Search

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]

A large shortcoming of current chemistry transport models for simulating the fate of ammonia in the atmosphere is the lack of a description of the bi-directional surfaceatmosphere exchange. In this paper, results of an update of the dry deposition module DEPAC in the LOTOS-EUROS model are discussed. It is shown that with the new description, which includes bi-directional surface-atmosphere exchange, the modeled ammonia concentrations increase almost everywhere, in particular in agricultural source areas. The reason is that by using a compensation point the ammonia life time and transport distance is increased. As a consequence, deposition of ammonia and ammonium decreases in agricultural source areas, while it increases in large nature areas and remote regions especially in Southern Scandinavia. The inclusion of a compensation point for water reduces the dry deposition over sea and allows reproducing the observed marine background concentrations at coastal locations to a better extend. A comparison with measurements shows that the model results better represent the measured ammonia concentrations. The concentrations in nature areas are slightly overestimated, while the concentrations in agricultural source areas are still underestimated. Although the introduction of the compensation point improves the model performance, the modeling of ammonia remains challenging. Important aspects are emission patterns in space and time as well as a proper approach to deal with the high concentration gradients in relation to model resolution. In short, the inclusion of a bi-directional surface atmosphere exchange is a significant step forward for modeling ammonia. [ABSTRACT FROM AUTHOR]

The article discusses a study conducted to update ECOCLIMAP-I in Europe. It is evident that ECOCLIMAP-I is a database designed to cater needs of meteorological community to examine natural and managed ecosystems in association with weather forecasting and climate change modelling. It discusses methods, datasets and characteristics of producing ECOCLIMAPII/Europe.

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]

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 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 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]

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]

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

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]

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]

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]

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

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

Satellite-derived high-resolution precipitation products (HRPP) have been developed to address the needs of the user community and are now available with 0.25° × 0.25° (or less) subdaily resolutions. This paper evaluates a number of commonly available satellite-derived HRPPs covering northwest Europe over a 6-yr period. Precipitation products include the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), the Climate Prediction Center (CPC) morphing (CMORPH) technique, the CPC merged microwave technique, the Naval Research Laboratory (NRL) blended technique, and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) technique. In addition, the Geosynchronous Operational Environmental Satellite (GOES) precipitation index (GPI) and the European Centre for Medium-Range Weather Forecasting (ECMWF) operational forecast model products are included for comparison. Surface reference data from the European radar network is used as ground truth, supported by the Global Precipitation Climatology Centre (GPCC) precipitation gauge analysis and gauge data over the United Kingdom. Measures of correlation, bias ratio, probability of detection, and false alarm ratio are used to evaluate the products. Results show that satellite products generally exhibit a seasonal cycle in correlation, bias ratio, probability of detection, and false alarm ratio, with poorer statistics during the winter. The ECMWF model also shows a seasonal cycle in the correlation, although the results are poorer during the summer, while the bias ratio, probability of detection, and false alarm ratio are consistent through all seasons. Importantly, all the satellite HRPPs underestimate precipitation over northwest Europe in all seasons. [ABSTRACT FROM AUTHOR]

ABSTRACT Aim This paper describes a set of integrative scenarios developed in the ALARM (Assessing LArge-scale environmental Risks for biodiversity with tested Methods) project. The ultimate aim of ALARM was to develop and test methods and protocols for the assessment of large-scale environmental risks to biodiversity and to evaluate mitigation options. Scenarios provide a tool for exploring such risks and the policy options to mitigate them; therefore they play a central role within the ALARM project. Methods Three integrative scenarios (liberalization, business as might be usual, sustainability) were developed and illustrated using the econometric model described in a subsequent paper. They are contextualized with projections from climate models and provide the input for model-based assessments of biodiversity trends. Additionally, three shock scenarios were developed (Gulf Stream collapse, peak oil, pandemic) to demonstrate the limits of linear extrapolation. As these extend beyond model capabilities, they are discussed semi-quantitatively based on modelling insights. Results Although the policy impacts on biodiversity are different for different pressures, biomes and species groups, some general trends could be identified. An extension of current EU policies will act as a brake on current trends by slowing down the loss of biodiversity in many cases and in most biomes, but it will be capable of neither halting nor of reversing the loss. Liberalization has the effect of accelerating biodiversity loss across the board, with few exceptions. A coherent sustainability scenario is clearly the most effective at preserving biodiversity, but the variant tested here still does not halt losses in all cases. Main conclusions Current EU policies for protecting biodiversity appear to be insufficient to reverse ongoing losses. Coherent sustainability strategies are effective at conserving biodiversity, but in order to assess losses and then reverse them, measures would need to be introduced that extend beyond the steps tested in the ALARM sustainability scenario. [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]

