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Common ragweed (Ambrosia artemisiifolia L.) is a highly allergenic and invasive plant in Europe. Its pollen can be transported over large distances and has been recognized as a significant cause of hayfever and asthma (D'Amato et al., 2007; Burbach et al., 2009). To simulate production and dispersion of common ragweed pollen, we implement a pollen emission and transport module in the Regional Climate Model (RegCM) version 4 using the framework of the Community Land Model (CLM) version 4.5. In the online model environment where climate is integrated with dispersion and vegetation production, pollen emissions are calculated based on the modelling of plant distribution, pollen production, species-specific phenology, flowering probability, and flux response to meteorological conditions. A pollen tracer model is used to describe pollen advective transport, turbulent mixing, dry and wet deposition. The model is then applied and evaluated on a European domain for the period 2000-2010. To reduce the large uncertainties notably due to ragweed density distribution on pollen emission, a calibration based on airborne pollen observations is used. Resulting simulations show that the model captures the gross features of the pollen concentrations found in Europe, and reproduce reasonably both the spatial and temporal patterns of flowering season and associated pollen concentrations measured over Europe. The model can explain 68.6, 39.2, and 34.3 % of the observed variance in starting, central, and ending dates of the pollen season with associated root mean square error (RMSE) equal to 4.7, 3.9, and 7.0 days, respectively. The correlation between simulated and observed daily concentrations time series reaches 0.69. Statistical scores show that the model performs better over the central Europe source region where pollen loads are larger. From these simulations health risks associated common ragweed pollen spread are then evaluated through calculation of exposure time above health-relevant threshold levels. The total risk area with concentration above 5 grains m-3 takes up 29.5 % of domain. The longest exposure time occurs on Pannonian Plain, where the number of days per year with the daily concentration above 20 grains m-3 exceeds 30. [ABSTRACT FROM AUTHOR]

The responses of crop functioning to changing climate and atmospheric CO2 concentration ([CO2]) could have large effects on food production, and impact carbon, water and energy fluxes, causing feedbacks to climate. To simulate the responses of temperate crops to changing climate and [CO2], accounting for the specific phenology of crops mediated by management practice, we present here the development of a process-oriented terrestrial biogeochemical model named ORCHIDEE-CROP (v0), which integrates a generic crop phenology and harvest module and a very simple parameterization of nitrogen fertilization, into the land surface model (LSM) ORCHIDEEv196, in order to simulate biophysical and biochemical interactions in croplands, as well as plant productivity and harvested yield. The model is applicable for a range of temperate crops, but it is tested here for maize and winter wheat, with the phenological parameterizations of two European varieties originating from the STICS agronomical model. We evaluate the ORCHIDEE-CROP (v0) model against eddy covariance and biometric measurements at 7 winter wheat and maize sites in Europe. The specific ecosystem variables used in the evaluation are CO2 fluxes (NEE), latent heat and sensible heat fluxes. Additional measurements of leaf area index (LAI), aboveground biomass and yield are used as well. Evaluation results reveal that ORCHIDEE-CROP (v0) reproduces the observed timing of crop development stages and the amplitude of pertaining LAI changes in contrast to ORCHIDEEv196 in which by default crops have the same phenology than grass. A near-halving of the root mean square error of LAI from 2.38 ± 0.77 to 1.08 ± 0.34 m² m-2 is obtained between ORCHIDEEv196 and ORCHIDEE-CROP (v0) across the 7 study sites. Improved crop phenology and carbon allocation lead to a general good match between modelled and observed aboveground biomass (with a normalized root mean squared error (NRMSE) of 11.0-54.2%), crop yield, as well as of the daily carbon and energy fluxes with NRMSE of ~9.0-20.1 and ~9.4-22.3 % for NEE, and sensible and latent heat fluxes, respectively. The model data mistfit for energy fluxes are within uncertainties of the measurements, which themselves show an incomplete energy balance closure within the range 80.6-86.3 %. The remaining discrepancies between modelled and observed LAI and other variables at specific sites are partly attributable to unrealistic representation of management events. In addition, ORCHIDEE-CROP (v0) is shown to have the ability to capture the spatial gradients of carbon and energy-related variables, such as gross primary productivity, NEE, sensible heat fluxes and latent heat fluxes, across the sites in Europe, an important requirement for future spatially explicit simulations. Further improvement of the model with an explicit parameterization of nutrition dynamics and of management, is expected to improve its predictive ability to simulate croplands in an Earth System Model. [ABSTRACT FROM AUTHOR]

In this study we present a series of sensitivity experiments conducted for the Last Glacial Maximum (LGM, ∼21 ka) over Europe using the regional climate Weather Research and Forecasting model (WRF). Using a four-step two-way nesting approach, we are able to reach a convection-permitting horizontal resolution over the inner part of the study area, covering central Europe and the Alpine region. The main objective of the paper is to evaluate a model version including a series of new developments better suitable for the simulation of paleo-glacial time slices with respect to the ones employed in former studies. The evaluation of the model is conducted against newly available pollen-based reconstructions of the LGM European climate and takes into account the effect of two main sources of model uncertainty: a different height of continental glaciers at higher latitudes of the Northern Hemisphere and different land cover. Model results are in good agreement with evidence from the proxies, in particular for temperatures. Importantly, the consideration of different ensemble members for characterizing model uncertainty allows for increasing the agreement of the model against the proxy reconstructions that would be obtained when considering a single model realization. The spread of the produced ensemble is relatively small for temperature, besides areas surrounding glaciers in summer. On the other hand, differences between the different ensemble members are very pronounced for precipitation, in particular in winter over areas highly affected by moisture advection from the Atlantic. This highlights the importance of the considered sources of uncertainty for the study of European LGM climate and allows for determining where the results of a regional climate model (RCM) are more likely to be uncertain for the considered case study. Finally, the results are also used to assess the effect of convection-permitting resolutions, at both local and regional scales, under glacial conditions. [ABSTRACT FROM AUTHOR]

The study focuses on the application of a dynamic ammonia emission into the Weather Research and Forecasting Chemistry model (WRF-Chem) and the influence on the simulated ammonia concentrations and the overall model performance. We have focused on agricultural ammonia sources and have analysed both hourly and daily patterns of ammonia emissions and concentrations at measurement sites located in agricultural areas or influenced by this activity. For selected episodes, we have also investigated the 3-D patterns of the ammonia concentrations in the atmosphere. The application of the dynamic ammonia emission into the WRF-Chem model (the "DYNAMIC" simulation) results in an improvement of the modelled daily ammonia concentrations in comparison to a static approach (the "BASE" simulation), which is currently widely used in chemical transport models. In the case of hourly resolution, we have observed an improvement for the DYNAMIC approach for the winter and autumn seasons, but for the entire year the modelled hourly ammonia peaks are shifted toward the afternoon hours if compared with measurements. This study indicates that the current description of the diurnal cycle of the ammonia concentration from fields is not accurate and more research is needed in order to improve the processes that describe the emission from fertilised fields. The results suggest that the governing processes in relation to the diurnal cycle are the atmospheric mixing and the emission strength. Therefore, an improved description of the diurnal profile of ammonia concentrations within atmospheric models requires a better description of the planetary boundary layer height and a stronger daily pattern of ammonia emission, e.g. through increased evaporation or increased fluxes from the surface. [ABSTRACT FROM AUTHOR]

