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Data-Driven Equation Discovery of a Cloud Cover Parameterization

Grundner, Arthur and Beucler, Tom and Gentine, Pierre and Eyring, Veronika (2024) Data-Driven Equation Discovery of a Cloud Cover Parameterization. Journal of Advances in Modeling Earth Systems, pp. 1-26. Wiley. doi: 10.1029/2023MS003763. ISSN 1942-2466.

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Official URL: https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023MS003763

Abstract

A promising method for improving the representation of clouds in climate models, and hence climate projections, is to develop machine learning-based parameterizations using output from global storm-resolving models. While neural networks can achieve state-of-the-art performance within their training distribution, they can make unreliable predictions outside of it. Additionally, they often require post-hoc tools for interpretation. To avoid these limitations, we combine symbolic regression, sequential feature selection, and physical constraints in a hierarchical modeling framework. This framework allows us to discover new equations diagnosing cloud cover from coarse-grained variables of global storm-resolving model simulations. These analytical equations are interpretable by construction and easily transferable to other grids or climate models. Our best equation balances performance and complexity, achieving a performance comparable to that of neural networks (R2 = 0.94) while remaining simple (with only 11 trainable parameters). It reproduces cloud cover distributions more accurately than the Xu-Randall scheme across all cloud regimes (Hellinger distances < 0.09), and matches neural networks in condensate-rich regimes. When applied and fine-tuned to the ERA5 reanalysis, the equation exhibits superior transferability to new data compared to all other optimal cloud cover schemes. Our findings demonstrate the effectiveness of symbolic regression in discovering interpretable, physically-consistent, and nonlinear equations to parameterize cloud cover.

Item URL in elib:https://elib.dlr.de/203075/
Document Type:Article
Title:Data-Driven Equation Discovery of a Cloud Cover Parameterization
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
Eyring, VeronikaDLR, IPAhttps://orcid.org/0000-0002-6887-4885UNSPECIFIED
Date:2024
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
DOI:10.1029/2023MS003763
Page Range:pp. 1-26
Publisher:Wiley
ISSN:1942-2466
Status:Published
Keywords:Cloud Cover, Parameterization, Machine Learning, Symbolic Regression, Equation Discovery, Physical Constraints, PySR, Sequential Feature Selection, Pareto Frontier
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:04 Mar 2024 11:03
Last Modified:04 Mar 2024 11:03

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