Bernard Fontaine
email: bernard.fontaine@u-bourgogne.fr

Samuel Louvet

Pascal Roucou

Fluctuations in annual cycles and inter-seasonal memory in West Africa: rainfall, soil moisture and heat fluxes

Summary:

Annual cycle and inter-seasonal persistence of surface-atmosphere water and heat fluxes are analyzed at a 5-day time step over the West African Monsoon (WAM) through observational precipitation estimates (CMAP), model datasets (NCEP/DOE level 2 reanalyses) and a Soil Water Index (SWI) from the ERS scatterometer. Coherent fluctuations (30-90 days) distinct from supra-synoptic variability (10-25 day periods) are first detected in the WAM precipitation and heat fluxes over the period . During all the northward excursion of the WAM rain band, a succession of four active phases (abrupt rainfall increases) occurs.

They are centered in the first days of March, mid-April, the second half of May and from the last week of June to mid-July (the Sahelian onset). A simple statistical approach shows that the Spring to Summer installation of the monsoon tends to be sensitive to these short periods.

Other analyses suggest the existence of lagged relationship between rainfall amounts registered in successive Fall, Spring (active periods) and Summer (top of the rainy season) implying land surface conditions. The spatial extension of the generated soil moisture anomalies reaches one maximum in March, mainly at the Guinean latitudes and over the Sahelian belt where the signal can persist until the next monsoon onset.

Typically after abnormal wet conditions in September-October two signals are observed: (1) more marked fluctuations in Spring with less (more) Sahelian rainfall in May (June and after) at the Sahelian-Sudanian latitudes; (2) wetter rainy seasons along the Guinean coast (in Spring and Summer with an advance in the mean date of the 'little dry season'). The reverse arises after abnormal dry conditions in autumn.

I. Introduction

Annual cycle and interannual to decadal rainfall fluctuations over West Africa and their association with the Sea Surface Temperature (SST) variability have been noted for a long time, first over the Atlantic Ocean and then at a quasi-global scale. It is clear that part of monsoon variability responds to external forcing, e.g. SST anomaly patterns of quasi-global or more regional scales. However, since the seminal works of [START_REF] Charney | Dynamics of deserts and droughts in the Sahel[END_REF][START_REF] Charney | A comparative study of the effects of albedo change on drought in semiarid regions[END_REF], many numerical experiments (for example in [START_REF] Zeng | Enhancement of interdecadal climate variability in the Sahel by vegetation interaction[END_REF][START_REF] Wang | Role of vegetation dynamics in enhancing the low-frequency variability of the Sahel rainfall[END_REF][START_REF] Wang | Role of vegetation dynamics in enhancing the low-frequency variability of the Sahel rainfall[END_REF] have recognized the role of land surface as a major and dominant factor at local and meso scales in spatial distribution of rain events. In this context, a lot of numerical studies have first focused on the close relationship linking soil moisture and precipitation mainly at the interannual time scale [START_REF] Xue | The influence of land surface properties on Sahel climate. Part I: Desertification[END_REF]. The strong interannual persistence of rainfall anomalies has been explained in terms of land-surface feedback due to the slow adjustment time of soils covered by vegetation south of Sahel [START_REF] Zeng | The role of vegetation-climate interaction and interannual variability in shaping the African savanna[END_REF]. [START_REF] Eltahir | Dynamics of wet and dry years in West Africa, Journal of Climate[END_REF] and [START_REF] Cook | Generation of the African Easterly Jet and Its Role in Determining West African Precipitation[END_REF] emphasized the role of the meridional moist static energy (MSE) and thermal gradients in low levels for WAM dynamics whereas [START_REF] Fontaine | An improvement of June-September rainfall forecasting in the Sahel based upon region April-May moist static energy content (1968-1997)[END_REF] pointed out the dominant role of Spring to Summer changes in MSE gradients for seasonal rainfall forecasting in West Africa. More recently, numerical [START_REF] Douville | Relevance of soil moisture for seasonal climate predictions: a preliminary study[END_REF] and empirical [START_REF] Philippon | The relationship between the Sahelian and previous 2nd Guinean rainy seasons: a monsoon regulation by soil wetness?[END_REF] studies have focused on the relevance of soil moisture for seasonal prediction. The latter authors have observed that abnormally wet Sahelian rainy seasons tend to be preceded by abnormally wet soils over the Sudanian-Guinean belt in northern Fall suggesting the possible existence of an in situ long-term memory controlled by soil moisture. However, using a simple water model [START_REF] Shinoda | Influence of Soil Moisture Anomaly on Temperature in the Sahel: A Comparison between Wet and Dry Decades[END_REF] conclude that the root-zone soil moisture does not act as a memory of Sahelian rainfall anomaly into the following rainy season and cannot be directly related to the long-term persistence of the drought.

The purpose of this study is to document the West African long-term memory, (i.e., abnormally wet Sahelian rainy seasons tend to be preceded by abnormally wet soils and rainfall over the Sudanian-Guinean belt during northern Fall), in terms of rainfall annual cycle and variability during the northward excursion of the WAM system in northern Spring. We will focus on the water and heat exchanges on the continent, since over ocean, surface-atmosphere interactions are triggered by the positive feedbacks in the strong coupled SST-wind system over the eastern equatorial Atlantic [START_REF] Mitchell | The annual cycle in equatorial convection and sea surface temperature[END_REF] and the air-sea coupling through vertical air-sea fluxes is more important at a decadal scale [START_REF] Chang | A decadal climate variation in the tropical Atlantic Ocean from thermodynamic air-sea interactions[END_REF] than at a seasonal or intra-seasonal scale [START_REF] Li | On the seasonal cycle of the equatorial Atlantic Ocean[END_REF]. By contrast, over land they are more due to varying land surface parameters such as vegetation and soil moisture which control locally the vertical heat and water fluxes.

A few recent studies have been devoted to the installation of the WAM system with an intra-seasonal approach. [START_REF] Grodsky | Coupled land/atmosphere interactions in the West African Monsoon[END_REF] and [START_REF] Louvet | Study of the first Guinean rainy season and role of the Subtropical Jet of the Northern Hemisphere[END_REF] have detailed the "pre-onset" and "onset" of the first Guinean rainy season while [START_REF] Sultan | Abrupt shift of the ITCZ over West Africa and intra-seasonal variability[END_REF], Janicot and[START_REF] Janicot | Intra-seasonal modulation of convection in the West African monsoon[END_REF]Sultan et al. (2003) focused on the abrupt latitudinal shift of the rain band over the Sahel and associated signals in moisture advection, atmospheric dynamics and convection. [START_REF] Matthews | Propagation mechanisms for the Madden-Julian oscillation[END_REF][START_REF] Matthews | Intraseasonal variability over tropical Africa during northern summer[END_REF] proposes an interesting hypothesis in which intra-seasonal fluctuations arise partly as a remote response to the 30-60 day Madden-Julian oscillations over the warm pool sector: the equatorial Kelvin wave response to this change propagates eastward and an equatorial Rossby wave response propagates westward. After a complete circuit of the equator they meet up twenty days later over Africa, where the negative mid-tropospheric temperature anomalies in the Kelvin and Rossby wave can favour deep convection. More recently, Louvet et al. (2003) have shown, through analyses of the CMAP dataset, that the different monsoon stages considered above (i.e, the Guinean pre-onset and onset, the Sahelian latitudinal shift of the rain band) are neither specific to a given latitudinal zone nor independent. They occur at the end of periods of pause in rainfall during the northward excursion of the rainbelt and could impact on the future rainy season.

So, rainfall over the WAM region appears to be controlled by long-term memory processes which explain part of the quasi decadal and interannual variability of the cumulative amounts registered during a rainy season. Nonetheless, there is no clear physical link between this large scale memory and the successive rain events of meso scale (i.e., squall lines, convective systems) or of synoptic scale. In this context variability during the northward excursion of the WAM system is important to consider because it impacts directly the dates of onsets and the length of the rainy seasons. We make the hypothesis that a significant part of rainfall in northern Spring is sensitive to the continental amounts registered during the preceding monsoon withdrawal, the other part responding to external forcing, often of oceanic origin (e.g. SSTs). Louvet et al. (2003) who documented the periods of pauses, we will focus on the active phases to detect critical periods during all the northern excursion of the WAM

By contrast with

II. The data

Rainfall data from CMAP are used in a version where observations from raingauges have been merged only with estimates from infrared and microwave data from satellites then analyzed on a 2.5 x 2.5 degree latitude/longitude grid. The data, available at a 5-day timescale over the period 1979-2001, depend mainly on observations since rainfall forecasts from the model are not assimilated in the selected CMAP version. The technique and algorithms are described in [START_REF] Spencer | Global oceanic precipitation from the MSU during 1979-91 and comparisons to other climatologies[END_REF] and [START_REF] Xie | Global precipitation: a 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs[END_REF]. It is noteworthy that this version is very close to the in situ daily rainfall amounts over the continent both in rhythm (linear correlation coefficients) and in amplitude (means and standard deviations) as shown in the Table 1 of Louvet et al. (2003) for the rainfall estimates compiled by IRD (Institut de Recherche pour le Développement), ASECNA (Agence pour la Sécurité de la Navigation Aérienne en Afrique et à Madagascar) and CIEH (Comité Inter-africain d'Etudes Hydrauliques) after interpolation on the 2.5° * 2.5° grid and calculation of 5-day averages .