Simple Summary: Ongoing climate change is causing temperatures to rise in both summer and winter, allowing insect pests to invade new areas and potentially causing economic and human health problems. Low winter temperatures are thought to be one of the main barriers to the colonization of higher latitudes. Climate models predict that winter temperatures will increase more than summer temperatures in temperate regions, which may allow insects from warmer regions to colonize the colder, higher latitudes in the future. Understanding how climate change will affect insect distributions is critical for many areas of human activity. This paper presents a method to assess the potential of insects to colonize colder regions under a warming winter scenario. The method is based on exposing insects to laboratory simulations of a warming winter climate. The applicability of the method is tested using the example of a Mediterranean pest, Sesamia nonagrioides, whose ability to colonize Central Europe is assessed. The results indicate that S. nonagrioides could survive Central European winters even under the current state of warming or under a warmer climate predicted for the near future. The presented method may be particularly useful in pest management to estimate overwinter survival and distribution of pests due to climate change. Ongoing climate change and anthropogenic pressure are having a profound influence on insects, causing species diversity to decline and populations to shrink. Insect pests invade new areas and cause economic and human health problems. Low temperatures in winter are thought to be one of the main barriers to the successful colonization of higher latitudes. Climate models predict that winter temperatures will increase more than summer temperatures in temperate and polar regions, potentially allowing species from warmer climates to colonize higher latitudes. Understanding how climate change will affect the distribution of insects is critical to many areas of human activity. One possible but seldom used way to predict likely range shifts of insects due to climate change is through simulation experiments. Here, I present and test a method to assess the potential of insect species from warmer regions to survive winters in colder regions under a warming winter scenario. The method is based on laboratory simulations of warming winters. The applicability of the method is demonstrated using the example of a Mediterranean pest, Sesamia nonagrioides, whose ability to survive Central European winters under a warming winter scenario is assessed. The method presented here is relatively simple, with potentially high accuracy of estimates. [ABSTRACT FROM AUTHOR]

Anthropogenic land use and land cover change (LULCC) is a major driver of environmental changes. The biophysical impacts of these changes on the regional climate in Europe are currently being extensively investigated within the World Climate Research Program (WCRP) Coordinated Downscaling Experiment (CORDEX) Flagship Pilot Study (FPS) Land Use and Climate Across Scales (LUCAS) using an ensemble of different regional climate models (RCMs) coupled with diverse land surface models (LSMs). In order to investigate the impact of realistic LULCC on past and future climates, high-resolution datasets with observed LULCC and projected future LULCC scenarios are required as input for the RCM–LSM simulations. To account for these needs, we generated the LUCAS Land Use and land Cover change (LUC) dataset version 1.1 at 0.1 ∘ resolution for Europe with annual LULC maps from 1950 to 2100 (10.26050/WDCC/LUC_hist_EU_v1.1, , 10.26050/WDCC/LUC_future_EU_v1.1,), which is tailored to use in state-of-the-art RCMs. The plant functional type (PFT) distribution for the year 2015 (i.e. the Modelling human LAND surface Modifications and its feedbacks on local and regional cliMATE – LANDMATE – PFT dataset) is derived from the European Space Agency Climate Change Initiative Land Cover (ESA-CCI LC) dataset. Details on the conversion method, cross-walking procedure, and evaluation of the LANDMATE PFT dataset are given in the companion paper by. Subsequently, we applied the land use change information from the Land-Use Harmonization 2 (LUH2) dataset, provided at 0.25 ∘ resolution as input for Coupled Modelling Intercomparison Project Phase 6 (CMIP6) experiments, to derive LULC distributions at high spatial resolution and at annual time steps from 1950 to 2100. In order to convert land use and land management change information from LUH2 into changes in the PFT distribution, we developed a land use translator (LUT) specific to the needs of RCMs. The annual PFT maps for Europe for the period 1950 to 2015 are derived from the historical LUH2 dataset by applying the LUT backward from 2015 to 1950. Historical changes in the forest type changes are considered using an additional European forest species dataset. The historical changes in the PFT distribution of LUCAS LUC follow closely the land use changes given by LUH2 but differ in some regions compared to other annual LULCC datasets. From 2016 onward, annual PFT maps for future land use change scenarios based on LUH2 are derived for different shared socioeconomic pathway (SSP) and representative concentration pathway (RCP) combinations used in the framework of CMIP6. The resulting LULCC maps can be applied as land use forcing to the new generation of RCM simulations for downscaling of CMIP6 results. The newly developed LUT is transferable to other CORDEX regions worldwide. [ABSTRACT FROM AUTHOR]

This paper studies the impact of land use and land cover change (LULCC) on the climate around 2500 years ago (2.5 ka), a period of rapid transitions across the European landscape. One global climate model was used to force two regional climate models (RCMs). The RCMs used two land cover descriptions. The first was from a dynamical vegetation model representing potential land cover, and the second was from a land cover description reconstructed from pollen data by statistical interpolation. The two different land covers enable us to study the impact of land cover on climate conditions. Since the difference in landscape openness between potential and reconstructed land cover is mostly due to LULCC, this can be taken as a measure of early anthropogenic effects on climate. Since the sensitivity to LULCC is dependent on the choice of climate model, we also use two RCMs. The results show that the simulated 2.5 ka climate was warmer than the simulated pre-industrial (PI, 1850 CE) climate. The largest differences are seen in northern Europe, where the 2.5 ka climate is 2–4 ∘ C warmer than the PI period. In summer, the difference between the simulated 2.5 ka and PI climates is smaller (0–3 ∘ C), with the smallest differences in southern Europe. Differences in seasonal precipitation are mostly within ± 10 %. In parts of northern Europe, the 2.5 ka climate is up to 30 % wetter in winter than that of the PI climate. In summer there is a tendency for the 2.5 ka climate to be drier than the PI climate in the Mediterranean region. The results also suggest that LULCC at 2.5 ka impacted the climate in parts of Europe. Simulations including reconstructed LULCC (i.e. those using pollen-derived land cover descriptions) give up to 1 ∘ C higher temperature in parts of northern Europe in winter and up to 1.5 ∘ C warmer in southern Europe in summer than simulations with potential land cover. Although the results are model dependent, the relatively strong response implies that anthropogenic land cover changes that had occurred during the Neolithic and Bronze Age could have affected the European climate by 2.5 ka. [ABSTRACT FROM AUTHOR]

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]

Agricultural adaptation to climate change is indispensable. However, the degree of adaptation depends on adaptive capacity levels and it only takes place if the appropriate resources are present. Cross-sectional climate response models ignore this requirement. This paper adapts the Ricardian method to control for a generic territorial adaptive capacity index. The results for a sample of over 60.000 European farms show a significant non-linear positive relationship between adaptive capacity and climate responsiveness and that some regions in Europe can increase their climate responsiveness significantly. This confirms that improvement of adaptive capacity is an important policy tool to enhance adaptation. [ABSTRACT FROM AUTHOR]

In this study we present a series of sensitivity experiments conducted for the Last Glacial Maximum (LGM, ~21000 years ago) over Europe using the regional climate Weather Research and Forecasting model (WRF). Using a 4 step 2-way nesting approach, we are able to reach a convection-permitting horizontal resolution over the inner part of the study area, covering Central Europe and the Alpine region. The main objective of the paper is to evaluate a model version including a series of new developments better suitable for the simulation of paleo-glacial time slices with respect to the ones employed in former studies. The evaluation of the model is conducted against newly available pollen-based reconstructions of the LGM European climate and takes into account the effect of two main sources of model uncertainty: a different height of continental glaciers at higher latitudes of the northern hemisphere and different land cover. Model results are in good agreement with evidence from the proxies, in particular for temperatures. Importantly, the consideration of different ensemble members for characterizing model uncertainty allows to increase the agreement of the model against the proxy reconstructions that would be obtained when considering a single model realization. The spread of the produced ensemble is relatively small for temperature, beside areas surrounding glaciers in summer. On the other hand, differences between the different ensemble members are very pronounced for precipitation, in particular in winter over areas highly affected by moisture advection from the Atlantic. This highlights the importance of the considered sources of uncertainty for the study of European LGM climate and allows to determine where the results of an RCM are more likely to be uncertain for the considered case study. Finally, the results are also used to demonstrate the added value of convection-permitting resolutions, at both local and regional scales, under glacial conditions. [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]

