311h96b013e_0.conda#06a0313ff3d2ec956a25767ccaf7c9f6
+https://conda.anaconda.org/conda-forge/linux-64/numcodecs-0.12.1-py311hb755f60_0.conda#38a2ff8ea433fe8792279b45e84b3730
+https://conda.anaconda.org/conda-forge/linux-64/pandas-2.1.4-py311h320fe9a_0.conda#e44ccb61b6621bf3f8053ae66eba7397
 https://conda.anaconda.org/conda-forge/linux-64/pango-1.50.14-heaa33ce_1.conda#cde553e0e32389e26595db4eacf859eb
-https://conda.anaconda.org/conda-forge/noarch/pooch-1.7.0-pyha770c72_3.conda#5936894aade8240c867d292aa0d980c6
-https://conda.anaconda.org/conda-forge/noarch/pylint-2.17.5-pyhd8ed1ab_0.conda#30dc94b05de470e3b579d73d64127656
-https://conda.anaconda.org/conda-forge/noarch/pyopenssl-23.2.0-pyhd8ed1ab_1.conda#34f7d568bf59d18e3fef8c405cbece21
+https://conda.anaconda.org/conda-forge/noarch/patsy-0.5.4-pyhd8ed1ab_0.conda#1184267eddebb57e47f8e1419c225595
+https://conda.anaconda.org/conda-forge/noarch/pooch-1.8.0-pyhd8ed1ab_0.conda#134b2b57b7865d2316a7cce1915a51ed
+https://conda.anaconda.org/conda-forge/noarch/pylint-plugin-utils-0.7-pyhd8ed1ab_0.tar.bz2#1657976383aee04dbb3ae3bdf654bb58
+https://conda.anaconda.org/conda-forge/noarch/pyopenssl-23.3.0-pyhd8ed1ab_0.conda#7819533e674dbbc51468f3228b9b1bb6
 https://conda.anaconda.org/conda-forge/noarch/pyroma-4.2-pyhd8ed1ab_0.conda#fe2aca9a5d4cb08105aefc451ef96950
-https://conda.anaconda.org/conda-forge/noarch/pytest-html-3.2.0-pyhd8ed1ab_1.tar.bz2#d5c7a941dfbceaab4b172a56d7918eb0
-https://conda.anaconda.org/conda-forge/noarch/requests-cache-1.1.0-pyhd8ed1ab_0.conda#57b89064c125bb9d0e533e018c3eb17a
+https://conda.anaconda.org/conda-forge/noarch/pytest-html-4.1.1-pyhd8ed1ab_0.conda#4d2040212307d18392a2687772b3a96d
+https://conda.anaconda.org/conda-forge/linux-64/pywavelets-1.4.1-py311h1f0f07a_1.conda#86b71ff85f3e4c8a98b5bace6d9c4565
+https://conda.anaconda.org/conda-forge/noarch/requests-cache-1.1.1-pyhd8ed1ab_0.conda#29bf13210ee541c59166cea092b91080
+https://conda.anaconda.org/conda-forge/linux-64/scipy-1.11.4-py311h64a7726_0.conda#9ac5334f1b5ed072d3dbc342503d7868
+https://conda.anaconda.org/conda-forge/noarch/seawater-3.3.4-py_1.tar.bz2#a9e101e1601faf5e5a119ab2bd7617a4
+https://conda.anaconda.org/conda-forge/linux-64/shapely-2.0.1-py311h54d622a_1.conda#a894c65b48676c4973e9ee8b59bceb9e
+https://conda.anaconda.org/conda-forge/noarch/snuggs-1.4.7-py_0.tar.bz2#cb83a3d6ecf73f50117635192414426a
 https://conda.anaconda.org/conda-forge/linux-64/tempest-remap-2.2.0-h43474b4_0.conda#fd815765a86daf44db1e15c6f6edf5e6
-https://conda.anaconda.org/conda-forge/noarch/tifffile-2023.7.18-pyhd8ed1ab_0.conda#34c210cbc9b4c4458d4eba5ee7726fcd
-https://conda.anaconda.org/conda-forge/noarch/virtualenv-20.24.2-pyhd8ed1ab_0.conda#a218f3be8ab6185a475c8168a86e18ae
-https://conda.anaconda.org/conda-forge/noarch/xarray-2023.7.0-pyhd8ed1ab_0.conda#2f18700699e1ea19aa1634ed57711677
-https://conda.anaconda.org/conda-forge/noarch/yapf-0.40.1-pyhd8ed1ab_0.conda#f269942e802d5e148632143d4c37acc9
 https://conda.anaconda.org/conda-forge/linux-64/aws-sdk-cpp-1.10.57-hbc2ea52_17.conda#452c7b08c21eea2ef01f4fd364d6affc
-https://conda.anaconda.org/conda-forge/noarch/cf_xarray-0.8.4-pyhd8ed1ab_0.conda#18472f8f9452f962fe0bcb1b8134b494
-https://conda.anaconda.org/conda-forge/noarch/distributed-2023.8.0-pyhd8ed1ab_0.conda#974b4a00b0e100e341cd9f179b05f574
+https://conda.anaconda.org/conda-forge/noarch/bokeh-3.3.2-pyhd8ed1ab_0.conda#c02a7e79365121bd3bcc25f1b65f48a9
+https://conda.anaconda.org/conda-forge/linux-64/cf-units-3.2.0-py311h1f0f07a_4.conda#1e105c1a8ea2163507726144b401eb1b
+https://conda.anaconda.org/conda-forge/noarch/distributed-2023.12.0-pyhd8ed1ab_0.conda#22d620e1079e99c34578cb0c615d2789
 https://conda.anaconda.org/conda-forge/linux-64/eccodes-2.30.2-h1f30a5c_0.conda#21ee8444a7f629924ea8cfe52a622cbd
 https://conda.anaconda.org/conda-forge/linux-64/esmf-8.4.2-nompi_h20110ff_0.conda#11f5169aeff54ad7277476be8ba19ff7
 https://conda.anaconda.org/conda-forge/linux-64/gdal-3.7.0-py311h281082f_2.conda#fde4fad3c517cc80f32995696f45198d
 https://conda.anaconda.org/conda-forge/linux-64/gtk2-2.24.33-h90689f9_2.tar.bz2#957a0255ab58aaf394a91725d73ab422
-https://conda.anaconda.org/conda-forge/noarch/jupyter_client-8.3.0-pyhd8ed1ab_0.conda#1d018ee4ab13217e2544f795eb0a6798
 https://conda.anaconda.org/conda-forge/linux-64/librsvg-2.56.3-h98fae49_0.conda#620e754f4344f4c27259ff460a2b9c50
-https://conda.anaconda.org/conda-forge/noarch/mapgenerator-1.0.7-pyhd8ed1ab_0.conda#d18db96ef2a920b0ecefe30282b0aecf
-https://conda.anaconda.org/conda-forge/noarch/myproxyclient-2.1.0-pyhd8ed1ab_2.tar.bz2#363b0816e411feb0df925d4f224f026a
+https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.8.2-py311h54ef318_0.conda#9f80753bc008bfc9b95f39d9ff9f1694
+https://conda.anaconda.org/conda-forge/noarch/myproxyclient-2.1.1-pyhd8ed1ab_0.conda#bcdbeb2b693eba886583a907840c6421
 https://conda.anaconda.org/conda-forge/noarch/nbformat-5.9.2-pyhd8ed1ab_0.conda#61ba076de6530d9301a0053b02f093d2
+https://conda.anaconda.org/conda-forge/linux-64/netcdf4-1.6.4-nompi_py311h4d7c953_100.conda#c03492d0342e512e58aa2d6c5fdaaa91
 https://conda.anaconda.org/conda-forge/noarch/pep8-naming-0.10.0-pyh9f0ad1d_0.tar.bz2#b3c5536e4f9f58a4b16adb6f1e11732d
-https://conda.anaconda.org/conda-forge/noarch/pre-commit-3.3.3-pyha770c72_0.conda#dd64a0e440754ed97610b3e6b502b6b1
-https://conda.anaconda.org/conda-forge/linux-64/psyplot-1.4.3-py311h38be061_1.tar.bz2#f0c9a1067c03e8f05e53ef0c5ad5fab3
-https://conda.anaconda.org/conda-forge/noarch/pylint-plugin-utils-0.7-pyhd8ed1ab_0.tar.bz2#1657976383aee04dbb3ae3bdf654bb58
-https://conda.anaconda.org/conda-forge/linux-64/python-stratify-0.3.0-py311h1f0f07a_0.conda#3a00b1b08d8c01b1a3bfa686b9152df2
+https://conda.anaconda.org/conda-forge/noarch/pre-commit-3.6.0-pyha770c72_0.conda#473a7cfca197da0a10cff3f6dded7d4b
+https://conda.anaconda.org/conda-forge/noarch/pylint-celery-0.3-py_1.tar.bz2#e29456a611a62d3f26105a2f9c68f759
+https://conda.anaconda.org/conda-forge/noarch/pylint-django-2.5.3-pyhd8ed1ab_0.tar.bz2#00d8853fb1f87195722ea6a582cc9b56
+https://conda.anaconda.org/conda-forge/noarch/pylint-flask-0.6-py_0.tar.bz2#5a9afd3d0a61b08d59eed70fab859c1b
+https://conda.anaconda.org/conda-forge/linux-64/python-stratify-0.3.0-py311h1f0f07a_1.conda#cd36a89a048ad2bcc6d8b43f648fb1d0
 https://conda.anaconda.org/conda-forge/linux-64/r-base-4.1.3-hfabd6f2_9.conda#0ab4cf54fbddc0cc9ff260c6f77f8c84
 https://conda.anaconda.org/conda-forge/linux-64/rasterio-1.3.8-py311h41e4db2_0.conda#b35deb26af1d7e0d98438c8ac5c6b7b2
-https://conda.anaconda.org/conda-forge/linux-64/scipy-1.11.1-py311h64a7726_0.conda#356da36102fc1eeb8a81e6d79e53bc7e
+https://conda.anaconda.org/conda-forge/linux-64/scikit-learn-1.3.2-py311hc009520_2.conda#9821f8e497a791858226f535e5e0be62
+https://conda.anaconda.org/conda-forge/noarch/sparse-0.14.0-pyhd8ed1ab_0.conda#ee01b310177a0612554b9d20e537fdbe
+https://conda.anaconda.org/conda-forge/linux-64/statsmodels-0.14.0-py311h1f0f07a_2.conda#8f1e772e5430ce48229740ec00a90b61
+https://conda.anaconda.org/conda-forge/noarch/tifffile-2023.12.9-pyhd8ed1ab_0.conda#454bc0aff84f35fa53ba9e0369737a9b
+https://conda.anaconda.org/conda-forge/noarch/xarray-2023.12.0-pyhd8ed1ab_0.conda#e9b31d3ab1b0dd5fd8c24419f6560b90
+https://conda.anaconda.org/conda-forge/noarch/zarr-2.16.1-pyhd8ed1ab_0.conda#59ec835edbee50266b7bdbadab7ba335
+https://conda.anaconda.org/conda-forge/linux-64/cartopy-0.22.0-py311h320fe9a_1.conda#10d1806e20da040c58c36deddf51c70c
+https://conda.anaconda.org/conda-forge/noarch/cf_xarray-0.8.6-pyhd8ed1ab_0.conda#2e33e3bdf2b1a79989ad792ac22104d3
+https://conda.anaconda.org/conda-forge/noarch/cmocean-3.0.3-pyhd8ed1ab_0.conda#eec7df83d725696d32c7bf99aff21d82
 https://conda.anaconda.org/conda-forge/noarch/dask-jobqueue-0.8.2-pyhd8ed1ab_0.conda#cc344a296a41369bcb05f7216661cec8
-https://conda.anaconda.org/conda-forge/noarch/esgf-pyclient-0.3.1-pyh1a96a4e_2.tar.bz2#64068564a9c2932bf30e9b4ec567927d
-https://conda.anaconda.org/conda-forge/noarch/esmpy-8.4.2-pyhc1e730c_1.conda#4067029ad6872d49f6d43c05dd1f51a9
+https://conda.anaconda.org/conda-forge/noarch/esgf-pyclient-0.3.1-pyhca7485f_3.conda#1d43833138d38ad8324700ce45a7099a
+https://conda.anaconda.org/conda-forge/noarch/esmpy-8.4.2-pyhc1e730c_4.conda#ddcf387719b2e44df0cc4dd467643951
 https://conda.anaconda.org/conda-forge/linux-64/fiona-1.9.4-py311hbac4ec9_0.conda#1d3445f5f7fa002a1c149c405376f012
 https://conda.anaconda.org/conda-forge/linux-64/graphviz-8.1.0-h28d9a01_0.conda#33628e0e3de7afd2c8172f76439894cb
-https://conda.anaconda.org/conda-forge/noarch/iris-3.6.1-pyha770c72_0.conda#36d615b339058273520990fc78239116
 https://conda.anaconda.org/conda-forge/linux-64/libarrow-12.0.1-h657c46f_7_cpu.conda#4de6e12428b7018f1f8a1e8dda555243
 https://conda.anaconda.org/conda-forge/linux-64/magics-4.14.2-hd3d5bb6_0.conda#3c571b994b6ce2b4d2c7b98be77a8ebe
 https://conda.anaconda.org/conda-forge/noarch/nbclient-0.8.0-pyhd8ed1ab_0.conda#e78da91cf428faaf05701ce8cc8f2f9b
+https://conda.anaconda.org/conda-forge/noarch/nc-time-axis-1.4.1-pyhd8ed1ab_0.tar.bz2#281b58948bf60a2582de9e548bcc5369
 https://conda.anaconda.org/conda-forge/linux-64/ncl-6.6.2-hf70af60_47.conda#ee27133164cb9f5e74681bdb8839688f
 https://conda.anaconda.org/conda-forge/linux-64/nco-5.1.6-hd62b316_0.conda#af7780f76ee37325d264327e21a478f5
-https://conda.anaconda.org/conda-forge/noarch/patsy-0.5.3-pyhd8ed1ab_0.tar.bz2#50ef6b29b1fb0768ca82c5aeb4fb2d96
-https://conda.anaconda.org/conda-forge/linux-64/psy-simple-1.4.1-py311h38be061_2.tar.bz2#4c9101d329f6bc09c2617a80e3eb9c89
-https://conda.anaconda.org/conda-forge/noarch/py-cordex-0.6.2-pyhd8ed1ab_0.conda#457fae5edf0703054b78f6bfb200e855
-https://conda.anaconda.org/conda-forge/noarch/pylint-celery-0.3-py_1.tar.bz2#e29456a611a62d3f26105a2f9c68f759
-https://conda.anaconda.org/conda-forge/noarch/pylint-django-2.5.3-pyhd8ed1ab_0.tar.bz2#00d8853fb1f87195722ea6a582cc9b56
-https://conda.anaconda.org/conda-forge/noarch/pylint-flask-0.6-py_0.tar.bz2#5a9afd3d0a61b08d59eed70fab859c1b
+https://conda.anaconda.org/conda-forge/noarch/prospector-1.10.3-pyhd8ed1ab_0.conda#f551d4d859a1d70c6abff8310a655481
+https://conda.anaconda.org/conda-forge/linux-64/psyplot-1.4.3-py311h38be061_1.tar.bz2#f0c9a1067c03e8f05e53ef0c5ad5fab3
+https://conda.anaconda.org/conda-forge/linux-64/py-xgboost-1.7.6-cuda118_py311h0be3a32_6.conda#e9989e03af742084940a11c7c3c395a5
 https://conda.anaconda.org/conda-forge/noarch/r-abind-1.4_5-r41hc72bb7e_1004.tar.bz2#831186670e5786df30f8ddeb5a623c5a
 https://conda.anaconda.org/conda-forge/linux-64/r-backports-1.4.1-r41h06615bd_1.tar.bz2#9a00c3283f8fb4bce68deffe08fbe09d
 https://conda.anaconda.org/conda-forge/noarch/r-bigmemory.sri-0.1.6-r41hc72bb7e_0.tar.bz2#926471a5be30d287a25f2d10446d6066
@@ -556,19 +557,18 @@ https://conda.anaconda.org/conda-forge/noarch/r-withr-2.5.0-r41hc72bb7e_1.tar.bz
 https://conda.anaconda.org/conda-forge/linux-64/r-xfun-0.39-r41ha503ecb_0.conda#555ee06849209b9471946da6f09bb98b
 https://conda.anaconda.org/conda-forge/noarch/r-xmlparsedata-1.0.5-r41hc72bb7e_1.tar.bz2#921c0ef7104d8df0ab506f1bb81a062c
 https://conda.anaconda.org/conda-forge/linux-64/r-yaml-2.3.7-r41h133d619_0.conda#4af88071a607237aa73a3cbd51788a39
-https://conda.anaconda.org/conda-forge/linux-64/scikit-image-0.21.0-py311hb755f60_0.conda#0a8cbf18f7e57e43b8e106630a0ca7df
-https://conda.anaconda.org/conda-forge/linux-64/scikit-learn-1.3.0-py311hc009520_0.conda#847797c4a9c0c3363ffcfe7571dedfee
-https://conda.anaconda.org/conda-forge/noarch/seaborn-base-0.12.2-pyhd8ed1ab_0.conda#cf88f3a1c11536bc3c10c14ad00ccc42
-https://conda.anaconda.org/conda-forge/noarch/sparse-0.14.0-pyhd8ed1ab_0.conda#ee01b310177a0612554b9d20e537fdbe
+https://conda.anaconda.org/conda-forge/linux-64/scikit-image-0.22.0-py311h320fe9a_2.conda#e94b7f09b52628b89e66cdbd8c3029dd
+https://conda.anaconda.org/conda-forge/noarch/seaborn-base-0.13.0-pyhd8ed1ab_0.conda#082666331726b2438986cfe33ae9a8ee
 https://conda.anaconda.org/conda-forge/linux-64/cdo-2.2.0-he026af2_4.conda#6c00b0a21b3de8a149eee137e83465d3
 https://conda.anaconda.org/conda-forge/linux-64/imagemagick-7.1.1_15-pl5321hf48ede7_0.conda#53c9f7169b61e615d5f41c8d70a72c00
+https://conda.anaconda.org/conda-forge/noarch/iris-3.7.0-pyha770c72_0.conda#dccc1f660bf455c239adaabf56b91dc9
 https://conda.anaconda.org/conda-forge/noarch/lime-0.2.0.1-pyhd8ed1ab_1.tar.bz2#789ce01416721a5533fb74aa4361fd13
-https://conda.anaconda.org/conda-forge/noarch/nbconvert-core-7.7.3-pyhd8ed1ab_0.conda#063c1fda5480050b8d989478c97a4c55
-https://conda.anaconda.org/conda-forge/noarch/prospector-1.10.2-pyhd8ed1ab_0.conda#2c536985982f7e531df8d640f554008a
-https://conda.anaconda.org/conda-forge/noarch/psy-maps-1.4.2-pyhd8ed1ab_0.tar.bz2#3ed13103dfd46f71dc870d188bd0b276
-https://conda.anaconda.org/conda-forge/linux-64/py-xgboost-1.7.6-cuda112py311h2344cce_0.conda#262d5d7254fd6e10e63170c4eb8d6ac0
+https://conda.anaconda.org/conda-forge/noarch/mapgenerator-1.0.7-pyhd8ed1ab_0.conda#d18db96ef2a920b0ecefe30282b0aecf
+https://conda.anaconda.org/conda-forge/noarch/nbconvert-core-7.12.0-pyhd8ed1ab_0.conda#4d67c68fd0d130091ada039bc2d81b33
+https://conda.anaconda.org/conda-forge/linux-64/psy-simple-1.4.1-py311h38be061_2.tar.bz2#4c9101d329f6bc09c2617a80e3eb9c89
+https://conda.anaconda.org/conda-forge/noarch/py-cordex-0.6.6-pyhd8ed1ab_0.conda#255f9eac03143526c8aed41d1d091c63
 https://conda.anaconda.org/conda-forge/linux-64/pyarrow-12.0.1-py311h39c9aba_7_cpu.conda#d513ab8d10ec5f3ee45b419c836195ec
-https://conda.anaconda.org/conda-forge/linux-64/pydot-1.4.2-py311h38be061_3.tar.bz2#64a77de29fde80aef5013ddf5e62a564
+https://conda.anaconda.org/conda-forge/linux-64/pydot-1.4.2-py311h38be061_4.conda#5c223cb0d9c05552bf9d1586a92720b2
 https://conda.anaconda.org/conda-forge/linux-64/r-askpass-1.1-r41h06615bd_3.tar.bz2#c8ec8683302ad9a2345cb31ab28e6c6b
 https://conda.anaconda.org/conda-forge/linux-64/r-bigmemory-4.6.1-r41h7525677_1.tar.bz2#6a956b57b027b49b7a9ca48031a8bbd6
 https://conda.anaconda.org/conda-forge/linux-64/r-checkmate-2.2.0-r41h57805ef_0.conda#dc314ad76563387e70e0117c5398a15a
@@ -595,12 +595,14 @@ https://conda.anaconda.org/conda-forge/linux-64/r-spam-2.9_1-r41hb20cf53_1.conda
 https://conda.anaconda.org/conda-forge/linux-64/r-timechange-0.2.0-r41h38f115c_0.conda#04a4229419d779a1e27395d70d493571
 https://conda.anaconda.org/conda-forge/linux-64/r-xml2-1.3.4-r41h1ad5fc0_1.conda#82c1446591783493d65273a158e8ce28
 https://conda.anaconda.org/conda-forge/linux-64/r-zoo-1.8_12-r41h133d619_0.conda#1d432d2eba171727afd03507faa5e2f6
-https://conda.anaconda.org/conda-forge/linux-64/statsmodels-0.14.0-py311h1f0f07a_1.conda#a1daa39fa0bfed4d91a3640c2274034a
-https://conda.anaconda.org/conda-forge/noarch/xesmf-0.7.1-pyhd8ed1ab_0.conda#3a9cc63d7bcbc8d738a0e92faf8b6c07
-https://conda.anaconda.org/conda-forge/noarch/dask-2023.8.0-pyhd8ed1ab_0.conda#0cd5f8a91edc681f4b87e8a0ce33a591
-https://conda.anaconda.org/conda-forge/noarch/nbconvert-pandoc-7.7.3-pyhd8ed1ab_0.conda#f44109e52a40b8149156f5ddd9c11b26
+https://conda.anaconda.org/conda-forge/noarch/seaborn-0.13.0-hd8ed1ab_0.conda#ebd31a95a7008b7e164dad9dbbb5bb5a
+https://conda.anaconda.org/conda-forge/noarch/xesmf-0.8.2-pyhd8ed1ab_0.conda#8e765a0eca0ce1cfa889cd9af82a23a8
+https://conda.anaconda.org/conda-forge/linux-64/xgboost-1.7.6-cuda118_py311h0be3a32_6.conda#f061993f8ed8ca90d2bb6a547a47109f
+https://conda.anaconda.org/conda-forge/noarch/nbconvert-pandoc-7.12.0-pyhd8ed1ab_0.conda#460d7cac50322a39b61a833885a6a8d5
 https://conda.anaconda.org/conda-forge/noarch/prov-2.0.0-pyhd3deb0d_0.tar.bz2#aa9b3ad140f6c0668c646f32e20ccf82
+https://conda.anaconda.org/conda-forge/noarch/psy-maps-1.4.2-pyhd8ed1ab_0.tar.bz2#3ed13103dfd46f71dc870d188bd0b276
 https://conda.anaconda.org/conda-forge/linux-64/psy-reg-1.4.0-py311h38be061_3.conda#6f7871722c07922028043144e8873b37
+https://conda.anaconda.org/conda-forge/noarch/pyarrow-hotfix-0.6-pyhd8ed1ab_0.conda#ccc06e6ef2064ae129fab3286299abda
 https://conda.anaconda.org/conda-forge/noarch/python-cdo-1.6.0-pyhd8ed1ab_0.conda#3fd1a0b063c1fbbe4b7bd5a5a7601e84
 https://conda.anaconda.org/conda-forge/linux-64/r-akima-0.6_2.3-r41h92ddd45_0.tar.bz2#bac0b7627ef744c98f4bc48885f52e72
 https://conda.anaconda.org/conda-forge/noarch/r-callr-3.7.3-r41hc72bb7e_0.tar.bz2#af0891cc9b87e2954c9a3c66f144992d
@@ -619,10 +621,8 @@ https://conda.anaconda.org/conda-forge/noarch/r-scales-1.2.1-r41hc72bb7e_1.tar.b
 https://conda.anaconda.org/conda-forge/linux-64/r-specsverification-0.5_3-r41ha503ecb_3.conda#2bc51f0d44b98092ba57cf2f8671b490
 https://conda.anaconda.org/conda-forge/linux-64/r-splancs-2.01_43-r41h8da6f51_1.tar.bz2#3a6aad0706541141d10e3b514467a080
 https://conda.anaconda.org/conda-forge/linux-64/r-vctrs-0.6.2-r41ha503ecb_0.conda#1f7610a1863648cab254a9f85bd29dcd
-https://conda.anaconda.org/conda-forge/noarch/seaborn-0.12.2-hd8ed1ab_0.conda#50847a47c07812f88581081c620f5160
-https://conda.anaconda.org/conda-forge/linux-64/xgboost-1.7.6-cuda112py311h2344cce_0.conda#bfa2be4c1df9bf875896b40b1735e6c2
-https://conda.anaconda.org/conda-forge/noarch/iris-esmf-regrid-0.7.0-pyhd8ed1ab_0.conda#de82eb8d09362babacafe6b7e27752ac
-https://conda.anaconda.org/conda-forge/noarch/nbconvert-7.7.3-pyhd8ed1ab_0.conda#f53d92ecd7d8563b006107f6a33e55c6
+https://conda.anaconda.org/conda-forge/noarch/dask-2023.12.0-pyhd8ed1ab_0.conda#5cfc00e93b71fba459bede86419d0f01
+https://conda.anaconda.org/conda-forge/noarch/nbconvert-7.12.0-pyhd8ed1ab_0.conda#364e28ab12477494e72839aaa588073d
 https://conda.anaconda.org/conda-forge/noarch/r-cyclocomp-1.1.0-r41hc72bb7e_1005.tar.bz2#800e1da5bf774be48934b8865dd78d33
 https://conda.anaconda.org/conda-forge/noarch/r-gridextra-2.3-r41hc72bb7e_1004.tar.bz2#71ebed7e976df735ff3443bb88bd154f
 https://conda.anaconda.org/conda-forge/noarch/r-httr-1.4.6-r41hc72bb7e_0.conda#53dbb769c96782db54bf2d414fc9b239
@@ -632,9 +632,10 @@ https://conda.anaconda.org/conda-forge/noarch/r-pillar-1.9.0-r41hc72bb7e_0.conda
 https://conda.anaconda.org/conda-forge/noarch/r-pkgload-1.3.2-r41hc72bb7e_0.tar.bz2#e23a1a8420ab52056d86a6f9691d23fa
 https://conda.anaconda.org/conda-forge/linux-64/r-purrr-1.0.1-r41h133d619_0.conda#d7404238cac0da3c97dc08503d116a2f
 https://conda.anaconda.org/conda-forge/noarch/r-r.cache-0.16.0-r41hc72bb7e_1.tar.bz2#aef451160d655cc630d8038d934dced3
-https://conda.anaconda.org/conda-forge/noarch/esmvalcore-2.9.0-pyhd8ed1ab_0.conda#1f80c9bce2737630da4e71332c9d39c1
+https://conda.anaconda.org/conda-forge/noarch/iris-esmf-regrid-0.9.0-pyhd8ed1ab_0.conda#570f2c6e387fd6dac5356a5152f91b3f
 https://conda.anaconda.org/conda-forge/noarch/r-climprojdiags-0.3.2-r41hc72bb7e_0.conda#9922b863cd10035cbb75e3c2edae64a7
 https://conda.anaconda.org/conda-forge/linux-64/r-tibble-3.2.1-r41h133d619_1.conda#3ae9b78fb1d8a44deed24a27cce33ebf
+https://conda.anaconda.org/conda-forge/label/esmvalcore_rc/noarch/esmvalcore-2.10.0rc1-pyh39db41b_0.conda#b973ee8c35712a7d21830ed06bdbc42d
 https://conda.anaconda.org/conda-forge/noarch/r-ggplot2-3.4.2-r41hc72bb7e_0.conda#c2b04f4ff351d84bf51fd5a77b5c9b6c
 https://conda.anaconda.org/conda-forge/noarch/r-rematch2-2.1.2-r41hc72bb7e_2.tar.bz2#f67eae0562ffc808b82f1590776c25f5
 https://conda.anaconda.org/conda-forge/noarch/r-styler-1.10.1-r41hc72bb7e_0.conda#c12b81cff8bb8745ffbe7aeb9dfd795f
@@ -648,11 +649,11 @@ https://conda.anaconda.org/conda-forge/linux-64/r-geomap-2.5_0-r41h06615bd_1.tar
 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/noarch/autodocsumm-0.2.6-pyhd8ed1ab_0.tar.bz2#4409dd7e06a62c3b2aa9e96782c49c6d
-https://conda.anaconda.org/conda-forge/noarch/nbsphinx-0.9.2-pyhd8ed1ab_0.conda#d1212b423fdd10d2da59601385561ff7
-https://conda.anaconda.org/conda-forge/noarch/pydata-sphinx-theme-0.13.3-pyhd8ed1ab_0.conda#07aca5f2dea315dcc16680d6891e9056
-https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-applehelp-1.0.6-pyhd8ed1ab_0.conda#5bba7b5823474cb3fcd4e4cbf942da61
-https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-devhelp-1.0.4-pyhd8ed1ab_0.conda#73dcd0eb2252cbd1530fd1e6e3cbbb03
-https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-htmlhelp-2.0.3-pyhd8ed1ab_0.conda#fb4d6329a57e20e03d7aecd18c7ca918
-https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-qthelp-1.0.5-pyhd8ed1ab_0.conda#85466265b76473cc1d02420056cbc4e3
-https://conda.anaconda.org/conda-forge/noarch/sphinx-7.1.2-pyhd8ed1ab_0.conda#d02bfa35cd4f2cd624289f64911cae9d
-https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-serializinghtml-1.1.7-pyhd8ed1ab_0.conda#01e35beea8aff61cdb445b90a7adf7d4
+https://conda.anaconda.org/conda-forge/noarch/nbsphinx-0.9.3-pyhd8ed1ab_0.conda#0dbaa7d08d3d79b2a1a4dd6a02cc4581
+https://conda.anaconda.org/conda-forge/noarch/pydata-sphinx-theme-0.14.4-pyhd8ed1ab_0.conda#c79b8443908032263ffb40ee6215e9e4
+https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-applehelp-1.0.7-pyhd8ed1ab_0.conda#aebfabcb60c33a89c1f9290cab49bc93
+https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-devhelp-1.0.5-pyhd8ed1ab_0.conda#ebf08f5184d8eaa486697bc060031953
+https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-htmlhelp-2.0.4-pyhd8ed1ab_0.conda#a9a89000dfd19656ad004b937eeb6828
+https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-qthelp-1.0.6-pyhd8ed1ab_0.conda#cf5c9649272c677a964a7313279e3a9b
+https://conda.anaconda.org/conda-forge/noarch/sphinx-7.2.6-pyhd8ed1ab_0.conda#bbfd1120d1824d2d073bc65935f0e4c0
+https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-serializinghtml-1.1.9-pyhd8ed1ab_0.conda#0612e497d7860728f2cda421ea2aec09
diff --git a/doc/sphinx/source/api/esmvaltool.diag_scripts.monitor.rst b/doc/sphinx/source/api/esmvaltool.diag_scripts.monitor.rst
index bb43997c43..3b1e3e6548 100644
--- a/doc/sphinx/source/api/esmvaltool.diag_scripts.monitor.rst
+++ b/doc/sphinx/source/api/esmvaltool.diag_scripts.monitor.rst
@@ -11,6 +11,7 @@ Examples
 --------
 
 * :ref:`recipe_monitor`
+* :ref:`recipe_model_evaluation`
 
 
 Diagnostic scripts
diff --git a/doc/sphinx/source/community/code_documentation.rst b/doc/sphinx/source/community/code_documentation.rst
index 82f8c3a8f7..1c211daf39 100644
--- a/doc/sphinx/source/community/code_documentation.rst
+++ b/doc/sphinx/source/community/code_documentation.rst
@@ -442,7 +442,10 @@ name to the list of authors in ``CITATION.cff`` and generate the entry for the
 ::
 
    pip install cffconvert
-   cffconvert --format zenodo --outfile .zenodo.json
+   cffconvert --infile CITATION.cff --format zenodo --outfile .zenodo.json
+
+Presently, this method unfortunately discards entries `communities`
+and `grants` from that file; please restore them manually.
 
 Note that authors of recipes and/or diagnostics also need to be added to the file
 `esmvaltool/config-references.yml <https://github.com/ESMValGroup/ESMValTool/blob/main/esmvaltool/config-references.yml>`__,
diff --git a/doc/sphinx/source/community/dataset.rst b/doc/sphinx/source/community/dataset.rst
index d70438d9bc..424d4d4694 100644
--- a/doc/sphinx/source/community/dataset.rst
+++ b/doc/sphinx/source/community/dataset.rst
@@ -65,10 +65,10 @@ The scientific reviewer needs to check this.
 Data availability
 =================
 
-Once your pull request has been approved by the reviewers, ask
-`@remi-kazeroni <https://github.com/remi-kazeroni>`_
+Once your pull request has been approved by the reviewers, ask a member of
+`@OBS-maintainers <https://github.com/orgs/ESMValGroup/teams/obs-maintainers>`_
 to add the new dataset to the data pool at DKRZ and CEDA-Jasmin.
-He is also the person in charge of merging CMORizer pull requests.
+This team is in charge of merging CMORizer pull requests.
 
 .. _dataset_checklist:
 
@@ -165,14 +165,14 @@ Run ``esmvaltool/recipes/examples/recipe_check_obs.yml`` for new dataset.
 RAW data
 --------
 
-Contact person in charge of ESMValTool data pool (`@remi-kazeroni`_) and
+Contact the team in charge of ESMValTool data pool (`@OBS-maintainers`_) and
 request to copy RAW data to RAWOBS/Tier2 (Tier3).
 
 
 CMORized data
 -------------
 
-Contact person in charge of ESMValTool data pool (`@remi-kazeroni`_) and
+Contact the team in charge of ESMValTool data pool (`@OBS-maintainers`_) and
 request to
 
 * Merge the pull request
diff --git a/doc/sphinx/source/community/diagnostic.rst b/doc/sphinx/source/community/diagnostic.rst
index 641a2da892..285815f7cf 100644
--- a/doc/sphinx/source/community/diagnostic.rst
+++ b/doc/sphinx/source/community/diagnostic.rst
@@ -179,6 +179,10 @@ and finally it will store provenance information. Provenance information is stor
 and provided that the provenance tree is small, also plotted in an SVG file for
 human inspection.
 In addition to provenance information, a caption is also added to the plots.
+
+Provenance information from the recipe is automatically recorded by ESMValCore, whereas
+diagnostic scripts must include code specifically to record provenance. See below for 
+documentation of provenance attributes that can be included in a recipe.
 When contributing a diagnostic, please make sure it records the provenance,
 and that no warnings related to provenance are generated when running the recipe.
 To allow the ESMValCore to keep track of provenance (e.g. which input files
@@ -233,20 +237,25 @@ Arbitrarily named other items are also supported.
 Please see the (installed version of the) file
 `esmvaltool/config-references.yml <https://github.com/ESMValGroup/ESMValTool/blob/main/esmvaltool/config-references.yml>`_
 for all available information on each item, see :ref:`esmvalcore:config-ref` for
-an introduction.
-In this file, the information is written in the form of ``key: value``.
-Note that we add the keys to the diagnostics.
-The keys will automatically be replaced by their values in the final provenance records.
-For example, in the ``config-references.yml`` there is a category for types of the plots:
+an introduction. It is also possible to add custom provenance information by adding items to each category in this file.
+In this file, the information is written in the form
+
+.. code-block:: console
+
+  key:
+    value: description
+
+for example
 
 .. code-block:: console
 
   plot_types:
     errorbar: error bar plot
 
-In the diagnostics, we add the key as:
-:code:`plot_types: [errorbar]`
-It is also possible to add custom provenance information by adding items to each category in this file.
+To use these items, include them in the provenance record dictionary in the form 
+:code:`key: [value]`
+i.e. for the example above as
+:code:`'plot_types': ['errorbar']`.
 
 In order to communicate with the diagnostic script, two interfaces have been defined,
 which are described in the `ESMValCore documentation <https://docs.esmvaltool.org/projects/esmvalcore/en/latest/interfaces.html>`_.
@@ -258,7 +267,7 @@ see the instructions and examples below on how to add provenance information:
 
 Recording provenance in a Python diagnostic script
 --------------------------------------------------
-Always use :meth:`esmvaltool.diag_scripts.shared.run_diagnostic` at the end of your script:
+Always use :func:`esmvaltool.diag_scripts.shared.run_diagnostic` at the end of your script:
 
 .. code-block:: python
 
@@ -266,16 +275,9 @@ Always use :meth:`esmvaltool.diag_scripts.shared.run_diagnostic` at the end of y
     with run_diagnostic() as config:
         main(config)
 
-And make use of a :class:`esmvaltool.diag_scripts.shared.ProvenanceLogger` to log provenance:
-
-.. code-block:: python
-
-  with ProvenanceLogger(cfg) as provenance_logger:
-        provenance_logger.log(diagnostic_file, provenance_record)
-
-The ``diagnostic_file`` can be obtained using :class:`esmvaltool.diag_scripts.shared.get_diagnostic_filename`.
-
-The ``provenance_record`` is a dictionary of provenance items, for example:
+Create a ``provenance_record`` for each diagnostic file (i.e. image or data 
+file) that the diagnostic script outputs. The ``provenance_record`` is a 
+dictionary of provenance items, for example:
 
 .. code-block:: python
 
@@ -294,10 +296,25 @@ The ``provenance_record`` is a dictionary of provenance items, for example:
         'statistics': ['mean'],
       }
 
+To save a matplotlib figure, use the convenience function 
+:func:`esmvaltool.diag_scripts.shared.save_figure`. Similarly, to save Iris cubes use 
+:func:`esmvaltool.diag_scripts.shared.save_data`. Both of these functions take
+``provenance_record`` as an argument and log the provenance accordingly.
 Have a look at the example Python diagnostic in
 `esmvaltool/diag_scripts/examples/diagnostic.py <https://github.com/ESMValGroup/ESMValTool/blob/main/esmvaltool/diag_scripts/examples/diagnostic.py>`_
 for a complete example.
 
+For any other files created, you will need to make use of a 
+:class:`esmvaltool.diag_scripts.shared.ProvenanceLogger` to log provenance. Include the
+following code directly after the file is saved:
+
+.. code-block:: python
+
+  with ProvenanceLogger(cfg) as provenance_logger:
+        provenance_logger.log(diagnostic_file, provenance_record)
+
+The full path of a ``diagnostic_file`` can be obtained using :class:`esmvaltool.diag_scripts.shared.get_diagnostic_filename`.
+
 Recording provenance in an NCL diagnostic script
 ------------------------------------------------
 Always call the ``log_provenance`` procedure after plotting from your NCL diag_script:
diff --git a/doc/sphinx/source/community/review.rst b/doc/sphinx/source/community/review.rst
index 41e596dda9..3429c0ef7a 100644
--- a/doc/sphinx/source/community/review.rst
+++ b/doc/sphinx/source/community/review.rst
@@ -69,7 +69,7 @@ GitHub.
 
 Pull requests are merged by the `@ESMValGroup/esmvaltool-coreteam`_.
 Specifically, pull requests containing a :ref:`CMORizer script<new-dataset>` can only be merged by
-`@remi-kazeroni`_, who will then add the CMORized data to the OBS data pool at
+a member of `@OBS-maintainers`_, who will then add the CMORized data to the OBS data pool at
 DKRZ and CEDA-Jasmin.
 The team member who does the merge first checks that both the technical and
 scientific reviewer approved the pull request and that the reviews were
@@ -215,7 +215,7 @@ their opinion and try to find a solution.
 .. _`@ESMValGroup/tech-reviewers`: https://github.com/orgs/ESMValGroup/teams/tech-reviewers
 .. _`@ESMValGroup/science-reviewers`: https://github.com/orgs/ESMValGroup/teams/science-reviewers
 .. _`@ESMValGroup/esmvaltool-coreteam`: https://github.com/orgs/ESMValGroup/teams/esmvaltool-coreteam
-.. _`@remi-kazeroni`: https://github.com/remi-kazeroni
+.. _`@OBS-maintainers`: https://github.com/orgs/ESMValGroup/teams/obs-maintainers
 .. _`pull request template`: https://raw.githubusercontent.com/ESMValGroup/ESMValTool/main/.github/pull_request_template.md
 .. _`Google meet`: https://meet.google.com
 .. _`Jitsi meet`: https://meet.jit.si
diff --git a/doc/sphinx/source/conf.py b/doc/sphinx/source/conf.py
index 6257289715..5ef08c3b06 100644
--- a/doc/sphinx/source/conf.py
+++ b/doc/sphinx/source/conf.py
@@ -153,7 +153,16 @@
 # Theme options are theme-specific and customize the look and feel of a theme
 # further.  For a list of options available for each theme, see the
 # documentation.
-# html_theme_options = {}
+#
+# Avoid the following warning issued by pydata_sphinx_theme:
+#
+# "WARNING: The default value for `navigation_with_keys` will change to `False`
+# in the next release. If you wish to preserve the old behavior for your site,
+# set `navigation_with_keys=True` in the `html_theme_options` dict in your
+# `conf.py` file.Be aware that `navigation_with_keys = True` has negative
+# accessibility implications:
+# https://github.com/pydata/pydata-sphinx-theme/issues/1492"
+html_theme_options = {"navigation_with_keys": False}
 
 # Add any paths that contain custom themes here, relative to this directory.
 # html_theme_path = []
diff --git a/doc/sphinx/source/faq.rst b/doc/sphinx/source/faq.rst
index 15d69192ca..10c72bd2cb 100644
--- a/doc/sphinx/source/faq.rst
+++ b/doc/sphinx/source/faq.rst
@@ -113,9 +113,15 @@ a symbolic link to it so it gets picked up at every re-run iteration:
 Can ESMValTool plot arbitrary model output?
 ===========================================
 
-Recipe :ref:`recipe_monitor` allows for the plotting of any preprocessed model.
-The plotting parameters are set through a yaml configuration file, and the
-type of plots to be generated are determined in the recipe.
+:ref:`recipe_model_evaluation` provides a set of recipes that can be used for a
+basic climate model evaluation with observational data.
+This is especially useful to get an overview of the general performance of a
+simulation.
+
+Furthermore, recipe :ref:`recipe_monitor` allows for the plotting of any
+preprocessed model.
+The plotting parameters are set through a yaml configuration file, and the type
+of plots to be generated are determined in the recipe.
 
 Moreover, recipe :ref:`recipes_psyplot_diag` and the corresponding diagnostic
 :ref:`psyplot_diag.py <api.esmvaltool.diag_scripts.psyplot_diag>` provide a
diff --git a/doc/sphinx/source/input.rst b/doc/sphinx/source/input.rst
index a28b91959a..ee913c5581 100644
--- a/doc/sphinx/source/input.rst
+++ b/doc/sphinx/source/input.rst
@@ -357,6 +357,9 @@ A list of the datasets for which a CMORizers is available is provided in the fol
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
 | MAC-LWP                      | lwp, lwpStderr (Amon)                                                                                |   3  | NCL             |
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
+| MERRA                        | cli, clivi, clt, clw, clwvi, hur, hus, lwp, pr, prw, ps, psl, rlut, rlutcs, rsdt, rsut, rsutcs, ta,  |   3  | NCL             |
+|                              | tas, ts, ua, va, wap, zg (Amon)                                                                      |      |                 |
++------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
 | MERRA2                       | sm (Lmon)                                                                                            |   3  | Python          |
 |                              | clt, pr, evspsbl, hfss, hfls, huss, prc, prsn, prw, ps, psl, rlds, rldscs, rlus, rlut, rlutcs, rsds, |      |                 |
 |                              | rsdscs, rsdt, tas, tasmin, tasmax, tauu, tauv, ts, uas, vas, rsus, rsuscs, rsut, rsutcs, ta, ua, va, |      |                 |
@@ -366,6 +369,8 @@ A list of the datasets for which a CMORizers is available is provided in the fol
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
 | MOBO-DIC_MPIM                | dissic (Omon)                                                                                        |   2  | Python          |
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
+| MOBO-DIC2004-2019            | dissic (Omon)                                                                                        |   2  | Python          |
++------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
 | MODIS                        | cliwi, clt, clwvi, iwpStderr, lwpStderr (Amon), od550aer (aero)                                      |   3  | NCL             |
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
 | MSWEP [#note1]_              | pr                                                                                                   |   3  | n/a             |
@@ -386,9 +391,11 @@ A list of the datasets for which a CMORizers is available is provided in the fol
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
 | NOAA-CIRES-20CR-V3           | clt, clwvi, hus, prw, rlut, rlutcs, rsut, rsutcs (Amon)                                              |   2  | Python          |
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
+| NOAA-MBL-CH4                 | ch4s (Amon)                                                                                          |   2  | Python          |
++------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
 | NOAAGlobalTemp               | tasa (Amon)                                                                                          |   2  | Python          |
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
-| NSIDC-0116-[nh|sh]           | usi, vsi (day)                                                                                       |   3  | Python          |
+| NSIDC-0116-[nh|sh] [#note4]_ | usi, vsi (day)                                                                                       |   3  | Python          |
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
 | OceanSODA-ETHZ               | areacello (Ofx), co3os, dissicos, fgco2, phos, spco2, talkos (Omon)                                  |   2  | Python          |
 +------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
@@ -428,6 +435,10 @@ A list of the datasets for which a CMORizers is available is provided in the fol
             can be found in the corresponding section of `recipe_check_obs.yml
             <https://github.com/ESMValGroup/ESMValTool/blob/main/esmvaltool/recipes/examples/recipe_check_obs.yml>`__.
 
+.. [#note4] The cmoriser requires PROJ>=9.3. Previous version of PROJ will return an error:
+            ``Internal Proj Error: proj_create: unhandled axis direction: UNKNOWN)``
+            You can check the version of PROJ in your conda environment by running:
+            ``conda list PROJ``.
 
