Air Quality Model Evaluation International Initiative Research Articles

Analysis of the WRF-Chem contributions to AQMEII phase2 with respect to aerosol radiative feedbacks on meteorology and pollutant distributions

As a contribution to phase2 of the Air Quality Model Evaluation International Initiative (AQMEII), eight different simulations for the year 2010 were performed with WRF-Chem for the European domain. The four simulations using RADM2 gas-phase chemistry and the MADE/SORGAM aerosol module are analyzed in this paper. The simulations included different degrees of aerosol–meteorology feedback, ranging from no aerosol effects at all to the inclusion of the aerosol direct radiative effect as well as aerosol cloud interactions and the aerosol indirect effect. In addition, a modification of the RADM2 gas phase chemistry solver was tested. The yearly simulations allow characterizing the average impact of the consideration of feedback effects on meteorology and pollutant concentrations and an analysis of the seasonality. Pronounced feedback effects were found for the summer 2010 Russian wildfire episode, where the direct aerosol effect lowered the seasonal mean solar radiation by 20 W m−3 and seasonal mean temperature by 0.25°. This might be considered as a lower limit as it must be taken into account that aerosol concentrations were generally underestimated by up to 50%. The high aerosol concentrations from the wildfires resulted in a 10%–30% decreased precipitation over Russia when aerosol cloud interactions were taken into account. The most pronounced and persistent feedback due to the indirect aerosol effect was found for regions with very low aerosol concentrations like the Atlantic and Northern Europe. The low aerosol concentrations in this area result in very low cloud droplet numbers between 5 and 100 droplets cm−1 and a 50–70% lower cloud liquid water path. This leads to an increase in the downward solar radiation by almost 50%. Over Northern Scandinavia, this results in almost one degree higher mean temperatures during summer. In winter, the decreased liquid water path resulted in increased long-wave cooling and a decrease of the mean temperature by almost the same amount. Precipitation over the Atlantic Ocean was found to be enhanced by up to 30% when aerosol cloud interactions were taken into account. The inclusion of aerosol cloud interactions can reduce the bias or improve correlations of simulated precipitation for some episodes and regions. However, the domain and time averaged performance statistics do not indicate a general improvement when aerosol feedbacks are taken into account. Except for conditions with either very low or very high aerosol concentrations, the impact of aerosol feedbacks on pollutant distributions was found to be smaller than the effect of the choice of the chemistry module or wet deposition implementation.

Evaluation of operational on-line-coupled regional air quality models over Europe and North America in the context of AQMEII phase 2. Part I: Ozone

The second phase of the Air Quality Model Evaluation International Initiative (AQMEII) brought together sixteen modeling groups from Europe and North America, running eight operational online-coupled air quality models over Europe and North America on common emissions and boundary conditions. With the advent of online-coupled models providing new capability to quantify the effects of feedback processes, the main aim of this study is to compare the response of coupled air quality models to simulate levels of O3 over the two continental regions. The simulated annual, seasonal, continental and sub-regional ozone surface concentrations and vertical profiles for the year 2010 have been evaluated against a large observational database from different measurement networks operating in Europe and North America. Results show a general model underestimation of the annual surface ozone levels over both continents reaching up to 18% over Europe and 22% over North America. The observed temporal variations are successfully reproduced with correlation coefficients larger than 0.8. Results clearly show that the simulated levels highly depend on the meteorological and chemical configurations used in the models, even within the same modeling system. The seasonal and sub-regional analyses show the models' tendency to overestimate surface ozone in all regions during autumn and underestimate in winter. Boundary conditions strongly influence ozone predictions especially during winter and autumn, whereas during summer local production dominates over regional transport. Daily maximum 8-h averaged surface ozone levels below 50–60 μg m−3 are overestimated by all models over both continents while levels over 120–140 μg m−3 are underestimated, suggesting that models have a tendency to severely under-predict high O3 values that are of concern for air quality forecast and control policy applications.

