Return periods and clustering of potential losses associated with European windstorms in a changing climate

Windstorms are one of the most damaging natural hazards in Western and Central Europe. A recent example was the windstorm series in winter 2013/2014, which affected primarily Great Britain. This indicates the importance of the estimation of potential losses linked to extreme windstorms as well as their return periods for present and future climate conditions. In particular, the occurrence of groups of windstorms (clustering) is of high interest, as they cause the top year losses. The present thesis consists of three studies. The first study quantifies the intensity of individual storms by potential losses estimated with empirical models. One model considers only impacts due to wind speeds (MI), while another also includes population density information as proxy for insured values within an area (LI). The models are applied to reanalysis data and general circulation model (GCM) data for recent (20C: 1960-2000) and future climate conditions for three Intergovernmental Panel on Climate Change climate scenarios (B1, A1B, A2: 2060-2100). Focus of the investigation is given on Europe. The projected tendencies for LI and MI are generally in accordance, with a correlation of about 99%, e.g. for Germany. However, the relationship between MI and LI is reduced when the evaluated area increases. Based on the identified event set, changes of intensity and return periods of single storm events are quantified. Return periods are estimated using the extreme value distribution with the peak over threshold method. Independent from the future climate scenario, results show shorter return periods and higher intensities for most countries. Nevertheless, changes are not always statistically significant. In the second study, a reliable method to quantify clustering of losses associated with historical storm series and return periods of clustered events are quantified for Germany. With this aim, the empirical storm loss model used in study 1 is further developed and applied to clearly separate potential losses associated with individual storms. Using reanalysis datasets and observations from German weather stations for 30 winters, event sets exceeding selected return levels (1-, 2- and 5-year) are analysed. The distribution of the chosen events over the winters is used as basis for the Poisson and the negative Binomial distribution. Additionally, about 4000 years of GCM simulations with current climate conditions (20C) are investigated. Results of reanalysis data differ between the methods: in particular, for less frequent series the Poisson distribution based assessments clearly deviate from empirical data. The negative Binomial distribution provides better estimates, even though a dependency on return levels of single storms and the dataset is identified. The consideration of about 4000 years GCM data provides similar estimates and a strong reduction of uncertainties. In the third study, the methods of study 2 are applied to quantify possible changes of clustering and return periods of high potential losses associated with multiple extreme storms in Europe. 21 countries and regions are investigated. Additionally,a second critical value for the event identification is used (fixed LI 20C for 1-, 2-, 5-year return level), and thus possible changes in intensity of events can be regarded. Reanalysis data for 40 years as well as simulations for 800 years each of the present (20C) and the future (A1B) scenario are investigated. As for Germany,for present day climate conditions results for other regions obtained with the negative Binomial distribution show better agreements with empirical data than the Poisson distribution. Future changes in return periods and clustering estimated with both empirical and with the negative Binomial distribution depend on the region, the return level, the method and number of events per winter. For fixed return levels (e.g. 1-year), only small changes in return periods of storm series are identified. Shorter return periods of storm clusters under future climate conditions are identified for Europe, except for the Mediterranean area considering a fixed LI of 20C. However, evidence is found that the projected changes may be within the range of natural climate variability. The detected change in clustering of extreme losses is of high interest for re-insurance companies, especially for the risk assessment. The three studies are an essential extend of the current state of research about future changes of clustering of losses associated with windstorms and may help to develop protection and mitigation measures for the infrastructure

The long-standing dilemma of European summer temperatures at the mid-Holocene and other considerations on learning from the past for the future using a regional climate model

