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Twenty-seven models participated in the Earth System Model–Snow Model Intercomparison Project (ESM-SnowMIP), the most data-rich MIP dedicated to snow modeling. Our findings do not support the hypothesis advanced by previous snow MIPs: evaluating models against more variables and providing evaluation datasets extended temporally and spatially does not facilitate identification of key new processes requiring improvement to model snow mass and energy budgets, even at point scales. In fact, the same modeling issues identified by previous snow MIPs arose: albedo is a major source of uncertainty, surface exchange parameterizations are problematic, and individual model performance is inconsistent. This lack of progress is attributed partly to the large number of human errors that led to anomalous model behavior and to numerous resubmissions. It is unclear how widespread such errors are in our field and others; dedicated time and resources will be needed to tackle this issue to prevent highly sophisticated models and their research outputs from being vulnerable because of avoidable human mistakes. The design of and the data available to successive snow MIPs were also questioned. Evaluation of models against bulk snow properties was found to be sufficient for some but inappropriate for more complex snow models whose skills at simulating internal snow properties remained untested. Discussions between the authors of this paper on the purpose of MIPs revealed varied, and sometimes contradictory, motivations behind their participation. These findings started a collaborative effort to adapt future snow MIPs to respond to the diverse needs of the community.

In soil moisture-limited evapotranspiration regimes, near-surface temperatures are strongly affected by the available soil water amount for evapotranspiration. Its spurious representation in climate models consequently results in an inaccurately simulated turbulent heat flux partitioning and associated temperature biases.Since the physical reasons for soil moisture induced temperature biases are different in every region and model, a new method is presented to reduce these biases systematically. To achieve this, a stochastic root depth variation is applied, whereby the root depths in each grid-box of the model domain are uniformly perturbed. Thus, the soil water supply for evapotranspiration is increased for 50 % of the grid-boxes in the model domain and reduced for the other 50 %. In energy-limited regimes, where soil moisture just slightly affects the near-surface temperatures, the turbulent heat flux partitioning is not affected. In moisture-limited regimes, the method has an asymmetric effect on evapotranspiration. In cases of overestimated supplies, the reduced root depths in 50 % of the model domain result in an overall evapotranspiration reduction. In cases of underestimated supplies, the opposite is the case. In cases of correctly simulated supplies, the evapotranspiration reduction in 50 % of the model domain and the evapotranspiration increase in the other 50 % balance each other on a climatological mean. In this way, the method affects the turbulent heat flux partitioning only if soil moisture is spuriously simulated in the model. The associated biases are then systematically reduced, independently of the underlying physical process, which caused the soil moisture deficiencies.

The IMK-TRO at the Karlsruhe Institute of Technology (KIT) presents in the HLRS annual report for 2018 selected projects in which the CRAY XE40 “Hazel Hen” was used. Different topics have been covered in 2018. In this report however, we focus on our very high resolution regional climate simulations within CORDEX FPS Convection and the simulation of present and future heat stress, especially in cities. The simulations are performed with the regional climate model COSMO-CLM (CCLM) and cover spatial resolutions from 50 to 2.8 km. The required Wall-Clock-Time (WCT) ranges from about 2000 to more than 30000 node-hours per simulated year.

Regional Climate Networks (RCNs) are used to identify heat waves and droughts in Germany and two subregions for the summer half years resp. summer seasons of the period 1951 to 2019. RCNs provide information for whole areas (in contrast to the point-wise information from standard indices), the underlying nodes can be distributed arbitrarily, they are easy to 5 construct and provide details otherwise difficult to avail of like extent, intensity and collective behaviour of extreme events. The RCNs were constructed on the regular 0.25 degree grid of the E-Obs data set. The season-wise correlation of time series of daily maximum temperature Tmax and precipitation were used to construct the adjacency matrix of the networks. Metrics to identify extremes were the edge density, the 90th percentile of the correlations and the average clustering coefficient, which turned out to be highly correlated; they increased considerably during extreme events. The standard indices for comparison 10 were the effective drought and heat index (EDI and EHI) respectively, based on the same time series, and complemented by other published data. Our results show that the RCNs are able to identify severe extremes in all cases and moderate extremes in most cases. An interesting finding is that during average years, the distribution of the node degrees is close to the Poisson distribution, characteristic of random networks, while for extreme years the distribution is more uniform and heavy tailed.

