Abstract
AbstractAs 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.
Highlights
Introduction and MotivationWinter windstorms represent one of the most dangerous and loss-intensive natural hazards for the European region
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 ECMWF Re-Analysis (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
In order to understand what drives the interannual variability of extreme winds in System 4 as well as in ERAInterim, we examined which of the nine large-scale mean sea level pressure (MSLP) variability patterns (EOFs) are most significant for extreme winds in the North Atlantic and European regions
Summary
Introduction and MotivationWinter windstorms represent one of the most dangerous and loss-intensive natural hazards for the European region. It would be of outmost value to provide useful predictions on seasonal scales as it would enable decision makers to take measures in order to minimize potential losses and most importantly to avoid casualties. The demand for these longer-term “weather forecasts” exceeding the common 10-day prediction period has generally increased considerably over the last decade. The reason for that is the nonlinearity of the atmospheric system that amplifies minuscule deviations in initial conditions into large disturbances at the end of a forecast period (Lorenz, 1963). The Atlantic Multidecadal Oscillation is known to have an impact on the decadal variability of the North Atlantic storm track (Nissen et al, 2014) and on its position (Woollings et al, 2012)
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