Geoscientific Model Development (Mar 2010)

Incremental testing of the Community Multiscale Air Quality (CMAQ) modeling system version 4.7

  • K. M. Foley,
  • 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

Journal volume & issue
Vol. 3, no. 1
pp. 205 – 226


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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 N<sub>2</sub>O<sub>5</sub> 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<sub>2.5</sub>) is dominated by overpredictions of unspeciated PM<sub>2.5</sub> (PM<sub>other</sub>) 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.