Earth-Science Reviews 79 (2006) 73 – 100
www.elsevier.com/locate/earscirev
North African dust emissions and transport
Sebastian Engelstaedter a,⁎, Ina Tegen b,1 , Richard Washington a,2
a
Climate Research Lab, Centre for the Environment, University of Oxford, South Parks Road, Oxford, OX1 3QY, UK
b
Institute for Tropospheric Research, Permoserstr. 15, 04318 Leipzig, Germany
Received 15 November 2005; accepted 23 June 2006
Available online 30 August 2006
Abstract
The need for a better understanding of the role of atmospheric dust in the climate system and its impact on the environment has
led to research of the underlying causes of dust variability in space and time in recent decades. North Africa is one of the largest
dust producing regions in the world with dust emissions being highly variable on time scales ranging from diurnal to multiannual.
Changes in the dust loading are expected to have an impact on regional and global climate, the biogeochemical cycle, and human
environments. The development of satellite derived products of global dust distributions has improved our understanding of dust
source regions and transport pathways in the recent years. Dust models are now capable of reproducing more realistic patterns of
dust distributions due to an improved parameterization of land surface conditions. A recent field campaign has improved our
understanding of the natural environment and emission processes of the most intense and persistent dust sources in the world, the
Bodélé Depression in Chad. In situ measurements of dust properties during air craft observations in and down wind of source
regions have led to new estimates of the radiative forcing effects which are crucial in predicting future climate change. With a focus
on the North African desert regions, this paper provides a review of the understanding of dust source regions, the variability of dust
emissions, climatic controls of dust entrainment and transport, the role of human impact on dust emission, and recent developments
of global and regional dust models.
© 2006 Elsevier B.V. All rights reserved.
Keywords: dust aerosols; emission; atmospheric transport; dust variability; North Africa; Sahel
1. Introduction
Research on dust emissions from desert regions and its
distribution in the atmosphere has seen a massively
increased focus over the last decade. An important driver
of this increased research effort stems from recognition of
⁎ Corresponding author. Tel.: +44 1865 285194; fax: +44 1865
275885.
E-mail addresses: [email protected]
(S. Engelstaedter), [email protected] (I. Tegen),
[email protected] (R. Washington).
1
Tel.: +49 341 2352146.
2
Tel.: +44 1865 272765.
0012-8252/$ - see front matter © 2006 Elsevier B.V. All rights reserved.
doi:10.1016/j.earscirev.2006.06.004
the impact which dust has on the climate system and the
large uncertainties associated with the role of dust in the
radiation budget. In order to reduce uncertainties in future
climate projections, the uncertainties associated with the
influence of dust on climate need to be reduced as a matter
of priority. The availability of remote sensing products
such as the Total Ozone Mapping Spectrometer absorbing
Aerosol Index (TOMS AI) (Herman et al., 1997) and the
Infrared Difference Dust Index (IDDI) (Legrand et al.,
2001) have, for the first time, allowed a global view of
dust to emerge. The effect has been to promote research,
particularly on the remote source regions for which
conventional surface based observations upon which
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dust work once relied so heavily, provided little or no
information.
North Africa is widely regarded as the Earth's largest
source of dust (e.g. Prospero et al., 2002; Washington et
al., 2003) but in the case of North Africa, the key dust
sources are located in particularly remote regions which,
in the absence of remote sensing data, would remain
largely unknown. Given the fast moving progress in dust
research, this paper provides an overview of key findings
for North African dust research, with a clear bias towards
work done since the global satellite products became
available in the late 1990s. Accordingly, the underlying
thesis of this paper is that the need to include the impact of
dust in global climate models (for the purposes of climate
change experiments) twinned with the simultaneous
availability of remote sensing products, reanalysis data
sets which provide products such as globally complete
three dimensional winds and targeted field and aircraft
campaigns, has changed the face of North African dust
research. Alongside this research enterprise and guided by
the remote sensing data are the related aircraft and
groundbased experiments which are helping to refine
observations of key variables for model development and
evaluation.
The extent of the North African deserts can be broadly
defined as the area covered from about 15°N to the
Mediterranean coast and from the Atlantic coast in the
west to the Red Sea in the east where annual rainfall is
usually below 200 mm yr− 1 (Fig. 1). The vast and mostly
uninhabited Saharan desert is interrupted by some
mountain regions, notably the Altas Mountains in
Morocco, the Hoggart Massif in southeast Algeria, the
Air Mountains in Niger and the Tibesti and Ennedi
mountains in Chad (Fig. 1). About 5000–6000 years ago,
the North African climate regime shifted abruptly from
wetter conditions with extensive vegetation, lakes and
wet lands to much drier conditions, eventually leading to
the development of the Saharan desert which, for the last
2000 years has, experienced both drier and some more
humid phases (Nicholson, 2001; Foley et al., 2003 and
references therein). Since the 1960s precipitation in the
Sahel has been mostly below average with extreme
drought periods in the 1970s and 1980s (Nicholson et al.,
1998; Hulme et al., 2001), which are thought to be
related to enhanced dust emissions in North Africa.
Fig. 2a and b show the mean TOMS AI for the whole
world for November–January and May–July. Emissions
from North Africa clearly stand out in terms of
Fig. 1. Mean annual rainfall in mm (1961–1990) over North Africa superimposed on topography. The main Saharan mountain regions are labelled.
Rainfall data were taken from New et al. (1999).
S. Engelstaedter et al. / Earth-Science Reviews 79 (2006) 73–100
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Fig. 2. Seasonal mean TOMS AI (×10) (1980–1992) for (a) November–January and (b) May–July.
magnitude and spatial extent. The Saharan sources are
considered by far the most active ones in the world,
although their contribution is mainly confined to
northern hemisphere summer (TOMS AI peaks at the
Gulf of Guinea coast in Fig. 2a and the west coast just
south of the Equator in Fig. 2b mainly result from
biomass burning). Apart from North Africa, major dust
activity is evident on the Arabian Peninsula, in Iran,
Turkmenistan, Afghanistan, Pakistan, Northern India,
the Namib and Kalahari desert and the Tarim Basin in
China. Some minor dust activity is shown in this satellite
product to occur in the Western United States, Mexico
and in central Australia. Estimates of the source strength
of the Sahara range from 130 to 760 Tg yr− 1 (Goudie and
Middleton, 2001), although a recent study estimates
annual Saharan dust emission to be about 1600 Tg yr− 1
(Ozer, 2001). As a comparison, estimates of global dust
emissions range from 1000 to 3000 Tg yr− 1 (Houghton
et al., 2001; Zender et al., 2004). There is considerable
evidence that North African dust emissions were highly
variable over the last decades (Goudie and Middleton,
1992; N'tchayi Mbourou et al., 1997; Prospero and
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Lamb, 2003). Such changes in the atmospheric dust
content are thought to impact significantly on regional
and global climate by altering the Earth's radiative
budget.
Efforts to identify dust source regions and attempts to
quantify the contribution of anthropogenic modified
sources are discussed in Section 2, transport pathways
and their environmental impacts in Section 3, and
temporal variability in Section 4 (including the diurnal
cycle, intraseasonal variability, the seasonal cycle, and
interannual and multiannual variability). Dust properties
and radiative effects of North Africa dust are discussed in
Section 5 and progress in modeling dust emissions and
transport (including estimates of emission and deposition
into the oceans) are discussed in Section 6.
2. Dust sources
2.1. Natural sources
A crucial criterion for the existence of a dust source is
the availability of fine deflatable material which can be
lifted from the ground when surface wind velocity
exceeds a certain threshold wind speed. This threshold
depends on surface roughness elements (e.g. rocks and
vegetation), grain size and soil moisture. Based on surface observations at eight stations in the western Sahara,
Fernandez-Partagas et al. (1986) report an average
threshold wind velocity of about 8 m s− 1 with values
varying from station to station between 5 m s− 1 and
12.5 m s− 1. Based on a comparison of IDDI and 10 m
surface wind speed from the European Center for
Medium range Weather Forecasting (ECMWF), Chomette et al. (1999) calculate threshold wind speeds between
6.63 ± 0.67 and 9.08 ± 1.08 m s− 1 for seven locations in
North Africa. Differences in these thresholds may result
from the precision of reanalysis products in simulating
the observed wind as well as the scale over which the
observations were made. The emissions of soil dust
particles cannot be easily measured directly because the
atmospheric lifetime of dust is relatively short and source
regions, particularly in the vast expanses of the Sahara
desert, often lie in remote desert areas where the
installation and maintenance of equipment for continuous
long-term measurements is not currently possible. In this
setting, remote observations, either at the surface and
downwind of sources, or from satellites, play a crucial
role.
Horizontal visibility is measured routinely at most
first-order meteorological stations. In desert regions,
visibility reduction often occurs as a result of dust in the
atmosphere and therefore provides an indirect measure of
the dust load in the near surface air layer. The reduction of
visibility below certain thresholds (e.g. 1 km, 5 km and
10 km) can be used as a classification scheme to derive
frequency distributions of atmospheric dust events
according to their relative intensity. Conclusions about
the location of dust sources based on visibility data can be
drawn by considerations about the wind direction and the
location of the meteorological station in relation to the
source. For a long time, visibility data have been the only
direct measurements available to document the spatial
and temporal characteristics of atmospheric dust and still
they provide valuable information about emissions and
near surface dust transport (Hoidale et al., 1977; Bertrand
et al., 1979; Middleton, 1984, 1986a,b; Littmann, 1991a,
b; Goudie and Middleton, 1992; N'tchayi Mbourou et al.,
1997; McTainsh et al., 1998; Ozer, 2001; Engelstaedter
et al., 2003).
