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Precipitation in Europe shows natural fluctuations that differ considerably between seasons and geographical regions. A number of studies have linked local or seasonal rainfall variability to various long-distance air pressure differences in north–south or west–east direction. This paper presents the first continent-wide analysis of European rainfall variability on a month-by-month and country-by-country basis. We calculated Pearson r values for unsmoothed monthly rainfall data of 39 European countries over the period 1950–2019 with five potential climatic drivers, namely the North Atlantic Oscillation (NAO), the Arctic Oscillation (AO), the North Sea Caspian Pattern (NCP), and two indices of Mediterranean Oscillation (MOI2, WeMOI). For a variety of countries and months we found substantial and statistically significant r values of up to r = 0.7 and more. The dynamic temporal-spatial evolution of the Pearson correlations was mapped out across the continent, tracking the gradual or abrupt expansion, displacement and subsequent waning of the various effects over the course of the year. The correlation analysis was complemented by best subset multiple regression, controlling for intercorrelation of the potential drivers. Our results may help to improve short- to midterm rainfall prognoses in Europe and provide important calibration data for the further refinement of climate models. [ABSTRACT FROM AUTHOR]

Determining the underlying mechanisms leading to extreme events in dynamical systems is a challenging task. Under mild hypotheses, large deviations theory predicts that, as one increases the threshold defining an extreme, dynamical trajectories which reach the extreme look more and more like one another: they converge towards a typical, i.e. most probable, trajectory called the instanton. In this paper, we use a 2000-year simulation of the IPSL-CM6A-LR model under a stationary pre-industrial climate to test this prediction on the case of hot extremes. We investigate whether the physical mechanisms leading to extreme temperatures at four locations in Europe are more similar with increasing extreme temperatures. Our results show that most physical variables exhibit the expected convergence towards a most probable trajectory, with some geographical and temporal variations. In particular, we observe the presence of a cut-off low in some trajectories, which suggests the existence of multiple pathways leading to extreme temperatures. These findings confirm the relevance of instanton dynamics to understand the physical mechanisms driving extreme events in climate models. [ABSTRACT FROM AUTHOR]

The accuracy of the climate impact models depends on the representative power of the climate model used in the analysis. Many problems in these impact models can be eliminated by the selection of the appropriate climate models as an input. The main purpose of this research is to find a methodology to select the more representative climate models. In recent years, the quantity and quality of climate models have significantly improved. In several projects, many different Global Climate Model (GCM)–Regional Climate Model (GCM) combinations have been carried out. One of these recent comprehensive projects is CORDEX. The monthly temperature outputs of various GCM-RCM combinations over Europe between the years 1976 and 2005 were used in this paper for the determination of the more representative combinations. E-OBS is used as the observation data set in this research. Using the proposed method, a clear difference between the more, moderately, and less representative model combinations could be obtained. To model the future temperature climatology of Europe, the best choice of the analysis method is using MPI as GCM and either REMO or CCLM5 as RCM over the whole domain. According to our analysis, the best representative climate model combination can be performed by running MPI-CCLM5 combination over the EURO-CORDEX domain. [ABSTRACT FROM AUTHOR]

Recently, much attention has been devoted to better understand the internal modes of variability of the climate system. This is particularly important in mid-latitude regions like the North-Atlantic, which is characterized by a large natural variability and is intrinsically difficult to predict. A suitable framework for studying the modes of variability of the atmospheric circulation is to look for recurrent patterns, commonly referred to as Weather Regimes. Each regime is characterized by a specific large-scale atmospheric circulation pattern, thus influencing regional weather and extremes over Europe. The focus of the present paper is the study of the Euro-Atlantic wintertime Weather Regimes in the climate models participating to the PRIMAVERA project. We analyse here the set of coupled historical simulations (hist-1950), which have been performed both at standard and increased resolution, following the HighresMIP protocol. The models' performance in reproducing the observed Weather Regimes is assessed in terms of different metrics, focussing on systematic biases and on the impact of resolution. We also analyse the connection of the Weather Regimes with the Jet Stream latitude and blocking frequency over the North-Atlantic sector. We find that—for most models—the regime patterns are better represented in the higher resolution version, for all regimes but the NAO-. On the other side, no clear impact of resolution is seen on the regime frequency of occurrence and persistence. Also, for most models, the regimes tend to be more tightly clustered in the increased resolution simulations, more closely resembling the observed ones. However, the horizontal resolution is not the only factor determining the model performance, and we find some evidence that biases in the SSTs and mean geopotential field might also play a role. [ABSTRACT FROM AUTHOR]

How climate change will unfold in the years to come is a central topic in today's environmental debate, in particular at the regional level. While projections using large ensembles of global climate models consistently indicate a future decrease in summer precipitation over southern Europe and an increase over northern Europe, individual models substantially modulate these distinct signals of change in precipitation. So far model improvements and higher resolution from regional downscaling have not been seen as able to resolve these disagreements. In this paper we assess whether 2 decades of investments in large ensembles of downscaling experiments with regional climate model simulations for Europe have contributed to a more robust model assessment of the future climate at a range of geographical scales. We study climate change projections of European seasonal temperature and precipitation using an ensemble-suite comprised by all readily available pan-European regional model projections for the twenty-first-century, representing increasing model resolution from ~ 50 to ~ 12 km grid distance, as well as lateral boundary and sea surface temperature conditions from a variety of global model simulations. Employing a simple scaling with global mean temperature change we identify emerging robust signals of future seasonal temperature and precipitation changes also found to resemble current observed trends, where these are judged to be statistically significant. [ABSTRACT FROM AUTHOR]

The Mediterranean basin is the richest biodiversity region in Europe and a global hotspot of biological diversity. In spite of that, anthropogenic climate change is one of the most serious concerns for nature conservation in this region. One of the climatic threats is represented by shifts of the Mediterranean climate and expansion of the arid climate. In this paper, we present an assessment of changes in the spatial range of the Mediterranean climate in Europe and the conversion into arid climate under different greenhouse gas forcings, namely RCP4.5 and RCP8.5. We used 11 simulations in two future 30-year periods of state-of-the-art regional climate models from EURO-CORDEX. Our results indicate that by the end of the century under RCP8.5 the present Mediterranean climate zone is projected to contract by 16%, i.e. an area (~ 157,000 km2) equivalent to half the size of Italy. This compares with the less severe scenario RCP4.5 that projected only a 3% reduction. In addition, the Mediterranean climate zone is projected to expand to other zones by an area equivalent to 24 and 50% of its present extent under RCP4.5 and RCP8.5, respectively. Our study indicates that expansion of the arid zone is almost always the cause for contraction of the Mediterranean zone. Under RCP8.5 the arid zone is projected to increase by more than twice its present extent, equivalent to three times the size of Greece. Results of this study are useful for identifying (1) priority zones for biodiversity conservation, i.e. stable Mediterranean climate zones, (2) zones requiring assisted adaptation, such as establishment of new protected areas, implementation of buffer zones around protected areas and creating ecological corridors connecting stable Mediterranean zones. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper presents a quantitative assessment of adaptation options in the context of forest fires in Europe under projected climate change. A standalone fire model (SFM) based on a state-of-the-art large-scale forest fire modelling algorithm is used to explore fuel removal through prescribed burnings and improved fire suppression as adaptation options. The climate change projections are provided by three climate models reflecting the SRES A2 scenario. The SFM's modelled burned areas for selected test countries in Europe show satisfying agreement with observed data coming from two different sources (European Forest Fire Information System and Global Fire Emissions Database). Our estimation of the potential increase in burned areas in Europe under 'no adaptation' scenario is about 200 % by 2090 (compared with 2000-2008). The application of prescribed burnings has the potential to keep that increase below 50 %. Improvements in fire suppression might reduce this impact even further, e.g. boosting the probability of putting out a fire within a day by 10 % would result in about a 30 % decrease in annual burned areas. By taking more adaptation options into consideration, such as using agricultural fields as fire breaks, behavioural changes, and long-term options, burned areas can be potentially reduced further than projected in our analysis. [ABSTRACT FROM AUTHOR]

