Pushing the frontiers in climate modelling and analysis with machine learning

Eyring, Veronika, Collins, William D. ORCID: https://orcid.org/0000-0002-4463-9848, Gentine, Pierre, Barnes, Elizabeth A., Barreiro, Marcelo, Beucler, Tom, Bocquet, Marc, Bretherton, Christopher S., Christensen, Hannah M., Dagon, Katherine, Gagne, David John, Hall, David, Hammerling, Dorit, Hoyer, Stephan, Iglesias-Suarez, Fernando, Lopez-Gomez, Ignacio, McGraw, Marie C., Meehl, Gerald A., Molina, Maria J., Monteleoni, Claire, Mueller, Juliane, Pritchard, Michael S., Rolnick, David, Runge, Jakob, Stier, Philip, Watt-Meyer, Oliver, Weigel, Katja, Yu, Rose and Zanna, Laure (2024) Pushing the frontiers in climate modelling and analysis with machine learning. Nature Climate Change, 14 (9). pp. 916-928. ISSN 1758-678X

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Abstract

Climate modelling and analysis are facing new demands to enhance projections and climate information. Here we argue that now is the time to push the frontiers of machine learning beyond state-of-the-art approaches, not only by developing machine-learning-based Earth system models with greater fidelity, but also by providing new capabilities through emulators for extreme event projections with large ensembles, enhanced detection and attribution methods for extreme events, and advanced climate model analysis and benchmarking. Utilizing this potential requires key machine learning challenges to be addressed, in particular generalization, uncertainty quantification, explainable artificial intelligence and causality. This interdisciplinary effort requires bringing together machine learning and climate scientists, while also leveraging the private sector, to accelerate progress towards actionable climate science.

Item Type:	Article
Additional Information:	Publisher Copyright: © Springer Nature Limited 2024.
Uncontrolled Keywords:	environmental science (miscellaneous),social sciences (miscellaneous),sdg 13 - climate action ,/dk/atira/pure/subjectarea/asjc/2300/2301
Faculty \ School:	Faculty of Science > School of Environmental Sciences
Related URLs:	https://www.scopus.com/pages/publication...
Depositing User:	LivePure Connector
Date Deposited:	24 Aug 2026 16:17
Last Modified:	24 Aug 2026 17:13
URI:	https://ueaeprints.uea.ac.uk/id/eprint/104313
DOI:	10.1038/s41558-024-02095-y

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