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This paper presents a novel dataset of regional climate model simulations over Europe that significantly improves our ability to detect changes in weather extremes under low and moderate levels of global warming. This is a unique and physically consistent dataset, as it is derived from a large ensemble of regional climate model simulations. These simulations were driven by two global climate models from the international HAPPI consortium. The set consists of 100×10-year simulations and 25×10-year simulations, respectively. These large ensembles allow for regional climate change and weather extremes to be investigated with an improved signal-to-noise ratio compared to previous climate simulations. To demonstrate how adaptation-relevant information can be derived from the HAPPI dataset, changes in four climate indices for periods with 1.5 and 2.0 ∘C global warming are quantified. These indices include number of days per year with daily mean near-surface apparent temperature of >28 ∘C (ATG28); the yearly maximum 5-day sum of precipitation (RX5day); the daily precipitation intensity of the 50-year return period (RI50yr); and the annual consecutive dry days (CDDs). This work shows that even for a small signal in projected global mean temperature, changes of extreme temperature and precipitation indices can be robustly estimated. For temperature-related indices changes in percentiles can also be estimated with high confidence. Such data can form the basis for tailor-made climate information that can aid adaptive measures at policy-relevant scales, indicating potential impacts at low levels of global warming at steps of 0.5 ∘C.

The added value of using regional climate models (RCMs) to downscale data from general circulation models (GCMs) has often been questioned and researched. Although several studies have used different methods to identify (and in some cases quantify) the added value, there is still a need to find a general metric that quantifies the added value of any variable. This paper builds on past studies to propose a new metric of added value in the simulation of present-day climate which measures the difference in the probability density functions (PDFs) at each grid-cell between a model and an observation source, and then compares the results of the RCM and GCM in order to spatially compute the added value index. The same method is also adapted to quantify the climate change downscaling signal in a way that is consistent with the present-day metric. These new metrics are tested on the daily precipitation output from the EURO-CORDEX and CORDEX-CORE projection ensembles and reveal an overall positive added value of RCMs, especially at the tail-end of the distribution. Higher added value is obtained in areas of complex topography and coast-lines, as well as in tropical regions. Areas with large added value in present-day climate are consistent with areas of significant climate change downscaling signal in the RCP 8.5 far future simulations, and when the analysis is repeated at a low-resolution. The use of different resolution observations shows that the added value tends to decrease when models are compared to low-resolution observation datasets.

Land cover in sub-polar and alpine regions of northern and eastern Europe have already begun changing due to natural and anthropogenic changes such as afforestation. This will impact the regional climate and hydrology upon which societies in these regions are highly reliant. This study aims to identify the impacts of afforestation/reforestation (hereafter afforestation) on snow and the snow-albedo effect, and highlight potential improvements for future model development. The study uses an ensemble of nine regional climate models for two different idealised experiments covering a 30-year period; one experiment replaces most land cover in Europe with forest while the other experiment replaces all forested areas with grass. The ensemble consists of nine regional climate models composed of different combinations of five regional atmospheric models and six land surface models. Results show that afforestation reduces the snow-albedo sensitivity index and enhances snow melt. While the direction of change is robustly modelled, there is still uncertainty in the magnitude of change. Greatest differences between models emerge in the snowmelt season. One regional climate model uses different land surface models which shows consistent changes between the three simulations during the accumulation period but differs in the snowmelt season. Together these results point to the need for further model development in representing both grass-snow and forest-snow interactions during the snowmelt season. Pathways to accomplishing this include 1) a more sophisticated representation of forest structure, 2) kilometer scale simulations, and 3) more observational studies on vegetation-snow interactions in Northern Europe.

