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Estimating the impact of climate change and emission scenarios on air pollution can be done using regional climate models (RCMs). Climate uncertainties are commonly estimated using RCM ensembles such as provided by EURO-CORDEX. Despite the strong relations between the weather and air pollutants, interactions are usually complex and require meteorological parameters that are not commonly available for the RCM ensembles. Pollution peaks, however, often coincide with stagnant atmospheric conditions that can be captured with widely-available RCM data. We first show that a commonly-used atmospheric stability index that uses rainfall, near-surface and 500 hPa wind speed, relates well to average and extreme air pollutant concentrations over Europe using Copernicus Atmosphere Monitoring Service (CAMS) data. We then provide an in-depth validation of 25 RCMs to reproduce the spatio-temporal features of air stagnation by comparison with ERA5. Overall the models were found to reproduce stagnant episodes fairly well, especially after bias correction. The systematic underestimation of stagnation frequency and duration is traced back to overestimated near-surface wind speed for a large group of models at high-elevation regions where the temporal correlations are also low. Regardless of the reference dataset, two model groups are identified that, independent on their resolution, give strongly different results in terms of orographic dependence of surface wind speed. These strong discrepancies underscore the need for bias correction when using RCM data for analysis of stagnation episodes.

The demand of city planners for quantitative information on the impact of climate change on the urban environment is increasing. However, such information is usually extracted from decadelong climate projections generated with global or regional climate models (RCMs). Because of their coarse resolution and unsuitable physical parameterization, however, their model output is not adequate to be used at city scale. A full dynamical downscaling to city level, on the other hand, is computationally too expensive for climatological time scales. A statistical–dynamical computationally inexpensive method is therefore proposed that approximates well the behavior of the full dynamical downscaling approach. The approach downscales RCM simulations using the combination of an RCM at high resolution (H-RES) and a land surface model (LSM). The method involves the setup of a database of urban signatures by running an H-RES RCM with and without urban parameterization for a relatively short period. Using an analog approach, these signatures are first selectively added to the long-term RCM data, which are then used as forcing for an LSM using an urban parameterization in a stand-alone mode. A comparison with a full dynamical downscaling approach is presented for the city of Brussels, Belgium, for 30 summers with the combined ALADIN–AROME model (ALARO-0) coupled to the Surface Externalisée model (SURFEX) as H-RES RCM and SURFEX as LSM. The average bias of the nocturnal urban heat island during heat waves is vanishingly small, and the RMSE is strongly reduced. Not only is the statistical–dynamical approach able to correct the heat-wave number and intensities, it can also improve intervariable correlations and multivariate and temporally correlated indices, such as Humidex.

Due to its high population density and strong industrialisation, Europe is subject to a high degree of air pollution. Reliable information on current and future air quality (AQ) is therefore necessary to develop policies. This information can be based on regional climate models (RCMs) such as used in CORDEX where weather-related uncertainties are estimated using RCM ensemble. Air polution peaks often occur during stagnant atmospheric conditions. We validate EURO-CORDEX RCMs to reproduce stagnant periods characterized by the Horton atmospheric stability index. We first prove the index's relation with both average and extreme air pollutant concentrations. The spatio-temporal features of air stagnant periods over continental Europe are then compared with reanalysis data from ERA5 for 25 RCM models. Overall a satisfactorily agreement is found for the stagnant periods despite a systematically underestimated frequency and stagnation duration. This bias is tracked back to the behavior of a large group of models over orographically complex regions. We show how bias correction can be used to improve the average, the variability and the duration of stagnation periods.

Sub-daily precipitation extremes can have a huge impact on society as they cause hazards such as flooding, erosion and landslides. For example, the July floods in Germany, Belgium and nearby countries, were one of the costliest events in Europe, with insured losses up to USD 13 billion. Climate change is expected to intensify precipitation extremes as atmospheric water content increases by 6-7% per degree of warming, underscoring the need to predict future hydrological hazards. Regional Climate Models (RCMs) typically run at a spatial resolution of 12-25 km, but they insufficiently describe the small-scale features of observed sub-daily precipitation extremes. The past decade, convection-permitting RCMs were developed which run at high resolution (1-4 km), and explicitly resolve deep convection. Confidence in future projections requires that RCMs adequately simulate the statistical features of observed sub-daily extreme precipitation and also represent the physical processes associated with convective events. We propose a diagnostic framework for simulated 1h-24h rainfall extremes that summarizes the overall RCM performance. This includes the following metrics: the seasonal/diurnal cycle, temperature and humidity dependency, temporal scaling, and spatiotemporal clustering. A substantial part of the work is devoted to the statistical modelling of the metrics with Extreme Value Theory (EVT). We illustrate the evaluation tool with convection-permitting RCM simulations over Belgium against high-frequency observations and assess the benefit of the convection-permitting RCMs with respect to coarser scales. Finally, we give some guidelines for bias correction of simulated precipitation extremes.

