Identifying the Atmospheric Drivers of Drought and Heat Using a Smoothed Deep Learning Approach
MCML Authors
Maximilian Weigert
* Former Member
David Rügamer
Prof. Dr.
Principal Investigator
Abstract
Maximilian Weigert
* Former Member
David Rügamer
Prof. Dr.
Principal Investigator
Abstract
Europe was hit by several, disastrous heat and drought events in recent summers. Besides thermodynamic influences, such hot and dry extremes are driven by certain atmospheric situations including anticyclonic conditions. Effects of climate change on atmospheric circulations are complex and many open research questions remain in this context, e.g., on future trends of anticyclonic conditions. Based on the combination of a catalog of labeled circulation patterns and spatial atmospheric variables, we propose a smoothed convolutional neural network classifier for six types of anticyclonic circulations that are associated with drought and heat. Our work can help to identify important drivers of hot and dry extremes in climate simulations, which allows to unveil the impact of climate change on these drivers. We address various challenges inherent to circulation pattern classification that are also present in other climate patterns, e.g., subjective labels and unambiguous transition periods.
inproceedings
Tackling Climate Change with ML @NeurIPS 2021
Workshop on Tackling Climate Change with Machine Learning at the 35th Conference on Neural Information Processing Systems. Virtual, Dec 06-14, 2021.Authors
M. Mittermeier • M. Weigert • D. RügamerLinks
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BibTeXKey: MWR21