a high-resolution simulation of a west african rainy season using...

A high-resolution simulation of a West African rainy season using a

regional climate model

H. Gallee,1,2 W. Moufouma-Okia,1 P. Bechtold,3 O. Brasseur,4 I. Dupays,5

P. Marbaix,6 C. Messager,1 R. Ramel,1 and T. Lebel1

Received 29 July 2003; revised 19 December 2003; accepted 6 January 2004; published 12 March 2004.

[1] The regional climate model Modele Atmospherique Regional (MAR) is applied toWest Africa and the year 1992 is simulated. MAR reproduces the observed intraseasonalvariations of rainfall. It is suggested that such a phenomenon is associated with oscillationsbetween a weak and a strong regime of the Hadley cell. The later is correlated with astronger meridional gradient of moist static energy in the planetary boundary layer and isresponsible for an enhanced convergence of this quantity and a subsequent increase ofconvection and rain. An enhanced consumption of moist static energy and finally aweakening of the meridional circulation result. The meridional gradient of the moist staticenergy is restored by surface processes. The model also simulates the observed abruptnorthward shift of the rainband in the first half of July. The spatial variability of thesimulated monthly mean rainfall is in good agreement with the observations, although themodel overestimates rainfall in some places from the beginning of August. Time series ofdaily mean rainfall are averaged over two 2.5� � 2.5� grid meshes in the Niameyregion and in the Oueme high valley. Maxima reaching up to 40 mm/day are found in bothareas, as in the observations. Atmospheric variables such as temperature and wind arebriefly compared with the European Center for Medium-Range Weather Forecastingreanalyses. The main (cold) biases are located where the hydrological cycle simulated byMAR is too strong. INDEX TERMS: 1620 Global Change: Climate dynamics (3309); 1655 Global

Change: Water cycles (1836); 3322 Meteorology and Atmospheric Dynamics: Land/atmosphere interactions;

KEYWORDS: regional climate model, rainfall, West Africa

Citation: Gallee, H., W. Moufouma-Okia, P. Bechtold, O. Brasseur, I. Dupays, P. Marbaix, C. Messager, R. Ramel, and T. Lebel

(2004), A high-resolution simulation of a West African rainy season using a regional climate model, J. Geophys. Res., 109,

D05108, doi:10.1029/2003JD004020.

1. Introduction

[2] The variability of the West African climate is closelylinked to the behavior of its hydrological cycle. It dependsmainly on the meridional displacement of a rainbandassociated with the Intertropical Convergence Zone (ITCZ).A strong variability over a wide range of space and time-scales characterizes the West African rainy regime [see, e.g.,Lebel et al., 2000]. The spatial variability is due to theconvective nature of the rain. The temporal variability maybe seen as a modulation of the seasonal cycle linkedto the position of the ITCZ and the activity of synoptic

disturbances. The rainy season in West Africa exhibits threedistinct periods [Le Barbe et al., 2002]. (1) The installationphase of the monsoon which occurs in March–June. It ischaracterized by an extension of the rainband from the coastnorthward. (2) The high rain period (July–September) whichstarts with an abrupt shift of the rainband core roughly from5�N to 10�N. (3) Finally, the gradual southward retreat of therainband in September–November. Looking at shorter time-scales, it is also found that the intensity of the West Africanrain is characterized by large and coherent oscillations withperiodicities of 15–20 days. A detailed description of theseoscillations and their link with the meteorological fields hasbeen provided recently by Sultan et al. [2003], suggestingthat they have a dynamical origin. The aim of this study is todevelop more insight into the modeled West African rainyregime at the intraseasonal timescale and in particularinto possible causes of these intraseasonal oscillations. Weinvestigate the hypothesis that the oscillations are linked tothe dynamics of the Hadley cell.[3] General circulation models (GCMs) have difficulties

in properly reproducing West African rainfall. For example,the monsoon onset is too early in the UK Met Office(UKMO) GCM [Clivar Africa Task Team (CATT), 1999].Lebel et al. [2000] show that the Laboratoire de Meteo-

JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 109, D05108, doi:10.1029/2003JD004020, 2004

1Laboratoire d’etude des Transferts en Hydrologie et Environnement,IRD, CNRS, Grenoble, France.

2Now at Laboratoire de Glaciologie et de Geophysique de l’Environne-ment, CNRS, Grenoble, France.

3European Center for Medium-Range Weather Forecast, Reading, UK.4Royal Meteorological Institute of Belgium, Brussels, Belgium.5Institut du Developpement et des Ressources en Informatique

Scientifique, Orsay, France.6Institut d’Astronomie et de Geophysique G. Lemaıtre, Louvain-a-

Neuve, Belgium.

Copyright 2004 by the American Geophysical Union.0148-0227/04/2003JD004020$09.00

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rologie Dynamique (LMD) GCM has the same problem andmay generate unrealistically large rain events. Other defi-ciencies in GCMs are overestimations of the Sahelian andSaharan rainfall [e.g., Douville et al., 2001; Vizy and Cook,2001]. The lack of precipitation simulated by GCMs overthe Fouta Djallon and Cameroon mountains is also aproblem, although it is judged less important [Taylor andClark, 2001; Clark et al., 2001]. A reason for this short-coming may be the coarse representation of the topographyowing to the coarse horizontal resolution of such models.[4] The use of a higher horizontal resolution in regional

