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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 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]

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]

In order to explore future air quality in Europe at the 2030 horizon, two emission scenarios developed in the framework of the Global Energy Assessment including varying assumptions on climate and energy access policies are investigated with an ensemble of six regional and global atmospheric chemistry transport models. A specific focus is given in the paper to the assessment of uncertainties and robustness of the projected changes in air quality. The present work relies on an ensemble of chemistry transport models giving insight into the model spread. Both regional and global scale models were involved, so that the ensemble benefits from medium-resolution approaches as well as global models that capture long-range transport. For each scenario a whole decade is modelled in order to gain statistical confidence in the results. A statistical downscaling approach is used to correct the distribution of the modelled projection. Last, the modelling experiment is related to a hind-cast study published earlier, where the performances of all participating models were extensively documented. The analysis is presented in an exposure-based framework in order to discuss policy relevant changes. According to the emission projections, ozone precursors such as NOx will drop down to 30% to 50% of their current levels, depending on the scenario. As a result, annual mean O3 will slightly increase in NOx saturated areas but the overall O3 burden will decrease substantially. Exposure to detrimental O3 levels for health (SOMO35) will be reduced down to 45% to 70% of their current levels. And the fraction of stations where present-day exceedences of daily maximum O3 is higher than 120 µg m-3 more than 25 days per year will drop from 43% down to 2 to 8%. We conclude that air pollution mitigation measures (present in both scenarios) are the main factors leading to the improvement, but an additional cobenefit of at least 40% (depending on the indicator) is brought about by the climate policy. [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]

Perturbed physics ensembles (PPEs) have been widely used to assess climate model uncertainties and have provided new estimates of climate sensitivity and parametric uncertainty in state-of-the-art climate models. So far, mainly global climate models were used to generate PPEs, and little work has been conducted with regional climate models. This paper discusses the parameter uncertainty in two PPEs of a regional climate model driven by reanalysis data for the present climate over Europe. The uncertainty is evaluated for the variables of 2-m temperature, precipitation, and total cloud cover, with a focus on the annual cycle, interannual variability, and selected extremes. The authors show that the simulated spread of the PPEs encompasses the observations at a regional scale in terms of the annual cycle and the interannual variability, provided observational uncertainty is taken into account. To rank the PPEs a new skill metric is proposed, which takes into account observational uncertainty and natural variability. The metric is a generalization of the climate prediction index (CPI) and is compared to metrics used in other studies. The consideration of observational uncertainty is particularly important for total cloud cover and reveals that current observations do not allow for a systematic evaluation of high precipitation intensities over the entire European domain. The skill framework is additionally used to identify important model parameters, which are of interest for an objective model calibration. [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]

In this paper we clearly demonstrate that changes in oceanic nutrients are a first order factor in determining changes in the primary production of the northwest European continental shelf on time scales of 5-10 yr. We present a series of coupled hydrodynamic ecosystem modelling simulations, using the POLCOMS-ERSEM system. These are forced by both reanalysis data and a single example of a coupled ocean-atmosphere general circulation model (OAGCM) representative of possible conditions in 2080-2100 under an SRES A1B emissions scenario, along with the corresponding present day control. The OA-GCM forced simulations show a substantial reduction in surface nutrients in the open-ocean regions of the model domain, comparing future and present day time-slices. This arises from a large increase in oceanic stratification. Tracer transport experiments identify a substantial fraction of on-shelf water originates from the open-ocean region to the south of the domain, where this increase is largest, and indeed the on-shelf nutrient and primary production are reduced as this water is transported on-shelf. This relationship is confirmed quantitatively by comparing changes in winter nitrate with total annual nitrate uptake. The reduction in primary production by the reduced nutrient transport is mitigated by on-shelf processes relating to temperature, stratification (length of growing season) and recycling. Regions less exposed to ocean-shelf exchange in this model (Celtic Sea, Irish Sea, English Channel, and Southern North Sea) show a modest increase in primary production (of 5-10 %) compared with a decrease of 0-20% in the outer shelf, Central and Northern North Sea. These findings are backed up by a boundary condition perturbation experiment and a simple mixing model. [ABSTRACT FROM AUTHOR]

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

The 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]

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]

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]

