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: ( 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.

Ching-Yao Lai

Pedram Hassanzadeh

Aditi Sheshadri

Maike Sonnewald

Raffaele Ferrari

Venkatramani Balaji

Annual Review of Condensed Matter Physics

自 2010 年出版的凝聚态物理年度回顾描述了凝聚态物理和相关学科最重要的进展。该杂志通过确定最近的发展并提出对该领域各个部分的批判性评估，为正在进行的研究做出贡献。

The Annual Review of Condensed Matter Physics has been published since 2010 and aims to describe the significant advancements in condensed matter physics and related fields. The journal contributes to ongoing research by identifying recent developments and providing critical assessments of various aspects of the field.

《Annual Review of Condensed Matter Physics》自2010年起出版，旨在描述凝聚态物理及相关学科的重要进展。该杂志通过识别最新的发展，并对该领域的各个部分进行批判性评估，为持续的研究提供贡献。

Category	Quartile
物理与天体物理	1
物理与天体物理, 物理凝聚态物理	1

Machine Learning for Climate Physics and Simulations

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Journal Info

Category	Quartile
PHYSICS, CONDENSED MATTER	1