Climate-Invariant Machine Learning

• URL: http://arxiv.org/abs/2112.08440v5
• Date: Wed, 17 Jan 2024 22:41:32 GMT
• Title: Climate-Invariant Machine Learning
• Authors: Tom Beucler, Pierre Gentine, Janni Yuval, Ankitesh Gupta, Liran Peng, Jerry Lin, Sungduk Yu, Stephan Rasp, Fiaz Ahmed, Paul A. O'Gorman, J. David Neelin, Nicholas J. Lutsko, Michael Pritchard
• Abstract summary: Current climate models require representations of processes that occur at scales smaller than model grid size. Recent machine learning (ML) algorithms hold promise to improve such process representations, but tend to extrapolate poorly to climate regimes they were not trained on. We propose a new framework - termed "climate-invariant" ML - incorporating knowledge of climate processes into ML algorithms.
• Score: 0.8831201550856289
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Projecting climate change is a generalization problem: we extrapolate the recent past using physical models across past, present, and future climates. Current climate models require representations of processes that occur at scales smaller than model grid size, which have been the main source of model projection uncertainty. Recent machine learning (ML) algorithms hold promise to improve such process representations, but tend to extrapolate poorly to climate regimes they were not trained on. To get the best of the physical and statistical worlds, we propose a new framework - termed "climate-invariant" ML - incorporating knowledge of climate processes into ML algorithms, and show that it can maintain high offline accuracy across a wide range of climate conditions and configurations in three distinct atmospheric models. Our results suggest that explicitly incorporating physical knowledge into data-driven models of Earth system processes can improve their consistency, data efficiency, and generalizability across climate regimes.

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