This paper presents a method for identifying a representative subset of global climate models (GCMs) for use in large-scale climate impact research. Based on objective criteria (GCM performance in reproducing the seasonal cycle of temperature and precipitation, and a subset ability to represent future inter-GCM variability), two candidate subsets are selected from a reference set of 16 GCMs. An additional subset based on subjective expert judgement is also analysed. The representativeness of the three subsets is validated (with respect to the reference set) and compared for future changes in temperature, precipitation and Palmer drought index Z (direct validation), and occurrence of the European corn borer and snow-cover characteristics implemented in the CLIMSAVE Integrated Assessment Platform (indirect validation). The direct validation indicates that one of the objective-based subsets (ECHAM5/MPI-OM, CSIRO-Mk3.0, HadGEM1, GFDL-CM2.1 and IPSL-CM4 models) provides the best choice for the Europe-wide climate change impact study. Its performance is balanced between regions, seasons and validation statistics. However, the expert-judgement-based subset achieved slightly better results in the indirect validation. The differences between the subsets and the reference set are generally much lower for the impact indices compared to their mean (across all GCMs in the subset) changes due to projected climate change. The ranking of the candidate subsets differs between regions, climatic characteristics and seasons, demonstrating that the subset suitability for a specific impact study depends on the target region and the roles of individual seasons and/or climatic variables on the processes being studied. [ABSTRACT FROM AUTHOR]

The term "weather whiplash" describes abrupt transitions from one persistent weather regime to another substantially different one, such as from a frigid cold spell to anomalous warmth. Weather whiplash events (WWEs) are often highly disruptive to agriculture, ecosystems, infrastructure, and human activities. While no consistent definition exists, we identify WWEs based on substantial shifts in the continental-scale, upper-atmosphere circulation. As first demonstrated in our earlier study focused on the NE Pacific/North American region, a WWE is detected when one persistent, large-scale pattern in 500 hPa height anomalies shifts to another distinctly different one. Patterns are identified using self-organizing maps (SOMs), which create a matrix of representative patterns in the data. In the present study, we apply this approach to identify WWEs in the North Atlantic/European sector. We analyze the occurrence of WWEs originating with long-duration events (four or more days) in each pattern. A WWE is detected when the pattern two days following a long-duration event is substantially different, measured with distance thresholds internal to the matrix. Changes in WWE frequency, past and future, are assessed objectively based on reanalysis output and climate model simulations. We find that future changes under RCP 8.5 forcing exhibit distinct trends, especially in summer months, while those based on reanalysis are less clear. Patterns featuring positive height anomalies in high latitudes are projected to produce more WWEs in the future, while patterns exhibiting negative anomalies produce fewer. Shifts in temperature and precipitation extremes associated with these WWEs are diagnosed. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper presents an evaluation of the multi-model simulations for the 4th Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) in terms of their ability to simulate the ERA40 circulation types over the Euro-Atlantic region in winter season. Two classification schemes, k-means and SANDRA, have been considered to test the sensitivity of the evaluation results to the classification procedure. The assessment allows establishing different rankings attending spatial and temporal features of the circulation types. Regarding temporal characteristics, in general, all AR4 models tend to underestimate the frequency of occurrence. The best model simulating spatial characteristics is the UKMO-HadGEM1 whereas CCSM3, UKMO-HadGEM1 and CGCM3.1(T63) are the best simulating the temporal features, for both classification schemes. This result agrees with the AR4 models ranking obtained when having analysed the ability of the same AR4 models to simulate Euro-Atlantic variability modes. This study has proved the utility of applying such a synoptic climatology approach as a diagnostic tool for models' assessment. The ability of the models to properly reproduce the position of ridges and troughs and the frequency of synoptic patterns, will therefore improve our confidence in the response of models to future climate changes. [ABSTRACT FROM AUTHOR]

In this paper, we examine the trends of temperature series in Europe, for the mean as well as for the variance in hot and cold seasons. To do so, we use as long and homogenous series as possible, provided by the European Climate Assessment and Dataset project for different locations in Europe, as well as the European ENSEMBLES project gridded dataset and the ERA40 reanalysis. We provide a definition of trends that we keep as intrinsic as possible and apply non-parametric statistical methods to analyse them. Obtained results show a clear link between trends in mean and variance of the whole series of hot or cold temperatures: in general, variance increases when the absolute value of temperature increases, i.e. with increasing summer temperature and decreasing winter temperature. This link is reinforced in locations where winter and summer climate has more variability. In very cold or very warm climates, the variability is lower and the link between the trends is weaker. We performed the same analysis on outputs of six climate models proposed by European teams for the 1961–2000 period (1950–2000 for one model), available through the PCMDI portal for the IPCC fourth assessment climate model simulations. The models generally perform poorly and have difficulties in capturing the relation between the two trends, especially in summer. [ABSTRACT FROM AUTHOR]

The increasing intensity and frequency of high-temperature events in response to climate change can potentially impact the aviation industry, since aircraft takeoff and landing performances depend on near-surface air temperature. Previous studies have combined climate data with aircraft technical data to estimate the future impact of rising high temperatures on aircraft takeoff. They found a decrease of maximum takeoff weights and the lengthening of takeoff distances. The Mediterranean region is a climate change "hot spot" area, specially concerned by extreme high-temperatures increase. In this study, the magnitude and trends of the daily maximum near-surface temperature extremes in summer were analysed over major airports in Southwestern Europe. Trends in the period 1961–2014 were analysed from observations and reanalysis. Future changes by 2021–2050 and 2071–2100, with respect to 1961–2005, were analysed from simulations performed with Regional and Global Climate Models (RCMs and GCMs). Before assessing future climate projections, climate models were evaluated in present climate, and the RCM and GCM ensembles were compared to each other. No clear added value was found for RCMs over GCMs in present climate at the airport scale in these terms. GCMs project larger temperature changes than RCMs over the same locations. Multi-model ensemble mean projected changes under the RCP8.5 scenario range between + 1.7 and + 3.2 ∘ C by the near term, and between + 4.9 and + 8.5 ∘ C by the long term, across the airports and the RCM and GCM ensembles. This increase of high-temperature extremes would impact airport operations. Adaptation or mitigation policies would become necessary. [ABSTRACT FROM AUTHOR]

European windstorms cause socioeconomic losses due to wind damage. Projections of future losses from such storms are subject to uncertainties from the frequency and tracks of the storms, their intensities and definitions thereof, and socio-economic scenarios. We use two storm severity indices applied to objectively identified extratropical cyclone footprints from a multi-model ensemble of state-of-the-art climate models under different future socio-economic scenarios. Here we show storm frequency increases across northern and central Europe, where the meteorological storm severity index more than doubles. The population-weighted storm severity index more than triples, due to projected population increases. Adapting to the increasing wind speeds using future damage thresholds, the population weighted storm severity index increases are only partially offset, despite a reduction in the meteorological storm severity through adaptation. Through following lower emissions scenarios, the future increase in risk is reduced, with the population-weighted storm severity index increase more than halved. New study by Little, @jencatto19, @_mattpriestley shows that #storm severity of European winter storms shows future increased #stormlosses in northwestern Europe, caused by changes in the location and intensity of storms, and increasing population. [ABSTRACT FROM AUTHOR]

