diff --git a/.circleci/config.yml b/.circleci/config.yml
index 5be43d58c6..5957a5e7e3 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -258,7 +258,7 @@ jobs:
     # Test conda package installation
     docker:
       - image: condaforge/mambaforge:latest
-    resource_class: medium
+    resource_class: large
     steps:
       - run:
           command: |
diff --git a/.github/workflows/run-tests-monitor.yml b/.github/workflows/run-tests-monitor.yml
index 1efe54a66a..c381bfca9e 100644
--- a/.github/workflows/run-tests-monitor.yml
+++ b/.github/workflows/run-tests-monitor.yml
@@ -42,6 +42,8 @@ jobs:
         run: |
           mamba --version 2>&1 | tee test_linux_artifacts_python_${{ matrix.python-version }}/conda_version.txt
           python -V 2>&1 | tee test_linux_artifacts_python_${{ matrix.python-version }}/python_version.txt
+      - name: Inspect environment
+        run: conda list
       - name: Install pytest-monitor
         run: pip install pytest-monitor
       - name: Install ESMValTool
@@ -87,6 +89,10 @@ jobs:
         run: |
           mamba --version 2>&1 | tee test_osx_artifacts_python_${{ matrix.python-version }}/conda_version.txt
           python -V 2>&1 | tee test_osx_artifacts_python_${{ matrix.python-version }}/python_version.txt
+      - name: Inspect environment
+        run: conda list
+      - name: Install git
+        run: mamba install -c conda-forge git
       - name: Install pytest-monitor
         run: pip install pytest-monitor
       - name: Install ESMValTool
diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
index 9eec648279..8291bc6914 100644
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -6,7 +6,6 @@ on:
   push:
     branches:
       - main
-      - fix_recipe_filler_bkwds_incompatibility
   schedule:
     - cron: '0 0 * * *'
 
@@ -42,6 +41,8 @@ jobs:
           python -V 2>&1 | tee test_linux_artifacts_python_${{ matrix.python-version }}/python_version.txt
           # this is how to export variables to the GITHUB var environment
           echo "pver0=$(python -V)" >> $GITHUB_ENV
+      - name: Inspect environment
+        run: conda list
       - name: Install ESMValTool
         run: pip install -e .[develop] 2>&1 | tee test_linux_artifacts_python_${{ matrix.python-version }}/install.txt
       - name: Install Julia dependencies
@@ -54,6 +55,8 @@ jobs:
           echo "Python minor version changed after Julia install"
           python -V
           exit 1
+      - name: Inspect environment
+        run: conda list
       - name: Run flake8
         run: flake8
       - name: Run tests
@@ -91,8 +94,18 @@ jobs:
         run: |
           mamba --version 2>&1 | tee test_osx_artifacts_python_${{ matrix.python-version }}/conda_version.txt
           python -V 2>&1 | tee test_osx_artifacts_python_${{ matrix.python-version }}/python_version.txt
+      - name: Inspect environment
+        run: conda list
+      - name: Determine if git
+        run: |
+          which git
+          git --version
+      - name: Install git
+        run: mamba install -c conda-forge git
       - name: Install ESMValTool
         run: pip install -e .[develop] 2>&1 | tee test_osx_artifacts_python_${{ matrix.python-version }}/install.txt
+      - name: Inspect environment
+        run: conda list
       - name: Run flake8
         run: flake8
       - name: Run tests
diff --git a/CITATION.cff b/CITATION.cff
index 5c253e3bb5..cd621538b7 100644
--- a/CITATION.cff
+++ b/CITATION.cff
@@ -175,6 +175,11 @@ authors:
     family-names: Hagemann
     given-names: Stefan
     orcid: "https://orcid.org/0000-0001-5444-2945"
+  -
+    affiliation: "University of Canterbury, New Zealand"
+    family-names: Hardacre
+    given-names: Catherine
+    orcid: "https://orcid.org/0000-0001-9093-4656"
   -
     affiliation: "ISAC-CNR, Italy"
     name-particle: von
diff --git a/README.md b/README.md
index 4fbe8aa84e..b196f7fbb8 100644
--- a/README.md
+++ b/README.md
@@ -5,8 +5,7 @@
 [![Chat on Matrix](https://matrix.to/img/matrix-badge.svg)](https://matrix.to/#/#ESMValGroup_Lobby:gitter.im)
 [![CircleCI](https://circleci.com/gh/ESMValGroup/ESMValTool/tree/main.svg?style=svg)](https://circleci.com/gh/ESMValGroup/ESMValTool/tree/main)
 [![Test in Full Development Mode](https://github.com/ESMValGroup/ESMValTool/actions/workflows/test-development.yml/badge.svg)](https://github.com/ESMValGroup/ESMValTool/actions/workflows/test-development.yml)
-[![Codacy Badge](https://app.codacy.com/project/badge/Coverage/79bf6932c2e844eea15d0fb1ed7e415c)](https://www.codacy.com/gh/ESMValGroup/ESMValTool?utm_source=github.com&utm_medium=referral&utm_content=ESMValGroup/ESMValTool&utm_campaign=Badge_Coverage)
-[![Codacy Badge](https://app.codacy.com/project/badge/Grade/79bf6932c2e844eea15d0fb1ed7e415c)](https://www.codacy.com/gh/ESMValGroup/ESMValTool?utm_source=github.com&utm_medium=referral&utm_content=ESMValGroup/ESMValTool&utm_campaign=Badge_Grade)
+[![Codacy Badge](https://app.codacy.com/project/badge/Grade/79bf6932c2e844eea15d0fb1ed7e415c)](https://app.codacy.com/gh/ESMValGroup/ESMValTool/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_grade)
 [![Docker Build Status](https://img.shields.io/docker/cloud/build/esmvalgroup/esmvaltool.svg)](https://hub.docker.com/r/esmvalgroup/esmvaltool/)
 [![Anaconda-Server Badge](https://img.shields.io/conda/vn/conda-forge/ESMValTool?color=blue&label=conda-forge&logo=conda-forge&logoColor=white)](https://anaconda.org/conda-forge/esmvaltool)
 ![stand with Ukraine](https://badgen.net/badge/stand%20with/UKRAINE/?color=0057B8&labelColor=FFD700)
diff --git a/conda-linux-64.lock b/conda-linux-64.lock
index ecaa328e4f..d975ed7293 100644
--- a/conda-linux-64.lock
+++ b/conda-linux-64.lock
@@ -1,97 +1,100 @@
 # Generated by conda-lock.
 # platform: linux-64
-# input_hash: e484ece0f2a2f8e9f3009df8b9de1be258c70868b9df17114bb859f15f903ccf
+# input_hash: f274c79b28e051195e24e39904d5f4bd4713f149f8e57358a8df008d2fb8342e
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-https://conda.anaconda.org/conda-forge/noarch/r-s2dverification-2.10.3-r41hc72bb7e_1.tar.bz2#2253f130c8dab435824d6ddb10a41c73
+https://conda.anaconda.org/conda-forge/linux-64/r-geomap-2.5_5-r43h57805ef_0.conda#dc942c6f2062894f7baf00fc6b907a79
+https://conda.anaconda.org/conda-forge/noarch/r-s2dverification-2.10.3-r43hc72bb7e_2.conda#13f4b1126272c8f195fc6ef38cc19d31
 https://conda.anaconda.org/conda-forge/noarch/autodocsumm-0.2.6-pyhd8ed1ab_0.tar.bz2#4409dd7e06a62c3b2aa9e96782c49c6d
 https://conda.anaconda.org/conda-forge/noarch/nbsphinx-0.9.3-pyhd8ed1ab_0.conda#0dbaa7d08d3d79b2a1a4dd6a02cc4581
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@@ -656,5 +675,5 @@ https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-applehelp-1.0.8-pyhd
 https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-devhelp-1.0.6-pyhd8ed1ab_0.conda#d7e4954df0d3aea2eacc7835ad12671d
 https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-htmlhelp-2.0.5-pyhd8ed1ab_0.conda#7e1e7437273682ada2ed5e9e9714b140
 https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-qthelp-1.0.7-pyhd8ed1ab_0.conda#26acae54b06f178681bfb551760f5dd1
-https://conda.anaconda.org/conda-forge/noarch/sphinx-7.2.6-pyhd8ed1ab_0.conda#bbfd1120d1824d2d073bc65935f0e4c0
+https://conda.anaconda.org/conda-forge/noarch/sphinx-7.3.7-pyhd8ed1ab_0.conda#7b1465205e28d75d2c0e1a868ee00a67
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diff --git a/doc/sphinx/source/community/release_strategy/release_strategy.rst b/doc/sphinx/source/community/release_strategy/release_strategy.rst
index b358fdb7f3..cae1c43807 100644
--- a/doc/sphinx/source/community/release_strategy/release_strategy.rst
+++ b/doc/sphinx/source/community/release_strategy/release_strategy.rst
@@ -54,9 +54,19 @@ With the following release schedule, we strive to have three releases per year a
 Upcoming releases
 ^^^^^^^^^^^^^^^^^
 
-- 2.11.0 (Release Manager: TBD)
+- 2.11.0 (Release Manager: Met Office: `Emma Hogan`_, `Chris Billows`_, `Ed Gillett`_)
 
-Planned for February or March 2024
++------------+------------+----------------------------------------+-------------------------------------+
+|  Planned   |    Done    |            Event                       |             Changelog               |
++============+============+========================================+=====================================+
+| 2024-04-22 |            | ESMValCore `Feature Freeze`_           |                                     |
++------------+------------+----------------------------------------+-------------------------------------+
+| 2023-05-03 |            | ESMValCore released                    |                                     |
++------------+------------+----------------------------------------+-------------------------------------+
+| 2023-05-06 |            | ESMValTool `Feature Freeze`_           |                                     |
++------------+------------+----------------------------------------+-------------------------------------+
+| 2023-05-17 |            | ESMValTool released                    |                                     |
++------------+------------+----------------------------------------+-------------------------------------+
 
 Past releases
 ^^^^^^^^^^^^^
@@ -647,3 +657,6 @@ Changelog
 .. _Manuel Schlund: https://github.com/schlunma
 .. _Javier Vegas-Regidor: https://github.com/jvegasbsc
 .. _Klaus Zimmermann: https://github.com/zklaus
+.. _Emma Hogan: https://github.com/ehogan
+.. _Chris Billows: https://github.com/chrisbillowsMO
+.. _Ed Gillett: https://github.com/mo-gill
diff --git a/doc/sphinx/source/develop/dataset.rst b/doc/sphinx/source/develop/dataset.rst
index 4a87a2dd93..f3c168a17c 100644
--- a/doc/sphinx/source/develop/dataset.rst
+++ b/doc/sphinx/source/develop/dataset.rst
@@ -74,7 +74,7 @@ The folder ``RAWOBS`` needs the subdirectories ``Tier1``, ``Tier2`` and
 ``Tier3``. The different tiers describe the different levels of restrictions
 for downloading (e.g. providing contact information, licence agreements)
 and using the observations. The unformatted (raw) observations
-should then be stored then in the appropriate of these three folders.
+should then be stored in the appropriate of these three folders.
 