Lightweight roofs are extremely sensitive to extreme snow loads, as confirmed by recently occurring failures all over Europe. Obviously, the problem is further emphasized in warmer climatic areas, where low design values are generally foreseen for snow loads. Like other climatic actions, representative values of snow loads provided in structural codes are usually derived by means of suitable elaborations of extreme statistics, assuming climate stationarity over time. As climate change impacts are becoming more and more evident over time, that hypothesis is becoming controversial, so that suitable adaptation strategies aiming to define climate resilient design loads need to be implemented. In the paper, past and future trends of ground snow load in Europe are assessed for the period 1950–2100, starting from high-resolution climate simulations, recently issued by the CORDEX program. Maps of representative values of snow loads adopted for structural design, associated with an annual probability of exceedance p = 2%, are elaborated for Europe. Referring to the historical period, the obtained maps are critically compared with the current European maps based on observations. Factors of change maps, referred to subsequent time windows are presented considering RCP4.5 and RCP8.5 emission trajectories, corresponding to medium and maximum greenhouse gas concentration scenarios. Factors of change are thus evaluated considering suitably selected weather stations in Switzerland and Germany, for which high quality point measurements, sufficiently extended over time are available. Focusing on the investigated weather stations, the study demonstrates that climate models can appropriately reproduce historical trends and that a decrease of characteristic values of the snow loads is expected over time. However, it must be remarked that, if on one hand the mean value of the annual maxima tends to reduce, on the other hand, its standard deviation tends to increase, locally leading to an increase of the extreme values, which should be duly considered in the evaluation of structural reliability over time. [ABSTRACT FROM AUTHOR]

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

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

Disentangling the effects of internal variability and anthropogenic forcing on regional climate trends remains a key challenge with far-reaching implications. Due to its largely unpredictable nature on timescales longer than a decade, internal climate variability limits the accuracy of climate model projections, introduces challenges in attributing past climate changes, and complicates climate model evaluation. Here, we highlight recent advances in climate modeling and physical understanding that have led to novel insights about these key issues. In particular, we synthesize new findings from large-ensemble simulations with Earth system models, observational large ensembles, and dynamical adjustment methodologies, with a focus on European climate. [ABSTRACT FROM AUTHOR]

Globally, the impacts of climate change can vary across different regions. This study uses a probability framework to evaluate recent historical (1976–2016) and near‐future projected (until 2049) climate change across Europe using Climate Research Unit and ensemble climate model datasets (under RCPs 2.6 and 8.5). A historical assessment shows that although the east and west of the domain experienced the largest and smallest increase in temperature, changes in precipitation are not as smooth as temperature. Results indicate that the maximum changes between distributions of the variables (temperature and precipitation) mainly occur at extreme percentiles (e.g., 10% and 90%). A group analysis of four subregions of Europe, namely east (G1), north (G2), west/south (G3), and UK/Ireland (G4), shows that G1 and G4 are expected to have the largest increase in temperature and precipitation extremes, respectively. Although maximum increases in temperature in G3 and G4 occur at larger percentiles, G1 and G2 experience maximum increases at both large and small percentiles. The maximum increase of precipitation over the study domain, however, occurs mainly at larger extremes. To quantify changes in the distribution of projection (2020–2049) relative to the historical reference (1976–2005), two measures are defined, namely a change in occurrences (KS statistic) and intensities at different quantiles (Δ). Results confirm that the temperature distribution tends to shift to higher temperatures. Changes in distribution and extremes of precipitation are spatially variable. Furthermore, extremes are expected to be intensified under RCP 8.5. The quantile analysis and defined measures reveal changes in the entire probability distribution, reflecting possible climate changes in the future. [ABSTRACT FROM AUTHOR]

