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Few studies explore extreme wintertime European cold waves (CW) despite their huge economic, and social impacts and a recent decade punctuated by CW (February 2012, January 2017 or March 2018), while Europe bets on mild winter to avoid an energy crisis in 2023. Here, we investigate potential early CW warning signals and model biases over the winters (November-to-March) of the 1950–2005 period, calculating the atmospheric and surface conditions of key climate variables (e.g. snow, incoming radiation, cloudiness, pressure, winds and sea surface temperatures variables) both in reanalyses and climate models, before and during CW events. We show that global coupled climate models systematically overestimate the number of CW in the present-day period. Until 30 days before CW, some robust and significant early patterns emerge, both in models and reanalyses: weak atmospheric blocking, anomalously negative North Sea surface temperatures (SST) and a deficit of incoming longwave radiation. Downward shortwave radiation is anomalously positive during and before CW which weakens arguments for direct negative solar forcing on winter extreme cold events in Europe. Climate models share in their great majority (> 80%) these patterns (for dynamical and radiative-related variables, spatial correlation r > 0.7) and can correctly simulate the sign of climate variables anomalies during and at least 7 days before CW. We find that excess of European snow cover and snow depth are unlikely to cause occurrences of CW, but the advection of cold air masses (North-East) emerges as a potential precursor signal of a majority of CW. In a context of climate change, fossil fuels scarcity and increased uncertainty due to geopolitical events, it is crucial to study the sensitivity of the energy, health and agriculture sectors to compound extreme weather, including cold waves. [ABSTRACT FROM AUTHOR]

In the autumn, the French Mediterranean area is frequently exposed to heavy precipitation events whose daily accumulation can exceed 300 mm. One of the key processes contributing to these precipitation amounts is deep convection, which can be explicitly resolved by state-of-the-art convection-permitting models to reproduce heavy rainfall events that are comparable to observations. This approach has been tested and performed at climate scale in several studies in recent decades for different areas. In this research, we investigate the added value of using an ensemble of three climate simulations at convection-permitting resolution (approx. 3 km) to replicate extreme precipitation events at both daily and shorter timescales over the south of France. These three convection-permitting simulations are performed with the Weather Research and Forecasting (WRF) Model. They are forced by three EURO-CORDEX simulations, which are also run with WRF at the resolution of 0.11 ∘ (approx. 12 km). We found that a convection-permitting approach provides a more realistic representation of extreme daily and 3-hourly rainfall in comparison with EURO-CORDEX simulations. Their similarity to observations allows use for climate change studies and its impacts. [ABSTRACT FROM AUTHOR]

In the autumn, the French Mediterranean area is frequently exposed to heavy precipitation events whose daily accumulation can exceed 300 mm. One of the key processes contributing to these precipitation amounts is the deep convection, which can be resolved explicitly by state-or-the-art convection-permitting model to reproduce heavy rainfall events that are comparable to observations. However, this approach has never been used in climate simulation for the Mediterranean coastal region. In this research, we investigate the added values of using three ensembles of climate simulations at convection-permitting resolution (approx. 3 km) in replicating extreme precipitation events in both daily and shorter time scale over the South of France. These three convection-permitting simulations are performed with the Weather Research and Forecasting Model (WRF). They are forced by three EURO-CORDEX simulations, which are also downscaled with WRF at the resolution of 0.11° (approx. 12 km). We found that a convection-permitting approach provides a more realistic representation of extreme daily and 3-hourly rainfall simulations in comparison with EURO-CORDEX simulations. Their similarity with observations allows a use for climate change studies and its impacts. [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]

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 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 at coastal regions where large 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]

Several major storms pounded western Europe in January 2018, generating large damages and casualties. The two most impactful ones, Eleanor and Friederike, are analysed here in the context of climate change. Near surface wind speed station observations exhibit a decreasing trend in the frequency of strong winds associated with such storms. High-resolution regional climate models, on the other hand, show no trend up to now and a small increase in storminess in future due to climate change. This shows that factors other than climate change, which are not in the climate models, caused the observed decline in storminess over land. A large part is probably due to increases in surface roughness, as shown for a small set of stations covering the Netherlands and in previous studies. This observed trend could therefore be independent from climate evolution. We concluded that human-induced climate change has had so far no significant influence on storms like the two mentioned. However, all simulations indicate that global warming could lead to a marginal increase (0 %–20 %) in the probability of extreme hourly winds until the middle of the century, consistent with previous modelling studies. This excludes other factors, such as surface roughness, aerosols, and decadal variability, which have up to now caused a much larger negative trend. Until these factors are correctly simulated by climate models, we cannot give credible projections of future storminess over land in Europe. [ABSTRACT FROM AUTHOR]

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

Several major storms pounded Western Europe in January 2018, generating large damages and casualties. The two most impactful ones, Eleanor and Friederike, are analyzed here in the context of climate change. Near surface wind speed station observations exhibit a decreasing trend of the frequency of strong winds associated with such storms. High-resolution regional climate models on the other hand show no trend up to now and a small increase in the future due to climate change. This shows that that factors other than climate change, which are not represented (well) in the climate models, caused the observed decline in storminess over land. A large part is probably due to increases in surface roughness, as shown for a small set of stations covering The Netherlands and in previous studies. This trend could therefore be independent from climate evolution. We concluded that human-induced climate change has had so far no significant influence on storms like the two studied. However, all simulations indicate that global warming could lead to a marginal increase (0-20%) of the probability of extreme hourly winds until the middle of the century, consistent with previous modelling studies. However, this excludes other factors, such as roughness, aerosols, and decadal variability, which have up to now caused a much larger negative trend. Until these factors are simulated well by climate models they cannot give credible projections of future storminess over land in Europe. [ABSTRACT FROM AUTHOR]

The article focuses on a study which aims to examine the attribution of wintertime anticyclonic stagnation on air pollution in western Europe. Topics being presented include climate simulations which shows a potential increase in frequency of stagnant wintertime conditions that prevailed over northwestern Europe in December 2016 which is significant in one multimodel ensemble.

