An intercomparison of the deposition models used in the CASTNET and CAPMoN networks

To assess long-term trends in atmospheric deposition, the U.S. operates the Clean Air Status and Trends Network (CASTNET) and Canada operates the Canadian Air and Precipitation Monitoring Network (CAPMoN). Both networks use modeled dry deposition velocities and measured atmospheric concentrations to compute estimates of dry deposition. While concentration measurements from the two networks are comparable, flux estimates can be significantly different due to differences in the model-estimated dry deposition velocities. This study intercompares the dry deposition velocity models used by the networks to identify those model inputs and model algorithms that are responsible for the differences in the dry deposition velocity predictions of the gaseous trace species ozone (O3), sulfur dioxide (SO2), and nitric acid (HNO3). The Big-Leaf Model (BLM) used for CAPMoN was inserted into the CASTNET modeling framework so that the on-site meteorological data obtained at the CASTNET sites could be used as input to both models. The models were run for four CASTNET sites that spanned different land use types and climatologies. The models were incrementally modified to assess the impacts of algorithmic differences on the predicted deposition velocities. While differences in aerodynamic resistance between the models contributed strongly to differences in predicted dry deposition velocities for HNO3, it is the non-stomatal (ground and cuticle) resistance parameterizations that cause the largest differences for other chemical species. The study points to the need for further consideration of these resistances. Additionally, comparisons of both models against recent independent flux data are needed to assess the accuracy of the models.