To describe the surface-atmosphere heat fluxes we have chosen the NCEP/DOE AMIP-II Reanalysis (R-2) because for this version of reanalysis, model precipitation has been replaced with observed (satellite + gauge) 5-day precipitation in a similar manner to CMAP and in the same spatial resolution [START_REF] Kanamitsu | NCEP-DOE AMIP-II reanalysis (R-2)[END_REF]. Moreover R-2 improves upon the NCEP/NCAR Reanalysis(R-1) by fixing the errors and by updating the parameterizations of the physical processes. However no direct assimilation of radiances, but (proper) use of SSM/I data and assimilation of rainfall data have been incorporated. The main improvements regarding the water cycle, as well as the effect of satellite and aircraft, on analyses are extensively discussed in [START_REF] Kanamitsu | NCEP-DOE AMIP-II reanalysis (R-2)[END_REF], in particular the correction method equivalent to using observed 5-day precipitation amounts in the hydrological calculations. [START_REF] Maurer | Evaluation of the Land Surface Water Budget in NCEP/NCAR and NCEP/DOE Reanalyses using an Off-line Hydrologic Model[END_REF] found that R-2 provides more accurate pictures of soil wetness, near surface temperature and surface hydrology budget over land, and radiation fluxes over Ocean.

By contrast the new boundary layer and convection schemes have modified the water vapor profile: R-2 has more moisture in low levels than R-1. In any case, over the West African continent the atmospheric and surface variables or parameters are more accurate; surface albedo over the Sahara goes also from 0.3 in R-1 to beyond 0.4 in accordance with observations.

Considering these aspects we will focus only on the surface parameters that are most closely linked to the water cycle over land at regional scale: i.e., the observed rainfall amounts and reanalyzed sensible and latent heat fluxes at a 5-day time-step over the period 1979-2001. We define first a West African Monsoon area (WAM: 5°-20°N; 10°W-10°E): this domain is very representative and concentrates the major part of the synoptic disturbances, organized convective and rainy systems (i.e, squall lines). The WAM box is then divided into three equal basic zones of 5° extension: a 'Sahelian' zone (SAH: 15°N-20°N) a 'Sudanian' zone (SUD: 10°N-15°N) and a 'Guinean' zone (GUI: 5°N-10°N).

Additionally, an independent dataset, the soil water index (SWI) from the Global Soil Moisture Archive 1992-2000 from ERS Scatterometer Data, has been retained for studying the land surface evolution. Indeed, SWI is not a direct measurement of soil moisture content but a satellite estimate based on the backscatter coefficient from the ground surface. Each SWI is expressed in percentages (relative measure) ranging between 0 (dry soil) and 1 (100% saturated soil). In fact the scatterometer estimates the top soil moisture (approximately the upper 5 centimetres of the soil) and calculation of SWI is based on this measurement for depicting the available water content in the rooting zone. SWI is influenced by vegetation and surface roughness, so methods have been developed to eliminate these effects (see for details, Wagner and[START_REF] Wagner | Large-Scale Soil Moisture Mapping in Western Africa using the ERS Scatterometer[END_REF][START_REF] Wagner | A Method for Estimating Soil Moisture from ERS Scatterometer and Soil Data[END_REF]. However such techniques allow derivation of the soil moisture content at a regional scale (50 km) through the conductivity of the top soil, at a time sampling of 1-2 observations per week over Africa. At this frequency the SWI depicts mainly the available top-soil water content. For the West African region there are 10,218 SWI estimates available at a ten-day time step over the period 1992-2000 (see for example graphs in Figures. 5 a, b and maps in Figures. 6b-d).

III. Mean annual cycles and fluctuations

Figure 1 presents mean annual evolutions of filtered rainfall records (RR), their time tendencies and the sensible (H), latent (LE) and total (H+LE) heat fluxes at the surface over the three latitudinal bands. It is noteworthy that RR is a filtered 'observed' signal after elimination of fluctuations < 5 pentads (30 days) by a low-pass butterworth filter (Murakami, 1979, while H and LE are 'raw 1d-f) the shapes of RR, H and LE differ: RR peaks also in August but the total heat flux remains nearly constant ( > 110 W/m 2 ) -from April when H reaches its first peak to October when LE starts its decrease-. Here also the H peak is clearly concomitant with the beginning of rainy season (Fig. 1d). It is noticed that from April to July the Sudanian belt (Fig. 1e) is marked by recurrent a succession of abrupt increases observed in 2/3 of years which indicates the phase-locking to the annual cycle (black bars). These 'active periods' are followed by short pauses (attenuations). At Guinean latitudes, rainfall amounts peak in June and September (Fig. 1g) according to the double annual displacement of the Solar apparent position and rain belt and exhibits similar active periods with less phase-locking. The LE flux remains nearly constant all along the year (80-100 W/m 2 ) and the H cycle is mono-modal and peaks in February (Fig. 1i).

Mean rainfall tendencies prove that the Spring to Summer installation of the monsoon is neither continuous nor linear but tends to be concentrated on specific short periods of the year. More precisely, there are four preferential periods of rainfall increase in West Africa: 1) the turn of February/March in the Guinean latitudes, 2) mid-April, 3) the end of May over the whole Guinean-Sudanian area, and 4) the turn of June/July over the Sudanian-Sahelian belt, which corresponds to the monsoon onset. However, these active phases can be affected by shorter fluctuations, as shown in Figure 2: if some peaks refer to the 13-16 and 20-25 day periods (linked to supra-synoptic variations), variations > 30 days (called hereafter intraseasonal) appear clearly more energetic and significant. Such variations can be induced by a large number of mechanisms linked to either internal atmospheric dynamics of synoptic scale (a few days) or climate mechanisms of longer periods due to interactions with the oceanic and land surfaces [START_REF] Grodsky | Coupled land/atmosphere interactions in the West African Monsoon[END_REF]Sultan andJanicot, 2000, 2003;[START_REF] Louvet | Study of the first Guinean rainy season and role of the Subtropical Jet of the Northern Hemisphere[END_REF].

The four abrupt rainfall increases can be viewed as periods of greater occurrence of monsoon surges in the West African monsoon during its northward penetration into the continent. This is well illustrated in Figure 3 which illustrates also the discontinuous shape of rain band and alternation in active phases and pauses from March to July. The mean annual evolution is rather simple. The first Guinean rainy season (> 6 mm/day south of 7°N in June) occurs after the total heat flux annual peak in February-March (> 130 W/m 2 at 6-7°N in Fig. 3b) which precedes a rapid increase in sensible heat flux over 10-15°N in March-April (Fig. 3c). In Summer the core of the Sudanian-Sahelian rain belt (> 7 mm/day at 8-12°N) is located just southward to the heat belt (H+LE maximums >120 W/m 2 at 13-15°N in Figure . 3b) leading the annual peak in LE flux (> 110 W/m 2 in October-November, Fig. 3d).

So the mean annual cycle of the West African monsoon cannot be reduced to a continuous latitudinal excursion of the rain band following the solar declination. It appears associated with two types of surface forcing which are largely out of phase: sensible energy north of 10°N (Fig. 3c) just before the first rainfall maximum over 'Sudan-Sahel' and latent energy in Autumn south of 10°N (Fig. 3d) after the second RR maximum during the monsoon withdrawal. In Spring, energy is pumped from the surface mainly through sensible-heating and is driving convection northward [START_REF] Fontaine | Spring to Summer changes in the West African monsoon through NCEP/NCAR reanalyses (1968-1998)[END_REF], but latent energy depends on the available soil moisture and is hence stronger during the second Guinean rainy season.

The correlation coeffcicients between the first three active phases (AP #1,2,3) over the 'Sudan-Sahel' region (10°N-18.5°N; 10°W-10°E) and the 5-day rainfall evolution are displayed in Figure 4 a-c, after elimination of all synoptic and supra-synoptic time fluctuations (< 6 pentads or 30 days) by a high-pass butterworth filter [START_REF] Murakami | Large scale aspects of deep convective activity over the Gate area[END_REF].

Mean timing and duration of AP#1-3 are reproduced at the bottom diagrams. For example, the link between the AP#1 (which is from pentad 11 to 14) and each 5-day rainfall amount along the year is shown in Figure 4a. It clearly appears that the highest positive values (> +0.7 or + 0.9) are always centered on these periods, suggesting both the important role of AP#1-3 for annual rainfall variability. For example, AP#1 is correlated with AP#3 over Sahel (Fig. 4a), whereas the Sudanian amounts during AP#2 and AP#3 periods are preceded by significant signals in February and March (Fig. 4b,c). The mean dates of AP occurrences are also depicted by the RR, H and LE signals in Figure 3 (look at the 1-5 mm/day, 40 and 80 W/m 2 lines in Fig. 3a,c, d respectively). Thus, from March to August, the successive APs could be controlled locally by the vertical fluxes of water and heat in a way where an enhanced hydrological cycle makes easier the AP generation. This arises the question of interannual variations in soil moisture availability during Spring and hence of the possible role of rainfall variations during monsoon withdrawal during the preceding Autumn.