The estimation of hub-height wind speed is critical to a comprehensive wind resource assessment, particularly for the evaluation of future energy mix scenarios. However, gridded datasets of wind speeds are often limited to near-surface winds, especially when it comes to climate model projections, which is a real limitation for using climate models. This study develops a transfer method to calculate 100 m wind speed using three machine learning methods, including the Least Absolute Shrinkage Selector Operator, Random Forest (RF) and extreme Gradient Boost (XGBoost). Compared with the traditional algorithm, based on empirical formulae, the tested machine learning-based algorithms allow much more accurate estimates of 100 m wind speeds. RF and XGBoost have good performance on the hourly scale, and correct the major biases of the classical, simplified algorithms, especially in the diurnal cycle of hub-height wind speeds. RF appears to be the best algorithm when compared with the reanalysis data. In addition, the machine learning transfer model is applied to 19 regional climate projections. Results show that the 100 m wind speed has decreased in most of Europe during 1979–2019, which is consistent with the observed stilling of surface winds in recent years. This trend is projected to increase in the future, under an uncurbed greenhouse gas emission scenario, which indicates adverse effects for the development of wind power generation in Europe. The approach established in this study can be applied to obtain numerical climate model outputs accurately, which is critical to the estimation of the long-term changes of global renewable energy resources. • A transfer model to calculate hub-height wind speed using machine learning methods. • Random Forest corrects the poor performance of the diurnal cycle simulation. • Wind power potential will decrease in the future along the 21st century in Europe. • Our methodology will benefit to wind power potential and drought risk assessment. [ABSTRACT FROM AUTHOR]

Climate change may alter the geographical pattern and intensity of near-surface winds which are the 'fuel' for wind turbines. In a context of fast current and planned development of wind power worldwide, investigating the impacts of climate change on wind power generation is necessary. This study aims at assessing future changes in the potential for wind power generation over the whole Europe and in the effective wind power production from national wind farms operating at the end of 2012 and planned by 2020. For this purpose, a simplified wind power generation model is applied to an ensemble of 15 regional climate projections achieved from 10 Regional Climate Models downscaling six Global Climate Models under the SRES A1B emission scenario from the ENSEMBLES project. The use of a relatively large multi-model ensemble allows the identification of robust changes and the estimation of a range of uncertainties associated with projected changes. We show with a high level of confidence that, under the A1B scenario, over most of Europe, changes in wind power potential will remain within ±15 and ±20 % by mid and late century respectively. Overall, we find a tendency toward a decrease of the wind power potential over Mediterranean areas and an increase over Northern Europe. Changes in multi-year power production will not exceed 5 and 15 % in magnitude at the European and national scale respectively for both wind farms in operation at the end of 2012 and planned by 2020. Therefore, climate change should neither undermine nor favor wind energy development in Europe. However, accounting for climate change effects in particular regions may help optimize the wind power development and energy mix plans. [ABSTRACT FROM AUTHOR]

The ability of a large ensemble of regional climate models to accurately simulate heat waves at the regional scale of Europe was evaluated. Within the EURO-CORDEX project, several state-of-the art models, including non-hydrostatic meso-scale models, were run for an extended time period (20 years) at high resolution (12 km), over a large domain allowing for the first time the simultaneous representation of atmospheric phenomena over a large range of spatial scales. Eight models were run in this configuration, and thirteen models were run at a classical resolution of 50 km. The models were driven with the same boundary conditions, the ERA-Interim re-analysis, and except for one simulation, no observations were assimilated in the inner domain. Results, which are compared with daily temperature and precipitation observations (ECA&D and E-OBS data sets) show that, even forced by the same re-analysis, the ensemble exhibits a large spread. A preliminary analysis of the sources of spread, using in particular simulations of the same model with different parameterizations, shows that the simulation of hot temperature is primarily sensitive to the convection and the microphysics schemes, which affect incoming energy and the Bowen ratio. Further, most models exhibit an overestimation of summertime temperature extremes in Mediterranean regions and an underestimation over Scandinavia. Even after bias removal, the simulated heat wave events were found to be too persistent, but a higher resolution reduced this deficiency. The amplitude of events as well as the variability beyond the 90th percentile threshold were found to be too strong in almost all simulations and increasing resolution did not generally improve this deficiency. Resolution increase was also shown to induce large-scale 90th percentile warming or cooling for some models, with beneficial or detrimental effects on the overall biases. Even though full causality cannot be established on the basis of this evaluation work, the drivers of such regional differences were shown to be linked to changes in precipitation due to resolution changes, affecting the energy partitioning. Finally, the inter-annual sequence of hot summers over central/southern Europe was found to be fairly well simulated in most experiments despite an overestimation of the number of hot days and of the variability. The accurate simulation of inter-annual variability for a few models is independent of the model bias. This indicates that internal variability of high summer temperatures should not play a major role in controlling inter-annual variability. Despite some improvements, especially along coastlines, the analyses conducted here did not allow us to generally conclude that a higher resolution is clearly beneficial for a correct representation of heat waves by regional climate models. Even though local-scale feedbacks should be better represented at high resolution, combinations of parameterizations have to be improved or adapted accordingly. [ABSTRACT FROM AUTHOR]

In order to fulfill the society demand for climate information at the spatial scale allowing impact studies, long-term high-resolution climate simulations are produced, over an area covering metropolitan France. One of the major goals of this article is to investigate whether such simulations appropriately simulate the spatial and temporal variability of the current climate, using two simulation chains. These start from the global IPSL-CM4 climate model, using two regional models (LMDz and MM5) at moderate resolution (15-20 km), followed with a statistical downscaling method in order to reach a target resolution of 8 km. The statistical downscaling technique includes a non-parametric method that corrects the distribution by using high-resolution analyses over France. First the uncorrected simulations are evaluated against a set of high-resolution analyses, with a focus on temperature and precipitation. Uncorrected downscaled temperatures suffer from a cold bias that is present in the global model as well. Precipitations biases have a season- and model-dependent behavior. Dynamical models overestimate rainfall but with different patterns and amplitude, but both have underestimations in the South-Eastern area (Cevennes mountains) in winter. A variance decomposition shows that uncorrected simulations fairly well capture observed variances from inter-annual to high-frequency intra-seasonal time scales. After correction, distributions match with analyses by construction, but it is shown that spatial coherence, persistence properties of warm, cold and dry episodes also match to a certain extent. Another aim of the article is to describe the changes for future climate obtained using these simulations under Scenario A1B. Results are presented on the changes between current and mid-term future (2021-2050) averages and variability over France. Interestingly, even though the same global climate model is used at the boundaries, regional climate change responses from the two models significantly differ. [ABSTRACT FROM AUTHOR]

