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This research explores the predictability of seasonal storminess in the North Sea and East China Sea using machine learning and the ACE2 weather model emulator, focusing on stratospheric and upper-tropospheric influences on winter storms. Using ERA5 reanalysis data (1940–2024), a storminess index based on storm frequency was developed to examine links with large-scale atmospheric fields.
For the North Sea, storminess was predicted using ACE2 and a Random Forest model. ACE2 is a machine-learning weather emulator developed by the Allen Institute for AI and trained on ERA5. ACE2 simulations show that lower stratospheric temperatures and stronger winds on 1 December increase emulated mean January surface wind speeds across much of the North Sea. The Random Forest model achieved its highest skill when predicting January storminess from December fields, with correlations of 0.55–0.60.
Predictability from both models follows the seasonal cycle of polar-vortex intensity. Skill peaks in winter, when stratosphere-troposphere coupling is strongest, with a lead time of 4-6 weeks. North Sea storminess is neither significantly correlated with storminess elsewhere nor persistent from month to month, suggesting regionally specific drivers operating on subseasonal timescales.
Overall, the results show that stratospheric conditions play an important role in North Sea winter storminess and that machine learning can improve subseasonal prediction in this region.
The East China Sea component is ongoing. Preliminary results indicate that a November sea-surface temperature and sea-level pressure pattern resembling La Niña is linked to December storm numbers. Strong relationships are also found between Southern Hemisphere high-latitude stratospheric temperature, zonal wind, and December storm activity. Together with evidence that La Niña favors a positive SAM, these results suggest a pathway linking La Niña, November SAM conditions, and enhanced December storminess in the East China Sea.