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15 JANUARY 2002 187L E B A R B E E T A L .

q 2002 American Meteorological Society

Rainfall Variability in West Africa during the Years 1950–90

LUC LE BARBE,* THIERRY LEBEL, AND DOMINIQUE TAPSOBA1

Laboratoire d’Etude des Transferts en Hydrologie et Environnement, IRD, Grenoble, France

(Manuscript received 29 January 2001, in final form 25 June 2001)

ABSTRACT

The study presented here makes use of about 300 daily rain gauges covering a 1 700 000 km2 area in orderto characterize the rainfall regimes of West Africa at hydrological scales. The rainfall regime is analyzed as acombination of two variables, the average number of events over a given period of time (nT) and the averagecumulative rainfall per event (h). These two parameters are a measure of the occurrence rate and magnitude ofthe convective storms that generate most of the rainfall in this region. They define the average water input tothe hydrological systems and the average time available for this water to be redistributed into the continentalhydrological cycle before a new input occurs. By analyzing for a period of 40 yr (1951–90), the space and timevariations of these two parameters, it is possible to better understand how the intraseasonal to decadal rainfallvariability may impact on the hydrological cycle. The analysis is carried out in two steps. First, the annual cycleand migrations of the weather zones characterizing the climate of West Africa are considered. This leads toevidence of a sudden and synchronous rain onset between 98 and 138N, which does not follow the classic schemeof a progressive migration of the rain zones, north and south with the sun. Second, the differences in the rainfallregimes between the two succeeding subperiods of 20 yr are obtained, the subperiod P1 (1951–70) being wetand the subperiod P2 (1971–90) being dry. The difference—averaged over the 168 by 128 study region—of themean interannual rainfall between the wet and the dry periods is 180 mm yr21. This difference is relativelyevenly distributed in space, with no clear meridional gradient. Between these two periods, the parameter nT

displays a systematic decrease, which appears well correlated to the decrease of the mean interannual rainfall.The variations of h are, by contrast, smaller in amplitude and more erratically distributed in space. When lookingat the intraseasonal scale, it appears that the rainfall deficit of the dry period is primarily linked to a deficit ofthe number of events occurring during the core of the rainy season over the Sahel, and during the first rainyseason for the region extending south to 98–108N. It is also shown that, in the south, the dry period is characterizedby a shift in time of the second rainy season. All these characteristics have strong implications in term ofagricultural and water resources management. They also raise questions about the traditional scheme used tocharacterize the dynamics of the West African monsoon.

1. Introduction

The semiarid regions of the world are known for theirunreliable rainfall, which has a large impact on the con-tinental hydrological cycle, the water resources and thefood security. The Sahel, extending across Africa fromthe Atlantic Ocean to the Indian Ocean is the largest ofthese regions. The famines that struck the Sahel in the1970s (1972–74) and in the 1980s (1983–85) haveprompted a number of authors (e.g., Folland et al. 1986;Fontaine and Janicot 1996; Hastenrath 1990; Lamb1983; Lamb and Peppler 1992; Nicholson 1981) to in-vestigate possible mechanisms responsible for these dra-

* Current affiliation: Direction de l’Hydraulique du Benin, Coto-nou, Benin.

1 Current affiliation: Chaire industrielle en hydrologie statistique,INRS-Eau/Hydro-Quebec/CRSNG, Sainte-Foy, Quebec, Canada.

Corresponding author address: Dr. Thierry Lebel, LTHE, BP 53F-38041, Grenoble Cedex 9, France.E-mail: [email protected]

matic events. In fact, these two sequences of a fewextremely dry years are part of a longer drought thatlasted from the end of the 1960s to the mid-1990s (LeBarbe and Lebel 1997a; D’Amato and Lebel 1998). Thisis well illustrated by Fig. 1, which shows a normalizedrainfall index computed over the whole Sahel from afew long stations in Fig. 1a and from a denser networkfor the last 50 years over southern Niger in Fig. 1b.

This unusual dry spell was not limited to the Sahel,defined as the region extending north to 128N, but ex-tended to regions more to the south as well (see, e.g.,Le Barbe and Lebel 1997b). This is clear from Fig. 2.The average rainfall deficit of the 1970s and 1980s withrespect to the 1950s and 1960s is about 180 mm for thearea covered by this figure, with a fairly regular distri-bution in space. An illustration of the effect of thedrought in the more humid parts of West Africa is theshortage of electricity that struck the large coastal cap-itals, like Abidjan and Cotonou, during the summers of1984 and 1998, following the dryness of the precedingrainy seasons. The low rainfall caused the reservoirs to

188 VOLUME 15J O U R N A L O F C L I M A T E

FIG. 1. (a) Evolution of the standardized rainfall index over the Sahel between 1921 and 1994;(b) evolution of the standardized rainfall index over Niger (south to 158N) between 1950 and1997. (The standardized rainfall index is an average of rainfall standardized at each station withrespect to the mean of the station: R* 5 (R 2 m)/s where m and s are the mean and standarddeviation of the rain series at the station.)

remain empty and the hydropower plants had to be shutdown. Such a large-scale phenomenon goes beyond therainfall variability that commonly characterizes tropicalrainfall regimes.