There are two main approaches to downscale global climate projections: dynamical and statistical downscaling. Both families have been widely evaluated, but intercomparison studies between the two families are scarce, and usually limited to temperature and precipitation. In this work, we present a comparison between a statistical downscaling model (SDM) based on machine learning and six regional climate models (RCMs) from EURO‐CORDEX, for five variables of interest: temperature, precipitation, wind, humidity and solar radiation under present climatic conditions. The study is conducted at a continental scale over Europe, with a spatial resolution of 0.11° and daily data. Both the SDM and the RCMs are driven by the ERA‐Interim reanalysis, and observations are taken from the gridded dataset E‐OBS. Several aspects have been evaluated: daily series, mean values and extremes, spatial patterns and also temporal aspects. Additionally, a multivariable index (fire weather index) derived from the fundamental variables has been included. The SDM has better scores than the RCMs for all the evaluated metrics with only a few exceptions, mainly related to an underestimation of the variance. After bias correction, both the SDM and the six RCMs present similar results, with no significant differences among them. Results presented here, combined with the low computational expense of SDMs and the limited availability of RCMs over some CORDEX domains, should motivate the consideration of statistical downscaling at the same level as RCMs by official providers of regional information, and its inclusion in reference sites. Nonetheless, further analysis on crucial aspects such as the impact on long‐term trends or the sensitivity of different methods to being driven by global climate models, is needed. [ABSTRACT FROM AUTHOR]

In recent years, the use of deep learning methods has rapidly increased in many research fields. Similarly, they have become a powerful tool within the climate scientific community. Deep learning methods have been successfully applied for different tasks, such as the identification of atmospheric patterns, weather extreme classification, or weather forecasting. However, due to the inherent complexity of atmospheric processes, the ability of deep learning models to simulate natural processes, particularly in the case of weather extremes, is still challenging. Therefore, a thorough evaluation of their performance and robustness in predicting precipitation fields is still needed, especially for extreme precipitation events, which can have devastating consequences in terms of infrastructure damage, economic losses, and even loss of life. In this study, we present a comprehensive evaluation of a set of deep learning architectures to simulate precipitation, including heavy precipitation events (>95th percentile) and extreme events (>99th percentile) over the European domain. Among the architectures analyzed here, the U‐Net network was found to be superior and outperformed the other networks in simulating precipitation events. In particular, we found that a simplified version of the original U‐Net with two encoder‐decoder levels generally achieved similar skill scores than deeper versions for predicting precipitation extremes, while significantly reducing the overall complexity and computing resources. We further assess how the model predicts through the attribution heatmaps from a layer‐wise relevance propagation explainability method. Plain Language Summary: With the increasing success of machine learning methods in Earth Sciences, deep learning is becoming a promising tool for building data‐driven models for meteorological applications. Yet, predicting extreme events, such as heavy rainfall, is still challenging. Here, we present an intercomparison of deep learning models to assess the ability of different architectures to predict precipitation events. While most of the models perform relatively well, we show that U‐Net‐based architectures outperformed the rest of the models. We additionally applied explainability methods to quantify which input features are the most important to predict precipitation events. Overall, the relative humidity showed the highest relevance values, followed by both wind components, particularly in western and southern Europe. Key Points: We present an intercomparison of deep learning models to assess the ability of different architectures to predict precipitation eventsU‐Net‐based architectures outperformed the rest of the modelsA layer‐wise relevance propagation explainability method quantify the most important feature to predict precipitation extreme [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]

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]

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]

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

Irrigation is a crucial land use practice to adapt agriculture to unsuitable climate and soil conditions. Aiming for improving 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 for analyzing the effects of irrigation on land-atmosphere interaction, including the effects and feedbacks of vegetation. We developed a new parameterization for irrigation and implemented it into the REgional climate MOdel REMO2020, coupled with the interactive MOsaicbased 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 South-West Europe including the Mediterranean region on 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 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]

