Articles | Volume 6, issue 5
https://doi.org/10.5194/gmd-6-1601-2013
https://doi.org/10.5194/gmd-6-1601-2013
Development and technical paper
 | 
25 Sep 2013
Development and technical paper |  | 25 Sep 2013

A method to represent ozone response to large changes in precursor emissions using high-order sensitivity analysis in photochemical models

G. Yarwood, C. Emery, J. Jung, U. Nopmongcol, and T. Sakulyanontvittaya

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Cited articles

Coe-Sullivan, D., Raffuse, S. M., Pryden, D. A., Craig, K. J., Reid, S. B., Wheeler, N. J. M, Chinkin, L. R., Larkin, N. K., Solomon, R., and Strand T.: Development and applications of Systems for Modeling Emissions and Smoke from Fires: The BlueSky Smoke Modeling Framework and SMARTFIRE, Presentation at the EPA 17th Annual International Emission Inventory Conference "Inventory Evolution – Portal to Improved Air Quality", Portland, OR, 2–5 June, 2008.
Cohan, D. S., Koo, B., and Yarwood, G.: Influence of uncertain reaction rates on ozone sensitivity to emissions, Atmos. Environ., 44, 3101–3109, 2010.
Dunker, A. M.: Efficient calculation of sensitivity coefficients for complex atmospheric models, Atmos. Environ., Part A, 15, 1155–1161, 1981.
Dunker, A. M., Yarwood, G., Ortmann, J. P., and Wilson, G. M.: The decoupled direct method for sensitivity analysis in a three-dimensional air quality model – Implementation, accuracy, and efficiency, Environ. Sci. Technol., 36, 2965–2976, 2002.
Emery, C., Jung, J., Downey, N., Johnson, J., Jimenez, J., Yarwood, G., and Morris, R.: Regional and global modeling estimates of policy relevant background ozone over the United States, Atmos. Environ., 47, 206–217, https://doi.org/10.1016/j.atmosenv.2011.11.012, 2012.
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