Search

It is well known that global temperatures have risen by about 1°C since the second half of the 19th century and that the major part of global warming experienced since the mid-20th century is due to anthropogenic greenhouse gas (GHG) emissions. The transportation sector contributes approximately 30% of the GHG emissions released in the European Union (EU) and is a significant driver of climate change. Therefore, most discussions and initiatives regarding transportation have been geared toward mitigation (reducing GHG emissions). However, transportation is exposed to climate change impacts at the same time. Since climate change will continue to unfold in the coming decades, mitigation alone is not enough to provide protection, and adaptation efforts will also be needed. Extreme weather events, which are expected to occur more frequently and violently in the wake of climate change over the decades to come, pose a considerable challenge to the resilience, reliability, and safety of transportation systems. It has become obvious that these challenges cannot be met with mitigation (reduction of GHG emissions) alone but have to be addressed by suitable adaptation measures. Appropriate actions will help to reduce the risk of bad investments and damage to transport infrastructure, and their identification is not trivial because of the often long lifespan (many decades) of infrastructure and the uncertainty involved in forecasting the extent of climate change’s impacts over long periods of time. It is therefore crucial to incorporate into transportation planning the design of appropriate measures for adaptation to the impact of climate change. However, for some reason (so-called barriers to adaptation), adaptation has rarely been adopted by stakeholders. The barriers include, for example, insufficient understanding of climate-related hazards and the vulnerability of the transport system to them; the lack of appropriate procedures; the lack of perception of the urgency; the impression that there is no need for a forward-looking, proactive integration of adaptation strategies into transport planning; the perception that the uncertainties are too great for adaptation planning; and budget challenges. Results of a survey among stakeholders in transportation—conducted in order to establish land transportation as the World Meteorological Organization’s new Service Delivery Target—revealed that stakeholders’ reluctance to implement the design of adaptation strategies into transportation planning, which was quite pronounced only a few years ago, has given way to general acceptance. The transport sector has a dual role—on the one hand, as a major driver of climate change, and on the other hand, as a sector vulnerable to climate change impacts. The consequences of climate change for transportation and the strategies for dealing with them by mitigation and adaptation are paramount. Mitigation and adaptation complement each other in attaining optimal protection of transportation against climate change’s impacts. Finally, the implementation of appropriate adaptation strategies needs to support decision makers in the design of forward-looking strategies that enhance the sustainability of infrastructure. An example of such implementation has occurred in the complex terrain of the European Alps.

Floodings and landslides are amongst the most devastating damage-processes worldwide. Associated risk levels are particularly high in topographically complex terrain. Along with the increase in climate-change induced extreme events, research devoted to the identification of so-called Climate Indices (CIs) describing weather phenomena triggering hazard-occurrences and -intensities gains rising emphasis. In this study we accomplish the first-time unification of the three most comprehensive cadastres on weather-induced hazard-processes, compiled and maintained by federal authorities. The therefrom resulting ‟event spaceˮ stretches seven decades from 1950 onwards and contains more than 20 000 hazard-occurrences, classified into different process-categories. Event data are analyzed together with a high-quality, daily-based dataset providing temperatures and precipitation-totals on a 1 km grid across the Austrian part of the European Alps. On the resulting unprecedented extent of extreme-weather triggered hazard-processes and gridded weather observations we are able to examine the hypothesis that daily sequences of precipitation-totals preceding damage-events allow for the detection of temporal weather-sequences uniquely allocatable to various hazard-categories in three orographically distinct regions in the European Alps. We pursue this research aim by analyzing for each hazard-category its quadratic form representing the physics contained in the observations. Resulting eigen-directions, invariant under its inherent second order tensor, are the sought-for total-sequences (CIs) and hence reject the alternative hypothesis. Therefore, precipitation total-sequences can be uniquely assigned to hazard-categories within each region. It is important to note that findings based on this novel, objective approach do not contradict, but rather add to attained research achievements by introducing this new perspective on the subject. Obtained CIs have substantial potential in research and applications. In civil defense, safeguarding critical infrastructure, early warning systems and the development of sustainable protection strategies, findings are in implementation by responsible decision-makers and in intense discussion with the European Freight and Logistics Leaders’ Forum.

EPISODES is an empirical statistical downscaling method which has been developed at the German national weather service, DWD (Kreienkamp et al. 2019). Its main aim is the downscaling of climate projections and climate predictions (seasonal to decadal) from global climate models (GCMs) to regional scale. A specific aim is to enhance ensembles based on dynamical downscaling and to improve robustness of deduced indices and statements.The methodology involves two main steps, first, analogue downscaling in connection with linear regression and, second, a sort of weather generator. An important precondition is the availability of long-term observation data sets of high quality and resolution. The synthetic time-series resulting from EPISODES are multivariate and consistent in space and time. The data provide daily values for selected surface variables and can be delivered on grid or station representation. As such, they meet the main requirements for applications in climate impact research. Thanks to low computational needs, EPISODES can provide climate projections within short time. This enables early insights in the local effects of climate change as projected by GCMs and allows flexibility in the selection of ensembles.While good results for EPISODES projections have already been achieved for Germany, the methodology needs to be adapted for the more complex terrain of the Alpine region. This is done in close collaboration of DWD and ZAMG (Austria). Among other tasks, the adaptions include a regionalization of the selection of relevant weather regimes, optimal fragmentation of the target region into climatic sub-zones and correction of precipitation class frequencies.The presentation will refer to the progress of the adaption process. In doing so the quality of downscaled climate projections is shown for a test ensemble in comparison with existing projections of the Austrian ÖKS15 data set and EURO-CORDEX. Reference: Kreienkamp, F., Paxian, A., Früh, B., Lorenz, P., Matulla, C.: Evaluation of the empirical–statistical downscaling method EPISODES. Clim Dyn 52, 991–1026 (2019). https://doi.org/10.1007/s00382-018-4276-2

