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Climate-invariant machine learning
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DOI：10.1126/sciadv.adj7250.png)
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Abstract
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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.
Keywords:
PREDICTING PROTEIN SOLUBILITY
MOLECULE FORCE SPECTROSCOPY
SECONDARY STRUCTURE
OPTICAL TWEEZERS
BINDING-SITE
NANOMECHANICS
DESIGN
LANGUAGE
SILK
HYDROGELS
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Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions.png)
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