All types of applications of stable water isotopes, for example, for the reconstruction of paleotemperatures or for climate model validation, rely on a proper understanding of the mechanisms determining the isotopic composition of water vapor and precipitation. In this study, we use the isotope‐enabled limited‐area model COSMOiso to characterize the impacts of continental evapotranspiration, rainout, and subcloud processes on δD of European water vapor and precipitation. To this end, we first confirm a reliable implementation of the most important isotope fractionation processes in COSMOiso by comparing 5 years of modeled δD values with multiplatform δD observations from Europe (remote sensing observations of the δD of water vapor around 2.6 km above ground level, in situ δD measurements in near‐surface water vapor, and δD precipitation data from the Global Network of Isotopes in Precipitation). Based on six 15 year sensitivity simulations, we then quantify the climatological impacts of the different fractionation processes on the δD values. We find δD of European water vapor and precipitation to be most strongly controlled by rainout. Superimposed to this are the effect of subcloud processes, which especially affects δD in precipitation under warm conditions, and the effect of continental evapotranspiration, which exerts an important control over the δD of near‐surface water vapor. In future studies, the validated COSMOiso model can be employed in a similar way for a comprehensive interpretation of European isotope records from climatologically different time periods.

The deterministic description of the subgrid-scale land–atmosphere interaction in regional climate model (RCM) simulations is changed by using stochastic soil and vegetation parameterizations. For this, the land–atmosphere interaction parameterized in a land surface model (LSM) is perturbed stochastically by adding a random value to the input parameters using a random number generator. In this way, a stochastic ensemble is created that represents the impact of the uncertainties in these subgrid-scale processes on the resolved scale circulation. In a first step, stochastic stand-alone simulations with the VEG3D LSM are performed to identify sensitive model parameters. Afterward, VEG3D is coupled to the Consortium for Small-Scale Modeling–Climate Limited-Area Modeling (COSMO-CLM) RCM and stochastically perturbed simulations driven by ERA-Interim (2001–10) are performed for the Coordinated Downscaling Experiment–European Domain (EURO-CORDEX) at a horizontal resolution of 0.22°. The simulation results reveal that the impact of stochastically varied soil and vegetation parameterizations on the simulated climate conditions differs regionally. In central Europe the impact on the mean temperature and precipitation characteristics is very weak. In southern Europe and North Africa, however, the resolved scale circulation is very sensitive to the local soil water conditions. Furthermore, it is demonstrated that the use of stochastic soil and vegetation parameterizations considerably improves the variability of monthly rainfall sums all over Europe by improving the representation of the land–atmosphere interaction in the stochastic ensemble on a daily basis. In particular, inland rainfall during summer is simulated much better.

In this study, regional climate simulations under present-day and mid-Holocene conditions are performed with an isotope-enabled RCM for Greenland. The capability of the applied isotope-enabled Regional Climate Model (RCM), COSMO_iso, to reproduce observed isotopic ratios in Greenland for these two periods is investigated by downscaling global ECHAM5-wiso present-day and MPI-ESM-wiso mid-Holocene simulations for the Arctic region. The RCM model results are subsequently compared to measured δ18O ratios from snow pit samples and ice cores. To our knowledge, this is the first time that a mid-Holocene isotope-enabled RCM simulation is performed for the Arctic region. Under present-day conditions, a downscaling with COSMO_iso to a spatial resolution of 50 km improves the agreement with the measured δ18O ratios for 11 of 16 observational data sets. A further increase in the spatial resolution to 7 km yield only improvements for the coastal areas with its complex terrain. Furthermore, by investigating the δ18O ratios in all COSMO_iso grid boxes located within the corresponding ECHAM5-wiso grid box, the observed isotopic ratios can be classified as a possible local δ18O ratio within the spatial uncertainties, derived by the regional downscaling approach. For the mid-Holocene, a fully coupled MPI-ESM-wiso time slice simulation is downscaled with COSMO_iso to a spatial resolution of 50 km. The model performance of MPI-ESM-wiso in the mid-Holocene is already on a high level for Greenland and a downscaling with COSMO_iso does not further improve the model-data agreement. But again, the range of the COSMO_iso_50km δ18O variability in the corresponding MPI-ESM-wiso grid boxes around each station is consistent with the observed δ18O values. The correct δ18O ratios are consequently already included but hidden in the MPI-ESM-wiso results, which just need to be extracted by a refinement with an RCM. Thus, an isotope-enabled GCM-RCM model chain with realistically implemented fractionating processes, constitutes a useful supplement to reconstruct regional paleo-climate conditions during the mid-Holocene in Greenland. Such model chains might also be applied to reveal the full potential of GCMs in regions and climate periods, in which large deviations to observed isotope ratios are simulated.