EPISODES is an empirical statistical downscaling (ESD) method, which has been initiated and developed by the German Weather Service (DWD). Having resulted in good evaluation scores for Germany, the methodology it is also set-up and adapted for Austria at ZAMG and, hence, for an alpine territory with complex topography.ESD methods are sparing regarding computational costs compared to dynamical downscaling models. Due to this advantage ESD can be applied in a short time frame and in a demand-based manner. It enables, e.g., processing ensembles of downscaled climate projections, which can be assessed either as stand-alone data set or to enhance ensembles based on dynamical methods. This helps improve the robustness of climatological statements for the purpose of climate impact research.Preconditions for achieving high-quality results by EPISODES are long-term, temporally consistent observation data sets and a best possible realistic reproduction of relevant large-scale weather conditions by the GCMs. Given these requirements, EPISODES produces high-quality multivariate and spatially/temporally consistent synthetic time series on regular grids or station locations. The output is provided for daily time steps and, at maximum, for the resolution of underlying observation data.The EPISODES method consists on mainly two steps: At first stage, univariate time series are produced on a coarse grid based on the analogue method and linear regression. It means that coarse scale atmospheric conditions of each single day as described by the GCM projections are assigned to a selection of at most similar daily weather situations of the observed past. From this selection new values are determined by linear regression for each day.The second stage of the EPISODES method works like a weather generator. Short-term anomalies based on first stage results, on the one hand, and on observations, on the other hand, are matched selecting the most similar day for all used meteorological parameters and coarse grid points at the same time. Together with the high-resolution climatological background of observations and the climatological shift as described by GCM projections the short-term variability are combined to synthetic daily values for each target grid point. This approach provides the desired characteristics of the downscaled climate projections such as multivariability and spatio-temporal consistency.Recent EPISODES evaluation results for daily precipitation and daily mean temperature are presented for the Austrian federal territory. Performance of the EPISODES ensemble will also be discussed in relation to existing ensembles based on dynamical methods which have already been widely used in climate impact studies in Austria: EURO-CORDEX and ÖKS15.

Climate change constitutes a main driver of altering population dynamics of spruce bark beetles (Ips typographus) all over Europe. Their swarming activity as well as development rate are strongly dependent on temperature and the availability of brood trees. Especially over the last years, the latter has substantially increased due to major drought events which led to a widespread weakening of spruce stands. Since both higher temperatures and longer drought periods are to be expected in Central Europe in the decades ahead, foresters face the challenges of maintaining sustainable forest management and safeguarding future yields. One approach used to foster decision support in silviculture relies on the identification of possible alternative tree species suitable for adapting to expected future climate conditions in threatened regions. In this study, we focus on the forest district of Horn, a region in Austria‘s north east that is beneficially influenced by the mesoclimate of the Pannonian basin. This fertile yet dry area has been severely affected by mass propagations of Ips typographus due to extensive droughts since 2017, and consequently has suffered from substantial forest damage in recent years. The urgent need for action was realized and has expedited the search for more robust alternative species to ensure sustainable silviculture in the area.The determination of suitable tree species is based on the identification of regions whose climatic conditions in the recent past are similar to those that are to be expected in the forest district of Horn in the future. To characterize these conditions, we consider 19 bioclimatic variables that are derived from monthly temperature and rainfall values. Using downscaled CMIP6 projections with a spatial resolution of 2.5 minutes, we determine future conditions in Horn throughout the 21st century. By employing 20-year periods from 2021 to 2100 for the scenarios SSP1-26, SSP2-45, SSP3-70 and SSP5-85, and comparing them to worldwide past climate conditions, we obtain corresponding bioclimatic regions for four future time slices until the end of the century. The Euclidian distance is applied as measure of similarity, effectively yielding similarity maps on a continuous scale. In order to account for the spatial variability within the forest district, this procedure is performed for the colder northwest and the warmer southeast of the area, individually seeking similar bioclimatic regions for each of these two subregions. Results point to Eastern Europe as well as the Po Valley in northern Italy as areas exhibiting the highest similarity to the future climate in this North-Eastern part of Austria.

The aim of this contribution is to present the design as well as findings of a survey targeted assessing the needs of stakeholders in the transportation domain with respect to climate change driven damages. This ‘User needs survey’ is one of the major objectives of multifarious collaborations investigating anticipatory asset protection strategies under accelerated climate change. The viability of these efforts is guaranteed by pairing up the scientific community (CIT, University of Vienna, BOKU, TU Vienna) with notable stakeholders (F&L, WMO, BMNT).The ‘User needs’ survey, was carried out in cooperation between the Climate Impact Team (CIT) the European Transport, Freight and Logistics Leaders Forum (F&L) and the World Meteorological Organization (WMO). The aim of the survey is to identify services that stakeholders in the realm of transportation themselves consider significant and beneficial. Therefore, findings should be of vital importance for -- (i) setting up meaningful climate services; (ii) selecting sustainable protection measures strengthening transportation system resilience in the face of future climate change; (iii) compiling the chapter on 'Land Transport' in WMO’s new Service Delivery Guide -- as they ensure the expediency of the services described.Presented results encompass: (i) an assessment of extreme events in terms of their damaging impacts on transport, freight and logistics by stakeholders; (ii) an assessment of the vulnerability of assets in transport, freight and logistics by stakeholders; (iii) an illustration of the extent of impacts climate-change (through shifts in extremes and associated threats) has had on transport, freight and logistics over the past decades; (iv) the stakeholders' expectations regarding future developments towards advancing climate-change and (v) an evaluation of time horizons (short, medium and long term) at which stakeholders need services. A summary completes this contribution.

In large parts of the world, an increasing number of damaging events caused by previously rare extreme weather phenomena is being observed. This poses a challenge to those responsible for civil protection of how to sustain current safety levels under accelerated climate change. The aim of this study is to contribute to meeting these challenges by providing methods to determine anticipatory strategies for decades of sustainable protection. This endeavor requires the identification of weather-related hazard processeson on the one, and the establishment of corresponding future hazard development corridors on the other hand. The former, so-called Climate Indices (CIs), are determined by blending damage events and spatiotemporal highly resolved meteorological data for three different regions in the Austrian Alpine region and six different process categories via multivariate statistical analyses. The derivation of hazard development corridors describing future changes in risk landscapes requires ensembles of regional climate projections, in which the occurrence of corresponding CIs is detected.Results are incorporated into the decision-making process and processed together with experts in civil protection. The determination of optimal, sustainable protection strategies is based on decision-theoretical techniques and the application of the expected utility theory (Bernoulli principle).The feasibility of integrating hazard development corridors into decision-making processes, as well as the satisfactory implementation of established procedures, is demonstrated by the most comprehensive civil protection project in Austria to date. The results are consistent and show significant differences between near (2036-2065) and far future (2071-2100) time periods, as well as between the threat levels corresponding to the "climate-friendly" path of humanity and those associated with the "business as usual" scenario. The results are in line with the European Floods Directive by ranking linear measures behind resettlement and retention measures.

