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Chapter 5: Dust variability over northern Africa
5.1. Introduction
The purpose of the analyses presented in this chapter is to describe the variability of
atmospheric dust loadings over Africa north of the Equator and to generate hypotheses
concerning relationships between dust loadings, rainfall in the Sahel and elements of the
regional circulation. This chapter and Chapter 6 both consider processes of dust generation
and the influence of rainfall on dust production. The Infra-red Difference Dust Index (IDDI)
is used to infer information concerning the spatial and temporal distributions of atmospheric
dust. Various dust-producing regions are identified and compared with the accepted sources
described in the literature. The seasonal variations in the activity of these source regions are
described.
Dust indices, consisting of spatially averaged IDDI values over a variety of regions and
representing a number of periods from one to twelve months, are calculated, and these
indices are used to compare mean atmospheric dust concentrations and dust variability over
different latitudinal bands of the Sahel-Sahara zone. Spatial and temporal relationships
between IDDI values in different regions are used to infer information concerning possible
mechanisms of large-scale dust production within the context of the broader body of
meteorological knowledge.
It has been claimed that the Sahel has become the most important source of wind-borne dust
in northern Africa (N’Tchayi et al., 1997). It has also been claimed that such a shift in the
maximum of dust production is due to changes in the Sahelian land surface resulting from
reduced rainfall and excessive land use (Tegen and Fung, 1995; Tegen et al., 1996). The
IDDI data offer an opportunity to examine these hypotheses, at least in part. Therefore an
important element of the work presented in this chapter is an investigation of the relative
importance of the Sahelian and Saharan regions as sources of airborne mineral dust, and of
relationships between dust production in these regions.
5.2. Scales of analysis
Ultimately it is hoped that the impact of dust on the climate of the northern African
subcontinent, and particularly on the climate of the Sahel, may be understood. The impact of
dust on the thermal structure and vertical and horizontal motion of the atmosphere is
Chapter 5: Dust variability over northern Africa
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addressed in Chapter 7. However, it is pertinent at this stage to consider the temporal and
spatial scales on which dust is most likely to interact with the processes that determine the
regional climate, in order to identify the regions and periods over which dust activity should
be assessed.
The geographical regions most likely to be important for Sahelian climate modification by
dust on a seasonal timescale are those that experience the greatest spring and summer
insolation. The intense heating of these regions contributes to the formation of the West
African Monsoon (WAM) (Barry and Chorley, 1995). These regions comprise the semi-arid,
arid and hyper-arid plateaux that stretch from the southern Sahel in the south to the central
Sahara in the north. It is postulated that large dust loadings over widespread areas in this
zone might reduce surface heating sufficiently to affect the dynamics of the WAM. Dust
events last for periods ranging from hours to days, and a single dust event is likely to have a
localised impact on the thermal structure of the atmosphere and possibly on the local
circulation. Any prolonged impact of atmospheric dust on the large-scale regional
circulation would arise from generally elevated dust levels over periods longer than those
associated with individual events, caused by a larger than usual number of events in a
particular period and/or a weakening of the processes which remove dust from the
atmosphere. It is therefore pertinent to examine variations in dust loadings over monthly and
seasonal periods, where the latter represent parts of the year characterised by broadly similar
climatic conditions. Annual variations in dust loadings over these periods should be
examined in order to investigate the relationships between dust production, rainfall and
other aspects of the regional climate. Seasonally averaged indices of dust loadings are
particularly useful in assessing the impact of cumulative rainfall amounts on subsequent
dust production (Section 5.7 and Chapter 6).
Single dust events can cover very large areas, and coherent dust signals in the IDDI data
may extend from the Sahel to the coastal regions bordering the Mediterranean (Figure 5.1).
Large-scale configurations of the atmospheric circulation which trigger dust events may
extend far outside the dust source regions (Kalu, 1979; Hastenrath, 1991). Dust that is
transported large distances may have a climatic impact far from its source (Li et al., 1996;
Alpert et al. 1998; Schollaert et al, 1998). Large dust loadings over given regions may
perturb the regional circulation, forming teleconnections with other regions. In this fashion
dust loadings over the Sahara may exert an influence on Sahelian climate. More directly,
dust which originates in the Sahara is transported over the Sahel by the prevailing trade
winds. In winter this circulation extends over the Sahara and Sahel down to the surface in
the form of the Harmattan; at Sahelian latitudes in summer, this transport is confined above
Chapter 5: Dust variability over northern Africa
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the oceanic monsoonal air layer in the form of the Saharan Air Layer (SAL). It is therefore
pertinent to examine variations in dust loadings throughout the Sahel-Sahara region, even if
the region of greatest interest is confined to the Sahel. Different regions act as dust sources
during different times of year, so it is also desirable to divide the Sahel-Sahara region into
smaller zones, and examine the variability of dust production in these zones individually.
Figure 5.1: Examples of IDDI signals covering very large areas. Left: Daily field for 18February 1989, illustrating a widespread and intense dust event. Right: Monthly meanIDDI field for January 1990, exhibiting elevated IDDI values extending from northernAlgeria to the northern Sahel. High IDDI values represent large dust loadings. Cloudyregions are masked (white). Scale in Kelvin
Dust over the oceans, specifically over the eastern tropical Atlantic, may have similar
remote impacts via the regional circulation (Schollaert et al, 1998). However, this analysis is
confined to an investigation of dust over land. Large dust loadings over land areas near to
the coast will reflect elevated dust levels over the adjacent ocean regions. Dust levels
generally will also be higher over land than over the oceans, due to the proximity of the
former to the sources of dust. It is expected that no great disadvantage results from the
consideration of mineral dust levels over land only.
The most significant effects of dust on shorter timescales will be those which influence the
dominant rain-producing processes, and are therefore likely to be manifest on timescales
comparable to the lifetimes of the convective disturbances (squall lines or disturbances line)
that bring the majority of the spring and summer rainfall to the Sahel (Tetzlaff and Peters,
1988; Rowell and Milford, 1993). It is plausible that on such short timescales (of the order
of hours – Rowell and Milford, 1993) and relatively small spatial scales (tens to hundreds of
kilometres) dust interferes with the dynamics of the convective cells that make up the
disturbance lines (DLs). Such processes would be most important in the relatively narrow
band of the Sahel, where DL formation is encouraged by surface moisture and the structure
Chapter 5: Dust variability over northern Africa
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of the monsoonal air layer (Tetzlaff and Peters, 1988). (DLs may extend over the Sahara, but
can only produce rainfall if they are embedded in the moist monsoonal air layer. Dust may
therefore modify the Saharan sections of DLs considerably, but such modifications will have
little or no hydrological impact unless their effects are transmitted to the more southerly
Sahelian cells of the DLs).
An analysis of the short-timescale interactions between atmospheric dust and DL generation
and evolution would require a modelling approach, which is outside the scope of this thesis.
It should also be noted that the IDDI dataset cannot detect dust in the presence of cloud. DLs
are associated with, and often identified by, widespread cloud cover, so it is impossible to
infer the degree of dust loading in the immediate vicinity of these phenomena. Means of
identifying and/or classifying DLs have been described by Hodges and Thorncroft (1997),
Lamb et al. (1998) and Diedhiou et al. (1998b). This study is a broad investigation of the
role of dust in the climate of Africa north of the equator.
Throughout this study, relationships between atmospheric dust and climate are inferred from
associations between indices representing dust loadings and a number of climatic variables.
These indices are created for given periods and geographical regions. Analyses range from
visual comparisons to studies of statistical relationships between IDDI series and
climatological series. The purpose of this chapter is to describe the variability of dust
production on monthly and seasonal timescales, in order to provide a context within which
to carry out the studies described in Chapters 6 and 7.
5.3. Study areas
For the reasons outlined in the previous section it is desirable to consider dust generation
and dust loadings over a very wide area covering most of Africa north of the equator. The
term “North Africa” is often used to describe the African countries bordering the
Mediterranean, whereas “West Africa” usually refers to the countries of the Sahel-Sudan
zone. The description “northern Africa” is therefore used here to describe the wider study
area, comprising all of Africa north of the equator. This study area is subdivided into
different zones in order to examine relationships between Sahelian and Saharan regions. A
“Sahara” zone is defined as the band between 20° and 30° N, and a “Sahel” zone between
10° and 20° N. (Figure 5.2).
Narrower latitude zones are also defined within the Sahel and Sahara zones (see Section
5.5). All the zones are defined to extend from the West African coast to 30° E. (They are not
Chapter 5: Dust variability over northern Africa
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defined over ocean regions). Although the choice of this eastern limit is fairly arbitrary, it is
informed by several factors. East of 30° N, dust loadings are unlikely to exert much of an
influence on the large-scale monsoon dynamics. The WAM is driven to a large extent by the
differential heating of the ocean and land, which will be dominant west of 10° E where the
east-west oriented Guinea Coast region ends. Some distance to the east of the 30° E
meridian, orography results in a climate different to that prevailing over the Saharan and
Sahelian regions, albeit subject to some of the same influences. Interaction between land and
ocean areas in these eastern regions will be dominated by the influence of the Indian Ocean
rather than that of the Atlantic (e.g. Camberlin, 1997). DL generation appears to be
infrequent east of 30° E. (Desbois et al., 1988).
Figure 5.2: The location of the “Sahara” and “Sahel” bands within the wider northernAfrican study area. The northern, western and eastern limits of the map are the limitsof the IDDI data. The western limit is defined by the West African coast.
5.4. The issue of land degradation
Certain studies (e.g. N’Tchayi et al., 1994, 1997) have provided evidence for an increase in
dust production in Sahelian regions over recent years. Other authors have modelled the
global dust cycle and have concluded that “…50 ± 20% of the total atmospheric dust
originates from disturbed soils” (Tegen and Fung, 1995). For the Sahel, the percentage
contribution of disturbed soils to the regional dust budget has not been quantified, and there
are insufficient observational data to verify model estimates. While deforestation and soil
erosion are undoubtedly characteristic of some Sahelian regions, their large-scale impact on
dust production, and particularly on the quantity of dust aerosols reaching high altitudes and
transported large distances, is very poorly understood. The causal relationships linking
changes in Sahelian soil properties with changes in the amount of dust exported from Africa
are therefore not well established. Mineralogical analyses of aerosol particles transported
Chapter 5: Dust variability over northern Africa
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over the Atlantic have identified Sahelian sources, characterised by a high clay content
when compared with Saharan sources (Bergametti et al., 1989). However, there is no
evidence that such particles have arisen from soils degraded by climatic desiccation or
human activity (Prospero et al, 1981; Ellis et al., 1993; Chiapello et al., 1997).
The northern limit of the Sahel is often placed approximately at the latitude of the 200 mm
or 250 mm isohyet (e.g. Glantz 1994). Such a definition is usually employed when the Sahel
is defined as the semi-arid region capable of supporting rain-fed agriculture (e.g. Mortimore,
1998). Thus it may be assumed that the northern limit of agriculture is approximately
coincident with the 200 mm isohyet, which ranges from some 17° N near the West African
coast to around 14° N in the region of Lake Chad for the period 1984-1993 (Figure 5.3).
Maps of soil degradation show degraded areas extending to around 17° N in Africa,
particularly west of about 10° E (UNEP, 1992, reproduced in Williams and Balling, 1996).
