The 21st century decline in damaging European windstorms

Abstract. A decline in damaging European windstorms has led to a reduction in insured losses in the 21st century. This decline is explored by identifying a damaging windstorm characteristic and investigating how and why this characteristic has changed in recent years. This novel exploration is based on 6103 high-resolution model-generated historical footprints (1979–2014), representing the whole European domain. The footprint of a windstorm is defined as the maximum wind gust speed to occur at a set of spatial locations over the duration of the storm. The area of the footprint exceeding 20 ms−1 over land, A20, is shown to be a good predictor of windstorm damage. This damaging characteristic has decreased in the 21st century, due to a statistically significant decrease in the relative frequency of windstorms exceeding 20 ms−1 in north-western Europe, although an increase is observed in southern Europe. This is explained by a decrease in the quantiles of the footprint wind gust speed distribution above approximately 18 ms−1 at locations in this region. In addition, an increased variability in the number of windstorm events is observed in the 21st century. Much of the change in A20 is explained by the North Atlantic Oscillation (NAO). The correlation between winter total A20 and winter-averaged mean sea-level pressure resembles the NAO pattern, shifted eastwards over Europe, and a strong positive relationship (correlation of 0.715) exists between winter total A20 and winter-averaged NAO. The shifted correlation pattern, however, suggests that other modes of variability may also play a role in the variation in windstorm losses.

Indices defined as where for grid point j, v j is gust speed, v 98j is 98 th percentile of gust speeds, and d j is population density.
Area A 20 is a good predictor of severity à Area A20 strongly related to L98 and a better classifier for largest loss storms  à NAO/NAM is likely to become slightly more positive (on average) SAM positive trend is likely to weaken as ozone depletion recovers à Medium confidence that projected changes in NAO and SAM are sensitive to boundary processes (stratosphere-troposphere interaction, ozone chemistry, response to Arctic sea ice loss), which are not yet well represented in many climate models • Area of wind gusts >20m/s is a good storm severity index for classifying high loss storms; • Total area of wind gusts >20m/s has decreased by 10% in winters from 2000-2013 compared to winters 1979-1999; • The decrease in total area is due to a significant reduction in wind speeds over much of Europe (apart from Iberian peninsula).Storm numbers have not shown any decrease; • This wind speed change can be accounted for by a decrease in the North Atlantic Oscillation.Total area of wind gusts is highly correlated with winter mean NAO; • The decrease in NAO is contrary to what one expects due to global warming so change is most likely natural variability.Indication that we are now heading back into a more positive phase of NAO.
Windstorm footprint estimation typically only uses standard meteorological observations which can have poor spatial coverage (e.g. at urban postcode scales); There are many other less conventional data sources that may be useful for inferring high gust speeds e.g.
Can this data be mined to improve footprint estimation?The stochastic model is a way of integrating these diverse data sources.
Let me know if you have any ideas about this or would like to be involved!
The Storm (1704) by Daniel Defoe second wind-gust speed at each location in a 72 hour period covering the passage of the storm, centred on the time at which the maximum wind speed over land occurs Example: Footprint for windstorm Daria (24th -26th January 1990) wind-gust (ms -1 ) 6301 storms identified in extended winters (October-March) using objective tracking algorithm (Hodges, 1995) Then footprints created for each storm by dynamically downscaling ERA-Interim using the Met Office 25km resolution North Atlantic-European operational NWP model.Footprint database: 1979/80-2013/14 Area with gusts > 20m/s (A 20 index) 6 Klawa, M. and Ulbrich, U.: A model for the estimation of storm losses and the identification of severe winter storms in Germany, Nat.Hazards Earth Syst.Sci., 3, 725-732, 2003.

àFigure 14 . 16 :
Figure 14.16: Summary of multi-model ensemble simulations of wintertime (Dec-Feb) mean NAO, NAM and SAM sea-level pressure indices for historical and RCP4.5 scenarios produced by 39 climate models participating in CMIP5.Panels a-c) show time series of the ensemble mean (black line) and inter-quartile range (grey shading) of the mean index for each model.