Reliable knowledge of the near-future climate change signal of extremes is important for adaptation and mitigation strategies. Especially compound extremes, like heat and drought occurring simultaneously, may have a greater impact on society than their univariate counterparts and have recently become an active field of study. In this paper, we use a 12-member ensemble of high-resolution (7 km) regional climate simulations with the regional climate model COSMO-CLM over central Europe to analyze the climate change signal and its uncertainty for compound heat and drought extremes in summer by two different measures: one describing absolute (i.e., number of exceedances of absolute thresholds like hot days), the other relative (i.e., number of exceedances of time series intrinsic thresholds) compound extreme events. Changes are assessed between a reference period (1971–2000) and a projection period (2021–2050). Our findings show an increase in the number of absolute compound events for the whole investigation area. The change signal of relative extremes is more region-dependent, but there is a strong signal change in the southern and eastern parts of Germany and the neighboring countries. Especially the Czech Republic shows strong change in absolute and relative extreme events.

Understanding changes in the climate of the late Pleistocene and the Holocene has long been a research topic. Studies rely on different sources of information, ranging from terrestrial and marine archives to a hierarchy of climate modeling activities. In contrast to the climate of the last millennium, novel approaches are necessary to bridge the different temporal and spatial representations of the various archives and the climate models, and to achieve a robust understanding of climate variability and climate processes on centennial-to-millennial time scales.

Regional decadal predictions have emerged in the past few years as a research field with high application potential, especially for extremes like heat and drought periods. However, up to now the prediction skill of decadal hindcasts, as evaluated with standard methods, is moderate and for extreme values even rarely investigated. In this study, we use hindcast data from a regional climate model (CCLM) for eight regions in Europe and quantify the skill of the model alternatively by constructing time-evolving climate networks and use the network correlation threshold (link strength) as a predictor for heat periods. We show that the skill of the network measure to estimate the low-frequency dynamics of heat periods is superior for decadal predictions with respect to the typical approach of using a fixed temperature threshold for estimating the number of heat periods in Europe.

The IMK-TRO (KIT) presents in the HLRS annual report for 2016–2017 projects and their results using the CRAY XC40 “Hazel Hen”. The research focuses on the very high resolution regional climate simulations including the modeling of land surface processes and urban climate, the generation of ensemble projections, and regional paleoclimate (PALMOD). The simulations are performed with the regional climate model (RCM) COSMO-CLM (CCLM) and cover spatial resolutions from 50 to 2.8 km. Within the projects, the standard CCLM is enhanced; for the analysis of the impact of different soil-vegetation transfer schemes (SVATs) VEG3D is coupled via OASIS3-MCT to CCLM. For the PALMOD project, a special isotope-enabled version CCLMiso is used. To highlight the added value, the results of the higher resolution climate predictions are compared to those of simulations with coarser resolutions. In addition, the impact of different global driving data sets is investigated. Climate projections are performed for two future time slices, 2021–2050 and 2071–2100. The urban climate and its change are also investigated using very high resolution simulations to enable the energetic optimisation of buildings. The required Wall-Clock-Times (WCT) range from 9 to 2000 node-hours per simulated year.

Ensembles of high-resolution regional climate model (RCM) simulations are crucial for assessing regional climate change and the associated uncertainties. This article presents an RCM ensemble generation technique which explores uncertainties arising from the positioning of synoptic systems in the large-scale atmospheric forcing by shifting the atmospheric fields from global climate model (GCM) runs horizontally. Here, we discuss how the so-called Atmospheric Forcing Shifting (AFS) affects temperature and precipitation over Europe for the period 1980–1984. We use ERA-40 reanalysis data in which the atmospheric fields are shifted to each direction by 25 and 50 km, respectively, to run RCM simulations with COSMO-CLM at 50-km resolution. The analysis of the AFS ensemble includes comparisons with E-OBS observations and COSMO-CLM runs driven by different GCMs. AFS has an evident effect on the spatiotemporal distributions of temperature and particularly precipitation, which is most pronounced during hydrological summer (May to October) when spatial weather patterns are more variable. Furthermore, AFS produces realistic changes in the likelihood and intensity of extreme precipitation. The changes induced by AFS depend strongly on orography, i.e. precipitation increases are likely to occur where moist air masses are shifted towards the windward mountain side and vice versa. Thus, increasing the RCM ensemble spread by means of AFS is a simple and useful method for sampling observed climate statistics and assessing the variability and changes in mean and extreme climate.