Recent diagnostic studies (Fontaine et al. 1998; Jan-icot et al. 1996; Ward 1998) have provided some in-teresting elements showing that the forcing of the WestAfrican rainfall by worldwide SSTs might have beenmodified during the 1960s. The continental surface con-ditions also play a role as shown by modeling studies(Polcher 1995; Eltahir and Gong 1996; Zheng and El-tahir 1998) and observations (Taylor and Lebel 1998).Diagnostic studies generally consider rainfall patternsat large spatial scales. This is because they either aimat developing tools for seasonal prediction or are limitedby the resolution of the models. From a hydrologicalpoint of view, it is essential, on the other hand, to con-sider the scales of the processes influencing the partitionof rainwater into the various components of the watercycle. This is also true when considering timescales.

The study presented here makes use of a set of 300daily rain gauges covering a 1 700 000 km2 area (148

3 108) in order to characterize the rainfall regimes atthe event scale and their modification when comparingthe wet and the dry periods mentioned above. It alsoprovides some interesting clues to the dynamics of therainy season(s) over West Africa.

2. Region of study and data used

In West Africa the datasets used in diagnostic studiesare usually 10-day or monthly accumulations. The cor-responding daily observations are not as readily avail-able, even though there has been a sufficient number ofrain gauges in operation since the midcentury to get agood picture of the spatial pattern of the rainfall regimesfor timescales less than 10 days. In French speakingcountries of West Africa, daily rain data have been reg-ularly collected by the national meteorological servicesand delivered to a database built by the Centre Inter-Etats d’Etudes Hydrauliques (CIEH) and the Office dela Recherche Scientifique et Technique Outre-Mer(ORSTOM). The CIEH–ORSTOM database contains datauntil 1984. This database was updated to 1990, with the


FIG. 2. Map of the absolute rainfall differences between the wet period P1 (1951–70) and thedry period P2 (1971–90) over the region of study. Isolines are in mm yr21.

help of AGRHYMET—the technical centre of the Com-ite permanent Inter-etats de Lutte contre la Secheresseau Sahel—for three Sahelian countries (Burkina Faso,Mali, Niger) and with the help of the respective mete-orological services for six other countries (Benin, IvoryCoast, Ghana, Guinea, Nigeria, and Togo). In the da-tabase that was assembled for our study, data from thebeginning of operation to 1990 are archived for eachstation. The number of stations has varied in time, asignificant increase having occurred at the end of the1940s. It is only at the beginning of the 1950s that anaverage density of about one station for 10 000 km2

was reached. We therefore chose to start our regionalstudy in 1951. In the entire database, the total numberof stations with less than 10% of missing data is 469during the first period covered by our study (wet periodP1, 1951–70). During the following 20 yr (dry periodP2, 1971–90) this number decreased to 433. This de-crease is essentially linked to the difficulty of gettingdata after 1984. This is especially the case for stationsnot present in the AGRHYMET database. The distri-bution of stations is the most homogeneous over the 1683 128 window shown in Fig. 3. It is also a window witha relatively stable number of stations: 323 stations withless than 10% of missing data during the period P1, and326 stations with less than 10% of missing data duringthe period P2. However this equality of numbers issomewhat misleading since, as seen in Fig. 3, there areseveral P1 stations that do not belong to the P2 dataset.This is compensated by an approximately equal numberof P2 stations not belonging to the P1 dataset. Figure 3also shows that the territories of Liberia, Guinea, Ghana,and northern Mali are only sparsely covered (in fact nodata were collected for Liberia). Therefore, in the fol-lowing, the analysis will focus on a window restrictedto 4.58–14.58N in latitude and 108W–48E in longitude,in order to eliminate border effects, especially to the

north and west of the 168 3 128 window. This restrictedwindow still samples the full range of West Africanclimates (from south to north: Guinean, Sudanian, andSahelian). It contains 277 stations with less than 10%of missing data during the period P1 and 299 during theperiod P2. This latter number corresponds to an averagedensity of one station for 5600 km2. In this 148 3 108window, the distribution of stations is more regular thananywhere else in the region. However, there remains acentral region of low density corresponding to Ghana.

3. From daily rainfall data to rain event statistics

Rainfall is by nature an intermittent process. In re-gions where convection is the dominant mechanism pro-ducing rain, as is the case in the Tropics, the rainfallregime is a succession of storms lasting a few hours(see D’Amato and Lebel 1998, for statistics of stormduration in the Sahel) and separated by longer interstormperiods. The associated rain fields are highly variablein space. The high space–time resolution of the Esti-mation des Precipitation par SATellite (EPSAT)–Nigerdataset allows us to see how this spatial variability ismaintained on timescales far longer than the storm scale(see Lebel et al. 1997; Lebel and Le Barbe 1997). Recentdata have confirmed these results. For instance, in 1998,two seasonal totals of, respectively, 450 and 1050 mmwere observed at two stations located 80 km apart in asimilar environment. The hydrological cycle is domi-nated by this intermittency. It must therefore be takeninto account in hydrological models.