There is an increasing need to obtain climate projections for cities using an ensemble approach for uncertainty estimation. Yet, current-day computational resources are too limited to dynamically downscale GCM ensembles to urban scale. Here, a recently developed and validated statistical-dynamical computationally-cheap method is employed to downscale ten EURO-CORDEX climate projections over Brussels (Belgium) covering the period 1971–2100. Results show that, under the Paris agreement, summer mean projected temperature in Brussels will rise by 3.6 °C to 4.1 °C [+ − 0.7 °C] on average. The Urban Heat Island (UHI) intensity does not increase under future global warming with even a slight decrease under heatwave (HW) conditions by 0.1 °C (±0.1 °C). However, the number of HW days is projected to be 30.6% and 158.9% higher for the 2 °C and 3 °C Global Warming Levels (GWL), respectively, as compared to 1.5 °C GWL. The heat stress during HW periods also follows the same trend: compared to a 1.5 °C GWL, the number of extreme heat stress days at 2 °C (3 °C) GWL will increase by 29% (91%) on average inside the city. The results can be used in support of adaptation measures, which should be considered for future resilience of the city of Brussels.

Sub-daily precipitation extremes are high-impact events that can result in flash floods, sewer system overload, or landslides. Several studies have reported an intensification of projected short-duration extreme rainfall in a warmer future climate. Traditionally, regional climate models (RCMs) are run at a coarse resolution using deep-convection parameterization for these extreme events. As computational resources are continuously ramping up, these models are run at convection-permitting resolution, thereby partly resolving the small-scale precipitation events explicitly. To date, a comprehensive evaluation of convection-permitting models is still missing. We propose an evaluation strategy for simulated sub-daily rainfall extremes that summarizes the overall RCM performance. More specifically, the following metrics are addressed: the seasonal/diurnal cycle, temperature and humidity dependency, temporal scaling and spatio-temporal clustering. The aim of this paper is: (i) to provide a statistical modeling framework for some of the metrics, based on extreme value analysis, (ii) to apply the evaluation metrics to a micro-ensemble of convection-permitting RCM simulations over Belgium, against high-frequency observations, and (iii) to investigate the added value of convection-permitting scales with respect to coarser 12-km resolution. We find that convection-permitting models improved precipitation extremes on shorter time scales (i.e, hourly or two-hourly), but not on 6h-24h time scales. Some metrics such as the diurnal cycle or the Clausius-Clapeyron rate are improved by convection-permitting models, whereas the seasonal cycle appears robust across spatial scales. On the other hand, the spatial dependence is poorly represented at both convection-permitting scales and coarser scales. Our framework provides perspectives for improving high-resolution atmospheric numerical modeling and datasets for hydrological applications.

The Clausius-Clapeyron (CC) relation expresses the exponential increase in the moisture-holding capacity of air of approximately 7%/degrees C. Earlier studies show that extreme hourly precipitation increases with daily mean temperature, consistent with the CC relation. Recent studies at specific locations found that for temperatures higher than around 12 degrees C, hourly precipitation extremes scale at rates higher than the CC scaling, a phenomenon that is often referred to as "super-CC scaling." These scalings are typically estimated by collecting rainfall data in temperature bins, followed by a linear fit or a visual inspection of the precipitation quantiles in each bin. In this study, a piecewise linear quantile regression model is presented for a more flexible, and robust estimation of the scaling parameters, and their associated uncertainties. Moreover, we use associated information criteria to prove statistically whether or not a pronounced super-CC scaling exists. The techniques were tested on stochastically simulated data and, when applied to hourly station data across Western Europe and Scandinavia, revealed large uncertainties in the scaling rates. Finally, goodness-of-fit measures indicated that the dew point temperature is a better scaling predictor than temperature.

