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Im Zentrum dieses Beitrags stehen Stürme, die weltweit, in Europa, im europäischen Alpenraum und in Österreich zu den zerstörerischsten Naturgefahren zählen. Die vorliegende Darstellung befasst sich sowohl physikalisch-konzeptionell mit dem Extremereignis »Sturm«, als auch strategisch mit dessen Auswirkungen und geeigneten Adaptionsmaßnahmen. Nach der notwendigen Begriffsbildung behandeln beide Foki folgende Aspekte: Beobachtung/Messung, historische Entwicklung, gegenwärtiger Stand (state-of the-art) des Wissens, Simulation/Warnung, künftig mögliche Entwicklungen, Schutzstrategien und schließlich Handlungsempfehlungen.

Severe winter storms in combination with precipitation extremes pose a serious threat to Europe. Located at the southeastern exit of the North Atlantic's storm track, European coastlines are directly exposed to impacts by high wind speeds, storm floods and coastal erosion. In this study we analyze potential changes in simulated winter storminess and extreme precipitation, which may occur under 1.5 or 2 degrees C warming scenarios. Here we focus on a first simulation suite of the atmospheric model CAM5 performed within the HAPPI project and evaluate how changes of the horizontal model resolution impact the results regarding atmospheric pressure, storm tracks, wind speed and precipitation extremes. The comparison of CAM5 simulations with different resolutions indicates that an increased horizontal resolution to 0.25 degrees not only refines regional-scale information but also improves large-scale atmospheric circulation features over the Euro-Atlantic region. The zonal bias in monthly pressure at mean sea level and wind fields, which is typically found in low-resolution models, is considerably reduced. This allows us to analyze potential changes in regional-to local-scale extreme wind speeds and precipitation in a more realistic way. Our analysis of the future response for the 2 degrees C warming scenario generally confirms previous model simulations suggesting a poleward shift and intensification of the meridional circulation in the Euro-Atlantic region. Additional analysis suggests that this shift occurs mainly after exceeding the 1.5 degrees C global warming level, when the midlatitude jet stream manifests a strengthening northeastward. At the same time, this northeastern shift of the storm tracks allows an intensification and northeastern expansion of the Azores high, leading to a tendency of less precipitation across the Bay of Biscay and North Sea. Regions impacted by the strengthening of the midlatitude jet, such as the northwestern coasts of the British Isles, Scandinavia and the Norwegian Sea, and over the North Atlantic east of Newfoundland, experience an increase in the mean as well as daily and sub-daily precipitation, wind extremes and storminess, suggesting an important influence of increasing storm activity in these regions in response to global warming.

Cyclones in the Arctic are detected and tracked in four different reanalysis data sets from 1981 to 2010. In great detail the spatial and seasonal patterns of changes are scrutinized with regards to their frequencies, depths, and sizes. We find common spatial patterns for their occurrences, with centers of main activity over the seas in winter, and more activity over land and over the North Pole in summer. The deep cyclones are more frequent in winter, and the number of weak cyclones peaks in summer. Overall, we find a good agreement of our tracking results across the different reanalyses. Regarding the frequency changes, we find strong decreases in the Barents Sea and along the Russian coast toward the North Pole and increases over most of the central Arctic Ocean and toward the Pacific in winter. Areas of increasing and decreasing frequencies are of similar size in winter. In summer there is a longish region of increase from the Laptev Sea toward Greenland, over the Canadian archipelago, and over some smaller regions west of Novaya Zemlya and over the Russia. The larger part of the Arctic experiences a frequency decrease. All the summer changes are found statistically unrelated to the winter patterns. In addition, the frequency changes are found unrelated to changes in cyclone depth and size. There is generally good agreement across the different reanalyses in the spatial patterns of the trend sign. However, the magnitudes of changes in a particular region may strongly differ across the data.

This study analyzes a method to construct a homogeneous, high-resolution global atmospheric hindcast. The method is the spectral nudging technique which was applied to a state-of-the-art general circulation model (ECHAM6, T255L95). Large spatial scales of the global climate model prognostic variables were spectrally nudged towards a reanalysis data set (NCEP1, T62L28) for the last decades. The main idea is the addition of dynamically consistent regional weather details to the coarse grid NCEP1 reanalysis. A large number of sensitivity experiments were performed, using different nudging e-folding times, vertical profiles, wave numbers, and variables. Comparisons with observations and several reanalyses showed a high dependency on the variations of the nudging configuration. At the global scale, the accordance is very high for extra-tropical regions and lower in the tropics. A wave number truncation of 30, a relatively short e-folding time of 50 min and a plateau-shaped nudging profile applied only to divergence and vorticity generally yielded the best results. This is one of the first global spectral nudging hindcast studies and the first applying an altitude-dependent profile to selected prognostic variables. The method can be applied to reconstruct the history of extreme events such as intense storms in the context of ongoing climate change.

This paper demonstrates two important aspects of regional dynamical downscaling of multidecadal atmospheric reanalysis. First, that in this way skillful regional descriptions of multidecadal climate variability may be constructed in regions with little or no local data. Second, that the concept of large‐scale constraining allows global downscaling, so that global reanalyses may be completed by additions of consistent detail in all regions of the world. Global reanalyses suffer from inhomogeneities. However, their large‐scale componenst are mostly homogeneous; Therefore, the concept of downscaling may be applied to homogeneously complement the large‐scale state of the reanalyses with regional detail—wherever the condition of homogeneity of the description of large scales is fulfilled. Technically, this can be done by dynamical downscaling using a regional or global climate model, which\'s large scales are constrained by spectral nudging. This approach has been developed and tested for the region of Europe, and a skillful representation of regional weather risks—in particular marine risks—was identified. We have run this system in regions with reduced or absent local data coverage, such as Central Siberia, the Bohai and Yellow Sea, Southwestern Africa, and the South Atlantic. Also, a global simulation was computed, which adds regional features to prescribed global dynamics. Our cases demonstrate that spatially detailed reconstructions of the climate state and its change in the recent three to six decades add useful supplementary information to existing observational data for midlatitude and subtropical regions of the world.