Despite the fact that visibility based analyses are often
limited (a) by the sparse distribution of meteorological
stations, especially in the Saharan desert where distances
between meteorological stations can reach up to about
2000 km (e.g. Engelstaedter et al., 2003), and (b) by
incomplete records due to underdeveloped infrastructure
or political instability, they highlight North Africa as one
of the most important dust emitting regions in the world,
and they enable us to narrow down the location of specific
source regions and to identify seasonal and long-term
variability in emission and transport. For instance,
Bertrand et al. (1979) analyze visibility data from 27
meteorological stations in the southern Sahara and show
that highest average values of visibility reduced below
5 km (190 h/month) are found in Bilma (Nigeria, located
at 18° 40′N/12° 55′E) suggesting that this station must be
located at or near a dust generating area. They also argue
for a second distinct source region in Mauritania because
of high frequencies of reduced visibility which have been
recorded downwind at Nouakchott and Nouadhibou
located at the Atlantic coast at 18° 5′N/15° 58′W and
20° 57′N/17° 2′W respectively. Their conclusions about
the general location of North African source regions agree
well with the findings of other visibility data based studies
(e.g. Middleton, 1986a; N'tchayi Mbourou et al., 1997;
Ozer, 2001), although these studies did not manage to
pinpoint the key dust sources which in the case of the
Bodélé Depression in Chad (one of the most intense dust
sources in the world) (Prospero et al., 2002; Washington et
al., 2003) is about 500 km from Bilma, and Bilma is not
located on the main Bodélé transport pathway.
A better approach to identifying source regions from
surface data lies in the use of dust trajectories. On this
basis, Kalu (1979), for example, argued for the
importance of a North African dust source in the area
S. Engelstaedter et al. / Earth-Science Reviews 79 (2006) 73–100
of Bilma and Faya Largeau (Chad, located at 17° 58′N/
19° 9′E), a region at the western slope of the Tibesti
massif described as a large alluvian plain. Undoubtedly
this work was closing in on the Bodélé Depression which
lies upwind of the Nigerian source studied by Kalu and
downwind of Faya Largeau (Chad).
Satellite derived products of atmospheric dust distributions have, to a large extent, overcome the coverage
problems characteristic of surface based observations
from meteorological stations notwithstanding unsolved
errors induced by land surface reflectance and cloud
cover, low frequency of measurements, the lack of longterm measurements and sensitivity to height of the dust
layer. Satellite observations in the visible range as measured, for instance, from the Advanced Very High Resolution Radiometer (AVHRR) (Ackerman and Chung,
1992) or Meteosat data can be used to retrieve dust
content over the ocean (Husar et al., 1997; Moulin et al.,
1997; Cakmur et al., 2001; Chiapello and Moulin, 2002)
but cannot as yet detect dust aerosols over bright surfaces
such as the Saharan desert. Herman et al. (1997) found
that retrievals from the Total Ozone Mapping Spectrometer (TOMS) from November 1978 to May 1993 on board
of the Nimbus 7 satellite (from July 1996 on the Earth
Probe satellite) can be used to detect absorbing aerosols
by looking at the spectral contrast of two UV channels
(340 nm and 380 nm for Nimbus 7; 331 and 360 nm for
Earth Probe), resulting in the TOMS absorbing aerosol
index (TOMS AI). The advantage of the TOMS AI is that
it can be applied over oceans as well as over land, which
improves our knowledge about the distribution of dust
sources in the world's desert regions (Middleton and
Goudie, 2001; Israelevich et al., 2002; Prospero et al.,
2002; Washington et al., 2003). This data set has proven
crucial for North Africa dust studies because it has
provided the basis for the identification of key source
regions. TOMS AI is not, however, without problems.
There is an ongoing debate about whether at all or how
well the TOMS AI is able to represent dust aerosols near
the surface at heights lower than about 1 km. Herman and
Celarier (1997) show that UV-absorbing aerosols are not
readily detected by the method applied to derive the
TOMS AI whereas Torres et al. (2002) argue that ‘colored’
aerosols such as mineral dust can be detected when close
to the ground. The length of the TOMS AI record, which
begins with the Nimbus 7 satellite in November 1978, has
nevertheless allowed satellite derived dust climatologies
to be mapped for the first time and this is the key advantage of the TOMS data.
Other satellite imagery, including the Moderate
Resolution Imaging Spectroradiometer (MODIS) and
the Multi-angle Imaging Spectroradiometer (MISR), both
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on board NASA's Terra satellite, have been used to
retrieve dust properties over land and ocean, with MODIS
being restricted to land surfaces with low reflectance (e.g.
Zhang and Christopher, 2003). NASA's Aqua satellite
also carries a MODIS instrument, thus, MODIS provides
dust aerosol information twice a day. A recent satellite
product is the Infrared Difference Dust Index (IDDI),
which uses reductions in atmospheric brightness temperatures derived from Meteosat IR-channel measurements to detect atmospheric dust distributions (Legrand
et al., 2001). It should be noted that both the TOMS AI
and IDDI product is sensitive to biomass burning aerosols
and a clear distinction between dust and biomass burning
aerosols is not always possible.
Using satellite retrievals and visibility measurements
to identify dust source regions is limited by the fact that
both data types are a measure of the atmospheric dust
content, which incorporates an emission and a transport
component. The transport component gets more important with increasing distance from the source. Although
it is very likely that high values of atmospheric dust
content occur over or close to a dust source where the
transport component is relatively low, it cannot be ruled
out that the data in some cases are misleading in terms of
the distribution of source regions. A theoretical example
would be that dust which has been transported away
from its source is kept suspended in the atmosphere by
certain weather conditions for a considerable long time
without being further advected. This might be detected
by satellites as a local hot spot which could be misinterpreted as a source region. Nevertheless, satellite data
provide the best estimates for the distribution of dust
source regions on a regional and global scale and have
made a vital contribution to the study of North Africa
dust.
Several studies have explored the TOMS AI data for the
purposes of identifying dust source regions, although a
variety of statistics have been employed for this purpose.
Prospero et al. (2002), for example, have specified an AI
threshold value of 1.0 for northern Africa, while Israelevich
et al. (2002) use long-time averages, Middleton and
Goudie (2001) use percentages of days with AI
values N 1.9, and Washington et al. (2003) calculate
Varimax rotated empirical orthogonal functions of the
correlation matrix of TOMS monthly anomalies, making
the assumption that source regions would show highest
shared temporal variance and non-source regions a much
lower (random) temporal variance. Reassuringly all these
approaches show similar results. All four studies highlight
the Bodélé Depression in Chad as the most important
source of dust emissions not only in the Sahara but in the
world (Fig. 3). This source, located between the Tibesti
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Fig. 3. Long-term mean TOMS AI (×10) (1980–1992).
massif and Lake Chad, shows highest global annual mean
value of TOMS AI exceeding 3.0 (Prospero et al., 2002;
Washington et al., 2003). A second large but less intensive
(AI N 2.4) dust source area is identified in the West Sahara
east of the Mauritanian coast near the Mali–Mauritanian
border stretching northeast towards the Mali–Algerian
boarder. Other minor dust source regions identified by the
TOMS AI are south of the northern (Tell) Atlas Mountains
in northern Algeria, the eastern Libyan Desert, and large
areas of Egypt and Sudan (Nubian desert).
On the basis of the sensitivity of the TOMS AI to
boundary layer height, Mahowald and Dufresne (2004)
suggest that previous studies using only TOMS AI averages or thresholds to determine dust sources underestimate
the importance of sources on the edges of deserts where
the boundary layer is generally lower than in central desert
and where emission may also be influenced by human
activity. The IDDI broadly points to the same source
regions (not shown), though the magnitude of emissions at
the West African and Egypt/Sudan sources is much higher.
The differences between TOMS AI and IDDI may be
partly explained by differences in the sampling techniques
used to calculate the two indices.
The environmental conditions and climatological
factors that make the Bodélé Depression in Chad and
large parts of West African such important dust sources
are beginning to be understood. Several possible pro-
cesses have been suggested for the importance of the
Chad basin as a major dust source. The Bodélé Dust
Experiment (BoDEx) (Washington et al., 2006a) showed
that regionally exposed diatomite deposits of an
expanded Lake Chad dating from the early Holocene
and Pleistocene (Ghienne et al., 2002) are the source of
intense dust emission visible on satellite images,
especially MODIS, from this region. BoDEx also
confirmed the existence of the Bodélé Low Level Jet
(LLJ), first described from NCEP and ERA reanalysis
data by Washington and Todd (2005), overlying the
Bodélé region that is at least partly forced by channeling
between the Tibesti and Ennedi Mountains (Washington
et al., 2006a; Washington et al., 2006b). The strength of
the LLJ and thus its potential for dust emissions is
controlled by synoptic scale variability associated with
the ridging of the Libyan High pressure system
(Washington and Todd, 2005), although the flow appears
to be highly ageostrophic over the depression. The
threshold wind speed for dust emission of ∼ 10 m s− 1
observed during BoDEx is in very good agreement with
the estimated threshold wind velocity of 10–11 m s− 1 by
Koren and Kaufman (2004) who analyzed MODIS data
from the Terra and Aqua satellites.
The importance of the western African source region
is much less understood mainly because there have been
no published field data from this region. The dust
S. Engelstaedter et al. / Earth-Science Reviews 79 (2006) 73–100
emitting area is much larger than the Chad source and
represents a large closed depression about 900 km in
length. Prospero et al. (2002) describe it as an area
extending from ∼ 17°–18°N, 8°–10°W to ENE to
∼ 26°N along the meridian which has extensive dune
systems and which is mainly uninhabited. Sources of
deflatable dust might be deposits of fine material from
dry Holocene lakebeds and/or paleo rivers, which transported sediments from bordering mountain areas. The
magnitude of average dust emission in this region, as
represented by the mean annual TOMS AI (Fig. 3), is
lower than emissions from the Bodélé source (Washington et al., 2003), but covers a larger area. Satellite
imagery shows that in the summer months large dust
clouds originate in West Africa, carrying dust westwards
towards the Atlantic Ocean, which results in high mean
values of reduced visibility at stations located at the
eastern North Africa coast, notably Nouakchott and
Nouadhibou in Mauritania (Bertrand et al., 1979;
Engelstaedter et al., 2003).