In the Northern Hemisphere, the seasonal snow cover plays a major role in the climate system via its effect on surface albedo and fluxes. The parameterization of snow-atmosphere interactions in climate models remains a source of uncertainty and biases in the representation of the local and global climate. Here, we evaluate the ability of an ensemble of regional climate models (RCMs) coupled to different land surface models to simulate the snow albedo effect over Europe, in winter and spring. We use a previously defined index, the Snow Albedo Sensitivity Index (SASI), to quantify the radiative forcing due to the snow albedo effect. By comparing RCM-derived SASI values with SASI calculated from reanalyses and satellite retrievals, we show that an accurate simulation of snow cover is essential for correctly reproducing the observed forcing over mid- and high-latitudes in Europe. The choice of parameterizations with first and foremost the choice of the land surface model but also the convection scheme and the planetary boundary layer, strongly influences the representation of SASI as it affects the ability of climate models to simulate snow cover correctly. The agreement between the datasets differs between the accumulation and ablation periods, with the latter one presenting the greatest challenge for the RCMs. Given the dominant role of land surface processes in the simulation of snow cover during the ablation period, the results suggest that the choice of the land surface model is more critical for the representation of SASI than the atmospheric model during this time period.

Anthropogenic land-use and land cover change (LULCC) is a major driver of environmental changes. The biophysical impacts of these changes on the regional climate in Europe are currently extensively investigated within the WCRP CORDEX Flagship Pilot Study (FPS) LUCAS – "Land Use and Climate Across Scales" using an ensemble of different Regional Climate Models (RCMs) coupled with diverse Land Surface Models (LSMs). In order to investigate the impact of realistic LULCC on past and future climates, high-resolution datasets with observed LULCC and projected future LULCC scenarios are required as input for the RCM-LSM simulations. To account for these needs, we generated the LUCAS LUC Version 1.0 at 0.1° resolution for Europe Hoffmann et al. (2021b,c). The plant functional type distribution for the year 2015 (i.e. LANDMATE PFT dataset) is derived from the European Space Agency Climate Change Initiative Land Cover (ESA-CCI LC) dataset. Details about the conversion method based on a cross-walking procedure and the evaluation of the LANDMATE PFT dataset are given in the companion paper by Reinhart et al. (submitted). Subsequently, we applied the land-use change information from the Land-Use Harmonization 2 (LUH2) dataset, provided at 0.25° resolution as input for CMIP6 experiments, to derive realistic LULC distribution at high spatial resolution and at annual timesteps from 1950 to 2100. In order to convert land use and land management change information from LUH2 into changes in the PFT distribution, we developed a Land Use Translator (LUT) specific to the needs of RCMs. The annual PFT maps for Europe for the period 1950 to 2015 are derived from the historical LUH2 dataset by applying the LUT backward from 2015 to 1950. Historical changes in the forest type changes are considered using an additional European forest species dataset. The historical changes in the PFT distribution of LUCAS LUC follow closely the land use changes given by LUH2 but differ in some regions compared to remotely-sensed PFT time series. From 2016 onward, annual PFT maps for future land use change scenarios based on LUH2 are derived for different Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs) combinations used in the framework of the Coupled Modelling Intercomparison Project Phase 6 (CMIP6). The resulting LULCC maps can be applied as land use forcing to the next generation of RCM simulations for downscaling of CMIP6 results. The newly developed LUT is transferable to other CORDEX regions world-wide.

Urban areas are prone to climate change impacts. A transition towards sustainable and climate-resilient urban areas is relying heavily on useful, evidence-based climate information on urban scales. However, current climate data and information produced by urban or climate models are either not scale compliant for cities, or do not cover essential parameters and/or urban-rural interactions under climate change conditions. Furthermore, although e.g. the urban heat island may be better understood, other phenomena, such as moisture change, are little researched. Our research shows the potential of regional climate models, within the EURO-CORDEX framework, to provide climate projections and information on urban scales for 11km and 3km grid size. The city of Berlin is taken as a case-study. The results on the 11km spatial scale show that the regional climate models simulate a distinct difference between Berlin and its surroundings for temperature and humidity related variables. There is an increase in urban dry island conditions in Berlin towards the end of the 21st century. To gain a more detailed understanding of climate change impacts, extreme weather conditions were investigated under a 2°C global warming and further downscaled to the 3km scale. This enables the exploration of differences of the meteorological processes between the 11km and 3km scales, and the implications for urban areas and its surroundings. The overall study shows the potential of regional climate models to provide climate change information on urban scales.