IV. The role of wet and dry conditions during monsoon withdrawal

So monsoon conditions in northern Spring could be sensitive to the continental amounts registered during the preceding monsoon withdrawal independently from external and/or oceanic (SSTs) forcing. We illustrate first this statement through the soil moisture index (SWI) estimates provided by the ERS Scatterometer at a 10-day frequency for the period 1992-2000 (see section 2). The mean and abnormally wet and dry years SWI annual evolutions for the entire WAM region are presented in Figure 5. It is noteworthy that SWI values increases from the end of February to mid-September, lagging the rainfall cycle with a time delay of one month (compared with Fig. 1). However the dry curve reaches its maximum before the wet one (first days of September against mid-September, respectively) and their synchronous differences are significant during Autumn.

Figure 6a shows the statistical impact of autumnal soil moisture variations registered over the Sudanian and Sahelian belts. It details, the relationship between September-December SWI anomalies in year 0 and soil moisture persistence in year +1 at a 10-day time-step and a subcontinental scale. The values are scaled in percentages of significant locations at p=0.05 regarding the wet minus dry SWI differences over 'Sudan-Sahel' relatively to the period 1992-2000 (wet: 1992, 93, 96, 97; dry: 1994, 95, 98, 99). So a value of 30% means that 30% of the 10,218 SWI Wet minus Dry differences (50km*50 Km) are locally significant. Notice first that the area significance exceeds largely the 5% threshold in Winter and reaches its maximum in March +1 (> 30%), at the end of the dry season. At this time of the year (end of dry season) SWI is close to its annual minimum over the WAM region and over the Guinean and Sudanian belts. More generally, except a few small areas near the Guinean coast, the mean SWI field in March exhibits very low values (Fig. 6b). In March (Fig. 6c), the significant differences are largely positive, except for the sub-Saharan regions and some areas over the Nigeria (south of 10°N and east of 10°E); they tend to persist until June over the Sahelian band (shadings along 15°N in Fig. 6d). This 6-month persistence could change the initial conditions for the next monsoon season.

To study more directly and over a larger period the impacts of rains during monsoon withdrawal, we have done composite analysis relative to September-October (SO) rainfall . We define a year as WET (DRY) when the SO rainfall amounts are higher (lower) than the mean: the 10 wettest and 10 driest SO years are listed in table 1. Figure 7 shows the WET minus DRY (W-D) differences in 5-day rainfall and heat fluxes over a 21month period (from January in year 0 to September in year +1). The results are tested locally using a paired Student t-test and globally (field significance) through 1000 Monte Carlo simulations.

The significant W-D rainfall estimates (Fig. 7a) concerns 8.8% of the data which is significant regarding field significance). After positive differences in September-October 0 over 'Sudan-Sahel' clear positive signals appear south of 10°N in March+1, July +1 and September+1 whereas months of May for both years (0 and +1) register negative W-D differences. These results show that rainfall amounts during monsoon withdrawal are statistically linked to the 1 0 timing and amplitude of precipitations during the next monsoon season. This might arise from changes in land surface (i.e., soil moisture and vegetation) since the H and LE differences (Fig. 7b,c) are clearly negative with H and positive with LE, according to variations in the H/LE Bowen ratio: the inverse H and LE behavior points to the role of variations in sensible versus latent partition of the net heat flux from the surface as a response to rainfall and soil wetness. [START_REF] Shinoda | Influence of Soil Moisture Anomaly on Temperature in the Sahel: A Comparison between Wet and Dry Decades[END_REF] The effect of soil moisture anomalies are logically more important in 'Sudan-Sahel' than near the Guinean coast, since over regions of low soil water content any small anomaly will change drastically the usual and will impact more the vegetation cover density. Figures 8 and9 illustrate the statistical impacts of September-October rainfall variations over 'Sudan-Sahel' on the future active phases and pauses, and on soil moisture evolution, respectively. Curves in Figure 8 contrast precipitation following the most abnormally wet and dry withdrawal periods (SO) listed in table 1 for the entire WAM region (Fig. 8a), the 8°N-12°N belt which exhibits a unique rainy season in Summer (Fig. 8b) and the 5°N-7°N coastal area with two wet seasons in Spring and Autumn separated by a 'little dry season' in Summer (Fig. 8c). When wetter conditions occur at the end of the preceding rainy season one observes successively (1) more marked fluctuations in Spring with typically less rainfall in May but more in June (Fig. 8a,b),1 (2) wetter rainy seasons in Summer (Fig. 8a,b), Spring and Autumn (Fig. 8c) along the Guinean coast where the little dry season occurs also in advance (by the end of July). By contrast, when SO precipitations of the preceding year are too rare, the rainy season is less abundant over the Sahelian and Sudanian belts; at the Guinean latitudes, the rainy seasons are also less abundant and separated by a marked little dry rainy season in August. Moreover, standard deviations of successive 5-day rainfall series extending from January to the end of August (the first 48 pentads in Fig. 8a-b) have been computed for the wet and dry cases: their mean values are significantly higher (lower) over WAM and Sudan-Sahel (8°N-12°N) at p=0.05. So, even though not directly tested here, more (less) marked active phases and pauses after wet (dry) SO conditions are clearly observed in Figures. 8a,b.

Soil moisture evolutions over selected regions following abnormally wet and dry September-October rainfall variations (years underlined in Table 1) are shown in Figure 9: except over Guinea (Fig. 9d), positive W-D differences persist all along the dry season; they are mostly significant from September 0 to the end of April-May +1 over WAM and Sudan-Sahel (Fig. 9a,b). The wet curve (solid line in Fig. 9b) exhibits also higher -but not significant-SWI values in July-September +1. This relationship is also partly due to the fact that SWI is calculated by assuming an exponential decay using a soil and climate characteristic constant: at local scale [START_REF] Gaze | Dry season water use patterns under Guiera senegalensis L. shrubs in a tropical savanna[END_REF] have shown from direct observations a rapid (2 months) depletion of soil moisture in the top 1 m of the soil. However, at regional scale, as shown in Figure 6, SWI persistence changes soil conditions during the dry season and hence the features of the future monsoon season.

V. Summary and Discussion

The general purpose of this study was to explore, through simple statistics, the possible control of rainfall fluctuations by an in-situ memory resulting from surface-atmosphere interactions over land, the other control being external forcing such as long-lived SST anomaly patterns. The first aim was to detect coherent signals at the surface-atmosphere interface through observed (CMAP data,) and modeled (NCEP/DOE reanalyses) heat and water vertical fluxes. The second one was to explain part of the link between abnormally wet soils in Fall using the Global Soil Moisture Archive from ERS Scatterometer and the next monsoon season and to document this long-term memory. We have then verified the hypothesis that active periods during the northern excursion of the WAM system are sensitive to rainfall amounts registered at the beginning of the preceding monsoon withdrawal. Most of the results derive from data analyses at the 5-day after elimination of the synoptic and sub-synoptic variability (< 30 days), except for the soil moisture estimates which are at a 10-day frequency. They can be summarized as follows.

Over the WAM region the net total heat flux reaches a maximum in March-April due to the annual peak in its sensible component, it remains then nearly constant until the end of September due to the increasing latent flux component (Fig. 1). Even though clear heat flux and rainfall fluctuations exist in the supra-synoptic frequencies (13-16 and 20-25 dayperiods), the most energetic signals are related to the intra-seasonal ones (30-90 days) (Figure .   2). The Spring to Summer period is marked by a maximum in sensible heat flux north of 10°N and a succession of active phases (APs with abrupt rainfall increases) (Fig. 3). This is attested both in observations (CMAP, in situ rainfall not shown here) and in the NCEP reanalyzed vertical heat fluxes. A simple approach shows that APs tend to be concentrated on preferential short periods during the Spring to Summer installation of the monsoon (Fig. 1). There are four main AP periods: the turn of February/March at the Guinean latitudes (AP#1), mid-April (AP#2), then the end of May (AP#3) over the Guinean-Sudanian area and the turn of June/July (AP#4) for the Sudanian and Sahelian belts. APs #1-3 are significantly linked together (Fig. 4).

The results suggest also the existence of a "memory effect" triggered by preceding September-October rainfall anomalies. For example, composite analyses performed on the soil moisture index (SWI) estimates at a 10-day frequency (Fig. 5,6) show that rainfall excesses during monsoon withdrawal in year 0 generate positive soil moisture anomalies until the months of April and May +1 over the Sahelian and Sudanian belts, respectively. This relationship is partly due to the fact that local SWI values are calculated by assuming an exponential decay in time. However, at regional scale, the spatial extension of SWI significant anomalies reaches one maximum at the end of the dry season (> 30% of the West African continent in March +1): such an inter-seasonal persistence is able to change the horizontal energy gradients in low levels and hence to alter the initial conditions for the next West African monsoon season. have shown that such soil moisture evolutions are sensitive to rainfall conditions in September-October during monsoon withdrawal. Such anomalies can affect significantly the timing and amplitude of the next monsoon season (in year +1) even if part of monsoon variability responds to external and/or larger forcing, e.g. SST anomalies. Typically after the wettest conditions (i.e., abnormal rainfall excess in September-October) significant inverse anomalies occur in the sensible (negative values) and latent (positive) heat fluxes from September 0 to April +1 over 'Sudan-Sahel'. Higher (lower) intraseasonal variance in the 5day rainfall January to August rainfall series is also observed after wet (dry) autumnal conditions, in coherency with more (less) marked active phases and pauses, along with: (1) Sahelian rainfall less (more) abundant in May (June); (2) wetter rainy seasons in Summer +1 at the Sahelian latitudes and in Spring and Autumn +1 along the Guinean coast where the 'little dry season' occurs in advance (at the end of July +1).