Climate models project an intensification of the wintertime North Atlantic Ocean storm track, over its downstream region, by the end of this century. Previous studies have suggested that ocean–atmosphere coupling plays a key role in this intensification, but the precise role of the different components of the coupling has not been explored and quantified. In this paper, using a hierarchy of ocean coupling experiments, we isolate and quantify the respective roles of thermodynamic (changes in surface heat fluxes) and dynamic (changes in ocean heat flux convergence) ocean coupling in the projected intensification of North Atlantic transient eddy kinetic energy (TEKE). We show that dynamic coupling accounts for nearly all of the future TEKE strengthening as it overcomes the much smaller effect of surface heat flux changes to weaken the TEKE. We further show that by reducing the Arctic amplification in the North Atlantic, ocean heat flux convergence increases the meridional temperature gradient aloft, causing a larger eddy growth rate and resulting in the strengthening of North Atlantic TEKE. Our results stress the importance of better monitoring and investigating the changes in ocean heat transport, for improving climate change adaptation strategies. Significance Statement: By the end of this century, the North Atlantic Ocean storm track is projected to intensify on its eastward flank. Such intensification will have large societal impacts, mostly over western Europe. Thus, it is critical to better understand the mechanism underlying the intensification of the storm track. Here we investigate the role of ocean coupling in the future intensification of the North Atlantic storm track and find that ocean heat transport processes are responsible for the strengthening of the storm track. Our results suggest that better monitoring the changes in ocean heat transport will hopefully improve climate change adaption strategies. [ABSTRACT FROM AUTHOR]

To improve high-resolution numerical environmental prediction, it is essential to represent ocean–atmosphere interactions properly, which is not the case in current operational regional forecasting systems used in western Europe. The objective of this paper is to present a new forecast-oriented coupled ocean–atmosphere system. This system uses the state-of-the-art numerical models AROME (cy43t2) and NEMO (v3.6) with a horizontal resolution of 2.5 km. The OASIS coupler (OASIS3MCT-4.0), implemented in the SurfEX surface scheme and in NEMO, is used to perform the communications between models. A sensitivity study of this system is carried out using 7 d simulations from 12 to 19 October 2018, characterized by extreme weather events (storms and heavy precipitation) in the area of interest. Comparisons with in situ and L3 satellite observations show that the fully coupled simulation reproduces the spatial and temporal evolution of the sea surface temperature and 10 m wind speed quantitatively well. Sensitivity analysis of ocean–atmosphere coupling shows that the use of an interactive and high-resolution sea surface temperature (SST), in contrast to actual numerical weather prediction (NWP) where SST is constant, modifies the atmospheric circulation and the location of heavy precipitation. Simulated oceanic fields show a large sensitivity to coupling when compared to the operational ocean forecast. The comparison to two distinct forced ocean simulations highlights that this sensitivity is mainly controlled by the change in the atmospheric model used to drive NEMO (AROME vs. IFS operational forecast), and less by the interactive air–sea exchanges. In particular, the oceanic boundary layer depths can vary by more than 40 % locally, between the two ocean-only experiments. This impact is amplified by the interactive coupling and is attributed to positive feedback between sea surface cooling and evaporation. [ABSTRACT FROM AUTHOR]

The parameterised description of subgrid-scale processes in the clear and cloudy boundary layer has a strong impact on the performance skill in any numerical weather prediction (NWP) or climate model and is still a prime source of uncertainty. Yet, improvement of this parameterised description is hard because operational models are highly optimised and contain numerous compensating errors. Therefore, improvement of a single parameterised aspect of the boundary layer often results in an overall deterioration of the model as a whole. In this paper, we will describe a comprehensive integral revision of three parameterisation schemes in the High Resolution Local Area Modelling – Aire Limitée Adaptation dynamique Développement InterNational (HIRLAM-ALADIN) Research on Mesoscale Operational NWP In Europe – Applications of Research to Operations at Mesoscale (HARMONIE-AROME) model that together parameterise the boundary layer processes: the cloud scheme, the turbulence scheme, and the shallow cumulus convection scheme. One of the major motivations for this revision is the poor representation of low clouds in the current model cycle. The newly revised parametric descriptions provide an improved prediction not only of low clouds but also of precipitation. Both improvements can be related to a stronger accumulation of moisture under the atmospheric inversion. The three improved parameterisation schemes are included in a recent update of the HARMONIE-AROME configuration, but its description and the insights in the underlying physical processes are of more general interest as the schemes are based on commonly applied frameworks. Moreover, this work offers an interesting look behind the scenes of how parameterisation development requires an integral approach and a delicate balance between physical realism and pragmatism. [ABSTRACT FROM AUTHOR]

The influence of climate on the distribution of taxa has been extensively investigated in the last two decades through Habitat Suitability Models (HSMs). In this context, the Worldclim database represents an invaluable data source as it provides worldwide climate surfaces for both historical and future time horizons. Thousands of HSMs‐based papers have been published taking advantage of Worldclim 1.4, the first online version of this repository. In 2017, Worldclim 2.1 was released. Here, we evaluated spatially explicit prediction mismatch at continental scale, focusing on Europe, between HSMs fitted using climate surfaces from the two Worldclim versions (between‐version differences). To this aim, we simulated occurrence probability and presence‐absence across Europe of four virtual species (VS) with differing climate‐occurrence relationships. For each VS, we fitted HSMs upon uncorrelated bioclimatic variables derived from each Worldclim version at three grid resolutions. For each factor combination, HSMs attaining sufficient discrimination performance on spatially independent test data were projected across Europe under current conditions and various future scenarios, and importance scores of the single variables were computed. HSMs failed in accurately retrieving the simulated climate‐occurrence relationships for the climate‐tolerant VS and the one occurring under a narrow combination of climatic conditions. Under current climate, noticeable between‐version prediction mismatch emerged across most of Europe for these two VSs, whose simulated suitability mainly depended upon diurnal or yearly variability in temperature; differently, between‐version differences were more clustered toward areas showing extreme values, like mountainous massifs or southern regions, for VSs responding to average temperature and precipitation trends. Under future climate, the chosen emission scenarios and Global Climate Models did not evidently influence between‐version prediction discrepancies, while grid resolution synergistically interacted with VSs' niche characteristics in determining extent of such differences. Our findings could help in re‐evaluating previous biodiversity‐related works relying on geographical predictions from Worldclim‐based HSMs. [ABSTRACT FROM AUTHOR]