Land-use and land cover (LULC) are continuously changing due to environmental changes and anthropogenic activities. Many observational and modeling studies show that LULC changes are important drivers altering land surface feedbacks and land-atmosphere exchange processes that have substantial impact on climate on the regional and local scale. Yet, most long-term regional climate modeling studies do not account for these changes. Therefore, within the WCRP CORDEX Flagship Pilot Study LUCAS (Land Use Change Across Scales) a new workflow was developed to generate high-resolution annual land cover change time series based on past reconstructions and future projections. First, the high-resolution global land cover dataset ESA-CCI LC (~300 m resolution) is aggregated and converted to a 0.1° resolution, fractional plant functional type (PFT) dataset. Second, the land use change information from the land-use harmonized dataset (LUH2), provided at 0.25° resolution as input for CMIP6 experiments, is translated into PFT changes employing a newly developed land use translator (LUT). The new LUT was first applied to the EURO-CORDEX domain. The resulting LULC maps for past and future - the LUCAS LUC dataset - can be applied as land use forcing to the next generation RCM simulations for downscaling CMIP6 by the EURO-CORDEX community and in the framework of FPS LUCAS. The dataset includes land cover and land management practices changes important for the regional and local scale such as urbanization and irrigation. The LUCAS LUC workflow is applied to further CORDEX domains, such as Australasia and North America. The resulting past and future land cover changes will be presented, and challenges regarding the application of the new workflow to different regions will be addressed. In addition, issues related to the implementation of the dataset into different RCMs will be discussed.

Projections of the 21st century potential future climate evolution, especially for precipitation, are associated with high uncertainty and variability. Knowledge of the variability of the projected precipitation and resulting run-offs and the sources of uncertainties form the basis for analysis and assessment of future water-management options as well as potential risks related to droughts and flood events. The variabilities related to climate modelling can only be assessed by using a comparatively large number of climate projections.In our research, we apply a large ensemble of regional climate model projections from the regional climate model REMO, driven by different global climate model simulations, at high temporal (hourly timestep) and high spatial (0.11 degree, or about 12.5 km) resolutions. Although the analysis of such big datasets involves considerable computational and storage capacities, this potentially helps to improve the simulation of future hydrological quantities in river catchments. For the analysis of the behaviour of small river catchments, we apply a semi-distributive hydrological model. Annual and winter average precipitation conditions show a robust and statistically significant increase especially for the RCP8.5 scenario. Precipitation ranges are compared with the ranges of runoff based on hydrological impact model runs driven by a set of simulated parameters from the regional climate model ensemble. The analyses are performed for a sub-catchment of the Lower Elbe system (Krückau catchment), which is a typical small basin (area < 200km2) close to the city of Hamburg in northern Germany. The model runs cover a long simulation period of 150 years (1950-2100) with a temporal resolution of 1 day. Short term model runs with a temporal resolution of 1 hour were carried out for annual and seasonal (summer/winter) maximum runoff derived from the long-term simulations.Average annual runoff shows an increase of 0 to 10 % for the RCP2.6 ensemble and an increase of 0 to 20 % for the RCP8.5 ensemble at the end of the 21st century. Annual and winter average conditions (precipitation sums and average runoff) of the RCP8.5 ensemble show a robust increase across different ensemble simulations. Extreme events however show high variability and no conclusive and robust trend. Analysis shows a good relation between average values of precipitation and average runoff (MQ). Future development of simulated annual maximum runoffs shows only a weak relation with future simulated precipitation extremes. However, summer maximum runoffs tend to show a relation with summer precipitation extremes. The behaviour of winter runoffs might be explained by altered future conditions of snow aggregation and melt in combination with high soil moisture. With increasing average and extreme temperatures, snow fall, snow accumulation and concentrated runoff caused by snow melt in spring are less likely to occur.One of the conclusions drawn is that especially for assessing extreme precipitation and its impacts on small hydrological catchments it is necessary to apply regional climate model projections with high spatial and temporal resolution where further improvement is expected by making use of the upcoming generation of climate simulations on convection permitting scale.

