Causal Climate Emulation with Bayesian Filtering

Sebastian Hickman, Ilija Trajković, Julia Kaltenborn, Francis Pelletier, Alexander T Archibald, Yaniv Gurwicz, Peer Nowack, David Rolnick, Julien Boussard

Keywords: Climate, causality, interpretability, physics, emulation

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 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.

Primary Area: Machine learning for sciences (e.g. climate, health, life sciences, physics, social sciences)

Submission Number: 20483