1European Commission, Joint Research Centre (JRC), Directorate for Energy,
Transport and Climate, Air and Climate Unit, Ispra (VA), Italy
2Enviroware srl, Concorezzo, MB, Italy
3Environmental Protection Agency, Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, Research Triangle Park, NC 27711, USA
4CETEMPS, University of L'Aquila, L'Aquila, Italy
5Dept. Physical and Chemical Sciences, University of L'Aquila, L'Aquila, Italy
6Eurasia Institute of Earth Sciences, Istanbul Technical University, Istanbul, Turkey
7Ricerca sul Sistema Energetico (RSE SpA), Milan, Italy
8University of Murcia, Department of Physics, Physics of the Earth, Campus
de Espinardo, Ed. CIOyN, 30100 Murcia, Spain
9Institute of Coastal Research, Chemistry Transport Modelling Group,
Helmholtz-Zentrum Geesthacht, Germany
10Aarhus University, Department of Environmental Science, Frederiksborgvej
399, 4000 Roskilde, Denmark
11INERIS, Institut National de l'Environnement Industriel et des Risques,
Parc Alata, 60550 Verneuil-en-Halatte, France
12Centre for Atmospheric and Instrumentation Research (CAIR), University of
Hertfordshire, Hatfield, UK
13Ricardo Energy & Environment, Gemini Building, Fermi Avenue, Harwell,
Oxon, OX11 0QR, UK
14CIEMAT. Avda. Complutense 40., 28040 Madrid, Spain
15Netherlands Organization for Applied Scientific Research (TNO), Utrecht,
16Ramboll Environ, 773 San Marin Drive, Suite 2115, Novato, CA 94998, USA
17Environmental Research Group, Kings' College London, London, UK
18Finnish Meteorological Institute, Atmospheric Composition Research Unit,
Received: 28 Jul 2016 – Discussion started: 07 Sep 2016
Abstract. Through the comparison of several regional-scale chemistry transport modeling systems that simulate meteorology and air quality over the European and North American continents, this study aims at (i) apportioning error to the responsible processes using timescale analysis, (ii) helping to detect causes of model error, and (iii) identifying the processes and temporal scales most urgently requiring dedicated investigations.
Revised: 09 Jan 2017 – Accepted: 17 Jan 2017 – Published: 28 Feb 2017
The analysis is conducted within the framework of the third phase of the Air Quality Model Evaluation International Initiative (AQMEII) and tackles model performance gauging through measurement-to-model comparison, error decomposition, and time series analysis of the models biases for several fields (ozone, CO, SO2, NO, NO2, PM10, PM2. 5, wind speed, and temperature). The operational metrics (magnitude of the error, sign of the bias, associativity) provide an overall sense of model strengths and deficiencies, while apportioning the error to its constituent parts (bias, variance, and covariance) can help assess the nature and quality of the error. Each of the error components is analyzed independently and apportioned to specific processes based on the corresponding timescale (long scale, synoptic, diurnal, and intraday) using the error apportionment technique devised in the former phases of AQMEII.
The application of the error apportionment method to the AQMEII Phase 3 simulations provides several key insights. In addition to reaffirming the strong impact of model inputs (emission and boundary conditions) and poor representation of the stable boundary layer on model bias, results also highlighted the high interdependencies among meteorological and chemical variables, as well as among their errors. This indicates that the evaluation of air quality model performance for individual pollutants needs to be supported by complementary analysis of meteorological fields and chemical precursors to provide results that are more insightful from a model development perspective. This will require evaluation methods that are able to frame the impact on error of processes, conditions, and fluxes at the surface. For example, error due to emission and boundary conditions is dominant for primary species (CO, particulate matter (PM)), while errors due to meteorology and chemistry are most relevant to secondary species, such as ozone. Some further aspects emerged whose interpretation requires additional consideration, such as the uniformity of the synoptic error being region- and model-independent, observed for several pollutants; the source of unexplained variance for the diurnal component; and the type of error caused by deposition and at which scale.
Solazzo, E., Bianconi, R., Hogrefe, C., Curci, G., Tuccella, P., Alyuz, U., Balzarini, A., Baró, R., Bellasio, R., Bieser, J., Brandt, J., Christensen, J. H., Colette, A., Francis, X., Fraser, A., Vivanco, M. G., Jiménez-Guerrero, P., Im, U., Manders, A., Nopmongcol, U., Kitwiroon, N., Pirovano, G., Pozzoli, L., Prank, M., Sokhi, R. S., Unal, A., Yarwood, G., and Galmarini, S.: Evaluation and error apportionment of an ensemble of atmospheric chemistry transport modeling systems: multivariable temporal and spatial breakdown, Atmos. Chem. Phys., 17, 3001-3054, doi:10.5194/acp-17-3001-2017, 2017.