Floodings are amongst the most devastating damage-processes worldwide. Along with the increase in climate change induced extreme events, research devoted to the identification of so-called Climate Indices (CIs) describing weather phenomena triggering hazard-occurrences gains rising emphasis. CIs have a wide potential for further investigation in both research and application as e.g. in public protection and the transport and logistic industry. The appearance of specific CIs in regional climate models (i.e., ‘hazard development corridors’) can serve as an input in decision-theoretic concepts aiming to sustain current safety levels in climate change induced altering risk landscapes (Matulla et al, submitted). Enigl et al, 2019 first objectively derived hazard-triggering precipitation totals for six process-categories and three climatologically as well as geomorphologically distinct regions in the Austrian part of the European Alps. This study aims at investigating a slightly different methodological approach for the objective determination of Climate Indices in the catchment area of the River Danube in Austria depending on catchment areas.

The Austrian Federal Railways (ÖBB) are operating about 4800 kilometers of railway track in all regions of Austria. Most parts of this infrastructure are exposed to various natural hazards like landslides, debris flows, rockfalls, floodings and avalanches but also extreme weather events like strong winds or extreme heat can disrupt railway traffic. The frequency of their occurrence is changing due to recent climate change.We use over 2000 events from 1990 to 2018 and a principal component approach to create an event space which lets us combine events and meteorological data on a fine spatial grid. This is necessary to detect characteristic climate-indices (CIs) in temporal series of meteorological parameters, like temperature or precipitation, that have negative effects on railway operation or trigger natural hazards that do so. The results are evaluated using various multivariate statistic methods to quantify the quality of the found CIs.After these steps we can estimate the frequency of CI occurrence in near (2036-2065) and remote future (2071-2100) by analyzing ensembles of downscaled GCM projections for different climate scenarios. The result are hazard-development-corridors that are a relative measure for the number of predicted hazard events during the two periods of time along the considered railway tracks.

An important consequence of climate change is the impact on the seasonal cycle of vegetation flora and fauna. Although it is generally understood that anthropogenic mechanisms play a major role in the warming trend of the climate and that the timing of such phases, especially spring timing events, depends largely on the temperature, the link has yet to be quantitatively shown for different kind of areas on a regional scale, due to high intrinsic noise. In this study, an end-to-end analysis to external forcings was carried out, linking the earlier timing of phenological spring timing events to changed climate conditions (increasing temperature) and this change in the environment to anthropogenic forcing, for the region of interest: Germany. Besides a large ensemble originating from six different General Circulation Models (GCMs), driven with various kinds of forcings, the E-OBS data set was used as observational reference for near-surface air temperature and the PEP725 for phenological observations. The latter contains over 100 different phenological phases, from which - after quality checking - 12 phases are evaluated. To overcome the scale differences, a quantile mapping bias correction approach was used to downscale the GCM data. The generation of simulated phenological time series was done with a temperature-day-sum model, which had to be calibrated and optimised beforehand. The signal-to-noise ratio was increased by generating samples of 50-year running trends, which make up the basis for the statistical evaluation. U-values from the Mann-Whitney U-Test represent the foundation, on which the null hypothesis for detection, that the observed changes can be explained with naturally forced climate conditions only, was tested. Based thereupon, a newly defined consistency factor was constructed, which allowed the assessment of anthropogenic impact on phenological phases. Observed changes in phenological phases were successfully attributed to anthropogenic impacts.

Our current knowledge on multi-decadal to centennial changes of snow in different parts of the world is based largely on observations of snow depth and depth of snowfall from national weather and hydrographic services. Studies analysing these snow observations in the European Alps are predominantly based on national data and are therefore limited by their respective borders in the detection of robust, spatiotemporal snow trends. In order to overcome this limitation, data from Austria and Switzerland, which cover a substantial fraction of the Alps when taken together, are merged for this study (196 station-records). Additionally, it is the first time that such an analysis is based on homogenized data. Our homogenization study shows that, although the detection of breaks in snow depth series works quite well with the existing methods, further research is needed to adequately correct snow depth series at a daily resolution. Roughly, 70% (139 station-records) of the snow depth series could be homogenized and are used for further trend analysis. The findings concern seven climatologically different areas that are identified by a regionalization (using empirical orthogonal functions) using station records from 1961 to 2012. These regions share a high degree of inner similarity and outer separation, and the temporal trends detected are rather different across the Swiss-Austrian domain. Regions in the south show a clear decrease in the snow depth of up to −12 cm/decade on average, while those in the northeast are characterized by almost no change. The declining trend in the southern regions intensifies as altitude increases. Comparisons of these variations in depth changes with concurrent changes in air temperature and precipitation totals reveal a clear dichotomy with respect to elevation. Snow depths in low elevated areas are highly sensitive to air temperature changes, whereas those at high elevations strongly depend on alterations in precipitation totals.

Lake surface temperature (LST) is a key characteristic of lakes, shaping the ecological properties of these inland water bodies and their environment. This study aims at establishing a long-term, high-quality, monthly LST dataset within the European Alps reaching back to 1880, which is provided to the scientific community for further research. Therefore monthly temperature records from Austrian lakes covering a period of about six decades are digitized from hydrological yearbooks. Clustering techniques (rotated empirical orthogonal functions and hierarchical cluster analysis) are used to identify groups of lakes signified by inner similarity and outer separation. These are not only used for an overall quality assessment, but also provide optimal starting conditions for the application of a homogenization procedure, warranting homogeneous LST data from 1950 onwards. LST reconstructions back to 1880 are derived from atmospheric covariates (provided by HISTALP) via sets of transfer-functions. Applied transfer-functions have passed a selection process ensuring mathematical, physical and quality requirements. They are selected from about 160 million candidates according to skill, which is determined through a comprehensive assessment based on validation experiments and several performance measures. Results show overall high skill following a seasonal cycle. From 1880 to 1950 LSTs feature generally slight increases accompanied by a succession of climate states. These are in alignment with outstanding climate periods and sustained, far-reaching repercussions triggered by significant events, which are known from historical documents. LST developments throughout the second half of the twentieth century up to date are characterized by a decline until the mid-1980s, indicating the impact of industrial aerosols. This behaviour is superseded by a steep increase, revealing the gradual unmasking of the anthropogenic greenhouse effect by the continuous reduction of aerosol loads in the atmosphere. The latter substantiates man-made climate change, whose prove is based on atmospheric variables by different data pertaining to the hydrosphere. Potential research hypotheses corresponding to various fields of science that may be investigated by using the here established LST dataset are considered. We hope that these data sets and associated findings will make a contribution to the broader research community.