The occurrence and intensity of heatwaves is expected to increase with climate change. Early warnings of hot summers have therefore a great socio-economical value. Previous studies have shown that hot summers are preceded by a Southern European rainfall deficit during winter, and higher spring temperatures. Changes in the surface energy budget are believed to drive this evolution, in particular changes in the latent and sensible heat fluxes. However these have rarely been investigated due to the lack of long-term reliable observation data. In this study, we analyzed several data-derived gridded products of latent and sensible heat fluxes, based on flux tower observations, together with re-analyses and regional climate model simulations over Europe. We find that warm summers are preceded by an increase in latent heat flux in early spring. During warm summers, an increase in available energy results in an excess of both latent and sensible heat fluxes over most of Europe, but a latent heat flux decrease over the Iberian Peninsula. This indicates that, on average, a summertime soil-moisture limited evapotranspiration regime only prevails in the Iberian Peninsula. In general, the models that we analyzed overestimate latent heat and underestimate sensible heat as compared to the flux tower derived data-product. Most models show considerable drying during warm seasons, leading to the establishment of a soil-moisture limited regime across Europe in summer. This over-estimation by the current generation of models of latent heat and hence of soil moisture deficit over Europe in summer has potential consequences for future summertime climate projections and the projected frequency of heat waves. We also show that a northward propagation of drought during warm summers is found in model results, a phenomenon which is also seen in the flux tower data-product. Our results lead to a better understanding of the role of latent and sensible heat flux in summer heatwaves, and provide a framework for benchmark of modeling studies. [ABSTRACT FROM AUTHOR]

Abstract: Accurate regional air pollution simulation relies strongly on the accuracy of the mesoscale meteorological simulation used to drive the air quality model. The framework of the Air Quality Model Evaluation International Initiative (AQMEII), which involved a large international community of modeling groups in Europe and North America, offered a unique opportunity to evaluate the skill of mesoscale meteorological models for two continents for the same period. More than 20 groups worldwide participated in AQMEII, using several meteorological and chemical transport models with different configurations. The evaluation has been performed over a full year (2006) for both continents. The focus for this particular evaluation was meteorological parameters relevant to air quality processes such as transport and mixing, chemistry, and surface fluxes. The unprecedented scale of the exercise (one year, two continents) allowed us to examine the general characteristics of meteorological models’ skill and uncertainty. In particular, we found that there was a large variability between models or even model versions in predicting key parameters such as surface shortwave radiation. We also found several systematic model biases such as wind speed overestimations, particularly during stable conditions. We conclude that major challenges still remain in the simulation of meteorology, such as nighttime meteorology and cloud/radiation processes, for air quality simulation. [Copyright &y& Elsevier]

Europe has been warming over the past 30 years. In particular all seasonal temperature records have been broken since 2003, which altered socio-economic and environmental systems. Since we expect this trend in both mean and extreme temperatures to continue along the twenty first century under enhanced radiative forcing, it is crucial to understand the underlying mechanisms of such climate variations to help in considering adaptation or mitigation strategies to reduce the impacts of a warmer climate. From a statistical analysis we show that the inter-annual variability of European seasonal temperatures can be reconstructed from North-Atlantic atmospheric circulation only, but not their recent trends and extreme seasons. Adding North-Atlantic sea-surface temperature (SST) as a predictor helps improving the reconstruction, especially in autumn and winter. Sensitivity experiments with the MM5 regional model over 2003-2007 suggest that the anomalous SST enhance European land temperatures through the upper-air advection of heat and water vapor, interacting with radiative fluxes over the continent. This mechanism is pronounced in autumn and winter, where estimates of SST influence as obtained from MM5 are in agreement with those obtained from statistical regressions. We find a lesser SST influence in spring and summer, where local surface and radiative feedbacks are the main amplifiers of recent extremes. [ABSTRACT FROM AUTHOR]

We explore the sensitivity of the simulation of photochemical smog to the turbulent mixing scheme, using two diffusion schemes and an original two-stream model (TSM) scheme, assuming in the column an updraft and a downdraft. In this latter scheme both updraft and downdraft concentrations are prognostic variables, unlike in previously proposed schemes. The comparisons are made using a one-dimensional column model, in a Eulerian or a Lagrangian mode. The diffusion schemes produce tilted concentration profiles for primary species, with higher concentrations near the surface and lower values at the top of the boundary layer, while TSM profiles yield more homogeneous concentrations in the planetary boundary layer (PBL). Ozone concentrations are also more homogeneous in the TSM PBL than in the diffusive PBL. Only deposition makes ozone concentrations slightly lower near the surface, while in diffusive case ozone is lower also due to titration by higher nitrogen oxide concentrations. The overall differences between the schemes remain small for ozone. Also, the development time and amplitude of an ozone city plume is not very sensitive to the choice of the mixing scheme. In the urban framework ozone build-up is slightly delayed by higher nitrogen oxide concentrations near the surface in the diffusive cases, but the plume development is similar to that of the TSM once the plume travels away from the emission area. Results also show that the sensitivity of ozone to nitrogen oxide and non-methane volatile organic compounds is itself not very sensitive to the mixing scheme. [Copyright &y& Elsevier]

Abstract: The extremely warm summer of 2003, with its August heat wave, is taken as a prototype of future summer weather in Europe. The stagnant circulation led to accumulation of heat and pollutants, increased forest fires, and induced high ozone and particulate matter levels. After a description of the meteorological conditions encountered, we review here the effects of the heat-wave meteorology on photochemistry, wild fires, and particulate matter, at the continental and urban scales. We discuss the extent to which this special summer can be taken for projecting air quality in a future warmer climate, especially in the perspective of changes in regional and global emissions. For ozone, the effect of regional reduction of emissions will dominate over summer climate change, but the increase in baseline ozone should significantly raise the mean ozone levels. [Copyright &y& Elsevier]

Abstract: Despite the major international efforts devoted to the understanding and to the future estimate of global climate change and its impact on regional scale processes, the evolution of the atmospheric composition in a changing climate is far to be understood. In particular, the future evolution of the concentration of near-surface pollutants determining air quality at a scale affecting human health and ecosystems is a subject of intense scientific research. This thematic issue reviews the current scientific knowledge of the consequences of global climate change on regional air quality and its related impact on the biosphere and on human mortality. This article provides a presentation of the key issues, summarizes the current knowledge, and introduces the thematic issue. [Copyright &y& Elsevier]

