Weather and Climate Dynamics
Weather and Climate Dynamics
WCD
Articles | Volume 5, issue 4
Articles | Volume 5, issue 4
https://doi.org/10.5194/wcd-5-1223-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wcd-5-1223-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 5, issue 4
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

Viewed

Total article views: 6,335 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
3,905 1,862 568 6,335 287 448
  • HTML: 3,905
  • PDF: 1,862
  • XML: 568
  • Total: 6,335
  • BibTeX: 287
  • EndNote: 448
Views and downloads (calculated since 18 Jan 2024)
Cumulative views and downloads (calculated since 18 Jan 2024)

Viewed (geographical distribution)

Total article views: 6,335 (including HTML, PDF, and XML) Thereof 6,180 with geography defined and 155 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

Latest update: 13 Oct 2026
Download
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.
Read more
Share