2019) The particle dry deposition component of total deposition from air quality models: right, wrong or uncertain?, Tellus B: Chemical and Physical Meteorology, 71:1, 1550324, ABSTRACT Dry deposition is an important loss process for atmospheric particles and can be a significant part of total deposition estimates calculated for critical loads analyses. However, algorithms used in large-scale air quality and atmospheric chemistry models to predict particle deposition velocity as a function of particle size are highly uncertain. Many of these algorithms, although derived from a common heritage, predict vastly different particle deposition velocities for a given particle diameter even under identical environmental conditions for major land use classes. Even more problematic, for vegetated landscapes (forests, in particular) the algorithms do not agree very well with available measurements. In this work, we perform a sensitivity study to estimate how significant the uncertainties in particle deposition algorithms may be in an air quality model's predictions of ground-level fine particle concentrations, particle deposition and overall total deposition of nitrogen and sulfur. Our results suggest that fine particle concentration predictions at the surface may vary by 5-15% depending on the choice of particle deposition velocity algorithm, while particle dry deposition is affected to a much greater extent with differences among algorithms >200%. Moreover, if accumulation mode particle dry deposition measurements over forests are correct, then dry particle deposition and total elemental deposition to these landscapes may be much larger than is typically simulated by current air quality and atmospheric chemistry models, calling into question commonly available estimates of total deposition and their use in critical loads analyses. Since accurate predictions of atmospheric particle concentrations and deposition are critically important for future air quality, weather and climate models and management of pollutant deposition to sensitive ecosystems, an investment in new dry deposition measurements in conjunction with integrated modelling efforts seems not only justified but vitally necessary to advance and improve the treatment of particle dry deposition processes in atmospheric models.

Abstract. This study applies the Gridpoint Statistical Interpolation (GSI) 3D-Var assimilation tool originally developed by the National Centers for Environmental Prediction (NCEP), to improve surface PM 2.5 predictions over the contiguous United States (CONUS) by assimilating aerosol optical depth (AOD) and surface PM 2.5 in version 5.1 of the Community Multi-scale Air Quality (CMAQ) modeling system. An optimal interpolation (OI) method implemented earlier (Tang et al., 2015) for the CMAQ modeling system is also tested for the same period (July 2011) over the same CONUS. Both GSI and OI methods assimilate surface PM 2.5 observations at 00:00, 06:00, 12:00 and 18:00 UTC, and MODIS AOD at 18:00 UTC. The assimilations of observations using both GSI and OI generally help reduce the prediction biases and improve correlation between model predictions and observations. In the GSI experiments, assimilation of surface PM 2.5 (particle matter with diameter < 2.5 µm) leads to stronger increments in surface PM 2.5 compared to its MODIS AOD assimilation at the 550 nm wavelength. In contrast, we find a stronger OI impact of the MODIS AOD on surface aerosols at 18:00 UTC compared to the surface PM 2.5 OI method. GSI produces smoother result and yields overall better correlation coefficient and root mean squared error (RMSE). It should be noted that the 3D-Var and OI methods used here have several big differences besides the data assimilation schemes. For instance, the OI uses relatively big model uncertainties, which helps yield smaller mean biases, but sometimes causes the RMSE to increase. We also examine and discuss the sensitivity of the assimilation experiments' results to the AOD forward operators.