Atmospheric Environment (v.53, #C)

Preface by S. Galmarini; S.T. Rao; D.G. Steyn (1-3).

Comparing emission inventories and model-ready emission datasets between Europe and North America for the AQMEII project by George Pouliot; Thomas Pierce; Hugo Denier van der Gon; Martijn Schaap; Michael Moran; Uarporn Nopmongcol (4-14).
This paper highlights the similarities and differences in how emission inventories and datasets were developed and processed across North America and Europe for the Air Quality Model Evaluation International Initiative (AQMEII) project and then characterizes the emissions for the two domains. We focus specifically on the creation of “model-ready” gridded emission datasets for 2006 across the two continental study domains. The practice of creating and processing the two inventories is discussed with a focus on emission factors, spatial allocation, temporal variability, speciation of PM and VOCs, and the mechanics of distributing the data and supporting emission algorithms to the modeling community. The spatial and temporal distribution on common scales is compared for the pollutants of primary concern: NOx, VOCs, SO2, PM2.5, CO, and NH3. Because of differences of population distribution, emissions across North America tend to be more heterogeneous in spatial coverage than in Europe. The temporal patterns in the estimated emissions are largely the result of assumptions used to characterize human activity, with the exception of “natural” emissions, which are modulated by meteorological variability, and emissions from large electric generating units in the U.S., which have the benefit of continuous emission monitors that provide hourly resolved profiles. Emission estimates in both study domains are challenged by several important but poorly characterized emission source sectors, notably road dust, agricultural operations, biomass burning, and road transport. Finally, this paper provides insight on the strengths and weaknesses of emission inventory preparation practices on both continents. One important outcome of this comparison of 2006 emissions between Europe and North America is the greater understanding provided into how the emission estimates developed for the AQMEII project impact regional air quality model performance.
Keywords: AQMEII; Regional air quality models; Emission inventories;

Evaluation of the meteorological forcing used for the Air Quality Model Evaluation International Initiative (AQMEII) air quality simulations by Robert Vautard; Michael D. Moran; Efisio Solazzo; Robert C. Gilliam; Volker Matthias; Roberto Bianconi; Charles Chemel; Joana Ferreira; Beate Geyer; Ayoe B. Hansen; Amela Jericevic; Marje Prank; Arjo Segers; Jeremy D. Silver; Johannes Werhahn; Ralf Wolke; S.T. Rao; Stefano Galmarini (15-37).
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.► Simulation of weather conditions for regional air quality prediction. ► Evaluation of an ensemble of mesoscale simulations for air quality. ► Intercomparison of weather models for air quality over North Atlantic and Europe.
Keywords: Air quality modeling; Ensemble modeling; Meteorological modeling; Model evaluation;

Trace gas/aerosol boundary concentrations and their impacts on continental-scale AQMEII modeling domains by Kenneth Schere; Johannes Flemming; Robert Vautard; Charles Chemel; Augustin Colette; Christian Hogrefe; Bertrand Bessagnet; Frederik Meleux; Rohit Mathur; Shawn Roselle; Rong-Ming Hu; Ranjeet S. Sokhi; S. Trivikrama Rao; Stefano Galmarini (38-50).
Keywords: Air quality modeling; Boundary concentrations; Model evaluation; AQMEII; GEMS;

ENSEMBLE and AMET: Two systems and approaches to a harmonized, simplified and efficient facility for air quality models development and evaluation by S. Galmarini; R. Bianconi; W. Appel; E. Solazzo; S. Mosca; P. Grossi; M. Moran; K. Schere; S.T. Rao (51-59).
The complexity of air quality modeling systems, air quality monitoring data make ad-hoc systems for model evaluation important aids to the modeling community. Among those are the ENSEMBLE system developed by the EC-Joint Research Center, and the AMET software developed by the US-EPA. These independent systems provide two examples of state of the art tools to support model evaluation. The two systems are described here mostly from the point of view of the support to air quality model users or developers rather than the technological point of view. While ENSEMBLE is a web based platform for model evaluation that allows the collection, share and treatment of model results as well as monitoring data, AMET is a standalone tool that works directly on single model data. The complementarity of the two approaches makes the two systems optimal for operational, diagnostic and probabilistic evaluations. ENSEMBLE and AMET have been extended in occasion of the AQMEII two-continent exercise and the new developments are described in this paper, together with those foreseen for the future.
Keywords: Model evaluation; Model community; Monitoring information; Information technology;