This study addresses the question of how complex topography in a low-mountain region affects the partitioning and the variability of the atmospheric water budget components (WBCs) as a function of synoptic-scale flow conditions. The WBCs are calculated for regions of different size and location in southwestern Germany and the summer months from 2005 to 2009 using the high-resolution regional climate model COSMO-CLM driven by Global Model (GME) analyses. Comparisons with observations from the Convective and Orographically-induced Precipitation Study (COPS) performed in summer 2007 show that the model is capable of simulating the atmospheric water budget reasonably (absolute mean error between 0.1 and 0.7 kg m−2 day−1). To investigate the influence of synoptic weather conditions, the daily WBCs are classified based on the inflow direction of the air masses and the cyclonality at 500 hPa. Using statistical tests, four groups out of the six synoptic conditions have significantly different distributions of the WBCs. This can be explained by differences in the air mass features and the influence of high/low pressure systems. The sensitivity of the modeled WBCs to topography and land cover is investigated by comparing a region in the flat upper Rhine Valley with one in the mountainous Black Forest/Swabian Jura. Compared to the Rhine Valley, increases of evapotranspiration (+5% to +16%), precipitation (+26% to +57%), and moisture convergence (+24% to +93%) are noticeable in the low-mountain region. Local modifications of the synoptic-scale flow, thermally induced winds, and land use cause this intensification of the atmospheric water budget, especially on the windward slopes of the mountains.

A five-member ensemble of regional climate model (RCM) simulations for Europe, with a high resolution nest over Germany, is analysed in a two-part paper: Part I (the current paper) presents the performance of the models for the control period, and Part II presents results for near future climate changes. Two different RCMs, CLM and WRF, were used to dynamically downscale simulations with the ECHAM5 and CCCma3 global climate models (GCMs), as well as the ERA40-reanalysis for validation purposes. Three realisations of ECHAM5 and one with CCCma3 were downscaled with CLM, and additionally one realisation of ECHAM5 with WRF. An approach of double nesting was used, first to an approximately 50 km resolution for entire Europe and then to a domain of approximately 7 km covering Germany and its near surroundings. Comparisons of the fine nest simulations are made to earlier high resolution simulations for the region with the RCM REMO for two ECHAM5 realisations. Biases from the GCMs are generally carried over to the RCMs, which can then reduce or worsen the biases. The bias of the coarse nest is carried over to the fine nest but does not change in amplitude, i.e. the fine nest does not add additional mean bias to the simulations. The spatial pattern of the wet bias over central Europe is similar for all CLM simulations, and leads to a stronger bias in the fine nest simulations compared to that of WRF and REMO. The wet bias in the CLM model is found to be due to a too frequent drizzle, but for higher intensities the distributions are well simulated with both CLM and WRF at the 50 and 7 km resolutions. Also the spatial distributions are close to high resolution gridded observations. The REMO model has low biases in the domain averages over Germany and no drizzle problem, but has a shift in the mean precipitation patterns and a strong overestimation of higher intensities. The GCMs perform well in simulating the intensity distribution of precipitation at their own resolution, but the RCMs add value to the distributions when compared to observations at the fine nest resolution.

An ensemble of high-resolution regional climate simulations is used to assess the effect of near future climate change on mean and extreme precipitation in a part of Central Europe with complex topography. The ensemble consists of high-resolution simulations with the COSMO-CLM (CLM) regional climate model (RCM) using several realizations of the driving general circulation models (GCMs) ECHAM5 and HadCM3. The study is focussed on the changes in the near future (2011–2040 compared to 1971–2000) which are relevant for planning purposes. The mean winter precipitation shows a spatially uniform increase, summertime mean precipitation is likely to decrease slightly. For extreme precipitation the simulations exhibit an increase on the average for both seasons, but for different reasons. The changes in winter are proportional to the increase in total precipitation, whereas in summer a broadening of the precipitation distribution is found. The spatial change patterns in summer are much more heterogeneous than in winter, with regions of significant increase and decrease sometimes close to each other. The plausibility of the findings is assessed in terms of ensemble consistency. The area mean changes found for the ensemble of CLM simulations were consistent with the change signals derived from a larger but coarser resolved ensembles using several RCMs and driving GCMs. In addition, it was found that the simulated near future precipitation changes in the study region during summer generally agree with trends observed during the last decades. Copyright © 2012 Royal Meteorological Society

In addition to precautionary or technical flood protection measures, short-term strategies of the operational management, i.e. the initiation and co-ordination of preventive measures during and/or before a flood event are crucially for the reduction of the flood damages. This applies especially for extreme flood events. These events are rare, but may cause a protection measure to be overtopped or even to fail and be destroyed. In such extreme cases, reliable decisions must be made and emergency measures need to be carried out to prevent even larger damages from occurring. Based on improved methods for meteorological and hydrological modelling a range of (physically based) extreme flood scenarios can be derived from historical events by modification of air temperature and humidity, shifting of weather fields and recombination of flood relevant event characteristics. By coupling the large scale models with hydraulic and geotechnical models, the whole flood-process-chain can be analysed right down to the local scale. With the developed GIS-based tools for hydraulic modelling FlowGIS and the Dike-Information-System, (IS-dikes) it is possible to quantify the endangering shortly before or even during a flood event, so the decision makers can evaluate possible options for action in operational mode.

