Identifying global dust source areas using high-resolution land surface form

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1 Click Here for Full Article JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 113,, doi: /2008jd010195, 2008 Identifying global dust source areas using high-resolution land surface form Charles D. Koven 1,2 and Inez Fung 3 Received 31 March 2008; revised 11 July 2008; accepted 30 July 2008; published 20 November [1] Dust originates from specific, landscape-scale (1 10 km) features; however, dust erodibility is commonly parameterized in global climate models using grid-cell-scale (1 5 ) relationships. We examine surface characteristics from the high- resolution (90 m at equator) global Shuttle Radar Topography Mission digital elevation model and aerosol optical thickness (AOT) measurements from the Multiangle Imaging Spectroradiometer (MISR) instrument to find landscape-scale characteristics common to dust producing regions. Regions with climatologically high aerosol optical thickness are associated with extremely low-slope landscapes at 5 km scale that also have high-elevation variance, likely due to aeolian features. We refer to these landscape characteristics as levelness and residual landscape roughness, and we extrapolate these relationships globally to identify geomorphologically based dust erodibility functions on the basis of levelness and residual landscape roughness criteria. We test these with the Dust Entrainment and Deposition model embedded in the Model for Atmospheric Transport and Chemistry global atmospheric tracer transport model, driven by reanalysis meteorology. The modeled global dust spatial distributions calculated with these erodibility parameters agree well with observations of MISR AOT and deposition and in the modeling framework used here represent an improvement over existing parameterizations in predicting the spatial patterns of dust sources in the Sahara and the relative amount of emission of sources in the Dust Belt to those in the Southern Hemisphere; however, agreement with observations is less good in high-relief areas such as the Asian dust source regions, possibly because of the coarse resolution of the meteorological fields used to drive the model. Citation: Koven, C. D., and I. Fung (2008), Identifying global dust source areas using high-resolution land surface form, J. Geophys. Res., 113,, doi: /2008jd Introduction [2] Mineral dust blown from bare soils is an important constituent of the Earth s atmosphere. Dust has a strong radiative effect on climate [Intergovernmental Panel on Climate Change, 2007], and, because it both reflects and absorbs shortwave and longwave radiation, changes both the total atmospheric column radiation gain and the vertical profile of radiative heating. The basic mechanism of dust emission is well understood: wind stresses impart momentum onto sand-sized particles, which then saltate and release dust through sandblasting [Gillette, 1978; Marticorena and Bergametti, 1995]. In addition to knowledge of atmospheric winds, modeling dust emission requires knowledge of soil properties, such as surface roughness height, saltation 1 Department of Environmental Science, Policy and Management and Berkeley Atmospheric Sciences Center, University of California, Berkeley, California, USA. 2 Now at Laboratoire des Sciences du Climat et de l Environnement, Gif-sur-Yvette, France. 3 Department of Earth and Planetary Sciences and Berkeley Atmospheric Sciences Center, University of California, Berkeley, California, USA. Copyright 2008 by the American Geophysical Union /08/2008JD010195$09.00 threshold friction velocity, and the ratio of vertical dust flux to horizontal sand saltation flux, for which global data sets are inadequate. A recent attempt to constrain the global dust cycle shows large uncertainty in the global dust budget, much of which results from uncertainties in quantification of the landscape characteristics that determine geographic variability in dust production [Cakmur et al., 2006]. In order to model future or past changes to the global dust cycle, it is critical to accurately locate dust source regions, so that changes in the climate or land surface can accurately project onto changes in atmospheric dust concentration. [3] Fundamentally, the problem of dust sources is one of sediment supply and thus suggests a critical role for geomorphology. While the basic characteristics of dust producing areas are qualitatively well understood to be regions where abundant alluvial sediments have accumulated in topographical minima [Prospero et al., 2002], a diversity of methods for quantifying these ideas leads to different geographical emphasis in modeled dust emissions [Ginoux et al., 2001; Tegen et al., 2002; Zender et al., 2003b; Grini et al., 2005; Cakmur et al., 2006]. In addition, there are large uncertainties because of subgrid-scale wind variation [Cakmur et al., 2004; Bouet et al., 2007]. In this paper, we focus on the geomorphic setting of dust sources, using the 1of19