A comprehensive performance assessment of the empirical–statistical downscaling (ESD) technique named EPISODES is presented. Pertaining evaluation analyses consist of multifarious validation experiments as well as various comparisons of EPISODES’ projections with those of three RCMs and two ESD methods based on the same GCM scenarios driven by two distinct representative concentration pathways (RCPs). EPISODES combines the downscaling of GCM simulations with a follow-up production of synthetic local time series. EPISODES is a comparably simple, computationally rather inexpensive technique, providing multi-variable and multi-site data that are suitable for being merged in an ensemble of RCM projections. This allows (e.g. for different RCPs) the compilation of large multi member ensembles derived from various GCM simulations via both main downscaling strategies (ESD and RCMs). Evaluation experiments reveal satisfying degrees of compliance between various results generated by EPISODES and observations. The grid cell bias for yearly values, for instance, is mostly less than $$0.1\,^\circ$$ C for temperature and 10% for precipitation totals. Recorded temperature values and precipitation totals corresponding to their 1st and the 99th percentiles are well represented by EPISODES too. Comparisons of various climate change signals derived by EPISODES and other downscaling approaches, present high levels of agreement as well. Many more findings referring to evaluation experiments and climate change projections are to be found in the paper as well as throughout the “Appendix”.

In the face of climate change, the assessment of land transport infrastructure exposure towards adverse climate events is of major importance for Europe's economic prosperity and social wellbeing. In this study, a climate index estimating rainfall patterns which trigger landslides in central Europe is analysed until the end of this century and compared to present-day conditions. The analysis of the potential future development of landslide risk is based on an ensemble of dynamically downscaled climate projections which are driven by the SRES A1B socio-economic scenario. Resulting regional-scale climate change projections across central Europe are concatenated with Europe's road and railway network. Results indicate overall increases of landslide occurrence. While flat terrain at low altitudes exhibits an increase of about 1 more potentially landslide-inducing rainfall period per year until the end of this century, higher elevated regions are more affected and show increases of up to 14 additional periods. This general spatial distribution emerges in the near future (2021–2050) but becomes more pronounced in the remote future (2071–2100). Since largest increases are to be found in Alsace, potential impacts of an increasing amount of landslides are discussed using the example of a case study covering the Black Forest mountain range in Baden-Württemberg by further enriching the climate information with additional geodata. The findings derived are suitable to support political decision makers and European authorities in transport, freight and logistics by offering detailed information on which parts of Europe's ground transport network are at particularly high risk concerning landslide activity.

Road authorities, freight, and logistic industries face a multitude of challenges in a world changing at an ever growing pace. While globalization, changes in technology, demography, and traffic, for instance, have received much attention over the bygone decades, climate change has not been treated with equal care until recently. However, since it has been recognized that climate change jeopardizes many business areas in transport, freight, and logistics, research programs investigating future threats have been initiated. One of these programs is the Conference of European Directors of Roads’ (CEDR) Transnational Research Programme (TRP), which emerged about a decade ago from a cooperation between European National Road Authorities and the EU. This paper presents findings of a CEDR project called CliPDaR, which has been designed to answer questions from road authorities concerning climate-driven future threats to transport infrastructure. Pertaining results are based on two potential future socio-economic pathways of mankind (one strongly economically oriented “A2” and one more balanced scenario “A1B”), which are used to drive global climate models (GCMs) producing global and continental scale climate change projections. In order to achieve climate change projections, which are valid on regional scales, GCM projections are downscaled by regional climate models. Results shown here originate from research questions raised by European Road Authorities. They refer to future occurrence frequencies of severely cold winter seasons in Fennoscandia, to particularly hot summer seasons in the Iberian Peninsula and to changes in extreme weather phenomena triggering landslides and rutting in Central Europe. Future occurrence frequencies of extreme winter and summer conditions are investigated by empirical orthogonal function analyses of GCM projections driven with by A2 and A1B pathways. The analysis of future weather phenomena triggering landslides and rutting events requires downscaled climate change projections. Hence, corresponding results are based on an ensemble of RCM projections, which was available for the A1B scenario. All analyzed risks to transport infrastructure are found to increase over the decades ahead with accelerating pace towards the end of this century. Mean Fennoscandian winter temperatures by the end of this century may match conditions of rather warm winter season experienced in the past and particularly warm future winter temperatures have not been observed so far. This applies in an even more pronounced manner to summer seasons in the Iberian Peninsula. Occurrence frequencies of extreme climate phenomena triggering landslides and rutting events in Central Europe are also projected to rise. Results show spatially differentiated patterns and indicate accelerated rates of increases.

International audience; AbstractKey messageAutumn and spring frost events caused wide variation in the survival of juvenile Douglas-fir in Austrian forest sites located in the transition zone from Atlantic to continental climate. Survival rate can be optimized by planting provenances originating from an altitudinal belt of 500–1400 m in North America. Neither the variety nor the climate of origin of planted Douglas-fir provenances influence its response to frost events.ContextUnderstanding the risks of frost during late spring and early autumn is crucial for planting non-native Douglas-fir (Pseudotsuga menziesii [Mirbel] Franco) as an alternative tree species under climate change in Europe.AimsWe investigate the role of early and late frost events on the survival of juvenile Douglas-fir and tested whether survival depends on seed origin.MethodsWith data from 19 provenance trials across Austria, we modeled the effects of early and late frost events on juvenile survival rate, accounting for random variations due to site condition and provenance origin.ResultsWide variations (37–93%) in the juvenile survival rate of Douglas-fir were mainly driven by early and late frost events (daily Tmin

Recommendations on suitable seed sources for reforestation are usually model based and include uncertainties arising from model assumptions, parameter estimation, and future climate scenarios. Due to the long-lived nature of forests, such uncertainties need to be considered when developing guidance for managers and policy makers. We evaluate the uncertainties and apply our recently developed genetically based models, Universal Response Functions (URFs) in Austria and Germany. The URFs predict growth performance (dominant height and basal area at age 24) of Douglas-fir (Pseudotsuga menziesii [Mirbel] Franco) populations, as a function of both environmental and genetic factors. We evaluated the URFs by comparing the predicted height growth performances with observations from independent provenance trial data across Europe. Also, the sensitivity of the URF variables and the overall model uncertainty were estimated and compared to the uncertainty due to climate change projections. Model evaluation suggests that the URFs perform better in Central and Southeastern Europe compared to maritime Western Europe. Summer drought and mean annual temperature of planting sites were the most sensitive variables of the models, whereas the mean annual temperature of seed origin was the least sensitive. Model uncertainty increased with mean annual temperature of the planting site. Uncertainty due to projected future climate was found to be higher than the model uncertainty. The URFs predicted that provenance regions of southwest Germany and southeast Austria below 1500 m altitude will be suitable, whereas Pannonian east of Austria will become less suitable for growing Douglas-fir in future. Current seed stands in North America providing planting materials for Europe under the legal framework of the Organization for Economic Cooperation and Development shall continue to provide the most suitable seed material also in the future.