Two photochemical smog modeling systems, UAM-V/SAIMM (the Variable-Grid UAM/Systems Applications International Mesoscale Model) and CHIMERE/ECMWF (European Center for Medium Range Weather Forecast), are applied to the same tropical domain (Bangkok Metropolitan Region) and the same episode (January 13-14, 1997) to evaluate their relative performance using the same anthropogenic emission database (emission data-base available at the Pollution Control Department [PCD] 1997). Ozone (O3) produced by both models meets U.S. Environment Protection Agency (EPA) suggested prediction criteria of mean normalized bias error and mean normalized gross error on January 14 but none on January 13. Both models are tested with various modified databases of precursors emissions from the PCD original database. Performance of UAM-V is the best when using the modified emission data with volatile organic compound (VOC), NOx, and CO mobile source emission reduced by 50%, 50%, and 20% from the original database. CHIMERE suggests a similar emission database except for the VOC emission, which is a reduction by 40% from the original PCD mobile source emission. Spatial and temporal variations of O3, CO, NOy (total reactive nitrogen), and Ox (NO2 + O3) predicted by both model systems using the modified emissions are compared with observations. The agreement between the two models is better than their agreement with observations. The difference between the results reflects the gaps in our current knowledge of photochemistry. With the respective modified emission database, CHIMERE produces comparable results with UAM-V for all considered parameters, and O3 simulated by both models does meet all of the EPA suggested criteria. Both models predict similar results of O3 productions with various combinations of anthropogenic and biogenic emissions in Bangkok and surrounding provinces. The simulated minumum O3 and maximum NOy in the city by both modeling systems with the modified emission database on January 14 are comparable to observed data; therefore, it is considered that the modeling tool can be used for studying the sensitivity of ozone formation to precursor emission reduction for Bangkok. Both model responses to emission reductions show similar trends, which indicate that the O3 formation in Bangkok is more sensitive to VOC than NOx emissions. [ABSTRACT FROM AUTHOR]

Abstract: The significance of natural sources in the concentrations of particles smaller than 10μm (PM10) in Europe is addressed. When considering only European anthropogenic emissions, chemistry-transport model simulations underestimate the PM10 concentrations by 30–50%, using the current knowledge about aerosol physics and chemistry. Along this article we hypothesize that the missing mass originates from natural sources like erosion dust entrainment and resuspension. The methodology consists in testing these hypotheses in the CHIMERE regional chemistry-transport model, and comparing the results with long series of PM10 measurements in Europe. It is demonstrated that the introduction of background Saharan dust boundary conditions greatly improves the model simulation over Southern Europe, and to a smaller extent also over northern Europe. However to accurately simulate acute episodes of Saharan dust transport time-resolved boundary conditions need to be used. Local erosion occurring over European regions is also considered. The introduction of a simplified bulk scheme for online calculation of mineral dust emission makes the model skill improve everywhere in Europe, indicating that this process can be significant in Europe. We finally assume that resuspension of material available on the ground, explains most of the remaining part of the missing part of PM10 average load. A simplified scheme for the corresponding emission, which depends on turbulence near the ground, is proposed. It also makes the model improve substantially, especially over Northern Europe. However sensitivity tests show that such an improvement can also be obtained by simply increasing the model secondary organic matter in aerosols by a factor of 3. We conclude that our results are consistent with the existence of a strong biogenic resuspension aerosol source, but more experimental work is required to ascertain this hypothesis. [Copyright &y& Elsevier]

The statistical model proposed by Vautard et al. is applied to the seasonal prediction of surface air temperatures over North America (Canada and the United States). This model is based on sea surface temperature predictors filtered by multichannel singular spectrum analysis (MSSA), which is equivalent here to a nonseasonal version of extended EOF analysis. Several versions of the MSSA model are proposed. The most successful one is based on a two-step procedure consisting in a prior prediction of filtered sea surface temperatures followed by a predictand specification stage. The MSSA model is compared with the recent prediction technique based on canonical correlation analysis (CCA). The former model turns out, in this application, to be more skillful in most seasons than the latter. The differences are, however, marginal. The authors argue that these differences are due to the nonseasonal nature of the MSSA model and to overfitting problems inherent to CCA. Another advantage of the MSSA model relative to CCA is the possibility of easily transforming deterministic continuous forecasts into probabilistic categorical forecasts. The geographical distribution of prediction skill across North America is studied. Canada turns out to be the country where skill is most significant. During winter, high skill values are also found over the southeastern United States. [ABSTRACT FROM AUTHOR]

Features a low-order model of the extratropical atmosphere built from long records of potential vorticity. Low-order potential vorticity model; Quasi-stationary weather regimes; Intraseasonal oscillations; Time-dependent solutions of the low-order model; Diagnostic results of the weather regime study.

Develops the theory of empirical normal modes for a shallow water fluid in the field of numerical weather predictions in the United States. Wave activities and normal modes for shallow water fluid; Numerical experiments; Statistical prediction using empirical normal modes.

Compares two different definitions of midlatitude weather regimes. Emergence of weather regimes in dynamical meteorology as a particular representation of atmospheric dynamics; Recurrent atmospheric patterns; Quasi-stationary patterns; Use of cluster analysis; Performance of analysis over Atlantic and Pacific sectors; Description of methodology used to identify quasi-stationary regimes.

Presents a study of low-frequency oscillations and weather regimes in the midlatitudes of the Northern Hemisphere. The multichannel singular spectrum analysis; The low-frequency oscillations and weather regimes; Results and conclusions.

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

Climate simulations at convection-permitting resolution have been used more frequently thanks to the development of computational power. Those simulations can produce realistic surface fields in regions of complex orography that play a vital role in initializing convective processes. They also resolve physically deep convection system in sub-grid scale. Consequently, the simulation of downpour events, especially in sub-daily time scale, is expected to be enhanced and better represented in a changing climate, which is necessary for climate impact studies. In this research, we investigate the advantages of using two ensembles of high resolution (approx. 3 km) simulations in replicating extreme precipitation in both daily and shorter time scale over the South of France during a historical period (1951-1980). These two convection-permitting simulations (CPS) are performed with the Weather Research and Forecasting Model (WRF). They are forced by EURO-CORDEX simulations, which are also downscaled with WRF at the resolution of 12-km and driven by HADGEM and IPSL models (i.e. the 3-km is not nested simultaneously within 12-km downscaling from GCMs). All simulations are evaluated using a metric of extreme indices against in-situ and gridded observations to quantify the added value of CPSs compared to convection-parameterized simulations. In addition, the analysis of the dependence of water holding capacity on temperature is also considered. These assessments would facilitate a more reliable attribution of extreme hourly precipitation to climate change, which has been a big challenge to scientific community so far. [ABSTRACT FROM AUTHOR]