Statistical downscaling methods (SDMs) are techniques used to downscale and/or bias‐correct climate model results to regional or local scales. The European network VALUE developed a framework to evaluate and inter‐compare SDMs. One of VALUE's experiments is the perfect predictor experiment that uses reanalysis predictors to isolate downscaling skill. Most evaluation papers for SDMs employ simple statistical diagnostics and do not follow a process‐based rationale. Thus, in this paper, a process‐based evaluation has been conducted for the more than 40 participating model output statistics (MOS, mostly bias correction) and perfect prognosis (PP) methods, for temperature and precipitation at 86 weather stations across Europe. The SDMs are analysed following the so‐called "regime‐oriented" technique, focussing on relevant features of the atmospheric circulation at large to local scales. These features comprise the North Atlantic Oscillation, blocking and selected Lamb weather types and at local scales the bora wind and the western Iberian coastal‐low level jet. The representation of the local weather response to the selected features depends strongly on the method class. As expected, MOS is unable to generate process sensitivity when it is not simulated by the predictors (ERA‐Interim). Moreover, MOS often suffers from an inflation effect when a predictor is used for more than one station. The PP performance is very diverse and depends strongly on the implementation. Although conditioned on predictors that typically describe the large‐scale circulation, PP often fails in capturing the process sensitivity correctly. Stochastic generalized linear models supported by well‐chosen predictors show improved skill to represent the sensitivities. [ABSTRACT FROM AUTHOR]

Large multiscenario multimodel ensembles (MMEs) of regional climate model (RCM) experiments driven by global climate models (GCMs) are made available worldwide and aim at providing robust estimates of climate changes and associated uncertainties. Due to many missing combinations of emission scenarios and climate models leading to sparse scenario–GCM–RCM matrices, these large ensembles, however, are very unbalanced, which makes uncertainty analyses impossible with standard approaches. In this paper, the uncertainty assessment is carried out by applying an advanced statistical approach, called QUALYPSO, to a very large ensemble of 87 EURO-CORDEX climate projections, the largest MME based on regional climate models ever produced in Europe. This analysis provides a detailed description of this MME, including (i) balanced estimates of mean changes for near-surface temperature and precipitation in Europe, (ii) the total uncertainty of projections and its partition as a function of time, and (iii) the list of the most important contributors to the model uncertainty. For changes in total precipitation and mean temperature in winter (DJF) and summer (JJA), the uncertainty due to RCMs can be as large as the uncertainty due to GCMs at the end of the century (2071–2099). Both uncertainty sources are mainly due to a small number of individual models clearly identified. Due to the highly unbalanced character of the MME, mean estimated changes can drastically differ from standard average estimates based on the raw ensemble of opportunity. For the RCP4.5 emission scenario in central–eastern Europe for instance, the difference between balanced and direct estimates is up to 0.8 ∘ C for summer temperature changes and up to 20 % for summer precipitation changes at the end of the century. [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]

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

In this paper, we present an estimate of the level of air pollution and radiation flux over the most important world heritage sites which are in danger according to the United Nations Educational, Scientific and Cultural Organization (UNESCO). According to UNESCO, there are 36 sites that are in danger. The influence of air pollution and radiation on these sites are presented in this work. To achieve the objective we used information about black carbon and shortwave radiation using models and remote sensing instruments from space. We found that the cultural heritage from Europe is more exposed to black carbon while the cultural heritage is exposed to a high level of a shortwave radiation flux. [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]

Anthropogenic land-use and land cover change (LULCC) is a major driver of environmental changes. The biophysical impacts of these changes on the regional climate in Europe are currently extensively investigated within the WCRP CORDEX Flagship Pilot Study (FPS) LUCAS - "Land Use and Climate Across Scales" using an ensemble of different Regional Climate Models (RCMs) coupled with diverse Land Surface Models (LSMs). In order to investigate the impact of realistic LULCC on past and future climates, high-resolution datasets with observed LULCC and projected future LULCC scenarios are required as input for the RCM-LSM simulations. To account for these needs, we generated the LUCAS LUC dataset Version 1.1 at 0.1° resolution for Europe with annual LULC maps from 1950-2100 (Hoffmann et al., 2022b, a), which is tailored towards the use in state-of-the-art RCMs. The plant functional type distribution (PFT) for the year 2015 (i.e., LANDMATE PFT dataset) is derived from the European Space Agency Climate Change Initiative Land Cover (ESA-CCI LC) dataset. Details about the conversion method, cross-walking procedure and the evaluation of the LANDMATE PFT dataset are given in the companion paper by Reinhart et al. (2022b). Subsequently, we applied the land-use change information from the Land-Use Harmonization 2 (LUH2) dataset, provided at 0.25? resolution as input for CMIP6 experiments, to derive LULC distribution at high spatial resolution and at annual timesteps from 1950 to 2100. In order to convert land use and land management change information from LUH2 into changes in the PFT distribution, we developed a Land Use Translator (LUT) specific to the needs of RCMs. The annual PFT maps for Europe for the period 1950 to 2015 are derived from the historical LUH2 dataset by applying the LUT backward from 2015 to 1950. Historical changes in the forest type changes are considered using an additional European forest species dataset. The historical changes in the PFT distribution of LUCAS LUC follow closely the land use changes given by LUH2 but differ in some regions compared to other annual LULCC datasets. From 2016 onward, annual PFT maps for future land use change scenarios based on LUH2 are derived for different Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs) combinations used in the framework of the Coupled Modelling Intercomparison Project Phase 6 (CMIP6). The resulting LULCC maps can be applied as land use forcing to the new generation of RCM simulations for downscaling of CMIP6 results. The newly developed LUT is transferable to other CORDEX regions world-wide. [ABSTRACT FROM AUTHOR]

The El Niño–Southern Oscillation (ENSO) teleconnection to Europe is projected to strengthen under global warming in most climate model simulations. However, given the current difference between recent observations and historical model simulations of tropical Pacific sea surface temperature trends, with models simulating an El Niño‐like warming in recent decades which is in disagreement with observations, it is important to understand the relative contributions of changes to the teleconnection forcing and background state to the overall teleconnection change. Using idealized climate model experiments, we show that both the eastward shift of El Niño precipitation and background state changes make contributions to the overall teleconnection change. These results suggest that the ENSO–Europe teleconnection can be expected to strengthen under global warming, even if ENSO precipitation anomalies do not shift eastwards as currently projected. However, the magnitude of the strengthening may depend on how much of an eastward shift does occur. Plain Language Summary: El Niño–Southern Oscillation (ENSO) can influence the weather and climate in Europe, and this connection is expected to become stronger under global warming according to most climate models. However, there is a difference between recent observations of sea surface temperatures in the Pacific Ocean and what climate models simulate. These differences could have important implications for future projections of ENSO's impact on Europe. To understand how likely the projected future strengthening of this link is, we looked at how two factors contribute to this connection change: (a) changes in rainfall associated with El Niño in the tropical Pacific and (b) overall changes in global climate elsewhere. Using a simplified climate model, we found that both these factors play a role in making the connection between El Niño and Europe stronger. This suggests that even if the specific patterns of El Niño rainfall don't change as projected, the link between El Niño and Europe will still get stronger due to global warming. However, how much stronger it gets might depend on the actual changes in El Niño rainfall, so it's important to figure out why there's a difference between observations and the model projections of Pacific Ocean temperature trends. Key Points: The El Niño–Southern Oscillation teleconnection to Europe is projected to strengthen under global warmingThis change is driven by an eastward shift of El Niño precipitation anomalies in the tropical Pacific and background state changes globallyBackground state changes have a greater impact on European circulation than precipitation changes, particularly over central‐eastern Europe [ABSTRACT FROM AUTHOR]