climate models (RCMs) allows a better simulation of therainy regime over topography [Semazzi et al., 1993]. Thedetails of the mesoscale convective systems (MCS) associ-ated with African waves are also better represented over thefiner grid of a RCM [Druyan and Fulakeza, 2000]. More-over, RCMs offer the possibility of simulating a morerealistic rainfall regime by adapting more closely the modelphysics to the region of interest [Giorgi and Mearns, 1999].This concerns, in particular, the representation of convec-tion, which is the main contribution to rainfall in WestAfrica. For example, Jenkins [1997] tested two parameter-izations of convection in his RCM when simulating therainy seasons of 1988 and 1990. He found that the Kuoconvective scheme is responsible for too much moisteningof the atmosphere and an underestimation of the precipita-tion amount. Other problems also exist in RCMs. Vizy andCook [2002] have modified MM5 in order to study theimpact of tropical Atlantic sea-surface temperature (SST) onthe West African rainy regime. In particular, clouds below850 hPa were assumed transparent to the incoming solarradiation in order to alleviate the impact of excess cloudfractions in the lower troposphere. Furthermore, the watervapor mixing ratios above 300 hPa remained fixed to theirinitial values in order to overcome the excess detrainment ofwater vapor by the convective parameterization they use.[5] Up to now, most modeling studies have focused on

the characteristics of the rainy regime during the core of therainy season. Particular attention has been given to thecauses of its variability from year to year. The impact ofthe tropical Atlantic SST has been discussed by imposingSST anomalies as boundary conditions in atmosphericmodels [Vizy and Cook, 2001, 2002]. When these SSTanomalies are exaggerated, the models are able to reproducethe hypothetized subsequent anomalies of the rainy regime.Messager et al. [2004] have suggested a strong impact ofthe SST variations in the Gulf of Guinea on West Africanprecipitation during drought years. Giannini et al. [2003]have shown that interdecadal variability of precipitationover the Sahel may be correlated with SST anomalies inthe Pacific and Indian Oceans. However, it has beensuggested that the influence of remote regions of the tropicson the atmospheric dynamics over West Africa is lessimportant than over East Africa [Rodwell and Hoskins,1996] or is relatively unimportant [Zheng and Eltahir,1998a]. Surface processes may modulate significantly theWest African climate. In this way the role of the continentalhydrological cycle has been emphasized by performingsimple two-dimensional (2-D) climate modeling studies.Xue et al. [1990] and Zheng and Eltahir [1998a, 1998b]have suggested that deforestation has a dramatic impact onrainfall. Zheng et al. [1999] have shown that land-atmo-

sphere interactions amplify rainfall anomalies induced bySST anomalies. Giannini et al. [2003] have calculated thatthe induced evaporation anomaly over land accounts for 1/3of the precipitation anomaly. Nevertheless, surface schemesused in climate models must be carefully calibrated over theSahelian region [Taylor and Clark, 2001].[6] The possible dynamical origin of the variability of the

West African rainy regime at the intraseasonal timescalewill be investigated in this paper. Plumb and Hou [1992]have suggested with an axisymmetric model that the dy-namics of the Hadley cell are characterized by two regimes:a radiative-convective equilibrium regime and an angularmomentum conserving regime. The former regime corre-sponds to a viscously driven Hadley circulation, while thelatter corresponds to a fully developed Hadley cell driven bythe latitudinal temperature gradient. The transition is char-acterized by a timescale much longer than 20 days, but thisdoes not prevent the Hadley cell dynamics from switchingfrom one regime to the other before a regime is fullyreached [Plumb and Hou, 1992].[7] Eltahir and Gong [1996] have suggested that the

occurrence of dry and wet years is explained by themechanism proposed by Plumb and Hou [1992]: Wet yearsresult from a stronger monsoon owing to an increase of thetemperature gradient between the Gulf of Guinea and thewarmer West African continent. Eltahir and Gong [1996]suggested that such an increase is due to colder SST. Zhenget al. [1999] suggested that it may be due to warmer SSTsresponsible for the advection of moister air over thecontinent, a subsequent increase of the downward infraredradiation, and an increase of the land surface temperature.The strengthening of the Hadley cell owing to an increase ofthe meridional temperature gradient over land has also beendocumented by Cook [2003].[8] We use here a RCM to develop more insight into the

influence of zonally averaged dynamics on the intraseasonaloscillations of the rainy regime. The RCM MAR is adaptedto West Africa and is used to perform high-resolution(40 km) simulations of the rainy regime that prevails duringthe year 1992. The model is described briefly in section 2.The simulation of year 1992 is described in section 3, and amechanism explaining the intraseasonal oscillations of therainfall is proposed. This year has been chosen because theannual rainfall over the Sahel is close to the annual rainfallaveraged over the period 1970–2000 and because detailedobservations exist. A simpler 2-D simulation of the WestAfrican monsoon is performed in section 4 in order to testthe hypothesis that 3-D dynamics are necessary for thesetypes of oscillations. We find in fact that the 2-D modeldoes exhibit intraseasonal oscillations. A summary of theresults and recommendations for future work are presentedin section 5.

2. Model Description

[9] MARwas first developed for polar regions. It has sincebeen applied to tropical and temperate regions [Brasseur etal., 1998; De Ridder and Gallee, 1998; Brasseur, 2001]. Ashort description is given here, emphasizing its adaptation forWest African climate simulations.[10] The dry version of the atmospheric model is fully

described by Gallee and Schayes [1994]. MAR is a hydro-

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static primitive equation model in which the vertical coor-dinate is the normalized pressure. No approximation ismade in the mass conservation equation (i.e., the compress-ible form is retained).[11] The representation of the hydrological cycle includes

a cloud microphysical model, with conservation equationsfor cloud droplet, raindrop, cloud ice crystal, and snow flakeconcentrations [Gallee, 1995]. The description of the cloudmicrophysical processes is essentially based on the Kessler[1969] parameterization. Ice microphysical processes areincluded on the basis of Lin et al. [1983]. The Fletcher[1962] equation for ice nuclei concentration is replaced withthe more realistic parameterization of Meyers et al. [1992].A prognostic equation for the ice crystal number is addedaccording to Levkov et al. [1992]. It allows to compute thesedimentation of ice cloud particles. We found in sensitivitytests that such improvements allow a better simulation ofthe West African climate.[12] Detailed solar and infrared radiation schemes are

used. The solar radiation scheme is from Fouquart andBonnel [1980]. The longwave radiation scheme follows awide-band formulation of the radiative transfer equation[Morcrette, 1984] and was designed for use in GCMs.Clouds’ properties are taken into account in the solar andinfrared radiation scheme by computing the liquid waterpath in each model layer from the concentration of clouddroplets and ice crystals. The atmospheric part of MAR isadapted to tropical regions by including the convectiveadjustment scheme of Bechtold et al. [2001].[13] The atmospheric part of MAR is coupled to the

