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Machine Learning for Clouds and Climate (Invited Chapter for the AGU Geophysical Monograph Series “Clouds and Climate”)
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  • Tom Beucler,
  • Imme Ebert-Uphoff,
  • Stephan Rasp,
  • Michael Pritchard,
  • Pierre Gentine
Tom Beucler
University of California, Irvine

Corresponding Author:[email protected]

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Imme Ebert-Uphoff
Cooperative Institute for Research in the Atmosphere
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Stephan Rasp
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Michael Pritchard
University of California
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Pierre Gentine
Columbia University
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Key Points: • Machine learning (ML) helps model the interaction between clouds and climate using large datasets. • We review physics-guided/explainable ML applied to cloud-related processes in the climate system. • We also provide a guide to scientists who would like to get started with ML. Abstract: Machine learning (ML) algorithms are powerful tools to build models of clouds and climate that are more faithful to the rapidly-increasing volumes of Earth system data than commonly-used semiempirical models. Here, we review ML tools, including interpretable and physics-guided ML, and outline how they can be applied to cloud-related processes in the climate system, including radiation, microphysics, convection, and cloud detection , classification, emulation, and uncertainty quantification. We additionally provide a short guide to get started with ML and survey the frontiers of ML for clouds and climate .