Multimodel estimates of intercontinental source-receptor relationships for ozone pollution

Fiore, A., F.J. Dentener, O. Wild, C. Cuvelier, M.G. Schultz, P. Hess, C. Textor, M. Schulz, R.M. Doherty, L.W. Horowitz, I.A. MacKenzie, M.G. Sanderson, D. Shindell, D.S. Stevenson, S. Szopa, R. Van Dingenen, G. Zeng, C. Atherton, D. Bergmann, I. Bey, G.R. Carmichael, W. Collins, B. Duncan, G. Faluvegi, G. Folberth, M. Gauss, S. Gong, D. Hauglustaine, T. Holloway, I.S.A. Isaksen, D.J. Jacob, J.E. Jonson, J.W. Kaminski, T.J. Keating, A. Lupu, E. Marmer, V. Montanaro, R.J. Park, G. Pitari, K.J. Pringle, J.A. Pyle, S. Schroeder, M.G. Vivanco, P. Wind, G. Wojcik, S. Wu, and A. Zuber (2009), Multimodel estimates of intercontinental source-receptor relationships for ozone pollution, J. Geophys. Res., 114, D04301, doi:10.1029/2008JD010816.
Abstract

Understanding the surface O3 response over a ‘‘receptor’’ region to emission changes over a foreign ‘‘source’’ region is key to evaluating the potential gains from an international approach to abate ozone (O3) pollution. We apply an ensemble of 21 global and hemispheric chemical transport models to estimate the spatial average surface O3 response over east Asia (EA), Europe (EU), North America (NA), and south Asia (SA) to 20% decreases in anthropogenic emissions of the O3 precursors, NOx, NMVOC, and CO (individually and combined), from each of these regions. We find that the ensemble mean surface O3 concentrations in the base case (year 2001) simulation matches available observations throughout the year over EU but overestimates them by >10 ppb during summer and early fall over the eastern United States and Japan. The sum of the O3 responses to NOx, CO, and NMVOC decreases separately is approximately equal to that from a simultaneous reduction of all precursors. We define a continental-scale ‘‘import sensitivity’’ as the ratio of the O3 response to the 20% reductions in foreign versus

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Research Program
Atmospheric Composition Modeling and Analysis Program (ACMAP)
Modeling Analysis and Prediction Program (MAP)