Evaluation and intercomparison of five major dry deposition algorithms in North America
Zhiyong Wu1, Donna Schwede2, Robert Vet3, Mike Shaw4, Ralf Staebler5 and Leiming Zhang6
Dry deposition of various pollutants needs to be quantified in air quality monitoring networks as well as in chemical transport models. The inferential method is the most commonly used approach in which the dry deposition velocity (Vd) is empirically parameterized as a function of meteorological and biological conditions and pollutant species’ chemical properties. Earlier model intercomparison studies suggested that existing dry deposition algorithms produce quite different Vd values, e.g., up to a factor of 2 for monthly to annual average values for ozone, and sulfur and nitrogen species (Flechard et al., 2011; Schwede et al., 2011; Wu et al., 2011). To further evaluate model discrepancies using available flux data, this study compared the five dry deposition algorithms commonly used in North America and evaluated the models using five-year Vd(O3) and Vd(SO2) data generated from concentration gradient measurements above a temperate mixed forest in Canada. The five algorithms include: (1) the one used in the Canadian Air and Precipitation Monitoring Network (CAPMoN) and several Canadian air quality models based on Zhang et al. (2003), (2) the one used in the US Clean Air Status and Trends Network (CASTNET) based on Meyers et al. (1998), (3) the one used in the Community Multiscale Air Quality (CMAQ) model described in Pleim and Ran (2011), (4) the Noah land surface model coupled with a photosynthesis-based Gas Exchange Model (Noah-GEM) described in Wu et al. (2011), and (5) the Wesely (1989) scheme, which is still widely used in many air-quality models. All algorithms were driven by the consistent on-site meteorology and with the same input parameters (e.g., leaf area index, roughness length, canopy height, etc.). The magnitudes of model uncertainties were quantified, and dominant factors causing the discrepancies were explored.
References:
Flechard et al. (2011): Atmospheric Chemistry and Physics, 11, 2703-2728.
Meyers et al. (1998): Journal of Geophysical Research, 103 (D17), 22645-22661.
Pleim and Ran (2011): Atmosphere 2011, 2(3), 271-302.
Schwede et al. (2011): Atmospheric Environment, 45, 1337-1346.
Wesely (1989): Atmospheric Environment, 23, 1293–1304.
Wu et al. (2011): Atmospheric Environment, 45, 2663–2674.
Zhang et al. (2003): Atmospheric Chemistry Physics 3, 2067-2082.
1Air Quality Research Division, Science and Technology Branch, Environment and Climate Change Canada, zhiyong.wu@canada.ca 2Atmospheric Modeling and Analysis Division, National Exposure Research Laboratory, US Environmental , Schwede.Donna@epa.gov 3Air Quality Research Division, Science and Technology Branch, Environment and Climate Change Canada, robert.vet@canada.ca 4Air Quality Research Division, Science and Technology Branch, Environment and Climate Change Canada, mike.shaw@canada.ca 5Air Quality Research Division, Science and Technology Branch, Environment and Climate Change Canada, ralf.staebler@canada.ca 6Air Quality Research Division, Science and Technology Branch, Environment and Climate Change Canada, leiming.zhang@canada.ca