Deciduous forests are characterized by a higher albedo, a reduced stomatal resistance, and a deeper root system in comparison to coniferous forests. As a consequence, less solar radiation is absorbed and evapotranspiration is potentially increased, making an increase in the deciduous forest fraction a potentially promising measure to mitigate the burdens of heat extremes for humans and nature. We analyze this potential by means of an idealized 30-year-long regional climate model (RCM) experiment, in which all coniferous forests in Europe are replaced by deciduous forests and compared to a simulation using the actual forest composition. Results show that an increase in the deciduous forest fraction reduces the heat intensity during heat periods in most regions of Europe. During heat periods, there is a slight reduction in the mean daily maximum 2 m temperatures simulated of about 0.2 K locally and 0.1 K non-locally. Regions with a high cooling potential are southwestern France and northern Turkey, where heat period intensities are reduced by up to 1 K. Warming effects are simulated in Scandinavia and eastern Europe. Although the cooling effect on heat period intensities is statistically significant over large parts of Europe, the magnitude of the temperature reduction is small. Consequently, an increase in the deciduous forest fraction only has a limited potential to reduce heat period intensities in Europe and can therefore only be considered as a supporting mitigation measure to complement more effective mitigation strategies. [ABSTRACT FROM AUTHOR]

In the 2022 summer, West-Central Europe and several other northern-hemisphere mid-latitude regions 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 West-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 pre-industrial 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, observationbased 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 in a 2 °C world, 2022-like soil drought conditions would become twice as likely for West-Central Europe compared to today, and would take place nearly every year across the northern extratropics. [ABSTRACT FROM AUTHOR]

Classifications by self‐organizing maps (SOMs) have been used to study links between extreme weather events and atmospheric circulation. However, SOMs may often provide a too generalized image of circulation, which is too coarse to properly resolve meteorological extremes. Several approaches have been used to circumvent this limitation, including an increase in the number of nodes and/or relaxation of the stress on topological ordering of nodes, through adjusting the neighbourhood radius. Here, we (1) test whether weighting of training fields based on extremity of their circulation could lead to SOMs that would better represent circulation extremes without impairing the topological ordering of nodes, and (2) we investigate whether Sammon maps (SM), which have so far been limited to validation of SOMs in climatology, could provide a viable alternative to SOMs in studying weather extremes. We show that removing a certain percentage of circulation fields closest to the climatological mean has a greater effect on the representation of circulation extremes than more than doubling the SOM array size. Additionally, this step allows for decreasing the neighbourhood radius, the parameter responsible for topological ordering, which would not otherwise be possible without impairing the topology of the map. The SOMs are compared with two newly developed methods that discretize SMs based on arbitrarily chosen and distribution‐based bins. We demonstrate the usefulness of both methods in climate model validation and show that both methods are able to capture circulation variability in its extremes in more detail than the SOMs. The distribution‐based method does not suffer from sample‐size issues and outperforms all other classifications in extracting ERA5 patterns leading to extreme temperature minima over central Europe. Our results suggest that classifications based on SM could be a viable alternative to SOMs when SOMs lack the ability to discriminate circulation outliers leading to temperature extremes. [ABSTRACT FROM AUTHOR]

Projections of changes in extreme droughts under future climate conditions are associated with large uncertainties, owing to the complex genesis of droughts and large model uncertainty in the atmospheric dynamics. In this study we investigate the impact of global warming on soil moisture drought severity in west-central Europe by employing pseudo global warming (PGW) experiments, which project the 1980–2020 period in a globally warmer world. The future analogues of present-day drought episodes allow for investigation of changes in drought severity conditional on the historic day-to-day evolution of the atmospheric circulation. The 2018 west-central European drought is the most severe drought in the 1980–2020 reference period in this region. Under 1.5, 2 and 3 ∘ C global warming, this drought episode experiences strongly enhanced summer temperatures but a fairly modest soil moisture drying response compared to the change in climatology. This is primarily because evaporation is already strongly moisture-constrained during present-day conditions, limiting the increase in evaporation and thus the modulation of the temperature response under PGW. Increasing precipitation in winter, spring and autumn limits or prevents an earlier drought onset and duration. Nevertheless, the drought severity, defined as the cumulative soil moisture deficit volume, increases considerably, with 20 % to 39 % under 2 ∘ C warming. The extreme drought frequency in the 1980–2020 period strongly increases under 2 ∘ C warming. Several years without noticeable droughts under present-day conditions show very strong drying and warming. This results in an increase in 2003-like drought occurrences, compounding with local summer temperature increases considerably above 2 ∘ C. Even without taking into account a (potentially large) dynamical response to climate change, drought risk in west-central Europe is strongly enhanced under global warming. Owing to increases in drought frequency, severity and compounding heat, a reduction in recovery times between drought episodes is expected to occur. Our physical climate storyline provides evidence complementing conventional large-ensemble approaches and is intended to contribute to the formulation of effective adaptation strategies. [ABSTRACT FROM AUTHOR]