Surface Vegetation Atmosphere Transfer (SVAT) schemeSoil Ice Snow Vegetation Atmosphere Transfer (SISVAT)[De Ridder and Gallee, 1998]. SISVAT is a vertical 1-Dmodel. The surface scheme includes soil-vegetation [DeRidder and Schayes, 1997], snow [Gallee et al., 2001], andan ice module [Lefebre et al., 2003]. SISVAT has beenvalidated over the Sahelian region (G. Derive et al., Eval-uation of the SISVAT land surface model over fallowsavannah in the Sahel, submitted to Journal of Hydrology,2003; G. Derive et al., Evaluation of the SISVAT landsurface model in the Sahel: Model sensitivity and influenceof the operationel parameter set on a millet crop, submittedto Journal of Applied Meteorology, 2003). Only the soil-vegetation module is described and used here. The soilhydrodynamic characteristics are constant along thesoil profile. The associated hydraulic properties are deducedthrough classical pedotransfer functions. The soil waterpotential and the hydraulic conductivity are defined accord-ing to Clapp and Hornberger [1978]. Four parameters arerequired for each soil type: the saturated water content, thewater potential at saturation, the hydraulic conductivity atsaturation, and the exponent of the water retention curve. Asoil type can be selected in the U.S. Department ofAgriculture (USDA) classification, according to the Foodand Agriculture Organization of the United Nations (FAO)global coverage. The ground surface albedo is assumed tobe a function of soil humidity, as in the work of McCumberand Pielke [1981]. The dry soil albedo is prescribed and is afunction of the soil type.[14] The vegetation is described through its plant type

which enables us to infer the parameter values: the dis-placement height, the roughness length for momentum, the

root fraction, the minimum stomatal resistance, and theglobal plant resistance. These characteristics can be selectedin the International Geosphere-Biosphere Program (IGBP)classification. The canopy spatial distribution and its tem-poral evolution are prescribed. The former is obtained fromthe IGBP global coverage data, and the later is describedthrough the green leaf area index (LAI) retrieved from theU.S. Geological Survey (USGS) monthly values of theNormalized Difference Vegetation Index (NDVI) data for1992 (see the Web site http://edcdaac.usgs.gov/glcc/glcc_version1.html#Africa). Note that the green leaf frac-tion (GLF) is the fraction of LAI that describes livingleaves, while the green LAI is the product of the GLF timesthe LAI, which is assumed to be equal to 3.[15] The coupling of SISVAT with the atmosphere is

performed through the exchange of radiative (solar, infra-red) and turbulent momentum and sensible and latent heatfluxes. Bulk aerodynamic formulae are used to describe theturbulent fluxes. The latent heat flux is subdivided into asoil contribution, which depends on the relative moisture atthe soil surface, a direct contribution from vegetation, whichis due to the evaporation of intercepted rain, and a transpi-ration contribution, which depends on the effective LAI andthe stomatal resistance. The effective LAI is the green LAIassociated with those leaves that receive sufficient radiationin the 0.4–0.7 mm wavelength in order for their stomata toopen. The stomatal resistance depends on the soil waterpotential [De Ridder and Schayes, 1997].[16] The SVAT energy and water budget are solved

separately for both soil and vegetation. The soil is discre-tized in seven layers down to 2.888 m, with a finerresolution near the surface. The thickness of the thinest soillayer is 2 mm. The vegetation is represented by one layer.

3. Simulation

[17] The model domain covers West Africa and neigh-boring areas, from 27�W to 15�E and from 5�S to 27�N (seeFigure 1). The horizontal resolution is 40 km. Note that thechoice of such a resolution is responsible for a smoothing ofthe model topography, especially in the region of theCameroon mountains. The vegetation is prescribed usingthe 1992 NDVI data of the USGS (see the preceding sectionfor more details). The ECMWF reanalyses (ERA-15) areused to initialize the soil humidity and meteorological fieldsand to force MAR through its lateral boundaries. They areupdated each 6 hours, and a linear interpolation is made inbetween. The homogeneity of the ECMWF data may beconsidered as acceptable over the integration domain.However, the vertical stratification may be biased towardinstability because of a slight overestimation of temperatureand humidity in the lower troposphere [Trenberth et al.,2001]. The overestimation of the humidity fields over WestAfrica may be responsible for an overestimation of thesimulated precipitation [Diongue et al., 2002]. MAR isinitialized with the ECMWF fields and may thereforeoverestimate the precipitation during the first days of theintegration. The simulation is started on 1 January 1992 andis performed over the whole year. First a short validation ismade by analyzing some thermodynamical variables. Adetailed analysis of the atmospheric variables simulatedby MAR will be the purpose of a forthcoming paper. Then


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the simulated rain is compared with existing observationsand possible causes of the intraseasonal oscillations areinferred.

3.1. Thermodynamics and Dynamics

[18] A short analysis of the meteorological variables isdone in this subsection. First, MAR and ECMWF fields arecompared with independent observations at some particularpoints. Then a comparison is made between MAR andECMWF fields over the whole domain.[19] The MAR and ECMWF temperature and humidity

deviations from the soundings at Bamako (12.53�N, 7.95W)are shown in Figure 2 for August 1992. They are obtainedby interpolating the simulated and observed temperatureand humidity profiles over a vertical regular grid each time asounding is available. The differences of the MAR andECMWF fields with the observed fields are then calculatedand finally averaged in time. Although MAR is forced byECMWF meteorological fields (ERA-15) through its lateralboundaries, it is expected that the planetary boundary layer(PBL) thermodynamical properties will adjust to the param-eterization of the MAR physics (turbulence, convection) atsome distance from the lateral boundaries [Giorgi andMearns, 1999]. Since it is known that the low tropospherichumidity is overestimated in ERA-15 [Diongue et al.,2002], it is expected from such a comparison that theMAR PBL is in better agreement with the soundings andthat it is drier than the ERA-15 PBL.[20] Bamako is preferred to Niamey because the temper-

ature deviation at Niamey is very similar to that of Bamako,but the humidity deviation is systemic at Bamako andrandom at Niamey, both for MAR and ECMWF. Comparedto Bamako, MAR (ECMWF) underestimates (overesti-mates) the temperature in the lowest 300 hPa. Nevertheless,the ERA-15 temperature vertical profile is generally inbetter agreement with the observations than MAR between900 and 700 hPa. In contrast, the ECMWF reanalysesexhibit a stronger positive deviation of specific humidityfrom the observation than MAR. Note that the positivedeviation of specific humidity in Niamey is stronger in

