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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]

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]

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 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]

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]

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]

Analyses of extreme weather events and their impacts often requires big data processing of ensembles of climate model simulations. Researchers generally proceed by downloading the data from the providers and processing the data files “at home” with their own analysis processes. However, the growing amount of available climate model and observation data makes this procedure quite awkward. In addition, data processing knowledge is kept local, instead of being consolidated into a common resource of reusable code. These drawbacks can be mitigated by using a web processing service (WPS). A WPS hosts services such as data analysis processes that are accessible over the web, and can be installed close to the data archives. We developed a WPS named ‘flyingpigeon’ that communicates over an HTTP network protocol based on standards defined by the Open Geospatial Consortium (OGC), to be used by climatologists and impact modelers as a tool for analyzing large datasets remotely. Here, we present the current processes we developed in flyingpigeon relating to commonly-used processes (preprocessing steps, spatial subsets at continent, country or region level, and climate indices) as well as methods for specific climate data analysis (weather regimes, analogues of circulation, segetal flora distribution, and species distribution models). We also developed a novel, browser-based interactive data visualization for circulation analogues, illustrating the flexibility of WPS in designing custom outputs. Bringing the software to the data instead of transferring the data to the code is becoming increasingly necessary, especially with the upcoming massive climate datasets. [ABSTRACT FROM AUTHOR]

In the framework of the IMPACT2C project, we have evaluated the future European particulate matter concentrations under the influence of climate change and anthropogenic emission reductions. To do so, 30-year simulations for present and future scenarios were performed with an ensemble of four regional Chemical Transport Models. +2 °C scenarios were issued from different regional climate simulations belonging to the CORDEX experiment (RCP4.5 scenario). Comparing present day simulations to observations shows that these simulations meet the requested quality criteria even if some biases do exist. Also, we showed that using regional climate models instead of meteorological reanalysis was not critical for the quality of our simulations. Present day as well as future scenarios show the large variability between models associated with different meteorology and process parameterizations. Future projections of PM concentrations show a large reduction of PM10 and PM2.5 concentrations in a +2 °C climate over the European continent (especially over Benelux), which can be mostly attributed to emission reduction policies. Under a current legislation scenario, annual PM10 could be reduced by between 1.8 and 2.9 μg m −3 (14.1–20.4%). If maximum technologically feasible emission reductions were implemented, further reductions of 1.4–1.9 μg m −3 (18.6–20.9%) are highlighted. Changes due to a +2 °C warming, in isolation from emission changes, are in general much weaker (−1.1 to +0.4 μg m −3 ,-0.3 to +5.1% for annual PM10 averaged over the European domain). Even if large differences exist between models, we have determined that the decrease of PM over Europe associated with emission reduction is a robust result. The patterns of PM changes resulting from climate change (for example the increase of PM over Spain and southern France and the decrease of PM10 over eastern Europe) are also robustly predicted even if its amplitude remains weak compared to changes associated with emission reductions. [ABSTRACT FROM AUTHOR]

The evolution of ozone and nitrogen dioxide over Europe between the present day and a future period with a +2 °C global warming relative to the pre-industrial climate was studied using four offline chemistry transport models, each driven by a different climate model. Given the recent outcome of the COP21 negotiations, understanding the implications of climate change around the +2 °C threshold has never been more pressing or relevant. One of the objectives of this study was to show how changes in anthropogenic emissions and +2 °C climate change are expected to affect future air quality, which may have important implications upon human health. It was found that a +2 °C climate change alone was responsible for a modest, and not statistically significant, increase in surface O 3 concentrations (of between −0.1–0.8 ppb in the summer averaged over the European domain) compared to the present climate. Two different emission scenarios were used for the future time period in order to provide an estimate of the extent of air pollution reductions that could occur if (a) all currently planned air quality legislation is implemented and (b) all maximum technologically feasible emission reductions are implemented. The results showed that summer O 3 could be reduced by between 4 and 5 ppb under a current legislation scenario, with at least 3 ppb of further reductions under the maximum mitigated scenario. Calculations of summer ozone enhancement were used as a metric to analyse the results after having removed background ozone level changes. In conclusion it was found that future air quality on a regional scale will depend upon the implementation of effective emission reduction policy; the positive effects of which should not be hindered by a +2 °C global warming. [ABSTRACT FROM AUTHOR]

Renewable energy resources, such as wind, are available worldwide. Locating areas with high and continual wind sources are crucial in pre-planning of wind farms. Vast offshore areas are characterized by higher and more reliable wind resources in comparison with continental areas. However, offshore wind energy production is in a quite preliminary phase. Elaborating the potential productivity of wind farms over such areas is challenging due to sparse in situ observations. The Mediterranean basin is not an exception. In this study we are proposing numerical simulations of near-surface wind fields from regional climate models (RCMs) in order to obtain and fill the gaps in observations over the Mediterranean basin. Four simulations produced with two regional climate models are examined here. Remote sensing observations (QuikSCAT satellite) are used to assess the skill of the simulated fields. A technique for estimating the potential energy from the wind fields over the region is introduced. The wind energy potential atlas and the map of a wind turbine's functional range are presented, locating the potentially interesting sub-regions for wind farms. The ability of models to reproduce the annual cycle and the probability density function of wind speed anomalies are detailed for specified sub-regions. [ABSTRACT FROM AUTHOR]