Extreme flood events are caused by long-lasting and/or intensive precipitation. The detailed knowledge of the distribution, intensity, and spatiotemporal variability of precipitation is, therefore, a prerequisite for hydrological flood modeling and flood risk management. For hydrological modeling, temporal and spatial high-resolution precipitation data can be provided by meteorological models. This study deals with the question of how small changes in the synoptic situation affect the characteristics of extreme forecasts. For that purpose, two historic extreme precipitation events were hindcasted using the Consortium for Small Scale Modeling (COSMO) model of the German Weather Service (DWD) with different grid resolutions (28, 7, and 2.8 km), where the domains with finer resolutions were nested into the ones with coarser resolution. The results show that the model is capable of simulating such extreme precipitation events in a satisfactory way. To assess the impact of small changes in the synoptic situations on extreme precipitation events, the large-scale atmospheric fields were shifted to north, south, east, and west with respect to the orography by about 28 and 56 km, respectively, in one series of runs while in another series, the relative humidity and temperature were increased to modify the amount of precipitable water. Both series were performed for the Elbe flood events in August 2002 and January 2003, corresponding to two very different synoptic situations. The results show that the modeled precipitation can be quite sensitive to small changes of the synoptic situation with changes in the order of 20% for the maximum daily precipitation and that the types of synoptic situations play an important role. While van Bebber weather conditions, of Mediterranean origin, were quite sensitive to modifications, more homogeneous weather patterns were less sensitive.

To study various aspects of the climate on the regional scale, IMK-TRO employs the regional climate model (RCM) COSMO-CLM on the High Performance Computing Systems (HPC) CRAY XE6 and CRAY XE40, operated by the HLRS. The research focus is on decadal predictability, extremes and high resolution experiments within individual research projects (MiKlip, KLIMOPASS and KLIWA). Also within MiKlip the effects of soil and vegetation processes on decadal climate predictions are investigated using a via OASIS3-MCT coupled system of COSMO-CLM and the Soil-Vegetation-Atmosphere-Transfer model (SVAT) VEG3D. Ensembles are built with different techniques to quantify the uncertainty of climate projections and predictions and to assess the quality of the models. KLIMOPASS includes projections of the future climate, considering periods up to the mid of the twenty-first century. High resolution (2.8 km) experiments are performed for the State of Baden–Wurttemberg to study extremes and their potential added value. Altogether simulations for Germany, Europe, and Africa are performed with varying temporal and spatial resolutions from 50 to 2.8 km. The required Wall-Clock-Time (WCT) reach from 100 to 650 node-hours per simulated year.

Continuous time series of soil water content over a period of more than 9 months for a midlatitude sandy loam soil covered by grass are calculated with the Campbell and the van Genuchten soil hydraulic functions and the Clapp–Hornberger, Cosby et al., and Rawls–Brakensiek parameter sets. The results are compared with soil water content observed at several soil depths, and the water balance components are evaluated. The Campbell soil hydraulic functions are often used by meteorologists, whereas the van Genuchten functions are widespread among hydrologists. The simulations are performed with the “VEG3D” soil–vegetation model in stand-alone mode forced by on-site meteorological observations. The soil water content and meteorological observations were obtained within the Regional Climate Project (REKLIP) at a site in the Rhine valley in southern Germany with 10-min temporal resolution. Apart from the different soil hydraulic functions and parameter sets, the effects of different lower boundary conditions and initializations on the simulations are compared in terms of statistical quantities like mean error, bias, correlation coefficient, and least squares fit. Large differences between the various combinations are found. For the situation considered in this paper, the van Genuchten–Clapp–Hornberger, the Campbell–Cosby et al., and the van Genuchten–Rawls–Brakensiek combinations give the best overall agreement with the observations.

