A multi-resolution assessment of the Community Multiscale Air Quality (CMAQ) model v4.7 wet deposition estimates for 2002–2006

Abstract

Abstract. This paper examines the operational performance of the Community Multiscale Air Quality (CMAQ) model simulations for 2002-2006 using both 36-km and 12-km horizontal grid spacing, with a primary focus on the performance of the CMAQ model in predicting wet deposition of sulfate (SO = 4 ), ammonium (NH + 4 ) and nitrate (NO − 3 ). Performance of the wet deposition estimates from the model is determined by comparing CMAQ predicted concentrations to concentrations measured by the National Acid Deposition Program (NADP), specifically the National Trends Network (NTN). For SO = 4 wet deposition, the CMAQ model estimates were generally comparable between the 36-km and 12-km simulations for the eastern US, with the 12-km simulation giving slightly higher estimates of SO = 4 wet deposition than the 36-km simulation on average. The result is a slightly larger normalized mean bias (NMB) for the 12-km simulation; however both simulations had annual biases that were less than ±15 % for each of the five years. The model estimated SO = 4 wet deposition values improved when they were adjusted to account for biases in the model estimated precipitation. The CMAQ model underestimates NH wet deposition tend to be slightly lower for the 36-km simulation as compared to the 12-km simulation, particularly in the spring. The underestimation of NO − 3 wet deposition in the spring and summer is due in part to a lack of lightning generated NO emissions in the upper troposphere, which can be a large source of NO in the spring and summer when lightning activity is the high. CMAQ model simulations that include production of NO from lightning show a significant improvement in the NO − 3 wet deposition estimates in the eastern US in the summer. Overall, performance for the 36-km and 12-km CMAQ model simulations is similar for the eastern US, while for the western US the performance of the 36-km simulation is generally not as good as either eastern US simulation, which is not entire unexpected given the complex topography in the western US.

Evaluation of a regional air-quality model with bidirectional NH<sub>3</sub> exchange coupled to an agroecosystem model

“…The monthly biases in the bidirectional wet deposition correlated well with the monthly meteorological precipitation biases (r 2 = 0.581, p < 0.05) while the base case NH x wet deposition biases did not significantly correlate with precipitation biases (r 2 = 0.08, p = 0.373). When the annual wet deposition is corrected for precipitation using PRISM interpolated precipitation data following Appel et al (2011), the absolute magnitude of the normalized bias in the bidirectional case is slightly reduced from 10.2 to −9.8 % and the absolute magnitude of the normalized bias in the base case increases from 1.9 to −16 % (Table 2). This indicates that the relatively unbiased wet deposition in the base case was likely due to meteorological model precipitation errors.…”

“…These precipitation biases are well documented and will be difficult to resolve in mesoscale models due to the localized/small scale nature of convective precipitation (Tost et al, 2010). Precipitation post processing techniques are necessary to account for potential precipitation biases in chemical transport models to illuminate the differences between biases propagated from errors in the simulated precipitation field and errors in the emissions, transport and fate in the chemical transport model (Appel et al, 2011). The bias introduced in NH x wet deposition estimates by the bidirectional model parametrization is largely mitigated if one accounts for the biases in the modeled precipitation of the driving meteorological model.…”

“…Assuming that the errors in precipitation and deposition are linear, an adjustment of the observed to estimated precipitation was applied to the CMAQ wet deposition fields to evaluate precipitation biases on a monthly deposition basis using PRISM interpolated observations (Eq. 9) to generate CONUS NH x wet deposition fields following Appel et al (2011).…”

Sensitivity of the Weather Research and Forecast/Community Multiscale Air Quality modeling system to MODIS LAI, FPAR, and albedo

“…Improving spatial and temporal distributions of modeled air pollutant concentrations and deposition, particularly O 3 , PM 2.5 , and NH 4 , will help reduce the uncertainties involved in quantifying risk assessment to human health and the environment. Despite significant advances in the modeling system over the past 10 years, there are still many uncertainties in the system [Foley et al, 2010;Appel et al, 2011a]. Many factors including emissions, transport, photolysis rates, photochemistry, and land surface exchange may contribute to errors in the current modeling system.…”

“…The PX LSM is mainly designed for air quality simulations using the WRF/CMAQ system where the WRF LSM parameters (e.g., stomatal and aerodynamic resistances) are consistently used in the AQ dry deposition model. WRF/CMAQ with the PX LSM scheme has been routinely used to retrospectively simulate for months to years continuously without reinitialization using the indirect soil moisture and temperature nudging schemes that leverages reanalysis fields [Appel et al, 2011a;Rogers et al, 2013;Hogrefe et al, 2014]. At the start of an extended run, the soil moisture fields can be very quickly and effectively spun up from simple generic initializations (e.g., from moisture availability factors by land use type) in about 5 days [Pleim and Gilliam, 2009].…”