Daily rainfall series provide a biased representationof rainfall intermittency in time. This is clear from thestatistics of the number of rainy days and rain events(storms) shown in Table 1. Using a high-resolution re-cord of rainfall at Niamey from an EPSAT–Niger gauge,it can be seen that in August the number of rain events


FIG. 3. Daily rain gauge network. Isohyets refer to the mean interannual rainfall (in mm) forthe period 1951–90.

TABLE 1. Comparison of the number of rainy days and of thenumber of rain events observed at the Niamey–ORSTOM recordingrain gauge station. Statistics are averages over 9 EPSAT–Niger yearsof measurement (1990–98).

May Jun Jul Aug Sep

Number of rainy daysNumber of rain events

5.92.4

7.85.1

11.412.1

13.917.3

7.77.0

is larger than the number of rainy days, but it is theopposite in June. This derives from the probability ofhaving two events in one day being larger than the prob-ability of having a rain event spread over two differentdays. In June, it is the opposite. More generally the ratiobetween the number of rain events and the number ofrainy days will depend on the location and period ofthe year considered. In Niamey for instance the diurnalcycle of rainfall is controlled by local convection, andby the life cycle of large mesoscale convective systems(MCSs) that originate several hundred kilometers east-ward in an area bounded by the Joss Plateau and theAir Mountains. D’Amato and Lebel (1998) have shownthat during the core of the rainy seasons large MCSsare the main source of rain in the region—this wasconfirmed by a recent study of Mathon and Laurent(2001)—while in the margins of the rainy season localrain is dominant. The statistics of Table 1 reflect thissituation.

In order to obtain a more accurate characterization ofthe time distribution of rain than that available directlyfrom daily rain series, Le Barbe and Lebel (1997a)—hereafter LBL97—have proposed the use of a Com-

pound Poisson Process (CPP) model allowing for thedisaggregation of a daily rain series into a rain eventseries (see, e.g., Rodriguez-Iturbe et al. 1984, for a dis-cussion on the modeling of temporal rainfall, and Bo etal. 1994, for an earlier work on the disaggregation ofrainfall statistics from daily to event scales). In thismodel, rainfall is represented as a succession of rainevents. The duration separating the starting times of twoconsecutive events is exponentially distributed. Therainfall accumulation H over one event also follows anexponential distribution. The distribution function of ac-cumulations over a period T, denoted HT, is the follow-ing:

2n 2HT Tf (H ) 5 e n e I (2Ïn H )/(Ïn H )T T 1 T T T T

for H . 0, (1)T

where I1(2 ) is the modified first-order BesselÏn HT T

function.The distribution is characterized by two parameters,

nT and h. While h is a scale parameter invariant withthe accumulation period T considered, nT is a recurrenceparameter, denoting the average number of events overperiod T. As such, nT is additive, that is for a periodT9, it becomes

n 5 (T9/T) n .T9 T (2)

Given its additive property, any rainfall series obey-ing a CPP with white noise exponential distribution maybe characterized by its average number of rain eventsday21—denoted n in the following—and by h, which isthe average rainfall per event, expressed in millimeters.


FIG. 4. The rainfall regime of Niamey (periods P1 and P2 treated separately):(top) Rainfall accumulations over successive periods of 10 days; (middle andbottom) The CPP process parameters—mean number of event per day and eventrainfall—averaged over the same periods of 10 days, respectively.

These two parameters fully describe the rainfall regimeat a given point in space. The distribution frequency ofthe rain depths, HT, for any period of accumulation, T,can be computed from these two parameters. Obviously,n and h vary during the course of the year, that is, theyare functions of time t and should be denoted n(t) andh(t). In the Sahel, for instance, the peak of the rainyseason occurs in August and corresponds to a greateroccurrence rate of the rain events. One desirable prop-erty of the CPP models is that it holds for nonstationaryprocesses. This is because the sum of two Poisson pro-cesses with two different occurrence rates is still a Pois-son process. Based on this property and in order toaccount for the time nonstationarity of rainfall in the

region, LBL97 have proposed a moving window methodfor inferring the parameters n(t) and h(t).

LBL97 successfully used and validated the CPP mod-el on 35 rainfall series in central Sahel. An example oftheir results is given in Fig. 4 for the station of Niamey,Niger. The model was run with a moving window of10 days, leading to computed time-averaged values ofn and h, plotted in Fig. 4 as a function of t (middle andbottom panels). This is a more relevant representationof the intraseasonal evolution of the rainfall regime, thanthe sole 10-day hyetogram1 also shown in Fig. 4 (top

1 Rainfall accumulation over a successive periods of days.


panel). It is clearly seen by comparing these three graphsthat it is the intraseasonal and interdecadal fluctuationsof n that control the seasonal cycle of rainfall and itsdeficit during the dry period. In contrast, the event rain-fall seasonal cycle is almost invariant between the two20-yr periods considered.

