Sub-seasonal prediction of storminess in the North Sea with machine learning methods

This research aims to predict storminess in the North Sea using machine learning methods, focusing on how the stratosphere and upper troposphere influence winter storms. Understanding what drives winter storminess is essential for improving sub-seasonal prediction skill in a region strongly affected by extratropical cyclones. Using ERA5 reanalysis data (1940–2024), we built a storminess index based on storm event frequency and examined its relationship with large-scale atmospheric fields.

We predict North Sea storminess using two approaches, one based on the ACE2 climate emulator and another on the Random Forest machine learning algorithm. For the ACE2 model, we used air temperature and zonal and meridional wind patterns at 70 hPa as predictors. For the Random Forest regression model, we used December air temperature, zonal wind at 70 hPa, and geopotential height at 200 hPa as predictors. In both cases, the predictand is North Sea storminess. The ACE2 simulations show that when we add the initial conditions of years with low January storminess, with the December 2015 (selected because December 2015 was followed by a stormy January) stratospheric anomalies (colder temperatures and stronger winds), January surface wind speeds increase generally about 0.5–3 m/s across much of the North Sea. This suggests a dynamical link between early winter stratospheric conditions and stronger surface storminess. The Random Forest model combined with PCA shows a correlation of 0.55–0.60 when the predictors are from December, and the predictand is from January (December–January). When we test other month pairs, the correlation is 0.20–0.36 for November–December and January–February, but it drops to negative values (–0.44 to –0.05) for October–November and February–March. This pattern follows the seasonal cycle of the polar vortex. The circumpolar westerly jet strengthens from autumn and peaks in winter, when predictability is highest. This higher skill is likely linked to stronger stratosphere–troposphere coupling between November and January, as polar vortex anomalies develop and begin to descend toward the surface.

Overall, this research shows that stratospheric conditions play an essential role in shaping North Sea winter storminess and that machine learning methods can improve sub-seasonal predictions in this region.