Aerosol and Clouds
Simulation of absorbing aerosol indices for African dust
Article first published online: 1 JUN 2005
Copyright 2005 by the American Geophysical Union.
Journal of Geophysical Research: Atmospheres (1984–2012)
Volume 110, Issue D18, 27 September 2005
How to Cite
2005), Simulation of absorbing aerosol indices for African dust, J. Geophys. Res., 110, D18S17, doi:10.1029/2004JD005276., , , and (
- Issue published online: 1 JUN 2005
- Article first published online: 1 JUN 2005
- Manuscript Accepted: 27 JAN 2005
- Manuscript Revised: 15 JAN 2005
- Manuscript Received: 25 JUL 2004
- African dust;
 It has been speculated that the vegetation change and human land use have modulated the dust sources in North Africa and contributed to the observed increase of desert dust since 1960s. However, the roles of surface disturbances on dust generation are not well constrained because of limitations in the available data and models. This study addresses this issue by simulating the Total Ozone Mapping Spectrometer (TOMS) Absorbing Aerosol Indices (AAIs) for model-predicted dust and comparing them with the observations. Model simulations are conducted for natural topographic depression sources with and without adding sources due to vegetation change and cultivation over North Africa. The simulated AAIs capture the previously reported properties of TOMS AAI as well as observed magnitude and spatial distribution reasonably well, although there are some important disagreements with observations. Statistical analyses of spatial and temporal patterns of simulated AAI suggest that simulations using only the natural topographic source capture the observed patterns better than those using 50% of surface disturbance sources. The AAI gradients between Sahara (north) and Sahel (south) suggest that the best mixture of surface disturbance sources is 20–25%, while spatial and temporal correlations suggest that the optimum mixture is 0–15% with the upper bound of 25–40%. However, sensitivity studies show that uncertainties associated with meteorology and source parameterization are large and may undermine the findings derived from the simulations. Additional uncertainties will arise because of model errors in sources, transport, and deposition. Such uncertainties in the model simulations need to be reduced in order to constrain the roles of different types of dust sources better using AAI simulation.