Building on LBL97 (see also Le Barbe and Lebel1989, for a preliminary test on rainfall series in a morehumid tropical climate), it was decided to apply the CPPmodel to all the series of our 148 by 108 window. Thefitting method originally proposed in LBL97 has beenrefined in order to obtain stable and smoothed param-eters. Also, the size of the moving window used to inferthe parameters of the model was increased to 15 days.This window size represents an optimum when workingon a 20-yr-long series, which is the length of the seriesused below. A shorter window makes the estimatessomewhat unstable. A longer window smoothes out fluc-tuations of the parameters too strongly with time. Themodel parameters n(t) and h(t) are thus represented bya discrete series of 24 parameters n15(i) and h15(i), wheren15 and h15 denote averages of n(t) and h(t) over 15 daysand i is a time index (i 5 1, 24). In the figures shownin section 4, the values n15(i) and h15(i) are centered onday number 8 1 (i 2 1) 3 15. In the following, unlessother specified, the subscript 15 will be omitted sincewe will be working only on 15-day averages of n(t) andh(t). A complete presentation of the algorithm may befound in Tapsoba (1997).

The choice—first introduced by LBL97—of the twoperiods P1 and P2 selected as references for wet con-ditions followed by dry conditions was followed herebecause it has the great advantage of producing twoperiods of equal length of time, which makes statisticalcomparisons simpler. Of course, the date of separationbetween the wet period and the dry period is somewhatarbitrary, but this does not seem to constitute a seriousissue since the passage from wet to dry years, even ifnot totally synchronic over the study area, was fairlyrapid.

The procedure described above for parameter esti-mation was identically applied to each of the 277 seriesof period P1 and to each of the 299 series of period P2.Then for each parameter and each of the 24 time steps,a spatial interpolation using an ‘‘error kriging’’ tech-nique (see Gurascio et al. 1975) was carried out in orderto create regular grids. The grid node spacing is 0.258by 0.258. Each resulting set of 24 grids may be seen asa space–time cube of resolution 0.258 by 0.258 by 15days. Four such cubes are available (two parameters, nand h, times two periods, P1 and P2). The combinationof the two cubes of period Pi describes the rainfall re-gime for that period. In Fig. 5, 2D cross sections of theP1 cubes are compared with maps of original data sta-tistics, that is the daily rainfall accumulated over onemonth, and the number of rainy days for that month.

Comparing the map of the number of rainy days withthe map of the number of rain events n31 (i.e., cumulated

over the 31 days of August) illustrates how the modelpermits us to obtain a better representation of the timeintermittency of the rain process. Over the Sahel, thenumber of rain events is larger—approximately by afactor of 125%—than the number of rainy days. Movingsouthward, the number of rain events become equal tothe number of rainy days around 98N. Farther south, thenumber of rainy days becomes far larger than the num-ber of events. This is well in agreement with the knownclimatology of the convective rain events of the region.As noted previously, in the Sahel in August, most ofthe rain events are associated with convective systemswith a separation time that can be less than one day.On the coast, at that time of year there are relativelyfew rain events but they are of a different nature andthe probability of having a rain event spread over twoconsecutive days is relatively large.

4. Dynamics of the rainy season

Figure 5 is a 2D-space cross section in the rainfallregime cubes, with a time integration of 1 month. Onecan also build time–space cross sections for selectedlongitudes. An example of such cross sections, com-puted here for the period P2 (the corresponding P1 crosssections will be shown in section 5 below), is given inFig. 6. On the left the longitude was set to 58W. On theright it is equal to 28E. The values so obtained are av-erages over one grid mesh in longitude, that is 0.258.These cross sections will be used as a basis for analysingthe dynamics of the rainy season in West Africa bylooking at the following: i) the onset of the rainy season;ii) the identification of weather zones; iii) the role ofthe topography.

a. The onset

The rain onset may be analyzed by looking either atthe n maps or the H maps in Fig. 6, H being the inter-annual average of the 15-day rainfall interpolated at thenodes of the grid (H is expressed in mm day21). Notethat, due to the algorithm used to compute n and h, wehave

H 5 n 3 h. (3)

The n map at both longitudes presents a similar pat-tern for the start of the rainy season. First, there is aprogressive increase of the number of rain events, prop-agating northward at an average rate of 2.58 month21.Note however that, near the coast, the isolines are almostvertical, and the reinforcement of the rainy season issynchronic up to 88–98N. That is, the peak in rain eventfrequency occurs during the first half of May and thedecrease occurs simultaneously between 88N and thecoast. During this phase of the onset, the patterns of then maps and the H maps are similar (cf., e.g., the isoline0.3 of the n map with the isoline 40 of the H map at58W). A striking feature in both series of maps is that


FIG. 5. Spatial representation over the study area of the rainfall parameter for the month of Aug (period P1, 1951–70). (top left) Rainfall, (top right) number of rainy days, (bottom left) number of events, and (bottom right) meanrainfall per event. Note the differences between the map of the number of rainy days and the map of the number ofrain events.

the onset of the Sahelian rainy season is not in continuitywith the onset of the first rainy season in the south. Thisis apparent on the n maps and H maps, but especiallyclear on the latter, when looking at the 5 mm day21

isoline (numbers in the figure are in 1/10 mm day21).There seems to be in fact two different dynamics. Oneis the onset of the rainy season that starts on the coastin February and is propagating regularly northward,reaching 138N in May. The other is an abrupt surge, inmid-June, of intensified rain occurring synchronouslybetween 98 and 138N. This space–time cell of high rainis clearly not connected with the space–time cell of highrain on the coast described above.

