1. Introduction

[2] Aeolian processes dominate the current surface environment on Mars [Leovy, 2002; Armstrong and Leovy, 2005]. Evidence of recent dust lifting, transport and deposition is abundant through observations of atmospheric dust opacity [Smith, 2004], dust devil tracks [Fisher et al., 2005] and albedo [Christensen, 1988; Smith, 2004; Kahre et al., 2005a].

[3] Airborne dust plays a critical role in the thermal and dynamical state of the Martian atmosphere. Suspended dust particles absorb solar radiation and absorb and emit infrared radiation. Atmospheric heating rates are strongly affected by atmospheric dust [Gierasch and Goody, 1968]. Atmospheric heating drives dynamical processes; thus the atmospheric dust load strongly influences the circulation [Haberle et al., 1982].

[4] The Martian atmosphere contains dust throughout the year, but the atmospheric dust load varies with season. The peak of the annual atmospheric dust optical depth occurs during southern spring and summer when Mars is near perihelion [Smith, 2004]. During these seasons, regional storms persist throughout the southern hemisphere and northern baroclinic zone. While the dust activity is heightened during southern spring and summer, a background haze of atmospheric dust is maintained throughout northern spring and summer. Mars Global Surveyor (MGS) Mars Orbiter Camera (MOC) observations indicate that small regional dust events persist throughout northern spring and summer, but they are less efficient at increasing the overall atmospheric dust load during northern spring and summer [Cantor et al., 2001]. On the basis of observations [Murphy and Nelli, 2002; Fisher et al., 2005] and general circulation modeling results [Basu et al., 2004], it is likely that dust devils are responsible for the presence of atmospheric dust during these seasons.

[5] Dust devils were first identified in Viking lander meteorology data [Ryan and Lucich, 1983] and Viking Orbiter images [Thomas and Gierasch, 1985]. More recently, dust devils and dust devil tracks have been found to be widespread in Mars Orbiter Camera images [Edgett and Malin, 2000; Balme et al., 2003; Fisher et al., 2005]. Balme et al. [2003] counted dust devil tracks and determined that the quantity of dust lifted by dust devils is insufficient to maintain the background atmospheric dust haze. Fisher et al. [2005] counted both dust devils and dust devil tracks in their survey and reached the conclusion that the quantity of dust lifted by dust devils could maintain the background atmospheric dust haze. Dust devil activity has been found to peak during local summer [Fisher et al., 2005]. Of the nine regions surveyed by Fisher et al. [2005], Amazonis contained the highest frequency of dust devil occurrence. We use these dust devil observations to constrain the dust devil lifting parameterizations that are implemented in the numerical model simulations described in this work.

[6] The observed Martian dust cycle depends upon the availability of mobile dust particles on the surface. Surface dust deposits are known to exist on Mars in at least the three low thermal inertia regions of Arabia, Tharsis, and Elysium [Christensen, 1986]. Based on observations of thermal inertia and rock abundance, these deposits are thought to be between 0.1 and 2 m deep [Christensen, 1986]. Observational estimates of deposition rates in these regions range from a few to almost 50 μm yr−1 and tend to be based on two assumptions: (1) after a global dust storm, dust that is deposited in the northern hemisphere is done so uniformly [Christensen, 1988]; and (2) once dust is deposited in the low thermal inertia regions, it is not subsequently removed [Christensen, 1988]. Christensen [1986] estimated an age of between 105 and 106 years for these regions based on the estimated thickness of the low thermal inertia deposits and an estimated rate of deposition. Since this timescale is similar to the period of orbital oscillations, it was postulated that these deposits are young and exhibit a cyclic behavior of deposition and then deflation [Christensen, 1986].

[7] Investigations of the Martian dust cycle with numerical general circulation models have to date varied greatly in project focus. Newman et al. [2002] focused on present‐day Mars. They studied the effects of implementing fully interactive dust (i.e., dust lifting and radiatively active dust transport) into the Oxford/LMD GCM. Two dust lifting mechanisms were employed: dust devil and wind stress lifting. Simulated wind stress lifting peaked during southern summer. Simulated dust devil lifting peaked during southern summer and had a secondary peak during northern summer.

[8] Haberle et al. [2003] addressed the history of the three low thermal inertia regions by analyzing the pattern of model predicted wind stress lifting over a wide range of obliquity. They found that these regions do not experience large amounts of wind stress deflation at any obliquity. However, their model did not include dust devil lifting or dust transport and deposition. As we will show, dust devil lifting plays a dominant role in determining the net dust gain or loss in these low thermal inertia regions.

[9] Basu et al. [2004] used a fully interactive version of the Geophysical Fluid Dynamics Laboratory (GFDL) GCM to study current‐day Martian climate. Dust devil lifting was shown to be the most likely mechanism for maintaining the background haze of atmospheric dust during northern spring and summer when wind stress lifting is minimal. The relative roles of dust devil and wind stress lifting on the spatial pattern of net dust deflation and deposition was not discussed. Such a discussion is included in this work.

[10] Newman et al. [2005] simulated the dust cycle under a range of orbital configurations with both radiatively active and radiatively inert (i.e., passive) dust transport. While dust devil lifting was included in their passive simulations, it was not included in their radiatively active simulations. On the basis of a passive simulation, they found the current dust deposition rate in one of the low thermal inertia continents, Arabia, to be approximately 1.5 μm yr−1. In this work, simulations that include radiatively active dust transport are used to study the net dust deposition/deflation rates in these low thermal inertia regions.

[11] The goal of this work is to increase our understanding of the physical processes that control the current Martian dust cycle and the depth evolution of the dust deposits in the low thermal inertia continents. Wind stress and dust devil lifting parameterizations are included in the NASA Ames Mars general circulation model to explore the respective roles of these dust lifting mechanisms on the dust cycle and on the spatial pattern of net surface dust deflation and deposition. We present a “best fit” baseline simulation that most closely reproduces available observations of atmospheric opacity, wind stress dust lifting events, and atmospheric temperatures during a nonglobal dust storm year. However, since many uncertainties exist in our understanding of the physical processes that drive the Martian dust cycle, we recognize that our baseline simulation does not capture all aspects of the “real” Mars. Therefore a large range of dust lifting parameters have been implemented in order to understand the model sensitivities upon the pattern of net surface dust deflation and deposition.

[12] The simulations presented here do not reproduce the interannual variability of global dust storms. We do not believe that the physical processes that drive interannual variability are well understood. Basu et al. [2004] simulate interannual variability with high wind stress lifting thresholds. As we show in section 4.2, when comparably high wind stress thresholds are implemented into the NASA Ames GCM, the spatial extent of observed dust lifting events [Cantor et al., 2001] is not well reproduced. Additionally, little discussion of the nature of the simulated interannual variability is presented by Basu et al. [2004], making direct comparisons between their results and ours difficult. Therefore we have chosen to not focus on interannual variability in this current work.

[13] Each simulation presented will be given an identifier; details concerning all simulations are listed in Table 1. Section 2 describes the model and the treatment of dust in the model. Section 3 presents the results from the baseline simulation which will be shown to be the best fit to the observed dust cycle. Multiple dust lifting schemes and their associated parameters will be explored in section 4. Section 5 describes the results of sensitivity studies performed to determine the robustness of the spatial pattern of net surface dust deposition and deflation.