Modelling serial clustering and inter-annual variability of European winter windstorms based on large-scale drivers

Area of Science:

• Climatology and Meteorology
• Natural Hazards Research
• Statistical Modeling

Background:

• Winter windstorms pose significant risks in Europe, causing substantial economic losses.
• Understanding the temporal variability and driving mechanisms of these storms is crucial for risk assessment.
• Seasonal clustering of windstorms is a key aspect of their variability.

Purpose of the Study:

• To analyze the temporal variability of European winter windstorms.
• To develop a statistical model linking storm counts to climate teleconnection patterns.
• To identify key drivers of inter-annual storm variability across different European regions.

Main Methods:

• A statistical model was developed using stepwise Poisson regression.
• The model relates winter storm counts to large-scale climate indices from ERA-20C reanalysis.
• Temporal variability and seasonal clustering of windstorms were analyzed.

Main Results:

• The North Atlantic Oscillation (NAO) and Scandinavian pattern (SCA) were identified as primary drivers for most European regions.
• Other teleconnections, such as the East Atlantic pattern, showed significance in specific regions.
• The model accurately estimates expected storm numbers and classifies seasons as active or inactive, with high skill over the British Isles and central Europe.

Conclusions:

• The developed statistical model effectively captures inter-annual variability in European winter windstorms.
• The model's predictive capability extends to regions with less frequent storm events.
• This research offers valuable insights for the actuarial sector and climate risk management.