2 Shuttle Radar Topography Mission (SRTM) data set [Farr and Kobrick, 2000; Rodríguez et al., 2005], which is a digital elevation model (DEM) that combines high resolution with global coverage and uniform observation methodology. DEMs are promising for the purpose of identifying dust source areas because elevation is a landscape characteristic that is relatively invariant with time, and less sensitive to ephemeral conditions such as vegetation than other remotely sensed surface properties such as reflectance. [4] Satellite observations (e.g., MODIS at native resolution) show that dust often arises as distinct plumes from specific landscapes at the scale of 1 10 km; these dust hot spots were noted and discussed by Gillette [1999]. We postulate that analysis of land surface form at this scale can be used to identify highly erodible regions. In particular, we hypothesize that low-slope landscapes are dust sources because they are low-energy depositional environments where fine sediments are likely to accumulate or have accumulated during prior climates. We also look for evidence of aeolian activity from the DEMs, as aeolian landscapes (e.g., dunes) are associated with the conditions needed for dust emission: dry, windy, with abundant free sediment for saltation Prior Studies [5] Prospero et al. [2002] describe many of the dust source regions, which they identify by the frequency of Total Ozone Mapping Spectrometer Aerosol Index (TOMS-AI) measurements being above a set threshold value. They point out that many dust source areas are related to fine-scale surface features and are located in topographic minima, and they suggest that alluvial and lacustrine sediments are responsible for dust production. They also point out a large-scale Dust Belt of sources that extends from the Sahara through the Middle East and Central Asia to China. In the Sahara, they describe a main source in a region known as the Bodélé Basin that is active throughout the year; they also note several smaller dust source regions. Outside of the Sahara but within their Dust Belt they identify regions in Iran, Arabia, near the Caspian and Aral Seas, and the Takla Makan desert of China. Outside of the Dust Belt they identify source regions in Patagonia, northern Botswana/Namibia, the Lake Eyre Basin in Australia, the Great Salt Lake in the US, and the Grand Desierto in northwestern Mexico. [6] Several researchers have used the basic relationships described by Prospero et al. [2002] as the basis of a dust erodibility parameterization with which to resolve the geographic variation in dust sources. Ginoux et al. [2001] argue that low- elevation areas adjacent to mountains are most likely to contain the alluvial and lacustrine sediments that are most effective at generating dust. They describe an empirical function that uses the elevation at 1 degree resolution relative to the elevation of surrounding grid cells to determine how productive dust source areas can be. As we show below, while there is clearly a relationship between topographic minima and dust sources, some strong dust sources such as that found in the southwestern Sahara are not located in coarse-scale topographic minima. [7] Tegen et al. [2002] model dust sources from dry lakebeds by using a water routing model [Coe, 1998] over a DEM to identify basins in which water would collect given unlimited precipitation. The HYDRA model they use has a resolution of 5 0, equivalent to 9 km at the equator. They define as potential paleolakes any regions that are internally drained at that resolution that are not currently lakes. Their preferential source function of potential paleolakes does appear to capture much of the spatial variability in dust source strength; however it is difficult to unambiguously define lakes or paleolakes in DEMs because water routing algorithms can produce anomalous internally drained areas when the slopes of the land surface decreases to the point where noise or unresolved roughness in the DEM approaches the actual relief [Coe, 1998]; these can be hand corrected in regions with clearly defined drainage networks, but this is difficult in places where drainage networks are ephemeral, poorly drained, or nonexistent, as is the case in remote desert regions that are the strongest dust sources. [8] Zender et al. [2003b] model several hypothetical dust source functions, using the Dust Entrainment and Deposition (DEAD) [Zender et al., 2003a] model embedded in the Model for Atmospheric Transport and Chemistry (MATCH) [Mahowald et al., 1997]. The DEAD model calculates the horizontal sand and vertical dust fluxes using the empirical equations of Iversen and White [1982] and Marticorena and Bergametti [1995]. One landscape-scale parameter, erodibility remains free, and Zender et al. [2003b] hypothesize four geomorphology-based erodibility fields: Uniform; Topographic (analogous to the S field of Ginoux et al. [2001]); Geomorphologic, where erodibility is set as the number of grid cells upstream of each grid cell; and Hydrologic, where erodibility is set as proportional to the amount of water that flows through a given grid cell, i.e., upstream area multiplied by upstream precipitation runoff. Grini et al. [2005] add to this list with two more hypotheses: a linear albedo hypothesis, where erodibility is hypothesized to be proportional to reflectivity; and an albedo-squared hypothesis, where erodibility is hypothesized to be proportional to the square of reflectivity; the argument behind the albedo parameterizations is that dunes and playas are the highest-albedo soils, and since these are also likely to be the most erodible, albedo is a proxy for erodibility. Zender et al. [2003b] argue that different erodibility factors correlate best with satellite observations in different regions; Grini et al. [2005] argue that the albedo-based erodibility factors are best, particularly in matching the dynamics of the western Saharan plume. [9] Cakmur et al. [2006] test several of the dust source functions proposed so far, using the GISS ModelE GCM [Miller et al., 2006; Schmidt et al., 2006]. Specifically, they compare the elevation [Ginoux et al., 2001], paleolake [Tegen et al., 2002], upstream area [Zender et al., 2003b], and albedo [Grini et al., 2005] erodibility parameterizations with a set of observations that includes AVHRR and TOMS satellites, AERONET ground-based sunphotometers [Holben et al., 1998], surface concentration, and deposition measurements [Ginoux et al., 2001; Kohfeld and Harrison, 2001]. They vary the total emission of dust in two separate size classes to optimize the model results to best match the set of observations, and find that different types of observations are better able to constrain the amount of each particle size class; for example, satellites are most sensitive to fine particles, while AERONET and surface 2of19

3 measurements can better constrain coarser particles and overall size distributions. Noting, as do Luo et al. [2003] and Zender et al. [2003b], the dependence of their results on model resolution and meteorological data sets used, they find best agreement with Ginoux et al. s [2001] source function, although other source functions can better match subsets of the data. In addition, even when constrained by all the available data types, they still find that a large range in the total annual dust flux, between 1000 and 3000 Tg/a (where a is years), is not inconsistent with the available observations Hypotheses [10] Efficient production of dust via the process of saltation requires several conditions be met. A large supply of fine sediment must be available for mobilization: both sand grains to saltate, and finer, silt- to clay-sized particles for long-range transport as dust. This sediment is more easily mobilized when not consolidated into surface crusts [Gillette, 1978], so loose sand deposits should be very efficient at saltating. In order for wind momentum to reach the surface, there needs to be an absence of vegetation. There must also be an absence of coarser gravel- to cobblesized sediment, since such particles both prevent wind momentum from reaching the surface and, over long periods of time, allow arid soils to form desert pavements that cause such surfaces to become net sinks of dust rather than sources [McFadden et al., 1998] in undisturbed landscapes. [11] Soils formed from bedrock or high-energy depositional environments (such as fans) are likely to have large amounts of gravel and cobbles, thus low-energy alluvial and lacustrine deposits are potentially most conducive to dust generation [Callot et al., 2000]. Overland flow energy is primarily related to slope and depth of flow during episodic flow events. Since we do not know what the relevant flows are for these landscapes that have evolved over a range of climatic conditions, flow depth is not a practical variable for a parameterization of dust sources. We therefore focus on slope as a measure of the energy available for aqueous sediment transport, thus the maximum particle size should decrease with decreasing slope. The tendency for alluvial landscapes to have graded profiles, where slope and elevation both decrease as they approach depositional regions would imply that lowest slope areas are also lowest elevation, however we show below that this is not necessarily the case, at least at coarse resolution, for poorly drained desert environments. [12] We hypothesize that low-slope environments are most conducive to dust formation. Low slopes should be associated with low-energy depositional environments such as alluvial and playa sediments, where there are abundant fine sediments for mobilization by the wind. These alluvial sediments may be relicts from past fluvial processes or modern due to episodic ephemeral flows. It is possible that these two categories of sediment behave differently, but we do not try to separate them here except to classify the landscapes on the basis of gross geomorphology. [13] We also hypothesize that landscapes with enough free sediment to form dust should also form aeolian landforms such as dunes [Grini et al., 2005]; thus landscapes whose topography indicates the presence of dunes should also be strong dust sources. We do not argue that all such landscapes produce dust; additional constraints such as vegetation and soil moisture will prevent, for example, paleodunes or vegetated river deltas from being active dust sources in the present climate. [14] The question of whether dunes are important to the dust cycle is an old one. The role of dunes as sources of dust is dismissed by Bagnold [1941], who distinguishes between alluvial dust-producing deserts and sand deserts, arguing that because of the highly sorted nature of dune sands there are too few fine particles for long-range transport. However, there are two ways that dunes could act as strong dust sources: self-abrasion of sand grain weathering rinds or scouring of the underlying surface over which the dunes migrate. Bullard and White [2005] describe experimental evidence for the former, noting that iron oxide content in dune sands decreases after saltation events. Chappell et al. [2008] show field evidence for the latter mechanism at the Bodélé Basin, where quartz dunes migrate over diatomite lake deposits to produce the largest global dust source; they also note that the dunes themselves alter the aerodynamic flow to increase dust emission. [15] We test these hypotheses by examining the slope and landscape-scale roughness of the land surface. We define below two dust erodibility parameterizations on the basis of these landscape-scale topographic data. Here, we define landscape-scale properties as those that appear at a resolution of km. [16] Our dust source parameterizations based on these local properties differ from prior studies in several important ways. The most fundamental is that we focus on topography at a specified landscape-scale intermediate between fieldbased experiments and GCM grid cells. Most prior studies have either tried to extrapolate from aerodynamic measurements made at individual field sites directly to regional or global models, which requires much more information than is available at present, or looked at relationships at the scale of GCM grid cells, for example by considering the relative location of grid cells within larger drainage basins or coarse fluvial networks. We seek to examine the land surface form and define properties at a scale that is intermediate between these two endmembers. Furthermore, there is a mechanistic justification for our arguing that slope should correspond to depositional energy and therefore particle size. While the basic idea that fluvial profiles grade to lower slopes at lower elevations would argue that low-elevation areas are also low slope, this is not necessarily true; some low-elevation areas do not have particularly low slopes, while some low-slope areas lie in large-scale elevation saddle points rather than minima. Our parameterizations do not assume the existence of a robust large-scale fluvial drainage structure, unlike erodibility parameterizations such as upstream area or lake area which implicitly do. This is important because dust emission may arise precisely as a result of the breakdown of normal fluvial processes due to lack of water; we would argue that dust represents a hand off (in space and/or time; possibly episodically) of sediment transport from fluvial to aeolian processes, thus should be strongest where drainage structures are not robust. A practical advantage is that our erodibility indices are relatively invariant to GCM grid resolution, unlike parameterizations that rely on the relation- 3of19