The CORDEX-CORE initiative was developed with the aim of producing homogeneous regional climate model (RCM) projections over domains world wide. In its first phase, two RCMs were run at 0.22° resolution downscaling 3 global climate models (GCMs) from the CMIP5 program for 9 CORDEX domains and two climate scenarios, the RCP2.6 and RCP8.5. The CORDEX-CORE simulations along with the CMIP5 GCM ensemble and the most recently produced CMIP6 GCM ensemble are analyzed, with focus on several temperature, heat, wet and dry hazard indicators for present day and mid-century and far future time slices. The CORDEX-CORE ensemble shows a better performance than the driving GCMs for several hazard indices due to its higher spatial resolution. For the far future time slice the 3 ensembles project an increase in all temperature and heat indices analyzed under the RCP8.5 scenario. The largest increases are always shown by the CMIP6 ensemble, except for Tx > 35 °C, for which the CORDEX-CORE projects higher warming. Extreme wet and flood prone maxima are projected to increase by the RCM ensemble over the la Plata basin in South America, the Congo basin in Africa, east North America, north east Europe, India and Indochina, regions where a better performance is obtained, whereas the GCM ensembles show small or negligible signals. Compound hazard hotspots based on heat, drought and wet indicators are detected in each continent worldwide in region like Central America, the Amazon, the Mediterranean, South Africa and Australia, where a linear relation is shown between the heatwave and drought change signal, and region like Arabian peninsula, the central and south east Africa region (SEAF), the north west America (NWN), south east Asia, India, China and central and northern European regions (WCE, NEU) where the same linear relation is found for extreme precipitation and HW increases. Although still limited, the CORDEX-CORE initiative was able to produce high resolution climate projections with almost global coverage and can provide an important resource for impact assessment and climate service activities.

The new Coordinated Output for Regional Evaluations (CORDEX-CORE) ensemble provides high-resolution, consistent regional climate change projections for the major inhabited areas of the world. It serves as a solid scientific basis for further research related to vulnerability, impact, adaptation and climate services in addition to existing CORDEX simulations. The aim of this study is to investigate and document the climate change information provided by the CORDEX-CORE simulation ensemble, as a part of the World Climate Research Programme (WCRP) CORDEX community. An overview of the annual and monthly mean climate change information in selected regions in different CORDEX domains is presented for temperature and precipitation, providing the foundation for detailed follow-up studies and applications. Initially, two regional climate models (RCMs), REMO and RegCM were used to downscale global climate model output. The driving simulations by AR5 global climate models (AR5-GCMs) were selected to cover the spread of high, medium, and low equilibrium climate sensitivity at a global scale. The CORDEX-CORE ensemble has doubled the spatial resolution compared to the previously existing CORDEX simulations in most of the regions (25$$\,\mathrm {km}$$km(0.22$$^{\circ }$$∘) versus 50$$\,\mathrm {km}$$km(0.44$$^{\circ }$$∘)) leading to a potentially improved representation of, e.g., physical processes in the RCMs. The analysis focuses on changes in the IPCC physical climate reference regions. The results show a general reasonable representation of the spread of the temperature and precipitation climate change signals of the AR5-GCMs by the CORDEX-CORE simulations in the investigated regions in all CORDEX domains by mostly covering the AR5 interquartile range of climate change signals. The simulated CORDEX-CORE monthly climate change signals mostly follow the AR5-GCMs, although for specific regions they show a different change in the course of the year compared to the AR5-GCMs, especially for RCP8.5, which needs to be investigated further in region specific process studies.

Regional Climate Models (RCMs) have undergone substantial development, resulting in increasingly reliable high-resolution simulations. Despite this, the added value of these simulations compared to their driving General Circulation Models (GCMs) has been a recurring issue. Past studies have used different techniques to quantify the added value of a RCM. A new method is now being presented, based on these past studies, that quantifies the added value and presents it spatially. The method was also adapted to assess the Downscaling Signal (DS) in climate change simulations and compare this to the added value.This new method has been used to assess the daily precipitation of the 55-model EURO-CORDEX ensemble and the CORDEX-CORE ensemble, focusing especially on the higher-end of the PDFs. This revealed an overall positive added value across all domains, especially in areas of complex topography, cost-lines, and tropical regions. This DS was similar to that of the added value when looking at RCP 8.5 far-future simulations.

