The variability of results from different automated methods of detection and tracking of extratropical cyclones is assessed in order to identify uncertainties related to the choice of method. Fifteen international teams applied their own algorithms to the same dataset—the period 1989–2009 of interim European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERAInterim) data. This experiment is part of the community project Intercomparison of Mid Latitude Storm Diagnostics (IMILAST; see www.proclim.ch/imilast/index.html). The spread of results for cyclone frequency, intensity, life cycle, and track location is presented to illustrate the impact of using different methods. Globally, methods agree well for geographical distribution in large oceanic regions, interannual variability of cyclone numbers, geographical patterns of strong trends, and distribution shape for many life cycle characteristics. In contrast, the largest disparities exist for the total numbers of cyclones, the detection of weak cyclones, and distribution in some densely populated regions. Consistency between methods is better for strong cyclones than for shallow ones. Two case studies of relatively large, intense cyclones reveal that the identification of the most intense part of the life cycle of these events is robust between methods, but considerable differences exist during the development and the dissolution phases.

Based on the availability of hemispheric gridded data sets from observations, analysis and global climate models, objective cyclone identification methods were developed and applied to these data sets. Due to the large amount of investigation methods combined with the variety of different datasets, a multitude of results exist, not only for the recent climate period but also for the next century, assuming anthropogenic changed conditions. Different thresholds, different physical quantities, and considerations of different atmospheric vertical levels add to a picture that is difficult to combine into a common view of cyclones, their variability and trends, in the real world and in GCM studies. Thus, this paper will give a comprehensive review of the actual knowledge on climatologies of mid-latitude cyclones for the Northern and Southern Hemisphere for the present climate and for its possible changes under anthropogenic climate conditions.

Flood estimation and flood management have traditionally been the domain of hydrologists, water resources engineers and statisticians, and disciplinary approaches abound. Dominant views have been shaped; one example is the catchment perspective: floods are formed and influenced by the interaction of local, catchment-specific characteristics, such as meteorology, topography and geology. These traditional views have been beneficial, but they have a narrow framing. In this paper we contrast traditional views with broader perspectives that are emerging from an improved understanding of the climatic context of floods. We come to the following conclusions: (1) extending the traditional system boundaries (local catchment, recent decades, hydrological/hydraulic processes) opens up exciting possibilities for better understanding and improved tools for flood risk assessment and management. (2) Statistical approaches in flood estimation need to be complemented by the search for the causal mechanisms and dominant processes in the atmosphere, catchment and river system that leave their fingerprints on flood characteristics. (3) Natural climate variability leads to time-varying flood characteristics, and this variation may be partially quantifiable and predictable, with the perspective of dynamic, climate-informed flood risk management. (4) Efforts are needed to fully account for factors that contribute to changes in all three risk components (hazard, exposure, vulnerability) and to better understand the interactions between society and floods. (5) Given the global scale and societal importance, we call for the organization of an international multidisciplinary collaboration and data-sharing initiative to further understand the links between climate and flooding and to advance flood research.

A storm loss model for Germany is presented. Input data to the model are the daily maximum gust wind speeds measured at stations distributed over the country. The individual daily peak gust values are scaled with the local climatological upper 2% quantile at each station. This scaling serves to take local conditions at the stations into account, and thus permits a simple spatial interpolation of the storm field. The next step is the computation of a loss index for each storm. It is based on the excess of (scaled) wind speed over the upper 2% quantile, and on population numbers in the individual districts within Germany, with the latter serving as a proxy for the spatial distribution of values that could be affected by a storm. Using wind speeds in excess of the percentile value also serves to take spatial heterogeneity of vulnerability against storms into account. The aggregated storm index gives an estimate of the severity of an individual storm. Finally, the relation between actual loss produced by a storm and the index is estimated using published annual insurance loss due to windstorm in Germany. Index values are accumulated for each year, and the relation to actual loss is computed. The average ratio for the whole reference period is eventually used. It is shown that the interannual variability of storm-related losses can be reproduced with a correlation coefficient of r = 0.96, and even individual storm damages can be estimated. Based on these evaluations we found that only 50 storms account for about 80% of insured storm losses between 1970 and 1997.