Articles | Volume 5, issue 4
https://doi.org/10.5194/wcd-5-1223-2024
https://doi.org/10.5194/wcd-5-1223-2024
Research article
 | 
09 Oct 2024
Research article |  | 09 Oct 2024

Arctic climate response to European radiative forcing: a deep learning study on circulation pattern changes

Sina Mehrdad, Dörthe Handorf, Ines Höschel, Khalil Karami, Johannes Quaas, Sudhakar Dipu, and Christoph Jacobi

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Latest update: 12 Dec 2024
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Short summary
This study introduces a novel deep learning (DL) approach to analyze how regional radiative forcing in Europe impacts the Arctic climate. By integrating atmospheric poleward energy transport with DL-based clustering of atmospheric patterns and attributing anomalies to specific clusters, our method reveals crucial, nuanced interactions within the climate system, enhancing our understanding of intricate climate dynamics.