 For each additional dataset, an entry needs to be made to the file 
 `datasets.yml
diff --git a/doc/sphinx/source/recipes/figures/aod_aeronet_assess/UKESM1-0-LL_CMIP_AERmon_historical_od440aer_gn_1988_2008_DJF.png b/doc/sphinx/source/recipes/figures/aod_aeronet_assess/UKESM1-0-LL_CMIP_AERmon_historical_od440aer_gn_1988_2008_DJF.png
new file mode 100644
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diff --git a/doc/sphinx/source/recipes/figures/aod_aeronet_assess/UKESM1-0-LL_CMIP_AERmon_historical_od440aer_gn_1988_2008_JJA.png b/doc/sphinx/source/recipes/figures/aod_aeronet_assess/UKESM1-0-LL_CMIP_AERmon_historical_od440aer_gn_1988_2008_JJA.png
new file mode 100644
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diff --git a/doc/sphinx/source/recipes/figures/aod_aeronet_assess/UKESM1-0-LL_CMIP_AERmon_historical_od440aer_gn_1988_2008_MAM.png b/doc/sphinx/source/recipes/figures/aod_aeronet_assess/UKESM1-0-LL_CMIP_AERmon_historical_od440aer_gn_1988_2008_MAM.png
new file mode 100644
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diff --git a/doc/sphinx/source/recipes/figures/aod_aeronet_assess/UKESM1-0-LL_CMIP_AERmon_historical_od440aer_gn_1988_2008_SON.png b/doc/sphinx/source/recipes/figures/aod_aeronet_assess/UKESM1-0-LL_CMIP_AERmon_historical_od440aer_gn_1988_2008_SON.png
new file mode 100644
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diff --git a/doc/sphinx/source/recipes/figures/aod_aeronet_assess/UKESM1-0-LL_CMIP_AERmon_historical_od440aer_gn_1988_2008_scatter.png b/doc/sphinx/source/recipes/figures/aod_aeronet_assess/UKESM1-0-LL_CMIP_AERmon_historical_od440aer_gn_1988_2008_scatter.png
new file mode 100644
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diff --git a/doc/sphinx/source/recipes/figures/aod_aeronet_assess/UKESM1-0-LL_CMIP_AERmon_historical_od440aer_gn_1994_2014_DJF.png b/doc/sphinx/source/recipes/figures/aod_aeronet_assess/UKESM1-0-LL_CMIP_AERmon_historical_od440aer_gn_1994_2014_DJF.png
new file mode 100644
index 0000000000..f653410e07
Binary files /dev/null and b/doc/sphinx/source/recipes/figures/aod_aeronet_assess/UKESM1-0-LL_CMIP_AERmon_historical_od440aer_gn_1994_2014_DJF.png differ
diff --git a/doc/sphinx/source/recipes/figures/aod_aeronet_assess/UKESM1-0-LL_CMIP_AERmon_historical_od440aer_gn_1994_2014_JJA.png b/doc/sphinx/source/recipes/figures/aod_aeronet_assess/UKESM1-0-LL_CMIP_AERmon_historical_od440aer_gn_1994_2014_JJA.png
new file mode 100644
index 0000000000..6474acc856
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diff --git a/doc/sphinx/source/recipes/figures/aod_aeronet_assess/UKESM1-0-LL_CMIP_AERmon_historical_od440aer_gn_1994_2014_MAM.png b/doc/sphinx/source/recipes/figures/aod_aeronet_assess/UKESM1-0-LL_CMIP_AERmon_historical_od440aer_gn_1994_2014_MAM.png
new file mode 100644
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diff --git a/doc/sphinx/source/recipes/figures/aod_aeronet_assess/UKESM1-0-LL_CMIP_AERmon_historical_od440aer_gn_1994_2014_SON.png b/doc/sphinx/source/recipes/figures/aod_aeronet_assess/UKESM1-0-LL_CMIP_AERmon_historical_od440aer_gn_1994_2014_SON.png
new file mode 100644
index 0000000000..ce4d3fac08
Binary files /dev/null and b/doc/sphinx/source/recipes/figures/aod_aeronet_assess/UKESM1-0-LL_CMIP_AERmon_historical_od440aer_gn_1994_2014_SON.png differ
diff --git a/doc/sphinx/source/recipes/figures/aod_aeronet_assess/UKESM1-0-LL_CMIP_AERmon_historical_od440aer_gn_1994_2014_scatter.png b/doc/sphinx/source/recipes/figures/aod_aeronet_assess/UKESM1-0-LL_CMIP_AERmon_historical_od440aer_gn_1994_2014_scatter.png
new file mode 100644
index 0000000000..9226bca81a
Binary files /dev/null and b/doc/sphinx/source/recipes/figures/aod_aeronet_assess/UKESM1-0-LL_CMIP_AERmon_historical_od440aer_gn_1994_2014_scatter.png differ
diff --git a/doc/sphinx/source/recipes/index.rst b/doc/sphinx/source/recipes/index.rst
index edcc48977a..0f0ce7667d 100644
--- a/doc/sphinx/source/recipes/index.rst
+++ b/doc/sphinx/source/recipes/index.rst
@@ -62,6 +62,7 @@ Atmosphere
    recipe_thermodyn_diagtool
    recipe_validation
    recipe_radiation_budget
+   recipe_aod_aeronet_assess
 
 Climate metrics
 ^^^^^^^^^^^^^^^
diff --git a/doc/sphinx/source/recipes/recipe_aod_aeronet_assess.rst b/doc/sphinx/source/recipes/recipe_aod_aeronet_assess.rst
new file mode 100644
index 0000000000..fec1bed761
--- /dev/null
+++ b/doc/sphinx/source/recipes/recipe_aod_aeronet_assess.rst
@@ -0,0 +1,161 @@
+.. _recipe_aod_aeronet_assess:
+
+AOD AeroNET Assess
+==================
+
+Overview
+--------
+
+This diagnostic evaluates model aerosol optical depth (AOD) against ground
+based observations from the AeroNET measurement network. Monthly mean AOD
+data is downloaded from the AeroNET website and formatted (CMORized) using the
+AERONET downloader and formatter within ESMValTool.
+
+Multiannual seasonal means are calculated from the model output and compared
+with a multiannual seasonal mean climatology generated from AeroNET
+observational data. At each AeroNET station the data are screened for validity
+according to the following default criteria:
+
+  * 1. Monthly means must be generated from at least one AOD observation in that
+    month.
+
+  * 2. Seasonal means for DJF, MAM, JJA and SON must be calculated from three 
+    monthly means, i.e. a monthly mean from December January and Feburary.
+
+  * 3. For a given year to be valid, there must be a seasonal mean for each climate
+    season i.e. DJF, MAM, JJA and SON.
+
+  * 4. For a multiannual seasonal means there must be at least five seasonaal means
+    over the time range of interest.
+
+NOTE: The code is designed to be flexible and the default criteria can be 
+changed according to the user's requirements (see the user settings below).  
+
+The evaluation is visualised by plotting model output as 2D filled contours and
+overlaying AeroNET observations at model grid cells co-located with the AeroNET
+measurement stations. Statistical data (root mean square error) is generated
+using AeroNET observations at model grid cells co-located with the AeroNET
+measurement stations.
+
+Available recipes and diagnostics
+---------------------------------
+
+Recipes are stored in esmvaltool/recipes/
+
+    * recipe_aod_aeronet_assess.yml
+
+Diagnostics are stored in esmvaltool/diag_scripts/aerosols/
+
+    * aod_aeronet_assess.py: Plot the AOD evaluation.
+    * aero_utils.py: Utility functions commonly used by aerosol assessment routines.
+
+
+User settings in recipe
+-----------------------
+
+#. Script aod_aeronet_assess.py
+
+   *Required settings for script*
+
+   * wavel: The wavelength of interest for the evaluation, currently set up for 440nm
+   * min_days_per_mon: The minimum number of days used to calculate the AOD monthly mean
+   * min_mon_per_seas: The minimum number of seasons used to calculate each
+     seasonal mean. This must be between 1 and 3.
+   * min_seas_per_year: The minimum number of seasonal means in each year. This
+     must be between 1 and 4.
+   * min_seas_per_clim: The minimum number of seasonal means used to calculate
+     the multiannual seasonal mean. This must be btween 1 and the number of years
+     of available AeroNET data.
+
+   *Optional settings for script*
+
+   * None
+
+   *Required settings for variables*
+
+   * None
+
+   *Optional settings for variables*
+
+   * None
+
+   *Required settings for preprocessor*
+
+   * None
+
+   *Optional settings for preprocessor*
+
+   * None
+
+   *Color tables*
+
+   * brewer_Spectral_11
+
+
+Variables
+---------
+
+* od440aer (atmos, monthly mean, longitude latitude time)
+
+
+Observations and reformat scripts
+---------------------------------
+
+* Note: (1) obs4MIPs data can be used directly without any preprocessing; (2) 
+  see headers of reformat scripts for non-obs4MIPs data for download
+  instructions.
+
+* The AeroNET data is downloaded from the AeroNET website using the downloader:
+
+  .. code-block:: yaml
+
+        $ esmvaltool data download AERONET.
+
+* The AeroNET data is formatteed (CMORized) using the formatter:
+
+  .. code-block:: yaml
+
+        $ esmvaltool data format AERONET.
+
+
+
+References
+----------
+* Holben B.N., T.F.Eck, I.Slutsker, D.Tanre, J.P.Buis, A.Setzer, E.Vermote, J.A.Reagan, Y.Kaufman, T.Nakajima, F.Lavenu, I.Jankowiak, and A.Smirnov, 1998: AERONET - A federated instrument network and data archive for aerosol characterization, Rem. Sens. Environ., 66, 1-16.
+
+* Holben, B.N., D.Tanre, A.Smirnov, T.F.Eck, I.Slutsker, N.Abuhassan, W.W.Newcomb, J.Schafer, B.Chatenet, F.Lavenue, Y.J.Kaufman, J.Vande Castle, A.Setzer, B.Markham, D.Clark, R.Frouin, R.Halthore, A.Karnieli, N.T.O'Neill, C.Pietras, R.T.Pinker, K.Voss, and G.Zibordi, 2001: An emerging ground-based aerosol climatology: Aerosol Optical Depth from AERONET, J. Geophys. Res., 106, 12 067-12 097.
+
+* Mulcahy, J. P., Johnson, C., Jones, C. G., Povey, A. C., Scott, C. E., Sellar, A., Turnock, S. T., Woodhouse, M. T., Abraham, N. L., Andrews, M. B., Bellouin, N., Browse, J., Carslaw, K. S., Dalvi, M., Folberth, G. A., Glover, M., Grosvenor, D. P., Hardacre, C., Hill, R., Johnson, B., Jones, A., Kipling, Z., Mann, G., Mollard, J., O’Connor, F. M., Palmiéri, J., Reddington, C., Rumbold, S. T., Richardson, M., Schitgens, N. A. J., Stier, P., Stringer, M., Tang, Y., Walton, J., Woodward, S., and Yool. A.: Description and evaluation of aerosol in UKESM1 and HadGEM3-GC3.1 CMIP6 historical simulations, Geosci. Model Dev., 13, 6383–6423, 2020
+
+Example plots
+-------------
+
+.. _fig_aod_aeronet_assess_1:
+.. figure::  /recipes/figures/aod_aeronet_assess/UKESM1-0-LL_CMIP_AERmon_historical_od440aer_gn_1994_2014_DJF.png
+   :align:   center
+
+   Evaluation of AOD at 440 nm from UKESM1 historical ensemble member r1i1p1f2 against the AeroNET climatology from ground-based observations for Dec-Jan-Feb. The multiannual seasonal mean is calculated for the model data for the period 1994-2014. The model output is overlaid with the observational climatology.
+
+.. _fig_aod_aeronet_assess_2:
+.. figure::  /recipes/figures/aod_aeronet_assess/UKESM1-0-LL_CMIP_AERmon_historical_od440aer_gn_1994_2014_MAM.png
+   :align:   center
+
+   Evaluation of AOD at 440 nm from UKESM1 historical ensemble member r1i1p1f2 against the AeroNET climatology from ground-based observations for Mar_Apr_May. The multiannual seasonal mean is calculated for the model data for the period 1994-2014. The model output is overlaid with the observational climatology.
+
+.. _fig_aod_aeronet_assess_3:
+.. figure::  /recipes/figures/aod_aeronet_assess/UKESM1-0-LL_CMIP_AERmon_historical_od440aer_gn_1994_2014_JJA.png
+   :align:   center
+
+   Evaluation of AOD at 440 nm from UKESM1 historical ensemble member r1i1p1f2 against the AeroNET climatology from ground-based observations for Jun-Jul-Aug. The multiannual seasonal mean is calculated for the model data for the period 1994-2014. The model output is overlaid with the observational climatology.
+
+.. _fig_aod_aeronet_assess_4:
+.. figure::  /recipes/figures/aod_aeronet_assess/UKESM1-0-LL_CMIP_AERmon_historical_od440aer_gn_1994_2014_SON.png
+   :align:   center
+
+   Evaluation of AOD at 440 nm from UKESM1 historical ensemble member r1i1p1f2 against the AeroNET climatology from ground-based observations for Sep-Oct-Nov. The multiannual seasonal mean is calculated for the model data for the period 1994-2014. The model output is overlaid with the observational climatology.
+
+.. _fig_aod_aeronet_assess_5:
+.. figure::  /recipes/figures/aod_aeronet_assess/UKESM1-0-LL_CMIP_AERmon_historical_od440aer_gn_1994_2014_scatter.png
+   :align:   center
+
+   Evaluation of AOD at 440 nm from UKESM1 historical ensemble member r1i1p1f2 against the AeroNET climatology from ground-based observations for Dec-Jan-Feb, Mar_Apr_May, Jun-Jul-Aug and Sep-Oct-Nov. The multiannual seasonal mean is calculated for the model data for the period 1994-2014.
diff --git a/doc/sphinx/source/recipes/recipe_clouds.rst b/doc/sphinx/source/recipes/recipe_clouds.rst
index 3106a616d7..d4497a5d4f 100644
--- a/doc/sphinx/source/recipes/recipe_clouds.rst
+++ b/doc/sphinx/source/recipes/recipe_clouds.rst
@@ -30,6 +30,9 @@ Four recipes are available to evaluate cloud climatologies from CMIP models.
    estimated as the relative temporal standard deviation from multi-year timeseries
    of data with the temporal standard deviations calculated from monthly anomalies
    after subtracting the climatological mean seasonal cycle.
+   Note that the satellite observations used in the original recipe (UWisc) is not
+   maintained anymore and has been superseeded by MAC-LWP (`Elsaesser et al., 2017`_).
+   We recommend using MAC-LWP.
 