UNEP (1992) attribute the majority of soil degradation in Africa to overgrazing. The zone of
degradation terminates just north of the Niger Bend in Mali, representing the approximate
northernmost limit of degradation in the Sahel. The extent of such degradation may have
been overestimated due to the degree of interpolation employed (Williams and Balling,
1996); certainly such estimates should be treated with caution. Nonetheless, such studies
suggest that dust production north of 17° N in the Sahel-Sahara region is unlikely to be the
result of human-induced soil degradation. It should be noted that the limit of 17° N for soil
degradation coincides with the rough definition of the northern limit of agriculture as being
equivalent to the 200 mm isohyet. The density of livestock associated with pastoral or other
human activity is very low in regions where annual rainfall is below about 250 mm, again
suggesting minimal human impact beyond 17° N. Indeed, the distribution of livestock
appears to be determined by the distribution of permanent human settlement, rather than the
extent of rangeland (Wint and Bourn, 1994), calling into question the notion that Sahelian
rangelands have been widely degraded by agricultural and pastoral activity. These issues
will be discussed further in Chapter 6.
Chapter 5: Dust variability over northern Africa
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Figure 5.3: Percentage reduction in rainfall between the wet period of 1950-1965 andthe period under investigation (1984-1993). Dashed contours are isohyets (in mm) forthe former period, solid contours are isohyets for the latter period. Based on the CRUgridded 1°°°°x1°°°° rainfall dataset (New et al., 1999).
It is plausible that degradation north of the limit of widespread human activity has resulted
from climatic desiccation. The isohyets in Figure 5.3 exhibit a southward shift by the order
of a degree latitude between 1950-65 and 1984-93. Such changes in rainfall may well lead to
changes in vegetation cover and hence land surface properties. However, the impacts of such
changes in land cover resulting from shifts in rainfall bands on dust production are
speculative. Nicholson and Tucker (1998) state that there is no evidence for widespread land
degradation on a regional scale in the Sahel, and earlier studies by Tucker et al. (1991,
1994) indicate that vegetation quickly recolonises desiccated areas in favourable rainfall
years (c.f. Chapter 2). It is reasonable under the circumstances to use the liberal UNEP
(1992) estimate of the extent of land degradation as a working value of the latitude beyond
which the degree of land degradation is negligible. The relationship between dust loadings
either side of this hypothetical northern limit of land degradation will be investigated in
Section 5.6.
5.5. Identification of dust source regions
The IDDI data are representative of aerosol loadings (Chapter 3), and over the arid and
semi-arid regions of northern Africa the dominant aerosols are mineral dust particles
mobilised from desert and dryland surfaces. Therefore, it may be assumed that high IDDI
values over the Sahel and Sahara are generally representative of high atmospheric dust
concentrations. Outside these regions, other aerosols, such as biomass burning products, will
Chapter 5: Dust variability over northern Africa
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also be important. The role of these particles is addressed where appropriate below, in the
discussions of particular geographical regions.
Concentrations of aerosols in the atmosphere will generally be greatest nearest to their
regions of origin, particularly if concentrations averaged over time are considered. Airborne
dust is likely to have the greatest impact on the radiative and optical properties of the
atmosphere near its source, where mean particle size and particle density are greatest.
Advection of dust will result in IDDI signals away from the major sources, but a judicious
and flexible use of scale and threshold values enables detailed regions of IDDI maxima to be
distinguished. These regions are interpreted as being broadly coincident with sources of dust
aerosols. The terms “IDDI values”, “dust loadings” and “dust production” are therefore
equivalent within the context of the following discussion, which identifies the major sources
of mineral dust in Africa from fields of IDDI data.
Various mean IDDI fields were created in order to examine dust variability over Africa.
Monthly means were created from the daily data. For each grid-cell, a monthly value was
calculated only if no more than 80 per cent of the daily values for that month were missing.
In other words, 6 daily values (or 5 values in the case of February) were required to
calculate a monthly mean value. Daily IDDI values greater than 50 Kelvin were treated as
missing data in order that any cloud values that had been misinterpreted as dust were not
used. The highest IDDI values that have been associated with dust outbreaks are in the range
40-45 Kelvin (Legrand, personal communication). Annual means were created from the
mean monthly values for cells where more than three months contained data. Seasonal
means were created provided one of the months contained data. Ten-year mean monthly and
annual fields were created, with missing data values returned to cells where fewer than four
years were represented. The percentages of data required to create the mean values were
chosen in order to strike a balance between the need for as wide a geographical coverage as
possible, and continuity in the resulting mean fields.
Figure 5.4 shows the mean annual field of IDDI values for 1984-1993. IDDI values greater
than 5K tend to delineate fairly distinct regions. The IDDI value of 5 K corresponds to dusty
conditions characterised by visibilities near the ground of less than 10 km, and is
approximately equivalent to aerosol optical depth (AOD) of 0.3-0.5 (Legrand, 1994, c.f.
Chapter 3). In Chapter 2 it was seen that an increase in AOD of 0.1 could correspond to a
forcing of several Wm-2, of the same order as the globally averaged enhanced greenhouse
gas forcing, albeit on a local or regional scale (Lacis and Mischenko, 1995). A mean IDDI
value of 5 K therefore represents a considerable time-averaged forcing of the local or
Chapter 5: Dust variability over northern Africa
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regional climate. Within these regions finer detail is apparent in the form of areas with IDDI
values in excess of 5.5 K. These thresholds are interpreted as indicating broad regions of
dust activity and major source regions respectively. The choice of thresholds is arbitrary
insofar as there is no physical basis for the identification of source regions with IDDI data
above a certain value. However, these IDDI values do reveal a good deal of fine structure in
the annual field, and define regions that broadly coincide with accepted regions in which
mineral dust is mobilised. Three major regions of high dust activity are apparent in the
Sahel-Sahara region; these are discussed below.
Figure 5.4: Annual average dust production over Africa, as indicated by time-averagedIDDI values. IDDI values were averaged for each annual twelve-month period(January-December), and the resulting fields averaged over the period 1984-1993.Large IDDI values indicate high dust loadings. Scale in Kelvin.
Chapter 5: Dust variability over northern Africa
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Figures 5.5 and 5.6 illustrate mean seasonal and mean monthly IDDI fields respectively.
These fields yield information concerning the seasonal variations in activity of the aerosol
source regions. A similar approach to that outlined above for the mean annual field is
adopted to identify source regions. For both mean monthly and mean seasonal fields the
threshold of 6 K delineates distinct and detailed regions of high IDDI values. This threshold
is interpreted as loosely defining dust sources. Where appropriate, the seasonality of
particular source regions is discussed in the short sections below which describe the
particular source regions. General patterns of seasonality are discussed at the end of this
section.
Figure 5.5: Mean seasonal IDDI fields over Africa in Kelvin, for the period 1984-1993.
Chapter 5: Dust variability over northern Africa
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Figure 5.6: Mean monthly fields of IDDI over Africa north of the Equator in Kelvinover the period 1984-1993. Scale in Kelvin.
Chapter 5: Dust variability over northern Africa
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5.5.1. The north-central Sahel
A region extending from about 5° to 20° E, and 13° to 18° N is apparent, with a peak IDDI
value at approximately 16° E, 17° N (Figure 5.4). A lower magnitude maximum in this
region is roughly centred on 9° E, 15° N. The region can thus be divided into two distinct
source areas. The eastern source represents the southern and eastern parts of the Erg of
Bilma, which straddle the border between Niger and Chad, and the alluvial plain to the
north-west of the town of Largeau in northern Chad. This region has been identified as an
important source of dust aerosols which are transported by the Harmattan winds (McTainsh,
1980; Drees et al., 1993). Dust storms in this region occur when intensification of
anticyclonic conditions over the northwestern and central Sahara lead to low-level pressure
surges (Kalu, 1979). The western source lies over central southern Niger, to the south of the
Aïr Massif. It is noted that the western sources are located in the vicinity of an area of
enhanced DL generation just west of the Aïr Mountains (Rowell and Milford, 1993). Both
eastern and western source regions lie within the central to northern Sahelian latitudes.
Mean monthly IDDI fields (Figure 5.6) indicate that the central Sahelian sources become
active in December, with maximum activity in January. Activity is still high in February,
particularly for the eastern source region, and dies down in March, becoming low in April.
The sources are reactivated in May, but activity is lower than in January and February. In
June the western sources merge with a zone of activity stretching westwards from 5° E and
associated with the northern limit of the ITCZ. A signal from the eastern sources is still
apparent, dying away in July.
5.5.2. The west Sahara and western Sahel
A large source region is apparent in the west of the Sahara, with maximum IDDI values
between about 7° - 0° W and 20° - 25° N. This coincides with the Erg Chech of northern
Mali and Mauritania and south-western Algeria, and a region characterised by smaller ergs
in northern Mali1. A secondary maximum is centred on a region of seasonal water courses
near to the coast located around the border between Western Sahara and Morocco, to the
south-west of the Tindouf and Draa hammadas. These regions coincide with the northern-
most extents of dust haze events as reported by Westphal et al. (1988). Coudé-Gaussen et al.
(1987) report a southern Moroccan origin for dust transported over the Canary Islands. A
dust plume reaching northwestern Europe in April 1983 contained particles originating in
1 Geographical data are taken from the VWK Africa North and West road map, 1: 4,000,000, P.O.Box 2105, Obertshausen 2, Germany.
Chapter 5: Dust variability over northern Africa
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Mauritania as well as in the southern Sahara/Sahel (Reiff et al., 1986). Chiapello et al.
(1997) found that dust transported over Sal, on the Cape Verde Islands, most frequently
originated in the north and west Sahara; the west Sahara region described here corresponds
to a broad source region characterised by a particular set of transport trajectories (sector 3 in
their description).
The West Sahara sources are most active in March. (Some low-level activity is also apparent
in February). The region of maximum IDDI values within the zone shifts from the east in
March to the west (southern Morocco/northern Western Sahara) in April and May. At this
time of year Saharan thermal lows develop south of the Atlas Mountains, influenced by the
strong east-west thermal contrast between the continental and oceanic regions (Moulin et
al., 1998a). These cyclones are responsible for transporting large amounts of dust to the
eastern Mediterranean. April coincides with the end of the Moroccan wet season, and the
consequent reduction in wet deposition will also act to enhance dust concentrations.
Activity reduces and shifts inland over western Algeria in June. A contribution to the annual
IDDI signal for this region arises from a zone of intense activity in the northern Sahel and
southern Sahara in June, July and August. This zone moves northwards over these months,
its central latitudes being approximately coincident with the expected position of the surface
discontinuity between the monsoon and Saharan air masses (Hastenrath, 1991). It is at these
locations that the signals are strongest, indicating dust production in the Saharan air mass,
and production in the Sahelian monsoonal air wedge, or advection from the Sahara, where
the monsoon layer is too shallow to allow significant rainfall to occur. A strong relationship
between the location of the transition zone from convective rainfall to a shallow monsoon
layer and the latitude of the maximum number of dusty days is reported by Alpert et al.
(1998).
In the summer, there will be a strong differential heating between the southern Sahara and
the waters off West Africa, resulting in the generation of cyclones which will interact with
the low-pressure zone associated with the ITCZ. Cyclogenesis may also be assisted by the
dynamics of the discontinuity region, as the Saharan air rises over the cooler monsoon air.
Dust may also enhance dry convection by heating the local atmosphere, resulting in a
positive feedback in the dust cycle (e.g. Chen et al., 1995). By September, dust production
has declined dramatically, although some residual activity is apparent over central
Mauritania and Mali.
Chapter 5: Dust variability over northern Africa
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5.5.3. The east Sahel-Sahara
The third major source region is approximately oriented in a north-south direction along the
30° longitude meridian, stretching from some 13° N in northern Sudan to around 25° N in
southern Egypt, terminating to the south of the oases of Dakhla and Kharga. Dust from the
northeastern Sudan has been transported to the eastern Mediterranean (Middleton, 1986).