The Belgian expertise with respect to climate research is very fragmented and led by both federal (like the Royal Meteorological Institute of Belgium, hereafter RMI) as well as regional scientific research institutions and universities. Furthermore, both in societal, research as well as policy context there is an increasing need for detailed, quantitative, reliable and consistent climate information and services, in particular linked to the expected climate changes and its impacts in different sectors (e.g. water, agriculture, energy, health, transport).To make all this scientific expertise and climate information available in a coordinated and, above all, user-friendly way, a Belgian climate centre or a one-stop shop for climate research and services would fully address the needs. With its long-standing scientific expertise and service provision, RMI plays a crucial role in fulfilling this growing need for climate information and services.Hence, RMI has elaborated a proposal on the creation of a Belgian climate centre. For the creation of such centre it is preferable to use existing collaborations from previous or ongoing research and service-provision activities in Belgium, requiring a structural cooperation in which each members’ expertise can be fully deployed. In the context of a previous research project, a large consortium of Belgian research institutions active in regional climate modelling, calculated for the first time a consistent set of high-resolution climate projections and sectoral impacts for Belgium. Nevertheless, it should be emphasized that the number of models used was far too limited to develop reliable information of the future climate. Moreover, this was a one-time project and there is currently an urgent need to update the climate projections to meet the new scientific requirements of the IPCC's new sixth Assessment Report.One of the activities of the centre would thus be the coordination of the climate research based on scientific research projects. Furthermore, in order to ensure the continuous development and provision of climate services based on this objective and scientifically based expertise coming from Belgian climate scientists, the climate centre would carry out two main core tasks: (i) the production and storage of climate information and (ii) the support for the development of climate services.The financing and implementation of a Belgian climate centre, would provide a structural framework for climate research and services, establishing partnerships with the various regions and communities, at both policy and research levels. In this way, RMI, as national meteorological institute and at the start of the climate services value chain, can guarantee a continuous scientific expertise and respond to the major needs for climate information and services at national and international level.

To support invasive alien species risk assessments, the Tracking Invasive Alien Species (TrIAS) project has developed an automated, open, workflow incorporating state-of-the-art species distribution modelling practices to create risk maps using the open source language R. It is based on Global Biodiversity Information Facility (GBIF) data and openly published environmental data layers characterizing climate and land cover. Our workflow requires only a species name and generates an ensemble of machine-learning algorithms (Random Forest, Boosted Regression Trees, K-Nearest Neighbors and AdaBoost) stacked together as a meta-model to produce the final risk map at 1 km2 resolution (Fig. 1). Risk maps are generated automatically for standard Intergovernmental Panel on Climate Change (IPCC) greenhouse gas emission scenarios and are accompanied by maps illustrating the confidence of each individual prediction across space, thus enabling the intuitive visualization and understanding of how the confidence of the model varies across space and scenario (Fig. 2). The effects of sampling bias are accounted for by providing options to: use the sampling effort of the higher taxon the modelled species belongs to (e.g., vascular plants), and to thin species occurrences. use the sampling effort of the higher taxon the modelled species belongs to (e.g., vascular plants), and to thin species occurrences. The risk maps generated by our workflow are defensible and repeatable and provide forecasts of alien species distributions under further climate change scenarios. They can be used to support risk assessments and guide surveillance efforts on alien species in Europe. The detailied modeling framework and code are available on GitHub: https://github.com/trias-project.