The past as an analogue for the future is one of the main motivations to use climate models for paleoclimate applications. Assessing possible model limitations in simulating past climate changes can lead to an improved understanding and representation of the response of the climate system to changes in the forcing, setting the basis for more reliable information for the future. In this study, the regional climate model (RCM) COSMO-CLM is used for the investigation of the mid-Holocene (MH, 6000 years ago) European climate, aiming to contribute to the solution of the long-standing debate on the reconstruction of MH summer temperatures for the region, and gaining more insights into the development of appropriate methods for the production of future climate projections. Two physically perturbed ensembles (PPEs) are first built by perturbing model physics and parameter values, consistently over two periods characterized by different forcing (i.e., the MH and pre-industrial, PI). The goal is to uncover possible processes associated with the considered changes that could deliver a response in MH summer temperatures closer to evidence from continental-scale pollen-based reconstructions. None of the investigated changes in model configuration produces remarkable differences with respect to the mean model behavior. This indicates a limited sensitivity of the model to changes in the climate forcing, in terms of its structural uncertainty. Additional sensitivity tests are further conducted for the MH, by perturbing the model initial soil moisture conditions at the beginning of spring. A strong spatial dependency of summer near-surface temperatures on the soil moisture available in spring is evinced from these experiments, with particularly remarkable differences evident over the Balkans and the areas north of the Black Sea. This emphasizes the role of soil–atmosphere interactions as one of the possible drivers of the differences in proxy-based summer temperatures evident between northern and southern Europe. A well-known deficiency of the considered land scheme of COSMO-CLM in properly retaining spring soil moisture, confirmed by the performed tests, suggests that more attention should be paid to the performance of the soil component of climate models applied to this case study. The consideration of more complex soil schemes may be required to help bridging the gap between models and proxy reconstructions. Finally, the distribution of the PPEs with changes in model configuration is analyzed for different variables. In almost all of the considered cases the results show that what is optimal for one period, in terms of a model configuration, is not the best for another characterized by different radiative forcing. These results raise concerns about the usefulness of automatic and objective calibration methods for RCMs, suggesting that a preferable approach is the production of small PPEs that target a set of model configurations, properly representing climate phenomena characteristic of the target region and that will be likely to contain the best model answer under different forcing

Serial clustering of extratropical cyclones over the North Atlantic and Europe under recent and future climate conditions

Under particular large-scale atmospheric conditions, several windstorms may affect Europe within a short time period. The occurrence of such cyclone families leads to large socioeconomic impacts and cumulative losses. The serial clustering of windstorms is analyzed for the North Atlantic/western Europe. Clustering is quantified as the dispersion (ratio variance/mean) of cyclone passages over a certain area. Dispersion statistics are derived for three reanalysis data sets and a 20-run European Centre Hamburg Version 5 /Max Planck Institute Version–Ocean Model Version 1 global climate model (ECHAM5/MPI-OM1 GCM) ensemble. The dependence of the seriality on cyclone intensity is analyzed. Confirming previous studies, serial clustering is identified in reanalysis data sets primarily on both flanks and downstream regions of the North Atlantic storm track. This pattern is a robust feature in the reanalysis data sets. For the whole area, extreme cyclones cluster more than nonextreme cyclones. The ECHAM5/MPI-OM1 GCM is generally able to reproduce the spatial patterns of clustering under recent climate conditions, but some biases are identified. Under future climate conditions (A1B scenario), the GCM ensemble indicates that serial clustering may decrease over the North Atlantic storm track area and parts of western Europe. This decrease is associated with an extension of the polar jet toward Europe, which implies a tendency to a more regular occurrence of cyclones over parts of the North Atlantic Basin poleward of 50°N and western Europe. An increase of clustering of cyclones is projected south of Newfoundland. The detected shifts imply a change in the risk of occurrence of cumulative events over Europe under future climate conditions

Applying an isotope-enabled regional climate model over the Greenland ice sheet: effect of spatial resolution on model bias