Activity in this region commences in the south in December, increasing in strength until
March and persisting until June.
5.5.4. Other Sahelo-Saharan sources
A minor region of activity is apparent at and to the north-east of a location centred on 14° E,
22.5° N. This area lies between the Plateau de Djado in northern Niger and the Idhan
Murzuq erg in south-western Libya, and is characterised by ephemeral watercourses and
elevations reaching some thousand metres. This region becomes active in December,
remaining a weak source until February, when activity is most intense. Notable activity is
also apparent in March and April, with the source switching off by May.
5.5.5. Seasonal patterns of dust production and non Sahelo-Saharan sources
The mean seasonally averaged fields of IDDI (Figure 5.5) illustrate broad patterns of dust
production throughout northern Africa. The most notable characteristic of the first six
months of the year is the switch of production from the eastern half of continental North
Africa in JFM to the west in AMJ. This is a broad general pattern: the East Sahel-Sahara
region remains active in AMJ, and the West Sahara region becomes active in March. Indeed,
March is the most active month as far as the West Sahara is concerned. However, within the
West Sahara region, maximum activity shifts from the east in March to the west in April and
May, reflecting the general seasonal pattern.
Within the Sahel-Sahara zone, activity remains concentrated almost exclusively west of 10°
E throughout JAS, and appears to be closely associated with the position of the ITCZ. For
example, the June source region lies to the north of the zone in which more than 40% of the
days in June are characterised as cloudy (Figure 5.7). The southernmost limit of the zone of
June high IDDI values (as defined as IDDI values greater than 6K) varies from about 12º to
15º N. These latitudes are approximately coincident with, or to the north of, the surface
discontinuity associated with the ITCZ (Hastenrath, 1991). As the ITCZ moves north in
July, the zone of dust production shifts to the north-west, to about 18º - 24º N and 13º W - 7º
Chapter 5: Dust variability over northern Africa
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E. The longitudinal extent of this region of activity shrinks in August to approximately
12ºW - 2º E, while its latitudinal extent is similar to that of June. The southern limit of the
region of high JAS IDDI values lies immediately to the north of the Niger Bend, and
contains the plains between 5° and 10° W which have been identified by Rowell and
Milford (1993) as areas of enhanced DL generation. (They also identify frequent DL events
over the most northerly regions of the Niger River).
OND is characterised by low IDDI values throughout continental North Africa. The
December activation of relatively small source regions in the central/northern Sahel and the
central Sahara results in a weak signal in these areas. October and November exhibit the
lowest IDDI values, representing an annual minimum in the seasonal dust cycle.
Figure 5.7: Mean June IDDI field (left), compared with mean June cloud frequencyfield (per cent of cloudy days in June – right). The scales are inverted with respect toone another so that yellow/red represents high dust loadings and low cloud frequency(i.e. aridity). Blue represents low dust levels and wet conditions in the IDDI and cloudfields respectively.
The Guinea Coast
Also notable in JAS is a zone of relatively high IDDI values along the Guinea Coast region,
extending in places to some 12° N and exhibiting a maximum in the east over Nigeria
(Figure 5.5). The period JAS corresponds to the “Little Dry Season” (Barry and Chorley,
1995) in this region and it might therefore be expected that widespread biomass burning
would be prevalent. Monthly maps of fire distribution are available for some years from the
World Fire Atlas, compiled by the European Space Agency and the European Space
Research Institute (ESA/ESRIN) as part of the Ionia programme
(http://shark1.esrin.esa.it/home.html; Arino and Melinotte, 1995; Arino et al., 1997).
These maps have been produced from AVHRR and ATSR satellite data. A visual
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comparison of the monthly IDDI fields with monthly fire maps for 1993 suggests that the
JAS high IDDI values over the Guinea Coast are not due to combustion products, as fires are
almost entirely absent from this region in this period according to the fire maps. At this time
of year detectable fires are concentrated between the Equator and 20°S, where IDDI values
are low. Strong fire signals in the ESA/ESRIN data occur over and to the east of the Guinea
Coast throughout the winter, with fires being most widespread in January. Again, the
regions of high IDDI values do not correspond to those characterised by fires; the January
1993 IDDI field exhibits low values over the Guinea Coast. However, the relationship
between the distribution of fires as detected by satellite remote sensing methods, and high
concentrations of biomass burning aerosol products is not necessarily straightforward.
The effect of smoke aerosols will be similar to that of sub-micron dust particles, resulting in
a cooling of the land surface due to a reduction in solar insolation. Both smoke and fine dust
are transparent in the infra-red part of the electromagnetic spectrum, and will not reduce the
OLR. Any IDDI signal resulting from the presence of these materials will therefore be solely
due to the cooling of the land surface, and is likely to be significant only when they are
present in large quantities. Dense clouds of smoke will obscure the ground from satellite
detectors operating in the visible part of the electromagnetic spectrum, hindering the
detection of the fires which produce them. It is therefore highly likely that fires resulting in
large quantities of smoke aerosols will not be represented in fire maps, explaining the
presence of strong localised IDDI signals which do not correspond to detected fires. It is
plausible that material from such fires is responsible for some of the high IDDI signals
apparent in Figures 5.4 to 5.6, providing at least a partial explanation for the summer Guinea
Coast signal.
Another plausible explanation for the high IDDI values over the Guinea Coast in summer is
that dust is transported from the Sahel-Sahara to a region of relatively stagnant air over this
region, where it remains in the atmosphere for some time. Between the Guinea Coast and the
Sahel-Sahara transition zone, dust will be removed from the atmosphere by rainfall,
resulting in short residence times, low aerosol concentrations and hence low IDDI values.
West central Africa
Large quantities of combustion aerosols also provide a plausible explanation for high IDDI
values over regions where dust is unlikely to be a major feature of the atmosphere. Such
values are seen over west central Africa (stretching from Gabon to the Democratic Republic
of Congo and southwards over Angola) in all the fields, and are largest in OND and JFM.
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Some biomass burning occurs in this region in these periods, particularly in October (based
on 1993 data from ESA/ESRIN). However, the frequency and density of fires during the
periods in question is far greater between 0° and 15° N, where IDDI values remain low.
Again, these discrepancies between the IDDI and fire data may be due to the complex
relationship between fire and smoke aerosol distributions.
The west-central African region is adjacent to an area of frequent cloud cover in JFM and
OND (i.e. southern hemisphere spring and summer), when the IDDI values are highest. It is
possible that some “cloud contamination,” i.e. misinterpretation of cloud values as IDDI
values occurs in these periods. This is most likely to occur when cloud is present throughout
the entire 15-day reference period used to produce a given set of IDDI values. When this
occurs, a cloud signal will be incorporated into the radiometric count values comprising the
“clear sky” field, resulting in erroneous reference values. While this will tend to reduce the
calculated IDDI values, it may also cause the cloud identification algorithm to fail, resulting
in cloud being misidentified as aerosol. It is possible that cloud contamination is also
responsible for some of the high values over the Guinea Coast.
Eastern Africa
Similar features occur over many of the eastern coastal regions of Africa south of 10º N,
particularly in AMJ and JAS. These regions contain no extensive deserts, but do include
semi-arid and dry sub-humid zones. The boreal summer high IDDI signal occurs during the
dry season in East Africa. It is possible that dust mobilisation occurs from disturbed soils in
these regions, although a complex biomass burning aerosol signal is again likely.
A prominent feature of the seasonal IDDI maps is the region of very high IDDI values over
the Horn of Africa in JAS. This signal in the IDDI data coincides with very high equivalent
aerosol optical thickness (EAOT) measurements over the Arabian Sea (Husar et al., 1997 –
based on data from July 1989 to June 1991, c.f. Chapter 2, Figure 2.5). The parts of Arabia
visible in the IDDI fields exhibit low IDDI values, suggesting that the material transported
over the Arabian Sea originates predominantly in the Horn of Africa. Mobilisation and
transport of dust is aided by the East African (or Somali) low-level jet, which is active at
this time of year as part of the summer monsoonal circulation (Hastenrath, 1991, Husar et
al., 1997). Transport of dust over large distances occurs above the monsoon inversion, in a
fashion analogous to the transport of Saharan dust above the trade wind inversion in the
Saharan air layer (Kalu, 1979; Sirocko et al., 1991).
Chapter 5: Dust variability over northern Africa
155
Southern Africa
Finally it is worth mentioning the southern hemisphere African deserts in terms of dust
sources as defined by the IDDI data. These regions do not stand out in the seasonal or
annual fields, although elevated IDDI values are apparent over the Kalahari in JFM. It is
striking that the Namib Desert does not appear to be a significant source of dust. The cold
Benguela Current to the immediate west of the desert results in a highly stable atmosphere
which is not conducive to the generation of the large convective events responsible for dust
mobilisation and transport elsewhere. While dust storms do occur over the sandy desert in
the Namibian interior, it appears that the spatial and time scales associated with these events
are such that they do not produce a major signal in the mean IDDI fields. The coastal
atmosphere is very different from that over West Africa, and it is likely that the atmospheric
environment over the Namib desert is such that dust aerosols are not carried to the
elevations necessary for long-range transport. Middleton (1997) states that dust mobilisation
and transport from the southern African deserts is poorly understood, but suggests that the
scale of such phenomena is not comparable with that which characterises the northern
African regions.
Summary
The above discussion illustrates some of the caveats to be considered when interpreting the
IDDI data. The question of whether the IDDI is a reliable means of detecting combustion
aerosols remains open, and will only be resolved when the relationship between detected
fires and the nature and distribution in the atmosphere of their products is better understood.
The physics of the interactions between IR radiation and such products should also be
investigated further.
It also appears that the IDDI is less reliable under persistently cloudy conditions. High IDDI
values which cannot be associated with dust or other aerosols are likely to be the result of
cloud contamination, where the IDDI method is not reliable due to high levels of cloud
throughout the period used to produce the reference images. Cloud contamination is most
likely to occur in humid regions, as suggested by the occurrence of very localised high IDDI
values at the edge of regions of high cloud frequency. Over the main regions of interest,
which are the semi-arid and arid zones of the Sahel and Sahara, such effects are likely to be
minimal. Indeed, the mean IDDI values over the Sahel and Sahara generally do not exhibit
the large-magnitude localised signals (often confined to a single 1° latitude x 1° longitude
grid-cell) seen near the edge of cloud fields in the equatorial regions. In the Sahel-Sahara
Chapter 5: Dust variability over northern Africa
156
zone, the dominant aerosols are dust particles. These will be preferentially detected
compared with other aerosol types due to the episodic nature of their production and
transport (resulting in a strong contrast with the background signal incorporated in the
reference field). The IDDI is therefore particularly suited to investigations of large dust
events; the strongest IDDI signals will result when such events contain a high proportion of
large dust particles, resulting in a reduction in both surface insolation and OLR.
Further work is required to decouple the effects of biomass burning products and cloud
contamination from the impacts of dust on the IDDI signal. However, over the Sahel and
Sahara, the IDDI appears to perform well, and detects large dust events and the major
sources of dust aerosols. It may therefore be used with confidence in studies of Saharan and
Sahelian aerosols and their relationships with the regional climate. Seasonal and
geographical variations in the IDDI data may also be used to infer information concerning
the nature of the major aerosol sources in northern Africa.