Since our climate is in a rapid changing phase, it is crucial to get information about the regional patterns of basic climatic parameters and indices. The EURO-CORDEX project provides high-quality regional climate model outputs, but these raw datasets are not convenient for the application in wider geoscience studies. According to the authors' knowledge, there is a lack of published spatial information about basic climate parameters and indices in Central Europe and especially in the broader Carpathian region therefore the basic aim of this study to fill this gap. The study presents the future trends in daily air temperature and precipitation and various climatic indices in the broader Carpathian Basin region during the 21st century. The indices are calculated using multi-model average temperature and precipitation data from EURO-CORDEX model simulations for the future time periods (2021-2050, 2071-2100) and emission scenarios (RCP4.5, RCP8.5). The indices present the future trends of the heat load, energy demand as well as extreme precipitation and drought characteristics. Based on the results the temperature increase is obvious and the heat load and energy demand quantifying indices follow the temperature trend. However, the trend is difficult to evaluate in the case of precipitation. The changes in the precipitation and the related indices can be considered small and appear within the regions. The future changes are the most considerable in the Carpathian Basin, but the entire examined region faces crucial changes in the following decades. [ABSTRACT FROM AUTHOR]

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

Inter‐variable dependencies are key properties to characterize many climate phenomena—such as compound events—and their future changes. Yet, climate simulations often have statistical biases. Hence, univariate (1dBC) and multivariate bias correction (MBC) methods are regularly applied. Inter‐variable properties (e.g., correlations) can be altered by BC corrections. Then, it is necessary to assess how underlying assumptions of BC methods on climate change affect the adjustments. This can lead to better choices of BC methods. Here, we investigate whether an MBC method should try reproducing, preserving or modifying the changes in rank correlations between daily temperature and precipitation over Europe. An original "perfect model experiment" is set up and applied to two different climate simulation ensembles over 2001–2100: 40 runs from the Community Earth System Model (CESM) global climate model and 11 runs from the Coupled Models Intercomparison Project (CMIP6) exercise. The results highlight that, within the multi‐run single Global Climate Models ensemble (CESM), accounting for correlation changes brings valuable information for long‐term projections but that a stationarity assumption provides less biased correlations, up to medium‐term projections (2060). For the multi‐model ensemble (CMIP6), the non‐stationarity assumption provides larger biases than the stationarity approach, up to the end of the century. Not correcting the model rank correlations (1dBC) provides the worst results. Whenever an ensemble is available, the best results come from accounting for the "robust" part of the change signal (i.e., average change from different runs). This pleads for using ensembles and their robust information, in order to perform robust bias corrections. Key Points: Multivariate bias correction methods can account for changes in correlations in climate simulations or can consider that they are stationaryA perfect model experiment is set up to evaluate consequences of these choices in terms of temperature versus precipitation rank correlationsResults on two ensembles show that both approaches are meaningful, depending on the underlying confidence put on climate simulations [ABSTRACT FROM AUTHOR]

This study investigates present and future European heat wave magnitudes, represented by the Heat Wave Magnitude Index-daily (HWMId), for regional climate models (RCMs) and the driving global climate models (GCMs) over Europe. A subset of the large EURO-CORDEX ensemble is employed to study sources of uncertainties related to the choice of GCMs, RCMs, and their combinations. We initially compare the evaluation runs of the RCMs driven by ERA-interim reanalysis to E-OBS (observation-based estimates), finding that the RCMs can capture most of the observed spatial and temporal features of HWMId. With their higher resolution compared to GCMs, RCMs can reveal spatial features of HWMId associated with small-scale processes (e.g., orographic effects); moreover, RCMs represent large-scale features of HWMId satisfactorily (e.g., by reproducing the general pattern revealed by E-OBS with high values at western coastal regions and low values at the eastern part). Our results indicate a clear added value of the RCMs compared to the driving GCMs. Forced with the emission scenario RCP8.5, all the GCM and RCM simulations consistently project a rise in HWMId at an exponential rate. However, the climate change signals projected by the GCMs are generally attenuated when downscaled by the RCMs, with the spatial pattern also altered. The uncertainty in a simulated future change of heat wave magnitudes following global warming can be attributed almost equally to the difference in model physics (as represented by different RCMs) and to the driving data associated with different GCMs. Regarding the uncertainty associated with RCM choice, a major factor is the different representation of the orographic effects. No consistent spatial pattern in the ensemble spread associated with different GCMs is observed between the RCMs, suggesting GCM uncertainties are transformed by RCMs in a complex manner due to the nonlinear nature of model dynamics and physics. In summary, our results support the use of dynamical downscaling for deriving regional climate realization regarding heat wave magnitudes. [ABSTRACT FROM AUTHOR]