One main conclusion seems to arise from the aboveanalysis. The onset dynamics does not follow the simplescheme of a regular northward movement with a timelag following the sun zenith position. On the other hand,the movement of the zone of high rainfall toward thecoast in September and October seems to follow thesun’s movement. This implies a need to reconsider the

classic scheme of an undisturbed pattern of weatherzones moving north and south with the sun.

b. The annual cycle

According to the pioneering work of Hamilton andArchbold (1945), the West African climate is charac-terized at any given time by four different weather zonesdenoted A, B, C, D from north to south and stretchingfrom east to west. The zones move with the sun, subjectto a time lag. There are two main features resulting fromthis description: i) the regular northward migration ofthe weather zones following the sun produces a pro-gressive onset of rain; ii) the seasonal cycle over WestAfrica is a progressive shift from a two rainy seasonregime on the coast to a single rainy season regime inthe north. The little dry season may last for a few weekson the coast. Its length decreases rapidly to the northand it is not felt much beyond 88N in our study area.Accordingly, the area of rain maximum starts from the


FIG. 6. Space–time diagrams of the rainfall parameters represented as cross sections at 58W(left) and 28E (right); period P2. Note the strong correlation between n (number of events day21)and H (rainfall in tenths of mm day21) at both long. The numbers on the x axis indicate the startof the month (i.e., the representation starts 1 Feb and ends 31 Oct).

coast, moves to the north, reaches its northern limit inthe Sahel and then retreats to the south. In a Hovmollerdiagram such as the one in Fig. 6, the two points aboveimply that

1) on average, the starting date of the rainy seasonshould regularly increase when moving northward;

2) the nucleus of rain maximum should be continuousand appear as an inverted V shape.

This is clearly not the case, as may be seen fromeither the n maps or the H maps of Fig. 6. The nonlineardynamics of the rain zones is very apparent when look-ing at the 0.3 and 0.4 isolines in the n maps or at the


FIG. 7. Daily mean number of rainfall events at three stations lying on a south to north axisaround long 28E, period P2, 1971–90.

40 (4 mm day21) and 50 (5 mm day21) isolines in theH maps. Strikingly, the first nucleus of rain maximumon the coast is disconnected from the Sahelian rain peak.On the other hand, in agreement with the classic visionsummarized above, the Sahelian rain peak is directlyconnected to the second rainfall peak to the south. Theclear cut between the first rainfall peak on the coast andthe Sahelian rainfall peak implies that there is no gradualtransition from a two rainy season regime in the southto a single rainy season regime in the north. Rather, asillustrated in Fig. 7, one can identify three rain maximafor the regions located between 78–88N and 108N.

Figure 7 represents the time evolution of n for thestations of Cotonou (5.58N), Parakou (9.58N), Benin,and Niamey (13.58N), all located on the 28E transect.While Cotonou displays a classic two rain season signal(with, however a secondary ‘‘shoulder’’ before the firstmaximum), Parakou is characterized by three rainfallmaxima occurring around 15 June, 20 July, and 15 Sep-tember. One interesting result appears when comparingthe Cotonou and Parakou signals: the first rain peakhappens at the same time at both stations, even thoughone is located 48N of the other. A similar feature wasfound for Abidjan and Yamassoukro in Ivory Coast (notshown). On the other hand, the Niamey maximum, thethird Parakou maximum, and the second Cotonou max-imum are regularly shifted in time, in agreement withthe classic vision of a progressive retreat of the rainmaximum to the south, accompanying the ITCZ migra-tion. Another interesting result appears in Fig. 8, wherethe n signal is plotted for the same three stations, butfor the wet period P1. Comparing the curves of periodP1 to those of period P2 shows that during the wet period,

1) the date of the first rain peak in Cotonou and Parakouis unchanged;

2) the third Parakou maximum and the second Cotonoumaximum are delayed by about 10 days in Parakouand 20 days in Cotonou;

3) in Niamey, the date of the maximum is unchanged,but a secondary maximum appears 20 days beforethe main maximum; this secondary maximum is syn-chronous with the second Parakou maximum.