 .. _inputdata_native_datasets:
 
diff --git a/doc/sphinx/source/recipes/figures/examples/IPCC_AR6_figure_9.3a_1850-2100.png b/doc/sphinx/source/recipes/figures/examples/IPCC_AR6_figure_9.3a_1850-2100.png
new file mode 100644
index 0000000000..67bccb204e
Binary files /dev/null and b/doc/sphinx/source/recipes/figures/examples/IPCC_AR6_figure_9.3a_1850-2100.png differ
diff --git a/doc/sphinx/source/recipes/figures/iht_toa/figure1_CERES-EBAF_CERES-EBAF.png b/doc/sphinx/source/recipes/figures/iht_toa/figure1_CERES-EBAF_CERES-EBAF.png
new file mode 100644
index 0000000000..18aa689123
Binary files /dev/null and b/doc/sphinx/source/recipes/figures/iht_toa/figure1_CERES-EBAF_CERES-EBAF.png differ
diff --git a/doc/sphinx/source/recipes/figures/iht_toa/figure2_CERES-EBAF_CERES-EBAF.png b/doc/sphinx/source/recipes/figures/iht_toa/figure2_CERES-EBAF_CERES-EBAF.png
new file mode 100644
index 0000000000..ae8afa7d25
Binary files /dev/null and b/doc/sphinx/source/recipes/figures/iht_toa/figure2_CERES-EBAF_CERES-EBAF.png differ
diff --git a/doc/sphinx/source/recipes/figures/iht_toa/figure3_CERES-EBAF_CERES-EBAF.png b/doc/sphinx/source/recipes/figures/iht_toa/figure3_CERES-EBAF_CERES-EBAF.png
new file mode 100644
index 0000000000..6562693268
Binary files /dev/null and b/doc/sphinx/source/recipes/figures/iht_toa/figure3_CERES-EBAF_CERES-EBAF.png differ
diff --git a/doc/sphinx/source/recipes/figures/iht_toa/figure4_CERES-EBAF_CERES-EBAF.png b/doc/sphinx/source/recipes/figures/iht_toa/figure4_CERES-EBAF_CERES-EBAF.png
new file mode 100644
index 0000000000..21076317ff
Binary files /dev/null and b/doc/sphinx/source/recipes/figures/iht_toa/figure4_CERES-EBAF_CERES-EBAF.png differ
diff --git a/doc/sphinx/source/recipes/figures/iht_toa/figure5_CERES-EBAF_CERES-EBAF.png b/doc/sphinx/source/recipes/figures/iht_toa/figure5_CERES-EBAF_CERES-EBAF.png
new file mode 100644
index 0000000000..e241ae07e3
Binary files /dev/null and b/doc/sphinx/source/recipes/figures/iht_toa/figure5_CERES-EBAF_CERES-EBAF.png differ
diff --git a/doc/sphinx/source/recipes/figures/iht_toa/figure6_CERES-EBAF_CERES-EBAF.png b/doc/sphinx/source/recipes/figures/iht_toa/figure6_CERES-EBAF_CERES-EBAF.png
new file mode 100644
index 0000000000..073ccff158
Binary files /dev/null and b/doc/sphinx/source/recipes/figures/iht_toa/figure6_CERES-EBAF_CERES-EBAF.png differ
diff --git a/doc/sphinx/source/recipes/figures/model_evaluation/annual_cycle_clt_southerocean_Amon.jpg b/doc/sphinx/source/recipes/figures/model_evaluation/annual_cycle_clt_southerocean_Amon.jpg
new file mode 100644
index 0000000000..0e1e8a4531
Binary files /dev/null and b/doc/sphinx/source/recipes/figures/model_evaluation/annual_cycle_clt_southerocean_Amon.jpg differ
diff --git a/doc/sphinx/source/recipes/figures/model_evaluation/map_swcre_MPI-ESM1-2-HR_Amon.jpg b/doc/sphinx/source/recipes/figures/model_evaluation/map_swcre_MPI-ESM1-2-HR_Amon.jpg
new file mode 100644
index 0000000000..f6abf01516
Binary files /dev/null and b/doc/sphinx/source/recipes/figures/model_evaluation/map_swcre_MPI-ESM1-2-HR_Amon.jpg differ
diff --git a/doc/sphinx/source/recipes/figures/model_evaluation/map_tas_MPI-ESM1-2-HR_Amon.jpg b/doc/sphinx/source/recipes/figures/model_evaluation/map_tas_MPI-ESM1-2-HR_Amon.jpg
new file mode 100644
index 0000000000..50b5ebbd20
Binary files /dev/null and b/doc/sphinx/source/recipes/figures/model_evaluation/map_tas_MPI-ESM1-2-HR_Amon.jpg differ
diff --git a/doc/sphinx/source/recipes/figures/model_evaluation/timeseries_rtnt_ambiguous_dataset_Amon.jpg b/doc/sphinx/source/recipes/figures/model_evaluation/timeseries_rtnt_ambiguous_dataset_Amon.jpg
new file mode 100644
index 0000000000..2b65fe97e7
Binary files /dev/null and b/doc/sphinx/source/recipes/figures/model_evaluation/timeseries_rtnt_ambiguous_dataset_Amon.jpg differ
diff --git a/doc/sphinx/source/recipes/figures/model_evaluation/variable_vs_lat_pr_Amon.jpg b/doc/sphinx/source/recipes/figures/model_evaluation/variable_vs_lat_pr_Amon.jpg
new file mode 100644
index 0000000000..4e252d7904
Binary files /dev/null and b/doc/sphinx/source/recipes/figures/model_evaluation/variable_vs_lat_pr_Amon.jpg differ
diff --git a/doc/sphinx/source/recipes/figures/monitor/hovmoeller_time_vs_lat_with_ref.png b/doc/sphinx/source/recipes/figures/monitor/hovmoeller_time_vs_lat_with_ref.png
new file mode 100644
index 0000000000..4abd6df04f
Binary files /dev/null and b/doc/sphinx/source/recipes/figures/monitor/hovmoeller_time_vs_lat_with_ref.png differ
diff --git a/doc/sphinx/source/recipes/figures/monitor/hovmoeller_z_vs_time_with_ref.png b/doc/sphinx/source/recipes/figures/monitor/hovmoeller_z_vs_time_with_ref.png
new file mode 100755
index 0000000000..734913c60b
Binary files /dev/null and b/doc/sphinx/source/recipes/figures/monitor/hovmoeller_z_vs_time_with_ref.png differ
diff --git a/doc/sphinx/source/recipes/figures/monitor/variable_vs_lat_with_ref.png b/doc/sphinx/source/recipes/figures/monitor/variable_vs_lat_with_ref.png
new file mode 100644
index 0000000000..31cb81135d
Binary files /dev/null and b/doc/sphinx/source/recipes/figures/monitor/variable_vs_lat_with_ref.png differ
diff --git a/doc/sphinx/source/recipes/index.rst b/doc/sphinx/source/recipes/index.rst
index 57c77e8b30..edcc48977a 100644
--- a/doc/sphinx/source/recipes/index.rst
+++ b/doc/sphinx/source/recipes/index.rst
@@ -21,6 +21,7 @@ large variety of input data.
 .. toctree::
    :maxdepth: 1
 
+   recipe_model_evaluation
    recipe_monitor
    recipe_psyplot
    recipe_seaborn
@@ -45,6 +46,7 @@ Atmosphere
    recipe_gier20bg
    recipe_heatwaves_coldwaves
    recipe_hyint
+   recipe_iht_toa
    recipe_impact
    recipe_modes_of_variability
    recipe_mpqb_xch4
@@ -99,6 +101,7 @@ IPCC
    recipe_ipccwg1ar6ch3
    recipe_ipccwg1ar5ch9
    recipe_collins13ipcc
+   recipe_examples
 
 Land
 ^^^^
diff --git a/doc/sphinx/source/recipes/recipe_examples.rst b/doc/sphinx/source/recipes/recipe_examples.rst
index 5874e2c1c4..e3c32c4337 100644
--- a/doc/sphinx/source/recipes/recipe_examples.rst
+++ b/doc/sphinx/source/recipes/recipe_examples.rst
@@ -8,20 +8,26 @@ Overview
 
 These are example recipes calling example diagnostic scripts.
 
-The recipe examples/recipe_python.yml produces time series plots of global mean
+The recipe ``examples/recipe_python.yml`` produces time series plots of global mean
 temperature and for the temperature in Amsterdam.
 It also produces a map of global temperature in January 2020.
 
-The recipe examples/recipe_extract_shape.yml produces a map of the mean
+The recipe ``examples/recipe_easy_ipcc.yml`` reproduces part of figure 9.3a from
+`IPCC AR6 - Climate Change 2021: The Physical Science Basis <https://www.ipcc.ch/report/sixth-assessment-report-working-group-i/>`__.
+It demonstrates how ESMValTool can be used to conveniently analyze
+many models on their native grid and is described in detail in the blog post
+`Analysis-ready climate data with ESMValCore <https://blog.esciencecenter.nl/easy-ipcc-powered-by-esmvalcore-19a0b6366ea7>`__.
+
+The recipe ``examples/recipe_extract_shape.yml`` produces a map of the mean
 temperature in the Elbe catchment over the years 2000 to 2002.
 Some example shapefiles for use with this recipe are available
 `here <https://github.com/ESMValGroup/ESMValTool/tree/main/esmvaltool/diag_scripts/shapeselect/testdata>`__,
 make sure to download all files with the same name but different extensions.
 
-The recipe examples/recipe_julia.yml produces a map plot with the mean temperature
+The recipe ``examples/recipe_julia.yml`` produces a map plot with the mean temperature
 over the year 1997 plus a number that is configurable from the recipe.
 
-The recipe examples/recipe_decadal.yml showcases how the ``timerange`` tag
+The recipe ``examples/recipe_decadal.yml`` showcases how the ``timerange`` tag
 can be used to load datasets belonging to the DCPP activity. Produces timeseries
 plots comparing the global mean temperature of a DCPP dataset with an observational
 dataset.
@@ -29,16 +35,16 @@ dataset.
 Available recipes and diagnostics
 ---------------------------------
 
-Recipes are stored in esmvaltool/recipes/
-
+Recipes are stored in `esmvaltool/recipes/ <https://github.com/ESMValGroup/ESMValTool/tree/main/esmvaltool/recipes>`__:
     * examples/recipe_python.yml
+    * examples/recipe_easy_ipcc.yml
     * examples/recipe_extract_shape.yml
     * examples/recipe_julia.yml
     * examples/recipe_decadal.yml
 
-Diagnostics are stored in esmvaltool/diag_scripts/
-
+Diagnostics are stored in `esmvaltool/diag_scripts/ <https://github.com/ESMValGroup/ESMValTool/tree/main/esmvaltool/diag_scripts>`__:
     * examples/diagnostic.py: visualize results and store provenance information
+    * examples/make_plot.py: Create a timeseries plot with likely ranges
     * examples/diagnostic.jl: visualize results and store provenance information
     * examples/decadal_example.py: visualize results and store provenance information
 
@@ -63,6 +69,7 @@ Variables
 ---------
 
 * tas (atmos, monthly, longitude, latitude, time)
+* tos (ocean, monthly, longitude, latitude, time)
 
 Example plots
 -------------
@@ -79,6 +86,12 @@ Example plots
 
    Amsterdam air temperature (multimodel mean of CMIP5 CanESM2 and CMIP6 BCC-ESM1).
 
+.. _easy_ipcc:
+.. figure:: /recipes/figures/examples/IPCC_AR6_figure_9.3a_1850-2100.png
+   :align:   center
+
+   Mean sea surface temperature anomaly (part of figure 9.3a from IPCC AR6).
+
 .. _elbe:
 .. figure::  /recipes/figures/examples/elbe.png
    :align:   center
diff --git a/doc/sphinx/source/recipes/recipe_iht_toa.rst b/doc/sphinx/source/recipes/recipe_iht_toa.rst
new file mode 100644
index 0000000000..87e182ef6a
--- /dev/null
+++ b/doc/sphinx/source/recipes/recipe_iht_toa.rst
@@ -0,0 +1,123 @@
+.. _recipes_iht_toa:
+
+Implied heat transport from Top of Atmosphere fluxes
+====================================================
+
+Overview
+--------
+
+This recipe calculates the implied horizontal heat transport (IHT) due to the
+spatial anomalies of radiative fluxes at the top of the atmosphere (TOA).
+The regional patterns of implied heat transport for different components of
+the TOA fluxes are calculated by solving the Poisson equation with the flux
+components as source terms.
+It reproduces the plots in `Pearce and Bodas-Salcedo (2023)`_ when the input
+data is CERES EBAF.
+
+
+Available recipes and diagnostics
+---------------------------------
+
+Recipes are stored in esmvaltool/recipes/
+
+* recipe_iht_toa.yml calculates the IHT maps for the following radiative fluxes:
+
+  * Total net, SW net, LW net (Figure 2).
+  * Total CRE, SW CRE, LW CRE (Figure 4).
+  * All-sky and clear-sky reflected SW (Figure 5).
+  * The meridional heat transports (MHT) of the fluxes above (Figures 1 and 3).
+
+Diagnostics are stored in esmvaltool/diag_scripts/iht_toa/
+
+* single_model_diagnostics.py: driver script that produces the plots.
+* poisson_solver.py: solver that calculates the IHTs.
+
+.. _`Pearce and Bodas-Salcedo (2023)`:  https://doi.org/10.1175/JCLI-D-22-0149.1
+
+User settings in recipe
+-----------------------
+There are no user settings in this recipe.
+
+Variables
+---------
+
+* rlut (atmos, monthly, longitude latitude time)
+* rlutcs (atmos, monthly, longitude latitude time)
+* rsutcs (atmos, monthly, longitude latitude time)
+* rsut (atmos, monthly, longitude latitude time)
+* rsdt (atmos, monthly, longitude latitude time)
+
+Observations and reformat scripts
+---------------------------------
+
+* CERES-EBAF
+
+References
+----------
+
+* Pearce, F. A., and A. Bodas-Salcedo, 2023: Implied Heat Transport from CERES
+  Data: Direct Radiative Effect of Clouds on Regional Patterns and Hemispheric
+  Symmetry. J. Climate, 36, 4019–4030, doi: 10.1175/JCLI-D-22-0149.1.
+
+Example plots
+-------------
+
+.. _fig_iht_toa_1:
+.. figure::  /recipes/figures/iht_toa/figure1_CERES-EBAF_CERES-EBAF.png
+   :align:   center
+
+   The implied heat transport due to the total net flux (blue), split into
+   the contributions from the SW (orange) and LW (green).
+
+.. _fig_iht_toa_2:
+.. figure::  /recipes/figures/iht_toa/figure2_CERES-EBAF_CERES-EBAF.png
+   :align:   center
+
+   The energy flux potentials for (a) TOT, (c) SW, and (e) LW fluxes,
+   alongside maps of the spatial anomalies of the fluxes [(b),(d),(f)
+   flux minus global average flux, respectively].
+   The implied heat transport is calculated as the gradient of the energy
+   flux potential, shown by the white vector arrows for a subset of points
+   to give the overall transport pattern.
+   Heat is directed from the blue minima of the potential field to
+   yellow maxima, with the magnitude implied by the density of contours.
+   All maps of the same type share the same color bar at the bottom
+   of the column.
+
+.. _fig_iht_toa_3:
+.. figure::  /recipes/figures/iht_toa/figure3_CERES-EBAF_CERES-EBAF.png
+   :align:   center
+
+   Direct radiative effects of clouds on the meridional heat transport.
+   (a) Contributions from TOT CRE (blue), SW CRE (orange), and LW CRE (green)
+   fluxes. (b) Contributions from all-sky and clear-sky OSR.
+   In (b), both curves have been multiplied by −1 such that positive heat
+   transport is northward.
+
+.. _fig_iht_toa_4:
+.. figure::  /recipes/figures/iht_toa/figure4_CERES-EBAF_CERES-EBAF.png
+   :align:   center
+
+   As in :numref:`fig_iht_toa_2`, but for the implied heat transport associated with
+   (a),(b) TOT CRE, (c),(d) SW CRE, and (e),(f) LW CRE fluxes.
+
+.. _fig_iht_toa_5:
+.. figure::  /recipes/figures/iht_toa/figure5_CERES-EBAF_CERES-EBAF.png
+   :align:   center
+
+   As in :numref:`fig_iht_toa_2`, but for (a), (b) clear-sky and (c), (d) all-sky reflected
+   SW flux.
+
+.. _fig_iht_toa_6:
+.. figure::  /recipes/figures/iht_toa/figure6_CERES-EBAF_CERES-EBAF.png
+   :align:   center
+
+   A measure of the symmetry between heat transport in the Northern and
+   Southern Hemispheres, calculated for the 12-month running mean of TOT MHT
+   in the regions: (a) the full hemisphere, (b) from the equator to 30°, and
+   (c) 30° to 90°.
+   Symmetry values obtained when including (blue) and excluding (orange)
+   the effect of clouds. The climatological symmetry values for the two cases
+   are shown as the black lines in each subplot, dashed and dotted,
+   respectively.
+   The standard deviations of the time series are shown in each plot.
diff --git a/doc/sphinx/source/recipes/recipe_model_evaluation.rst b/doc/sphinx/source/recipes/recipe_model_evaluation.rst
new file mode 100644
index 0000000000..9e199815e0
--- /dev/null
+++ b/doc/sphinx/source/recipes/recipe_model_evaluation.rst
@@ -0,0 +1,98 @@
+.. _recipe_model_evaluation:
+
+General model evaluation
+========================
+
+Overview
+--------
+
+These recipes and diagnostics provide a basic climate model evaluation with
+observational data.
+This is especially useful to get an overview of the performance of a
+simulation.
+The diagnostics used here allow plotting arbitrary preprocessor output, i.e.,
+arbitrary variables from arbitrary datasets.
+
+
+Available recipes and diagnostics
+---------------------------------
+
+Recipes are stored in `recipes/model_evaluation`
+
+* recipe_model_evaluation_basics.yml
+* recipe_model_evaluation_clouds_clim.yml
+* recipe_model_evaluation_clouds_cycles.yml
+* recipe_model_evaluation_precip_zonal.yml
+
+Diagnostics are stored in `diag_scripts/monitor/`
+
+* :ref:`multi_datasets.py
+  <api.esmvaltool.diag_scripts.monitor.multi_datasets>`:
+  Monitoring diagnostic to show multiple datasets in one plot (incl. biases).
+
+
+User settings
+-------------
+
+It is recommended to use a vector graphic file type (e.g., SVG) for the output
+format when running this recipe, i.e., run the recipe with the command line
+option ``--output_file_type=svg`` or use ``output_file_type: svg`` in your
+:ref:`esmvalcore:user configuration file`.
+Note that map and profile plots are rasterized by default.
+Use ``rasterize: false`` in the recipe to disable
+this.
+
+
+Recipe settings
+~~~~~~~~~~~~~~~
+
+A list of all possible configuration options that can be specified in the
+recipe is given for each diagnostic individually (see links given for the
+available diagnostics in the previous section).
+
+
+Variables
+---------
+
+Any, but the variables' number of dimensions should match the ones expected by
+each diagnostic (see links given for the available diagnostics in the previous
+section).
+
+
+Example plots
+-------------
+
+.. _fig_1:
+.. figure::  /recipes/figures/model_evaluation/map_tas_MPI-ESM1-2-HR_Amon.jpg
+   :align:   center
+   :width:   14cm
+
+Global climatology of 2m near-surface air temperature.
+
+.. _fig_2:
+.. figure::  /recipes/figures/model_evaluation/map_swcre_MPI-ESM1-2-HR_Amon.jpg
+   :align:   center
+   :width:   14cm
+
+Global climatology of the shortwave cloud radiative effect (SWCRE).
+
+.. _fig_3:
+.. figure::  /recipes/figures/model_evaluation/timeseries_rtnt_ambiguous_dataset_Amon.jpg
+   :align:   center
+   :width:   14cm
+
+Time series of the global mean top-of-the-atmosphere net radiative flux.
+
+.. _fig_4:
+.. figure::  /recipes/figures/model_evaluation/variable_vs_lat_pr_Amon.jpg
+   :align:   center
+   :width:   14cm
+
+Zonal mean precipitation.
+
+.. _fig_5:
+.. figure::  /recipes/figures/model_evaluation/annual_cycle_clt_southerocean_Amon.jpg
+   :align:   center
+   :width:   14cm
+
+Annual cycle of Southern Ocean total cloud cover.
diff --git a/doc/sphinx/source/recipes/recipe_monitor.rst b/doc/sphinx/source/recipes/recipe_monitor.rst
index 0358ec36a7..ee3b9b44fa 100644
--- a/doc/sphinx/source/recipes/recipe_monitor.rst
+++ b/doc/sphinx/source/recipes/recipe_monitor.rst
@@ -18,19 +18,18 @@ Available recipes and diagnostics
 
 Recipes are stored in `recipes/monitor`
 
-   * recipe_monitor.yml
-   * recipe_monitor_with_refs.yml
+* recipe_monitor.yml
+* recipe_monitor_with_refs.yml
 
 Diagnostics are stored in `diag_scripts/monitor/`
 
-   * :ref:`monitor.py <api.esmvaltool.diag_scripts.monitor.monitor>`:
-     Monitoring diagnostic to plot arbitrary preprocessor output.
-   * :ref:`compute_eofs.py <api.esmvaltool.diag_scripts.monitor.compute_eofs>`:
-     Monitoring diagnostic to plot EOF maps and associated PC timeseries.
-   * :ref:`multi_datasets.py
-     <api.esmvaltool.diag_scripts.monitor.multi_datasets>`:
-     Monitoring diagnostic to show multiple datasets in one plot (incl.
-     biases).
+* :ref:`monitor.py <api.esmvaltool.diag_scripts.monitor.monitor>`:
+  Monitoring diagnostic to plot arbitrary preprocessor output.
+* :ref:`compute_eofs.py <api.esmvaltool.diag_scripts.monitor.compute_eofs>`:
+  Monitoring diagnostic to plot EOF maps and associated PC timeseries.
+* :ref:`multi_datasets.py
+  <api.esmvaltool.diag_scripts.monitor.multi_datasets>`:
+  Monitoring diagnostic to show multiple datasets in one plot (incl. biases).
 
 
 User settings
@@ -210,3 +209,24 @@ Zonal mean profile of ta including a reference dataset.
    :width:   14cm
 
 1D profile of ta including a reference dataset.
+
+.. _fig_variable_vs_lat_with_ref:
+.. figure::  /recipes/figures/monitor/variable_vs_lat_with_ref.png
+   :align:   center
+   :width:   14cm
+
+Zonal mean pr including a reference dataset.
+
+.. _fig_hovmoeller_z_vs_time_with_ref:
+.. figure::  /recipes/figures/monitor/hovmoeller_z_vs_time_with_ref.png
+   :align:   center
+   :width:   14cm
+
+Hovmoeller plot (pressure vs. time) of ta including a reference dataset.
+
+.. _fig_hovmoeller_time_vs_lat_with_ref:
+.. figure:: /recipes/figures/monitor/hovmoeller_time_vs_lat_with_ref.png
+   :align:   center
+   :width:   14cm
+
+Hovmoeller plot (time vs. latitude) of tas including a reference dataset
diff --git a/doc/sphinx/source/utils.rst b/doc/sphinx/source/utils.rst
index 5e46d06d81..e5e7b00553 100644
--- a/doc/sphinx/source/utils.rst
+++ b/doc/sphinx/source/utils.rst
@@ -246,7 +246,8 @@ Optionally, the following parameters can be edited:
 * ``partition``, *str*: Name of the DKRZ partition used to run jobs. Default is ``interactive`` to minimize computing cost compared to ``compute`` for which nodes cannot be shared.
 * ``memory``, *str*: Amount of memory requested for each run. Default is ``64G`` to allow to run 4 recipes on the same node in parallel.
 * ``time``, *str*: Time limit. Default is ``04:00:00`` to increase the job priority. Jobs can run for up to 8 hours and 12 hours on the compute and interactive partitions, respectively.
-
+* ``default_max_parallel_tasks``, *int*: Default is ``8`` which works for most recipes. For other cases, an entry needs to be made to the ``MAX_PARALLEL_TASKS`` dictionary (see below).
+  
 The script will generate a submission script for all recipes using by default the ``interactive`` queue and with a time limit of 4h. In case a recipe
 may require of additional resources, they can be defined in the ``SPECIAL_RECIPES`` dictionary. The recipe name has to be given as a ``key`` in which the
 values are another dictionary. 
@@ -263,8 +264,8 @@ given by the slurm flags ``--mem``, ``--constraint`` or ``--ntasks``. In general
         },
    }
 
-Some recipes can only be run with ``--max_parallel_tasks=1`` for various reasons (memory issues, diagnostic issues, CMIP3 data used).
-These recipes need to be added to the ``ONE_TASK_RECIPES`` list.
+Some recipes can only be run with a number of tasks less than ``default_max_parallel_tasks`` for various reasons (memory issues, diagnostic issues, CMIP3 data used).
+These recipes need to be added to the ``MAX_PARALLEL_TASKS`` dictionary with a specific ``max_parallel_tasks`` value.
 
 Note that the script has been optimized to use standard SLURM settings to run most recipes while minimizing the computational cost of the jobs and tailored runtime settings for resource-intensive recipes.
 It is only necessary to edit this script for recipes that have been added since the last release and cannot be run with the default settings.
diff --git a/environment.yml b/environment.yml
index be78716bd2..f84253afd9 100644
--- a/environment.yml
+++ b/environment.yml
@@ -4,7 +4,7 @@ channels:
   # The release candidate channel should only be activated
   # during the rc phase right before the next release of the
   # ESMValCore.
-  # - conda-forge/label/esmvalcore_rc
+  - conda-forge/label/esmvalcore_rc
   - conda-forge
   - nodefaults
 
@@ -25,7 +25,7 @@ dependencies:
   - ecmwf-api-client
   - eofs
   - esmpy
-  - esmvalcore 2.9.*
+  - esmvalcore =2.10.0rc1
   - fiona
   - fire
   - gdal
@@ -39,6 +39,7 @@ dependencies:
   - natsort
   - nc-time-axis
   - netCDF4
+  - numba
   - numpy !=1.24.3  # severe masking bug
   - packaging
   - openpyxl
diff --git a/environment_osx.yml b/environment_osx.yml
index d767113193..76f80d319e 100644
--- a/environment_osx.yml
+++ b/environment_osx.yml
@@ -4,7 +4,7 @@ channels:
   # The release candidate channel should only be activated
   # during the rc phase right before the next release of the
   # ESMValCore.
-  # - conda-forge/label/esmvalcore_rc
+  - conda-forge/label/esmvalcore_rc
   - conda-forge
   - nodefaults
 
@@ -25,7 +25,7 @@ dependencies:
   - ecmwf-api-client
   - eofs
   - esmpy
-  - esmvalcore 2.9.*
+  - esmvalcore =2.10.0rc1
   - fiona
   - fire
   - gdal
diff --git a/esmvaltool/cmorizers/data/cmor_config/MOBO-DIC2004-2019.yml b/esmvaltool/cmorizers/data/cmor_config/MOBO-DIC2004-2019.yml
new file mode 100644
index 0000000000..7e80dc6634
--- /dev/null
+++ b/esmvaltool/cmorizers/data/cmor_config/MOBO-DIC2004-2019.yml
@@ -0,0 +1,19 @@
+---
+# Common global attributes for Cmorizer output
+attributes:
+  dataset_id: MOBO-DIC2004-2019
+  version: '2.3'
+  tier: 2
+  modeling_realm: reanaly
+  project_id: OBS6
+  source: 'https://www.nodc.noaa.gov/archive/arc0211/0277099/2.3/data/0-data/'
+  reference: 'mobo_dic2004_2019'
+
+# Variables to cmorize
+variables:
+  dissic:
+    filename: MPI_MOBO-DIC_2004-2019_v2.nc
+    mip: Omon
+    raw_name: DIC
+    raw_units: '1e-6 mol kg-1'
+    comment: 'The original units of this variable are mumol/kg. To convert to the CMOR units mol/m3, we assume a constant sea water density of 1032 kg/m3, which is approximately the sea water density for T=4°C, salinity=35PSU, and p=100bar according to the UNESCO formula (UNESCO, 1981, Tenth report of the joint panel on oceanographic tables and standards, UNESCO Technical Papers in Marine Science, see https://www.wkcgroup.com/tools-room/seawater-density-calculator/ and https://link.springer.com/content/pdf/bbm:978-3-319-18908-6/1.pdf).'
diff --git a/esmvaltool/cmorizers/data/cmor_config/NOAA-MBL-CH4.yml b/esmvaltool/cmorizers/data/cmor_config/NOAA-MBL-CH4.yml
new file mode 100644
index 0000000000..23e84a657d
--- /dev/null
+++ b/esmvaltool/cmorizers/data/cmor_config/NOAA-MBL-CH4.yml
@@ -0,0 +1,18 @@
+---
+# Common global attributes for Cmorizer output
+attributes:
+  dataset_id: NOAA-MBL-CH4
+  version: '1.0'
+  tier: 2
+  modeling_realm: atmos
+  project_id: OBS6
+  source: 'https://gml.noaa.gov/webdata/ccgg/trends/ch4/ch4_mm_gl.csv'
+  reference: 'noaa-mbl-ch4'
+
+# Variables to cmorize
+variables:
+  ch4s:
+    filename: ch4_mm_gl.csv
+    mip: Amon
+    raw_name: ch4
+    raw_units: 'nmol mol-1'
diff --git a/esmvaltool/cmorizers/data/datasets.yml b/esmvaltool/cmorizers/data/datasets.yml
index 5f7834832b..5ca2002c5d 100644
--- a/esmvaltool/cmorizers/data/datasets.yml
+++ b/esmvaltool/cmorizers/data/datasets.yml
@@ -845,6 +845,13 @@ datasets:
       individually or by using the option "download all".
       Data is freely available, but a registration is required.
 
+  MERRA:
+    tier: 3
+    source: https://goldsmr3.gesdisc.eosdis.nasa.gov/data/MERRA_MONTHLY/
+    last_access: 2023-02-01
+    info: |
+      Use automatic download. That will download monthly data.
+
   MERRA2:
     tier: 3
     source: https://goldsmr4.gesdisc.eosdis.nasa.gov/data/MERRA2_MONTHLY/ https://goldsmr5.gesdisc.eosdis.nasa.gov/data/MERRA2_MONTHLY/
@@ -875,6 +882,13 @@ datasets:
     info: |
       Download the file MOBO-DIC_MPIM_monthly_clim.nc
 
+  MOBO-DIC2004-2019:
+    tier: 2
+    source: https://www.nodc.noaa.gov/archive/arc0211/0277099/2.3/data/0-data/
+    last_access: 2023-10-09
+    info: |
+      Download the file MPI_MOBO-DIC_2004-2019_v2.nc
+
   MODIS:
     tier: 3
     source: https://ladsweb.modaps.eosdis.nasa.gov/search/order
@@ -1017,7 +1031,7 @@ datasets:
         miscSI-MO/pr_wtr.eatm.mon.mean.nc
         prsSI-MO/shum.mon.mean.nc
         miscMO/tcdc.eatm.mon.mean.nc
-        ntatFlxSI-MO/ulwrf.ntat.mon.mean.nc  
+        ntatFlxSI-MO/ulwrf.ntat.mon.mean.nc
         ntatFlxSI-MO/uswrf.ntat.mon.mean.nc
         ntatFlxSI-MO/csulf.ntat.mon.mean.nc
         ntatFlxSI-MO/csusf.ntat.mon.mean.nc
@@ -1030,6 +1044,14 @@ datasets:
       Download the following files:
       [SOURCE]/gridded/NOAAGlobalTemp_v5.0.0_gridded_s188001_e202205_c20220608T133245.nc
 
+  NOAA-MBL-CH4:
+    tier: 2
+    source: https://gml.noaa.gov/webdata/ccgg/trends/ch4/ch4_mm_gl.csv
+    last_access: 2023-07-17
+    info: |
+      Download the following file:
+      https://gml.noaa.gov/webdata/ccgg/trends/ch4/ch4_mm_gl.csv
+
   NSIDC-0116-sh:
     tier: 3
     source: https://nsidc.org/data/NSIDC-0116
diff --git a/esmvaltool/cmorizers/data/downloaders/datasets/merra.py b/esmvaltool/cmorizers/data/downloaders/datasets/merra.py
new file mode 100644
index 0000000000..df1d0ff7e9
--- /dev/null
+++ b/esmvaltool/cmorizers/data/downloaders/datasets/merra.py
@@ -0,0 +1,57 @@
+"""Script to download MERRA."""
+
+from datetime import datetime
+
+from dateutil import relativedelta
+
+from esmvaltool.cmorizers.data.downloaders.wget import NASADownloader
+
+
+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
+    """
+    if not start_date:
+        start_date = datetime(1979, 1, 1)
+    if not end_date:
+        end_date = datetime(2015, 12, 31)
+    loop_date = start_date
+
+    downloader = NASADownloader(
+        config=config,
+        dataset=dataset,
+        dataset_info=dataset_info,
+        overwrite=overwrite,
+    )
+
+    while loop_date <= end_date:
+        year = loop_date.year
+        downloader.download_folder(
+            "https://goldsmr3.gesdisc.eosdis.nasa.gov/data/MERRA_MONTHLY/"
+            f"MAIMNXINT.5.2.0/{year}/")
+        downloader.download_folder(
+            "https://goldsmr3.gesdisc.eosdis.nasa.gov/data/MERRA_MONTHLY/"
+            f"MAIMCPASM.5.2.0/{year}/")
+        downloader.download_folder(
+            "https://goldsmr3.gesdisc.eosdis.nasa.gov/data/MERRA_MONTHLY/"
+            f"MATMNXRAD.5.2.0/{year}/")
+        downloader.download_folder(
+            "https://goldsmr3.gesdisc.eosdis.nasa.gov/data/MERRA_MONTHLY/"
+            f"MATMFXCHM.5.2.0/{year}/")
+
+        loop_date += relativedelta.relativedelta(years=1)
diff --git a/esmvaltool/cmorizers/data/downloaders/datasets/mobo_dic2004_2019.py b/esmvaltool/cmorizers/data/downloaders/datasets/mobo_dic2004_2019.py
new file mode 100644
index 0000000000..1299981811
--- /dev/null
+++ b/esmvaltool/cmorizers/data/downloaders/datasets/mobo_dic2004_2019.py
@@ -0,0 +1,39 @@
+"""Script to download MOBO-DIC2004-2019."""
+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,
+    )
+
+    downloader.download_file(
+        "https://www.nodc.noaa.gov/archive/arc0211/0277099/2.3/data/0-data/"
+        "MPI_MOBO-DIC_2004-2019_v2.nc",
+        wget_options=[],
+    )
diff --git a/esmvaltool/cmorizers/data/downloaders/datasets/noaa_mbl_ch4.py b/esmvaltool/cmorizers/data/downloaders/datasets/noaa_mbl_ch4.py
new file mode 100644
index 0000000000..3cbf701c97
--- /dev/null
+++ b/esmvaltool/cmorizers/data/downloaders/datasets/noaa_mbl_ch4.py
@@ -0,0 +1,38 @@
+"""Script to download NOAA-MBL-CH4."""
+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,
+    )
+
+    downloader.download_file(
+        "https://gml.noaa.gov/webdata/ccgg/trends/ch4/ch4_mm_gl.csv",
+        wget_options=[],
+    )
diff --git a/esmvaltool/cmorizers/data/downloaders/wget.py b/esmvaltool/cmorizers/data/downloaders/wget.py
index 413066c60b..2afcca1d5a 100644
--- a/esmvaltool/cmorizers/data/downloaders/wget.py
+++ b/esmvaltool/cmorizers/data/downloaders/wget.py
@@ -103,8 +103,8 @@ def download_folder(self, server_path, wget_options=None):
         """
         if wget_options is None:
             wget_options = []
-        wget_options = self._wget_common_options + ["-np", "--accept=nc,nc4"
-                                                    ] + wget_options
+        wget_options = self._wget_common_options + [
+            "-np", "--accept=nc,nc4,hdf"] + wget_options
         super().download_folder(server_path, wget_options)
 
     def download_file(self, server_path, wget_options=None):
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/esacci_watervapour.py b/esmvaltool/cmorizers/data/formatters/datasets/esacci_watervapour.py
index c5ec55bb16..0985e5d6e6 100644
--- a/esmvaltool/cmorizers/data/formatters/datasets/esacci_watervapour.py
+++ b/esmvaltool/cmorizers/data/formatters/datasets/esacci_watervapour.py
@@ -23,7 +23,7 @@
 import os
 
 import iris
-from esmvalcore.cmor.check import _get_time_bounds
+from esmvalcore.cmor.fixes import get_time_bounds
 from esmvalcore.preprocessor import concatenate
 
 from ...utilities import (
@@ -83,7 +83,7 @@ def cmorization(in_dir, out_dir, cfg, cfg_user, start_date, end_date):
             yearly_cube = concatenate(monthly_cubes)
             # Fix monthly time bounds
             time = yearly_cube.coord('time')
-            time.bounds = _get_time_bounds(time, 'mon')
+            time.bounds = get_time_bounds(time, 'mon')
             save_variable(yearly_cube,
                           var,
                           out_dir,
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/merra.ncl b/esmvaltool/cmorizers/data/formatters/datasets/merra.ncl
new file mode 100644
index 0000000000..b57bca6a09
--- /dev/null
+++ b/esmvaltool/cmorizers/data/formatters/datasets/merra.ncl
@@ -0,0 +1,315 @@
+; #############################################################################
+; ESMValTool CMORizer for NASA MERRA reanalysis v5.2.0
+; #############################################################################
+;
+; Tier
+;    Tier 3: restricted dataset (registration required).
+;
+; Source
+;    EarthData via https://goldsmr3.gesdisc.eosdis.nasa.gov/data/MERRA_MONTHLY/
+;
+; Last access
+;    20230201
+;
+; Download and processing instructions
+; (requires EarthData login; see https://urs.earthdata.nasa.gov/)
+;    Use ESMValTool automatic download:
+;       esmvaltool data download --config_file <config-user.yml> MERRA
+;
+; Modification history
+;    20230818-lauer_axel: added output of clwvi (iwp + lwp)
+;    20230201-lauer_axel: written
+;
+; #############################################################################
+loadscript(getenv("esmvaltool_root") + \
+           "/data/formatters/interface.ncl")
+
+begin
+
+  ; Script name (for logger)
+  DIAG_SCRIPT = "merra.ncl"
+
+  ; Source name
+  OBSNAME = "MERRA"
+
+  ; Tier
+  TIER = 3
+
+  ; Period (complete years only)
+  YEAR1 = get_year(start_year, 1979)
+  YEAR2 = get_year(end_year, 2015)
+
+  ; Selected variable (standard name)
+  VAR = (/"zg", "wap", "cli", "clw", "hus", "hur", "ta", "ua", "va",  \
+          "ps", "psl",                                                \
+          "clivi", "clwvi", "lwp", "prw",                             \
+          "clt", "rlut", "rlutcs", "rsdt", "rsut", "rsutcs", "ts",    \
+          "tas", "pr"/)
+
+  ; Name in the raw data
+  NAME = (/"H", "OMEGA", "QI", "QL", "QV", "RH", "T", "U", "V",  \  ; 3d asm
+           "PS", "SLP",                                          \
+           "TQI", "TQI", "TQL", "TQV",                           \  ; 2d int
+           "CLDTOT", "LWTUP", "LWTUPCLR", "SWTDN", "SWTNT",      \  ; 2d rad
+           "SWTNTCLR", "TS",                                     \
+           "T2M", "PRECLSC"/)                                       ; 2d chm
+
+  ; unit conversion factor
+  CONVFAC = (/1.0, 1.0, 1.0, 1.0, 1.0, 100.0, 1.0, 1.0, 1.0,  \  ; 3d asm
+             1.0, 1.0,                                        \
+             1.0, 1.0, 1.0, 1.0,                              \  ; 2d int
+             100.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,             \  ; 2d rad
+             1.0, 1.0/)                                          ; 2d chm
+
+  ; additional attribute "positive" (for radiative fluxes)
+  POSITIVE = (/"", "", "", "", "", "", "", "", "", "", "",    \  ; 3d asm
+               "", "", "", "",                                \  ; 2d int
+               "", "up", "up", "down", "up", "up", "",        \  ; 2d rad
+               "", ""/)                                          ; 2d chm
+
+  ; names source files
+  SOURCEFILE = (/"instM_3d_asm_Cp.", "instM_3d_asm_Cp.", \  ; 3d asm
+                 "instM_3d_asm_Cp.", "instM_3d_asm_Cp.", \
+                 "instM_3d_asm_Cp.", "instM_3d_asm_Cp.", \
+                 "instM_3d_asm_Cp.", "instM_3d_asm_Cp.", \
+                 "instM_3d_asm_Cp.", "instM_3d_asm_Cp.", \
+                 "instM_3d_asm_Cp.",                     \
+                 "instM_2d_int_Nx.", "instM_2d_int_Nx.", \  ; 2d int
+                 "instM_2d_int_Nx.", "instM_2d_int_Nx.", \
+                 "tavgM_2d_rad_Nx.", "tavgM_2d_rad_Nx.", \  ; 2d rad
+                 "tavgM_2d_rad_Nx.", "tavgM_2d_rad_Nx.", \
+                 "tavgM_2d_rad_Nx.", "tavgM_2d_rad_Nx.", \
+                 "tavgM_2d_rad_Nx.",                     \
+                 "tavgM_2d_chm_Fx.", "tavgM_2d_chm_Fx."/)   ; 2d chm
+
+  ; dataset doi numbers
+  DOI = (/"10.5067/YX0AVASQRTNW", "10.5067/YX0AVASQRTNW",  \  ; 3d asm
+          "10.5067/YX0AVASQRTNW", "10.5067/YX0AVASQRTNW",  \
+          "10.5067/YX0AVASQRTNW", "10.5067/YX0AVASQRTNW",  \
+          "10.5067/YX0AVASQRTNW", "10.5067/YX0AVASQRTNW",  \
+          "10.5067/YX0AVASQRTNW", "10.5067/YX0AVASQRTNW",  \
+          "10.5067/YX0AVASQRTNW",                          \
+          "10.5067/QL0PGBK2CYJS", "10.5067/QL0PGBK2CYJS",  \  ; 2d int
+          "10.5067/QL0PGBK2CYJS", "10.5067/QL0PGBK2CYJS",  \
+          "10.5067/6UX3EDUNVUFK", "10.5067/6UX3EDUNVUFK",  \  ; 2d rad
+          "10.5067/6UX3EDUNVUFK", "10.5067/6UX3EDUNVUFK",  \
+          "10.5067/6UX3EDUNVUFK", "10.5067/6UX3EDUNVUFK",  \
+          "10.5067/6UX3EDUNVUFK",                          \
+          "10.5067/IYDN3LNZ63UE", "10.5067/IYDN3LNZ63UE"/)    ; 2d chm
+
+  ; MIP
+  MIP = "Amon"
+
+  ; Frequency
+  FREQ = "mon"
+
+  ; CMOR table
+  CMOR_TABLE = getenv("cmor_tables") + \
+    (/"/cmip5/Tables/CMIP5_" + MIP, "/cmip5/Tables/CMIP5_" + MIP,  \  ; 3d asm
+      "/cmip5/Tables/CMIP5_" + MIP, "/cmip5/Tables/CMIP5_" + MIP,  \
+      "/cmip5/Tables/CMIP5_" + MIP, "/cmip5/Tables/CMIP5_" + MIP,  \
+      "/cmip5/Tables/CMIP5_" + MIP, "/cmip5/Tables/CMIP5_" + MIP,  \
+      "/cmip5/Tables/CMIP5_" + MIP, "/cmip5/Tables/CMIP5_" + MIP,  \
+      "/cmip5/Tables/CMIP5_" + MIP,                                \
+      "/cmip5/Tables/CMIP5_" + MIP, "/cmip5/Tables/CMIP5_" + MIP,  \  ; 2d int
+      "/custom/CMOR_lwp.dat", "/cmip5/Tables/CMIP5_" + MIP,        \
+      "/cmip5/Tables/CMIP5_" + MIP, "/cmip5/Tables/CMIP5_" + MIP,  \  ; 2d rad
+      "/cmip5/Tables/CMIP5_" + MIP, "/cmip5/Tables/CMIP5_" + MIP,  \
+      "/cmip5/Tables/CMIP5_" + MIP, "/cmip5/Tables/CMIP5_" + MIP,  \
+      "/cmip5/Tables/CMIP5_" + MIP,                                \
+      "/cmip5/Tables/CMIP5_" + MIP, "/cmip5/Tables/CMIP5_" + MIP/)    ; 2d chm
+
+  ; Type
+  TYPE = "reanaly"
+
+  ; Version
+  VERSION = "5.2.0"
+
+  ; Global attributes
+  SOURCE = "https://goldsmr3.gesdisc.eosdis.nasa.gov/data/MERRA_MONTHLY/"
+  COMMENT = "Goddard Earth Sciences Data and Information Services Center " + \
+            "(GES DISC)"
+
+end
+
+begin
+
+  ; Loop over variables
+  do vv = 0, dimsizes(VAR) - 1
+
+    log_info("Processing " + VAR(vv) + " (" + MIP + ")")
+    if (isvar("output")) then
+      delete(output)
+    end if
+
+    do yy = YEAR1, YEAR2
+      do mm = 1, 12
+
+        datestr = tostring(yy)
+        if (mm .lt. 10) then
+          datestr = datestr + "0"
+        end if
+        datestr = datestr + tostring(mm)
+
+        fname = systemfunc("ls " + input_dir_path + "MERRA???.prod.assim." + \
+                           SOURCEFILE(vv) + datestr + ".hdf")
+
+        f = addfile(fname, "r")
+        tmp = f->$NAME(vv)$
+
+        ; Extract time range
+        tmp&TIME_EOSGRID@calendar = "standard"
+        date = cd_calendar(tmp&TIME_EOSGRID, 0)
+        if ((date(0, 0) .ne. yy) .or. (date(0, 1) .ne. mm)) then
+          error_msg("f", DIAG_SCRIPT, "", \
+                    "date in input file does not match date in filename: " + \
+                    fname)
+        end if
+
+        delete(date)
+
+        if (.not.isvar("output")) then
+          dims = dimsizes(tmp)
+          ; overwrite time dimension
+          dims(0) = 12
+          output = new(dims, float)
+          delete(dims)
+          rank = dimsizes(dimsizes(output))
+
+          output!0 = "time"
+          if (rank.eq.4) then
+            output!1 = "plev"
+            output!2 = "lat"
+            output!3 = "lon"
+            output&plev = tmp&Height_EOSGRID * 100.  ; [hPa] --> [Pa]
+          elseif (rank.eq.3)
+            output!1 = "lat"
+            output!2 = "lon"
+          end if
+
+          output&time = fspan(1, 12, 12)
+          output&time@calendar = "standard"
+          output&time@units = "days since 1950-01-01 00:00:00"
+
+          output&lat = tmp&YDim_EOSGRID
+          output&lon = tmp&XDim_EOSGRID
+
+        end if
+
+        ; Unpack variable according to metadata information
+        if (isatt(tmp, "scale_factor") .or. isatt(tmp, "add_offset")) then
+          tmp = tmp * tmp@scale_factor + tmp@add_offset
+        end if
+
+        if (rank.eq.4) then
+          output(mm - 1, :, :, :) = (/ tmp(0, :, :, :) /)
+        else
+          output(mm - 1, :, :) = (/ tmp(0, :, :) /)
+        end if
+
+        delete(tmp)
+
+        ; calcuation of outgoing fluxes: out = in - net
+        if ((VAR(vv) .eq. "rsut") .or. (VAR(vv) .eq. "rsutcs")) then
+          tmp = f->SWTDN
+          if (isatt(tmp, "scale_factor") .or. isatt(tmp, "add_offset")) then
+            tmp = tmp * tmp@scale_factor + tmp@add_offset
+          end if
+          output(mm - 1, :, :) = (/ tmp(0, :, :) /) - output(mm - 1, :, :)
+
+          delete(tmp)
+        end if
+
+        ; calcuation of total precipitation flux = large-scale+convective+anvil
+        if (VAR(vv) .eq. "pr") then
+          tmp = f->PRECCON  ; surface precipitation flux from convection
+          if (isatt(tmp, "scale_factor") .or. isatt(tmp, "add_offset")) then
+            tmp = tmp * tmp@scale_factor + tmp@add_offset
+          end if
+          output(mm - 1, :, :) = output(mm - 1, :, :) + tmp(0, :, :)
+          delete(tmp)
+          tmp = f->PRECANV  ; surface precipitation flux from anvils
+          if (isatt(tmp, "scale_factor") .or. isatt(tmp, "add_offset")) then
+            tmp = tmp * tmp@scale_factor + tmp@add_offset
+          end if
+          output(mm - 1, :, :) = output(mm - 1, :, :) + tmp(0, :, :)
+          delete(tmp)
+        end if
+
+        ; calculation of clwvi
+        if (VAR(vv) .eq. "clwvi") then
+          tmp = f->TQL
+          if (isatt(tmp, "scale_factor") .or. isatt(tmp, "add_offset")) then
+            tmp = tmp * tmp@scale_factor + tmp@add_offset
+          end if
+          output(mm - 1, :, :) = output(mm - 1, :, :) + tmp(0, :, :)
+          delete(tmp)
+        end if
+
+        delete(f)
+
+      end do  ; loop over months (mm)
+
+      ; Convert units
+      if (CONVFAC(vv) .ne. 1.0) then
+        output = output * CONVFAC(vv)
+      end if
+
+      ; Format coordinates
+      format_coords(output, yy + "0101", yy + "1231", FREQ)
+
+      ; Set variable attributes
+      tmp = format_variable(output, VAR(vv), CMOR_TABLE(vv))
+      delete(output)
+      output = tmp
+      delete(tmp)
+
+      ; set additional attribute "positive" for radiative fluxes
+      if (POSITIVE(vv) .ne. "") then
+        output@positive = POSITIVE(vv)
+      end if
+
+      ; Calculate coordinate bounds
+      bounds = guess_coord_bounds(output, FREQ)
+
+      ; Set global attributes
+      REF = "doi: " + DOI(vv)
+      gAtt = set_global_atts(OBSNAME, TIER, SOURCE, REF, COMMENT)
+
+      ; Output file
+      DATESTR = yy + "01-" + yy + "12"
+      fout = output_dir_path + \
+        str_join((/"OBS", OBSNAME, TYPE, VERSION, \
+                   MIP, VAR(vv), DATESTR/), "_") + ".nc"
+
+      ; Add height coordinate to tas variable (required by the new backend)
+      if (VAR(vv).eq."tas") then
+        output@coordinates = "height"
+      end if
+
+      ; Write variable
+      write_nc(fout, VAR(vv), output, bounds, gAtt)
+      delete(gAtt)
+      delete(output)
+      delete(bounds)
+
+      ; Add height coordinate to tas variable (required by CMOR checker)
+      if (VAR(vv).eq."tas") then
+        height = 2.d
+        height!0 = "ncl_scalar"
+        height@units = "m"
+        height@axis = "Z"
+        height@positive = "up"
+        height@long_name = "height"
+        height@standard_name = "height"
+        w = addfile(fout, "w")
+        w->height = height
+        delete(w)
+      end if
+
+        ; ---------------------------------------------------------------------
+
+    end do  ; loop over years (yy)
+  end do  ; loop over variables (vv)
+
+end
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/mobo_dic2004_2019.py b/esmvaltool/cmorizers/data/formatters/datasets/mobo_dic2004_2019.py
new file mode 100644
index 0000000000..570e20f715
--- /dev/null
+++ b/esmvaltool/cmorizers/data/formatters/datasets/mobo_dic2004_2019.py
@@ -0,0 +1,17 @@
+"""ESMValTool CMORizer for MOBO-DIC2004-2019 data.
+
+Tier
+    Tier 2: other freely-available dataset.
+
+Source
+    https://www.nodc.noaa.gov/archive/arc0211/0277099/2.3/data/0-data/
+
+Last access
+    20231009
+
+Download and processing instructions
+    Download the file MPI_MOBO-DIC_2004-2019_v2.nc
+
+"""
+
+from .mobo_dic_mpim import cmorization  # noqa
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/mobo_dic_mpim.py b/esmvaltool/cmorizers/data/formatters/datasets/mobo_dic_mpim.py
index 68884c22ae..9ae096104f 100644
--- a/esmvaltool/cmorizers/data/formatters/datasets/mobo_dic_mpim.py
+++ b/esmvaltool/cmorizers/data/formatters/datasets/mobo_dic_mpim.py
@@ -20,6 +20,7 @@
 from pathlib import Path
 
 import iris
+import numpy as np
 from cf_units import Unit
 from dask import array as da
 from iris import NameConstraint
@@ -30,6 +31,9 @@
 logger = logging.getLogger(__name__)
 
 
+TIME_UNITS = Unit('days since 1950-01-01 00:00:00', calendar='standard')
+
+
 def _callback_fix_missing_value(cube, field, _):
     """Create masked array from missing_value."""
     if hasattr(field.cf_data, 'missing_value'):
@@ -40,20 +44,18 @@ def _callback_fix_missing_value(cube, field, _):
 
 def _fix_climatological_time(cube):
     """Fix climatology coordinate."""
-    time_units = Unit('days since 1950-01-01 00:00:00', calendar='standard')
-
     # Following the doc the covered time period of the climatology is
     # January 2004 to December 2017 (Use 2011 as the "mean" year). See
     # https://www.ncei.noaa.gov/access/metadata/landing-page/bin/
     # iso?id=gov.noaa.nodc%3A0221526
-    time_points = time_units.date2num(
+    time_points = TIME_UNITS.date2num(
         [datetime(2011, m, 15) for m in range(1, 13)]
     )
     time_bounds = [
         [datetime(2004, m, 1), datetime(2017, m + 1, 1)] for m in range(1, 12)
     ]
     time_bounds.append([datetime(2004, 12, 1), datetime(2018, 1, 1)])
-    time_bounds = time_units.date2num(time_bounds)
+    time_bounds = TIME_UNITS.date2num(time_bounds)
 
     # Add new time coordinate to cube
     time_coord = DimCoord(
@@ -62,7 +64,7 @@ def _fix_climatological_time(cube):
         standard_name='time',
         long_name='time',
         var_name='time',
-        units=time_units,
+        units=TIME_UNITS,
         climatological=True,
     )
     cube.remove_coord('month of the year')
@@ -73,6 +75,49 @@ def _fix_climatological_time(cube):
     cube.add_cell_method(CellMethod('mean over years', coords=time_coord))
 
 
+def _fix_time(cube):
+    """Fix time coordinate."""
+    julian_day_coord = cube.coord('Julian Day')
+
+    # Calculate bounds of new time coordinate
+    # print(str(julian_day_coord.units))
+    datetime_base = datetime.strptime(
+        str(julian_day_coord.units).partition(' since ')[2],
+        '%Y-%m-%d %H:%M:%S',
+    )
+    base_year = datetime_base.year
+    base_month = datetime_base.month
+    all_months = list(julian_day_coord.points.astype(int)) + [
+        julian_day_coord.points.astype(int).max() + 1  # 1 more month for bnds
+    ]
+    bounds_datetimes = [
+        datetime(base_year + (m - 1) // 12, base_month + (m - 1) % 12, 1)
+        for m in all_months
+    ]
+    time_bounds = np.stack(
+        (
+            TIME_UNITS.date2num(bounds_datetimes[:-1]),
+            TIME_UNITS.date2num(bounds_datetimes[1:]),
+        ),
+        axis=-1,
+    )
+
+    # Calculate time points as mean of bounds
+    time_points = np.mean(time_bounds, axis=1)
+
+    # Add new time coordinate to cube
+    time_coord = DimCoord(
+        time_points,
+        bounds=time_bounds,
+        standard_name='time',
+        long_name='time',
+        var_name='time',
+        units=TIME_UNITS,
+    )
+    cube.remove_coord('Julian Day')
+    cube.add_dim_coord(time_coord, 0)
+
+
 def _fix_var_metadata(var_info, cmor_info, cube):
     """Fix variable metadata.
 