4 ships between individual grid cells, and relatively invariant over time, unlike, e.g., albedo, which can change as function of variables such as soil moisture or vegetation cover that are already explicitly represented in dust models. [17] In section 2 we focus our initial investigation over the Saharan-Arabian region, where the global dust cycle is strongest and most clearly separated from other aerosols and therefore allows the best comparison of landscape characteristics with overlying dust concentrations. We explore the geographic patterns of dust and land surface topography in section 3, and qualitatively compare Multiangle Imaging Spectroradiometer (MISR) and SRTM statistics to suggest mechanistic relationships which we use to define dust erodibility parameters. However, because it is necessary to consider variation in surface wind stresses as well as the influence of advected versus locally produced dust, we cannot quantitatively test these parameters except within a global dust model. In section 4, we apply the erodibility parameters globally, and test our source parameterizations using a dust model embedded in an atmospheric transport model to directly compare modeled vs observed dust. 2. Climatology of Saharan-Arabian Aerosol [18] To identify dust source regions, we first use satellite observations of dust over land. We base our spatial analysis on the Multiangle Imaging Spectroradiometer instrument [Diner et al., 2001; Martonchik et al., 1998, 2002] We use monthly mean data for the period. We choose MISR over other satellite aerosol optical thickness retrievals because MISR is able to detect aerosol over high-albedo desert surfaces which are the most important for dust production; the TOMS instrument can also be used over bright desert regions, but because it uses UV light the retrieval is sensitive to the dust vertical profile [Mahowald and Dufresne, 2004]. We focus on the Saharan-Arabian region because satellite aerosol observations can be most likely determined to be dust there because of the presence of huge dust sources and a clear spatial and seasonal separation of dust from other aerosol sources. [19] We are interested in identifying source regions of dust; for a first estimate we assume that the mean dust burden above a point in the land surface is proportional to its source strength. This clearly is a weak assumption, however, because dust burden is a function of sources, removals, and transport, therefore some areas which do not contribute any dust at all could be difficult to separate from true source areas because they lie downwind of strong source areas. Nonetheless, aerosol maxima should correspond approximately to dust source regions. In addition, dust emission depends on other spatially and time-varying properties such as wind stress and vegetation. Because we focus on the Saharan-Arabian region here, vegetation variability is likely not playing a role. We will directly consider the role of transport and surface wind stress below by quantitatively comparing modeled versus observed dust. [20] Figure 1 shows seasonal mean MISR aerosol optical thickness over the Saharan-Arabian region for the four seasons, averaged over the period During Northern Hemisphere winters (Figure 1a), aerosol optical thickness is highest south of the Sahara because of biomass burning; within the desert, aerosol optical thickness is high northeast of Lake Chad in the Bodélé Basin region [Prospero et al., 2002], as well as in some other spots. Spring and summer (Figures 1b 1c) both show higher aerosol optical thickness in the Sahara than in the Sahel. In addition to the Bodélé Basin dust seen in the winter months, a second source region in the western Sahara, in northern Mali and Mauritania, is clearly visible; this is somewhat broader than the Bodélé Basin plume [Middleton and Goudie, 2001]. Dust activity drops off sharply in the fall months (Figure 1d). In addition to the Saharan sources, a central Arabian dust maxima is also visible, peaking in the summer months. Over the oceans, the broad North Atlantic aerosol plume can be seen to move seasonally as described by Husar et al. [1997]. [21] The shift in aerosol sources with seasons reflects different mechanisms: smoke from biomass burning from the Sahel in the winter and mineral dust from the Sahara in the summer. The dust sources themselves have differing seasonality as well, with the Bodélé Basin more active throughout the year than the Mali/Mauritania source, which is much more active during the summer [Koven and Fung, 2006]. Engelstaedter and Washington [2007] ascribe this seasonality to the strength of turbulence arising from seasonal heating; this is also supported by the modeling study of Cakmur et al. [2004]. [22] In addition to the two major dust sources in the Bodélé Basin and the Mali/Mauritania border, several smaller dust source areas can be identified in the satellite observations, including the Zone of Chotts in Algeria [Mahowald et al., 2003] and several source regions in Libya. 3. Topographical Signatures of Saharan-Arabian Dust Source Areas 3.1. Methods [23] WeusetheSRTM[Farr and Kobrick, 2000; Rodríguez et al., 2005] DEM for our analysis of Saharan-Arabian geomorphology. This DEM was created using synthetic aperture radar during a 10-day space shuttle mission in Prior to this, much of the Sahara was poorly mapped because of its remoteness, and multiple data sets gave conflicting values and sharp discontinuities in surface elevation statistics. The SRTM data set has the advantage that it derives from a single source, has high and well-characterized quality, and covers the entire globe between latitudes 60 N 60 S [Rodríguez et al., 2005, 2006; Guth, 2006]. We use the SRTM3 data, which has a resolution of 3 arc sec, corresponding to 90 m at the equator [Slater et al., 2006]. There are some gaps in coverage over the Sahara due to areas of low backscatter or high local slope [Hall et al., 2005]. [24] To test our hypotheses about surface morphology playing a role on dust emission, we divide the DEM into many small spatial domains or windows, over each of which we fit quadratic surfaces using 2-D quadratic regression [Evans, 1980; Wood, 1996] z ¼ ax 2 þ by 2 þ cxy þ dx þ ey þ f ; ð1þ 4of19