In order to investigate the impact of spatial resolution on the discrepancy between simulated δ$^{18}$O and observed δ$^{18}$O in Greenland ice cores, regional climate simulations are performed with the isotope-enabled regional climate model (RCM) COSMO_iso. For this purpose, isotope-enabled general circulation model (GCM) simulations with the ECHAM5-wiso general circulation model (GCM) under present-day conditions and the MPI-ESM-wiso GCM under mid-Holocene conditions are dynamically downscaled with COSMO_iso for the Arctic region. The capability of COSMO_iso to reproduce observed isotopic ratios in Greenland ice cores for these two periods is investigated by comparing the simulation results to measured δ$^{18}$O ratios from snow pit samples, Global Network of Isotopes in Precipitation (GNIP) stations and ice cores. To our knowledge, this is the first time that a mid-Holocene isotope-enabled RCM simulation is performed for the Arctic region. Under present-day conditions, a dynamical downscaling of ECHAM5-wiso (1.1∘×1.1∘) with COSMO_iso to a spatial resolution of 50 km improves the agreement with the measured δ$^{18}$O ratios for 14 of 19 observational data sets. A further increase in the spatial resolution to 7 km does not yield substantial improvements except for the coastal areas with its complex terrain. For the mid-Holocene, a fully coupled MPI-ESM-wiso time slice simulation is downscaled with COSMO_iso to a spatial resolution of 50 km. In the mid-Holocene, MPI-ESM-wiso already agrees well with observations in Greenland and a downscaling with COSMO_iso does not further improve the model–data agreement. Despite this lack of improvement in model biases, the study shows that in both periods, observed δ$^{18}$O values at measurement sites constitute isotope ratios which are mainly within the subgrid-scale variability of the global ECHAM5-wiso and MPI-ESM-wiso simulation results. The correct δ$^{18}$O ratios are consequently not resolved in the GCM simulation results and need to be extracted by a refinement with an RCM. In this context, the RCM simulations provide a spatial δ$^{18}$O distribution by which the effects of local uncertainties can be taken into account in the comparison between point measurements and model outputs. Thus, an isotope-enabled GCM–RCM model chain with realistically implemented fractionating processes constitutes a useful supplement to reconstruct regional paleo-climate conditions during the mid-Holocene in Greenland. Such model chains might also be applied to reveal the full potential of GCMs in other regions and climate periods, in which large deviations relative to observed isotope ratios are simulated

Loss potentials associated with European windstorms under future climate conditions

Possible changes in the frequency and intensity of windstorms under future climate conditions during the 21st century are investigated based on an ECHAM5 GCM multi-scenario ensemble. The intensity of a storm is quantified by the associated estimated loss derived with using an empirical model. The geographical focus is ‘Core Europe’, which comprises countries of Western Europe. Possible changes of losses are analysed by comparing ECHAM5 GCM data for recent (20C, 1960 to 2000) and future climate conditions (B1, A1B, A2; 2060 to 2100), each with 3 ensemble members. Changes are quantified using both rank statistics and return periods (RP) estimated by fitting an extreme value distribution using the peak over threshold method to potential storm losses. The estimated losses for ECHAM5 20C and reanalysis events show similar statistical features in terms of return periods. Under future climate conditions, all climate scenarios show an increase in both frequency and magnitude of potential losses caused by windstorms for Core Europe. Future losses that are double the highest ECHAM5 20C loss are identified for some countries. While positive changes of ranking are significant for many countries and multiple scenarios, significantly shorter RPs are mostly found under the A2 scenario for return levels correspondent to 20 yr losses or less. The emergence time of the statistically significant changes in loss varies from 2027 to 2100. These results imply an increased risk of occurrence of windstorm-associated losses, which can be largely attributed to changes in the meteorological severity of the events. Additionally, factors such as changes in the cyclone paths and in the location of the wind signatures relative to highly populated areas are also important to explain the changes in estimated losses

Robustness of serial clustering of extratropical cyclones to the choice of tracking method