 4) Recipe family recipe_lauer22jclim_*.yml is an extension of
    recipe_lauer13jclim.yml for evaluation of cloud radiative forcing
@@ -49,6 +52,7 @@ Four recipes are available to evaluate cloud climatologies from CMIP models.
 .. _`Flato et al., 2013`: https://www.ipcc.ch/site/assets/uploads/2018/02/WG1AR5_Chapter09_FINAL.pdf
 .. _`Lauer and Hamilton (2013)`: https://journals.ametsoc.org/view/journals/clim/26/11/jcli-d-12-00451.1.xml
 .. _`Lauer et al. (2023)`: https://journals.ametsoc.org/view/journals/clim/36/2/JCLI-D-22-0181.1.xml
+.. _`Elsaesser et al., 2017`: https://journals.ametsoc.org/view/journals/clim/30/24/jcli-d-16-0902.1.xml
 
 
 Available recipes and diagnostics
diff --git a/environment.yml b/environment.yml
index e04c2711c1..34f567214a 100644
--- a/environment.yml
+++ b/environment.yml
@@ -50,6 +50,7 @@ dependencies:
   - psy-reg
   - psy-simple
   - pyproj >=2.1
+  - pys2index  # only from conda-forge
   - python >=3.9
   - python-cdo
   - python-dateutil
@@ -68,7 +69,7 @@ dependencies:
   - xgboost >1.6.1  # github.com/ESMValGroup/ESMValTool/issues/2779
   - xlsxwriter
   - zarr
-  # Python packages needed for testing
+  # Python packages needed for unit testing
   - flake8
   - pytest >=3.9,!=6.0.0rc1,!=6.0.0
   - pytest-cov
@@ -85,6 +86,7 @@ dependencies:
   # Python packages needed for development
   - codespell
   - docformatter
+  - imagehash
   - isort
   - pre-commit
   - prospector
@@ -103,7 +105,7 @@ dependencies:
   - cdo
   - r-base >=3.5
   - r-abind
-  - r-akima <=0.6-2.3  # see https://github.com/ESMValGroup/ESMValTool/issues/2695
+  - r-akima
   - r-climdex.pcic
   - r-climprojdiags
   - r-docopt
diff --git a/environment_osx.yml b/environment_osx.yml
index 058e82aadf..5593c7afe0 100644
--- a/environment_osx.yml
+++ b/environment_osx.yml
@@ -50,6 +50,7 @@ dependencies:
   - psy-reg
   - psy-simple
   - pyproj>=2.1
+  - pys2index  # only from conda-forge
   - python >=3.9
   - python-cdo
   - python-dateutil
@@ -68,7 +69,7 @@ dependencies:
   - xgboost >1.6.1  # github.com/ESMValGroup/ESMValTool/issues/2779
   - xlsxwriter
   - zarr
-  # Python packages needed for testing
+  # Python packages needed for unit testing
   - flake8
   - pytest >=3.9,!=6.0.0rc1,!=6.0.0
   - pytest-cov
@@ -85,6 +86,7 @@ dependencies:
   # Python packages needed for development
   - codespell
   - docformatter
+  - imagehash
   - isort
   - pre-commit
   - prospector
diff --git a/esmvaltool/cmorizers/data/cmor_config/ESACCI-WATERVAPOUR.yml b/esmvaltool/cmorizers/data/cmor_config/ESACCI-WATERVAPOUR.yml
index 400fb7e8da..eda705c000 100644
--- a/esmvaltool/cmorizers/data/cmor_config/ESACCI-WATERVAPOUR.yml
+++ b/esmvaltool/cmorizers/data/cmor_config/ESACCI-WATERVAPOUR.yml
@@ -1,34 +1,35 @@
 ---
 # Common global attributes for Cmorizer output
 
-
-# Input
-# CDR-2
-filename: 'ESACCI-WATERVAPOUR-L3S-TCWV-*-05deg-{year}{month}-fv3.1.nc'
-# CDR-1
-# filename: 'dataset3_1/CDR-1/monthlies/ESACCI-WATERVAPOUR-L3?-TCWV-*-05deg-{year}{month}-fv3.1.nc'
 attributes:
   dataset_id: ESACCI-WATERVAPOUR
-  # CDR-2
-  version: 'CDR2-L3S-05deg_fv3.1'
-  # CDR-1
-  # version: 'CDR1-L3-05deg_fv3.1'
+  version: CDR2-L3-COMBI-05deg-fv3.1
   tier: 3
   modeling_realm: sat
-  project_id: OBS
-  source: "ftp.brockmann-consult.de, access currently restricted"
+  project_id: OBS6
+  source: https://wui.cmsaf.eu/safira/action/viewDoiDetails?acronym=COMBI_V001
   reference: ["esacci-watervapour"]
-  comment: "Preliminary data."
+  comment: "This CMORizer is for the CDR2 version of dataset (global coverage).
+            Download is possible after registration and ordering of the data."
 
 # Variables to cmorize (here use only filename prefix)
 variables:
-  prw:
+# monthly frequency
+  prw_mon:
+    short_name: prw
     mip: Amon
     raw: tcwv
-    # Output automatially added: Amon_<var>_200910-200910.nc
-    # CDR-2
-    file: OBS_ESACCI-WATERVAPOUR-CDR2-L3S-TCWV-05deg
-    # CDR-1
-    # file: OBS_ESACCI-WATERVAPOUR-CDR1-L3-TCWV-05deg
-    start_year: 2003
+    frequency: mon
+    filename: HTWmm{year}*000000313WVCCI01GL.nc
+    start_year: 2002
+    end_year: 2017
+
+# daily frequency 
+  prw_day:
+    short_name: prw
+    mip: Eday
+    raw: tcwv
+    frequency: day
+    filename: HTWdm{year}*000000313WVCCI01GL.nc
+    start_year: 2002
     end_year: 2017
diff --git a/esmvaltool/cmorizers/data/datasets.yml b/esmvaltool/cmorizers/data/datasets.yml
index 58702b7535..6ed10a4340 100644
--- a/esmvaltool/cmorizers/data/datasets.yml
+++ b/esmvaltool/cmorizers/data/datasets.yml
@@ -534,14 +534,12 @@ datasets:
 
   ESACCI-WATERVAPOUR:
     tier: 3
-    source: Marc Schröder, ftp.brockmann-consult.de
-    last_access: 2021-03-29
+    source: https://wui.cmsaf.eu/safira/action/viewDoiDetails?acronym=COMBI_V001
+    last_access: 2024-02-21
     info: |
-      Currently still restricted because preliminary.
-      Download and processing instructions:
-        FTP server: ftp.brockmann-consult.de, access currently restricted
-                    data/tcwv/dataset3_1/CDR-*/...
-        All files need to be in one directory, not in yearly subdirectories.
+      CDR2 requires registration at EUMETSAT CM SAF, the information on how to 
+          download the order will be emailed once the order is ready.
+      All files need to be in one directory, not in yearly subdirectories.
 
   ESDC:
     tier: 2
diff --git a/esmvaltool/cmorizers/data/formatters/datasets/esacci_watervapour.py b/esmvaltool/cmorizers/data/formatters/datasets/esacci_watervapour.py
index 0985e5d6e6..d4901007cc 100644
--- a/esmvaltool/cmorizers/data/formatters/datasets/esacci_watervapour.py
+++ b/esmvaltool/cmorizers/data/formatters/datasets/esacci_watervapour.py
@@ -1,24 +1,27 @@
 """ESMValTool CMORizer for ESACCI-WATERVAPOUR data.
 
 Tier
-   Tier 3: currently still restricted because preliminary.
+   Tier 3: CDR2 requires registration at EUMETSAT CM SAF.
 
 Source
-   Marc Schröder, ftp.brockmann-consult.de
+   https://wui.cmsaf.eu/safira/action/viewDoiDetails?acronym=COMBI_V001
 
 Last access
-   20210329
+   20240221
 
 Download and processing instructions
-   FTP server: ftp.brockmann-consult.de, access currently restricted
-               data/tcwv/dataset3_1/CDR-*/...
+   CDR2 requires registration at EUMETSAT CM SAF, the information on how to
+        download the order will be emailed once the order is ready.
    All files need to be in one directory, not in yearly subdirectories.
 