The object of research is the methodology of existing models of climate change and the identification of areas that are suitable for agricultural production. The consequences of natural disasters (floods, tornadoes, tornadoes, earthquakes, volcanic eruptions etc.) are the destruction of local ecosystems and the local economy, the emergence of threats to food and water supply, the creation of social conflicts, internal and external migration. Damage from natural disasters is estimated at billions of dollars. The economies of countries in which natural disasters are being synchronized cannot recover on their own and need international assistance, especially the placement of climate refugees in other countries. An analysis of recent research and publications shows that its authors do not even pose such a problem. In the period from 2011 to 2020 alone, the number of natural disasters in the world increased 7 times, in particular in Europe – 41 times. The study shows that the concept of anthropogenic impact on climate change, sustainable development and possible adaptation through environmental and energy and resource conservation measures must be considered erroneous. International spending on climate change adaptation programs around the world is inefficient. The theoretical basis on which these concepts have been built so far needs to be revised, and the environmental policy of all countries of the world is subject to radical transformation. The practical significance of research is to improve the model of climate change, which is used to develop forecast scenarios for world events, based on the dependence of CO2 emissions. from anthropogenic human activity. Since the results of any mathematical model depend on the input data and the correlation of the relationships embedded in such a model, the percentage of reliability and probability of implementation of such a model is also estimated. Therefore, climate models developed at the initiative of the International Group of Experts on Climate Change cannot be used as a basis for substantiating recommendations for the transformation of agriculture in accordance with climate change. [ABSTRACT FROM AUTHOR]

It is well established that using kilometer scale grid resolution for simulations of weather systems in weather and climate models enhances their realism. This study explores heavy- and extreme-precipitation characteristics over the Nordic region generated by the regional climate model HARMONIE-Climate (HCLIM). Two model setups of HCLIM are used: ERA-Interim-driven HCLIM12 spanning over Europe at 12 km grid spacing with a convection parameterization scheme and HCLIM3 spanning over the Nordic region with 3 km grid spacing and explicitly resolved deep convection. The HCLIM simulations are evaluated against a unique and comprehensive set of gridded and in situ observation datasets for the warm season from April to September regarding their ability to reproduce sub-daily and daily heavy-precipitation statistics across the Nordic region. Both model setups are able to capture the daily heavy-precipitation characteristics in the analyzed region. At the sub-daily scale, HCLIM3 clearly improves the statistics of occurrence of the most intense heavy-precipitation events and the amplitude and timing of the diurnal cycle of these events compared to its forcing of HCLIM12. Extreme value analysis shows that HCLIM3 provides added value in capturing sub-daily return levels compared to HCLIM12, which fails to produce the most extreme events. The results indicate clear benefits of the convection-permitting model in simulating heavy and extreme precipitation in the present-day climate, therefore, offering a motivating way forward to investigate the climate change impacts in the region. [ABSTRACT FROM AUTHOR]

Abstract: The paper presents a methodology for estimating high quantiles of distributions of daily temperature in a non-stationary context, based on peaks-over-threshold analysis with a time-dependent threshold expressed in terms of regression quantiles. The extreme value models are applied to estimate 20-yr return values of maximum daily temperature over Europe in transient global climate model (GCM) simulations for the 21st century. A comparison of scenarios of changes in the 20-yr return temperatures based on the non-stationary peaks-over-threshold models with conventional stationary models is performed. It is demonstrated that the application of the stationary extreme value models in temperature data from GCM scenarios yields results that may be to a large extent biased, while the non-stationary models lead to spatial patterns that are robust and enable one to detect areas where the projected warming in the tail of the distribution of daily temperatures is largest. The method also allows splitting the projected warming of extremely high quantiles into two parts that reflect change in the location and scale of the distribution of extremes, respectively. Spatial patterns of the two components differ significantly in the examined climate change projections over Europe. [Copyright &y& Elsevier]