5 Figure 1. MISR seasonal mean aerosol optical thickness for the Saharan-Arabian region for (a) December February, (b) March May, (c) June August, and (d) September November. Boxes in Figure 1c correspond to the sources of the two major summer dust plumes, in the Bodélé Basin and Mali/ Mauritania. The Bodélé plume is much less seasonal than the western Mali/Mauritania plume. where x and y are the latitude and longitude coordinates and a, b, c, d, e, and f are the coefficients of the fit surface for the selected window. We calculate slope, S, as pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi S ¼ arctan d 2 þ e 2 and roughness, R, as the standard deviation of the error between the best fit surface, z, and the actual DEM points, z obs, over the fitting window ð2þ R ¼ rmsðz obs zþ: ð3þ [25] We use quadratic rather than linear surfaces to investigate whether curvature plays a role in dust source geomorphology; also we want to exclude large-scale curvature from our estimate of roughness. The use of quadratic regression surfaces rather than linear has no effect on the slope measurement, which is identical for both methods. As we discuss below, the relevant environments for dust production are extremely low slope, and therefore also low curvature, at scales larger than 1 km, where the difference between linear and quadratic surfaces is negligible. [26] Because landscape properties are scale dependent, we calculate these values over a range of window sizes, from 5 5 pixels ( m) to pixels ( m). We report S in units of radians, and roughness in units of meters. [27] We aggregate from the small fitting windows to the regional and global scales by calculating the mean slope, roughness, elevation, and curvature for averaging windows. Thus we can use the statistics of the landscape on small, local scales, but compare regional and global variations in these landscape-scale properties Morphology of Saharan-Arabian Region and Relationship to Dust Loadings [28] Maps of mean elevation, slope at two resolutions, and roughness from the SRTM data set over the Saharan- Arabian region are shown in Figure 2. Most of the region is relatively low elevation, with only a few mountain ranges (e.g., Atlas, Tibesti, Aıuml;r) extending above 1000 m 5of19

6 Figure 2. Maps of Saharan-Arabian surface form for (a) mean elevation, (b) mean slope over 450 m averaging windows, (c) mean slope over 4500 m averaging windows, and (d) mean residual landscape roughness over 4500 m averaging windows. Dust maxima from Figure 1 show up as as low-slope regions in Figure 2c but as relatively high- slope regions in Figure 2b and high roughness in Figure 2d as comparatively. Boxes correspond to dust plume sources in Figure 1. elevation. At 450 m resolution, much of the region has slopes on the order of 10 2 ; while the coarser resolution slope measurements show several regions with much gentler slope on the order of The boxes identifying the source regions of the two major dust plumes in Figure 1c are also shown in Figure 2c. Both the Mali/Mauritania and Bodélé source regions correspond to broad, low-slope environments at the 4500 m scale; the low-slope area extending south of the Bodélé Basin near Lake Chad is less arid and thus is not expected to be a large dust source in the current climate. In addition, many smaller sources visible in Figure 1c also correspond to low-slope areas in Figure 2c, e.g., the Zone of Chotts, Algeria; and areas in Libya, Saudi Arabia, and Sudan. Roughness measurements appear to closely follow the high-resolution slope measurements. [29] In Figure 3, slope (at two resolutions), roughness, and elevation are plotted against each other with data points colored on the basis of their mean seasonal dust loading. Figure 3a shows the basic relationship in fluvial-dominated landscapes where slope tends to grade down from highelevation, high-slope regions to low-slope, low-elevation regions. Several different gradational trends can be seen: fluvial systems that grade down to their terminations at the coast (i.e., sea level), and fluvial systems that grade down to their terminations in internally drained basins at higher elevations. Comparing the dust loading along these different trends, the higher dust in regions where slope grades to elevation minima above sea level supports the idea of Ginoux et al. [2001] that internally drained basins are important dust sources. Low- elevation regions that are steep are not associated with dust. [30] Figure 3b shows the logarithm of coarse-resolution (4500 m) slope versus roughness over the Saharan-Arabian region. First, a main trend of points extends from steep slope, high-roughness environments to gentle slope, smoother environments. These points all have low dust loadings, suggesting that these upland landscapes are not 6of19

7 dust sources. The main trend stops at a slope of around 0.002; we interpret this to mean a shift from fluvial-erosive landscapes to alluvial/lacustrine-depositional landscapes. [31] At lower slopes and/or higher roughness values than the main trend of landscapes in Figure 3b is a second cluster of points. These low-slope, high-roughness landscapes appear to have higher dust loadings (mean aerosol optical thickness (AOT) 0.5) than the points in the main trend (AOT < 0.5). We interpret these as being aeolian landscapes, with the additional roughness arising from aeolian landforms such as sand dunes or yardangs, however it is possible that the different relationship between slope and roughness for these points may also be due to differences in lithology. [32] Evidence for the roughness arising from dune-like, periodic landforms can be found in Figure 3c, which shows slope at 4500 m resolution plotted versus slope at 450 m resolution. Again, the same relationship appears as in Figure 3b, where the high-roughness, high-dust region in Figure 3c shows higher slopes at fine resolution than at coarse resolution. The explanation for this is that when the sampling window is large, the surface fitting captures several dunes; because dunes are periodic, the slope ignores the individual dunes and samples the slope of the surrounding landscape while the dunes add to the unresolved roughness after subtracting the fitted surface. As the sampling window shrinks, individual dunes start to influence the slope more so than small-scale features in less periodic landscapes, thus slope increases more in dune landscapes than nondune landscapes. This is why the dust source areas such as the Bodélé appear as low slope at the coarse resolution but not the fine resolution. [33] One possible weakness with our approach is that the error in the SRTM data set is higher in dune landscapes because of extremely low backscatter from dry sand in dunes [Rodríguez et al., 2005, 2006]. This noise may further contribute to roughness in addition to that present from the dunes themselves. However, several observations provide confidence in our method. The first is that the estimate of error used by Rodríguez et al. [2005, 2006] may itself be biased high in the presence of dunes, because it is calculated as residual high-frequency variability after subtracting a smooth background estimate. Second, we see the same pattern in Figure 3b as in Figure 3c; the slopes would not increase in rough regions unless the variation has a correlation length scale on the order of the fine-scale sampling window, as we would expect for dunes, with wavelengths of order km [Breed and Grow, 1979], but not for Figure 3. Plots of surface form in Saharan-Arabian region (30 W 60 E, N); colors indicate the dust overlying each point. (a) Mean slope over 4500 m averaging window versus elevation, (b) mean slope over 4500 m averaging window vresus mean roughness over 4500 m averaging window, and (c) mean slope over 4500 m averaging window versus mean slope over 450 m averaging window. Lines in Figure 3b correspond to the parameterized levelness and residual landscape roughness thresholds described in section 3.3; the solid line corresponds to R 0 in equation (4) and the dashed line corresponds to the slope threshold. 7of19