Inter-variable correlations (e.g., between daily temperature and precipitation) are key statistical properties to characterise probabilities of simultaneous climate events and compound events. Their correct simulations from climate models, both in values and in changes over time, is then a prerequisite to investigate their future changes and associated impacts. Therefore, this study first evaluates the capabilities of one 11-single run multi-model ensemble (CMIP6) and one 40-member single model initial-condition large ensemble (CESM) over Europe to reproduce the characteristics of a reanalysis dataset (ERA5) in terms of temperature–precipitation correlations and their historical changes. Next, the ensembles' correlations for the end of the 21st century are compared. Over the historical period, both CMIP6 and CESM ensembles have season-dependent and spatially structured biases. Moreover, the inter-variable correlations from both ensembles mostly appear stationary. Thus, although reanalysis displays significant correlation changes, none of the ensembles can reproduce them, with internal variability representing only 30% on the inter-model variability. However, future correlations show significant changes over large spatial patterns. Yet, those patterns are rather different for CMIP6 and CESM, reflecting a large uncertainty in changes. In addition, for historical and future projections, an analysis conditional on atmospheric circulation regimes is performed. The conditional correlations given the regimes are found to be the main contributor to the biases in correlation over the historical period, and to the past and future changes of correlation. These results highlight the importance of the large-scale circulation regimes and the need to understand their physical relationships with local-scale phenomena associated to specific inter-variable correlations. [ABSTRACT FROM AUTHOR]

Three consecutive dry summers in western Europe (2018–2019–2020) had widespread negative impacts on society and ecosystems, and started societal debate on (changing) drought vulnerability and adaptation measures. We investigate the occurrence of multi-year droughts in the Rhine basin, with a focus on event probability in the present and in future warmer climates. Additionally, we investigate the temporally compounding physical drivers of multi-year drought events. A combination of multiple reanalysis datasets and multi-model large ensemble climate model simulations was used to provide a robust analysis of the statistics and physical processes of these rare events. We identify two types of multi-year drought events (consecutive meteorological summer droughts and long-duration hydrological droughts), and show that these occur on average about twice in a 30 year period in the present climate, though natural variability is large (zero to five events can occur in a single 30 year period). Projected decreases in summer precipitation and increases in atmospheric evaporative demand, lead to a doubling of event probability at 1 ∘ C additional global warming relative to present-day and an increase in the average length of events. Consecutive meteorological summer droughts are forced by two, seemingly independent, summers of lower than normal precipitation and higher than normal evaporative demand. The soil moisture response to this temporally compound meteorological forcing has a clear multi-year imprint, resulting in a relatively larger reduction of soil moisture content in the second year of drought, and potentially more severe drought impacts. Long-duration hydrological droughts start with a severe summer drought followed by lingering meteorologically dry conditions. This limits and slows down the hydrological recovery of soil moisture content, leading to long-lasting drought conditions. This initial exploration provides avenues for further investigation of multi-year drought hazard and vulnerability in the region, which is advised given the projected trends and vulnerability of society and ecosystems. [ABSTRACT FROM AUTHOR]

Although prolonged heat periods have become a recurring feature of European climate, little knowledge is available on dominant spatial patterns of heatwaves and their influence on moisture-related processes. Increased knowledge will help to improve heatwave and drought prediction and mitigation. This study uses hierarchical agglomerative clustering to derive nine dominating spatial heatwave patterns from a 50-member regional climate model (Canadian Regional Climate Model version 5, CRCM5-LE). The heatwave patterns correspond well with clusters derived from an observational data set (E-OBS) and with extreme historical heatwave events. Moreover, we analyse the occurrence of heatwaves in the identified spatial patterns regarding a soil moisture deficit present before and after the event. We show that negative soil moisture anomalies in the preceding winter/spring (JFMA) can serve as a predictor for heatwaves in South Europe. For North Europe, we find a negative correlation between the number of heatwave days in summer and autumn (OND) soil moisture content. [ABSTRACT FROM AUTHOR]

European climate is associated with variability and changes in the mid-latitude atmospheric circulation. In this study, we aim to investigate potential future change in circulation over Europe by using the EURO-CORDEX regional climate projections at 0.11° grid mesh. In particular, we analyze future change in 500-hPa geopotential height (Gph), 500-hPa wind speed and mean sea level pressure (MSLP) addressing different warming levels of 1 °C, 2 °C and 3 °C, respectively. Simple scaling with the global mean temperature change is applied to the regional climate projections for monthly mean 500-hPa Gph and 500-hPa wind speed. Results from the ensemble mean of individual models show a robust increase in 500-hPa Gph and MSLP in winter over Mediterranean and Central Europe, indicating an intensification of anticyclonic circulation. This circulation change emerges robustly in most simulations within the coming decade. There are also enhanced westerlies which transport warm and moist air to the Mediterranean and Central Europe in winter and spring. It is also clear that, models showing different responses to circulation depend very much on the global climate model ensemble member in which they are nested. For all seasons, particularly autumn, the ensemble mean is much more correlated with the end of the century than most of the individual models. In general, the emergence of a scaled pattern appears rather quickly. [ABSTRACT FROM AUTHOR]

The impacts of Arctic stratospheric polar vortex (SPV) on wintertime precipitation over the Northern Hemisphere are analyzed based on various datasets. Two groups of ensemble climate model experiments with the SPV nudged towards strong and weak states are performed to clarify stratospheric impacts on changes in precipitation. During weak SPV events, precipitation rates over the western and southeastern parts of North Pacific Ocean, the southern part of North Atlantic Ocean, and Southern Europe are larger, whereas the total precipitation rates over the central North Pacific, the northern part of North Atlantic and Northern Europe are smaller than those during strong SPV events. The SPV-induced changes in precipitation over the North Atlantic are stronger than those over the North Pacific. The convective (large-scale) precipitation changes play a major role in the total precipitation changes over the southern (northern) parts of middle latitudes associated with SPV changes. The tropospheric zonal wind deceleration around 60°N associated with weak SPV events is responsible for lower-tropospheric anomalous cyclonic flows over the two oceans at middle latitudes. The anomalous cyclonic flows lead to more large-scale precipitation in the southeastern parts of the oceans and less large-scale precipitation over Northern Europe and the central North Pacific during weak SPV events. The stratosphere–troposphere coupling over the North Atlantic is stronger than that over the North Pacific, leading to stronger large-scale precipitation responses over the former region. In addition, convective precipitation rates between 30 and 45°N are basically larger during weak SPV events than during strong SPV events. This is because more baroclinic waves associated with a southward shift of storm tracks during weak SPV events cause more heat exchanges between the lower latitudes and higher latitudes. Consequently, the upper tropospheric temperature and static ability between 30 and 45°N are reduced, leading to larger convective available potential energy and more convective precipitation during weak SPV events. [ABSTRACT FROM AUTHOR]