The comparison between Figs. 7 and 8 provides somefood for thought regarding how the rainfall regimeswere modified during the dry spell, and will be discussedin section 5. Further examination of Fig. 8 suggests ascheme of dry/rainy seasons that departs from the classicvision in the following ways. The first rainy season isthe rainy season directly associated with the northwardmigration of the sun between February and June, whichcreates a first maximum of received solar energy. Thesecond peak corresponds to a reinforcement of rainfalloccurring simultaneously between 98 and 138N. Itshould be noted that this is the period where mobileMCSs become the prominent form of rain-generatingsystems in the region (see D’Amato and Lebel 1998;Mathon and Laurent 2001 for a climatology of convec-tive systems over the Sahelian area). Thus, it appearsthat, rather than progressively ‘‘invading’’ the northernregions, the zone of deep convection becomes active allof a sudden north of 98N. Finally, the third peak isassociated with the southward migration of the sun andthe second maximum in received solar energy.

c. Factors influencing the dynamics of the WestAfrican monsoon

The results presented above do not support the clas-sical vision of the West African monsoon (WAM) beingdriven by a continuous dynamics. While this vision had


FIG. 8. Same as Fig. 7, except for period P1, 1951–70.

not been seriously challenged until recently, our findingprovides some support to the nonlinear monsoon con-cept proposed by Eltahir and Gong (1996). This conceptis a development, for the case of a moist atmosphere,of the dynamical theory for the response to subtropicalthermal forcing of a dry zonally symmetric atmosphereformulated by Plumb and Hou (1992). The scheme ofEltahir and Gong (1996) may explain the existence ofthe two climatic regimes identified here. The theorystates that the meridional distribution of boundary layerentropy controls the partition between a radiative–con-vective equilibrium regime and an angular momentumconserving regime. While the first corresponds to dryconditions, the second is associated to a healthy mon-soon circulation. From our study it may be hypothesizedthat the brutal jump of the rainfall occurrence rate atthe end of June is linked to the achievement of thethreshold above which the angular momentum conserv-ing regime becomes effective. When this threshold isreached the meridional circulation develops rapidly andthe conditions for rainfall to occur are satisfied overseveral degrees of latitude. This mechanism is shapingthe annual cycle over this region. It is worth noting thatthe seasonal forecasting method developed by Fontaineet al. (1999) on the basis of the theory proposed byEltahir and Gong (1996) produces significantly betterscores than the alternative stochastic methods that onlyuse various SST predictors. The method is based on thecomputation of the meridional gradient of moist staticenergy in April over the region, this gradient being de-pendent on the SSTs and on the continental surface con-ditions. There is thus some converging evidence thatthe surface conditions, both over the ocean and over thecontinent, strongly modulate the atmosphere dynamicson a regional scale, producing a nonlinear behavior ofthe WAM.

The relief is another factor that may play a significantrole in the dynamics of the WAM. Figure 9 shows the2D maps of n for 6 dates at 30-day intervals for the dryperiod P2. The influence of the two mountain ranges,the Fouta-Djalon in the west and the Atakora in theeast—crossing Togo and ending in northwest Benin—is clearly visible in the maps of day 165 (mid-June) andday 195 (mid-July). During this first part of the rainyseason, the maxima of rainfall occurrences are locatedon the upwind side (west–southwest) of these two rang-es. Associated with these maxima are areas of lowerrainfall located on the lee side of the two mountainranges. Then, after mid-July, the regime of large con-vective systems moving from east to west becomesprominent and the location of the maxima associatedwith the ranges is shifted to the east. It seems that inthe latter type of circulation regime, the barrier effectof the topography tends to disappear or even to act inthe opposite way, shielding the southwest side of themountains. A very similar pattern is observed for thewet period P1 (not shown). At a larger scale, it is worthnoting that a recent modeling simulation experiment(Semazzi and Sun 1997) has pointed to the possible roleof the Atlas Mountains and the Ahaggar Plateau in in-ducing an orographic circulation over the Sahelian re-gion. These mountains are located at the critical latitudeof the descending branch of the Hadley cell, the locationof which is directly related to the equilibrium conditionsin the theory of Plumb and Hou (1992). It consequentlycannot be excluded that this unique feature of the WestAfrican topography might interact with the equilibriumconditions controlling the monsoon circulation in caseof a significant and durable modification of the generalcirculation, thus resulting in an abrupt climate changeover the region. Enhanced and sustained observations


FIG. 9. 2D maps of the mean number of rain events day21 (n) over the study area for the dryperiod P2. Day 165 is mid-Jun, day 195 is mid-Jul, day 225 is mid-Aug.

over the whole transect extending from the Guinea Gulfto the Sahara are a necessity to control such theories.