5.5.6. Character of the source regions
The dust sources in the northern central Sahel, the west Sahara, east Sahel-Sahara and the
Niger-Libya border region all lie in areas that are characterised by sandy desert (erg) and/or
ephemeral watercourses (or wadi systems). These regions constitute the most significant
dust sources in Africa. The area to the south of the Aïr Massif, in the western part of the
northern Central Sahel source region, is characterised predominantly by temporary
watercourse, although it includes the westernmost part of the Erg du Teneré. It also includes
the northernmost limits of the zone of settlement, and therefore of agriculture. It is therefore
the most likely candidate, out of all the Saharan and Sahelian sources, for a region
containing anthropogenic sources. However, significant settlements are absent from much of
the zone, and the density of wadi systems and proximity to the Aïr highlands suggests that
erodible material is likely to be supplied in large part by hydrological action.
The distribution of dust signals in the IDDI fields suggests that large-scale dust production
occurs predominantly in response to the prevailing climatological conditions and, outside of
eastern Africa, in regions where human activity is limited or negligible. It is possible,
however, that material from anthropogenically degraded soils does not produce a strong
signal in the IDDI data, resulting in an underestimation of the extent of the major source
regions. Aerosols from degraded soils are likely to be very different in nature from those
deflated from arid to hyper-arid desert regions. Dust consisting of such aerosols will contain
more organic material and have a higher clay content, resulting in a high proportion of small
Chapter 5: Dust variability over northern Africa
157
( < 2 µm) aerosol particles (McTainsh and Walker, 1982). Organic material has been
detected in dust deposited in Niger (Drees et al., 1993) and northern Nigeria (McTainsh and
Walker, 1982). However, it is not clear whether the organic input is due to desiccation of
vegetated areas or if it is a long-term feature of the soil-dust cycle.
Small aerosol particles from clay-rich soils will have a weak attenuating effect on the
outgoing longwave radiation (OLR). Thus the IDDI signal from such material will be
predominantly the result of surface cooling. McTainsh and Walker (1982) report a tendency
for lower visibility and reduced solar radiation to be associated with finer mean particle
sizes. The correlation of IDDI values with measured visibilities (Legrand et al., 1994; c.f.
Chapter 3) suggests that the IDDI are capable of detecting such fine material. Recent
modelling studies indicate no difference in the temperature and IR radiance at the top of the
atmosphere (TOA) between fine dust in a elevated layer and fine dust extending from the
ground to 1.5 km (Legrand, personal communication.). A lack of layering of locally
produced fine aerosol particles over the Sahel is therefore unlikely to lead to poor detection
of such material. It is plausible that fine particles from degraded Sahelian soils remain near
the surface, and have a small effect on the net IR radiance at the TOA. However, this is
speculative, and aerosols which remained at low levels would not contribute to the budget of
aerosols exported from Africa, as this transport occurs above about 1.5 km (Alpert et al.,
1998). The IDDI data therefore call into question the assumption that aerosols from
degraded soils contribute significantly to the budget of material exported from northern
Africa.
5.6. Sahel and Sahara IDDI indices: a comparison
Sahelian and Saharan zones are defined in order to compare dust production in the two
regions. The Sahel is defined as the latitudinal band lying between 10º and 20º N, between
the West Africa coast and 30° E. The Sahara is defined as the band lying between 20º and
30º N within the same longitudinal band as the Sahel. The large longitudinal range is
justified, as the primary purpose of this section is to examine changes in the nature of dust
production with latitude, i.e. as the inhabited Sahel gives way to the effectively uninhabited
Sahara. Nonetheless, the definitions of the “Sahel” and “Sahara” are arbitrary, and the
“Sahel” region extends into the zone in which agriculture and human impact on the land
surface are minimal or non-existent (Section 5.3). For these reasons the bands were further
divided (Figure 5.8) into the South Sahel (10°-15° N.), North Sahel (15°-20° N; effectively
the Sahel-Sahara transition zone), South Sahara (20°-25° N.) and North Sahara (25°-30°N.)
Chapter 5: Dust variability over northern Africa
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IDDI indices were created for these regions for monthly, “seasonal” and annual periods.
Seasonal periods were chosen to represent different prevailing meteorological conditions
and different dominant patterns of dust distributions. The “wet season” was liberally defined
as May to October and the “dry season” as November to April. The dry season was divided
into the “early dry season” (November and December), and the “late dry season” (January to
April). The annual period used here is the twelve-month period from the beginning of
October to the end of September. In order to create a period which, cumulative effects aside,
will be affected by only one wet season, an index from the beginning of October to the end
of April (before the onset of the next year’s rains) was also created. (This period is very
similar to the dry season period, and allows the impact of including October into the period
between wet seasons to be assessed).
Figure 5.8: Bands for which IDDI values were zonally average to create IDDI indices.
The indices were constructed by averaging monthly fields of IDDI data over the periods, or
“seasons”, in question. These temporally averaged fields were then spatially averaged over
the regions of interest. The spatial averaging process was also carried out for individual
months. The resulting timeseries are ten years in length, or nine years in the case of periods
that span two years, such as October-September.
Chapter 5: Dust variability over northern Africa
159
5.6.1. Mean zonal variations of IDDI
Mean monthly, seasonal and annual IDDI values were created by averaging the yearly
values that constitute the IDDI-index timeseries described above. The resulting mean
monthly values are plotted against month for the various zones defined above, in Figure 5.9.
The numerical values of the means for the periods comprising more than one month are
given in Table 5.1.
Figure 5.9: Mean spatially averaged IDDI values, in hundredths of a degree Kelvin,over various latitudinal bands (see text for definitions), for individual months.
Figure 5.9 effectively shows the mean seasonal variation in dust production for the different
zones. The seasonal behaviour of the Sahel and Sahara zones is broadly similar, although
some striking differences are apparent. Saharan IDDI values, and by inference dust
production, peak during the period February-May, with the highest values apparent in April.
During this period they exceed the Sahel IDDI values, which peak in May and June. A
comparable Sahel maximum is also apparent in March. From June to September there is a
downward trend in IDDI for both regions, with the Sahel values exceeding the Sahara
values. Sahelian IDDI values fall dramatically below those of the Sahara in November, most
likely in response to the previous wet season’s rainfall, via the effects of moisture and/or
vegetation on soil cohesion and vulnerability to deflation.
These seasonal variations in IDDI values, and by inference in dust production, are reflected
to varying degrees over the narrower zonal bands. The largest IDDI values are located over
the South and North Sahara in March and April respectively. These values represent both
geographical and seasonal maxima in dust loadings. IDDI values over the Saharan regions
also exceed values over the Sahelian regions in November. The November Saharan values
reflect the largest dust loadings at a time when dust production is at a minimum, whereas the
Chapter 5: Dust variability over northern Africa
160
March/April values reflect maxima during a high-activity season. Minima in the North
Sahara series which are not reflected in the South Sahara series occur in February and
August, and represent a decoupling of dust production in these two zones. During February
such a decoupling is likely to be the result of mobilisation mechanisms connected with
Atlantic and Mediterranean cyclones extending over North Africa.
Geographical region
Period Sahel Sahara South
Sahel
North
Sahel
South
Sahara
North
Sahara
JFM 510 534 478 541 539 429
AMJ 520 527 493 545 516 539
JAS 485 461 463 505 482 438
OND 410 438 396 424 438 438
Wet 487 471 467 506 476 464
Dry 475 510 447 502 512 507
Early Dry 412 446 391 433 451 440
Late Dry 506 541 476 536 542 541
Oct-Sep 482 491 458 505 495 486
Oct-Apr 466 498 457 489 499 497
Table 5.1: Mean IDDI values, in hundredths of a degree Kelvin over various latitudinalbands (see text for definitions), for different periods. Values representing meridionalmaxima in dust production are shown in bold.
The maximum value in the North Sahel series occurs in June and is comparable with those
in the Saharan series in March and April. From June to September, and during December
and January, the North Sahel exhibits the highest values of all the zones, indicating a
meridional maximum in dust production between 15° and 20° N. These wet season dust
maxima are consistent with the observations of N’Tchayi et al. (1997, c.f. Chapter 2). The
lowest values of all the series occur over the North and South Sahel in November, with the
Chapter 5: Dust variability over northern Africa
161
latter zone exhibiting lower values than the former. It is expected that the impact of rainfall
on vegetative cover will be greater in the South Sahel than in the North Sahel. Soil moisture
and vegetation take longer to return to their pre-wet season states further south (Hess et al.,
1996), as the role of vegetation becomes more important and soil moisture contents rise.
Rainfall impacts on vegetation will be most manifest over regions and periods usually
characterised by widespread vegetative cover. Therefore the larger these regions and the
longer the periods, the greater is the potential for changes in rainfall to alter land-
atmosphere interaction by perturbing the system from its normal state.
The mean seasonal and annual IDDI values (Table 5.1) yield information concerning the
most active dust-producing zones on longer timescales. Most importantly from the
perspective of the debate concerning whether the region of maximum dust production has
moved from the Sahara to the Sahel in recent years, the largest value for the annual
(October–September) period occurs over the North Sahel. This situation is reflected during
the wet season, JFM, AMJ and JAS, while the zone of maximum production shifts
northwards to the Sahara in OND. In AMJ, IDDI values fall over the South Sahara and rise
to a value comparable with that for the North Sahel over the North Sahara. Thus in AMJ,
there are two meridional maxima in dust production, suggesting two independent and
geographically distinct sets of deflationary processes. April is characterised by the passage
of Atlantic cyclones over the west Sahara at the end of the Moroccan wet season (Moulin et
al., 1998), which explain the high IDDI values over the North Sahara. In the North Sahel,
AMJ includes the historical period of maximum dust-event frequency (April), as well as the
period to which the annual dust maximum has shifted in some regions (June) (N’Tchayi et
al, 1997).
When the wet season is removed from the annual period to leave the October–April period,
the South Saharan signal is dominant, although the difference in IDDI values between the
South Sahara and the North Sahel is not large (0.1 K or 2%). The dry season, when analysed
as a whole, or as the early and late dry season, also yields maxima over the South Sahara.
The shift from dry season maximum over the South Sahara to wet season maximum over the
North Sahel suggests that changes in climate or land-surface associated with a general
increase in Sahelian dust production are likely to be associated with spring and summer
deflation as much as with dry season dust mobilisation. The largest percentage difference
between South Sahara and North Sahel IDDI values occurs in the wet-season (6 %), further
supporting the notion that wet-season processes contribute to the enhanced annual Sahelian
dust signal. The nature of these processes will be discussed in the following section.
Chapter 5: Dust variability over northern Africa
162
5.6.2. Locating the meridional maximum of dust production
IDDI values for the South Sahel do not exceed those of the North Sahel in any month
(Figure 5.9), suggesting that, within the 10°-20° N. band, dust production is maximum north
of 15° N throughout the year. The North Sahel band contains the northern limit of the zones
of rainfed agriculture and potential land degradation, the northernmost extent of which has
been tentatively placed at around 17° N. (see Section 5.3). It is therefore instructive to
examine IDDI values to the north and south of this limit within the North Sahel zone.
In order to locate the meridional maximum of dust production within the Sahel more
precisely, the distribution of sources between the northern and southern sections of the 15°-
20° N band was examined. Zonal averages were created for a southern band between 15° N
and 17° N. This band will contain the regions where any land degradation is prevalent. As
the limit of land degradation will vary with longitude, possibly extending north of 17° N in
places, the northernmost band was defined as the region from 18° N to 20° N, leaving a gap
of 1° between the zones. The zonally averaged IDDI values for these regions are plotted in
Figure 5.10. It is assumed that any maxima in the spatially averaged IDDI representing these
2° latitude bands which are coincident with maxima in the North Sahel for the 5° bands
represent the latitudes of maximum dust production within the latitudinal range 10°-30° N.
