Data-Driven Equation Discovery of a Cloud Cover Parameterization

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...

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Bibliographic Details
Published in:arXiv.org 2024-02
Main Authors: Grundner, Arthur, Beucler, Tom, Gentine, Pierre, Eyring, Veronika
Format: Article
Language:English
Subjects:
Climate models
Cloud cover
Constraint modelling
Machine learning
Microbalances
Neural networks
Nonlinear equations
Parameterization
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Summary: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 (\(R^2=0.94\)) 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 \(
ISSN:2331-8422