Extra-tropical windstorms are one of the costliest natural hazards affecting Europe, and windstorms that develop a sting jet are extremely damaging. A sting jet is a mesoscale core of very high wind speeds that occurs in Shapiro–Keyser type cyclones, and high-resolution models are required to adequately model sting jets. Here, we develop a low-cost methodology to automatically detect sting jets, using the characteristic warm seclusion of Shapiro–Keyser cyclones and the slantwise descent of high wind speeds, within pan-European 2.2 km convection-permitting climate model (CPM) simulations. The representation of wind gusts is improved with respect to ERA-Interim reanalysis data compared to observations; this is linked to better representation of cold conveyor belts and sting jets in the CPM. Our analysis indicates that Shapiro–Keyser cyclones, and those that develop sting jets, are the most damaging windstorms in present and future climates. The frequency of extreme windstorms is projected to increase by 2100 and a large contribution comes from sting jet storms. Furthermore, extreme wind speeds and their future changes are underestimated in the global climate model (GCM) compared to the CPM. We conclude that the CPM adds value in the representation of extreme winds and surface wind gusts and can provide improved input for impact models compared to coarser resolution models. [ABSTRACT FROM AUTHOR]

We present "EU-Trees4F", a dataset of current and future potential distributions of 67 tree species in Europe at 10 km spatial resolution. We provide both climatically suitable future areas of occupancy and the future distribution expected under a scenario of natural dispersal for two emission scenarios (RCP 4.5 and RCP 8.5) and three time steps (2035, 2065, and 2095). Also, we provide a version of the dataset where tree ranges are limited by future land use. These data-driven projections were made using an ensemble species distribution model calibrated using EU-Forest, a comprehensive dataset of tree species occurrences for Europe, and driven by seven bioclimatic parameters derived from EURO-CORDEX regional climate model simulations, and two soil parameters. "EU-Trees4F", can benefit various research fields, including forestry, biodiversity, ecosystem services, and bio-economy. Possible applications include the calibration or benchmarking of dynamic vegetation models, or informing forest adaptation strategies based on assisted tree migration. Given the multiple European policy initiatives related to forests, this dataset represents a timely and valuable resource to support policymaking. Measurement(s) trees occurences Technology Type(s) computational modeling technique Factor Type(s) trees • emission scenario • time step Sample Characteristic - Organism tree Sample Characteristic - Environment forest biome Sample Characteristic - Location Europe Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.17144627 [ABSTRACT FROM AUTHOR]

The majority of regional climate change assessments for the Euro-CORDEX region is based on high resolution atmosphere models. These models use prescribed lower boundary conditions, such as sea surface temperatures (SST) from global ocean General Circulation Models (GCMs), that do not respond to changes simulated by the regional atmosphere model, thus lacking an important feedback to the atmosphere. However, research during the past decade indicated that the use of coupled atmosphere–ocean models can lead to significantly altered model solutions compared to standalone atmosphere models for the present day climate imposing some uncertainty on the widely used uncoupled future scenarios. We here present the first multi-model and multi scenario (RCP2.6, RCP4.5, RCP8.5) ensemble of future climate change scenarios downscaled with a coupled atmosphere—ocean model in which sea surface temperature and sea ice fields are explicitly simulated by a coupled state-of-the-art high resolution ocean model and communicated to the atmosphere at 3-hourly time steps. Our ensemble generally confirms results of previous uncoupled ensembles over land areas implying that the coupling effect is restricted mainly to the coupled area and the adjacent coastal zone. By contrast, over the North Sea and Baltic Sea small scale processes point to important coupling effects that mediate the response to climate change and that can not be simulated by uncoupled models. Our results therefore impose general uncertainty on the usage of regional climate change data from uncoupled ensembles over marine areas such as for purposes of offshore wind or mussel farming, the planing of marine protected areas, and marine recreation along the coastal zone. It further sets in question the usage of uncoupled scenario data (such as Euro-CORDEX) to force high resolution ocean models. Comparing coupled and uncoupled hindcast simulations reveals that the coupling effect over land is most pronounced during the warm season when prescribed and modelled sea surface temperatures (SST) differ strongest. In addition, a generally weaker wind regime in summer damps the heat dispersion in the atmosphere so that air temperature anomalies can extent further over land compared to winter. Future projections are discussed under consideration of land-sea warming characteristics for selected climate indices as well as mean seasonal climate change. At the end of the century a clear land-sea pattern is seen in all scenarios with stronger warming over land than over open sea areas. On average land areas warm at a rate 1.5 times faster than areas over the open ocean. Over the coupled area, i.e. the North Sea and Baltic Sea tropical nights are impacted strongest and the Baltic Sea turns out to be a hot spot in future climate. This has been unrecognized in previous studies using high resolution atmosphere models with prescribed SSTs from global models which do not represent small scale ocean processes in the Baltic Sea adequately. [ABSTRACT FROM AUTHOR]

The effect of model calibration on the projection of climate change impact on hydrological indicators was assessed by employing variants of a pan-European hydrological model driven by forcing data from an ensemble of climate models. The hydrological model was calibrated using three approaches: calibration at the outlets of major river basins, regionalization through calibration of smaller scale catchments with unique catchment characteristics, and building a model ensemble by sampling model parameters from the regionalized model. The large-scale patterns of the change signals projected by all model variants were found to be similar for the different indicators. Catchment scale differences were observed between the projections of the model calibrated for the major river basins and the other two model variants. The distributions of the median change signals projected by the ensemble model were found to be similar to the distributions of the change signals projected by the regionalized model for all hydrological indicators. The study highlights that the spatial detail to which model calibration is performed can highly influence the catchment scale detail in the projection of climate change impact on hydrological indicators, with an absolute difference in the projections of the locally calibrated model and the model calibrated for the major river basins ranging between 0 and 55% for mean annual discharge, while it has little effect on the large-scale pattern of the projection. [ABSTRACT FROM AUTHOR]

Convection-permitting climate models have shown superior performance in simulating important aspects of the precipitation climate including extremes and also to give partly different climate change signals compared to coarser-scale models. Here, we present the first long-term (1998–2018) simulation with a regional convection-permitting climate model for Fenno-Scandinavia. We use the HARMONIE-Climate (HCLIM) model on two nested grids; one covering Europe at 12 km resolution (HCLIM12) using parameterized convection, and one covering Fenno-Scandinavia with 3 km resolution (HCLIM3) with explicit deep convection. HCLIM12 uses lateral boundaries from ERA-Interim reanalysis. Model results are evaluated against reanalysis and various observational data sets, some at high resolutions. HCLIM3 strongly improves the representation of precipitation compared to HCLIM12, most evident through reduced "drizzle" and increased occurrence of higher intensity events as well as improved timing and amplitude of the diurnal cycle. This is the case even though the model exhibits a cold bias in near-surface temperature, particularly for daily maximum temperatures in summer. Simulated winter precipitation is biased high, primarily over complex terrain. Considerable undercatchment in observations may partly explain the wet bias. Examining instead the relative occurrence of snowfall versus rain, which is sensitive to variance in topographic heights it is shown that HCLIM3 provides added value compared to HCLIM12 also for winter precipitation. These results, indicating clear benefits of convection-permitting models, are encouraging motivating further exploration of added value in this region, and provide a valuable basis for impact studies. [ABSTRACT FROM AUTHOR]