However, we have to mention that external forcing (teleconnections), long-lived SST anomaly patterns and vegetation cover have not been directly considered here. Part of the relationship involving soil moisture could also be due to the fact that local SWI values are calculated by assuming an exponential decay in time. Nonetheless, most of the preceding results are significant and have good internal consistency. They suggest that rainfall anomalies occurring at the end of the rainy season are of paramount importance for land-surface properties (soil, vegetation) during the dry season then in Spring and Summer for monsoon dynamics and precipitation. This arises yet the questions of (1) the capacity of semi-arid ecosystems to benefit from a water surplus at a multi-month lag, and of (2) the chain of processes linking these new surface parameters to monsoon dynamics. The first point has been recently investigated by Schwimning et al. (2004) and [START_REF] Martiny | Interannual persistence effects in vegetation dynamics of semi-arid Africa[END_REF] using the Normalized Difference Vegetation Index (NDVI). These authors indicate first that clearly exists a memory of the past precipitation; second, they show that soil moisture favours increased seeds, production and more abundant foliar precession, whereas enhanced infiltration, decreased run-off and biogeochemical cycles facilitate vegetation growth.

The second question (forcing on monsoon dynamics in Spring) implies the existence of slow varying surface parameters (vegetation) at sub-continental scale and of more rapid feedbacks between soil moisture and the vertical fluxes at local scales. In fact large areas of vegetation cover (density, quality) change surface albedo and roughness but limit also direct evaporation and hence maintain humidity in low levels. Such a wetness excess exerts 2 types of forcing on the low troposphere: at local scale it strengthens the vertical moist static energy (MSE) gradients and thus the vertical instability; at regional scale it influences monsoon dynamics by rearranging the horizontal MSE gradients (Emmanuel, 1995[START_REF] Eltahir | Dynamics of wet and dry years in West Africa, Journal of Climate[END_REF][START_REF] Zheng | The role of vegetation in the dynamics of West African monsoons[END_REF][START_REF] Fontaine | Seasonal evolution of boundary layer heat content in the West African monsoon from the NCEP/NCAR reanalysis (1968-1998)[END_REF] and associated rainfall amounts [START_REF] Philippon | The relationship between the Sahelian and previous 2nd Guinean rainy seasons: a monsoon regulation by soil wetness?[END_REF]. Although in a numerical model these gradients are not able to produce the observed structure of the African Easterly Jet (AEJ), as shown by [START_REF] Thorncroft | Maintenance of the African easterly jet[END_REF], they organize the monsoon flux and can contribute to the AEJ dynamics through temperature gradients magnified by meridional soil moisture gradients (Cook,1999).

In fact observations and atmospheric reanalyses show that (i) the relaxation of MSE gradients over land (southward orientated) in Spring is linked to the northward excursion of the system into the continent, and (ii) both the sensible-heating driven convection in low levels over the Sahel and the adiabatic ascents due to latent heat release in the high troposphere tend to reinforce before an abnormally wet rainy season [START_REF] Fontaine | Spring to Summer changes in the West African monsoon through NCEP/NCAR reanalyses (1968-1998)[END_REF].

So, the chain of processes at the end of the dry season could be as follows. (1) abnormally wetter soils in March (Fig. 6a,c) decrease surface albedo, but increase surface roughness, air moisture and low level convergence; (2) decreasing albedo reinforces locally the net solar radiation (so the upward radiation from the soil decreases) but lessens air temperature near the surface (so the boundary layer thickness diminishes); (3) the net surface radiation strengthens and therefore the surface-atmosphere heat flux. In the West African regional context this will reinforce MSE in low levels between the areas where soil moisture is abnormally high and the dry Sahara. Such a situation is favourable to monsoon circulation and to its northward penetration into the continent. In particular this will exert a differential forcing on the vertical atmospheric heating arrangement: the sensible flux will heat the near-surface layers whereas the latent flux will warm the atmosphere above the condensation level; this will impact monsoon dynamics at regional scale through the generation of horizontal thermal and moisture gradients and hence will affect both the amplitude and timing of precipitation.

TABLES

Table 1: The 10 most abnormally wet and dry September-October periods over the entire WAM domain and the Sahelian, Sudanian, Guinean belts defined in section 2 ; underscores point out the years retained for the two-year evolution of SWI composites over 1992[START_REF] Matthews | Propagation mechanisms for the Madden-Julian oscillation[END_REF]. Wet Sep-Oct periods WAM: 1979198019811984[START_REF] Folland | Sahel rainfall and worldwide sea temperature[END_REF]1987198819891994[START_REF] Gpcc | The Global Precipitation Climatology Centre. Information on World Wide Web under[END_REF]SUD-SAH : 197919811984[START_REF] Folland | Sahel rainfall and worldwide sea temperature[END_REF]19881994[START_REF] Emanuel | On thermally direct circulations in moist atmospheres[END_REF][START_REF] Eltahir | Dynamics of wet and dry years in West Africa, Journal of Climate[END_REF][START_REF] Gpcc | The Global Precipitation Climatology Centre. Information on World Wide Web under[END_REF][START_REF] Cook | Generation of the African Easterly Jet and Its Role in Determining West African Precipitation[END_REF]SAH: 197919801984[START_REF] Folland | Sahel rainfall and worldwide sea temperature[END_REF]1994[START_REF] Emanuel | On thermally direct circulations in moist atmospheres[END_REF][START_REF] Eltahir | Dynamics of wet and dry years in West Africa, Journal of Climate[END_REF]1997[START_REF] Gpcc | The Global Precipitation Climatology Centre. Information on World Wide Web under[END_REF][START_REF] Cook | Generation of the African Easterly Jet and Its Role in Determining West African Precipitation[END_REF]SUD: 197919811984[START_REF] Folland | Sahel rainfall and worldwide sea temperature[END_REF]19881994[START_REF] Emanuel | On thermally direct circulations in moist atmospheres[END_REF][START_REF] Gpcc | The Global Precipitation Climatology Centre. Information on World Wide Web under[END_REF][START_REF] Cook | Generation of the African Easterly Jet and Its Role in Determining West African Precipitation[END_REF]GUI: 197919801981198219841987198819891994[START_REF] Emanuel | On thermally direct circulations in moist atmospheres[END_REF]Dry Sep-Oct periods WAM: 1983198519901991[START_REF] Mitchell | The annual cycle in equatorial convection and sea surface temperature[END_REF][START_REF] Spencer | Global oceanic precipitation from the MSU during 1979-91 and comparisons to other climatologies[END_REF][START_REF] Eltahir | Dynamics of wet and dry years in West Africa, Journal of Climate[END_REF]1997[START_REF] Cook | Generation of the African Easterly Jet and Its Role in Determining West African Precipitation[END_REF]2001SUD-SAH: 19821983198519901991[START_REF] Mitchell | The annual cycle in equatorial convection and sea surface temperature[END_REF][START_REF] Spencer | Global oceanic precipitation from the MSU during 1979-91 and comparisons to other climatologies[END_REF]1997[START_REF] Matthews | Propagation mechanisms for the Madden-Julian oscillation[END_REF]2001SAH: 19821983198519871988198919901991[START_REF] Mitchell | The annual cycle in equatorial convection and sea surface temperature[END_REF]2001SUD: 19821983198519901991[START_REF] Mitchell | The annual cycle in equatorial convection and sea surface temperature[END_REF][START_REF] Spencer | Global oceanic precipitation from the MSU during 1979-91 and comparisons to other climatologies[END_REF]1997[START_REF] Matthews | Propagation mechanisms for the Madden-Julian oscillation[END_REF]2001GUI: 1983198519901991[START_REF] Mitchell | The annual cycle in equatorial convection and sea surface temperature[END_REF][START_REF] Eltahir | Dynamics of wet and dry years in West Africa, Journal of Climate[END_REF]1997[START_REF] Cook | Generation of the African Easterly Jet and Its Role in Determining West African Precipitation[END_REF][START_REF] Matthews | Propagation mechanisms for the Madden-Julian oscillation[END_REF]2001 Table 2: Squared first rank autocorrelation coefficients (R2) computed on the raw 5-day RR, H, LE and H/LE time series averaged over the 5°N-12°N Sudanian-Guinean belt for different bimonthly periods. Each time series corresponds to the successive 5-day raw (non filtered) values selected over a 2-month period (i.e., the JF time series corresponds to the first twelve pentad values of each year (23 years), hence to all the 12*23 January-February pentads; the MA series corresponds to the pentad numbers 13 to 24 of each year…). Asters mark the significant values at the p=0.05 level taking into account the equivalent number of degrees of freedom in the series. Period 1979Period -2001 (b) as above but for the second active period (AP#2);

(c) as above but for the third active period (AP#3). regarding the abnormally wet and dry September-December periods over 'Sudan-Sahel' (see Table 1). The Wet minus Dry differences are tested using a paired Student t-test at p=0.05. regarding the abnormally wet and dry September-December periods over 'Sudan-Sahel' (see Table 1). The Wet minus Dry differences are tested using a paired Student t-test at p=0.05.

