AIMIP Phase 1: systematic evaluations of AI weather and climate models
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| Format: | Preprint |
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2026
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| author | Henn, Brian Bretherton, Christopher S. Koldunov, Nikolay Lessig, Christian Molina, Maria J. Arcomano, Troy Watt-Meyer, Oliver Couairon, Guillaume Singh, Renu Brunstein, Robert Hasson, Yana Jost, Antonia Brenowitz, Noah Manshausen, Peter Cresswell-Clay, Nathaniel Durran, Dale Hall, Kyle Joseph Chen Yuval, Janni Kochkov, Dmitrii Hoyer, Stephan Lopez-Gomez, Ignacio |
| author_facet | Henn, Brian Bretherton, Christopher S. Koldunov, Nikolay Lessig, Christian Molina, Maria J. Arcomano, Troy Watt-Meyer, Oliver Couairon, Guillaume Singh, Renu Brunstein, Robert Hasson, Yana Jost, Antonia Brenowitz, Noah Manshausen, Peter Cresswell-Clay, Nathaniel Durran, Dale Hall, Kyle Joseph Chen Yuval, Janni Kochkov, Dmitrii Hoyer, Stephan Lopez-Gomez, Ignacio |
| contents | We present the AI weather and climate model intercomparison project (AIMIP), phase 1. Drawing from the rich tradition of intercomparisons in climate model development, we specify a common experiment, output data format, and training constraints (namely, training against historical reanalysis data) for AIMIP Phase 1 models. We aim to identify differences in modeling frameworks and AI architectural choices that influence model behavior, and build trust in AI weather and climate models through open data and evaluation. AIMIP Phase 1 models must simulate the atmosphere given specified historical sea surface temperatures over 1979-2024. We evaluate the models' performance using five major evaluation criteria: biases, trends, response to El Niño-related sea surface temperature anomalies, temporal variability, and out-of-sample generalization tests. We find that the AI models are able to simulate the historical climate and response to forcing as well as a conventional physically-based model, but some AI models underestimate historical warming trends, and their predictions diverge in the out-of-sample generalization tests. We describe the AIMIP Phase 1 dataset that is publicly available for additional evaluations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_06944 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AIMIP Phase 1: systematic evaluations of AI weather and climate models Henn, Brian Bretherton, Christopher S. Koldunov, Nikolay Lessig, Christian Molina, Maria J. Arcomano, Troy Watt-Meyer, Oliver Couairon, Guillaume Singh, Renu Brunstein, Robert Hasson, Yana Jost, Antonia Brenowitz, Noah Manshausen, Peter Cresswell-Clay, Nathaniel Durran, Dale Hall, Kyle Joseph Chen Yuval, Janni Kochkov, Dmitrii Hoyer, Stephan Lopez-Gomez, Ignacio Atmospheric and Oceanic Physics We present the AI weather and climate model intercomparison project (AIMIP), phase 1. Drawing from the rich tradition of intercomparisons in climate model development, we specify a common experiment, output data format, and training constraints (namely, training against historical reanalysis data) for AIMIP Phase 1 models. We aim to identify differences in modeling frameworks and AI architectural choices that influence model behavior, and build trust in AI weather and climate models through open data and evaluation. AIMIP Phase 1 models must simulate the atmosphere given specified historical sea surface temperatures over 1979-2024. We evaluate the models' performance using five major evaluation criteria: biases, trends, response to El Niño-related sea surface temperature anomalies, temporal variability, and out-of-sample generalization tests. We find that the AI models are able to simulate the historical climate and response to forcing as well as a conventional physically-based model, but some AI models underestimate historical warming trends, and their predictions diverge in the out-of-sample generalization tests. We describe the AIMIP Phase 1 dataset that is publicly available for additional evaluations. |
| title | AIMIP Phase 1: systematic evaluations of AI weather and climate models |
| topic | Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2605.06944 |