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 physically-based causal relationships. Here, we develop an interpretable climate model emulator based on causal representation learning. We derive a novel 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.

翻译：传统的气候变化模型采用复杂的耦合方程组来模拟整个地球系统的物理过程。这些模拟计算成本极高，限制了我们对气候变化的预测及其因果关系的分析。机器学习有潜力快速模拟气候模型数据，但现有方法无法纳入基于物理的因果关系。本文基于因果表示学习，开发了一种可解释的气候模型模拟器。我们提出了一种新方法，其中包含用于稳定长期自回归模拟的贝叶斯滤波器。我们证明，该模拟器能够学习准确的气候动力学，并通过一个现实的合成数据集以及来自两个广泛应用的气候模型的数据，展示了其各组成部分的重要性。