Isoprene is a highly reactive volatile organic compound emitted by vegetation, known to be a precursor of secondary organic aerosols and to enhance tropospheric ozone formation under polluted conditions. Isoprene emissions respond strongly to changes in meteorological parameters such as temperature and solar radiation. In addition, the increasing CO2 concentration has a dual effect, as it causes both a direct emission inhibition as well as an increase in biomass through fertilization. In this study we used the MEGAN (Model of Emissions of Gases and Aerosols from Nature) emission model coupled with the MOHYCAN (Model of HYdrocarbon emissions by the CANopy) canopy model to calculate the isoprene fluxes emitted by vegetation in the recent past (1979–2014) and in the future (2070–2099) over Europe at a resolution of 0.1∘×0.1∘. As a result of the changing climate, modeled isoprene fluxes increased by 1.1 % yr−1 on average in Europe over 1979–2014, with the strongest trends found over eastern Europe and European Russia, whereas accounting for the CO2 inhibition effect led to reduced emission trends (0.76 % yr−1). Comparisons with field campaign measurements at seven European sites suggest that the MEGAN–MOHYCAN model provides a reliable representation of the temporal variability of the isoprene fluxes over timescales between 1 h and several months. For the 1979–2014 period the model was driven by the ECMWF ERA-Interim reanalysis fields, whereas for the comparison of current with projected future emissions, we used meteorology simulated with the ALARO regional climate model. Depending on the representative concentration pathway (RCP) scenarios for greenhouse gas concentration trajectories driving the climate projections, isoprene emissions were found to increase by +7 % (RCP2.6), +33 % (RCP4.5), and +83 % (RCP8.5), compared to the control simulation, and even stronger increases were found when considering the potential impact of CO2 fertilization: +15 % (RCP2.6), +52 % (RCP4.5), and +141 % (RCP8.5). However, the inhibitory CO2 effect goes a long way towards canceling these increases. Based on two distinct parameterizations, representing strong or moderate inhibition, the projected emissions accounting for all effects were estimated to be 0–17 % (strong inhibition) and 11–65 % (moderate inhibition) higher than in the control simulation. The difference obtained using the two CO2 parameterizations underscores the large uncertainty associated to this effect.

The arid ecosystem is fragile and sensitive to the changes in climate and CO2 concentration. Exploring the responses of the arid ecosystem to the changes under different representative concentration pathways (RCPs) is of particular significance for the sustainable development of the ecosystem. In this study, the dynamics of net primary productivity (NPP), evapotranspiration (ET), and water use efficiency (WUE) for arid ecosystems in Tianshan North Slope are explored by running the arid ecosystem model at 25 km resolution under RCP2.6, RCP4.5, and RCP8.5. The climate in Tianshan North Slope presents a wet-warming trend during 2006&ndash, 2055 under each RCP scenario with temporal and spatial heterogeneity. In response to the changes in climate and CO2, the regional annual NPP and ET increased during 2006&ndash, 2055 by a respectively maximum rate of 2.15 g C m&minus, 2 year&minus, 1 and 0.52 mm year&minus, 1 under RCP8.5. Both the NPP and ET share a similar temporal and spatial heterogeneity with climate change. Different vegetation types respond differently to the changes under different RCP scenarios with increasing WUE. Under each RCP, the non-phreatophyte, phreatophyte, and grass are more sensitive to the changes than in the others, and the broadleaf forest and cropland are less sensitive to the changes.

The change in forest productivity was simulated in six stands in Wallonia (Belgium) following different climate scenarios using a process-based and spatially explicit tree growth model. Simulations revealed a strong and positive impact of the CO 2 fertilization while the negative effect of the transpiration deficit was compensated by longer vegetation periods. The site modulated significantly the forest productivity, mainly through the stand and soil characteristics. Forest net primary production (NPP) reflects forest vitality and is likely to be affected by climate change. Simulating the impact of changing environmental conditions on NPP and two of its main drivers (transpiration deficit and vegetation period) in six Belgian stands and decomposing the site effect. Based on the tree growth model HETEROFOR, simulations were performed for each stand between 2011 and 2100 using three climate scenarios and two CO2 modalities (constant vs time dependent). Then, the climate conditions, soils and stands were interchanged to decompose the site effect in these three components. In a changing climate with constant atmospheric CO2, NPP values remained constant due to a compensation of the negative effect of increased transpiration deficit by a positive impact of longer vegetation periods. With time-dependent atmospheric CO2, NPP substantially increased, especially for the scenarios with higher greenhouse gas (GHG) emissions. For both atmospheric CO2 modalities, the site characteristics modulated the temporal trends and accounted in total for 56 to 73% of the variability. Long-term changes in NPP were primarily driven by CO2 fertilization, reinforced transpiration deficit, longer vegetation periods and the site characteristics.