Land surface models (LSMs) use the atmospheric grid as their basic spatial decomposition because their main objective is to provide the lower boundary conditions to the atmosphere. Lateral water flows at the surface on the other hand require a much higher spatial discretization as they are closely linked to topographic details. We propose here a methodology to automatically tile the atmospheric grid into hydrological coherent units which are connected through a graph. As water is transported on sub-grids of the LSM, land variables can easily be transferred to the routing network and advected if needed. This is demonstrated here for temperature. The quality of the river networks generated, as represented by the connected hydrological transfer units, are compared to the original data in order to quantify the degradation introduced by the discretization method. The conditions the sub-grid elements impose on the time step of the water transport scheme are evaluated, and a methodology is proposed to find an optimal value. Finally the scheme is applied in an off-line version of the ORCHIDEE (Organising Carbon and Hydrology In Dynamic Ecosystems) LSM over Europe to show that realistic river discharge and temperatures are predicted over the major catchments of the region. The simulated solutions are largely independent of the atmospheric grid used thanks to the proposed sub-grid approach. [ABSTRACT FROM AUTHOR]

Climate models can produce accurate representations of the most important modes of climate variability, but they cannot be expected to follow their observed time evolution. This makes direct comparison of simulated and observed variability difficult and creates uncertainty in estimates of forced change. We investigate the role of three modes of climate variability, the North Atlantic Oscillation, El Niño–Southern Oscillation and the Southern Annular Mode, as pacemakers of climate variability since 1781, evaluating where their evolution masks or enhances forced climate trends. We use particle filter data assimilation to constrain the observed variability in a global climate model without nudging, producing a near-free-running model simulation with the time evolution of these modes similar to those observed. Since the climate model also contains external forcings, these simulations, in combination with model experiments with identical forcing but no assimilation, can be used to compare the forced response to the effect of the three modes assimilated and evaluate the extent to which these are confounded with the forced response. The assimilated model is significantly closer than the "forcing only" simulations to annual temperature and precipitation observations over many regions, in particular the tropics, the North Atlantic and Europe. The results indicate where initialised simulations that track these modes could be expected to show additional skill. Assimilating the three modes cannot explain the large discrepancy previously found between observed and modelled variability in the southern extra-tropics but constraining the El Niño–Southern Oscillation reconciles simulated global cooling with that observed after volcanic eruptions. [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]

Ural blocking (UB) is a prominent mode of variability of the Northern Hemisphere atmospheric circulation, particularly in fall. It can persist for several days and exert a lagged influence on the wintertime NH circulation, providing predictability at the subseasonal time scale. Using two atmospheric models, we explore how the early winter atmospheric circulation responds to a 2‐week persistent UB anomaly imposed in early November. Experiments are carried out with two different configurations of Barents‐Kara (BK) sea‐ice concentration to examine whether it plays a role in how UB impacts atmospheric variability. In both models, the UB anomaly is followed by a weakening of the stratospheric polar vortex and a negative phase of the North Atlantic Oscillation (NAO), which lasts up to 2 months after the forcing is released. Interestingly, the response is more persistent under low BK sea‐ice conditions, that is, BK sea ice modulates the atmospheric response to UB. Additional experiments with prescribed sea ice concentration anomalies alone suggest that BK sea ice exerts a limited influence on early winter NH atmospheric variability. The response to UB involves a weakening of the polar vortex that persists longer under low BK sea ice, which explains the more persistent response in that configuration. Our study highlights that UB variability in November is a robust precursor for early winter NAO/polar stratosphere anomalies, and this may be more relevant in the context of declining Arctic sea‐ice extent. Provided that climate models accurately capture this teleconnection, it has the potential to improve subseasonal predictions of the NH wintertime climate. Plain Language Summary: Predicting subseasonal fluctuations of the polar stratosphere in winter (or polar vortex) is critical to anticipate weather extreme events over populated areas of the Northern Hemisphere. In this study, we highlight that a persisting high‐pressure system over the Europe/Ural region in fall (known as Ural blocking [UB]) can force a delayed response at the surface several weeks later, that resembles the negative phase of the North Atlantic Oscillation (NAO). This is found in two different atmospheric models in which the UB pattern is imposed during the first 2 weeks of November, using a regional relaxation of the troposphere. We also investigate the response to fall/early winter Barents‐Kara sea ice decline, which has been hypothesized to have a similar impact on the polar vortex/NAO, but we find that it is of secondary importance compared to the influence of UB. Key Points: Two‐week persistent Ural blocking (UB) anomalies induce a weaker polar vortex and negative North Atlantic OscillationRole of Barents‐Kara (BK) sea ice is secondary compared to the influence of UBHowever, BK sea ice modulates the persistence of the response to UB [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]