  system. Recent availability of latest version of the NCEP/DOE reanalyses level II, the CPC Merged Analysis of Precipitation (CMAP) and Soil Moisture Archive from ERS Scatterometer data offers such an opportunity at the very beginning of the 2006-2007 AMMA field experiments.

  model products' which reflect how the NCEP model deals with soil moisture holding capacity. Over the Sahel (Fig.1 a-c) RR, LE and H+LE reach their annual maximum in the second part of August following the annual maximum in H which occurs by the end of June in clear concomitance with the beginning of the rainy season as shown by the sudden rainfall increase called 'Sahelian onset' in Figure. 1b. Over the Sudanian belt (Fig.

  detail these processes over the Sahel through hourly soil moisture observations: basically increased (decreased) precipitation is accumulated as increased (decreased) soil moisture with a time lag, leading to decreased (increased) sensible heating and simultaneously increased (decreased) latent heating and finally to low (high) daily maximum temperature. The significant W-D signals at the p=0.05 level in Figures. 7b, c concern 12.7% and 20.7% of the H (negative differences) and LE (positive ones) diagrams, respectively. As for rainfall these panels are globally significant but the signals occur mainly from September 0 to March-April+1 over 'Sudan-Sahel' showing that before the rainy season, the sensible versus latent partition depends on local moisture availability. Table 2 displays evolution of squared first rank autocorrelation coefficients (R2) in the 5-day series as an estimate of simple time persistence (see the caption). It can be noticed that the highest values, centered on the months of November-February; are obtained with the heat fluxes (R2 > 40%) and vanish after May: since no rainfall occur in northern Winter, the H/LE partition depends directly on soil reservoir capacity and/or vegetation coverage. By contrast after the first rains these land surface parameters change locally and persistence decreases abruptly as shown in the Table.

Figure 1 :

 1 Figure 1: Left (a,d,g) Mean Sahelian annual evolutions (1979-2001) of filtered rainfall records (curve and error-bars) in mm/day after elimination of synoptic and supra-synoptic fluctuations (< 5 pentads); middle (b,e,h) rainfall tendencies (differences between successive pentads) in mm/day; right (c,f,i), the sensible (H, dashed line), latent (LE, thin line) and total (H+LE, bold line) net heat fluxes at the surface (curves) in W/m 2 bands at a 5-day time-step. The black bars in b,e,h mark the beginning of rainfall changes observed at least in 2/3 of years with an absolute difference > +/-0.05 mm/day between 3 successive pentads. (c,d) for the Sudanian belt with rainfall changes > +/-0.18 mm/day;(e,f) for the Guinean belt with rainfall changes > +/-0.21 mm/day.

Figure 2 :

 2 Figure 2: (a) Power spectral density estimates of the detrended 5-day rainfall anomaly time series averaged over the Sahelian belt after elimination of variations linked to the mean annual cycle (annual and semi-annual periods) and inter-annual scales by a low-pass butterworth filter (< 30 pentads, 150 days). Asters mark the significant peaks regarding a Monte Carlo procedure (1000 random rank permutations in the series). Period 1979-2001. (b): as (a) but for the Sudanian belt;(c): as (a) but for the Guinean belt.

Figure 3 :

 3 Figure 3: (a): Mean annual evolutions of continental rainfall in mm/day over the WAM domain (5°N-20°N; 10°W-10°E) as a function of latitudes, after elimination of all fluctuations < 30 days. Maximums are shaded. Period 1979-2001. (b) as above but for the total heat flux in W/m 2 (c) as above but for the sensible heat flux in W/m 2 (d) as above but for the latent heat flux in W/m 2

Figure 5 :

 5 Figure 5: (a) Interannual evolution of the ERS soil water index (SWI) averaged over WAM.

Figure 6 :

 6 Figure 6: Typical results regarding the ERS soil water index (SWI) evolution. (a): Annual evolution at a 10-day time-step of percentages of significant areas over the West African continent (period 1992-2000). (b): Mean SWI field in March; (c,d ) Significant wet minus dry SWI differences in March (c) and June (d)

Figure 7 :

 7 Figure 7: (a) wet minus dry rainfall differences over a 19 month period (January0 to September+1) regarding the 10 wettest and 10 driest yearly rainfall amounts averaged over the WAM region. Local differences are tested using a paired Student t-test at p=0.05 with negative (positive) differences in dashed (solid) lines, and significant values in grey. Field significances at p=0.05 and 0.01, respectively 6.82% and 8.14%, are evaluated using Monte Carlo procedures in which the wet versus dry correspondences are shuffled randomly 1000 times. Period 1979-2001. (b) As above but for the sensible heat flux; field significances at p=0.05 and 0.01 are respectively 9.06% and 11.55%.(c) As above but for the latent heat flux; field significances at p=0.05 and 0.01 are respectively 9.58% and 12.60%.

  Figure 8:(a) Annual evolution of rainfall following the abnormally wet (solid line) and dry (dashed line) withdrawal periods (SO) over 'Sudan-Sahel' (see selected years in Table1)

Figure 1 :

 1 Figure 1: Left (a,d,g) Mean Sahelian annual evolutions (1979-2001) of filtered rainfall records (curve and error-bars) in mm/day after elimination of synoptic and supra-synoptic fluctuations (< 5 pentads); middle (b,e,h) rainfall tendencies (differences between successive pentads) in mm/day; right (c,f,i), the sensible (H, dashed line), latent (LE, thin line) and total (H+LE, bold line) net heat fluxes at the surface (curves) in W/m 2 bands at a 5-day time-step. The black bars in b,e,h mark the beginning of rainfall changes observed at least in 2/3 of years with an absolute difference > +/-0.05 mm/day between 3 successive pentads. (c,d) for the Sudanian belt with rainfall changes > +/-0.18 mm/day;(e,f) for the Guinean belt with rainfall changes > +/-0.21 mm/day.

Figure 2 :

 2 Figure 2: (a) Power spectral density estimates of the detrended 5-day rainfall anomaly time series averaged over the Sahelian belt after elimination of variations linked to the mean annual cycle (annual and semi-annual periods) and inter-annual scales by a low-pass butterworth filter (< 30 pentads, 150 days). Asters mark the significant peaks regarding a Monte Carlo procedure (1000 random rank permutations in the series). Period 1979-2001. (b): as (a) but for the Sudanian belt;(c): as (a) but for the Guinean belt.

Figure 3 :

 3 Figure 3: (a): Mean annual evolutions of continental rainfall in mm/day over the WAM domain (5°N-20°N; 10°W-10°E) as a function of latitudes, after elimination of all fluctuations < 30 days. Maximums are shaded. Period 1979-2001.

Figure 6 :

 6 Figure 6: Typical results regarding the ERS soil water index (SWI) evolution. (a): Annual evolution at a 10-day time-step of percentages of significant areas over the West African continent (period 1992-2000). (b): Mean SWI field in March; (c,d ) Significant wet minus dry SWI differences in March (c) and June (d)

Figure 7 :

 7 Figure 7: (a) wet minus dry rainfall differences over a 19 month period (January0 to September+1) regarding the 10 wettest and 10 driest yearly rainfall amounts averaged over the WAM region. Local differences are tested using a paired Student t-test at p=0.05 with negative (positive) differences in dashed (solid) lines, and significant values in grey. Field significances at p=0.05 and 0.01, respectively 6.82% and 8.14%, are evaluated using Monte Carlo procedures in which the wet versus dry correspondences are shuffled randomly 1000 times. Period 1979-2001. (b) As above but for the sensible heat flux; field significances at p=0.05 and 0.01 are respectively 9.06% and 11.55%.(c) As above but for the latent heat flux; field significances at p=0.05 and 0.01 are respectively 9.58% and 12.60%.

Figure 8 :

 8 Figure 8: (a) Annual evolution of rainfall following the abnormally wet (solid line) and dry (dashed line) withdrawal periods (SO) over 'Sudan-Sahel' (see selected years inTable 1)

Table 1

 1

	Period 1979-2001.

)

for the WAM domain. The differences are tested using a paired Student t-test at p=0.05 with negative (positive) differences in dashed (solid) lines, and significant values marked by asters.

Table 1 )

 1

periods (SOND) over 'Sudan-Sahel' defined in Table1. The Wet minus Dry differences are tested using a paired Student t-test at p=0.05 with significant values marked by asters. Period 1992-2000.(b) as above but for the Sudan-Sahel belt (c) as above but for the Guinean belt

Acknowledgements:

The Authors are thankful to the American NOAA for providing the CMAP and NCEP/NCAR (National Center for Environnemental Prediction and National Center for Atmospheric Research), data level II, and to the Global Precipitation Climatology Centre for additional rainfall estimates. They are thankful to the 2 anonymous reviewers for their very interesting suggestions and to Dr. S. Sijikumar to his careful reading of the manuscript. This work has been supported by the PNEDC and PNRH French programs (INSUE-CNRS) and is a contribution to the AMMA international project. GPCC (1998): The Global Precipitation Climatology Centre. Information on World Wide Web under http://www.dwd.de/research/gpcc/ . Based on an French initiative, AMMA was built by an international scientific group and is currently funded by a large number of agencies, especially from France, UK, US and Africa. It has been the beneficiary of a major financial contribution from the European Community's Sixth Framework Research Programme. Detailed information on scientific coordination and funding is available on the AMMA International web site http://www.amma-international.org 2 * 2 for the WAM domain. The differences are tested using a paired Student t-test at p=0.05 with negative (positive) differences in dashed (solid) lines, and significant values marked by asters. Period 1979Period -2001. (b) . (b) for the 12°N-8°N belt;

(c) for the coastal Guinean region (5°N-7°N).