Humans have made such dramatic and permanent changes to Earth's landscapes that much of it is now substantially and irreversibly altered from its preanthropogenic state. Remote islands, until recently isolated from humans, offer insights into how these landscapes evolved in response to human-induced perturbations. However, little is known about when and how remote systems were colonized because archaeological data and historical records are scarce and incomplete. Here, we use a multiproxy approach to reconstruct the initial colonization and subsequent environmental impacts on the Azores Archipelago. Our reconstructions provide unambiguous evidence for widespread human disturbance of this archipelago starting between 700-60+50 and 850-60+60 Common Era (CE), ca. 700 y earlier than historical records suggest the onset of Portuguese settlement of the islands. Settlement proceeded in three phases, during which human pressure on the terrestrial and aquatic ecosystems grew steadily (i.e., through livestock introductions, logging, and fire), resulting in irreversible changes. Our climate models suggest that the initial colonization at the end of the early Middle Ages (500 to 900 CE) occurred in conjunction with anomalous northeasterly winds and warmer Northern Hemisphere temperatures. These climate conditions likely inhibited exploration from southern Europe and facilitated human settlers from the northeast Atlantic. These results are consistent with recent archaeological and genetic data suggesting that the Norse were most likely the earliest settlers on the islands. [ABSTRACT FROM AUTHOR]

This study investigates, through regional climate modelling, the surface mass concentration and AOD (aerosol optical depth) evolution of the various (anthropogenic and natural) aerosols over the Euro-Mediterranean region between the end of the 20th century and the mid-21st century. The direct aerosol radiative forcing (DRF) as well as the future Euro-Mediterranean climate sensitivity to aerosols have also been analysed. Different regional climate simulations were carried out with the CNRM-ALADIN63 regional climate model, driven by the global CNRM-ESM2-1 Earth system model (used in CMIP6) and coupled to the TACTIC (Tropospheric Aerosols for ClimaTe In CNRM) interactive aerosol scheme. These simulations follow several future scenarios called shared socioeconomic pathways (SSP 1-1.9, SSP 3-7.0 and SSP 5-8.5), which have been chosen to analyse a wide range of possible future scenarios in terms of aerosol or particle precursor emissions. Between the historical and the future period, results show a total AOD decrease between 30 % and 40 % over Europe for the three scenarios, mainly due to the sulfate AOD decrease (between -85 and -93 %), that is partly offset by the nitrate and ammonium particles AOD increase (between +90 and +120 %). According to these three scenarios, nitrate aerosols become the largest contributor to the total AOD during the future period over Europe, with a contribution between 43.5 % and 47.5 %. It is important to note that one of the precursors of nitrate and ammonium aerosols, nitric acid, has been implemented in the model as a constant climatology over time. Concerning natural aerosols, their contribution to the total AOD increases slightly between the two periods. The different evolution of aerosols therefore impacts their DRF, with a significant sulfate DRF decrease between 2.4 and 2.8 W m -2 and a moderate nitrate and ammonium DRF increase between 1.3 and 1.5 W m -2 , depending on the three scenarios over Europe. These changes, which are similar under the different scenarios, explain about 65 % of the annual shortwave radiation change but also about 6 % (in annual average) of the warming expected over Europe by the middle of the century. This study shows, with SSP 5-8.5, that the extra warming attributable to the anthropogenic aerosol evolution over Central Europe and the Iberian Peninsula during the summer period is due to "aerosol–radiation" as well as "aerosol–cloud" interaction processes. The extra warming of about 0.2 ∘ C over Central Europe is explained by a surface radiation increase of 5.8 W m -2 over this region, due to both a surface aerosol DRF decrease of 4.4 W m -2 associated with a positive effective radiative forcing due to aerosol–radiation interactions (ERFari) of 2.7 W m -2 at the top of the atmosphere (TOA) and a cloud optical depth (COD) decrease of 1.3. In parallel, the simulated extra warming of 0.2 ∘ C observed over the Iberian Peninsula is due to a COD decrease of 1.3, leading to a positive effective radiative forcing due to aerosol–cloud interactions (ERFaci) of 2.6 W m -2 at the TOA but also to an atmospheric dynamics change leading to a cloud cover decrease of about 1.7 % and drier air in the lower layers, which is a signature of the semi-direct forcing. This study thus highlights the necessity of taking into account the evolution of aerosols in future regional climate simulations. [ABSTRACT FROM AUTHOR]