5. Wet spell and dry spell in West Africa

The severity of the drought that struck the Sahel inthe years 1971–74 generated several studies looking forfactors explaining this climatic event. From the earlywork of Charney et al. (1977) using GCMs to recent

studies using regional models (see, e.g., Zheng and El-tahir 1998) a series of modeling exercices were carriedout regarding the potential impact of vegetation deg-radation on the West African rainfall. The role of theoceans was also investigated (e.g., Folland et al. 1986;Lamb and Peppler 1992; Semazzi et al. 1996; Ward1998). Despite the improved capabilities of these mod-els, a proper evaluation of the respective roles of thecontinental surface conditions and of the tropical oceans


FIG. 10. Space–time diagrams of the rainfall regime represented as cross sections at 58W (left)and 28E (right). Period P1. As in Fig. 6, note the strong correlation between the n (number ofevents day21) and the H (accumulated rainfall in mm) at both long. Also note the similarity ofthe patterns between the two figures. During the dry years, the cumulative rainfall and the numberof events decreased but the general space–time pattern was preserved.

(and of their interactions as well) remains to beachieved. This will constitute a major challenge for thecoming years, as underlined by Nicholson (2000) andin the recent report produced by the CLIVAR AfricaTask Team (2000, hereafter CATT). A prerequisite tosuch studies is to characterize as precisely as possiblewhat changes occur in the rainfall regimes when shifting

from the wet to the dry period. The comparative analysiscarried out below on the rain event statistics of thesetwo periods is a contribution to that task.

To this end, we compare the maps of Fig. 6—thatwere analyzed previously—with similar maps (shownin Fig. 10) computed for the wet period P1. For a givenlongitude, the respective patterns of the n maps of each


FIG. 11. Space–time distributions of the number of events obtained for period P1 and P2 aresuperimposed (top) Cross section at 28E, (bottom) cross section at 58W). The distribution forperiod P1 is represented by the color image while the distribution for period P2 is represented bythe contour map. This representation highlights the time shift of the rainy seasons. While thisshift remains moderate for the first rainy season in the south it amounts to almost 15 days forthe peak of the Sahelian rainy season.

period are strikingly similar. This is also true for the hmaps. Compared to the wet period, the n values of thedry period are decreased by 0.1–0.2 events per day,while the H values are decreased by 1–1.5 mm day21,but the location and date of the maxima and minimaremain unchanged. This is true for both longitudes ex-amined here (58W and 28E). On the other hand, the hmaps display in all cases some patchiness. It is difficultto identify well-defined structures. While the decreaseof n during the dry period is systematic, the areas whereh values decreased are globally balanced by areas ofincreased h. Previous studies focusing on the Sahel (e.g.,

LBL97; D’Amato and Lebel 1998) have shown that therainfall variability and the variability of the number ofevents are strongly correlated at both interannual anddecadal timescales. The above comparison confirmsthese first results and extends them to the Soudanianarea of West Africa. At the same time, the n map ofperiod P2 is not a simple homothetic replication of then map of period P1. In order to visualize the differencesbetween the two periods, two maps (shown in Fig. 11)were constructed. In each map (one for each longitude),the isolines refer to period P2 while the color image isdrawn from the P1 values. This representation confirms


a significant shift of the position of the second zone ofrain maximum, whereas the position of the first zone ofrain maximum is only slightly shifted. On the coast thetiming of the first rainy season is advanced by aboutone week during the dry years. As for the timing of thesecond rain zone, it is seen that its maximum occursabout 20 days earlier at all latitudes between 68 and108N. This time shift is progressively decreases to thenorth: at 148N it is reduced to about one week. It isremarkable that these values are very similar whethercomputed on the 28E map or on the 58W map. Lookingback at the diagrams of Figs. 7 and 8, it is seen that asimilar overall trend is observed on point rainfall series.However, the values are slightly different, as mentionedabove in section 4. They indicate a regular decrease ofthe time shift when moving away from the coast (20days in Cotonou, 10 days in Parakou, and no lag inNiamey). Given the smoothing effect produced by themapping of Fig. 11, it is not possible at this point toascertain whether the early timing of the second rainyseason during the dry years must be considered as con-stant over the 68–108N region or whether, as indicatedby Figs. 7 and 8, there is a zonal gradient.

A summary of the main changes that occurred in therain event statistics between the wet and dry seasons isas follows.

1) Even though changes in the mean event rainfall (h)happened here and there, these changes are not spa-tially organized; areas of decreased h are balancedby areas of increased h and, when expressed in rel-ative values, the range of these changes are small.

2) In contrast, changes in the mean number of event(n) are extremely well organized in space. A decreaseis observed systematically. It accounts for most ofthe global rainfall deficit between the two periodsexcept possibly on the coast for the little dry season.

3) The deficit of the number of rain events is mostpronounced during the core of the rainy season inthe Sahel and during the first rainy season on thecoast.

4) The total length of the rainy season is almost un-changed in the Sahel. Farther south, the date of onsetis not modified much, but the second rainy seasonstarts earlier and finishes earlier. In total the lengthof this second rainy season is diminished, but by nomore than 10 days.

5) There is a very distinctive and global time shift ofthe second rain zone extending from the Sahel to thecoast between August and October. The whole struc-ture is shifted by 10–20 days with its maximum oc-curring earlier during the dry years.