This paper deals with the subject of elaborating a guideline on the use of climate data, which have been made available to the climate-change and climate-impact community as well as to the general public. The therein descripted data is a gridded dataset on 1 km nominal resolution for Austria, consisting of observational data as well as of bias corrected climate model output. It covers the area of Austria. The aim of this paper is to make reference to this guideline that is by now accessible for those interested in using the dataset as well as for those creating datasets planning to provide guidance to their data users.

This paper provides local scale temperature scenarios for Austria from the middle up to the end of the 21st century. Climate simuations based on the IPCC emission pathways A2 and A1B have been carried out with the global climate models ECHAM5 (three A1B and two A2 realizations) and HadGEM2 (three A1B realizations). The corresponding large scale projections of sea level pressure and 850 hPa temperature fields are statistically downscaled to stations spread across Austria using a perfect prognosis (PP) approach and Multiple Regression Models. The downscaling performance is assessed by a split sample test. Simulated time series are compared to actual measurements by means of the simulated variance, the root mean square and the mean error. Performances are highest during the cold season and sites located in valleys exhibit somewhat lower values. In summer performances show about a 10 percent lower skill than in winter. Downscaled local scale scenarios differ between seasons, scenarios, GCMs and regions in Austria. A1B estimates derived from ECHAM5 indicate a winter-temperature increase of approximately 3 °C at the end of the 21st century compared to present conditions, which is about one degree above the HadGEM2 based projections. This situation is reversed in summer: the HadGEM2 based projections show a warming of about 4 °C while those derived from ECHAM5 indicate a 0.5 °C lower warming. Statistically downscaled winter warming rates at stations

In the face of climate change, the assessment of land transport infrastructure exposure towards adverse climate events is of major importance for Europe's economic prosperity and social wellbeing. Robust and reliable information on the extent of climate change and its projected future impacts on roads and railways are of prime importance for proactive planning and the implementation of targeted adaptation strategies. Among various menacing natural hazards, landslides stand out as most destructive hazards to the functional effectivity and structural integrity of land-bound transport systems, since they cause long-lasting downtimes and exceedingly expensive repair works. Periods of heavy precipitation persisting over several days are known to be a major trigger for increased landslide activity. Along with climate change such events can be expected to increase in frequency, duration and intensity over the decades to come. In this study, a Climate Index (CI) picturing rainfall patterns which trigger landslides in Central Europe is analyzed until the end of this century and compared to present day conditions. The analysis of potential future developments is based on an ensemble of dynamically downscaled climate projections which are driven by the SRES A1B socio-economic scenario. Resulting regional scale climate change projections across Central Europe are concatenated with Europe's road and railway network. Results indicate overall increases of landslide occurrences. While flat terrain at low altitudes exhibits increases of about two more landslide events per year until the end of this century, higher elevated regions are more affected and show increases of up to eight additional events. This general spatial distribution emerges already in the near future (2021–2050) but gets more pronounced in the remote future (2071–2100). Largest increases are to be found in the Alsace. Consequently, potential impacts of increasing landslide events are discussed using the example of a case study covering the Black Forest mountain range in Baden-Württemberg by further enriching the climate information with and additional geodata. Derived findings are suitable to support political decision-makers and European authorities in transport, freight and logistics by offering detailed information on which parts of Europe's land-bound transport network are at particularly high risk concerning landslide activity. This study supports proactive development of adaption strategies and the realization of cost-efficient and effective protection programmes as well as the generation of guidelines for climate proofing. This refers to the design of transport networks, intermodal logistics as well as the setting up of maintenance and reinforcement strategies in order to safeguard one of the most essential backbones of Europe's economic prosperity.

Bark beetles are the major biotic disturbance factor threatening Norway spruce (Picea abies [L.] karst.) forests in Austria. The increase of bark beetle salvage after large storm damages is well known. However, over the recent two decades salvage from bark beetle damages in Austria has increased and varied between 0.6 and 3.0 million m3, where at regional scale a complex interplay of initial beetle population density, forest conditions, weather phenomena such as drought periods, various other disturbance agents such as snow and storm and forest management have been hypothesized as major determinants. This points at the need to develop tools to assess the risks of damage from bark beetle disturbances at the operational scale of forest stands, so that adaptation measures can be developed and implemented in a targeted approach. In the current analysis, binomial generalized linear mixed models (GLMMs) were used to assess the effects of site, stand and climate conditions on the probability, and linear mixed models (LMMs) for the intensity of bark beetle disturbance events at forest stand level. The database used for model development combined 10-year forest management plans and related harvest records of four management units of the Austrian Federal Forests covering in total more than 40,000 ha of forest, and a gridded climate data set provided by the Austrian Central Institution for Meteorology and Geodynamics. In the models, timber stock volume and previous disturbances had the largest impacts on bark beetle damage. Potential bark beetle generations estimated from a beetle phenology model were also a useful predictor. While the model of disturbance probability correctly classified 90% of all cases in the data set (specificity 95%, sensitivity 29%), the model for damage intensity explained only low shares of the variation in the recorded damage data (full model R2 = 0.45, fixed-effects-only model R2 = 0.13; cross validation in the four forest management units yielded similar R2 values). Benefits and limitations of the data set are critically discussed and conclusions for operational forest management are drawn.

This study presents a new gridded dataset providing absolute monthly mean temperatures across the Greater Alpine Region (GAR) of Europe at a spatial resolution of 5 arcmin (6 × 9 km in the region) from 1780 to 2008. The starting point was a set of long-term homogenized station time series. To assure the quality of the analyses back in time, when the station density decreases, missing measurements were reconstructed by an Empirical Orthogonal Function analysis that can deal with gappy data. It is shown that the reconstructed values comprise similar statistical features to the observations and that the method produces no breaks between the reconstructions and the observations. The compound anomaly dataset was then interpolated separately for two different altitude ranges to preserve anomaly gradients between high and low elevations. This allowed for the derivation of individual anomalies at each grid point in GAR. Finally, these smooth anomalies were blended with the highly resolved monthly mean absolute temperature fields, provided by a project of the European Climate Support Network. The added value of this new high resolution and long-term temperature dataset is shown and discussed using the examples of the height of the 0 °C altitude and vertical lapse rates. For the first time, these and other features are now available for more than two centuries in a topographically complex region like GAR.