Changes in atmospheric circulation under increasing greenhouse gas concentrations are important because of their implications for weather extremes and associated societal risks. However, uncertainties in models and future projections are still large and drivers behind circulation changes are not well understood. Particularly for Europe, a potential weakening of the Atlantic meridional overturning circulation (AMOC) is considered important as it affects SST patterns and ocean–atmosphere heat fluxes and, subsequently, European climate. Here we detect and characterize changes in atmospheric circulation patterns over the North Atlantic under increasing CO2 concentrations in simulations of a very high-resolution, fully coupled climate model (CM2.6) with a realistic representation of the AMOC. We use an objective clustering technique (self-organizing maps) and validate the model's clusters against reanalysis data. We compare the frequency of those patterns in a CO2 doubling experiment, characterized by an AMOC decline, with those in a preindustrial run, and find statistically significant changes. The most robust findings are 1) a ~30% increase in zonal flow regimes in February, relevant for flood risk in northwestern Europe, and 2) a ~60% increase in anticyclonic (high pressure) circulation directly west of the United Kingdom in August, relevant for western and central European drought. A robust decrease in the frequency of Scandinavian blocking is also seen across most months and seasons. Despite the uncertainties regarding atmospheric circulation response to climate change, our findings contribute to the increasing evidence for the emergence of robust high-impact changes over Europe. [ABSTRACT FROM AUTHOR]

In this study, we aim to better understand the current and future projections of precipitation extremes in Europe in the context of climatic variability over a long-term period from the last millennium to the end of the twenty- first century. The daily gridded precipitation data from five global climate models (GCMs) of phase 5 of the Coupled Model Intercomparison Project (CMIP5) are chosen to investigate natural variability and precipitation extremes during the last millennium (850-1849), the historical (1850-2005) period, and two representative concentration pathway (RCP2.6 and RCP8.5) scenarios (2006-99). First, the seasonal and annual precipitation and extreme statistics from GCMs are evaluated using reconstruction and observation. Second, the spatial and temporal patterns of extreme precipitation from GCMs are investigated from the last millennium to the end of the twenty-first century. Meanwhile, the characteristic changes of extreme precipitation for the five regions of Europe are further analyzed. The results revealed the following: GCMs underestimate extreme precipitation and overestimate mean precipitation compared with the observations from European Climate Assessment and Dataset (ECA&D) ; the majority of Europe except southern Europe will most likely have large magnitude increases in the extreme precipitation and mean precipitation in the future under both RCP scenarios; there is no systematic change of precipitation extremes from the last millennium to the historical period from all GCMs; and larger magnitude increases are shown in 100- and 200-yr than in 5- and 10-yr return period precipitation from both RCP scenarios. In addition, short-duration extreme precipitation willmost likely increase more than longer-duration extremes. [ABSTRACT FROM AUTHOR]

The paper analyses effects of changes in temperatures on heating and cooling demands in Europe until 2050. Specifically, the study addresses changes in trends (10-year mean) and extremes (10-year min/max). The analysis is based on two GHG emission climate scenarios (RCP2.6 and RCP4.5) and eight high-resolution regional climate models and results are provided as relative and absolute changes on grid and country scales. Population density is used as proxy for spatial distribution of demands. Projected future temperatures are proportional with RCP scenario and distance into the future and the highest relative changes occur towards north-eastern Europe and for high-altitude areas. The temperature changes lead to general decreased heating demands and corresponding increased cooling demands. In general, higher spreads are seen between demand change ratios for individual models when addressing extremes as opposed to trends: The general 2010–2050 change ratios for heating between countries are 0.85–95 for model means and average 0.69 for the extreme analysis. For cooling, corresponding ratios are 1.25–1.5 for model means and average 2.76 for model maxima. For absolute demand changes, some countries are projected to experience significant changes e.g. exceeding a doubling in cooling demands. The results are suggested as a basis for energy system analyses. [ABSTRACT FROM AUTHOR]

