Abstract
Traditional models of climate change use complex systems of coupled equations to simulate physical processes across the Earth system. These simulations are highly computationally expensive, limiting our predictions of climate change and analyses of its causes and effects. Machine learning has the potential to quickly emulate data from climate models, but current approaches are not able to incorporate physics-informed causal relationships. Here, we develop an interpretable climate model emulator based on causal representation learning. We derive a physics-informed approach including a Bayesian filter for stable long-term autoregressive emulation. We demonstrate that our emulator learns accurate climate dynamics, and we show the importance of each one of its components on a realistic synthetic dataset and data from two widely deployed climate models.
Abstract (translated)
传统的气候模型使用复杂的耦合方程系统来模拟地球系统的物理过程。这些模拟计算成本高昂，限制了我们对气候变化及其原因和影响的预测与分析能力。机器学习有潜力快速模拟气候模型的数据，但目前的方法无法纳入基于物理学的因果关系。在这里，我们开发了一种基于因果表示学习的可解释气候模型仿真器。我们提出一种物理知识导向的方法，包括用于长期稳定自回归仿真的贝叶斯滤波器。我们在一个现实合成数据集和两个广泛应用的气候模型的数据上证明了我们的仿真器能够准确地学习气候动态，并展示了该仿真器每个组件的重要性。
URL
https://arxiv.org/abs/2506.09891