Knowing that internal climate variability is the principal driver of Mediterranean droughts during the last millennium, in this study, we investigate circulation patterns in the Euro-Atlantic domain associated with multi-year droughts over the Mediterranean region in CMIP5-PMIP3 and CESM-LME simulations. The focus is on the natural variability of droughts during 850–2005, thus excluding the anthropogenic trends from 1850 CE onward. The results re-confirm that Mediterranean drought occurrence during the last millennium is associated with internal variability. In terms of the temporal variability, all climate models exhibit a multi-decadal anti-phase occurrence of droughts between the western and eastern Mediterranean agreeing with some proxy records. This anti-phase occurrence of droughts can be explained by the dominant circulation patterns in each region: western Mediterranean droughts are dominated by a high-pressure system and ridge over central Europe and an NAO-like pattern, while eastern Mediterranean droughts are linked to positive pressure anomalies in the southern and eastern Mediterranean, negative NAO, negative EA and EA-WR-like patterns. However, these modes of climate variability are strongly model-dependent, i.e. each model has its preferred circulation patterns that occur more frequently during droughts, suggesting that the main drivers of droughts differ between the models. The models' preference is therefore a potential source of uncertainties in Mediterranean droughts in model-proxy comparison and may have implications for future climate projections. [ABSTRACT FROM AUTHOR]

Winter windstorms are among the most significant natural hazards in Europe linked to fatalities and substantial economic damages. 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 CMIP6 with the climate-risk assessment model CLIMADA to obtain projections of future change in windstorm-induced damages over Europe. We conduct an uncertainty-sensitivity analysis, and find large uncertainties in the projected changes in the damages, with climate model uncertainty being the dominant factor of uncertainty in the projections. We investigate spatial patterns of the future changes in windstorm damages and find an increase in the damages 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 windstorm damages, with damages with return periods of 100 years under current climate conditions becoming damages 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]

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

The North Atlantic Oscillation ("NAO") and the East Atlantic Pattern ("EAP") dominate winter atmospheric variability over the North Atlantic. Unlike the NAO, seasonal predictability of the EAP has remained elusive. A multi‐model ensemble of seasonal predictions yields skillful forecasts of the EAP in late autumn and early winter, complementing NAO prediction skill in winter. The shift in prediction skill from EAP to NAO reflects the ability of the ensemble to forecast the evolving influence of the El Niño South Oscillation on the North Atlantic region. In early winter, the ensemble correctly forecasts the key tropical–extratropical teleconnections, resulting in skillful predictions of the EAP and western European temperatures and rainfall. However, the modeled tropical–extratropical teleconnections are weak compared to observations, contributing to a signal to noise error in predictions of the EAP. Improving the strength of such teleconnections would improve predictions of the EAP and associated surface climate. Plain Language Summary: Predicting the most likely weather patterns ahead of the autumn and winter can be very useful for resilience planning. To date, skillful forecasts have only been possible for the most common pattern of winter atmospheric variability over the North Atlantic and European region, called the North Atlantic Oscillation ("NAO"), with no significant skill for the second pattern, called the East Atlantic Pattern ("EAP"). Using seasonal predictions from a range of coupled ocean—atmosphere models, we demonstrate significant skill in the EAP, in late autumn and early winter. The associated temperature and precipitation variability across western Europe is also skilfully predicted. We further show that skill shifts from the EAP to the NAO over the autumn to winter and reflects the evolving influence of the El Niño South Oscillation, a large‐scale atmosphere‐ocean feature of the tropical Pacific. However, climate models underestimate the magnitude of the predictable signal of the EAP, as found previously for the NAO. Resolving this error would further improve seasonal predictions of the late autumn and early winter period. Key Points: Seasonal prediction skill of the East Atlantic Pattern and associated surface climate is demonstrated in late autumn and early winterA seasonal shift in prediction skill reflects the evolving influence of the El Niño Southern Oscillation on the North Atlantic regionModeled tropical–extratropical teleconnections are weak, contributing to a signal to noise error in East Atlantic Pattern predictions [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]