Evaluation of operational online-coupled regional air quality models over Europe and North America in the context of AQMEII phase 2. Part II: Particulate matter

The second phase of the Air Quality Model Evaluation International Initiative (AQMEII) brought together seventeen modeling groups from Europe and North America, running eight operational online-coupled air quality models over Europe and North America using common emissions and boundary conditions. The simulated annual, seasonal, continental and sub-regional particulate matter (PM) surface concentrations for the year 2010 have been evaluated against a large observational database from different measurement networks operating in Europe and North America. The results show a systematic underestimation for all models in almost all seasons and sub-regions, with the largest underestimations for the Mediterranean region. The rural PM10 concentrations over Europe are underestimated by all models by up to 66% while the underestimations are much larger for the urban PM10 concentrations (up to 75%). On the other hand, there are overestimations in PM2.5 levels suggesting that the large underestimations in the PM10 levels can be attributed to the natural dust emissions. Over North America, there is a general underestimation in PM10 in all seasons and sub-regions by up to ∼90% due mainly to the underpredictions in soil dust. SO42− levels over EU are underestimated by majority of the models while NO3− levels are largely overestimated, particularly in east and south Europe. NH4+ levels are also underestimated largely in south Europe. SO4 levels over North America are particularly overestimated over the western US that is characterized by large anthropogenic emissions while the eastern USA is characterized by underestimated SO4 levels by the majority of the models. Daytime AOD levels at 555 nm is simulated within the 50% error range over both continents with differences attributed to differences in concentrations of the relevant species as well as in approaches in estimating the AOD. Results show that the simulated dry deposition can lead to substantial differences among the models. Overall, the results show that representation of dust and sea-salt emissions can largely impact the simulated PM concentrations and that there are still major challenges and uncertainties in simulating the PM levels.

<i>E pluribus unum</i>*: ensemble air quality predictions

Abstract. In this study we present a novel approach for improving the air quality predictions using an ensemble of air quality models generated in the context of AQMEII (Air Quality Model Evaluation International Initiative). The development of the forecasting method makes use of modelled and observed time series (either spatially aggregated or relative to single monitoring stations) of ozone concentrations over different areas of Europe and North America. The technique considers the underlying forcing mechanisms on ozone by means of spectrally decomposed previsions. With the use of diverse applications, we demonstrate how the approach screens the ensemble members, extracts the best components and generates bias-free forecasts with improved accuracy over the candidate models. Compared to more traditional forecasting methods such as the ensemble median, the approach reduces the forecast error and at the same time it clearly improves the modelled variance. Furthermore, the result is not a mere statistical outcome depended on the quality of the selected members. The few individual cases with degraded performance are also identified and analysed. Finally, we show the extensions of the approach to other pollutants, specifically particulate matter and nitrogen dioxide, and provide a framework for its operational implementation. *One out of many

Space-time analysis of the Air Quality Model Evaluation International Initiative (AQMEII) Phase 1 air quality simulations

This study presents an evaluation of summertime ozone concentrations over North America (NA) and Europe (EU) using the database generated from Phase 1 of the Air Quality Model Evaluation International Initiative (AQMEII). The analysis focuses on identifying temporal and spatial features that can be used to stratify operational model evaluation metrics and to test the extent to which the various modeling systems can replicate the features seen in the observations. Using a synoptic map typing approach, it is demonstrated that model performance varies with meteorological conditions associated with specific synoptic-scale flow patterns over both eastern NA and EU. For example, the root mean square error of simulated daily maximum 8-hr ozone was twice as high when cloud fractions were high compared with when cloud fractions were low over eastern NA. Furthermore, results show that over both NA and EU the regional models participating in AQMEII were able to better reproduce the observed variance in ambient ozone levels than the global model used to specify chemical boundary conditions, although the variance simulated by almost all regional models is still less that the observed variance on all spatiotemporal scales. In addition, all modeling systems showed poor correlations with observed fluctuations on the intraday time scale over both NA and EU. Furthermore, a methodology is introduced to distinguish between locally influenced and regionally representative sites for the purpose of model evaluation. Results reveal that all models have worse model performance at locally influenced sites. Overall, the analyses presented in this paper show how observed temporal and spatial information can be used to stratify operational model performance statistics and to test the modeling systems’ ability to replicate observed temporal and spatial features, especially at scales the modeling systems are designed to capture.Implications: The analyses presented in this paper demonstrate how observed temporal and spatial information can be used to stratify operational model performance and to test the modeling systems’ ability to replicate observed temporal and spatial features. Decisions for the improvement of regional air quality models should be based on the information derived from only regionally representative sites.