The Hengduan Mountains and Tibetan Plateau possess unique topographical characteristics that serve as an effective blocking of the movement of the westerly wind in the middle and lower troposphere towards East China. This study examines results from a regional climate model (REMO) at the resolutions of 25 and 50 km for the period 1980–2012. The model is run using lateral boundary conditions from ERA-Interim (European Centre for Medium-Range Weather Forecasts interim reanalysis). There are only a few differences between 25 and 50 km in land surface/vegetation characteristics, but the major differences in this region are due to the orography. Results show that the high-resolution simulation performance is poor in winter, when southwesterly wind prevails, whereas it performs well in summer, when the westerly wind is substantially weakened in southern China. In comparison to the ERA-Interim wind field, the high-resolution simulation overestimates the air flow over the Hengduan Mountains near the ground surface, which influences the transport of atmospheric water vapor in the downstream region, i.e., over southern China. Specifically, with the help of the overestimated southwesterly wind, the amount of atmospheric water vapor transported increases considerably perennially by up to 20% in southern China, while it decreases remarkably by more than 5% throughout the year in a large area of Central and North China. These features lead to excessive precipitation and underestimated cloud cover in southern China, which probably causes the overestimated 2-m temperature in southern China. Our study emphasizes that, in such high-resolution-model studies for East Asia, special attention should be paid to the near-surface winds over the Hengduan Mountains.

The core of climate services is to provide high quality climate-related information and data that are beneficial for the users. Between the provision of data and the application of climate services, a chain of providers and subsequent users exists. It is an ongoing challenge for providers to conclusively define what users perceive as beneficial regarding the quality of climate model output. This study aims (1) to understand the needs of users with regard to the quality of climate data and information, and (2) to enable providers to assess the quality of climate data input and derived products. From a large-scale survey, we distilled three main user groups: (i) Donna data (data user/product provider), (ii) Pete product (product user/product provider) and (iii) Nick non (potential-user). The survey results show that all three user groups struggle—amongst other things—with identifying reliable climate model output, that is relevant to their needs. They also desire guidance on how to evaluate the quality of climate model data to determine the suitability of the selected dataset for their purpose. Addressing this central need is breaking new ground. The evaluation of quality in the field of climate services in terms of climate model output is of high relevance to both climate model data users and providers of tailored climate information and not restricted to scientific standards and technical quality. We present a customized and tested tool (“QUACK”) as one of the first hands-on, scientifically-based and at the same time user-oriented guidelines on how to assure data quality and to self-evaluate the processing of the data.

The Land Use and Climate Across Scales Flagship Pilot Study (LUCAS FPS) is a coordinated community effort to improve the integration of Land Use Change (LUC) in Regional Climate Models (RCMs) and to quantify the biogeophysical effects of LUC on local to regional climate in Europe. In the first phase of LUCAS, nine RCMs are used to explore the biogeophysical impacts of re-/afforestation over Europe. Namely, two idealized experiments representing respectively a non-forested and a maximally forested Europe are compared in order to quantify spatial and temporal variations in the regional climate sensitivity to forestation. We find some robust features in the simulated response to forestation. In particular, all models indicate a year-round decrease in surface albedo, which is most pronounced in winter and spring at high latitudes. This results in a winter warming effect, which is relatively robust across models. However, there are also a number of strongly diverging responses. For instance, there is no agreement on the sign of temperature changes in summer with some RCMs predicting a widespread cooling from forestation, a widespread warming, or a mixed response. A large part of the inter-model spread is attributed to the representation of land processes. In particular, differences in the partitioning of sensible and latent heat are identified as a key source of uncertainty. In contrast, for precipitation, the representation of atmospheric processes dictates more directly the simulated response. In conclusion, the multi-model approach we use here has the potential to deliver more robust and reliable information to stakeholders involved in land use planning, as compared to results based on single models. However, given the contradictory responses identified, our results also show that there are still fundamental uncertainties that need to be tackled to better anticipate the possible intended or unintended consequences of LUC on regional climates.

The ERA15 Reanalysis (1979-1993) has been dynamically downscaled over Central Europe using 4 different regional climate models. The regional simulations were analysed with respect to 2m temperature and total precipitation, the main input parameters for hydrological applications. Model results were validated against three reference data sets (ERA15, CRU, DWD) and uncertainty ranges were derived. For mean annual 2 m temperature over Germany, the simulation bias lies between -1.1°C and +0.9°C depending on the combination of model and reference data set. The bias of mean annual precipitation varies between -31 and +108 mm/year. Differences between RCM results are of the same magnitude as differences between the reference data sets.