As urban environments have a specific climate that poses extra challenges (e.g. increased heat stress during heat waves), gaining detailed insight into the urban climate is important. This paper presents the high-accuracy MOCCA (MOnitoring the City's Climate and Atmosphere) network, which is monitoring the urban climate of the city of Ghent since July 2016. The study illustrates the complementarity between modelling and observing the urban climate. Two different modelling approaches are used: 1 km resolution runs of the SURFEX land surface model and 100 m resolution runs of the computationally cheaper UrbClim boundary layer model. On the one hand, urban models are able to simulate the spatial variability of the urban climate. As such, these models serve as a tool to help deciding on the locations of the measurement stations. On the other hand, the MOCCA observations are used to validate the high-resolution urban model experiments for the summer (July-August-September) of 2016. Our results demonstrate that the models capture the nighttime intra-urban temperature differences, but they are not able to reproduce the observed daytime temperature differences which are determined by the micro-scale environment.

Convection-permitting models (CPMs) have been proven successful in simulating extreme precipitation statistics. However, when such models are used to study climate change, contrasting sensitivities with respect to resolution (CPM vs. models with parameterized convection) are found for different parts of the world. In this study, we explore to which extent this contrasting sensitivity is due to the specific characteristics of the model or due to the characteristics of the region. Therefore, we examine the results of 360 years of climate model data from two different climate models (COSMO-CLM driven by EC-EARTH and ALARO-0 driven by CNRM ARPEGE) both at convection-permitting scale (CPS, ~ 3 km resolution) and non-convection-permitting scale (non-CPS, 12.5 km resolution) over two distinct regions (flatland vs. hilly region) in Belgium. We found that both models show an overall consistent scale-dependency of the future increase in hourly extreme precipitation for day-time. More specifically, both models yield a larger discrepancy in the day-time climate change signal between CPS and non-CPS for extreme precipitation over flatland (Flanders) than for orographically induced extreme precipitation (Ardennes). This result is interesting, since both RCMs are very different (e.g., in terms of model physics and driving GCM) and use very different ways to represent deep convection processes. Despite those model differences, the scale-dependency of projected precipitation extremes is surprisingly similar in both models, suggesting that the this scale-dependency is more dependent on the characteristics of the region, than on the model used. ispartof: Climate Dynamics vol:54 issue:3-4 pages:1267-1280 status: Published online

Dynamical downscaling in a continuous approach using initial and boundary conditions from a reanalysis or a global climate model is a common method for simulating the regional climate. The simulation potential can be improved by applying an alternative approach of reinitialising the atmosphere, combined with either a daily reinitialised or a continuous land surface. We evaluated the dependence of the simulation potential on the running mode of the regional climate model ALARO coupled to the land surface model Météo-France SURFace EXternalisée (SURFEX), and driven by the ERA-Interim reanalysis. Three types of downscaling simulations were carried out for a 10-year period from 1991 to 2000, over a western European domain at 20 km horizontal resolution: (1) a continuous simulation of both the atmosphere and the land surface, (2) a simulation with daily reinitialisations for both the atmosphere and the land surface and (3) a simulation with daily reinitialisations of the atmosphere while the land surface is kept continuous. The results showed that the daily reinitialisation of the atmosphere improved the simulation of the 2 m temperature for all seasons. It revealed a neutral impact on the daily precipitation totals during winter, but the results were improved for the summer when the land surface was kept continuous. The behaviour of the three model configurations varied among different climatic regimes. Their seasonal cycle for the 2 m temperature and daily precipitation totals was very similar for a Mediterranean climate, but more variable for temperate and continental climate regimes. Commonly, the summer climate is characterised by strong interactions between the atmosphere and the land surface. The results for summer demonstrated that the use of a daily reinitialised atmosphere improved the representation of the partitioning of the surface energy fluxes. Therefore, we recommend using the alternative approach of the daily reinitialisation of the atmosphere for the simulation of the regional climate.

This study explores whether climate models with higher spatial resolutions provide higher accuracy for precipitation simulations and/or different climate change signals. The outputs from two convection-permitting climate models (ALARO and CCLM) with a spatial resolution of 3–4 km are compared with those from the coarse-scale driving models or reanalysis data for simulating/projecting daily and sub-daily precipitation quantiles. Validation of historical design precipitation statistics derived from intensity–duration–frequency (IDF) curves shows a better match of the convection-permitting model results with the observations based IDF statistics compared to the driving GCMs and reanalysis data. This is the case for simulation of local subdaily precipitation extremes during the summer season, while the convection-permitting models do not appear to bring added value to simulation of daily precipitation extremes. Results moreover indicate that one has to be careful in assuming spatial-scale independency of climate change signals for the delta change downscaling method, as high resolution models may show larger changes in extreme precipitation. These larger changes appear to be dependent on the timescale, since such intensification is not observed for daily timescales for both the ALARO and CCLM models. ispartof: Hydrology and Earth System Sciences vol:20 issue:9 pages:3843-3857 status: published