By transporting warm and salty water poleward, the Gulf Stream system maintains a mild climate in northwestern Europe while also facilitating the dense water formation that feeds the deep ocean. The sensitivity of North Atlantic circulation to future greenhouse gas emissions seen in climate models has prompted an increasing effort to monitor the various ocean circulation components in recent decades. Here, we synthesize available ocean transport measurements from several observational programs in the North Atlantic and Nordic Seas, as well as an ocean state estimate (ECCOv4-r4), for an enhanced understanding of the Gulf Stream and its poleward extensions as an interconnected circulation system. We see limited coherent variability between the records on interannual timescales, highlighting the local oceanic response to atmospheric circulation patterns and variable recirculation timescales within the gyres. On decadal timescales, we find a weakening subtropical circulation between the mid-2000s and mid-2010s, while the inflow and circulation in the Nordic Seas remained stable. Differing decadal trends in the subtropics, subpolar North Atlantic, and Nordic Seas warrant caution in using observational records at a single latitude to infer large-scale circulation change. [ABSTRACT FROM AUTHOR]

Winter windstorms are among the most significant natural hazards in Europe linked to fatalities and substantial damage. However, projections of windstorm impact in Europe under climate change are highly uncertain. This study combines climate projections from 30 general circulation models participating in Phase 6 of the Coupled Model Intercomparison Project (CMIP6) with the climate risk assessment model CLIMADA to obtain projections of windstorm-induced damage over Europe in a changing climate. We conduct an uncertainty–sensitivity analysis and find large uncertainties in the projected changes in the damage, with climate model uncertainty being the dominant factor of uncertainty in the projections. We investigate the spatial patterns of the climate change-induced modifications in windstorm damage and find an increase in the damage in northwestern and northern central Europe and a decrease over the rest of Europe, in agreement with an eastward extension of the North Atlantic storm track into Europe. We combine all 30 available climate models in an ensemble-of-opportunity approach and find evidence for an intensification of future climate windstorm damage, in which damage with return periods of 100 years under current climate conditions becomes damage with return periods of 28 years under future SSP585 climate scenarios. Our findings demonstrate the importance of climate model uncertainty for the CMIP6 projections of windstorms in Europe and emphasize the increasing need for risk mitigation due to extreme weather in the future. [ABSTRACT FROM AUTHOR]

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

Precipitation projections in transient climate change scenarios have been extensively studied over multiple climate model generations. Although these simulations have also been used to make projections at specific Global Warming Levels (GWLs), dedicated simulations are more appropriate to study changes in a stabilizing climate. Here, we analyze precipitation projections in six multi‐century experiments with fixed atmospheric concentrations of greenhouse gases, conducted with the UK Earth System Model and which span a range of GWLs between 1.5 and 5°C of warming. Regions are identified where the sign of precipitation trends in high‐emission transient projections is reversed in the stabilization experiments. For example, stabilization reverses a summertime precipitation decline across Europe. This precipitation recovery occurs concurrently with changes in the pattern of Atlantic sea surface temperature trends due to a slow recovery of the Atlantic Meridional Overturning Circulation in the stabilization experiments, along with changes in humidity and atmospheric circulation. Plain Language Summary: Climate model projections consistently predict that summer precipitation over Europe is expected to decline in the future as global temperatures rise under continued global warming. In our study, we use new climate model simulations that simulate a world where atmospheric concentrations of greenhouse gases are no longer increasing and the rise in global temperatures has slowed down. We show that the summer rainfall decline across Europe can, to some extent, be reversed if global temperatures were to stabilize. This has important implications for adaptation and planning decisions, particularly in so‐called climate change "hot‐spots" such as the Mediterranean. Key Points: Climate stabilization experiments show significant differences in projected precipitation compared to high‐emission transient scenariosNorthern European and Mediterranean projected summer drying is partially reversedEuropean summer precipitation changes are consistent with the atmospheric response to Atlantic SST changes [ABSTRACT FROM AUTHOR]

Mid‐tropospheric deep depressions in summer over the North Atlantic are shown to have strongly increased in the eastern and strongly decreased in the western North Atlantic region. This evolution is linked to a change in baroclinicity in the west of the North Atlantic ocean and over the North American coast, likely due to the increased surface temperature there. Deep depressions in the Eastern North Atlantic are linked to a temperature pattern typical of extreme heat events in the region. The same analysis is applied to a sample of CMIP6 model outputs, and no such trends are found. This study suggests a link between the observed increase of summer extreme heat events in the region and the increase of the number of Atlantic depressions. The failure of CMIP6 models to reproduce these events can consequently also reside in an incorrect reproduction of this specific feature of midlatitude atmospheric dynamics. Plain Language Summary: Extreme temperatures events in Western Europe have been rising fast, and current global climate models are not able to reproduce this excess. There are different hypotheses to explain this discrepancy. One is that the large‐scale atmospheric dynamics, responsible for the local weather, is not correctly represented by the models: indeed, the frequency and amplitude of some specific weather phenomena have been shown to be insufficiently reproduced, especially in summer. Here, we study one such phenomenon, namely the transient deep depressions, or extratropical cyclones, that travel across the Atlantic basin. A significant large increase of the number of these events is found in summer in the region of the North Atlantic off the western European coast. Depressions in that region are accompanied by high temperatures in continental western Europe. An ensemble of state of the art climate models are also analyzed and none of them is able to correctly reproduce the frequency of deep depressions nor their large trend, which suggests a common origin with the insufficient prediction of western European extreme heat events. Great caution should be used when analyzing climate change predictions in the region, and even more so when studying changes in complex dynamical phenomena. Key Points: Deep depression occurrences have significantly increased over the eastern side, and decreased over the western side of the North AtlanticDeep depressions are linked to high surface temperature patterns in western continental Europe but have little impact on the mean warmingGlobal Climate Models fail to reproduce the observed trends in deep depressions correctly [ABSTRACT FROM AUTHOR]

The concentration of carbonaceous aerosols, black carbon (BC) and organic aerosol (OA), in the atmosphere is related to co-emitted or co-produced trace gases. In this study, we investigate the most relevant proportional relationships between both BC and OA with the following trace gases: carbon monoxide (CO), formaldehyde (HCHO), nitrogen dioxide (NO2), ozone (O3), and sulfur dioxide (SO2). One motivation for selecting these trace gases is that they can be observed using remote sensing measurements from satellite instrumentation, and could therefore be used to predict spatial changes in the amounts of BC and OA. Airborne measurements are optimal for the analysis of both the composition of aerosols and trace gases in different environments ranging from unpolluted oceanic air masses to those in heavily polluted city plumes. The two aircraft campaigns of the EMeRGe (Effect of Megacities on the Transport and Transformation of Pollutants on the Regional to Global Scales) project have created a unique database, with flight plans dedicated to studying city plumes in two regions, Europe (2017) and East Asia (2018), along with identical instrumental payload. Using linear regression analysis, three relevant relationships between carbonaceous aerosol and trace gases are identified: - The BC/OA ratio observed in the Asian campaign is three times higher (≈ 0.3) than in the European campaign (≈ 0.1), whereas the Pearson correlation coefficient (R) between BC and OA is much higher in Europe (R ≈ 0.8) than in Asia (R ≈ 0.6). - The CO/BC ratio is also observed higher in the Asian campaign (≈ 240) than in the European campaign (≈ 170), whereas the R-value between CO and BC is similar for both campaigns (R ≈ 0.7). - The HCHO/OA ratio is similar in both campaigns (≈0.32), but the observed R-values between HCHO and OA is higher in Europe than in the Asia (R ≈ 0.7 compared to ≈ 0.3). By focusing on heavily polluted air masses sampled downwind in the city plumes, the ratios between the observed carbonaceous aerosols and the five trace gases change, and the R-values increase with O3 for both BC and OA (R ≈ 0.5). To assess the performance of atmospheric models with respect to the most relevant observed relationships, an air quality model ensemble is used to represent the current state of atmospheric modeling, consisting of two global and two regional simulations. The evaluation shows that these proportional relationships are not satisfactorily reproduced by the model ensemble. The relationships between BC and OA or between CO and BC are modeled with stronger correlations than the observed ones, and their higher ratios observed in Asia compared to Europe are not reproduced. Furthermore, the modeled HCHO/OA ratio is underestimated in the Asian campaign and overestimated in the European campaign. This analysis of the proportional relationships between carbonaceous aerosols and trace gases implies that the observed relationships can be used to constrain models and improve anthropogenic emission inventories. In addition, it implies that information about the lower tropospheric concentration of carbonaceous aerosols can potentially be inferred from satellite retrievals of trace gases, particularly in the plumes from megacities. [ABSTRACT FROM AUTHOR]

