Suchergebnisse

Abstract. Extratropical cyclones are the most damaging
natural hazard to affect western Europe. Serial clustering occurs when many
intense cyclones affect one specific geographic region in a short period of
time which can potentially lead to very large seasonal losses. Previous
studies have shown that intense cyclones may be more likely to cluster than
less intense cyclones. We revisit this topic using a high-resolution climate
model with the aim to determine how important clustering is for windstorm-related losses. The role of windstorm clustering is investigated using a quantifiable metric
(storm severity index, SSI) that is based on near-surface meteorological
variables (10 m wind speed) and is a good proxy for losses. The SSI is used
to convert a wind footprint into losses for individual windstorms or seasons.
918 years of a present-day ensemble of coupled climate model simulations from
the High-Resolution Global Environment Model (HiGEM) are compared to
ERA-Interim reanalysis. HiGEM is able to successfully reproduce the
wintertime North Atlantic/European circulation, and represent the large-scale
circulation associated with the serial clustering of European windstorms. We
use two measures to identify any changes in the contribution of clustering to
the seasonal windstorm loss as a function of return period. Above a return period of 3 years, the accumulated seasonal loss from HiGEM is
up to 20 % larger than the accumulated seasonal loss from a set of random
resamples of the HiGEM data. Seasonal losses are increased by
10 %–20 % relative to randomized seasonal losses at a return period
of 200 years. The contribution of the single largest event in a season to the
accumulated seasonal loss does not change with return period, generally
ranging between 25 % and 50 %. Given the realistic dynamical representation of cyclone clustering in HiGEM,
and comparable statistics to ERA-Interim, we conclude that our estimation of
clustering and its dependence on the return period will be useful for
informing the development of risk models for European windstorms,
particularly for longer return periods.

Abstract. Windstorms are the most damaging natural hazard across western Europe. Risk modellers are limited by the observational data record to only ∼ 60 years of comprehensive reanalysis data that are dominated by considerable inter-annual variability. This makes estimating return periods of rare events difficult and sensitive to the choice of the historical period used. This study proposes a novel statistical method for estimating wind gusts across Europe based on observed windstorm footprints. A good description of extreme wind speeds is obtained by assuming that gust speed peaks over threshold are distributed exponentially, i.e. a generalised Pareto distribution having a zero shape parameter. The threshold and tail scale parameter are estimated at each location and used to calculate estimates of the 10- and 200-year return levels. The North Atlantic Oscillation (NAO) is particularly important for modulating lower return levels and modulating the threshold, with a less detectable influence on rarer extremes and the tail scale parameter. The length of historical data required to have the lowest error in estimating return levels is quantified using both observed and simulated time series of the historical NAO. For reducing errors in estimating 200-year return levels of an independent 10-year period, a data catalogue of at least 20 years is required. For lower return levels the NAO has a stronger influence on estimated return levels, and so there is more variability in estimates. Using theoretical estimates of future NAO states, return levels are largely outside the historical uncertainty, indicating significant increases in risk potential from windstorms in the next 100 years. Our method presents a framework for assessing high-return-period events across a range of hazards without the additional complexities of a full catastrophe model.