Articles | Volume 18, issue 18
https://doi.org/10.5194/gmd-18-6341-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-6341-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
25 Sep 2025
Methods for assessment of models |
| 25 Sep 2025
| 25 Sep 2025
The updated Multi-Model Large Ensemble Archive and the Climate Variability Diagnostics Package: new tools for the study of climate variability and change
Research School of Earth Sciences, Australian National University, Canberra, Australia
Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, USA
Adam S. Phillips
Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, CO, USA
Clara Deser
Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, CO, USA
Robert C. Jnglin Wills
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
Flavio Lehner
Department of Earth and Atmospheric Sciences, Cornell University, Ithaca, NY, USA
Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, CO, USA
Polar Bears International, Bozeman, MT, USA
John Fasullo
Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, CO, USA
Julie M. Caron
Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, CO, USA
Lukas Brunner
Research Unit Sustainability and Climate Risk, Center for Earth System Research and Sustainability (CEN), University of Hamburg, Hamburg, Germany
Urs Beyerle
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
Jemma Jeffree
Research School of Earth Sciences, Australian National University, Canberra, Australia
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Cited
12 citations as recorded by crossref.
- Developing Guidelines for working with Multi-Model Ensembles in CMIP A. Katzenberger et al. https://doi.org/10.5194/esd-17-495-2026
- 外强迫下的南极放大效应：基于大样本集合模拟的量化<?A3B2 pi6?>分析 江. 朱 et al. https://doi.org/10.1360/N072025-0315
- Intensifying hydroclimatic swings under a warming climate: Disentangling anthropogenic climate change and internal variability in North America W. Na et al. https://doi.org/10.1016/j.gloplacha.2025.105171
- Advances in aggregation methods for multi-model ensembles in climate science: A comprehensive review M. Shiru et al. https://doi.org/10.1016/j.pce.2026.104391
- Adapting analogue forecasting to compare ENSO remote influences across different models and regions J. Jeffree et al. https://doi.org/10.1007/s00382-026-08258-1
- New framework for benchmarking decadal predictions leveraging the PCMDI Metric Package with interactive visualization J. Choi et al. https://doi.org/10.5194/gmd-19-6189-2026
- Present-day tropical precipitation and cloud feedbacks determine future equatorial Pacific trends S. Stevenson et al. https://doi.org/10.1126/sciadv.aea8070
- A Python diagnostics package for evaluation of Madden–Julian Oscillation (MJO) teleconnections in subseasonal-to-seasonal (S2S) forecast systems C. Stan et al. https://doi.org/10.5194/gmd-18-7969-2025
- Quantifying the time of emergence of the anthropogenic signal in the global land carbon sink N. Li et al. https://doi.org/10.5194/bg-23-767-2026
- Temperature variability projections remain uncertain after constraining them to best performing Large Ensembles of individual Climate Models L. Suarez-Gutierrez & N. Maher https://doi.org/10.1038/s41467-025-67005-y
- Antarctic amplification in external forcing: Insights from large ensemble simulations J. Zhu et al. https://doi.org/10.1007/s11430-025-1900-4
- Climate Modes evaluation datasets from CMIP6 pre-industrial control simulations and observations S. Mohapatra et al. https://doi.org/10.5194/essd-18-5463-2026
12 citations as recorded by crossref.
- Developing Guidelines for working with Multi-Model Ensembles in CMIP A. Katzenberger et al. https://doi.org/10.5194/esd-17-495-2026
- 外强迫下的南极放大效应：基于大样本集合模拟的量化<?A3B2 pi6?>分析 江. 朱 et al. https://doi.org/10.1360/N072025-0315
- Intensifying hydroclimatic swings under a warming climate: Disentangling anthropogenic climate change and internal variability in North America W. Na et al. https://doi.org/10.1016/j.gloplacha.2025.105171
- Advances in aggregation methods for multi-model ensembles in climate science: A comprehensive review M. Shiru et al. https://doi.org/10.1016/j.pce.2026.104391
- Adapting analogue forecasting to compare ENSO remote influences across different models and regions J. Jeffree et al. https://doi.org/10.1007/s00382-026-08258-1
- New framework for benchmarking decadal predictions leveraging the PCMDI Metric Package with interactive visualization J. Choi et al. https://doi.org/10.5194/gmd-19-6189-2026
- Present-day tropical precipitation and cloud feedbacks determine future equatorial Pacific trends S. Stevenson et al. https://doi.org/10.1126/sciadv.aea8070
- A Python diagnostics package for evaluation of Madden–Julian Oscillation (MJO) teleconnections in subseasonal-to-seasonal (S2S) forecast systems C. Stan et al. https://doi.org/10.5194/gmd-18-7969-2025
- Quantifying the time of emergence of the anthropogenic signal in the global land carbon sink N. Li et al. https://doi.org/10.5194/bg-23-767-2026
- Temperature variability projections remain uncertain after constraining them to best performing Large Ensembles of individual Climate Models L. Suarez-Gutierrez & N. Maher https://doi.org/10.1038/s41467-025-67005-y
- Antarctic amplification in external forcing: Insights from large ensemble simulations J. Zhu et al. https://doi.org/10.1007/s11430-025-1900-4
- Climate Modes evaluation datasets from CMIP6 pre-industrial control simulations and observations S. Mohapatra et al. https://doi.org/10.5194/essd-18-5463-2026
Saved (final revised paper)
Latest update: 25 Aug 2026
Short summary
We present the new Multi-Model Large Ensemble Archive (MMLEAv2) and introduce the newly updated Climate Variability Diagnostics Package version 6 (CVDPv6), which is designed specifically for use with large ensembles. For highly variable quantities, we demonstrate that a model might perform evaluation poorly or favourably compared to the single realisation of the world that the observations represent, highlighting the need for large ensembles for model evaluation.
We present the new Multi-Model Large Ensemble Archive (MMLEAv2) and introduce the newly updated...
