Could Machine Learning Break the Convection Parameterization Deadlock?

Gentine, P.; Pritchard, M.; Rasp, S.; Reinaudi, G.; Yacalis, G.

doi:10.1029/2018GL078202

Title: Could Machine Learning Break the Convection Parameterization Deadlock?

Journal Article · Tue Jun 12 00:00:00 EDT 2018 · Geophysical Research Letters

DOI:https://doi.org/10.1029/2018GL078202· OSTI ID:1452657

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^[3]; Reinaudi, G. ^[1];

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Earth and Environmental Engineering, Columbia University, New York NY USA
Earth System Science, University of California, Irvine CA USA
Faculty of Physics, LMU Munich, Munich Germany

Representing unresolved moist convection in coarse-scale climate models remains one of the main bottlenecks of current climate simulations. Many of the biases present with parameterized convection are strongly reduced when convection is explicitly resolved (i.e., in cloud resolving models at high spatial resolution approximately a kilometer or so). We here present a novel approach to convective parameterization based on machine learning, using an aquaplanet with prescribed sea surface temperatures as a proof of concept. A deep neural network is trained with a superparameterized version of a climate model in which convection is resolved by thousands of embedded 2-D cloud resolving models. The machine learning representation of convection, which we call the Cloud Brain (CBRAIN), can skillfully predict many of the convective heating, moistening, and radiative features of superparameterization that are most important to climate simulation, although an unintended side effect is to reduce some of the superparameterization’s inherent variance. Since as few as three months’ high-frequency global training data prove sufficient to provide this skill, the approach presented here opens up a new possibility for a future class of convection parameterizations in climate models that are built “top-down,” that is, by learning salient features of convection from unusually explicit simulations.

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Research Organization:: Univ. of California, Irvine, CA (United States)

Sponsoring Organization:: USDOE; National Science Foundation (NSF)

Grant/Contract Number:: SC0012548; SC0012152

OSTI ID:: 1452657

Alternate ID(s):: OSTI ID: 1452658; OSTI ID: 1510994

Journal Information:: Geophysical Research Letters, Journal Name: Geophysical Research Letters Vol. 45 Journal Issue: 11; ISSN 0094-8276

Publisher:: American Geophysical UnionCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 197 works

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