Cyclone clusters are a frequent synoptic feature in the Euro-Atlantic area. Recent studies have 24 shown that serial clustering of cyclones generally occurs on both flanks and downstream 25 regions of the North Atlantic storm track, while cyclones tend to occur more regulary on the 26 eastern side of the North Atlantic basin near Newfoundland. This study explores the 27 sensitivity of serial clustering to the choice of cyclone tracking method using cyclone track 28 data from 15 methods derived from ERA-Interim data (1979-2010). Clustering is estimated by 29 the dispersion (ratio of variance to mean) of winter (DJF) cyclones passages near each grid 30 point over the Euro-Atlantic area. The mean number of cyclone counts and their variance are 31 compared between methods, revealing considerable differences, particularly for the latter. 32 Results show that all different tracking methods qualitatively capture similar large-scale 33 spatial patterns of underdispersion / overdispersion over the study region. The quantitative 34 differences can primarily be attributed to the differences in the variance of cyclone counts 35 between the methods. Nevertheless, overdispersion is statistically significant for almost all 36 methods over parts of the Eastern North Atlantic and Western Europe, and is therefore 37 considered as a robust feature. The influence of the North Atlantic Oscillation on cyclone 38 clustering displays a similar pattern for all tracking methods, with one maximum near Iceland 39 and another between the Azores and Iberia. The differences in variance between methods are 40 not related with different sensitivities to the NAO, which can account to over 50% of the 41 clustering in some regions. We conclude that the general features of underdispersion / 42 overdispersion of extra-tropical cyclones over the North Atlantic and Western Europe is 43 robust to the choice of tracking method. The same is true for the influence of the North 44 Atlantic Oscillation on cyclone dispersion

Projections of global warming-induced impacts on winter storm losses in the German private household sector

We present projections of winter storm-induced 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 3-member ensemble of ECHAM5/MPI-OM1 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 statistical-dynamical downscaling, considering frequency changes of the occurrence of storm-prone 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 district-scale resolution. Compared to 1971–2000, the downscaling methods indicate an increase of 10-year return values (i.e. 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 fit-uncertainty), the return-level 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 losses

Return periods of losses associated with European windstorm series in a changing climate

Possible future changes of clustering and return periods (RPs) of European storm series with high potential losses are quantified. Historical storm series are identified using 40 winters of reanalysis. Time series of top events (1, 2 or 5 year return levels (RLs)) are used to assess RPs of storm series both empirically and theoretically. Additionally, 800 winters of general circulation model simulations for present (1960-2000) and future (2060-2100) climate conditions are investigated. Clustering is identified for most countries, and estimated RPs are similar for reanalysis and present day simulations. Future changes of RPs are estimated for fixed RLs and fixed loss index thresholds. For the former, shorter RPs are found for Western Europe, but changes are small and spatially heterogeneous. For the latter, which combines the effects of clustering and event ranking shifts, shorter RPs are found everywhere except for Mediterranean countries. These changes are generally not statistically significant between recent and future climate. However, the RPs for the fixed loss index approach are mostly beyond the range of pre-industrial natural climate variability. This is not true for fixed RLs. The quantification of losses associated with storm series permits a more adequate windstorm risk assessment in a changing climate

Statistical modelling of grapevine yield in the Port Wine region under present and future climate conditions

The impact of projected climate change on wine production was analysed for the Demarcated Region of Douro, Portugal. A statistical grapevine yield model (GYM) was developed using climate parameters as predictors. Statistically significant correlations were identified between annual yield and monthly mean temperatures and monthly precipitation totals during the growing cycle. These atmospheric factors control grapevine yield in the region, with the GYM explaining 50.4% of the total variance in the yield time series in recent decades. Anomalously high March rainfall (during budburst, shoot and inflorescence development) favours yield, as well as anomalously high temperatures and low precipitation amounts in May and June (May: flowering and June: berry development). The GYM was applied to a regional climate model output, which was shown to realistically reproduce the GYM predictors. Finally, using ensemble simulations under the A1B emission scenario, projections for GYM-derived yield in the Douro Region, and for the whole of the twenty-first century, were analysed. A slight upward trend in yield is projected to occur until about 2050, followed by a steep and continuous increase until the end of the twenty-first century, when yield is projected to be about 800 kg/ha above current values. While this estimate is based on meteorological parameters alone, changes due to elevated CO2 may further enhance this effect. In spite of the associated uncertainties, it can be stated that projected climate change may significantly benefit wine yield in the Douro Valley