It may be argued that, at a 2° latitude resolution, localised zones of higher values may exist
at other latitudes, the signals of which have been lost due to the zonal averaging process. For
this to be the case these other zones would have to be sharply defined, narrow in zonal
extent and surrounded by low IDDI values. The distribution of high-value regions in the
IDDI fields suggests that this is not the case.
Figure 5.10: Mean spatially averaged IDDI values over the northern and southernextremities of the North Sahel zone. Units in hundredths of a degree Kelvin.
Chapter 5: Dust variability over northern Africa
163
The 18°-20° N band exhibits higher IDDI values than the 15º-17º N band in July and
August. Values in the two bands are very similar in April, September and November (with
differences of less than 1 %). Throughout most of the year (January-March, May-June,
October and December) values are greater over the 15°-17° N band, indicating higher dust
loadings over the latitudes containing potentially degraded areas. In order to examine
whether these maxima within the North Sahel represent meridional maxima in dust
production over all the zones discussed, the periods in which the North Sahel exhibits the
highest IDDI values of all the 5° bands are recalled. These are June-September and
December-January. Therefore, in June, December and January, the higher values over the
15°-17° N band indicate a meridional maximum in dust production within the zone that is
potentially subject to degradation, albeit at the edge of this zone where the extent of
degradation is unclear. The groups of months exhibiting similar distributions of meridional
IDDI maxima are discussed in turn below.
At this point it is appropriate to address the implications of choosing the above two 2º
bands, rather than other such domains within the North Sahel. IDDI values over the chosen
regions were compared with those over other zones in order to test the assumption that
maximum values associated with either of these bands represent meridional maxima. IDDI
values for the 16°-18° N and 17°-19° N zones (not shown) lie between those for the 15°-17°
N and 18°-20° N zones or exhibit negligible deviations from them. Thus, considering these
other possible bands, it can still be said that the high IDDI values over the bands at the
extremities of the North Sahel zone represent meridional maxima at a 2° latitude resolution.
July and August: 18º-20º N maximum
July and August represent the peak of the wet season, when the organised lines of westward-
travelling convective disturbances (DLs) crossing the Sahel are most frequent and intense.
DLs play a major role in rainfall generation and are known to mobilise dust (Kalu, 1979).
Rowell and Milford (1993) identified DLs in August 1985 which were generated as far
north as 20° N. They found regions of enhanced DL generation just west of the Aïr
Mountains between 16° and 19° N and also around the northernmost part of the river Niger.
They state that DLs from the former region were short lived, and that DLs generated north
of about 14° N generally do not exist for more than a few hours.
The greater the convective activity of the DLs, the more likely the convective cells which
comprise them will be to transport dust to high altitudes, where it will produce a large IDDI
signal. However, the more intense the convection, the more likely the cells are to produce
Chapter 5: Dust variability over northern Africa
164
rainfall which will remove the dust from the atmosphere. Therefore dust will be quickly
removed where the unstable monsoon layer is thick enough to allow convection to high
altitudes, and where the surface is moist enough from past rainfall to provide a moisture
supply to the convective cells. The opposing processes of convective dust generation and
rainfall washout mean that the zone of maximum IDDI will occur close to the edge of the
rainfall zone, where convection is as intense as is possible without being strong enough to
produce large amounts of rainfall. This is likely to be the case within a few hundred
kilometres of the surface discontinuity between the Saharan and the monsoon air masses,
which is located near 20° N during August (Hastenrath, 1991). Dust mobilised by the DLs in
this region may reach relatively high altitudes without being removed from the air by
rainfall, so will remain in the atmosphere for some time, resulting in a strong signal in the
mean IDDI fields. A southerly shift in the isohyets, as has been apparent in the Sahel since
the onset of dry conditions in the late 1960s (Figure 5.3), will also entail southward
displacement of the zone in which the relationship between dust mobilisation and rainfall
removal of dust particles shifts to favour high atmospheric dust concentrations.
The above conceptual model is supported by the fact that the zone of maximum IDDI values
in August (Figure 5.6) lies immediately to the north of the regions of enhanced DL
generation described by Rowell and Milford (1993). The southernmost limit of the distinct
zone where IDDI values are greater than 5 K is located at around 18° N., and the entire high-
IDDI zone is confined to the west of the Air Mountains. The shift in maximum IDDI values
from the 15°-17° N band in May and June to the 18°-20° N band in July and August is also
compatible with this model, as the zone of maximum wet-season dust loadings will migrate
with the surface discontinuity.
The reasons for the large northwards extent of the summer zone of high-IDDI values (to
some 26° N in July and August, see Figure 5.6) are not clear. DL activity is unlikely to
extend far enough north to explain the dust signals much beyond the surface discontinuity. It
is possible that advection transports dust northwards, although this would require that large
amounts of dust are transferred across the boundary between the monsoonal and Saharan air
masses, and then transported further north within the Saharan air mass. This seems unlikely,
as the prevailing motion within this air mass is towards the southwest. The DLs that bring
rainfall to the Sahel require a surface moisture source to develop fully, and are reinforced by
the dynamics of the moist air layer and northward surges of monsoonal air. However, these
DLs are triggered by African easterly waves, which may extend for some thousands of
kilometres in a north-south direction (Hastenrath, 1992; Rowell and Milford, 1993). It is
Chapter 5: Dust variability over northern Africa
165
plausible, although speculative, that instabilities in the easterly waves give rise to DL-like
activity within the Saharan air mass, which results in meteorological conditions capable of
mobilising dust. An alternative explanation is that the northeasterly surface winds in the
Sahara are stronger in the vicinity of the surface discontinuity. Dust transport near the
discontinuity may be assisted by the enhanced vertical motion resulting from the rising of
the Saharan air above the monsoon air.
December, January and June: 15º-17º N maximum
The monthly IDDI fields (Figure 5.6) indicate that the 15º-17º N band contains the southern
regions of the Bilma-Faya-Largeau source region and the sources to the south of the Aïr
Mountains. These sources are most active from December to February. As discussed earlier
in this chapter, the former area is a well-known dust source, and the latter sources are likely
to be the consequence of pluvial processes in highland regions. At these latitudes, intense
rainfall events are characteristic of the summer months, but annual rainfall amounts are
relatively low. A possible explanation for the maximum in dust production in some months
in this zone is that the balance between rainfall erosion and vegetation cover is such that
summer production of erodible material by fluvial processes is at a maximum in this zone.
While an anthropogenic influence cannot be discounted, the mean relationship between
rainfall and land cover may therefore be sufficient explanation for the maximum in dust
production in this band. Dust concentrations will be kept low during the summer months by
wet deposition, and the erodible material will undergo the most significant transport as a
result of winter mechanisms operating in a dry atmosphere. In June, a combination of
intense mobilisation events related to convective activity (DLs) and relatively bare soils
(before the summer vegetation becomes established) is likely to be behind the meridional
maximum in dust concentrations.
February, March, May and October: Local maximum at 15º-17º N
In February, North Sahel IDDI values are similar to those for the South Sahara, which
represent a meridional maximum in dust concentrations at a 5º-latitude resolution. A similar
situation exists in March, although the differences between the North Sahel and the South
Sahara are somewhat greater, and the North Sahara values are much greater than in
February. In October, dust concentrations are greatest over the Saharan regions, with very
similar values for the North and South Sahel. Thus, in these three months, the general trend
is for dust concentrations to decline from the South Sahara to the South Sahel. However,
superimposed on this general decline is an increase in dust concentrations over the band
Chapter 5: Dust variability over northern Africa
166
from 15º-17º N. This is indicative of a zone of increased dust production, the signal from
which is superimposed on the regional trend of decreasing dust concentrations away from
the southern and central Saharan regions, from which dust is advected by the Harmattan.
The monthly fields indicate strong Saharan sources in February and March, with high
activity also occurring in the Bilma-Faya-Largeau region. The reasons for the local
meridional dust maximum are therefore similar to those behind the regional maximum in
December and January. High activity is also apparent in the eastern Sahel-Sahara sources,
which extend to the 15º-17º N zone. A reactivation of the Bilma-Faya-Largeau sources is
apparent in May, with maximum activity between about 14º and 17º N. Although the IDDI
signal in this region is weaker than in December and January, the central Saharan sources
are relatively inactive in May. The pattern of source activity in October is similar to that in
January and February, albeit on a much reduced scale in terms of intensity and geographical
extent of dust production.
Summary
In January, May and June, the meridional maximum in dust production appears to lie within
the zone that may be characterised by some degree of land degradation. It is possible that the
volumes of airborne dust originating in the 15°-17° N are augmented by deflation from
degraded soils. It is also possible that material from degraded soils makes a large enough
contribution to the dust budget of the region to cause the meridional maximum in dust
production. Degraded soils may be more susceptible to deflation in May and June than in
most other months because of the prevalence of deflationary meteorological processes
associated with early DLs. This susceptibility will continue into the JAS period, when these
processes will be more effective, but when levels of dust will be kept low by increased
rainfall washout. Increases in vegetation cover as the summer progresses are likely to reduce
dust mobilisation. During July and August the meridional maximum of dust production is
located between 18° and 20° N. As this band lies outside the zone of potential degradation it
is concluded that dust mobilisation here is a response to the level and nature of the DL
activity.
This interpretation suggests that caution should be applied before attributing the May/June
meridional maximum in the 15°-17° N zone to soil degradation, as the deflationary
processes (related to DL activity) will be of maximum efficacy in this zone in these months.
However, the state of the soil will affect the response of the land to such processes. The fact
that higher IDDI levels are observed over the 15°-17° N zone for eight out of twelve months,
regardless of the location of the meridional IDDI maximum, implies that dust concentrations
Chapter 5: Dust variability over northern Africa
167
are higher in the marginal zone characterised by some human impact on the land surface
than in the unutilised zone representing the Sahel-Sahara transition zone. Several possible
factors that might be responsible for this phenomenon are postulated:
i. Removal of vegetation and disturbance of topsoil by human activity may reduce the
cohesion of the soil and increase its vulnerability to deflation.
ii. Dying off of vegetation in response to dry conditions may have a similar, although less
dramatic, effect to human activity.
iii. Higher rainfall levels than in the more northerly zone may cause increased water
erosion, ensuring a constant supply of erodible material. This process may act on
undisturbed soils, particularly after one or more drought years when vegetation cover
has been reduced and the organic content of the soil diminished. Rainfall on loose, dry
soils will enhance their erodibility still further. The impact of such water erosion is
likely to depend on the soil state, the intensity of the rain (i.e. droplet size) and the
timing of the rainfall. Rainfall that is sustained over a period of weeks or months will
initially enhance erosion, but will ultimately stabilise the land surface by encouraging
vegetation cover. Isolated rainstorms will cause significant erosion without otherwise
altering the land surface. Rainfall events, flash floods and ephemeral river flow are
important mechanisms of sediment mobilisation in drylands (Baird, 1997; Reid and
Frostick, 1997).
Although the processes described in point (iii) above will have a greater impact on degraded
or desiccated soils, they will also be a naturally occurring feature of the climatically
marginal extremities of a monsoonal regime such as that characterising West Africa. The
southward shift in the isohyets over the course of the late twentieth century desiccation is
likely to be associated with a southward movement of this marginal zone, which will entail a
similar shift in the zone in which pluvial and geomorphological processes are associated
with significant dust production throughout most of the year, and with elevated dust
concentrations in the wet season.