@@ -121,7 +166,10 @@ def _extract_variable(var_info, cmor_info, attrs, filepath, out_dir):
     _fix_var_metadata(var_info, cmor_info, cube)
 
     # Fix coordinates
-    _fix_climatological_time(cube)
+    if cube.coords('month of the year'):  # MOBO-DIC_MPIM
+        _fix_climatological_time(cube)
+    elif cube.coords('Julian Day'):  # MOBO-DIC2004-2019
+        _fix_time(cube)
     cube.coord('depth').units = 'm'
     utils.fix_coords(cube, overwrite_time_bounds=False)
 
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/noaa_mbl_ch4.py b/esmvaltool/cmorizers/data/formatters/datasets/noaa_mbl_ch4.py
new file mode 100644
index 0000000000..30011229de
--- /dev/null
+++ b/esmvaltool/cmorizers/data/formatters/datasets/noaa_mbl_ch4.py
@@ -0,0 +1,159 @@
+"""ESMValTool CMORizer for NOAA-MBL-CH4 data.
+
+Tier
+    Tier 2: other freely-available dataset.
+
+Source
+    https://gml.noaa.gov/ccgg/trends_ch4/
+
+Last access
+    20230717
+
+Download and processing instructions
+    Download the file:
+    wget https://gml.noaa.gov/webdata/ccgg/trends/ch4/ch4_mm_gl.csv
+"""
+
+import logging
+import warnings
+from pathlib import Path
+
+from datetime import datetime
+import iris
+import pandas as pd
+from cf_units import Unit
+import numpy as np
+
+from esmvaltool.cmorizers.data import utilities as utils
+
+logger = logging.getLogger(__name__)
+
+LAT_COORD = iris.coords.DimCoord([0.],
+                                 bounds=[[-90.0, 90.0]],
+                                 var_name='lat',
+                                 standard_name='latitude',
+                                 long_name='latitude',
+                                 units='degrees')
+LON_COORD = iris.coords.DimCoord([180.0],
+                                 bounds=[[0., 360.]],
+                                 var_name='lon',
+                                 standard_name='longitude',
+                                 long_name='longitude',
+                                 units='degrees')
+
+
+def _fix_var_metadata(var_info, cmor_info, cube):
+    """Fix variable metadata."""
+    if 'raw_units' in var_info:
+        cube.units = var_info['raw_units']
+
+    cube.convert_units(cmor_info.units)
+
+    utils.fix_var_metadata(cube, cmor_info)
+    return cube
+
+
+def _get_time_coord(year, month):
+    """Get time coordinate."""
+    point = datetime(year=year, month=month, day=15)
+    bound_low = datetime(year=year, month=month, day=1)
+    if month == 12:
+        month_bound_up = 1
+        year_bound_up = year + 1
+    else:
+        month_bound_up = month + 1
+        year_bound_up = year
+    bound_up = datetime(year=year_bound_up, month=month_bound_up, day=1)
+    time_units = Unit('days since 1950-01-01 00:00:00', calendar='standard')
+    time_coord = iris.coords.DimCoord(
+        time_units.date2num(point),
+        bounds=time_units.date2num([bound_low, bound_up]),
+        var_name='time',
+        standard_name='time',
+        long_name='time',
+        units=time_units,
+    )
+    return time_coord
+
+
+def _get_cube(row, column_name):
+    """Create :class:`iris.cube.Cube` from :class:`pandas.Series`."""
+    time_coord = _get_time_coord(int(row['year']), int(row['month']))
+    lat_coord = LAT_COORD.copy()
+    lon_coord = LON_COORD.copy()
+    data = np.ma.masked_invalid(row[column_name])
+    cube = iris.cube.Cube(
+        data.reshape((1, 1, 1)),
+        dim_coords_and_dims=[(time_coord, 0), (lat_coord, 1), (lon_coord, 2)],
+        units='ppb',
+    )
+    return cube
+
+
+def _fix_coords(cube):
+    """Fix coordinates."""
+    utils.fix_dim_coordnames(cube)
+
+    return cube
+
+
+def _extract_variable(var_info, cmor_info, attrs, filepath, out_dir):
+    """Extract variable."""
+    var = cmor_info.short_name
+
+    # Load data
+    with warnings.catch_warnings():
+        warnings.filterwarnings(
+            action='ignore',
+            message="Skipping global attribute 'units': 'units' is not a "
+                    "permitted attribute",
+            category=UserWarning,
+            module='iris',
+        )
+        skiprows = 0
+        with open(filepath, 'r', encoding='utf-8') as csv:
+            for line in csv:
+                if line.startswith("#"):
+                    skiprows = skiprows + 1
+
+        data_frame = pd.read_csv(filepath, header=skiprows)
+
+        # Extract cube
+        cubes = iris.cube.CubeList()
+        for (_, row) in data_frame.iterrows():
+            cube = _get_cube(row, 'average')
+            cubes.append(cube)
+        cube = cubes.concatenate_cube()
+        cube.var_name = var
+
+    # Fix coordinates
+    cube = _fix_coords(cube)
+
+    # Fix variable metadata
+    cube = _fix_var_metadata(var_info, cmor_info, cube)
+
+    # Fix global metadata
+    utils.set_global_atts(cube, attrs)
+
+    # Save variable
+    utils.save_variable(
+        cube,
+        var,
+        out_dir,
+        attrs,
+        unlimited_dimensions=['time'],
+    )
+
+
+def cmorization(in_dir, out_dir, cfg, cfg_user, start_date, end_date):
+    """Cmorization func call."""
+    cmor_table = cfg['cmor_table']
+    glob_attrs = cfg['attributes']
+
+    # Run the cmorization
+    for (var, var_info) in cfg['variables'].items():
+        filepath = Path(in_dir) / var_info['filename']
+        logger.info("CMORizing variable '%s' from file %s", var, filepath)
+        glob_attrs['mip'] = var_info['mip']
+        cmor_info = cmor_table.get_variable(var_info['mip'], var)
+        _extract_variable(var_info, cmor_info, glob_attrs, filepath, out_dir)
diff --git a/esmvaltool/cmorizers/data/formatters/utilities.ncl b/esmvaltool/cmorizers/data/formatters/utilities.ncl
index 42632889ef..4c8cf6444e 100644
--- a/esmvaltool/cmorizers/data/formatters/utilities.ncl
+++ b/esmvaltool/cmorizers/data/formatters/utilities.ncl
@@ -209,11 +209,11 @@ begin
     ; Reset date (middle of the month)
     tt = 0
     do yy = year1, year2
-      date(tt, 0) = yy
       yy@calendar = calendar
       m1 = where(yy.eq.year1, month1, 1)
       m2 = where(yy.eq.year2, month2, 12)
       do mm = m1, m2
+        date(tt, 0) = yy
         date(tt, 1) = mm
         dm = days_in_month(yy, mm) / 2. + 1
         date(tt, 2) = toint(dm)
@@ -245,14 +245,14 @@ begin
     ; Reset date (middle of the day)
     tt = 0
     do yy = year1, year2
-      date(tt, 0) = yy
       m1 = where(yy.eq.year1, month1, 1)
       m2 = where(yy.eq.year2, month2, 12)
       do mm = m1, m2
-        date(tt, 1) = mm
         d1 = where(yy.eq.year1 .and. mm.eq.m1, day1, 1)
         d2 = where(yy.eq.year2 .and. mm.eq.m2, day2, days_in_month(yy, mm))
         do dd = d1, d2
+          date(tt, 0) = yy
+          date(tt, 1) = mm
           date(tt, 2) = dd
           tt = tt + 1
         end do
diff --git a/esmvaltool/config-references.yml b/esmvaltool/config-references.yml
index baaaa44c96..16725c3764 100644
--- a/esmvaltool/config-references.yml
+++ b/esmvaltool/config-references.yml
@@ -120,10 +120,19 @@ authors:
     institute: DLR, Germany
     orcid: https://orcid.org/0000-0001-7058-5938
     github: LisaBock
+  bodas-salcedo_alejandro:
+    name: Bodas-Salcedo, Alejandro
+    institute: MetOffice, UK
+    orcid:
   bojovic_dragana:
     name: Bojovic, Dragana
     institute: BSC, Spain
     orcid: https://orcid.org/0000-0001-7354-1885
+  bonnet_pauline:
+    name: Bonnet, Pauline
+    institute: DLR, Germany
+    orcid: https://orcid.org/0000-0003-3780-0784
+    github: Paulinebonnet111
   brunner_lukas:
     name: Brunner, Lukas
     institute: ETH Zurich, Switzerland
@@ -252,6 +261,11 @@ authors:
     name: Hempelmann, Nils
     institute: IPSL, France
     orcid:
+  heuer_helge:
+    name: Heuer, Helge
+    institute: DLR, Germany
+    email: helge.heuer@dlr.de
+    orcid: https://orcid.org/0000-0003-2411-7150
   hogan_emma:
     name: Hogan, Emma
     institute: MetOffice, UK
@@ -288,10 +302,20 @@ authors:
     name: Koirala, Sujan
     institute: MPI-BGC, Germany
     orcid: https://orcid.org/0000-0001-5681-1986
+  kraft_jeremy:
+    name: Kraft, Jeremy
+    institute: DLR, Germany
+    orcid:
+    github: jeremykraftdlr
   krasting_john:
     name: Krasting, John
     institute: NOAA, USA
     orcid: https://orcid.org/0000-0002-4650-9844
+  kuehbacher_birgit:
+    name: Kuehbacher, Birgit
+    institute: DLR, Germany
+    email: birgit.kuehbacher@dlr.de
+    orcid:
   lejeune_quentin:
     name: Lejeune, Quentin
     institute: Climate Analytics, Germany
@@ -391,6 +415,10 @@ authors:
     name: Pandde, Amarjiit
     institute: Univ. of Arizona, USA
     orcid:
+  pearce_francesca:
+    name: Pearce, Francesca
+    institute: MetOffice, UK
+    orcid:
   perez-zanon_nuria:
     name: Perez-Zanon, Nuria
     institute: BSC, Spain
@@ -446,6 +474,11 @@ authors:
     name: Sandstad, Marit
     institute: Cicero, Norway
     orcid:
+  sarauer_ellen:
+    name: Sarauer, Ellen
+    institute: DLR, Germany
+    orcid:
+    github: ellensarauer
   serva_federico:
     name: Serva, Federico
     institute: CNR, Italy
diff --git a/esmvaltool/diag_scripts/austral_jet/asr.ncl b/esmvaltool/diag_scripts/austral_jet/asr.ncl
index c12b66f166..5d855ad393 100644
--- a/esmvaltool/diag_scripts/austral_jet/asr.ncl
+++ b/esmvaltool/diag_scripts/austral_jet/asr.ncl
@@ -289,7 +289,7 @@ begin
     work_dir = output@work_dir
     opt = diag_script_info
     log_info(opt@wdiag)
-    plot_path = "missing"
+    plot_path = "n/a"
     plot_type = ""
 
     ; Iterate over all datasets
diff --git a/esmvaltool/diag_scripts/austral_jet/main.ncl b/esmvaltool/diag_scripts/austral_jet/main.ncl
index 446133e1d5..c078830687 100644
--- a/esmvaltool/diag_scripts/austral_jet/main.ncl
+++ b/esmvaltool/diag_scripts/austral_jet/main.ncl
@@ -731,7 +731,7 @@ begin
   if (output) then
     work_dir := output@work_dir
     opt = diag_script_info
-    plot_path = "missing"
+    plot_path = "n/a"
     plot_type = ""
 
     ; Iterate over all desired diagnostics
diff --git a/esmvaltool/diag_scripts/carbon_ec/carbon_beta.ncl b/esmvaltool/diag_scripts/carbon_ec/carbon_beta.ncl
index eb43b10cd3..0d6319ea85 100644
--- a/esmvaltool/diag_scripts/carbon_ec/carbon_beta.ncl
+++ b/esmvaltool/diag_scripts/carbon_ec/carbon_beta.ncl
@@ -279,11 +279,7 @@ begin
   create_legend_lines(leg@annots, leg, plot_dir + \
                       DIAG_SCRIPT + "_legend", "markers")
 
-  if (file_type .ne. "png") then
-    plotname = plot_dir + plot_file + "." + file_type
-  else
-    plotname = plot_dir + plot_file + ".000001.png"
-  end if
+  plotname = plot_dir + plot_file + "." + file_type
 
   ; Call provenance logger
   log_provenance(ncdf_outfile, \
diff --git a/esmvaltool/diag_scripts/carbon_ec/carbon_co2_cycle.ncl b/esmvaltool/diag_scripts/carbon_ec/carbon_co2_cycle.ncl
index 7876ffdb62..f37b7bc23a 100644
--- a/esmvaltool/diag_scripts/carbon_ec/carbon_co2_cycle.ncl
+++ b/esmvaltool/diag_scripts/carbon_ec/carbon_co2_cycle.ncl
@@ -492,11 +492,7 @@ begin
   ; Write NetCDF output
   ncdf_outfile = ncdf_write(CO2var, new_path)
 
-  if (file_type .ne. "png") then
-    plotname = plot_dir + plot_file + "." + file_type
-  else
-    plotname = plot_dir + plot_file + ".000001.png"
-  end if
+  plotname = plot_dir + plot_file + "." + file_type
 
   ; -----------------------------------------------------------
   ; Call provenance logger
diff --git a/esmvaltool/diag_scripts/carbon_ec/carbon_constraint.ncl b/esmvaltool/diag_scripts/carbon_ec/carbon_constraint.ncl
index 94d5b8145c..61f1f5c5a3 100644
--- a/esmvaltool/diag_scripts/carbon_ec/carbon_constraint.ncl
+++ b/esmvaltool/diag_scripts/carbon_ec/carbon_constraint.ncl
@@ -331,7 +331,7 @@ begin
 
   ; Call provenance logger
   log_provenance(ncdf_outfile, \
-                 output_dir + "." + file_type, \
+                 output_dir + plot_file + "." + file_type, \
                  XStg + " vs " + YStg, \
                  (/"anomaly", "corr", "stddev"/), \
                  (/"trop", "global"/),\
diff --git a/esmvaltool/diag_scripts/carbon_ec/carbon_gammaHist.ncl b/esmvaltool/diag_scripts/carbon_ec/carbon_gammaHist.ncl
index 1479620173..589b0d72e8 100644
--- a/esmvaltool/diag_scripts/carbon_ec/carbon_gammaHist.ncl
+++ b/esmvaltool/diag_scripts/carbon_ec/carbon_gammaHist.ncl
@@ -336,7 +336,7 @@ begin
 
   ; Call provenance logger
   log_provenance(ncdf_outfile, \
-                 output_dir + "." + file_type, \
+                 output_dir + plot_file + "." + file_type, \
                  XStg + " vs " + YStg, \
                  (/"anomaly", "corr", "stddev"/), \
                  (/"trop", "global"/),\
diff --git a/esmvaltool/diag_scripts/examples/diagnostic.py b/esmvaltool/diag_scripts/examples/diagnostic.py
index 6ef63d13f1..a9e4f8667e 100644
--- a/esmvaltool/diag_scripts/examples/diagnostic.py
+++ b/esmvaltool/diag_scripts/examples/diagnostic.py
@@ -20,6 +20,9 @@
 
 def get_provenance_record(attributes, ancestor_files):
     """Create a provenance record describing the diagnostic data and plot."""
+    # Associated recipe uses contains a caption string with placeholders
+    # like {long_name} that are now populated from attributes dictionary.
+    # Note that for simple recipes, caption can be set here as a simple string
     caption = attributes['caption'].format(**attributes)
 
     record = {
diff --git a/esmvaltool/diag_scripts/examples/make_plot.py b/esmvaltool/diag_scripts/examples/make_plot.py
new file mode 100644
index 0000000000..796d55cd49
--- /dev/null
+++ b/esmvaltool/diag_scripts/examples/make_plot.py
@@ -0,0 +1,76 @@
+"""Python example diagnostic."""
+import logging
+from pathlib import Path
+
+import iris
+import matplotlib.pyplot as plt
+
+from esmvaltool.diag_scripts.shared import run_diagnostic, save_figure
+
+logger = logging.getLogger(Path(__file__).stem)
+
+
+def main(cfg):
+    """Plot part of figure_9.3a from IPCC AR6."""
+    colors = {
+        'historical-ssp126': '#2a3652',
+        'historical-ssp585': '#78333a',
+    }
+    fill_colors = {
+        'historical-ssp126': '#d2d5dc',
+        'historical-ssp585': '#ddced2',
+    }
+    labels = {
+        'historical-ssp126': 'Historical and SSP1-2.6',
+        'historical-ssp585': 'Historical and SSP5-8.5',
+    }
+
+    # Group input data by experiment
+    groups = {}
+    for filename, attributes in cfg['input_data'].items():
+        exp = attributes['exp']
+        if exp not in groups:
+            groups[exp] = {}
+        groups[exp][attributes['dataset']] = filename
+
+    # Loop over experiments to populate plot
+    for exp, group in groups.items():
+        mean = iris.load_cube(group['MultiModelMean'])
+        iris.quickplot.plot(
+            mean,
+            color=colors.get(exp),
+            label=labels.get(exp, exp),
+        )
+
+        p17 = iris.load_cube(group['MultiModelPercentile17'])
+        p83 = iris.load_cube(group['MultiModelPercentile83'])
+        time_coord = mean.coord('time')
+        time_axis = time_coord.units.num2date(time_coord.core_points())
+        plt.fill_between(
+            time_axis,
+            p17.core_data(),
+            p83.core_data(),
+            color=fill_colors.get(exp),
+            label='Likely (17% - 83%) ranges',
+        )
+
+    plt.title('Sea surface temperature anomaly')
+    plt.legend(loc='upper left')
+
+    filename = 'IPCC_AR6_figure_9.3a_1850-2100'
+    provenance_record = {
+        'caption': "Part of figure 9.3a from IPCC AR6.",
+        'authors': [
+            'kalverla_peter',
+            'andela_bouwe',
+        ],
+        'references': ['fox-kemper21ipcc'],
+        'ancestors': list(cfg['input_data'].keys()),
+    }
+    save_figure(filename, provenance_record, cfg, dpi=300)
+
+
+if __name__ == '__main__':
+
+    with run_diagnostic() as config:
+        main(config)
diff --git a/esmvaltool/diag_scripts/iht_toa/poisson_solver.py b/esmvaltool/diag_scripts/iht_toa/poisson_solver.py
new file mode 100644
index 0000000000..886d5fc616
--- /dev/null
+++ b/esmvaltool/diag_scripts/iht_toa/poisson_solver.py
@@ -0,0 +1,293 @@
+# (C) Crown Copyright 2023, the Met Office.
+"""Poisson solver for the full ocean-atmosphere column.
+
+The Poisson equation is solved by numerically using the bi-conjugate
+gradient stabilized (BiCGSTAB) method.
+
+The solution is achieved when the difference between the input field (radiative
+flux) and the Laplacian of the output field is less than the stated tolerance.
+If the solver fails to converge, the tolerance can be increased.
+
+Convergence is achieved faster by using a preconditioner on the output field.
+
+The heat transport is calculated as the gradient of the energy flux potential,
+the output of the Poisson solver.
+"""
+
+import numpy as np
+from numba import jit
+
+
+def swap_bounds(array):
+    """Extend the array by one in all directions.
+
+    As the array is periodic it allows for easier computations at
+    boundaries.
+    """
+    shape0, shape1 = np.array(array.shape) - 2
+    wrap_point = int(shape1 / 2 + 1)
+    for i in range(1, shape1 + 1):
+        array[0, i] = array[1, wrap_point]
+        array[shape0 + 1, i] = array[shape0, wrap_point]
+        wrap_point += 1
+        if wrap_point > shape1:
+            wrap_point = 1
+
+    array[:, 0] = array[:, shape1]
+    array[:, shape1 + 1] = array[:, 1]
+
+    return array
+
+
+def dot_prod(a_matrix, b_matrix):
+    """Calculate dot product of two matrices only over source term size."""
+    shape0, shape1 = np.array(a_matrix.shape) - 2
+    return (a_matrix[1:shape0 + 1, 1:shape1 + 1] *
+            b_matrix[1:shape0 + 1, 1:shape1 + 1]).sum()
+
+
+def precon(x_matrix, m_matrix):
+    """Preconditioner.
+
+    This is a wrapper to two steps that are optimised using jit.
+    It implements the preconditioning step of van der Vorst, H. A., 1992.
+    https://doi.org/10.1137/0913035.
+    """
+    cx_matrix = np.zeros(np.array(x_matrix.shape))
+    precon_a(x_matrix, m_matrix[1], m_matrix[2], m_matrix[4], cx_matrix)
+    cx_matrix = swap_bounds(cx_matrix)
+    precon_b(m_matrix[0], m_matrix[3], cx_matrix)
+    cx_matrix = swap_bounds(cx_matrix)
+    return cx_matrix
+
+
+@jit(nopython=True)
+def precon_a(x_matrix, m_w, m_s, m_p, cx_matrix):
+    """First step of preconditioner."""
+    shape0, shape1 = np.array(cx_matrix.shape) - 2
+    for j in range(1, shape0 + 1):
+        for i in range(1, shape1 + 1):
+            cx_matrix[j, i] = m_p[j, i] * (x_matrix[j, i] -
+                                           m_s[j, i] * cx_matrix[j - 1, i] -
+                                           m_w[j, i] * cx_matrix[j, i - 1])
+
+
+@jit(nopython=True)
+def precon_b(m_e, m_n, cx_matrix):
+    """Second step of preconditioner."""
+    shape0, shape1 = np.array(cx_matrix.shape) - 2
+    for j in range(shape0, 0, -1):
+        for i in range(shape1, 0, -1):
+            cx_matrix[j, i] = (cx_matrix[j, i] -
+                               m_e[j, i] * cx_matrix[j, i + 1] -
+                               m_n[j, i] * cx_matrix[j + 1, i])
+
+
+class SphericalPoisson:
+    """Poisson solver over the sphere.
+
+    Solve Poisson equation for a given source term (forcing) and
+    calculate meridional heat transport (MHT).
+    """
+
+    def __init__(self, logger, source, tolerance=2.0e-4):
+        """Initialise solver with source field, metrics and matrices."""
+        self.logger = logger
+        self.source = source
+        self.tolerance = tolerance
+        self.energy_flux_potential = None
+        self.meridional_heat_transport = None
+        logger.info("Initialising Poisson solver.")
+        self.set_matrices()
+
+    def set_matrices(self):
+        """Calculate A and M matrices.
+
+        A is the matrix that defines the five-point stencil (Eq. 8). The
+        A_matrix are the values are the contributions from each of the
+        four neighbouring cells: e,w,s,n,p.
+        """
+        # Calculate metrics hpi and hvj
+        src_shape = np.array(self.source.shape)
+        hpi = np.zeros(src_shape[0])
+        hvj = np.zeros(src_shape[0] + 1)
+        deltay = np.pi / src_shape[0]
+        yyy = -0.5 * np.pi + 0.5 * deltay
+        hvj[0] = 0.0
+        for j in range(0, src_shape[0]):
+            hpi[j] = np.cos(yyy)
+            hvj[j + 1] = np.cos(yyy + 0.5 * deltay)
+            yyy += deltay
+        hvj[-1] = 0.0
+
+        # Storing the full matrix
+        a_matrix = np.zeros((5, *src_shape))
+
+        # ILU factors
+        m_matrix = np.zeros((5, *(src_shape + 1)))
+
+        # Spherical Laplacian variables
+        aaa = 1.0 / ((2.0 * np.pi / src_shape[1])**2.)
+        bbb = 1.0 / ((np.pi / src_shape[0])**2.)
+
+        # First calculate the Poisson equations 5-point stencil
+        # A_w is the contribution from i-1, A_e is from i+1,
+        # A_s is j-1, A_n is j+1, and A_p is the diagonal
+        for j in range(0, src_shape[0]):
+            txa = aaa / hpi[j]**2.0
+            tyb = bbb / hpi[j]
+
+            for i in range(0, src_shape[1]):
+                a_matrix[0, j, i] = txa
+                a_matrix[1, j, i] = txa
+                a_matrix[2, j, i] = tyb * hvj[j]
+                a_matrix[3, j, i] = tyb * hvj[j + 1]
+                a_matrix[4, j, i] = -a_matrix[0:4, j, i].sum()
+
+        # ILU/SIP preconditioner factors: alf = 0.0 is ILU
+        alf = 0.9
+        m_matrix[4] += 1.0
+
+        for j in range(1, src_shape[0] + 1):
+            for i in range(1, src_shape[1] + 1):
+                m_matrix[2, j, i] = (a_matrix[2, j - 1, i - 1] /
+                                     (1.0 + alf * m_matrix[0, j - 1, i]))
+
+                m_matrix[1, j, i] = (a_matrix[1, j - 1, i - 1] /
+                                     (1.0 + alf * m_matrix[3, j, i - 1]))
+
+                m_matrix[4, j, i] = (a_matrix[4, j - 1, i - 1] -
+                                     m_matrix[2, j, i] *
+                                     (m_matrix[3, j - 1, i] -
+                                     alf * m_matrix[0, j - 1, i]) -
+                                     m_matrix[1, j, i] *
+                                     (m_matrix[0, j, i - 1] -
+                                     alf * m_matrix[3, j, i - 1]))
+
+                m_matrix[4, j, i] = 1.0 / m_matrix[4, j, i]
+
+                m_matrix[0, j, i] = ((a_matrix[0, j - 1, i - 1] -
+                                     alf * m_matrix[2, j, i] *
+                                     m_matrix[0, j - 1, i]) *
+                                     m_matrix[4, j, i])
+
+                m_matrix[3, j, i] = ((a_matrix[3, j - 1, i - 1] -
+                                     alf * m_matrix[1, j, i] *
+                                     m_matrix[3, j, i - 1]) *
+                                     m_matrix[4, j, i])
+
+        self.a_matrix = a_matrix
+        self.m_matrix = m_matrix
+
+    def solve(self, max_iterations=1000):
+        """Solve equation for the source term.
+
+        Bi-conjugate gradient stabilized numerical solver: van der
+        Vorst, H. A., 1992: Bi-cgstab: A fast and smoothly converging
+        variant of bi-cg for the solution of nonsymmetric linear
+        systems. SIAM Journal on Scientific and Statistical Computing,
+        https://doi.org/10.1137/0913035.
+        This solver implements the preconditioned Bi-CGSTAB algorithm,
+        described in page 638 of that paper.
+        """
+        bbb = np.zeros(np.array(self.source.shape) + 2)
+        xxx = np.zeros(np.array(self.source.shape) + 2)
+        bbb[1:-1, 1:-1] = self.source
+        bbb = swap_bounds(bbb)
+
+        sc_err = dot_prod(bbb, bbb)
+
+        # Group some temporal variables
+        stv = {
+            'alf': 1.0,
+            'omg': 1.0,
+            'nrm': 1.0,
+            'rrr': bbb - self.calc_ax(xxx)
+        }
+        stv['crrr'] = stv['rrr'].copy()
+
+        ppp = np.zeros(np.array(self.source.shape) + 2)
+        vvv = np.zeros(np.array(self.source.shape) + 2)
+
+        iteration = 0
+        while iteration < max_iterations:
+            rho = dot_prod(stv['rrr'], stv['crrr'])
+
+            bet = (rho / stv['nrm']) * (stv['alf'] / stv['omg'])
+
+            ttt = stv['rrr'] - bet * stv['omg'] * vvv
+
+            sss = precon(ttt, self.m_matrix)
+            ppp = sss + bet * ppp
+
+            vvv = self.calc_ax(ppp)
+            stv['nrm'] = dot_prod(stv['crrr'], vvv)
+
+            stv['alf'] = rho / stv['nrm']
+            sss = stv['rrr'] - stv['alf'] * vvv
+
+            csss = precon(sss, self.m_matrix)
+            ttt = self.calc_ax(csss)
+
+            stv['omg'] = dot_prod(ttt, sss) / dot_prod(ttt, ttt)
+
+            xxx = xxx + stv['alf'] * ppp + stv['omg'] * csss
+            stv['rrr'] = sss - stv['omg'] * ttt
+
+            stv['nrm'] = rho
+
+            if abs(stv['omg']) < 1.0e-16:
+                self.logger.info('Terminating Poisson solver.')
+                break
+
+            err = np.sqrt(dot_prod(stv['rrr'], stv['rrr']) / sc_err)
+            if err < self.tolerance:
+                self.logger.info('Poisson solver has converged.')
+                break
+
+            iteration += 1
+
+            if iteration == max_iterations:
+                raise RuntimeError('Poisson solver has not converged.')
+
+        self.energy_flux_potential = xxx
+
+    def calc_meridional_heat_transport(self):
+        """Meridional heat transport of energy flux potential.
+
+        Calculate of the meridional heat transport using the gradient of
+        the energy flux potential. Equation (11) in Pearce and Bodas-
+        Salcedo (2023).
+        """
+        deltax = 2.0 * np.pi / self.source.shape[1]
+        deltay = np.pi / self.source.shape[0]
+        yvalues = np.arange(-0.5 * np.pi + 0.5 * deltay, 0.5 * np.pi, deltay)
+        grad_phi = np.gradient(self.energy_flux_potential, deltay, axis=0)
+        grad_phi = grad_phi[1:-1, 1:-1]
+        self.meridional_heat_transport = np.sum((grad_phi.T *
+                                                 np.cos(yvalues) *
+                                                 deltax).T, axis=1)
+
+    def calc_ax(self, x_matrix):
+        """Matrix calculation of the Laplacian equation, LHS of Eq.
+
+        (9) in Pearce and Bodas-Salcedo (2023).
+        """
+        # Laplacian equation
+        src_shape = np.array(self.source.shape)
+        ax_matrix = np.zeros(src_shape + 2)
+        x_matrix = swap_bounds(x_matrix)
+        shape0, shape1 = src_shape
+        ax_matrix[1:shape0 + 1, 1:shape1 + 1] = (
+            self.a_matrix[2, 0:shape0, 0:shape1] *
+            x_matrix[0:shape0, 1:shape1 + 1] +
+            self.a_matrix[1, 0:shape0, 0:shape1] *
+            x_matrix[1:shape0 + 1, 0:shape1] +
+            self.a_matrix[0, 0:shape0, 0:shape1] *
+            x_matrix[1:shape0 + 1, 2:shape1 + 2] +
+            self.a_matrix[3, 0:shape0, 0:shape1] *
+            x_matrix[2:shape0 + 2, 1:shape1 + 1] +
+            self.a_matrix[4, 0:shape0, 0:shape1] *
+            x_matrix[1:shape0 + 1, 1:shape1 + 1])
+        ax_matrix = swap_bounds(ax_matrix)
+        return ax_matrix
diff --git a/esmvaltool/diag_scripts/iht_toa/single_model_diagnostics.py b/esmvaltool/diag_scripts/iht_toa/single_model_diagnostics.py
new file mode 100644
index 0000000000..fc917a8aa5
--- /dev/null
+++ b/esmvaltool/diag_scripts/iht_toa/single_model_diagnostics.py
@@ -0,0 +1,993 @@
+# (C) Crown Copyright 2023, the Met Office.
+"""Single model diagnostics.
+
+Apply Poisson solver to input fluxes and produce plots.
+"""
+
+import datetime
+import logging
+from copy import deepcopy
+
+import cartopy.crs as ccrs
+import iris
+import iris.plot as iplt
+import matplotlib.dates as mdates
+import matplotlib.pyplot as plt
+import numpy as np
+from iris import NameConstraint
+from matplotlib import gridspec, rcParams
+from poisson_solver import SphericalPoisson
+
+from esmvaltool.diag_scripts.shared import (
+    group_metadata,
+    run_diagnostic,
+    save_figure,
+)
+
+# Initialise logger
+logger = logging.getLogger(__name__)
+
+rcParams.update({
+    'font.size': 14,
+    'xtick.major.pad': 10,
+    'ytick.major.pad': 10,
+    'xtick.major.size': 10,
+    'ytick.major.size': 10,
+    'xtick.minor.size': 5,
+    'ytick.minor.size': 5,
+    'axes.linewidth': 2,
+    'lines.markersize': 8,
+    'lines.linewidth': 2
+})
+
+# Figure captions
+caption = {
+    'F1': 'Figure 1. The implied heat transport due to TOA net flux (blue), '
+          'split into the contributions from SW (orange) and LW (green).',
+    'F2': 'Figure 2. The TOA energy flux potentials for (a) TOT, (c) '
+          'SW, and (e) LW net fluxes, alongside maps of the spatial anomalies '
+          'of the input fluxes [(b),(d),(f)]. The implied heat transport is '
+          'the gradient of the energy flus potential, shown by the white '
+          'vector arrows (with the same magnitude scale across all subplots). '
+          'Heat is directed from the blue minima of the potential field to '
+          'yellow maxima, with the magnitude implied by the density of '
+          'contours. All maps of the same type share the same color bar at '
+          'the bottom of the column so that it is possible to directly '
+          'compare the results from different fluxes.',
+    'F3': 'Figure 3. Direct radiative effects of clouds on the meridional '
+          'heat transport. (a) Contributions from TOT CRE (blue), SW CRE '
+          '(orange), and LW CRE (green). (b) Contributions from all-sky and '
+          'clear-sky OSR. Both curves have been multiplied by -1 such that '
+          'positive heat transport is northward.',
+    'F4': 'Figure 4. As in Figure 2, but for cloud radiative effects.',
+    'F5': 'Figure 5. As in Figure 2, but for energy flux potentials and '
+          'spatial radiative anomalies associated with all-sky and clear-sky '
+          'outgoing shortwave radiation. ',
+    'F6': 'Figure 6. A measure of the symmetry between heat transport in the '
+          'Northern and Southern Hemispheres, calculated for the 12-month '
+          'running mean of MHT in (a) the full hemisphere, (b) from the '
+          'equator to 30 deg latitude, and (c) between 30 and 90 deg '
+          'latitude. Symmetry values obtained when including (blue) and '
+          'excluding (orange) the effect of clouds are shown. The '
+          'climatological symmetry values for the two cases are shown as '
+          'black lines in each subplot. The standard deviations of the '
+          'time series are shown in each subplot.',
+}
+
+
+def get_provenance_record(filenames, figure_caption):
+    """Return a provenance record describing the plot.
+
+    Parameters
+    ----------
+    filenames : list of strings
+        The filenames containing the data used to create the plot.
+    figure_caption : string
+        Detailed description of the figure.
+
+    Returns
+    -------
+    dictionary
+        The provenance record describing the plot.
+    """
+    record = {
+        'ancestors': filenames,
+        'caption': figure_caption,
+        'references': ['pearce23jclim']
+    }
+    return record
+
+
+def matching_strings(list_of_strings, substrings):
+    """Return subset of ``list_of_strings`` with matches in ``substrings``.
+
+    Parameters
+    ----------
+    list_of_strings : list of strings
+        List of strings to be searched.
+    substrings : list of strings
+        The list of search strings.
+
+    Returns
+    -------
+    list
+        The elements in ``list_of_strings`` that contain
+        any of the substrings.
+    """
+    matches = []
+    for element in list_of_strings:
+        for var in substrings:
+            if var in element:
+                matches.append(element)
+    return matches
+
+
+def area_average(cube, latitude='latitude', longitude='longitude', mdtol=1):
+    """Return area-weighted average of a cube.
+
+    Parameters
+    ----------
+    cube : :class:`iris.cube.Cube`
+        Input cube.
+    latitude : string
+        Name of latitude coordinate in ``cube``.
+    longitude : string
+        Name of longitude coordinate in ``cube``.
+    mdtol : float
+        Tolerance to missing data, between 0 and 1.
+
+
+    Returns
+    -------
+    :class:`iris.cube.Cube`
+        Collapsed cube with the weighted average.
+    """
+    if cube.coord(latitude).bounds is None:
+        cube.coord(latitude).guess_bounds()
+    if cube.coord(longitude).bounds is None:
+        cube.coord(longitude).guess_bounds()
+    grid_areas = iris.analysis.cartography.area_weights(cube)
+    cube_avg = cube.collapsed([longitude, latitude],
+                              iris.analysis.MEAN,
+                              weights=grid_areas,
+                              mdtol=mdtol)
+    return cube_avg
+
+
+def weight_zm(cube, latitude=None):
+    """Weight zonal-mean by normalised gridbox areas.
+
+    Parameters
+    ----------
+    cube : :class:`iris.cube.Cube`
+        Input cube.
+    latitude : tuple
+        Four-element tuple defining the latitude range.
+        The last two elements must be False, e.g.
+        latitude=(-90, 0, False, False).
+
+    Returns
+    -------
+    :class:`numpy.array`
+        Zonal-mean in the selected latitude range, weighted
+        by the normalised areas.
+    """
+    if cube.coord('latitude').bounds is None:
+        cube.coord('latitude').guess_bounds()
+    areas_data = iris.analysis.cartography.area_weights(cube, normalize=True)
+    cube_areas = iris.cube.Cube(areas_data,
+                                long_name="normalised_area",
+                                var_name="area", units="1",
+                                dim_coords_and_dims=[(cube.coords()[0], 0)])
+    if latitude is not None:
+        cube = cube.intersection(latitude=latitude)
+        cube_areas = cube_areas.intersection(latitude=latitude)
+    return cube.data * cube_areas.data
+
+
+def call_poisson(flux_cube, latitude='latitude', longitude='longitude'):
+    """Call the Poisson solver with the data in ``flux_cube`` as source term.
+
+       Return the energy flux potential and implied meridional heat transport
+       as cubes.
+
+    Parameters
+    ----------
+    flux_cube : :class:`iris.cube.Cube`
+        Input cube.
+    latitude : string
+        Name of latitude coordinate in ``cube``.
+    longitude : string
+        Name of longitude coordinate in ``cube``.
+
+    Returns
+    -------
+    efp_cube: :class:`iris.cube.Cube`
+        Energy flux potential cube.
+    mht_cube: :class:`iris.cube.Cube`
+        Implied meridional heat transport associated
+        with the source flux field.
+    """
+    earth_radius = 6371e3  # Earth's radius in m
+    if flux_cube.coord(latitude).bounds is None:
+        flux_cube.coord(latitude).guess_bounds()
+    if flux_cube.coord(longitude).bounds is None:
+        flux_cube.coord(longitude).guess_bounds()
+
+    # Remove average of flux field to account for storage term
+    grid_areas = iris.analysis.cartography.area_weights(flux_cube)
+    data_mean = flux_cube.collapsed(["longitude", "latitude"],
+                                    iris.analysis.MEAN,
+                                    weights=grid_areas).data
+    data = flux_cube.data - data_mean
+
+    logger.info("Calling spherical_poisson")
+    sphpo = SphericalPoisson(logger,
+                             source=data * (earth_radius**2.0),
+                             tolerance=2.0e-4)
+    sphpo.solve()
+    sphpo.calc_meridional_heat_transport()
+    logger.info("Ending spherical_poisson")
+
+    # Energy flux potential
+    efp_cube = iris.cube.Cube(sphpo.energy_flux_potential[1:-1, 1:-1],
+                              long_name=f"energy_flux_potential"
+                                        f"_of_{flux_cube.var_name}",
+                              var_name=f"{flux_cube.var_name}_efp",
+                              units='J s-1',
+                              dim_coords_and_dims=[(flux_cube.coords()[0], 0),
+                                                   (flux_cube.coords()[1], 1)])
+
+    # MHT data cube
+    collapsed_longitude = iris.coords.AuxCoord(180.0,
+                                               bounds=(0.0, 360.0),
+                                               long_name='longitude',
+                                               standard_name='longitude',
+                                               units='degrees')
+    dim_coords_and_dims = [(flux_cube.coord('latitude'), 0)]
+    aux_coords_and_dims = [(flux_cube.coord('time'), None),
+                           (collapsed_longitude, None)]
+    mht_cube = iris.cube.Cube(sphpo.meridional_heat_transport,
+                              long_name=f"meridional_heat_transport_of"
+                                        f"_{flux_cube.var_name}",
+                              var_name=f"{flux_cube.var_name}_mht",
+                              units='W',
+                              dim_coords_and_dims=dim_coords_and_dims,
+                              aux_coords_and_dims=aux_coords_and_dims)
+    return efp_cube, mht_cube
+
+
+def symmetry_metric(cube):
+    """Calculate symmetry metrics for a zonal-mean cube.
+
+    It returns the symmetry metric S, as defined in Pearce and
+    Bodas-Salcedo, JClim, 2023, for 3 regions: entire hemisphere,
+    tropics (0 to 30 deg latitude) and extratropics
+    (30 to 90 degrees latitude). Perfectly symmetrical latitude
+    bands give S=0.
+
+    Parameters
+    ----------
+    cube : :class:`iris.cube.Cube`
+        Input cube.
+
+    Returns
+    -------
+    hemisphere: float
+        Metric for the whole hemisphere.
+    tropics: float
+        Metric for the tropics.
+    extra_tropics: float
+        Metric for the extra-tropics.
+    """
+    hemisphere = np.abs(
+        weight_zm(cube, latitude=(0, 90, False, False))[::-1] +
+        weight_zm(cube, latitude=(-90, 0, False, False))).sum()
+    tropics = np.abs(
+        weight_zm(cube, latitude=(0, 30, False, False))[::-1] +
+        weight_zm(cube, latitude=(-30, 0, False, False))).sum()
+    extra_tropics = np.abs(
+        weight_zm(cube, latitude=(30, 90, False, False))[::-1] +
+        weight_zm(cube, latitude=(-90, -30, False, False))).sum()
+    return hemisphere, tropics, extra_tropics
+
+
+def format_plot(axes, label, title):
+    """Format plots in quiver panel.
+
+    Parameters
+    ----------
+    axes : :class:`matplotlib.axes.Axes`
+        Input axes.
+    label : string
+        Top-left plot label.
+    title : string
+        Plot title.
+    """
+    axes.set_xticks(np.arange(-180, 190, 60))
+    axes.set_xticklabels(['180', '120W', '60W', '0', '60E', '120E', '180'])
+    axes.set_yticks(np.arange(-90, 100, 30))
+    axes.set_yticklabels(['90S', '60S', '30S', 'Eq', '30N', '60N', '90N'])
+    axes.annotate(label, xy=(0, 1.05), xycoords='axes fraction', color='k')
+    axes.set_title(title)
+
+
+class ImpliedHeatTransport:
+    """Class that solves implied heat transport for an input dataset.
+
+    These are the physical meanings of the main acronyms
+    used in the variable names:
+       FLX: radiative flux
+       EFP: energy flux potential
+       MHT: meridional heat transport
+    """
+
+    def __init__(self, flx_files):
+        """Calculate all the diagnostics for all fluxes in ``flx_files``.
+
+        Parameters
+        ----------
+        flx_files : list
+            List of files with input data.
+        """
+        self.flx_files = flx_files
+
+        # Create cube lists for the different datasets
+        self.flx_clim = iris.cube.CubeList()
+        self.mht_clim = iris.cube.CubeList()
+        self.efp_clim = iris.cube.CubeList()
+        self.mht_rolling_mean = iris.cube.CubeList()
+        self.symmetry_metric = iris.cube.CubeList()
+
+        # Calculate 12-month rolling means for time series.
+        self.flx_rolling_mean = iris.cube.CubeList()
+        for flx_file in flx_files:
+            flx = iris.load_cube(flx_file)
+            if len(flx.shape) == 3:
+                self.flx_rolling_mean.append(
+                    flx.rolling_window('time', iris.analysis.MEAN, 12))
+            else:
+                self.flx_clim.append(flx)
+
+        # Compute derived fluxes
+        self.derived_fluxes()
+
+        # Calculate Energy Flux Potential and Meridional Heat Transport
+        # for each flux component
+        self.compute_efp_and_mht()
+
+        # Times series of MHT symmetry metric
+        self.mht_symmetry_metrics()
+
+        self.print()
+
+    def compute_efp_and_mht(self):
+        """Calculate Energy Flux Potential and meridional heat transport.
+
+        Loop over input data and calculate EFP and MHT of the
+        climatologies of radiative fluxes and the 12-month
+        rolling means of radiative fluxes.
+        """
+        # Loop over climatologies
+        for flx in self.flx_clim:
+            efp, mht = call_poisson(flx)
+            self.efp_clim.append(efp)
+            self.mht_clim.append(mht)
+        # Loop over rolling means
+        for flx_rm in self.flx_rolling_mean:
+            mht_series = iris.cube.CubeList()
+            for flx in flx_rm.slices_over('time'):
+                efp, mht = call_poisson(flx)
+                mht_series.append(mht)
+            # Append MHT rolling mean after merging time series.
+            self.mht_rolling_mean.append(mht_series.merge_cube())
+
+    def derived_fluxes(self):
+        """Calculate derived radiative fluxes.
+
+        rlnt_clim: climatology of net LW TOA
+        rtntcs_clim: climatology of clear-sky net TOA
+        rtntcs_rolling_mean: 12-month rolling mean of rtntcs
+        """
+        # Derived TOA climatologies: rlnt_clim, rtntcs_clim
+        rlnt_clim = -1.0 * self.flx_clim.extract_cube(
+            NameConstraint(var_name="rlut"))
+        rlnt_clim.var_name = "rlnt"
+        rlnt_clim.long_name = "radiative_flux_of_rlnt"
+        self.flx_clim.append(rlnt_clim)
+        rtntcs_clim = (self.flx_clim.extract_cube(
+                       NameConstraint(var_name="rsdt")) -
+                       self.flx_clim.extract_cube(
+                       NameConstraint(var_name="rsutcs")) -
+                       self.flx_clim.extract_cube(
+                       NameConstraint(var_name="rlutcs")))
+        rtntcs_clim.var_name = "rtntcs"
+        rtntcs_clim.long_name = "radiative_flux_of_rtntcs"
+        self.flx_clim.append(rtntcs_clim)
+        # Annual rolling means clear-sky net total TOA
+        rtntcs_rolling_mean = (self.flx_rolling_mean.extract_cube(
+                               NameConstraint(var_name="rsdt")) -
+                               self.flx_rolling_mean.extract_cube(
+                               NameConstraint(var_name="rsutcs")) -
+                               self.flx_rolling_mean.extract_cube(
+                               NameConstraint(var_name="rlutcs")))
+        rtntcs_rolling_mean.var_name = "rtntcs"
+        rtntcs_rolling_mean.long_name = "radiative_flux_of_rtntcs"
+        self.flx_rolling_mean.append(rtntcs_rolling_mean)
+
+    def print(self):
+        """Print variable names of all cubes in an IHT object."""
+        logger.info("=== implied_heat_transport object ===")
+        logger.info(self.mht_clim)
+        info_message = "Long name: %s; Variable: %s."
+        for climatology in self.mht_clim:
+            logger.info(info_message,
+                        climatology.long_name,
+                        climatology.var_name)
+
+        logger.info(self.efp_clim)
+        for climatology in self.efp_clim:
+            logger.info(info_message,
+                        climatology.long_name,
+                        climatology.var_name)
+
+        logger.info(self.flx_clim)
+        for climatology in self.flx_clim:
+            logger.info(info_message,
+                        climatology.long_name,
+                        climatology.var_name)
+
+        logger.info(self.mht_rolling_mean)
+        for rolling_mean in self.mht_rolling_mean:
+            logger.info(info_message,
+                        rolling_mean.long_name,
+                        rolling_mean.var_name)
+
+        logger.info(self.symmetry_metric)
+        for metric in self.symmetry_metric:
+            logger.info(info_message,
+                        metric.long_name,
+                        metric.var_name)
+
+        logger.info(self.flx_files)
+
+    def mht_symmetry_metrics(self):
+        """Calculate symmetry metrics.
+
+        Produce 12-month rolling means for all monthly time series
+        of MHT.
+        """
+        petaunit = 1.0e15
+        for mht_series in self.mht_rolling_mean:
+            time_coord = mht_series.coord('time')
+            ntime = time_coord.shape[0]
+            hemisphere = np.zeros(ntime)
+            tropics = np.zeros(ntime)
+            extra_tropics = np.zeros(ntime)
+            for i in np.arange(ntime):
+                hemisphere[i], tropics[i], extra_tropics[i] = (
+                    symmetry_metric(mht_series[i]))
+            # Create the cubes for each metric
+            long_name = f"symmetry_hemisphere_of_{mht_series.long_name}"
+            var_name = f"s_hem_{mht_series.var_name}"
+            cube_h = iris.cube.Cube(hemisphere / petaunit,
+                                    long_name=long_name,
+                                    var_name=var_name,
+                                    units="PW",
+                                    dim_coords_and_dims=[(time_coord, 0)])
+            long_name = f"symmetry_tropics_of_{mht_series.long_name}"
+            var_name = f"s_tro_{mht_series.var_name}"
+            cube_t = iris.cube.Cube(tropics / petaunit,
+                                    long_name=long_name,
+                                    var_name=var_name,
+                                    units="PW",
+                                    dim_coords_and_dims=[(time_coord, 0)])
+            long_name = f"symmetry_extratropics_of_{mht_series.long_name}"
+            var_name = f"s_ext_{mht_series.var_name}"
+            cube_e = iris.cube.Cube(extra_tropics / petaunit,
+                                    long_name=long_name,
+                                    var_name=var_name,
+                                    units="PW",
+                                    dim_coords_and_dims=[(time_coord, 0)])
+            self.symmetry_metric.append(cube_h)
+            self.symmetry_metric.append(cube_t)
+            self.symmetry_metric.append(cube_e)
+
+    def mht_plot(self, var_names, legend_label, ylim=(-10, 10)):
+        """Produce a single multi-line plot of MHT components.
+
+        MHT is presented in PW. Up to three variables are on each plot.
+
+        Parameters
+        ----------
+        var_names : list
+            Variable names to plot, e.g. ["rtnt_mht", "rsnt_mht"].
+        legend_label : list
+            List of labels for each line.
+        ylim : tuple
+            y axis limits.
+        """
+        plt.figure()
+        for i, vname in enumerate(var_names):
+            mht = self.mht_clim.extract_cube(NameConstraint(var_name=vname))
+            mht.convert_units('PW')
+            plt.plot(mht.coord('latitude').points,
+                     mht.data,
+                     label=legend_label[i])
+        plt.hlines(0, -90, 90, color='k', linestyles=':')
+        plt.vlines(0, -10, 10, color='k', linestyles=':')
+        plt.xlim(-90, 90)
+        plt.ylim(ylim[0], ylim[1])
+        plt.xticks(np.arange(-90, 120, 30))
+        plt.xlabel('Latitude')
+        plt.ylabel('MHT (PW)')
+        plt.legend()
+        plt.tight_layout()
+
+    def cre_mht_plot(self, left, right, ylim=(-1.5, 1.5)):
+        """Produce two multiline plots of MHT components.
+
+        Parameters
+        ----------
+        left : dictionary
+            Dictionary with variable names and labels for
+            the LHS plot, e.g.
+            {'vname': ['netcre_mht', 'swcre_mht', 'lwcre_mht'],
+            'legend': ['Net CRE', 'SW CRE', 'LW CRE']}
+        right : dictionary
+            As ``left`` but for the RHS plot
+        ylim : tuple
+            y axis limits.
+        """
+        plt.figure(figsize=(11, 5))
+        ax1 = plt.subplot(121)
+        for i, vname in enumerate(left['vname']):
+            mht = self.mht_clim.extract_cube(NameConstraint(var_name=vname))
+            mht.convert_units('PW')
+            ax1.plot(mht.coord('latitude').points,
+                     mht.data,
+                     label=left['legend'][i])
+        ax1.axhline(0, color='k', ls=':')
+        ax1.axvline(0, color='k', ls=':')
+        ax1.set_xlim(-90, 90)
+        ax1.set_xticks(np.arange(-90, 120, 30))
+        ax1.set_xlabel('Latitude')
+        ax1.set_ylim(ylim[0], ylim[1])
+        ax1.set_ylabel('MHT (PW)')
+        ax1.annotate('(a)',
+                     xy=(0.01, 0.95),
+                     xycoords='axes fraction',
+                     color='k')
+        plt.legend()
+
+        ax2 = plt.subplot(122)
+        col = ['C3', 'C7']
+        for i, vname in enumerate(right['vname']):
+            mht = self.mht_clim.extract_cube(NameConstraint(var_name=vname))
+            mht.convert_units('PW')
+            ax2.plot(mht.coord('latitude').points,
+                     -mht.data,
+                     label=right['legend'][i],
+                     color=col[i])
+        ax2.axhline(0, color='k', ls=':')
+        ax2.axvline(0, color='k', ls=':')
+        ax2.set_xlim(-90, 90)
+        ax2.set_xticks(np.arange(-90, 120, 30))
+        ax2.set_xlabel('Latitude')
+        ax2.set_ylim(ylim[0], ylim[1])
+        ax2.set_ylabel('MHT (PW)')
+        ax2.annotate('(b)',
+                     xy=(0.01, 0.95),
+                     xycoords='axes fraction',
+                     color='k')
+        plt.legend(loc='lower right')
+        plt.tight_layout()
+
+    def quiver_start(self, ntot, step):
+        """Calculate start point for quiver plot.
+
+        Parameters
+        ----------
+        ntot : int
+            Total number of points of the full vector.
+        step : int
+            Sampling step.
+        """
+        start = (ntot - 2 - ((ntot - 2) // step) * step) // 2
+        return start
+
+    def quiver_maps_data(self, vnames, change_sign):
+        """Obtain data for one row of plots.
+
+        Parameters
+        ----------
+        vnames : list
+            Two-element list with the names of the EFP and
+            flux variables.
+        change_sign : list
+            Two-element list of booleans to indicate if
+            the variable has to be plotted with the sign changed.
+        """
+        efp = self.efp_clim.extract_cube(NameConstraint(var_name=vnames[0]))
+        flx = self.flx_clim.extract_cube(NameConstraint(var_name=vnames[1]))
+        # The choice of origin for efp is arbitrary,
+        # we choose the unweighted mean.
+        efp = efp - efp.collapsed(efp.coords(), iris.analysis.MEAN)
+        flx = flx - area_average(flx)
+        if change_sign[0]:
+            efp = -efp
+        if change_sign[1]:
+            flx = -flx
+        efp.convert_units("PW")
+        v_component, u_component = np.gradient(efp.data)
+        u_component = u_component[1:-1, 1:-1]
+        v_component = v_component[1:-1, 1:-1]
+        return {'efp': efp, 'flx': flx, 'uuu': u_component, 'vvv': v_component}
+
+    def quiver_subplot(self, dargs):
+        """Produce panel with maps of EFPs and fluxes.
+
+        Plot figures with energy flux potential and gradient in the left-hand
+        column and the corresponding source term in the right-hand column.
+
+        Parameters
+        ----------
+        dargs : dictionary
+            Dictionary with variable names and plot configuration
+            data.
+        """
+        mshgrd = np.meshgrid(self.flx_clim[0].coord('longitude').points,
+                             self.flx_clim[0].coord('latitude').points)
+        nrows = len(dargs['var_name'])
+        # Calculate sampling for vector plot
+        dxy = [mshgrd[0].shape[1] // 20, mshgrd[0].shape[0] // 10]
+        startx = self.quiver_start(mshgrd[0].shape[1], dxy[0])
+        starty = self.quiver_start(mshgrd[0].shape[0], dxy[1])
+
+        # Set grid layout depending on number of rows.
+        # Place figures every grid_step rows in the grid.
+        grid_step = 7
+        if nrows == 3:
+            plt.figure(figsize=(10, 10))
+            grds = gridspec.GridSpec(22, 2)
+            grds.update(wspace=0.25, hspace=1.5)
+        elif nrows == 2:
+            plt.figure(figsize=(10, 6.5))
+            grds = gridspec.GridSpec(15, 2)
+            grds.update(wspace=0.25, hspace=1.5)
+        elif nrows == 1:
+            plt.figure(figsize=(12, 4))
+            grds = gridspec.GridSpec(8, 2)
+            grds.update(wspace=0.25, hspace=1.5)
+
+        cbs = []
+        for i in range(nrows):
+            data = self.quiver_maps_data(dargs['var_name'][i],
+                                         dargs['change_sign'][i])
+            plt.subplot(grds[i * grid_step:(i * grid_step) + grid_step, 0],
+                        projection=ccrs.PlateCarree(central_longitude=0))
+            cbs.append(
+                iplt.contourf(data['efp'],
+                              levels=np.linspace(dargs['vmin'], dargs['vmax'],
+                                                 dargs['nlevs'])))
+            plt.gca().coastlines()
+            if i == 0:
+                qqq = plt.quiver(mshgrd[0][starty::dxy[1], startx::dxy[0]],
+                                 mshgrd[1][starty::dxy[1], startx::dxy[0]],
+                                 data['uuu'][starty::dxy[1], startx::dxy[0]],
+                                 data['vvv'][starty::dxy[1], startx::dxy[0]],
+                                 pivot='mid',
+                                 color='w',
+                                 width=0.005)
+            else:
+                plt.quiver(mshgrd[0][starty::dxy[1], startx::dxy[0]],
+                           mshgrd[1][starty::dxy[1], startx::dxy[0]],
+                           data['uuu'][starty::dxy[1], startx::dxy[0]],
+                           data['vvv'][starty::dxy[1], startx::dxy[0]],
+                           pivot='mid',
+                           scale=qqq.scale,
+                           color='w')
+            format_plot(plt.gca(), dargs['label'][i][0], dargs['title'][i][0])
+
+            plt.subplot(grds[i * grid_step:(i * grid_step) + grid_step, 1],
+                        projection=ccrs.PlateCarree(central_longitude=0))
+            cbs.append(
+                iplt.contourf(data['flx'],
+                              levels=np.linspace(dargs['wmin'], dargs['wmax'],
+                                                 dargs['nwlevs']),
+                              cmap='RdBu_r'))
+            plt.gca().coastlines()
+            format_plot(plt.gca(), dargs['label'][i][1], dargs['title'][i][1])
+
+        plt.subplot(grds[-1, 0])
+        plt.colorbar(cbs[0],
+                     cax=plt.gca(),
+                     orientation='horizontal',
+                     label='Energy flux potential (PW)')
+        plt.subplot(grds[-1, 1])
+        plt.colorbar(cbs[1],
+                     cax=plt.gca(),
+                     orientation='horizontal',
+                     label=r'Flux (Wm$^{-2}$)',
+                     ticks=np.linspace(dargs['wmin'], dargs['wmax'],
+                                       dargs['nwlevs'])[1::dargs['wlevstep']])
+
+        if nrows == 3:
+            plt.subplots_adjust(left=0.1, right=0.94, top=1.0, bottom=0.11)
+        elif nrows == 2:
+            plt.subplots_adjust(left=0.11, right=0.9, top=1.0, bottom=0.13)
+        elif nrows == 1:
+            plt.subplots_adjust(left=0.11, right=0.9, top=1.0, bottom=0.20)
+
+    def plot_symmetry_time_series(self):
+        """Produce Figure 6.
+
+        All-sky and clear-sky time series of the symmetry metrics for
+        three regions: globe, tropics and extra-tropics.
+        """
+        var_list = [["s_hem_rtnt_mht", "s_hem_rtntcs_mht"],
+                    ["s_tro_rtnt_mht", "s_tro_rtntcs_mht"],
+                    ["s_ext_rtnt_mht", "s_ext_rtntcs_mht"]]
+        col = ['C0', 'C1']
+        label = [
+            r'Global: 0$^\mathrm{o}$ - 90$^\mathrm{o}$',
+            r'Tropics: 0$^\mathrm{o}$ - 30$^\mathrm{o}$',
+            r'Extratropics: 30$^\mathrm{o}$ - 90$^\mathrm{o}$'
+        ]
+        legend_label = ["TOA net all-sky", "TOA net clear-sky"]
+
+        plt.figure(figsize=(6, 12))
+        for count, (var_name_1, var_name_2) in enumerate(var_list):
+            yy0 = self.symmetry_metric.extract_cube(
+                NameConstraint(var_name=var_name_1))
+            yy1 = self.symmetry_metric.extract_cube(
+                NameConstraint(var_name=var_name_2))
+            axx = plt.subplot(3, 1, count + 1)
+            dtx = [
+                datetime.datetime.strptime(str(cell[0]), '%Y-%m-%d %H:%M:%S')
+                for cell in yy0.coord('time').cells()
+            ]
+            plt.plot(dtx, yy0.data, lw=4, linestyle='-', label=legend_label[0])
+            plt.plot(dtx, yy1.data, lw=4, linestyle='-', label=legend_label[1])
+            axx.annotate(rf'$\sigma$: {np.std(yy0.data):5.3f}', (0.05, 0.55),
+                         xycoords='axes fraction',
+                         color=col[0])
+            axx.annotate(rf'$\sigma$: {np.std(yy1.data):5.3f}', (0.05, 0.45),
+                         xycoords='axes fraction',
+                         color=col[1])
+            axx.set_ylim(0, 0.8)
+            axx.set_ylabel(r'$S$ (PW)')
+            axx.xaxis.set_major_locator(mdates.YearLocator(3, month=1, day=1))
+            axx.xaxis.set_major_formatter(mdates.DateFormatter('%Y'))
+            axx.xaxis.set_minor_locator(mdates.YearLocator())
+            axx.set_title(label[count])
+            if count == 0:
+                plt.legend(loc=5)
+        plt.tight_layout()
+
+
+def efp_maps(iht, model, experiment, config):
+    """Produce Figures 2, 4, and 5.
+
+    Parameters
+    ----------
+    iht : :class: ImpliedHeatTransport
+        Object with the recipe datasets.
+    model : string
+        Model name.
+    experiment : string
+        Experiment name.
+    config : dict
+        The ESMValTool configuration.
+    """
+    # Figure 2
+    iht.quiver_subplot(
+        {
+            'var_name': [['rtnt_efp', 'rtnt'], ['rsnt_efp', 'rsnt'],
+                         ['rlnt_efp', 'rlnt']],
+            'title': [['$P_{TOA}^{TOT}$', r'$\Delta F_{TOA}^{TOT}$'],
+                      ['$P_{TOA}^{SW}$', r'$\Delta F_{TOA}^{SW}$'],
+                      ['$P_{TOA}^{LW}$', r'$\Delta F_{TOA}^{LW}$']],
+            'label': [['(a)', '(b)'], ['(c)', '(d)'], ['(e)', '(f)']],
+            'change_sign': [[False, False], [False, False], [False, False]],
+            'wmin':
+            -180,
+            'wmax':
+            180,
+            'nwlevs':
+            19,
+            'wlevstep':
+            4,
+            'vmin':
+            -1.2,
+            'vmax':
+            1.2,
+            'nlevs':
+            11
+        })
+    flx_files = matching_strings(iht.flx_files, ['rtnt/',
+                                                 'rsut/',
+                                                 'rlut/'])
+    provenance_record = get_provenance_record(flx_files, caption['F2'])
+    figname = f"figure2_{model}_{experiment}"
+    save_figure(figname, provenance_record, config)
+    # Figure 4
+    iht.quiver_subplot(
+        {
+            'var_name': [['netcre_efp', 'netcre'], ['swcre_efp', 'swcre'],
+                         ['lwcre_efp', 'lwcre']],
+            'title': [['$P_{TOA}^{TOTCRE}$', r'$\Delta CRE_{TOA}^{TOT}$'],
+                      ['$P_{TOA}^{SWCRE}$', r'$\Delta CRE_{TOA}^{SW}$'],
+                      ['$P_{TOA}^{LWCRE}$', r'$\Delta CRE_{TOA}^{LW}$']],
+            'label': [['(a)', '(b)'], ['(c)', '(d)'], ['(e)', '(f)']],
+            'change_sign': [[False, False], [False, False], [False, False]],
+            'wmin':
+            -60,
+            'wmax':
+            60,
+            'nwlevs':
+            13,
+            'wlevstep':
+            2,
+            'vmin':
+            -0.3,
+            'vmax':
+            0.3,
+            'nlevs':
+            11
+        })
+    flx_files = matching_strings(iht.flx_files, ['netcre/',
+                                                 'swcre/',
+                                                 'lwcre/'])
+    provenance_record = get_provenance_record(flx_files, caption['F4'])
+    figname = f"figure4_{model}_{experiment}"
+    save_figure(figname, provenance_record, config)
+    # Figure 5
+    iht.quiver_subplot(
+        {
+            'var_name': [['rsutcs_efp', 'rsutcs'], ['rsut_efp', 'rsut']],
+            'title': [['$P_{TOA}^{SWup, clr}$',
+                       r'$\Delta F_{TOA}^{SWup, clr}$'],
+                      ['$P_{TOA}^{SWup, all}$',
+                       r'$\Delta F_{TOA}^{SWup, all}$']],
+            'label': [['(a)', '(b)'], ['(c)', '(d)']],
+            'change_sign': [[True, True], [True, True]],
+            'wmin': -100,
+            'wmax': 100,
+            'nwlevs': 21,
+            'wlevstep': 3,
+            'vmin': -0.35,
+            'vmax': 0.35,
+            'nlevs': 11
+        })
+    flx_files = matching_strings(iht.flx_files, ['rsut/', 'rsutcs/'])
+    provenance_record = get_provenance_record(flx_files, caption['F5'])
+    figname = f"figure5_{model}_{experiment}"
+    save_figure(figname, provenance_record, config)
+
+
+def mht_plots(iht, model, experiment, config):
+    """Produce Figures 1 and 3.
+
+    Parameters
+    ----------
+    iht : :class: ImpliedHeatTransport
+        Object with the recipe datasets.
+    model : string
+        Model name.
+    experiment : string
+        Experiment name.
+    config : dict
+        The ESMValTool configuration.
+    """
+    # Figure 1
+    iht.mht_plot(["rtnt_mht", "rsnt_mht", "rlnt_mht"], ['Net', 'SW', 'LW'])
+    flx_files = matching_strings(iht.flx_files, ['rtnt/', 'rsut/', 'rlut/'])
+    provenance_record = get_provenance_record(flx_files, caption['F1'])
+    figname = f"figure1_{model}_{experiment}"
+    save_figure(figname, provenance_record, config)
+    # Figure 3
+    iht.cre_mht_plot(
+        {
+            'vname': ['netcre_mht', 'swcre_mht', 'lwcre_mht'],
+            'legend': ['Net CRE', 'SW CRE', 'LW CRE']
+        }, {
+            'vname': ['rsut_mht', 'rsutcs_mht'],
+            'legend': ['-1 x OSR (all-sky)', '-1 x OSR (clear-sky)']
+        })
+    flx_files = matching_strings(iht.flx_files, ['netcre/', 'swcre/', 'lwcre/',
+                                                 'rsut/', 'rsutcs/'])
+    provenance_record = get_provenance_record(flx_files, caption['F3'])
+    figname = f"figure3_{model}_{experiment}"
+    save_figure(figname, provenance_record, config)
+
+
+def symmetry_plots(iht, model, experiment, config):
+    """Produce Figure 6.
+
+    Parameters
+    ----------
+    iht : :class: ImpliedHeatTransport
+        Object with the recipe datasets.
+    model : string
+        Model name.
+    experiment : string
+        Experiment name.
+    config : dict
+        The ESMValTool configuration.
+    """
+    iht.plot_symmetry_time_series()
+    flx_files = matching_strings(iht.flx_files, ['rtnt_monthly',
+                                                 'rsutcs_monthly',
+                                                 'rlutcs_monthly',
+                                                 'rsdt_monthly'])
+    provenance_record = get_provenance_record(flx_files, caption['F6'])
+    figname = f"figure6_{model}_{experiment}"
+    save_figure(figname, provenance_record, config)
+
+
+def plot_single_model_diagnostics(iht_dict, config):
+    """Produce plots for a single model and experiment.
+
+    Parameters
+    ----------
+    iht_dict : dict
+        iht_dict is a two-level dictionary: iht_dict[model][experiment]
+    config : dict
+        The ESMValTool configuration.
+    """
+    for model, iht_model in iht_dict.items():
+        logger.info("Plotting model: %s", model)
+        for experiment, iht_experiment in iht_model.items():
+            logger.info("Plotting experiment: %s", experiment)
+            mht_plots(iht_experiment, model, experiment, config)
+            efp_maps(iht_experiment, model, experiment, config)
+            symmetry_plots(iht_experiment, model, experiment, config)
+
+
+def main(config):
+    """Produce all the recipe's plots.
+
+    Produce Figures 1 to 6 of Pearce and Bodas-Salcedo (2023) for each
+    model and dataset combination.
+
+    Parameters
+    ----------
+    config : dict
+        The ESMValTool configuration.
+    """
+    input_data = deepcopy(list(config['input_data'].values()))
+    input_data = group_metadata(input_data, 'dataset', sort='variable_group')
+
+    # Arrange input flux files in a 2-level dictionary [model_name][dataset]
+    flux_files = {}
+    for model_name, datasets in input_data.items():
+        flux_files[model_name] = {}
+        for dataset in datasets:
+            if dataset['dataset'] in flux_files[model_name]:
+                flux_files[model_name][dataset['dataset']].append(
+                    dataset['filename'])
+            else:
+                flux_files[model_name][dataset['dataset']] = [
+                    dataset['filename']
+                ]
+
+    # Create dictionary of implied_heat_transport objects.
+    # It's a 2-level dictionary like flux_files.
+    # This is where all the calculations are done.
+    iht = {}
+    for model_name, datasets in flux_files.items():
+        logger.info("Model %s", model_name)
+        iht[model_name] = {}
+        for dataset_name, files in datasets.items():
+            logger.info("Dataset %s", dataset_name)
+            iht[model_name][dataset_name] = ImpliedHeatTransport(files)
+
+    # Produce plots
+    plot_single_model_diagnostics(iht, config)
+
+
+if __name__ == '__main__':
+
+    with run_diagnostic() as configuration:
+        main(configuration)
diff --git a/esmvaltool/diag_scripts/ipcc_ar5/ch12_calc_IAV_for_stippandhatch.ncl b/esmvaltool/diag_scripts/ipcc_ar5/ch12_calc_IAV_for_stippandhatch.ncl
index df1ea2375a..e92d60ab43 100644
--- a/esmvaltool/diag_scripts/ipcc_ar5/ch12_calc_IAV_for_stippandhatch.ncl
+++ b/esmvaltool/diag_scripts/ipcc_ar5/ch12_calc_IAV_for_stippandhatch.ncl
@@ -410,7 +410,6 @@ begin
 
