Causal Climate Emulation with Bayesian Filtering

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.

Authors

• Sebastian H. M. Hickman
• Ilija Trajković
• Julia Kaltenborn
• Francis Pelletier
• Alex Archibald
• Yaniv Gurwicz
• Peer Nowack
• David Rolnick Mila - Quebec AI Institute McGill University
• Julien Boussard

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