5.6.3. Commonality of Sahelian and Saharan dust loadings
The above results are not incompatible with the notion that at least some of the dust
produced in the Sahel zone is the result of soil degradation. However, the seasonal
variations in dust levels appear to reflect the migration of meteorological conditions, most
notably the surface discontinuity associated with the movement of the ITCZ. For six months
Chapter 5: Dust variability over northern Africa
168
of the year dust loadings are greatest south of 20° N. During four of these six months the
meridional maximum of dust production is located in the zone from 15° to 17° N, while for
the remaining two months this maximum lies between 18° and 20° N. Thus it appears that
much of the annual dust production in northern Africa occurs in the zone including the
northern limits of human activity and the transition zone between the Sahel and the Sahara,
but south of the hyper-arid desert regions. It is uncertain to what extent dust from this region
is mobilised from soils that have been degraded through human activity (such as
overgrazing), past agricultural activity or climate-induced desiccation, although the role of
the last of these processes is likely to be significant. In order to ascertain the degree to which
soil degradation has affected dust production in this and more southerly regions, field
studies would be required. However, the IDDI data allow us to examine the extent to which
variations in dust production in these marginal regions reflect those in regions where soil
degradation is unlikely to be an important factor in deflation. These latter regions are those
in the interior of the Sahara, where rainfall and human/animal impacts are negligible.
In order to achieve a comparison of dust variability over different latitudinal zones, time
series of IDDI values averaged over the different zones described in the previous section
were correlated with each other. Each correlation was between a pair of series representing
the same period or month. In order to minimise the likelihood of high correlations resulting
from single, spatially continuous source regions straddling the boundaries between zones,
indices representing adjacent zones were not correlated. The statistical significance of the
correlations was tested using the method of Ebisuzaki (1997), which accounts for
autocorrelation in the timeseries (see Chapter 3). The resulting correlations are presented
below in Tables 5.2 and 5.3.
When IDDI indices representing periods longer than one month are correlated between the
various zones, statistically significant results are particularly notable in AMJ (Table 5.2).
Relationships in this period are also likely to be responsible for part of the signal
represented by the significant results for the wet season (May-October) and for the annual
period January-December. An April signal may also be reflected in some of the late dry
season (January-April) results. The lack of significant correlations in the dry (November-
April), early dry (November-December) and OND periods may reflect a decoupling between
the Sahelian and Saharan regions due to the fact that, in the period immediately following
the wet season, Sahel dust production is significantly influenced by the soil state as
determined by rainfall. This influence could be in the form of soil moisture content and/or
vegetation coverage and state, and could include an anthropogenic component.
Chapter 5: Dust variability over northern Africa
169
Regions for which correlations were calculated
Period S. Sahel -
S. Sahara
S. Sahel -
N. Sahara
N. Sahel -
N. Sahara
15-17°°°°N -
S. Sahara
15-17°°°°N -
N. Sahara
18-20°°°°N -
N. Sahara
JFM 0.43 0.24 0.21 0.16 -0.04 0.48
AMJ 0.90* 0.58* 0.58 0.95* 0.64* 0.50
JAS 0.50 0.41 0.62 0.02 0.06 0.80
OND 0.62 0.11 0.00 0.62 -0.14 0.14
Wet 0.63* 0.64* 0.68 0.87* 0.63 0.66
Dry 0.60 0.40 0.03 0.29 -0.04 0.12
Early Dry 0.36 0.24 0.18 0.56 0.02 0.30
Late Dry 0.56* 0.54* 0.62 0.29 0.35 0.83*
Oct-Apr 0.76 0.44 0.17 0.45 0.06 0.28
Oct-Sep 0.86* 0.63 0.38 0.57 0.18 0.48
May-Apr 0.72 0.63 0.50 0.58 0.34 0.62*
Jan-Dec 0.69* 0.56* 0.45 0.32 0.14 0.67
Table 5.2: Correlations between series of yearly IDDI indices, representing variousperiods, over different latitudinal zones, for 1984-1993. Correlations significant at the5% significance level are shown in bold; those significant at the 1% level are markedwith an asterisk.
The monthly-index correlations show a less coherent pattern than the aggregated period
results (Table 5.3). The largest number of significant results occurs in May, and there is,
arguably, a clustering of significant correlations around the March-May period. The
relationship between the North Sahel and the North Sahara in July and September is
plausibly due to African easterly waves which will be manifest as DL activity within the
monsoonal air mass. The correlation between the South Sahel and South Sahara in August is
also likely to be due to DL activity; both these regions will be characterised by the edges of
the zone in which DLs are generated (Rowell and Milford, 1993).
Chapter 5: Dust variability over northern Africa
170
Regions for which correlations were calculated
Month S. Sahel –
S. Sahara
S. Sahel –
N. Sahara
N. Sahel –
N. Sahara
15-17°N –
S. Sahara
15-17°N –
N. Sahara
18-20°N –
N. Sahara
Jan 0.54 0.18 0.57 0.72* 0.34 0.71*
Feb 0.53 0.45 0.49 0.44 0.49 -0.41
Mar 0.48* 0.22 -0.12 -0.09 -0.14 -0.05
Apr 0.75* 0.35 0.30 0.74* 0.36 0.24
May 0.76* 0.57* 0.53 0.86* 0.46 0.52
Jun 0.68 0.40 0.27 0.62 0.33 0.17
Jul 0.26 0.12 0.89* 0.46 0.78 0.84*
Aug 0.64* 0.44 0.01 0.18 -0.16 0.08
Sep -0.28 0.10 0.93* 0.23 0.57 0.92*
Oct 0.34 0.53 0.25 0.77 0.19 0.30
Nov 0.50 0.19 0.18 0.71* 0.10 0.26
Dec 0.65* 0.53 0.45 0.63 0.30 0.57
Table 5.3: Correlations between series of yearly IDDI indices, representing individualmonths, over different latitudinal zones, for 1984-1993. Key as for Figure 5.2.
Correlations were also calculated between the IDDI-index timeseries calculated over the
10°-19° N. and 21°-30° N zones, which represent the aggregated Sahel and Sahara
respectively. A gap of 2° latitude was left between the zones in order to reduce the effect of
coherent regions of IDDI overlapping the two zones. The resulting correlations are shown in
Table 5.4. As with the narrower latitudinal bands described above, strong correlations are
apparent in the AMJ season, and in the individual months comprising this season. These
relationships are probably largely responsible for the high wet season correlation, and
possibly for the late dry season result. They also offer an explanation as to why the January-
December annual period exhibits a significant result, whereas the May-April annual period
does not. The former period contains the AMJ period whereas the latter period starts a third
Chapter 5: Dust variability over northern Africa
171
of the way through it, distributing each coherent, continuous AMJ period of high
correlations between two twelve-month periods. Although the October-September annual
period, which also contains the AMJ season, does not exhibit a significant result, the
correlation is high. It is possible that this high correlation represents a real relationship but
was not classified as significant due to high autocorrelation in the timeseries.
a.
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
0.57 0.56 0.07 0.67* 0.87* 0.89* 0.63 0.41 0.47 0.60 0.48 0.65
b.
JFM AMJ JAS OND Wet Dry EarlyDry
LateDry
Oct-Apr
Oct-Sep
May-Apr
Jan-Dec
0.28 0.92* 0.55 0.35 0.86* 0.40 0.42 0.58* 0.50 0.72 0.63 0.63*
Table 5.4: Correlations between series of yearly IDDI indices representing theaggregated Sahel and Sahara zones, for (a) individual months and (b) periods ofbetween two and twelve months duration. Key as for Figure 5.2.
The frequency of significant correlations between the various zones described here suggests
that the mechanisms of dust production are often large-scale in nature, extending throughout
Sahelian and Saharan latitudes. The fact that the overwhelming majority of correlations are
positive also suggests that there exists a systematic commonality to the data representing
dust levels over the various latitudinal zones. A significant degree of commonality in dust
loadings may be due to advection of mineral aerosols from the Sahara over the Sahel.
However, it is difficult to explain correlations between these two regions in terms of
advection for those periods when the majority of production occurs in the southernmost of
the zones being correlated, as advection is generally towards the south and west. During the
boreal summer, correlations are apparent between regions under the influence of two
different air masses: advection of dust from the northern Saharan airmass into the southern
monsoonal airmass is unlikely, although a Saharan signal may result from dust-laden
Saharan air overlying the monsoonal airmass to the south.
However, the significant correlations between latitudinal zones are not ubiquitous, meaning
that zonal variations in dust production are significant. The decoupling of the Saharan and
Chapter 5: Dust variability over northern Africa
172
Sahelian dust signals is evident in those periods when significant correlations between
Saharan and Sahelian zones are infrequent or non-existent. Such a period is that immediately
following the Sahel wet season, suggesting that rainfall does have an impact on dust
production. This is in keeping with the findings of other authors who have noted elevated
dust levels in air originating over northern Africa after dry episodes in the Sahel (e.g.
Prospero and Nees, 1986). The decoupling of Saharan and Sahelian dust signals in
individual years is discussed further in the following section.
5.7. Interannual variability of the IDDI
Interannual variability of dust production in zones located in the Sahel-Sahara region was
examined by plotting the timeseries of IDDI indices representing selected yearly or sub-
yearly periods for these zones (Figures 5.12 to 5.15). The timeseries used were those
employed in the correlation analysis described in the previous section. In this section
selected timeseries are plotted in the form of standardised anomalies with respect to the
period 1984-1993 for each series. For each period (e.g. wet season, AMJ), the timeseries
representing mean IDDI over the aggregated Sahel and Sahara zones are presented.
Timeseries for the two narrower zones (e.g. South Sahel, North Sahara) which exhibit the
strongest commonality (see section 5.5), or which might best serve to illuminate the
processes driving the generation of dust, are also plotted in each case. Individual monthly
anomalies for the aggregated Sahel and Sahara only are also presented (Figures 5.16 and
5.17). For each period for which timeseries are plotted, years containing particularly large-
magnitude anomalies are noted. IDDI and climatological data from these anomalous years
will be used to produce composite fields representing high and low dust years in Chapter 6.
As the following sections contain some discussion of the relationships between dust
loadings and rainfall, rainfall anomalies for the period 1983-1993 are plotted in Figure 5.11
(below). 1983 is included because of its potential impact on dust production in the Sahel
during 1984. Both annual and August anomalies are shown: most of the variability in
Sahelian rainfall is due to variations in August-September rainfall totals (Nicholson and
Palao, 1993), and August is the month of maximum rainfall. Discussions of dust-rainfall
relationships based only on short series of anomalies must be fairly speculative. However,
such speculation is employed in order to form hypotheses concerning dust-climate
interactions that will provide a context for the investigations described in the following
chapters.
Chapter 5: Dust variability over northern Africa
173
The three driest years in the Sahel between 1984 and 1993 were, in descending order of
aridity, 1984, 1987 and 1990 (Figure 5.11). The three wettest years in the same period were,
in order of decreasing rainfall anomaly magnitude, 1988, 1989 and 1991. Only 1988
exhibited annual rainfall above the twentieth century mean, and the 1988 anomaly was only
marginally greater than zero.
Figure 5.11: Rainfall anomalies for the spatially aggregated Sahel (10°°°°-20°°°° N; WestAfrican coast - 30°°°° E) based on the mean for the period 1983-1993. Units are standarddeviations.
