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Large‐Scale Drivers and Seasonal Predictability of Extreme Wind Speeds Over the North Atlantic and Europe
Author(s) -
Walz Michael A.,
Donat Markus G.,
Leckebusch Gregor C.
Publication year - 2018
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
eISSN - 2169-8996
pISSN - 2169-897X
DOI - 10.1029/2017jd027958
Subject(s) - predictability , climatology , environmental science , meteorology , wind speed , north atlantic oscillation , extreme value theory , geography , statistics , geology , mathematics
As extreme wind speeds are responsible for large socioeconomic losses in the European domain, a skillful prediction would be of great benefit for disaster prevention as well as the actuarial community. Here we evaluate the patterns of atmospheric variability and the seasonal predictability of extreme wind speeds (e.g., >95th percentile) in the European domain in the dynamical seasonal forecast system European Centre for Medium‐Range Weather Forecasts (ECMWF) System 4 and compare to the predictability using a statistical prediction model. Further we compare the seasonal forecast system with ECMWF Re‐Analysis (ERA)‐Interim in order to advance the understanding of the large‐scale conditions that generate extreme winds. The dominant mean sea level pressure patterns of atmospheric variability show distinct differences between reanalysis and System 4 as most patterns in System 4 are extended downstream in comparison to ERA‐Interim. This dissimilar manifestation of the patterns across the two models leads to substantially different drivers associated with the generation of extreme winds: While the prominent pattern of the North Atlantic Oscillation could be identified as the main driver in the reanalysis, extreme winds in System 4 appear to be related to different large‐scale atmospheric pressure patterns. Thus, our results suggest that System 4 does not seem to capture the potential predictability of extreme winds that exists in the real world. This circumstance is likely related to the unrealistic representation of the atmospheric patterns driving these extreme winds. Hence, our study points to potential improvements of dynamical prediction skill by improving the simulation of large‐scale atmospheric variability.