Since 2019, Central South America (CSA) has been reeling under drought conditions, with the last 4 months of 2022 receiving only 44% of the average total precipitation. Simultaneously to the drought, a series of record-breaking heat waves has affected the region. The rainfall deficit during October–November-December (OND) is highly correlated with the Niño3.4 index, indicating that the OND 2022 rainfall deficit is partly driven by La Niña, as observed in previous droughts in this region. To identify whether human-induced climate change was also a driver of the OND 2022 rainfall deficit, we analysed precipitation over the most impacted region. Our findings revealed a pattern of decreased rainfall over the past 40 years, although we cannot definitively conclude whether this trend exceeds what would be expected from natural variations. To clarify if this trend can be attributed to climate change, we looked at 1-in-20-year low rainfall events over the same region in climate models. The models show that the severity of low rainfall events decreases (i.e. they become wetter, the opposite of the trend observed in most weather records), although this trend is again not significant and is compatible with natural variability. Therefore, we cannot attribute the low rainfall to climate change. Moreover, our analysis of effective precipitation potential (evapotranspiration minus rainfall) shows that, in climate models, the increase in temperature does partly compensate for the increase in rainfall but only to offset the wetting, and does not lead to a significant climate change signal in effective precipitation. However, higher temperatures in the region, which have been attributed to climate change, decreased water availability in the models in late 2022, indicating that climate change probably reduced water availability over this period also in the observations, increasing agricultural drought, although this study did not quantify this effect. This means that even though the reduced rainfall is within the natural variability, the consequences of drought are becoming more severe due to the strong increase in extreme heat. The case of the OND 2022 rainfall deficit and the ongoing drought in CSA is a clear example of the interplay between climate variability and human-induced climate change. This shows the importance of considering not only those aspects associated with climate change but also climate variability in order to understand and attribute particular events or trends at the regional level. [ABSTRACT FROM AUTHOR]

A large area including the central-northern part of Argentina, southern Bolivia, central Chile, and most of Paraguay and Uruguay, experienced record-breaking temperatures during two consecutive heatwaves in late November and early December 2022. During the second heatwave, nine locations in northern Argentina registered their highest maximum temperature of December since at least 1961. Our analysis based on observational and reanalysis datasets indicate that South America, like the rest of the world, has experienced heatwaves increasingly frequently in recent years. The December 2022 heatwave has an estimated return time of 1 in 20 years in the current climate, meaning it has about a 5% chance of happening each year. To estimate how human-caused climate change has influenced the likelihood and intensity of the observed heatwave, we combined climate models with the observation-based data. We found that human-caused climate change made the event about 60 times more likely. A heatwave with a return period of 20 years would be about 1.4 °C less hot in a world without anthropogenic global warming. Heatwaves this early in the summer season pose a substantial risk to human health and are potentially lethal. This risk is aggravated by climate change, but also by other factors such as an aging population, urbanisation and the built environment, and individual behavior and susceptibility to the heat. This highlights the importance of attribution studies in a region already threatened and vulnerable to climate change. [ABSTRACT FROM AUTHOR]

We examine long-term trends in the historical record of extreme precipitation events occurring over the French Mediterranean area. Extreme events are considered in terms of their intensity, frequency, extent and precipitated volume. Changes in intensity are analysed via an original statistical approach where the annual maximum rainfall amounts observed at each measurement station are aggregated into a univariate time-series according to their dependence. The mean intensity increase is significant and estimated at + 22% (+ 7 to + 39% at the 90% confidence level) over the 1961-2015 period. Given the observed warming over the considered area, this increase is consistent with a rate of about one to three times that implied by the Clausius-Clapeyron relationship. Changes in frequency and other spatial features are investigated through a Generalised Linear Model. Changes in frequency for events exceeding high thresholds (about 200 mm in 1 day) are found to be significant, typically near a doubling of the frequency, but with large uncertainties in this change ratio. The area affected by severe events and the water volume precipitated during those events also exhibit significant trends, with an increase by a factor of about 4 for a 200 mm threshold, again with large uncertainties. All diagnoses consistently point toward an intensification of the most extreme events over the last decades. We argue that it is difficult to explain the diagnosed trends without invoking the human influence on climate. [ABSTRACT FROM AUTHOR]

Towards the end of June 2021, temperature records were broken by several degrees Celsius in several cities in the Pacific Northwest areas of the US and Canada, leading to spikes in sudden deaths and sharp increases in emergency calls and hospital visits for heat-related illnesses. Here we present a multi-model, multi-method attribution analysis to investigate the extent to which human-induced climate change has influenced the probability and intensity of extreme heat waves in this region. Based on observations, modelling and a classical statistical approach, the occurrence of a heat wave defined as the maximum daily temperature (TXx) observed in the area 45–52 ∘ N, 119–123 ∘ W, was found to be virtually impossible without human-caused climate change. The observed temperatures were so extreme that they lay far outside the range of historical temperature observations. This makes it hard to state with confidence how rare the event was. Using a statistical analysis that assumes that the heat wave is part of the same distribution as previous heat waves in this region led to a first-order estimation of the event frequency of the order of once in 1000 years under current climate conditions. Using this assumption and combining the results from the analysis of climate models and weather observations, we found that such a heat wave event would be at least 150 times less common without human-induced climate change. Also, this heat wave was about 2 ∘ C hotter than a 1-in-1000-year heat wave would have been in 1850–1900, when global mean temperatures were 1.2 ∘ C cooler than today. Looking into the future, in a world with 2 ∘ C of global warming (0.8 ∘ C warmer than today), a 1000-year event would be another degree hotter. Our results provide a strong warning: our rapidly warming climate is bringing us into uncharted territory with significant consequences for health, well-being and livelihoods. Adaptation and mitigation are urgently needed to prepare societies for a very different future. [ABSTRACT FROM AUTHOR]