Influence of grid resolution and meteorological forcing on simulated European air quality: A sensitivity study with the modeling system COSMO–MUSCAT

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.

Operational model evaluation for particulate matter in Europe and North America in the context of AQMEII

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 (PM 10 and PM 2.5) and its chemical components. Modeled concentrations of gaseous PM precursors, SO 2 and NO 2, 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 PM 10 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, PM 2.5 and its major components (SO 4, NH 4, NO 3, 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 PM 2.5 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 PM 2.5 was found to be much better estimated by the models than PM 10, no model was found to consistently match the observations for all locations throughout the entire year. © 2012 Elsevier Ltd.

Model evaluation and ensemble modelling of surface-level ozone in Europe and North America in the context of AQMEII

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. © 2012 Elsevier Ltd.

Comparing emission inventories and model-ready emission datasets between Europe and North America for the AQMEII project

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: NO x , VOCs, SO 2 , PM 2.5 , CO, and NH 3 . 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.

Evaluation of the meteorological forcing used for the Air Quality Model Evaluation International Initiative (AQMEII) air quality simulations

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.

Examination of the Community Multiscale Air Quality (CMAQ) model performance over the North American and European domains

Abstract 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 (O 3 ), fine particulate matter (PM 2.5 ) and total particulate matter (PM 10 ) for the two continents has been assessed. The model underestimates daytime (8am–8pm LST) O 3 mixing ratios by 13% in the winter for North America, primarily due to an underestimation of daytime O 3 mixing ratios in the middle and lower troposphere from the lateral boundary conditions. The model overestimates winter daytime O 3 mixing ratios in Europe by an average of 8.4%. The model underestimates daytime O 3 by 4–5% in the spring for both continents, while in the summer daytime O 3 is overestimated by 9.8% for North America and slightly underestimated by 1.6% for Europe. The model overestimates daytime O 3 in the fall for both continents, grossly overestimating daytime O 3 by over 30% for Europe. The performance for PM 2.5 varies both seasonally and geographically for the two continents. For North American, PM 2.5 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, PM 2.5 is underestimated throughout the entire year, with the NMB ranging from −24% in the fall to −55% in the winter. PM 10 is underestimated throughout the year for both North America and Europe, with remarkably similar performance for both continents. The domain average NMB for PM 10 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.

Modeling Europe with CAMx for the Air Quality Model Evaluation International Initiative (AQMEII)

Abstract The CAMx photochemical grid model was used to model ozone (O 3 ) 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 SO 2 . SO 2 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 NO 2 was better in winter than summer. The tendency to under-predict daytime NOx and O 3 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 O 3 was biased low and this was attributed to a low bias in the O 3 boundary conditions. PM 10 was widely under-predicted in both winter and summer. The poor PM 10 was influenced by under-estimation of coarse PM emissions. Sensitivities of O 3 concentrations to precursor emissions are quantified using the decoupled direct method in CAMx. The results suggest that O 3 production over the central and southern Europe during summer is mostly NOx-limited but for a more northerly city, London, O 3 production can be limited either by NOx or VOC depending upon daily meteorological conditions.

Trace gas/aerosol boundary concentrations and their impacts on continental-scale AQMEII modeling domains

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.

Impact of biogenic emissions on air quality over Europe and North America

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.

