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Non-Linear Dimensionality Reduction With a Variational Encoder Decoder to Understand Convective Processes in Climate Models

Behrens, Gunnar and Beucler, Tom and Gentine, Pierre and Iglesias-Suarez, Fernando and Pritchard, Michael and Eyring, Veronika (2022) Non-Linear Dimensionality Reduction With a Variational Encoder Decoder to Understand Convective Processes in Climate Models. Journal of Advances in Modeling Earth Systems, 14 (8). Wiley. doi: 10.1029/2022MS003130. ISSN 1942-2466.

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Official URL: https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2022MS003130


Deep learning can accurately represent sub-grid-scale convective processes in climate models, learning from high resolution simulations. However, deep learning methods usually lack interpretability due to large internal dimensionality, resulting in reduced trustworthiness in these methods. Here, we use Variational Encoder Decoder structures (VED), a non-linear dimensionality reduction technique, to learn and understand convective processes in an aquaplanet superparameterized climate model simulation, where deep convective processes are simulated explicitly. We show that similar to previous deep learning studies based on feed-forward neural nets, the VED is capable of learning and accurately reproducing convective processes. In contrast to past work, we show this can be achieved by compressing the original information into only five latent nodes. As a result, the VED can be used to understand convective processes and delineate modes of convection through the exploration of its latent dimensions. A close investigation of the latent space enables the identification of different convective regimes: (a) stable conditions are clearly distinguished from deep convection with low outgoing longwave radiation and strong precipitation; (b) high optically thin cirrus-like clouds are separated from low optically thick cumulus clouds; and (c) shallow convective processes are associated with large-scale moisture content and surface diabatic heating. Our results demonstrate that VEDs can accurately represent convective processes in climate models, while enabling interpretability and better understanding of sub-grid-scale physical processes, paving the way to increasingly interpretable machine learning parameterizations with promising generative properties.

Item URL in elib:https://elib.dlr.de/187922/
Document Type:Article
Title:Non-Linear Dimensionality Reduction With a Variational Encoder Decoder to Understand Convective Processes in Climate Models
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Behrens, GunnarDLR, IPAhttps://orcid.org/0000-0002-5921-5327UNSPECIFIED
Beucler, TomInstitute of Earth Surface Dynamics, University of Lausanne, Lausanne, Switzerlandhttps://orcid.org/0000-0002-5731-1040UNSPECIFIED
Gentine, PierreDepartment of Earth and Environmental Engineering, Columbia University, New York, USAhttps://orcid.org/0000-0002-0845-8345UNSPECIFIED
Iglesias-Suarez, FernandoDLR, IPAhttps://orcid.org/0000-0003-3403-8245UNSPECIFIED
Pritchard, MichaelDepartment of Earth System Science, University of California Irvine, Irvine, CA, USAhttps://orcid.org/0000-0002-0340-6327UNSPECIFIED
Eyring, VeronikaDLR, IPAhttps://orcid.org/0000-0002-6887-4885UNSPECIFIED
Date:August 2022
Journal or Publication Title:Journal of Advances in Modeling Earth Systems
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In ISI Web of Science:Yes
Keywords:machine learning, generative deep learning, convection, parameterization, explainable artificial intelligence, dimensionality reduction
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: Behrens, Gunnar
Deposited On:27 Sep 2022 14:22
Last Modified:05 Dec 2022 15:22

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