Projections of global warminginduced impacts on w

Authors: Hermann Held FriedrichWilhelm Gerstengarbe Tobias Pardowitz Joaquim G Pinto Uwe Ulbrich Kai Born Markus G Donat Melanie K Karremann Gregor C Leckebusch Patrick Ludwig Katrin M Nissen Hermann Österle Boris F Prahl Peter C Werner Daniel J Befort Olaf Burghoff

Publish Date: 2013/09/18

Volume: 121, Issue: 2, Pages: 195-207

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Abstract

We present projections of winter storminduced insured losses in the German residential building sector for the 21st century With this aim two structurally most independent downscaling methods and one hybrid downscaling method are applied to a 3member ensemble of ECHAM5/MPIOM1 A1B scenario simulations One method uses dynamical downscaling of intense winter storm events in the global model and a transfer function to relate regional wind speeds to losses The second method is based on a reshuffling of present day weather situations and sequences taking into account the change of their frequencies according to the linear temperature trends of the global runs The third method uses statisticaldynamical downscaling considering frequency changes of the occurrence of stormprone weather patterns and translation into loss by using empirical statistical distributions The A1B scenario ensemble was downscaled by all three methods until 2070 and by the statistical dynamical methods until 2100 Furthermore all methods assume a constant statistical relationship between meteorology and insured losses and no developments other than climate change such as in constructions or claims management The study utilizes data provided by the German Insurance Association encompassing 24 years and with districtscale resolution Compared to 1971–2000 the downscaling methods indicate an increase of 10year return values ie loss ratios per return period of 6–35  for 2011–2040 of 20–30  for 2041–2070 and of 40–55  for 2071–2100 respectively Convolving various sources of uncertainty in one confidence statement data loss model storm realization and Pareto fituncertainty the returnlevel confidence interval for a return period of 15 years expands by more than a factor of two Finally we suggest how practitioners can deal with alternative scenarios or possible natural excursions of observed lossesWinter storms represent one of the major natural hazards Based on three decades of data 1982–2011 from the EMDAT1 database eg GuhaSapir et al 2012 extratropical storms account for a worldwide average of annual economic loss of USD 107 bn in 2011 prices According to Kron et al 2012 the cumulative worldwide losses 1980–2012 amount for USD 178 bn 2012 prices For Europe current estimates for a single 10year event reach USD 7 bn 2006 prices increasing up to USD 30 bn for events with a return level of once per 100 years Schwierz et al 2010 Changes in winter storm statistics related to global warming have been studied in several investigations in particular referring to the EuroAtlantic area eg Ulbrich et al 2009 Additionally several studies indicate that losses from winter storms may increase in future decades over Western Europe eg Donat et al 2011a Leckebusch et al 2007 Pinto et al 2012 Schwierz et al 2010 Hence winter storms clearly have the potential for a relevant global warming impact category Further the international debate on an adequate climate policy is shifting from a mitigationoriented focus towards a broader perspective including more of the impact and adaptation side2 With this aim we suggest a new methodological approach for combining different impact assessment techniques and integrating the related measures of uncertainty We suggest that the sequence demonstrated here for the special case of storm losses in Germany will be relevant for impact modeling in generalLosses caused by European winter storms are particularly relevant for western and central European countries eg DellaMarta and Pinto 2009 Haylock 2011 Leckebusch et al 2007 Average annual stormrelated insured losses to residential buildings in Germany add up to about EUR 11 bn GDV 2012 The link between extreme wind speeds and induced insured losses is dependent on the spatial distribution of winter storm hazard intensities and the vulnerabilities of local buildings eg Donat et al 2011b Prahl et al 2012 As there is no unique mechanism quantifying the vulnerabilities which may differ between regions the assessment of any ‘winter storm intensity—loss’relation must rely on statistical data In that regard insurance companies hold the most reliable and systematic records Annual values of insured losses for Germany had been used in previous works eg Leckebusch et al 2007 Pinto et al 2007 For the first time the German Insurance Association Gesamtverband der Deutschen Versicherungswirtschaft eV GDV provided highly spatiotemporally resolved data on storm and hail losses from the German residential building sector for a scientific project Currently about 93  of private buildings in Germany are insured For that reason we assume that the results derived from that dataset are representative for the sector as a wholeIn this article we provide a compilation of results from the project focusing on globalwarming induced changes of winter storm losses over Germany based on ensemble projections with one coupled global climate model GCM At the same time the subsequent methodological advancements are highlighted The key sources of uncertainty are estimated in a causal chain from global warming projections to economic losses including the step of regionalization We utilize three different approaches to regionalization of winter storms As these approaches produce different results by applying different methodologies to one common driver ECHAM5/MPIOM1 GCM scenario runs they constitute a 3model ensemble in some sense Basically the three approaches correspond to dynamical downscaling Statistical Downscaling and a hybrid of dynamical and Statistical Downscaling Firstly the applied methods are described in more detail in the following Section and specific results are given in Section 3 Secondly we attempt a semiquantitative discussion of all major uncertainties and to a large extent their synergetic effects along the causal chain as outlined in Section 4 Thirdly the uncertainties are evaluated based on both academic and stakeholder insurance industry standards The implications of the latter are further incorporated into the summary in Section 5

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