We present investigations of the interactions between surface dust reservoirs on Mars and the observed Martian dust cycle. Both observational and numerical modeling techniques are utilized to study the effects of surface dust reservoirs on the Martian dust cycle, the evolution of these surface dust reservoirs in response to the dust cycle, the thermodynamic response to the redistribution of surface dust; and the role of dust lifting mechanisms on the growth/depletion of these surface dust reservoirs and the observed dust cycle. A version of the NASA Ames General Circulation Model (GCM) that includes the lifting (due to wind stress and dust devils), transport, and sedimentation of radiatively active dust is the main numerical tool used in this study. Simulated results indicate that the three low thermal inertia regions (Arabia, Tharsis, and Elysium) are not currently net accumulation regions. In fact, these regions could be weak net dust deflation regions. This simulated deflation is due to parameterized dust devil lifting. The dust devil threshold independent dust lifting scheme predicts dust devil lifting that is spatially and temporally consistent with dust devil observations. A surface wind stress lifting threshold of 22.5 mN m-2 and the KMH stress dependent dust lifting parameterization produces a spatial pattern of lifting much more consistent with observations than do results for smaller or larger threshold values. When exhaustable surface dust reservoirs are implemented in the model, the resulting simulated dust cycle is not consistent with the observed average dust cycle. This suggests that the redistribution of dust on multi-annual timescales does not drive the interannual variability of global dust storms. Changes in the surface dust distribution (as quantified through albedo pattern changes) due to a global dust storm can be monitored with Earth-based telescopes; adaptive optics capability extends the observing season. These changes are possibly thermodynamically important, which argues for the inclusion of temporally varying surface properties in future numerical simulations.
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