A new ensemble of climate and climate change simulations covering all major inhabited regions with a spatial resolution of about 25 km, from the WCRP CORDEX COmmon Regional Experiment (CORE) Framework, has been established in support of the growing demands for climate services. The main objective of this study is to assess the quality of the simulated climate and its fitness for climate change projections by REMO (REMO2015), a regional climate model of Climate Service Center Germany (GERICS) and one of the RCMs used in the CORDEX-CORE Framework. The CORDEX-CORE REMO2015 simulations were driven by the ECMWF ERA-Interim reanalysis and the simulations were evaluated in terms of biases and skill scores over ten CORDEX Domains against the Climatic Research Unit (CRU) TS version 4.02, from 1981 to 2010, according to the regions defined by the K&ouml, ppen&ndash, Trewartha (K&ndash, T) Climate Classification types. The REMO simulations have a relatively low mean annual temperature bias (about &plusmn, 0.5 K) with low spatial standard deviation (about &plusmn, 1.5 K) in the European, African, North and Central American, and Southeast Asian domains. The relative mean annual precipitation biases of REMO are below &plusmn, 50 % in most domains, however, spatial standard deviation varies from &plusmn, 30 % to &plusmn, 200 %. The REMO results simulated most climate types relatively well with lowest biases and highest skill score found in the boreal, temperate, and subtropical regions. In dry and polar regions, the REMO results simulated a relatively high annual biases of precipitation and temperature and low skill. Biases were traced to: missing or misrepresented processes, observational uncertainty, and uncertainties due to input boundary forcing.

The transferability of the regional climate model REMO with a standard setup over different regions of the world has been evaluated. The study is based on the idea that the modeling parameters and parameterizations in a regional climate model should be robust to adequately simulate the major climatic characteristic of different regions around the globe. If a model is not able to do that, there might be a chance of an “overtuning” to the “home-region”, which means that the model physics are tuned in a way that it might cover some more fundamental errors, e.g., in the dynamics. All simulations carried out in this study contribute to the joint effort by the international regional downscaling community called COordinated Regional climate Downscaling EXperiment (CORDEX). REMO has been integrated over six CORDEX domains forced with the so-called perfect boundary conditions obtained from the global reanalysis dataset ERA-Interim for the period 1989 to 2008. These six domains include Africa, Europe, North America, South America, West Asia and the Mediterranean region. Each of the six simulations was conducted with the identical model setup which allows investigating the transferability of a single model to regions with substantially different climate characteristics. For the consistent evaluation over the different domains, a new evaluation framework is presented by combining the Köppen-Trewartha climate classification with temperature-precipitation relationship plots and a probability density function (PDF) skill score method. The evaluation of the spatial and temporal characteristics of simulated precipitation and temperature, in comparison to observational datasets, shows that REMO is able to simulate the mean annual climatic features over all the domains quite reasonably, but still some biases remain. The regions over the Amazon and near the coast of major upwelling regions have a significant warm bias. Wet and dry biases appear over the mountainous regions and East Africa, respectively. The temperature over South America and precipitation over the tundra and highland climate of West Asia are misrepresented. The probable causes leading to these biases are discussed and ideas for improvements are suggested. The annual cycle of precipitation and temperature of major catchments in each domain are also well represented by REMO. The model has performed well in simulating the inter- and intra-seasonal characteristics of different climate types in different regions. Moreover, the model has a high ability in representing the general characteristics of different climate types as measured by the probability density function (PDF) skill score method. Although REMO seems to perform best over its home domain in Europe (domain of development and testing), the model has simulated quite well the climate characteristics of other regions with the same set of parameterization options. Therefore, these results lead us to the conclusion that REMO is well suited for long-term climate change simulations to examine projected future changes in all these regions.

