ClimateBench v2.0: Probabilistic Climate Model Benchmarking

arXiv:2610.04558v1 Announce Type: cross Abstract: We present ClimateBench v2, a standardized protocol for evaluating climate models on diagnostics expected to be informative for their skill in projecting mid-century regional temperature and precipitation changes. The protocol is designed to evaluate any physics-based, data-driven, or hybrid climate model on equal footing using a common set of observational and out-of-distribution tests. We define three tiers of evaluation. Tier I establishes physical credibility through entry-ticket tests of energy conservation, coupled (co-)variability, and basic forced responses. Tier II scores models against post-2015 observations of surface temperature, precipitation, radiative fluxes, sea ice, and key modes of variability using fair CRPS as the primary probabilistic score, complemented by distributional and ensemble-consistency diagnostics. Tier III tests out-of-distribution generalization through paleoclimate simulations spanning the Last Interglacial, Last Glacial Maximum, and Mid-Holocene, and through perfect-model experiments in which data-driven models must predict the future climate of existing Earth system models from historical data alone. We reserve all observational data after 2015 for testing, and submissions must include multiple ensemble members to enable probabilistic evaluation. This reservation exploits a new opportunity provided by the decade of observations accumulated since the end of the CMIP6 historical experiment, which constitutes an out-of-sample record of forced climate change (and internal variability) for the current generation of models, and we quantify, in an idealized setting, the information it carries about mid-century warming. We provide the evaluation code, observational reference datasets, and perfect-model training data as an open benchmark to drive measurable progress in climate projection across all modeling approaches.

Authors:Duncan Watson-Parris, Willa Tobin, Aytaç Paçal, Manuel Schlund, V. Balaji, Kevin Bowman, Chris Bretherton, Peter M. Caldwell, Will Chapman, William D. Collins, Gregory S. Elsaesser, Pierre Gentine, Helene Hewitt, Stephan Hoyer, Ralph Keeling, Nikolay Koldunov, David M. Lawrence, Christian Lessig, Daniel J. Lunt, J. David Neelin, Mike Pritchard, Sarah Purkey, Gavin Schmidt, Tapio Schneider, Michael Schulz, Tiffany Shaw, Isla R. Simpson, Graeme Stephens, Aneesh C. Subramanian, Joao Teixeira, Jessica Tierney, Andrew I. L. Williams, Laure Zanna, Veronika Eyring, Rose Yu

Abstract:We present ClimateBench v2, a standardized protocol for evaluating climate models on diagnostics expected to be informative for their skill in projecting mid-century regional temperature and precipitation changes. The protocol is designed to evaluate any physics-based, data-driven, or hybrid climate model on equal footing using a common set of observational and out-of-distribution tests. We define three tiers of evaluation. Tier I establishes physical credibility through entry-ticket tests of energy conservation, coupled (co-)variability, and basic forced responses. Tier II scores models against post-2015 observations of surface temperature, precipitation, radiative fluxes, sea ice, and key modes of variability using fair CRPS as the primary probabilistic score, complemented by distributional and ensemble-consistency diagnostics. Tier III tests out-of-distribution generalization through paleoclimate simulations spanning the Last Interglacial, Last Glacial Maximum, and Mid-Holocene, and through perfect-model experiments in which data-driven models must predict the future climate of existing Earth system models from historical data alone. We reserve all observational data after 2015 for testing, and submissions must include multiple ensemble members to enable probabilistic evaluation. This reservation exploits a new opportunity provided by the decade of observations accumulated since the end of the CMIP6 historical experiment, which constitutes an out-of-sample record of forced climate change (and internal variability) for the current generation of models, and we quantify, in an idealized setting, the information it carries about mid-century warming. We provide the evaluation code, observational reference datasets, and perfect-model training data as an open benchmark to drive measurable progress in climate projection across all modeling approaches.

Atmospheric and Oceanic Physics (physics.ao-ph); Machine Learning (cs.LG)

arXiv:2610.04558 [physics.ao-ph]

(or arXiv:2610.04558v1 [physics.ao-ph] for this version)

https://doi.org/10.48550/arXiv.2610.04558 arXiv-issued DOI via DataCite (pending registration)

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ClimateBench v2.0: Probabilistic Climate Model Benchmarking

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