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Concorezzo, Italy

Solazzo E.,European Commission - Joint Research Center Ispra | Galmarini S.,European Commission - Joint Research Center Ispra | Bianconi R.,Enviroware srl | Rao T.,U.S. Environmental Protection Agency
NATO Science for Peace and Security Series C: Environmental Security | Year: 2013

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 for the Air Quality Model Evaluation International Initiative (AQMEII), whose main goals are model inter-comparison and model evaluation. 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. Analyses of PM10 time series show a large model underestimation throughout the year. Moreover, a large variability among models in predictions of emissions, deposition, and concentration of PM and its precursors has been found. © Springer Science+Business Media Dordrecht 2014. Source


Curci G.,University of LAquila | Hogrefe C.,U.S. Environmental Protection Agency | Bianconi R.,Enviroware srl | Im U.,European Commission - Joint Research Center Ispra | And 20 more authors.
Atmospheric Environment | Year: 2015

The calculation of aerosol optical properties from aerosol mass is a process subject to uncertainty related to necessary assumptions on the treatment of the chemical species mixing state, density, refractive index, and hygroscopic growth. In the framework of the AQMEII-2 model intercomparison, we used the bulk mass profiles of aerosol chemical species sampled over the locations of AERONET stations across Europe and North America to calculate the aerosol optical properties under a range of common assumptions for all models. Several simulations with parameters perturbed within a range of observed values are carried out for July 2010 and compared in order to infer the assumptions that have the largest impact on the calculated aerosol optical properties. We calculate that the most important factor of uncertainty is the assumption about the mixing state, for which we estimate an uncertainty of 30-35% on the simulated aerosol optical depth (AOD) and single scattering albedo (SSA). The choice of the core composition in the core-shell representation is of minor importance for calculation of AOD, while it is critical for the SSA. The uncertainty introduced by the choice of mixing state choice on the calculation of the asymmetry parameter is the order of 10%. Other factors of uncertainty tested here have a maximum average impact of 10% each on calculated AOD, and an impact of a few percent on SSA and g. It is thus recommended to focus further research on a more accurate representation of the aerosol mixing state in models, in order to have a less uncertain simulation of the related optical properties. © 2014 The Authors. Source


Brunner D.,Empa - Swiss Federal Laboratories for Materials Science and Technology | Savage N.,UK Met Office | Jorba O.,Barcelona Supercomputing Center | Eder B.,U.S. Environmental Protection Agency | And 33 more authors.
Atmospheric Environment | Year: 2015

Air pollution simulations critically depend on the quality of the underlying meteorology. In phase 2 of the Air Quality Model Evaluation International Initiative (AQMEII-2), thirteen modeling groups from Europe and four groups from North America operating eight different regional coupled chemistry and meteorology models participated in a coordinated model evaluation exercise. Each group simulated the year 2010 for a domain covering either Europe or North America or both. Here were present an operational analysis of model performance with respect to key meteorological variables relevant for atmospheric chemistry processes and air quality. These parameters include temperature and wind speed at the surface and in the vertical profile, incoming solar radiation at the ground, precipitation, and planetary boundary layer heights. A similar analysis was performed during AQMEII phase 1 (Vautard et al., 2012) for offline air quality models not directly coupled to the meteorological model core as the model systems investigated here. Similar to phase 1, we found significant overpredictions of 10-m wind speeds by most models, more pronounced during night than during daytime. The seasonal evolution of temperature was well captured with monthly mean biases below 2 K over all domains. Solar incoming radiation, precipitation and PBL heights, on the other hand, showed significant spread between models and observations suggesting that major challenges still remain in the simulation of meteorological parameters relevant for air quality and for chemistry-climate interactions at the regional scale. © 2014 The Authors. Source


Forkel R.,Karlsruhe Institute of Technology | Balzarini A.,RSE SpA | Baro R.,University of Murcia | Bianconi R.,Enviroware srl | And 12 more authors.
Atmospheric Environment | Year: 2015

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 20Wm-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 100dropletscm-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. © 2014 The Authors. Source


Solazzo E.,European Commission - Joint Research Center Ispra | Bianconi R.,Enviroware srl | Vautard R.,French Climate and Environment Sciences Laboratory | Appel K.W.,U.S. Environmental Protection Agency | And 33 more authors.
Atmospheric Environment | Year: 2012

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. Source

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