The representation of groundwater is simplified in most regional climate models (RCMs), potentially leading to biases in the simulations. This study introduces a unique dataset from the regional Terrestrial Systems Modelling Platform (TSMP) driven by the Max Planck Institute Earth System Model at Low Resolution (MPI-ESM-LR) boundary conditions in the context of dynamical downscaling of global climate models (GCMs) for climate change studies. TSMP explicitly simulates full 3D soil and groundwater dynamics together with overland flow, including complete water and energy cycles from the bedrock to the top of the atmosphere. By comparing the statistics of heat events, i.e., a series of consecutive days with a near-surface temperature exceeding the 90th percentile of the reference period, from TSMP and those from GCM–RCM simulations with simplified groundwater dynamics from the COordinated Regional Climate Downscaling EXperiment (CORDEX) for the European domain, we aim to improve the understanding of how groundwater representation affects heat events in Europe. The analysis was carried out using RCM outputs for the summer seasons of 1976–2005 relative to the reference period of 1961–1990. While our results show that TSMP simulates heat events consistently with the CORDEX ensemble, there are some systematic differences that we attribute to the more realistic representation of groundwater in TSMP. Compared to the CORDEX ensemble, TSMP simulates fewer hot days (i.e., days with a near-surface temperature exceeding the 90th percentile of the reference period) and lower interannual variability and decadal change in the number of hot days on average over Europe. TSMP systematically simulates fewer heat waves (i.e., heat events lasting 6 d or more) compared to the CORDEX ensemble; moreover, they are shorter and less intense. The Iberian Peninsula is particularly sensitive with respect to groundwater. Therefore, incorporating an explicit 3D groundwater representation in RCMs may be a key in reducing biases in simulated duration, intensity, and frequency of heat waves in Europe. The results highlight the importance of hydrological processes for the long-term regional climate simulations and provide indications of possible potential implications for climate change projections. [ABSTRACT FROM AUTHOR]

For over 6 months, the 2014–2015 effusive eruption at Holuhraun, Iceland, injected considerable amounts of sulfur dioxide (SO2) into the lower troposphere with a daily rate of up to one-third of the global emission rate, causing extensive air pollution across Europe. The large injection of SO2 , which oxidises to form sulfate aerosol (SO42-), provides a natural experiment offering an ideal opportunity to scrutinise state-of-the-art general circulation models' (GCMs) representation of aerosol–cloud interactions (ACIs). Here we present Part 1 of a two-part model inter-comparison using the Holuhraun eruption as a framework to analyse ACIs. We use SO2 retrievals from the Infrared Atmospheric Sounding Interferometer (IASI) instrument and ground-based measurements of SO2 and SO42- mass concentrations across Europe, in conjunction with a trajectory analysis using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model, to assess the spatial and chemical evolution of the volcanic plume as simulated by five GCMs and a chemical transport model (CTM). IASI retrievals of plume altitude and SO2 column load reveal that the volcanic perturbation is largely contained within the lower troposphere. Compared to the satellite observations, the models capture the spatial evolution and vertical variability of the plume reasonably well, although the models often overestimate the plume altitude. HYSPLIT trajectories are used to attribute to Holuhraun emissions 111 instances of elevated sulfurous surface mass concentrations recorded at European Monitoring and Evaluation Programme (EMEP) stations during September and October 2014. Comparisons with the simulated concentrations show that the modelled ratio of SO2 to SO42- during these pollution episodes is often underestimated and overestimated for the young and mature plume, respectively. Models with finer vertical resolutions near the surface are found to better capture these elevated sulfurous ground-level concentrations. Using an exponential function to describe the decay of observed surface mass concentration ratios of SO2 to SO42- with plume age, the in-plume oxidation rate constant is estimated as 0.032 ± 0.002 h-1 (1.30 ± 0.08 d e -folding time), with a near-vent ratio of 25 ± 5 (µgm-3 of SO2 / µgm-3 of SO42-). The majority of the corresponding derived modelled oxidation rate constants are lower than the observed estimate. This suggests that the representation of the oxidation pathway/s in the simulated plumes is too slow. Overall, despite their coarse spatial resolutions, the six models show reasonable skill in capturing the spatial and chemical evolution of the Holuhraun plume. This capable representation of the underlying aerosol perturbation is essential to enable the investigation of the eruption's impact on ACIs in the second part of this study. [ABSTRACT FROM AUTHOR]

Heavy rainfall is the main driver of soil erosion by water, which is a threat to soil and water resources across the globe. As a consequence of climate change, precipitation – especially extreme precipitation – is increasing in a warmer world, leading to an increase in rainfall erosivity. However, conventional global climate models struggle to represent extreme rain events and cannot provide precipitation data at the high spatiotemporal resolution that is needed for an accurate estimation of future rainfall erosivity. Convection-permitting simulations (CPSs), on the other hand, provide high-resolution precipitation data and a better representation of extreme rain events, but they are mostly limited to relatively small spatial extents and short time periods. Here, we present, for the first time, rainfall erosivity in a large modeling domain such as central Europe based on high-resolution CPS climate data generated with the regional climate model COSMO-CLM using the Representative Concentration Pathway 8.5 (RCP8.5) emission scenario. We calculated rainfall erosivity for the past (1971–2000), present (2001–2019), near future (2031–2060) and far future (2071–2100). Our results showed that future increases in rainfall erosivity in central Europe can be up to 84 % in the region's river basins. These increases are much higher than previously estimated based on regression with mean annual precipitation. We conclude that despite remaining limitations, CPSs have an enormous and currently unexploited potential for climate impact studies on soil erosion. Thus, the soil erosion modeling community should closely follow the recent and future advances in climate modeling to take advantage of new CPSs for climate impact studies. [ABSTRACT FROM AUTHOR]

In the 2022 summer, western–central Europe and several other regions in the northern extratropics experienced substantial soil moisture deficits in the wake of precipitation shortages and elevated temperatures. Much of Europe has not witnessed a more severe soil drought since at least the mid-20th century, raising the question whether this is a manifestation of our warming climate. Here, we employ a well-established statistical approach to attribute the low 2022 summer soil moisture to human-induced climate change using observation-driven soil moisture estimates and climate models. We find that in western–central Europe, a June–August root zone soil moisture drought such as in 2022 is expected to occur once in 20 years in the present climate but would have occurred only about once per century during preindustrial times. The entire northern extratropics show an even stronger global warming imprint with a 20-fold soil drought probability increase or higher, but we note that the underlying uncertainty is large. Reasons are manifold but include the lack of direct soil moisture observations at the required spatiotemporal scales, the limitations of remotely sensed estimates, and the resulting need to simulate soil moisture with land surface models driven by meteorological data. Nevertheless, observation-based products indicate long-term declining summer soil moisture for both regions, and this tendency is likely fueled by regional warming, while no clear trends emerge for precipitation. Finally, our climate model analysis suggests that under 2 ∘ C global warming, 2022-like soil drought conditions would become twice as likely for western–central Europe compared to today and would take place nearly every year across the northern extratropics. [ABSTRACT FROM AUTHOR]