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

Many climate extremes, including heatwaves and heavy precipitation events, are projected to worsen under climate change, with important impacts for society. Future projections required for adaptation are often based on climate model simulations. Given finite resources, trade-offs must be made concerning model resolution, ensemble size, and level of model complexity. Here we focus on the resolution component. A given resolution can be achieved over a region using either global climate models (GCMs) or at lower cost using regional climate models (RCMs) that dynamically downscale coarser GCMs. Both approaches to increasing resolution may better capture small-scale processes and features (downscaling effect), but increased GCM resolution may also improve the representation of the large-scale atmospheric circulation (upscaling effect). The size of this upscaling effect is therefore important for deciding modelling strategies. Here we evaluate the benefits of increased model resolution for both global and regional climate models for simulating temperature, precipitation, and wind extremes over Europe at resolutions that could currently be realistically used for coordinated sets of climate projections at the pan-European scale. First we examine the benefits of regional downscaling by comparing EURO-CORDEX simulations at 12.5 and 50 km resolution to their coarser CMIP5 driving simulations. Secondly, we compare global-scale HadGEM3-A simulations at three resolutions (130, 60, and 25 km). Finally, we separate out resolution-dependent differences for HadGEM3-A into downscaling and upscaling components using a circulation analogue technique. Results suggest limited benefits of increased resolution for heatwaves, except in reducing hot biases over mountainous regions. Precipitation extremes are sensitive to resolution, particularly over complex orography, with larger totals and heavier tails of the distribution at higher resolution, particularly in the CORDEX vs. CMIP5 analysis. CMIP5 models underestimate precipitation extremes, whilst CORDEX simulations overestimate compared to E-OBS, particularly at 12.5 km, but results are sensitive to the observational dataset used, with the MESAN reanalysis giving higher totals and heavier tails than E-OBS. Wind extremes are somewhat stronger and heavier tailed at higher resolution, except in coastal regions where large coastal grid boxes spread strong ocean winds further over land. The circulation analogue analysis suggests that differences with resolution for the HadGEM3-A GCM are primarily due to downscaling effects. [ABSTRACT FROM AUTHOR]

The science of event attribution has emerged to routinely answer the question whether and to what extent human-induced climate change altered the likelihood and intensity of recently observed extreme weather events. In Europe a pilot program to operationalize the method started in November 2019, highlighting the demand for timely information on the role of climate change when it is needed most: in the direct aftermath of an extreme event. Independent of whether studies are provided operationally or as academic studies, the necessity of good observational data and well-verified climate models imply most attributions are currently made for highly developed countries only. Current attribution assessments therefore provide very little information about those events and regions where the largest damages and socio-economic losses are incurred. Arguably, these larger damages signify a much greater need for information on how the likelihood and intensity of such high-impact events have been changing and are likely to change in a warmer world. In short, why do we not focus event attribution research efforts on the whole world, and particularly events in the developing world? The reasons are not just societal and political but also scientific. We simply cannot attribute these events in the same probabilistic framework employed in most studies today. We outline six focus areas to lessen these barriers, but we will not overcome them in the near future. [ABSTRACT FROM AUTHOR]

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

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

Aim: Statistical species distribution models (SDMs) are the most common tool to predict the impact of climate change on biodiversity. They can be tuned to fit relationships at various levels of complexity (defined here as parameterization complexity, number of predictors, and multicollinearity) that may co‐determine whether projections to novel climatic conditions are useful or misleading. Here, we assessed how model complexity affects the performance of model extrapolations and influences projections of species ranges under future climate change. Location: Europe. Taxon: 34 European tree species. Methods: We sampled three replicates of predictor sets for all combinations of 10 levels (n = 3–12) of environmental variables (climate, terrain, soil) and 10 levels of multicollinearity. We used these sets for each species to fit four SDM algorithms at three levels of parameterization complexity. The >100,000 resulting SDM fits were then evaluated under environmental block cross‐validation and projected to environmental conditions for 2061–2080 considering four climate models and two emission scenarios. Finally, we investigated the relationships of model design with model performance and projected distributional changes. Results: Model complexity affected both model performance and projections of species distributional change. Fits of intermediate parameterization complexity performed best, and more complex parameterizations were associated with higher projected loss of current ranges. Model performance peaked at 10–11 variables but increasing number of variables had no consistent effect on distributional change projections. Multicollinearity had a low impact on model performance but distinctly increased projected loss of current ranges. Main conclusions: SDM‐based climate change impact assessments should be based on ensembles of projections, varying SDM algorithms as well as parameterization complexity, besides emission scenarios and climate models. The number of predictor variables should be kept reasonably small and the classical threshold of maximum absolute Pearson correlation of 0.7 restricts collinearity‐driven effects in projections of species ranges. [ABSTRACT FROM AUTHOR]

