Machine Learning for Climate Physics and Simulations

doi:10.1146/annurev-conmatphys-043024-114758

Machine Learning for Climate Physics and Simulations

2024-11-26

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DOI：10.1146/annurev-conmatphys-043024-114758

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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: (a) ML for climate physics and (b) 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.

Keywords:

climate

physics-informed machine learning

machine learning-informed

physics

equation discovery

parameterization

emulator

Machine Learning for Climate Physics and Simulations

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