Previous studies indicate that the temperature changes from a day to the following day are not normally distributed either in winter or in summer. However, this study is the first to investigate the causes of the asymmetry in these temperature changes. We hypothesised that the asymmetries are driven by the passages of atmospheric fronts and by the radiative processes during anticyclonic circulation conditions. We used the maximum and minimum temperatures observed at the Praha-Karlov station, the records of front passages, and 7 different catalogues of circulation types over central Europe. We demonstrated that the asymmetries in the large temperature changes (5 °C and larger) were related to the passages of all three front types in winter and to the passages of cold fronts in summer. The asymmetries in the slight temperature changes (up to 3 °C) were mainly caused by cold advection from the north and the northeast in winter and by warm advection from the south, the southeast, and the east in summer. We suppose that our results can be generalized; that is, the passages of atmospheric fronts and cold/warm advection are the likely causes of the asymmetries of the day-to-day temperature changes elsewhere in Europe. The presented findings can be applied as a tool for the validation of climate model outputs. [ABSTRACT FROM AUTHOR]

We assess the differences of future climate changes over Europe in summer as projected by state-of-the-art regional climate models (RCM, from the EURO-Coordinated Regional Downscaling Experiment) and by their forcing global climate models (GCM, from the Coupled Model Intercomparison Project Phase 5) and study the associated physical mechanisms. We show that important discrepancies at large-scales exist between global and regional projections. The RCMs project at the end of the 21st century over a large area of Europe a summer warming 1.5–2 K colder, and a much smaller decrease of precipitation of 5%, versus 20% in their driving GCMs. The RCMs generally simulate a much smaller increase in shortwave radiation at surface, which directly impacts surface temperature. In addition to differences in cloud cover changes, the absence of time-varying anthropogenic aerosols in most regional simulations plays a major role in the differences of solar radiation changes. We confirm this result with twin regional simulations with and without time-varying anthropogenic aerosols. Additionally, the RCMs simulate larger increases in evapotranspiration over the Mediterranean sea and larger increases/smaller decreases over land, which contribute to smaller changes in relative humidity, with likely impacts on clouds and precipitation changes. Several potential causes of these differences in evapotranspiration changes are discussed. Overall, this work suggests that the current EURO-CORDEX RCM ensemble does not capture the upper part of the climate change uncertainty range, with important implications for impact studies and the adaptation policies that they inform. [ABSTRACT FROM AUTHOR]

In recent decades, unprecedented extreme summer heat waves have occurred in Europe, and they have exhibited an increasing trend since 1970s. Although previous studies have suggested that these recent hot European summers could have been instigated by the underlying surface thermal conditions, the possible influence of shrinking Arctic sea ice and Eurasian snow cover on heat waves are not well understood. Herein, we present evidence obtained via observational analyses and numerical experiments indicating that the interdecadal increase in European heat waves is closely linked to the reductions in Arctic sea ice concentration (ASIC) and Eurasian snow cover fraction (EASC) across mid–high latitudes via the excitation of the anomalous Eurasian wave train. The combined effects of declined ASIC and EASC, accompanied by the drier soil and the stronger heat flux, tend to weaken the poleward temperature gradient at mid–high latitudes and affect the midlatitude jet stream and transient eddy activities. These dynamic and thermodynamic circulations increase the likelihood of more persistent European blocking events that favor frequent and strengthened heat waves. Further projection analysis of simulations from 13 CMIP5 climate models suggests that Europe may experience more hot summers as the ASIC and EASC continue to decline over the next century. [ABSTRACT FROM AUTHOR]

Impact models are a major source of information for quantifying the consequences of future climate change for humans and the environment. To provide plausible outputs, these models require unbiased high resolution meteorological data as input for atmospheric conditions. State of the art regional climate models (RCMs) often fail to provide such data, since they can exhibit large systematic biases. Therefore, bias correction methods (BCMs) have become a common tool in climate impact studies. Bias correction, however, comes with strong assumptions and limitations, often resulting from the fact that most BCMs are unable to appropriately calibrate a time dependent and conditional transfer function. To address this problem, we introduce here regression quantile mapping (RQM), a bias correction approach based on (linear) regression models which allow to design transfer functions based on expert knowledge. The new RQM algorithm is described in full detail in its basic (linear model) version and applied to RCM generated precipitation data for entire Europe. Based on the latter example, we provide a thorough comparison with another established BCM, quantile delta mapping (QDM) regarding the seasonal characteristics of precipitation sums. Our results demonstrate that RQM already achieves good results when a simple linear model is used. The relationship between precipitation and temperature was properly evaluated by RQM and representation of seasonal variations and key characteristics of precipitation were improved for most seasons. Systematic biases where reduced significantly during this process. Particular improvements in comparison with QDM are found regarding the shape and width of the distribution of bias corrected model precipitation. Furthermore, the representation of precipitation extremes within the data was largely improved when RQM was used instead of QDM. [ABSTRACT FROM AUTHOR]

Uncertainties in climate model ensembles are still relatively large. Besides scenario and model response uncertainty, natural variability is another important source of uncertainty. To study regional natural variability on timescales of several decades and more, observations are often too sparse and short. Regional Climate Models (RCMs) can be used to overcome this lack of useful data at high spatial resolutions. In this study, we compare a new 50-member single RCM large ensemble (CRCM5-LE) with an ensemble of 22 EURO-CORDEX models for seasonal temperature and precipitation at 0.11° grid size over Europe—all driven by the RCP 8.5 scenario. This setup allows us to quantify the contribution of natural/model-internal variability on the total uncertainty of a multi-model ensemble. The variability of climate change signals within the two ensembles is compared for three future periods (2020–2049, 2040–069 and 2070–2099). Results show that the single model spread is usually smaller than the multi-model spread for temperature. Similar variabilities can mostly be found in the near future (and to a lesser extent in the mid future) during winter and spring, especially for northern and central parts of Europe. The contribution of internal variability is generally higher for precipitation. In the near future almost all seasons and regions show similar variabilities. In the mid and far future only fall, summer and spring still show similar variabilites. There is a significant decrease of the contribution of internal variability over time for both variables. However, even in the far future for most regions and seasons 25–75% of the overall variability can be explained by internal variability. [ABSTRACT FROM AUTHOR]

Considering the recent extreme precipitation in southeast Europe, it has become necessity to investigate the impact of climate change on extreme precipitation. The aim of this study was to determine the change in precipitation quantiles with longer return periods under changing climate conditions. The study was conducted using the daily records gathered at 11 precipitation stations within the Lim River Basin, Serbia. The simulated precipitation datasets were collected from three regional climate models for the baseline period (1971–2000), as well as the future period (2006–2055) under the 2.6, 4.5 and 8.5 representative concentration pathways. The raw precipitation data from the climate models were transformed by employing four bias correction methods. Using the bias-corrected precipitation, an ensemble of annual maximum daily precipitation was developed. A weighted ensemble approach was applied to estimate the weights of each ensemble member favorizing the members whose quantiles were closer to observed measurements. The mixed general extreme value distribution was used to derive the projected quantiles with 100, 50, 25, 10, five and two year return periods based on the estimated quantiles and the normalized weights of all ensemble members. An overall increase of 69% and 56% for the 100 and 50 year return periods, respectively, can be expected within the northern part of the basin. Similarly, an overall increase of 50–57% and 39–42% for the 100 and 50 year return periods, respectively, may be expected for the central and southern parts of the Lim River Basin. [ABSTRACT FROM AUTHOR]