Soil water contents, calculated with seven soil hydraulic parameterizations, that is, soil hydraulic functions together with the corresponding parameter sets, are compared with observational data. The parameterizations include the Campbell/Clapp–Hornberger parameterization that is often used by meteorologists and the van Genuchten/Rawls–Brakensiek parameterization that is widespread among hydrologists. The observations include soil water contents at several soil depths and atmospheric surface data; they were obtained within the Regio Klima Projekt (REKLIP) at three sites in the Rhine Valley in southern Germany and cover up to 3 yr with 10-min temporal resolution. Simulations of 48-h episodes, as well as series of daily simulations initialized anew every 24 h and covering several years, were performed with the “VEG3D” soil–vegetation model in stand-alone mode; furthermore, 48-h episodes were simulated with the model coupled to a one-dimensional atmospheric model. For the cases and soil types considered in this paper, the van Genuchten/Rawls–Brakensiek model gives the best agreement between observed and simulated soil water contents on average. Especially during episodes with medium and high soil water content, the van Genuchten/Rawls–Brakensiek model performs better than the Campbell/Clapp–Hornberger model.

The High Performance Computing System (HPC) CRAY XE6 operated by HLRS is a powerful tool to study various aspects of the regional climate. Employing the regional climate model (RCM) COSMO-CLM, the research focus of IMK-TRO is on the regional atmospheric water cycle, especially on extremes, and different goals are pursued in individual research projects (MiKlip, KLIMOPASS and KLIWA). The simulation regions comprise Germany, Europe, and Africa with resolutions varying from 50 km to 2.8 km. Furthermore, different time spans are investigated. Decadal simulations are performed to assess decadal regional climate predictability. Projections of the future climate consider periods up to the end of the twenty-first century. To quantify the uncertainty of climate projections and predictions as well as the quality of the models, ensembles are built by different techniques. The Soil-Vegetion-Atmosphere-Transfer model (SVAT) VEG3D is coupled to COSMO-CLM via OASIS3-MCT to investigate the effect of soil and vegetation processes on decadal climate predictions. High resolution (2.8 km) experiments are performed for the State of Baden–Wurttemberg to study the potential added value and extremes. Computational capacities from 100 to 650 node–hours per simulated year (Wall Clock Time) are required for these simulations.

The prediction of climate on time scales of years to decades is attracting the interest of both climate researchers and stakeholders. The German Ministry for Education and Research (BMBF) has launched a major research programme on decadal climate prediction called MiKlip (Mittelfristige Klimaprognosen, Decadal Climate Prediction) in order to investigate the prediction potential of global and regional climate models (RCMs). In this paper we describe a regional predictive hindcast ensemble, its validation, and the added value of regional downscaling. Global predictions are obtained from an ensemble of simulations by the MPI-ESM-LR model (baseline 0 runs), which were downscaled for Europe using the COSMO-CLM regional model. Decadal hindcasts were produced for the 5 decades starting in 1961 until 2001. Observations were taken from the E-OBS data set. To identify decadal variability and predictability, we removed the long-term mean, as well as the long-term linear trend from the data. We split the resulting anomaly time series into two parts, the first including lead times of 1–5 years, reflecting the skill which originates mainly from the initialisation, and the second including lead times from 6–10 years, which are more related to the representation of low frequency climate variability and the effects of external forcing. We investigated temperature averages and precipitation sums for the summer and winter half-year. Skill assessment was based on correlation coefficient and reliability. We found that regional downscaling preserves, but mostly does not improve the skill and the reliability of the global predictions for summer half-year temperature anomalies. In contrast, regionalisation improves global decadal predictions of half-year precipitation sums in most parts of Europe. The added value results from an increased predictive skill on grid-point basis together with an improvement of the ensemble spread, i.e. the reliability.

Using the CRAY XE-6 at the HLRS high performance computing facilities provides the possibility to study various aspects of the regional climate employing the regional climate model COSMO-CLM. The research activities of the group “Regional Climate and Water Cycle” at the KIT focus on the regional atmospheric water cycle and, especially, on extremes and different goals are pursued in the individual research projects. Different regions and orographies are studied using different resolutions from 50 to 3 km. Furthermore, different time spans are investigated and computational capacities from 2 to 500 node-hours per year (Wall Clock Time) are required. The analyses comprise decadal climate simulations of Germany, Europe and Africa to assess regional decadal climate predictability. Further, climate projections are carried out for Baden-Wurttemberg (Germany) and novel ensemble generating techniques are implemented to better describe the involved uncertainties. High resolution (3 km) experiments are performed for Baden-Wurttemberg to study extremes and the effects of climate change on soil erosion. Moreover, the possibilities of adaption to climate change for Baden-Wurttemberg are analysed, with focus on extremes and combination of extremes (such as dry soil and extreme precipitation).