Combining proxy information and climate model simulations reconciles these sources of information about past climates. This, in turn, strengthens our understanding of past climatic changes. The analogue or proxy surrogate reconstruction method is a computationally cheap data assimilation approach, which searches in a pool of simulated climate states the best fit to proxy data. We use the approach to reconstruct European summer mean temperature from the 13th century until present using the Euro 2k set of proxy records and a pool of global climate simulation output fields. Our focus is on quantifying the uncertainty of the reconstruction, because previous applications of the analogue method rarely provided uncertainty ranges. We show several ways of estimating reconstruction uncertainty for the analogue method, which take into account the non-climate part of the variability in each proxy record. In general, our reconstruction agrees well at multi-decadal timescales with the Euro 2k reconstruction, which was conducted with two different statistical methods and no information from model simulations. In both methodological approaches, the decades around the year 1600 CE were the coldest. However, the approaches disagree on the warmest pre-industrial periods. The reconstructions from the analogue method also represent the local variations of the observed proxies. The diverse uncertainty estimates obtained from our analogue approaches can be locally larger or smaller than the estimates from the Euro 2k effort. Local uncertainties of the temperature reconstructions tend to be large in areas that are poorly covered by the proxy records. Uncertainties highlight the ambiguity of field-based reconstructions constrained by a limited set of proxies. [ABSTRACT FROM AUTHOR]

Simulations of water fluxes at high spatial resolution that consistently cover historical observations, seasonal forecasts, and future climate projections are key to providing climate services aimed at supporting operational and strategic planning, and developing mitigation and adaptation policies. The End-to-end Demonstrator for improved decision-making in the water sector in Europe (EDgE) is a proof-of-concept project funded by the Copernicus Climate Change Service program that addresses these requirements by combining a multimodel ensemble of state-of-the-art climate model outputs and hydrological models to deliver sectoral climate impact indicators (SCIIs) codesigned with private and public water sector stakeholders from three contrasting European countries. The final product of EDgE is a water-oriented information system implemented through a web application. Here, we present the underlying structure of the EDgE modeling chain, which is composed of four phases: 1) climate data processing, 2) hydrological modeling, 3) stakeholder codesign and SCII estimation, and 4) uncertainty and skill assessments. Daily temperature and precipitation from observational datasets, four climate models for seasonal forecasts, and five climate models under two emission scenarios are consistently downscaled to 5-km spatial resolution to ensure locally relevant simulations based on four hydrological models. The consistency of the hydrological models is guaranteed by using identical input data for land surface parameterizations. The multimodel outputs are composed of 65 years of historical observations, a 19-yr ensemble of seasonal hindcasts, and a century-long ensemble of climate impact projections. These unique, high-resolution hydroclimatic simulations and SCIIs provide an unprecedented information system for decision-making over Europe and can serve as a template for water-related climate services in other regions. [ABSTRACT FROM AUTHOR]

Wintertime midlatitude cyclone activity and precipitation are projected to increase across northern Europe and decrease over southern Europe, particularly over the western Mediterranean. Greater confidence in these regional projections may be established by their replication in state-of-the-art, high-resolution global climate models that resolve synoptic-scale dynamics. We evaluated the representation of the wintertime eddy-driven and subtropical jet streams, extratropical cyclone activity, and precipitation across the North Atlantic and Europe under historical (1985–2011) and RCP8.5 sea surface temperature forcing in an ensemble of atmosphere-only HadGEM3-GA3.0 simulations, where horizontal atmospheric resolution is increased from 135 to 25 km. Under RCP8.5, increased (decreased) frequency of northern (southern) eddy-driven jet occurrences and a basinwide poleward shift in the upper-level westerly flow are simulated. Increasing atmospheric resolution significantly enhances these climate change responses. At 25-km resolution, these enhanced changes in large-scale circulation amplify increases (decreases) in extratropical cyclone track density and mean intensity across the northern (southern) Euro-Atlantic region under RCP8.5. These synoptic changes with resolution impact the overall climate change response of mean and heavy winter precipitation: wetter (drier) conditions in northern (southern) Europe are also amplified at 25-km resolution. For example, the reduction in heavy precipitation simulated over the Iberian Peninsula under RCP8.5 is ~15% at 135 km but ~30% at 25-km resolution. Conversely, a shift to more frequent high extratropical cyclone (ETC)-associated precipitation rates is simulated over Scandinavia under RCP8.5, which is enhanced at 25 km. This study provides evidence that global atmospheric resolution may be a crucial consideration in European winter climate change projections. [ABSTRACT FROM AUTHOR]