The improvement in resolution of climate models has always been mentioned as one of the most important factors when investigating past climatic conditions, especially in order to evaluate and compare the results against proxy data. Despite this, only a few studies have tried to directly estimate the possible advantages of highly resolved simulations for the study of past climate change. Motivated by such considerations, in this paper we present a set of high-resolution simulations for different time slices of the mid-to-late Holocene performed over Europe using the state-of-the-art regional climate model COSMO-CLM. After proposing and testing a model configuration suitable for paleoclimate applications, the aforementioned midto- late Holocene simulations are compared against a new pollen-based climate reconstruction data set, covering almost all of Europe, with two main objectives: testing the advantages of high-resolution simulations for paleoclimatic applications, and investigating the response of temperature to variations in the seasonal cycle of insolation during the mid-tolate Holocene. With the aim of giving physically plausible interpretations of the mismatches between model and reconstructions, possible uncertainties of the pollen-based reconstructions are taken into consideration. Focusing our analysis on near-surface temperature, we can demonstrate that concrete advantages arise in the use of highly resolved data for the comparison against proxyreconstructions and the investigation of past climate change. Additionally, our results reinforce previous findings showing that summertime temperatures during the mid-to-late Holocene were driven mainly by changes in insolation and that the model is too sensitive to such changes over Southern Europe, resulting in drier and warmer conditions. However, in winter, the model does not correctly reproduce the same amplitude of changes evident in the reconstructions, even if it captures the main pattern of the pollen data set over most of the domain for the time periods under investigation. Through the analysis of variations in atmospheric circulation we suggest that, even though the wintertime discrepancies between the two data sets in some areas are most likely due to high pollen uncertainties, in general the model seems to underestimate the changes in the amplitude of the North Atlantic Oscillation, overestimating the contribution of secondary modes of variability. [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 the first ensemble modelling experiment in relation to birch pollen in Europe. The sevenmodel European ensemble of MACC-ENS, tested in trial simulations over the flowering season of 2010, was run through the flowering season of 2013. The simulations have been compared with observations in 11 countries, all members of the European Aeroallergen Network, for both individual models and the ensemble mean and median. It is shown that the models successfully reproduced the timing of the very late season of 2013, generally within a couple of days from the observed start of the season. The end of the season was generally predicted later than observed, by 5 days or more, which is a known feature of the source term used in the study. Absolute pollen concentrations during the season were somewhat underestimated in the southern part of the birch habitat. In the northern part of Europe, a recordlow pollen season was strongly overestimated by all models. The median of the multi-model ensemble demonstrated robust performance, successfully eliminating the impact of outliers, which was particularly useful since for most models this was the first experience of pollen forecasting. [ABSTRACT FROM AUTHOR]

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

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

Irrigation is a crucial land use practice to adapt agriculture to unsuitable climate and soil conditions. Aiming to improve the growth of plants, irrigation modifies the soil condition, which causes atmospheric effects and feedbacks through land–atmosphere interaction. These effects can be quantified with numerical climate models, as has been done in various studies. It could be shown that irrigation effects, such as air temperature reduction and humidity increase, are well understood and should not be neglected on local and regional scales. However, there is a lack of studies including the role of vegetation in the altered land–atmosphere interaction. With the increasing resolution of numerical climate models, these detailed processes have a chance to be better resolved and studied. This study aims to analyze the effects of irrigation on land–atmosphere interaction, including the effects and feedbacks of vegetation. We developed a new parameterization for irrigation, implemented it into the REgional climate MOdel (REMO2020), and coupled it with the interactive MOsaic-based VEgetation module (iMOVE). Following this new approach of a separate irrigated fraction, the parameterization is suitable as a subgrid parameterization for high-resolution studies and resolves irrigation effects on land, atmosphere, and vegetation. Further, the parameterization is designed with three different water application schemes in order to analyze different parameterization approaches and their influence on the representation of irrigation effects. We apply the irrigation parameterization for southwestern Europe including the Mediterranean region at a 0.11 ∘ horizontal resolution for hot extremes. The simulation results are evaluated in terms of the consistency of physical processes. We found direct effects of irrigation, like a changed surface energy balance with increased latent and decreased sensible heat fluxes, and a surface temperature reduction of more than -4 K as a mean during the growing season. Further, vegetation reacts to irrigation with direct effects, such as reduced water stress, but also with feedbacks, such as a delayed growing season caused by the reduction of the near-surface temperature. Furthermore, the results were compared to observational data, showing a significant bias reduction in the 2 m mean temperature when using the irrigation parameterization. [ABSTRACT FROM AUTHOR]

By transporting warm and salty water poleward, the Gulf Stream system maintains a mild climate in northwestern Europe while also facilitating the dense water formation that feeds the deep ocean. The sensitivity of North Atlantic circulation to future greenhouse gas emissions seen in climate models has prompted an increasing effort to monitor the various ocean circulation components in recent decades. Here, we synthesise available ocean transport measurements from several observational programs in the North Atlantic and Nordic Seas, as well as an ocean state estimate (ECCOv4-r4), for an enhanced understanding of the Gulf Stream and its poleward extensions as an interconnected circulation system. We see limited coherence between the records on interannual time scales, highlighting the local oceanic response to atmospheric circulation patterns and variable recirculation time scales within the gyres. On decadal time scales, we find a weakening subtropical circulation between the mid-2000s and mid-2010s, while the inflow and circulation in the Nordic Seas remained stable. Differing decadal trends in the subtropics, subpolar North Atlantic, and Nordic Seas warrant caution in using observational records at a single latitude to infer large-scale circulation change. [ABSTRACT FROM AUTHOR]

The PRIMAVERA project aimed to develop a new generation of advanced and well-evaluated high-resolution global climate models. As part of PRIMAVERA, seven different climate models were run in both standard and higher-resolution configurations, with common initial conditions and forcings to form a multi-model ensemble. The ensemble simulations were run on high-performance computers across Europe and generated approximately 1.6 PiB (pebibytes) of output. To allow the data from all models to be analysed at this scale, PRIMAVERA scientists were encouraged to bring their analysis to the data. All data were transferred to a central analysis facility (CAF), in this case the JASMIN super-data-cluster, where it was catalogued and details made available to users using the web interface of the PRIMAVERA Data Management Tool (DMT). Users from across the project were able to query the available data using the DMT and then access it at the CAF. Here we describe how the PRIMAVERA project used the CAF's facilities to enable users to analyse this multi-model dataset. We believe that PRIMAVERA's experience using a CAF demonstrates how similar, multi-institute, big-data projects can efficiently share, organise and analyse large volumes of data. [ABSTRACT FROM AUTHOR]