This study presents the application of an interactive downscaling in Europe using iLOVECLIM (a model of intermediate complexity), increasing its atmospheric resolution from 5.56 to 0.25 ∘ kilometric. A transient simulation using the appropriate climate forcings for the entire Holocene (11.5–0 ka BP) was done for both the standard version of the model and with an interactive downscaling applied. Our results show that simulations from downscaling present spatial variability that agrees better with proxy-based reconstructions and other climate models as compared to the standard model. The downscaling scheme simulates much higher (by at least a factor of 2) precipitation maxima and provides detailed information in mountainous regions. We focus on examples from the Scandes mountains, the Alps, the Scottish Highlands, and the Mediterranean. The higher spatial resolution of the downscaling provides a more realistic overview of the topography and gives local climate information, such as precipitation and temperature gradient, that is important for paleoclimate studies. With downscaling, we simulate similar trends and spatial patterns of the precipitation changes reconstructed by other proxy studies (for example in the Alps) as compared to the standard version. Our downscaling tool is numerically cheap, implying that our model can perform kilometric, multi-millennial simulations and is suitable for future studies. [ABSTRACT FROM AUTHOR]

This study analyzes a simulation of the Arctic winter 2016–2017 with focus on multi‐step vertical coupling (MSVC) by primary, secondary, and higher‐order gravity waves (GWs). We employ the HIgh Altitude Mechanistic general Circulation Model with nudging of the large scales to MERRA‐2 reanalysis. Simulation results confirm the well‐known effects from primary GWs in the winter middle atmosphere regarding strong westward GW drag and a warm winter polar stratopause during the strong‐vortex period in late December 2016, as well as weak eastward GW drag and mesospheric cooling during the sudden stratospheric warming (SSW) in late January and early February 2017. Since the amplitudes of the primary GWs that dissipate in the middle atmosphere are weaker for a reversed or weakened polar vortex, the theory for secondary GW generation predicts reduced MSVC in this case. This is confirmed by strongly reduced secondary and higher‐order GW amplitudes during the SSW and the weak‐vortex period in February. The wintertime higher‐order GWs show partial concentric ring structures above their sources in the lower thermosphere. We find that mostly those higher‐order GWs propagate to higher altitudes that have horizontal propagation directions against the tidal winds. The simulated GWs at 300 km height and observed perturbations of total electron content over Europe and North America during selected days of low geomagnetic activity show very good agreement regarding (a) the wave characteristics and (b) the reduction of amplitudes during the SSW and early February as compared to late December. Plain Language Summary: We analyze the Arctic winter 2016–2017 using a whole atmosphere model with resolved gravity waves (GWs) and nudging of the large scales to MERRA‐2 reanalysis. Model results confirm the well‐known effects from primary GWs in the winter middle atmosphere regarding strong westward GW drag and a warm winter polar stratopause during the strong‐vortex period in late December 2016, as well as weak eastward GW drag and mesospheric cooling during the sudden stratospheric warming (SSW), which started in late January 2017 and was followed by a weak vortex in February. Since secondary and higher‐order GWs are the predominant GWs in the winter mesopause region and thermosphere, the vertical coupling from the lower to the upper atmosphere by GWs is termed multi‐step vertical coupling (MSVC). We find that MSVC is strongly reduced during and after the SSW. The simulated GWs at 300 km height and observed total electron content (TEC) perturbations over Europe and North America during days of low geomagnetic activity show very good agreement regarding the wave characteristics. Furthermore, we find a clear reduction in the TEC perturbation amplitudes during and after the SSW. This agreement between model and observations strongly suggests that quiet‐time TEC perturbations in the winter thermosphere are caused by GWs from MSVC. Key Points: Wintertime thermospheric gravity waves (GWs) simulated by a whole atmosphere model are strongly reduced during the January 2017 sudden stratospheric warming (SSW) and the subsequent weak‐vortex periodObserved quiet‐time total electron content (TEC) perturbations show reduced amplitudes during the SSW and weak‐vortex period as wellPropagation directions, wavelengths, and periods of the simulated thermospheric GWs agree with observed quiet‐time TEC perturbations [ABSTRACT FROM AUTHOR]