The impact of meteorological forcings on gas phase air pollutants (ozone and nitrogen dioxide) over Europe was studied using four offline chemistry transport models (CTMs) as part of the IMPACT2C project. This study uses long (20- and 30-year) simulations to evaluate the present-day performance of the CTMs, which is a necessary first step before undertaking any analysis of future air quality impacts. Two sets of meteorological forcings were used for each model: reanalysis of past observation data (ERA-Interim) and Global Climate Model (GCM) output. The results for the simulations forced by reanalysis data were assessed in relation to AirBase v7 measurement data, and it was determined that all four models slightly overpredict annual O 3 values (mean biases range between 0.7 and 6.6 ppb) and three out of the four models underpredict observed annual NO 2 (mean biases range between −3.1 and −5.2 ppb). The simulations forced by climate models result in spatially averaged monthly concentrations of O 3 that are generally between 0 and 5 ppb higher than the values obtained from simulations forced by reanalysis data; therefore it was concluded that the use of climate models introduces an additional bias to the results, but this bias tends not to be significant in the majority of cases. The bias in O 3 results appears to be correlated mainly to differences in temperature and boundary layer height between the two types of simulations, whereas the less significant bias in NO 2 is negatively correlated to temperature and boundary layer height. It is also clear that the selection of chemical boundary conditions is an important factor in determining the variability of O 3 model results. These results will be used as a baseline for the interpretation of future work, which will include an analysis of future climate scenarios upon European air quality. [ABSTRACT FROM AUTHOR]

Abstract: Ten state-of-the-science regional air quality (AQ) modeling systems have been applied to continental-scale domains in North America and Europe for full-year simulations of 2006 in the context of Air Quality Model Evaluation International Initiative (AQMEII), whose main goals are model inter-comparison and evaluation. Standardised modeling outputs from each group have been shared on the web-distributed ENSEMBLE system, which allows statistical and ensemble analyses to be performed. In this study, the one-year model simulations are inter-compared and evaluated with a large set of observations for ground-level particulate matter (PM10 and PM2.5) and its chemical components. Modeled concentrations of gaseous PM precursors, SO2 and NO2, have also been evaluated against observational data for both continents. Furthermore, modeled deposition (dry and wet) and emissions of several species relevant to PM are also inter-compared. The unprecedented scale of the exercise (two continents, one full year, fifteen modeling groups) allows for a detailed description of AQ model skill and uncertainty with respect to PM. Analyses of PM10 yearly time series and mean diurnal cycle show a large underestimation throughout the year for the AQ models included in AQMEII. The possible causes of PM bias, including errors in the emissions and meteorological inputs (e.g., wind speed and precipitation), and the calculated deposition are investigated. Further analysis of the coarse PM components, PM2.5 and its major components (SO4, NH4, NO3, elemental carbon), have also been performed, and the model performance for each component evaluated against measurements. Finally, the ability of the models to capture high PM concentrations has been evaluated by examining two separate PM2.5 episodes in Europe and North America. A large variability among models in predicting emissions, deposition, and concentration of PM and its precursors during the episodes has been found. Major challenges still remain with regards to identifying and eliminating the sources of PM bias in the models. Although PM2.5 was found to be much better estimated by the models than PM10, no model was found to consistently match the observations for all locations throughout the entire year. [Copyright &y& Elsevier]

Abstract: At the onset of the 2010 statutory deadline for the respect of the European National Emission Ceiling directive, two questions arise. Will the engaged regulations for the respect of ozone air quality thresholds be fully efficient on the most polluted regions? How can we design the continuation of ozone control in those areas? This study is based on refined 3D modelling studies over a French Mediterranean region. It compares 2001 and 2003 situations with several prospective 2010 emission scenarios with, for the first time, the evaluation of local action plans. The degree of compliance with air quality regulation is investigated and the impact of emission control on the local potential for ozone formation is discussed. The results show that current efforts on emissions, although substantial and efficient, are not sufficient yet to abrogate all the ozone threshold exceedances. They also highlight the gap between regulatory and effective emission control, as well as the need for regional regulations to complete national efforts. Finally, the simulations indicate that large-scale emission control significantly helps reducing rural ozone (−20 ppbv) but affects much fewer (−2 to −10 ppbv) the highest peaks. The continuation and the strengthening of ozone policies under their current form in such regions are considered. [Copyright &y& Elsevier]

Biogenic VOC emission estimates from the earth''s surface are crucial input parameters in air quality models. Knowledge accumulated in the last years about BVOC source distributions and chemical compound species emission profiles in Europe as well as the demand of air quality modellers for a finer resolution in space and time of BVOC estimates have led to the set-up of new emission modelling systems. An updated fast BVOC emission modelling platform explicitly considering the seasonality of emission potentials and leaf temperature gradients in forest canopies by the semi-empirical emission module (seBVOC) will be proposed and used for estimating hourly values of chemical compound-specific emissions in Europe (33–68° north; 10° west to 40° east) in the years 1997, 2000, 2001, and 2003. Spatial resolution will be 10km by 10km. The database used contains latest land and forest distributions, updated foliar biomass densities, leaf area indices (LAI), and plant as well as chemical compound-specific emission potentials, if available. Meteorological input parameters for the respective years will be generated using the non-hydrostatic meteorological model MM5. Highest BVOC emissions occur in daytime hours around noon from the end of May to mid-August in the Mediterranean area and from the mid of June to the end of July in the boreal forests. Comparison of 3 BVOC model approaches will reveal that for July 2003, the European isoprene and monoterpene totals range from 1124Gg to 1446Gg and from 338Gg to 1112Gg, respectively. Small-scale deviations may be as high as ±0.6Mgkm−2 for July 2003, reflecting the current uncertainty range for BVOC estimates. Key sources of errors in inventories are still insufficiently detailed land use data for some areas and lacking chemically speciated plant-specific emission potentials in particular in boreal, south-eastern, and northern African landscapes. The hourly emissions of isoprene, speciated terpenes, and oxyVOC have been made available by the NatAir database. [Copyright &y& Elsevier]