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JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 113, D22204, doi:10.1029/2008JD010195, 2008
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Identifying global dust source areas using
high-resolution land surface form
Charles D. Koven1,2 and Inez Fung3
Received 31 March 2008; revised 11 July 2008; accepted 30 July 2008; published 20 November 2008.
[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, D22204, doi:10.1029/2008JD010195.
1. 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.
0148-0227/08/2008JD010195$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
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Shuttle Radar Topography Mission (SRTM) data set [Farr
and Kobrick, 2000; Rodrı́guez 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.
1.1. 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
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given unlimited precipitation. The HYDRA model they use
has a resolution of 50, 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
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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.
1.2. 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
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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 0.5– 5 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-
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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 2000– 2005 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 2000 – 2005. During
Northern Hemisphere winters (Figure 1a), aerosol optical
thickness is highest south of the Sahara because of biomass
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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] We use the SRTM [Farr and Kobrick, 2000; Rodrı́guez
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
2000. 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 [Rodrı́guez 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]
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z ¼ ax2 þ by2 þ cxy þ dx þ ey þ f ;
ð1Þ
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KOVEN AND FUNG: DUST SOURCES FROM HI-RES SURFACE FORM
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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
S ¼ arctan
pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
d 2 þ e2
ð2Þ
and roughness, R, as the standard deviation of the error
between the best fit surface, z, and the actual DEM points,
zobs, over the fitting window
R ¼ rmsðzobs 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 (450 450 m) to 50 50 pixels
(4500 4500 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 0.5 0.5° 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.
3.2. 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 SaharanArabian 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
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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 102; while the coarser resolution
slope measurements show several regions with much gentler slope on the order of 103. 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
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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 [Rodrı́guez 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 Rodrı́guez 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 0.5 – 2 km [Breed and Grow, 1979], but not for
Figure 3. Plots of surface form in Saharan-Arabian
region (30°W –60°E, 10– 32°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 R0
in equation (4) and the dashed line corresponds to the slope
threshold.
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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 Rodrı́guez 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.
3.3. 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, as the
fraction of land surface with slope below an empirically
determined threshold of 102.75 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
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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
Rres ¼ log R R0 * log S;
ð4Þ
where Rres is the residual landscape roughness, R is the
roughness, and R0 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 Rres, for the Sahara
and the globe, are shown in Figure 4.
[39] The Rres 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 Rres 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 Rres we consider here is different from the
surface roughness, z0, 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
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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
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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 0.55. 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 Rres (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
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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 2000 – 2005. 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.
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[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.
4.2. 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
2000– 2005 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 13– 32° N), as well as against the monthly means
for all desert regions (defined as having a maximum NDVI
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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 (r2 = 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 1.8– 2.1 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
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Table 1. Regression Results of Modeled Dust Optical Thickness and MISR Aerosol Optical Thickness for the
2000 – 2005 Perioda
Sahara JJA
2
Correlation (r )
Levelness
Roughness
Inverse elevation
Albedo
Upstream area
High-resolution upstream area
Deserts
Total Dust (Pg/a)
0.52
0.57
0.48
0.45
0.53
0.23
1.85
1.76
2.05
1.55
2.12
2.79
(±0.16)
(±0.13)
(±0.19)
(±0.15)
(±0.18)
(±0.46)
2
Correlation (r )
0.51
0.52
0.43
0.50
0.44
0.45
Total Dust (Pg/a)
1.99
1.73
2.04
1.45
1.93
2.09
(±0.03)
(±0.03)
(±0.04)
(±0.03)
(±0.05)
(±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
r2 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 (r2 =
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 (0.43 – 0.47).
Totals for the implied magnitude of the dust cycle range
from 1.5 – 2.1 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 r2 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 (0.7– 0.8). 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 DIRTMAP data is highly weighted at the upper end to observations near Chinese source regions. All of the model runs
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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
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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.
4.3. 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.
4.3.1. 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
Rres (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 Rres 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 Measurementsa
Levelness
Roughness
Inverse Elevation
Albedo
Upstream area
High-resolution upstream area
Ginoux et al. [2001]
Deposition
DIRTMAP
Deposition
0.72
0.79
0.73
0.69
0.76
0.78
0.44
0.46
0.65
0.55
0.58
0.55
a
As shown in Figure 9. Results in bold show the highest correlations
against the observations.