MAR than in the ECMWF model (not shown). Neverthe-less, a domain average of the humidity vertical soundingsindicate that MAR is drier than the ECMWF reanalyses inthe PBL.[21] In short, the deviations of MAR from the soundings

at Niamey and Bamako in the lowest 300 hPa of theatmosphere exhibit a significant underestimation (overesti-mation) of the temperature (specific humidity). This may bedue to a too small (too large) surface sensible (latent) heatflux.[22] The agreement between MAR and the observations

is better in the middle and upper troposphere. This gives usconfidence in the assimilation of large-scale meteorologicalfields at the lateral boundaries of the model. Furthermore,no accumulation of humidity is found in the simulatedupper troposphere, giving us confidence in its parameteri-zation of convection.

Figure 1. Model domain and topography. Distance inkilometers. Latitude and longitude in degrees. ‘‘B’’ refers toBamako and ‘‘N’’ to Niamey.

Figure 2. (top) Temperature (in �C) and (bottom) specifichumidity (in g/kg) deviation from the observations in theMAR (solid line) and ECMWF (dashed line) verticalsoundings at Bamako (12.53�N, 7.95�W). The consideredperiod is August 1992.


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[23] The temperature bias (i.e., temperature differencebetween the MAR simulation and the ECMWF reanalyses)in the 850 hPa layer for August 1992 (Figure 3) is nowconsidered. Two significant cold anomalies are found overthe continent, over the eastern side of the Fouta Djallon, andon the southern side of the Aır highlands, at roughly 18�N.It is also found that the first anomaly corresponds to arainfall anomaly (see below for more details) and a maxi-mum of the simulated surface latent heat flux, while thesecond corresponds to a maximum in the simulated cloudoptical depth in the 17–19�N latitude band. A minimum inthe OLR is also found for the 17–19�N latitude band on thesouthern side of the Aır highlands. Both anomalies havemoved northward from June to August (not shown). Theseanomalies could be a consequence of an overestimatedatmospheric hydrological cycle simulated by the model inthese areas. Indeed, an overestimation of both the cloudcover and the latent heat flux lead to an underestimation ofthe surface and boundary layer temperatures.[24] The simulation reproduces the main features of the

atmospheric circulation over West Africa. The zonal com-ponent of the wind averaged between 10�W and 10�E isshown in Figure 4 for the MAR (upper panel) and theECMWF reanalyses (lower panel), respectively. The agree-ment is sufficiently good for our purposes. The monsoonflow is reasonably well simulated by MAR both in depthand northward extent, even if associated westerlies in thefirst 1000 m above the surface are slightly stronger thanthose from ECMWF. The strength of the African EasterlyJet (AEJ) is slightly underestimated. A minimum in theeasterly flow of approximately 7 m/s is simulated around16�N, 600 hPa (Figure 4, upper panel). The reason for thisunderestimation may be the low temperatures produced bythe model at 850 hPa, in particular over the Southern Saharaat 10�E, leading to an underestimation of the north-southtemperature gradient in the area of the AEJ. The location,height, and strength of the Tropical Easterly Jet are quitewell simulated (6�N, 200 hPa and 14 m/s respectively). Thisskill is important because it gives confidence in the resultsand will allow the study of the variability of synoptic

disturbances and their interactions with MCSs over WestAfrica.[25] In conclusion, it is found that MAR reproduces

reasonably well the main characteristics of the West Africanatmosphere. The impact of atmospheric dynamics on thesimulated West African rainfall is examined in the nextsubsection.

3.2. Rainy Regime

[26] The simulated rainy regime is analyzed in thissection. A classical comparison with some existing obser-vations is made first. The aim is to validate the spatialvariability of the simulated rainfall. In previous studies,such a comparison is made in general for the summermonths. Here a more refined analysis is done byperforming comparisons between the simulation andobservations for time periods before and after the ob-served abrupt shift of the rainband core (i.e., June andAugust, respectively). Then more detailed comparisons ofthe temporal variability of rainfall are made at two typicallocations. The aim is to focus on timescales shorter than10 days. Finally, an analysis is made of the modelbehavior at intraseasonal timescales larger than 10 days.

Figure 3. The 850 hPa temperature bias (MAR-ECMWF),August 1992. The contour interval is 1�C. The region withhigh topography is shaded.

Figure 4. Vertical cross sections of mean August 1992zonal wind. (top) MAR; (bottom) ECMWF.


