Evaluation and Intercomparison of Five North American Dry Deposition Algorithms at a Mixed Forest Site

• Main Authors: Zhiyong Wu, Donna B. Schwede, Robert Vet, John T. Walker, Mike Shaw, Ralf Staebler, Leiming Zhang
• Format: Article
• Language: English
• Published: American Geophysical Union (AGU) 2018-07-01
• Series: Journal of Advances in Modeling Earth Systems
• Subjects: dry deposition algorithms; model intercomparison; surface resistance; ozone; sulfur dioxide; ensemble modeling
• Online Access: https://doi.org/10.1029/2017MS001231

Abstract To quantify differences between dry deposition algorithms commonly used in North America, five models were selected to calculate dry deposition velocity (Vd) for O3 and SO2 over a temperate mixed forest in southern Ontario, Canada, where a 5‐year flux database had previously been developed. The models performed better in summer than in winter with correlation coefficients for hourly Vd between models and measurements being approximately 0.6 and 0.3, respectively. Differences in mean Vd values between models were on the order of a factor of 2 in both summer and winter. All models produced lower Vd values than the measurements of O3 in summer and SO2 in summer and winter, although the measured Vd may be biased. There was not a consistent tendency in the models to overpredict or underpredict for O3 in winter. Several models produced magnitudes of the diel variation of Vd (O3) comparable to the measurements, while all models produced slightly smaller diel variations than the measurements of Vd (SO2) in summer. A few models produced larger diel variations than the measurements of Vd for O3 and SO2 in winter. Model differences were mainly due to different surface resistance parameterizations for stomatal and nonstomatal uptake pathways, while differences in aerodynamic and quasi‐laminar resistances played only a minor role. It is recommended to use ensemble modeling results for ecosystem impact assessment studies, which provides mean values of all the used models and thus can avoid too much overestimations or underestimations.