Humans change the climate in many ways. In addition to greenhouse gases, climate model inputs thus include a number of other forcings like land-use change. While studies typically investigate the joint effects of all forcings, we here isolate the impact of afforestation and deforestation on winds in the lowermost 350 m of the atmosphere to quantify the relevance of the lower boundary condition for large-scale wind energy assessments. We use vertically resolved sub-daily output from two regional climate models instead of extrapolating near-surface winds with simplified profiles. Comparing two extreme scenarios, we report that afforestation reduces wind speeds by more than 1 m/s in many locations across Europe even 300 m above ground and thus remains relevant at hub heights of current and future wind turbines. We show that standard extrapolation with modified parameters approximates long-term means well but fails to capture essential spatio-temporal details, such as changes in the daily cycle, and is thus insufficient to estimate wind energy potentials. Using adjacent climate model levels to account for spatio-temporal wind profile complexity, we report that wind energy capacity factors are strongly impacted by afforestation and deforestation: they differ by more than 0.1 in absolute terms and up to 50 % in relative terms. Our results confirm earlier studies that land use change impacts on wind energy can be severe and that they are generally misrepresented with common extrapolation techniques. [ABSTRACT FROM AUTHOR]

Central European winters have warmed markedly since the mid-20th century. Yet cold winters are still associated with severe societal impacts on energy systems, infrastructure and public health. It is therefore crucial to anticipate storylines of worst-case cold winter conditions, and to understand whether an extremely cold winter, such as the coldest winter in the historical record of Germany in 1963 (−6.3 °C or −3.4 σ seasonal DJF temperature anomaly relative to 1981–2010), is still possible in a warming climate. Here, we first show based on multiple attribution methods that a winter of similar circulation conditions to 1963 would still lead to an extreme seasonal cold anomaly of about −4.9 to −4.7 °C (best estimates across methods) under present-day climate. This would rank as second-coldest winter in the last 75 years. Second, we conceive storylines of worst-case cold winter conditions based on two independent rare event sampling methods (climate model boosting and empirical importance sampling): winter as cold as 1963 is still physically possible in Central Europe today, albeit very unlikely. While cold winter hazards become less frequent and less intense in a warming climate overall, it remains crucial to anticipate the possibility of an extreme cold winter to avoid potential maladaptation and increased vulnerability. [ABSTRACT FROM AUTHOR]

Background: The ticks Ixodes ricinus and Dermacentor reticulatus are two of the most important vectors in Europe. Climate niche modelling has been used in many studies to attempt to explain their distribution and to predict changes under a range of climate change scenarios. The aim of this study was to assess the ability of different climate niche modelling approaches to explain the known distribution of I. ricinus and D. reticulatus in Europe. Methods: A series of climate niche models, using different combinations of input data, were constructed and assessed. Species occurrence records obtained from systematic literature searches and Global Biodiversity Information Facility data were thinned to different degrees to remove sampling spatial bias. Four sources of climate data were used: bioclimatic variables, WorldClim, TerraClimate and MODIS satellite-derived data. Eight different model training extents were examined and three modelling frameworks were used: maximum entropy, generalised additive models and random forest models. The results were validated through internal cross-validation, comparison with an external independent dataset and expert opinion. Results: The performance metrics and predictive ability of the different modelling approaches varied significantly within and between each species. Different combinations were better able to define the distribution of each of the two species. However, no single approach was considered fully able to capture the known distribution of the species. When considering the mean of the performance metrics of internal and external validation, 24 models for I. ricinus and 11 models for D. reticulatus of the 96 constructed were considered adequate according to the following criteria: area under the receiver-operating characteristic curve > 0.7; true skill statistic > 0.4; Miller's calibration slope 0.25 above or below 1; Boyce index > 0.9; omission rate < 0.15. Conclusions: This comprehensive analysis suggests that there is no single 'best practice' climate modelling approach to account for the distribution of these tick species. This has important implications for attempts to predict climate-mediated impacts on future tick distribution. It is suggested here that climate variables alone are not sufficient; habitat type, host availability and anthropogenic impacts, not included in current modelling approaches, could contribute to determining tick presence or absence at the local or regional scale. [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 rural-to-urban transformation (RUT) is the process of turning rural or natural land-surface into urban one which brings important modifications in the surface causing well know effects like the urban heat island (UHI), reduced wind-speeds, increased boundary layer heights and so on. Moreover, with concentrated human activities RUT introduces new emission source which greatly perturbs the local and regional air-pollution. Particulate matter (PM) is one of key pollutants responsible for deterioration of urban air-quality and is still a major issue in European cities with frequent exceedances of limit values. Here we introduce a regional chemistry-climate model (regional climate model RegCM coupled offline to chemistry transport model CAMx) study which quantifies how the process of RUT modified the PM concentrations over central Europe including the underlying controlling mechanisms that contribute to the final PM pollution. Apart from the two most studied ones, i) the urban emissions and ii) the urban canopy meteorological forcing (UCMF, i.e. the impact of modified meteorological conditions on air-quality) we analyze also two less studied contributors to the RUT's impact on air-quality: iii) the impact of modified dry-deposition velocities due to urbanized land-use and iv) the impact of modified biogenic emissions due to urbanization induced vegetation modifications and changes in meteorological conditions which affect these emissions. To calculate the magnitude of each of these RUT contributors, we perform a cascade of simulations were each contributor is added one-by-one to the reference state while focus is given on PM2.5 (particulate matter with diameter less then 2.5 µm). We also look at their primary and secondary components, namely primary elemental carbon (PEC), sulphates (PSO4), nitrates (PNO3), ammonium (PNH4) and secondary organic aerosol (SOA). The validation using surface measurements showed a systematic negative bias for the total PM2.5 which is probably caused by underestimated organic aerosol and partly by the negative bias in sulphates and elemental carbon. For ammonium and nitrate, the underestimation is limited to the warm season while for winter, the model tends to overestimate their concentrations. However, in each case, the annual cycle is reasonably captured. We evaluated the RUT impact on PM2.5 over an ensemble of 19 central European cities and found that the total impact of urbanization is about 2–3 and 1–1.5 µgm−3 in winter and summer, respectively. This is mainly driven by the impact of emissions alone causing a slightly higher impact (1.5–3.5 and 1.2–2 µgm−3 in winter and summer), while the effect of UCMF was a decrease at about 0.2–0.5 µgm−3 (in both seasons) which was mainly controlled by enhanced vertical eddy-diffusion while increases were modelled over rural areas. The transformation of rural land-use into urban one caused an increase of dry-deposition velocities by around 30–50 % which alone resulted in a decrease of PM2.5 by 0.1–0.25 µgm−3 in both seasons. Finally, the impact of biogenic emission modification due to modified land-use and meteorological conditions caused a decrease of summer PM2.5 of about 0.1 µgm−3 while the winter effects were negligible. The total impact of urbanization on aerosol components is modelled to be (values indicate winter and summer averages) 0.4 and 0.3 µgm−3 for PEC, 0.05 and 0.02 µgm−3 for PSO4, 0.1 and 0.08 µgm−3 for PNO3, 0.04 and 0.03 µgm−3 for PNH4 and 0 and 0.05 µgm−3 for SOA. The main contributor of each of these components was the impact of emissions which was usually larger than the total impact due to the fact that UCMF counteracted with a decrease. For each aerosol component the impact of modified DV was a clear decrease of concentration and finally, the modifications of biogenic emissions impacted predominantly SOA causing a summer decrease while a very small secondary effect of secondary inorganic aerosol was modelled too (they increased). In summary, we showed that when analyzing the impact of urbanization on PM pollution, apart from the impact of emissions and the urban canopy meteorological forcing, one has to consider also the effect of modified land-use and its impact on dry-deposition. These were shown to be important in both seasons. For the effect of modified biogenic emissions, our calculations showed that it acts on PM2.5 predominantly trough SOA modifications which turned to be important only during 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]