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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*109 kg m2 s1, or 150 t km2 a1. This is more than an
order of magnitude larger than estimated fluvial erosion
rates of 8 t km2 a1 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.
4.3.2. 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
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central Australia, southwestern Africa, and a broad belt
extending from central Asia through the Mediterranean to
the northern and western Sahara; all of which are already
semiarid to arid, and thus could become greater dust
sources.
[69] In particular, drying is predicted to occur in the Mali/
Mauritania source region. Of the two major Saharan
plumes, our model suggests that this is responsible for the
bulk of dust transported west over the Caribbean; thus if
source strength rather than transport alone accounted for the
increased dust concentrations observed between the 1960s
and 1980s then the increase was presumably in this region,
and could conceivably continue to increase in the future.
5. Conclusions
[70] We consider the effect of fine-scale geomorphology
on dust production, by comparing local statistics of a highresolution global DEM with observations of aerosol optical
thickness from the MISR instrument. We note that highaerosol areas are associated with regions within deserts that
have low slopes and relatively high residual elevation
variance after subtracting a best-fit surface. We hypothesize
that the low slopes indicate low-energy alluvial and lacustrine deposition, which imply an abundance of fine sediment for deflation and a lack of coarse sediment that could
form an armoring desert pavement. We further hypothesize
that the high roughness at low slope is a result of aeolian
landforms such as yardangs and sand dunes; and that these
landscapes are either themselves sources of dust or else
represent an availability of wind-blown sediment, with the
dunes themselves representing sediment that has been
mobilized by wind but redeposited locally and built up.
[71] To separate the influence of wind stress variance and
transport effects, we create 2 new erodibility indices, and
apply them to a dust model embedded in an atmospheric
transport model to calculate predicted dust distributions. We
then compare the modeled dust optical thickness with the
MISR observations of aerosol optical thickness, for the
Saharan-Arabian region and over deserts globally. High
correlations between the modeled and observed dust support
our hypothesis that the fine-scale geomorphology is a useful
measure of dust erodibility, both over the Saharan-Arabian
region and elsewhere.
[72] In this paper we treat the landscape properties of
levelness and residual landscape roughness as proxies for
erodibility, rather than explicitly considering how they
would influence the dust generation process. Future
research will focus on these dynamics, for example by
considering how landscape slope might effect the size
distribution of soil aggregates, or how dunes and other
large roughness elements might channel wind momentum to
the surface.
[73] In our comparison between modeled and observed
dust, the residual landscape roughness erodibility parameterization shows the highest correlation with the MISR
observations, both for the Saharan-Arabian summer plume
and for deserts globally, however the benefit is modest
relative to other erodibility parameterizations. In comparison to the deposition data, the roughness erodibility also
shows the highest correlation to Ginoux et al.’s [2001] data,
but correlates poorly to the DIRTMAP data due to under-
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estimating the strength of dust sources in China. This
geomorphologically based parameterization has the benefit
that it is not tied to model resolution, but is instead
dependent on a landscape property specified at a scale that
is relevant to the landforms themselves. Thus it should be
amenable for use in both global and regional dust models.
Lastly, it is relatively insensitive to biotic factors and can
therefore be used to examine potential source regions that
are not currently desert but could dry in the future.
[74] The observation that dust arises from low-slope,
high-roughness environments supports the mechanistic idea
that dust results from a hand off of sediment from fluvial to
aeolian transport. This hand off can happen over short
timescales because of intermittency of streams, over long
timescales because of climate change, or in space because of
climatic gradients. As sediment is carried to progressively
lower slopes in deserts, fluvial processes lose the energy to
erode and move sediment, and aeolian processes take over.
The association of dust sources with dunes indicates a
shared origin for dust and sand, and likely reflects both
the sorting of particle sizes by wind as well as production of
dust by sand abrasion.
[75] Acknowledgments. We thank the MISR science team for aerosol
optical thickness data. We thank Charlie Zender for providing the DEAD
model and input data sets. We thank Tom Farr and the SRTM team for
SRTM data. We thank Ron Miller for thoughtful review and discussion. We
thank Scott Doney and Taylor Perron for thoughtful discussion. This work
was supported by NASA Headquarters under the Earth System Science
Fellowship grant NNG04GQ76H.
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I. Fung, Department of Earth and Planetary Sciences and Berkeley
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C. Koven, Laboratoire des Sciences du Climat et de l’Environnement, Point
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