8 random noise. Third is that our roughness estimates do not show the crosshatching pattern due to variation in sampling frequency shown in the error estimates of Rodríguez et al. [2005, 2006], but instead appear to more closely correspond to actual landscapes. Lastly, our roughness-derived erodibility index (discussed below and shown in Figure 4b) corresponds to the major ergs (dune fields) of the Sahara. [34] Grini et al. [2005] also argue for the importance of sand dunes as dust sources in their numerical experiments using albedo as a proxy for dust erodibility. Sand dunes show the highest nonice albedos in the MODIS land imagery [Tsvetsinskaya et al., 2002], and Grini et al. [2005] show that land surface albedo is a good proxy for sand dunes. Our roughness method is similar, in that it is a proxy for sand dunes; however it is a proxy for the height of dunes rather than the composition of sand and underlying land surface that is captured by albedo. The two methods also appear to produce similar spatial patterns, in particular they show two major centers of Saharan activity (Bodélé and Mali/Mauritania sources) corresponding to the two source regions seen in the satellite data. Field observations suggest a relationship between dune height and sand transport rates [Lancaster, 1988], so it is plausible that the roughness we observe may be more directly related to sandblasting activity than albedo. [35] Another possibility is that dunes do not act as dust sources themselves, but that they reflect instead the presence of available sediment for aeolian transportation. In this scenario, alluvial or lacustrine sources of sediment would be sorted by the wind, with the larger particles only able to move slowly as dunes, while the smaller particles would be available for long-range transport as dust. One way to investigate this would be to look for dunes that have migrated away from currently active dust source regions, rather than lying directly in source regions Construction of a Dust Source Function [36] For modeling purposes, we seek to define an erodibility function [Zender et al., 2003b], which captures the large-scale geographic variation in land surface properties that determine how effective a region is in producing dust. The suggestion in Figure 3b is that areas with low slopes and high roughness relative to slope are associated with high dust burdens. We try to quantify these relationships as separate hypotheses: that dust erodibility is related to the levelness or inverse of the slope (we avoid use of the word flatness here as it connotes both levelness and smoothness, which are separate and different characteristics); and that dust erodibility is related to residual landscape roughness, which reflects the presence of aeolian landforms in desert landscapes. [37] On the basis of the relationships described above, we define levelness-based source erodibility function, L, asthe fraction of land surface with slope below an empirically determined threshold of radians at 4.5 km scale (identified as the vertical dashed line in Figure 3b). This threshold empirically corresponds to the regions identified as dust source areas in the preceding section. A heuristic justification for a threshold is based on the idea that, for a given sediment grain size and flow depth, a minimum slope is required to initiate fluvial sediment transport. Our threshold slope is in agreement with minimum slopes for shallow flows identified by Howard [1980], which we expect to limit hydrologic sediment transport via episodic runoff events in arid environments. An interesting prediction of this slope threshold is that coastal river deltas which are very low slope but not dust sources, e.g., that of the Nile, are not limited by landscape properties in their dust production but instead by vegetation or soil moisture, and thus could become strong dust sources if those conditions changed. [38] We also define a roughness-based source erodibility function, based on the relationships shown in Figure 3b. The DEM data indicates that there is a general relationship between slope and roughness, as defined by the main trend (solid line) in Figure 3b. Furthermore, the dust climatology indicates that dusty areas tend to lie above this main trend, with increasing dustiness the rougher the landscape for a given slope. From this relationship, we define residual landscape roughness to be the distance above the main trend of data R res ¼ log R R 0 * logs; where R res is the residual landscape roughness, R is the roughness, and R 0 is a coefficient for roughness as a function of slope based on least squares fitting of the main trend of data in Figure 3c. Maps of L and R res, for the Sahara and the globe, are shown in Figure 4. [39] The R res statistic also captures the lowest slope regions, because the observed relationship between elevation and roughness in the SRTM data saturates at the lowest slopes; this may represents a change in land surface processes and/or the threshold of error in the SRTM observations. The R res statistic is high for both extremely low-slope environments, as well as those for which high roughness indicates the presence of dunes, thus it captures two separate but important components of dust sources, the presence of both fine particles and saltators. [40] It is important to clarify that the landscape roughness, R and R res we consider here is different from the surface roughness, z 0, used to model boundary layer turbulence. The former is a measure of the height of the landscape surface deviation at the scale of 1 km, whereas the latter is the height of individual roughness elements on the scale of individual plants and soil aggregates, 1 m for the landscapes we are interested in here. Callot et al. [2000] describes the distinction of macroroughness, mesoroughness, and microroughness corresponding to different scale of roughness such as dunes, shrubs, and sand grains, respectively; SRTM can only measure the coarsest macroroughness, while the mesoroughness and microroughness set the aerodynamic criteria of roughness height. The aerodynamic effect of the macroroughness discussed below is not modeled explicitly here, and is instead used as a proxy for erodibility; however, recent measurements suggest that the macro roughness itself may play an important role in the aerodynamics of dune sources such as the Bodélé Basin [Chappell et al., 2008]. [41] For the Saharan-Arabian region, maps of L (Figure 4a) show two strong centers of activity in the Sahara: in the Bodélé Basin and in the Mali/Mauritania plume region. In addition, the Nile and Tigris-Euphrates delta regions show up as potential dust source regions. Outside the Saharan-Arabian ð4þ 8of19

9 Figure 4. Maps of erodibility used to drive the model runs. Vegetated areas masked out using NDVI threshold. The (a) levelness, (b) residual landscape roughness, (c) inverse elevation [Ginoux et al., 2001], (d) albedo [Grini et al., 2005], (e) upstream area [Zender et al., 2003b], and (f) high-resolution upstream area. Because the absolute magnitude of the erodibility is arbitrary, all values here are normalized relative to the maximum value of each field. region, several other low-slope areas appear: in Asia, very level regions include the Indus Valley and Thar desert in the western Indian subcontinent, the Caspian and Aral sea regions of Central Asia, the Takla Makan desert in northwestern China. In sub-saharan Africa, level regions include a broad region in Botswana and Namibia. Much of Australia shows up weakly in the levelness index, with a maximum over the Lake Eyre Basin. In South America, a fluvial system east of the Andes between latitudes 30 and 35 S, (which agrees well with descriptions of dust source areas by 9of19

