Intraseasonal prediction of monthly storminess in the North Sea with the ACE2 atmospheric emulator and Random Forests

Abstract. This research explores the predictability of seasonal storminess in the North Sea using machine learning methods and the weather model emulator ACE2, focusing on how the stratosphere and upper troposphere influence winter storms. Understanding the drivers of winter storminess is essential for improving sub-seasonal prediction skill in regions strongly affected by extratropical cyclones. Using ERA5 reanalysis data (1940–2024), we built a storminess index based on storm-event frequency, examined its relationship with large-scale atmospheric fields, and explored its predictability on seasonal timescales. We aim to predict North Sea storminess using two approaches: one based on the ACE2 climate emulator and another on the Random Forest machine learning algorithm. The ACE2 simulations show that by decreasing the stratospheric temperature and increasing the wind speed on 1 December, we can increase the emulated mean January surface wind speeds by about 0.5–3 m s−1 across much of the North Sea. Similar sensitivity emulations initialised at the start of other months, e.g. 1 November, failed to produce a meaningful response in the ensuing month. This suggests a dynamic link between early-winter stratospheric conditions and increased mid-winter surface storminess. For the Random Forest regression model, monthly means of air temperature, zonal wind at 70 hPa, and geopotential height at 200 hPa were used as predictors after dimensionality reduction using Principal Components Analysis (PCA). The highest skill was obtained when predicting January storminess from December fields, with a correlation of about 0.55–0.60, while predictive skill was substantially weaker and sometimes negative for other month-to-month predictions. This seasonal predictability pattern, derived from both the ACE2 emulations and the Random Forest model, follows the seasonal cycle of the polar vortex's average intensity. 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 that peaks in late December and extends until mid-February, as polar vortex anomalies develop and begin to descend toward the surface. Both indicate that the seasonal predictability of storminess peaks in mid-winter, with a predictability lead time of about 4 to 6 weeks. It fades for the earlier and late winter periods. We found that North Sea storminess, measured as the monthly or seasonal number of storms, is not statistically correlated with storminess in any other region of the globe. This suggests that although individual seasons or months may show increased storminess over large areas, the statistical predictors of North Sea storminess must have a small-scale regional character specific to the North Sea. We also found that the North Sea storminess shows no statistical persistence from month to month. This suggests that although individual winters may display North Sea storminess across all winter months, predictors of North Sea monthly storminess should also have a short-term subseasonal temporal scale. Together, these two findings indicate that sea-surface temperatures are not an adequate statistical predictor of North Sea storminess, although they may play a role in individual winters. 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.