 Modification history
+   20240221-malinina_elizaveta: Adjust for daily cmorization and updated
+                                filenames, remove CDR1 due to irrelevance.
    20210607-weigel_katja: Fix for monthly time bounds.
    20210408-weigel_katja: written.
 """
 
+import glob
 import logging
 import os
 
@@ -60,30 +63,30 @@ def extract_variable(var_info, raw_info, attrs, year):
 
 def cmorization(in_dir, out_dir, cfg, cfg_user, start_date, end_date):
     """Cmorize data."""
-    # cmor_table = cfg['cmor_table']
     glob_attrs = cfg['attributes']
 
     # run the cmorization
-    for var, vals in cfg['variables'].items():
+    for var_name, vals in cfg['variables'].items():
+        var = vals['short_name']
         var_info = cfg['cmor_table'].get_variable(vals['mip'], var)
         glob_attrs['mip'] = vals['mip']
-        raw_info = {'name': vals['raw'], 'file': vals['file']}
-        inpfile = os.path.join(in_dir, cfg['filename'])
-        logger.info("CMORizing var %s from file type %s", var, inpfile)
-        # years = range(vals['start_year'], vals['end_year'] + 1)
-        months = ["0" + str(mo) for mo in range(1, 10)] + ["10", "11", "12"]
+        raw_info = {'name': vals['raw']}
+        inpfile_pattern = os.path.join(in_dir, vals['filename'])
+        logger.info("CMORizing var %s from file type %s", var, inpfile_pattern)
         for year in range(vals['start_year'], vals['end_year'] + 1):
-            monthly_cubes = []
-            for month in months:
-                raw_info['file'] = inpfile.format(year=year, month=month)
+            data_cubes = []
+            year_inpfile_pattern = inpfile_pattern.format(year=year)
+            inpfiles = sorted(glob.glob(year_inpfile_pattern))
+            for inpfile in inpfiles:
+                raw_info['file'] = inpfile
                 logger.info("CMORizing var %s from file type %s", var,
                             raw_info['file'])
-                monthly_cubes.append(
+                data_cubes.append(
                     extract_variable(var_info, raw_info, glob_attrs, year))
-            yearly_cube = concatenate(monthly_cubes)
+            yearly_cube = concatenate(data_cubes)
             # Fix monthly time bounds
             time = yearly_cube.coord('time')
-            time.bounds = get_time_bounds(time, 'mon')
+            time.bounds = get_time_bounds(time, vals['frequency'])
             save_variable(yearly_cube,
                           var,
                           out_dir,
diff --git a/esmvaltool/config-references.yml b/esmvaltool/config-references.yml
index 285ace740e..b5f43bc911 100644
--- a/esmvaltool/config-references.yml
+++ b/esmvaltool/config-references.yml
@@ -252,6 +252,10 @@ authors:
     name: Hansson, Ulf
     institute: SMHI, Sweden
     orcid:
+  hardacre_catherine:
+    name: Hardacre, Catherine
+    institute: University of Canterbury, New Zealand
+    orcid: https://orcid.org/0000-0001-9093-4656
   hassler_birgit:
     name: Hassler, Birgit
     institute: DLR, Germany
diff --git a/esmvaltool/diag_scripts/aerosols/aero_utils.py b/esmvaltool/diag_scripts/aerosols/aero_utils.py
new file mode 100644
index 0000000000..623a85f2aa
--- /dev/null
+++ b/esmvaltool/diag_scripts/aerosols/aero_utils.py
@@ -0,0 +1,193 @@
+"""Part of the ESMValTool Aerosol diagnostics.
+
+This module contains utility functions commonly used by aerosol
+assessment routines.
+"""
+
+import iris
+import numpy as np
+
+
+class AeroAnsError(Exception):
+
+    """Exception class for errors raised when model data is checked in the
+    extract_pt module.
+    """
+
+
+def add_bounds(cube):
+    """Add bounds to a cube's latitude and longitude coordinates.
+
+    Parameters
+    ----------
+    cube : Iris cube
+        Iris cube with latitude and longitude coordinates.
+
+    Returns
+    -------
+    cube : Iris cube.
+        Iris cube with bounds added to the latitude and longitude coordinates.
+    """
+
+    if not cube.coord('latitude').has_bounds():
+        cube.coord('latitude').guess_bounds()
+    if not cube.coord('longitude').has_bounds():
+        cube.coord('longitude').guess_bounds()
+
+    return cube
+
+
+def extract_pt(icube, pt_lat, pt_lon, height=None, level=None, nearest=False):
+    """Extracts given location(s) (3-D) from a cube.
+
+    Method
+    ------
+    Uses Iris module Analysis.Interpolate to extract values,
+    initially based on horizontal coordinates, and then based on
+    height, if specified.
+
+    If height ('altitude') is requested, checks if cube heights
+    include orography, i.e. HybridHeights have been derived.
+
+    Parameters
+    ----------
+    icube : Iris cube
+    pt_lat, pt_lon : Float or list/array of floats. Latitude and longitude
+                     coordinates of desired points.
+    args:
+        height  : Float or list/array of floats. Altitude (above geoid) of
+                  point. Initialized to None.
+        level   : Integer . Model level or pseudo level or tile number.
+                  Initialized to None, meaning that all available levels in
+                  the cube are used.
+        nearest : Boolean. Specify whether to use 'nearest neighbour', instead
+                  of 'linear' method while extracting data. Default is False.
+
+    Returns
+    -------
+    data_out : List
+        List of single point values, corresponding to each point specified.
+
+    Raises
+    ------
+    AeroAnsError : If the number of latitude and longitude points are
+        mismatched. OR if both and level and height are passed as args.
+        OR if the cube contains a time coordinate. OR if a pseudo level
+        coordinate is requested, but not present in the cube. OR if the numbers
+        of latitude/longitude and height points are mismatched. OR if height
+        is requested but the cube does not contain an altitude coordinate.
+    """
+
+    # Check that input data is a (single) cube
+    if not isinstance(icube, iris.cube.Cube):
+        raise AeroAnsError('Extract_pt:First argument must be a single cube')
+
+    # Check if the cube contains a time dimension, which is
+    # currently unsupported.
+    if icube.coords()[0].name() == 'time':
+        raise AeroAnsError(
+            'Extract_pt:Cannot handle time dimension at present')
+
+    # Check that equal number of lat/lon pairs are passed in point coordinates.
+    # Convert arguments to lists for easier processing if necessary.
+    pt_lat1 = []
+    pt_lon1 = []
+
+    if not isinstance(pt_lat, list):
+        pt_lat1.append(pt_lat)
+        pt_lon1.append(pt_lon)
+
+    else:
+        for n_lat in np.arange(len(pt_lat)):
+            pt_lat1.append(pt_lat[n_lat])
+            pt_lon1.append(pt_lon[n_lat])
+
+    if len(pt_lat1) != len(pt_lon1):
+        raise AeroAnsError('Extract_pt:Mismatch in number of lat/long values')
+
+    # Check that both level and height haven't been requested.
+    if level is not None and height is not None:
+        raise AeroAnsError('Extract_pt: Both Level and Height requested')
+
+    # Check that the cube has a level coordinate if level has been requested.
+    if level is not None and not icube.coord(
+            'model_level_number') and not icube.coord('pseudo_level'):
+        raise AeroAnsError('Extract_pt:Level requested, but not found in cube')
+
+    # Check that the number of height points is equal to the number of
+    # lat/lon pairs. Convert the argument to a list for easier
+    # processing if necessary.
+    if height is not None:
+        pt_hgt = []
+
+#        if isinstance(height, list):
+#            pt_hgt.extend(height)
+#        else:
+#            pt_hgt.append(height)
+        pt_hgt.extend(height) if \
+            isinstance(height, list) else \
+            pt_hgt.append(height)
+
+        if len(pt_lat1) != len(pt_hgt):
+            raise AeroAnsError(
+                'Extract_pt:Mismatch in number of points for lat/long/height')
+
+        # Check that heights have been merged with orography.
+        if not icube.coords('altitude'):
+            raise AeroAnsError(
+                'Extract_pt:Height requested but input data does not contain \
+                "Altitude" coordinate')
+
+        # Store the min and max altitudes from cube data so that user
+        # cannot request points located below/ above that.
+        # Will extract =min/max if beyond limits.
+        hgt_min = icube.coord('altitude').points.min()
+        hgt_max = icube.coord('altitude').points.max()
+
+    # ---------- Finished checks -- begin processing -------------------------
+
+    # If level specified, extract slice first
+    if level is not None:
+
+        try:
+            icube = icube.extract(
+                iris.Constraint(model_level_number=level))
+
+        except Exception:
+            print('Model level number not available. Use pseudo level')
+
+        else:
+            icube = icube.extract(
+                iris.Constraint(pseudo_level=level))
+
+    # Extract values for specified points lat/lon
+    # NOTE: Does not seem to handle multiple points if 3-D
+    data_out = []
+
+    # Set lat/lon coordinates for model grid cell interpolation
+    for n_lat1 in np.arange(len(pt_lat1)):
+        latlon_coords = [('latitude', pt_lat1[n_lat1]),
+                         ('longitude', pt_lon1[n_lat1])]
+
+        if nearest:
+            tcube = icube.interpolate(latlon_coords, iris.analysis.Nearest())
+        else:
+            tcube = icube.interpolate(latlon_coords, iris.analysis.Linear())
+
+        # If height specified, interpolate to requested height
+        if height is not None:
+
+            # Set vertical coordinates for model grid cell interpolation
+            point = max(pt_hgt[n_lat1], hgt_min)
+            point = min(pt_hgt[n_lat1], hgt_max)
+            hgt_coords = [('altitude', point)]
+
+            if nearest:
+                tcube = tcube.interpolate(hgt_coords, iris.analysis.Nearest())
+            else:
+                tcube = tcube.interpolate(hgt_coords, iris.analysis.Linear())
+
+        # Append processed data point
+        data_out.append(tcube.data)
+
+    return data_out
diff --git a/esmvaltool/diag_scripts/aerosols/aod_aeronet_assess.py b/esmvaltool/diag_scripts/aerosols/aod_aeronet_assess.py
new file mode 100644
index 0000000000..27ab6b2714
--- /dev/null
+++ b/esmvaltool/diag_scripts/aerosols/aod_aeronet_assess.py
@@ -0,0 +1,448 @@
+"""Implement the AOD climatology metric from ground-based
+   AeroNet observations.
+"""
+import logging
+import os
+
+import iris
+import iris.plot as iplt
+import matplotlib.cm as mpl_cm
+import matplotlib.lines as mlines
+import matplotlib.pyplot as plt
+import numpy as np
+import scipy
+from aero_utils import add_bounds, extract_pt
+from matplotlib import colors, gridspec
+from numpy import ma
+
+from esmvaltool.diag_scripts.shared import group_metadata, run_diagnostic
+from esmvaltool.diag_scripts.shared._base import get_plot_filename
+
+logger = logging.getLogger(os.path.basename(__file__))
+fontsizedict = {"title": 25, "axis": 20, "legend": 18, "ticklabel": 18}
+
+
+def get_provenance_record(filenames):
+    """Return a provenance record describing the metric.
+
+    Parameters
+    ----------
+    filenames : List of strings
+        The filenames containing the data used to create the metric.
+
+    Returns
+    -------
+    dictionary
+        The provenance record describing the metric.
+    """
+    record = {
+        "ancestors": filenames,
+    }
+
+    return record
+
+
+def plot_aod_mod_obs(md_data, obs_data, aeronet_obs_cube, plot_dict):
+    """Plot AOD contour overlaid with Aeronet climatology.
+
+    Parameters
+    ----------
+    md_data : Iris cube
+        Model AOD as a cube with latitude and longitude coordinates.
+    obs_data : List.
+        Observations of AOD from each AeroNET station.
+    aeronet_obs_cube : Iris cube.
+        Holds information about Aeronet measurement stations including
+        station names, station latitude and station longitude.
+    plot_dict : Dictionary.
+        Holds plotting settings.
+    """
+    # Plot model data
+    cf_plot = iplt.contourf(md_data,
+                            plot_dict["Levels"],
+                            colors=plot_dict["Colours"],
+                            extend="max")
+
+    # Latitude and longitude of stations.
+    anet_aod_lats = aeronet_obs_cube.coord("latitude").points
+    anet_aod_lons = ((aeronet_obs_cube.coord("longitude").points + 180) % 360 -
+                     180)
+
+    # Loop over stations
+    for istn, stn_data in enumerate(obs_data):
+        if ma.is_masked(stn_data):
+            continue
+
+        # Find position of the observed AOD on the colorscale.
+        # np.searchsorted returns index at which inserting new value will
+        # maintain a sorted array. We use the color to the left of index.
+        cid = np.searchsorted(plot_dict["Levels"], stn_data)
+        cid = max(0, cid - 1)  # filter out zero and max when seeking 'left'
+        cid = min(len(plot_dict["Colours"]) - 1, cid)
+        pcol = plot_dict["Colours"][cid]
+
+        # Overlay contourf with observations
+        plt.plot(
+            anet_aod_lons[istn],
+            anet_aod_lats[istn],
+            color=pcol,
+            marker="o",
+            markeredgecolor="k",
+            markeredgewidth=2,
+            markersize=9,
+        )
+
+    # Decorate the plot
+    plt.title(plot_dict["Title"], size=24)
+    colbar = plt.colorbar(cf_plot, orientation="horizontal")
+    colbar.set_ticks(plot_dict["Levels"])
+    colbar.set_ticklabels(plot_dict["tick_labels"])