   ; collect meta-data
   nc_file = ncdf_outfile
-  plot_file = work_dir + "/None"
   caption = "Inter-annual variability based on piControl runs."
   statistics = ("var")
   domains = ("global")
@@ -418,7 +417,7 @@ begin
   authors = (/"lorenz_ruth"/)
   references = (/"collins13ipcc"/)
   infiles = metadata_att_as_array(info_items, "filename")
-  log_provenance(nc_file, plot_file, caption, statistics, domains, \
+  log_provenance(nc_file, "n/a", caption, statistics, domains, \
                  plot_types, authors, references, infiles)
 
   leave_msg(DIAG_SCRIPT, "")
diff --git a/esmvaltool/diag_scripts/mder/select_for_mder.ncl b/esmvaltool/diag_scripts/mder/select_for_mder.ncl
index 8c9198a8f6..d07cda657f 100644
--- a/esmvaltool/diag_scripts/mder/select_for_mder.ncl
+++ b/esmvaltool/diag_scripts/mder/select_for_mder.ncl
@@ -97,7 +97,7 @@ begin
   AUTHORS = (/"wenzel_sabrina", "schlund_manuel"/)
   DOMAIN = diag_script_info@domain
   PLOT_TYPE = ""
-  PLOT_FILE = "missing"
+  PLOT_FILE = "n/a"
   REFERENCES = (/"wenzel16jclim"/)
 
   ; Get reference dataset(s)
diff --git a/esmvaltool/diag_scripts/monitor/monitor_base.py b/esmvaltool/diag_scripts/monitor/monitor_base.py
index 135027f374..21dc159619 100644
--- a/esmvaltool/diag_scripts/monitor/monitor_base.py
+++ b/esmvaltool/diag_scripts/monitor/monitor_base.py
@@ -97,7 +97,9 @@ def __init__(self, config):
         )
         plot_folder = plot_folder.replace('{plot_dir}',
                                           self.cfg[names.PLOT_DIR])
-        self.plot_folder = os.path.abspath(plot_folder)
+        self.plot_folder = os.path.abspath(
+            os.path.expandvars(os.path.expanduser(plot_folder))
+        )
         self.plot_filename = config.get(
             'plot_filename',
             '{plot_type}_{real_name}_{dataset}_{mip}_{exp}_{ensemble}')
@@ -293,11 +295,7 @@ def get_plot_folder(self, var_info):
             'real_name': self._real_name(var_info['variable_group']),
             **var_info
         }
-        folder = os.path.expandvars(
-            os.path.expanduser(
-                list(_replace_tags(self.plot_folder, info))[0]
-            )
-        )
+        folder = list(_replace_tags(self.plot_folder, info))[0]
         if self.plot_folder.startswith('/'):
             folder = '/' + folder
         if not os.path.isdir(folder):
diff --git a/esmvaltool/diag_scripts/monitor/multi_datasets.py b/esmvaltool/diag_scripts/monitor/multi_datasets.py
index 6ac399652b..abfed90f9d 100644
--- a/esmvaltool/diag_scripts/monitor/multi_datasets.py
+++ b/esmvaltool/diag_scripts/monitor/multi_datasets.py
@@ -31,7 +31,7 @@
       datasets need to be given on the same horizontal and vertical grid (you
       can use the preprocessors :func:`esmvalcore.preprocessor.regrid` and
       :func:`esmvalcore.preprocessor.extract_levels` for this). Input data
-      needs to be 2D with dimensions `latitude`, `height`/`air_pressure`.
+      needs to be 2D with dimensions `latitude`, `altitude`/`air_pressure`.
 
       .. warning::
 
@@ -42,7 +42,28 @@
 
     - 1D profiles (plot type ``1d_profile``): for each variable separately, all
       datasets are plotted in one single figure. Input data needs to be 1D with
-      single dimension `height` / `air_pressure`
+      single dimension `altitude` / `air_pressure`
+    - Variable vs. latitude plot (plot type ``variable_vs_lat``):
+      for each variable separately, all datasets are plotted in one
+      single figure. Input data needs to be 1D with single
+      dimension `latitude`.
+    - Hovmoeller Z vs. time (plot type ``hovmoeller_z_vs_time``): for each
+      variable and dataset, an individual figure is plotted. If a reference
+      dataset is defined, also include this dataset and a bias plot into the
+      figure. Note that if a reference dataset is defined, all input datasets
+      need to be given on the same temporal and vertical grid (you can use
+      the preprocessors :func:`esmvalcore.preprocessor.regrid_time` and
+      :func:`esmvalcore.preprocessor.extract_levels` for this). Input data
+      needs to be 2D with dimensions `time`, `altitude`/`air_pressure`.
+    - Hovmoeller time vs. latitude or longitude (plot type
+      ``hovmoeller_time_vs_lat_or_lon``): for each variable and dataset, an
+      individual figure is plotted. If a reference dataset is defined, also
+      include this dataset and a bias plot into the figure. Note that if a
+      reference dataset is defined, all input datasets need to be given on the
+      same temporal and horizontal grid (you can use the preprocessors
+      :func:`esmvalcore.preprocessor.regrid_time` and
+      :func:`esmvalcore.preprocessor.regrid` for this). Input data
+      needs to be 2D with dimensions `time`, `latitude`/`longitude`.
 
 Author
 ------
@@ -60,12 +81,16 @@
 figure_kwargs: dict, optional
     Optional keyword arguments for :func:`matplotlib.pyplot.figure`. By
     default, uses ``constrained_layout: true``.
+group_variables_by: str, optional (default: 'short_name')
+    Facet which is used to create variable groups. For each variable group, an
+    individual plot is created.
 plots: dict, optional
     Plot types plotted by this diagnostic (see list above). Dictionary keys
-    must be ``timeseries``, ``annual_cycle``, ``map``, ``zonal_mean_profile``
-    or ``1d_profile``.
-    Dictionary values are dictionaries used as options for the corresponding
-    plot. The allowed options for the different plot types are given below.
+    must be ``timeseries``, ``annual_cycle``, ``map``, ``zonal_mean_profile``,
+    ``1d_profile``, ``variable_vs_lat``, ``hovmoeller_z_vs_time``,
+    ``hovmoeller_time_vs_lat_or_lon``. Dictionary values are dictionaries used
+    as options for the corresponding plot. The allowed options for the
+    different plot types are given below.
 plot_filename: str, optional
     Filename pattern for the plots.
     Defaults to ``{plot_type}_{real_name}_{dataset}_{mip}_{exp}_{ensemble}``.
@@ -119,6 +144,10 @@
     ``{project}`` that vary between the different datasets will be transformed
     to something like  ``ambiguous_project``. Examples: ``title: 'Awesome Plot
     of {long_name}'``, ``xlabel: '{short_name}'``, ``xlim: [0, 5]``.
+time_format: str, optional (default: None)
+    :func:`~datetime.datetime.strftime` format string that is used to format
+    the time axis using :class:`matplotlib.dates.DateFormatter`. If ``None``,
+    use the default formatting imposed by the iris plotting function.
 
 Configuration options for plot type ``annual_cycle``
 ----------------------------------------------------
@@ -196,13 +225,17 @@
     ``plot_func``. String arguments can include facets in curly brackets which
     will be derived from the corresponding dataset, e.g., ``{project}``,
     ``{short_name}``, ``{exp}``. Examples: ``default: {levels: 2}, CMIP6:
-    {vmin: 200, vmax: 250}``.
+    {vmin: 200, vmax: 250}``. In addition to the normalization_ options
+    supported by the plot function, the option ``norm: centered`` can be
+    specified. In this case, the keywords ``vcenter`` and ``halfrange`` should
+    be used instead of ``vmin`` or ``vmax`` (see
+    :class:`~matplotlib.colors.CenteredNorm`).
 plot_kwargs_bias: dict, optional
     Optional keyword arguments for the plot function defined by ``plot_func``
     for plotting biases. These keyword arguments update (and potentially
     overwrite) the ``plot_kwargs`` for the bias plot. This option has no effect
     if no reference dataset is given. See option ``plot_kwargs`` for more
-    details. By default, uses ``cmap: bwr``.
+    details. By default, uses ``cmap: bwr`` and ``norm: centered``.
 projection: str, optional (default: 'Robinson')
     Projection used for the map plot. Needs to be a valid projection class of
     :mod:`cartopy.crs`. Keyword arguments can be specified using the option
@@ -219,10 +252,9 @@
     ``{project}``, ``{short_name}``, ``{exp}``.  Examples: ``title: 'Awesome
     Plot of {long_name}'``, ``xlabel: '{short_name}'``, ``xlim: [0, 5]``.
 rasterize: bool, optional (default: True)
-    If ``True``, use `rasterization
-    <https://matplotlib.org/stable/gallery/misc/rasterization_demo.html>`_ for
-    map plots to produce smaller files. This is only relevant for vector
-    graphics (e.g., ``output_file_type=pdf,svg,ps``).
+    If ``True``, use rasterization_ for map plots to produce smaller files.
+    This is only relevant for vector graphics (e.g., ``output_file_type:
+    pdf,svg,ps``).
 show_stats: bool, optional (default: True)
     Show basic statistics on the plots.
 x_pos_stats_avg: float, optional (default: 0.0)
@@ -270,7 +302,7 @@
 plot_func: str, optional (default: 'contourf')
     Plot function used to plot the profiles. Must be a function of
     :mod:`iris.plot` that supports plotting of 2D cubes with coordinates
-    latitude and height/air_pressure.
+    latitude and altitude/air_pressure.
 plot_kwargs: dict, optional
     Optional keyword arguments for the plot function defined by ``plot_func``.
     Dictionary keys are elements identified by ``facet_used_for_labels`` or
@@ -280,13 +312,17 @@
     ``plot_func``. String arguments can include facets in curly brackets which
     will be derived from the corresponding dataset, e.g., ``{project}``,
     ``{short_name}``, ``{exp}``. Examples: ``default: {levels: 2}, CMIP6:
-    {vmin: 200, vmax: 250}``.
+    {vmin: 200, vmax: 250}``. In addition to the normalization_ options
+    supported by the plot function, the option ``norm: centered`` can be
+    specified. In this case, the keywords ``vcenter`` and ``halfrange`` should
+    be used instead of ``vmin`` or ``vmax`` (see
+    :class:`~matplotlib.colors.CenteredNorm`).
 plot_kwargs_bias: dict, optional
     Optional keyword arguments for the plot function defined by ``plot_func``
     for plotting biases. These keyword arguments update (and potentially
     overwrite) the ``plot_kwargs`` for the bias plot. This option has no effect
     if no reference dataset is given. See option ``plot_kwargs`` for more
-    details. By default, uses ``cmap: bwr``.
+    details. By default, uses ``cmap: bwr`` and ``norm: centered``.
 pyplot_kwargs: dict, optional
     Optional calls to functions of :mod:`matplotlib.pyplot`. Dictionary keys
     are functions of :mod:`matplotlib.pyplot`. Dictionary values are used as
@@ -295,10 +331,9 @@
     ``{project}``, ``{short_name}``, ``{exp}``.  Examples: ``title: 'Awesome
     Plot of {long_name}'``, ``xlabel: '{short_name}'``, ``xlim: [0, 5]``.
 rasterize: bool, optional (default: True)
-    If ``True``, use `rasterization
-    <https://matplotlib.org/stable/gallery/misc/rasterization_demo.html>`_ for
-    profile plots to produce smaller files. This is only relevant for vector
-    graphics (e.g., ``output_file_type=pdf,svg,ps``).
+    If ``True``, use rasterization_ for profile plots to produce smaller files.
+    This is only relevant for vector graphics (e.g., ``output_file_type:
+    pdf,svg,ps``).
 show_stats: bool, optional (default: True)
     Show basic statistics on the plots.
 show_y_minor_ticklabels: bool, optional (default: False)
@@ -352,14 +387,209 @@
 show_y_minor_ticklabels: bool, optional (default: False)
     Show tick labels for the minor ticks on the Y axis.
 
+Configuration options for plot type ``variable_vs_lat``
+-------------------------------------------------------
+gridline_kwargs: dict, optional
+    Optional keyword arguments for grid lines. By default, ``color: lightgrey,
+    alpha: 0.5`` are used. Use ``gridline_kwargs: false`` to not show grid
+    lines.
+legend_kwargs: dict, optional
+    Optional keyword arguments for :func:`matplotlib.pyplot.legend`. Use
+    ``legend_kwargs: false`` to not show legends.
+plot_kwargs: dict, optional
+    Optional keyword arguments for :func:`iris.plot.plot`. Dictionary keys are
+    elements identified by ``facet_used_for_labels`` or ``default``, e.g.,
+    ``CMIP6`` if ``facet_used_for_labels: project`` or ``historical`` if
+    ``facet_used_for_labels: exp``. Dictionary values are dictionaries used as
+    keyword arguments for :func:`iris.plot.plot`. String arguments can include
+    facets in curly brackets which will be derived from the corresponding
+    dataset, e.g., ``{project}``, ``{short_name}``, ``{exp}``. Examples:
+    ``default: {linestyle: '-', label: '{project}'}, CMIP6: {color: red,
+    linestyle: '--'}, OBS: {color: black}``.
+pyplot_kwargs: dict, optional
+    Optional calls to functions of :mod:`matplotlib.pyplot`. Dictionary keys
+    are functions of :mod:`matplotlib.pyplot`. Dictionary values are used as
+    single argument for these functions. String arguments can include facets in
+    curly brackets which will be derived from the datasets plotted in the
+    corresponding plot, e.g., ``{short_name}``, ``{exp}``. Facets like
+    ``{project}`` that vary between the different datasets will be transformed
+    to something like  ``ambiguous_project``. Examples: ``title: 'Awesome Plot
+    of {long_name}'``, ``xlabel: '{short_name}'``, ``xlim: [0, 5]``.
+
+Configuration options for plot type ``hovmoeller_z_vs_time``
+------------------------------------------------------------
+cbar_label: str, optional (default: '{short_name} [{units}]')
+    Colorbar label. Can include facets in curly brackets which will be derived
+    from the corresponding dataset, e.g., ``{project}``, ``{short_name}``,
+    ``{exp}``.
+cbar_label_bias: str, optional (default: 'Δ{short_name} [{units}]')
+    Colorbar label for plotting biases. Can include facets in curly brackets
+    which will be derived from the corresponding dataset, e.g., ``{project}``,
+    ``{short_name}``, ``{exp}``. This option has no effect if no reference
+    dataset is given.
+cbar_kwargs: dict, optional
+    Optional keyword arguments for :func:`matplotlib.pyplot.colorbar`. By
+    default, uses ``orientation: vertical``.
+cbar_kwargs_bias: dict, optional
+    Optional keyword arguments for :func:`matplotlib.pyplot.colorbar` for
+    plotting biases. These keyword arguments update (and potentially overwrite)
+    the ``cbar_kwargs`` for the bias plot. This option has no effect if no
+    reference dataset is given.
+common_cbar: bool, optional (default: False)
+    Use a common colorbar for the top panels (i.e., plots of the dataset and
+    the corresponding reference dataset) when using a reference dataset. If
+    neither ``vmin`` and ``vmix`` nor ``levels`` is given in ``plot_kwargs``,
+    the colorbar bounds are inferred from the dataset in the top left panel,
+    which might lead to an inappropriate colorbar for the reference dataset
+    (top right panel). Thus, the use of the ``plot_kwargs`` ``vmin`` and
+    ``vmax`` or ``levels`` is highly recommend when using this ``common_cbar:
+    true``. This option has no effect if no reference dataset is given.
+fontsize: int, optional (default: 10)
+    Fontsize used for ticks, labels and titles. For the latter, use the given
+    fontsize plus 2. Does not affect suptitles.
+log_y: bool, optional (default: True)
+    Use logarithmic Y-axis.
+plot_func: str, optional (default: 'contourf')
+    Plot function used to plot the profiles. Must be a function of
+    :mod:`iris.plot` that supports plotting of 2D cubes with coordinates
+    latitude and altitude/air_pressure.
+plot_kwargs: dict, optional
+    Optional keyword arguments for the plot function defined by ``plot_func``.
+    Dictionary keys are elements identified by ``facet_used_for_labels`` or
+    ``default``, e.g., ``CMIP6`` if ``facet_used_for_labels: project`` or
+    ``historical`` if ``facet_used_for_labels: exp``. Dictionary values are
+    dictionaries used as keyword arguments for the plot function defined by
+    ``plot_func``. String arguments can include facets in curly brackets which
+    will be derived from the corresponding dataset, e.g., ``{project}``,
+    ``{short_name}``, ``{exp}``. Examples: ``default: {levels: 2}, CMIP6:
+    {vmin: 200, vmax: 250}``. In addition to the normalization_ options
+    supported by the plot function, the option ``norm: centered`` can be
+    specified. In this case, the keywords ``vcenter`` and ``halfrange`` should
+    be used instead of ``vmin`` or ``vmax`` (see
+    :class:`~matplotlib.colors.CenteredNorm`).
+plot_kwargs_bias: dict, optional
+    Optional keyword arguments for the plot function defined by ``plot_func``
+    for plotting biases. These keyword arguments update (and potentially
+    overwrite) the ``plot_kwargs`` for the bias plot. This option has no effect
+    if no reference dataset is given. See option ``plot_kwargs`` for more
+    details. By default, uses ``cmap: bwr`` and ``norm: centered``.
+pyplot_kwargs: dict, optional
+    Optional calls to functions of :mod:`matplotlib.pyplot`. Dictionary keys
+    are functions of :mod:`matplotlib.pyplot`. Dictionary values are used as
+    single argument for these functions. String arguments can include facets in
+    curly brackets which will be derived from the corresponding dataset, e.g.,
+    ``{project}``, ``{short_name}``, ``{exp}``.  Examples: ``title: 'Awesome
+    Plot of {long_name}'``, ``xlabel: '{short_name}'``, ``xlim: [0, 5]``.
+rasterize: bool, optional (default: True)
+    If ``True``, use rasterization_ for profile plots to produce smaller files.
+    This is only relevant for vector graphics (e.g., ``output_file_type:
+    pdf,svg,ps``).
+show_stats: bool, optional (default: True)
+    Show basic statistics on the plots.
+show_y_minor_ticklabels: bool, optional (default: False)
+    Show tick labels for the minor ticks on the Y axis.
+x_pos_stats_avg: float, optional (default: 0.01)
+    Text x-position of average (shown on the left) in Axes coordinates. Can be
+    adjusted to avoid overlap with the figure. Only relevant if ``show_stats:
+    true``.
+x_pos_stats_bias: float, optional (default: 0.7)
+    Text x-position of bias statistics (shown on the right) in Axes
+    coordinates. Can be adjusted to avoid overlap with the figure. Only
+    relevant if ``show_stats: true``.
+time_format: str, optional (default: None)
+    :func:`~datetime.datetime.strftime` format string that is used to format
+    the time axis using :class:`matplotlib.dates.DateFormatter`. If ``None``,
+    use the default formatting imposed by the iris plotting function.
+
+Configuration options for plot type ``hovmoeller_time_vs_lat_or_lon``
+---------------------------------------------------------------------
+cbar_label: str, optional (default: '{short_name} [{units}]')
+    Colorbar label. Can include facets in curly brackets which will be derived
+    from the corresponding dataset, e.g., ``{project}``, ``{short_name}``,
+    ``{exp}``.
+cbar_label_bias: str, optional (default: 'Δ{short_name} [{units}]')
+    Colorbar label for plotting biases. Can include facets in curly brackets
+    which will be derived from the corresponding dataset, e.g., ``{project}``,
+    ``{short_name}``, ``{exp}``. This option has no effect if no reference
+    dataset is given.
+cbar_kwargs: dict, optional
+    Optional keyword arguments for :func:`matplotlib.pyplot.colorbar`. By
+    default, uses ``orientation: vertical``.
+cbar_kwargs_bias: dict, optional
+    Optional keyword arguments for :func:`matplotlib.pyplot.colorbar` for
+    plotting biases. These keyword arguments update (and potentially overwrite)
+    the ``cbar_kwargs`` for the bias plot. This option has no effect if no
+    reference dataset is given.
+common_cbar: bool, optional (default: False)
+    Use a common colorbar for the top panels (i.e., plots of the dataset and
+    the corresponding reference dataset) when using a reference dataset. If
+    neither ``vmin`` and ``vmix`` nor ``levels`` is given in ``plot_kwargs``,
+    the colorbar bounds are inferred from the dataset in the top left panel,
+    which might lead to an inappropriate colorbar for the reference dataset
+    (top right panel). Thus, the use of the ``plot_kwargs`` ``vmin`` and
+    ``vmax`` or ``levels`` is highly recommend when using this ``common_cbar:
+    true``. This option has no effect if no reference dataset is given.
+fontsize: int, optional (default: 10)
+    Fontsize used for ticks, labels and titles. For the latter, use the given
+    fontsize plus 2. Does not affect suptitles.
+plot_func: str, optional (default: 'contourf')
+    Plot function used to plot the profiles. Must be a function of
+    :mod:`iris.plot` that supports plotting of 2D cubes with coordinates
+    latitude and height/air_pressure.
+plot_kwargs: dict, optional
+    Optional keyword arguments for the plot function defined by ``plot_func``.
+    Dictionary keys are elements identified by ``facet_used_for_labels`` or
+    ``default``, e.g., ``CMIP6`` if ``facet_used_for_labels: project`` or
+    ``historical`` if ``facet_used_for_labels: exp``. Dictionary values are
+    dictionaries used as keyword arguments for the plot function defined by
+    ``plot_func``. String arguments can include facets in curly brackets which
+    will be derived from the corresponding dataset, e.g., ``{project}``,
+    ``{short_name}``, ``{exp}``. Examples: ``default: {levels: 2}, CMIP6:
+    {vmin: 200, vmax: 250}``. In addition to the normalization_ options
+    supported by the plot function, the option ``norm: centered`` can be
+    specified. In this case, the keywords ``vcenter`` and ``halfrange`` should
+    be used instead of ``vmin`` or ``vmax`` (see
+    :class:`~matplotlib.colors.CenteredNorm`).
+plot_kwargs_bias: dict, optional
+    Optional keyword arguments for the plot function defined by ``plot_func``
+    for plotting biases. These keyword arguments update (and potentially
+    overwrite) the ``plot_kwargs`` for the bias plot. This option has no effect
+    if no reference dataset is given. See option ``plot_kwargs`` for more
+    details. By default, uses ``cmap: bwr`` and ``norm: centered``.
+pyplot_kwargs: dict, optional
+    Optional calls to functions of :mod:`matplotlib.pyplot`. Dictionary keys
+    are functions of :mod:`matplotlib.pyplot`. Dictionary values are used as
+    single argument for these functions. String arguments can include facets in
+    curly brackets which will be derived from the corresponding dataset, e.g.,
+    ``{project}``, ``{short_name}``, ``{exp}``.  Examples: ``title: 'Awesome
+    Plot of {long_name}'``, ``xlabel: '{short_name}'``, ``xlim: [0, 5]``.
+rasterize: bool, optional (default: True)
+    If ``True``, use rasterization_ for profile plots to produce smaller files.
+    This is only relevant for vector graphics (e.g., ``output_file_type:
+    pdf,svg,ps``).
+show_y_minor_ticks: bool, optional (default: True)
+    Show minor ticks for time on the Y axis.
+show_x_minor_ticks: bool, optional (default: True)
+    Show minor ticks for latitude or longitude on the X axis.
+time_format: str, optional (default: None)
+    :func:`~datetime.datetime.strftime` format string that is used to format
+    the time axis using :class:`matplotlib.dates.DateFormatter`. If ``None``,
+    use the default formatting imposed by the iris plotting function.
+
 .. hint::
 
    Extra arguments given to the recipe are ignored, so it is safe to use yaml
    anchors to share the configuration of common arguments with other monitor
    diagnostic script.
 
