Pushing the frontiers in climate modelling and analysis with machine learning

Veronika Eyring (), William D. Collins (), Pierre Gentine, Elizabeth A. Barnes, Marcelo Barreiro, Tom Beucler, Marc Bocquet, Christopher S. Bretherton, Hannah M. Christensen, Katherine Dagon, David John Gagne, David Hall, Dorit Hammerling, Stephan Hoyer, Fernando Iglesias-Suarez, Ignacio Lopez-Gomez, Marie C. McGraw, Gerald A. Meehl, Maria J. Molina, Claire Monteleoni, Juliane Mueller, Michael S. Pritchard, David Rolnick, Jakob Runge, Philip Stier, Oliver Watt-Meyer, Katja Weigel, Rose Yu and Laure Zanna
Additional contact information
Veronika Eyring: Institut für Physik der Atmosphäre
William D. Collins: Lawrence Berkeley National Laboratory
Pierre Gentine: Columbia University
Elizabeth A. Barnes: Colorado State University
Marcelo Barreiro: Universidad de la República
Tom Beucler: University of Lausanne
Marc Bocquet: École des Ponts and EdF R&D
Christopher S. Bretherton: Allen Institute for Artificial Intelligence
Hannah M. Christensen: University of Oxford
Katherine Dagon: NSF National Center for Atmospheric Research
David John Gagne: NSF National Center for Atmospheric Research
David Hall: NVIDIA Corporation
Dorit Hammerling: Colorado School of Mines
Stephan Hoyer: Google Research
Fernando Iglesias-Suarez: Institut für Physik der Atmosphäre
Ignacio Lopez-Gomez: Google Research
Marie C. McGraw: Colorado State University
Gerald A. Meehl: NSF National Center for Atmospheric Research
Maria J. Molina: NSF National Center for Atmospheric Research
Claire Monteleoni: University of Colorado Boulder
Juliane Mueller: National Renewable Energy Laboratory
Michael S. Pritchard: NVIDIA Corporation
David Rolnick: McGill University
Jakob Runge: Institut für Datenwissenschaften
Philip Stier: University of Oxford
Oliver Watt-Meyer: Allen Institute for Artificial Intelligence
Katja Weigel: Institut für Physik der Atmosphäre
Rose Yu: San Diego
Laure Zanna: New York University

Nature Climate Change, 2024, vol. 14, issue 9, 916-928

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41558-024-02095-y

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