Bernard Fontaine

Samuel Louvet

Pascal Roucou

Definition and predictability of an OLR based West African monsoon onset

come

based on other datasets such as the CMAP and GPCP rainfall estimates and seem more realistic, especially during years when the latitudinal shift is unclear and delayed. However the respective means (30 June for ODs based on OLR vs 25 June for ODs based on CMAP and GPCP) and standard deviations (15.6 days vs 8.1) are slightly different. These differences illustrate the fact that the monsoon onset corresponds to a 10-15 day transition in the West African monsoon seasonal evolution which does not affect concomitantly all variables describing the system.

The onset is clearly linked to the time evolution of a few key-descriptors of the monsoon system at regional scale such as the installation of northward meridional gradients of moist static energy in low levels and of the monsoon cell over the continent. It is also associated with specific variations of the relative vorticity in low troposphere and of the velocity potential in upper levels over the Sahel region. Moreover OD time series exhibits good predictability. Multivariate linear regressions have been performed in a leave-one-out crossvalidation way using OLR and selected atmospheric predictors in May. The best results, explain 60% of the OD variance and are obtained with 2 types of predictors: the OLR gradients between the Gulf of Guinea and the African continent, and the northward migration of the West African Monsoon cell.

I. Introduction:

The West African Monsoon consists of a low-level moist flow originating from the equatorial and southern tropical Atlantic basin and converging onto the continent. Following by about 4-6 weeks the Solar zenithal position, this inter tropical convergence zone (ITCZ), moves therefore northward in spring then southward in autumn and concentrates most of moist convergence in low troposphere along with the major part of organized convective systems and precipitation. As a result, the annual course of rainy seasons is closely associated with the meridional excursion of this monsoon 'system'. So, the Guinean regions record two rainy seasons centered on May-June and September-October whereas areas northward to 8°N register a single rainy season in July-September when ITCZ and its associated rain band reach their northernmost location [START_REF] Amma | African Monsoon Multidisciplinary Analysis : description of work[END_REF][START_REF] Ward | Seasonal-to-Decadal Predictability and Prediction of West African Climate[END_REF]. During the northward migration of the system the starting of the Guinean then of the Sahelian rainy seasons is usually associated with clear increases in moist convergence, convection and precipitation at regional scale [START_REF] Louvet | Active phases and pauses during the installation of the West African monsoon through 5-day CMAP rainfall data (1979-2001[END_REF]Sultan and Janicot 2003). These phenomena called monsoon onsets are nevertheless different: the Guinean onset is mainly due to local increase in convection just southward to the line coast by 5°-6°N; the Sahelian onset results from an abrupt northward shift of the ITCZ from 5°N to 10°N and is preceded in May-June by a preonset stage. The preonset corresponds to the northward shift up to 15°N of the confluence line between moist southwesterly monsoon winds and dry northeasterly Harmattan allowing isolated convective systems to develop ITCZ (Janicot andSultan 2001, Sultan et al. 2003).

The problem of definition and predictability of onsets has been investigated using several approaches. For example, Sultan and Janicot (2003) utilized the daily NCEP/NCAR reanalyses and the daily OLR low-filtered data to remove variability lower than 10 days and applied a semi-empirical criterion based on the synchronous evolution of OLR series averaged over the Sahelian and Guinean belts. [START_REF] Louvet | Active phases and pauses during the installation of the West African monsoon through 5-day CMAP rainfall data (1979-2001[END_REF] used Principal Components Analysis (PCA) with a Varimax orthogonal rotation performed on the 5-day CMAP data after application of low pass filter (< 1 month) for detecting the main active and inactive phases of the monsoon through an automatic criterion based on the time evolution of series. More recently, [START_REF] Fontaine | Sudan-Sahel rainfall onset: Definition of an objective index, types of years, and experimental hindcasts[END_REF] Although conditioned by different purposes and choices, the above approaches give similar ODs in terms of temporal means (24-30 June) and standard deviations (8-10 days). This partly results from use of time filtering and latitudinal averaging along the meridional plane which artificially reduce the real interannual variability of the dates of onset and tend also to artificially reinforce the potential skill of OD statistical prediction. Moreover the 3 approaches allow description of only a small part of variability within the ITCZ core. For example, they do not take directly into account the observed abrupt latitudinal shift in deep convection at the moment of onset since they integrate external information issued from its northern (the Sahel) and southern (the Gulf of Guinean) margins.

The purpose of this article is first to compare different new approaches of monsoon onset detection through selected variables and datasets describing precipitation, convection and atmospheric dynamics. Its first aim is to propose a more direct and precise depiction of the onset at a 5-day time scale without any time filtering and spatial averaging along the meridional plane. The second one is to define a few regional atmospheric descriptors describing the basic key-features of the West African monsoon linked to the onset. The third aim is to analyze the potential predictability of onset dates. The work is based on the empirical evidence that the latitudinal shift in deep convection within ITCZ averaged between 10W and 10E can be considered as a robust and basic signal of the monsoon onset at regional scale. In the following section the data and methodological approaches will be briefly described while sections 3 and 4 will present selected diagnostics and hindcasts. Section 5 will summarize the results.

II. Data and methods

The Sudan-Sahel onset is here defined at the daily and 5-day time-steps using a few basic variables describing the mean processes associated with the West African monsoon, i.e., convection, precipitation, energy and dynamics.

The Outgoing Longwave Radiation (OLR) data are first selected for depicting the onset through convection within the ITCZ. This is estimated from the daily NOAA Interpolated OLR in a 2.5°*2.5° resolution. In this version all gaps have been filled with sophisticated temporal and spatial interpolation as described in Liebmann and Smith (Bulletin of the 1b,c).

Westward to this window i.e., over the Atlantic, or eastward to 10°E, the northward shift is less observed (Fig. 1a,d,e). So all following results will refer to the 10°W-10°E window.

It if not. Then, we calculate the corresponding sums over each 5-day periods and average by latitude to portray the mean position of the ITCZ core between 10°W and 10°E. In that context, the 5-day values range from 0, meaning no day with OLR < 240 (or < 180) for all longitudes, to 5 meaning 5 days with OLR < 240 (or < 180) at every longitude. The results are then converted in percentages of occurrence for convenience. These percentages will be used for computing OLR based dates of onset.

Rainfall data have been selected from satellite products like the CPC Merged Analysis of Precipitation (CMAP, [START_REF] Xie | Global precipitation: a 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs[END_REF] and the Global Precipitation Climatology Project (GPCP, [START_REF] Adler | The version 2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979-present)[END_REF][START_REF] Xie | GPCP pentad precipitation analyses: An experimental dataset based on gauge observations and satellite estimates[END_REF]. These Satellite estimates are available on a 2.5

x 2.5 degree latitude/longitude grid at a 5-day time-scale over the period 1979-2005. It is obviously of paramount importance to estimate the validity of these data versus the real world. [START_REF] Fontaine | Sudan-Sahel rainfall onset: Definition of an objective index, types of years, and experimental hindcasts[END_REF] showed that for onset diagnosis and predictability purposes the two datasets fit rather well over the West African domain: the CMAP-GPCP relative differences expressed in percentages are negative over land and positive over the Gulf of Guinea but always low (< 10%); temporal correlations are also very high (> 0.96). More details are available in [START_REF] Gruber | The comparison of two merger rain gauge -Satellite precipitation datasets[END_REF] and [START_REF] Yin | Comparison of the GPCP and CMAP merged gauge satellite monthly precipitation products for the period 1979-2001[END_REF] who compare the two precipitation datasets at larger scale.

It is more difficult to compare directly satellite estimates with land-based rainfall data at a daily or 5-day scale. The main reason is that a large number of West African stations suffer from many gaps over the last decades and especially after 2004. For example rainfall stations located between 10W and 10E referenced in the Global Surface Summary of Day (GSOD), one of the most complete land-based data base, show that for many stations, 50% or more of the daily precipitation amounts can be considered as doubtful (incomplete or underestimated) over recent periods. However reliable regional indexes can be defined with selected stations over both the Guinean (05N-7.5N: 37 stations) and Sudano-Sahelian (8.5N-12N: 38 stations) regions for the period 1979-2004. When synoptic variability (< 10 days) is eliminated these rainfall indexes are positively correlated with the respective GPCP and CMAP indices although the results are very sensitive to the time filter retained.

The CMAP and GPCP satellite estimates show also good agreement with land-based rainfall data provided by the CILSS and IRD for the Sudan-Sahel belt (see Louvet et al., 2007 for more details). Over these regions GPCP is generally closer to observations than CMAP in terms of correlation (intra-seasonal rhythms) whereas CMAP is better in terms of bias (pentad anomalies) and of statistical distribution. Obviously such comparisons are challenging over areas documented by a small number of rainfall stations as in Western Sudan [START_REF] Ali | Estimation des pluies au Sahel: utilisation d'un modèle d'erreur pour évaluer les réseaux sol et produits satellitaires[END_REF]2005). In any case, the gauge data dominates the final GPCP and CMAP analysis over land areas (Dr. Xie's personal communication).

