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A tracking method for tropical cyclones (TCs) is presented and their characteristics for data sets with a lower horizontal resolution, e.g., the ERA‐40 Reanalysis data set from 1958 to 2001 are explored. The tracking method uses sea level pressure, relative vorticity and wind speed at 850 hPa, and vertical wind shear. The method, assessed in the Atlantic basin, identifies a realistic number of TCs. However, the ERA‐40 TCs compared with best track data from the U.S. National Hurricane Center are too weak to reach hurricane character, i.e., the tracked TCs do not show hurricanes of category three to five. Another caveat is that the life cycle of central pressure values is often not realistically reproduced by ERA‐40 TCs. To correct the life cycle of the central pressure, a two‐step statistical downscaling approach is applied to the ERA‐40 TCs which strongly improves the finding of major hurricanes.

Current estimates of the European windstorm climate and their associated losses are often hampered by either relatively short, coarse resolution or inhomogeneous datasets. This study tries to overcome some of these shortcomings by estimating the European windstorm climate using dynamical seasonal-to-decadal (s2d) climate forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF). The current s2d models have limited predictive skill of European storminess, making the ensemble forecasts ergodic samples on which to build pseudoclimates of 310-396 yr in length. Extended winter (October-April) windstorm climatologies are created using scalar extreme wind indices considering only data above a high hreshold. The method identifies up to 2363 windstorms in s2d data and up to 380 windstorms in the 40-yr ECMWF Re-Analysis (ERA-40). Classical extreme value analysis (EVA) techniques are used to determine the windstorm climatologies. Differences between the ERA-40 and s2d windstorm climatologies require the application of calibration techniques to result in meaningful comparisons. Using a combined dynamical-statistical sampling technique, the largest influence on ERA-40 return period (RP) uncertainties is the sampling variability associated with only 45 seasons of storms. However, both maximum likelihood (ML) and L-moments (LM) methods of fitting a generalized Pareto distribution result in biased parameters and biased RP at sample sizes typically obtained from 45 seasons of reanalysis data. The authors correct the bias in the ML and LM methods and find that the ML-based ERA-40 climatology overestimates the RP of windstorms with RPs between 10 and 300 yr and underestimates the RP of windstorms with RPs greater than 300 yr. A 50-yr event in ERA-40 is approximately a 40-yr event after bias correction. Biases in the LM method result in higher RPs after bias correction although they are small when compared with those of the ML method. The climatologies are linked to the Swiss Reinsurance Company (Swiss Re) European windstorm loss model. New estimates of the risk of loss are compared with those from historical and stochastically generated windstorm fields used by Swiss Re. The resulting loss-frequency relationship matches well with the two independently modeled estimates and clearly demonstrates the added value by using alternative data and methods, as proposed in this study, to estimate the RP of high RP losses. ABSTRACT Current estimates of the European windstorm climate and their associated losses are often hampered by either relatively short, coarse resolution or inhomogeneous datasets. This study tries to overcome some of these shortcomings by estimating the European windstorm climate using dynamical seasonal-to-decadal (s2d) climate forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF). The current s2d models have limited predictive skill of European storminess, making the ensemble forecasts ergodic samples on which to build pseudoclimates of 310-396 yr in length. Extended...

Decision makers need better insights about solutions to accelerate adaptation efforts. Defining the concept of solution space and revealing the forces and strategies that influence this space will enable decision makers to define pathways for adaptation action.

Abstract. Droughts can induce important building damages due to shrinking and swelling of soils, leading to costs as large as for floods in some regions. Previous studies have focused on damage data analysis, geological or constructional aspects. Here, a study investigating the climatic aspects of soil subsidence damage is presented for the first time. We develop a simple model to examine if the meteorology has a considerable impact on the interannual variability of damages from soil subsidence in France. We find that the model is capable of reproducing yearly drought-induced building damages for the time period 1989-2002, thus suggesting a strong meteorological influence. Furthermore, our results reveal a doubling of damages in these years compared to , mainly as a consequence of increasing temperatures. This indicates a link to climate change. We also apply the model to the extreme summer of 2003, which caused a further increase in damage by a factor four, according to a preliminary damage estimate. The simulation result for that year shows strong damage underestimation, pointing to additional sources of vulnerability. Damage data suggest a higher sensitivity to soil subsidence of regions first affected by drought in the 2003 summer, possibly due to a lack of preparedness and adaptation. This is of strong concern in the context of climate change, as densely populated regions in Central Europe and North America are expected to become newly affected by drought in the future.