A regional-scale case study has been carried out to assess the possible climatic benefits of forest cover increase in Europe. For the end of the 21st century (2071-2090) it has been investigated, whether the projected climate change could be reduced assuming potential afforestation of the continent. The magnitude of the biogeophysical effects of enhanced forest cover on temperature and precipitation means and extremes have been analyzed relative to the magnitude of the climate change signal applying the regional climate model REMO. The simulation results indicate that in the largest part of the temperate zone potential afforestation may reduce the projected climate change through cooler and moister conditions, thus could contribute to the mitigation of the projected climate change for the entire summer period. The largest relative effect of forest cover increase can be expected in northern Germany, Poland and Ukraine. Here, the projected precipitation decrease could be fully compensated, the temperature increase could be relieved by up to 0.5 °C, and the probability of extremely warm and dry days could be reduced. Results can help to identify the areas, where forest cover increase could be the most effective from climatic point of view. Thus they can build an important basis of the future adaptation strategies and forest policy.

The aim of this study was to develop an advanced parameterization of the snow-free land surface albedo for climate modelling describing the temporal variation of surface albedo as a function of vegetation phenology on a monthly time scale. To estimate the effect of vegetation phenology on snow-free land surface albedo, remotely sensed data products from the Moderate-Resolution Imaging Spectroradiometer (MODIS) on board the NASA Terra platform measured during 2001 to 2004 are used. The snow-free surface albedo variability is determined by the optical contrast between the vegetation canopy and the underlying soil surface. The MODIS products of the white-sky albedo for total shortwave broad bands and the fraction of absorbed photosynthetically active radiation (FPAR) are analysed to separate the vegetation canopy albedo from the underlying soil albedo. Global maps of pure soil albedo and pure vegetation albedo are derived on a 0.5° regular latitude/longitude grid, re-sampling the high-resolution information from remote sensing-measured pixel level to the model grid scale and filling up gaps from the satellite data. These global maps show that in the northern and mid-latitudes soils are mostly darker than vegetation, whereas in the lower latitudes, especially in semi-deserts, soil albedo is mostly higher than vegetation albedo. The separated soil and vegetation albedo can be applied to compute the annual surface albedo cycle from monthly varying leaf area index. This parameterization is especially designed for the land surface scheme of the regional climate model REMO and the global climate model ECHAM5, but can easily be integrated into the land surface schemes of other regional and global climate models.

In this study the regional climate model of the German Max-Planck-Institute for Meteorology (REMO) is used to analyse the effect of monthly varying vegetation on the simulated climate in Europe. For this investigation the annual cycle of vegetation is implemented in the land surface parameterization scheme of REMO. As input data source a new global dataset of land surface parameters is used. It contains monthly varying vegetation parameter values for leaf area index, fractional vegetation cover and background surface albedo. This dataset is adapted to both standard REMO model domains at 0.5 degree and 0.l6 degree horizontal resolution focusing Europe. For both resolutions present-day climate simulations are performed to examine the sensitivity of REMO to the modified vegetation parameterization. The simulation results are compared to corresponding reference simulations where vegetation parameter values are held constant in time. A validation is done by the comparison of the model results with several gridded observational datasets. A significant influence of monthly varying vegetation on the regional climate can be demonstrated. Vertical surface fluxes, near surface temperature and precipitation are strongly affected. The temporal analysis of the results reveals that the vegetation effect on the simulated climate occurs mainly in the summer season. In general, the simulated near-surface climate becomes cooler and wetter during the growing season. Concerning the spatial resolution, main effects can be detected in eastern Europe and the Hungarian lowlands. In these regions the more realistic vegetation treatment improves the simulated mean annual cycles of 2 m temperature and precipitation with respect to the observations.

A new high-resolution regional climate change ensemble has been established for Europe within the World Climate Research Program Coordinated Regional Downscaling Experiment (EURO-CORDEX) initiative. The first set of simulations with a horizontal resolution of 12.5 km was completed for the new emission scenarios RCP4.5 and RCP8.5 with more simulations expected to follow. The aim of this paper is to present this data set to the different communities active in regional climate modelling, impact assessment and adaptation. The EURO-CORDEX ensemble results have been compared to the SRES A1B simulation results achieved within the ENSEMBLES project. The large-scale patterns of changes in mean temperature and precipitation are similar in all three scenarios, but they differ in regional details, which can partly be related to the higher resolution in EURO-CORDEX. The results strengthen those obtained in ENSEMBLES, but need further investigations. The analysis of impact indices shows that for RCP8.5, there is a substantially larger change projected for temperature-based indices than for RCP4.5. The difference is less pronounced for precipitation-based indices. Two effects of the increased resolution can be regarded as an added value of regional climate simulations. Regional climate model simulations provide higher daily precipitation intensities, which are completely missing in the global climate model simulations, and they provide a significantly different climate change of daily precipitation intensities resulting in a smoother shift from weak to moderate and high intensities., Regional Environmental Change, 14 (2), ISSN:1436-3798, ISSN:1436-378X