Model evaluation and ensemble modelling of surface-level ozone in Europe and North America in the context of AQMEII by Efisio Solazzo; Roberto Bianconi; Robert Vautard; K. Wyat Appel; Michael D. Moran; Christian Hogrefe; Bertrand Bessagnet; Jørgen Brandt; Jesper H. Christensen; Charles Chemel; Isabelle Coll; Hugo Denier van der Gon; Joana Ferreira; Renate Forkel; Xavier V. Francis; George Grell; Paola Grossi; Ayoe B. Hansen; Amela Jeričević; Lukša Kraljević; Ana Isabel Miranda; Uarporn Nopmongcol; Guido Pirovano; Marje Prank; Angelo Riccio; Karine N. Sartelet; Martijn Schaap; Jeremy D. Silver; Ranjeet S. Sokhi; Julius Vira; Johannes Werhahn; Ralf Wolke; Greg Yarwood; Junhua Zhang; S.Trivikrama Rao; Stefano Galmarini (60-74).
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.
Keywords: AQMEII; Clustering; Error minimization; Multi-model ensemble; Ozone; Model evaluation;

Operational model evaluation for particulate matter in Europe and North America in the context of AQMEII by Efisio Solazzo; Roberto Bianconi; Guido Pirovano; Volker Matthias; Robert Vautard; Michael D. Moran; K. Wyat Appel; Bertrand Bessagnet; Jørgen Brandt; Jesper H. Christensen; Charles Chemel; Isabelle Coll; Joana Ferreira; Renate Forkel; Xavier V. Francis; Georg Grell; Paola Grossi; Ayoe B. Hansen; Ana Isabel Miranda; Uarporn Nopmongcol; Marje Prank; Karine N. Sartelet; Martijn Schaap; Jeremy D. Silver; Ranjeet S. Sokhi; Julius Vira; Johannes Werhahn; Ralf Wolke; Greg Yarwood; Junhua Zhang; S. Trivikrama Rao; Stefano Galmarini (75-92).
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.
Keywords: AQMEII; Regional air quality model; Particulate matter; Model evaluation; PM2.5 speciation;

Investigating impacts of chemistry and transport model formulation on model performance at European scale by G. Pirovano; A. Balzarini; B. Bessagnet; C. Emery; G. Kallos; F. Meleux; C. Mitsakou; U. Nopmongcol; G.M. Riva; G. Yarwood (93-109).
The CAMx and CHIMERE chemistry and transport models were applied over Europe for the year 2006 in the framework of the AQMEII inter-comparison exercise. Model simulations used the same input data set thus allowing model performance evaluation to focus on differences related to model chemistry and physics. Model performance was investigated according to different conditions, such as monitoring station classification and geographical features. An improved evaluation methodology, based on the Wilcoxon statistical test, was implemented to provide objectivity in the comparison of model performance.The models demonstrated similar geographical variations in model performance with just a few exceptions. Both models displayed great performance variability from region to region and within the same region for NO2 and PM10. Station type is relevant mainly for pollutants directly influenced by low level emission sources, such as NO2 and PM10.The analysis of the differences between CAMx and CHIMERE results revealed that both physical and chemical processes influenced the model performance. Particularly, differences in vertical diffusion coefficients (Kz) and 1st layer wind speed can affect the surface concentration of primary compounds, especially for stable conditions. Differently, differences in the vertical profiles of Kz strongly influenced the impact of aloft sources on ground level concentrations of both primary pollutants such as SO2 as well as PM10 compounds. CAMx showed stronger photochemistry than CHIMERE giving rise to higher ozone concentrations that agreed better with observations. Nonetheless, in some areas the more effective photochemistry showed by CAMx actually compensated for an underestimation in the background concentration.Finally, PM10 performance was rather poor for both models in most regions. CAMx performed always better than CHIMERE in terms of bias, while CHIMERE score for correlation was always higher than CAMx. As already mentioned, vertical mixing is one cause of such discrepancies in correlation. Differently, the stronger underestimation experienced by CHIMERE was mainly influenced by temporal smoothing of the boundary conditions, underestimation of low level emissions (mainly related to fires) and more intense depletion by wet deposition.
Keywords: Model performance evaluation; Wilcoxon ranked test; Model intercomparison; Ozone; Particulate matter; CAMx; CHIMERE;