+.. _rasterization: https://matplotlib.org/stable/gallery/misc/
+   rasterization_demo.html
+.. _normalization: https://matplotlib.org/stable/users/explain/colors/
+   colormapnorms.html
+
 """
 import logging
+import warnings
 from copy import deepcopy
 from pathlib import Path
 from pprint import pformat
@@ -367,14 +597,21 @@
 import cartopy.crs as ccrs
 import iris
 import matplotlib as mpl
+import matplotlib.dates as mdates
 import matplotlib.pyplot as plt
 import numpy as np
 import seaborn as sns
 from iris.analysis.cartography import area_weights
 from iris.coord_categorisation import add_year
 from iris.coords import AuxCoord
+from matplotlib.colors import CenteredNorm
 from matplotlib.gridspec import GridSpec
-from matplotlib.ticker import FormatStrFormatter, LogLocator, NullFormatter
+from matplotlib.ticker import (
+    AutoMinorLocator,
+    FormatStrFormatter,
+    LogLocator,
+    NullFormatter,
+)
 from sklearn.metrics import r2_score
 
 import esmvaltool.diag_scripts.shared.iris_helpers as ih
@@ -397,16 +634,19 @@ def __init__(self, config):
         """Initialize class member."""
         super().__init__(config)
 
-        # Get default stettings
+        # Get default settings
         self.cfg = deepcopy(self.cfg)
         self.cfg.setdefault('facet_used_for_labels', 'dataset')
         self.cfg.setdefault('figure_kwargs', {'constrained_layout': True})
+        self.cfg.setdefault('group_variables_by', 'short_name')
         self.cfg.setdefault('savefig_kwargs', {
             'bbox_inches': 'tight',
             'dpi': 300,
             'orientation': 'landscape',
         })
         self.cfg.setdefault('seaborn_settings', {'style': 'ticks'})
+        logger.info("Using facet '%s' to group variables",
+                    self.cfg['group_variables_by'])
         logger.info("Using facet '%s' to create labels",
                     self.cfg['facet_used_for_labels'])
 
@@ -414,7 +654,7 @@ def __init__(self, config):
         self.input_data = self._load_and_preprocess_data()
         self.grouped_input_data = group_metadata(
             self.input_data,
-            'short_name',
+            self.cfg['group_variables_by'],
             sort=self.cfg['facet_used_for_labels'],
         )
 
@@ -436,7 +676,10 @@ def __init__(self, config):
             'annual_cycle',
             'map',
             'zonal_mean_profile',
-            '1d_profile'
+            '1d_profile',
+            'variable_vs_lat',
+            'hovmoeller_z_vs_time',
+            'hovmoeller_time_vs_lat_or_lon',
         ]
         for (plot_type, plot_options) in self.plots.items():
             if plot_type not in self.supported_plot_types:
@@ -453,14 +696,15 @@ def __init__(self, config):
                 self.plots[plot_type].setdefault('legend_kwargs', {})
                 self.plots[plot_type].setdefault('plot_kwargs', {})
                 self.plots[plot_type].setdefault('pyplot_kwargs', {})
+                self.plots[plot_type].setdefault('time_format', None)
 
-            if plot_type == 'annual_cycle':
+            elif plot_type == 'annual_cycle':
                 self.plots[plot_type].setdefault('gridline_kwargs', {})
                 self.plots[plot_type].setdefault('legend_kwargs', {})
                 self.plots[plot_type].setdefault('plot_kwargs', {})
                 self.plots[plot_type].setdefault('pyplot_kwargs', {})
 
-            if plot_type == 'map':
+            elif plot_type == 'map':
                 self.plots[plot_type].setdefault(
                     'cbar_label', '{short_name} [{units}]')
                 self.plots[plot_type].setdefault(
@@ -478,6 +722,9 @@ def __init__(self, config):
                 self.plots[plot_type]['plot_kwargs_bias'].setdefault(
                     'cmap', 'bwr'
                 )
+                self.plots[plot_type]['plot_kwargs_bias'].setdefault(
+                    'norm', 'centered'
+                )
                 if 'projection' not in self.plots[plot_type]:
                     self.plots[plot_type].setdefault('projection', 'Robinson')
                     self.plots[plot_type].setdefault(
@@ -491,7 +738,7 @@ def __init__(self, config):
                 self.plots[plot_type].setdefault('x_pos_stats_avg', 0.0)
                 self.plots[plot_type].setdefault('x_pos_stats_bias', 0.92)
 
-            if plot_type == 'zonal_mean_profile':
+            elif plot_type == 'zonal_mean_profile':
                 self.plots[plot_type].setdefault(
                     'cbar_label', '{short_name} [{units}]')
                 self.plots[plot_type].setdefault(
@@ -509,6 +756,9 @@ def __init__(self, config):
                 self.plots[plot_type]['plot_kwargs_bias'].setdefault(
                     'cmap', 'bwr'
                 )
+                self.plots[plot_type]['plot_kwargs_bias'].setdefault(
+                    'norm', 'centered'
+                )
                 self.plots[plot_type].setdefault('pyplot_kwargs', {})
                 self.plots[plot_type].setdefault('rasterize', True)
                 self.plots[plot_type].setdefault('show_stats', True)
@@ -518,7 +768,7 @@ def __init__(self, config):
                 self.plots[plot_type].setdefault('x_pos_stats_avg', 0.01)
                 self.plots[plot_type].setdefault('x_pos_stats_bias', 0.7)
 
-            if plot_type == '1d_profile':
+            elif plot_type == '1d_profile':
                 self.plots[plot_type].setdefault('aspect_ratio', 1.5)
                 self.plots[plot_type].setdefault('gridline_kwargs', {})
                 self.plots[plot_type].setdefault('legend_kwargs', {})
@@ -529,6 +779,69 @@ def __init__(self, config):
                 self.plots[plot_type].setdefault(
                     'show_y_minor_ticklabels', False
                 )
+            elif plot_type == 'variable_vs_lat':
+                self.plots[plot_type].setdefault('gridline_kwargs', {})
+                self.plots[plot_type].setdefault('legend_kwargs', {})
+                self.plots[plot_type].setdefault('plot_kwargs', {})
+                self.plots[plot_type].setdefault('pyplot_kwargs', {})
+
+            elif plot_type == 'hovmoeller_z_vs_time':
+                self.plots[plot_type].setdefault('cbar_label',
+                                                 '{short_name} [{units}]')
+                self.plots[plot_type].setdefault('cbar_label_bias',
+                                                 'Δ{short_name} [{units}]')
+                self.plots[plot_type].setdefault('cbar_kwargs',
+                                                 {'orientation': 'vertical'})
+                self.plots[plot_type].setdefault('cbar_kwargs_bias', {})
+                self.plots[plot_type].setdefault('common_cbar', False)
+                self.plots[plot_type].setdefault('fontsize', 10)
+                self.plots[plot_type].setdefault('log_y', True)
+                self.plots[plot_type].setdefault('plot_func', 'contourf')
+                self.plots[plot_type].setdefault('plot_kwargs', {})
+                self.plots[plot_type].setdefault('plot_kwargs_bias', {})
+                self.plots[plot_type]['plot_kwargs_bias'].setdefault(
+                    'cmap', 'bwr')
+                self.plots[plot_type]['plot_kwargs_bias'].setdefault(
+                    'norm', 'centered'
+                )
+                self.plots[plot_type].setdefault('pyplot_kwargs', {})
+                self.plots[plot_type].setdefault('rasterize', True)
+                self.plots[plot_type].setdefault('show_stats', True)
+                self.plots[plot_type].setdefault('show_y_minor_ticklabels',
+                                                 False)
+                self.plots[plot_type].setdefault('time_format', None)
+                self.plots[plot_type].setdefault('x_pos_stats_avg', 0.01)
+                self.plots[plot_type].setdefault('x_pos_stats_bias', 0.7)
+
+            elif plot_type == 'hovmoeller_time_vs_lat_or_lon':
+                self.plots[plot_type].setdefault(
+                    'cbar_label', '{short_name} [{units}]')
+                self.plots[plot_type].setdefault(
+                    'cbar_label_bias', 'Δ{short_name} [{units}]')
+                self.plots[plot_type].setdefault(
+                    'cbar_kwargs', {'orientation': 'vertical'}
+                )
+                self.plots[plot_type].setdefault('cbar_kwargs_bias', {})
+                self.plots[plot_type].setdefault('common_cbar', False)
+                self.plots[plot_type].setdefault('fontsize', 10)
+                self.plots[plot_type].setdefault('plot_func', 'contourf')
+                self.plots[plot_type].setdefault('plot_kwargs', {})
+                self.plots[plot_type].setdefault('plot_kwargs_bias', {})
+                self.plots[plot_type]['plot_kwargs_bias'].setdefault(
+                    'cmap', 'bwr'
+                )
+                self.plots[plot_type]['plot_kwargs_bias'].setdefault(
+                    'norm', 'centered'
+                )
+                self.plots[plot_type].setdefault('pyplot_kwargs', {})
+                self.plots[plot_type].setdefault('rasterize', True)
+                self.plots[plot_type].setdefault(
+                    'show_y_minor_ticks', True
+                )
+                self.plots[plot_type].setdefault(
+                    'show_x_minor_ticks', True
+                )
+                self.plots[plot_type].setdefault('time_format', None)
 
         # Check that facet_used_for_labels is present for every dataset
         for dataset in self.input_data:
@@ -587,10 +900,10 @@ def _add_stats(self, plot_type, axes, dim_coords, dataset,
         # Different options for the different plots types
         fontsize = 6.0
         y_pos = 0.95
-        if plot_type == 'map':
-            x_pos_bias = self.plots[plot_type]['x_pos_stats_bias']
-            x_pos = self.plots[plot_type]['x_pos_stats_avg']
-        elif plot_type in ['zonal_mean_profile']:
+        if all([
+                'x_pos_stats_avg' in self.plots[plot_type],
+                'x_pos_stats_bias' in self.plots[plot_type],
+        ]):
             x_pos_bias = self.plots[plot_type]['x_pos_stats_bias']
             x_pos = self.plots[plot_type]['x_pos_stats_avg']
         else:
@@ -757,9 +1070,17 @@ def _get_plot_kwargs(self, plot_type, dataset, bias=False):
                 plot_kwargs[key] = val
 
         # Default settings for different plot types
-        if plot_type in ('timeseries', 'annual_cycle', '1d_profile'):
+        if plot_type in ('timeseries', 'annual_cycle', '1d_profile',
+                         'variable_vs_lat'):
             plot_kwargs.setdefault('label', label)
 
+        if plot_kwargs.get('norm') == 'centered':
+            norm = CenteredNorm(
+                vcenter=plot_kwargs.pop('vcenter', 0.0),
+                halfrange=plot_kwargs.pop('halfrange', None),
+            )
+            plot_kwargs['norm'] = norm
+
         return deepcopy(plot_kwargs)
 
     def _load_and_preprocess_data(self):
@@ -822,6 +1143,7 @@ def _plot_map_with_ref(self, plot_func, dataset, ref_dataset):
                 axes_data.gridlines(**gridline_kwargs)
             axes_data.set_title(self._get_label(dataset), pad=3.0)
             self._add_stats(plot_type, axes_data, dim_coords_dat, dataset)
+            self._process_pyplot_kwargs(plot_type, dataset)
 
             # Plot reference dataset (top right)
             # Note: make sure to use the same vmin and vmax than the top left
@@ -838,6 +1160,7 @@ def _plot_map_with_ref(self, plot_func, dataset, ref_dataset):
                 axes_ref.gridlines(**gridline_kwargs)
             axes_ref.set_title(self._get_label(ref_dataset), pad=3.0)
             self._add_stats(plot_type, axes_ref, dim_coords_ref, ref_dataset)
+            self._process_pyplot_kwargs(plot_type, ref_dataset)
 
             # Add colorbar(s)
             self._add_colorbar(plot_type, plot_data, plot_ref, axes_data,
@@ -981,6 +1304,7 @@ def _plot_zonal_mean_profile_with_ref(self, plot_func, dataset,
             else:
                 axes_data.get_yaxis().set_minor_formatter(NullFormatter())
             self._add_stats(plot_type, axes_data, dim_coords_dat, dataset)
+            self._process_pyplot_kwargs(plot_type, dataset)
 
             # Plot reference dataset (top right)
             # Note: make sure to use the same vmin and vmax than the top left
@@ -995,6 +1319,7 @@ def _plot_zonal_mean_profile_with_ref(self, plot_func, dataset,
             axes_ref.set_title(self._get_label(ref_dataset), pad=3.0)
             plt.setp(axes_ref.get_yticklabels(), visible=False)
             self._add_stats(plot_type, axes_ref, dim_coords_ref, ref_dataset)
+            self._process_pyplot_kwargs(plot_type, ref_dataset)
 
             # Add colorbar(s)
             self._add_colorbar(plot_type, plot_data, plot_ref, axes_data,
@@ -1105,49 +1430,401 @@ def _plot_zonal_mean_profile_without_ref(self, plot_func, dataset):
 
         return (plot_path, {netcdf_path: cube})
 
-    def _process_pyplot_kwargs(self, plot_type, dataset):
-        """Process functions for :mod:`matplotlib.pyplot`."""
-        pyplot_kwargs = self.plots[plot_type]['pyplot_kwargs']
-        for (func, arg) in pyplot_kwargs.items():
-            if isinstance(arg, str):
-                arg = self._fill_facet_placeholders(
-                    arg,
-                    dataset,
-                    f"pyplot_kwargs of {plot_type} '{func}: {arg}'",
-                )
-            if arg is None:
-                getattr(plt, func)()
-            else:
-                getattr(plt, func)(arg)
-
-    @staticmethod
-    def _check_cube_dimensions(cube, plot_type):
-        """Check that cube has correct dimensional variables."""
-        expected_dimensions_dict = {
-            'annual_cycle': (['month_number'],),
-            'map': (['latitude', 'longitude'],),
-            'zonal_mean_profile': (['latitude', 'air_pressure'],
-                                   ['latitude', 'altitude']),
-            'timeseries': (['time'],),
-            '1d_profile': (['air_pressure'],
-                           ['altitude']),
+    def _plot_hovmoeller_z_vs_time_without_ref(self, plot_func, dataset):
+        """Plot Hovmoeller Z vs. time for single dataset without reference."""
+        plot_type = 'hovmoeller_z_vs_time'
+        logger.info(
+            "Plotting Hovmoeller Z vs. time without reference dataset"
+            " for '%s'", self._get_label(dataset))
 
-        }
-        if plot_type not in expected_dimensions_dict:
-            raise NotImplementedError(f"plot_type '{plot_type}' not supported")
-        expected_dimensions = expected_dimensions_dict[plot_type]
-        for dims in expected_dimensions:
-            cube_dims = [cube.coords(dim, dim_coords=True) for dim in dims]
-            if all(cube_dims) and cube.ndim == len(dims):
-                return dims
-        expected_dims_str = ' or '.join(
-            [str(dims) for dims in expected_dimensions]
-        )
-        raise ValueError(
-            f"Expected cube that exactly has the dimensional coordinates "
-            f"{expected_dims_str}, got {cube.summary(shorten=True)}")
+        # Make sure that the data has the correct dimensions
+        cube = dataset['cube']
+        dim_coords_dat = self._check_cube_dimensions(cube, plot_type)
 
-    @staticmethod
+        # Create plot with desired settings
+        with mpl.rc_context(self._get_custom_mpl_rc_params(plot_type)):
+            fig = plt.figure(**self.cfg['figure_kwargs'])
+            axes = fig.add_subplot()
+            plot_kwargs = self._get_plot_kwargs(plot_type, dataset)
+            plot_kwargs['axes'] = axes
+            plot_hovmoeller = plot_func(cube, **plot_kwargs)
+
+            # Print statistics if desired
+            self._add_stats(plot_type, axes, dim_coords_dat, dataset)
+
+            # Setup colorbar
+            fontsize = self.plots[plot_type]['fontsize']
+            colorbar = fig.colorbar(plot_hovmoeller,
+                                    ax=axes,
+                                    **self._get_cbar_kwargs(plot_type))
+            colorbar.set_label(self._get_cbar_label(plot_type, dataset),
+                               fontsize=fontsize)
+            colorbar.ax.tick_params(labelsize=fontsize)
+
+            # Customize plot
+            axes.set_title(self._get_label(dataset))
+            fig.suptitle(f"{dataset['long_name']} ({dataset['start_year']}-"
+                         f"{dataset['end_year']})")
+            z_coord = cube.coord(axis='Z')
+            axes.set_ylabel(f'{z_coord.long_name} [{z_coord.units}]')
+            if self.plots[plot_type]['log_y']:
+                axes.set_yscale('log')
+                axes.get_yaxis().set_major_formatter(
+                    FormatStrFormatter('%.1f'))
+            if self.plots[plot_type]['show_y_minor_ticklabels']:
+                axes.get_yaxis().set_minor_formatter(
+                    FormatStrFormatter('%.1f'))
+            else:
+                axes.get_yaxis().set_minor_formatter(NullFormatter())
+            if self.plots[plot_type]['time_format'] is not None:
+                axes.get_xaxis().set_major_formatter(
+                    mdates.DateFormatter(self.plots[plot_type]['time_format']))
+            axes.set_xlabel('time')
+            self._process_pyplot_kwargs(plot_type, dataset)
+
+            # Rasterization
+            if self.plots[plot_type]['rasterize']:
+                self._set_rasterized([axes])
+
+        # File paths
+        plot_path = self.get_plot_path(plot_type, dataset)
+        netcdf_path = get_diagnostic_filename(Path(plot_path).stem, self.cfg)
+
+        return (plot_path, {netcdf_path: cube})
+
+    def _plot_hovmoeller_z_vs_time_with_ref(self, plot_func, dataset,
+                                            ref_dataset):
+        """Plot Hovmoeller Z vs. time for single dataset with reference."""
+        plot_type = 'hovmoeller_z_vs_time'
+        logger.info(
+            "Plotting Hovmoeller z vs. time with reference dataset"
+            " '%s' for '%s'", self._get_label(ref_dataset),
+            self._get_label(dataset))
+
+        # Make sure that the data has the correct dimensions
+        cube = dataset['cube']
+        ref_cube = ref_dataset['cube']
+        dim_coords_dat = self._check_cube_dimensions(cube, plot_type)
+        dim_coords_ref = self._check_cube_dimensions(ref_cube, plot_type)
+
+        # Create single figure with multiple axes
+        with mpl.rc_context(self._get_custom_mpl_rc_params(plot_type)):
+            fig = plt.figure(**self.cfg['figure_kwargs'])
+            gridspec = GridSpec(5,
+                                4,
+                                figure=fig,
+                                height_ratios=[1.0, 1.0, 0.4, 1.0, 1.0])
+
+            # Options used for all subplots
+            plot_kwargs = self._get_plot_kwargs(plot_type, dataset)
+            fontsize = self.plots[plot_type]['fontsize']
+
+            # Plot dataset (top left)
+            axes_data = fig.add_subplot(gridspec[0:2, 0:2])
+            plot_kwargs['axes'] = axes_data
+            plot_data = plot_func(cube, **plot_kwargs)
+            axes_data.set_title(self._get_label(dataset), pad=3.0)
+            z_coord = cube.coord(axis='Z')
+            axes_data.set_ylabel(f'{z_coord.long_name} [{z_coord.units}]')
+            if self.plots[plot_type]['log_y']:
+                axes_data.set_yscale('log')
+                axes_data.get_yaxis().set_major_formatter(
+                    FormatStrFormatter('%.1f'))
+            if self.plots[plot_type]['show_y_minor_ticklabels']:
+                axes_data.get_yaxis().set_minor_formatter(
+                    FormatStrFormatter('%.1f'))
+            else:
+                axes_data.get_yaxis().set_minor_formatter(NullFormatter())
+            if self.plots[plot_type]['time_format'] is not None:
+                axes_data.get_xaxis().set_major_formatter(
+                    mdates.DateFormatter(self.plots[plot_type]['time_format']))
+            self._add_stats(plot_type, axes_data, dim_coords_dat, dataset)
+            self._process_pyplot_kwargs(plot_type, dataset)
+
+            # Plot reference dataset (top right)
+            # Note: make sure to use the same vmin and vmax than the top left
+            # plot if a common colorbar is desired
+            axes_ref = fig.add_subplot(gridspec[0:2, 2:4],
+                                       sharex=axes_data,
+                                       sharey=axes_data)
+            plot_kwargs['axes'] = axes_ref
+            if self.plots[plot_type]['common_cbar']:
+                plot_kwargs.setdefault('vmin', plot_data.get_clim()[0])
+                plot_kwargs.setdefault('vmax', plot_data.get_clim()[1])
+            plot_ref = plot_func(ref_cube, **plot_kwargs)
+            axes_ref.set_title(self._get_label(ref_dataset), pad=3.0)
+            plt.setp(axes_ref.get_yticklabels(), visible=False)
+            self._add_stats(plot_type, axes_ref, dim_coords_ref, ref_dataset)
+            self._process_pyplot_kwargs(plot_type, ref_dataset)
+
+            # Add colorbar(s)
+            self._add_colorbar(plot_type, plot_data, plot_ref, axes_data,
+                               axes_ref, dataset, ref_dataset)
+
+            # Plot bias (bottom center)
+            bias_cube = cube - ref_cube
+            axes_bias = fig.add_subplot(gridspec[3:5, 1:3],
+                                        sharex=axes_data,
+                                        sharey=axes_data)
+            plot_kwargs_bias = self._get_plot_kwargs(plot_type,
+                                                     dataset,
+                                                     bias=True)
+            plot_kwargs_bias['axes'] = axes_bias
+            plot_bias = plot_func(bias_cube, **plot_kwargs_bias)
+            axes_bias.set_title(
+                f"{self._get_label(dataset)} - {self._get_label(ref_dataset)}",
+                pad=3.0,
+            )
+            axes_bias.set_xlabel('time')
+            axes_bias.set_ylabel(f'{z_coord.long_name} [{z_coord.units}]')
+            cbar_kwargs_bias = self._get_cbar_kwargs(plot_type, bias=True)
+            cbar_bias = fig.colorbar(plot_bias,
+                                     ax=axes_bias,
+                                     **cbar_kwargs_bias)
+            cbar_bias.set_label(
+                self._get_cbar_label(plot_type, dataset, bias=True),
+                fontsize=fontsize,
+            )
+            cbar_bias.ax.tick_params(labelsize=fontsize)
+            self._add_stats(plot_type, axes_bias, dim_coords_dat, dataset,
+                            ref_dataset)
+
+            # Customize plot
+            fig.suptitle(f"{dataset['long_name']} ({dataset['start_year']}-"
+                         f"{dataset['end_year']})")
+            self._process_pyplot_kwargs(plot_type, dataset)
+
+            # Rasterization
+            if self.plots[plot_type]['rasterize']:
+                self._set_rasterized([axes_data, axes_ref, axes_bias])
+
+        # File paths
+        plot_path = self.get_plot_path(plot_type, dataset)
+        netcdf_path = (get_diagnostic_filename(
+            Path(plot_path).stem + "_{pos}", self.cfg))
+        netcdf_paths = {
+            netcdf_path.format(pos='top_left'): cube,
+            netcdf_path.format(pos='top_right'): ref_cube,
+            netcdf_path.format(pos='bottom'): bias_cube,
+        }
+
+        return (plot_path, netcdf_paths)
+
+    def _plot_hovmoeller_time_vs_lat_or_lon_with_ref(self, plot_func, dataset,
+                                                     ref_dataset):
+        """Plot the hovmoeller profile for single dataset with reference."""
+        plot_type = 'hovmoeller_time_vs_lat_or_lon'
+        logger.info("Plotting Hovmoeller plots with reference dataset"
+                    " '%s' for '%s'",
+                    self._get_label(ref_dataset), self._get_label(dataset))
+
+        # Make sure that the data has the correct dimensions
+        cube = dataset['cube']
+        ref_cube = ref_dataset['cube']
+        dim_coords_dat = self._check_cube_dimensions(cube, plot_type)
+        self._check_cube_dimensions(ref_cube, plot_type)
+
+        # Create single figure with multiple axes
+        with mpl.rc_context(self._get_custom_mpl_rc_params(plot_type)):
+            fig = plt.figure(**self.cfg['figure_kwargs'])
+            gridspec = GridSpec(5, 4, figure=fig,
+                                height_ratios=[1.0, 1.0, 0.4, 1.0, 1.0])
+
+            # Options used for all subplots
+            plot_kwargs = self._get_plot_kwargs(plot_type, dataset)
+            fontsize = self.plots[plot_type]['fontsize']
+
+            # Plot dataset (top left)
+            axes_data = fig.add_subplot(gridspec[0:2, 0:2])
+            plot_kwargs['axes'] = axes_data
+            coord_names = [coord[0].name() for coord in cube.dim_coords]
+            if coord_names[0] == "time":
+                coord_names.reverse()
+            plot_kwargs['coords'] = coord_names
+            plot_data = plot_func(cube, **plot_kwargs)
+            axes_data.set_title(self._get_label(dataset), pad=3.0)
+            axes_data.set_ylabel('time')
+            if self.plots[plot_type]['time_format'] is not None:
+                axes_data.get_yaxis().set_major_formatter(mdates.DateFormatter(
+                    self.plots[plot_type]['time_format']))
+            if self.plots[plot_type]['show_y_minor_ticks']:
+                axes_data.get_yaxis().set_minor_locator(AutoMinorLocator())
+            if self.plots[plot_type]['show_x_minor_ticks']:
+                axes_data.get_xaxis().set_minor_locator(AutoMinorLocator())
+            self._process_pyplot_kwargs(plot_type, dataset)
+
+            # Plot reference dataset (top right)
+            # Note: make sure to use the same vmin and vmax than the top left
+            # plot if a common colorbar is desired
+            axes_ref = fig.add_subplot(gridspec[0:2, 2:4], sharex=axes_data,
+                                       sharey=axes_data)
+            plot_kwargs['axes'] = axes_ref
+            if self.plots[plot_type]['common_cbar']:
+                plot_kwargs.setdefault('vmin', plot_data.get_clim()[0])
+                plot_kwargs.setdefault('vmax', plot_data.get_clim()[1])
+            plot_ref = plot_func(ref_cube, **plot_kwargs)
+            axes_ref.set_title(self._get_label(ref_dataset), pad=3.0)
+            plt.setp(axes_ref.get_yticklabels(), visible=False)
+            self._process_pyplot_kwargs(plot_type, ref_dataset)
+
+            # Add colorbar(s)
+            self._add_colorbar(plot_type, plot_data, plot_ref, axes_data,
+                               axes_ref, dataset, ref_dataset)
+
+            # Plot bias (bottom center)
+            bias_cube = cube - ref_cube
+            axes_bias = fig.add_subplot(gridspec[3:5, 1:3], sharex=axes_data,
+                                        sharey=axes_data)
+            plot_kwargs_bias = self._get_plot_kwargs(plot_type, dataset,
+                                                     bias=True)
+            plot_kwargs_bias['axes'] = axes_bias
+            plot_kwargs_bias['coords'] = coord_names
+            plot_bias = plot_func(bias_cube, **plot_kwargs_bias)
+            axes_bias.set_title(
+                f"{self._get_label(dataset)} - {self._get_label(ref_dataset)}",
+                pad=3.0,
+            )
+            axes_bias.set_ylabel('time')
+            if 'latitude' in dim_coords_dat:
+                axes_bias.set_xlabel('latitude [°N]')
+            elif 'longitude' in dim_coords_dat:
+                axes_bias.set_xlabel('longitude [°E]')
+            cbar_kwargs_bias = self._get_cbar_kwargs(plot_type, bias=True)
+            cbar_bias = fig.colorbar(plot_bias, ax=axes_bias,
+                                     **cbar_kwargs_bias)
+            cbar_bias.set_label(
+                self._get_cbar_label(plot_type, dataset, bias=True),
+                fontsize=fontsize,
+            )
+            cbar_bias.ax.tick_params(labelsize=fontsize)
+
+            # Customize plot
+            fig.suptitle(f"{dataset['long_name']} ({dataset['start_year']}-"
+                         f"{dataset['end_year']})")
+            self._process_pyplot_kwargs(plot_type, dataset)
+
+            # Rasterization
+            if self.plots[plot_type]['rasterize']:
+                self._set_rasterized([axes_data, axes_ref, axes_bias])
+
+        # File paths
+        plot_path = self.get_plot_path(plot_type, dataset)
+        netcdf_path = (
+            get_diagnostic_filename(Path(plot_path).stem + "_{pos}", self.cfg)
+        )
+        netcdf_paths = {
+            netcdf_path.format(pos='top_left'): cube,
+            netcdf_path.format(pos='top_right'): ref_cube,
+            netcdf_path.format(pos='bottom'): bias_cube,
+        }
+
+        return (plot_path, netcdf_paths)
+
+    def _plot_hovmoeller_time_vs_lat_or_lon_without_ref(self, plot_func,
+                                                        dataset):
+        """Plot time vs zonal or meridional Hovmoeller without reference."""
+        plot_type = 'hovmoeller_time_vs_lat_or_lon'
+        logger.info("Plotting Hovmoeller plots without reference dataset"
+                    " for '%s'", self._get_label(dataset))
+
+        # Make sure that the data has the correct dimensions
+        cube = dataset['cube']
+        dim_coords_dat = self._check_cube_dimensions(cube, plot_type)
+
+        # Create plot with desired settings
+        with mpl.rc_context(self._get_custom_mpl_rc_params(plot_type)):
+            fig = plt.figure(**self.cfg['figure_kwargs'])
+            axes = fig.add_subplot()
+            plot_kwargs = self._get_plot_kwargs(plot_type, dataset)
+            plot_kwargs['axes'] = axes
+
+            # Make sure time is on y-axis
+            plot_kwargs['coords'] = list(reversed(dim_coords_dat))
+            plot_hovmoeller = plot_func(cube, **plot_kwargs)
+
+            # Setup colorbar
+            fontsize = self.plots[plot_type]['fontsize']
+            colorbar = fig.colorbar(plot_hovmoeller, ax=axes,
+                                    **self._get_cbar_kwargs(plot_type))
+            colorbar.set_label(self._get_cbar_label(plot_type, dataset),
+                               fontsize=fontsize)
+            colorbar.ax.tick_params(labelsize=fontsize)
+
+            # Customize plot
+            axes.set_title(self._get_label(dataset))
+            fig.suptitle(f"{dataset['long_name']} ({dataset['start_year']}-"
+                         f"{dataset['end_year']})")
+            if 'latitude' in dim_coords_dat:
+                axes.set_xlabel('latitude [°N]')
+            elif 'longitude' in dim_coords_dat:
+                axes.set_xlabel('longitude [°E]')
+            axes.set_ylabel('time')
+            if self.plots[plot_type]['time_format'] is not None:
+                axes.get_yaxis().set_major_formatter(mdates.DateFormatter(
+                    self.plots[plot_type]['time_format'])
+                )
+            if self.plots[plot_type]['show_y_minor_ticks']:
+                axes.get_yaxis().set_minor_locator(AutoMinorLocator())
+            if self.plots[plot_type]['show_x_minor_ticks']:
+                axes.get_xaxis().set_minor_locator(AutoMinorLocator())
+            self._process_pyplot_kwargs(plot_type, dataset)
+
+            # Rasterization
+            if self.plots[plot_type]['rasterize']:
+                self._set_rasterized([axes])
+
+        # File paths
+        plot_path = self.get_plot_path(plot_type, dataset)
+        netcdf_path = get_diagnostic_filename(Path(plot_path).stem, self.cfg)
+        return (plot_path, {netcdf_path: cube})
+
+    def _process_pyplot_kwargs(self, plot_type, dataset):
+        """Process functions for :mod:`matplotlib.pyplot`."""
+        pyplot_kwargs = self.plots[plot_type]['pyplot_kwargs']
+        for (func, arg) in pyplot_kwargs.items():
+            if isinstance(arg, str):
+                arg = self._fill_facet_placeholders(
+                    arg,
+                    dataset,
+                    f"pyplot_kwargs of {plot_type} '{func}: {arg}'",
+                )
+            if arg is None:
+                getattr(plt, func)()
+            else:
+                getattr(plt, func)(arg)
+
+    @staticmethod
+    def _check_cube_dimensions(cube, plot_type):
+        """Check that cube has correct dimensional variables."""
+        expected_dimensions_dict = {
+            'annual_cycle': (['month_number'],),
+            'map': (['latitude', 'longitude'],),
+            'zonal_mean_profile': (['latitude', 'air_pressure'],
+                                   ['latitude', 'altitude']),
+            'timeseries': (['time'],),
+            '1d_profile': (['air_pressure'],
+                           ['altitude']),
+            'variable_vs_lat': (['latitude'],),
+            'hovmoeller_z_vs_time': (['time', 'air_pressure'],
+                                     ['time', 'altitude']),
+            'hovmoeller_time_vs_lat_or_lon': (['time', 'latitude'],
+                                              ['time', 'longitude']),
+        }
+        if plot_type not in expected_dimensions_dict:
+            raise NotImplementedError(f"plot_type '{plot_type}' not supported")
+        expected_dimensions = expected_dimensions_dict[plot_type]
+        for dims in expected_dimensions:
+            cube_dims = [cube.coords(dim, dim_coords=True) for dim in dims]
+            if all(cube_dims) and cube.ndim == len(dims):
+                return dims
+        expected_dims_str = ' or '.join(
+            [str(dims) for dims in expected_dimensions]
+        )
+        raise ValueError(
+            f"Expected cube that exactly has the dimensional coordinates "
+            f"{expected_dims_str}, got {cube.summary(shorten=True)}")
+
+    @staticmethod
     def _fill_facet_placeholders(string, dataset, description):
         """Fill facet placeholders."""
         try:
@@ -1170,21 +1847,21 @@ def _get_multi_dataset_facets(datasets):
                 multi_dataset_facets[key] = f'ambiguous_{key}'
         return multi_dataset_facets
 
-    @staticmethod
-    def _get_reference_dataset(datasets, short_name):
+    def _get_reference_dataset(self, datasets):
         """Extract reference dataset."""
+        variable = datasets[0][self.cfg['group_variables_by']]
         ref_datasets = [d for d in datasets if
                         d.get('reference_for_monitor_diags', False)]
         if len(ref_datasets) > 1:
             raise ValueError(
                 f"Expected at most 1 reference dataset (with "
                 f"'reference_for_monitor_diags: true' for variable "
-                f"'{short_name}', got {len(ref_datasets):d}")
+                f"'{variable}', got {len(ref_datasets):d}")
         if ref_datasets:
             return ref_datasets[0]
         return None
 
-    def create_timeseries_plot(self, datasets, short_name):
+    def create_timeseries_plot(self, datasets):
         """Create time series plot."""
         plot_type = 'timeseries'
         if plot_type not in self.plots:
@@ -1226,8 +1903,15 @@ def create_timeseries_plot(self, datasets, short_name):
         # Default plot appearance
         multi_dataset_facets = self._get_multi_dataset_facets(datasets)
         axes.set_title(multi_dataset_facets['long_name'])
-        axes.set_xlabel('Time')
-        axes.set_ylabel(f"{short_name} [{multi_dataset_facets['units']}]")
+        axes.set_xlabel('time')
+        # apply time formatting
+        if self.plots[plot_type]['time_format'] is not None:
+            axes.get_xaxis().set_major_formatter(
+                mdates.DateFormatter(self.plots[plot_type]['time_format']))
+        axes.set_ylabel(
+            f"{multi_dataset_facets[self.cfg['group_variables_by']]} "
+            f"[{multi_dataset_facets['units']}]"
+        )
         gridline_kwargs = self._get_gridline_kwargs(plot_type)
         if gridline_kwargs is not False:
             axes.grid(**gridline_kwargs)
@@ -1267,7 +1951,7 @@ def create_timeseries_plot(self, datasets, short_name):
             provenance_logger.log(plot_path, provenance_record)
             provenance_logger.log(netcdf_path, provenance_record)
 
-    def create_annual_cycle_plot(self, datasets, short_name):
+    def create_annual_cycle_plot(self, datasets):
         """Create annual cycle plot."""
         plot_type = 'annual_cycle'
         if plot_type not in self.plots:
@@ -1298,7 +1982,10 @@ def create_annual_cycle_plot(self, datasets, short_name):
         multi_dataset_facets = self._get_multi_dataset_facets(datasets)
         axes.set_title(multi_dataset_facets['long_name'])
         axes.set_xlabel('Month')
-        axes.set_ylabel(f"{short_name} [{multi_dataset_facets['units']}]")
+        axes.set_ylabel(
+            f"{multi_dataset_facets[self.cfg['group_variables_by']]} "
+            f"[{multi_dataset_facets['units']}]"
+        )
         axes.set_xticks(range(1, 13), [str(m) for m in range(1, 13)])
         gridline_kwargs = self._get_gridline_kwargs(plot_type)
         if gridline_kwargs is not False:
@@ -1339,7 +2026,7 @@ def create_annual_cycle_plot(self, datasets, short_name):
             provenance_logger.log(plot_path, provenance_record)
             provenance_logger.log(netcdf_path, provenance_record)
 
-    def create_map_plot(self, datasets, short_name):
+    def create_map_plot(self, datasets):
         """Create map plot."""
         plot_type = 'map'
         if plot_type not in self.plots:
@@ -1349,7 +2036,7 @@ def create_map_plot(self, datasets, short_name):
             raise ValueError(f"No input data to plot '{plot_type}' given")
 
         # Get reference dataset if possible
-        ref_dataset = self._get_reference_dataset(datasets, short_name)
+        ref_dataset = self._get_reference_dataset(datasets)
         if ref_dataset is None:
             logger.info("Plotting %s without reference dataset", plot_type)
         else:
@@ -1415,7 +2102,7 @@ def create_map_plot(self, datasets, short_name):
                 for netcdf_path in netcdf_paths:
                     provenance_logger.log(netcdf_path, provenance_record)
 
-    def create_zonal_mean_profile_plot(self, datasets, short_name):
+    def create_zonal_mean_profile_plot(self, datasets):
         """Create zonal mean profile plot."""
         plot_type = 'zonal_mean_profile'
         if plot_type not in self.plots:
@@ -1425,7 +2112,7 @@ def create_zonal_mean_profile_plot(self, datasets, short_name):
             raise ValueError(f"No input data to plot '{plot_type}' given")
 
         # Get reference dataset if possible
-        ref_dataset = self._get_reference_dataset(datasets, short_name)
+        ref_dataset = self._get_reference_dataset(datasets)
         if ref_dataset is None:
             logger.info("Plotting %s without reference dataset", plot_type)
         else:
@@ -1493,7 +2180,7 @@ def create_zonal_mean_profile_plot(self, datasets, short_name):
                 for netcdf_path in netcdf_paths:
                     provenance_logger.log(netcdf_path, provenance_record)
 
-    def create_1d_profile_plot(self, datasets, short_name):
+    def create_1d_profile_plot(self, datasets):
         """Create 1D profile plot."""
         plot_type = '1d_profile'
         if plot_type not in self.plots:
@@ -1525,7 +2212,10 @@ def create_1d_profile_plot(self, datasets, short_name):
 
         # Default plot appearance
         axes.set_title(multi_dataset_facets['long_name'])
-        axes.set_xlabel(f"{short_name} [{multi_dataset_facets['units']}]")
+        axes.set_xlabel(
+            f"{multi_dataset_facets[self.cfg['group_variables_by']]} "
+            f"[{multi_dataset_facets['units']}]"
+        )
         z_coord = cube.coord(axis='Z')
         axes.set_ylabel(f'{z_coord.long_name} [{z_coord.units}]')
 
@@ -1595,21 +2285,252 @@ def create_1d_profile_plot(self, datasets, short_name):
             provenance_logger.log(plot_path, provenance_record)
             provenance_logger.log(netcdf_path, provenance_record)
 
+    def create_variable_vs_lat_plot(self, datasets):
+        """Create Variable as a function of latitude."""
+        plot_type = 'variable_vs_lat'
+        if plot_type not in self.plots:
+            return
+        if not datasets:
+            raise ValueError(f"No input data to plot '{plot_type}' given")
+        logger.info("Plotting %s", plot_type)
+        fig = plt.figure(**self.cfg['figure_kwargs'])
+        axes = fig.add_subplot()
+
+        # Plot all datasets in one single figure
+        ancestors = []
+        cubes = {}
+        for dataset in datasets:
+            ancestors.append(dataset['filename'])
+            cube = dataset['cube']
+            cubes[self._get_label(dataset)] = cube
+            self._check_cube_dimensions(cube, plot_type)
+
+            # Plot data
+            plot_kwargs = self._get_plot_kwargs(plot_type, dataset)
+            plot_kwargs['axes'] = axes
+            iris.plot.plot(cube, **plot_kwargs)
+
+        # Default plot appearance
+        multi_dataset_facets = self._get_multi_dataset_facets(datasets)
+        axes.set_title(multi_dataset_facets['long_name'])
+        axes.set_xlabel('latitude [°N]')
+        axes.set_ylabel(
+            f"{multi_dataset_facets[self.cfg['group_variables_by']]} "
+            f"[{multi_dataset_facets['units']}]"
+        )
+        gridline_kwargs = self._get_gridline_kwargs(plot_type)
+        if gridline_kwargs is not False:
+            axes.grid(**gridline_kwargs)
+
+        # Legend
+        legend_kwargs = self.plots[plot_type]['legend_kwargs']
+        if legend_kwargs is not False:
+            axes.legend(**legend_kwargs)
+
+        # Customize plot appearance
+        self._process_pyplot_kwargs(plot_type, multi_dataset_facets)
+
+        # Save plot
+        plot_path = self.get_plot_path(plot_type, multi_dataset_facets)
+        fig.savefig(plot_path, **self.cfg['savefig_kwargs'])
+        logger.info("Wrote %s", plot_path)
+        plt.close()
+
+        # Save netCDF file
+        netcdf_path = get_diagnostic_filename(Path(plot_path).stem, self.cfg)
+        var_attrs = {
+            n: datasets[0][n] for n in ('short_name', 'long_name', 'units')
+        }
+        io.save_1d_data(cubes, netcdf_path, 'latitude', var_attrs)
+
+        # Provenance tracking
+        caption = (f"{multi_dataset_facets['long_name']} vs. latitude for "
+                   f"various datasets.")
+        provenance_record = {
+            'ancestors': ancestors,
+            'authors': ['sarauer_ellen'],
+            'caption': caption,
+            'plot_types': ['line'],
+            'long_names': [var_attrs['long_name']],
+        }
+        with ProvenanceLogger(self.cfg) as provenance_logger:
+            provenance_logger.log(plot_path, provenance_record)
+            provenance_logger.log(netcdf_path, provenance_record)
+
+    def create_hovmoeller_z_vs_time_plot(self, datasets):
+        """Create Hovmoeller Z vs. time plot."""
+        plot_type = 'hovmoeller_z_vs_time'
+        if plot_type not in self.plots:
+            return
+
+        if not datasets:
+            raise ValueError(f"No input data to plot '{plot_type}' given")
+
+        # Get reference dataset if possible
+        ref_dataset = self._get_reference_dataset(datasets)
+        if ref_dataset is None:
+            logger.info("Plotting %s without reference dataset", plot_type)
+        else:
+            logger.info("Plotting %s with reference dataset '%s'", plot_type,
+                        self._get_label(ref_dataset))
+
+        # Get plot function
+        plot_func = self._get_plot_func(plot_type)
+
+        # Create a single plot for each dataset (incl. reference dataset if
+        # given)
+        for dataset in datasets:
+            if dataset == ref_dataset:
+                continue
+            ancestors = [dataset['filename']]
+            if ref_dataset is None:
+                (plot_path,
+                 netcdf_paths) = (self._plot_hovmoeller_z_vs_time_without_ref(
+                     plot_func, dataset))
+                caption = (
+                    f"Hovmoeller Z vs. time plot of {dataset['long_name']} "
+                    f"of dataset "
+                    f"{dataset['dataset']} (project {dataset['project']}) "
+                    f"from {dataset['start_year']} to {dataset['end_year']}.")
+            else:
+                (plot_path,
+                 netcdf_paths) = (self._plot_hovmoeller_z_vs_time_with_ref(
+                     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']}) from "
+                    f"{dataset['start_year']} to {dataset['end_year']}.")
+                ancestors.append(ref_dataset['filename'])
+
+            # If statistics are shown add a brief description to the caption
+            if self.plots[plot_type]['show_stats']:
+                caption += (
+                    " The number in the top left corner corresponds to the "
+                    "spatiotemporal mean.")
+
+            # Save plot
+            plt.savefig(plot_path, **self.cfg['savefig_kwargs'])
+            logger.info("Wrote %s", plot_path)
+            plt.close()
+
+            # Save netCDFs
+            for (netcdf_path, cube) in netcdf_paths.items():
+                io.iris_save(cube, netcdf_path)
+
+            # Provenance tracking
+            provenance_record = {
+                'ancestors': ancestors,
+                'authors': ['kuehbacher_birgit', 'heuer_helge'],
+                'caption': caption,
+                'plot_types': ['vert'],
+                'long_names': [dataset['long_name']],
+            }
+            with ProvenanceLogger(self.cfg) as provenance_logger:
+                provenance_logger.log(plot_path, provenance_record)
+                for netcdf_path in netcdf_paths:
+                    provenance_logger.log(netcdf_path, provenance_record)
+
+    def create_hovmoeller_time_vs_lat_or_lon_plot(self, datasets):
+        """Create the Hovmoeller plot with time vs latitude or longitude."""
+        plot_type = 'hovmoeller_time_vs_lat_or_lon'
+        if plot_type not in self.plots:
+            return
+
+        if not datasets:
+            raise ValueError(f"No input data to plot '{plot_type}' given")
+
+        # Get reference dataset if possible
+        ref_dataset = self._get_reference_dataset(datasets)
+        if ref_dataset is None:
+            logger.info("Plotting %s without reference dataset", plot_type)
+        else:
+            logger.info("Plotting %s with reference dataset '%s'", plot_type,
+                        self._get_label(ref_dataset))
+
+        # Get plot function
+        plot_func = self._get_plot_func(plot_type)
+
+        # Create a single plot for each dataset (incl. reference dataset if
+        # given)
+        for dataset in datasets:
+            if dataset == ref_dataset:
+                continue
+            ancestors = [dataset['filename']]
+            if ref_dataset is None:
+                (plot_path, netcdf_paths) = (
+                    self._plot_hovmoeller_time_vs_lat_or_lon_without_ref(
+                        plot_func,
+                        dataset)
+                )
+                caption = (
+                    f"Hovmoeller plot of {dataset['long_name']} of dataset "
+                    f"{dataset['dataset']} (project {dataset['project']}) "
+                    f"from {dataset['start_year']} to {dataset['end_year']}."
+                )
+            else:
+                (plot_path, netcdf_paths) = (
+                    self._plot_hovmoeller_time_vs_lat_or_lon_with_ref(
+                        plot_func, dataset, ref_dataset)
+                )
+                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']}) from "
+                    f"{dataset['start_year']} to {dataset['end_year']}."
+                )
+                ancestors.append(ref_dataset['filename'])
+
+            # Save plot
+            plt.savefig(plot_path, **self.cfg['savefig_kwargs'])
+            logger.info("Wrote %s", plot_path)
+            plt.close()
+
+            # Save netCDFs
+            for (netcdf_path, cube) in netcdf_paths.items():
+                io.iris_save(cube, netcdf_path)
+
+            # Provenance tracking
+            provenance_record = {
+                'ancestors': ancestors,
+                'authors': ['schlund_manuel', 'kraft_jeremy', 'ruhe_lukas'],
+                'caption': caption,
+                'plot_types': ['zonal'],
+                'long_names': [dataset['long_name']],
+            }
+            with ProvenanceLogger(self.cfg) as provenance_logger:
+                provenance_logger.log(plot_path, provenance_record)
+                for netcdf_path in netcdf_paths:
+                    provenance_logger.log(netcdf_path, provenance_record)
+
     def compute(self):
         """Plot preprocessed data."""
-        for (short_name, datasets) in self.grouped_input_data.items():
-            logger.info("Processing variable %s", short_name)
-            self.create_timeseries_plot(datasets, short_name)
-            self.create_annual_cycle_plot(datasets, short_name)
-            self.create_map_plot(datasets, short_name)
-            self.create_zonal_mean_profile_plot(datasets, short_name)
-            self.create_1d_profile_plot(datasets, short_name)
+        for (var_key, datasets) in self.grouped_input_data.items():
+            logger.info("Processing variable %s", var_key)
+            self.create_timeseries_plot(datasets)
+            self.create_annual_cycle_plot(datasets)
+            self.create_map_plot(datasets)
+            self.create_zonal_mean_profile_plot(datasets)
+            self.create_1d_profile_plot(datasets)
+            self.create_variable_vs_lat_plot(datasets)
+            self.create_hovmoeller_z_vs_time_plot(datasets)
+            self.create_hovmoeller_time_vs_lat_or_lon_plot(datasets)
 
 
 def main():
     """Run diagnostic."""
     with run_diagnostic() as config:
-        MultiDatasets(config).compute()
+        with warnings.catch_warnings():
+            warnings.filterwarnings(
+                'ignore',
+                message="Using DEFAULT_SPHERICAL_EARTH_RADIUS",
+                category=UserWarning,
+                module='iris',
+            )
+            MultiDatasets(config).compute()
 
 
 if __name__ == '__main__':
diff --git a/esmvaltool/diag_scripts/perfmetrics/collect.ncl b/esmvaltool/diag_scripts/perfmetrics/collect.ncl
index 503a9c50d8..a2b14ab733 100644
--- a/esmvaltool/diag_scripts/perfmetrics/collect.ncl
+++ b/esmvaltool/diag_scripts/perfmetrics/collect.ncl
@@ -502,8 +502,9 @@ begin
   domains = (/"global"/)
 
   ; Call provenance logger
-  log_provenance(ncdf_outfile, plotpath, caption, statistics, domains, \
-                 plottype, authors, references, data_files)
+  log_provenance(ncdf_outfile, plotpath + "." + file_type, caption, \
+                 statistics, domains, plottype, authors, references, \
+                 data_files)
 
   leave_msg(DIAG_SCRIPT, "")
 
diff --git a/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig2.ncl b/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig2.ncl
index aa0db4d6a6..034d3a1287 100644
--- a/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig2.ncl
+++ b/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig2.ncl
@@ -158,7 +158,7 @@ begin
 