A high resolution (1 km x 1 km) data set of monthly wind velocities over Germany for the time period 1951-2001 is provided. The data have been reduced to a reference level using a so called 'relative altitude' scheme, interpolated using a simple Inverse Distance Weighting approach and retransformed to the actual topography. However, no parametrization of land use or surface roughness has been integrated in the modelling process of this data and therefore the data is not suitable to serve as criteria for planning wind energy sites. A Cross-Validation scheme applied to this data set yields a mean error of 0.1 m/s for the time period 1951-2001. Regarding the area mean of wind velocities a linear trend of -0.05 m/s is obvious for this period. This negative linear trend changes to a positive one when shorter time scales are considered, e.g. +0.3 m/s for the 1981-2001 period. However, all these temporal trends are not significant. Thus, they could be the result of random features within the dataset and are not further interpreted. These monthly derived wind velocities serve as a reference data set for regional climate model evaluations. The climate models used are two different versions of the hydrostatic regional climate model REMO as well as the nonhydrostatic CLM and MM5 models. All models are capable to reproduce the temporal and spatial variability of the observations to a great extent. Projections of changes in wind velocity have been carried out with these regional climate models. All of these projections show a significant increase in wind velocities over the full model domain, especially over the Baltic Sea and the North Sea, during winter and a decrease during summer. Regarding changes in annual means an increase of up to 1.0 m/s for the Baltic Sea and a decrease in wind velocitites of the same magnitude for the Mediterranean is projected as an average for the 2070-2099 period.

In the context of the EU-Project BALANCE (http://balance-eu.info) the regional climate model REMO was used for extensive calculations of the Barents Sea climate to investigate the vulnerability of this region to climate change. The regional climate model REMO simulated the climate change of the Barents Sea Region between 1961 and 2100 (Control and Climate Change run, CCC-Run). REMO on ~50 km horizontal resolution was driven by the transient ECHAM4/OPYC3 IPCC SRES B2 scenario. The output of the CCC-Run was applied to drive the dynamic vegetation model LPJ-GUESS. The results of the vegetation model were used to repeat the CCC-Run with dynamic vegetation fields. The feedback effect of the modified vegetation on the climate change signal is investigated and discussed with focus on precipitation, temperature and snow cover. The effect of the offline coupled vegetation feedback run is much lower than the greenhouse gas effect.

The seasonal variation of snow-free land surface albedo was integrated into the land surface schemes of the global climate model ECHAM5 and the regional climate model REMO to test the sensitivity of climate models to the advanced surface albedo parameterisation. This new albedo parameterisation was developed in previous studies describing the monthly varying surface albedo fields as a function of vegetation phenology derived from MODIS data products. Three model simulations were performed in order to study the sensitivity of the simulated climate to the seasonal background albedo variations: 1. control simulation with the old time-invariant surface background albedo, 2. experiment 1 with the new mean time-invariant surface background albedo field, 3. experiment 2 with the monthly varying surface background albedo as a function of the leaf area index. The analysis of the simulation results demonstrates the influence of the new albedo parameterisation on the model simulations. Strong effects occur over Europe with the regional and the global model simulations responding differently to seasonal background albedo variations. In contrast to the global simulation, where the large-scale conditions are changed by the new albedo parameterisation, in the regional simulations the circulation patterns within the model domain are not influenced. Here, the external forcing via the lateral boundaries of the regional model domain suppress changes in the large-scale circulation that might occur without the external forcing. In the regional model simulations only local effects occur mainly during the summer season, when the vertical energy exchange at the land surface is enhanced compared to the winter season. The comparison of the regional and global simulation results over Europe reveals that the global model shows higher sensitivity to the changed albedo parameterization with respect to the simulated annual temperature and precipitation cycle than the regional model. For both the regional and the global study the temperature and precipitation deviations from the observations are larger than the differences between the three model simulations.