Long‐term groundwater droughts are known to persist over timescales from multiple years up to decades. The mechanisms leading to drought persistence are, however, only partly understood. Applying a unique terrestrial system modeling platform in a probabilistic simulation framework over Europe, we discovered an important positive feedback mechanism from groundwater into the atmosphere that may increase drought persistence at interannual time scales over large continental regions. In the feedback loop, groundwater drought systematically increases net solar radiation via a cloud feedback, which, in turn, increases the drying of the land. In commonly applied climate and Earth system models, this feedback cannot be simulated due to a lack of groundwater memory effects in the representation of terrestrial hydrology. Thus, drought persistence and compound events may be underestimated in current climate projections. Plain Language Summary: Depending on the climate zone, droughts can persist for a very long time. In generally dry regions, interactions between the drought and the atmosphere become quickly apparent with a missing moisture supply from the surface. In more humid areas like the mid‐latitudes, the effects are more hidden because there is more guarantee for moisture supply from the ocean. Still, some feedbacks prolong droughts that are often overlooked in climate modeling. We show with a model that includes the whole water cycle from the groundwater to the cloud top that drought conditions can change the properties of the clouds with changes in the energy cycle. The clouds become higher and transmit more solar energy to the surface. The surplus of energy at the surface leads to more evaporation and prolonged droughts. Key Points: Previously neglected drought feedbacks initiated by water deficits in the subsurface are prolonging water deficitsThe shortfall of subsurface water leads to higher clouds via changes in the sensible heat fluxHigher clouds let more solar radiation reach the ground contributing to drought persistence [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]

The biogeophysical effects of severe forestation are quantified using a new ensemble of regional climate simulations over North America and Europe. Following the protocol outlined for the Land-Use and Climate Across Scales (LUCAS) intercomparison project, two sets of simulations are compared, FOREST and GRASS, which respectively represent worlds where all vegetation is replaced by trees and grasses. Three regional climate models were run over North America. One of them, the Canadian Regional Climate Model (CRCM5), was also run over Europe in an attempt to bridge results with the original LUCAS ensemble, which was confined to Europe. Overall, the CRCM5 response to forestation reveals strong inter-continental similarities, including a pronounced wintertime and springtime warming concentrated over snow-masking evergreen forests. Crucially, these northern evergreen needleleaf forests populate lower, hence sunnier, latitudes in North America than in Europe. Snow masking reduces albedo similarly over both continents, but stronger insolation amplifies the net shortwave radiation and hence warming simulated over North America. In the summertime, CRCM5 produces a mixed response to forestation, with warming over northern needleleaf forests and cooling over southern broadleaf forests. The partitioning of the turbulent heat fluxes plays a major role in determining this response, but it is not robust across models over North America. Implications for the inter-continental transferability of the original LUCAS results are discussed. [ABSTRACT FROM AUTHOR]

Despite a large number of studies, out of all drivers of radiative forcing, the effect of aerosols has the largest uncertainty in global climate model radiative forcing estimates. There have been studies of aerosol optical properties in climate models, but the effects of particle number size distribution need a more thorough inspection. We investigated the trends and seasonality of particle number concentrations in nucleation, Aitken, and accumulation modes at 21 measurement sites in Europe and the Arctic. For 13 of those sites, with longer measurement time series, we compared the field observations with the results from five climate models, namely EC-Earth3, ECHAM-M7, ECHAM-SALSA, NorESM1.2, and UKESM1. This is the first extensive comparison of detailed aerosol size distribution trends between in situ observations from Europe and five earth system models (ESMs). We found that the trends of particle number concentrations were mostly consistent and decreasing in both measurements and models. However, for many sites, climate models showed weaker decreasing trends than the measurements. Seasonal variability in measured number concentrations, quantified by the ratio between maximum and minimum monthly number concentration, was typically stronger at northern measurement sites compared to other locations. Models had large differences in their seasonal representation, and they can be roughly divided into two categories: for EC-Earth and NorESM, the seasonal cycle was relatively similar for all sites, and for other models the pattern of seasonality varied between northern and southern sites. In addition, the variability in concentrations across sites varied between models, some having relatively similar concentrations for all sites, whereas others showed clear differences in concentrations between remote and urban sites. To conclude, although all of the model simulations had identical input data to describe anthropogenic mass emissions, trends in differently sized particles vary among the models due to assumptions in emission sizes and differences in how models treat size-dependent aerosol processes. The inter-model variability was largest in the accumulation mode, i.e. sizes which have implications for aerosol–cloud interactions. Our analysis also indicates that between models there is a large variation in efficiency of long-range transportation of aerosols to remote locations. The differences in model results are most likely due to the more complex effect of different processes instead of one specific feature (e.g. the representation of aerosol or emission size distributions). Hence, a more detailed characterization of microphysical processes and deposition processes affecting the long-range transport is needed to understand the model variability. [ABSTRACT FROM AUTHOR]