Global climate change will increase the frequency of hot temperatures, impairing health and productivity for millions of working people and raising labor costs. In mainland China, high-temperature subsidies (HTSs) are allocated to employees for each working day in extremely hot environments, but the potential heat-related increase in labor cost has not been evaluated so far. Here, we estimate the potential HTS cost in current and future climates under different scenarios of socioeconomic development and radiative forcing (Representative Concentration Pathway), taking uncertainties from the climate model structure and bias correction into account. On average, the total HTS in China is estimated at 38.6 billion yuan/y (US $6.22 billion/y) over the 1979-2005 period, which is equivalent to 0.2% of the gross domestic product (GDP). Assuming that the HTS standards (per employee per hot day) remain unchanged throughout the 21st century, the total HTS may reach 250 billion yuan/y in the 2030s and 1,000 billion yuan/y in 2100. We further show that, without specific adaptation, the increased HTS cost is mainly determined by population growth until the 2030s and climate change after the mid-21st century because of increasingly frequent hot weather. Accounting for the likely possibility that HTS standards follow the wages, the share of GDP devoted to HTS could become as high as 3% at the end of 21st century. [ABSTRACT FROM AUTHOR]

Regional Climate Models are widely used tools to add detail to the coarse resolution of global simulations. However, these are known to be affected by biases. Usually, published model evaluations use a reduced number of variables, frequently precipitation and temperature. Due to the complexity of the models, this may not be enough to assess their physical realism (e.g. to enable a fair comparison when weighting ensemble members). Furthermore, looking at only a few variables makes difficult to trace model errors. Thus, in many previous studies, these biases are described but their underlying causes and mechanisms are often left unknown. In this work the ability of a multi-physics ensemble in reproducing the observed climatologies of many variables over Europe is analysed. These are temperature, precipitation, cloud cover, radiative fluxes and total soil moisture content. It is found that, during winter, the model suffers a significant cold bias over snow covered regions. This is shown to be related with a poor representation of the snow-atmosphere interaction, and is amplified by an albedo feedback. It is shown how two members of the ensemble are able to alleviate this bias, but by generating a too large cloud cover. During summer, a large sensitivity to the cumulus parameterization is found, related to large differences in the cloud cover and short wave radiation flux. Results also show that small errors in one variable are sometimes a result of error compensation, so the high dimensionality of the model evaluation problem cannot be disregarded. [ABSTRACT FROM AUTHOR]

The mechanisms leading to the occurrence of extreme weather and climate events are varied and complex. They generally encompass a combination of dynamic and thermodynamic processes, as well as drivers external to the climate system, such as anthropogenic greenhouse gas emissions and land use change. Here we present the ExtremeX multi-model intercomparison experiment, which was designed to investigate the contribution of dynamic and thermodynamic processes to recent weather and climate extremes. The numerical experiments are performed with three Earth system models: CESM, MIROC, and EC-Earth. They include control experiments with interactive atmosphere and land surface conditions, as well as experiments wherein the atmospheric circulation, soil moisture, or both are constrained using observation-based data. The temporal evolution and magnitude of temperature anomalies during heatwaves are well represented in the experiments with a constrained atmosphere. However, the magnitude of mean climatological biases in temperature and precipitation are not greatly reduced in any of the constrained experiments due to persistent or newly introduced biases. This highlights the importance of error compensations and tuning in the standard model versions. To show one possible application, ExtremeX is used to identify the main drivers of heatwaves and warm spells. The results reveal that both atmospheric circulation patterns and soil moisture conditions substantially contribute to the occurrence of these events. Soil moisture effects are particularly important in the tropics, the monsoon areas, and the Great Plains of the United States, whereas atmospheric circulation effects are major drivers in other midlatitude and high-latitude regions. [ABSTRACT FROM AUTHOR]

In October 2020, Central Vietnam was struck by heavy rain resulting from a sequence of 5 tropical depressions and typhoons. The immense amount of water led to extensive flooding and landslides that killed more than 200 people, injured more than 500 people, and caused direct damages valued at approximately 1.2 billion USD. Here, we quantify how the intensity of the precipitation leading to such exceptional impacts is attributable to anthropogenic climate change. First, we define the event as the regional maximum of annual maximum 15-day average rainfall (Rx15day). We then analyse the trend in Rx15day over Central Vietnam from the observations and simulations in the PRIMAVERA and CORDEX-CORE ensembles, which pass our evaluation tests, by applying the generalised extreme value (GEV) distribution in which location and scale parameters exponentially covary with increasing global temperatures. Combining these observations and model results, we find that the 2020 event, occurring about once every 80 years (at least 17 years), has not changed in either probability of occurrence (a factor 1.0, ranging from 0.4 to 2.4) or intensity (0%, ranging from −8 to +8%) in the present climate in comparison with early-industrial climate. This implies that the effect of human-induced climate change contributing to this persistent extreme rainfall event is small compared to natural variability. However, given the scale of damage of this hazard, our results underline that more investment in disaster risk reduction for this type of rainfall-induced flood hazard is of importance, even independent of the effect of anthropogenic climate change. Moreover, as both observations and model simulations will be extended with the passage of time, we encourage more climate change impact investigations on the extreme in the future that help adaptation and mitigation plans and raise awareness in the country. [ABSTRACT FROM AUTHOR]

Towards the end of June 2021, temperature records were broken by several degrees Celsius in several cities in the Pacific northwest areas of the U.S. and Canada, leading to spikes in sudden deaths, and sharp increases in hospital visits for heat-related illnesses and emergency calls. Here we present a multi-model, multi-method attribution analysis to investigate to what extent human-induced climate change has influenced the probability and intensity of extreme heatwaves in this region. Based on observations and modeling, the occurrence of a heatwave with maximum daily temperatures (TXx) as observed in the area 45° N-52° N, 119° W-123° W, was found to be virtually impossible without human-caused climate change. The observed temperatures were so extreme that they lie far outside the range of historically observed temperatures. This makes it hard to quantify with confidence how rare the event was. In the most realistic statistical analysis, which uses the assumption that the heatwave was a very low probability event that was not caused by new nonlinearities, the event is estimated to be about a 1 in 1000 year event in today's climate. With this assumption and combining the results from the analysis of climate models and weather observations, an event, defined as daily maximum temperatures (TXx) in the heatwave region, as rare as 1 in a 1000 years would have been at least 150 times rarer without human-induced climate change. Also, this heatwave was about 2 °C hotter than a 1 in 1000-year heatwave that at the beginning of the industrial revolution would have been (when global mean temperatures were 1.2 °C cooler than today). Looking into the future, in a world with 2 °C of global warming (0.8 °C warmer than today), a 1000-year event would be another degree hotter. It would occur roughly every 5 to 10 years in such global warming conditions. Our results provide a strong warning: our rapidly warming climate is bringing us into uncharted territory with significant consequences for health, well-being, and livelihoods. Adaptation and mitigation are urgently needed to prepare societies for a very different future. [ABSTRACT FROM AUTHOR]

