Recent field observations demonstrate that a significant discrepancy exists between models and measurements of large dust aerosol particles at remote sites. We assess the fraction of this bias explained by assumptions involving four different dust production processes. These include dust source size distribution (constant or dynamically changing according to saltation and sandblasting theory), wind speed distributions (using mean wind or a probability density function (PDF)), parent soil aggregate size distribution, and the discretization (number of bins) in the dust size distribution. The Dust Entrainment and Deposition global model is used to simulate the measurements from the Puerto Rican Dust Experiment (PRIDE) (2000). Using wind speed PDFs from observed National Centers for Environmental Prediction winds results in small changes in downwind size distribution for the production which neglects sandblasting, but it results in significant changes when production includes sandblasting. Saltation-sandblasting generally produces more large dust particles than schemes which neglect sandblasting. Parent soil aggregate size distribution is an important factor when calculating size-distributed dust emissions. Changing from a soil with large grains to a soil with smaller grains increases by 50% the fraction of large aerosols (D >5 μm) modeled at Puerto Rico. Assuming that the coarse medium sand typical of West Africa dominates all source regions produces the best agreement with PRIDE observations.

The dominant process in producing fine dust aerosols during saltation is thought to be sandblasting. Recent studies claim that due to competing physical processes, emission efficiencies of dust aerosols oscillate with increasing wind friction speed. These oscillations can result in order of magnitude changes in dust mass emissions. Our work shows that emission efficiencies, and hence emissions of dust aerosols are smooth functions of the wind friction speed for natural soil size distributions. This rules out oscillations as an explanation for scatter in experimental data. We show and explain the reasons for the oscillations.

Global atmospheric dust is simulated using the Dust Entrainment and Deposition (DEAD) model in combination with the global-scale Oslo chemical transport model CTM2 using meteorological data for 1996. Dust sources are calculated using both mean wind speeds with model resolution T63 and subgrid wind speeds. Different data sets are used to describe soil erodibility. We explain how the different assumptions about dust production affect atmospheric dust burden and deposition. Some aspects of the annual dust cycle, such as the east Asian dust emissions, are largely dependent on the data used to determine soil erodibility. Other aspects, such as the timing of the maximum in the African plume at Northern Hemisphere summer, are well modeled with all data sets applied here. We show that the daily variation in optical depth at Cape Verde on the west coast of Africa is well simulated when we assume that erodibility is correlated with surface reflectivity from Moderate-Resolution Imaging Spetroradiometer (MODIS) satellite data. Using a subgrid probability density function of wind speed to drive the dust sources facilitates dust emissions in areas with low wind speeds. Dust concentrations in remote areas are sensitive to the parameterization of wet deposition. Our results point out the need for a detailed soil erodibility data set for global dust modeling, and they suggest that surface reflectivity is potentially valuable for producing or evaluating such data sets.