Apart from the West African and the Bodélé dust
sources which stand out clearly in terms of intensity
(Fig. 3) Prospero et al. (2002) identified several smaller
sources in North Africa which are clearly visible in the
TOMS AI record but have lower values. Our knowledge
about the natural environments and processes involved
in dust emission at these minor dust sources is very low
as most research so far has been concentrated on understanding the major dust sources in the central Sahara. A
persistently active source region is located south of the
northern (Tell) Atlas Mountains centered at about 33°N
and 7°W with peak emissions occurring between June
and August. The region is characterized by a system of
ephemeral and salt lakes (Chotts) which may play an
important role in modulating dust emissions. Mahowald
et al. (2003) found a weak but significant relationship
between periods of lake inundation and TOMS AI in
this region, but the interpretation of the results remains
difficult. Another large active source region is located in
the eastern Libyan Desert covering an area in a SW–NE
direction from about ∼23°N, 16°W to ∼ 29°N, 23°W
and extending eastwards into Egypt. This region is,
similar to other minor source regions identified by
Prospero et al. (2002) in Egypt, Sudan, Ethiopia and
Niger, characterized by wadis, topographic depressions
and ephemeral lakes and drainage systems. Also, the
shrinking of Lake Chad over the last decades, leading to
the exposition of approximately 20,000 km2 of lake
sediments between 1960s and 1980s (Birkett, 2000; Coe
and Foley, 2001), has been suggested as a potential
source of dust emissions (Mahowald et al., 2002).
However, the contribution of Lake Chad source is likely
79
to be negligible compared to the major dust source of the
Bodélé Depression about 700 km north of Lake Chad
(Prospero et al., 2002; Washington et al., 2003).
In general, all natural North African dust sources are
located north of ∼ 15°N in regions that receive rainfall
less than 200 mm yr− 1 (compare Fig. 2) (Prospero et al.,
2002). Most sources seem to be associated with paleo or
fluvial deposits associated with geomorphological features such as dry or periodically flooded lakes, wadis,
drainage systems, alluvial fans or playas. These are
often linked to topographic depressions into which
sediments are transported from the surrounding mountains or higher elevated areas. Source regions do not
seem to be associated with the large dune systems of
North Africa.
In summary, surface based observations, owing to the
sparse distribution, have not been sufficient to pin point
dust sources. Major progress in identifying key dust
regions in North Africa arose from the release of longterm satellite derived estimates of dust, particularly
TOMS AI, which could then be used as a basis for
motivating field experiments such as BoDEx in these
remote regions. Similar field based work in the West
African source will do much to uncover the processes
peculiar to that key source.
2.2. Anthropogenically modified sources
Zender et al. (2004) defines anthropogenic soil dust
as that part of the dust load that is produced due to
human activity. They also identify two ways in which
humans can influence dust emissions, (1) by land use
which changes soil surface conditions that modify the
potential for dust emission (e.g. by agriculture, mining,
livestock, vehicles or water management) and (2) by
modifying climate, which in turn modifies dust emissions, for example, by changes in surface winds or
vegetation growth. A good example for anthropogenic
dust of the first kind is the intensive agricultural land
use, which lead to extreme periods of dust emission in
the dry ‘Dust Bowl’ years in the 1930s in the United
States (Worster, 1979; Svobida, 1986). Such human
impact may contribute significantly to regional dust
emissions.
In the case of North Africa, it is the Sahel where the
question of anthropogenic emissions have received the
most attention. Dust emissions here have increased significantly during the drought periods of the last decades (e.g.
Ozer, 2001). The population in the Sahel has increased by
about 3% annually during the last decades, leading to an
increased demand for food (Sterk, 2003). This, in turn, has
resulted in overcultivation, overgrazing, deforestisation
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and mismanagement of irrigated cropland which has been
put forward as main reasons for desertification in the
Sahel due to human activities (Thomas and Middleton,
1994). The resultant increase of disturbed soils has been
hypothesized to be partly responsible for the increase in
dust emissions (Mahowald et al., 2002). The extent to
which changes in dust emissions can be attributed to
anthropogenically disturbed soil surfaces or natural
climate variability (e.g. changes in precipitation or wind
speed) is difficult to disentangle and estimates can only be
made by using numerical models that simulate the emission and transport of dust (for a more detailed description
of dust models see Section 6). Only few studies have
attempted estimates of the contribution of anthropogenically disturbed soils on global dust emission by comparing scenarios of modeled dust emissions for different
test cases allowing for higher emissions in regions with
anthropogenically disrupted soils, e.g. croplands or
pastures, with observations from either satellite aerosol
retrievals or dust storm frequencies. The resulting contributions to global dust loads from anthropogenic soil dust
sources from such estimates range from 0% to 50%
(Tegen and Fung, 1995; Sokolik and Toon, 1996; Tegen
et al., 2004; Mahowald et al., 2004; Yoshioka et al., 2005),
numbers which also reflect the uncertainties involved in
these analyses and the limited means by which the
anthropogenic contribution can be quantified. Accordingly these values should be used with caution and discussed carefully when referenced in the literature. The
main constraints hinge on difficulties models have in
reproducing spatial and temporal pattern of dust emissions as well as the availability of land use and wind data
sets. Luo et al. (2003), for example, show that the use of
two different meteorological data sets (NCEP/NCAR and
NASA DAO) in their dust model resulted in global dust
emission estimates which differed more than the total
estimated anthropogenic dust mass. From this perspective
it becomes clear that we are not yet able to make reliable
estimates of anthropogenic dust. Although, dust emissions in natural deserts, especially the Sahara, are very
large, anthropogenically disturbed soils may not make a
large contribution to total dust emissions, but they remain
important to humans as they occur in inhabited areas,
where human health and soil fertility are adversely
affected by enhanced dust emissions.
3. Dust transport and its environmental impacts
Satellite imagery and atmospheric dust concentration
measurements confirm that dust emitted from desert
source regions can be transported over large distances in
the atmosphere, affecting life, ecosystems and climate far
away from its source. Three major pathways of dust
transport trajectories from North African sources can be
distinguished. First, dust is transported over large distances across the Atlantic Ocean to the United States, the
Caribbean and South America (Swap et al., 1992; Perry
et al., 1997; Prospero and Lamb, 2003); secondly, dust is
transported towards the Mediterranean and Europe
(Franzen et al., 1994; Moulin et al., 1998; Avila and
Penuelas, 1999; Borbely-Kiss et al., 2004); and thirdly,
dust is transported towards the eastern Mediterranean and
the Middle East (Yaalon and Ganor, 1979; Ganor, 1994;
Kubilay et al., 2000; Israelevich et al., 2003) (compare
Fig. 2). Additionally, a recent study suggests transcontinental dust transport from North Africa and the Middle
East to East Asia which can lead to dusty conditions in
Japan (Tanaka et al., 2005).
Since Charles Darwin first analyzed dust samples
from vessels on the Atlantic Ocean in the first half of the
19th century, the transport and deposition of dust from
North Africa across the North Atlantic Ocean has been
described by many (Schuetz, 1979; Swap et al., 1992;
Chiapello et al., 1995; Ellis and Merrill, 1995; Gatz and
Prospero, 1996; Moulin et al., 1997; Prospero and Lamb,
2003). Dust aerosol concentration measurements at
Barbados, Bermuda and Miami show that large areas
of the North Atlantic Ocean are affected by atmospheric
dust, with a maximum in June–July and a minimum
normally in December–February (Prospero, 1996). In
summer months large dust plumes emerge from the west
coast of the North African continent, following a pattern
of occurrence of 3 to 5 days, which are associated with
the passage ways of easterly waves crossing North
Africa from east to west (Prospero and Carlson, 1981;
Prospero and Nees, 1986; Jones et al., 2003, 2004). The
structure of these dust plumes is very complex, and some
trajectories also carry dust to the western European
continent (Middleton and Goudie, 2001). It takes about a
week for the dust clouds to cross the tropical North
Atlantic Ocean from Africa to the Caribbean (Ott et al.,
1991). In the winter season, dust transport follows more
southward trajectories driven by strong northeasterly
winds (Harmattan), bringing dust to most parts of
Nigeria, the Gulf of Guinea (Washington et al., 2006a)
and as far as the Amazon Basin in South America (Swap
et al., 1992; Kaufman et al., 2005).
Dust transported from North Africa across the
Atlantic Ocean is thought to interact with natural and
human environments far away from its sources. For
instance, the deposition of North African dust serves as
an important source of nutrients and may alter the biogeochemical cycle of oceanic and terrestrial ecosystems,
such as in the North Atlantic Ocean (Talbot et al., 1986;
S. Engelstaedter et al. / Earth-Science Reviews 79 (2006) 73–100
Jickells et al., 2005) and the Amazon Basin (Swap et al.,
1992). Low level dust in the Saharan Air Layer (SAL)
seems to inhibit the strengthening of weak tropical
waves or disturbances and to weaken pre-existing
Atlantic tropical cyclones (Dunion and Velden, 2004).
Saharan dust has also been linked to environmental
hazards, including blooms of toxic algae known as ‘red
tides’ (Walsh and Steidinger, 2001) and the decline of
several species of amphibians in eastern Puerto Rico
(Stallard, 2001). Airborne dust from the Saharan desert
can also be a threat to human health, for instance, by
creating conditions which support the spread of
epidemics such as meningitis in the Sahel (Sultan
et al., 2005), or by increasing dust concentrations which
exceed regional air quality limits (e.g. South-eastern
America) (Gatz and Prospero, 1996; Prospero, 1999).
Dust has also been associated with a number of aviation
disasters in North Africa. Viable fungi and bacteria
found in aerosol samples at Barbados have been
associated with trans-Atlantic summer dust transport
which may cause allergic reactions or asthma (Prospero
et al., 2005). Their long-term variability and role in the
climate system is however still uncertain.
Next we consider the transport pathway northwards to
the Mediterranean. Israelevich et al. (2002) used the
TOMS AI to analyze the generation and transport of dust
to the Mediterranean Basin. He found that in spring and
summer the air over North Africa is almost permanently
loaded with significant amounts of dust, and low-pressure systems called Sharav cyclones mobilize the suspended dust and transports it northwards and eastwards
along the Mediterranean coast. Dust storm events start
appearing in the western Mediterranean sea and move
eastwards (7–8°/day), most of them traveling at least as
far as the eastern Mediterranean coast. For the eastern
Mediterranean Israelevich et al. (2003) identify three
periods of increased atmospheric dust in spring (March–
May), summer (July–August) and autumn (September–
November) that show distinct differences in the particle
size distributions and the real and imaginary parts of the
refractive indices. During these periods size distributions
indicate the existence of both fine and coarse modes. The
size distribution of the coarse mode in summer and
autumn (∼ 3 μm) is twice as large as in spring (∼ 1.5 μm).