10 Figure 5. Map of combined global erodibility indices; purple shading is levelness, green shading is residual landscape roughness, and black shading indicates both level and rough. (a) Nondesert regions have been masked using a maximum NDVI threshold of Boxes correspond to known source regions, most of which are in the global Dust Belt: Salt Lake (S.L.), Great Plains (G.P.), Pampas, Senegal (Sen.), Mali/Mauritania (M./M.), Bodélé, Chotts, Libya (Lib.), Iraq, Arabia, Caspian (Casp.), Aral, Thar, Takla Makan, Kalahari, and Lake Eyre (L.E.). (b) Not masked; all potential global sources are shown. Zarate [2003]), as well as small but distinct spots in the Andean Salars. In North America, small dust source areas include the Colorado River delta, the Great Salt Lake desert in Utah, and the dust bowl region of the Southern Great Plains. [42] Maps of R res (Figure 4b) show several areas of roughness in the Saharan-Arabian region that correspond to major ergs. Again, the Bodélé Basin and the Mali/ Mauritania source region appear, as do major ergs in Libya, Algeria, and Egypt. Two main dune areas on the Arabian peninsula appear, as do many ergs in Central Asia and western China. The Lake Eyre Basin appears in Australia appears strongly. [43] We have defined two separate landscape characteristics that we posit to be related to wind erodibility and dust production. Figure 5 shows both erodibility indices combined for comparison of the differences in spatial patterns of levelness and roughness (rough areas are shown in green, level areas are purple, and both rough and level areas are black). To differentiate between current and potential dust source areas, Figure 5a has nondesert regions masked using an NDVI threshold to identify only sources that could contribute dust in the present climate, while Figure 5b has no such mask. There are clear relationships between the low-slope and high-roughness environments in deserts, so that they tend to exist at the same location (shown as black, e.g., Mali/Mauritania source region, Bodélé Basin), or adjacent to each other (adjacent purple and green areas, e.g., Takla Makan Desert, Eastern China deserts). [44] Figure 6 also shows mean total aerosol optical thickness from the MISR satellite, globally, for comparison with dust source areas. Many of the regions identified as erodible by these two parameterizations correspond to maxima in desert aerosol. [45] Figure 5b shows areas with topographic characteristics similar to current dust sources and therefore we suggest they could become dust sources in alternate climate states. The residual landscape roughness statistic seems to give too high values over forested regions, suggesting it is either affected by SRTM elevation offset because of vegetation [Hofton et al., 2006], or else there is a different relationship between slope and roughness in these areas. However, it is likely still useful at desert margin regions that are most susceptible to becoming dust sources. Slope appears to be robust even in the presence of vegetation in predicting areas of alluvial sediment. Notable areas that 10 of 19

11 Figure 6. Global mean MISR aerosol optical thickness. All aerosols, including dust, pollution, and others. In addition to Saharan dust sources, other important dust source regions can be seen in the Takla Makan Desert in western China, along with several smaller deserts to the east. Mixed dust pollution aerosol can be seen in South Asia. have high levelness values in Figure 5b but are masked in Figure 5a are major alluvial deposits such as the Mississippi valley in North America, Ganges-Brahmaputra valley in South Asia, and the Paraná valley in South America. This suggests such areas could potentially be or have been dust sources if the climate were radically drier or if floodplains were abandoned because of river incision following sea level drop. 4. Dust Emission and Transport Model 4.1. Model Setup [46] One goal of this research is to better parameterize the variability in dust source area erodibility for use in climate models. To test our erodibility indices derived in the previous section, we apply them in a global dust model, and compare with observations of dust in the atmosphere. We use the DEAD dust model [Zender et al., 2003a] embedded within the MATCH [Mahowald et al., 1997] transport model. The model uses assimilated meteorology to calculate wind-dependent dust emission for four different size classes of dust, and then calculates transport and removal through wet and dry deposition. We ran the model using NCEP [Kalnay et al., 1996] meteorology for the period We compare monthly mean model results with observations from the MISR satellite, which is operational for the period March 2000 onward. Because the dust acts as a passive tracer in the model, feedbacks between dust and meteorology, which may decrease dust emission [Perlwitz et al., 2001], are not considered. [47] In addition to erodibility and meteorologic data, the DEAD model requires several other input variables. We use the IGBP soil texture map for percent sand and clay [Global Soil Data Task Group, 2000], and Myneni et al. s [1997] AVHRR leaf area index data sets to determine vegetation index. Surface types are based on Olson s [1992] data set transformed onto the LSM [Bonan, 1996] categorization. [48] In addition to the levelness and residual landscape roughness erodibility indices, we also ran the model using several other published erodibility indices for comparison, shown in Figures 4c 4e. We used the inverse elevation erodibility index of Ginoux et al. [2001] as regridded to NCEP resolution by Zender et al. [2003b] (Figure 4c), the linear albedo erodibility index of Grini et al. [2005] (Figure 4d), and the upstream area erodibility index of Zender et al. [2003b] (Figure 4e). Lastly, we also created a second upstream area erodibility on the basis of the highresolution SRTM DEM (Figure 4f). To do this, we calculated flow paths directly on the high-resolution data using the method of Moore et al. [1991]; we then transfer this to the coarse resolution of the transport model by taking the maximum flow accumulation within each grid cell. The purpose of this was to consider the effect of resolution on the alluvial hypothesis of Zender et al. [2003b]; also, the high-resolution flow accumulation data set is in some sense more realistic than the coarse-resolution data set in that it shows the Nile as the dominant river in the Saharan region Comparison of Model Results and Observations [49] Figure 7 shows maps of the annual mean aerosol optical thickness for each of the cases. All the modeled cases show a similar gross geographic pattern, with the majority of global dust transported west from the Sahara over the Atlantic, smaller sources in the Middle East and Asia, and much smaller sources outside of the Dust Belt. To differentiate the cases, we regress the calculated AOT against the MISR monthly mean values, regridded to the T62 NCEP resolution. [50] We use MISR data over Africa as a qualitative motivation for the exploration earlier in the paper; we use the regional and global data here, along with deposition observations, as a quantitative test of our hypothesis. For each run, we optimally estimated a scalar multiplier to yield a slope of one for the best-fit regression line between the modeled and MISR aerosol optical thickness. We make this choice of data sets against which to optimize because MISR has a high and relatively uniform sampling density over dust source regions, and thus contains detailed information on the spatial distribution of dust sources. Other studies have used different optimization algorithms and data sets, e.g., Zender et al. [2003b] use a scalar multiplication factor so that the total annual dust emission is equal to an a priori set value, while Cakmur et al. [2006] separately optimize fine and coarse dust to best fit a diverse set of observations, including satellite, sunphotometer, deposition, and filter measurements. [51] Table 1 shows the MATCH-MISR correlations for the various cases, as well as the implied total magnitude of the dust cycle required to set the regression coefficient to 1 for each case. For each case, we average across the years for both model and data to avoid error due to interannual variability in transport and emission, as well as to gain statistical significance given the relatively long repeat interval and small spatial footprint of the MISR instrument. We regress the modeled dust optical depths against both the climatological summer (JJA) mean values over the Saharan-Arabian region (longitudes 30 W 60 E, latitudes N), as well as against the monthly means for all desert regions (defined as having a maximum NDVI 11 of 19

