Improved Model Estimates of Desert Dust Aerosol Surface Concentration

Desert dust aerosols are the most abundant aerosol by mass in the atmosphere, and fine-mode aerosol particles (diameter ≤ 2.5µm) have significant impacts on air quality and human health. Observational measurements of surface dust concentration are sparse. Therefore, simulations from global climate and chemical transport models provide a useful alternative method to obtain surface concentration estimates in regions where observations are not available. However, models have difficulty simulating accurate dust surface concentrations, in part due to an inaccurate representation of dust particle size distribution at each stage of the dust cycle. A recent study developed an analytical framework that constrains dust particle size distribution using model, observation, and experimental datasets. Here, we use the new analytical framework to develop a correction method for the model surface dust concentration that accounts for the bias in dust particle size distributions. To evaluate the accuracy of the bias correction, we compare two independent measurement datasets with the bias corrected dust surface concentrations. The two independent measurements are (1) surface dust concentrations taken during the Atmosphere-Ocean Chemistry Experiment and the sea-air exchange program, and (2) PM_2.5 measurements over dust dominated regions that are reported elsewhere in literature. Preliminary results show that bias corrected surface dust concentrations are in better agreement with observational datasets than the original model estimates. As such, the new bias corrected dataset has the potential to provide a more accurate estimation of regional dust surface concentrations, especially over regions where observations are unavailable. Therefore, it can be used to better understand the spatial distribution of surface dust concentrations and more accurately predict the effects dust aerosols have on human health.