The dependence on wavelength of the real part of the
refractive index in summer and autumn is almost the
same but differs significantly for spring; whereas the
imaginary part of the refractive index shows significant
differences between spring/summer and autumn. These
changes in the physical and optical properties suggest
variations in the chemical composition of the dust which
may be related to changes in the source and transport
81
pathways. By looking at the geographical distribution of
the TOMS AI Israelevich et al. (2003) suggest different
sources and transport pathways for the three dust periods
(Fig. 4). They suggest that in spring, dust is transported
from Chad and maybe a source in Algeria along the
North African coast to the eastern Mediterranean, a
transport pathway associated with Sharav cyclones. In
summer, dust originates probably from sources on both
sides of the Red Sea. Locating the origin of dust in
autumn is difficult. Dust is transported from the Libyan
coast towards eastern Mediterranean, but dust transport
to the Libyan coast is possibly from Chad, sources
Fig. 4. Distribution of largest aerosol index values observed at any
given point above North Africa and Mediterranean. Black areas denote
main dust sources. Arrows show schematically the trajectories of the
dust transport. From top to bottom: data for March–May, July–
August, and September–November. With permission from Israelevich
et al. (2003).
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nearby the Red Sea, and from Libyan sources. Also,
autumn dust trajectories pass over small but strong
sources near Benghazi (31°N, 21°E), Libya.
Using Meteosat retrievals of dust optical depths,
Moulin et al. (1998) show that the northwards transport
of dust follows a seasonal pattern, shifting from the eastern
to the western Mediterranean basin during March to
August, while during autumn and winter there is only very
little dust. Three different major cyclogenesis situations
are thought to be responsible for this pattern (Fig. 5). The
transport pathways suggested by Moulin et al. (1998)
broadly agrees with the findings of Israelevich et al. (2003)
for dust transport to the eastern Mediterranean.
Using air mass back trajectory analysis, Kubilay et al.
(2000) suggests that the source regions for the dust in
the eastern Mediterranean are both in North Africa and
in the Middle East, although the North African Sahara is
more common (Goudie and Middleton, 2001). It is also
suggested that dust transport towards the North Atlantic
Ocean from July to November occurs at higher altitudes
than the more southwards orientated dust transport
between March and May (Chiapello et al., 1995). Dust
from the Sahara is frequently observed in southern
Europe (e.g. Mattsson and Nihlen, 1996; Carinanos et
al., 2004; Rogora et al., 2004; Lyamani et al., 2005) but
it is also transported farther north towards central
Europe (e.g. Bücher and Dessens, 1992; Schwikowski
et al., 1995; Coen et al., 2004; Vukmirovic et al., 2004)
and even as far as England (Thomas, 1982; Burt, 1991;
Ryall et al., 2002) and Scandinavia (Franzen et al.,
1994). Dust deposition in the Alps has been suggested to
affect the environment and climate by reducing glaciers
albedo leading to an increased glacier melting (Franzen
et al., 1994), which in turn reduces the regional land
surface albedo, and by altering the chemistry of Alpine
lakes (Psenner, 1999).
Prior to the advent of satellite derived dust data from
Meteosat (e.g. Moulin et al., 1997) and the TOMS AI
data (e.g. Middleton and Goudie, 2001), work on transport pathways invariably relied on samples made at
target regions (e.g. Barbados, Amazon, Swiss Alps)
with back trajectories computed from weather forecast
related wind products or else inferred from climatological winds. Satellite data allows much firmer analysis of
pathways to be undertaken to the extent that transport
quantities rather than pathways alone can now be
estimated (e.g. Kaufman et al., 2005). This, combined
with the reanalysis data (NCEP and ERA-40), means
that a fuller assessment of transport is now possible and
that these ‘observed’ pathways can be compared with
those of dust models (see Section 6). In the case of the
Sahara, where transport occurs over uninhabited regions
and then primarily out over the Atlantic Ocean, both the
satellite data and the reanalysis wind data have provided
a rich source of opportunity to constrain transport and
steer aircraft campaigns such as the Dust and Biomass
Experiment (DABEX) and Dust Outflow and Deposition to Ocean experiment (DODO) to fly on the pathways commonly followed by dust. In the absence of
satellite products, such analysis is barely possible.
4. Temporal variability
Fig. 5. Main meteorological synoptic situation for dust generation and
transport during spring and summer in the Mediterranean. Locations of
major low (L) and high (H) pressure centers are shown. The
cyclogenesis is adapted from Alpert et al. (1990). The plates show
Sharav cyclons in April (top), the coupling between Saharan low and
Libyan high in June (middle), and the effect of the Balearic
cyclogenesis in August (bottom). The frequency (Fr.) of dust
mobilization over North Africa has been estimated from IDDI derived
Meteosat images for the period 1984–1994 using the method of
Legrand et al. (1994). Dust optical depth (O.D.) is estimated from
available Meteosat imagery (1984–1994). The lower and upper
horizontal dashed lines correspond to latitudes 30° and 40° N,
respectively. With permission from Moulin et al. (1998).
Like many quantities in the atmosphere, North
African dust is highly variable in space and time. At
the same time, the existence of temporal variability
provides the opportunity to establish controls on the
variability and thereby to understand the causes of dust
emission and transport better. The observed dust
variability and its controls are discussed with respect
to the time scales on which they occur (diurnal to
multiannual) in Sections 4.1 4.2 4.3 4.4 4.5. Although
changes in quaternary accumulation rates in terrestrial,
S. Engelstaedter et al. / Earth-Science Reviews 79 (2006) 73–100
marine and ice core sediment records suggest that
aeolian dust emissions are highly variable over
geological time scales (e.g. Kohfeld and Harrison,
2001), especially during glacial and inter-glacial stages,
here we discuss the observational record only.
4.1. Diurnal cycle
Because satellite measurements provide data about
atmospheric dust distributions at most only once or
twice a day (depending on the orbital overpass), only
synoptic data based on hourly observations from
meteorological stations (e.g. visibility or frequency of
dust events) provide reliable information about the
diurnal cycle of dust emissions. The analysis of 3-hourly
visibility data for the period 1951–1997 from the West
African stations Niamey, Zinder, Gao, Tombouctou and
Bilma shows that the maximum frequency of dust
storms is recorded during the day between 9:00 and
15:00, and minimum frequency in the night between
21:00 and 3:00 (Ozer, 2001). The increased occurrence
of dust emissions during daytime can be explained by
enhanced convection in a deepened boundary layer
during the day. In a similar study, N'tchayi Mbourou et
al. (1997) also show that the dust peak occurs during the
day, but the magnitude and the synoptic hour vary from
station to station. Stations located farther away from the
source regions seem to experience only weak variations
in dust occurrence within the diurnal cycle, whereas
stations located at or near the source area are
characterized by much larger variability (N'tchayi
Mbourou et al., 1997). For the Sahelian stations of
Tombouctou and Agadez (Ozer, 2001) and FayaLargeau (Bertrand et al., 1979), the difference between
the maximum value of dust occurrence during day and
minimum value during night is especially large. These
analyses suggest that convection in the boundary layer
and its diurnal variation is the dominant influence on
dust concentrations at or near the source region. The
diurnal cycle of dust concentrations at stations located
farther away from the dust sources is more strongly
influenced by transport, which is less influenced by
changes in the boundary layer depth.
4.2. Intraseasonal variability
Only little is known about the intraseasonal variability of North African dust emissions ranging on time
scales of several days. It has been hypothesized that
African Easterly Waves (AEW), a synoptic-scale feature
of the North African circulation system which prominent in the boreal summer, may play an important role in
83
dust mobilization (Jones et al., 2003, 2004). AEWs
originate close to the Ethiopian Highlands and propagate westwards at periods of 3–5 days with a maximum
amplitude of 1–2 m s− 1 at about 700 hPa (Burpee,
1972) and are related to tropical cyclone activity
(Thorncroft and Hodges, 2001). Jones et al. (2003)
suggest that about 20% of the dust entrainment into the
atmosphere and 10–20% of the seasonal variability of
dust concentrations across the Atlantic Ocean are
associated with AEW activity, which highlights the
importance of intraseasonal variations of atmospheric
circulation patterns in modulating dust production and
transport processes. However, it has also been suggested
that in return the radiative effects of North African dust
aerosols are responsible for differences in the amplitude
of AEWs (Jones et al., 2004), a possible feed-back
mechanism.
Transient eddies in the subtropics have also been
associated with intraseasonal variability of dust emissions (Washington and Todd, 2005). On the basis of
lagged regression between a daily index of Bodélé dust
and NCEP circulation data, Washington and Todd
(2005) have traced variability of dust emissions from
this key source in Chad to anticyclones ridging through
the Mediterranean and Libya occurring on times scales
of a few days. The frequency and strength of these
systems are sufficient to impose themselves on monthly
anomalies of geopotential height fields over North
Africa (Washington and Todd, 2005).
The understanding of the intraseasonal variability of
dust in the Sahel is likely to receive a major boost with
the African Multidisciplinary Monsoon Analysis
(AMMA), the largest land based climate experiment
yet undertaken, which has a period of special observation (SOP) during the summer of 2006.
4.3. Annual cycle
Satellite and observational data shows that North
African dust emissions follow a strong annual cycle.
Here we describe the annual dust cycle as derived from
the TOMS AI (Fig. 6). In general, five dust source
regions can be distinguished with respect to their
different seasonal behavior, (a) the Bodélé Depression,
(b) West Africa, covering large areas of Mauritania,
Mali and southern Algeria (hereafter called WA source),
(c) North-West Africa, covering a region from northeast
Algeria (south of Altas Mountains) to central Algeria
(hereafter called NWA source), (d) the Libyan Desert,
including sources in south, central and northeast Libya,
and (e) the Nubian desert, covering several sources from
central Egypt to central Sudan and a source on the
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Fig. 6. Monthly mean TOMS AI (× 10) (1980–1992).
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eastern Sudanese coast (hereafter Nubian source).
Unlike many other sources, the Bodélé Depression is
active throughout the whole year with a peak in May and
lowest values in August (Fig. 6), although analysis of
MODIS dust plumes emanating from the Bodélé point
to January to March as the peak months (Washington
and Todd, 2005). A minimum in most data sources is
reached in July and August. The annual dust cycle of the
NWA sources is very similar to that of the WA sources
which in the TOMS AI data shows an increase in dust
activity from March onwards, peaking in summer (June/
July) and subsiding in September (Fig. 6). The dust
source regions in south, central and northeast Libya
peak from April to June. The Nubian dust sources are
active in the summer between April and August with
highest values at the eastern Sudanese coast in June/
July. Lowest dust values in North Africa seem to prevail
in October/November. A visual comparison with the
works of N'tchayi Mbourou et al. (1997) who plot the
averaged number of hours with horizontal visibility
reduced below 5 km at 53 stations in North Africa for
the seasons January–March, April–June, July–September and October–December (not shown) shows that the
spatial pattern of the annual dust cycle is very similar.
