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.

中文翻译：

---

用于气候物理学和模拟的机器学习


我们讨论了机器学习 （ML） 和气候物理学交叉领域的新兴进展和机遇，重点介绍了 ML 技术的使用，包括有监督、无监督和方程发现，以加速气候知识的发现和模拟。我们描述了两个不同但互补的方面：（a） 用于气候物理学的 ML 和 （b） 用于气候模拟的 ML。尽管基于物理的 ML 模型（例如基于 ML 的天气预报）在数据丰富且静止时已证明是成功的，但 ML 模型的物理知识和可解释性在小数据/非平稳状态下至关重要，以确保泛化性。由于缺乏观测，长期的未来气候属于小数据模式。因此，用于气候物理学的 ML 在应对 ML 用于气候模拟的挑战方面发挥着关键作用。我们强调气候物理学、ML 理论和数值分析之间需要合作，以实现可靠的基于 ML 的气候应用模型。