There is now a wide range of forecasts and observations of seasonal climatic conditions that can be used across a range of application sectors, including hydrological risk forecasting, planning and management. As we rely more on seasonal climate forecasts, it becomes essential to also assess its quality to ensure its intended use. In this study, we provide the most comprehensive assessment of seasonal temperature and precipitation ensemble forecasts of the EUROSIP multi-model forecasting system over Europe. The forecasts from the four individual climate models within the EUROSIP are assessed using both deterministic and probabilistic approaches. One equally and two unequally Weighted Multi-Models (WMMs) are also constructed from the individual models, for both climate variables, and their respective forecasts are also assessed. Consistent with existing literature, we find limited seasonal climate prediction skill over Europe. A simple equally WMM system performs better than both unequally WMM combination systems. However, the equally WMM system does not always outperform the single best model within the EUROSIP multi-model. Based on the results, it is recommended to assess seasonal temperature and precipitation forecast of individual climate models as well as their multi-model mean for a comprehensive overview of the forecast skill. [ABSTRACT FROM AUTHOR]

A detailed analysis is carried out to assess the HadGEM3-A global atmospheric model skill in simulating extreme temperatures, precipitation and storm surges in Europe in the view of their attribution to human influence. The analysis is performed based on an ensemble of 15 atmospheric simulations forced with observed sea surface temperature of the 54 year period 1960-2013. These simulations, together with dual simulations without human influence in the forcing, are intended to be used in weather and climate event attribution. The analysis investigates the main processes leading to extreme events, including atmospheric circulation patterns, their links with temperature extremes, land-atmosphere and troposphere-stratosphere interactions. It also compares observed and simulated variability, trends and generalized extreme value theory parameters for temperature and precipitation. One of the most striking findings is the ability of the model to capture North-Atlantic atmospheric weather regimes as obtained from a cluster analysis of sea level pressure fields. The model also reproduces the main observed weather patterns responsible for temperature and precipitation extreme events. However, biases are found in many physical processes. Slightly excessive drying may be the cause of an overestimated summer interannual variability and too intense heat waves, especially in central/northern Europe. However, this does not seem to hinder proper simulation of summer temperature trends. Cold extremes appear well simulated, as well as the underlying blocking frequency and stratosphere-troposphere interactions. Extreme precipitation amounts are overestimated and too variable. The atmospheric conditions leading to storm surges were also examined in the Baltics region. There, simulated weather conditions appear not to be leading to strong enough storm surges, but winds were found in very good agreement with reanalyses. The performance in reproducing atmospheric weather patterns indicates that biases mainly originate from local and regional physical processes. This makes local bias adjustment meaningful for climate change attribution. [ABSTRACT FROM AUTHOR]

The objective of the present work is to compare the projections of surface solar radiation (SSR) simulated by four regional climate models (CCLM, RCA4, WRF, ALADIN) with the respective fields of their ten driving CMIP5 global climate models. First the annual and seasonal SSR changes are examined in the regional and in the global climate models based on the RCP8.5 emission scenarios. The results show significant discrepancies between the projected SSR, the multi-model mean of RCMs indicates a decrease in SSR of −0.60 W/m per decade over Europe, while the multi-model mean of the associated GCMs used to drive the RCMs gives an increase in SSR of +0.39 W/m per decade for the period of 2006-2100 over Europe. At seasonal scale the largest differences appear in spring and summer. The different signs of SSR projected changes can be interpreted as the consequence of the different behavior of cloud cover in global and regional climate models. Cloudiness shows a significant decline in GCMs with −0.24% per decade which explains the extra income in SSR, while in case of the regional models no significant changes in cloudiness can be detected. The reduction of SSR in RCMs can be attributed to increasing atmospheric absorption in line with the increase of water vapor content. Both global and regional models overestimate SSR in absolute terms as compared to surface observations, in line with an underestimation of cloud cover. Regional models further have difficulties to adequately reproduce the observed trends in SSR over the past decades. [ABSTRACT FROM AUTHOR]

An ensemble of six pairs of RCM experiments performed at 25 and 50 km for the period 1961-2000 over a large European domain is examined in order to evaluate the effects of resolution on the simulation of daily precipitation statistics. Application of the non-parametric two-sample Kolmorgorov-Smirnov test, which tests for differences in the location and shape of the probability distributions of two samples, shows that the distribution of daily precipitation differs between the pairs of simulations over most land areas in both summer and winter, with the strongest signal over southern Europe. Two-dimensional histograms reveal that precipitation intensity increases with resolution over almost the entire domain in both winter and summer. In addition, the 25 km simulations have more dry days than the 50 km simulations. The increase in dry days with resolution is indicative of an improvement in model performance at higher resolution, while the more intense precipitation exceeds observed values. The systematic increase in precipitation extremes with resolution across all models suggests that this response is fundamental to model formulation. Simple theoretical arguments suggest that fluid continuity, combined with the emergent scaling properties of the horizontal wind field, results in an increase in resolved vertical transport as grid spacing decreases. This increase in resolution-dependent vertical mass flux then drives an intensification of convergence and resolvable-scale precipitation as grid spacing decreases. This theoretical result could help explain the increasingly, and often anomalously, large stratiform contribution to total rainfall observed with increasing resolution in many regional and global models. [ABSTRACT FROM AUTHOR]

Daily precipitation statistics as simulated by the ERA-Interim-driven EURO-CORDEX regional climate model (RCM) ensemble are evaluated over two distinct regions of the European continent, namely the European Alps and Spain. The potential added value of the high-resolution 12 km experiments with respect to their 50 km resolution counterparts is investigated. The statistics considered consist of wet-day intensity and precipitation frequency as a measure of mean precipitation, and three precipitation-derived indicators (90th percentile on wet days-90pWET, contribution of the very wet days to total precipitation-R95pTOT and number of consecutive dry days-CDD). As reference for model evaluation high resolution gridded observational data over continental Spain (Spain011/044) and the Alpine region (EURO4M-APGD) are used. The assessment and comparison of the two resolutions is accomplished not only on their original horizontal grids (approximately 12 and 50 km), but the high-resolution RCMs are additionally regridded onto the coarse 50 km grid by grid cell aggregation for the direct comparison with the low resolution simulations. The direct application of RCMs e.g. in many impact modelling studies is hampered by model biases. Therefore bias correction (BC) techniques are needed at both resolutions to ensure a better agreement between models and observations. In this work, the added value of the high resolution (before and after the bias correction) is assessed and the suitability of these BC methods is also discussed. Three basic BC methods are applied to isolate the effect of biases in mean precipitation, wet-day intensity and wet-day frequency on the derived indicators. Daily precipitation percentiles are strongly affected by biases in the wet-day intensity, whereas the dry spells are better represented when the simulated precipitation frequency is adjusted to the observed one. This confirms that there is no single optimal way to correct for RCM biases, since correcting some distributional features typically leads to an improvement of some aspects but to a deterioration of others. Regarding mean seasonal biases before the BC, we find only limited evidence for an added value of the higher resolution in the precipitation intensity and frequency or in the derived indicators. Thereby, evaluation results considerably depend on the RCM, season and indicator considered. High resolution simulations better reproduce the indicators' spatial patterns, especially in terms of spatial correlation. However, this improvement is not statistically significant after applying specific BC methods. [ABSTRACT FROM AUTHOR]

