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

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Cited articles

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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.
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