Coherent Modes of Northern Hemisphere Wind Extremes and Their Links to Global Large-Scale Drivers

Abstract. Analogously to the well-known seasonal atmospheric circulation patterns that capture coherent large-scale regions of synchronous variability (e.g. North Atlantic Oscillation) we aim in this study to identify spatially coherent modes of stormy seasons over the Northern Hemisphere land regions. Locally, stormy seasons are defined here as seasons with a high number of days having daily wind speed higher than the local climatological 95th-percentile derived from ERA5 reanalysis data. Applying a principal component analysis (PCA) to seasonal October-through-March (ONDJFM) local storm indices reveals a leading mode of hemispheric variability characterised by a meridional dipole structure.

Regions north of 50° N (Europe–Asia) fluctuate coherently, in opposite phase to those farther south. Correlation analyses between the principal component time series and global spatial fields of sea surface temperature (SST), mean sea level pressure (MSLP), and skin temperature (i.e. surface temperature at radiative equilibrium; SKT) identify teleconnections to the North Atlantic Oscillation (NAO) and Pacific SST anomalies, indicating that known climate modes modulate the large-scale spatial coherence of seasonal extreme wind frequency. These teleconnections to large-scale modes arise in the months preceding the target season ONDJFM, suggesting potential predictability at seasonal timescales.

To explore physical causality between SKT and storminess modes related to the atmospheric response to SKT-anomalies, we use the atmospheric emulator ACE2 driven at the surface by the relevant patterns of SKT identified in the SKT–storm correlation analysis. The ACE2 emulator is a recently released artificial-intelligence emulator trained with ERA5 reanalysis. The emulator experiments reproduce the observed storm variability pattern and yield a split jet-stream response with both poleward and equatorward branches. ACE2 simulations driven by the relevant patterns of SKT do tend to produce more stormy seasons in the regions identified by the mentioned PCA analysis. These results support a causal link between coherent large-scale patterns of seasonal storminess and large-scale surface temperature gradients.

Our findings bridge statistical climate variability with physical processes, offering a framework for understanding how continental storm risks respond to changes in global surface temperature. We note that this framework can also be applied to other extreme events.