Abstract. Dust aerosol plays an important role in the radiative budget and hydrological cycle, but large uncertainties remain for simulating dust emission and dry deposition processes in models. In this study, we investigated dust simulation sensitivity to two dust emission schemes and three dry deposition schemes for a severe dust storm during May 2017 over East Asia using the Weather Research and Forecasting model coupled with chemistry (WRF-Chem). Results showed that simulated dust loading is very sensitive to different dry deposition schemes, with the relative difference in dust loading using different dry deposition schemes ranging from 20 %–116 %. Two dust emission schemes are found to produce significantly different spatial distributions of dust loading. The difference in dry deposition velocity in different dry deposition schemes comes from the parameterization of collection efficiency from impaction and rebound effect. An optimal combination of dry deposition scheme and dust emission scheme has been identified to best simulate the dust storm in comparison with observation. The optimal dry deposition scheme accounts for the rebound effect and its collection efficiency from impaction changes with the land use categories and therefore has a better physical treatment of dry deposition velocity. Our results highlight the importance of dry deposition schemes for dust simulation.

The radiative forcing associated with aerosol-cloud interactions, traditionally referred to as aerosol indirect effects, indirectly by modifying the microphysical properties of clouds, affecting their reflectivity and persistence, contributes the largest uncertainty to total radiative forcing estimates (Boucher et al., 2013). For liquid clouds, reducing droplet size and increasing reflectance of clouds due to increased droplet number for a constant liquid water path, namely the "Twomey" effect (Twomey, 1977), is relatively well understood (Christensen et al., 2020; Diamond et al., 2020; Liu & Li, 2019). However, aerosol effects on the amount of boundary layer clouds that cover large areas of the oceans and strongly reflect incoming solar radiation are still not well-documented (Bellouin et al., 2020), especially the magnitude of the aerosol influence on cloud fraction (CF) (Ghan et al., 2016; Gryspeerdt et al., 2016). Understanding how aerosols affect cloud cover helps to reduce the considerable uncertainty of the radiative forcing associated with aerosol-cloud interactions (Fan et al., 2016) because of the strong correlation of CF to other cloud properties and their large impact on radiation. The long-term satellite observations provide excellent opportunities for quantifying cloud-mediated aerosol radiative effects (

Large uncertainties remain in the key physical processes associated with aerosol-cloud interactions (ACI) in models. With the help of A-Train satellite observations, the Weather Research and Forecasting Model with chemistry (WRF-Chem) model with two microphysical schemes, Morrison (MOR) and Lin (LIN), is evaluated by quantifying the susceptibilities of cloud properties, precipitation characteristics, and warm rain process to aerosols for marine stratocumulus over the Southeast Pacific. We reduced the meteorological control on clouds by stratifying them using cloud geometric thickness. Our results show that while the cloud fraction increases with increasing cloud droplet number concentration (N d) in observation and simulations, the susceptibility of cloud fraction to N d in simulations are only half of that in the observation. The cloud liquid water path increases with N d in simulations but decreases slightly in the observation. Compared with the observations, the warm rain in WRF-Chem simulations is generally less suppressed by aerosols, and it initiates at a much smaller cloud droplet effective radius (R e). The conversion from cloud to rain is substantially faster in simulations compared to satellite observations. The conversion rate accelerates at R e ≈ 13 μm in observations and at R e ≈ 9 μm in simulations.

Abstract. Dust aerosol plays an important role in the radiative budget and hydrological cycle, but large uncertainties remain for simulating dust emission and dry deposition processes in models. In this study, we investigated dust simulation sensitivity to two dust emission schemes and three dry deposition schemes using Weather Research and Forecasting model coupled with chemistry (WRF-Chem). Results showed that simulated dust loading is very sensitive to different dry deposition schemes, with the relative difference of dust loading using different dry deposition schemes range from 20 %–116 %. Two dust emission schemes are found to produce significantly different spatial distribution of dust loading. The difference of dry deposition velocity in different dry deposition schemes comes from the parameterization of collection efficiency from impaction and rebound effect. An optimal combination of dry deposition scheme and dust emission scheme has been identified to best simulate the dust storm in comparison with observation and to include better physical treatment of dust emission and surface collection processes. The optimal dry deposition scheme accounts for the rebound effect and the collection efficiency from impaction changes with the land use categories and therefore has a better physical treatment of dry deposition velocity. Our results highlight the importance of dry deposition schemes for dust simulation.

Cloud droplet number concentration (N d ) plays an essential role in understanding cloud physics and quantifying the effective radiative forcing associated with aerosol-cloud interactions. N d is the bridge intimately connecting aerosols and cloud properties. Changes in aerosols, including the aerosol number and physicochemical properties, modify cloud microphysical and macrophysical properties for a given dynamical condition. Specifically, Twomey (1977) pointed out that more (larger N d ) and smaller droplets caused by more aerosols increases the cloud albedo under a constant liquid water path. More (larger N d ) and smaller droplets slow down the collision-coalescence process (Rosenfeld et al., 2012), hence increasing the cloud lifetime (Albrecht, 1989).