   ; Call provenance logger
   log_provenance(ncdf_outfile, \
-                 plotpath, \
+                 plotpath + "." + output_type(), \
                  "Russell et al 2018 figure 2", \
                  "mean", \
                  "sh", \
diff --git a/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig3b-2.ncl b/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig3b-2.ncl
index c182468947..c912663321 100644
--- a/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig3b-2.ncl
+++ b/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig3b-2.ncl
@@ -274,7 +274,7 @@ begin
     delete(last_index)
     delete(out_var)
     log_provenance(ncdf_outfile, \
-                   plotpath, \
+                   plotpath + "." + output_type(), \
                    "Russell et al 2018 figure 3b part 2", \
                    "mean", \
                    "sh",   \
diff --git a/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig3b.ncl b/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig3b.ncl
index 0b6b9d155f..fd4cf3f43b 100644
--- a/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig3b.ncl
+++ b/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig3b.ncl
@@ -264,7 +264,7 @@ begin
     delete(last_index)
     delete(out_var)
     log_provenance(ncdf_outfile, \
-                   plotpath, \
+                   plotpath + "." + output_type(), \
                    "Russell et al 2018 figure 3b", \
                    "mean", \
                    "sh", \
diff --git a/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig4.ncl b/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig4.ncl
index 10f94a62b2..4e62c7b552 100644
--- a/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig4.ncl
+++ b/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig4.ncl
@@ -368,7 +368,7 @@ begin
     delete(totaltransport)
     ; Call provenance logger
     log_provenance(ncdf_outfile, \
-                   plotpath, \
+                   plotpath + "." + file_type, \
                    "Russell et al 2018 figure 4", \
                    "mean", \
                    "sh", \
diff --git a/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig5.ncl b/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig5.ncl
index bddd26791b..5a60ea03d9 100644
--- a/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig5.ncl
+++ b/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig5.ncl
@@ -169,7 +169,7 @@ begin
     delete(dataset)
 
     log_provenance(ncdf_outfile, \
-                   plotpath, \
+                   plotpath + "." + output_type(), \
                    "Russell et al 2018 figure 5 -polar", \
                    "mean", \
                    "sh", \
diff --git a/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig5g.ncl b/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig5g.ncl
index 128fb25f43..67d8c53491 100644
--- a/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig5g.ncl
+++ b/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig5g.ncl
@@ -226,7 +226,7 @@ begin
 
     ; Call provenance logger
     log_provenance(ncdf_outfile, \
-                   plotpath, \
+                   plotpath + "." + output_type(), \
                    "Russell et al 2018 figure 5g", \
                    "mean", \
                    "sh",   \
diff --git a/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig6a.ncl b/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig6a.ncl
index 29bbe4ace7..206d2909cc 100644
--- a/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig6a.ncl
+++ b/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig6a.ncl
@@ -699,7 +699,7 @@ begin
     ncdf_outfile = ncdf_write(out_var, nc_filename)
 
     log_provenance(ncdf_outfile, \
-                   plotpath, \
+                   plotpath + "." + output_type(), \
                    "Russell et al 2018 figure 6 part a", \
                    "mean", \
                    "sh",   \
diff --git a/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig6b.ncl b/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig6b.ncl
index bbbf72c72d..2287b62adf 100644
--- a/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig6b.ncl
+++ b/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig6b.ncl
@@ -678,7 +678,7 @@ begin
     ncdf_outfile = ncdf_write(out_var, nc_filename)
 
     log_provenance(ncdf_outfile, \
-                   plotpath, \
+                   plotpath + "." + output_type(), \
                    "Russell et al 2018 figure 6b", \
                    "mean", \
                    "sh", \
diff --git a/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig7h.ncl b/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig7h.ncl
index 4a698f455e..249712d29f 100644
--- a/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig7h.ncl
+++ b/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig7h.ncl
@@ -164,7 +164,7 @@ begin
     delete(var_lon_avg)
     delete(dataset)
     log_provenance(ncdf_outfile, \
-                   plotpath, \
+                   plotpath + "." + output_type(), \
                    "Russell et al 2018 figure 7h", \
                    "mean", \
                    "sh", \
diff --git a/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig7i.ncl b/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig7i.ncl
index dd56bd4a41..7abcc9e7a9 100644
--- a/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig7i.ncl
+++ b/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig7i.ncl
@@ -202,7 +202,7 @@ begin
     end if
 
     log_provenance(ncdf_outfile, \
-                   plotpath, \
+                   plotpath + "." + output_type(), \
                    "Russell et al 2018 figure 7i", \
                    "mean", \
                    "sh", \
diff --git a/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig9a.ncl b/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig9a.ncl
index 99cf97b2ce..8bef8cb204 100644
--- a/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig9a.ncl
+++ b/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig9a.ncl
@@ -317,7 +317,7 @@ begin
     ncdf_outfile = ncdf_write(outvar, nc_filename)
 
     log_provenance(ncdf_outfile, \
-                   plotpath, \
+                   plotpath + "." + output_type(), \
                    "Russell et al 2018 figure 9a", \
                    "mean", \
                    "sh", \
diff --git a/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig9b.ncl b/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig9b.ncl
index bade43d9cf..8afd05425a 100644
--- a/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig9b.ncl
+++ b/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig9b.ncl
@@ -335,7 +335,7 @@ begin
     ncdf_outfile = ncdf_write(outvar, nc_filename)
 
     log_provenance(ncdf_outfile, \
-                   plotpath, \
+                   plotpath + "." + output_type(), \
                    "Russell et al 2018 figure 9b", \
                    "mean", \
                    "sh", \
diff --git a/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig9c.ncl b/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig9c.ncl
index e669bac579..0e5c828a6a 100644
--- a/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig9c.ncl
+++ b/esmvaltool/diag_scripts/russell18jgr/russell18jgr-fig9c.ncl
@@ -331,7 +331,7 @@ begin
     ncdf_outfile = ncdf_write(outvar, nc_filename)
 
     log_provenance(ncdf_outfile, \
-                   plotpath, \
+                   plotpath + "." + output_type(), \
                    "Russell et al 2018 figure 9c", \
                    "mean", \
                    "sh", \
diff --git a/esmvaltool/diag_scripts/russell18jgr/russell18jgr-polar.ncl b/esmvaltool/diag_scripts/russell18jgr/russell18jgr-polar.ncl
index e01771babf..b9c8f1faaa 100644
--- a/esmvaltool/diag_scripts/russell18jgr/russell18jgr-polar.ncl
+++ b/esmvaltool/diag_scripts/russell18jgr/russell18jgr-polar.ncl
@@ -232,7 +232,7 @@ begin
     ncdf_outfile = ncdf_write(dataset, nc_filename)
     delete(dataset)
     log_provenance(ncdf_outfile, \
-                   plotpath, \
+                   plotpath + "." + output_type(), \
                    "Russell et al 2018 polar plot " + var0, \
                    "mean", \
                    "sh", \
diff --git a/esmvaltool/diag_scripts/tebaldi21esd/calc_IAV_hatching.ncl b/esmvaltool/diag_scripts/tebaldi21esd/calc_IAV_hatching.ncl
index 8484a00871..f8f0d83511 100644
--- a/esmvaltool/diag_scripts/tebaldi21esd/calc_IAV_hatching.ncl
+++ b/esmvaltool/diag_scripts/tebaldi21esd/calc_IAV_hatching.ncl
@@ -399,7 +399,6 @@ begin
 
   ; collect meta-data
   nc_file = ncdf_outfile
-  plot_file = plot_dir + "/None"
   caption = "Inter-annual variability based on piControl runs."
   statistics = ("var")
   domains = ("global")
@@ -407,7 +406,7 @@ begin
   authors = (/"lorenz_ruth"/)
   references = (/"collins13ipcc"/)
   infiles = metadata_att_as_array(info_items, "filename")
-  log_provenance(nc_file, plot_file, caption, statistics, domains, \
+  log_provenance(nc_file, "n/a", caption, statistics, domains, \
                  plot_types, authors, references, infiles)
 
   leave_msg(DIAG_SCRIPT, "")
diff --git a/esmvaltool/diag_scripts/validation.py b/esmvaltool/diag_scripts/validation.py
index d72cc5e1ad..52c8d1a561 100644
--- a/esmvaltool/diag_scripts/validation.py
+++ b/esmvaltool/diag_scripts/validation.py
@@ -29,27 +29,11 @@
 
 def _get_provenance_record(cfg, plot_file, caption, loc):
     """Create a provenance record describing the diagnostic data and plot."""
-    all_input_files = [
-        k for k in cfg["input_data"].keys() if k.endswith(".nc")
-    ]
-    if "_vs_" in plot_file:
-        model_1 = plot_file.split("_vs_")[0].split("_")[-1]
-        if plot_file.endswith(".png"):
-            model_2 = plot_file.split("_vs_")[1].strip(".png")
-        elif plot_file.endswith(".nc"):
-            model_2 = plot_file.split("_vs_")[1].strip(".nc")
-        ancestor_1 = [
-            k for k in all_input_files if model_1 in os.path.basename(k)
-        ][0]
-        ancestor_2 = [
-            k for k in all_input_files if model_2 in os.path.basename(k)
-        ][0]
-        ancestor_files = [ancestor_1, ancestor_2]
-    else:
-        model = os.path.basename(plot_file).split("_")[0]
-        ancestor_files = [
-            k for k in all_input_files if model in os.path.basename(k)
-        ]
+    ancestor_files = []
+    for dataset in cfg['input_data'].values():
+        if (dataset['alias'] in plot_file and
+                dataset['short_name'] in plot_file):
+            ancestor_files.append(dataset['filename'])
     record = {
         'caption': caption,
         'statistics': ['mean'],
@@ -72,9 +56,9 @@ def _get_provenance_record(cfg, plot_file, caption, loc):
 def plot_contour(cube, cfg, plt_title, file_name):
     """Plot a contour with iris.quickplot (qplot)."""
     if len(cube.shape) == 2:
-        qplt.contourf(cube, cmap='RdYlBu_r', bbox_inches='tight')
+        qplt.contourf(cube, cmap='RdYlBu_r')
     else:
-        qplt.contourf(cube[0], cmap='RdYlBu_r', bbox_inches='tight')
+        qplt.contourf(cube[0], cmap='RdYlBu_r')
     plt.title(plt_title)
     plt.gca().coastlines()
     plt.tight_layout()
@@ -138,7 +122,10 @@ def plot_latlon_cubes(cube_1,
     # plot each cube
     var = data_names.split('_')[0]
     if not obs_name:
-        cube_names = [data_names.split('_')[1], data_names.split('_')[3]]
+        cube_names = [
+            data_names.replace(f'{var}_', '').split('_vs_')[i] for i in
+            range(2)
+        ]
         for cube, cube_name in zip(cubes, cube_names):
             if not season:
                 plot_file_path = os.path.join(
@@ -179,23 +166,40 @@ def plot_zonal_cubes(cube_1, cube_2, cfg, plot_data):
     # xcoordinate: latotude or longitude (str)
     data_names, xcoordinate, period = plot_data
     var = data_names.split('_')[0]
-    cube_names = [data_names.split('_')[1], data_names.split('_')[3]]
+    cube_names = data_names.replace(var + '_', '').split('_vs_')
     lat_points = cube_1.coord(xcoordinate).points
     plt.plot(lat_points, cube_1.data, label=cube_names[0])
     plt.plot(lat_points, cube_2.data, label=cube_names[1])
+    plt.title(f'Annual Climatology of {var}' if period == 'alltime'
+              else f'{period} of {var}')
     if xcoordinate == 'latitude':
-        plt.title(period + ' Zonal Mean for ' + var + ' ' + data_names)
+        axis = plt.gca()
+        axis.set_xticks([-60, -30, 0, 30, 60],
+                        labels=['60\N{DEGREE SIGN} S',
+                                '30\N{DEGREE SIGN} S',
+                                '0\N{DEGREE SIGN}',
+                                '30\N{DEGREE SIGN} N',
+                                '60\N{DEGREE SIGN} N'])
     elif xcoordinate == 'longitude':
-        plt.title(period + ' Meridional Mean for ' + var + ' ' + data_names)
+        axis = plt.gca()
+        axis.set_xticks([0, 60, 120, 180, 240, 300, 360],
+                        labels=['0\N{DEGREE SIGN} E',
+                                '60\N{DEGREE SIGN} E',
+                                '120\N{DEGREE SIGN} E',
+                                '180\N{DEGREE SIGN} E',
+                                '240\N{DEGREE SIGN} E',
+                                '300\N{DEGREE SIGN} E',
+                                '0\N{DEGREE SIGN} E'])
     plt.xlabel(xcoordinate + ' (deg)')
-    plt.ylabel(var)
+    plt.ylabel(f'{var} [{str(cube_1.units)}]')
     plt.tight_layout()
     plt.grid()
     plt.legend()
+    png_name = f'{xcoordinate}_{period}_{data_names}.png'
     if xcoordinate == 'latitude':
-        png_name = 'Zonal_Mean_' + xcoordinate + '_' + data_names + '.png'
+        png_name = 'Zonal_Mean_' + png_name
     elif xcoordinate == 'longitude':
-        png_name = 'Merid_Mean_' + xcoordinate + '_' + data_names + '.png'
+        png_name = 'Merid_Mean_' + png_name
     plot_file_path = os.path.join(cfg['plot_dir'], period, png_name)
     plt.savefig(plot_file_path)
     save_plotted_cubes(
@@ -252,13 +256,13 @@ def coordinate_collapse(data_set, cfg):
         if 'mask_threshold' in cfg:
             thr = cfg['mask_threshold']
             data_set.data = np.ma.masked_array(data_set.data,
-                                               mask=(mask_cube.data > thr))
+                                               mask=mask_cube.data > thr)
         else:
             logger.warning('Could not find masking threshold')
             logger.warning('Please specify it if needed')
             logger.warning('Masking on 0-values = True (masked value)')
             data_set.data = np.ma.masked_array(data_set.data,
-                                               mask=(mask_cube.data == 0))
+                                               mask=mask_cube.data == 0)
 
     # if zonal mean on LON
     if analysis_type == 'zonal_mean':
diff --git a/esmvaltool/diag_scripts/xco2_analysis/delta_T.ncl b/esmvaltool/diag_scripts/xco2_analysis/delta_T.ncl
index af7498d75b..f83f56cb31 100644
--- a/esmvaltool/diag_scripts/xco2_analysis/delta_T.ncl
+++ b/esmvaltool/diag_scripts/xco2_analysis/delta_T.ncl
@@ -332,7 +332,7 @@ begin
   ncdf_outfile = ncdf_write(delta_t_detr, outfile_netcdf_dtgr_detr)
 
   log_provenance(ncdf_outfile, \
-                 outfile_netcdf_dtgr_detr, \
+                 outfile_dtgr_detr + "." + file_type, \
                  "Sensitivity of interannual variability of " \
                  + var0 + " growth rate in the " + region + ", " + \
                  start_year + "-" + end_year + "to the interannual " \
diff --git a/esmvaltool/diag_scripts/xco2_analysis/global_maps.ncl b/esmvaltool/diag_scripts/xco2_analysis/global_maps.ncl
index 9aa205db32..d1748425d4 100644
--- a/esmvaltool/diag_scripts/xco2_analysis/global_maps.ncl
+++ b/esmvaltool/diag_scripts/xco2_analysis/global_maps.ncl
@@ -472,7 +472,7 @@ begin
 
   ; Provenance
   log_provenance(ncdf_outfile, \
-                 outfile, \
+                 outfile + "." + file_type, \
                  "Maps of mean Seasonal Cycle Amplitude of " + var0 + " " \
                  + DATASETS(imod) + " for " + start_year + "-" \
                  + end_year + ". Top: SCA with observational sampling (left)" \
@@ -587,7 +587,7 @@ begin
 
   ; Provenance
   log_provenance(ncdf_outfile, \
-                 outfile_scap, \
+                 outfile_scap + "." + file_type, \
                  "Maps of mean Seasonal Cycle Amplitude of " \
                  + var0 + " for " + start_year + "-" \
                  + end_year + ". Similar to Gier et al 2020, Fig 5.", \
diff --git a/esmvaltool/diag_scripts/xco2_analysis/main.ncl b/esmvaltool/diag_scripts/xco2_analysis/main.ncl
index 8f585e5f62..0739e52edb 100644
--- a/esmvaltool/diag_scripts/xco2_analysis/main.ncl
+++ b/esmvaltool/diag_scripts/xco2_analysis/main.ncl
@@ -438,7 +438,7 @@ begin
 
   ; Provenance
   log_provenance(ncdf_outfile, \
-                 outfile_ts_panels, \
+                 outfile_ts_panels + "." + file_type, \
                  "Timeseries, growth rate and seasonal cycle of " \
                  + var0 + " for " + region + ", " + start_year + "-" \
                  + end_year + ". Using masking: " + opt_mask \
@@ -550,7 +550,7 @@ begin
 
   ; Provenance
   log_provenance(ncdf_outfile, \
-                 outfile_grbp, \
+                 outfile_grbp + "." + file_type, \
                  "Growth Rate histogram of " \
                  + var0 + " for " + region + ", " + start_year + "-" \
                  + end_year + ". Using masking: " + opt_mask \
@@ -653,7 +653,7 @@ begin
 
   ; Provenance
   log_provenance(ncdf_outfile, \
-                 outfile_sabp, \
+                 outfile_sabp + "." + file_type, \
                  "Seasonal Cycle Amplitude histogram of " \
                  + var0 + " for " + region + ", " + start_year + "-" \
                  + end_year + ". Using masking: " + opt_mask, \
diff --git a/esmvaltool/diag_scripts/xco2_analysis/panel_plots.ncl b/esmvaltool/diag_scripts/xco2_analysis/panel_plots.ncl
index bcd4c82002..08fe30ad0f 100644
--- a/esmvaltool/diag_scripts/xco2_analysis/panel_plots.ncl
+++ b/esmvaltool/diag_scripts/xco2_analysis/panel_plots.ncl
@@ -360,7 +360,7 @@ begin
 
   ; Provenance
   log_provenance(ncdf_outfile, \
-                 outfile_scaplot, \
+                 outfile_scaplot + "." + file_type, \
                  "Trend of Seasonal Cycle Amplitude with " \
                  + var0 + " for " + region + ", " + start_year + "-" \
                  + end_year + ". Using masking: " + opt_mask \
@@ -391,7 +391,7 @@ begin
 
   ; Provenance
   log_provenance(ncdf_outfile, \
-                 outfile_grplot, \
+                 outfile_grplot + "." + file_type, \
                  "Trend of Growth Rate with " \
                  + var0 + " for " + region + ", " + start_year + "-" \
                  + end_year + ". Using masking: " + opt_mask \
diff --git a/esmvaltool/diag_scripts/xco2_analysis/sat_masks.ncl b/esmvaltool/diag_scripts/xco2_analysis/sat_masks.ncl
index c54df52d90..2bed5c74a6 100644
--- a/esmvaltool/diag_scripts/xco2_analysis/sat_masks.ncl
+++ b/esmvaltool/diag_scripts/xco2_analysis/sat_masks.ncl
@@ -258,7 +258,7 @@ begin
 
   ; Provenance
   log_provenance(ncdf_outfile, \
-                 outfile, \
+                 outfile + "." + file_type, \
                  var0 + "fractional data coverage " + start_year + "-" \
                  + end_year + ". Similar to Gier et al 2020, Fig 1.", \
                  (/"mean"/), \
@@ -377,7 +377,7 @@ begin
     delete(res)
 
     log_provenance(ncdf_outfile, \
-                   outfile_c3s, \
+                   outfile_c3s + "." + file_type, \
                    var0 + "fractional data coverage for different obs " \
                    + "periods. Similar to Gier et al 2020, Fig 8.", \
                    (/"mean"/), \
diff --git a/esmvaltool/diag_scripts/xco2_analysis/station_comparison.ncl b/esmvaltool/diag_scripts/xco2_analysis/station_comparison.ncl
index 53734d94db..23155e36fa 100644
--- a/esmvaltool/diag_scripts/xco2_analysis/station_comparison.ncl
+++ b/esmvaltool/diag_scripts/xco2_analysis/station_comparison.ncl
@@ -285,7 +285,7 @@ begin
 
     ; Provenance
     log_provenance(ncdf_outfile, \
-                   outfile_stations_masked, \
+                   outfile_stations_masked + "." + file_type, \
                    "Time series of station " + station_array&station(istat) \
                    + ", dotted denoting areas masked like observations.", \
                    (/""/), \
@@ -334,7 +334,7 @@ begin
 
     ; Provenance
     log_provenance(ncdf_outfile, \
-                   outfile_stations_obs, \
+                   outfile_stations_obs + "." + file_type, \
                    "Time series of station " + station_array&station(istat) \
                    + ", for " + var0 + " and " + var1 + ".", \
                    (/"mean"/), \
@@ -501,7 +501,7 @@ begin
 
         ; Provenance
     log_provenance(ncdf_outfile, \
-                   outfile_map, \
+                   outfile_map + "." + file_type, \
                    "Time series for satellite, multi-model mean and station " \
                    + var0 + " and " + var1 \
                    + ". Similar to Gier et al, 2020 Fig. 2", \
diff --git a/esmvaltool/interface_scripts/logging.ncl b/esmvaltool/interface_scripts/logging.ncl
index 3e1b10b0b2..4cb5ff1498 100644
--- a/esmvaltool/interface_scripts/logging.ncl
+++ b/esmvaltool/interface_scripts/logging.ncl
@@ -336,6 +336,12 @@ begin
   if (outfile .ne. "n/a") then
     suffix = get_file_suffix(outfile, 0)
 
+    if (ismissing(suffix)) then
+      error_msg("f", scriptname, funcname, "got invalid value for outfile " + \
+                "(path to figure): '" + outfile + "'; expected path to a " + \
+                "file or 'n/a'")
+    end if
+
     ; For PNGs, additionally check for existence of files like
     ; "plot_file.000001.png", "plot_file.000002.png", etc. and save
     ; provenance record for each of them
@@ -354,7 +360,10 @@ begin
           break
         end if
       end do
-    else
+    end if
+
+    ; For all other cases, use outfile
+    if (.not. isvar("all_outfiles")) then
       all_outfiles = outfile
     end if
 
@@ -362,6 +371,13 @@ begin
     original_entry = outstring
     do outfile_idx = 0, dimsizes(all_outfiles) - 1
       file_to_add := all_outfiles(outfile_idx)
+      if (.not. fileexists(outfile)) then
+        error_msg("f", scriptname, funcname, "outfile (path to figure) '" + \
+                  file_to_add + "' does not exist (for PNGs, this " + \
+                  "function also searches for 'FILE.000001.png', " + \
+                  "'FILE.000002.png', etc.); if no plot file is available " + \
+                  "use 'n/a'")
+      end if
       new_entry = (/original_entry/)
       new_entry(0) = "? " + file_to_add
       outstring := array_append_record(outstring, new_entry, 0)
diff --git a/esmvaltool/recipes/bock20jgr/recipe_bock20jgr_fig_1-4.yml b/esmvaltool/recipes/bock20jgr/recipe_bock20jgr_fig_1-4.yml
index aaca95d116..1ae9066ec3 100644
--- a/esmvaltool/recipes/bock20jgr/recipe_bock20jgr_fig_1-4.yml
+++ b/esmvaltool/recipes/bock20jgr/recipe_bock20jgr_fig_1-4.yml
@@ -400,11 +400,10 @@ diagnostics:
     variables:
       pr:
         <<: *var_cmip6_bias
-        reference_dataset: GPCP-SG
+        reference_dataset: GPCP-V2.3
         additional_datasets: *cmip6_tas
     additional_datasets:
-      - {dataset: GPCP-SG, project: obs4MIPs, level: L3, version: v2.3,
-         tier: 1}
+      - {dataset: GPCP-V2.3, project: obs4MIPs, tier: 1}
     scripts:
       model_bias:
         <<: *model_bias_settings
@@ -416,15 +415,14 @@ diagnostics:
     variables:
       pr:
         <<: *var_cmip6_bias
-        reference_dataset: GPCP-SG
+        reference_dataset: GPCP-V2.3
         project: CMIP5
         ensemble: r1i1p1
         start_year: 1985
         end_year: 2004
         additional_datasets: *cmip5_tas
     additional_datasets:
-      - {dataset: GPCP-SG, project: obs4MIPs, level: L3, version: v2.3,
-         tier: 1}
+      - {dataset: GPCP-V2.3, project: obs4MIPs, tier: 1}
     scripts:
       model_bias:
         <<: *model_bias_settings
@@ -436,7 +434,7 @@ diagnostics:
     variables:
       pr:
         <<: *var_cmip6_bias
-        reference_dataset: GPCP-SG
+        reference_dataset: GPCP-V2.3
         mip: A1
         project: CMIP3
         modeling_realm: atm
@@ -447,8 +445,7 @@ diagnostics:
         end_year: 1999
         additional_datasets: *cmip3_tas
     additional_datasets:
-      - {dataset: GPCP-SG, project: obs4MIPs, level: L3, version: v2.3,
-         tier: 1, mip: Amon}
+      - {dataset: GPCP-V2.3, project: obs4MIPs, tier: 1, mip: Amon}
     scripts:
       model_bias:
         <<: *model_bias_settings
@@ -460,11 +457,10 @@ diagnostics:
     variables:
       pr:
         <<: *var_cmip6_bias
-        reference_dataset: GPCP-SG
+        reference_dataset: GPCP-V2.3
         additional_datasets: *cmip6_highresmip_low
     additional_datasets:
-      - {dataset: GPCP-SG, project: obs4MIPs, level: L3, version: v2.3,
-         tier: 1}
+      - {dataset: GPCP-V2.3, project: obs4MIPs, tier: 1}
     scripts:
       model_bias:
         <<: *model_bias_settings
@@ -476,11 +472,10 @@ diagnostics:
     variables:
       pr:
         <<: *var_cmip6_bias
-        reference_dataset: GPCP-SG
+        reference_dataset: GPCP-V2.3
         additional_datasets: *cmip6_highresmip_high
     additional_datasets:
-      - {dataset: GPCP-SG, project: obs4MIPs, level: L3, version: v2.3,
-         tier: 1}
+      - {dataset: GPCP-V2.3, project: obs4MIPs, tier: 1}
     scripts:
       model_bias:
         <<: *model_bias_settings
diff --git a/esmvaltool/recipes/bock20jgr/recipe_bock20jgr_fig_6-7.yml b/esmvaltool/recipes/bock20jgr/recipe_bock20jgr_fig_6-7.yml
index c54ffc5f1e..93e4284ec5 100644
--- a/esmvaltool/recipes/bock20jgr/recipe_bock20jgr_fig_6-7.yml
+++ b/esmvaltool/recipes/bock20jgr/recipe_bock20jgr_fig_6-7.yml
@@ -359,7 +359,7 @@ diagnostics:
     variables:
       pr:
         preprocessor: ppNOLEV1
-        reference_dataset: GPCP-SG
+        reference_dataset: GPCP-V2.2
         alternative_dataset: GHCN
         mip: Amon
         project: CMIP5
@@ -393,8 +393,7 @@ diagnostics:
       - {dataset: FGOALS-f3-L, ensemble: r1i1p1f1, grid: gr, project: CMIP6}
       - {dataset: MCM-UA-1-0, grid: gn, ensemble: r1i1p1f1, project: CMIP6}
 
-      - {dataset: GPCP-SG, project: obs4MIPs, level: L3,
-         version: v2.2, tier: 1}
+      - {dataset: GPCP-V2.2, project: obs4MIPs, level: L3, tier: 1}
       - {dataset: GHCN, project: OBS, type: ground, version: 1, tier: 2}
     scripts:
       grading:
@@ -530,8 +529,7 @@ diagnostics:
       - {dataset: CIESM, grid: gr, ensemble: r1i1p1f1, project: CMIP6}
       - {dataset: FGOALS-f3-L, ensemble: r1i1p1f1, grid: gr, project: CMIP6}
 
-      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B,
-         version: Ed2-8, tier: 1, start_year: 2001, end_year: 2015}
+      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, tier: 1, start_year: 2001, end_year: 2015}
     scripts:
       grading:
         <<: *grading_settings
@@ -568,8 +566,7 @@ diagnostics:
       - {dataset: CanESM5-CanOE, ensemble: r1i1p2f1, grid: gn, project: CMIP6}
       - {dataset: FGOALS-f3-L, ensemble: r1i1p1f1, grid: gr, project: CMIP6}
 
-      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B,
-         version: Ed2-8, tier: 1, start_year: 2001, end_year: 2015}
+      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, tier: 1, start_year: 2001, end_year: 2015}
     scripts:
       grading:
         <<: *grading_settings
@@ -611,8 +608,7 @@ diagnostics:
       - {dataset: FGOALS-f3-L, ensemble: r1i1p1f1, grid: gr, project: CMIP6}
       - {dataset: MCM-UA-1-0, grid: gn, ensemble: r1i1p1f1, project: CMIP6}
 
-      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B,
-         version: Ed2-8, tier: 1, start_year: 2001, end_year: 2015}
+      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, tier: 1, start_year: 2001, end_year: 2015}
     scripts:
       grading:
         <<: *grading_settings
@@ -628,7 +624,7 @@ diagnostics:
     variables:
       hus:
         preprocessor: pp400
-        reference_dataset: AIRS
+        reference_dataset: AIRS-2-1
         alternative_dataset: ERA-Interim
         mip: Amon
         project: CMIP5
@@ -652,10 +648,8 @@ diagnostics:
       - {dataset: FGOALS-f3-L, ensemble: r1i1p1f1, grid: gr, project: CMIP6}
       - {dataset: MCM-UA-1-0, grid: gn, ensemble: r1i1p1f1, project: CMIP6}
 
-      - {dataset: AIRS, project: obs4MIPs, level: L3,
-         version: RetStd-v5, tier: 1, start_year: 2003, end_year: 2010}
-      - {dataset: ERA-Interim, project: OBS6, type: reanaly,
-         version: 1, tier: 3}
+      - {dataset: AIRS-2-1, project: obs4MIPs, level: L3, tier: 1, start_year: 2003, end_year: 2010}
+      - {dataset: ERA-Interim, project: OBS6, type: reanaly, version: 1, tier: 3}
     scripts:
       grading:
         <<: *grading_settings
@@ -694,8 +688,7 @@ diagnostics:
       - {dataset: CIESM, grid: gr, ensemble: r1i1p1f1, project: CMIP6}
       - {dataset: FGOALS-f3-L, ensemble: r1i1p1f1, grid: gr, project: CMIP6}
 
-      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B,
-         version: Ed2-8, tier: 1, start_year: 2001, end_year: 2015}
+      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, tier: 1, start_year: 2001, end_year: 2015}
     scripts:
       grading:
         <<: *grading_settings
@@ -744,8 +737,7 @@ diagnostics:
       - {dataset: FGOALS-f3-L, ensemble: r1i1p1f1, grid: gr, project: CMIP6}
       - {dataset: MCM-UA-1-0, grid: gn, ensemble: r1i1p1f1, project: CMIP6}
 
-      - {dataset: ERA-Interim, project: OBS6, type: reanaly,
-         version: 1, tier: 3}
+      - {dataset: ERA-Interim, project: OBS6, type: reanaly, version: 1, tier: 3}
       - {dataset: NCEP-NCAR-R1, project: OBS6, type: reanaly, version: 1, tier: 2}
     scripts:
       grading:
@@ -1085,7 +1077,7 @@ diagnostics:
     variables:
       pr:
         <<: *var_settings
-        reference_dataset: GPCP-SG
+        reference_dataset: GPCP-V2.2
         alternative_dataset: GHCN
     additional_datasets:
       - {<<: *cmip3, dataset: bccr_bcm2_0, institute: BCCR}
@@ -1113,7 +1105,7 @@ diagnostics:
       - {dataset: FGOALS-f3-L, ensemble: r1i1p1f1, grid: gr, project: CMIP6}
       - {dataset: MCM-UA-1-0, grid: gn, ensemble: r1i1p1f1, project: CMIP6}
 
-      - {dataset: GPCP-SG, project: obs4MIPs, level: L3, version: v2.2, tier: 1}
+      - {dataset: GPCP-V2.2, project: obs4MIPs, level: L3, tier: 1}
       - {dataset: GHCN, project: OBS, type: ground, version: 1, tier: 2}
 
   psl_cor:
@@ -1175,8 +1167,7 @@ diagnostics:
       - {dataset: FGOALS-f3-L, ensemble: r1i1p1f1, grid: gr, project: CMIP6}
       - {dataset: MCM-UA-1-0, grid: gn, ensemble: r1i1p1f1, project: CMIP6}
 
-      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, version: Ed2-8,
-         tier: 1, start_year: 2001, end_year: 2015}
+      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, tier: 1, start_year: 2001, end_year: 2015}
 
   swcre_cor:
     <<: *corr_diag
@@ -1199,8 +1190,7 @@ diagnostics:
       - {dataset: CanESM5-CanOE, ensemble: r1i1p2f1, grid: gn, project: CMIP6}
       - {dataset: FGOALS-f3-L, ensemble: r1i1p1f1, grid: gr, project: CMIP6}
 
-      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, version: Ed2-8,
-         tier: 1, start_year: 2001, end_year: 2015}
+      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, tier: 1, start_year: 2001, end_year: 2015}
 
   fig_7:
     title: Collection of pattern correlations
diff --git a/esmvaltool/recipes/bock20jgr/recipe_bock20jgr_fig_8-10.yml b/esmvaltool/recipes/bock20jgr/recipe_bock20jgr_fig_8-10.yml
index 652490ca09..a01020e709 100644
--- a/esmvaltool/recipes/bock20jgr/recipe_bock20jgr_fig_8-10.yml
+++ b/esmvaltool/recipes/bock20jgr/recipe_bock20jgr_fig_8-10.yml
@@ -415,8 +415,7 @@ diagnostics:
         derive: true
         additional_datasets: *cmip3_all
     additional_datasets:
-      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, version: Ed2-7,
-         start_year: 2001, end_year: 2010, tier: 1}
+      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, start_year: 2001, end_year: 2010, tier: 1}
     scripts:
       clim:
         script: clouds/clouds_ipcc.ncl
diff --git a/esmvaltool/recipes/clouds/recipe_clouds_bias.yml b/esmvaltool/recipes/clouds/recipe_clouds_bias.yml
index 0d28235431..d424397b22 100644
--- a/esmvaltool/recipes/clouds/recipe_clouds_bias.yml
+++ b/esmvaltool/recipes/clouds/recipe_clouds_bias.yml
@@ -171,11 +171,10 @@ diagnostics:
     variables:
       pr:
         preprocessor: clim
-        reference_dataset: GPCP-SG
+        reference_dataset: GPCP-V2.2
         mip: Amon
     additional_datasets:
-      - {dataset: GPCP-SG, project: obs4MIPs, level: L3, version: v2.2,
-         start_year: 1986, end_year: 2005, tier: 1}
+      - {dataset: GPCP-V2.2, project: obs4MIPs, level: L3, start_year: 1986, end_year: 2005, tier: 1}
     scripts:
       clim:
         <<: *clim_settings
@@ -198,11 +197,10 @@ diagnostics:
     variables:
       clt:
         preprocessor: clim
-        reference_dataset: MODIS
+        reference_dataset: MODIS-1-0
         mip: Amon
     additional_datasets:
-      - {dataset: MODIS, project: obs4MIPs, level: L3, version: C5,
-         start_year: 2001, end_year: 2010, tier: 1}
+      - {dataset: MODIS-1-0, project: obs4MIPs, level: L3, start_year: 2001, end_year: 2010, tier: 1}
     scripts:
       clim:
         <<: *clim_settings
diff --git a/esmvaltool/recipes/clouds/recipe_clouds_ipcc.yml b/esmvaltool/recipes/clouds/recipe_clouds_ipcc.yml
index 0df31e074e..2fb14b4795 100644
--- a/esmvaltool/recipes/clouds/recipe_clouds_ipcc.yml
+++ b/esmvaltool/recipes/clouds/recipe_clouds_ipcc.yml
@@ -145,8 +145,7 @@ diagnostics:
         mip: Amon
         derive: true
     additional_datasets:
-      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, version: Ed2-7,
-         start_year: 2001, end_year: 2010, tier: 1}
+      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, start_year: 2001, end_year: 2010, tier: 1}
     scripts:
       clim: &clim_settings
         script: clouds/clouds_ipcc.ncl
@@ -168,8 +167,7 @@ diagnostics:
         mip: Amon
         derive: true
     additional_datasets:
-      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, version: Ed2-7,
-         start_year: 2001, end_year: 2010, tier: 1}
+      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, start_year: 2001, end_year: 2010, tier: 1}
     scripts:
       clim:
         <<: *clim_settings
@@ -188,8 +186,7 @@ diagnostics:
         mip: Amon
         derive: true
     additional_datasets:
-      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, version: Ed2-7,
-         start_year: 2001, end_year: 2010, tier: 1}
+      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, start_year: 2001, end_year: 2010, tier: 1}
     scripts:
       clim:
         <<: *clim_settings
diff --git a/esmvaltool/recipes/clouds/recipe_lauer13jclim.yml b/esmvaltool/recipes/clouds/recipe_lauer13jclim.yml
index 2a7051b5d5..3d741e485d 100644
--- a/esmvaltool/recipes/clouds/recipe_lauer13jclim.yml
+++ b/esmvaltool/recipes/clouds/recipe_lauer13jclim.yml
@@ -168,11 +168,10 @@ diagnostics:
     variables:
       clt:
         preprocessor: clim
-        reference_dataset: MODIS
+        reference_dataset: MODIS-1-0
         mip: Amon
     additional_datasets:
-      - {dataset: MODIS, project: obs4MIPs, level: L3, version: C5,
-         start_year: 2001, end_year: 2010, tier: 1}
+      - {dataset: MODIS-1-0, project: obs4MIPs, level: L3, start_year: 2001, end_year: 2010, tier: 1}
     scripts:
       clim:
         <<: *clim_settings
@@ -187,11 +186,10 @@ diagnostics:
     variables:
       pr:
         preprocessor: clim
-        reference_dataset: GPCP-SG
+        reference_dataset: GPCP-V2.2
         mip: Amon
     additional_datasets:
-      - {dataset: GPCP-SG, project: obs4MIPs, level: L3, version: v2.2,
-         start_year: 1986, end_year: 2005, tier: 1}
+      - {dataset: GPCP-V2.2, project: obs4MIPs, level: L3, start_year: 1986, end_year: 2005, tier: 1}
     scripts:
       clim:
         <<: *clim_settings
@@ -211,8 +209,7 @@ diagnostics:
         mip: Amon
         derive: true
     additional_datasets:
-      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, version: Ed2-7,
-         start_year: 2001, end_year: 2010, tier: 1}
+      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, start_year: 2001, end_year: 2010, tier: 1}
     scripts:
       clim:
         <<: *clim_settings
@@ -232,8 +229,7 @@ diagnostics:
         mip: Amon
         derive: true
     additional_datasets:
-      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, version: Ed2-7,
-         start_year: 2001, end_year: 2010, tier: 1}
+      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, start_year: 2001, end_year: 2010, tier: 1}
     scripts:
       clim:
         <<: *clim_settings
@@ -253,11 +249,10 @@ diagnostics:
     variables:
       clt:
         preprocessor: clim
-        reference_dataset: MODIS
+        reference_dataset: MODIS-1-0
         mip: Amon
     additional_datasets:
-      - {dataset: MODIS, project: obs4MIPs, level: L3, version: C5,
-         start_year: 2001, end_year: 2010, tier: 1}
+      - {dataset: MODIS-1-0, project: obs4MIPs, level: L3, start_year: 2001, end_year: 2010, tier: 1}
     scripts:
       clim: &taylor_settings
         script: clouds/clouds_taylor.ncl
@@ -314,8 +309,7 @@ diagnostics:
         mip: Amon
         derive: true
     additional_datasets:
-      - {dataset: UWisc, project: OBS, type: sat, version: v2,
-         start_year: 1988, end_year: 2007, tier: 3}
+      - {dataset: UWisc, project: OBS, type: sat, version: v2, start_year: 1988, end_year: 2007, tier: 3}
     scripts:
       clim:
         <<: *taylor_settings
@@ -335,8 +329,7 @@ diagnostics:
         mip: Amon
         derive: true
     additional_datasets:
-      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, version: Ed2-7,
-         start_year: 2001, end_year: 2010, tier: 1}
+      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, start_year: 2001, end_year: 2010, tier: 1}
     scripts:
       clim:
         <<: *taylor_settings
@@ -356,8 +349,7 @@ diagnostics:
         mip: Amon
         derive: true
     additional_datasets:
-      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, version: Ed2-7,
-         start_year: 2001, end_year: 2010, tier: 1}
+      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, start_year: 2001, end_year: 2010, tier: 1}
     scripts:
       clim:
         <<: *taylor_settings
@@ -372,11 +364,10 @@ diagnostics:
     variables:
       pr:
         preprocessor: clim
-        reference_dataset: GPCP-SG
+        reference_dataset: GPCP-V2.2
         mip: Amon
     additional_datasets:
-      - {dataset: GPCP-SG, project: obs4MIPs, level: L3, version: v2.2,
-         start_year: 1986, end_year: 2005, tier: 1}
+      - {dataset: GPCP-V2.2, project: obs4MIPs, level: L3, start_year: 1986, end_year: 2005, tier: 1}
     scripts:
       clim:
         <<: *taylor_settings
@@ -402,8 +393,7 @@ diagnostics:
         mip: Amon
         derive: true
     additional_datasets:
-      - {dataset: UWisc, project: OBS, type: sat, version: v2,
-         start_year: 1988, end_year: 2007, tier: 3}
+      - {dataset: UWisc, project: OBS, type: sat, version: v2, start_year: 1988, end_year: 2007, tier: 3}
     scripts:
       clim: &intera_settings
         script: clouds/clouds_interannual.ncl
@@ -435,11 +425,10 @@ diagnostics:
     variables:
       clt:
         preprocessor: clim
-        reference_dataset: MODIS
+        reference_dataset: MODIS-1-0
         mip: Amon
     additional_datasets:
-      - {dataset: MODIS, project: obs4MIPs, level: L3, version: C5,
-         start_year: 2001, end_year: 2010, tier: 1}
+      - {dataset: MODIS-1-0, project: obs4MIPs, level: L3, start_year: 2001, end_year: 2010, tier: 1}
     scripts:
       clim:
         <<: *intera_settings
@@ -459,8 +448,7 @@ diagnostics:
         mip: Amon
         derive: true
     additional_datasets:
-      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, version: Ed2-7,
-         start_year: 2001, end_year: 2010, tier: 1}
+      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, start_year: 2001, end_year: 2010, tier: 1}
     scripts:
       clim:
         <<: *intera_settings
@@ -480,8 +468,7 @@ diagnostics:
         mip: Amon
         derive: true
     additional_datasets:
-      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, version: Ed2-7,
-         start_year: 2001, end_year: 2010, tier: 1}
+      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, start_year: 2001, end_year: 2010, tier: 1}
     scripts:
       clim:
         <<: *intera_settings
@@ -496,11 +483,10 @@ diagnostics:
     variables:
       pr:
         preprocessor: clim
-        reference_dataset: GPCP-SG
+        reference_dataset: GPCP-V2.2
         mip: Amon
     additional_datasets:
-      - {dataset: GPCP-SG, project: obs4MIPs, level: L3, version: v2.2,
-         start_year: 1986, end_year: 2005, tier: 1}
+      - {dataset: GPCP-V2.2, project: obs4MIPs, level: L3, start_year: 1986, end_year: 2005, tier: 1}
     scripts:
       clim:
         <<: *intera_settings
diff --git a/esmvaltool/recipes/examples/recipe_check_obs.yml b/esmvaltool/recipes/examples/recipe_check_obs.yml
index ec7101d555..6f791bcd24 100644
--- a/esmvaltool/recipes/examples/recipe_check_obs.yml
+++ b/esmvaltool/recipes/examples/recipe_check_obs.yml
@@ -576,6 +576,16 @@ diagnostics:
     scripts: null
 
 
+  MOBO-DIC2004-2019:
+    description: MOBO-DIC2004-2019 check
+    variables:
+      dissic:
+    additional_datasets:
+      - {dataset: MOBO-DIC2004-2019, project: OBS6, mip: Omon, tier: 2,
+         type: reanaly, version: '2.3', start_year: 2004, end_year: 2019}
+    scripts: null
+
+
   NCEP-NCAR-R1:
     description: NCEP-NCAR-R1 check
     variables:
@@ -670,6 +680,14 @@ diagnostics:
          type: reanaly, version: v2, start_year: 1871, end_year: 2012}
     scripts: null
 
+  NOAA-MBL-CH4:
+    description: NOAA marine boundary layer CH4 check
+    variables:
+      ch4s:
+    additional_datasets:
+      - {dataset: NOAA-MBL-CH4, project: OBS6, mip: Amon, type: atmos, version: 1.0, tier: 2,
+         start_year: 1983, end_year: 2023}
+    scripts: null
 
   NOAA-CIRES-20CR-V3:
     description: NOAA-CIRES-20CR-V3 check
@@ -1636,6 +1654,38 @@ diagnostics:
          type: sat, version: v1, start_year: 1988, end_year: 2016}
     scripts: null
 
+  MERRA:
+    description: MERRA check
+    variables:
+      cli:
+      clivi:
+      clt:
+      clw:
+      clwvi:
+      hur:
+      hus:
+      lwp:
+      pr:
+      prw:
+      ps:
+      psl:
+      rlut:
+      rlutcs:
+      rsdt:
+      rsut:
+      rsutcs:
+      ta:
+      tas:
+      ts:
+      ua:
+      va:
+      wap:
+      zg:
+    additional_datasets:
+      - {dataset: MERRA, project: OBS, tier: 3, mip: Amon,
+         type: reanaly, version: 5.2.0, start_year: 1979, end_year: 2015}
+    scripts: null
+
 
