| 06 Feb 2026
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.
A review of egusphere-2026-523
“Coherent Modes of Northern Hemisphere Wind Extremes and Their Links to Global Large-Scale Drivers”
by Kai Bellinghausen, Birgit Hünicke, and Eduardo Zorita
General comments
The manuscript reports on research efforts regarding the connection of a hemispheric variability pattern of winter storm occurrence with known large scale atmospheric variability modes like NAO, PNA and ENSO. The idea is that the particular areas affected by more than the average number of damaging storms in a specific winter could be associated with other areas less affected. The phase of such a storm occurrence variability pattern may be related to large scale atmospheric modes and other drivers that could be predictable. Using daily mean 10 m wind speeds from ERA5 reanalysis as a proxy for storm damage, the number of days with stronger wind than the local 95th percentile for each month of the year is counted per winter. The resulting data for the Northern Hemisphere are put into a PCA. The resulting index is then related to indices of the known patterns and correlated to the spatial distribution of parameters like the global SST, also considering that there may be indications for a predictability of the storm occurrence pattern from the anomalies. The authors also make use of a recently released statistical climate emulator called ACE2 which had been trained with ERA5 reanalysis. ACE2 simulation is driven by patterns of SST (or of skin temperature?), comprising both land and ocean areas. The paper claims to have identified connections between the storminess index and SST-patterns, suggesting a relation to ENSO and the AMOC without an explicit testing. It tests the predictive potential of the global anomaly patterns found, and finds no significant skill (chapter 6). It explores a surrogate climate simulations with the statistical climate emulator ACE2, losing the focus on the specific approaches of the previous sections (e.g, looking now into other seasons than winter, different aggregations, spatial extents of analyzed regions). In this section statistical relationships with variability patterns like PNA and NAO which was missing in the earlier part. Here the paper turns into an evaluation effort for ACE2, with multiple aspects mentioned.
While the overall initial idea of the paper is interesting, I see a couple of aspects of the approach and its realization that must be re-thought by the authors. I wonder, for example, why the authors regard it a good idea to leave the role of extratropical cyclones out in their study, which are the local and regional link between windstorms and large-scale pressure pattern variability. Another question is the definition of the threshold wind speed for months of the year. Wind damage thresholds for buildings will depend on wind force, but be largely independent of the month, so that storm relevance will be overestimated for the autumn and spring events. While the authors claim that the resulting patterns look similar (line 259), I still see no good reason for the chosen monthly separation.
With respect to the evaluation of ACE2, it appears that the authors conduct a couple of analysis approaches without a clearly founded and explained hypothesis behind it. One specific question is if a different version of the emulator ACE2 is used than what is cited. According to the publication of Watt-Meyer et al. (2025) and many locations in the text, ACE2 is using SSTs as input. However, the authors state that they use skin temperature because this parameter is needed by ACE2 (section 8.2). The authors could not convince me that the emulator can provide additional information on the searched links of storminess pattern and pressure variability patterns, and thus avoid the effort of running full numerical models. It is clearly not independent from the ERA5 reanalysis, and the fact that the emulator’s training with 6 hourly reanalysis data is no guarantee that it will produce independent data (avoiding circularity) for a quantification of relationships in the aggregated winter seasons. Isn’t the essential result or ACE2 merely a re-shuffling of SST and weather situations in ERA5, combining such situations which fit to each other? Given the steering of ACE2 by SSTs (or is it global skin temperatures?) and other patterns of variables (section 7.1.1) , I would not consider this a basis for independent free climate simulations.
Given my above comments, I think that the work required to come up with a manuscript which is acceptable for WCD clearly exceeds what can be done during a revision. I thus recommend rejection of the present submission, but would like to encourage the authors to follow the initial idea mentioned, which I regard interesting and relevant.
Specific Comments
1. Definition of the storm variability index
It is not clear if the different signs of variable storm activity in Fig. 2 represent a real dipole, or if it is really a monopole of variable activity which appears as a dipole because of the applied PCA. A correlation map (teleconnection analysis) showing the areas with strongest negative correlations may be elucidating in this respect.
2. Given the hemispheric scale of the PCA applied, different regional variability patterns (for example, related to the NAO or alternatively to the PNA) enter the PCA. The methodology can have the effect of putting different variability patterns of storm activity into one (see Ambaum, Hoskins and Stephenson, 2001, for a related discussion on NAO, PNA and AO). Thus, it is not clear if the hemispheric approach hides the existence of regional patterns which could have a much stronger relationship to the pressure patterns. Fig. 8 points at an intermittent link between the patterns, which has been documented in some studies some time ago, for example in conjunction with the so-called “storm track”.
3. Chapter 5 produces some handwaving reasons for variability without actually looking into the relevant data (e.g., AMOC, SSTs and baroclinic instability). These could be explicitly checked as the respective data are available.
4. I would have expected that physical concepts like geostrophic or thermal wind as well as baroclinicity are used in the analysis and the interpretation.
Other comments
A couple of the cited references are apparently used without having read them, and this makes me speculate if the authors could have simply used generative AI for producing parts of the text. For example, Zheng et al discuss mechanisms for wind stilling with respect to energy production, but not hazardous storms. Cited work on extratropical storms is erroneously also linked to tropical storms in the text. Delworth is referenced in terms of a link between the NAO and westerly flow as if it was a statistical coincidence, while geostrophic wind or thermal wind are certainly useful approaches in this respect. Pfleiderer’s and Klotzbach’s papers on hurricanes are cited in the context of some “predictive skill from favourable climatic conditions and established teleconnections” as a basis of statistical models, far from the mid-latitude issues which are mainly in the paper’s focus.
Line 82-86: There seems to be a surprising misconception of the authors with respect to the basic mechanisms of tropical and extratropical cyclones. I wonder how this can have made it into the submitted text.
Line 119: What is the basis of the speculation that “regions with similar extreme wind variability may experience comparable shifts due to climate change”?
Section 2.3: What is the pattern of the NAO-pattern associated with the downloaded index? For which months was it computed from the monthly indices at https://www.cpc.ncep.noaa.gov/products/precip/CWlink/pna/nao.shtml? What is the role of the seasonal changes for the analysis conducted?
Section 3: The section is very difficult to understand, and the sense of the procedure applied with linear trend subtraction and polynomial fit does not make apparent sense to me. How does the pattern of exceedances and the time series typically look like for subsections of the time series? It is necessary to have an idea of the data eventually going into the correlations. It is not clear in how far known storms are found in the time series.
Line 259: This is a manuscript related to meteorology. The word "trough" is linked to a specific atmospheric feature and should not be used here for describing a time series.
Section 4: It appears that the outcome of the procedure from section 3 and 4 results in rather useless patterns, with just 8% of the variance explained by the largest 5 (!) modes.
The citation of Bellomo et al on line 292 is obviously misleading, as these authors look into 4*CO2” experiments which do not represent small AMOC reductions.