Climate-invariant machine learning

Beucler, Tom; Gentine, Pierre; Yuval, Janni; Gupta, Ankitesh; Peng, Liran; Lin, Jerry; Yu, Sungduk; Rasp, Stephan; Ahmed, Fiaz; O’Gorman, Paul A.; Neelin, J. David; Lutsko, Nicholas J.; Pritchard, Michael

doi:10.1126/sciadv.adj7250

Title: Climate-invariant machine learning

Journal Article · 06 February 2024 · Science Advances

DOI: https://doi.org/10.1126/sciadv.adj7250 · OSTI ID:2472228

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University of Lausanne (Switzerland); University of California, Irvine, CA (United States); OSTI
Columbia University, New York, NY (United States)
Massachusetts Institute of Technology, Cambridge, MA (United States)
University of California, Irvine, CA (United States)
Google Research, Mountain View, CA (United States)
University of California, Los Angeles, CA (United States)
University of California, San Diego, La Jolla, CA (United States)
University of California, Irvine, CA (United States); NVIDIA, Santa Clara, CA (United States)

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 that they were not trained on. To get the best of the physical and statistical worlds, we propose a 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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Research Organization:: University of California, San Diego, CA (United States); University of California, Irvine, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC); National Science Foundation (NSF); Schmidt Family Foundation

Grant/Contract Number:: SC0022255; SC0022331

OSTI ID:: 2472228

Journal Information:: Science Advances, Journal Name: Science Advances Journal Issue: 6 Vol. 10; ISSN 2375-2548

Publisher:: AAASCopyright Statement

Country of Publication:: United States

Language:: English

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