This study explores the possibility of reconstructing the weather of Southeast Asia for the last decades using an atmospheric regional climate model, the Climate version of the Lokal-Modell (CLM). For this purpose global National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalyses data were dynamically downscaled to 50 km and in a double-nesting approach to 18-km grid distance. To prevent the regional model from deviating significantly from the reanalyses with respect to large-scale circulation and large-scale weather phenomena, a spectral nudging technique was used. The performance of this technique in dealing with Southeast Asian typhoons is now examined by considering an ensemble of one simulated typhoon case. This analysis is new insofar as it deals with simulations done in the climate mode (so that any skill of reproducing the typhoon is not related to details of initial conditions), is done in ensemble mode (the same development is described by several simulations), and is done with a spectral nudging constraint (so that the observed large-scale state is enforced in the model domain). This case indicates that tropical storms that are coarsely described by the reanalyses are correctly identified and tracked; considerably deeper core pressure and higher wind speeds are simulated compared to the driving reanalyses. When the regional atmospheric model is run without spectral nudging, significant intraensemble variability occurs; also additional, nonobserved typhoons form. Thus, the insufficiency of lateral boundary conditions alone for determining the details of the dynamic developments in the interior becomes very clear. The same lateral boundary conditions are consistent with different developments in the interior. Several sensitivity experiments were performed concerning varied grid distances, different initial starting dates of the simulations, and changed spectral nudging parameters.

International audience; Original Jung, M.; Vetter, M.; Herold, M.; Churkina, G.; Reichstein, M.; Zaehle, S.; Ciais, P.; Viovy, N.; Bondeau, A.; Chen, Y.; Trusilova, K.; Feser, F.; Heimann, M.: [1] Continental to global-scale modeling of the carbon cycle using process-based models is subject to large uncertainties. These uncertainties originate from the model structure and uncertainty in model forcing fields; however, little is known about their relative importance. A thorough understanding and quantification of uncertainties is necessary to correctly interpret carbon cycle simulations and guide further model developments. This study elucidates the effects of different state-of-the-art land cover and meteorological data set options and biosphere models on simulations of gross primary productivity (GPP) over Europe. The analysis is based on (1) three different process-oriented terrestrial biosphere models (Biome-BGC, LPJ, and Orchidee) driven with the same input data and one model (Biome-BGC) driven with (2) two different meteorological data sets (ECMWF and REMO), (3) three different land cover data sets (GLC2000, MODIS, and SYNMAP), and (4) three different spatial resolutions of the land cover (0.25°fractional, 0.25°d ominant, and 0.5°dominant). We systematically investigate effects on the magnitude, spatial pattern, and interannual variation of GPP. While changing the land cover map or the spatial resolution has only little effect on the model outcomes, changing the meteorological drivers and especially the model results in substantial differences. Uncertainties of the meteorological forcings affect particularly strongly interannual variations of simulated GPP. By decomposing modeled GPP into their biophysical and ecophysiological components (absorbed photosynthetic active radiation (APAR) and radiation use efficiency (RUE), respectively) we show that differences of interannual GPP variations among models result primarily from differences of simulating RUE. Major discrepancies appear to be related to the feedback through the carbon-nitrogen interactions in one model (Biome-BGC) and water stress effects, besides the modeling of croplands. We suggest clarifying the role of nitrogen dynamics in future studies and revisiting currently applied concepts of carbon-water cycle interactions regarding the representation of canopy conductance and soil processes. Citation: Jung, M., et al. (2007), Uncertainties of modeling gross primary productivity over Europe: A systematic study on the effects of using different drivers and terrestrial biosphere models, Global Biogeochem. Cycles, 21, GB4021

Forecasting and early warning systems are important investments to protect lives, properties and livelihood. While early warning systems are frequently used to predict the magnitude, location and timing of potentially damaging events, these systems rarely provide impact estimates, such as the expected amount and distribution of physical damage, human consequences, disruption of services or financial loss. Complementing early warning systems with impact forecasts has a two‐fold advantage: it would provide decision makers with richer information to take informed decisions about emergency measures, and focus the attention of different disciplines on a common target. This would allow capitalizing on synergies between different disciplines and boosting the development of multi‐hazard early warning systems. This review discusses the state‐of‐the‐art in impact forecasting for a wide range of natural hazards. We outline the added value of impact‐based warnings compared to hazard forecasting for the emergency phase, indicate challenges and pitfalls, and synthesize the review results across hazard types most relevant for Europe. Plain language summary Forecasting and early warning systems are important investments to protect lives, properties and livelihood. While such systems are frequently used to predict the magnitude, location and timing of potentially damaging events, they rarely provide impact estimates, such as the expected physical damage, human consequences, disruption of services or financial loss. Extending hazard forecast systems to include impact estimates promises many benefits for the emergency phase, for instance, for organising evacuations. We review and compare the state‐of‐the‐art of impact forcasting across a wide range of natural hazards, and outline opportunities and key challenges for research and development of impact forecasting.