Abstract: More than ten state-of-the-art regional air quality models have been applied as part of the Air Quality Model Evaluation International Initiative (AQMEII). These models were run by twenty independent groups in Europe and North America. Standardised modelling outputs over a full year (2006) from each group have been shared on the web-distributed ENSEMBLE system, which allows for statistical and ensemble analyses to be performed by each group. The estimated ground-level ozone mixing ratios from the models are collectively examined in an ensemble fashion and evaluated against a large set of observations from both continents. The scale of the exercise is unprecedented and offers a unique opportunity to investigate methodologies for generating skilful ensembles of regional air quality models outputs. Despite the remarkable progress of ensemble air quality modelling over the past decade, there are still outstanding questions regarding this technique. Among them, what is the best and most beneficial way to build an ensemble of members? And how should the optimum size of the ensemble be determined in order to capture data variability as well as keeping the error low? These questions are addressed here by looking at optimal ensemble size and quality of the members. The analysis carried out is based on systematic minimization of the model error and is important for performing diagnostic/probabilistic model evaluation. It is shown that the most commonly used multi-model approach, namely the average over all available members, can be outperformed by subsets of members optimally selected in terms of bias, error, and correlation. More importantly, this result does not strictly depend on the skill of the individual members, but may require the inclusion of low-ranking skill-score members. A clustering methodology is applied to discern among members and to build a skilful ensemble based on model association and data clustering, which makes no use of priori knowledge of model skill. Results show that, while the methodology needs further refinement, by optimally selecting the cluster distance and association criteria, this approach can be useful for model applications beyond those strictly related to model evaluation, such as air quality forecasting. [Copyright &y& Elsevier]

Abstract: Over twenty modeling groups are participating in the Air Quality Model Evaluation International Initiative (AQMEII) in which a variety of mesoscale photochemical and aerosol air quality modeling systems are being applied to continental-scale domains in North America and Europe for 2006 full-year simulations for model inter-comparisons and evaluations. To better understand the reasons for differences in model results among these participating groups, each group was asked to use the same source of emissions and boundary concentration data for their simulations. This paper describes the development and application of the boundary concentration data for this AQMEII modeling exercise. The European project known as GEMS (Global and regional Earth-system Monitoring using Satellite and in-situ data) has produced global-scale re-analyses of air quality for several years, including 2006 (http://gems.ecmwf.int). The GEMS trace gas and aerosol data were made available at 3-hourly intervals on a regular latitude/longitude grid of approximately 1.9° resolution within 2 “cut-outs” from the global model domain. One cut-out was centered over North America and the other over Europe, covering sufficient spatial domain for each modeling group to extract the necessary time- and space-varying (horizontal and vertical) concentrations for their mesoscale model boundaries. Examples of the impact of these boundary concentrations on the AQMEII continental simulations are presented to quantify the sensitivity of the simulations to boundary concentrations. In addition, some participating groups were not able to use the GEMS data and instead relied upon other sources for their boundary concentration specifications. These are noted, and the contrasting impacts of other data sources for boundary data are presented. How one specifies four-dimensional boundary concentrations for mesoscale air quality simulations can have a profound impact on the model results, and hence, this aspect of data preparation must be performed with considerable care. [Copyright &y& Elsevier]

European surface temperatures have increased during the past decades. According to climate projections, this warming is expected to continue in future years under enhanced radiative forcings. In addition to the mean increase, changes in temperature variability are likely to occur, with more frequent extreme seasons such as those recently observed in the past decade (e.g. summer 2003, autumn 2006). Yet, most of the processes driving such long-term tendencies remain unidentified and unexplored. A particularly important issue is how changes in the atmospheric dynamics over the North-Atlantic and Europe (NAE) contribute to trends in both mean and extreme temperatures. The high occurrence of the positive phase of the North Atlantic Oscillation (NAO) in the 1980s-1990s suggested that the European warming could result from a re-organization in the main structures of the NAE dynamics. However, the 2000s have rather revealed an inconsistency between atmospheric circulation conditions and European temperatures. Here we investigate the relationship between sea-level pressure (SLP) and 2-m temperature (T2m) using a flow-analogues method applied over both observations and future projections. We use 13 models of the Third phase of the Coupled Model Intercomparison Project (CMIP3) over 1961-2000, 2046-2065 and 2081-2100, from which we extract seasonal subensembles with respect to their representation of observed SLP-T2m seasonal relationships. First we show that the distribution of SLP does not undergo major changes in future climate according to these seasonal model-ensembles, albeit a general decline of the variability is observed for all seasons. Then, using the flow-analogues, we conclude that the projected European warming appears disconnected from changes in the NAE dynamics. Only in winter a slight shift towards positive NAO conditions could partially contribute to the future temperature increase. Finally, a focus over unusually warm/cold seasons reveals that future temperature extremes should likely to be associated with similar atmospheric circulations as observed during recent ones. [ABSTRACT FROM AUTHOR]

Drought in spring and early summer has been shown to precede anomalous hot summer temperature. In particular, drought in the Mediterranean region has been recently shown to precede and to contribute to the development of extreme heat in continental Europe. In this paper, this mechanism is investigated by performing integrations of a regional mesoscale model at the scale of the European continent in order to reproduce hot summer inception, starting with different initial values of soil moisture south of 46°N. The mesoscale model is driven by the large-scale atmospheric conditions corresponding to the 10 hottest summers on record from the European Climate Assessment dataset. A northward progression of heat and drought from late spring to summer is observed from the Mediterranean regions, which leads to a further increase of temperature during summer in temperate continental Europe. Dry air formed over dry soils in the Mediterranean region induces less convection and diminished cloudiness, which gets transported northward by occasional southerly wind, increasing northward temperature and vegetation evaporative demand. Later in the season, drier soils have been established in western and central Europe where they further amplify the warming through two main feedback mechanisms: 1) higher sensible heat emissions and 2) favored upper-air anticyclonic circulation. Drier soils in southern Europe accelerate the northward propagation of heat and drying, increasing the probability of strong heat wave episodes in the middle or the end of the summer. [ABSTRACT FROM AUTHOR]

