Filip von der Lippe

• Lippe, Filip von der; Carlsen, Tim; Storelvmo, Trude & David, Robert Oscar (2026). Shortening of the Arctic cold air outbreak season detected by a phenomenological machine learning approach. Atmospheric Chemistry and Physics (ACP). ISSN 1680-7316. 26(2), p. 1565–1585. doi: 10.5194/acp-26-1565-2026. Full text in Research Archive Show summary

  Abstract. Marine cold air outbreaks (CAOs) frequently occur in the Arctic when cold air moves over the relatively warm ocean, resulting in large turbulent fluxes, instability and cloud formation. Given the high frequency of CAOs during the Arctic winter, the associated clouds have a large impact on the region's radiative balance. Due to Arctic warming, the prevalence of CAOs and their clouds may change, impacting the Arctic radiative balance and potentially amplifying or mitigating local and global warming. To better understand how CAO clouds respond to Arctic warming, this study has developed a phenomenological CAO cloud classification tool that utilizes machine learning methods to identify closed and open cell clouds in CAOs from MODIS satellite imagery. This new approach achieves better performance in identifying CAO clouds compared to the marine cold air outbreak index calculated using MERRA-2 reanalysis, with accuracies of 85.4 % and 78.0 %, respectively. The new approach has revealed frequent CAO cloud formation in regions of high sea surface temperatures, with occurrence maxima along the Norwegian coast and the Northern Atlantic region south of Iceland. Furthermore, the approach reveals trends in CAO cloud cover that suggest a shortening of the CAO season, characterized by an approximate 10 %, increase in cloud coverage during winter and a nearly 20 % decrease during the shoulder months over the past 25 years. These trends suggest a positive radiative feedback during winter in response to climate change, underscoring the importance of further investigating these clouds to understand the trajectory of future Arctic climate.

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