%0 journal article %@ 1680-7316 %A Solazzo, E., Bianconi, R., Hogrefe, C., Curci, G., Alyuz, U., Balzarini, A., Baro, R., Bellasio, R., Bieser, J., Brandt, J., Christensen, J.H., Colette, A., Francis, X., Fraser, A., Garcia Vivanco, M., Jimenez-Guerrero, P., Im, U., Manders, A., Nopmongcol, U., Kitwiroon, N., Pirovano, G., Pozzoli, L., Prank, M., Sokhi, R.S., Tuccella, P., Unal, A., Yarwood, G., Galmarini, S. %D 2017 %J Atmospheric Chemistry and Physics %N 4 %P 3001-3054 %R doi:10.5194/acp-17-3001-2017 %T Evaluation and error apportionment of an ensemble of atmospheric chemistry transport modeling systems: multivariable temporal and spatial breakdown %U https://doi.org/10.5194/acp-17-3001-2017 4 %X 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 (emissions and boundary conditions) and poor representation of the stable boundary layer on model bias, results also highlighted the high inter-dependencies 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. The error embedded in the emissions is dominant for primary species (CO, PM, NO) and largely outweighs the error from any other source. The uncertainty in meteorological fields is most relevant to ozone. Some further aspects emerged whose interpretation requires additional consideration, such as, among others, 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.