Improved Estimates of the European Winter Windstorm Climate and the Risk of Reinsurance Loss Using Climate Model Data

Abstract.A refined model for the calculation of storm losses is presented, making use of high-resolution insurance loss records for Germany and allowing loss estimates on a spatial level of administrative districts and for single storm events. Storm losses are calculated on the basis of wind speeds from both ERA-Interim and NCEP reanalyses. The loss model reproduces the spatial distribution of observed losses well by taking specific regional loss characteristics into account. This also permits high-accuracy estimates of total cumulated losses, though slightly underestimating the country-wide loss sums for storm "Kyrill", the most severe event in the insurance loss records from 1997 to 2007. A larger deviation, which is assigned to the relatively coarse resolution of the NCEP reanalysis, is only found for one specific rather smallscale event, not adequately captured by this dataset.The loss model is subsequently applied to the complete reanalysis period to extend the storm event catalogue to cover years when no systematic insurance records are available. This allows the consideration of loss-intensive storm events back to 1948, enlarging the event catalogue to cover the recent 60+ years, and to investigate the statistical characteristics of severe storm loss events in Germany based on a larger sample than provided by the insurance records only. Extreme value analysis is applied to the loss data to estimate the return periods of loss-intensive storms, yielding a return period for storm "Kyrill", for example, of approximately 15 to 21 years.Correspondence to: M. G. Donat

Section: Summary Discussion and Conclusionmentioning

confidence: 99%

High-resolution refinement of a storm loss model and estimation of return periods of loss-intensive storms over Germany

Donat

Pardowitz²,

Leckebusch

et al. 2011

“…Della-Marta et al (2010) found that estimation using L-moments resulted in lower biases in parameter estimates for small sample sizes than using maximum likelihood estimates. Figure 5 shows the return period of modelled loss for the historical and 22 RCM storm sets.…”

Section: The Rcm Storm Sets and Their Damagesmentioning

confidence: 94%

“…An alternative method of calibration that permits the possibility of higher wind speeds than has been observed in history would be to fit extreme value distributions to the highest wind speeds in the RCM and historical storm sets and then quantile match the wind speeds with reference to these distributions. Della-Marta et al (2010) used such an approach to calibrate indices of storms between two models. It would be worth considering here, but the geographically-dependent calibration used here would require a robust automatic threshold detection algorithm for peak-over-threshold extreme value distributions.…”

Section: The Rcm Storm Sets and Their Damagesmentioning

confidence: 99%

European extra-tropical storm damage risk from a multi-model ensemble of dynamically-downscaled global climate models

Haylock¹

2011

Abstract. Uncertainty in the return levels of insured loss from European wind storms was quantified using storms derived from twenty-two 25 km regional climate model runs driven by either the ERA40 reanalyses or one of four coupled atmosphere-ocean global climate models. Storms were identified using a model-dependent storm severity index based on daily maximum 10 m wind speed. The wind speed from each model was calibrated to a set of 7 km historical storm wind fields using the 70 storms with the highest severity index in the period 1961-2000, employing a two stage calibration methodology. First, the 25 km daily maximum wind speed was downscaled to the 7 km historical model grid using the 7 km surface roughness length and orography, also adopting an empirical gust parameterisation. Secondly, downscaled wind gusts were statistically scaled to the historical storms to match the geographically-dependent cumulative distribution function of wind gust speed.The calibrated wind fields were run through an operational catastrophe reinsurance risk model to determine the return level of loss to a European population density-derived property portfolio. The risk model produced a 50-yr return level of loss of between 0.025 % and 0.056 % of the total insured value of the portfolio.

“…Wind storm occurrence has particularly high variability (Bärring and von Storch, 2004;Wang et al, 2009). Hence, a reliable estimation of long-term changes requires large samples, which can also be obtained from ensemble simulations (see, for example, Della-Marta et al, 2010;Donat et al, 2010a).…”

Section: Introductionmentioning

confidence: 99%

Future changes in European winter storm losses and extreme wind speeds inferred from GCM and RCM multi-model simulations

Donat

Leckebusch²,

Wild³

et al. 2011

126

Abstract. Extreme wind speeds and related storm loss potential in Europe have been investigated using multi-model simulations from global (GCM) and regional (RCM) climate models. Potential future changes due to anthropogenic climate change have been analysed from these simulations following the IPCC SRES A1B scenario. The large number of available simulations allows an estimation of the robustness of detected future changes. All the climate models reproduced the observed spatial patterns of wind speeds, although some models displayed systematic biases. A storm loss model was applied to the GCM and RCM simulated wind speeds, resulting in realistic mean loss amounts calculated from 20th century climate simulations, although the inter-annual variability of losses is generally underestimated. In future climate simulations, enhanced extreme wind speeds were found over northern parts of Central and Western Europe in most simulations and in the ensemble mean (up to 5%). As a consequence, the loss potential is also higher in these regions, particularly in Central Europe. Conversely, a decrease in extreme wind speeds was found in Southern Europe, as was an associated reduction in loss potential. There was considerable spread in the projected changes of individual ensemble members, with some indicating an opposite signature to the ensemble mean. Downscaling of the large-scale simulations with RCMs has been shown to be an important source of uncertainty. Even RCMs with identical boundary forcings can show a wide range of potential changes. The robustness of the projected changes was estimated using two different measures. First, the inter-model standard deviation was calculated; however, it is sensitive to outliers and thus displayed large uncertainty ranges. Second, a multimodel combinatorics approach considered all possible subensembles from GCMs and RCMs, hence taking into account Correspondence to: M. G. Donat (m.donat@unsw.edu.au) the arbitrariness of model selection for multi-model studies. Based on all available GCM and RCM simulations, for example, a 25% mean increase in risk of loss for Germany has been estimated for the end of the 21st century, with a 90% confidence range of +15 to +35%.