Model evaluation studies are essential for determining model performance as well as assessing model deficiencies, and are the focus of the Air Quality Model Evaluation International Initiative (AQMEII). The chemistry-transport model system COSMO–MUSCAT participates in this initiative. In this paper the robustness and variability of the model results against changes in the model setup are analyzed. Special focus is given to the formation of secondary particulate matter and the ability to reproduce unusually high levels of PM10 in Central Europe caused by long-range transported smoke of fires in western Russia. Seven different model configurations are investigated in this study. The COSMO–MUSCAT results are evaluated in comparison with ground-based measurements in Central Europe. The analysis is performed for two selected periods in April/May 2006 and October 2006 which are characterized by elevated concentrations of PM. Furthermore, the sensitivity of the results is studied against the used grid resolution and the meteorological forcing. Here, COSMO–MUSCAT is applied with different horizontal grid sizes and, alternatively, forced by reanalysis data with finer resolution. The use of finer grid resolutions in COSMO–MUSCAT has direct consequences on the meteorological forcing as well as on the calculated emission and deposition rates. The presented results suggest a large impact of the meteorological effects on the PM concentrations. The more accurate spatial appointment of the emissions and deposition fluxes seems to be of little consequence compared to the meteorological forcing.
Keywords: Model evaluation; Ammonium sulfate; Ammonium nitrate; PM10; COSMO–MUSCAT;

Impact of biogenic emissions on air quality over Europe and North America by Karine N. Sartelet; Florian Couvidat; Christian Seigneur; Yelva Roustan (131-141).
This study aims to compare the relative impact of biogenic emissions on ozone (O3) and particulate matter (PM) concentrations between North America (NA) and Europe. The simulations are conducted with the Polyphemus air quality modeling system over July and August 2006. Prior to the sensitivity study on the impact of biogenic emissions on air quality, the modeling results are compared to observational data, as well as to the concentrations obtained by other modeling teams of the Air Quality Model Evaluation International Initiative (AQMEII) study.Over Europe, three distinct emission inventories are used. Model performance is satisfactory for O3, PM10 and PM2.5 with all inventories with respect to the criteria described in the literature. Furthermore, the rmse and errors are lower than the average rmse and errors of the AQMEII simulations. Over North America, the model performance satisfies the criteria described in the literature for O3, PM10 and PM2.5. Polyphemus results are within the range of the AQMEII model results. Although the rmse and errors are higher than the average of the AQMEII simulations for O3, they are lower for PM10 and PM2.5.The impact of biogenic and anthropogenic emissions on O3 and PM concentrations is studied by removing alternatively biogenic and anthropogenic emissions in distinct simulations. Because biogenic species interact strongly with NOx, the impact of biogenic emissions on O3 concentrations varies with variations of the Volatile Organic Compound (VOC)/NOx ratio. This impact is larger over NA than Europe. O3 decreases by 10–11% on average over Europe and 20% over NA. Locally, the relative impact is also higher in NA (60% maximum) than in Europe (35% maximum). O3 decreases near large urban centers where biogenic emissions are large (e.g. Los Angeles, Chicago, Houston in NA, Milan in Europe).Most of secondary organic aerosols (SOA) formed at the continental scale over Europe and NA are biogenic aerosols. Eliminating biogenic emissions reduces SOA by 72–88% over Europe and by 90% over NA. However, biogenic SOA are not only impacted by biogenic but also by anthropogenic emissions: eliminating all anthropogenic emissions affects oxidant levels and the absorbing carbon mass, reducing the formation of SOA by 15–16% over Europe and by about 10% over NA; Furthermore, locally, the reduction may be as large as 50%, especially over large urban centers in Europe and NA.
Keywords: Biogenic; Anthropogenic; Emission; Ozone; Particles; PM;