The CORDEX.be project created the foundations for Belgian climate services by producing high-resolution Belgian climate information that (a) incorporates the expertise of the different Belgian climate modeling groups and that (b) is consistent with the outcomes of the international CORDEX (“COordinated Regional Climate Downscaling Experiment”) project. The key practical tasks for the project were the coordination of activities among different Belgian climate groups, fostering the links to specific international initiatives and the creation of a stakeholder dialogue. Scientifically, the CORDEX.be project contributed to the EURO-CORDEX project, created a small ensemble of High-Resolution (H-Res) future projections over Belgium at convection-permitting resolutions and coupled these to seven Local Impact Models. Several impact studies have been carried out. The project also addressed some aspects of climate change uncertainties. The interactions and feedback from the stakeholder dialogue led to different practical applications at the Belgian national level. Keywords: Regional downscaling, Climate impact modeling, Statistical downscaling, Dynamical downscaling, Local Impact Models, Climate change, EURO-CORDEX, Uncertainty estimation, Regional Climate Model, Climate Belgium, Water vapour observations

A new research project AFTER “Impacts of climate change and cli- mateextremesonAgricultureandForestryintheEurope-Russia-Turkey Region” started in 2018. It was designed to bridge the gap between supplyanddemandofstate-of-the-artclimateinformationtoassessthe impact of changing climate on agriculture and forestry.

Daily summer precipitation over Belgium from the Aire Limitée Adaptation Dynamique Développement International (ALADIN) model and a version of the model that has been updated with physical parameterizations, the so-called ALARO-0 model [ALADIN and AROME (Application de la Recherche à l'Opérationnel à Meso-Echelle) combined model, first baseline version released in 1998], are compared with respect to station observations for the period 1961–90. The 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40) is dynamically downscaled using both models on a horizontal resolution of 40 km, followed by a one-way nesting on high spatial resolutions of 10 and 4 km. This setup allows us to explore the relative importance of spatial resolution versus parameterization formulation on the model skill to correctly simulate extreme daily precipitation. Model performances are assessed through standard statistical errors and density, frequency, and quantile distributions as well as extreme value analysis, using the peak-over-threshold method and generalized Pareto distribution. The 40-km simulations of ALADIN and ALARO-0 show similar results, both reproducing the observations reasonably well. For the high-resolution simulations, ALARO-0 at both 10 and 4 km is in better agreement with the observations than ALADIN. The ALADIN model consistently produces too high precipitation rates. The findings demonstrate that the new parameterizations within the ALARO-0 model are responsible for a correct simulation of extreme summer precipitation at various horizontal resolutions. Moreover, this study shows that ALARO-0 is a good candidate model for regional climate modeling.

The potential of the implementation of the land surface model SURFEX in the atmospheric ALARO-0 model configuration of the ALADIN system is tested in a continuous regional climate simulation. This contribution is evaluated with respect to the regional climate simulated by the original setup of ALARO-0 with ISBA. Next, an assessment has been performed to evaluate the continuous setup with an upper air daily reinitialised setup, where the surface is kept in a freely continuous mode. The results show that the introduction of SURFEX improves or has a neutral impact on the 2 m temperature and the daily total precipitation. More importantly, the use of an upper air daily reinitialised atmosphere outperforms the setup with a continuous atmosphere, for the 2 m temperature in winter and summer, and for the summer daily total precipitation. The differences between the two downscaling setups in the 2 m temperature and precipitation interact with the soil moisture. This coupling is strong for the continental areas, which motivates the use of a coupled land-atmosphere model to optimise the representation of the climate.