So we choose to define rainfall-based dates of onset mainly through the CPCP and CMAP data. Additionally to obtain a better benchmark of onset database closer to the 'real world' we will compare with ODs based on selected GSOD stations over the Guinean (37 stations) and Sudan-Sahel regions (38 stations).

Atmospheric data have been provided by the NCEP/DOE AMIP-II Reanalysis, the 2 nd second version (R-2) of NCEP/NCAR reanalysis [START_REF] Kanamitsu | NCEP-DOE AMIP-II reanalysis (R-2) Bull[END_REF] over the period 1979-2005.

They are available at the same spatial resolution than the OLR, CMAP and GPCP datasets. In this version of reanalysis, model precipitation has been replaced with observed (satellite + gauge) 5-day precipitation in a similar manner to CMAP [START_REF] Kanamitsu | NCEP-DOE AMIP-II reanalysis (R-2) Bull[END_REF]. Moreover R-2 improves upon the NCEP/NCAR Reanalysis(R-1) by fixing the errors and by updating the parameterizations of the physical processes. Use of SSM/I data and assimilation of rainfall data have been also incorporated. The main improvements regarding the water cycle, as well as the effect of satellite and aircraft, on analyses are extensively discussed in [START_REF] Kanamitsu | NCEP-DOE AMIP-II reanalysis (R-2) Bull[END_REF], in particular the correction method equivalent to using observed 5-day precipitation amounts in the hydrological calculations. [START_REF] Maurer | Evaluation of the Land Surface Water Budget in NCEP/NCAR and NCEP/DOE Reanalyses using an Off-line Hydrologic Model[END_REF] found that R-2 provides more accurate pictures of soil wetness, near surface temperature and surface hydrology budget over land, and radiation fluxes over Ocean. By contrast the new boundary layer and convection schemes have modified the water vapor profile: R-2 has more moisture in low levels than R-1.

For convenience atmospheric variables and indexes used in the present article will be described in section 4.

III. An OLR index for the West African monsoon onset Hovmöller diagrams in Figure 2 (left panels a,c,e) display mean annual evolutions of OLR values versus percentages of occurrences of moderate and strong moist convection using the OLR thresholds defined above. The superimposed black curves mark OLR minimums and maximums of convection. Clearly, a 180 W/m2 threshold allows better determination of the meridional shift of deep convection within ITCZ (Fig. 2e) than the 240 W/m2 limit (Fig. 2c)

or than the raw values (Fig. 2a). The northward displacement of moist convective areas in Fig. before the onset -25-30% in April-May against less than 20% after-: this change is not attested with moderate convection (Fig. 2d). Only the 180 W/m2 threshold correctly monitor both the abrupt meridional shift and the subsequent decrease in convective maximums within ITCZ.

The advantage of using low OLR thresholds such as 180 W/m2 or 200 W/m2 for capturing better the meridional shift of ITCZ has also been confirmed through composite and clustering analyses (not shown here).

An OLR-based onset date (OD) can then be defined when the 3 following conditions occur successively: i) convective maxima are already located at -or northward to-5°N during 2-3 pentads; ii) part of the strongest maxima shifts abruptly to 10°N where percentages of OLR < 180 W/m2 are lower than 25%; iii) they remain further at that latitude or move northward.

Figure 3a presents these OLR based onset dates against the CMAP and GPCP OD estimates in bold, solid and dash, respectively. ODcmap and ODgpcp time series are highly correlated (r=+ 0.85): the curves are largely superimposed on the figure because their yearly differences equal zero in 92% of years. These 2 series can therefore be averaged for defining new ODs through precipitation: ODpre= (ODcmap+ODgpcp) / 2, where the subscript ' pre' means precipitation. For comparison dates of onset based on the selected GSOD rain gauges mentioned in the 'data and methods' section are also defined. This additional descriptor, called ODgsod, is computed by making each year the rainfall difference between the Sudano-Sahelian and Guinean areas documented by 38 and 37 selected stations respectively, and after elimination of the synoptic variability (< 12 days) with a butterworth filter: the onset date is then the first pentad of any 20-day or longer period registering a positive difference.

Tables 1 and2 The right panels in Fig. 5 display the major changes observed in OLR, WAMI and MSE evolutions for a period of sixty days centered each year on the date of monsoon onset to verify whether coherent leading signals exist in the 5-day tendencies before the monsoon jump.

Tendencies are defined as the differences between 2 consecutive pentads. Each 5-day tendency is judged significant if it exceeds the mean June-July tendency +/-2 standard deviations. This is indicated by markers in Fig. 5b,d,e.

At time of onset (OD on the x-axis) OLR diminishes at 10°N whereas the values slightly augment southward mainly between 5°N and the equator (Fig 5b). This signal is preceded by a modification of monsoon dynamics: ten days before OD, all WAMI indexes increases strongly, especially over the Sahelian belt and reach a first maximum at the onset (Fig. 5d).

Similarly, the MSE flux convergence increases over the Sahel whereas divergence occurs southward, as shown in Fig. 5f with the Guinean minimum.

In order to see if such signals could be useful for prediction purposes with sufficient leadingtime, we calculate correlation coefficients between ODolr and some selected atmospheric and OLR indexes over a 60-day period preceding each OD. Markers are superimposed when the values are judged significant at P=0.05, taking into account autocorrelation in the series. Fig. 6a displays the results obtained with meridional OLR gradient over the continent, defined as the differences between the 10°N-5°N and 5°N-Eq: a northward gradient is defined positive.

Clear changes in correlation appear 30 days before the onset date. A late onset tends to be preceded by 1) a southward OLR gradient anomaly over land between 10N and 5N, and 2) a northward gradient over the Gulf of Guinea from equator to 5N. An early onset is associated with the reverse signals: a northward/southward OLR gradient over land/ocean.

Significant correlations are also obtained with the WAMI and Conv(MSE) meridional gradients, defined as the differences between the Sahelian and Guinean belts (Fig. 6b). These In figure 7a, one can notice that onsets are concomitant with RV minimum in Central Sahel.

This indicates a northward migration of cyclonic curl within the Saharan heat low associated with RV maximums which strongly develops at solstice. Southward to that region the irrotational component of the low-level flow can reinforce. Figure 5b ). This confirms that the onset cannot be reduced to a sudden change affecting all variables describing the monsoon circulation: it is a 10-15 day transition period separating 2 quasi-equilibrium periods centered in May-June and July-September which does not affect simultaneously all variables at a daily or 5-day time scale.

The question of ODolr predictability is now examined at a 5-day timestep through multivariate linear regressions using evolution in May, i.e. pentads # 25-30 of a few key atmospheric variables. Two indexes linked to the northward displacement of the monsoon system appeared efficient: 1) the OLR differences between 5°N and the equator for estimating displacements in deep moist convection over the Gulf of Guinea; 2) the WAMI difference between Sahel and Guinea for measuring the northward migration of the monsoon cell. Table 3 shows that when the regression is performed in a leave-one-out cross-validation way [START_REF] Michaelsen | Cross-validation in statistical climate forecast models[END_REF], 60% of the OD variance is explained by the northward evolution of these 2 descriptors. Notice that the selected predictors are not collinear since the variance inflation factor is low, largely < 5 in Table 3. Signs of coefficients show that late ODs are associated with 1) OLR gradient anomalies oriented southward in May which depicts a northward gradient in moist convection anomalies; 2) northward WAMI gradient anomalies in May, meaning a northernmost location of the monsoon cell over West Africa. Early ODs are obviously linked to the reverse signals.

Additional analyses have been performed with the same predictors by dividing the series into different training and validation periods. Despite some decrease in the variance explained the results show generally good agreement. For example when the validation period refers to 1992-2004 (training period =1979-1991) 1979-2004. 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 ODcmap ODgsod 202 182 192 187 182 217 167 187 182 187 202 192 217 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 (1977).

  compared the sensitivity of onset dates (ODs) to the 5-day GPCP and CMAP rainfall estimates. They define a Northern index (NI) averaging precipitation between 7.5°N and 20°N and a Southern Index (SI) for the region between 7.5°N and the equator after elimination of time variability < 15 days. In such a context the onset date (OD) is the first pentad registering a positive Onset Indice (OI) where OI is the normalized difference between the NI and SI standardized indexes, in CMAP and/or GPCP: OI= stan [ stan(SSI) -stan(GI) ].

  2e is more obvious and more precise in time and latitude: it occurs clearly at the turn of June/July between 5°N and 12°N.Right panels in Fig.2 b,d,f detail the latitudinal evolutions of OLR and convection: the onset follows a period of OLR increase from the end of May to mid-June, as shown by the values higher than 220 W/m2 in Fig.2b; this is followed by a clear decrease in both moderate and strong convection within ITCZ, i.e., values < 70% and 15 % in Fig.2d,f respectively. In particular, Fig.2fshows that when the ITCZ reaches 10°N, the geographic extent of the strongest convective signals (OLR < 180 W/m2) is smaller than what is observed at 5°N

  display the basic statistics and ODs obtained with the 3 indexes: they differ regarding their means and variances but are positively correlated: r=+0.76 between ODpre and ODolr; r=+0.39 between ODpre and ODgsod. The lower correlation and higher standard deviation registered with ODgsod are easy to explain: in some cases daily values at station are not reported or underestimated. In Mali and Benin for instance only 50% or less of the GSOD daily values correspond really to the summation of 4 reports of 6-hour precipitation amount, or of 2 reports of 12-hour precipitation amount or of 1 report of 24-hour precipitation amount.As a result the correct geographic coverage can change with days, months and years, which limits the precision of ODgsod.

