[1] Predicting mineral aerosol distributions is a difficult task due to the episodic nature of the sources and transport. Here we show comparisons between a 22-year simulation of mineral aerosols and satellite and in situ observations. Our results suggest that the model does a good job of predicting atmospheric mineral aerosol distributions, with some discrepancies. In addition, there are differences between our model results and previously published results [e.g., Ginoux et al., 2001]. We conduct several tests of the sensitivity of mineral aerosol simulations to the meteorological data sets and mobilization parameterizations in order to understand the differences. Comparisons between model simulations using National Center for Atmospheric Research/National Center for Environmental Prediction (NCEP/NCAR) and National Aeronautics and Space Administration Data Assimilation Office (NASA DAO) reanalysis data sets show that the model results with the two data sets are fairly consistent but with some important differences. The sensitivity analysis shows that differences between simulated dust near Australia are likely due to differences in both source parameterization and surface winds. Differences over East Asia are dominated by differences in meteorology. The sensitivity analysis also shows that we cannot tell from comparisons with observations whether the cultivation source is active nor eliminate it because of the large uncertainty in meteorology and source parameterization.

[1] Mineral aerosols are important atmospheric constituents owing to their interactions with climate and biogeochemistry. The interannual variability in atmospheric mineral aerosols is evaluated using a model simulation of 1979-2000 and mineral aerosol observations. Overall, the variability in monthly means between different years is not as large as the variability within a month for column amount, surface concentration, and deposition fluxes. The magnitude of the variability predicted in the model varies spatially and appears similar to that seen in the available observations, although the model is not always able to simulate observed high-and low-dust years. The area over which the interannual variability in the observing station data should be representative is estimated in the model simulation and is shown to be regional in extent. However, correlations between modeled surface concentrations at the stations and modeled deposition in the surrounding region is often low, suggesting that the observations of the variability of surface concentrations are difficult to extrapolate to variability in regional deposition fluxes. The correlations between modeled monthly mean optical depth and modeled deposition or mobilization are low to moderate (0.2-0.6) over much of the globe, indicating the difficulty of estimating mobilization or deposition fluxes from satellite retrievals of optical depth. In both the model and observations there are relationships between climate indices (e.g., North Atlantic Oscillation, El Niño, and Pacific Decadal Oscillation) and dust, although a 22-year simulation is not long enough to well characterize this relationship. In this model, simulation of 1979-2000, dust concentration variability appears to be dominated by transport variability and/or transport and source covariance rather than source strength variability.

[1] Atmospheric mineral aerosols influence climate and biogeochemistry, and thus understanding the impact of humans on mineral aerosols is important. Our longest continuous record of in situ atmospheric desert dust measurements comes from Barbados, which shows fluctuations of a factor of 4 in surface mass concentrations between the 1960s and the 1980s [Prospero and Nees, 1986]. Understanding fluctuations this large should help us understand how natural and anthropogenic factors change mineral aerosol sources, transport, distributions, and deposition, although we are limited in our ability to interpret the results as there is a quantitative record only at one location. We test the hypothesis that dry topographic lows (and not disturbed sources such as cultivated areas or new desert regions) are the sources of desert dust, using a hierarchy of models as well meteorological data sets to look at decadal scale changes in the North Atlantic desert dust. We find that the inclusion of a disturbed source improves our simulations in many (but not all) comparisons. Unfortunately, we are severely limited by the accuracy of the available data sets and models in making definitive statements about the role of disturbed sources or anthropogenic activity in changing the atmospheric desert dust cycle. Processes that might change the size or intensity of desert dust sources in North Africa (such as new sources due to desertification or land use) may be difficult to distinguish from topographic low sources in models due to their similar geographical locations and impact on atmospheric aerosol distributions.