The scope of regional climate simulations carried out at the Institute for Meteorology and Climate Research (IMK) of Karlsruhe Institute of Technology (KIT) using the regional climate model (RCM) COSMO-CLM (CCLM) has been extended during the last years. From the focus first on Southwest Germany the area of interest has then been extended to the whole of Germany for the assessment of changes in flood risk for medium and small mountainous river catchments (CEDIM project: www.cedim.de), and within the frame of CORDEX (Coordinated Regional climate Downscaling Experiment, http://wcrp.ipsl.jussieu.fr/SF_RCD_CORDEX.html) also to the whole African continent. CORDEX aims to provide a framework to evaluate and benchmark RCMs and to design a set of experiments to produce climate projections for use in impact and adaption studies and as input to the IPCC 5th Assessment Report (AR5).

A series of regional climate simulations using the regional climate model COSMO-CLM (CCLM) have been carried out at the Institute for Meteorology and Climate Research (IMK) of Karlsruhe Institute of Technology (KIT) (Panitz et al. 2010). These simulations span the years 1971–2000 to represent the climate of the recent past and the years 2011–2040 to analyse the climate change in Southwest Germany during the next few decades. A second set of simulations covering all of Germany, have been carried out for the CEDIM-project “Hochwassergefahr durch Klimawandel”, to assess possible changes in flood hazard in the near future. Model validation, results for changes in temperature and precipitation statistics, and statistics on the computational resources used at the HLRS facitilites are presented.

To determine return values at various return periods for extreme daily precipitation events over complex orography, an appropriate threshold value and distribution function are required. The return values are calculated using the peak-over-threshold approach in which only a reduced sample of precipitation events exceeding a predefined threshold is analyzed. To fit the distribution function to the sample, the L-moment method is used. It is found that the deviation between the fitted return values and the plotting positions of the ranked precipitation events is smaller for the kappa distribution than for the generalized Pareto distribution. As a second focus, the ability of regional climate models to realistically simulate extreme daily precipitation events is assessed. For this purpose the return values are derived using precipitation events exceeding the 90th percentile of the precipitation time series and a fit of a kappa distribution. The results of climate simulations with two different regional climate models are analyzed for the 30-yr period 1971–2000: the so-called consortium runs performed with the climate version of the Lokal Modell (referred to as the CLM-CR) at 18-km resolution and the Regional Model (REMO)–Umweltbundesamt (UBA) simulations at 10-km resolution. It was found that generally the return values are overestimated by both models. Averaged across the region the overestimation is higher for REMO–UBA compared to CLM-CR.

In its Fourth Assessment Report (AR4) the Intergovernmental Panel on Climate Change (IPCC) states that it is very likely that many land regions, especially on the Northern Hemisphere, will warm during the 21st century due to the general climate change, which itself is caused by the increase of anthropogenic greenhouse gases [7]. On the global scale the IPCC estimates that for the next two decades a warming of about 0.2○ C per decade can be expected on the basis of the SRES (Special Reports on Emission Scenarios, [11]) emission scenarios. Even if the concentrations of all greenhouse gases were kept at the year 2000 level, a further warming of about 0.1○ C per decade can be expected. For many land regions of the globe it is estimated that the annual mean temperature increase will be higher than the global mean. This variability of climatic change persists over all scales, from global to regional, and more or less for all climatological observables. On the spatial scale of global climate models (about 200 km) for example, the largest warming in Europe is likely to happen in the northern part in winter and in the Mediterranean area in summer [2]. Annual precipitation is very likely to increase in most of northern Europe and decrease in most of the Mediterranean area. In Central Europe, precipitation is likely to increase in winter but decrease in summer, but the agreement between the results of various models is quite low there. Extremes of daily precipitation are very likely to increase throughout Europe [1]; compared to Northern and Southern Europe, however, climatic change for Central Europe is more difficult to assess due to sometimes conflicting tendencies.

To assess a possible future change in flood and drought risks for medium and small-scale river catchments, one needs to have data of a higher spatial and temporal resolution than what is provided by the global climate models. The COSMO-CLM regional climate model has to this purpose been used to downscale a set of global climate simulations to a 7 km horizontal resolution. In order to assess some of the uncertainties involved in near future scenario simulations, several different global simulations are downscaled to produce an ensemble of high resolution data. This will then be used as input to hydrological catchment models to assess future changes in flood risk for three catchments in Germany, within the CEDIM-project “Flood hazard in a changing climate” (Hochwassergefahr durch Klimawandel).