+    plt.gca().coastlines(color="#525252")
+
+    # Statistics on plot
+    plt.figtext(
+        0.12,
+        0.27,
+        (f'''Global mean AOD={plot_dict["Mean_aod"]:.3f}; RMSE='''
+         f'''{plot_dict["RMS_aod"]:.3f}; Stn mean: md='''
+         f'''{plot_dict["Stn_mn_md"]:.3f}; obs='''
+         f'''{plot_dict["Stn_mn_obs"]:.3f}'''),
+        size=16,
+    )
+
+
+def aod_analyse(model_data, aeronet_obs_cube, clim_seas, wavel):
+    """Evaluates AOD vs Aeronet, generates plots and returns evaluation
+    metrics.
+
+    Parameters
+    ----------
+    model_data : Iris Cube.
+        Contains model output of AOD with coordinates; time, latitude and
+        longitude.
+    aeronet_obs_cube : Iris Cube.
+        Contains information about Aeronet measurement stations including
+        station names, station latitude and station longitude.
+    clim_seas : List.
+       Strings to denote climate seasons ["DJF", "MAM", "JJA", "SON"]
+    wavel : String.
+        AOD wavelength, default = 440nm - translates to pseudo-level.
+
+    Returns
+    -------
+    figures : List.
+        Contains figure instances for the seasonal contour plots overlaid with
+        observations of AOD from AeroNET.
+    fig_scatter : Figure object.
+        The scatter plot comparing modelled and observed AOD at 440nm.
+    """
+    # Convert wave length nm -> um
+    wv_mi = str(float(wavel) / 1000.0)
+
+    # Get model run id
+    if "parent_source_id" in model_data.attributes:
+        model_id = model_data.attributes["parent_source_id"]
+    else:
+        model_id = "Multi-Model-Mean"
+
+    # Add bounds for lat and lon if not present
+    model_data = add_bounds(model_data)
+
+    # Co-locate model grid points with measurement sites --func from aero_utils
+    anet_aod_lats = aeronet_obs_cube.coord("latitude").points.tolist()
+    anet_aod_lons = aeronet_obs_cube.coord("longitude").points.tolist()
+    aod_at_anet = extract_pt(model_data, anet_aod_lats, anet_aod_lons)
+
+    # Set up seasonal contour plots
+    figures = []
+
+    clevs = [0.0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 2.0]
+    clabs = [
+        "0.0", "", "0.1", "", "0.2", "", "0.3", "", "0.4", "", "0.5", "2.0"
+    ]
+    cmapr = mpl_cm.get_cmap("brewer_Spectral_11")
+    cmap = colors.ListedColormap(cmapr(range(cmapr.N))[-1::-1])
+    colours = cmap.colors
+
+    # Set up the figure for scatter plotting
+    fig_scatter = plt.figure(figsize=(10, 10))
+    gs_scatter = gridspec.GridSpec(ncols=1, nrows=1)
+    ax_scatter = fig_scatter.add_subplot(gs_scatter[0, 0])
+    col_scatter = ["#081d58", "#41ab5d", "#fe9929", "#7f0000"]
+    leg_scatter = []
+
+    # Loop over seasons
+    for season in aeronet_obs_cube.slices_over("clim_season"):
+
+        # Match Aeronet obs season with model season number
+        model_sn = [c.lower() for c in clim_seas
+                    ].index(season.coord("clim_season").points[0])
+        model_season = model_data[model_sn]
+
+        logger.info('Analysing AOD for %s: %s', {model_id},
+                    {clim_seas[model_sn]})
+
+        # Generate statistics required - area-weighted mean
+        grid_areas = iris.analysis.cartography.area_weights(model_season)
+        global_mean = model_season.collapsed(
+            ["latitude", "longitude"],
+            iris.analysis.MEAN,
+            weights=grid_areas,
+        )
+
+        # Extract model and obs data for season number (model_sn)
+        seas_anet_obs = season.data
+        seas_anet_md = np.array([x[model_sn] for x in aod_at_anet])
+
+        # Match model data with valid obs data
+        valid_indices = ma.where(seas_anet_obs)
+        valid_obs = seas_anet_obs[valid_indices]
+        valid_md = seas_anet_md[valid_indices]
+
+        # Model - obs statistics (diff, model mean and RMS, r2)
+        diff = valid_md - valid_obs
+        stn_mn_obs = np.mean(valid_obs)
+        stn_mn_md = np.mean(valid_md)
+        rms_aod = np.sqrt(np.mean(diff**2))
+        linreg = scipy.stats.linregress(valid_obs, valid_md)
+
+        # Plot scatter of co-located model and obs data
+        ax_scatter.scatter(valid_obs, valid_md, color=col_scatter[model_sn])
+
+        # Legend
+        label = f"{clim_seas[model_sn]} = {linreg.rvalue**2:.2f}"
+        leg_scatter.append(
+            mlines.Line2D(
+                [0],
+                [0],
+                marker="o",
+                color="w",
+                label=label,
+                markersize=15,
+                markerfacecolor=col_scatter[model_sn],
+            ))
+
+        # Plot contours overlaid with obs for this run and season
+        fig_cf = plt.figure(figsize=(11, 8), dpi=300)
+
+        n_stn = str(len(valid_obs))
+        title = ("\nTotal Aerosol Optical Depth at " + wv_mi + " microns" +
+                 "\n" + model_id + ", " + clim_seas[model_sn] +
+                 ", N stations=" + n_stn)
+
+        # Plot dictionary
+        plot_dict = {
+            "Mean_aod": global_mean.data,
+            "Stn_mn_obs": stn_mn_obs,
+            "Stn_mn_md": stn_mn_md,
+            "RMS_aod": rms_aod,
+            "Levels": clevs,
+            "Colours": colours,
+            "tick_labels": clabs,
+            "Title": title,
+            "Season": clim_seas[model_sn],
+        }
+        plot_aod_mod_obs(model_season, seas_anet_obs, aeronet_obs_cube,
+                         plot_dict)
+
+        figures.append(fig_cf)
+
+    # Decorate the scatter plot
+    line = mlines.Line2D([0, 1], [0, 1], color="#696969")
+    transform = ax_scatter.transAxes
+    line.set_transform(transform)
+    ax_scatter.add_line(line)
+
+    ax_scatter.set(
+        xlim=(0, 1),
+        xticks=np.linspace(0.0, 1.0, num=6),
+        ylim=(0, 1),
+        yticks=np.linspace(0.0, 1.0, num=6),
+    )
+    ax_scatter.set_xlabel("AeroNET AOD", fontsize=fontsizedict["axis"])
+    ax_scatter.set_ylabel(model_id + " AOD", fontsize=fontsizedict["axis"])
+
+    ax_scatter.tick_params(axis="both",
+                           which="major",
+                           labelsize=fontsizedict["ticklabel"])
+
+    ax_scatter.set_title(
+        "Model vs obs: Total Aerosol Optical Depth \n at " + wv_mi +
+        " microns",
+        fontsize=fontsizedict["title"],
+    )
+
+    ax_scatter.legend(
+        handles=leg_scatter,
+        loc="lower right",
+        title="Seasonal R2",
+        title_fontsize=fontsizedict["legend"],
+        fontsize=fontsizedict["legend"],
+    )
+
+    return figures, fig_scatter
+
+
+def preprocess_aod_obs_dataset(obs_dataset):
+    """Calculate a multiannual seasonal mean AOD climatology.
+
+    Observational AOD timeseries data from AeroNET are used to generate a
+    multiannual seasonal mean climatology for each AeroNET station. The
+    user sets thresholds (or uses the default settings) to specify the
+    amount of valid data required for the climatology. At this stage
+    ESMValTool preprocessors are unsuitable for pre-processing the AeroNET
+    AOD observations because of the bespoke nature and application of the
+    filtering thresholds.
+
+    Parameters
+    ----------
+    obs_dataset : ESMValTool dictionary. Holds meta data for the observational
+        AOD dataset.
+
+    Returns
+    -------
+     multiannual_seaonal_mean : Iris cube. Preprocessed observational
+         AOD climatology.
+    """
+    obs_cube = iris.load_cube(obs_dataset[0]["filename"])
+
+    # Set up thresholds for generating the multi annual seasonal mean
+    min_days_per_mon = 1
+    min_mon_per_seas = 3
+    min_seas_per_year = 4
+    min_seas_per_clim = 5
+
+    # Add the clim_season and season_year coordinates.
+    iris.coord_categorisation.add_year(obs_cube, 'time', name='year')
+
+    iris.coord_categorisation.add_season(obs_cube, 'time', name='clim_season')
+
+    iris.coord_categorisation.add_season_year(obs_cube,
+                                              'time',
+                                              name='season_year')
+
+    # Copy obs cube and mask all months with fewer
+    # "Number of days" than given threshold.
+    num_days_var = obs_cube.ancillary_variable("Number of days")
+    masked_months_obs_cube = obs_cube.copy(data=ma.masked_where(
+        num_days_var.data < min_days_per_mon, obs_cube.data))
+
+    # Aggregate (mean) by season.
+    # The number of unmasked months per season is counted,
+    # and where there are fewer unmasked months than the
+    # given threshold, the computed mean is masked.
+    annual_seasonal_mean = masked_months_obs_cube.aggregated_by(
+        ['clim_season', 'season_year'],
+        iris.analysis.MEAN,
+    )
+    annual_seasonal_count = masked_months_obs_cube.aggregated_by(
+        ['clim_season', 'season_year'],
+        iris.analysis.COUNT,
+        function=lambda values: ~ma.getmask(values),
+    )
+    annual_seasonal_mean.data = ma.masked_where(
+        annual_seasonal_count.data < min_mon_per_seas,
+        annual_seasonal_mean.data,
+    )
+
+    # Aggregate (mean) by multi-annual season.
+    # The number of unmasked seasons per multi-annual season
+    # is counted, and where there are fewer unmasked seasons
+    # than the given threshold, the computed multi-annual
+    # season is masked.
+    multi_annual_seasonal_mean = annual_seasonal_mean.aggregated_by(
+        'clim_season',
+        iris.analysis.MEAN,
+    )
+    clim_season_agg_count = annual_seasonal_mean.aggregated_by(
+        'clim_season',
+        iris.analysis.COUNT,
+        function=lambda values: ~ma.getmask(values),
+    )
+    multi_annual_seasonal_mean.data = ma.masked_where(
+        clim_season_agg_count.data < min_seas_per_clim,
+        multi_annual_seasonal_mean.data,
+    )
+    year_agg_count = multi_annual_seasonal_mean.aggregated_by(
+        'year',
+        iris.analysis.COUNT,
+        function=lambda values: ~ma.getmask(values),
+    )
+
+    counter = range(len(
+        multi_annual_seasonal_mean.coord('clim_season').points))
+    for iseas in counter:
+        multi_annual_seasonal_mean.data[iseas, :] = ma.masked_where(
+            year_agg_count.data[0, :] < min_seas_per_year,
+            multi_annual_seasonal_mean.data[iseas, :],
+        )
+
+    return multi_annual_seasonal_mean
+
+
+def main(config):
+    """Produce the AOD climatology metric from ground-based AeroNet
+    observations.
+
+    Parameters
+    ----------
+    wavel : String.
+        User defined. Default is "440".
+    config : dict
+        The ESMValTool configuration.
+    """
+    input_data = config["input_data"]
+    datasets = group_metadata(input_data.values(), "dataset")
+
+    # Default wavelength
+    wavel = "440"
+
+    # Produce climatology for observational dataset
+    obs_dataset = datasets.pop(config["observational_dataset"])
+    obs_cube = preprocess_aod_obs_dataset(obs_dataset)
+
+    for model_dataset, group in datasets.items():
+        # 'model_dataset' is the name of the model dataset.
+        # 'group' is a list of dictionaries containing metadata.
+        logger.info("Processing data for %s", model_dataset)
+        logger.info(group)
+
+        for attributes in group:
+            logger.info(attributes["filename"])
+
+            input_file = attributes["filename"]
+            provenance_record = get_provenance_record(input_file)
+            logger.info(provenance_record)
+            cube = iris.load_cube(input_file)
+
+            # Set up for analysis and plotting
+            seasons = ["DJF", "MAM", "JJA", "SON"]
+
+            plot_file_prefix = (model_dataset + "_" + attributes["activity"] +
+                                "_" + attributes["mip"] + "_" +
+                                attributes["exp"] + "_" +
+                                attributes["short_name"] + "_" +
+                                str(attributes["start_year"]) + "_" +
+                                str(attributes["end_year"]) + "_")
+
+            # Analysis and plotting for model-obs comparison
+            figures, fig_scatter = aod_analyse(cube,
+                                               obs_cube,
+                                               seasons,
+                                               wavel=wavel)
+
+        # Save the scatter plot
+        output_file = plot_file_prefix + "scatter"
+        output_path = get_plot_filename(output_file, config)
+        fig_scatter.savefig(output_path)
+
+        # Save the contour plots
+        for ifig, seas_fig in enumerate(figures):
+            output_file = plot_file_prefix + seasons[ifig]
+            output_path = get_plot_filename(output_file, config)
+            seas_fig.savefig(output_path)
+
+
+if __name__ == "__main__":
+    with run_diagnostic() as CONFIG:
+        main(CONFIG)
diff --git a/esmvaltool/diag_scripts/monitor/multi_datasets.py b/esmvaltool/diag_scripts/monitor/multi_datasets.py
index abfed90f9d..a760a312f6 100644
--- a/esmvaltool/diag_scripts/monitor/multi_datasets.py
+++ b/esmvaltool/diag_scripts/monitor/multi_datasets.py
@@ -946,15 +946,24 @@ def _add_stats(self, plot_type, axes, dim_coords, dataset,
                 mean.data,
                 dataset['units'],
             )
-        axes.text(x_pos, y_pos, f"{mean.data:.2f}{cube.units}",
-                  fontsize=fontsize, transform=axes.transAxes)
+        if np.abs(mean.data) >= 0.1:
+            mean_val = f"{mean.data:.2f} {cube.units}"
+        else:
+            mean_val = f"{mean.data:.2e} {cube.units}"
+        axes.text(
+            x_pos, y_pos, mean_val, fontsize=fontsize, transform=axes.transAxes
+        )
         if ref_cube is None:
             return
 
