  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: (1) ML for climate physics and (2) ML for
climate simulations. While physics-free ML-based models, such as ML-based
weather forecasting, have demonstrated success when data is abundant and
stationary, the physics knowledge and interpretability of ML models become
crucial in the small-data/non-stationary 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.