   MERRA2:
     description: MERRA2 check
diff --git a/esmvaltool/recipes/examples/recipe_easy_ipcc.yml b/esmvaltool/recipes/examples/recipe_easy_ipcc.yml
new file mode 100644
index 0000000000..40040e2835
--- /dev/null
+++ b/esmvaltool/recipes/examples/recipe_easy_ipcc.yml
@@ -0,0 +1,128 @@
+documentation:
+  title: Easy IPCC
+  description: Reproduce part of IPCC AR6 figure 9.3a.
+  references:
+    - fox-kemper21ipcc
+  authors:
+    - kalverla_peter
+    - andela_bouwe
+  maintainer:
+    - andela_bouwe
+
+preprocessors:
+  easy_ipcc:
+    custom_order: true
+    anomalies:
+      period: month
+      reference:
+        start_year: 1950
+        start_month: 1
+        start_day: 1
+        end_year: 1979
+        end_month: 12
+        end_day: 31
+    area_statistics:
+      operator: mean
+    annual_statistics:
+      operator: mean
+    convert_units:
+      units: 'degrees_C'
+    ensemble_statistics:
+      statistics:
+        - operator: mean
+    multi_model_statistics:
+      statistics:
+        - operator: mean
+        - operator: percentile
+          percent: 17
+        - operator: percentile
+          percent: 83
+      span: full
+      keep_input_datasets: false
+      ignore_scalar_coords: true
+
+diagnostics:
+  AR6_Figure_9.3:
+    variables:
+      tos_ssp585:
+        short_name: tos
+        exp: ['historical', 'ssp585']
+        project: CMIP6
+        mip: Omon
+        preprocessor: easy_ipcc
+        timerange: '1850/2100'
+      tos_ssp126:
+        short_name: tos
+        exp: ['historical', 'ssp126']
+        project: CMIP6
+        mip: Omon
+        timerange: '1850/2100'
+        preprocessor: easy_ipcc
+    scripts:
+      Figure_9.3a:
+        script: examples/make_plot.py
+
+datasets:
+  - {dataset: ACCESS-CM2, ensemble: 'r(1:5)i1p1f1', grid: gn}
+  - {dataset: ACCESS-ESM1-5, ensemble: 'r(1:40)i1p1f1', grid: gn}
+  - {dataset: AWI-CM-1-1-MR, ensemble: r1i1p1f1, grid: gn}
+  - {dataset: BCC-CSM2-MR, ensemble: r1i1p1f1, grid: gn}
+  # - {dataset: CAMS-CSM1-0, ensemble: 'r(1:2)i1p1f1', grid: gn} # available data does not fully cover timerange
+  - {dataset: CAS-ESM2-0, ensemble: r1i1p1f1, grid: gn}
+  - {dataset: CAS-ESM2-0, ensemble: r3i1p1f1, grid: gn}
+  - {dataset: CESM2, ensemble: r4i1p1f1, grid: gn}
+  - {dataset: CESM2, ensemble: 'r(10:11)i1p1f1', grid: gn}
+  - {dataset: CESM2-WACCM, ensemble: r1i1p1f1, grid: gn}
+  - {dataset: CIESM, ensemble: r1i1p1f1, grid: gn}
+  - {dataset: CMCC-CM2-SR5, ensemble: r1i1p1f1, grid: gn}
+  - {dataset: CMCC-ESM2, ensemble: r1i1p1f1, grid: gn}
+  - {dataset: CNRM-CM6-1, ensemble: 'r(1:6)i1p1f2', grid: gn}
+  - {dataset: CNRM-CM6-1-HR, ensemble: r1i1p1f2, grid: gn}
+  - {dataset: CNRM-ESM2-1, ensemble: 'r(1:5)i1p1f2', grid: gn}
+  - {dataset: CanESM5, ensemble: 'r(1:25)i1p(1:2)f1', grid: gn}
+  - {dataset: CanESM5-1, ensemble: 'r1i1p(1:2)f1', grid: gn, institute: CCCma}
+  - {dataset: CanESM5-CanOE, ensemble: 'r(1:3)i1p2f1', grid: gn}
+  - {dataset: EC-Earth3, ensemble: r1i1p1f1, grid: gn}
+  - {dataset: EC-Earth3, ensemble: r4i1p1f1, grid: gn}
+  - {dataset: EC-Earth3, ensemble: r6i1p1f1, grid: gn}
+  # - {dataset: EC-Earth3, ensemble: r9i1p1f1, grid: gn}  # download failure of ssp585
+  - {dataset: EC-Earth3, ensemble: r11i1p1f1, grid: gn}
+  - {dataset: EC-Earth3, ensemble: r15i1p1f1, grid: gn}
+  # - {dataset: EC-Earth3, ensemble: 'r(101:150)i1p1f1', grid: gn}  # available data does not fully cover timerange
+  - {dataset: EC-Earth3-Veg, ensemble: 'r(1:4)i1p1f1', grid: gn}
+  - {dataset: EC-Earth3-Veg, ensemble: r6i1p1f1, grid: gn}
+  # - {dataset: EC-Earth3-Veg-LR, ensemble: 'r(1:3)i1p1f1', grid: gn}  # mismatch between i and j coordinate names between historical and ssp experiment
+  - {dataset: FGOALS-f3-L, ensemble: 'r(1:3)i1p1f1', grid: gn}
+  - {dataset: FGOALS-g3, ensemble: 'r(1:4)i1p1f1', grid: gn}
+  - {dataset: FIO-ESM-2-0, ensemble: 'r(1:3)i1p1f1', grid: gn}
+  - {dataset: GFDL-ESM4, ensemble: r1i1p1f1, grid: gn}
+  - {dataset: GISS-E2-1-G, ensemble: 'r(1:4)i1p5f1', grid: gn, supplementary_variables: [{short_name: areacella, mip: fx, exp: piControl, ensemble: r1i1p5f1}, {short_name: areacello, skip: true}]}
+  - {dataset: GISS-E2-1-G, ensemble: 'r(1:5)i1p1f2', grid: gn, supplementary_variables: [{short_name: areacella, mip: fx, exp: piControl, ensemble: r1i1p1f1}, {short_name: areacello, skip: true}]}
+  - {dataset: GISS-E2-1-G, ensemble: 'r(1:5)i1p3f1', grid: gn, supplementary_variables: [{short_name: areacella, mip: fx, exp: piControl, ensemble: r1i1p3f1}, {short_name: areacello, skip: true}]}
+  - {dataset: GISS-E2-1-H, ensemble: 'r(1:5)i1p1f2', grid: gn, supplementary_variables: [{short_name: areacella, mip: fx, exp: piControl, ensemble: r1i1p1f1}, {short_name: areacello, skip: true}]}
+  - {dataset: GISS-E2-1-H, ensemble: 'r(1:5)i1p3f1', grid: gn, supplementary_variables: [{short_name: areacella, mip: fx, exp: piControl, ensemble: r1i1p3f1}, {short_name: areacello, skip: true}]}
+  - {dataset: GISS-E2-2-G, ensemble: 'r(1:5)i1p3f1', grid: gn, supplementary_variables: [{short_name: areacella, mip: fx, exp: piControl, ensemble: r1i1p1f1}, {short_name: areacello, skip: true}]}
+  - {dataset: HadGEM3-GC31-LL, ensemble: r1i1p1f3, grid: gn}
+  - {dataset: HadGEM3-GC31-MM, ensemble: r1i1p1f3, grid: gn}
+  # - {dataset: IITM-ESM, ensemble: r1i1p1f1, grid: gn}  # available data does not fully cover timerange
+  - {dataset: INM-CM4-8, ensemble: r1i1p1f1, grid: gr1}
+  - {dataset: INM-CM5-0, ensemble: r1i1p1f1, grid: gr1}
+  - {dataset: IPSL-CM6A-LR, ensemble: 'r(1:4)i1p1f1', grid: gn}
+  - {dataset: IPSL-CM6A-LR, ensemble: r6i1p1f1, grid: gn}
+  - {dataset: IPSL-CM6A-LR, ensemble: r14i1p1f1, grid: gn}
+  # - {dataset: KACE-1-0-G, ensemble: 'r(1:3)i1p1f1', grid: gr}  # unstructured grid but no cell area information available
+  # - {dataset: KIOST-ESM, ensemble: r1i1p1f1, grid: gr1}  # historical and ssp126 experiment are on different grids
+  - {dataset: MCM-UA-1-0, ensemble: r1i1p1f2, grid: gn}
+  - {dataset: MIROC-ES2H, ensemble: r1i1p4f2, grid: gn}
+  - {dataset: MIROC-ES2L, ensemble: 'r(1:10)i1p1f2', grid: gn}
+  - {dataset: MIROC6, ensemble: 'r(1:50)i1p1f1', grid: gn}
+  - {dataset: MPI-ESM1-2-HR, ensemble: 'r1i1p1f1', grid: gn}
+  # - {dataset: MPI-ESM1-2-HR, ensemble: 'r(1:2)i1p1f1', grid: gn}  # second ensemble member causes warnings about large graphs in `concatenate` preprocessor step
+  - {dataset: MPI-ESM1-2-LR, ensemble: 'r(1:30)i1p1f1', grid: gn}
+  - {dataset: MRI-ESM2-0, ensemble: 'r(1:5)i1p1f1', grid: gn}
+  # - {dataset: NESM3, ensemble: 'r(1:2)i1p1f1', grid: gn}  # cannot be used due to https://github.com/ESMValGroup/ESMValCore/issues/2101
+  # - {dataset: NorESM2-LM, ensemble: r1i1p1f1, grid: gn}  # duplicated areacello file with wrong name
+  - {dataset: NorESM2-MM, ensemble: r1i1p1f1, grid: gn}
+  - {dataset: TaiESM1, ensemble: r1i1p1f1, grid: gn}
+  - {dataset: UKESM1-0-LL, ensemble: 'r(1:4)i1p1f2', grid: gn}
+  - {dataset: UKESM1-0-LL, ensemble: r8i1p1f2, grid: gn}
diff --git a/esmvaltool/recipes/examples/recipe_preprocessor_test.yml b/esmvaltool/recipes/examples/recipe_preprocessor_test.yml
index df97678ac7..cce4e89df6 100644
--- a/esmvaltool/recipes/examples/recipe_preprocessor_test.yml
+++ b/esmvaltool/recipes/examples/recipe_preprocessor_test.yml
@@ -75,10 +75,16 @@ preprocessors:
     multi_model_statistics: false
 
 
-  # Simple ensemble mean and median for multiple models
+  # Simple ensemble mean, median, and percentiles for multiple models
   preprocessor_5:
     ensemble_statistics:
-      statistics: [mean, median]
+      statistics:
+        - mean
+        - median
+        - operator: percentile
+          percent: 5
+        - operator: percentile
+          percent: 95
       exclude: [GFDL-ESM2G]
 
   # Calculate ensemble means, then multi-model mean
diff --git a/esmvaltool/recipes/examples/recipe_python.yml b/esmvaltool/recipes/examples/recipe_python.yml
index a6309d826e..d85e1ae437 100644
--- a/esmvaltool/recipes/examples/recipe_python.yml
+++ b/esmvaltool/recipes/examples/recipe_python.yml
@@ -29,7 +29,7 @@ documentation:
 
 datasets:
   - {dataset: BCC-ESM1, project: CMIP6, exp: historical, ensemble: r1i1p1f1, grid: gn}
-  - {dataset: bcc-csm1-1, project: CMIP5, exp: historical, ensemble: r1i1p1}
+  - {dataset: bcc-csm1-1, version: v1, project: CMIP5, exp: historical, ensemble: r1i1p1}
 
 preprocessors:
   # See https://docs.esmvaltool.org/projects/esmvalcore/en/latest/recipe/preprocessor.html
diff --git a/esmvaltool/recipes/ipccwg1ar5ch9/recipe_flato13ipcc_figures_92_95.yml b/esmvaltool/recipes/ipccwg1ar5ch9/recipe_flato13ipcc_figures_92_95.yml
index 298b2bbc07..80286e716a 100644
--- a/esmvaltool/recipes/ipccwg1ar5ch9/recipe_flato13ipcc_figures_92_95.yml
+++ b/esmvaltool/recipes/ipccwg1ar5ch9/recipe_flato13ipcc_figures_92_95.yml
@@ -267,10 +267,10 @@ diagnostics:
     variables:
       pr:
         preprocessor: clim
-        reference_dataset: GPCP-SG
+        reference_dataset: GPCP-V2.2
         mip: Amon
     additional_datasets:
-      - {dataset: GPCP-SG, project: obs4MIPs, level: L3, version: v2.2,
+      - {dataset: GPCP-V2.2, project: obs4MIPs, level: L3,
          start_year: 1986, end_year: 2005, tier: 1}
       - {dataset: ACCESS1-0, project: CMIP5, exp: historical, ensemble: r1i1p1,
          start_year: 1986, end_year: 2005}
@@ -385,7 +385,7 @@ diagnostics:
         mip: Amon
         derive: true
     additional_datasets:
-      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, version: Ed2-7,
+      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B,
          start_year: 2001, end_year: 2010, tier: 1}
       - {dataset: ACCESS1-0, project: CMIP5, exp: historical, ensemble: r1i1p1,
          start_year: 1986, end_year: 2005}
diff --git a/esmvaltool/recipes/ipccwg1ar5ch9/recipe_flato13ipcc_figures_938_941_cmip6.yml b/esmvaltool/recipes/ipccwg1ar5ch9/recipe_flato13ipcc_figures_938_941_cmip6.yml
index dad979e8aa..e67625b257 100644
--- a/esmvaltool/recipes/ipccwg1ar5ch9/recipe_flato13ipcc_figures_938_941_cmip6.yml
+++ b/esmvaltool/recipes/ipccwg1ar5ch9/recipe_flato13ipcc_figures_938_941_cmip6.yml
@@ -322,13 +322,13 @@ diagnostics:
     variables:
       pr:
         preprocessor: clima_nomask_pr
-        reference_dataset: GPCP-SG
+        reference_dataset: GPCP-V2.2
         mip: Amon
         ensemble: r1i1p1
         start_year: 1980
         end_year: 2005
     additional_datasets:
-      - {dataset: GPCP-SG, project: OBS, project: obs4MIPs, level: L3, version: v2.2, tier: 1}
+      - {dataset: GPCP-V2.2, project: obs4MIPs, tier: 1}
       # - {dataset: CRU, project: OBS, type: reanaly, version: TS4.02, tier: 2}
     scripts:
       clim:
diff --git a/esmvaltool/recipes/model_evaluation/recipe_model_evaluation_basics.yml b/esmvaltool/recipes/model_evaluation/recipe_model_evaluation_basics.yml
new file mode 100644
index 0000000000..06f8ad3b12
--- /dev/null
+++ b/esmvaltool/recipes/model_evaluation/recipe_model_evaluation_basics.yml
@@ -0,0 +1,260 @@
+# ESMValTool
+---
+documentation:
+  title: Basic Model Evaluation.
+  description: >
+    Show plots of several variables that can be used for basic model
+    evaluations ("sanity checks").
+  authors:
+    - hassler_birgit
+    - lauer_axel
+    - bonnet_pauline
+    - schlund_manuel
+  maintainer:
+    - hassler_birgit
+
+
+# Note: The following models are just examples
+datasets:
+  - {project: CMIP6, dataset: MPI-ESM1-2-HR, exp: historical, ensemble: r1i1p1f1, grid: gn}
+  - {project: CMIP6, dataset: MPI-ESM1-2-LR, exp: historical, ensemble: r1i1p1f1, grid: gn}
+
+# Note: for some observational datasets, we use preset time ranges due to
+# their limited temporal availability
+timerange_for_models: &time_period
+  timerange: '2003/2007'  # can be specified, this is just an example
+
+
+preprocessors:
+
+  timeseries_regular: &pp_timeseries_regular
+    area_statistics:
+      operator: mean
+
+  timeseries_regular_ann:
+    <<: *pp_timeseries_regular
+    annual_statistics:
+      operator: mean
+
+  timeseries_regular_pr:
+    <<: *pp_timeseries_regular
+    convert_units:
+      units: mm day-1
+
+  full_climatology: &pp_full_climatology
+    climate_statistics:
+      period: full
+    regrid:
+      target_grid: 2x2
+      scheme:
+        reference: esmf_regrid.schemes:ESMFAreaWeighted
+
+  full_climatology_pr:
+    <<: *pp_full_climatology
+    convert_units:
+      units: mm day-1
+
+  zonal_mean:
+    custom_order: true  # makes preprocessor much faster since input for extract_levels is smaller
+    climate_statistics:
+      period: full
+    extract_levels:
+      levels: {cmor_table: CMIP6, coordinate: plev19}
+      scheme: linear
+      coordinate: air_pressure
+    regrid:
+      scheme:
+        reference: esmf_regrid.schemes:ESMFAreaWeighted
+      target_grid: 2x2
+    zonal_statistics:
+      operator: mean
+
+
+diagnostics:
+
+  # Climatologies - maps (full climatology)
+
+  plot_maps_with_references_tas:
+    description: Plot climatology maps including reference datasets for tas.
+    variables:
+      tas:
+        <<: *time_period
+        mip: Amon
+        preprocessor: full_climatology
+        additional_datasets:
+          - {project: native6, dataset: ERA5, type: reanaly, version: v1, tier: 3, reference_for_monitor_diags: true}
+    scripts:
+      plot: &plot_multi_dataset_default
+        plot_folder: '{plot_dir}'
+        plot_filename: '{plot_type}_{real_name}_{dataset}_{mip}'
+        script: monitor/multi_datasets.py
+        plots:
+          map:
+            common_cbar: true
+
+  plot_maps_with_references_pr:
+    description: Plot climatology maps including reference datasets for pr.
+    variables:
+      pr:
+        <<: *time_period
+        mip: Amon
+        preprocessor: full_climatology_pr
+        additional_datasets:
+          - {project: OBS, dataset: GPCP-SG, type: atmos, version: 2.3, tier: 2, reference_for_monitor_diags: true}
+    scripts:
+      plot:
+        <<: *plot_multi_dataset_default
+        script: monitor/multi_datasets.py
+        plots:
+          map:
+            common_cbar: true
+            plot_kwargs:
+              default:
+                cmap: Blues
+
+  # Climatologies (zonal means)
+
+  plot_zonal_mean_profiles_with_references_ta:
+    description: Plot 2D zonal mean profiles including reference datasets.
+    variables:
+      ta:
+        <<: *time_period
+        mip: Amon
+        preprocessor: zonal_mean
+        additional_datasets:
+          - {project: native6, dataset: ERA5, type: reanaly, version: v1, tier: 3, reference_for_monitor_diags: true}
+    scripts:
+      plot:
+        <<: *plot_multi_dataset_default
+        script: monitor/multi_datasets.py
+        plots:
+          zonal_mean_profile:
+            common_cbar: true
+
+  plot_zonal_mean_profiles_with_references_ua:
+    description: Plot 2D zonal mean profiles including reference datasets.
+    variables:
+      ua:
+        <<: *time_period
+        mip: Amon
+        preprocessor: zonal_mean
+        additional_datasets:
+          - {project: native6, dataset: ERA5, type: reanaly, version: v1, tier: 3, reference_for_monitor_diags: true}
+    scripts:
+      plot:
+        <<: *plot_multi_dataset_default
+        script: monitor/multi_datasets.py
+        plots:
+          zonal_mean_profile:
+            common_cbar: true
+            plot_kwargs:
+              default:
+                cmap: Blues
+
+  plot_zonal_mean_profiles_with_references_hus:
+    description: Plot 2D zonal mean profiles including reference datasets.
+    variables:
+      hus:
+        <<: *time_period
+        mip: Amon
+        preprocessor: zonal_mean
+        additional_datasets:
+          - {project: native6, dataset: ERA5, type: reanaly, version: v1, tier: 3, reference_for_monitor_diags: true}
+    scripts:
+      plot:
+        <<: *plot_multi_dataset_default
+        script: monitor/multi_datasets.py
+        plots:
+          zonal_mean_profile:
+            common_cbar: true
+            plot_kwargs:
+              default:
+                cmap: Blues
+
+  # Time series of global averages (monthly)
+
+  plot_multiple_timeseries:
+    description: Plot time series including reference datasets.
+    variables:
+      tas:
+        <<: *time_period
+        mip: Amon
+        preprocessor: timeseries_regular
+        additional_datasets:
+          - {project: native6, dataset: ERA5, type: reanaly, version: v1, tier: 3, reference_for_monitor_diags: true}
+      clt:
+        <<: *time_period
+        mip: Amon
+        preprocessor: timeseries_regular
+        additional_datasets:
+          - {project: OBS, dataset: ESACCI-CLOUD, type: sat, version: AVHRR-AMPM-fv3.0, tier: 2, reference_for_monitor_diags: true}
+      rsut:
+        <<: *time_period
+        mip: Amon
+        preprocessor: timeseries_regular
+        additional_datasets:
+          - {project: OBS, dataset: CERES-EBAF, type: sat, version: Ed4.1, tier: 2, reference_for_monitor_diags: true}
+      rlut:
+        <<: *time_period
+        mip: Amon
+        preprocessor: timeseries_regular
+        additional_datasets:
+          - {project: OBS, dataset: CERES-EBAF, type: sat, version: Ed4.1, tier: 2, reference_for_monitor_diags: true}
+      rtnt:
+        derive: true
+        force_derivation: true
+        mip: Amon
+        preprocessor: timeseries_regular_ann
+        timerange: '1995/2014'
+      prw:
+        <<: *time_period
+        mip: Amon
+        preprocessor: timeseries_regular
+        # timerange MUST NOT start before 2003 since the observations are not available before 2003
+        additional_datasets:
+          - {project: OBS, dataset: ESACCI-WATERVAPOUR, type: sat, version: CDR2-L3S-05deg_fv3.1, tier: 3, timerange: '2003/2007', reference_for_monitor_diags: true}
+    scripts:
+      plot:
+        <<: *plot_multi_dataset_default
+        group_variables_by: variable_group
+        script: monitor/multi_datasets.py
+        plots:
+          timeseries:
+            annual_mean_kwargs: false
+            plot_kwargs:
+              MPI-ESM1-2-HR:
+                color: C0
+              MPI-ESM1-2-LR:
+                color: C1
+              ERA5:
+                color: black
+              ESACCI-CLOUD:
+                color: black
+              CERES-EBAF:
+                color: black
+              ESACCI-WATERVAPOUR:
+                color: black
+
+  plot_multiple_timeseries_pr:
+    description: Plot time series including reference datasets.
+    variables:
+      pr:
+        <<: *time_period
+        mip: Amon
+        preprocessor: timeseries_regular_pr
+        additional_datasets:
+          - {project: OBS, dataset: GPCP-SG, type: atmos, version: 2.3, tier: 2, reference_for_monitor_diags: true}
+    scripts:
+      plot:
+        <<: *plot_multi_dataset_default
+        script: monitor/multi_datasets.py
+        plots:
+          timeseries:
+            annual_mean_kwargs: false
+            plot_kwargs:
+              MPI-ESM1-2-HR:
+                color: C0
+              MPI-ESM1-2-LR:
+                color: C1
+              GPCP-SG:
+                color: black
diff --git a/esmvaltool/recipes/model_evaluation/recipe_model_evaluation_clouds_clim.yml b/esmvaltool/recipes/model_evaluation/recipe_model_evaluation_clouds_clim.yml
new file mode 100644
index 0000000000..fd2d08781f
--- /dev/null
+++ b/esmvaltool/recipes/model_evaluation/recipe_model_evaluation_clouds_clim.yml
@@ -0,0 +1,226 @@
+# ESMValTool
+---
+documentation:
+  title: Model evaluation with focus on clouds.
+  description: >
+    Plot climatologies several cloud-related variables of multi-year
+    simulations.
+  authors:
+    - bonnet_pauline
+    - lauer_axel
+    - hassler_birgit
+    - schlund_manuel
+  maintainer:
+    - lauer_axel
+
+
+# Note: the following models are just examples
+datasets:
+  - {project: CMIP6, dataset: MPI-ESM1-2-HR, exp: historical, ensemble: r1i1p1f1, grid: gn}
+  - {project: CMIP6, dataset: MPI-ESM1-2-LR, exp: historical, ensemble: r1i1p1f1, grid: gn}
+
+# Note: for some observational datasets, we use preset time ranges due to
+# their limited temporal availability
+timerange_for_models: &time_period
+  timerange: '2005/2014'  # can be specified, this is just an example
+
+
+preprocessors:
+
+  full_climatology: &full_climatology_diag
+    climate_statistics:
+      period: full
+    regrid:
+      target_grid: 2x2
+      scheme:
+        reference: esmf_regrid.schemes:ESMFAreaWeighted
+
+  full_climatology_pr:
+    <<: *full_climatology_diag
+    convert_units:
+      units: mm day-1
+
+  zonal_mean:
+    custom_order: true  # makes preprocessor much faster since input for extract_levels is smaller
+    climate_statistics:
+      period: full
+    extract_levels:
+      levels: {cmor_table: CMIP6, coordinate: plev19}
+      scheme: linear
+      coordinate: air_pressure
+    regrid:
+      scheme:
+        reference: esmf_regrid.schemes:ESMFAreaWeighted
+      target_grid: 2x2
+    zonal_statistics:
+      operator: mean
+
+
+diagnostics:
+
+  plot_clt_maps:
+    description: Plot clt climatology maps including reference datasets.
+    variables:
+      clt:
+        <<: *time_period
+        mip: Amon
+        preprocessor: full_climatology
+        additional_datasets:
+          - {dataset: ESACCI-CLOUD, project: OBS, type: sat, version: AVHRR-AMPM-fv3.0, tier: 2, reference_for_monitor_diags: true}
+    scripts:
+      plot: &plot_multi_dataset_default
+        script: monitor/multi_datasets.py
+        plot_folder: '{plot_dir}'
+        plot_filename: '{plot_type}_{real_name}_{dataset}_{mip}'
+        plots:
+          map:
+            common_cbar: true
+
+  plot_lwcre_maps:
+    description: Plot lwcre climatology maps including reference datasets.
+    variables:
+      lwcre:
+        <<: *time_period
+        mip: Amon
+        preprocessor: full_climatology
+        derive: true
+        additional_datasets:
+          - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, timerange: '2001/2010', tier: 1, reference_for_monitor_diags: true}
+    scripts:
+      plot:
+        <<: *plot_multi_dataset_default
+        plots:
+          map:
+            common_cbar: true
+
+  plot_swcre_maps:
+    description: Plot swcre climatology maps including reference datasets.
+    variables:
+      swcre:
+        <<: *time_period
+        mip: Amon
+        preprocessor: full_climatology
+        derive: true
+        additional_datasets:
+          - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, tier: 1, timerange: '2001/2010', reference_for_monitor_diags: true}
+    scripts:
+      plot:
+        <<: *plot_multi_dataset_default
+        plots:
+          map:
+            common_cbar: true
+
+  plot_lwp_maps:
+    description: Plot lwp climatology maps including reference datasets.
+    variables:
+      lwp:
+        <<: *time_period
+        mip: Amon
+        preprocessor: full_climatology
+        derive: true
+        additional_datasets:
+          - {project: native6, dataset: ERA5, type: reanaly, version: v1, tier: 3, reference_for_monitor_diags: true}
+    scripts:
+      plot:
+        <<: *plot_multi_dataset_default
+        plots:
+          map:
+            common_cbar: true
+            fontsize: 6
+
+  plot_clivi_maps:
+    description: Plot clivi climatology maps including reference datasets.
+    variables:
+      clivi:
+        <<: *time_period
+        mip: Amon
+        preprocessor: full_climatology
+        additional_datasets:
+          - {project: native6, dataset: ERA5, type: reanaly, version: v1, tier: 3, reference_for_monitor_diags: true}
+    scripts:
+      plot:
+        <<: *plot_multi_dataset_default
+        plots:
+          map:
+            common_cbar: true
+
+  plot_prw_maps:
+    description: Plot prw climatology maps including reference datasets.
+    variables:
+      prw:
+        <<: *time_period
+        mip: Amon
+        preprocessor: full_climatology
+        additional_datasets:
+          - {dataset: ESACCI-WATERVAPOUR, project: OBS, type: sat, version: CDR2-L3S-05deg_fv3.1, tier: 3, timerange: '2003/2017', reference_for_monitor_diags: true}
+    scripts:
+      plot:
+        <<: *plot_multi_dataset_default
+        plots:
+          map:
+            common_cbar: true
+
+  plot_pr_maps:
+    description: Plot prw climatology maps including reference datasets.
+    variables:
+      pr:
+        <<: *time_period
+        mip: Amon
+        preprocessor: full_climatology_pr
+        additional_datasets:
+          - {dataset: GPCP-SG, project: OBS, type: atmos, version: 2.3, tier: 2,
+             timerange: '2003/2017', reference_for_monitor_diags: true}
+    scripts:
+      plot:
+        <<: *plot_multi_dataset_default
+        plots:
+          map:
+            common_cbar: true
+
+  plot_clw_profiles:
+    description: Plot clw vertical profiles including reference datasets.
+    variables:
+      clw:
+        <<: *time_period
+        mip: Amon
+        preprocessor: zonal_mean
+        additional_datasets:
+          - {dataset: CLOUDSAT-L2, project: OBS, type: sat, version: P1-R05-gridbox-average-noprecip, timerange: '2006/2017', tier: 3, reference_for_monitor_diags: true}
+    scripts:
+      plot:
+        <<: *plot_multi_dataset_default
+        plots:
+          profile:
+            common_cbar: true
+
+  plot_cli_profiles:
+    description: Plot cli vertical profiles including reference datasets.
+    variables:
+      cli:
+        <<: *time_period
+        mip: Amon
+        preprocessor: zonal_mean
+        additional_datasets:
+          - {dataset: CALIPSO-ICECLOUD, project: OBS, type: sat, version: 1-00, timerange: '2007/2015', tier: 3, reference_for_monitor_diags: true}
+    scripts:
+      plot:
+        <<: *plot_multi_dataset_default
+        plots:
+          profile:
+            common_cbar: true
+
+  plot_cl_profiles:
+    description: Plot cl vertical profiles including reference datasets.
+    variables:
+      cl:
+        <<: *time_period
+        mip: Amon
+        preprocessor: zonal_mean
+        additional_datasets:
+          - {project: native6, dataset: ERA5, type: reanaly, version: v1, tier: 3, reference_for_monitor_diags: true}
+    scripts:
+      plot:
+        <<: *plot_multi_dataset_default
+        plots:
+          profile:
+            common_cbar: true
diff --git a/esmvaltool/recipes/model_evaluation/recipe_model_evaluation_clouds_cycles.yml b/esmvaltool/recipes/model_evaluation/recipe_model_evaluation_clouds_cycles.yml
new file mode 100644
index 0000000000..ed52fd7d3c
--- /dev/null
+++ b/esmvaltool/recipes/model_evaluation/recipe_model_evaluation_clouds_cycles.yml
@@ -0,0 +1,178 @@
+# ESMValTool
+---
+documentation:
+  title: Model evaluation with focus on clouds.
+  description: >
+    Plot annual cycles of several cloud-related variables of multi-year simulations.
+  authors:
+    - lauer_axel
+    - schlund_manuel
+  maintainer:
+    - lauer_axel
+
+
+# Note: the following models are just examples
+datasets:
+  - {project: CMIP6, dataset: MPI-ESM1-2-HR, exp: historical, ensemble: r1i1p1f1, grid: gn}
+  - {project: CMIP6, dataset: MPI-ESM1-2-LR, exp: historical, ensemble: r1i1p1f1, grid: gn}
+
+# Note: for some observational datasets, we use preset time ranges due to
+# their limited temporal availability
+timerange_for_models: &time_period
+  timerange: '2000/2014'  # can be specified, this is just an example
+
+
+preprocessors:
+
+  pp_global: &global_settings
+    area_statistics:
+      operator: mean
+    climate_statistics:
+      period: month
+
+  pp_SEPacific:
+    <<: *global_settings
+    extract_region:
+      start_longitude: 265
+      end_longitude: 275
+      start_latitude: -25
+      end_latitude: -5
+    mask_landsea:
+      mask_out: land
+
+  pp_SouthernOcean:
+    <<: *global_settings
+    extract_region:
+      start_longitude: 0
+      end_longitude: 360
+      start_latitude: -65
+      end_latitude: -30
+    mask_landsea:
+      mask_out: land
+
+  pp_StormTracks:
+    <<: *global_settings
+    extract_region:
+      start_longitude: 0
+      end_longitude: 360
+      start_latitude: 45
+      end_latitude: 60
+
+
+diagnostics:
+
+  anncyc:
+    description: Plot annual cycles including reference datasets.
+    variables:
+      clt_global: &clt_settings
+        <<: *time_period
+        preprocessor: pp_global
+        short_name: clt
+        mip: Amon
+        additional_datasets:
+          - {dataset: ESACCI-CLOUD, project: OBS, type: sat, version: AVHRR-AMPM-fv3.0, tier: 2}
+      clt_tropics:
+        <<: *clt_settings
+      clt_sepacific:
+        <<: *clt_settings
+        preprocessor: pp_SEPacific
+      clt_southerocean:
+        <<: *clt_settings
+        preprocessor: pp_SouthernOcean
+      clt_stormtracks:
+        <<: *clt_settings
+        preprocessor: pp_StormTracks
+      clivi_global: &clivi_settings
+        <<: *time_period
+        preprocessor: pp_global
+        short_name: clivi
+        mip: Amon
+        additional_datasets:
+          - {dataset: ESACCI-CLOUD, project: OBS, type: sat, version: AVHRR-AMPM-fv3.0, tier: 2}
+      clivi_tropics:
+        <<: *clivi_settings
+      clivi_sepacific:
+        <<: *clivi_settings
+        preprocessor: pp_SEPacific
+      clivi_southerocean:
+        <<: *clivi_settings
+        preprocessor: pp_SouthernOcean
+      clivi_stormtracks:
+        <<: *clivi_settings
+        preprocessor: pp_StormTracks
+      lwp_global: &lwp_settings
+        <<: *time_period
+        preprocessor: pp_global
+        short_name: lwp
+        derive: true
+        mip: Amon
+        additional_datasets:
+          - {dataset: ESACCI-CLOUD, project: OBS, type: sat, version: AVHRR-AMPM-fv3.0, tier: 2}
+      lwp_tropics:
+        <<: *lwp_settings
+      lwp_sepacific:
+        <<: *lwp_settings
+        preprocessor: pp_SEPacific
+      lwp_southerocean:
+        <<: *lwp_settings
+        preprocessor: pp_SouthernOcean
+      lwp_stormtracks:
+        <<: *lwp_settings
+        preprocessor: pp_StormTracks
+      swcre_global: &swcre_settings
+        <<: *time_period
+        preprocessor: pp_global
+        short_name: swcre
+        derive: true
+        mip: Amon
+        additional_datasets:
+          - {dataset: CERES-EBAF, project: OBS, type: sat, version: Ed4.1, tier: 2}
+      swcre_tropics:
+        <<: *swcre_settings
+      swcre_sepacific:
+        <<: *swcre_settings
+        preprocessor: pp_SEPacific
+      swcre_southerocean:
+        <<: *swcre_settings
+        preprocessor: pp_SouthernOcean
+      swcre_stormtracks:
+        <<: *swcre_settings
+        preprocessor: pp_StormTracks
+      lwcre_global: &lwcre_settings
+        <<: *time_period
+        preprocessor: pp_global
+        short_name: lwcre
+        derive: true
+        mip: Amon
+        additional_datasets:
+          - {dataset: CERES-EBAF, project: OBS, type: sat, version: Ed4.1, tier: 2}
+      lwcre_tropics:
+        <<: *lwcre_settings
+      lwcre_sepacific:
+        <<: *lwcre_settings
+        preprocessor: pp_SEPacific
+      lwcre_southerocean:
+        <<: *lwcre_settings
+        preprocessor: pp_SouthernOcean
+      lwcre_stormtracks:
+        <<: *lwcre_settings
+        preprocessor: pp_StormTracks
+    scripts:
+      allplots:
+        script: monitor/multi_datasets.py
+        plot_folder: '{plot_dir}'
+        plot_filename: '{plot_type}_{real_name}_{mip}'
+        group_variables_by: variable_group
+        plots:
+          annual_cycle:
+            legend_kwargs:
+              loc: upper right
+            plot_kwargs:
+              MPI-ESM1-2-HR:
+                color: C0
+              MPI-ESM1-2-LR:
+                color: C1
+              ESACCI-CLOUD:
+                color: black
+            pyplot_kwargs:
+              title: '{short_name}'
diff --git a/esmvaltool/recipes/model_evaluation/recipe_model_evaluation_precip_zonal.yml b/esmvaltool/recipes/model_evaluation/recipe_model_evaluation_precip_zonal.yml
new file mode 100644
index 0000000000..6bd1231046
--- /dev/null
+++ b/esmvaltool/recipes/model_evaluation/recipe_model_evaluation_precip_zonal.yml
@@ -0,0 +1,72 @@
+# ESMValTool
+---
+documentation:
+  title: Model evaluation with focus on precipitation.
+  description: >
+    Plot zonal mean plots of precipitation.
+  authors:
+    - lauer_axel
+    - schlund_manuel
+  maintainer:
+    - lauer_axel
+
+
+# Note: the following models are just examples
+datasets:
+  - {project: CMIP6, dataset: MPI-ESM1-2-HR, exp: historical, ensemble: r1i1p1f1, grid: gn}
+  - {project: CMIP6, dataset: MPI-ESM1-2-LR, exp: historical, ensemble: r1i1p1f1, grid: gn}
+
+# Note: for some observational datasets, we use preset time ranges due to
+# their limited temporal availability
+timerange_for_models: &time_period
+  timerange: '2000/2014'  # can be specified, this is just an example
+
+
+preprocessors:
+
+  pp_zonal:
+    regrid:
+      target_grid: 2x2
+      scheme:
+        reference: esmf_regrid.schemes:ESMFAreaWeighted
+    zonal_statistics:
+      operator: mean
+    climate_statistics:
+      operator: mean
+      period: full
+    convert_units:
+      units: mm day-1
+
+
+diagnostics:
+
+  zonal:
+    description: Plot annual cycles including reference datasets.
+    variables:
+      pr:
+        <<: *time_period
+        preprocessor: pp_zonal
+        mip: Amon
+        additional_datasets:
+          - {dataset: ERA5, project: native6, type: reanaly, version: 'v1', tier: 3}
+          - {dataset: GPCP-SG, project: obs4MIPs, level: L3, version: v2.3, tier: 1}
+    scripts:
+      allplots:
+        script: monitor/multi_datasets.py
+        plot_folder: '{plot_dir}'
+        plot_filename: '{plot_type}_{real_name}_{mip}'
+        group_variables_by: variable_group
+        plots:
+          variable_vs_lat:
+            legend_kwargs:
+              loc: upper right
+            plot_kwargs:
+              MPI-ESM1-2-HR:
+                color: C0
+              MPI-ESM1-2-LR:
+                color: C1
+              ERA5:
+                color: black
+                linestyle: dotted
+              GPCP-SG:
+                color: black
diff --git a/esmvaltool/recipes/monitor/recipe_monitor_with_refs.yml b/esmvaltool/recipes/monitor/recipe_monitor_with_refs.yml
index 0d1415979a..681277310c 100644
--- a/esmvaltool/recipes/monitor/recipe_monitor_with_refs.yml
+++ b/esmvaltool/recipes/monitor/recipe_monitor_with_refs.yml
@@ -1,12 +1,17 @@
 # ESMValTool
 ---
 documentation:
-  title: Model Monitoring
+  title: Example recipe for model monitoring with reference datasets.
   description: |
     Show plots that include multiple datasets that can be used to monitor
     (ongoing) model simulations.
   authors:
     - schlund_manuel
+    - heuer_helge
+    - kraft_jeremy
+    - kuehbacher_birgit
+    - ruhe_lukas
+    - sarauer_ellen
     - winterstein_franziska
   maintainer:
     - schlund_manuel
@@ -14,8 +19,8 @@ documentation:
 
 datasets:
   # Note: plot_label currently only used by diagnostic plot_multiple_annual_cycles
-  - {project: CMIP6, dataset: EC-Earth3, exp: historical, ensemble: r1i1p1f1, grid: gr, plot_label: 'EC-Earth3 historical'}
-  - {project: CMIP6, dataset: CanESM5,   exp: historical, ensemble: r1i1p1f1, grid: gn, plot_label: 'Reference (CanESM5 historical)', reference_for_monitor_diags: true}
+  - {project: CMIP6, dataset: MPI-ESM1-2-HR, exp: historical, ensemble: r1i1p1f1, grid: gn, plot_label: 'MPI-ESM1-2-HR historical'}
+  - {project: CMIP6, dataset: MPI-ESM1-2-LR, exp: historical, ensemble: r1i1p1f1, grid: gn, plot_label: 'Reference (MPI-ESM1-2-LR historical)', reference_for_monitor_diags: true}
 
 
 preprocessors:
@@ -67,6 +72,36 @@ preprocessors:
       scheme: linear
       coordinate: air_pressure
 
+  var_vs_lat:
+    climate_statistics:
+      operator: mean
+    regrid:
+      target_grid: 2x2
+      scheme: linear
+    zonal_statistics:
+      operator: mean
+    convert_units:
+      units: mm day-1
+
+  global_mean_extract_levels:
+    custom_order: true
+    extract_levels:
+      levels: {cmor_table: CMIP6, coordinate: alt16}
+      scheme: linear
+      coordinate: altitude
+    regrid:
+      target_grid: 2x2
+      scheme: linear
+    area_statistics:
+      operator: mean
+
+  zonal_mean_2d:
+    regrid:
+      target_grid: 2x2
+      scheme: linear
+    zonal_statistics:
+      operator: mean
+
 
 diagnostics:
 
@@ -87,9 +122,9 @@ diagnostics:
             annual_mean_kwargs:
               linestyle: '--'
             plot_kwargs:
-              EC-Earth3:  # = dataset since 'facet_used_for_labels' is 'dataset' by default
+              MPI-ESM1-2-HR:  # = dataset since 'facet_used_for_labels' is 'dataset' by default
                 color: C0
-              CanESM5:
+              MPI-ESM1-2-LR:
                 color: black
 
   plot_multiple_annual_cycles:
@@ -108,9 +143,9 @@ diagnostics:
             legend_kwargs:
               loc: upper right
             plot_kwargs:
-              'EC-Earth3 historical':  # = plot_label since 'facet_used_for_labels: plot_label'
+              'MPI-ESM1-2-HR historical':  # = plot_label since 'facet_used_for_labels: plot_label'
                 color: C0
-              'Reference (CanESM5 historical)':
+              'Reference (MPI-ESM1-2-LR historical)':
                 color: black
             pyplot_kwargs:
               title: Near-Surface Air Temperature on Northern Hemisphere
@@ -150,6 +185,7 @@ diagnostics:
             plot_kwargs_bias:
               levels: [-10.0, -7.5, -5.0, -2.5, 0.0, 2.5, 5.0, 7.5, 10.0]
 
+
   plot_1D_profiles_with_references:
     description: Plot 1D profiles including reference datasets.
     variables:
@@ -164,7 +200,58 @@ diagnostics:
         plots:
           1d_profile:
             plot_kwargs:
-              EC-Earth3:  # = dataset since 'facet_used_for_labels' is 'dataset' by default
+              MPI-ESM1-2-HR:  # = dataset since 'facet_used_for_labels' is 'dataset' by default
                 color: C0
-              CanESM5:
+              MPI-ESM1-2-LR:
                 color: black
+
+  plot_variable_vs_latitude:
+    description: Creates a single-panel variable plot over latitude.
+    variables:
+      pr:
+        preprocessor: var_vs_lat
+        mip: Amon
+        timerange: '20000101/20030101'
+    scripts:
+      plot:
+        <<: *plot_multi_dataset_default
+        script: monitor/multi_datasets.py
+        plots:
+          variable_vs_lat:
+
+  plot_hovmoeller_z_vs_time:
+    description: Plot Hovmoeller Z vs. time including reference datasets.
+    variables:
+      ta:
+        preprocessor: global_mean_extract_levels
+        mip: Amon
+        timerange: '2000/2004'
+    scripts:
+      plot:
+        <<: *plot_multi_dataset_default
+        script: monitor/multi_datasets.py
+        plots:
+          hovmoeller_z_vs_time:
+            plot_func: contourf
+            common_cbar: true
+            time_format: '%Y'
+            log_y: false
+            pyplot_kwargs:
+              ylim: [0, 20000]
+
+  plot_time_vs_lat_with_references:
+    description: Plot Hovmoeller time vs. latitude including reference datasets.
+    variables:
+      tas:
+        mip: Amon
+        preprocessor: zonal_mean_2d
+        timerange: '2000/2004'
+    scripts:
+      plot:
+        <<: *plot_multi_dataset_default
+        script: monitor/multi_datasets.py
+        plots:
+          hovmoeller_time_vs_lat_or_lon:
+            common_cbar: true
+            show_x_minor_ticks: false
+            time_format: '%Y'
diff --git a/esmvaltool/recipes/recipe_ecs_constraints.yml b/esmvaltool/recipes/recipe_ecs_constraints.yml
index 3655503e8a..0abc4f20e6 100644
--- a/esmvaltool/recipes/recipe_ecs_constraints.yml
+++ b/esmvaltool/recipes/recipe_ecs_constraints.yml
@@ -522,9 +522,9 @@ diagnostics:
         mip: Amon
         start_year: 1986
         end_year: 2005
-        reference_dataset: GPCP-SG
+        reference_dataset: GPCP-V2.2
         additional_datasets:
-          - {dataset: GPCP-SG, project: obs4MIPs, level: L3, version: v2.2, tier: 1}
+          - {dataset: GPCP-V2.2, project: obs4MIPs, level: L3, tier: 1}
     additional_datasets:
       - {dataset: ACCESS1-0}
       - {dataset: ACCESS1-3}
@@ -593,9 +593,9 @@ diagnostics:
         mip: Amon
         start_year: 2003
         end_year: 2005
-        reference_dataset: AIRS
+        reference_dataset: AIRS-2-1
         additional_datasets:
-          - {dataset: AIRS, project: obs4MIPs, level: L3, version: RetStd-v5, tier: 1}
+          - {dataset: AIRS-2-1, project: obs4MIPs, level: L3, tier: 1}
     additional_datasets:
       - {dataset: ACCESS1-0}
       - {dataset: ACCESS1-3}
@@ -755,7 +755,7 @@ diagnostics:
         end_year: 2005
         reference_dataset: CERES-EBAF
         additional_datasets:
-          - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, version: Ed2-7, tier: 1}
+          - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, tier: 1}
       rsutcs:
         <<: *var_settings_cmip5
         preprocessor: default
@@ -764,7 +764,7 @@ diagnostics:
         end_year: 2005
         reference_dataset: CERES-EBAF
         additional_datasets:
-          - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, version: Ed2-7, tier: 1}
+          - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, tier: 1}
       rsdt:
         <<: *var_settings_cmip5
         preprocessor: default
@@ -773,7 +773,7 @@ diagnostics:
         end_year: 2005
         reference_dataset: CERES-EBAF
         additional_datasets:
-          - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, version: Ed2-7, tier: 1}
+          - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, tier: 1}
     additional_datasets:
       - {dataset: ACCESS1-0}
       - {dataset: ACCESS1-3}
@@ -1175,9 +1175,9 @@ diagnostics:
         exp: [historical, rcp85]
         start_year: 2005
         end_year: 2010
-        reference_dataset: 'AIRS-2-0|MLS-AURA'
+        reference_dataset: AIRS-2-0|MLS-AURA
         additional_datasets:
-          - {dataset: AIRS-2-0, project: obs4MIPs, level: L3, version: v2, tier: 1}
+          - {dataset: AIRS-2-0, project: obs4MIPs, level: L3, tier: 1}
           - {dataset: MLS-AURA, project: OBS6, type: sat, version: '004', tier: 3}
     additional_datasets:
       - {dataset: ACCESS1-0}
diff --git a/esmvaltool/recipes/recipe_ecs_scatter.yml b/esmvaltool/recipes/recipe_ecs_scatter.yml
index a052caf1c4..eae1034e42 100644
--- a/esmvaltool/recipes/recipe_ecs_scatter.yml
+++ b/esmvaltool/recipes/recipe_ecs_scatter.yml
@@ -126,14 +126,14 @@ diagnostics:
       - atmos
     variables:
       pr:
-        reference_dataset: TRMM-L3
+        reference_dataset: TRMM
         mip: Amon
         exp: historical
         start_year: 1986
         end_year: 2005
         project: CMIP5
         additional_datasets:
-          - {dataset: TRMM-L3, project: obs4MIPs, level: v7, version: 7A,
+          - {dataset: TRMM, project: obs4MIPs, level: v7,
              start_year: 1998, end_year: 2013, tier: 1}
           - {dataset: ACCESS1-0, ensemble: r1i1p1}
           # - {dataset: ACCESS1-3, ensemble: r1i1p1}
@@ -162,11 +162,11 @@ diagnostics:
           - {dataset: MRI-CGCM3, ensemble: r1i1p1}
           - {dataset: NorESM1-M, ensemble: r1i1p1}
       prStderr:
-        reference_dataset: TRMM-L3
+        reference_dataset: TRMM
         mip: Amon
         frequency: mon
         additional_datasets:
-          - {dataset: TRMM-L3, project: obs4MIPs, level: v7, version: 7A,
+          - {dataset: TRMM, project: obs4MIPs, level: v7,
              start_year: 1998, end_year: 2013, tier: 1}
     scripts:
       ecs_scatter:
@@ -188,14 +188,14 @@ diagnostics:
     variables:
       hus:
         preprocessor: pp500
-        reference_dataset: AIRS
+        reference_dataset: AIRS-2-1
         mip: Amon
         exp: historical
         start_year: 1986
         end_year: 2005
         project: CMIP5
         additional_datasets:
-          - {dataset: AIRS, project: obs4MIPs, level: L3, version: RetStd-v5,
+          - {dataset: AIRS-2-1, project: obs4MIPs, level: L3,
              start_year: 2003, end_year: 2010, tier: 1}
           - {dataset: ACCESS1-0, ensemble: r1i1p1}
           # - {dataset: ACCESS1-3, ensemble: r1i1p1}
@@ -225,11 +225,11 @@ diagnostics:
           - {dataset: NorESM1-M, ensemble: r1i1p1}
       husStderr:
         preprocessor: pp500
-        reference_dataset: AIRS
+        reference_dataset: AIRS-2-1
         mip: Amon
         frequency: mon
         additional_datasets:
-          - {dataset: AIRS, project: obs4MIPs, level: L3, version: RetStd-v5,
+          - {dataset: AIRS-2-1, project: obs4MIPs, level: L3,
              start_year: 2003, end_year: 2010, tier: 1}
     scripts:
       ecs_scatter:
@@ -396,24 +396,24 @@ diagnostics:
         exp: historical
         project: CMIP5
         additional_datasets:
-          - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, version: Ed2-7,
-             start_year: 2001, end_year: 2005, tier: 1}
+          - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, start_year: 2001, end_year: 2005,
+             tier: 1}
       rsutcs:
         reference_dataset: CERES-EBAF
         mip: Amon
         exp: historical
         project: CMIP5
         additional_datasets:
-          - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, version: Ed2-7,
-             start_year: 2001, end_year: 2005, tier: 1}
+          - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, start_year: 2001, end_year: 2005,
+             tier: 1}
       rsdt:
         reference_dataset: CERES-EBAF
         mip: Amon
         exp: historical
         project: CMIP5
         additional_datasets:
-          - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, version: Ed2-7,
-             start_year: 2001, end_year: 2005, tier: 1}
+          - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, start_year: 2001, end_year: 2005,
+             tier: 1}
     scripts:
       ecs_scatter:
         diag: covrefl
@@ -433,15 +433,14 @@ diagnostics:
       - atmos
     variables:
       pr:
-        reference_dataset: TRMM-L3
+        reference_dataset: TRMM
         start_year: 1998
         end_year: 2013
         exp: historical
         mip: Amon
         project: CMIP6
         additional_datasets:
-          - {dataset: TRMM-L3, project: obs4MIPs, level: v7,
-             version: 7A, tier: 1}
+          - {dataset: TRMM, project: obs4MIPs, level: v7, tier: 1}
           - {dataset: BCC-CSM2-MR, grid: gn, ensemble: r1i1p1f1}
           - {dataset: BCC-ESM1, grid: gn, ensemble: r1i1p1f1}
           - {dataset: CAMS-CSM1-0, grid: gn, ensemble: r1i1p1f1}
@@ -457,14 +456,13 @@ diagnostics:
           - {dataset: MIROC6, grid: gn, ensemble: r1i1p1f1}
           - {dataset: MRI-ESM2-0, grid: gn, ensemble: r1i1p1f1}
       prStderr:
-        reference_dataset: TRMM-L3
+        reference_dataset: TRMM
         mip: Amon
         frequency: mon
         start_year: 1998
         end_year: 2013
         additional_datasets:
-          - {dataset: TRMM-L3, project: obs4MIPs, level: v7,
-             version: 7A, tier: 1}
+          - {dataset: TRMM, project: obs4MIPs, level: v7, tier: 1}
     scripts:
       ecs_scatter:
         diag: itczidx
@@ -484,15 +482,14 @@ diagnostics:
     variables:
       hus:
         preprocessor: pp500
-        reference_dataset: AIRS
+        reference_dataset: AIRS-2-1
         start_year: 2003
         end_year: 2010
         exp: historical
         mip: Amon
         project: CMIP6
         additional_datasets:
-          - {dataset: AIRS, project: obs4MIPs, level: L3,
-             version: RetStd-v5, tier: 1}
+          - {dataset: AIRS-2-1, project: obs4MIPs, level: L3, tier: 1}
           - {dataset: BCC-CSM2-MR, grid: gn, ensemble: r1i1p1f1}
           - {dataset: BCC-ESM1, grid: gn, ensemble: r1i1p1f1}
           - {dataset: CAMS-CSM1-0, grid: gn, ensemble: r1i1p1f1}
@@ -509,14 +506,13 @@ diagnostics:
           - {dataset: MRI-ESM2-0, grid: gn, ensemble: r1i1p1f1}
       husStderr:
         preprocessor: pp500
-        reference_dataset: AIRS
+        reference_dataset: AIRS-2-1
         mip: Amon
         frequency: mon
         start_year: 2003
         end_year: 2010
         additional_datasets:
-          - {dataset: AIRS, project: obs4MIPs, level: L3,
-             version: RetStd-v5, tier: 1}
+          - {dataset: AIRS-2-1, project: obs4MIPs, level: L3, tier: 1}
     scripts:
       ecs_scatter:
         diag: humidx
@@ -689,8 +685,7 @@ diagnostics:
         mip: Amon
         project: CMIP6
         additional_datasets:
-          - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B,
-             version: Ed2-7, tier: 1}
+          - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, tier: 1}
           - {dataset: BCC-CSM2-MR, grid: gn, ensemble: r1i1p1f1}
           - {dataset: BCC-ESM1, grid: gn, ensemble: r2i1p1f1}
           # - {dataset: CAMS-CSM1-0, grid: gn, ensemble: r1i1p1f1}
@@ -713,8 +708,7 @@ diagnostics:
         mip: Amon
         project: CMIP6
         additional_datasets:
-          - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B,
-             version: Ed2-7, tier: 1}
+          - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, tier: 1}
           - {dataset: BCC-CSM2-MR, grid: gn, ensemble: r1i1p1f1}
           - {dataset: BCC-ESM1, grid: gn, ensemble: r2i1p1f1}
           # - {dataset: CAMS-CSM1-0, grid: gn, ensemble: r1i1p1f1}
@@ -737,8 +731,7 @@ diagnostics:
         mip: Amon
         project: CMIP6
         additional_datasets:
-          - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B,
-             version: Ed2-7, tier: 1}
+          - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, tier: 1}
           - {dataset: BCC-CSM2-MR, grid: gn, ensemble: r1i1p1f1}
           - {dataset: BCC-ESM1, grid: gn, ensemble: r2i1p1f1}
           # - {dataset: CAMS-CSM1-0, grid: gn, ensemble: r1i1p1f1}
diff --git a/esmvaltool/recipes/recipe_heatwaves_coldwaves.yml b/esmvaltool/recipes/recipe_heatwaves_coldwaves.yml
index 6481e33971..aae6491103 100644
--- a/esmvaltool/recipes/recipe_heatwaves_coldwaves.yml
+++ b/esmvaltool/recipes/recipe_heatwaves_coldwaves.yml
@@ -27,7 +27,7 @@ datasets:
   # - {dataset: IPSL-CM5A-MR, type: exp, project: CMIP5, exp: rcp85,
   #    ensemble: r1i1p1, start_year: 2020, end_year: 2040}
   - {dataset: bcc-csm1-1, type: exp, project: CMIP5, exp: historical,
-     ensemble: r1i1p1, start_year: 1971, end_year: 2000}
+     ensemble: r1i1p1, start_year: 1971, end_year: 2000, version: v1}
   - {dataset: bcc-csm1-1, type: exp, project: CMIP5, exp: rcp85,
      ensemble: r1i1p1, start_year: 2060, end_year: 2080}
 