Air quality simulations for North America - MM5–CAMx modelling performance for main gaseous pollutants

Abstract 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.

The AQMEII two-continent Regional Air Quality Model evaluation study: Fueling ideas with unprecedented data

Although strong collaborations in the air pollution field haveexistedamongtheNorthAmerican(NA)andEuropean(EU)countriesover the past five decades, regional-scale air quality model devel-opments and model performance evaluations have been carried outindependently unlike in the climate or global air quality modelingcommunity.Recognizingtheneedtobuildinternationalcooperationforrapidlyadvancing thescienceinregional-scalephotochemicalairqualitymodelingsystems,ateamofscientistsembarkedonaprojectknown as Air Quality Model Evaluation International Initiative(AQMEII) in 2009 (Galmarini et al., 2010; Rao et al., 2010, http://aqmeii.jrc.ec.europa.eu/). The aim of AQMEII is to provide a perma-nent forum for constantly monitoring the state of advancement ofregional-scale air quality models and model evaluation methodolo-gies, identifying knowledge gaps in air quality science, and devel-oping methodologies to quantify and communicate uncertainties inmodeling results.As of now, more than 23 modeling groups from both sides of theAtlantic Ocean are currently participating. Concentrations of ozoneand fine particulate matter, and deposition of sulfate and nitrogenfrom various regional-scale models are submitted to the EuropeanUnionJointResearchCenter’sENSEMBLEmodelevaluationplatform(Galmarini et al., 2004). Data from several thousands of surfacemonitoring stations (chemical and meteorological), ozone profiles,and MOZAIC aircraft measurements from the two continents havebeen gathered and harmonized on the ENSEMBLE system for directweb-consultation and comparison with model data. The AQMEIIdatabase is complete, unique, and unprecedented.GiventheoriginalscopesofAQMEII,wewouldliketoprovideanopportunity for anyone, outside the AQMEII community, interestedin contributing toward the model evaluation science. Therefore,weinvitethosethatarenotcurrentlyparticipatinginAQMEIItosubmitproposals to us for developing innovative model evaluation tech-niques using unprecedented AQMEII data. Cross- or inter- disci-plinary novel approaches are expected to result from thiscollaboration. The open questions on regional scale air qualitymodel evaluation have been outlined during the 1st AQMEIIworkshop held in Stresa Italy in April 2009, (Galmarini et al., 2010).These relate to: Synthetic evaluation index definition, Specific model performance metrics; Graphical representations; Quantifying the nature and magnitude of model uncertainties; Reconciliation of the different nature of model results andobservation uncertainties; Reconciliation of the different space-time representations ofmonitoring data, model outputs, and exposure; Ways to effectively communicate the linked space-time natureofairpollutionfields,andtheconceptofprobabilisticmodeling. Detailed assessments of physical/chemical processes in modelsThe above are just few examples of the areas in which muchwork is still needed. There are in fact a large number of challengingissues especially relating to the so-called probabilistic evaluation(Dennisetal.,2010)thataremuchunexploredandthathavealargepotential in real-world applications. Those interested in havingaccess to the AQMEII data are encouraged to contact the authors tohelp bring together the best of minds for assessing state-of-sciencein the current generation models.DisclaimerAlthough this article has been reviewed and approved forpublication by the European Union Joint Research Center and theU.S. Environmental Protection Agency, it does not necessarilyreflect their views and policies.References

Air Quality Model Evaluation International Initiative (AQMEII): Advancing the State of the Science in Regional Photochemical Modeling and Its Applications

aMErICan METEOrOLOGICaL SOCIETy | 23 AffiliAtions: Rao—Atmospheric Modeling and Analysis Division, National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina; GalmaRini—Institute of Environment and Sustainability, European Commission, Joint Research Center, Ispra, Italy; Puckett—Air Quality Research Division, Environment Canada, Toronto, Ontario, Canada Corresponding Author: Dr. S. T. Rao, Atmospheric Modeling and Analysis Division, National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711 E-mail: rao.st@epa.gov