This study is based on the study from Matulla et al. (2009) where the glacier under estimation has been Peyto Glacier in Canada. The same methods have been used for five Austrian glaciers; projections of glacier mass balance are generated from ensembles of general circulation model (GCM) simulations by the use of direct statistical downscaling. Thereby, the general features of the atmospheric circulation over an expanded geographical region covering the European Alps are linked empirically to winter and summer mass balance records measured at five glaciers in Austria. The projections are taken from an ensemble of ECHAM5-MPIOM simulations forced with the IPCC-SRES scenarios A1B and B1. Results based on the statistical downscaling indicate decreasing balances for both winter and summer. These results suggest continued frontal recession and downwasting of the alpine glaciers in this region until 2100. For Jamtalferner, these suggestions reach reductions of about 1000 mm water equivalent in summer. Copyright © 2012 Royal Meteorological Society

Solid precipitation (mainly snow, but snow and ice pellets or hail as well), is an important parameter for climate studies. But as this parameter usually is not available operationally before the second part of the 20th century and nowadays is not reported by automatic stations, information usable for long term climate studies is rare. Therefore a proxy for the fraction of solid precipitation based on a nonlinear relationship between the percentage of solid precipitation and monthly mean temperature was developed for the Greater Alpine Region of Europe and applied to the existing longterm high resolution temperature and precipitation grids (5 arcmin). In this paper the method is introduced and some examples of the resulting datasets available at monthly resolution for 1800–2003 are given.

Due to an administrative error the incorrect, unrevised version of this paper has appeared online initially and for some time. The original article has now been corrected.

We apply a direct downscaling approach to generate ensembles of local-scale glacier mass balance projections from coarse-scale general circulation model (GCM) data. The general modes of the atmospheric circulation over a large geographical region are linked statistically to Peyto Glacier's winter and summer balance separately. Our study focuses on the generation of ensemble projections derived from simulations with ECHAM5-MPIOM forced with Intergovernmental Panel on Climate Change - Special Report on Emission Scenarios (IPCC-SRES) A1B and B1. The resulting ensembles of mass balance projections show a moderate increase in winter balance and a steep decrease in summer balance. Together these results suggest continued frontal recession and downwasting of Peyto Glacier and a shift of the equilibrium line altitude by at least 100 m above that estimated for the 1966–2001 period suggesting that very little of the glacier will remain by 2100. Copyright © 2008 Crown in the right of Canada. Published by John Wiley & Sons, Ltd.

Annual and seasonal statistics of local air pressure characteristics have already been used as proxies for storminess across Northern Europe. We present an update of such proxies for Northern Europe and an unprecedented analysis for Central Europe which together considerably extends the current knowledge of European storminess. Calculations are completed for three sets of stations, located in North-Western, Northern and Central Europe. Results derived from spatial differences (geo- strophic winds) and single station pressure changes per 24 h support each other. Geostrophic winds' high percentiles (95th, 99th) were relatively high during the late nineteenth and the early twentieth century; after that they leveled off somewhat, to get larger again in the late twentieth century. The decrease happens suddenly in Central Europe and over several decades in Northern Europe. The subsequent rise is most pronounced in North-Western Europe, while slow and steady in Central Europe. Europe's storm climate has undergone significant changes throughout the past 130 years and comprises significant variations on a quasi- decadal timescale. Most recent years feature average or calm conditions, supporting claims raised in earlier studies with new evidence. Aside from some dissimilarity, a gen- eral agreement between the investigated regions appears to be the most prominent feature. The capability of the NAO index to explain storminess across Europe varies in space and with the considered period.

This study examines the performance of the analog method for downscaling daily precipitation. The evaluation is performed for (1) a number of similarity measures for searching analogs, (2) various ways to include the past atmospheric evolution, and (3) different truncations in EOF space. It is carried out for two regions with complex topographic structures, and with distinct climatic characteristics, namely, California’s Central Valley (together with the Sierra Nevada) and the European Alps. NCEP/NCAR reanalysis data are used to represent the large scale state of the atmosphere over the regions. The assessment is based on simulating daily precipitation for 103 stations for the month of January, for the years 1950–2004 in the California region, and for 70 stations in the European Alps (January 1948–2004). Generally, simulated precipitation is in better agreement with observations in the California region than in the European Alps. Similarity measures such as the Euclidean norm, the sum of absolute differences and the angle between two atmospheric states perform better than measures which introduce additional weightings to principal components (e.g., the Mahalanobis distance). The best choice seems dependent upon the target variable. Lengths of wet spells, for instance, are best simulated by using the angular similarity measure. Overall, the Euclidean norm performs satisfactorily in most cases and hence is a reasonable first choice, whereas the use of Mahalanobis distance is less advisable. The performance of the analog method improves by including large-scale information for bygone days, particularly, for the simulation of wet and dry spells. Optimal performance is obtained when about 85–90% of the total predictor variability is retained.

This study assesses the impact of a changing climate on fish fauna by comparing the past mean state of fish assemblage to a possible future mean state. It is based on (1) local scale observations along an Inner-Alpine river called Mur, (2) an IPCC emission scenario (IS92a), implemented by atmosphere-ocean global circulation model (AOGCM) ECHAM4/OPYC3, and (3) a model-chain that links climate research to hydrobiology. The Mur River is still in a near-natural condition and water temperature in summer is the most important aquatic ecological constraint for fish distribution. The methodological strategy is (1) to use downscaled air temperature and precipitation scenarios for the first half of the twenty-first century, (2) to establish a model that simulates water temperature by means of air temperature and flow rate in order to generate water temperature scenarios, and (3) to evaluate the impact on fish communities using an ecological model that is driven by water temperature. This methodology links the response of fish fauna to an IPCC emission scenario and is to our knowledge an unprecedented approach. The downscaled IS92a scenarios show increased mean air temperatures during the whole year and increased precipitation totals during summer, but reduced totals for the rest of the annual cycle. These changes result in scenarios of increased water temperatures, an altered annual cycle of flow rate, and, in turn, a 70 m displacement in elevation of fish communities towards the river's head. This would enhance stress on species that rely on low water temperatures and coerce cyprinid species into advancing against retreating salmonids. Hyporhithral river sectors would turn into epipotamal sectors. Grayling (Thymallus thymallus) and Danube salmon (Hucho hucho), presently characteristic for the Mur River, would be superceded by other species. Native brown trout (Salmo trutta), already now under pressure of competition, may be at risk of losing its habitat in favour of invaders like the exotic rainbow trout (Oncorhynchus mykiss), which are better adapted to higher water temperatures. Projected changes in fish communities suggest an adverse influence on salmonid sport fishing and a loss in its high economic value.