Using the regional climate model ALARO-0, the Royal Meteorological Institute of Belgium and Ghent University have performed two simulations of the past observed climate within the framework of the Coordinated Regional Climate Downscaling Experiment (CORDEX). The ERA-Interim reanalysis was used to drive the model for the period 1979–2010 on the EURO-CORDEX domain with two horizontal resolutions, 0.11 and 0.44°. ALARO-0 is characterised by the new microphysics scheme 3MT, which allows for a better representation of convective precipitation. In Kotlarski et al. (2014) several metrics assessing the performance in representing seasonal mean near-surface air temperature and precipitation are defined and the corresponding scores are calculated for an ensemble of models for different regions and seasons for the period 1989–2008. Of special interest within this ensemble is the ARPEGE model by the Centre National de Recherches Météorologiques (CNRM), which shares a large amount of core code with ALARO-0. Results show that ALARO-0 is capable of representing the European climate in an acceptable way as most of the ALARO-0 scores lie within the existing ensemble. However, for near-surface air temperature, some large biases, which are often also found in the ARPEGE results, persist. For precipitation, on the other hand, the ALARO-0 model produces some of the best scores within the ensemble and no clear resemblance to ARPEGE is found, which is attributed to the inclusion of 3MT. Additionally, a jackknife procedure is applied to the ALARO-0 results in order to test whether the scores are robust, meaning independent of the period used to calculate them. Periods of 20 years are sampled from the 32-year simulation and used to construct the 95 % confidence interval for each score. For most scores, these intervals are very small compared to the total ensemble spread, implying that model differences in the scores are significant.

A new high-resolution dynamical downscaling strategy to examine how rural and urban areas respond to change in future climate, is presented. The regional climate simulations have been performed with a new version of the limited-area model of the ARPEGE-IFS system running at 4-km resolution coupled with the Town Energy Balance scheme (TEB). In order to downscale further the regional climate projections to a urban scale, at 1 km resolution, a stand-alone surface scheme is employed in offline mode. We performed downscaling simulations according to three model set-ups: (i) reference run, where TEB is not activated neither in 4-km simulations nor in 1-km urban simulation, (ii) offline run, where TEB is activated only for 1-km urban simulation and (iii) inline run, where TEB is activated both for regional and urban simulations. The applicability of this method is demonstrated for Brussels Capital Region, Belgium. For present climate conditions, another set of simulations were performed using European Center for Medium-Range Weather Forecasts global reanalysis ERA40 data. Results from our simulations indicate that the reference and offline runs have comparable values of daytime and nocturnal urban heat island (UHI) and lower values than the inline run. The inline values are closer to observations. In the future climate, under and A1B emission scenario, the three downscaling methods project a decrease of daytime UHI between −0.24 and −0.20 °C, however, their responses are different for nocturnal UHI: (i) reference run values remains unaltered, (ii) for the offline runs, the frequency of present climate weak nocturnal UHI decreases to the benefit of negative UHIs leading to a significant decrease in the nocturnal UHI over the city, (iii) for the inline run, nocturnal UHIs stays always positive but the frequency of the strong UHI decreases significantly in the future by 1 °C.

The AFTER project was initiated to investigate the “Impact of climate change and climate extremes on the Agriculture and Forestry in the Europe-Russia-Turkey Region”. In this context high-resolution climate data was produced by running the RCMs ALARO-0 and REMO at a resolution of 25 km over the Central Asia (CAS)-CORDEX domain. The models were evaluated by comparing the output of ERA-Interim driven runs with CRU data over the 1980-2017 period. Different climate variables that will be used later as input for crop and vegetation models are validated. First validation results show that both RCMs reproduce realistic spatial patterns for temperature and precipitation, with biases in an acceptable range. Similar precipitation biases appear for both models with a strong wet winter bias in the east and a dry summer bias in the south-western part of the CAS-CORDEX domain. In contrast, there are differences between the RCMs in terms of spatial bias patterns for air temperature. For both variables large biases are observed over the mountainous areas but these could be largely attributed to the observational error. The climate change results over the CAS-CORDEX domain using CMIP5 driven simulations enable an assessment of climate impacts and extreme events. The REMO model is driven by three GCMs (MPIESM, HadGEM and NorESM), while ALARO is driven by CNMR-CM5. The comparison of the historical period with the future model output up to 2100 allows to estimate the response of the climate over Central Asia under the scenarios RCP2.6, RCP4.5 (only for ALARO) and RCP8.5. The AFTER consortium bridges the gap between supply and demand of climate information by implementing the newly created climate data directly into crop and vegetation models. For specific sub-regions of Latvia, Turkey and major crop areas of Russia detailed studies will be done. In addition stakeholders from different countries in the CAS-CORDEX domain are involved in discussing the scientific results.