diff --git a/esmvaltool/recipes/recipe_iht_toa.yml b/esmvaltool/recipes/recipe_iht_toa.yml
new file mode 100644
index 0000000000..19e05f5ac2
--- /dev/null
+++ b/esmvaltool/recipes/recipe_iht_toa.yml
@@ -0,0 +1,119 @@
+# ESMValTool
+# recipe_transport.yml
+---
+documentation:
+  title: Implied heat transport
+
+  description: |
+    Horizontal implied heat transport of the entire column derived from
+    TOA radiative fluxes.
+
+#    The 'single_model' script  produces Figures 1 to 6 in Pearce and
+#    Bodas-Salcedo (2023) for each of the datasets chosen.
+
+  authors:
+    - pearce_francesca
+    - bodas-salcedo_alejandro
+
+  maintainer:
+    - bodas-salcedo_alejandro
+
+  references:
+    - pearce23jclim
+
+datasets:
+  # The CERES-EBAF observations reproduce the results of Pearce and
+  # Bodas-Salcedo (2023), but it can be applied to model data.
+  - {dataset: CERES-EBAF, project: obs4MIPs, tier: 1, start_year: 2001, end_year: 2010}
+  # - {dataset: HadGEM3-GC31-LL, project: CMIP6, exp: amip, ensemble: r1i1p1f3, grid: gn, mip: Amon, start_year: 1990, end_year: 2005}
+
+preprocessors:
+  # It is recommended to use a low resolution grid as the
+  # calculations are computationally expensive
+  common_grid:
+    regrid:
+      target_grid: 5.0x5.0
+      scheme: area_weighted
+
+  climate_mean_global:
+    regrid:
+      target_grid: 5.0x5.0
+      scheme: area_weighted
+    climate_statistics:
+      operator: mean
+
+diagnostics:
+
+  implied_heat_transport:
+    title: TOA implied heat transports
+    description: Energy flux potential and implied heat transports for TOA radiative fluxes.
+    themes:
+      - phys
+    realms:
+      - atmos
+    variables:
+      rtnt:
+        derive: true
+        short_name: rtnt
+        mip: Amon
+        preprocessor: climate_mean_global
+      rtnt_monthly:
+        derive: true
+        short_name: rtnt
+        mip: Amon
+        preprocessor: common_grid
+      rsnt:
+        derive: true
+        short_name: rsnt
+        mip: Amon
+        preprocessor: climate_mean_global
+      rlut:
+        short_name: rlut
+        mip: Amon
+        preprocessor: climate_mean_global
+      rlutcs:
+        short_name: rlutcs
+        mip: Amon
+        preprocessor: climate_mean_global
+      rlutcs_monthly:
+        short_name: rlutcs
+        mip: Amon
+        preprocessor: common_grid
+      rsutcs:
+        short_name: rsutcs
+        mip: Amon
+        preprocessor: climate_mean_global
+      rsutcs_monthly:
+        short_name: rsutcs
+        mip: Amon
+        preprocessor: common_grid
+      rsut:
+        short_name: rsut
+        mip: Amon
+        preprocessor: climate_mean_global
+      rsdt:
+        short_name: rsdt
+        mip: Amon
+        preprocessor: climate_mean_global
+      rsdt_monthly:
+        short_name: rsdt
+        mip: Amon
+        preprocessor: common_grid
+      netcre:
+        derive: true
+        short_name: netcre
+        mip: Amon
+        preprocessor: climate_mean_global
+      swcre:
+        derive: true
+        short_name: swcre
+        mip: Amon
+        preprocessor: climate_mean_global
+      lwcre:
+        derive: true
+        short_name: lwcre
+        mip: Amon
+        preprocessor: climate_mean_global
+    scripts:
+      single_model:
+        script: iht_toa/single_model_diagnostics.py
diff --git a/esmvaltool/recipes/recipe_kcs.yml b/esmvaltool/recipes/recipe_kcs.yml
index cbb7e85592..6eebb2c0e6 100644
--- a/esmvaltool/recipes/recipe_kcs.yml
+++ b/esmvaltool/recipes/recipe_kcs.yml
@@ -64,7 +64,11 @@ preprocessors:
       standardize: false
     multi_model_statistics:
       span: full
-      statistics: [p10, p90]
+      statistics:
+        - operator: percentile
+          percent: 10
+        - operator: percentile
+          percent: 90
   preprocessor_local: &extract_NL
     extract_point:
       longitude: 6.25
@@ -91,7 +95,7 @@ diagnostics:
       global_matching:
         script: kcs/global_matching.py
         scenario_years: [2050, 2085]
-        scenario_percentiles: [P10, P90]
+        scenario_percentiles: [Percentile10, Percentile90]
 
   local_resampling:
     description: >
diff --git a/esmvaltool/recipes/recipe_perfmetrics_CMIP5.yml b/esmvaltool/recipes/recipe_perfmetrics_CMIP5.yml
index ee7c8e96e1..f1ff96eb64 100644
--- a/esmvaltool/recipes/recipe_perfmetrics_CMIP5.yml
+++ b/esmvaltool/recipes/recipe_perfmetrics_CMIP5.yml
@@ -613,7 +613,7 @@ diagnostics:
     variables:
       pr:
         preprocessor: ppNOLEV1
-        reference_dataset: GPCP-SG
+        reference_dataset: GPCP-V2.2
         mip: Amon
         project: CMIP5
         exp: historical
@@ -670,8 +670,7 @@ diagnostics:
       - {dataset: MRI-ESM1}
       - {dataset: NorESM1-M}
       - {dataset: NorESM1-ME}
-      - {dataset: GPCP-SG, project: obs4MIPs, level: L3,
-         version: v2.2, tier: 1}
+      - {dataset: GPCP-V2.2, project: obs4MIPs, level: L3, tier: 1}
     scripts:
       grading:
         <<: *grading_settings
@@ -811,8 +810,7 @@ diagnostics:
       - {dataset: MRI-CGCM3}
       - {dataset: NorESM1-M}
       - {dataset: NorESM1-ME}
-      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B,
-         version: Ed2-7, tier: 1}
+      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, tier: 1}
     scripts:
       latlon:
         <<: *latlon_settings
@@ -883,8 +881,7 @@ diagnostics:
       - {dataset: MRI-CGCM3}
       - {dataset: NorESM1-M}
       - {dataset: NorESM1-ME}
-      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B,
-         version: Ed2-7, tier: 1}
+      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, tier: 1}
     scripts:
       latlon:
         <<: *latlon_settings
@@ -958,8 +955,7 @@ diagnostics:
       - {dataset: MRI-CGCM3}
       - {dataset: NorESM1-M}
       - {dataset: NorESM1-ME}
-      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B,
-         version: Ed2-7, tier: 1}
+      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, tier: 1}
     scripts:
       grading:
         <<: *grading_settings
@@ -975,7 +971,7 @@ diagnostics:
     variables:
       hus:
         preprocessor: pp400
-        reference_dataset: AIRS
+        reference_dataset: AIRS-2-1
         alternative_dataset: ERA-Interim
         mip: Amon
         project: CMIP5
@@ -1024,10 +1020,9 @@ diagnostics:
       - {dataset: MRI-ESM1}
       - {dataset: NorESM1-M}
       - {dataset: NorESM1-ME}
-      - {dataset: AIRS, project: obs4MIPs, level: L3,
-         version: RetStd-v5, tier: 1}
-      - {dataset: ERA-Interim, project: OBS6, type: reanaly,
-         version: 1, tier: 3}
+      - {dataset: AIRS-2-1, project: obs4MIPs, level: L3, tier: 1}
+      - {dataset: ERA-Interim, project: OBS6,
+         type: reanaly, version: 1, tier: 3}
     scripts:
       grading:
         <<: *grading_settings
@@ -1090,8 +1085,7 @@ diagnostics:
       - {dataset: MRI-CGCM3}
       - {dataset: NorESM1-M}
       - {dataset: NorESM1-ME}
-      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B,
-         version: Ed2-7, tier: 1}
+      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, tier: 1}
     scripts:
       grading:
         <<: *grading_settings
diff --git a/esmvaltool/recipes/recipe_perfmetrics_CMIP5_4cds.yml b/esmvaltool/recipes/recipe_perfmetrics_CMIP5_4cds.yml
index b9e6b1ad1e..6e80cef108 100644
--- a/esmvaltool/recipes/recipe_perfmetrics_CMIP5_4cds.yml
+++ b/esmvaltool/recipes/recipe_perfmetrics_CMIP5_4cds.yml
@@ -1025,7 +1025,7 @@ diagnostics:
     variables:
       hus:
         preprocessor: pp400
-        reference_dataset: AIRS
+        reference_dataset: AIRS-2-1
         alternative_dataset: ERA-Interim
         mip: Amon
         project: CMIP5
@@ -1074,9 +1074,9 @@ diagnostics:
       - {dataset: MRI-ESM1}
       - {dataset: NorESM1-M}
       - {dataset: NorESM1-ME}
-      - {dataset: AIRS, project: obs4MIPs, level: L3, version: RetStd-v5, tier: 1}
-      - {dataset: ERA-Interim, project: OBS6, type: reanaly,
-         version: 1, tier: 3}
+      - {dataset: AIRS-2-1, project: obs4MIPs, level: L3, tier: 1}
+      - {dataset: ERA-Interim, project: OBS6,
+         type: reanaly, version: 1, tier: 3}
     scripts:
       grading:
         <<: *grading_settings
@@ -1271,7 +1271,7 @@ diagnostics:
     variables:
       pr:
         preprocessor: ppNOLEV1
-        reference_dataset: GPCP-SG
+        reference_dataset: GPCP-V2.2
         mip: Amon
         project: CMIP5
         exp: historical
@@ -1328,7 +1328,7 @@ diagnostics:
       - {dataset: MRI-ESM1}
       - {dataset: NorESM1-M}
       - {dataset: NorESM1-ME}
-      - {dataset: GPCP-SG, project: obs4MIPs, level: L3, version: v2.2, tier: 1}
+      - {dataset: GPCP-V2.2, project: obs4MIPs, level: L3, tier: 1}
     scripts:
       grading:
         <<: *grading_settings
@@ -1477,7 +1477,7 @@ diagnostics:
       - {dataset: MRI-CGCM3}
       - {dataset: NorESM1-M}
       - {dataset: NorESM1-ME}
-      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, version: Ed2-7, tier: 1}
+      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, tier: 1}
     scripts:
       grading:
         <<: *grading_settings
@@ -1544,7 +1544,7 @@ diagnostics:
       - {dataset: MRI-CGCM3}
       - {dataset: NorESM1-M}
       - {dataset: NorESM1-ME}
-      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, version: Ed2-7, tier: 1}
+      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, tier: 1}
     scripts:
       grading:
         <<: *grading_settings
diff --git a/esmvaltool/recipes/recipe_perfmetrics_land_CMIP5.yml b/esmvaltool/recipes/recipe_perfmetrics_land_CMIP5.yml
index 834b97e158..11ed80bbaa 100644
--- a/esmvaltool/recipes/recipe_perfmetrics_land_CMIP5.yml
+++ b/esmvaltool/recipes/recipe_perfmetrics_land_CMIP5.yml
@@ -420,7 +420,7 @@ diagnostics:
       rlus:
         short_name: rlus
         preprocessor: ppNOLEV1
-        reference_dataset: CERES-EBAF
+        reference_dataset: CERES-EBAF_Surface
         mip: Amon
         project: CMIP5
         exp: historical
@@ -460,9 +460,7 @@ diagnostics:
       - {dataset: MRI-CGCM3}
       - {dataset: NorESM1-M}
       - {dataset: NorESM1-ME}
-
-      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B,
-         version: Ed2-8, tier: 1, start_year: 2001, end_year: 2015}
+      - {dataset: CERES-EBAF_Surface, project: obs4MIPs, level: L3B, tier: 1, start_year: 2001, end_year: 2013}
     scripts:
       grading:
         <<: *grading_settings
@@ -478,7 +476,7 @@ diagnostics:
       rlds:
         short_name: rlds
         preprocessor: ppNOLEV1
-        reference_dataset: CERES-EBAF
+        reference_dataset: CERES-EBAF_Surface
         mip: Amon
         project: CMIP5
         exp: historical
@@ -519,9 +517,7 @@ diagnostics:
       - {dataset: MRI-CGCM3}
       - {dataset: NorESM1-M}
       - {dataset: NorESM1-ME}
-
-      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B,
-         version: Ed2-8, tier: 1, start_year: 2001, end_year: 2015}
+      - {dataset: CERES-EBAF_Surface, project: obs4MIPs, level: L3B, tier: 1, start_year: 2001, end_year: 2013}
     scripts:
       grading:
         <<: *grading_settings
@@ -537,7 +533,7 @@ diagnostics:
       rsus:
         short_name: rsus
         preprocessor: ppNOLEV1
-        reference_dataset: CERES-EBAF
+        reference_dataset: CERES-EBAF_Surface
         mip: Amon
         project: CMIP5
         exp: historical
@@ -577,9 +573,7 @@ diagnostics:
       - {dataset: MRI-CGCM3}
       - {dataset: NorESM1-M}
       - {dataset: NorESM1-ME}
-
-      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B,
-         version: Ed2-8, tier: 1, start_year: 2001, end_year: 2015}
+      - {dataset: CERES-EBAF_Surface, project: obs4MIPs, level: L3B, tier: 1, start_year: 2001, end_year: 2013}
     scripts:
       grading:
         <<: *grading_settings
@@ -595,7 +589,7 @@ diagnostics:
       rsds:
         short_name: rsds
         preprocessor: ppNOLEV1
-        reference_dataset: CERES-EBAF
+        reference_dataset: CERES-EBAF_Surface
         mip: Amon
         project: CMIP5
         exp: historical
@@ -637,8 +631,7 @@ diagnostics:
       - {dataset: NorESM1-M}
       - {dataset: NorESM1-ME}
 
-      - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B,
-         version: Ed2-8, tier: 1, start_year: 2001, end_year: 2015}
+      - {dataset: CERES-EBAF_Surface, project: obs4MIPs, level: L3B, tier: 1, start_year: 2001, end_year: 2013}
     scripts:
       grading:
         <<: *grading_settings
diff --git a/esmvaltool/recipes/recipe_psyplot.yml b/esmvaltool/recipes/recipe_psyplot.yml
index 4a07f4c558..c34ee203f0 100644
--- a/esmvaltool/recipes/recipe_psyplot.yml
+++ b/esmvaltool/recipes/recipe_psyplot.yml
@@ -2,13 +2,13 @@
 # recipe_psyplot.yml
 ---
 documentation:
-  title: >
-    Create arbitrary Psyplot plots.
+  title: Example recipe for the Psyplot diagnostic.
 
   description: >
     This recipes showcases the use of the Psyplot diagnostic that provides a
     high-level interface to Psyplot for ESMValTool recipes. For each input
-    dataset, an individual plot is created.
+    dataset, an individual plot is created. With the Psyplot diagnostic,
+    arbitrary Psyplot plots can be created.
 
   authors:
     - schlund_manuel
diff --git a/esmvaltool/recipes/recipe_quantilebias.yml b/esmvaltool/recipes/recipe_quantilebias.yml
index 57eee9d05e..2da81b5960 100644
--- a/esmvaltool/recipes/recipe_quantilebias.yml
+++ b/esmvaltool/recipes/recipe_quantilebias.yml
@@ -20,8 +20,8 @@ documentation:
     - c3s-magic
 
 datasets:
-   - {dataset: CanESM2, project: CMIP5, exp: historical, ensemble: r1i1p1, start_year: 1979, end_year: 2005}
-   - {dataset: GPCP-SG, project: obs4MIPs, level: L3, version: v2.3, tier: 1, start_year: 1979, end_year: 2005}
+  - {dataset: CanESM2, project: CMIP5, exp: historical, ensemble: r1i1p1, start_year: 1979, end_year: 2005}
+  - {dataset: GPCP-V2.3, project: obs4MIPs, level: L3, tier: 1, start_year: 1979, end_year: 2005}
 
 preprocessors:
   mask_regrid_preproc:
@@ -37,7 +37,7 @@ diagnostics:
     variables:
       pr:
         preprocessor: mask_regrid_preproc
-        reference_dataset: "GPCP-SG"
+        reference_dataset: "GPCP-V2.3"
         mip: Amon
 
     scripts:
diff --git a/esmvaltool/recipes/recipe_radiation_budget.yml b/esmvaltool/recipes/recipe_radiation_budget.yml
index 2f56cb4f9c..4a81307283 100644
--- a/esmvaltool/recipes/recipe_radiation_budget.yml
+++ b/esmvaltool/recipes/recipe_radiation_budget.yml
@@ -52,13 +52,13 @@ diagnostics:
         mip: Amon
         preprocessor: single_value
         additional_datasets:
-          - {dataset: CERES-EBAF, project: obs4mips, level: L3B, version: Ed2-7,
+          - {dataset: CERES-EBAF, project: obs4mips, level: L3B,
              start_year: 2000, end_year: 2010, tier: 1}
       rsutcs:
         mip: Amon
         preprocessor: single_value
         additional_datasets:
-          - {dataset: CERES-EBAF, project: obs4mips, level: L3B, version: Ed2-7,
+          - {dataset: CERES-EBAF, project: obs4mips, level: L3B,
              start_year: 2000, end_year: 2010, tier: 1}
       rsds:
         mip: Amon
@@ -70,13 +70,13 @@ diagnostics:
         mip: Amon
         preprocessor: single_value
         additional_datasets:
-          - {dataset: CERES-EBAF, project: obs4mips, level: L3B, version: Ed2-7,
+          - {dataset: CERES-EBAF, project: obs4mips, level: L3B,
              start_year: 2000, end_year: 2010, tier: 1}
       rlutcs:
         mip: Amon
         preprocessor: single_value
         additional_datasets:
-          - {dataset: CERES-EBAF, project: obs4mips, level: L3B, version: Ed2-7,
+          - {dataset: CERES-EBAF, project: obs4mips, level: L3B,
              start_year: 2000, end_year: 2010, tier: 1}
       rlds:
         mip: Amon
diff --git a/esmvaltool/recipes/recipe_schlund20esd.yml b/esmvaltool/recipes/recipe_schlund20esd.yml
index 28d9e83375..041ae07e41 100644
--- a/esmvaltool/recipes/recipe_schlund20esd.yml
+++ b/esmvaltool/recipes/recipe_schlund20esd.yml
@@ -858,9 +858,9 @@ diagnostics:
         mip: Amon
         start_year: 1986
         end_year: 2005
-        reference_dataset: GPCP-SG
+        reference_dataset: GPCP-V2.2
         additional_datasets:
-          - {dataset: GPCP-SG, project: obs4MIPs, level: L3, version: v2.2, tier: 1}
+          - {dataset: GPCP-V2.2, project: obs4MIPs, level: L3, tier: 1}
     additional_datasets:
       - {dataset: ACCESS1-0}
       - {dataset: ACCESS1-3}
@@ -919,9 +919,9 @@ diagnostics:
         mip: Amon
         start_year: 1986
         end_year: 2005
-        reference_dataset: GPCP-SG
+        reference_dataset: GPCP-V2.2
         additional_datasets:
-          - {dataset: GPCP-SG, project: obs4MIPs, level: L3, version: v2.2, tier: 1}
+          - {dataset: GPCP-V2.2, project: obs4MIPs, level: L3, tier: 1}
     additional_datasets:
       - {dataset: ACCESS-CM2,      ensemble: r1i1p1f1, grid: gn, institute: CSIRO-ARCCSS}
       - {dataset: ACCESS-ESM1-5,   ensemble: r1i1p1f1, grid: gn}
@@ -994,9 +994,9 @@ diagnostics:
         mip: Amon
         start_year: 2003
         end_year: 2005
-        reference_dataset: AIRS
+        reference_dataset: AIRS-2-1
         additional_datasets:
-          - {dataset: AIRS, project: obs4MIPs, level: L3, version: RetStd-v5, tier: 1}
+          - {dataset: AIRS-2-1, project: obs4MIPs, level: L3, tier: 1}
     additional_datasets:
       - {dataset: ACCESS1-0}
       - {dataset: ACCESS1-3}
@@ -1055,9 +1055,9 @@ diagnostics:
         mip: Amon
         start_year: 2003
         end_year: 2005
-        reference_dataset: AIRS
+        reference_dataset: AIRS-2-1
         additional_datasets:
-          - {dataset: AIRS, project: obs4MIPs, level: L3, version: RetStd-v5, tier: 1}
+          - {dataset: AIRS-2-1, project: obs4MIPs, level: L3, tier: 1}
     additional_datasets:
       - {dataset: ACCESS-CM2,      ensemble: r1i1p1f1, grid: gn, institute: CSIRO-ARCCSS}
       - {dataset: ACCESS-ESM1-5,   ensemble: r1i1p1f1, grid: gn}
@@ -1283,7 +1283,7 @@ diagnostics:
         end_year: 2005
         reference_dataset: CERES-EBAF
         additional_datasets:
-          - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, version: Ed2-7, tier: 1}
+          - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, tier: 1}
       rsutcs:
         <<: *var_settings_cmip5
         preprocessor: default
@@ -1292,7 +1292,7 @@ diagnostics:
         end_year: 2005
         reference_dataset: CERES-EBAF
         additional_datasets:
-          - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, version: Ed2-7, tier: 1}
+          - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, tier: 1}
       rsdt:
         <<: *var_settings_cmip5
         preprocessor: default
@@ -1301,7 +1301,7 @@ diagnostics:
         end_year: 2005
         reference_dataset: CERES-EBAF
         additional_datasets:
-          - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, version: Ed2-7, tier: 1}
+          - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, tier: 1}
     additional_datasets:
       - {dataset: ACCESS1-0}
       - {dataset: ACCESS1-3}
@@ -1380,7 +1380,7 @@ diagnostics:
         end_year: 2005
         reference_dataset: CERES-EBAF
         additional_datasets:
-          - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, version: Ed2-7, tier: 1}
+          - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, tier: 1}
       rsutcs:
         <<: *var_settings_cmip6
         preprocessor: default
@@ -1389,7 +1389,7 @@ diagnostics:
         end_year: 2005
         reference_dataset: CERES-EBAF
         additional_datasets:
-          - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, version: Ed2-7, tier: 1}
+          - {dataset: CERES-EBAF, project: obs4MIPs, level: L3B, tier: 1}
       rsdt:
         <<: *var_settings_cmip6
         preprocessor: default
@@ -1885,7 +1885,7 @@ diagnostics:
         end_year: 2010
         reference_dataset: 'AIRS-2-0|MLS-AURA'
         additional_datasets:
-          - {dataset: AIRS-2-0, project: obs4MIPs, level: L3, version: v2, tier: 1}
+          - {dataset: AIRS-2-0, project: obs4MIPs, level: L3, tier: 1}
           - {dataset: MLS-AURA, project: OBS6, type: sat, version: '004', tier: 3}
     additional_datasets:
       - {dataset: ACCESS1-0}
@@ -1940,7 +1940,7 @@ diagnostics:
         end_year: 2010
         reference_dataset: 'AIRS-2-0|MLS-AURA'
         additional_datasets:
-          - {dataset: AIRS-2-0, project: obs4MIPs, level: L3, version: v2, tier: 1}
+          - {dataset: AIRS-2-0, project: obs4MIPs, level: L3, tier: 1}
           - {dataset: MLS-AURA, project: OBS6, type: sat, version: '004', tier: 3}
     additional_datasets:
       - {dataset: ACCESS-CM2,      ensemble: r1i1p1f1, grid: gn, institute: CSIRO-ARCCSS}
diff --git a/esmvaltool/recipes/recipe_seaborn.yml b/esmvaltool/recipes/recipe_seaborn.yml
index faf0f07085..983efae0be 100644
--- a/esmvaltool/recipes/recipe_seaborn.yml
+++ b/esmvaltool/recipes/recipe_seaborn.yml
@@ -2,14 +2,14 @@
 # recipe_seaborn.yml
 ---
 documentation:
-  title: >
-    Create arbitrary Seaborn plots.
+  title: Example recipe for the Seaborn diagnostic.
 
   description: >
     This recipe showcases the use of the Seaborn diagnostic that provides a
     high-level interface to Seaborn for ESMValTool recipes. For this, the input
     data is arranged into a single `pandas.DataFrame`, which is then used as
-    input for the Seaborn function defined by the option `seaborn_func`.
+    input for the Seaborn function defined by the option `seaborn_func`. With
+    the Seaborn diagnostic, arbitrary Seaborn plots can be created.
 
   authors:
     - schlund_manuel
diff --git a/esmvaltool/recipes/recipe_smpi_4cds.yml b/esmvaltool/recipes/recipe_smpi_4cds.yml
index 5f946986b4..3a60419d69 100644
--- a/esmvaltool/recipes/recipe_smpi_4cds.yml
+++ b/esmvaltool/recipes/recipe_smpi_4cds.yml
@@ -236,9 +236,9 @@ diagnostics:
       pr:
         <<: *variable_settings
         preprocessor: ppNOLEV
-        reference_dataset: GPCP-SG
+        reference_dataset: GPCP-V2.2
     additional_datasets:
-      - {dataset: GPCP-SG, project: obs4MIPs, level: L3, version: v2.2, tier: 1}
+      - {dataset: GPCP-V2.2, project: obs4MIPs, level: L3, tier: 1}
     scripts:
       grading:
         script: perfmetrics/main.ncl
diff --git a/esmvaltool/references/fox-kemper21ipcc.bibtex b/esmvaltool/references/fox-kemper21ipcc.bibtex
new file mode 100644
index 0000000000..e26a480f46
--- /dev/null
+++ b/esmvaltool/references/fox-kemper21ipcc.bibtex
@@ -0,0 +1,11 @@
+@inbook{fox-kemper21ipcc,
+  author={B. Fox-Kemper and H.T. Hewitt and C. Xiao and G. Aðalgeirsdóttir and S.S. Drijfhout and T.L. Edwards and N.R. Golledge and M. Hemer and R.E. Kopp and G. Krinner and A. Mix and D. Notz and S. Nowicki and I.S. Nurhati and L. Ruiz and J.-B. Sallée and A.B.A. Slangen and Y. Yu},
+  editor={V. Masson-Delmotte and P. Zhai and A. Pirani and S.L. Connors and C. Pean and S. Berger and N. Caud and Y. Chen and L. Goldfarb and M.I. Gomis and M. Huang and K. Leitzell and E. Lonnoy and J.B.R. Matthews and T.K. Maycock and T. Waterfield and O. Yelekci and R. Yu and B. Zhou},
+  title={Ocean, Cryosphere and Sea Level Change},
+  booktitle={Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change},
+  year={2021},
+  publisher={Cambridge University Press},
+  address={Cambridge, UK and New York, NY, USA},
+  pages = {1211--1362},
+  doi={10.1017/9781009157896.011}
+}
diff --git a/esmvaltool/references/merra.bibtex b/esmvaltool/references/merra.bibtex
new file mode 100644
index 0000000000..aa1df2c53d
--- /dev/null
+++ b/esmvaltool/references/merra.bibtex
@@ -0,0 +1,13 @@
+@article{merra,
+	doi = {10.1175/JCLI-D-11-00015.1},
+	url = {https://journals.ametsoc.org/view/journals/clim/24/14/jcli-d-11-00015.1.xml},
+	year = 2011,
+	month = {jul},
+	publisher = {American Meteorological Society},
+	volume = {24},
+	number = {14},
+	pages = {3624--3648},
+	author = {Michele M. Rienecker and Max J. Suarez and Ronald Gelaro and Ricardo Todling and Julio Bacmeister and Emily Liu and Michael G. Bosilovich and Siegfried D. Schubert and Lawrence Takacs and Gi-Kong Kim and Stephen Bloom and Junye Chen and Douglas Collins and Austin Conaty and Arlindo da Silva and Wei Gu and Joanna Joiner and Randal D. Koster and Robert Lucchesi and Andrea Molod and Tommy Owens and Steven Pawson and Philip Pegion and Christopher R. Redder and Rolf Reichle and Franklin R. Robertson and Albert G. Ruddick and Meta Sienkiewicz and Jack Woollen},
+	title = {{MERRA}: {NASA}’s Modern-Era Retrospective Analysis for Research and Applications},
+	journal = {Journal of Climate}
+}
diff --git a/esmvaltool/references/mobo_dic2004_2019.bibtex b/esmvaltool/references/mobo_dic2004_2019.bibtex
new file mode 100644
index 0000000000..928fc33afd
--- /dev/null
+++ b/esmvaltool/references/mobo_dic2004_2019.bibtex
@@ -0,0 +1,12 @@
+@article{mobo_dic2004_2019,
+  doi = {10.1029/2022gb007677},
+  url = {https://doi.org/10.1029/2022gb007677},
+  year = {2023},
+  month = may,
+  publisher = {American Geophysical Union ({AGU})},
+  volume = {37},
+  number = {5},
+  author = {L. Keppler and P. Landschützer and S. K. Lauvset and N. Gruber},
+  title = {Recent Trends and Variability in the Oceanic Storage of Dissolved Inorganic Carbon},
+  journal = {Global Biogeochemical Cycles}
+}
diff --git a/esmvaltool/references/noaa-mbl-ch4.bibtex b/esmvaltool/references/noaa-mbl-ch4.bibtex
new file mode 100644
index 0000000000..e4c6a17cbd
--- /dev/null
+++ b/esmvaltool/references/noaa-mbl-ch4.bibtex
@@ -0,0 +1,9 @@
+@misc{noaa-mbl-ch4,
+        doi = {https://doi.org/10.15138/P8XG-AA10},
+	url = {https://gml.noaa.gov/ccgg/trends_ch4/},
+	year = 2023,
+        month = {jul},
+	author = {Lan, X., K.W. Thoning, and E.J. Dlugokencky},
+	title = {Trends in globally-averaged CH4, N2O, and SF6 determined from NOAA Global Monitoring Laboratory measurements. Version 2023-09},
+        howpublished = {via website https://gml.noaa.gov/ccgg/trends_ch4/, provided by the NOAA/GML, Boulder, Colorado, USA}
+}
diff --git a/esmvaltool/references/pearce23jclim.bibtex b/esmvaltool/references/pearce23jclim.bibtex
new file mode 100644
index 0000000000..ebfac532ad
--- /dev/null
+++ b/esmvaltool/references/pearce23jclim.bibtex
@@ -0,0 +1,13 @@
+@article{pearce23jclim,
+	doi = {10.1175/JCLI-D-22-0149.1},
+	url = {https://doi.org/10.1175/JCLI-D-22-0149.1},
+	year = 2023,
+	month = {jun},
+	publisher = {American Meteorological Society},
+	volume = {36},
+	number = {9},
+	pages = {1--30},
+	author = {Francesca Pearce and Alejandro Bodas-Salcedo},
+	title = {Implied heat transport from {CERES} data: direct radiative effect of clouds on regional patterns and hemispheric symmetry},
+	journal = {Journal of Climate}
+}
diff --git a/esmvaltool/utils/batch-jobs/generate.py b/esmvaltool/utils/batch-jobs/generate.py
index dd7a46d83f..fc9deb8339 100644
--- a/esmvaltool/utils/batch-jobs/generate.py
+++ b/esmvaltool/utils/batch-jobs/generate.py
@@ -51,6 +51,9 @@
 # Full path to config_file
 # If none, ~/.esmvaltool/config-user.yml is used
 config_file = ''
+# Set max_parallel_tasks
+# If none, read from config_file
+default_max_parallel_tasks = 8
 
 # List of recipes that require non-default SLURM options set above
 SPECIAL_RECIPES = {
@@ -74,12 +77,17 @@
     },
     'recipe_climate_change_hotspot': {
         'partition': '#SBATCH --partition=compute \n',
+        'memory': '#SBATCH --constraint=512G \n',
     },
     'recipe_collins13ipcc': {
         'partition': '#SBATCH --partition=compute \n',
         'time': '#SBATCH --time=08:00:00 \n',
         'memory': '#SBATCH --constraint=512G \n',
     },
+    'recipe_daily_era5': {
+        'partition': '#SBATCH --partition=compute \n',
+        'memory': '#SBATCH --constraint=512G \n',
+    },
     'recipe_eady_growth_rate': {
         'partition': '#SBATCH --partition=compute \n',
     },
@@ -125,6 +133,7 @@
     },
     'recipe_ipccwg1ar6ch3_fig_3_43': {
         'partition': '#SBATCH --partition=compute \n',
+        'time': '#SBATCH --time=08:00:00 \n',
     },
     'recipe_lauer22jclim_fig3-4_zonal': {
         'partition': '#SBATCH --partition=compute \n',
@@ -137,6 +146,7 @@
     },
     'recipe_mpqb_xch4': {
         'partition': '#SBATCH --partition=compute \n',
+        'memory': '#SBATCH --constraint=512G \n',
     },
     'recipe_perfmetrics_CMIP5': {
         'partition': '#SBATCH --partition=compute \n',
@@ -164,6 +174,9 @@
     'recipe_schlund20jgr_gpp_change_rcp85': {
         'partition': '#SBATCH --partition=compute \n',
     },
+    'recipe_sea_surface_salinity': {
+        'partition': '#SBATCH --partition=compute \n',
+    },
     'recipe_smpi': {
         'partition': '#SBATCH --partition=compute \n',
     },
@@ -174,6 +187,9 @@
         'partition': '#SBATCH --partition=compute \n',
         'time': '#SBATCH --time=08:00:00 \n',
     },
+    'recipe_thermodyn_diagtool': {
+        'partition': '#SBATCH --partition=compute \n',
+    },
     'recipe_wenzel16jclim': {
         'partition': '#SBATCH --partition=compute \n',
     },
@@ -182,26 +198,28 @@
     },
 }
 
+# These recipes cannot be run with the default number of parallel
+# tasks (max_parallel_tasks=8).
 # These recipes either use CMIP3 input data
 # (see https://github.com/ESMValGroup/ESMValCore/issues/430)
-# and recipes where tasks require the full compute node memory.
-ONE_TASK_RECIPES = [
-    'recipe_bock20jgr_fig_1-4',
-    'recipe_bock20jgr_fig_6-7',
-    'recipe_bock20jgr_fig_8-10',
-    'recipe_flato13ipcc_figure_96',
-    'recipe_flato13ipcc_figures_938_941_cmip3',
-    'recipe_ipccwg1ar6ch3_fig_3_9',
-    'recipe_ipccwg1ar6ch3_fig_3_42_a',
-    'recipe_ipccwg1ar6ch3_fig_3_43',
-    'recipe_check_obs',
-    'recipe_collins13ipcc',
-    'recipe_lauer22jclim_fig3-4_zonal',
-    'recipe_lauer22jclim_fig5_lifrac',
-    'recipe_smpi',
-    'recipe_smpi_4cds',
-    'recipe_wenzel14jgr',
-    ]
+# or require a large fraction of the compute node memory.
+MAX_PARALLEL_TASKS = {
+    'recipe_bock20jgr_fig_1-4': 1,
+    'recipe_bock20jgr_fig_6-7': 1,
+    'recipe_bock20jgr_fig_8-10': 1,
+    'recipe_flato13ipcc_figure_96': 1,
+    'recipe_flato13ipcc_figures_938_941_cmip3': 1,
+    'recipe_ipccwg1ar6ch3_fig_3_9': 1,
+    'recipe_ipccwg1ar6ch3_fig_3_42_a': 1,
+    'recipe_ipccwg1ar6ch3_fig_3_43': 1,
+    'recipe_check_obs': 1,
+    'recipe_collins13ipcc': 1,
+    'recipe_lauer22jclim_fig3-4_zonal': 3,
+    'recipe_lauer22jclim_fig5_lifrac': 3,
+    'recipe_smpi': 1,
+    'recipe_smpi_4cds': 1,
+    'recipe_wenzel14jgr': 1,
+}
 
 
 def generate_submit():
@@ -272,9 +290,12 @@ def generate_submit():
             else:
                 file.write(f'esmvaltool run --config_file '
                            f'{str(config_file)} {str(recipe)}')
-            if recipe.stem in ONE_TASK_RECIPES:
-                file.write(' --max_parallel_tasks=1')
-
+            # set max_parallel_tasks
+            max_parallel_tasks = MAX_PARALLEL_TASKS.get(
+                recipe.stem,
+                default_max_parallel_tasks,
+            )
+            file.write(f' --max_parallel_tasks={max_parallel_tasks}\n')
         if submit:
             subprocess.check_call(['sbatch', filename])
 
diff --git a/esmvaltool/utils/batch-jobs/parse_recipes_output.py b/esmvaltool/utils/batch-jobs/parse_recipes_output.py
index a8436d3c31..9df80c6cc0 100644
--- a/esmvaltool/utils/batch-jobs/parse_recipes_output.py
+++ b/esmvaltool/utils/batch-jobs/parse_recipes_output.py
@@ -7,10 +7,8 @@
 a GitHub issue or any other such documentation.
 """
 import datetime
-import os
-
 import glob
-
+import os
 
 # User change needed
 # directory where SLURM output files (.out and .err) are
@@ -81,11 +79,13 @@ def display_in_md():
     todaynow = datetime.datetime.now()
     print(f"## Recipe running session {todaynow}\n")
     with open("all_recipes.txt", "r", encoding='utf-8') as allrecs:
-        all_recs = [rec.strip() for rec in allrecs.readlines()]
+        all_recs = [
+            os.path.basename(rec.strip()) for rec in allrecs.readlines()
+        ]
 
     # parse different types of recipe outcomes
     recipe_list, failed, missing_dat = parse_output_file()
-    print("### Succesfully run recipes\n\n")
+    print("### Successfully ran recipes\n\n")
     print(f"{len(recipe_list)} out of {len(all_recs)}\n")
     for rec in recipe_list:
         print("- " + rec)
diff --git a/esmvaltool/utils/draft_release_notes.py b/esmvaltool/utils/draft_release_notes.py
index d00e752455..13bcb4324c 100644
--- a/esmvaltool/utils/draft_release_notes.py
+++ b/esmvaltool/utils/draft_release_notes.py
@@ -10,6 +10,7 @@
 """
 import datetime
 from pathlib import Path
+from zoneinfo import ZoneInfo
 
 import dateutil
 import esmvalcore
@@ -41,23 +42,24 @@
 }
 
 PREVIOUS_RELEASE = {
-    'esmvalcore': datetime.datetime(2022, 10, 13, 18),
-    'esmvaltool': datetime.datetime(2022, 10, 28, 18),
+    'esmvalcore':
+    datetime.datetime(2023, 7, 4, 11, tzinfo=ZoneInfo("CET")),
+    'esmvaltool':
+    datetime.datetime(2022, 10, 28, 18, tzinfo=ZoneInfo("CET")),
 }
 
-
 LABELS = {
     'esmvalcore': (
+        'backwards incompatible change',  # important, keep at the top
+        'deprecated feature',  # important, keep at the top
+        'bug',  # important, keep at the top
         'api',
-        'backwards incompatible change',
-        'bug',
         'cmor',
         'containerization',
         'community',
+        'dask',
         'deployment',
-        'deprecated feature',
         'documentation',
-        'enhancement',
         'fix for dataset',
         'installation',
         'iris',
@@ -65,20 +67,21 @@
         'release',
         'testing',
         'UX',
-        'variable derivation'
+        'variable derivation',
+        'enhancement',  # uncategorized, keep at the bottom
     ),
     'esmvaltool': (
-        'backwards incompatible change',
-        'bug',
+        'backwards incompatible change',  # important, keep at the top
+        'deprecated feature',  # important, keep at the top
+        'bug',  # important, keep at the top
         'community',
-        'deprecated feature',
         'documentation',
         'diagnostic',
         'preprocessor',
         'observations',
         'testing',
         'installation',
-        'enhancement',
+        'enhancement',  # uncategorized, keep at the bottom
     )
 }
 
@@ -87,6 +90,7 @@
     'deprecated feature': 'Deprecations',
     'bug': 'Bug fixes',
     'cmor': 'CMOR standard',
+    'dask': 'Computational performance improvements',
     'diagnostic': 'Diagnostics',
     'fix for dataset': 'Fixes for datasets',
     'observations': 'Observational and re-analysis dataset support',
@@ -210,7 +214,8 @@ def _compose_note(pull):
 
 
 def main():
-    """Entry point for the scrip."""
+    """Entry point for the script."""
+
     def display(lines, out):
         text = "\n".join(lines) + "\n"
         out.write(text)
diff --git a/esmvaltool/utils/recipe_filler.py b/esmvaltool/utils/recipe_filler.py
index 90001674b1..40f637c6d5 100755
--- a/esmvaltool/utils/recipe_filler.py
+++ b/esmvaltool/utils/recipe_filler.py
@@ -63,15 +63,6 @@
 CFG = {}
 
 
-# standard libs from esmvalcore ported here to avoid private func import
-def load_config_developer(cfg_file=None):
-    """Load the config developer file and initialize CMOR tables."""
-    cfg_developer = read_config_developer_file(cfg_file)
-    for key, value in cfg_developer.items():
-        CFG[key] = value
-    read_cmor_tables(CFG)
-
-
 def _purge_file_handlers(cfg: dict) -> None:
     """Remove handlers with filename set.
 
@@ -258,7 +249,7 @@ def read_config_user_file(config_file, folder_name, options=None):
     cfg['run_dir'] = os.path.join(cfg['output_dir'], 'run')
 
     # Read developer configuration file
-    load_config_developer(cfg['config_developer_file'])
+    read_cmor_tables(cfg['config_developer_file'])
 
     return cfg
 
@@ -300,16 +291,31 @@ def read_config_user_file(config_file, folder_name, options=None):
 }
 
 
+def _get_download_dir(yamlconf, cmip_era):
+    """Get the Download Directory from user config file."""
+    if 'download_dir' in yamlconf:
+        return os.path.join(yamlconf['download_dir'], cmip_era)
+    return False
+
+
 def _get_site_rootpath(cmip_era):
     """Get site (drs) from config-user.yml."""
     config_yml = get_args().config_file
     with open(config_yml, 'r') as yamf:
         yamlconf = yaml.safe_load(yamf)
     drs = yamlconf['drs'][cmip_era]
-    rootdir = yamlconf['rootpath'][cmip_era]
+
+    download_dir = _get_download_dir(yamlconf, cmip_era)
+    rootdir = [yamlconf['rootpath'][cmip_era], ]
+
+    if download_dir:
+        rootdir.append(download_dir)
     logger.debug("%s root directory %s", cmip_era, rootdir)
     if drs == 'default' and 'default' in yamlconf['rootpath']:
-        rootdir = yamlconf['rootpath']['default']
+        rootdir = [yamlconf['rootpath']['default'], ]
+        if download_dir:
+            rootdir.append(download_dir)
+
         logger.debug("Using drs default and "
                      "default: %s data directory", rootdir)
 
@@ -336,6 +342,7 @@ def _determine_basepath(cmip_era):
         rootpaths = _get_site_rootpath(cmip_era)[1]
     else:
         rootpaths = [_get_site_rootpath(cmip_era)[1]]
+
     basepaths = []
     for rootpath in rootpaths:
         if _get_input_dir(cmip_era) != os.path.sep:
diff --git a/esmvaltool/utils/testing/regression/compare.py b/esmvaltool/utils/testing/regression/compare.py
index ca96b65a68..a4ee33c5d3 100644
--- a/esmvaltool/utils/testing/regression/compare.py
+++ b/esmvaltool/utils/testing/regression/compare.py
@@ -80,7 +80,7 @@ def diff_attrs(ref: dict, cur: dict) -> str:
             msg.append(f"missing attribute '{key}'")
         elif not np.array_equal(ref[key], cur[key]):
             msg.append(f"value of attribute '{key}' is different: "
-                       f"expected '{cur[key]}' but found '{ref[key]}'")
+                       f"expected '{ref[key]}' but found '{cur[key]}'")
     for key in cur:
         if key not in ref:
             msg.append(f"extra attribute '{key}' with value '{cur[key]}'")
@@ -96,12 +96,15 @@ def diff_array(ref: np.ndarray, cur: np.ndarray) -> str:
     msg = []
     if cur.shape != ref.shape:
         msg.append("data has different shape")
+    elif np.issubdtype(ref.dtype, np.inexact) and np.issubdtype(
+            cur.dtype, np.inexact):
+        if not np.array_equal(ref, cur, equal_nan=True):
+            if np.allclose(ref, cur, equal_nan=True):
+                msg.append("data is almost but not quite the same")
+            else:
+                msg.append("data is different")
     elif not np.array_equal(ref, cur):
-        if np.issubdtype(ref.dtype, np.inexact) and np.issubdtype(
-                cur.dtype, np.inexact) and np.allclose(ref, cur):
-            msg.append("data is almost but not quite the same")
-        else:
-            msg.append("data is different")
+        msg.append("data is different")
     return as_txt(msg)
 
 
diff --git a/setup.py b/setup.py
index 834da8a70b..9aa0346b90 100755
--- a/setup.py
+++ b/setup.py
@@ -45,6 +45,7 @@
         'natsort',
         'nc-time-axis',
         'netCDF4',
+        'numba',
         'numpy!=1.24.3',  # severe masking bug
         'packaging',
         'openpyxl',
diff --git a/tests/integration/test_recipe_filler.py b/tests/integration/test_recipe_filler.py
index a0f3f85333..b78ac8c5f8 100644
--- a/tests/integration/test_recipe_filler.py
+++ b/tests/integration/test_recipe_filler.py
@@ -8,14 +8,8 @@
 import pytest
 import yaml
 
-import esmvaltool.utils.recipe_filler as recipe_filler
-from esmvalcore.cmor.table import read_cmor_tables
 from esmvaltool.utils.recipe_filler import run
 
-# Initialize with standard config developer file
-std_config = recipe_filler.read_config_developer_file()
-# Initialize CMOR tables
-read_cmor_tables(std_config)
 
 # Load test configuration
 with open(os.path.join(os.path.dirname(__file__),