5.7.1. Annual IDDI anomalies
The annual IDDI anomalies were created for the period from October to September. This
period was chosen in order to capture a signal characterising a twelve-month period
beginning at the end of the wet season. Although such a period will capture the effects of
two wet seasons, it will contain a dry season that is subject to the influence of a single year’s
rainfall, if cumulative effects are neglected. Figure 5.12 shows that the Sahelian anomalies
tend reflect the Saharan anomalies from the 1985/86 12-month period onwards. However,
although the correlation between the two timeseries is high (0.72) it is not statistically
significant at the 5% level. The apparent correspondence is particularly strong between the
South Sahel and the South Sahara, and the correlation rises to 0.86 between these two zones,
significant at the 1% level.
Chapter 5: Dust variability over northern Africa
174
Figure 5.12: Standardised anomalies of IDDI values averaged over twelve monthperiods from October to the following September, then spatially averaged for specifiedregions (see text for details.) Anomalies are with respect to the 9-year mean of annualaverage IDDI values.
Over the period for which data are available, the largest positive annual IDDI anomaly for
the Sahel occurs in 1984/85, immediately after the very dry year of 1984 (the driest year for
the aggregated Sahel this century). The other two largest positive anomalies occur in
1987/88 and 1990/91, following the two next driest years in the 1984-1993 period. This
pattern is slightly stronger in the South Sahel, where both rainfall and vegetation cover are
greater and therefore likely to play a larger role in determining land-surface properties.
However, the largest magnitude negative IDDI anomalies in the Sahel occur in 1985/86,
1989/90 and 1991/92. The first and last of these periods immediately follow those in which
IDDI is at a maximum following very dry summers. The obvious inference to be drawn from
this is that the intervening wet seasons of 1985 and 1991 have led to a recovery in soil
cohesion and vegetation cover, leading to a land-surface less vulnerable to deflation.
However, both these years are characterised by relatively low rainfall. The deficit in 1985 is
of considerable magnitude, and precedes the least dusty 12-month period in the series. Four
possible interpretations of these anomalies are postulated:
i. Even relatively low rainfall amounts may be sufficient to prompt a recovery in soil
cohesion and protection by vegetation. It is likely that the response of vegetation to
rainfall variations is not linear, and that a rainfall threshold exists, below which soil
becomes particularly vulnerable to deflation. Such a threshold is likely to be
determined by the amount of moisture required for vegetation to exhibit significant
growth during the wet season. It is possible that this minimum-rainfall threshold was
breached in 1984, 1987 and 1990, but not in other years in the 1984-1993 period.
Chapter 5: Dust variability over northern Africa
175
These three dry years all exhibit rainfall deficits in excess of 1.8 standard deviations
from the 1901-1996 mean. The next largest deficit occurred in 1985 and had a
magnitude of approximately 1.5 standard deviations. This would suggest that any
hypothetical “vulnerability threshold” would lie between 1.5 and 1.8 standard
deviations if measured in terms of rainfall deficits from the twentieth century mean. Of
course such an approach does not account for spatial variations in rainfall, and it
would be desirable to perform such determinations of vulnerability thresholds on
smaller spatial scales. Such determinations would need to be verified by field
measurements, and would require more years of IDDI data than are available at
present.
ii. The differences in the sign of anomalies in dry years may be the result of different
spatial patterns of drought. If the 1985 and 1991 rainfall deficits are the result of more
localised drought conditions than occurred in 1984 and 1990 it is likely that other
regions experienced enough rainfall for a recovery in the soil and vegetation to occur
which stabilised the soil, reducing deflation. This is especially likely if large-
magnitude rainfall anomalies in 1985 and 1991 occurred in regions away from the
major dust sources.
iii. It is possible that the majority of vulnerable material in Sahelian regions is removed by
deflation in the months following the wet season. If no new erodible material is
created before the following dry season deflation will be minimal, even after a
particularly dry summer. Water is an important agent for the concentration of erodible
material in environments where deflationary processes are prevalent (Middleton,
1997). Hence after two consecutive dry summers dust levels will be low, most of the
erodible material having been removed after the first dry summer. This hypothesis
provides a plausible model for the periods 1984-1986 and 1990-1992, when large
positive IDDI anomalies in one 12-month period are followed by notable negative
anomalies in the next 12-month period.
iv. The relatively high degree of commonality between Sahelian and Saharan IDDI
anomalies suggests that large-scale processes and phenomena connected with the
atmospheric circulation may exert a common influence on both regions. These
processes might dominate over rainfall influences and be responsible for the pattern of
anomalies, with large anomalies after dry summers being coincidental. It is plausible
that at least some of the apparent commonality between the Sahel and Sahara is due to
advection from the latter to the former. Advection is probably responsible for the
Chapter 5: Dust variability over northern Africa
176
relatively low background field of non-zero IDDI values which prevails throughout the
year. However, the discrete and pronounced localised regions of high IDDI apparent in
the mean monthly fields are indicative of areas coinciding with or close to sources,
suggesting that the strongest signals are characteristic of recently mobilised dust,
rather than dust which has been advected very large distances. Theoretical and
observational considerations indicate that both particle size and aerosol density will be
greater closer to source regions (e.g. Gillies et al., 1996), producing a large IDDI
signal as the result of both scattering of shortwave radiation (although the mean
particle size will be high, concentrations of sub-micron particles will be high) and
attenuation of longwave radiation. The fact that the highest zonal IDDI values
commonly occur in the northerly Sahelian latitudes also suggest that the zonal IDDI
signals are responses to generation rather than large-scale advection; if dust over the
Sahel originated in the Sahara, we should expect the highest dust loadings over the
latter region, close to the sources.
5.7.2. Wet season IDDI anomalies
Figure 5.13: Standardised anomalies of IDDI values averaged over a liberally definedwet-season from May to October, then spatially averaged for specified regions (see textfor details.) Anomalies are with respect to the 10-year mean of seasonal average IDDIvalues.
Wet season (May-October) IDDI anomalies are presented in Figure 5.13 for the aggregated
Sahel and Sahara, and also for the South Sahel and North Sahara. The latter two regions are
chosen due to the fact that their IDDI indices are significantly correlated (see Table 5.2), and
the fact that they are separated by some 10° latitude. The higher correlation between IDDI
indices for the 15°-17° N and South Sahara zones may be explained by the fact that both
Chapter 5: Dust variability over northern Africa
177
these zones are characterised by the mobile band of high IDDI values around and to the
north of the surface discontinuity associated with the summer position of the ITCZ. The
correlation between the South Sahel and South Sahara may also be explained in this manner,
although any teleconnections that exist between the Sahel and Sahara may also contribute to
the apparent relationship.
The large separation of the South Sahel and North Sahara further precludes advection as a
linking factor (especially since the relationship of each region’s zonal IDDI with that of the
closer non-adjacent region is weaker). This suggests a possible atmospheric teleconnection
between the two zones, resulting from the regional circulation, provided the validity of such
a statistical comparison of two such short timeseries is accepted.
The most notable anomaly in the Sahel (aggregated and South) is the large positive anomaly
of 1991. Although characterised by a rainfall deficit relative to the twentieth century mean,
1991 was the third wettest year in the 1984-1993 period for which IDDI data are available.
Only 1988 and 1989 were wetter, with 1988 being the wettest. These are the only other
years to exhibit positive IDDI anomalies in the South Sahel. Anomalies are also positive,
although small, in the aggregated Sahel. In this region the anomalies for 1987 and 1988 are
similar, despite the fact that 1987 was very dry and 1988 relatively wet. In the South Sahel,
1987 exhibits a small negative anomaly. Positive Saharan anomalies are greatest in 1988 and
1991.
The largest magnitude negative anomalies in all four regions occur in 1985 and 1990, years
characterised by large rainfall deficits which follow a very dry year and a relatively wet year
respectively. The wet-season IDDI anomaly for 1984, the driest year this century in the
Sahel, is negative and small in the South Sahara and positive but near-zero for the other
regions.
The pattern of wet season IDDI anomalies suggests that dust production is not a simple
function of rainfall. If any pattern is evident from this short timeseries of anomalies, it is that
there is a tendency for wet years to be characterised by dusty summers, and dry years to
exhibit low summer dust loadings. These findings are consistent with the argument that
summer dust production is largely due to activity related to African Easterly Waves,
particularly to DL activity in the Sahelian, and possibly southern Saharan, regions. The
interpretation that summer dust production is controlled by such large-scale phenomena that
may span many degrees of longitude and latitude is supported by the fact that there are
striking similarities between the Sahelian and Saharan wet-season IDDI anomaly series.
Chapter 5: Dust variability over northern Africa
178
Note the statistically significant correlation of 0.86 between the wet season IDDI indices for
the aggregated Sahel and Sahara zones.
For dust levels to be positively related to DL activity, the dust-raising activity of the DLs
must dominate over the removal of dust by rainfall, the amount of which is related to the
frequency of DLs of intermediate to high intensity (Lamb et al., 1998). Therefore a
theoretical rainfall threshold may be postulated, above which the washout of dust by DL-
induced rainfall dominates over vertical dust transport by DL-related convective
disturbances. This suggests that the nature of the relationship between summer dust loadings
and DL activity will depend on the aridity of the region in question, or the amount of rainfall
that the DLs generate. Lamb et al. (1998) present evidence that the decline in rainfall over
the Sahel since the l950s has coincided with a general decrease in the frequency of large and
well organised (and intense) DLs, and an increase in the frequency of small and
disorganised (and weak) DLs. It may be speculated that at least part of the increase in dust
production over the past few decades is due to a change in the ratio of mobilisation to
washout, which is a result of the changes in the nature of the DLs. Lamb et al. (1998) also
report an increase in the post-1965 frequency of DL absence. Periods characterised by
weaker DLs may be characterised by lower rainfall and elevated dust levels for the reasons
outlined above, provided the DLs are still strong enough to result in deflation. Periods of DL
absence are likely to be marked by reductions in both rainfall and atmospheric dust loadings
for similar reasons.
This conceptual model would be consistent with increased dust aerosol levels over the
Sahel, where the decline in rainfall effects the deflation/washout ratio, and also with
unchanged or reduced levels of dust over the Sahara. As rainfall over the Sahara is
negligible, the deflation/washout ratio will be unaffected. However, the occurrence of fewer
(or of generally weaker) DLs may lead to a reduction in dust generation over the Sahara,
without these changes being offset by reduced washout. This model is consistent with the
observations of N’Tchayi et al. (1997), who have observed a decrease in the frequency of
dust events at some Saharan sites (c.f. Chapter 2). Although a detailed analysis of the
frequency, distribution and intensity of DLs is outside the scope of this thesis, the
relationship between easterly wave activity and dust production will be assessed in a more
quantitative fashion in Chapter 6.
Chapter 5: Dust variability over northern Africa
179
5.7.3. Early dry season (November-December) IDDI anomalies
IDDI values for the Sahel over the November-December period should be more highly
dependent on rainfall in the previous wet season than IDDI values in other periods, as
vegetation and soil moisture content (both determined by the amount of rainfall in the
previous few months) will not have reached their minimum extent and level respectively.
Land surface properties will therefore retain a wet season “signature”. None of the Sahara-
Sahel pairs of zonally averaged anomalies are significantly correlated, suggesting that the
preferential impact of rainfall on the Sahelian soils acts to decouple dust production in this
region from that in the Sahara. The lack of commonality is reflected in the series of yearly
anomalies, although some similarities between the Sahel and Sahara are apparent (Figure
5.14). The impact of the 1984 drought in the Sahel is evident from the large positive
anomaly apparent for this year in the Sahel IDDI series, contrasting with a negative anomaly
in the Sahara series. The South Sahel and South Sahara series are also presented in order to
provide a comparison between a rainfall dominated region and an arid region.