Cyclonic windstorms are one of the most important natural hazards for Europe, but robust climate projections of the position and the strength of the North Atlantic storm track are not yet possible, bearing significant risks to European societies and the (re)insurance industry. Previous studies addressing the problem of climate model uncertainty through statistical comparisons of simulations of the current climate with (re-)analysis data show large disagreement between different climate models, different ensemble members of the same model and observed climatologies of intense cyclones. One weakness of such evaluations lies in the difficulty to separate influences of the climate model's basic state from the influence of fast processes on the development of the most intense storms, which could create compensating effects and therefore suggest higher reliability than there really is. This work aims to shed new light into this problem through a cost-effective 'seamless' approach of hindcasting 20 historical severe storms with the two global climate models, ECHAM6 and GA4 configuration of the Met Office Unified Model, run in a numerical weather prediction mode using different lead times, and horizontal and vertical resolutions. These runs are then compared to re-analysis data. The main conclusions from this work are: (a) objectively identified cyclone tracks are represented satisfactorily by most hindcasts; (b) sensitivity to vertical resolution is low; (c) cyclone depth is systematically under-predicted for a coarse resolution of T63 by both climate models; (d) no systematic bias is found for the higher resolution of T127 out to about three days, demonstrating that climate models are in fact able to represent the complex dynamics of explosively deepening cyclones well, if given the correct initial conditions; (e) an analysis using a recently developed diagnostic tool based on the surface pressure tendency equation points to too weak diabatic processes, mainly latent heating, as the main source for the under-prediction in the coarse-resolution runs. Finally, an interesting implication of these results is that the too low number of deep cyclones in many free-running climate simulations may therefore be related to an insufficient number of storm-prone initial conditions. This question will be addressed in future work. [ABSTRACT FROM AUTHOR]

In Portugal, the precipitation regimes present one of the highest volumes of extreme precipitation occurrence in Europe, and one of the largest mean precipitation spatial gradient (annual observed values above 2,500 mm in the NW and under 400 mm in the SE). Moreover, southern Europe is one of the most vulnerable regions in the world to climate change. In the ENSEMBLES framework many climate change assessment studies were performed, but none focused on Portuguese precipitation. An extensive evaluation and ranking of the RCMs results addressing the representation of mean precipitation and frequency distributions was performed through the computation of statistical errors and frequency distribution scores. With these results, an ensemble was constructed; giving the same weight to mean precipitation and distribution model skills. This ensemble reveals a good ability to describe the precipitation regime in Portugal, and enables the evaluation of the eventual impact of climate change on Portuguese precipitation according to the A1B scenario. The mean seasonal precipitation is expected to decrease substantially in all seasons, excluding winter. This reduction is statistically significant; it spans from less than 20 % in the north to 40 % in the south in the intermediate seasons, and is above 50 % in the largest portion of mainland in summer. At a basin level the precipitation diminishes in all months for all the basins with exception of December. Total precipitation PDFs reveal an important decrease of the contribution from low to moderate/high precipitation bins, and a striking rise for days with extreme rainfall, up to 30 %. [ABSTRACT FROM AUTHOR]

Future anthropogenic climate change is likely to increase the air temperature ( T) across Europe and increase the frequency, duration and magnitude of severe heat stress events. Heat stress events are generally associated with clear-sky conditions and high T, which give rise to high radiant heat load, i.e. mean radiant temperature ( T). In urban environments, T is strongly influenced by urban geometry. The present study examines the effect of urban geometry on daytime heat stress in three European cities (Gothenburg in Sweden, Frankfurt in Germany and Porto in Portugal) under present and future climates, using T as an indicator of heat stress. It is found that severe heat stress occurs in all three cities. Similar maximum daytime T is found in open areas in all three cities despite of the latitudinal differences in average daytime T. In contrast, dense urban structures like narrow street canyons are able to mitigate heat stress in the summer, without causing substantial changes in T in the winter. Although the T averages are similar for the north-south and east-west street canyons in each city, the number of hours when T exceeds the threshold values of 55.5 and 59.4 °C-used as indicators of moderate and severe heat stress-in the north-south canyons is much higher than that in the east-west canyons. Using statistically downscaled data from a regional climate model, it is found that the study sites were generally warmer in the future scenario, especially Porto, which would further exacerbate heat stress in urban areas. However, a decrease in solar radiation in Gothenburg and Frankfurt reduces T in the spring, while the reduction in T is somewhat offset by increasing T in other seasons. It suggests that changes in the T under the future scenario are dominated by variations in T. Nonetheless, the intra-urban differences remain relatively stable in the future. These findings suggest that dense urban structure can reduce daytime heat stress since it reduces the number of hours of high T in the summer and does not cause substantial changes in average and minimum T in the winter. In dense urban settings, a more diverse urban thermal environment is also preferred to compensate for reduced solar access in the winter. The extent to which the urban geometry can be optimized for the future climate is also influenced by local urban characteristics. [ABSTRACT FROM AUTHOR]

We compared soil moisture from the soil water balance model for European Water Accounting (swbEWA) with in situ observations from nine locations in three European climatic zones (continental, Mediterranean and maritime temperate), for different periods between 2003 and 2011. Despite the simplicity of the swbEWA model, the patterns of temporal changes in soil moisture content are well represented at all locations. Annual averages show that the model overestimates the soil moisture content, and that overestimations are the smallest when measurements are obtained from more than one depth. These results suggest that the relationship between simulated and observed soil moisture also depends on the number of measurements and the depth over which they are taken. In the continental climate, where snow cover and frozen soil influence soil moisture, we observe higher root mean square error values in winter months. However, in the Mediterranean and maritime temperate climates, we do not observe clear common seasonal patterns in the soil moisture profile, which makes it difficult to relate the model's accuracy to climate. With the percentage of correctness and probability of detection measures, we tested the model performance in simulating dry versus non-dry events. The percentage of the correctly classified dry and non-dry events is higher than 84 % at all locations, whereas the probability to detect dry events is significantly lower, exceeding 50 % at only four out of nine stations. The frequency distribution of consecutive days with dry soil (CDDS) confirms the model performance: higher number of short dry periods (with less than 20 days of soil moisture near wilting point) are reproduced and observed in continental climates, whereas long dry periods (longer than 50 days) are noted in the Mediterranean climate. Overall, the statistical measures suggest that the model produces the highest accuracy in summer months at the stations in continental climates, whereas in the Mediterranean climate, the accuracy is slightly higher in the colder seasons. [ABSTRACT FROM AUTHOR]

This study aims at sharpening the existing knowledge of expected seasonal mean climate change and its uncertainty over Europe for the two key climate variables air temperature and precipitation amount until the mid-twentyfirst century. For this purpose, we assess and compensate the global climate model (GCM) sampling bias of the ENSEMBLES regional climate model (RCM) projections by combining them with the full set of the CMIP3 GCM ensemble. We first apply a cross-validation in order to assess the skill of different statistical data reconstruction methods in reproducing ensemble mean and standard deviation. We then select the most appropriate reconstruction method in order to fill the missing values of the ENSEMBLES simulation matrix and further extend the matrix by all available CMIP3 GCM simulations forced by the A1B emission scenario. Cross-validation identifies a randomized scaling approach as superior in reconstructing the ensemble spread. Errors in ensemble mean and standard deviation are mostly less than 0.1 K and 1.0 % for air temperature and precipitation amount, respectively. Reconstruction of the missing values reveals that expected seasonal mean climate change of the ENSEMBLES RCM projections is not significantly biased and that the associated uncertainty is not underestimated due to sampling of only a few driving GCMs. In contrast, the spread of the extended simulation matrix is partly significantly lower, sharpening our knowledge about future climate change over Europe by reducing uncertainty in some regions. Furthermore, this study gives substantial weight to recent climate change impact studies based on the ENSEMBLES projections, since it confirms the robustness of the climate forcing of these studies concerning GCM sampling. [ABSTRACT FROM AUTHOR]