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In particular, a possible mechanism for the intraseasonaloscillations is provided.3.2.1. Spatial Distribution of Rainfall[27] A comparison of the 3-D MAR simulation and the

Global Precipitation Climatology Center (GPCC) 1992 cli-matology (see http://www.dwd.de/en/FundE/Klima/KLIS/int/GPCC) is shown for two months, June in Figure 5 andAugust in Figure 6. For June, i.e., preceding the observedmonsoon jump, the main characteristics of the observedclimatology are reasonably well simulated. The rainband iswell located, although the model slightly underestimates therainfall. Maxima are simulated over the mountains. Themaximum over the Fouta Djallon, in the western part ofthe domain, is simulated far into the continent while it isobserved over the coast. It has been found in 2-D experi-ments that this simulated maximum results from a complexinteraction between the simulated monsoon flux, sea-breezecirculations at the coast, and anabatic flow over the moun-tains (not shown). The maximum over the Cameroon high-lands (on the eastern part of the model domain) is betterlocated. This maximum occurs during the first half of June,while that over the Fouta Djallon is the consequence of an

increase in precipitation during the second half of thismonth. Looking more closely at the evolution of the rain-band, it is found that such a strengthening is the first step of asubsequent abrupt northward shift of the rainband core at theend of June.[28] The spatial distribution of rain during the high rain

period (July–September) is illustrated by the August rain-fall in Figure 6. The August rainfall is overestimated by themodel, except on the western side of the Fouta Djallon. Asin the observations, the core of the rainband (8 mm/dayisohyet) is located farther northward than in June, with amaximum northward location along the Greenwich merid-ian. The northern edge of the simulated rainband (2 mm/dayisohyet) is located too far north (20�N). Moreover, thedouble maximum in rain over the southwestern coast isnot captured by the model. Only one marked maximum issimulated on the coast, while the other is simulated inland.In contrast with the observation, no maximum is simulatedover the Cameroon highlands. Rather, a maximum issimulated on the western side of the Jos Plateau in thecentral part of Nigeria. Nevertheless, the apparent discrep-

Figure 5. June 1992 mean rainfall (mm/day). (top) MARsimulation; (bottom) GPCC climatology. Contour intervals:2 mm/day for a mean rainfall less or equal to 4 mm/day and4 mm/day otherwise.

Figure 6. August 1992 mean rainfall (mm/day). (top)MAR simulation; (bottom) GPCC climatology. Contourintervals: 2 mm/day for a mean rainfall less or equal to4 mm/day and 4 mm/day otherwise.


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ancy between the observations and the simulation over theJos Plateau could be due to a lack of observations over theJos Plateau. A more detailed analysis of the model precip-itation pattern (not shown) reveals that the maxima over theFouta Djallon and the Jos Plateau are caused by the frequentsimulation of MCS in that area. This is in agreement withsatellite observations made by Mathon and Laurent [2001].Finally, the rainfall is overestimated between 8�N and 10�N,on the western side of the Atakora highlands in the vicinityof the Greenwich meridian.3.2.2. Rainfall Frequency Histograms[29] Here the question is if MAR is able to simulate

the characteristics of the West African rainfall at spatialscales corresponding to the resolution of a GCM. Notethe difficulty to simulate correctly rainfall on a particulargrid point. The task is more difficult in climate models

than for example in cloud resolving models because thesimulated variables are the solutions of a boundary valueproblem and not of an initial value problem. Furthermore,the climatic simulations are conducted over a muchlonger period than those made with a cloud resolvingmodel. Both constraints cause the amplification of errors,especially when rainfall is considered. Continuing such areasoning would lead to the conclusion that it is impos-sible to simulate accurately a particular rain event on alimited space/time domain when considering a climatesimulation. Rather, it is better to raise the question toknow which characteristics of the rainfall the model isable to simulate. A first step is to average MAR rainfallover such a large area as 2.5� � 2.5�, allowing a morefavorable comparison with the observations. Such an areacorresponds to the grid mesh of the National Centers forEnvironmental Prediction (NCEP)-National Center forAtmospheric Research (NCAR) reanalyses, which proba-bly provide the best simulation of West African precip-itation as far as a GCM is concerned. The purpose of thissection is a preliminary evaluation of which character-istics of the rainfall MAR is able to simulate whenconsidering an area corresponding to the finest resolvablearea described in the NCEP-NCAR reanalyses.[30] Frequency histograms of the simulated daily mean

rainfall have been constructed for the regions includingNiamey and the Oueme high valley, which are the twotests sites of the Couplage de l’Atmosphere et du CycleHydrologique (CATCH) observing system. They havebeen compared with the Institut de Recherches pour leDeveloppment (IRD) observations. These gauge data aredescribed in Appendix A. Model outputs and IRD obser-vations have been averaged over 2.5� � 2.5� grid meshescentered respectively at (12.5�N, 2.5�E) and (10�N,2.5�E).[31] A first analysis of the simulated rainfall time series

reveals that their variability at the seasonal timescale com-pares favorably with the observations (not shown). Rainfallpeaks in the first half of July and in September over theOueme, while it peaks in the second half of August aroundNiamey.[32] The frequency histograms of the simulated daily

mean rainfall are compared with the observations inFigure 7. The simulated daily rainfall maxima amountto no more than 43 mm/day, as in the observations and incontrast with some GCM studies [Lebel et al., 2000].Nevertheless, the model overestimates the frequency oflight rain events. This comparison raises the question atwhich temporal and spatial scales MAR is able tosimulate the characteristics of the West African rainfall.Such a question is important if one considers the forcingof hydrological models. This will be the subject of afuture study.[33] A high rainfall event was simulated on 24 August

1992 in the area of Niamey and it passed over the Niameygrid box in the morning. This rain event corresponds to thepassage of a MCS originating from the Jos Plateau locatedto the east of Niamey (not shown). A high rain event wasalso observed on 21 August 1992 in the same area. Theobserved event did not originate from the Jos Plateau butfrom the Aır Mountain. Nevertheless, it is interesting to notethat such an event is simulated during a wet phase of the

Figure 7. Daily rainfall frequency histograms. Shadingshows simulation. White areas show IRD observations.Units on the horizontal axis, mm/day; on the vertical axis,number of events. (top) Niamey area; (bottom) Oueme highvalley.


