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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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Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Approximation by superpositions of a sigmoidal function
Cybenko, G. (1989) · Mathematics of Control, Signals, and Systems · doi:10.1007/bf02551274 · Cited by 13,883
Untitled work
(2017) · Cited by 11,625
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
Simonyan, K., Vedaldi, A., & Zisserman, A. (2013) · arXiv · doi:10.48550/arxiv.1312.6034 · Cited by 5,370
Deep learning to represent subgrid processes in climate models
Rasp, S., Pritchard, M. S., & Gentine, P. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1810286115 · Cited by 1,044
On large-scale circulations in convecting atmospheres
Emanuel, K., Neelin, J. D., & Bretherton, C. S. (1994) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.49712051902 · Cited by 874
Self-citation
Fundamentals of synchronization in chaotic systems, concepts, and applications
Pecora, L. M., Carroll, T. L., Johnson, G. A., Mar, D. J., & Heagy, J. F. (1997) · Chaos: An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.166278 · Cited by 862
Tropical Super Clusters within Intraseasonal Variations over the Western Pacific
Nakazawa, T. (1988) · Journal of the Meteorological Society of Japan Ser II · doi:10.2151/jmsj1965.66.6_823 · Cited by 654
Nonhydrostatic icosahedral atmospheric model (NICAM) for global cloud resolving simulations
Satoh, M., Matsuno, T., Tomita, H., Miura, H., Nasuno, T., & Iga, S. (2007) · Journal of Computational Physics · doi:10.1016/j.jcp.2007.02.006 · Cited by 651
A nonlinear dynamical perspective on model error: A proposal for non‐local stochastic‐dynamic parametrization in weather and climate prediction models
Palmer, T. N. (2001) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.49712757202 · Cited by 469

Show all 37 references

Research works printed but not linked: 4

10.1175/1520-0442(2004)017<1517:RBWVPA>2.0.CO;2 · DOI
ECMWF, 2018, IFS Documentation CY41R1, 1
Goodfellow I., 2016, Deep learning, adaptive computation and machine learning
Hanin B.(2017).Universal function approximation by deep neural nets with bounded width and ReLU activations. arXiv e‐prints.

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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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