Cited by 232Meteorological Phenomena and SimulationsClimate variability and modelsAtmospheric aerosols and clouds

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Spatially Extended Tests of a Neural Network Parametrization Trained by Coarse‐Graining

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Brenowitz, Noah D., Bretherton, Christopher S.·2019·Journal of Advances in Modeling Earth Systems

DOI 10.1029/2019ms001711

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Abstract

General circulation models (GCMs) typically have a grid size of 25–200 km. Parametrizations are used to represent diabatic processes such as radiative transfer and cloud microphysics and account for subgrid‐scale motions and variability. Unlike traditional approaches, neural networks (NNs) can readily exploit recent observational data sets and global cloud‐system resolving model (CRM) simulations to learn subgrid variability. This article describes an NN parametrization trained by coarse‐graining a near‐global CRM simulation with a 4‐km horizontal grid spacing. The NN predicts the residual heating and moistening averaged over (160 km) 2 grid boxes as a function of the coarse‐resolution fields within the same atmospheric column. This NN is coupled to the dynamical core of a GCM with the same 160‐km resolution. A recent study described how to train such an NN to be stable when coupled to specified time‐evolving advective forcings in a single‐column model, but feedbacks between NN and GCM components cause spatially extended simulations to crash within a few days. Analyzing the linearized response of such an NN reveals that it learns to exploit a strong synchrony between precipitation and the atmospheric state above 10 km. Removing these variables from the NN's inputs stabilizes the coupled simulations, which predict the future state more accurately than a coarse‐resolution simulation without any parametrizations of subgrid‐scale variability, although the mean state slowly drifts.

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Authors: Brenowitz, N. D., & Bretherton, C. S.
Affiliations: University of Washington (US)
Corresponding author: Brenowitz, Noah D.
ORCID: Brenowitz, Noah D.
Bretherton, Christopher S.
Venue: Journal of Advances in Modeling Earth Systems
Publisher: American Geophysical Union (AGU)
Published: 2019-08
Volume: 11
Issue: 8
Pages: 2728-2744
ISSN: 1942-2466 (ISSN-L)
DOI: 10.1029/2019ms001711
OpenAlex: W2930756996
Cited by: 231
References: 37
Citations by year: 2026: 21 · 2025: 16 · 2024: 45 · 2023: 50 · 2022: 30 · 2021: 47 · 2020: 20 · 2019: 2
FWCI: 9.88
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (am, 21 d)
creativecommons.org/licenses/by/4.0/ (vor, 21 d)
Locations: Journal of Advances in Modeling Earth Systems (publishedVersion, cc-by)
arXiv (Cornell University) (submittedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
arXiv (Cornell University) (cc-by)
Funders: Alfred P. Sloan Foundation (3835)
Gordon and Betty Moore Foundation (2013-10-29)
U.S. Department of Energy (DE-SC0012451, DE-SC0016433)
Washington Research Foundation
Topics: Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Meteorological Phenomena and Simulations
Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
Physical Sciences › Environmental Science › Global and Planetary Change › Atmospheric aerosols and clouds
Keywords: neural network parametrization, coarse-graining, cloud-resolving model, subgrid-scale processes, linear stability analysis, model drift

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Spatially Extended Tests of a Neural Network Parametrization Trained by Coarse‐Graining

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