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monsoon and that it occurs at the same time as in theobservations (see Figure 8 and details below).3.2.3. Seasonal Cycle[34] The seasonal cycle of precipitation is analyzed in

order to assess how the model is able to simulate the

different subperiods of the rainy season. The month ofJanuary is considered as a spin up period and is notanalyzed. The simulated rainfall are compared with theIRD observations and the NCEP reanalysis [Kalnay et al.,1996]. The data are displayed in Figure 8 after beingaveraged between 10�W to 10�E and filtered using arunning mean with a 10-day window. The comparison withthe ECMWF simulated rainfall is not shown here because itoverestimates the rainfall by a factor of almost 3 in someplaces. This could be because the reanalyzed humidity fieldsare overestimated in the West African low troposphere[Trenberth et al., 2001; Diongue et al., 2002], and to a lessextent, to the use of a diagnostic scheme for estimating theprecipitation in the ECMWF model. Indeed, diagnosticschemes do not take into account the fall of raindrops andtheir subsequent reevaporation in undersaturated air layers[Ferretti et al., 2000].[35] The main characteristics of the seasonal cycle are

reasonably reproduced by MAR. The onset of the monsoonoccurs in March in the coastal zone. The installation phaseis characterized by the occurrence of successive rainfallevents in that area. Nevertheless, the observed maxima inthe coastal area in May–June are not simulated. Simulatedmaxima are found at 7�N and later at 8�N. This problem iseven more pronounced in some GCMs which simulate rainover West Africa during the installation phase as far north asduring the high rain period [CATT, 1999].[36] The last rain maximum of the installation phase is the

strongest and occurs at the end of May before the transitionto the high rain period (Figure 8). As in the observations, anabrupt shift is simulated in the rainy regime at the end ofJune. This is the beginning of the high rain period. Anadditional shift of the rainband core is simulated north to13–14�N in the last 10 days of August 1992. This feature isfound in the IRD observations and the simulation, althoughit is more marked in the latter. It occurs in conjunction witha strong rain event in the area of Niamey (see the precedingsubsection for more details). Finally, a rather regular retreatphase of the rainband is simulated by the model from theend of September, as in the observations. Nevertheless,rainfall is overestimated by the model during that phase.This could be due to the overestimation of the latent heatflux by the model, partly because of an overestimation ofthe green LAI. Latent heat fluxes and rainfall are improvedwhen, in a short term sensitivity simulation, the green LAIis decreased. The intraseasonal oscillations are still simu-lated, so this improvment does not change our analysis ofthe intraseasonal variability. A similar improvement is alsoobtained by Taylor and Clark [2001] when they modify thedescription of the Sahelian vegetation cover in their GCM.Of course, this improvement must be confirmed in MARand this will be reported in a future study.3.2.4. Intraseasonal Variability[37] Possible causes of the intraseasonal variability have

been investigated by comparing the behavior of thesimulated daily rainfall (RR) to other variables of themodel. All variables are sampled each 6 hours and arethen averaged between 10�W and 10�E. They are finallyfiltered using a running mean with a 10-day window inorder to remove the influences of the easterly waves andthe inertial instabilities. Note that the latter are character-ized by a timescale of 4–5 days [see, e.g., Tomas and

Figure 8. The 1992 rainy regime (mm/day). (top) IRDobservation (only between 5�N and 15�N). (middle) MARsimulation. (bottom) NCEP reanalyses. Contour intervals:2 mm/day for a mean rainfall less or equal to 4 mm/day and4 mm/day otherwise.


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Webster, 1997]. The period between 10 July and 20 Augusthas been chosen because the monsoon circulation isrelatively stationary. First, the Hovmoller diagram ofrainfall is compared with that of the meridional windspeed and of the meridional gradient of moist static energy(MSE) (Figure 9). The two latter variables are averagedover the PBL (defined here for simplicity as the 8 lowest slayers of the model, from s = 0.86 to s = 1). The reasonwhy the moist static energy has been considered as arelevant variable is that it is very similar to the equivalentpotential temperature qe [Emanuel, 1994]. Furthermore,Emanuel et al. [1994] deduced from observations thatmost tropical convective systems are nearly in statisticalequilibrium with their environment. They have argued(1) that qe in the tropical subcloud layer is nearly equalto q*e, i.e., the equivalent potential temperature that the airwould have if it were saturated at the same temperatureand pressure, and (2) that q*e is nearly constant with heightin the tropical atmosphere. This means that the verticallapse rate of temperature is moist adiabatic.[38] Two intraseasonal oscillations of the rainfall are

included in Figure 9. An increase (decrease) of the rainfallis led by an increase (decrease) of the meridional gradient ofMSE between the coast and 15�N. The variations of themeridional gradient of MSE slightly lead those of themeridional wind speed, and the latter lead those of rainfall.[39] Additional comparisons between the model varia-

bles have been performed. Following the idea of Eltahirand Gong [1996], the meridional gradient of the moiststatic energy MSE and the meridional wind V are averagedover the planetary boundary layer (from s = 0.86 to s = 1)in order to characterize the Hadley cell dynamics. Ananalysis of surface turbulent heat fluxes (sensible heat(SH) flux and latent heat (LH) flux) is also performed toprovide some insight into the role of surface-atmosphereinteractions. Each of the abovementioned variables issampled every 6 hours, then averaged over an areabetween 10�W and 10�E, and finally filtered using arunning mean with a 10-day window.

[40] The normalized temporal variations of rainfall, SHand LH averaged over the 6�N to 14�N latitude band, arecompared with the gradients of MSE and V across thatlatitude band (see Figure 10). This region has been chosenbecause it corresponds to that part of the continent wherethe meridional gradient of MSE is positive (see Figure 9).The use of gradients over such a large latitude band alsoallows us to assess the general characteristics of the Hadleycell dynamics over West Africa.[41] The results are consistent with the theory proposed

by Plumb and Hou [1992] and Emanuel [1995], whoidentified two regimes of the zonally averaged meridionalcirculation: a radiative-convective equilibrium regime andan angular momentum conserving regime. The former ischaracterized by a weak meridional qe gradient in the PBL(or very similarly MSE gradient) and by a viscously drivendirect meridional circulation. The latter needs an efficientHadley circulation counterbalancing a stronger MSE gra-dient in order to maintain an atmospheric angular momen-tum smaller than the angular momentum at Earth’s surface.The timescale needed by a primitive equation modelbefore fully developing such circulations is rather long(roughly 100 days; see Plumb and Hou [1992, Figure 6]).Here we hypothetize that MAR oscillates between theseregimes without reaching them fully. The former regimecorresponds in MAR to a weaker meridional gradient ofMSE and a weaker northerly wind in the PBL, mainlysouthward of 8�N (Figure 9). The latter corresponds toan intensification of the monsoon and the precipitation[Eltahir and Gong, 1996]. The ‘‘rapid’’ oscillations couldbe explained by a rapid restoration of the MSE gradient bythe surface processes and by the efficient consumption ofMSE by the convective processes (see below for moredetails).

