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Deep Learning Based Cloud Cover Parameterization for ICON

Grundner, Arthur and Beucler, Tom and Gentine, Pierre and Iglesias-Suarez, Fernando and Giorgetta, Marco A. and Eyring, Veronika (2022) Deep Learning Based Cloud Cover Parameterization for ICON. Journal of Advances in Modeling Earth Systems, 14 (12), e2021MS002959. Wiley. doi: 10.1029/2021MS002959. ISSN 1942-2466.

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Official URL: https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021MS002959

Abstract

A promising approach to improve cloud parameterizations within climate models and thus climate projections is to use deep learning in combination with training data from storm-resolving model (SRM) simulations. The ICOsahedral Non-hydrostatic (ICON) modeling framework permits simulations ranging from numerical weather prediction to climate projections, making it an ideal target to develop neural network (NN) based parameterizations for sub-grid scale processes. Within the ICON framework, we train NN based cloud cover parameterizations with coarse-grained data based on realistic regional and global ICON SRM simulations. We set up three different types of NNs that differ in the degree of vertical locality they assume for diagnosing cloud cover from coarse-grained atmospheric state variables. The NNs accurately estimate sub-grid scale cloud cover from coarse-grained data that has similar geographical characteristics as their training data. Additionally, globally trained NNs can reproduce sub-grid scale cloud cover of the regional SRM simulation. Using the game-theory based interpretability library SHapley Additive exPlanations, we identify an overemphasis on specific humidity and cloud ice as the reason why our column-based NN cannot perfectly generalize from the global to the regional coarse-grained SRM data. The interpretability tool also helps visualize similarities and differences in feature importance between regionally and globally trained column-based NNs, and reveals a local relationship between their cloud cover predictions and the thermodynamic environment. Our results show the potential of deep learning to derive accurate yet interpretable cloud cover parameterizations from global SRMs, and suggest that neighborhood-based models may be a good compromise between accuracy and generalizability.

Item URL in elib:https://elib.dlr.de/192830/
Document Type:Article
Title:Deep Learning Based Cloud Cover Parameterization for ICON
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Grundner, ArthurDLR, IPAhttps://orcid.org/0000-0002-3765-242XUNSPECIFIED
Beucler, TomUniversity of Lausanne, Lausanne, Switzerlandhttps://orcid.org/0000-0002-5731-1040UNSPECIFIED
Gentine, PierreColumbia University, New York, USAhttps://orcid.org/0000-0002-0845-8345UNSPECIFIED
Iglesias-Suarez, FernandoDLR, IPAhttps://orcid.org/0000-0003-3403-8245UNSPECIFIED
Giorgetta, Marco A.MPI für Meteorologie, Hamburghttps://orcid.org/0000-0002-4278-1963UNSPECIFIED
Eyring, VeronikaDLR, IPAhttps://orcid.org/0000-0002-6887-4885UNSPECIFIED
Date:8 December 2022
Journal or Publication Title:Journal of Advances in Modeling Earth Systems
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:14
DOI:10.1029/2021MS002959
Page Range:e2021MS002959
Publisher:Wiley
Series Name:Machine Learning Application to Earth System Modeling
ISSN:1942-2466
Status:Published
Keywords:Cloud Cover, Parameterization, Machine Learning, Neural Network, Explainable AI, SHAP
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Atmospheric and climate research
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Atmospheric Physics > Earth System Model Evaluation and Analysis
Deposited By: Grundner, Arthur
Deposited On:21 Dec 2022 15:50
Last Modified:21 Dec 2022 15:50

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