         # Weighted RMSE
         rmse = (cube - ref_cube).collapsed(dim_coords, iris.analysis.RMS,
                                            weights=weights)
-        axes.text(x_pos_bias, y_pos, f"RMSE={rmse.data:.2f}{cube.units}",
+        if np.abs(rmse.data) >= 0.1:
+            rmse_val = f"{rmse.data:.2f} {cube.units}"
+        else:
+            rmse_val = f"{rmse.data:.2e} {cube.units}"
+        axes.text(x_pos_bias, y_pos, f"RMSE={rmse_val}",
                   fontsize=fontsize, transform=axes.transAxes)
         logger.info(
             "Area-weighted RMSE of %s for %s = %f%s",
@@ -1193,8 +1202,7 @@ def _plot_map_with_ref(self, plot_func, dataset, ref_dataset):
                             ref_dataset)
 
             # Customize plot
-            fig.suptitle(f"{dataset['long_name']} ({dataset['start_year']}-"
-                         f"{dataset['end_year']})")
+            fig.suptitle(dataset['long_name'])
             self._process_pyplot_kwargs(plot_type, dataset)
 
             # Rasterization
@@ -1249,8 +1257,7 @@ def _plot_map_without_ref(self, plot_func, dataset):
 
             # Customize plot
             axes.set_title(self._get_label(dataset))
-            fig.suptitle(f"{dataset['long_name']} ({dataset['start_year']}-"
-                         f"{dataset['end_year']})")
+            fig.suptitle(dataset['long_name'])
             self._process_pyplot_kwargs(plot_type, dataset)
 
             # Rasterization
@@ -1351,8 +1358,7 @@ def _plot_zonal_mean_profile_with_ref(self, plot_func, dataset,
                             ref_dataset)
 
             # Customize plot
-            fig.suptitle(f"{dataset['long_name']} ({dataset['start_year']}-"
-                         f"{dataset['end_year']})")
+            fig.suptitle(dataset['long_name'])
             self._process_pyplot_kwargs(plot_type, dataset)
 
             # Rasterization
@@ -1404,8 +1410,7 @@ def _plot_zonal_mean_profile_without_ref(self, plot_func, dataset):
 
             # Customize plot
             axes.set_title(self._get_label(dataset))
-            fig.suptitle(f"{dataset['long_name']} ({dataset['start_year']}-"
-                         f"{dataset['end_year']})")
+            fig.suptitle(dataset['long_name'])
             axes.set_xlabel('latitude [°N]')
             z_coord = cube.coord(axis='Z')
             axes.set_ylabel(f'{z_coord.long_name} [{z_coord.units}]')
@@ -1463,8 +1468,7 @@ def _plot_hovmoeller_z_vs_time_without_ref(self, plot_func, dataset):
 
             # Customize plot
             axes.set_title(self._get_label(dataset))
-            fig.suptitle(f"{dataset['long_name']} ({dataset['start_year']}-"
-                         f"{dataset['end_year']})")
+            fig.suptitle(dataset['long_name'])
             z_coord = cube.coord(axis='Z')
             axes.set_ylabel(f'{z_coord.long_name} [{z_coord.units}]')
             if self.plots[plot_type]['log_y']:
@@ -1590,8 +1594,7 @@ def _plot_hovmoeller_z_vs_time_with_ref(self, plot_func, dataset,
                             ref_dataset)
 
             # Customize plot
-            fig.suptitle(f"{dataset['long_name']} ({dataset['start_year']}-"
-                         f"{dataset['end_year']})")
+            fig.suptitle(dataset['long_name'])
             self._process_pyplot_kwargs(plot_type, dataset)
 
             # Rasterization
@@ -1699,8 +1702,7 @@ def _plot_hovmoeller_time_vs_lat_or_lon_with_ref(self, plot_func, dataset,
             cbar_bias.ax.tick_params(labelsize=fontsize)
 
             # Customize plot
-            fig.suptitle(f"{dataset['long_name']} ({dataset['start_year']}-"
-                         f"{dataset['end_year']})")
+            fig.suptitle(dataset['long_name'])
             self._process_pyplot_kwargs(plot_type, dataset)
 
             # Rasterization
@@ -1752,8 +1754,7 @@ def _plot_hovmoeller_time_vs_lat_or_lon_without_ref(self, plot_func,
 
             # Customize plot
             axes.set_title(self._get_label(dataset))
-            fig.suptitle(f"{dataset['long_name']} ({dataset['start_year']}-"
-                         f"{dataset['end_year']})")
+            fig.suptitle(dataset['long_name'])
             if 'latitude' in dim_coords_dat:
                 axes.set_xlabel('latitude [°N]')
             elif 'longitude' in dim_coords_dat:
@@ -1842,7 +1843,7 @@ def _get_multi_dataset_facets(datasets):
         multi_dataset_facets = {}
         for key in all_keys:
             if all(d.get(key) == datasets[0].get(key) for d in datasets):
-                multi_dataset_facets[key] = datasets[0][key]
+                multi_dataset_facets[key] = datasets[0].get(key)
             else:
                 multi_dataset_facets[key] = f'ambiguous_{key}'
         return multi_dataset_facets
@@ -2058,8 +2059,7 @@ def create_map_plot(self, datasets):
                 )
                 caption = (
                     f"Map plot of {dataset['long_name']} of dataset "
-                    f"{dataset['dataset']} (project {dataset['project']}) "
-                    f"from {dataset['start_year']} to {dataset['end_year']}."
+                    f"{dataset['alias']}."
                 )
             else:
                 (plot_path, netcdf_paths) = (
@@ -2067,10 +2067,8 @@ def create_map_plot(self, datasets):
                 )
                 caption = (
                     f"Map plot of {dataset['long_name']} of dataset "
-                    f"{dataset['dataset']} (project {dataset['project']}) "
-                    f"including bias relative to {ref_dataset['dataset']} "
-                    f"(project {ref_dataset['project']}) from "
-                    f"{dataset['start_year']} to {dataset['end_year']}."
+                    f"{dataset['alias']} including bias relative to "
+                    f"{ref_dataset['alias']}."
                 )
                 ancestors.append(ref_dataset['filename'])
 
@@ -2135,8 +2133,7 @@ def create_zonal_mean_profile_plot(self, datasets):
                 )
                 caption = (
                     f"Zonal mean profile of {dataset['long_name']} of dataset "
-                    f"{dataset['dataset']} (project {dataset['project']}) "
-                    f"from {dataset['start_year']} to {dataset['end_year']}."
+                    f"{dataset['alias']}."
                 )
             else:
                 (plot_path, netcdf_paths) = (
@@ -2145,10 +2142,8 @@ def create_zonal_mean_profile_plot(self, datasets):
                 )
                 caption = (
                     f"Zonal mean profile of {dataset['long_name']} of dataset "
-                    f"{dataset['dataset']} (project {dataset['project']}) "
-                    f"including bias relative to {ref_dataset['dataset']} "
-                    f"(project {ref_dataset['project']}) from "
-                    f"{dataset['start_year']} to {dataset['end_year']}."
+                    f"{dataset['alias']} including bias relative to "
+                    f"{ref_dataset['alias']}."
                 )
                 ancestors.append(ref_dataset['filename'])
 
@@ -2389,20 +2384,17 @@ def create_hovmoeller_z_vs_time_plot(self, datasets):
                      plot_func, dataset))
                 caption = (
                     f"Hovmoeller Z vs. time plot of {dataset['long_name']} "
-                    f"of dataset "
-                    f"{dataset['dataset']} (project {dataset['project']}) "
-                    f"from {dataset['start_year']} to {dataset['end_year']}.")
+                    f"of dataset {dataset['alias']}."
+                )
             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']}.")
+                    f"of dataset {dataset['alias']} including bias relative "
+                    f"to {ref_dataset['alias']}."
+                )
                 ancestors.append(ref_dataset['filename'])
 
