Articles | Volume 6, issue 3
https://doi.org/10.5194/wcd-6-995-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wcd-6-995-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
26 Sep 2025
Research article | Highlight paper |
| 26 Sep 2025
| 26 Sep 2025
Learning predictable and informative dynamical drivers of extreme precipitation using variational autoencoders
Fiona R. Spuler
CORRESPONDING AUTHOR
Department of Meteorology, University of Reading, Reading, UK
The Alan Turing Institute, London, UK
Marlene Kretschmer
CORRESPONDING AUTHOR
Department of Meteorology, University of Reading, Reading, UK
Leipzig Institute for Meteorology, Leipzig University, Leipzig, Germany
Magdalena Alonso Balmaseda
European Centre for Medium-Range Weather Forecasts, Reading, UK
Yevgeniya Kovalchuk
Centre for Advanced Research Computing, University College London, London, UK
Theodore G. Shepherd
Department of Meteorology, University of Reading, Reading, UK
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Linda van Garderen, Frauke Feser, and Theodore G. Shepherd
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The storyline method is used to quantify the effect of climate change on a particular extreme weather event using a global atmospheric model by simulating the event with and without climate change. We present the method and its successful application for the climate change signals of the European 2003 and the Russian 2010 heatwaves.
Marlene Kretschmer, Giuseppe Zappa, and Theodore G. Shepherd
Weather Clim. Dynam., 1, 715–730, https://doi.org/10.5194/wcd-1-715-2020, https://doi.org/10.5194/wcd-1-715-2020, 2020
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The winds in the polar stratosphere affect the weather in the mid-latitudes, making it important to understand potential changes in response to global warming. However, climate model projections disagree on how this so-called polar vortex will change in the future. Here we show that sea ice loss in the Barents and Kara (BK) seas plays a central role in this. The time when the BK seas become ice-free differs between models, which explains some of the disagreement regarding vortex projections.
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Executive editor
The occurrence and predictability of extreme events is often modulated by regional dynamical drivers, but their identification is not straightforward. This paper by Fiona Spuler et al. presents an innovate approach to dimensionality reduction targeted on regionally occurring phenomena. Their approach is based on a machine-learning method demonstrating, in a broad sense, that these method can identify physically interpretable drivers of targeted phenomena. The authors discuss the tradeoff between regime informativeness of local precipitation extremes and predictability of the regimes at subseasonal lead times. The novel machine-learning-based approach may find useful application beyond atmospheric and climate science.
The occurrence and predictability of extreme events is often modulated by regional dynamical...
Short summary
Large-scale atmospheric dynamics modulate the occurrence of extreme events and can improve their prediction. We present a generative machine learning method to identify key dynamical drivers of an impact variable in the form of targeted circulation regimes. Applied to extreme precipitation in Morocco, we show that these targeted regimes are more predictive of the impact while preserving their own predictability and physical consistency.
Large-scale atmospheric dynamics modulate the occurrence of extreme events and can improve their...