Identifying populations within tree species potentially adapted to future climatic conditions is an important requirement for reforestation and assisted migration programmes. Such populations can be identified either by empirical response functions based on correlations of quantitative traits with climate variables or by climate envelope models that compare the climate of seed sources and potential growing areas. In the present study, we analyzed the intraspecific variation in climate growth response of Douglas-fir planted within the non-analogous climate conditions of Central and continental Europe. With data from 50 common garden trials, we developed Universal Response Functions (URF) for tree height and mean basal area and compared the growth performance of the selected best performing populations with that of populations identified through a climate envelope approach. Climate variables of the trial location were found to be stronger predictors of growth performance than climate variables of the population origin. Although the precipitation regime of the population sources varied strongly none of the precipitation related climate variables of population origin was found to be significant within the models. Overall, the URFs explained more than 88% of variation in growth performance. Populations identified by the URF models originate from western Cascades and coastal areas of Washington and Oregon and show significantly higher growth performance than populations identified by the climate envelope approach under both current and climate change scenarios. The URFs predict decreasing growth performance at low and middle elevations of the case study area, but increasing growth performance on high elevation sites. Our analysis suggests that population recommendations based on empirical approaches should be preferred and population selections by climate envelope models without considering climatic constrains of growth performance should be carefully appraised before transferring populations to planting locations with novel or dissimilar climate.

International audience; Abstract• ContextAmong natural disturbances, wind storms cause the greatest damage to forests in Austria.• AimThe aim of this study is to quantify the effects of site, stand and meteorological attributes on the wind disturbance regime at the operational scale of forest stands.• MethodsWe used binomial generalized linear mixed models (GLMMs) to quantify the probability of damage events and linear mixed models (LMMs) to explain the damage intensity at the forest stand level in four management units with a total forest area of approximately 28,800 ha.• ResultsTimber stock volume, stand age, elevation, previous disturbances, wind gust speed and frozen state of soil contributed in explaining probability of wind damage. While the model of disturbance probability correctly classified 90 % of all cases in the data set (specificity 95 %, sensitivity 26 %), the model for damage intensity explained only low percentages of the variation in the observed damage data (full model R2 = 0.38, fixed effects-only model R2 = 0.09; cross-validation in the four forest management units yielded similar R2 values).• ConclusionThe developed models indicated that decreasing the proportion of Norway spruce (Picea abies [L.] Karst), limiting stand age and reducing the timber stock in course of tending treatments in stands exposed to wind disturbance can mitigate the risk and the expected damage intensity. High gust speeds and salvage cuts after earlier damage increase the probability of further wind disturbance events.

This paper describes the HISTALP database, consisting of monthly homogenised records of temperature, pressure, precipitation, sunshine and cloudiness for the ‘Greater Alpine Region’ (GAR, 4–19°E, 43–49°N, 0–3500m asl). The longest temperature and air pressure series extend back to 1760, precipitation to 1800, cloudiness to the 1840s and sunshine to the 1880s. A systematic QC procedure has been applied to the series and a high number of inhomogeneities (more than 2500) and outliers (more than 5000) have been detected and removed. The 557 HISTALP series are kept in different data modes: original and homogenised, gap-filled and outlier corrected station mode series, grid-1 series (anomaly fields at 1° × 1°, lat × long) and Coarse Resolution Subregional (CRS) mean series according to an EOF-based regionalisation. The leading climate variability features within the GAR are discussed through selected examples and a concluding linear trend analysis for 100, 50 and 25-year subperiods for the four horizontal and two altitudinal CRSs. Among the key findings of the trend analysis is the parallel centennial decrease/increase of both temperature and air pressure in the 19th/20th century. The 20th century increase (+1.2 °C/+ 1.1 hPa for annual GAR-means) evolved stepwise with a first peak near 1950 and the second increase (1.3 °C/0.6hPa per 25 years) starting in the 1970s. Centennial and decadal scale temperature trends were identical for all subregions. Air pressure, sunshine and cloudiness show significant differences between low versus high elevations. A long-term increase of the high-elevation series relative to the low-elevation series is given for sunshine and air pressure. Of special interest is the exceptional high correlation near 0.9 between the series on mean temperature and air pressure difference (high-minus low-elevation). This, further developed via some atmospheric statics and thermodynamics, allows the creation of ‘barometric temperature series’ without use of the measures of temperature. They support the measured temperature trends in the region. Precipitation shows the most significant regional and seasonal differences with, e.g., remarkable opposite 20th century evolution for NW (9% increase) versus SE (9% decrease). Other long- and short-term features are discussed and indicate the promising potential of the new database for further analyses and applications. Copyright © 2006 Royal Meteorological Society.

In this study, we set out to investigate the linkage of frost frequency to monthly mean temperature and its sensitivity to temperature changes. According to other related studies, the linkage between frost frequency and monthly mean temperature is approximated month per month via hyperbolic tangent functions. These models are validated using three validation experiments including split sample tests and temporal cross-validation. As there are quality-checked station data in Austria, whose temporal resolution and length allow for such a validation procedure, the validation experiments are conducted there. After the performance of the empirical models is evaluated and found adequate, the hyperbolic tangent approach is applied to about 500 stations within the so called Greater Alpine region (GAR), which extends from about 4 °E to 18 °E and from 44 °N to 49 °N. Using these models, it is possible to derive the sensitivity of frost frequency for any location for which the annual temperature cycle is known. This strategy is explicitly demonstrated for the Po Plain, where vertical temperature profiles on a monthly base are on hand as well as in Austria, where spatially high resolved maps of monthly mean temperature are available. Moreover, at stations for which long-term homogenised series of monthly mean temperature are available, reconstructions of frost frequency via the empirical models are done, returning to historical periods where no measurements of minimum temperature exist. On the basis of these findings, the impact of a possible future warming can be assessed, which is essential with regard to glaciers, permafrost and avalanches. Reduction in frost might bring positive economic aspects for agriculture, but negative consequences for low level skiing areas. Copyright © 2005 Royal Meteorological Society.