Figure 9. Hovmoller Diagram of the rainfall (mm/day,gray scale), the meridional gradient of the moist static energyaveraged over the PBL (black contour interval: 2 J/kg/kmbelow 10 and 1 J/kg/km above), and meridional wind speed(3 and 4 m/s thick dashed contours are plotted).

Figure 10. Normalized temporal variations of the rainfallRR (solid line), the sensible and latent heat fluxes SH andLH (axis and long dash, respectively), and the south to northgradient of the moist static energy (points). These variablesare averaged between 6�N and 14�N. Temporal variations of(VV6�N – �VV14�N), where VV is the meridional wind speed(short dash). All variables are also averaged between 10�Wand 10�E.


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[42] The simulated stronger (weaker) north to southgradients of the meridional wind appear to be the conse-quence of stronger (weaker) south to north MSE gradients.Indeed, the first maximum of the meridional gradient ofMSE on Figure 10 develops on 20 July and is followed afew days later by maxima of the meridional wind speed andrainfall. The meridional gradient of MSE leads the merid-ional wind speed and rainfall until a second maximum,which is simulated on 10 August for these variables. Recallthat the simulated meridional flux of MSE is stronger at 6�Nthan at 14�N (Figure 9), indicating the convergence of MSEin that latitude band. Rainfall (RR) may be viewed on Figure10 as a rather immediate consequence of the meridionalconvergence of MSE. Consequently, it could be argued thata positive feedback is switched on when the meridionalgradient of MSE is sufficiently large. In this case themeridional convergence of MSE is enhanced because of astrengthening of the Hadley cell. An increase of convectionresults, particularly northward of the maximum of the MSEmeridional gradient. The feedback switches off after sometime because MSE is consumed by the convection. Aweakening of the meridional gradient of MSE and asubsequent weakening of the meridional circulation result.[43] The restoration of the meridional gradient of MSE

may be explained by looking at the surface processes. Notefirst that both LH and SH contribute to the MSE, and a moredetailed analysis of the results (not shown) indicate that,during the period considered, the sum of LH and SHincreases by roughly 40 W/m2 from 6�N to 14�N inresponse to the increase of the radiation absorbed by thesurface. A consequence of this behavior is the more rapidincrease of MSE from south to north over the abovemen-tioned latitude zone and a restoration of the meridionalgradient of MSE.[44] An alternative cause of the intraseasonal oscilla-

tions may be the release of inertial instability. As alreadymentioned, the timescale for having an inertial instabilityis only 4–5 days [Tomas and Webster, 1997], so such aprocess is not a good candidate. Rather, it could beviewed as a process triggering convection when theconvergence of MSE is sufficiently strong. Also, easterlywaves are not a good candidate since their timescale istoo short. Furthermore, the variance of these waves is nota good indicator of the rainfall variability. Convection andrainfall can occur without any easterly waves present, andnot all easterly waves are associated with rainfall [Lebelet al., 2003].[45] The feedback loop responsible for the intraseasonal

oscillations during the high rain period is thus determinedby the surface processes and the response of the Hadley celldynamics. Both processes are relatively slow [Plumb andHou, 1992] when compared to easterly waves and intertialinstabilities, for example. This explains the relatively longtimescale of these oscillations. Note, however, that such anexplanation is not complete when considering the abruptshift of the rainband in late June to early July. Sultan andJanicot [2003] suggest that such a shift could be influencedby more complex 3-D processes. However, the shift was notfound in the ECMWF simulated rainfall in 1992, but thiscould be due to the excessive humidity found in the lowtroposphere of ERA-15, enhancing convection and rain inthe ECMWF model.

[46] In this section a mechanism is proposed for explain-ing the intraseasonal oscillations which occurred during thehigh rain period of 1992 (end of June to September).Oscillations occur between a weak and a strong regime ofthe Hadley cell. The later is correlated with a strongermeridional gradient of moist static energy in the planetaryboundary layer and is responsible for an enhanced conver-gence of this quantity and a subsequent increase of convec-tion and rain. An enhanced consumption of moist staticenergy and finally a weakening of the meridional circulationresult.

4. A 2-D Simulation of the West African RainyRegime

[47] An idealized 2-D simulation of the West Africanclimate has been performed with MAR in order to testhypotheses of the intraseasonal rainfall variability. Indeed,we started our explanation from the zonally symmetric pointof view of Plumb and Hou [1992]. Although this point ofview has been justified in the past for West Africa [Zhengand Eltahir, 1998a], recent observations suggest a signifi-cant divergence in the zonal transport of humidity at thelevel of the AEJ [Fontaine et al., 2003]. If such a nonzonalprocess is responsible for the intraseasonal oscillations, thenthe feedback loop described above should not be effective ina 2-D zonally symmetric dynamical framework. Moregenerally, the intraseasonal oscillations could be generatedoutside the MAR domain and transmitted into the domainthrough its lateral boundaries. In order to eliminate thesepossibilities, we will test a zonally averaged frameworkwith Neuman boundary conditions on temperature and zerowind speed in the latitudinal limits. In this case thelatitudinal boundaries of this domain will not be allowedto assimilate meteorological conditions from outside. Thecharacteristics of the domain are those of the 10�W–10�Elongitude band, thus including a part of West Africa. Themodel dynamics and physical parameterizations are other-wise those included in the 3-D simulation. Only a shortdescription of the 2-D zonally averaged version of themodel is given here. A more detailed study will be thesubject of a separate paper.[48] The equations of MAR have also been adapted to