Thirty-three snowpack models of varying complexity and purpose were evaluated across a wide range of hydrometeorological and forest canopy conditions at five Northern Hemisphere locations, for up to two winter snow seasons. Modeled estimates of snow water equivalent (SWE) or depth were compared to observations at forest and open sites at each location. Precipitation phase and duration of above-freezing air temperatures are shown to be major influences on divergence and convergence of modeled estimates of the subcanopy snowpack. When models are considered collectively at all locations, comparisons with observations show that it is harder to model SWE at forested sites than open sites. There is no universal "best'' model for all sites or locations, but comparison of the consistency of individual model performances relative to one another at different sites shows that there is less consistency at forest sites than open sites, and even less consistency between forest and open sites in the same year. A good performance by a model at a forest site is therefore unlikely to mean a good model performance by the same model at an open site (and vice versa). Calibration of models at forest sites provides lower errors than uncalibrated models at three out of four locations. However, benefits of calibration do not translate to subsequent years, and benefits gained by models calibrated for forest snow processes are not translated to open conditions.

The impact of highway emissions on O(3) and NO(x) was assessed using a validated air-quality model (MCCM-Multiscale Climate Chemistry Model) applied to a region of Southern Germany. During a measuring campaign around the City of Augsburg (Southern Germany) in May 2001 modelling calculations were performed, including a scenario in which the highway emissions (ca. 80,000 vehicles/day) between Munich and Augsburg (distance ca. 40 km) were switched off. Based on the change in emissions, the variations in O(3)- and NO(x)-concentrations as well as the size of the influenced area within the region of interest are discussed. Additionally, an episode in August 2001 with elevated ozone values was modelled for comparison with the May episode. The model results show a very pronounced reduction of O(3)- and an increase of NO(2)-concentrations after passing the highway within a distance of more than 40 km. After turning off the highway emissions it could be demonstrated that the emission reduction causes a significant increase in the ozone levels downwind of the highway in the magnitude of 10-20 ppbv depending on the time of the day.

Seit einigen Jahren haben numerische Stromungs- und Ausbreitungsmodelle, welche vordem fast nur im universitaren Bereich schwerpunktmasig zu Forschungszwecken eingesetzt wurden, Eingang in die gutachterliche Praxis gefunden. Dies ist zum einen bedingt durch gestiegene Anforderungen, zum anderen durch die Verfugbarkeit solcher Modelle und die stark angestiegene Leistung von Personal Computern und Workstations. Im Bereich Lufthygiene kam z.B. in den 70er und 80er Jahren der Hauptanteil an Luftschadstoffen aus Kraftwerken und Industrieanlagen, welche aus hohen Schornsteinen emittierten. Unter diesen Umstanden konnten mit relativ einfachen Modellen, wie es z.B. in der TA Luft beschrieben ist, zumindest realistische Grosenordnungen erzielt werden. Inzwischen haben sich die Fragestellungen gewandelt, oft handelt es sich um bodennahe Quellen (z.B. Kfz- Verkehr), die Quellen liegen in topographisch gegliedertem Gelande, und es interessieren spezielle Stromungen, z.B. Kaltluftabflusse (Frischuftversorgung, Geruchsprobleme); in solchen Fallen kann mit einfachen Modellen, z.B. Gaus- Modellen, oft keine zuverlassige Aussage gemacht werden.

The projected climate change signals of a five-member high resolution ensemble, based on two global climate models (GCMs: ECHAM5 and CCCma3) and two regional climate models (RCMs: CLM and WRF) are analysed in this paper (Part II of a two part paper). In Part I the performance of the models for the control period are presented. The RCMs use a two nest procedure over Europe and Germany with a final spatial resolution of 7 km to downscale the GCM simulations for the present (1971–2000) and future A1B scenario (2021–2050) time periods. The ensemble was extended by earlier simulations with the RCM REMO (driven by ECHAM5, two realisations) at a slightly coarser resolution. The climate change signals are evaluated and tested for significance for mean values and the seasonal cycles of temperature and precipitation, as well as for the intensity distribution of precipitation and the numbers of dry days and dry periods. All GCMs project a significant warming over Europe on seasonal and annual scales and the projected warming of the GCMs is retained in both nests of the RCMs, however, with added small variations. The mean warming over Germany of all ensemble members for the fine nest is in the range of 0.8 and 1.3 K with an average of 1.1 K. For mean annual precipitation the climate change signal varies in the range of −2 to 9 % over Germany within the ensemble. Changes in the number of wet days are projected in the range of ±4 % on the annual scale for the future time period. For the probability distribution of precipitation intensity, a decrease of lower intensities and an increase of moderate and higher intensities is projected by most ensemble members. For the mean values, the results indicate that the projected temperature change signal is caused mainly by the GCM and its initial condition (realisation), with little impact from the RCM. For precipitation, in addition, the RCM affects the climate change signal significantly.