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

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

doi:10.5194/acp-26-1565-2026

Articles | Volume 26, issue 2

https://doi.org/10.5194/acp-26-1565-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/acp-26-1565-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 26, issue 2

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

Data sets

Shortening Arctic cold air outbreak season Filip Severin von der Lippe https://doi.org/10.5281/zenodo.18352136

MODIS 1km Calibrated Radiances Product MODIS Characterization Support Team (MCST) https://doi.org/10.5067/MODIS/MOD021KM.061

Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data N. DiGirolamo et al. https://doi.org/10.5067/MPYG15WAA4WX

MERRA-2 tavg1_2d_slv_Nx: 2d,1-Hourly,Time-Averaged,Single- Level, Assimilation,Single-Level Diagnostics V5.12.4 Global Modeling and Assimilation Office (GMAO) https://doi.org/10.5067/VJAFPLI1CSIV

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.