Examination of the Community Multiscale Air Quality (CMAQ) model performance over the North American and European domains by K. Wyat Appel; Charles Chemel; Shawn J. Roselle; Xavier V. Francis; Rong-Ming Hu; Ranjeet S. Sokhi; S.T. Rao; Stefano Galmarini (142-155).
The CMAQ modeling system has been used to simulate the air quality for North America and Europe for the entire year of 2006 as part of the Air Quality Model Evaluation International Initiative (AQMEII). The operational model performance of tropospheric ozone (O3), fine particulate matter (PM2.5) and total particulate matter (PM10) for the two continents has been assessed. The model underestimates daytime (8am–8pm LST) O3 mixing ratios by 13% in the winter for North America, primarily due to an underestimation of daytime O3 mixing ratios in the middle and lower troposphere from the lateral boundary conditions. The model overestimates winter daytime O3 mixing ratios in Europe by an average of 8.4%. The model underestimates daytime O3 by 4–5% in the spring for both continents, while in the summer daytime O3 is overestimated by 9.8% for North America and slightly underestimated by 1.6% for Europe. The model overestimates daytime O3 in the fall for both continents, grossly overestimating daytime O3 by over 30% for Europe. The performance for PM2.5 varies both seasonally and geographically for the two continents. For North American, PM2.5 is overestimated in the winter and fall, with an average Normalized Mean Bias (NMB) greater than −30%, while performance in the summer is relatively good, with an average NMB of −4.6%. For Europe, PM2.5 is underestimated throughout the entire year, with the NMB ranging from −24% in the fall to −55% in the winter. PM10 is underestimated throughout the year for both North America and Europe, with remarkably similar performance for both continents. The domain average NMB for PM10 ranges between −45% and −65% for the two continents, with the largest underestimation occurring in the summer for North American and the winter for Europe.
Keywords: CMAQ; Ozone; Particulate matter; Air quality modeling; Model evaluation; AQMEII;

An integrated model study for Europe and North America using the Danish Eulerian Hemispheric Model with focus on intercontinental transport of air pollution by J. Brandt; J.D. Silver; L.M. Frohn; C. Geels; A. Gross; A.B. Hansen; K.M. Hansen; G.B. Hedegaard; C.A. Skjøth; H. Villadsen; A. Zare; J.H. Christensen (156-176).
The Danish Eulerian Hemispheric Model (DEHM) is a 3D long-range atmospheric chemistry-transport model with a horizontal domain covering the Northern Hemisphere. For the AQMEII (Air Quality Modelling Evaluation International Initiative) inter-comparison exercise, the model was set up with two two-way nested domains simultaneously – one covering Europe and one covering North America. In this paper, the model configuration used in AQMEII will be described, including a discussion of model results and evaluation for the year 2006 against available measurements in Europe for different chemical species. The evaluation of DEHM for Europe shows that the model gives satisfying results for species such as ozone, nitrogen-dioxide, sulphur-dioxide and secondary inorganic aerosols. The evaluation also points to certain processes where DEHM can be improved, such as biogenic emissions of isoprene, mass closure for particulate matter, wet deposition, and description of vertical mixing during winter. Furthermore, special attention is given to the intercontinental transport of air pollution between North America (NA) and Europe (EU). We estimate the contributions to the total air pollution levels at continental scale from the anthropogenic emissions in the two areas, with a focus on ozone and particulate matter using a tagging method, taking into account the non-linear effects of atmospheric chemistry. We conclude that for this specific year, the intercontinental transport between NA and EU is small for the annual or seasonal mean values – for ozone the contributions are typically around 3% (∼1 ppb) from NA to EU and around 1% (∼0.3 ppb) from EU to NA. For particles the contributions from NA to EU is around 0.9% (∼0.05 μg m−3) and from EU to NA around 1.4% (∼0.05 μg m−3).► We model the air pollution in North America and Europe for the year 2006 using DEHM. ► We evaluate the model results against measurements for Europe for all available species. ► The evaluation leads to conclusions on where to improve the model. ► We calculated the intercontinental transport of air pollution between North America and Europe. ► We found that the exchange of air pollution between the two continents is small on the annual basis.
Keywords: Model documentation; Model evaluation; Intercontinental transport; Ozone; PM;

