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|Title:||Model evaluation and ensemble modelling of surface-level ozone in Europe and North America in the context of AQMEII|
|Authors:||SOLAZZO EFISIO; GALMARINI Stefano; BIANCONI Roberto; PIROVANO G.; VAUTARD Robert; APPEL W.; BESSAGNET B.; BRANDT J.; Christensen J.H.; Chemel Charles; COLL I.; FERREIRA J.; FORKEL R.; FRANCIS X.v.; GRELL G.; GROSSI P.; HANSEN A; MIRANDA A.i.; MORAN M.d.; NOPMONGCOL U.; SARTELET K.n.; SCHAAP Martijn; SILVER J.d.; Sokhi Ranjeet S.; VIRA J.; WERHAHN J.; WOLKE R.; YARWOOD G.; ZHANG J.; RAO S. Trivikrama; PRANK M.; HOGREFE Christian; VAN DER GON Hugo Denier; JERICEVIC A.; RICCIO A.; KRALJEVIC Luksa|
|Citation:||ATMOSPHERIC ENVIRONMENT vol. 53 p. 60-74|
|Publisher:||PERGAMON-ELSEVIER SCIENCE LTD|
|Type:||Articles in periodicals and books|
|Abstract:||More than ten state-of-the-art regional air quality models have participated in the Air Quality Model Evaluation International Initiative (AQMEII), in which a variety of mesoscale air quality modeling systems have been applied to continental-scale domains in North America and Europe for 2006 full-year simulations. The main goal of AQMEII is model inter-comparisons and evaluations. Standardised modelling 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 simulations issued from the models are inter-compared and evaluated with a large set of observations for ground level aerosol (PM10 and PM2.5) and its components, in both the continents. To facilitate the discussion and interpretation of the results, three sub-regions for each continental domain have been selected and analyses, with focus on spatially-averaged concentration. The unprecedented scale of the exercise (two continents, one year, over twenty groups) allows for a detailed description of model’s skill and uncertainty. Analysis of PM10 yearly time series and daily cycles indicates that large positive biases exist for all the investigated region and time of the year. We seek possible causes of PM bias in the emission and deposition balance, and in the bias induced by meteorological factors, such as the wind speed. PM2.5 and its major components are then analysed, and model performances highlighted. Finally, capability of models to capture high PM concentrations is also evaluated by looking at two separate PM2.5 episodes in Europe and North America. In particular, we found a large variability among models in predicting emissions, deposition, and PM concentration (especially PM10). Major challenges still remain to eliminate the sources of PM bias. Although PM2.5 is, by far, better estimated than PM10, no model was found to consistently match the observations under of variety of scenarios (sub-region and time of the year).|
|JRC Institute:||Sustainable Resources|
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