Articles | Volume 16, issue 19
Atmos. Chem. Phys., 16, 12667–12701, 2016
Atmos. Chem. Phys., 16, 12667–12701, 2016
Research article
12 Oct 2016
Research article | 12 Oct 2016

Presentation of the EURODELTA III intercomparison exercise – evaluation of the chemistry transport models' performance on criteria pollutants and joint analysis with meteorology

Bertrand Bessagnet1, Guido Pirovano2, Mihaela Mircea3, Cornelius Cuvelier4, Armin Aulinger5, Giuseppe Calori6, Giancarlo Ciarelli7, Astrid Manders8, Rainer Stern9, Svetlana Tsyro10, Marta García Vivanco11, Philippe Thunis12, Maria-Teresa Pay13, Augustin Colette1, Florian Couvidat1, Frédérik Meleux1, Laurence Rouïl1, Anthony Ung1, Sebnem Aksoyoglu7, José María Baldasano13, Johannes Bieser5, Gino Briganti3, Andrea Cappelletti3, Massimo D'Isidoro3, Sandro Finardi6, Richard Kranenburg8, Camillo Silibello6, Claudio Carnevale14, Wenche Aas15, Jean-Charles Dupont16, Hilde Fagerli10, Lucia Gonzalez17, Laurent Menut18, André S. H. Prévôt7, Pete Roberts17, and Les White19 Bertrand Bessagnet et al.
  • 1INERIS, National Institute for Industrial Environment and Risks, Parc Technologique ALATA, 60550 Verneuil-en-Halatte, France
  • 2RSE S.p.A., via Rubattino 54, 20134 Milan, Italy
  • 3ENEA, Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), Via Martiri di Monte Sole 4, 40129 Bologna, Italy
  • 4Ex European Commission, Joint Research Centre JRC Institute for Environment and Sustainability, 21020 Ispra (Va), Italy
  • 5HZG, Helmholtz-Zentrum Geesthacht, Institute for Coastal Research, Max-Planck-Straße 1, 21502 Geesthacht, Germany
  • 6ARIANET Srl, Via Gilino n.9 20128, Milan, Italy
  • 7PSI, LAC, Paul Scherrer Institute, 5232 Villigen PSI, Switzerland
  • 8TNO, Dept. Climate, Air and Sustainability, P.O. Box 80015, 3508 TA Utrecht, the Netherlands
  • 9Freie Universität Berlin, Institut für Meteorologie Troposphärische Umweltforschung Carl-Heinrich-Becker Weg 6–10, 12165 Berlin, Germany
  • 10Climate Modelling and Air Pollution Division, Research and Development Department, Norwegian Meteorological Institute (MET Norway) P.O. Box 43, Blindern, 0313 Oslo, Norway
  • 11CIEMAT, Atmospheric Pollution Unit, Avda. Complutense, 22, 28040 Madrid, Spain
  • 12European Commission, Joint Research Centre JRC Institute for Environment and Sustainability 21020 Ispra (Va), Italy
  • 13BSC, Barcelona Supercomputing Center, Centro Nacional de Supercomputación, Nexus II Building, Jordi Girona, 29, 08034 Barcelona, Spain
  • 14Department of Electronics for the Automation, University of Brescia, via Branze 38, 25123 Brescia, Italy
  • 15Norwegian Institute for Air Research (NILU), Box 100, 2027 Kjeller, Norway
  • 16Institut Pierre-Simon Laplace, CNRS-Ecole Polytechnique, 91128 Palaiseau, Paris, France
  • 17CONCAWE, Boulevard du Souverain 165, 1160 Brussels, Belgium
  • 18Laboratoire de Météorologie Dynamique, École Polytechnique, ENS, UPMC, CNRS, Institut Pierre-Simon Laplace, 91128 Palaiseau, France
  • 19AERIS EUROPE Ltd., Strouds Church Lane, West Sussex RH17 7AY, UK

Abstract. The EURODELTA III exercise has facilitated a comprehensive intercomparison and evaluation of chemistry transport model performances. Participating models performed calculations for four 1-month periods in different seasons in the years 2006 to 2009, allowing the influence of different meteorological conditions on model performances to be evaluated. The exercise was performed with strict requirements for the input data, with few exceptions. As a consequence, most of differences in the outputs will be attributed to the differences in model formulations of chemical and physical processes. The models were evaluated mainly for background rural stations in Europe. The performance was assessed in terms of bias, root mean square error and correlation with respect to the concentrations of air pollutants (NO2, O3, SO2, PM10 and PM2.5), as well as key meteorological variables. Though most of meteorological parameters were prescribed, some variables like the planetary boundary layer (PBL) height and the vertical diffusion coefficient were derived in the model preprocessors and can partly explain the spread in model results. In general, the daytime PBL height is underestimated by all models. The largest variability of predicted PBL is observed over the ocean and seas. For ozone, this study shows the importance of proper boundary conditions for accurate model calculations and then on the regime of the gas and particle chemistry. The models show similar and quite good performance for nitrogen dioxide, whereas they struggle to accurately reproduce measured sulfur dioxide concentrations (for which the agreement with observations is the poorest). In general, the models provide a close-to-observations map of particulate matter (PM2.5 and PM10) concentrations over Europe rather with correlations in the range 0.4–0.7 and a systematic underestimation reaching −10 µg m−3 for PM10. The highest concentrations are much more underestimated, particularly in wintertime. Further evaluation of the mean diurnal cycles of PM reveals a general model tendency to overestimate the effect of the PBL height rise on PM levels in the morning, while the intensity of afternoon chemistry leads formation of secondary species to be underestimated. This results in larger modelled PM diurnal variations than the observations for all seasons. The models tend to be too sensitive to the daily variation of the PBL. All in all, in most cases model performances are more influenced by the model setup than the season. The good representation of temporal evolution of wind speed is the most responsible for models' skillfulness in reproducing the daily variability of pollutant concentrations (e.g. the development of peak episodes), while the reconstruction of the PBL diurnal cycle seems to play a larger role in driving the corresponding pollutant diurnal cycle and hence determines the presence of systematic positive and negative biases detectable on daily basis.

Short summary
The EURODELTA III exercise allows a very comprehensive intercomparison and evaluation of air quality models' performance. On average, the models provide a rather good picture of the particulate matter (PM) concentrations over Europe even if the highest concentrations are underestimated. The meteorology is responsible for model discrepancies, while the lack of emissions, particularly in winter, is mentioned as the main reason for the underestimations of PM.
Final-revised paper