Figure 5.14: Standardised anomalies of IDDI values averaged over the early dry season(November-December) which immediately succeeds the wet-season, then spatiallyaveraged for specified regions (see text for details.) Anomalies are with respect to the10-year mean of the bi-monthly average IDDI values.
The most striking aspect of the Sahel series is that anomalies are consistently positive from
1984 to 1988, after which they are negative or (in the case of the aggregated Sahel in 1992)
barely exceed zero. Analysis of individual monthly IDDI anomalies (Figure 5.17)
demonstrates that this change in the IDDI series is due to an even more pronounced change
in the December values. The short length of the series means that caution is required in
assigning any significance to these results. However, a tentative interpretation is that soil
Chapter 5: Dust variability over northern Africa
180
cohesion underwent a recovery as a result of the cumulative effects of the relatively wet
summers of 1988 and 1989. Although the 1989 rainfall anomaly was negative, the deficit
was small, and it is plausible that there was enough rain to reinforce a recovery prompted by
the positive rainfall anomaly of 1988. The fact that the November-December IDDI anomaly
in 1988 was positive suggests that this single wet year was insufficient to cause a recovery
in soil structure. An alternative, and not incompatible, interpretation is that relatively
vigorous rainfall acting on degraded soils produced a large amount of deflatable material
which was removed in the subsequent dry season, causing relatively high dust loadings over
the Sahel in late 1988. However, before the negative IDDI anomaly of 1989 is ascribed
wholly to a rainfall-induced recovery of degraded soils, it should be noted that large
negative anomalies are also evident in the Sahara in 1989, suggesting a possible explanation
involving large-sale aspects of the region atmospheric circulation. This interpretation is
strengthened by the fact that the large negative anomaly over the Sahara occurs within a
series of consecutive positive anomalies, from which it represents a significant departure.
Such large-scale processes are probably confined to 1989, as suggested by the return of the
Sahara to positive anomalies in 1990, and by the contrary nature of the Saharan and Sahelian
signals in 1990 and 1991.
5.7.4. Late dry season (January-April) IDDI anomalies
Figure 5.15: Standardised anomalies of IDDI values averaged the late dry season(January-April), then spatially averaged for specified regions (see text for details.)Anomalies are with respect to the 10-year mean of January-April average IDDI values.
The impact of one summer’s rainfall on the following late dry season dust production is
uncertain, and likely to be complex. Around the beginning of the calendar year the moisture
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content of the soil will have diminished to the point at which it plays a negligible role in
determining the cohesive properties of the soil. It is possible that the previous year’s rainfall
has an indirect effect on soil cohesion via the amount of organic debris resulting from the
decay of vegetation. This notion is supported by the existence of a dramatic peak in the
IDDI anomaly series in this period for 1985 (Figure 5.15), after the severe drought of 1984.
However, this phenomenon is not apparent after other very dry years, suggesting that the
effects of drought on dust production in this period may be cumulative. In other words the
dust production cycle is likely to be quite robust during the first few months of the year,
with rainfall impacts only becoming apparent after a number of consecutive dry summers.
Persistent drought may lead to a reduction in soil stability via the cumulative effects of
drought stress on vegetation and consequently on the organic matrix of the soil. The
relatively small magnitude of the IDDI anomalies (excepting 1985) when compared with the
other periods discussed here suggests that the mechanisms that control dust production
during this part of the year are not as variable as those which determine production at other
times. As well as the direct effects of the previous year’s rainfall being minimal, so will be
the effects of variability of DL activity, as DLs are largely confined to the wet season. The
Sahel-Sahara zone will be predominantly influenced by the large-scale Harmattan
circulation, and it is likely that changes in dust production will be dependent on variability
in Harmattan wind strength. More specifically, production will depend on the frequency
with which the wind exceeds the threshold velocity required for deflation to occur. This will
depend on the properties of the soil, allowing for the influence of drought and degradation
on soil cohesion.
It should be remembered that this period is characterised by high dust loadings throughout
the Sahel-Sahara zone, and is the dustiest period in the Sahara. The frequency of dust
mobilisation events and the amounts of dust in the atmosphere are likely to be high enough
to ensure that factors which might be important in determining the variations in dust levels
in less dusty periods are of relatively little importance.
5.7.5. Individual monthly IDDI anomalies
When monthly IDDI anomalies are plotted by year (Figure 5.16), extended periods with
sustained low or high IDDI values, and hence dust loadings, are apparent. The most notable
of these are discussed below, in chronological order.
In the Sahel the period from September 1984 to April 1985 exhibits large positive IDDI
anomalies, apart from in January 1985, which is characterised by a near-zero anomaly. This
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182
is most likely to be a manifestation of the immediate and cumulative effects of the drought
that culminated in the record 1984 rainfall deficit.
Figure 5.16: Standardised anomalies of monthly IDDI over the Sahel and Sahara.Standardisation is performed using the mean and standard deviation for the seriesrepresenting each individual month.
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183
The first half of 1986 is characterised by negative IDDI anomalies in both the Sahel and
Sahara, suggesting that large-scale circulation mechanisms (probably related to the strength
of the Harmattan winds) are responsible for dust levels over this period.
July, August and September 1988 exhibit notable positive anomalies over the Sahel and
Sahara. These are the wettest three months of the year, reinforcing the hypothesis that
summer dust production is largely the result of DL activity which is in turn associated with
rainfall. The anomalies are greater over the Sahara, suggesting that the role of the DLs in
mobilising dust in the Sahel is offset by washout of the dust from the atmosphere by rainfall.
March to August 1991 in the Sahel, and March to July 1991 in the Sahara, are characterised
by persistently positive and often large anomalies. May 1991 stands out as a particularly
dusty month over both regions. The negative IDDI anomalies in January and February, and
the common pattern between the zones, suggests that the large-scale circulation is likely to
be responsible for the nature of the anomalies.
1992 is similar in some respects to 1986, in that IDDI anomalies are generally negative in
the first half of the year in the Sahel, and until August in the Sahara
Figure 5.17 presents the same monthly IDDI anomalies as Figure 5.16, but arranged by
month rather than year. Although not a great deal of new information can be gleaned from
this alternative format, it is useful in that it allows us to identify months which are
particularly anomalous in relation to the same month throughout the ten-year series. Such
months are candidates for studies based on comparisons of IDDI anomaly fields with
anomaly fields of other climatological variables. Such studies should provide information
concerning the mechanisms that are responsible for particular patterns of dust loadings over
the Sahel and Sahara. Anomalous months are identified as those which exhibit intermediate
to large magnitude anomalies over both the Sahel and the Sahara. Generally a large-
magnitude anomaly over one region is reflected by an anomaly which is of the same sign
and of a significant magnitude over the other region. The data in table 5.7 are used in the
following chapter to identify months of high and low IDDI loadings from which to create
composite IDDI fields for comparison with climatological data representing the same
periods.
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184
Figure 5.17: Standardised anomalies of monthly IDDI over the Sahel and Sahara (as inFigure 5.16), plotted for each month.
5.8. Summary
The IDDI provides useful information concerning the temporal and spatial distribution of
dust loadings, which allows us to identify the major sources of mineral dust in Africa. These
sources show a high degree of seasonality, which is indicative of seasonal shifts in the
regional atmospheric circulation. Mean monthly IDDI fields dramatically demonstrate the
seasonal evolution of North African dust sources, illustrating the importance of the northern
and central Sahelian latitudes from December to March and in May and June, and of the
Sahara from February to May. A shift of dust production from the central Sahel in May to
the west Sahel-Sahara in July and August is apparent. The degree to which regions of high
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185
summer dust loadings are geographically constrained by the position of the surface
discontinuity between the monsoonal and Saharan air masses is striking.
There is a tendency for dust levels over Sahelian regions to reflect those over the Sahara at
certain times of the year, particularly during the spring and summer. There is a significant
amount of evidence to suggest that the major determinant of dust loadings in these periods is
the amount of disturbance line (DL) activity. However, the results also suggest that dust
generation mechanisms over Saharan and Sahelian regions may become decoupled under
certain conditions, particularly when persistent drought acts to preferentially enhance the
erodibility of Sahelian soils. This is particularly evident for the two months immediately
following the end of the wet season; the November-December period is the only time during
which no significant correlations are apparent between zonally averaged IDDI for the Sahel
and Sahara regions examined in this study.
It is speculated that at least some of the observed increase in dust levels over the past few
decades may be related to changes in the nature of the rain-producing DLs. The relationship
between DL activity and dust levels over the Sahel in summer will depend on the amount of
wet deposition resulting from rainfall. This in turn will depend on the frequency of rain
events, i.e. on the aridity of the region. It is possible that observed changes in DL activity
have led to a situation in which dust mobilisation has remained relatively constant while
dust-removal processes have become less effective. Of course dust mobilisation may have
increased, or even diminished – what is important is the relative roles of mobilisation and
removal processes.
Dry season Sahelian dust concentrations are likely to have increased in part as a result of the
southward shift of the pluvial and geomorphological zones characteristic of the boundary
between the semi-arid Sahel and the arid Sahara. Within this zone, the ratio of rainfall
erosion of soils to rainfall stabilisation of soils (via the initiation of vegetation growth) will
be high relative to the wetter regions further south. Whether a shift in this ratio constitutes
land degradation or desertification is arguable. Tucker et al. (1991, 1994) and Nicholson and
Tucker (1998) have found that vegetation extent in the Sahel varies considerably on an
interannual timescale in response to rainfall (c.f. Chapter 2), and does not constitute
systematic desertification or land degradation. Similarly, variations in dust production may
be largely a manifestation of the interannual variability, with the increases in dry-season
aerosol concentrations being partly due to the increased number of dry years since the
1960s, and the consequent prevalence of desert-boundary type land-atmosphere interactions.
Of course, the response of the surface geomorphology to climate will be less instantaneous
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186
than that of vegetation, and the removal of soil material is not necessarily reversible in the
short term, so the dry season dust response to interannual rainfall variability will be more
complex than that of vegetation, with rainfall impacts on dust production manifesting
themselves over several years rather than any single season.
The hypothesised changes described above will ultimately also be a result of the weaker wet
season convective events. These may be the result of a southwards shift in the trajectories of
the DLs, or of a reduction in their meridional extent, with the weaker convection associated
with the edges of these systems being responsible for reduced rainfall in the Sahelian
margins. However, Lamb et al. (1998) report a general decline in the frequency and intensity
of such systems, and the work reviewed in Chapter 2 suggests that reduced rainfall is
associated with more complex climate changes than a simple southwards shift in climatic
zones.
Evidence for such climatic changes also suggests the possibility of changes in the
mechanisms of dust mobilisation and transport throughout the year, and changes in rainfall
are likely to be only a partial explanation for increases in atmospheric dust loadings. The
commonality of IDDI indices over Sahelian and Saharan regions, and the low variability of
January-April IDDI in the Sahel, suggest that dust production in the Sahel during the early
part of the calendar year is determined by the large-scale circulation rather than the
condition of the Sahelian soils. Exceptions to this general rule may occur after several years
of extreme drought.
Analysis of timeseries of IDDI indices over different zones for different periods and for
individual months has identified months and periods characterised by anomalous dust
loadings. Where the anomalies are common to Sahelian and Saharan regions they indicate
the likelihood of large-scale dust-mobilisation processes connected with patterns of the
region circulation. Analyses of circulation patterns in conjunction with fields of IDDI data
will be presented in Chapter 6, which will deal with the climatology associated with dust
production, and spatial relationships between rainfall and subsequent dust production.