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Annual Review of Condensed Matter Physics

Volume 16, 2025

Machine Learning for Climate Physics and Simulations

  • Ching-Yao Lai1, Pedram Hassanzadeh2, Aditi Sheshadri3, Maike Sonnewald4, Raffaele Ferrari5 and Venkatramani Balaji6
  • 1Department of Geophysics, Stanford University, Stanford, California, USA; email: [email protected] 2Department of Geophysical Sciences and Committee on Computational and Applied Mathematics, University of Chicago, Chicago, Illinois, USA 3Department of Earth System Science, Stanford University, Stanford, California, USA 4Department of Computer Science, University of California, Davis, California, USA 5Department of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA 6Schmidt Sciences, New York, NY, USA
  • Vol. 16:343-365 (Volume publication date March 2025)
  • First published as a Review in Advance on November 26, 2024
  • Copyright © 2025 by the author(s).
    This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See credit lines of images or other third-party material in this article for license information.

Abstract

We discuss the emerging advances and opportunities at the intersection of machine learning (ML) and climate physics, highlighting the use of ML techniques, including supervised, unsupervised, and equation discovery, to accelerate climate knowledge discoveries and simulations. We delineate two distinct yet complementary aspects: () ML for climate physics and () ML for climate simulations. Although physics-free ML-based models, such as ML-based weather forecasting, have demonstrated success when data are abundant and stationary, the physics knowledge and interpretability of ML models become crucial in the small-data/nonstationary regime to ensure generalizability. Given the absence of observations, the long-term future climate falls into the small-data regime. Therefore, ML for climate physics holds a critical role in addressing the challenges of ML for climate simulations. We emphasize the need for collaboration among climate physics, ML theory, and numerical analysis to achieve reliable ML-based models for climate applications.

Keyword(s): climate, emulator, equation discovery, machine learning-informed physics, parameterization, physics-informed machine learning
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2025-03-10
2025-06-18
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Machine Learning for Climate Physics and Simulations
Annual Review of Condensed Matter Physics 16, 343 (2025); https://doi.org/10.1146/annurev-conmatphys-043024-114758
/content/journals/10.1146/annurev-conmatphys-043024-114758
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