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

TL;DR

This paper demonstrates that Variational Encoder Decoders can effectively compress and interpret convective processes in climate models, achieving accurate reproduction with only five latent variables and revealing distinct convective regimes.

Contribution

It introduces the use of VEDs for non-linear dimensionality reduction in climate modeling, enabling interpretability and understanding of convective processes with minimal latent dimensions.

Findings

VEDs accurately reproduce convective processes

Five latent nodes suffice for effective compression

Latent space reveals distinct convective regimes

Abstract

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