The impact of the drought on the hydrological cycleand the agriculture are not independent of the abovechanges. In the Sahel the greatest threat to agricultureseems to be an increased risk of dry spells during thecore of the rainy season at periods where the millet maybe most sensitive to it. On the other hand, a reduction

in the length of the rainy season does not appear to bevery significant and thus the quest for new grain vari-eties with a shorter growing cycle may not be so nec-essary. From a hydrological point of view, the increasein the mean time between two successive rain events,with little changes in the mean event rainfall probablyimplies that, all other things being equal, the averageresulting runoff might not change much. Since the soildries out rapidly in the Sahel, the initial wetness con-ditions when the rain starts are not critically modifiedwhen increasing the average time between two eventsby 20% or so, or by an increased probability of longerdry spells. The vegetation, however, will suffer. It maythus be inferred that a long series of dry years combinedwith demographic pressure and the demand for cookingwood, will have a serious impact on the vegetation, thusmodifying the soil surface conditions. This, in turn, maychange the ratio between rainfall and runoff. There arethus interacting consequences of long lasting droughtsthat require further attention by both observation pro-grams and climate modelers.

In the south it seems that the deeper impact for theagriculture is the advance in the timing of the secondrainy season and its overall shortening. Also, the im-portance of the first rainy season on the coast is sig-nificantly reduced. The combination of these two factorsmight have significant consequences for agriculturalplanning.

6. Discussion and perspectives

The main result reported in this study is that analyzingthe rainfall regimes of West Africa in terms of rain eventoccurrence rate and intensity provides an improvedcharacterization of the weather dynamics of this region.In particular, it questions the classic vision of a pro-gressive transition from a two rainy season regime onthe coast to a single rainy season in the Sahel. Thesudden surge of a maximum of rain event occurrencefrequency between 108 and 148N, starting in mid-June,has, to our knowledge, never been pointed out before.It raises some intriguing questions regarding the equi-librium between atmospheric moisture advection on thecontinent and energetic factors that may create the re-quired conditions for the formation of fast-moving con-vective complexes that account for most of the rainfallin July and August at these latitudes. This is in line withthe nonlinear monsoon theory developed by Eltahir andGong (1996) and the recent work of Fontaine et al.(1999) that shows that seasonal forecasts of the rainyseason over West Africa may be improved by includingthe horizontal moist static energy gradient as a predictor.

Another important result, already found for the Sahelby Le Barbe and Lebel (1997a) is that most of the rain-fall deficit of the dry period 1971–90 is correlated witha general decrease of the occurrence rate of rain events.This, along with the fact that the rainfall deficit wasequal to 180–200 mm all over West Africa, indicates


that there was no crucial difference in nature betweenthe Sahelian drought and the drought of the Soudano–Guinean regions. It does not appear that the explanationof such features resides in an abnormally southwardposition of the ITCZ during the dry spell.

Given the method used in this paper it was not pos-sible to investigate the interannual variability of theWest African rainfall. While D’Amato and Lebel (1998)have shown from high-resolution rain data that in theSahel, the interannual variability of the seasonal rainfallwas also related to the variability in the number of me-soscale convective systems, other works have pointedto other factors. Janicot (1992) has shown that distin-guishing two regions north and south of 108N, allowsa better description of the interannual variability of theWest African rainfall. More recently Janicot et al. (1996)have shown that the correlation field between the worldocean SSTs and the Sahelian rainfall, computed at theinterannual scale, changed markedly at the turning pointof the end of the 1960s. Another important point toconsider from an hydrological perspective is the oc-currence of extreme rainfall. The global model and ap-proach used here did not permit us to tackle that ques-tion. It is not clear whether fluctuations in the magnitudeand occurrence of extreme events are similar, whetherlooking at the interannual variability or focusing on thecontrast between the two periods analyzed here. Thereobviously remain room for work in this respect. Itshould be noticed, however that the data currently avail-able in West Africa are not sufficient to analyze in detailthe various modes of variability of rainfall. It is thusimportant to obtain accurate and high-resolution datathat will allow us to study the intensity, spatial exten-sion, and structure of the rain fields at various timescalesin order to better characterize their variability from theintraseasonal to the decadal scales. Specific observingsystems will be required for that purpose, as recom-mended by CATT (2000). Such systems will also beextremely useful in validating satellite estimates andmodel outputs.

Presently, simulations by global or regional modelsare not able to reproduce the rainfall variability at thescale of the rain event. GCM resolution is too coarsefor that purpose and their internal variability is far toolarge (see, e.g., Semazzi et al. 1996). It is thus an im-portant goal for modeling studies to be able to identifyrain events in regional models realistically, so as to com-pare their statistics to those of the observations. It isnecessary to attain the rain event scale in climate mod-eling in order to study the impact of climate variabilityon the water resources under various climatic scenarios.

Acknowledgments. We are grateful to the various na-tional meteorological services that helped the Institutde Recherche pour le Developpement (IRD, formerlyORSTOM) to build the daily rainfall database used inthis study. Special thanks are due to Elfatih Eltahir andan anonymous reviewer for constructive comments on

section 4 of the paper, and to Nick Hall for helping toput it in proper English. This research was funded byIRD.

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