In the future, climate change is expected to affect the spatial distribution of most tree species in Europe. The European beech (Fagus sylvatica), a drought-sensitive tree species, is currently distributed throughout Europe, where it is an ecologically and economically important species. In Slovenia, the European beech represents 33% of the growing stock, but such a proportion greatly varies across Europe. Whether such a variation is related to the climate environmental gradients or because of historical or management decisions is an as-yet unexplored question. For this study, we employed the Slovenian Forests Service inventory, where the proportion of beech in the forest stock has been monitored in 341,341 forest stands across the country. Modeled climate data from the SLOCLIM database, calculated for each of the stands, was also used to test the hypothesis that although beech forests have always been influenced by human activity, the dominance of beech trees in forest stands is at least partially dictated by the climate. The results showed the distribution of the main climate variables (annual precipitation, the share of summer and spring precipitation, and annual maximum and minimum temperatures) and how they affect the current dominance of beech trees at the stand level. Due to the large number and variability of forest stands studied, the results should be transferable to better understand and manage the climatic suitability and risks of Fagus sylvatica. The modeled data is publicly available in the web repository Zenodo. [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 strong multidecadal variability in North Atlantic (NA) winter atmospheric circulation is poorly understood and appears too weak in climate models. Recent research has shown peak atmospheric multidecadal variability over the NA in late winter, particularly March, linked to Atlantic multidecadal variability (AMV) of the ocean. Here a range of NA atmospheric circulation indices are assessed to provide a comprehensive picture of early‐to‐late winter low‐frequency variability and its representation in the latest generation of climate models (Coupled Model Intercomparison Project Phase 6 [CMIP6]). As found for CMIP5, CMIP6 models exhibit too‐weak multidecadal NA atmospheric variability compared to reanalysis data over the period 1862–2005. Consistent with previous research, the eastern part of the NA westerly jet (U700NA) exhibits peak low‐frequency variability in March. However, for NA‐wide jet speed and the NAO, low‐frequency variability and model‐reanalysis discrepancies are strongest in January and February, associated with too‐weak NA ocean‐atmosphere linkages. Plain Language Summary: The strong low‐frequency multidecadal variability of large‐scale atmospheric circulation in the North Atlantic (NA) drives variability in climate conditions over western Europe. Reliably reproducing this variability in climate models is of high importance for estimating future climatic conditions. However, a current issue in climate modeling is that models generally exhibit weak winter multidecadal variability compared to estimates based on observationally constrained reconstructions (reanalyses). In particular recent research has shown that strong low‐frequency atmospheric variability and associated discrepancies between models and reanalyses are most pronounced in late winter, particularly March. The first step in this study is to establish that the current generation of climate models still exhibit the long‐standing issue that simulated multidecadal variability of NA atmospheric circulation is weaker than in reanalyses. A subseasonal analysis shows that, consistent with previous research, the eastern part of the NA westerly jet exhibits peak low‐frequency variability in March. However, for NA‐wide indices, tropospheric westerly jet speed and the NAO, low‐frequency variability is strongest in January and February. Evidence is shown suggesting that these inter‐index differences in subseasonal variability are associated with differences in monthly correlations with NA subpolar gyre SSTs. Key Points: Climate models exhibit systematically weak winter multidecadal North Atlantic (NA) atmospheric variability relative to reanalysis outputThe subseasonal timing of 20th century winter variability is sensitive to the choice of atmospheric circulation indexOnset of strong variability occurs earliest for NA jet speed (December), followed by NAO (January) and eastern‐NA jet speed (February) [ABSTRACT FROM AUTHOR]