Explaining the spatial and temporal characteristics of the
annual dust cycle in North Africa remains difficult,
although satellite data have provided the means by
which the first annual cycles of the source regions can
be estimated.
4.3.1. Impact of rainfall
The annual dust cycle may be partly explained by
seasonal changes in the positions of the Intertropical
Convergence Zone (ITCZ) and associated rainfall
distributions. The influence of the monsoon rain is
evident in the distinct southern boundary at about 15° to
17° N from May to September (Fig. 6), which follows
the northwards advance and retreat of the ITCZ in the
summer months. The ITCZ is characterized by the
convergence of humid southeasterly winds and dry
northeast trade winds resulting in a band of heavy
precipitation, which moves north starting in February,
reaching its most northern position in June/August at
about 20°N (Hastenrath, 1991) and retreating southwards until December. This seasonal pattern of the
atmospheric general circulation is responsible for the
rainy (monsoon) season, which occurs in the summer in
the Sahel. Rainfall reduces the atmospheric dust content
in two ways, (1) it increases soil moisture and vegetation
cover, both reducing dust emission from the ground and
(2) it is cleansing the atmosphere by removing dust and
smoke particles (wet deposition).
85
Fig. 7. The annual cycle of TOMS AI (filled contours) and rainfall
(black line contours) in mm yr− 1 displayed as a function of latitude
from 5°N to 37°N taken at 0.5°W representing a section through North
Africa from the southern coast of Ghana through Burkina Faso, Mali
and Algeria to the Mediterranean Sea. TOMS AI values were
calculated over 1982–1990 and rainfall over 1961–1990. Rainfall
data were taken from New et al. (1999).
The relationship between the annual cycle of dust
and rainfall is illustrated in Fig. 7 showing a latitude
section of the TOMS AI (filled contours) through the
Sahel and Sahara at 0.5°W from 5°N near the southern
coast of Ghana through the Sahel and Sahara to the
Mediterranean at 37°N. Highest aerosol concentrations
are evident in the central Sahara between ∼ 15 and 28°N
in summer (May–September) with the peak centered
around 21.5°N and somewhat lower values near the
Mediterranean coast between 5 and 10°N in winter
(December–February). In the transition between winter
and summer (February–April), the region between 10
and 15°N shows lower but still significant amounts of
absorbing aerosols in the atmosphere. Near the
Mediterranean coast high TOMS AI values occur only
in summer (June–August). Superimposed on the TOMS
AI is rainfall (back contours) which in the south show
highest values in the summer (up to 2500 mm yr− 1 at
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S. Engelstaedter et al. / Earth-Science Reviews 79 (2006) 73–100
∼ 11°N in August) with intensity and length of the rainy
season decreasing northwards towards the Sahel. In the
central Sahara rainfall is very low b 250 mm yr− 1. Close
to the Mediterranean coast rainfall values are increasing
again to above 500 mm yr− 1 but contrary to the south of
the Sahara where the rainfall season is in summer
highest rainfall occurs from autumn to spring (September–May).
It is thought that to a large degree the high TOMS AI
values in the south in winter are caused by biomass
burning in the Sahel–Soudano region which takes place
in the driest season of the year (Herman et al., 1997).
However, biomass burning aerosols get mixed with dust
as in winter the atmospheric dust transport from the
Sahara and Sahel follows more southwards trajectories
towards the Gulf of Guinea (see Section 3). Towards the
Sahel the length of the dry/burning season increases.
Atmospheric dust content is likely to increase compared
to smoke as population density decreases (resulting in
fewer fires) and surfaces are more prone to dust
emission due to lower vegetation cover. Low values of
TOMS AI from the equator up to 15°N correspond well
with high values of rainfall which removes aerosols
from the atmosphere or inhibit production of aerosols by
soil deflation or biomass burning. North of ∼ 15°N
rainfall rates are too low (b 200 mm yr− 1) to scavenge
atmospheric dust and smoke. In the central Sahara the
rainfall season (though short and with very low rainfall)
occurs almost simultaneously with the dust season. Peak
rainfall seems to lag behind peak dust concentrations by
1–2 months. This may be explained by dry convection
and associated near surface disturbances which occur in
the central Sahara in summer (see following section
‘Role of the atmospheric circulation’ for more details).
The observed relationship between rainfall and dust/
smoke described here for the longitude 0.5°W seems to
be representative for most parts of North Africa (not
shown). Rainfall controls atmospheric aerosols north
and south of the Sahara but not in the central Sahara
where the major dust sources are.
Fig. 8 shows monthly mean number of hours of
visibility reduced below 10 km due to atmospheric dust
(white bars), rainfall (black bars) and respective TOMS
AI values (grey bars) for nine North African stations
which are located from southern coast (Gulf of Guinea)
to Mediterranean coast in the north centered around the
Greenwich Meridian: Abidjan (5.25°N, 3.93°W), Bobo
87
Bioulasso (11.17°N, 4.3°W), Dori (14.03°N, 0.05°W),
Tombouctou (16.72°N, 3.0°W), Tessalit (20.2°N,
0.98°E), Adrar (27.88°N, 0.28°W), El Golea
(30.57°N, 2.87°E), Touggourt (33.12°N, 6.13°E) and
Oran (35.63°N, 0.6°W). Although both visibility and
TOMS AI data are used as a measure of the atmospheric
dust content, the nature of these data sets is very
different. For instance, visibility is measured horizontally thus represents horizontal dust content, is sampled
in 3-hourly time steps and represents a point measurement in space whereas the TOMS AI is a measure of
aerosols content in the atmospheric column (vertical), is
derived from a single measurement per day (at local
noon) and corresponds to an area of several thousand
square kilometers. Despite these differences, the annual
cycle of the two data sets match reasonably well at most
stations (Fig. 8). At Abidjan, the most southern station,
the annual cycle of the number of hours with reduced
visibility and TOMS AI are very similar both peaking in
winter (January–December) when rainfall is lowest. At
Bodo Bioulasso the peak of the TOMS AI annual cycle
already starts shifting from winter to spring and at Dori
the annual cycle reverses completely with highest values
in summer (June). This shift in the dust peak from winter
to summer is also evident in the number of hours with
reduced visibility from south (Abidjan) northwards
lagging slightly behind the shift in TOMS AI. However,
high values of reduced visibility seem to prevail in
winter, progressively declining towards the north,
resulting in a semi-annual cycle until Tessalit at
20.2°N. Rainfall values peak in summer and decline
progressively towards the north until Tessalit. At the
Saharan stations Adar, El Golea and Touggourt which
are not affected by the monsoon rainfall both reduced
visibility and TOMS AI peak in summer, although the
TOMS AI peak generally 2–3 month later then reduced
visibility. Whereas the intensity of the TOMS AI does
not change significantly at these three stations, reduced
visibility shows highest values at El Golea and lowest
values at Touggourt. At Oran located near the Mediterranean coast, atmospheric dust seems to be controlled by
a semi-annual rainfall cycle peaking in February and
November which results in high numbers of hours of
reduced visibility in summer.
The seasonality of the North African rainfall distribution seems to have an effect on the dust cycle in the
Sahel and close to the Mediterranean coast, but the
Fig. 8. Monthly means of number of hours with visibility reduced below 10 km (white bars), TOMS AI multiplied by 100 (grey bars) and rainfall in
mm (black bars). All monthly means are for the period 1983–1987. The monthly mean TOMS AI was calculated from the 1.25° by 1° grid box that
covers the area where the respective station is located in. The left scale applies to all data sets. Visibility measurements and rainfall data are based on
N'tchayi Mbourou et al. (1997).
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overall observed dust variability cannot be explained by
rainfall patterns alone. Changes in the atmospheric
general circulation, as observed in the changing position
of the ITCZ, control rainfall patterns, but may also have
an effect on other factors, for instance the occurrence of
surface wind extremes.
4.3.2. Role of the atmospheric circulation
We have shown that rainfall patterns driven by the
monsoon seem to have an impact on the annual cycle
of dust in the Sahel and southern fringes of the Sahara.
However, the most intense source areas are located
farther north in the hyper-arid regions of North Africa
where rainfall and vegetation cover is extremely low
(Figs. 2 and 3). The annual cycle of dust in these
regions is probably therefore controlled by changes in
the near surface wind. The relationship between the
annual cycle of wind and its impact on dust emission
is, however, only rarely addressed in the literature (e.g.
Ozer, 2001).
During winter, most parts of North Africa are
characterized by strong near surface trade winds from
N to NE, known as the Harmattan winds, which
converge with moist maritime air from the equator near
5–10°N building the ITCZ (Fig. 9a). Towards the
summer this convergence belt shifts northwards, reaches
its most northern position between 15 and 23°N in
August (Fig. 9b), before retreating back south until
December. The convergence belt moves much farther
north than the monsoon rainfall belt. All major dust
sources in the western-central Sahara (e.g. as defined by
TOMS AI, Fig. 2) are located in regions that are affected
by the crossing of the convergence belt during summer
when dust emission are highest. This timely coincidence
suggests that the converging winds may incorporate
processes such as dry, deep convection which favor the
emission of dust by enhancing near surface turbulence.
Dry convection is thermally driven by the slope of
potential temperature with height. It is likely that dry
convection is also associated with near surface atmospheric disturbances increasing the frequency by which
wind speed exceeds the threshold velocity. This may also
explain why monthly mean wind speed (as defined by
ERA-40, Fig. 9) at the convergence line is very low
(usually below 1 m s− 1) whereas dust emissions are high
because an increase in gusty winds at the surface would
not have much affect on mean wind speed. Together, this
would lead to increased dust emissions and an increased
vertical transport of dust into higher altitudes. These
considerations agree well with the findings of Alpert et
al. (2006) who show that over the Sahara region the
annual cycle of daily TOMS AI is highly correlated
(0.98) with integrated daily solar insulation which
controls surface heating and thus dry convection.
