A Photochemical Model Comparison Study: CAMx and CMAQ Performance in Central California

Because of the complex topographic and geographic features of Central California, it is difficult to numerically simulate the production of photochemical smog in this region. To date, our attempts to simulate the ozone episodes that occurred in this region during the summers of 1999 and 2000 have tended to under-predict ozone, especially in locations near the observed ozone peaks. To identify and understand the reasons for this under-prediction, we have undertaken a two-part study. First, we compared the predictions of two photochemical models, CAMx and CMAQ, to ensure the under-prediction problem was not inherent in our choice of photochemical models. Second, we examined the sensitivity of both models to pollutant emissions and other inputs known to be uncertain in the models. This study found that ozone was underestimated with either choice of photochemical models. In addition, the sensitivity tests provided valuable clues as to the possible reasons for the underestimation.

In the first part of the study, we designed our model setups for CAMx and CMAQ to be as similar to one another as possible. The modeled domain covered most of California with a 4x4 km2 horizontal grid and an expanding vertical grid that varied from 25 m to 1000 m in the lower atmosphere. Two modeling periods were examined: mid July 1999 and late July to early August 2000. During the first modeling period, only routinely collected data were available for model evaluation; however, for the second period, supplemental data were available from the 2000 Central California Ozone Study. For both CAMx and CMAQ, the meteorological inputs were generated from MM5 with four-dimensional data assimilation; the emissions inventory estimates were generated by the California Air Resources Board; and the pollutant boundary conditions were derived from field-study observations collected during the summers of 1990 and 2000. For both episodes, we found that ozone was under-predicted for both choices of photochemical models. Though the location of some of the simulated ozone peaks varied with the choice of model, both CAMx and CMAQ under-predicted ozone by about 20-30 ppb. Performance statistics for oxides of nitrogen (NOx), volatile organic compounds (VOC), and carbon monoxide (CO) also showed under-prediction throughout the modeling domain.

In the second part of this study, we first examined the sensitivity of each model to changes in precursor emissions of NOx and both biogenic and anthropogenic VOC. Our simulations showed that peak ozone was sensitive to both anthropogenic and biogenic emissions. Next, we examined the sensitivity of each model to ground albedo, ambient temperatures, and winds within the range of their estimated uncertainty. Ground albedo is known to increase with wavelength and changes with surface characteristics; however, neither CAMx nor CMAQ account for both facts. Sensitivity tests revealed that a more accurate description of ground albedo in the model could increase peak ozone by about 6 ppb. Since local temperatures at locations with peak ozone observed were consistently underestimated in the baseline MM5 run by 3-5 C, we conducted a simulation with temperatures artificially increased by 5 C, and found that peak ozone increased by about 6-8 ppb for both models. Finally, we examined the effect of different chemical mechanisms on each model. Simulations showed that by using the SAPRC99 chemical mechanism instead of the CB4 chemical mechanism peak ozone increased by about 10 ppb. Additional comparisons will be undertaken and presented to compare the relative sensitivities of CAMx and CMAQ to the chemical mechanism and to each of the model inputs discussed above.