Articles | Volume 26, issue 2
https://doi.org/10.5194/acp-26-1565-2026
https://doi.org/10.5194/acp-26-1565-2026
Research article
 | 
29 Jan 2026
Research article |  | 29 Jan 2026

Shortening of the Arctic cold air outbreak season detected by a phenomenological machine learning approach

Filip Severin von der Lippe, Tim Carlsen, Trude Storelvmo, and Robert Oscar David

Viewed

Total article views: 1,953 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,761 156 36 1,953 38 37
  • HTML: 1,761
  • PDF: 156
  • XML: 36
  • Total: 1,953
  • BibTeX: 38
  • EndNote: 37
Views and downloads (calculated since 12 Aug 2025)
Cumulative views and downloads (calculated since 12 Aug 2025)

Viewed (geographical distribution)

Total article views: 1,953 (including HTML, PDF, and XML) Thereof 1,913 with geography defined and 40 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 29 Jan 2026
Download
Short summary
This paper investigates how clouds associated with Arctic marine cold air outbreaks (CAOs) respond to climate change. By utilizing machine learning methods and remote sensing data from the past 25 years, the study identifies trends indicating a shortening of the CAO season. This has implications for the Arctic energy balance, underscoring the importance of further investigating these clouds to understand the trajectory of future Arctic climate.
Share
Altmetrics
Final-revised paper
Preprint