12 Figure 7. Calculated mean dust optical depth for each of the experimental runs. of less than 0.5 over the observation record) globally. We scale all AOT values, calculated by DEAD at 670 nm, to the MISR wavelength of 550nm, using a bulk angstrom parameter of 0.1 [Holben et al., 2001]. Correlations are generally higher and the implied magnitudes of the global dust cycle are lower for the summer Sahara-Arabian case; this is consistent with the observation that the summer Saharan dust plume is less mixed with other aerosol than desert aerosol generally. [52] For the summer Saharan-Arabian region, the roughness erodibility shows the highest correlation (r 2 = 0.57) to MISR, followed by upstream area (0.53) and levelness (0.52), with lower correlation for the inverse elevation (0.48), albedo (0.45), and high-resolution upstream area (0.23). The implied total magnitude of the dust cycle of the high correlation cases lies in the range Pg/a, which lies well within the range of previous estimates [e.g., Ginoux et al., 2001; Cakmur et al., 2006]. It is difficult to 12 of 19

13 Table 1. Regression Results of Modeled Dust Optical Thickness and MISR Aerosol Optical Thickness for the Period a Sahara JJA Deserts Correlation (r 2 ) Total Dust (Pg/a) Correlation (r 2 ) Total Dust (Pg/a) Levelness (±0.16) (±0.03) Roughness (±0.13) (±0.03) Inverse elevation (±0.19) (±0.04) Albedo (±0.15) (±0.03) Upstream area (±0.18) (±0.05) High-resolution upstream area (±0.46) (±0.08) a First two columns are for summer means over Saharan-Arabian region. Last two columns are the monthly means averaged over all years for desert regions globally. Total budgets are calculated after scaling the total dust cycle so that regression line has slope of one. Results in bold show the highest correlations against the observations. determine the statistical significance of the differences in the r 2 values here, adding to the uncertainty of the analysis. We discuss the spatial patterns of these differences in section 4.3. [53] For the monthly comparison over desert regions globally, the roughness erodibility is again highest (r 2 = 0.516), but close to the levelness (0.511) and albedo erodibility indices (0.50). The inverse elevation and both upstream area indices are all somewhat lower ( ). Totals for the implied magnitude of the dust cycle range from Pg/a for the high correlation cases; again within the range of previous estimates. [54] Figure 7, showing the calculated dust optical thickness, and Figure 8, showing the annual mean difference between modeled dust optical depth and observed MISR total aerosol optical depth, can be used to qualitatively assess the differences between the model runs. Over the Sahara, the levelness, roughness, inverse elevation, and albedo runs all show two main plumes, one in the western Sahara and one in the Bodélé Basin; the comparison with MISR shows that all overestimate the strength of the western plume relative to the Bodélé plume. In addition, they locate the origins of the plumes in slightly different places: the inverse elevation puts the western plume slightly to the north, the levelness and roughness both extend the western plume slightly too far west, and the albedo appears to extend the western plume both too far north and west. All four do a reasonable job over the Arabian peninsula. [55] The two upstream area runs both add an eastern Saharan source region associated with the Nile valley, which is not strongly present in the satellite data. The high-resolution upstream area run puts this as the strongest source in the Sahara, while it is much weaker in the coarseresolution run. They both do a reasonable job with the location and strength of the Bodélé plume, but put the western plume too far north. The large difference between the two underscores the extreme sensitivity of this statistic to resolution and method of basin filling. [56] Over Asia, the levelness, roughness, and albedo all underestimate the Takla Makan dust relative to the Saharan/ Arabian sources, while the inverse elevation run calculates too much dust over Central Asia. It is difficult to compare the runs with satellite data over south and east Asia because of the known mixing between dust and pollution aerosols there. The two upstream area runs are more similar here, as both slightly underestimate the Takla Makan and overestimate the Central Asian sources. [57] For the Southern Hemisphere, all but the highresolution upstream area run have the largest source in the Lake Eyre basin of Australia, which is consistent with the MISR observations. The magnitude of the Australian plume relative to the Dust Belt plumes is higher for the inverse elevation and upstream area erodibilities than for the levelness, residual landscape roughness, and albedo; comparison with MISR (Figure 8) suggests that the smaller magnitude of the Australian source predicted by the residual landscape roughness and albedo runs (Figures 8b and 8d) is more accurate and that the upstream area and inverse elevation runs (Figures 8c and 8e) are approximately a factor of 2 too high there. Thus our best estimate of dust emission from Australia, 97 Tg/a from the residual landscape roughness run, is lower than other values reported in the literature [e.g., Luo et al., 2003; Li et al., 2008]. This is an important point because, despite the small contribution of Australia to the global dust budget, the lack of major sources in the Southern Hemisphere in the current climate means that Australia plays an important role as the source of dust delivered to iron-limited Southern Ocean marine ecosystems. [58] We also compare the modeled deposition fluxes with Ginoux et al. s [2001] observations and the DIRTMAP (version 2) record [Kohfeld and Harrison, 2001]. The results are shown in Figure 9. The data is plotted logarithmically to cover the wide dynamic range of dust deposition rates. The modeled dust for all of the specified source erodibilities underestimate the deposition at highestdeposition sites, which are primarily in western China. This is likely due to an underestimation of coarse loess material in the model, resulting from specifying size distributions that are more representative of transported dust than locally produced aeolian sediment. It is also likely that the NCEP winds do not capture the mesoscale circulation downwind of the Tibetan Plateau critical for lofting dust. [59] Table 2 shows the r 2 correlation between the modeled and observed logarithm of deposition for Ginoux et al. s [2001] and the DIRTMAP [Kohfeld and Harrison, 2001] deposition data sets. The model forced by roughness and the high-resolution upstream area correlate most strongly with Ginoux et al. s [2001] deposition, though all of the runs have similar levels of correlation ( ). The model forced by inverse elevation erodibility correlates most strongly with the DIRTMAP data set, and the range of correlations is much larger. As discussed above, the DIRT- MAP data is highly weighted at the upper end to observations near Chinese source regions. All of the model runs 13 of 19

14 Figure 8. Difference between predicted dust AOT and observed MISR total AOT for annual mean. Note that MISR includes substantial contributions from nondust aerosol in nondesert regions. All MATCH predictions overestimate the Western Saharan source relative to the Bodélé source and underestimate the dust source in the Takla Makan relative to the Saharan sources. here underestimate deposition in the Chinese loess region relative to the Saharan and Arabian sources, particularly the levelness and roughness erodibilities. [60] The results listed above indicate that none of the dust erodibility indices presented here performs better than the rest for all criteria. The roughness erodibility presented in this study performs well in comparison to the regional and global MISR data as well as Ginoux et al. s [2001] deposition data, except for the underestimation of dust emission in western China. In addition, only one model, with its assumptions (e.g., specified dust size distribution and lack of anthropogenic dust sources) and one set of reanalysis atmospheric winds at a single resolution was used to simulate all of the cases; there will be large uncertainties 14 of 19

