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

Media type: E-Article

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

Contributor: Behrens, Gunnar [Author]; Beucler, Tom [Author]; Gentine, Pierre [Author]; Iglesias‐Suarez, Fernando [Author]; Pritchard, Michael [Author]; Eyring, Veronika [Author]; 3 Institute of Earth Surface Dynamics University of Lausanne Lausanne Switzerland [Author]; 2 Department of Earth and Environmental Engineering Columbia University New York NY USA [Author]; 1 Deutsches Zentrum für Luft‐ und Raumfahrt (DLR) Institut für Physik der Atmosphäre Oberpfaffenhofen Germany [Author]; 5 Department of Earth System Science University of California Irvine Irvine CA USA [Author]

imprint: GEO-LEOe-docs (FID GEO), 2022-08-13

Language: English

DOI: https://doi.org/10.1029/2022MS003130

Keywords: machine learning ; parameterization ; convection ; dimensionality reduction ; explainable artificial intelligence ; generative deep learning

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Description: 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. ; Plain Language Summary: Deep neural nets are hard to interpret due to their hundred thousand or million trainable parameters without further postprocessing. We demonstrate in this paper the usefulness of a network type that is designed to drastically reduce this high ...

Access State: Open Access

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