Despite an increasing understanding of potential climate change impacts in Europe, the associated uncertainties remain a key challenge. In many impact studies, the assessment of uncertainties is underemphasised, or is not performed quantitatively. A key source of uncertainty is the variability of climate change projections across different regional climate models (RCMs) forced by different global circulation models (GCMs). This study builds upon an indicator-based NUTS-2 level assessment that quantified potential changes for three climate-related hazards: heat stress, river flood risk, and forest fire risk, based on five GCM/RCM combinations, and non-climatic factors. First, a sensitivity analysis is performed to determine the fractional contribution of each single input factor to the spatial variance of the hazard indicators, followed by an evaluation of uncertainties in terms of spread in hazard indicator values due to inter-model climate variability, with respect to (changes in) impacts for the period 2041-70. The results show that different GCM/RCM combinations lead to substantially varying impact indicators across all three hazards. Furthermore, a strong influence of inter-model variability on the spatial patterns of uncertainties is revealed. For instance, for river flood risk, uncertainties appear to be particularly high in the Mediterranean, whereas model agreement is higher for central Europe. The findings allow for a hazard-specific identification of areas with low vs. high model agreement (and thus confidence of projected impacts) within Europe, which is of key importance for decision makers when prioritising adaptation options. [ABSTRACT FROM AUTHOR]

Soil physical characteristics can influence terrestrial hydrology and the energy balance and may thus affect land-atmosphere exchanges. However, only few studies have investigated the importance of soil textures for climate. In this study, we examine the impact of soil texture specification in a regional climate model. We perform climate simulations over Europe using soil maps derived from two different sources: the soil map of the world from the Food and Agricultural Organization and the European Soil Database from the European Commission Joint Research Center. These simulations highlight the importance of the specified soil texture in summer, with differences of up to 2 °C in mean 2-m temperature and 20 % in precipitation resulting from changes in the partitioning of energy at the land surface into sensible and latent heat flux. Furthermore, we perform additional simulations where individual soil parameters are perturbed in order to understand their role for summer climate. These simulations highlight the importance of the vertical profile of soil moisture for evapotranspiration. Parameters affecting the latter are hydraulic diffusivity parameters, field capacity and plant wilting point. Our study highlights the importance of soil properties for climate simulations. Given the uncertainty associated with the geographical distribution of soil texture and the resulting differences between maps from different sources, efforts to improve existing databases are needed. In addition, climate models would benefit from tackling unresolved issues in land-surface modeling related to the high spatial variability in soil parameters, both horizontally and vertically, and to limitations of the concept of soil textural class. [ABSTRACT FROM AUTHOR]

Clear precipitation trends have been observed in Europe over the past century. In winter, precipitation has increased in north-western Europe. In summer, there has been an increase along many coasts in the same area. Over the second half of the past century precipitation also decreased in southern Europe in winter. An investigation of precipitation trends in two multi-model ensembles including both global and regional climate models shows that these models fail to reproduce the observed trends. In many regions the model spread does not cover the trend in the observations. In contrast, regional climate model (RCM) experiments with observed boundary conditions reproduce the observed precipitation trends much better. The observed trends are largely compatible with the range of uncertainties spanned by the ensemble, indicating that the boundary conditions of RCMs are responsible for large parts of the trend biases. We find that the main factor in setting the trend in winter is atmospheric circulation, for summer sea surface temperature (SST) is important in setting precipitation trends along the North Sea and Atlantic coasts. The causes of the large trends in atmospheric circulation and summer SST are not known. For SST there may be a connection with the well-known ocean circulation biases in low-resolution ocean models. A quantitative understanding of the causes of these trends is needed so that climate model based projections of future climate can be corrected for these precipitation trend biases. [ABSTRACT FROM AUTHOR]

Climate models predict substantial summer precipitation reductions in Europe and the Mediterranean region in the twenty-first century, but the extent to which these models correctly represent the mechanisms of summertime precipitation in this region is uncertain. Here an analysis is conducted to compare the observed and simulated impacts of the dominant large-scale driver of summer rainfall variability in Europe and the Mediterranean, the summer North Atlantic Oscillation (SNAO). The SNAO is defined as the leading mode of July-August sea level pressure variability in the North Atlantic sector. Although the SNAO is weaker and confined to northern latitudes compared to its winter counterpart, with a southern lobe located over the UK, it significantly affects precipitation in the Mediterranean, particularly Italy and the Balkans (correlations of up to 0.6). During high SNAO summers, when strong anticyclonic conditions and suppressed precipitation prevail over the UK, the Mediterranean region instead is anomalously wet. This enhanced precipitation is related to the presence of a strong upper-level trough over the Balkans-part of a hemispheric pattern of anomalies that develops in association with the SNAO-that leads to mid-level cooling and increased potential instability. Neither this downstream extension nor the surface influence of the SNAO is captured in the two CMIP3 models examined (HadCM3 and GFDL-CM2.1), with weak or non-existent correlations between the SNAO and Mediterranean precipitation. Because these models also predict a strong upward SNAO trend in the future, the error in their representation of the SNAO surface signature impacts the projected precipitation trends. In particular, the attendant increase in precipitation that, based on observations, should occur in the Mediterranean and offset some of the non-SNAO related drying does not occur. Furthermore, the fact that neither the observed SNAO nor summer precipitation in Europe/Mediterranean region exhibits any significant trend so far (for either the full century or the recent half of the record) does not increase our confidence in these model projections. [ABSTRACT FROM AUTHOR]

Various combinations of thirteen regional climate models (RCM) and six general circulation models (GCM) were used in FP6-ENSEMBLES. The response to the SRES-A1B greenhouse gas concentration scenario over Europe, calculated as the difference between the 2021-2050 and the 1961-1990 means can be viewed as an expected value about which various uncertainties exist. Uncertainties are measured here by variance explained for temperature and precipitation changes over eight European sub-areas. Three sources of uncertainty can be evaluated from the ENSEMBLES database. Sampling uncertainty is due to the fact that the model climate is estimated as an average over a finite number of years (30) despite a non-negligible interannual variability. Regional model uncertainty is due to the fact that the RCMs use different techniques to discretize the equations and to represent sub-grid effects. Global model uncertainty is due to the fact that the RCMs have been driven by different GCMs. Two methods are presented to fill the many empty cells of the ENSEMBLES RCM × GCM matrix. The first one is based on the same approach as in FP5-PRUDENCE. The second one uses the concept of weather regimes to attempt to separate the contribution of the GCM and the RCM. The variance of the climate response is analyzed with respect to the contribution of the GCM and the RCM. The two filling methods agree that the main contributor to the spread is the choice of the GCM, except for summer precipitation where the choice of the RCM dominates the uncertainty. Of course the implication of the GCM to the spread varies with the region, being maximum in the South-western part of Europe, whereas the continental parts are more sensitive to the choice of the RCM. The third cause of spread is systematically the interannual variability. The total uncertainty about temperature is not large enough to mask the 2021-2050 response which shows a similar pattern to the one obtained for 2071-2100 in PRUDENCE. The uncertainty about precipitation prevents any quantitative assessment on the response at grid point level for the 2021-2050 period. One can however see, as in PRUDENCE, a positive response in winter (more rain in the scenario than in the reference) in northern Europe and a negative summer response in southern Europe. [ABSTRACT FROM AUTHOR]