15 Figure 9. Plots of calculated versus observed deposition. The highest dust deposition sites are located near Asian source regions, where dust is underestimated by this model at all erodibilities. due to model biases embedded in the comparisons. Other studies making similar comparisons of dust source area prescriptions with other models, e.g., Cakmur et al. [2006] found different ranking of source parameterizations. This underscores the model dependence of these results Discussion [61] Figures 8a 8b shows that, for the two major Saharan sources, the Mali/Mauritania dust is overestimated relative to the Bodélé dust using the levelness and roughness erodibility parameters, as compared with the MISR data; the albedo and inverse elevation indices also overestimate the western Saharan dust relative to the Bodélé dust (Figures 8c and 8d). One possible reason for this is that the Bodélé basin has, in addition to highly erodible sediments, a strong meteorologic component, the orographically induced Low Level Bodélé Jet [Washington and Todd, 2005]. The coarse-resolution NCEP winds used to forced the MATCH + DEAD model do not resolve this feature well [Todd et al., 2008], so it is expected that our simulated dust distributions over the Bodélé would be weak relative to the observations. The low bias in dust from western China relative to the Sahara is also pronounced in both the levelness and roughness cases, as well as the albedo case and somewhat less so in the inverse elevation and upstream area cases. The extremely pronounced topography in this region plays a major role in determining dust emission [Liu and Westphal, 2001], with the coarse global model unable to resolve the relevant meteorology Role of Dust in the Desert Sediment Cycle [62] The two geomorphically defined erodibility indices which we define in this paper give very similar dust distributions to each other when used in the model. One reason for this can be seen in the combined map of L and R res (Figure 5), which shows that landscapes with high levelness and roughness tend to be adjacent and/or colocated in deserts. Hence the relatively coarse T62 resolution may further blur them together. [63] In addition, the spatial patterns of L and R res are qualitatively similar to other dust erodibility functions that appear in the literature, such as the inverse elevation relationship of Ginoux et al. [2001], the paleolakes of Tegen et al. [2004], and the albedo-based erodibility maps by Grini et al. [2005]. This is not surprising as all erodibility functions are empirically trying to capture the observed variability in the satellite dust observations, and it indicates that in general, dust source areas are low slope, low elevation, poorly drained, high albedo, and tend to have aeolian roughness features. [64] The spatial similarity indicates a more general mechanism at work in desert environments that leads to strong dust sources. In highland environments, fluvial erosion leads to basic landscape morphologies that are similar to nondesert environments, i.e., organized stream networks for eroding and transporting sediment. But at lower elevations, slopes decrease, reducing the power available to fluvial erosion. Stream networks become less organized as fluvial processes act to deposit rather than erode sediment. This leaves aeolian processes to dominate in removing sediment from and thus shaping these low fluvial energy environments. Dunes and other aeolian features further interrupt the stream network resulting in scattered ephemeral lakes and streams that collect fine grained sediments; the only possible way to erode these sediments is as dust. Table 2. Correlation Between Logarithm of Modeled and Observed Deposition Measurements a Ginoux et al. [2001] Deposition DIRTMAP Deposition Levelness Roughness Inverse Elevation Albedo Upstream area High-resolution upstream area a As shown in Figure 9. Results in bold show the highest correlations against the observations. 15 of 19

16 Figure 10. Predicted fractional change in precipitation from the first to the last decades of the 21st century, according to the multimodel mean of the Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios A1B scenario. Overlain are contours of erodibility for the (a) levelness and (b) residual landscape roughness cases. Note that drying of more than 10% is expected in central Australia, southwestern Africa, and along a belt extending from Central Asia through the northern and western Sahara. [65] The various erodibility parameterizations discussed here and in the literature all emphasize different aspects of the same general sediment cycle. The levelness parameter introduced here identifies broad areas where low-energy alluvial deposits have built up into large reservoirs of sediment over long periods of time; the roughness parameter adds to this the idea that aeolian processes have reworked much of this sediment into dunes. The inverse elevation parameter of Ginoux et al. [2001], because it emphasizes high-relief areas, may identify places where ephemeral erosion and deposition is most active rather than where paleodeposition has built up large deposits. Similarly, the upstream area parameter of Zender et al. [2003b] emphasizes deposits along floodplains and lakes, while the albedo parameter of Grini et al. [2005] identifies specific environments (dunes and playas) that are also reservoirs of erodible sediments. [66] In linking dust sources to the geomorphology of source regions, one question is whether dust emission is relevant to the overall sediment and erosion budgets of these landscapes. To assess the significance of dust in the sediment budgets of some of the stronger source areas in the Sahara, we compare modeled dust emission rates from the Mali/Mauritania dust source area with measured sediment fluxes from the climatically similar but fluvial-dominated landscape of the adjacent Senegal River basin. If we accept the magnitude of the total budget of the levelness and roughness models as calculated above, then the net dust emission minus settling from the region is approximately 5*10 9 kg m 2 s 1, or 150 t km 2 a 1. This is more than an order of magnitude larger than estimated fluvial erosion rates of 8 t km 2 a 1 as measured by sediment fluxes in the Senegal River [Milliman and Syvitski, 1992]; this reinforces the idea that, at least in the current climate, aeolian processes have essentially taken over the sediment budgets of these dust-emitting landscapes, especially in low-slope, arid environments where fluvial processes are unable to dispose of fine sediment created by erosion Implications for Future Climate-Change Scenarios [67] One critical question is how dust sources may change in the future as climate and/or land use shifts. There have been strong changes in dustiness in the past, for example the fourfold increase in dust reaching Barbados from the Sahara between the 1960s and 1980s [Prospero and Lamb, 2003], which may have been partly due to increased source strengths, the dust bowl that resulted from drought and land use change in the Great Plains during the 1930s, or the recent increase in dust from northern China. The control of dust emission by the hydrological cycle is a complex problem, and model results indicate that dust emission changes in response to vegetation changes more than to changes in soil moisture or wind stress, with carbon fertilization potentially offsetting decreases in precipitation in controlling the distribution of vegetation [Mahowald et al., 2006]. However, past variation of vegetation in the Sahel has been shown to be controlled primarily by precipitation, with CO2 fertilization playing only a minor role [Hickler et al., 2005]. Furthermore, Zender and Kwon [2005] show that interannual variability of dust sources can be tied to interannual variation in precipitation of both signs, for example with the Bodélé Basin more active after dry seasons, indicating a soil moisture limitation, while the Zone of Chotts in Algeria more active after wet seasons, indicating a sediment supply limitation. [68] We are particularly interested in where drying because of climate change may occur in regions where our geomorphic analysis identifies regions as having characteristics consistent with high dust erodibility. Figure 10 shows the predicted fractional change in precipitation from the multimodel mean of the Intergovernmental Panel on Climate Change AR4 Special Report on Emissions Scenarios A1B scenario between the first and last decades of the 21st century; overlain are contours of erodibility of the levelness (Figure 10a) and roughness (Figure 10b) criteria. Reduction of precipitation of more than 10% is predicted to occur in 16 of 19