An Interpretable Latent Space reveals changing dynamics of European heatwaves
Tamara Happ\'e, Jasper Wijnands, Paolo Scussolini, Peter Pfleiderer, and Dim Coumou

TL;DR
This paper uses deep learning to classify and interpret European heatwaves based on atmospheric circulation, revealing how different heatwave types are changing over time and providing insights into future trends.
Contribution
It introduces a novel use of Variational Autoencoders for analyzing heatwave circulation patterns and provides interpretability methods to understand latent space changes over time.
Findings
Atlantic Plume heatwaves are increasing in frequency.
Atlantic High heatwaves are decreasing in frequency.
Circulation features associated with heatwaves are consistent across models and observations.
Abstract
Due to climate change, heatwaves are becoming more frequent and intense, with western Europe experiencing the strongest trends in the Northern Hemisphere mid-latitudes. Part of the temperature trends are caused by circulation changes, which are not accurately captured in climate models. Here we deploy Deep Learning techniques to classify European heatwaves based on their atmospheric circulation and to study their associated changes over time. We use a Variational Autoencoder (VAE) to reduce the dimensionality of the heatwave samples, after which we cluster them on their extraced features. The VAE is trained on large ensemble climate model simulations and we show that the VAE generalizes well to observed heatwave circulations in ERA5 reanalysis, without the need for transfer learning. The circulation features relevant for heatwaves in ERA5 are consistent with the climate model heatwaves.…
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Taxonomy
TopicsClimate variability and models · Meteorological Phenomena and Simulations · Tree-ring climate responses