Like the western sources, the Bodélé Depression is
dominated by the northeasterly Harmattan winds of
North Africa which are shown by the NCEP reanalysis
data to be present in the lowest 100 hPa of the
atmosphere in all months except July and August,
when the intertropical convergence zone moves sufficiently far north to bring light and variable winds over
the Bodélé. Near surface wind speeds follow a clear
annual cycle with a maximum in the winter months and
a minimum between July and September. Dust tends to
follow a semi-annual cycle. Peaks in the winter/spring
surface and near surface winds and dustiness are
contemporaneous however, as is the low dust production and weak winds during high summer.
Other factors, such as changes in soil moisture or the
availability of deflatable sediments may also play a role
in controlling the annual dust cycle in North Africa.
Also, the role of the annual cycle of vegetation cover in
the Sahel in controlling dust emissions is not well
understood. Many open questions remain in explaining
the full annual dust cycle, in particular, why the months
October and November appear as relatively dust free
and how the combined effects of rainfall and disturbances affect dust emissions.
4.4. Interannual variability
Satellite derived data sets are now sufficiently long
to support comprehensive analyses of dust on
interannual time scales. The year-to-year variations in
North African dust transport over the northern tropical
Atlantic (15–30°N and 5–30°W), using combined
aerosol observations from TOMS/Nimbus-7 (1979–
1993) and Meteosat/VIS (1984–1997), have, for
example, been analyzed over nearly 20 years (Chiapello and Moulin, 2002). The analysis shows that there
is a high year-to-year variability in winter (December–
March) dust optical thickness (DOT) over the study
area, with years (e.g. 1986) where dust is almost absent
and years (e.g. 1989) where dust is almost as high as in
summer. The mean dust optical depth between the
years 1984 and 1992 varies in winter by a factor of 3,
whereas summer and annual values vary only by a
factor of 1.5, which suggests that Saharan winter dust
emissions impact significantly on the interannual
variability of dust transport over the northern tropical
Atlantic. Using combined TOMS and Meteosat data
over 22 years (1979–2000), Chiapello et al. (2005)
show that dust optical thickness over the north tropical
Atlantic is in good agreement with dust concentrations
S. Engelstaedter et al. / Earth-Science Reviews 79 (2006) 73–100
89
Fig. 9. Monthly mean ERA-40 10 m surface wind speed (1980–92) in m s− 1 and direction for (a) January and (b) August. Regions with altitudes
higher than 800 m are shaded grey. The position of converging winds at the ITCZ is marked orange. (For interpretation of the references to colour in
this figure legend, the reader is referred to the web version of this article.)
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measured at Barbados (Prospero and Lamb, 2003) at
both monthly and annual time scales, suggesting that
the Barbados dust record is representative for transatlantic dust transport during both summer and winter.
By looking at the climatology of TOMS AI (1979–
1992) at major dust source regions in North Africa (as
defined in Prospero et al., 2002), Barkan et al. (2004)
demonstrate that for the West African sources in
Mauritania and Mali there is a well defined discontinuity
between the period 1979–1982 (less dusty conditions)
and the period 1983–1992 (more dusty conditions).
Their study showed that in general North African dust
sources were particularly dusty in the years 1983–1984
(mainly 1984) and 1987–1988 (mainly 1988), whereas
in the year 1986 there was only little dust. The dust
sources in Libya, Sudan and Ethiopia show highest
TOMS AI values in 1991 (Barkan et al., 2004).
Several studies found a connection between the
atmospheric dust content in North Africa and rainfall
data from the previous year or years. For example, the
correlation coefficient for the relationship between
precipitation and the frequency of days with dust haze
at Bilma is − 0.65, and increased to − 0.88 when dust
haze was correlated to rainfall of the three preceding
years (Bertrand et al., 1979). Similarly, the correlation
between rainfall and IDDI data for the period May to
October suggests that in the Sahel, enhanced dust
emissions in March and April are correlated with
reduced rainfall in the previous year (Brooks and
Legrand, 2000). Annual dust concentrations measured
in the African trade winds at Barbados also show a
stronger correlation (r = 0.75) to Sahelian rainfall from
the previous year (Prospero and Lamb, 2003). A
possible explanation for this relationship might be the
slow adjustment time of vegetation to the faster
changes in precipitation, which is also observed in
satellite data (Goward and Prince, 1995) and in the
results of a coupled atmosphere–land–vegetation
model for the West African Sahel (Zeng et al.,
1999). Drought conditions in the Sahel affect dust
emission and transport in both summer and winter
from year to year with atmospheric dust being closely
linked to previous-year rainfall (Moulin and Chiapello,
2004; Chiapello et al., 2005) which suggests control of
dust emissions by a change in vegetation cover in the
Sahel. However, the interactions between vegetation
and precipitation and the role of vegetation in
controlling dust emissions are only poorly understood.
Interannual variability in dust from the Bodélé
seems to be closely related to modulation in the
strength of the Bodélé Low Level Jet. (BLLJ). From
the TOMS AOT record for the Bodélé Depression,
Washington and Todd (2005) studied the two most
dusty and least dusty January, February, March and
April months for the period 1979–1992, yielding
8 high and 8 low dust sampling months. Composite
zonal wind anomalies were calculated from ERA-40
data based on these samples of high minus low TOMS
AOT months. Results indicate a strengthening of the
BLLJ by about 3 m s− 1 over the Bodélé during the
high dust months relative to the low dust months,
representing a deviation of about 40% relative to the
mean BLLJ zonal velocity. A subsequent analysis of
Bodélé dust plumes identified from MODIS showed
that March dust plume events between 2002 and 2004
inclusive all showed an enhanced BLLJ structure
(Washington et al., 2006a).
4.5. Multiannual variability and trends
Multiannual variations in North African dust emissions in recent decades have been observed using data
from meteorological stations (Bertrand et al., 1979;
Goudie and Middleton, 1992; Ozer, 2001), satellite data
(Chiapello and Moulin, 2002; Moulin and Chiapello,
2004; Chiapello et al., 2005) and dust concentration
measurements (Prospero and Lamb, 2003). Both
satellite and surface observations show that dust
emissions can vary significantly on longer time scales
with most data showing a general increase in dustiness
over the last few decades (Bertrand et al., 1979; Goudie
and Middleton, 1992; Ozer, 2001). For instance, Ozer
(2001) shows that dust storm frequencies in the dry
season (October–April) increased over large areas of
West Africa by factors of up to 10 between 1951 and
1997. Also, dust concentrations measured at Barbados
were lowest in the late 1960s, and values have been
higher ever since (Fig. 10) (Prospero and Lamb, 2003).
In trying to explain the observed variability it has been
suggested that long-term trends in rainfall play an
important role in modulating dust emissions in North
Africa. Phases of increased dustiness seem to be associated with reduced rainfall conditions especially in the
Sahel (Prospero and Nees, 1986; Goudie and Middleton,
1992; Moulin and Chiapello, 2004; Chiapello et al.,
2005).
Well-known modes of variability in the atmosphere,
such as the North Atlantic Oscillation (NAO) and the
El Nino Southern Oscillation (ENSO) and the related
Pacific Decadal Oscillation (PDO) are characterized by
multiannual variability. As these atmospheric phenomena affect rainfall patterns and the general circulation,
it might be expected that the NAO and ENSO are also
related to dust variability. Indeed, it has been suggested
S. Engelstaedter et al. / Earth-Science Reviews 79 (2006) 73–100
91
Fig. 10. Monthly mean dust concentrations measured at Barbados (1965–1998). Arrows indicate years when there were strong El Nino events as
identified by the NOAA CIRES El Nino web site. With permission from Prospero and Lamb (2003).
that the variability of North African winter dust concentrations are associated with the NAO (Chiapello
and Moulin, 2002) and ENSO (Prospero and Nees,
1986). An earlier finding that also interannual variations in summer dust transport are correlated to the
variability of the NAO (Moulin et al., 1997) has been
revised by Moulin and Chiapello (2004) who showed
that over a longer time period summer dust concentrations are more strongly correlated to the previous-year
Sahel Drought (SD) index than to the NAO (the SD
index being a phenomena independent from the NAO).
The influence of the NAO on North African dust
emissions dominates in winter and seems to be
geographically restricted to eastern Atlantic north of
15°N, southern Mauritania and the Bodélé Depression
in Chad (Chiapello et al., 2005). In a model study,
Ginoux et al. (2004) find a similar relationship
between the NAO and the North Atlantic and Africa
dust loading in winter suggesting that correlation can
be attributed to emissions from the Sahel.
Only little is known about long-term changes of
wind speed patterns in North Africa and their effect on
dust emission and transport. However, two independent studies suggest that periods of low rainfall can
have an effect on winds, such as an increase in the
frequency by which wind exceeds the threshold wind
speed in Nigeria (Ozer, 2001) or a stronger lower
troposphere easterly flow over the Sahel (Newell and
Kidson, 1984). Many questions remain in explaining
the observed multiannual variability of North African
dust. For instance, which processes establish the link
between large-scale patterns, such as NAO and ENSO,
and dust emissions and transport, or, why lead periods
of reduced rainfall to an increase in dust occurrences?
In this respect, the reanalysis data sets will prove
invaluable, particularly over the data sparse Sahara.
5. Dust properties and radiative effects
The presence of dust in the atmosphere can lead to
changes in the earth's radiation budget (‘radiative
forcing’). A positive forcing, i.e. a net increase in
downward radiative fluxes, tends to warm whereas a
negative forcing tends to cool the atmosphere. Estimates
of the radiative forcing of aerosols including mineral
dust are highly uncertain so that the net effect of aerosol
forcing on the earth's surface temperature remains unclear (Kaufman et al., 2002).