             # If statistics are shown add a brief description to the caption
@@ -2467,8 +2459,7 @@ def create_hovmoeller_time_vs_lat_or_lon_plot(self, datasets):
                 )
                 caption = (
                     f"Hovmoeller plot of {dataset['long_name']} of dataset "
-                    f"{dataset['dataset']} (project {dataset['project']}) "
-                    f"from {dataset['start_year']} to {dataset['end_year']}."
+                    f"{dataset['alias']}."
                 )
             else:
                 (plot_path, netcdf_paths) = (
@@ -2477,10 +2468,8 @@ def create_hovmoeller_time_vs_lat_or_lon_plot(self, datasets):
                 )
                 caption = (
                     f"Hovmoeller plot of {dataset['long_name']} of dataset "
-                    f"{dataset['dataset']} (project {dataset['project']}) "
-                    f"including bias relative to {ref_dataset['dataset']} "
-                    f"(project {ref_dataset['project']}) from "
-                    f"{dataset['start_year']} to {dataset['end_year']}."
+                    f"{dataset['alias']} including bias relative to "
+                    f"{ref_dataset['alias']}."
                 )
                 ancestors.append(ref_dataset['filename'])
 
diff --git a/esmvaltool/diag_scripts/seaice_drift/seaice_drift.py b/esmvaltool/diag_scripts/seaice_drift/seaice_drift.py
index fd586f6b7e..6791254109 100644
--- a/esmvaltool/diag_scripts/seaice_drift/seaice_drift.py
+++ b/esmvaltool/diag_scripts/seaice_drift/seaice_drift.py
@@ -540,7 +540,8 @@ def __init__(self, polygon=None, lat=None, lon=None):
 
         polygon.append(polygon[0])
         self.transformer = Transformer.from_crs("WGS84",
-                                                "North_Pole_Stereographic")
+                                                "North_Pole_Stereographic",
+                                                always_xy=True)
 
         transformed = []
         for lon_val, lat_val in polygon:
diff --git a/esmvaltool/recipes/clouds/recipe_lauer13jclim.yml b/esmvaltool/recipes/clouds/recipe_lauer13jclim.yml
index 3d741e485d..9acbcfcbd1 100644
--- a/esmvaltool/recipes/clouds/recipe_lauer13jclim.yml
+++ b/esmvaltool/recipes/clouds/recipe_lauer13jclim.yml
@@ -142,6 +142,13 @@ diagnostics:
         mip: Amon
         derive: true
     additional_datasets:
+        # The original recipe uses the UWisc dataset that is not 
+        # maintained anymore by the authors and has been supersided
+        # by MAC-LWP (Elsaesser et al., 2017)
+        # https://doi.org/10.1175/JCLI-D-16-0902.1
+        # We recommend using MAC-LWP
+      #- {dataset: MAC-LWP, project: OBS, type: sat, version: v1,
+      #   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:
diff --git a/esmvaltool/recipes/clouds/recipe_lauer22jclim_fig1_clim.yml b/esmvaltool/recipes/clouds/recipe_lauer22jclim_fig1_clim.yml
index f63f527da4..0ecd79e458 100644
--- a/esmvaltool/recipes/clouds/recipe_lauer22jclim_fig1_clim.yml
+++ b/esmvaltool/recipes/clouds/recipe_lauer22jclim_fig1_clim.yml
@@ -238,8 +238,8 @@ diagnostics:
         end_year: 2005
         reference_dataset: ESACCI-WATERVAPOUR
         additional_datasets:
-          - {dataset: ESACCI-WATERVAPOUR, project: OBS, type: sat,
-             version: CDR2-L3S-05deg_fv3.1, tier: 3, start_year: 2003,
+          - {dataset: ESACCI-WATERVAPOUR, project: OBS6, type: sat,
+             version: CDR2-L3-COMBI-05deg-fv3.1, tier: 3, start_year: 2003,
              end_year: 2017}
           - {dataset: ISCCP-FH, project: OBS, type: sat, version: v0, tier: 2,
              start_year: 1984, end_year: 2016}
@@ -393,8 +393,8 @@ diagnostics:
         end_year: 2014
         reference_dataset: ESACCI-WATERVAPOUR
         additional_datasets:
-          - {dataset: ESACCI-WATERVAPOUR, project: OBS, type: sat,
-             version: CDR2-L3S-05deg_fv3.1, tier: 3,
+          - {dataset: ESACCI-WATERVAPOUR, project: OBS6, type: sat,
+             version: CDR2-L3-COMBI-05deg-fv3.1, tier: 3,
              start_year: 2003, end_year: 2017}
           - {dataset: ISCCP-FH, project: OBS, type: sat, version: v0, tier: 2,
              start_year: 1984, end_year: 2016}
diff --git a/esmvaltool/recipes/clouds/recipe_lauer22jclim_fig1_clim_amip.yml b/esmvaltool/recipes/clouds/recipe_lauer22jclim_fig1_clim_amip.yml
index 65818e71e4..8ae73c9da5 100644
--- a/esmvaltool/recipes/clouds/recipe_lauer22jclim_fig1_clim_amip.yml
+++ b/esmvaltool/recipes/clouds/recipe_lauer22jclim_fig1_clim_amip.yml
@@ -253,8 +253,8 @@ diagnostics:
         end_year: 2005
         reference_dataset: ESACCI-WATERVAPOUR
         additional_datasets:
-          - {dataset: ESACCI-WATERVAPOUR, project: OBS, type: sat,
-             version: CDR2-L3S-05deg_fv3.1, tier: 3,
+          - {dataset: ESACCI-WATERVAPOUR, project: OBS6, type: sat,
+             version: CDR2-L3-COMBI-05deg-fv3.1, tier: 3,
              start_year: 2003, end_year: 2017}
           - {dataset: ISCCP-FH, project: OBS, type: sat, version: v0, tier: 2,
              start_year: 1984, end_year: 2016}
@@ -408,8 +408,8 @@ diagnostics:
         end_year: 2014
         reference_dataset: ESACCI-WATERVAPOUR
         additional_datasets:
-          - {dataset: ESACCI-WATERVAPOUR, project: OBS, type: sat,
-             version: CDR2-L3S-05deg_fv3.1, tier: 3,
+          - {dataset: ESACCI-WATERVAPOUR, project: OBS6, type: sat,
+             version: CDR2-L3-COMBI-05deg-fv3.1, tier: 3,
              start_year: 2003, end_year: 2017}
           - {dataset: ISCCP-FH, project: OBS, type: sat, version: v0, tier: 2,
              start_year: 1984, end_year: 2016}
diff --git a/esmvaltool/recipes/clouds/recipe_lauer22jclim_fig2_taylor.yml b/esmvaltool/recipes/clouds/recipe_lauer22jclim_fig2_taylor.yml
index 8bce0895cb..c36e633164 100644
--- a/esmvaltool/recipes/clouds/recipe_lauer22jclim_fig2_taylor.yml
+++ b/esmvaltool/recipes/clouds/recipe_lauer22jclim_fig2_taylor.yml
@@ -136,8 +136,8 @@ diagnostics:
         exp: historical
         reference_dataset: ESACCI-WATERVAPOUR
         additional_datasets:
-          - {dataset: ESACCI-WATERVAPOUR, project: OBS, type: sat,
-             version: CDR2-L3S-05deg_fv3.1, tier: 3,
+          - {dataset: ESACCI-WATERVAPOUR, project: OBS6, type: sat,
+             version: CDR2-L3-COMBI-05deg-fv3.1, tier: 3,
              start_year: 2003, end_year: 2017}
           - {dataset: ISCCP-FH, project: OBS, type: sat, version: v0, tier: 2,
              start_year: 1984, end_year: 2016}
diff --git a/esmvaltool/recipes/clouds/recipe_lauer22jclim_fig2_taylor_amip.yml b/esmvaltool/recipes/clouds/recipe_lauer22jclim_fig2_taylor_amip.yml
index 0c36bc8dc9..426746571b 100644
--- a/esmvaltool/recipes/clouds/recipe_lauer22jclim_fig2_taylor_amip.yml
+++ b/esmvaltool/recipes/clouds/recipe_lauer22jclim_fig2_taylor_amip.yml
@@ -135,8 +135,8 @@ diagnostics:
         exp: amip
         reference_dataset: ESACCI-WATERVAPOUR
         additional_datasets:
-          - {dataset: ESACCI-WATERVAPOUR, project: OBS, type: sat,
-             version: CDR2-L3S-05deg_fv3.1, tier: 3,
+          - {dataset: ESACCI-WATERVAPOUR, project: OBS6, type: sat,
+             version: CDR2-L3-COMBI-05deg-fv3.1, tier: 3,
              start_year: 2003, end_year: 2017}
           - {dataset: ISCCP-FH, project: OBS, type: sat, version: v0, tier: 2,
              start_year: 1984, end_year: 2016}
diff --git a/esmvaltool/recipes/clouds/recipe_lauer22jclim_fig6_interannual.yml b/esmvaltool/recipes/clouds/recipe_lauer22jclim_fig6_interannual.yml
index f5c76aad79..cb8912eee9 100644
--- a/esmvaltool/recipes/clouds/recipe_lauer22jclim_fig6_interannual.yml
+++ b/esmvaltool/recipes/clouds/recipe_lauer22jclim_fig6_interannual.yml
@@ -226,8 +226,8 @@ diagnostics:
         end_year: 2005
         reference_dataset: ESACCI-WATERVAPOUR
         additional_datasets:
-          - {dataset: ESACCI-WATERVAPOUR, project: OBS, type: sat,
-             version: CDR2-L3S-05deg_fv3.1, tier: 3,
+          - {dataset: ESACCI-WATERVAPOUR, project: OBS6, type: sat,
+             version: CDR2-L3-COMBI-05deg-fv3.1, tier: 3,
              start_year: 2003, end_year: 2017}
           - {dataset: ISCCP-FH, project: OBS, type: sat, version: v0, tier: 2,
              start_year: 1984, end_year: 2016}
@@ -372,8 +372,8 @@ diagnostics:
         end_year: 2014
         reference_dataset: ESACCI-WATERVAPOUR
         additional_datasets:
-          - {dataset: ESACCI-WATERVAPOUR, project: OBS, type: sat,
-             version: CDR2-L3S-05deg_fv3.1, tier: 3,
+          - {dataset: ESACCI-WATERVAPOUR, project: OBS6, type: sat,
+             version: CDR2-L3-COMBI-05deg-fv3.1, tier: 3,
              start_year: 2003, end_year: 2017}
           - {dataset: ISCCP-FH, project: OBS, type: sat, version: v0, tier: 2,
              start_year: 1984, end_year: 2016}
diff --git a/esmvaltool/recipes/clouds/recipe_lauer22jclim_fig7_seas.yml b/esmvaltool/recipes/clouds/recipe_lauer22jclim_fig7_seas.yml
index aedba080b7..998f38b4cb 100644
--- a/esmvaltool/recipes/clouds/recipe_lauer22jclim_fig7_seas.yml
+++ b/esmvaltool/recipes/clouds/recipe_lauer22jclim_fig7_seas.yml
@@ -231,8 +231,8 @@ diagnostics:
         end_year: 2005
         reference_dataset: ESACCI-WATERVAPOUR
         additional_datasets:
-          - {dataset: ESACCI-WATERVAPOUR, project: OBS, type: sat,
-             version: CDR2-L3S-05deg_fv3.1, tier: 3,
+          - {dataset: ESACCI-WATERVAPOUR, project: OBS6, type: sat,
+             version: CDR2-L3-COMBI-05deg-fv3.1, tier: 3,
              start_year: 2003, end_year: 2017}
           - {dataset: ISCCP-FH, project: OBS, type: sat, version: v0, tier: 2,
              start_year: 1984, end_year: 2016}
@@ -384,8 +384,8 @@ diagnostics:
         end_year: 2014
         reference_dataset: ESACCI-WATERVAPOUR
         additional_datasets:
-          - {dataset: ESACCI-WATERVAPOUR, project: OBS, type: sat,
-             version: CDR2-L3S-05deg_fv3.1, tier: 3,
+          - {dataset: ESACCI-WATERVAPOUR, project: OBS6, type: sat,
+             version: CDR2-L3-COMBI-05deg-fv3.1, tier: 3,
              start_year: 2003, end_year: 2017}
           - {dataset: ISCCP-FH, project: OBS, type: sat, version: v0, tier: 2,
              start_year: 1984, end_year: 2016}
diff --git a/esmvaltool/recipes/examples/recipe_check_obs.yml b/esmvaltool/recipes/examples/recipe_check_obs.yml
index 7fd27bcd25..820ceddbd0 100644
--- a/esmvaltool/recipes/examples/recipe_check_obs.yml
+++ b/esmvaltool/recipes/examples/recipe_check_obs.yml
@@ -1601,8 +1601,11 @@ diagnostics:
     variables:
       prw:
     additional_datasets:
-      - {dataset: ESACCI-WATERVAPOUR, project: OBS, mip: Amon, tier: 3,
-         type: sat, version: CDR2-L3S-05deg_fv3.1,
+      - {dataset: ESACCI-WATERVAPOUR, project: OBS6, mip: Amon, tier: 3,
+         type: sat, version: CDR2-L3-COMBI-05deg-fv3.1,
+         start_year: 2003, end_year: 2017}
+      - {dataset: ESACCI-WATERVAPOUR, project: OBS6, mip: Eday, tier: 3,
+         type: sat, version: CDR2-L3-COMBI-05deg-fv3.1,
          start_year: 2003, end_year: 2017}
     scripts: null
 