take into account the sphericity of Earth, but it appeared in asensitivity test that the model is not sensitive to such amodification. The horizontal resolution is 40 km, and themodel domain extends from 45�S to 45�N, including theAtlantic Ocean and West Africa. This domain extent wasalso chosen by Xue et al. [1990] in their study of the WestAfrican climate with a 2-D primitive equation model. Thetopography is the zonal average of the West Africantopography between 10�W and 10�E. The distribution ofvegetation is realistic. SST are prescribed from the clima-tological annual cycle averaged between 10�W and 10�E.The weekly means of SST observed by Reynolds and Smith[1995] have been used. The model is initiated at rest using atypical climatology for temperature and humidity. Thesimulation is started on 1st January and is conducted for2 years. The simulated climate of the second year is verysimilar to that of the first year, so 1 year of simulation isprobably sufficient. The main characteristics of the tropicalatmospheric dynamics are simulated by the model with a


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Subtropical Westerly Jet (SWJ), a Tropical Easterly Jet(TEJ), a southwesterly monsoonal flow, and an AfricanEasterly Jet (AEJ). The AEJ is replaced sometimes by atongue of easterly winds which joins the TEJ and thesurface easterly winds over the Sahara. The model is ableto simulate realistic annual cycles of rainfall. The simulatedrainy regime for the high rain period of the first year isshown in Figure 11. Note that the intraseasonal variations ofthe rainfall have similar phase at 6, 8, 11, and 13�N. Thisconfirms the global influence of the Hadley cell dynamicson the intraseasonal variability of rainfall.[49] The rainfall time series RR averaged over the 0�N–

20�N latitude band is compared to the meridional conver-gence of MSE, V, SH, and SL as for the 3-D experiments.The comparison is qualitatively similar: an increase of MSEconvergence is associated with a strengthening of theHadley cell and leads an increase of rainfall. The behaviorof SH and SL in the 2-D experiment is qualitatively similarto that of the 3-D one.[50] As this simulation does not include the full dynamics

of the system, it suggests that the interactions between theHadley cell dynamics, convection, and the surface processesmay be a cause for intraseasonal oscillations of the rainyregime in West Africa. Furthermore, it suggests that these

oscillations may be studied using a RCM having a domainlimited to West Africa.

5. Conclusions

[51] In this paper the adaptation of the regional climatemodel MAR to West Africa is made by improving its cloudmicrophysical scheme and by coupling it to the convectiveadjustment scheme of Bechtold et al. [2001]. The year 1992is simulated. A validation of the simulated rainy regime atdifferent timescales is performed: seasonal, monthly, anddaily timescales. The model produces a reasonable simula-tion of observed oscillations of the rainy regime as well asan abrupt northward shift of the rainband in the second halfof July. We propose that these oscillations are caused bytransitions between two regimes of the Hadley cell: a strongand a weak regime in conjunction with respectively astronger and a weaker meridional gradient of the moiststatic energy averaged over the PBL thickness. Strongerconvergence of the moist static energy during the strongregime is responsible for an additional enhancement of themeridional gradient of the moist static energy averaged overthe PBL, and a subsequent enhancement of the above-mentioned convergence. Intensified convective rainfall

Figure 11. Two-dimensional simulation of the West African rainfall. From bottom to top: 6�N, 8�N,11�N, and 13�N. Units are mm/day.


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results, and convection consumes the excess moist staticenergy. The meridional gradient of moist static energy issubsequently weakened. Finally, it is progressively restoredby the surface turbulent heat fluxes, which are larger wherethe radiation absorbed by the surface is larger.[52] An acceptable agreement is found between the sim-

ulated and observed rain amounts over the rainy season,although the model overestimates the rainfall during thehigh rain period (July–September) and the retreat phase ofthe rainy season. Time series of the mean daily rainfall areconstructed over 2.5� � 2.5� grid meshes. The simulatedmaxima amount to roughly 40 mm/day, as in the observa-tions. Such marked maxima may correspond to the simula-tion of westward propagating MCS. However, too manylight rain events are simulated by the model. Finally, a shortanalysis of the atmospheric variables indicates that themodel is too cold in some places and that this bias maybe due to the overly strong hydrological cycle simulated bythe model. Nevertheless, such a model is an useful tool forrepresenting in a more realistic way the fine (time, spatial)scale structure of the rainy regime at the climatic timescale.This may allow us to understand more easily the short-comings of climate models over West Africa. Finally, thiswill provide a forcing of the surface part of the modeledhydrological cycle in this region.

Appendix A: Precipitation and IRD Observations

[53] The IRD observations in 1992 are daily rainfall for230 gauges located in West Africa on the domain 3�N–20�N/18�W–25�E. These daily values have been interpo-lated in space on the NCEP 2.5� � 2.5� grid by assigningeach station daily value to the nearest grid point andaveraging all the values related to each grid point. Thestations are located between the latitudes 5�N and 15�N onthe NCEP grid.

[54] Acknowledgments. This research was funded as part of thePPL3-COUMEHY project and supported by the French program ACI-GRID 2001 (Ministere de la Recherche). All major computations wererealized with IDRIS computing resources. Nick Hall and Arona Diedhiouare acknowledged for their thoughtful and constructive remarks. Theauthors thank the ECMWF, NCEP, GPCC, and IRD institutions for theirdata sets.

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en Hydrologie et Environnement, 1023-1025 Rue de la Piscine, BP 53,F-38041 Grenoble Cedex 9, France. ([email protected]; [email protected]; [email protected])P. Marbaix, Universite catholique de Louvain (UCL), Institut d’astro-

nomie et de geophysique G. Lemaıtre (ASTR), Chemin du Cyclotron 2,B-1348 Louvain-la-Neuve, Belgium. ([email protected])W. Moufouma-Okia, Centre d’Enseignement et de Recherche en

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a high-resolution simulation of a west african rainy season using...

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