Modeling Europe with CAMx for the Air Quality Model Evaluation International Initiative (AQMEII) by Uarporn Nopmongcol; Bonyoung Koo; Edward Tai; Jaegun Jung; Piti Piyachaturawat; Chris Emery; Greg Yarwood; Guido Pirovano; Christina Mitsakou; George Kallos (177-185).
The CAMx photochemical grid model was used to model ozone (O3) and particulate matter (PM) over a European modeling domain for calendar year 2006 as part of the Air Quality Model Evaluation International Initiative (AQMEII). The CAMx base case utilized input data provided by AQMEII for emissions, meteorology and boundary conditions. Sensitivity of model outputs to input data was investigated by using alternate input data and changing other important modeling assumptions including the schemes to represent photochemistry, dry deposition and vertical mixing. Impacts on model performance were evaluated by comparisons with ambient monitoring data. Base case model performance for January and July 2006 exhibited under-estimation trends for all pollutants both in winter and summer, except for SO2. SO2 generally had little bias although some over-estimation occurred at coastal locations and this was attributed to incorrect vertical distribution of emissions from marine vessels. Performance for NOx and NO2 was better in winter than summer. The tendency to under-predict daytime NOx and O3 in summer may result from insufficient NOx emissions or overstated daytime dilution (e.g., too deep planetary boundary layer) or monitors that are located near sources (e.g., roadside monitors). Winter O3 was biased low and this was attributed to a low bias in the O3 boundary conditions. PM10 was widely under-predicted in both winter and summer. The poor PM10 was influenced by under-estimation of coarse PM emissions. Sensitivities of O3 concentrations to precursor emissions are quantified using the decoupled direct method in CAMx. The results suggest that O3 production over the central and southern Europe during summer is mostly NOx-limited but for a more northerly city, London, O3 production can be limited either by NOx or VOC depending upon daily meteorological conditions.
Keywords: AQMEII; Photochemical modeling; Sensitivity analysis; Decoupled direct method; Ozone; PM10; PM2.5; CAMx; MM5; WRF; MEGAN;

Improving the horizontal transport in the lower troposphere with four dimensional data assimilation by Robert C. Gilliam; James M. Godowitch; S. Trivikrama Rao (186-201).
The physical processes involved in air quality modeling are governed by dynamically-generated meteorological model fields. This research focuses on reducing the uncertainty in the horizontal transport in the lower troposphere by improving the four dimensional data assimilation (FDDA) strategy in retrospective meteorological modeling. In particular, characterization of winds in the nocturnal low-level jet and overlying residual layer is crucial to accurately model regional-scale ozone transport in the key airsheds of the US. Since model errors in wind speed and direction lead to spatial displacements of pollution plumes, observations not routinely used in previous retrospective modeling are introduced through FDDA in an effort to help reduce this transport uncertainty. Prior to the main modeling sensitivity, an observational uncertainty analysis was pursued to identify uncertainties in the wind speed and direction in the lower 1-km of the troposphere that are inherent in the observational data sets used in FDDA. Comparisons of observations among various platforms (radar wind profilers, radiosonde soundings and weather radar profiles) taken in close proximity revealed that an uncertainty of approximately 1.8 m s−1 for wind speed and about 20° for wind direction was intrinsic to the measurements. In the modeling sensitivities, some minimal improvement of modeled winds within the convective planetary boundary layer (PBL) was found when surface analysis nudging of wind was eliminated. Improvements in the nocturnal jet and residual layer winds at night are demonstrated as a reaction to the use of new observations in the data assimilation in layers above the stable PBL. There is also evidence that the assimilated observations above the convective PBL during the day led to improvements of winds within the PBL, which may relieve the need of nudging within the PBL, including surface analysis nudging.► We examine the uncertainty of observed and modeled winds in the lower troposphere. ► Uncertainty of wind observations was found to be about 1.8 m s−1 and 20–25°. ► Assimilation of wind profile observations reduced modeled transport uncertainty. ► Nudging in the planetary boundary layer using surface wind may not be necessary. ► Our nudging method reduces modeled transport uncertainty to that of the observations.
Keywords: Pollution transport; Observational uncertainty; Wind speed and direction errors; Nocturnal low-level jet; Four-dimensional data assimilation (FDDA); Air quality modeling;