Dust aerosols directly affect climate by scattering and
absorbing solar radiation and out-going thermal radiation
(direct radiative effect) (Tegen et al., 1996; Haywood and
Boucher, 2000; Harrison et al., 2001; Haywood et al.,
2001; Sokolik et al., 2001) and indirectly by processes that
change the physical and microphysical properties of
clouds such as brightness, lifetime, structure, size, area of
coverage or precipitation productivity (indirect radiative
effect) (Levin et al., 1996; Han et al., 1998; Wurzler et al.,
2000; Rosenfeld et al., 2001). It has also been found that
dust aerosols in clouds can act as ice condensation nuclei,
which can cause changes in the microphysical and
radiative properties, latent heating and precipitation
productivity in clouds (Levin et al., 1996; DeMott et al.,
2003; Sassen et al., 2003). Using daily satellite data over
the North Atlantic Ocean, Koren et al. (2005), found a
strong correlation between the presence of aerosols
(including desert dust from North Africa) and the
structural properties of convective clouds suggesting a
systematic invigoration (5% increase in cloud fraction and
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S. Engelstaedter et al. / Earth-Science Reviews 79 (2006) 73–100
an increase in cloud top height) of convective clouds by
aerosols.
To a large degree the magnitude of the direct radiative
forcing of dust aerosols depends on the dust physical,
chemical and optical properties, including particle size
distribution, color, shape, chemical and mineralogical
composition, which determine light absorption and scattering coefficients. Whether these radiative properties
are similar from one source to the other or different is not
well constrained as measurements are only available for
some source regions. The lack of information about
radiative properties of dust from different source areas
has led to the organization of coordinated field
experiments including in situ measurements and simultaneous satellite measurements. For North African dust,
such field experiments include the Saharan Dust
Experiment (SHADE) (Tanre et al., 2003), the Mediterranean Israeli Dust Experiment (MEIDEX) (Alpert et al.,
2004), the Bodélé Dust Experiment (BoDEx) (Washington et al., 2006a), the Dust and Biomass Experiment
(DABEX) (http://metresearch.net/DABEX) and the
Saharan Mineral Dust Experiment (SAMUM) (http://
www.tropos.de/samum) scheduled for 2006. Measurements have been taken at source regions (e.g. BoDEx) or
downwind of sources (e.g. SHADE).
During BoDEx some dust properties were measured at
one of the world's most intense sources, the Bodélé
Depression in Chad. Retrievals of the size distributions
from measurements by a Cimel C-318 sun-sky spectral
radiometer, an instrument which is also used in the NASA
Aerosol Robotic Network (AERONET) (Holben et al.,
1998), show a dominant coarse mode centered around
1.5–2.0 μm and minor fine mode centered close to
0.5 μm (Todd et al., in press). The coarse mode is similar
to other available observations for North Africa, such as
long-term observations from AERONET (Dubovik et al.,
2002) or short-term in situ measurements (Highwood et
al., 2003). The fine mode, however, is rather unusual for
desert dust and may be related to the source material of
the depression which is dominated by fragmented fossil
diatoms from a dry lake bed, or may be a residual from the
retrieval method. The net radiative effect of a relatively
moderate (compared to other dust events derived from
MODIS imagery (Washington et al., 2006a)) dust event
during 10–12 March 2005 reduced near surface maximum temperature by about 7 °C (Todd et al., in press).
During BoDEx the mean single scattering albedo for
locally produced dust on days when AOT440 exceeds 0.3
ranges from 0.994 to 0.98 at 441 and 1022 nm
respectively (Todd et al., in press). On 5 of March,
when the source of dust was further east in the region of
the Ennedi mountains, single scattering albedo values
were considerably lower. In agreement with other recent
publications (e.g. Kaufman et al., 2001; Dubovik et al.,
2002) these findings suggest that absorption of solar
radiation by Saharan dust is weak at wavelengths greater
than 550 nm whereas absorption in the blue part of the
spectrum can be pronounced at some source regions
which will affect the magnitude of the radiative forcing
effect (Todd et al., in press). These estimates of the
radiative properties of dust were derived either from
satellite retrievals or sunphotometer measurements,
which cannot clearly isolate the ‘pure’ dust, as atmospheric aerosols are mostly mixtures of different species.
Laboratory measurements of dust samples (as summarized e.g. in Sokolik and Toon, 1996) showed that dust
absorbed more at solar wavelengths than had been indicated by the optical remote and in-situ measurements.
This discrepancy has not yet been resolved.
In order to determine the dust parameters relevant for
the direct radiative effects, SHADE took place during the
period 19–29 of September 2000, where in situ and
remote sensing instruments on aircrafts were coordinated
with satellite overpasses and surface observations in the
Cape Verde area (Tanre et al., 2003). These regions lie
downwind of the North African dust sources in summer. A
subsequent analysis showed that during a dust event
occurring on 25 of September 2000 outgoing terrestrial
radiation was reduced by 6.5 Wm− 2 at the TOA whereas
downwelling terrestrial radiation increased by 11.5 Wm− 2
at the surface (Highwood et al., 2003). Over the ocean, the
direct radiative effect reached very large values in the
short wave spectrum (approximately −130 Wm− 2 on 25
of September) compared to other aerosols types (Haywood et al., 2003). The modeled estimates of the direct
radiative effects during SHADE show a much stronger
solar radiative impact than thermal (terrestrial) impact
with an estimate of the global and diurnal mean net
radiative forcing at the TOA of approximately
−0.4 Wm− 2, having a cooling effect (Myhre et al.,
2003). The major uncertainty in current dust forcing
estimates is the imaginary part of the refractive index of
dust, which largely controls its single scattering albedo
that is responsible for the partition of absorbed and
scattered light. A comparison between sea surface
temperatures and the occurrence of dust events shows
that the direct radiative effect of dust over the Atlantic
Ocean off the West African coast can also lead to cooler
sea surface temperatures (Schollaert and Merrill, 1998).
In general, dust particles are less efficient absorbers
than smoke particles. Retrievals from satellites and
sunphotometers indicate that absorption accounts for
5% of the aerosol optical thickness for Saharan dust
(Dubovik et al., 2002; Kaufman et al., 2002). The
S. Engelstaedter et al. / Earth-Science Reviews 79 (2006) 73–100
estimates of the direct radiative of effect dust aerosols
demonstrate that atmospheric dust significantly alters the
earth's radiation budget. Most climate models, however,
do not account for this alteration. For instance, Haywood
et al. (2005) suggest that under cloud-free conditions over
North Africa atmospheric mineral dust can result in a
radiative forcing of up to 50 Wm− 2 which would explain
the discrepancy between satellite and model observations
of outgoing longwave radiation at the TOA. They go on
to suggest that the inclusion of dust in numerical weather
prediction would improve forecasts.
6. Modeling of North African dust
An important achievement of the last decade has been
the development of numerical models that include the
simulation of emission, transport and deposition of Saharan
dust aerosol in order to quantify its export to the North
Atlantic and Mediterranean, as well as to study its effects on
the climate system. In this section we review the progress
made in numerical modeling efforts, paying attention to the
ways in which emission is simulated, go on to consider the
range of global dust emission estimates and dust transport
and conclude with comments on dust deposition.
Modeling the Saharan dust cycle within global models
(e.g. Tegen and Fung, 1994) was initially limited by the
assumption of uniform surface properties for the whole
Saharan desert and computation of emission and transport
using climatological wind fields, which did not reproduce
individual dust events. A significant improvement in the
modeling of Saharan dust was achieved by accounting for
regional variability in surface properties (Marticorena et al.,
1997). The parameterization of topographical depressions
as areas of preferential dust emission and the use of
reanalysis winds to calculate emission and atmospheric
transport of Saharan dust aerosol, permitting the simulation
of individual dust events, brought further improvements
(Ginoux et al., 2001; Tegen et al., 2002; Zender et al.,
2003). Such dust models could be used to reproduce dust
events during the recent field experiments PRIDE (Colarco
et al., 2003) and SHADE (Myhre et al., 2003), so helping to
interpret the results from the field measurements. Recent
model efforts include the forecasting of Saharan dust storm
occurrence and dust transport to Europe (Nickovic et al.,
2001; Barnum et al., 2004).
Saharan dust emission is mostly computed as saltation process, by which moving sand particles and clay
aggregates that are mobilized by wind dislocate
micrometer-sized dust particles when impacting on the
ground (e.g. Alfaro et al., 1997; Shao, 2001). Dust fluxes
are thus functions of surface wind speed or wind friction
velocity, together with soil properties like surface rough-
93
ness, texture, and wetness, as well as vegetation cover.
The ratios of horizontal saltation flux and vertical dust
fluxes depend on the availability of fine soil material and
can vary by several orders of magnitude (Marticorena
et al., 1997). While the dependence of dust emission on
surface wind friction velocity and surface properties like
texture and surface roughness is determined from wind
tunnel experiments and theoretical considerations (e.g.
Marticorena and Bergametti, 1995; Alfaro et al., 1997;
Fecan et al., 1999; Shao, 2001), the parameterization of
these processes in large-scale regional or global models
is limited by the quality of available input data for both
meteorological fields and soil properties. This is particularly a limitation in the case of the Sahara where dust
sources such as the Bodélé and the West Africa source
are extremely remote. Soil data sets for such regions are
likely to be interpolated over extensive regions, and the
reanalysis data sets used to drive the winds are initialized
by very few observations. Nevertheless, the reanalysis
data has allowed more realistic simulations as well as
specific events or specific periods to be analysed.
Mean annual dust emission estimates that have been
published over the last three decades for North Africa and
the total globe are summarized in Table 1. Numerical
modeling efforts have played a crucial role in this work.
Estimated annual emission for North Africa range from
170 to 1600 Tg yr− 1 and for the whole world from 1100 to
5000 Tg yr− 1. Estimates of global dust emission from
different studies over the last few years varied only
between 1000 and 2000 Tg yr− 1, but emission estimates
for North Africa still vary greatly, with most studies
reporting Saharan dust emissions between 500 and
1000 Tg yr− 1. Some studies (e.g. Ozer, 2001; Ginoux
et al., 2004) calculated estimates of annual dust emission
for North Africa which are the same order of magnitude
(or even higher) than estimates from other studies for the
whole world. Overall, the differences in the estimates
reflect the differences in the model parameterization and
input parameters and point to the need for further model
development and evaluation.
Atmospheric transport of dust is computed with tracer
schemes, which are embedded either directly in climate
models (Perlwitz et al., 2001), or in offline models driven
with assimilated meteorological fields, facilitating direct
comparisons with observations (e.g. Ginoux et al., 2001;
Tegen et al., 2002; Luo et al., 2003). Particle deposition
is simulated as a combination of sedimentation, turbulent
mixing to the surface, and wet deposition, often parameterized using a simple scavenging ratio (Buat-Menard
and Duce, 1986). Models using multi-year reanalysis
data (Mahowald et al., 2002; Ginoux et al., 2004) have
been used to understand the observational records of
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S. Engelstaedter et al. / Earth-Science Reviews 79 (2006) 73–100
Table 1
Estimates of mean annual dust emission (E) for North Africa and the
world. The data compilation is based on the works of Goudie and
Middleton (2001), Zender et al. (2004) and Mahowald et al. (2005).