The current state of the art in three-dimensional chemistry-transport models allows them to be considered as mature and reliable enough to be combined with observations networks for implementing integrated air quality monitoring systems over large territories. A cooperative initiative of tour research and operational organizations in France has led to the creation of an integrated air quality platform providing near-real-time and forecasted information using last model developments. Since summer 2003, this system, Prévisions et Observations de la Qualité de l'Air en France et en Europe (PRBV AIR; available online at www.prevair. org), has been delivering daily air quality observations short-term (0-3 days) forecasts, and real-time analyses of ozone, nitrogen dioxide, and particulate matter (PM) at the global and European scales, with a focus over France. It has become a powerful scientific and communication tool to inform the public, sensitive population about the potential occurrence of air pollutant concentrations exceeding regulatory thresholds. The architecture of the operational system is described in this paper. Its setup allows the partners to cope with stringent operational constraints regarding the timely availability of the forecasts and the reliability and the quality of the products delivered. The system relies on a chain of numerical models and methods, and on real-time air quality observations, it uses National Centers for Environmental Predictions (NCEP) and Météo-France weather forecasts. Methods used for producing air pollutant concentration forecasts and analyses are described, as well as the final products, with case examples. A synthesis of the operational system skill results obtained over three consecutive years of operation (2003-06) is given. Lessons learned are presented and issues related to the use of the PREV'AIR system as a complementary tool for air quality management are discussed. [ABSTRACT FROM AUTHOR]

Abstract: Ground-level ozone concentrations in the atmospheric boundary layer over Spain are still exceeding thresholds established in EU legislation to protect human health and prevent damage to ecosystems. The increasing role that air quality models play in air quality management requires comparison between model results and previous observations in order to determine the capacity of the model to reproduce past events. The CHIMERE chemistry-transport model has been used by several research groups to estimate air pollutant concentrations in different European countries. An evaluation of the model performance of the CHIMERE air quality model was carried out for the spring and summer periods of 2003–2005 in Spain, using EMEP emissions. This evaluation has demonstrated a fair agreement between observed and modelled ozone values for background stations, with a mean normalized absolute error below 15% for rural background air quality sites. This value lays inside the range proposed in EPA''s guideline for an acceptable level of model performance. In spite of this acceptable model performance, further studies need to be carried out to explain some underestimation found over Madrid surroundings. [Copyright &y& Elsevier]

The mechanisms leading to the occurrence of extreme weather and climate events are varied and complex. They generally encompass a combination of dynamical and thermodynamical processes, as well as drivers external to the climate system, such as anthropogenic greenhouse gas emissions and land-use change. Here we present the ExtremeX multi-model intercomparison experiment, which was designed to investigate the contribution of dynamic and thermodynamic processes to recent weather and climate extremes. The numerical experiments are performed with three Earth System Models: CESM, MIROC, and EC-Earth. They include control experiments with interactive atmosphere and land surface conditions, and experiments where either the atmospheric circulation, soil moisture or both are constrained using observation-based values. The temporal evolution and magnitude of temperature anomalies during heatwaves is well represented in the experiments with constrained atmosphere. However, mean climatological biases in temperature and precipitation are not substantially reduced in any of the constrained experiments, highlighting the importance of error compensations and tuning in the standard model versions. The results further reveal that both atmospheric circulation patterns and soil moisture conditions substantially contribute to the occurrence of heat extremes. Soil moisture effects are particularly important in the tropics, the monsoon areas and the Great Plains of the United States. [ABSTRACT FROM AUTHOR]

The last few years have seen an explosion of interest in extreme event attribution, the science of estimating the influence of human activities or other factors on the probability and other characteristics of an observed extreme weather or climate event. This is driven by public interest, but also has practical applications in decision-making after the event and for raising awareness of current and future climate change impacts. The World Weather Attribution (WWA) collaboration has over the last 5 years developed a methodology to answer these questions in a scientifically rigorous way in the immediate wake of the event when the information is most in demand. This methodology has been developed in the practice of investigating the role of climate change in two dozen extreme events world-wide. In this paper, we highlight the lessons learned through this experience. The methodology itself is documented in a more extensive companion paper. It covers all steps in the attribution process: the event choice and definition, collecting and assessing observations and estimating probability and trends from these, climate model evaluation, estimating modelled hazard trends and their significance, synthesis of the attribution of the hazard, assessment of trends in vulnerability and exposure, and communication. Here, we discuss how each of these steps entails choices that may affect the results, the common problems that can occur and how robust conclusions can (or cannot) be derived from the analysis. Some of these developments also apply to other attribution methodologies and indeed to other problems in climate science. [ABSTRACT FROM AUTHOR]

In this study, we perform an evaluation of PRIMAVERA high-resolution (25-50 km) Global Climate Models (GCMs) relative to CORDEX Regional Climate Models (RCMs) over Europe (12-50 km resolutions). It is the first time such assessment is performed for regional climate information using ensembles of GCMs and RCMs at similar horizontal resolutions. We perform this exercise for the distribution of daily precipitation contributions to rainfall bins over Europe under current climate conditions. Both ensembles are evaluated against high quality national gridded observations in terms of resolution and station density. We show that PRIMAVERA GCMs simulate very similar distribution to CORDEX RCMs that CMIP5 cannot because of their coarse resolutions. PRIMAVERA and CORDEX ensembles generally show similar strengths and weaknesses. They are of good quality in summer and autumn in most European regions, but tend to overestimate precipitation in winter and spring. PRIMAVERA show improvements in the latter bias by reducing mid-rain rate biases in Central and Eastern Europe. Moreover, CORDEX simulate less light rainfall than PRIMAVERA in most regions and seasons, which improves this common GCM bias. Finally, PRIMAVERA simulate less heavy precipitation than CORDEX in most regions and seasons, especially in summer. PRIMAVERA appear to be closer to observations. However, when we apply an averaged precipitation undercatch error of 20 %, CORDEX become closer to these synthetic datasets. Considering 50 km resolution GCM or RCM datasets over Europe results in large benefits compared to CMIP5 models for impact studies at the regional scale. The effect of increasing resolution from 50 km to 12 km in CORDEX simulations is, in comparison, small in most regions and seasons outside mountainous regions (due to the importance of orography) and coastal regions (mostly depending on the resolution of the land-sea contrast). Now that GCMs are able to reach the level of information provided by CORDEX RCMs run at similar resolutions, there is an opportunity to better coordinate GCM and RCM simulations for future model intercomparison projects. [ABSTRACT FROM AUTHOR]

The article presents a study which examines the contribution of anthropogenic climate change to the likelihood of an extreme summer heat wave, like the summer 2017 in western Europe and Euro-Mediterranean. It explores the return period and changing risk of heat waves as well as looks at the 3-day-mean area-averaged daily maximum temperatures. The trends in the likelihood of 3-day heat waves are noted.