@@ -1966,4 +1969,4 @@ diagnostics:
     additional_datasets:
       - {dataset: UWisc, project: OBS, mip: Amon, tier: 3,
          type: sat, version: v2, start_year: 1988, end_year: 2007}
-    scripts: null
\ No newline at end of file
+    scripts: null
diff --git a/esmvaltool/recipes/model_evaluation/recipe_model_evaluation_basics.yml b/esmvaltool/recipes/model_evaluation/recipe_model_evaluation_basics.yml
index 06f8ad3b12..9b7c187140 100644
--- a/esmvaltool/recipes/model_evaluation/recipe_model_evaluation_basics.yml
+++ b/esmvaltool/recipes/model_evaluation/recipe_model_evaluation_basics.yml
@@ -212,7 +212,7 @@ diagnostics:
         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}
+          - {project: OBS6, dataset: ESACCI-WATERVAPOUR, type: sat, version: CDR2-L3-COMBI-05deg-fv3.1, tier: 3, timerange: '2003/2007', reference_for_monitor_diags: true}
     scripts:
       plot:
         <<: *plot_multi_dataset_default
diff --git a/esmvaltool/recipes/model_evaluation/recipe_model_evaluation_clouds_clim.yml b/esmvaltool/recipes/model_evaluation/recipe_model_evaluation_clouds_clim.yml
index fd2d08781f..aaf964dc24 100644
--- a/esmvaltool/recipes/model_evaluation/recipe_model_evaluation_clouds_clim.yml
+++ b/esmvaltool/recipes/model_evaluation/recipe_model_evaluation_clouds_clim.yml
@@ -3,8 +3,8 @@
 documentation:
   title: Model evaluation with focus on clouds.
   description: >
-    Plot climatologies several cloud-related variables of multi-year
-    simulations.
+    Plot climatologies and zonal mean profiles of several cloud-related
+    variables of multi-year simulations.
   authors:
     - bonnet_pauline
     - lauer_axel
@@ -152,7 +152,7 @@ diagnostics:
         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}
+          - {dataset: ESACCI-WATERVAPOUR, project: OBS6, type: sat, version: CDR2-L3-COMBI-05deg-fv3.1, tier: 3, timerange: '2003/2017', reference_for_monitor_diags: true}
     scripts:
       plot:
         <<: *plot_multi_dataset_default
diff --git a/esmvaltool/recipes/model_evaluation/recipe_model_evaluation_clouds_cycles.yml b/esmvaltool/recipes/model_evaluation/recipe_model_evaluation_clouds_cycles.yml
index ed52fd7d3c..8139a04dfc 100644
--- a/esmvaltool/recipes/model_evaluation/recipe_model_evaluation_clouds_cycles.yml
+++ b/esmvaltool/recipes/model_evaluation/recipe_model_evaluation_clouds_cycles.yml
@@ -3,7 +3,8 @@
 documentation:
   title: Model evaluation with focus on clouds.
   description: >
-    Plot annual cycles of several cloud-related variables of multi-year simulations.
+    Plot annual cycles of several cloud-related variables of multi-year
+    simulations.
   authors:
     - lauer_axel
     - schlund_manuel
diff --git a/esmvaltool/recipes/model_evaluation/recipe_model_evaluation_precip_zonal.yml b/esmvaltool/recipes/model_evaluation/recipe_model_evaluation_precip_zonal.yml
index 6bd1231046..97802deb47 100644
--- a/esmvaltool/recipes/model_evaluation/recipe_model_evaluation_precip_zonal.yml
+++ b/esmvaltool/recipes/model_evaluation/recipe_model_evaluation_precip_zonal.yml
@@ -3,7 +3,7 @@
 documentation:
   title: Model evaluation with focus on precipitation.
   description: >
-    Plot zonal mean plots of precipitation.
+    Plot zonal mean precipitation.
   authors:
     - lauer_axel
     - schlund_manuel
diff --git a/esmvaltool/recipes/recipe_aod_aeronet_assess.yml b/esmvaltool/recipes/recipe_aod_aeronet_assess.yml
new file mode 100644
index 0000000000..0fc82ec864
--- /dev/null
+++ b/esmvaltool/recipes/recipe_aod_aeronet_assess.yml
@@ -0,0 +1,65 @@
+# ESMValTool
+# recipe_aod_aeronet_assess.yml
+---
+documentation:
+  description: |
+    Recipe to plot seasonal maps of global aerosol optical depth (AOD) at 440nm.
+
+  title: Recipe that runs an AOD diagnostic
+
+  authors:
+    - hogan_emma
+    - lillis_jon
+    - hardacre_catherine
+
+  maintainer:
+    - hogan_emma
+    - lillis_jon
+    - hardacre_catherine
+
+  projects:
+    - esmval
+
+preprocessors:
+  ma_season_mean:
+    regrid:
+      target_grid: 2.5x2.5
+      scheme: nearest
+    climate_statistics:
+      operator: mean
+      period: season
+      seasons: ['DJF', 'MAM', 'JJA', 'SON']
+    multi_model_statistics:
+      span: overlap
+      statistics: [mean]
+
+diagnostics:
+  od440aer_climatologies:
+    description: Visualise spatial multi-annual seasonal means AOD at 440nm.
+    variables:
+
+      od440aer: &var_od440aer
+        mip: AERmon
+        short_name: od440aer
+        start_year: 1994
+        end_year: 2014
+        additional_datasets:
+          - {dataset: AERONET, project: OBS6, mip: AERmon, tier: 3, type: atmos, version: 20231021}
+
+      od440aer_season:
+        <<: *var_od440aer
+        preprocessor: ma_season_mean
+        additional_datasets:
+          - {dataset: UKESM1-0-LL, project: CMIP6, mip: AERmon, exp: historical, ensemble: r1i1p1f2, grid: gn}
+          - {dataset: HadGEM3-GC31-LL, project: CMIP6, mip: AERmon, exp: historical, ensemble: r1i1p1f3, grid: gn}
+          - {dataset: EC-Earth3-AerChem, project: CMIP6, mip: AERmon, exp: historical, ensemble: r1i1p1f1, grid: gn}
+#          - {dataset: NorESM2-LM, project: CMIP6, mip: AERmon, exp: historical, ensemble: r1i1p1f1, grid: gn}
+          - {dataset: GFDL-ESM4, project: CMIP6, mip: AERmon, exp: historical, ensemble: r1i1p1f1, grid: gr1}
+          - {dataset: MPI-ESM-1-2-HAM, project: CMIP6, mip: AERmon, exp: historical, ensemble: r1i1p1f1, grid: gn}
+
+    scripts:
+      aeronet:
+        script: aerosols/aod_aeronet_assess.py
+        observational_dataset: AERONET
+        quickplot:
+          plot_type: plot
diff --git a/esmvaltool/recipes/recipe_autoassess_landsurface_surfrad.yml b/esmvaltool/recipes/recipe_autoassess_landsurface_surfrad.yml
index 0ddb86c181..3d77ed02c1 100644
--- a/esmvaltool/recipes/recipe_autoassess_landsurface_surfrad.yml
+++ b/esmvaltool/recipes/recipe_autoassess_landsurface_surfrad.yml
@@ -42,7 +42,7 @@ diagnostics:
         force_derivation: false
         mip: Amon
         additional_datasets:
-          - {dataset: CERES-EBAF,  project: obs4MIPs,  level: L3B,  version: Ed2-7,  start_year: 2001,  end_year: 2012, tier: 1}
+          - {dataset: CERES-EBAF,  project: obs4MIPs,  level: L3B,  start_year: 2001,  end_year: 2012, tier: 1}
       sftlf:
         mip: fx
       rlns: # Surf LW net all sky
@@ -50,7 +50,7 @@ diagnostics:
         force_derivation: false
         mip: Amon
         additional_datasets:
-          - {dataset: CERES-EBAF,  project: obs4MIPs,  level: L3B,  version: Ed2-7,  start_year: 2001,  end_year: 2012, tier: 1}
+          - {dataset: CERES-EBAF,  project: obs4MIPs,  level: L3B,  start_year: 2001,  end_year: 2012, tier: 1}
       sftlf:
         mip: fx
     scripts:
diff --git a/esmvaltool/recipes/recipe_cmug_h2o.yml b/esmvaltool/recipes/recipe_cmug_h2o.yml
index a44358f664..fbd70e7036 100644
--- a/esmvaltool/recipes/recipe_cmug_h2o.yml
+++ b/esmvaltool/recipes/recipe_cmug_h2o.yml
@@ -81,8 +81,8 @@ diagnostics:
         additional_datasets:
           # - {dataset: ESACCI-WV, project: OBS, type: sat,
           #    version: HOAPS-v4.0, start_year: 2001, end_year: 2012, tier: 2}
-          - {dataset: ESACCI-WATERVAPOUR, project: OBS, type: sat,
-             version: CDR2-L3S-05deg_fv3.1, start_year: 2003, end_year: 2014, tier: 3}
+          - {dataset: ESACCI-WATERVAPOUR, project: OBS6, type: sat,
+             version: CDR2-L3-COMBI-05deg-fv3.1, start_year: 2003, end_year: 2014, tier: 3}
     additional_datasets:
       - {dataset: GFDL-CM3, exp: piControl, start_year: 1, end_year: 150}
       - {dataset: GFDL-CM3, exp: abrupt4xCO2, start_year: 1, end_year: 150}
diff --git a/setup.py b/setup.py
index c39ec221fd..5a4d8bc62c 100755
--- a/setup.py
+++ b/setup.py
@@ -57,6 +57,7 @@
         'psy-reg',
         'psy-simple',
         'pyproj>=2.1',
+        # 'pys2index',  # issues installing from PyPI (wheel doesn't build)
         'python-dateutil',
         'pyyaml',
         'rasterio',
@@ -75,8 +76,8 @@
         'xlsxwriter',
         'zarr',
     ],
-    # Test dependencies
-    # Execute `pip install .[test]` once and the use `pytest` to run tests
+    # Test dependencies (unit tests)
+    # Execute `pip install .[test]` once and then use `pytest` to run tests
     'test': [
         'flake8',
         'pytest>=3.9,!=6.0.0rc1,!=6.0.0',
@@ -99,6 +100,7 @@
     'develop': [
         'codespell',
         'docformatter',
+        'imagehash',
         'isort',
         'pre-commit',
         'prospector[with_pyroma]!=1.1.6.3,!=1.1.6.4',