⁎The estimate is based on the sum of emissions from the individual
source regions (W. Africa, C. Africa and E. Africa).+The estimate is
based on the sum of emissions from the individual sources.
Reference
Peterson and Junge (1971)
Jaenicke (1979)
Schütz (1980)
Schütz et al. (1981)
D'Almeida (1986)
Tegen and Fung (1994)
Duce (1995)
Tegen and Fung (1995)
Andreae (1996)
Marticorena and
Bergametti (1996)
Prospero (1996)
Swap et al. (1996)
Mahowald et al. (1999)
Callot et al. (2000)
Ozer (2001)
Penner et al. (2001)
Ginoux et al. (2001)
Chin et al. (2002)
Werner et al. (2002)
Tegen et al. (2002)
Zender et al. (2003)
Luo et al. (2003)
Mahowald and Luo
(2003)
Ginoux et al. (2004)
Miller et al. (2004)
Tegen et al. (2004)
Kaufman et al. (2005)
Jickells et al. (2005)
E for North Africa
(Tg yr− 1)
E globally
(Tg yr− 1)
500
260
Up to 5000
260
630–710
1800–2000
3000
1000–2000
1222
1500
586–665
170
130–460
3000
760
1600
1114⁎
1430
479–589
2150
1814
1650
1060 ± 194
1100
1490 ± 160
1654
1654
2073+
1018
1921
240 ± 80
1790
The data compilation is based on the works of Goudie and Middleton
(2001), Zender et al. (2004) and Mahowald et al. (2005). ⁎The estimate
is based on the sum of emissions from the individual source regions
(W. Africa, C. Africa and E. Africa).+The estimate is based on the sum
of emissions from the individual sources.
Saharan dust, in order to understand the effect of changes
in the various controls of the dust cycle.
While dust models are now able to capture major
patterns like the vast increase in dust deposition during
glacial periods which are related to decreased vegetation
cover and increased surface wind speeds at glacials
(Mahowald et al., 1999; Werner et al., 2002), attempting
to use such models to predict future changes in dust
emission and transport results in widely different results,
where even the sign of the change is unclear both for
global and regional changes (Mahowald and Luo, 2003;
Tegen et al., 2004; Woodward et al., 2005). A major
difference in these model results is the predicted vege-
tation coverage for the Saharan desert. The model results
showing the greatest decrease in dust production used an
equilibrium vegetation model that predicted the Sahara to
become partly vegetated under greenhouse warming conditions. While this diversity in model results for future
dust change estimates implies that still unresolved problems in the parameterization of the dust emission exist in
global models, it also shows that future climate model
scenarios for those parameters on which dust emissions
are particularly sensitive, like variability in surface wind
speeds or precipitation, as well as changes in vegetation
cover, still remain difficult to predict.
The dust particles can be removed from the atmosphere
either by gravitational settling (dry deposition) or by
removal through precipitation (scavenging, wet deposition). Over the oceans wet deposition is the main process
of removing dust particles from the atmosphere. Wet
deposition processes are, however, difficult to simulate in
dust models because the interactions between dust and
clouds are not well understood and not many accurate
measurements are available (Mahowald et al., 2005).
Therefore, many models (e.g. Duce et al., 1991; Tegen and
Fung, 1994; Mahowald et al., 2003) use simple scavenging ratios between 200 and 1000 based on observations
from Duce et al. (1991). Depending on the scavenging
ratio the wet deposition rates can be highly variable.
Table 2 shows mean annual dust deposition estimates
into the global oceans and into various ocean basins.
Estimates of dust deposition into the global oceans range
Table 2
Estimates of mean annual dust deposition to the global oceans (GO),
North Atlantic Ocean (NAO), South Atlantic Ocean (SAO), North
Pacific Ocean (NPO), South Pacific Ocean (SPO), North Indian Ocean
(NIO) and South Indian Ocean (SIO). Duce et al. (1991) use a
scavenging ratio (SR) of 1000 except for NAO (SR = 200) whereas
Prospero (1996) using the same model applied a SR of 200 globally.
⁎Estimates are based on the sum of the individual ocean basins.
Reference
Duce et al. (1991)
Prospero (1996)
Ginoux et al. (2001)
Zender et al. (2003)
Luo et al. (2003)
Ginoux et al. (2004)
Tegen et al. (2004)
Kaufman et al.
(2005)
Jickells et al. (2005)
Deposition (Tg yr− 1)
GO
NAO
SAO NPO SPO NIO SIO
910
358
478⁎
314
428⁎
505⁎
422⁎
220
220
184
178
230
161
259
140 ± 40
24
5
20
29
30
20
35
480
96
92
31
35
117
56
39
8
28
8
20
28
11
100
20
154
36
113
164
61
17
72
29
118
134⁎ 202
44
9
12
15
Duce et al. (1991) use a scavenging ratio (SR) of 1000 except for NAO
(SR = 200) whereas Prospero (1996) using the same model applied a
SR of 200 globally.
⁎Estimates are based on the sum of the individual ocean basins.
S. Engelstaedter et al. / Earth-Science Reviews 79 (2006) 73–100
from 134 to 910 Tg yr− 1. Duce et al. (1991) and Prospero
(1996) use the same model for the calculations but
scavenging ratios of 1000 and 200 respectively. This
results in dust deposition estimates into the global oceans
of 910 Tg yr− 1 and 358 Tg yr− 1 which differ by a factor of
about 2.5 and reflect the uncertainties in these estimates.
Deposition estimates of North African dust into the North
Atlantic ranges from 140 Tg yr− 1 to 259 Tg yr− 1. In some
studies (Prospero, 1996; Luo et al., 2003; Zender et al.,
2003; Tegen et al., 2004) annual dust deposition into the
North Atlantic Ocean is more than the deposition into all
the other ocean basins. In comparison to model-based
estimates, Kaufman et al. (2005) use MODIS aerosol data
from the Terra satellite to estimate the amounts of dust
transport from North Africa. They estimate an annual dust
transport of 240 ± 80 Tg towards the Atlantic Ocean
whereby 140 ± 40 Tg are deposited in the Atlantic Ocean,
50 Tg fertilize the Amazon Basin and 50 Tg are
transported to the Caribbean. 20 Tg stay in Africa or are
transported towards Europe. In their calculations the total
uncertainty of ±35% in the dust fluxes results from the
uncertainty of ±30% in dust concentration and ±15% in
wind speed near the continents. As deposition calculations are based on dust flux divergence, the assumption is
made that the errors in the dust flux are correlated and
therefore are also 35%. Their estimate of 140 ± 40 Tg dust
deposition into the North Atlantic Ocean is lower than all
model-based deposition estimates (compare Table 2).
Recent models used to calculate dust emission, transport and deposition seem to agree quite well in their
estimates of global dust emissions but differ significantly
in their estimates of dust deposition into the individual
ocean basins (Tables 1 and 2). A possible explanation for
this may be differences in prescribed dust particle sizes or
the poor representation of wet deposition processes in the
models which can lead to large differences in wet deposition lifetimes ranging from 10 to 56 days (Ginoux et al.,
2001; Mahowald et al., 2002, 2005).
7. Summary
In the decade up to 2006, the research effort in North
African dust has increased threefold from an average of
21 refereed papers per year between 1995 and 1997
inclusive to an average of 61 between 2003 and 2005.
An underlying argument of this paper is that the need to
include the impact of dust in global climate models (for
the purposes of climate change experiments) twinned
with the simultaneous availability of remote sensing
products and reanalysis data sets, has changed the face
of North African dust research. Alongside this research
enterprise and guided by the remote sensing data are the
95
related aircraft and groundbased experiments which are
helping to refine observations of key variables for model
development and evaluation.
Before the use of satellite data (about 1995) only surface observations such as visibility measurements could
be used to derive information about the geographical distribution of sources and dust transport away from them.
These were distant from remote desert regions making the
differentiation between dust emission and transport very
difficult. The few satellite records available provided only
information about dust over the oceans and were too short
to study long-term variability. Dust concentration measurements at Barbados (since 1965) arguably gave some
of the best indications about North African dust
variability, although clearly these measurements were
only at a single location far away from the source.
Since the availability of satellite data that provide
long-term information about dust distributions over land
and oceans, much progress has been made in the study of
atmospheric dust. Intense source regions, including the
Bodélé Depression and the West African sources in the
heat low region of the Sahara which peak during summer,
have been recognized and some of the characteristics
responsible for these sources, such as the Bodélé Low
Level Jet, have been identified. Field campaigns and
aircraft observations which have been targeted on the
basis of the satellite data have done much to improve our
understanding of the physical, chemical and optical
properties of dust which are responsible for interactions
with climate. Overall, dust aerosols are now much more
recognized for the role they might play modulating the
climate system on a local and global scale.
The introduction of numerical models which simulate dust emission, transport and deposition has stimulated dust research in many ways. Models have led to
estimates of the sensitivity of the radiation budget to
changes in dust properties. They also have been central
to estimates of dust emission and deposition, although
both the radiative impact and dust emissions are characterized by large uncertainties. If the progress in the
next decade is as rapid as during the last, then many of
these uncertainties are likely to be reduced.
North Africa has been recognized as the largest dust
emitting region in the world (Fig. 2). It is the combination
of sources rich in deflatable material, energetic wind
systems, and a coupling to wind systems that facilitate
long range transport and thereby influencing remote
ecosystems which further raises the importance of this
great source. Africa is an ideal lab to study dust-climate
processes not only because it is such a large source but
also, because of the efficient transport mechanisms, it
affects many adjacent continental and ocean/sea regions.
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S. Engelstaedter et al. / Earth-Science Reviews 79 (2006) 73–100
An important overall goal is to understand the role of
dust in the climate system. The need to quantify how
dust emission and transport will change in the future and
how these changes will affect the Earth's climate and
environment will no doubt be a crucial driver of the
scientific effort in decades to come.
Acknowledgements
We would like to thank two anonymous referees for
their useful comments.
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