Effect of aerosol-radiation feedback on regional air quality – A case study with WRF/Chem by Renate Forkel; Johannes Werhahn; Ayoe Buus Hansen; Stuart McKeen; Steven Peckham; Georg Grell; Peter Suppan (202-211).
Numerical simulations were performed in order to investigate the impact of the direct effect of aerosol particles on radiation and the indirect aerosol effect on meteorological variables and subsequent distributions of near surface ozone and PM10 over Europe. The fully coupled meteorology-chemistry community model WRF/Chem has been applied for June and July 2006 for a baseline case without any aerosol feedback on meteorology, a simulation with the direct effect included, and a simulation including the direct as well as the indirect aerosol effect. The impact of the subsequent changes in temperature, boundary layer height, and clouds that were triggered by the direct effect of aerosol on radiation (“semi-direct effect”) was found to dominate the direct effect of aerosol particles on solar radiation. Over Central Europe the mean reduction of global radiation alone was mostly 3–7 W m−2, but changes in cloud cover due to semi-direct effects resulted in monthly mean changes between ±50 W m−2. The inclusion of the indirect aerosol effect resulted in a pronounced decrease of cloud water content by up to 70% and a significantly higher mean rain water content over the North Atlantic. Although generally plausible, the effect appears to be too strong due to too low simulated aerosol particle numbers in this area. Regional changes in precipitation between −100% and 100% were simulated over the European continent. For the simulation including only the direct aerosol effect these changes are almost entirely due to semi-direct effects. Mean ozone mixing ratios over Europe in July were modified by up to 4 ppb or 10% over continental Europe, mostly related to changes in cloud cover. For PM10 the inclusion of the direct effect resulted for the considered episode in a mean decrease by 20–50% due to an increased atmospheric boundary layer height except for the regions with high PM10 concentrations. When the indirect aerosol effect was additionally taken into account an increase of the monthly PM10 concentration by 1–3 μg m−3 was found for July 2006 over large parts of continental Europe.
Keywords: WRF/Chem; Online coupled model; Feedback; Aerosol; Semi-direct effect; Indirect effect;

Air quality simulations for North America - MM5–CAMx modelling performance for main gaseous pollutants by J. Ferreira; A. Rodriguez; A. Monteiro; A.I. Miranda; M. Dios; J.A. Souto; G. Yarwood; U. Nopmongcol; C. Borrego (212-224).
In the scope of the Air Quality Model Evaluation International Initiative (AQMEII) the air quality modelling system MM5–CAMx was applied to the North American (NA) domain for calendar year 2006. The simulation domain was defined according to the spatial resolution and the coordinate system of the emission databases provided and the common grid required by AQMEII for ensemble analysis. A Lambert Conformal Projection grid of around 5500 km by 3580 km with 24 × 24 km2 horizontal resolution was defined. Emissions available through AQMEII have been prepared to feed the CAMx model. Meteorological inputs were developed by the application of the meteorological model MM5, which was initialized by 1° resolution NCEP-FNL global data and run for the whole year of 2006. A spatial and temporal analysis of results based on the 2D surface fields and time series for regional monitoring stations was performed for the main gaseous pollutants. A detailed statistical analysis and evaluation against observations was carried out, considering three different sub-domains over North America, in order to comprehend the differences between the East, West and Central part. The exploitation of modelling results was based on the capabilities and analysis tools available through the ENSEMBLE software, developed and upgraded for AQMEII. Results have shown a good agreement between observed and modelled concentrations of O3 (especially regarding peaks) and NO2 and a weaker performance of the air quality model for CO and SO2. However, the model tends to underestimate O3 and overestimate NO2 and CO at night as a consequence of meteorology (weak vertical mixing due to underestimation of the Planetary Boundary Layer (PBL) height). This paper intends to be a valuable contribution to the overall AQMEII exercise since it aims to evaluate the performance of individual models to be used in the ensemble approach for the areas of interest.
Keywords: Air quality model performance; Air quality assessment; North America;