Geoscientific Model Development www.geosci-model-dev.net 1991-959X 1991-9603 3 1 2010 10.5194/gmd-3-205-2010 http://www.geosci-model-dev.net/3/205/2010/ http://www.geosci-model-dev.net/3/205/2010/gmd-3-205-2010.html http://www.geosci-model-dev.net/3/205/2010/gmd-3-205-2010.pdf 205 226 2010-03-26 Incremental testing of the Community Multiscale Air Quality (CMAQ) modeling system version 4.7 K. M. Foley foley.kristen@epa.gov S. J. Roselle K. W. Appel P. V. Bhave J. E. Pleim T. L. Otte R. Mathur G. Sarwar J. O. Young R. C. Gilliam C. G. Nolte J. T. Kelly A. B. Gilliland J. O. Bash Atmospheric Modeling and Analysis Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, RTP, NC, USA now at: Regional Air Quality Modeling Section, Planning and Technical Support Division, Air Resources Board, California Environmental Protection Agency, Sacramento, CA, USA This paper describes the scientific and structural updates to the latest release of the Community Multiscale Air Quality (CMAQ) modeling system version 4.7 (v4.7) and points the reader to additional resources for further details. The model updates were evaluated relative to observations and results from previous model versions in a series of simulations conducted to incrementally assess the effect of each change. The focus of this paper is on five major scientific upgrades: (a) updates to the heterogeneous N₂O₅ parameterization, (b) improvement in the treatment of secondary organic aerosol (SOA), (c) inclusion of dynamic mass transfer for coarse-mode aerosol, (d) revisions to the cloud model, and (e) new options for the calculation of photolysis rates. Incremental test simulations over the eastern United States during January and August 2006 are evaluated to assess the model response to each scientific improvement, providing explanations of differences in results between v4.7 and previously released CMAQ model versions. Particulate sulfate predictions are improved across all monitoring networks during both seasons due to cloud module updates. Numerous updates to the SOA module improve the simulation of seasonal variability and decrease the bias in organic carbon predictions at urban sites in the winter. Bias in the total mass of fine particulate matter (PM_2.5) is dominated by overpredictions of unspeciated PM_2.5 (PM_other) in the winter and by underpredictions of carbon in the summer. The CMAQv4.7 model results show slightly worse performance for ozone predictions. However, changes to the meteorological inputs are found to have a much greater impact on ozone predictions compared to changes to the CMAQ modules described here. Model updates had little effect on existing biases in wet deposition predictions. Aiyyer,~A., Cohan,~D., Russell,~A., Stockwell,~W., Tanrikulu,~S., Vizuete, ~W., and Wilczak,~J.: Final report: Third peer review of the CMAQ model, online available at: http://www.cmascenter.org/r_and_d/cmaq_review_process.cfm, 2007. Appel,~K W., Bhave,~P V., Gilliland,~A B., Sarwar,~G., and Roselle,~S J.: Evaluation of the Community Multiscale Air Quality (CMAQ) model version 4.5: Sensitivities impacting model performance; Part II – particulate matter, Atmos. 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