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

Data sets

Arctic Climate Response to European Radiative Forcing: A Deep Learning Study on Circulation Pattern Changes (control run daily dataset) Sina Mehrdad and Sudhakar Dipu https://doi.org/10.5281/zenodo.10245983

Arctic Climate Response to European Radiative Forcing: A Deep Learning Study on Circulation Pattern Changes (Experiment run daily dataset) Sina Mehrdad and Sudhakar Dipu https://doi.org/10.5281/zenodo.10246439

Model code and software

Sinamhr/Code_examples_radiation_paper: Arctic Climate Response to European Radiative Forcing: A Deep Learning Study on Circulation Pattern Changes (related codes) Sina Mehrdad https://doi.org/10.5281/zenodo.13371084

Code_examples_radiation_paper Sina Mehrdad https://github.com/Sinamhr/Code_examples_radiation_paper

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