Large-scale climate forcings on local precipitation in Cameroon are analysed during the little rainy season (March-June). Variables found to have strong influence are used to downscale GCM projected rainfall for 2010-2049. In particular, 2 IPCC IS92a scenarios, simulated by the ECHAM4/OPYC3 climate model, are investigated. First, monthly precipitation data from 1951-1990 at 33 meteorological stations are grouped into homogeneous rainfall regions using self-organising feature maps (SOFMs). SOFMs identified 3 groups of stations with related time-series variability. Then, an empirical orthogonal function procedure, followed by canonical correlation analysis (CCA), is used to derive statistical relationships between the homogeneous regions and large-scale variables from the NCEP/NCAR Reanalysis Project. A CCA model is established for every region. Numerous fields at different pressure levels are used as macro-scale predictors. All possible combinations of 2 predictors are systematically tested in 3 validation experiments. Those combinations that perform well in the experiments are used to derive local-scale precipitation scenarios from the general circu- lation model (GCM) climate projection experiments. Different combinations of large-scale variables enter the model depending on region. A composite analysis suggests that precipitation is related to an advective (convective) phenomenon in the northern (southern) part of the study domain. More- over, precipitation changes based on 2 IS92a emission scenarios as simulated by ECHAM4/OPYC3 are calculated. The trace-gas-only and the trace-gas-plus-sulphate integrations induce changes ranging locally from + 44 to -10% and from + 36 to -9% respectively, relative to the 1951-1990 control period.

In this study we set out to investigate the possibility of linking phenological phases throughout the vegetation cycle, as a local-scale biological phenomenon, directly with large-scale atmospheric variables via two different empirical downscaling techniques. In recent years a number of methods have been developed to transfer atmospheric information at coarse General Circulation Model's grid resolutions to local scales and individual points. Here multiple linear regression (MLR) and canonical correlation analysis (CCA) have been selected as downscaling methods. Different validation experiments (e.g. temporal cross-validation, split-sample tests) are used to test the performance of both approaches and compare them for time series of 17 phenological phases and air temperatures from Central Europe as microscale variables. A number of atmospheric variables over the North Atlantic and Europe are utilized as macroscale predictors. The period considered is 1951-1998. Temporal cross-validation reveals that the CCA model generally performs better than MLR, which explains 20%-50% of the phenological variances, whereas the CCA model shows a range from 40% to over 60% throughout most of the vegetation cycle. To show the validity of employing phenological observations for downscaling purposes both methods (MLR and CCA) are also applied to gridded local air temperature time series over Central Europe. In this case there is no obvious superiority of the CCA model over the MLR model. Both models show explained variances from 40% to over 70% in the temporal cross-validation experiment. The results of this study indicate that time series of phenological occurrence dates are very compatible with the needs of empirical downscaling originally developed of local-scale atmospheric variables.

Regional risk assessments for the potential effects of climate change rely on plausible small-scale climate change scenario data. To bridge the gap between the coarse scale of general circulation models and the local scale of approximately 11 000 sample sites of the Austrian National Forest Inventory (AFI), a seasonally stratified statistical downscaling procedure was applied to a con- trol run and 2 transient experiments of ECHAM4/OPYC3, which are based on the trace gas only or trace gas plus sulphate scenario IPCC IS92a. We fitted multiple linear regression (MLR) models for the micro-scale monthly precipitation and temperature for each AFI point. The meteorological data at the AFI sites were obtained by interpolation of measurements from the dense network of Austrian weather stations for the period 1961-1995. The macro-scale predictors were principal components of monthly NCEP/NCAR reanalysis data (850 and 700 hPa geopotential height, 850 hPa temperature and 700 hPa relative humidity). The results show spatial and temporal heterogeneity for both tem- perature and precipitation. In case of temperature MLR leads to increases from +1.4 to +4.0oC (trace gas only integration) and from +1.1 to +2.9oC (trace gas plus sulphate integration) for a period of about 55 yr relative to the 1961-1995 climatology. The regionalized precipitation changes are both negative and positive. Values range from -44 to +26% (trace gas only integration) and from -29 to +26% (trace gas plus sulphate integration). As expected, the explained variability for temperature was higher than for precipitation and depended on the season. From a validation experiment for model calibration we conclude that MLR shows reliable results for temperature. Even in the case of precipitation the method seems to yield plausible results. Both temperature and precipitation were better reproduced for winter than for summer.

In order to include pionic degrees of freedom in the description of nuclear many-body systems, the chiral {sigma} model in the nonlinear representation is investigated. The renormalizability of the model, which is obtained from the linear {sigma} model by a field transformation, is studied in the context of the equivalence theorem. It is shown that in any expansion scheme which is based on self-consistent mean scalar fields, the nonlinear {sigma} model should be considered as unrenormalizable (even if the {sigma} mass is kept finite), and new counterterms have to be introduced in each order. The resulting equation of state in the one-loop (Hartree) approximation is calculated, and the corresponding pion-nucleus optical potential is discussed. {copyright} {ital 1997} {ital The American Physical Society}

This study investigates storminess in northern Italy and the northern Adriatic Sea through the examination of several storm proxies. These proxies are based on homogenized daily mean pressure series given at a set of stations (Genoa, Milan, Padua, Turin, and Hvar). The application of widely accepted and well-known methods on pressure series allows for a long-term year-to-year analysis of the intra-seasonal storm variability. As storminess is usually more intense throughout the cold season, our analysis is limited to the October–March period of each year. The following proxies are considered in this study: First, we assess the statistics of geostrophic wind speed. These statistics are derived from two adjacent triangles that are located across the Adriatic Sea (Padua–Hvar–Genoa) and in northern Italy (Genoa–Padua–Turin). Second, we evaluate annual statistics of time series of pressure tendency. Last, intra-seasonal low percentiles of pressure are also made use of. These proxies are used to describe the evolution of the storm climate far back in time, covering in some cases a 260-year long period. The proxies show pronounced interannual and interdecadal variability, but no sustained long-term trend.

The methodological framework of a large-scale risk assessment for Austrian forests under scenarios of climatic change is presented. A recently developed 3D-patch model is initialized with ground-true soil and vegetation data from sample plots of the Austrian Forest Inventory (AFI). Temperature and precipitation data of the current climate are interpolated from a network of more than 600 weather stations to the sample plots of the AFI. Vegetation development is simulated under current climate (‘control run’) and under climate change scenarios starting from today's forest composition and structure. Similarity of species composition and accumulated biomass between these two runs at various points in time were used as assessment criteria. An additive preference function which is based on Saaty’s AHP is employed to synthesize these criteria to an overall index of the adaptation potential of current forests to a changing climate. The presented methodology is demonstrated for a small sample from the Austrian Forest Inventory. The forest model successfully simulated equilibrium species composition under current climatic conditions spatially explicit in a heterogenous landscape based on ground-true data. At none of the simulated sites an abrupt forest dieback did occur due to climate change impacts. However, substantial changes occured with regard to species composition of the potential natural vegetation (PNV).