Figure

  Figure 3b displays the mean OLR field during the last pentad of June a time of onset. The major convective areas are signaled by values < 240 W/m2. In complement the composite map in figure 3c describes OLR evolution around the onset date by making the OLR difference after and before each ODolr: here positive/negative values stand for local increases/decreases in OLR, and the shadings point out the significant differences at the p= 0.05 level. Significant signals are encountered in the northern subtropics marked by high OLR

  gradients capture respectively the northward migrations of the monsoon cell and of the MSE flux convergence. All curves exhibit significant signals 5-20-day before onsets: a late/early onset is preceded by positive/negative WAMI gradients and by negative/positive Conv(MSE) gradient. Monsoon onsets are also directly linked to the evolution of dynamical atmospheric variables over the Sahelian belt. This can be illustrated through the relative vorticity in low tropospheric levels (RV at 925 hPa) and the velocity potential in upper levels (VP at 200 hPa) averaged over the Western, Central and Eastern Sahel regions. Here RV features atmospheric dynamics in low levels in terms of wind curl and is positive with cyclonic motion. By contrast VP refers to larger-scale atmospheric motion in high troposphere, since the VP gradients are proportional to the irrotational flux: VP is positive when large-scale divergence occurs in this flux. The diagrams 7a,b present the mean evolution of these quantities over a 300-day time window centered each year on ODolr.

  shows a VP maximum at onset which expresses the upsurge of the divergent component of the flow in high levels: this tends to reinforce the upper branch of the monsoon cell which is directly linked to the upper and lower flows via latent heat release over regions of deep convection. Thus most onsets are captured both by variations in moist static energy which measures the quantity of available energy in the atmosphere and by the WAMI index directly linked to the lower and upper branch of the monsoon cell.

Figure

  Figure8adisplays the mean annual evolution of the monsoon system as a function of MSE gradients and WAMI. Additionally, the period during which conv(MSE) is greater over the Sahel than over Sudan is marked with circles whereas the beginning and end of this season are indicated by pentads #33 and #53, respectively (between mid-June and the end of September). Notice that on the average pentad # 36 i.e., end of June, is the first one registering the 3 following conditions: grad(MSE) > 0, WAMI > 0.1 in standardized units and conv(MSE) stronger over Sahel than southward. So it is assumed that the onset can be also captured by the sole MSE and WAMI descriptors. It is hence possible to define an atmospheric onset date: ODatm occurs when, for a period of at least 20 days, the MSE gradient between Sudan-Sahel and Guinea is oriented northward while the WAMI index is clearly positive (> 0.1) according to the installation of the monsoon cell over the continent, as a positive WAMI depicts by construction SW winds in low levels and easterlies at 200 hPa.

Figure

  Figure 8b presents the respective ODatm versus ODpre and ODolr series in Julian days over the period 1979-2004: ODatm is marked with squares while ODolr and ODpre are displayed with asters and circles, respectively: clearly the onset dates depend on the used criterion. The figure illustrates also the internal consistency of all descriptors: ODatm and ODolr series register about the same mean (day#181) and standard deviation (13 days) and are positively correlated (r=+0.52). This confirms that the onset cannot be reduced to a sudden change

  the correlation between observations and hindcasted series is +0.65; a very similar value is obtained when the training and validation periods are permuted. If the training refers to 20 consecutive years, correlations over the validation periods (each of 6 years) can show stronger variations due to the small length of the series.When the training is defined over 1979-1998, the correlation on the validation period 1999-2004 is +0.77.V. Conclusion.The purpose of this article was to propose a direct approach of the latitudinal shift in deep convection within ITCZ, without any time filtering and spatial averaging along the meridional plane. It used mainly an interpolated version of the NOAA OLR at a 5-day time-step over the domain Eq.-20°N; 10°W-10°E. This choice is based on the evidence that moist convective signals of regional scale cannot be directly derived from spatial indexes computed with raw values of OLR in W/m2, because any average will tend to merge both atmospheric and surface emissions and not the only influence of highly reflective cloud systems. At time of onset, OLR values increase (i.e., convection decreases) first on the eastern Atlantic northward to the equator and westward to 5°E, then they decreases (i.e., convection increases) on Central Sahel eastward to 5°E.Efficiencies of specific OLR thresholds, 240 W/m2 and 180 W/m2, have then been discussed.Theoretical and empirical evidence exists that indexes based on OLR < 180 W/m2 capture better deep convective areas inside the ITCZ than OLR < 240 W/m2. The strongest convective signals with OLR < 180 W/m2 allowed us to correctly portray several basic features of the Sahelian onset in particular its meridional shift and the subsequent decrease in convection maximums within ITCZ.So, Onset Dates (ODs) have been defined with OLR signals < 180 W/m2 without any time filtering or spatial averaging in latitude as follows: i) convective maxima are located at -or northward to-5°N during 2-3 pentads; ii) part of these strongest maxima shifts abruptly to 10°N where percentages of areas with OLR < 180 W/m2 are lower than 25%; iii) they remain further at that latitude or move northward.. They show high correlation (r=+0.76) with ODs based on CMAP and GPCP rainfall estimates but differ in means (30 June with OLR versus 25 June with precipitation) and standard deviations (15.6 days with OLR against 8.1 with precipitation). This larger variability is more realistic because the monsoon onset does not affect simultaneously all variables of the system at a 5-day time step and cannot be reduced to an abrupt change in monsoon circulation: it is essentially a 10-15 day transition which separates 2 quasi-equilibrium periods of the monsoon system centered on May-June and July-August-September. It has been more complex to define accurate ODs with land-based rain gauges using due to the scarcity of the station network and to the quality of many reported amounts in the GSOD dataset.OD time series based on the northward migration of deep convective signals inside the ITCZ depict better onsets than ODs based on large spatial averages or defined with other variables such as satellite estimates or land-based precipitation. This appears well when the onset is unclear or delayed, as inyears 1988, 1995 and 1998. ODs occur when convection over the Gulf of Guinea decreases while monsoon dynamics changes over the continent. This relationship has been portrayed with a few selected descriptors describing atmospheric dynamics at regional scale, such as the monsoon cell depicted by the WAMI index, moist static energy gradients, flux convergence and relative vorticity in low levels, velocity potential at 200 hPa. In fact most onsets can be captured by the sole variations of moist static energy gradients and of the monsoon cell.Onset dates based on deep convective signals exhibit also good predictability. Multivariate linear regressions have been performed through both a leave-one-out cross-validation way and different training and validation periods using 2 basic atmospheric indexes in May: OLR differences between 5°N and the equator and WAMI differences between Sahel and Guinea.

Figure captions Figure 1 :

 captions1 Figure captions

Figure 2 :

 2 Figure 2 : Comparison between selected April-October signals in OLR averaged in the 10°W-10°E window as a function of latitude: (a) OLR mean, (b) OLR minimum; (c,e) percentages of occurrences with OLR < 240 and < 180 W/m2, respectively; (d,f) percentages of occurrences for convection maximums. The superimposed bold curve on left panels marks the core of ITCZ using OLR minimums (a) and convection maximums (c,e). Period 1979-2005.

Figure 3 :

 3 Figure 3: (a) Time series of the different onset dates: ODcmap, ODgpcp and ODolr; (b) mean OLR field in W/m2 around the onset date; (c) OLR tendency = mean field differencebetween OLR observed after and before ODolr each year (see Table1); (d) as (c) but for ODpre. In c,d the shadings point out the significant differences at p= 0.05 regarding a Student t-test.Period 1979Period -2005. .

Figure 4 :

 4 Figure 4: Hovmöller diagrams averaged in the 10°W-10°E window for the years 1988, 1995 and 1998: OLR values (left) and maximums of convection (right). The superimposed '+' and '*' mark ODpre and ODolr, respectively.

Figure 5 :

 5 Figure 5: Mean composite evolution and tendencies before and after ODs for selected indexes averaged in the 10°W-10°E window : (a,b), OLR at 10°N, 5°N and equator; (c,d) WAMI and Conv(MSE) (e,f) over central Sahel, central Sudan and central Guinea. Markers are superimposed when a local (5-day) tendency is greater than mean tendency plus 2 standard deviations in June-July.

Figure 6 :

 6 Figure 6: Time evolution of correlation coefficients between ODolr and selected indexes averaged in the 10°W-10°E window: a) OLR difference between 10°N and 5°N (dashed line) and between 5°N and equator (solid); b) WAMI and Conv(MSE) differences between central Sahel and Guinea (dashed and solid lines respectively).

Figure 7 :

 7 Figure 7: Time evolution of atmospheric dynamics stratified against ODolr (lag time in days)for the Western (dashed line), Central (red bolded line) and Eastern Sahel (dash-dotted line).(a) Relative vorticity at 925 hPa in s -1 ; (b) Velocity potential at 200 hPa in s -1 . The onset date (OD on x-axis) is also indicated by markers on the curves.

Figure 8 :

 8 Figure 8: a) Mean annual evolution of the meridional MSE gradient between [10N-15N] and [05N-10N] and of the WAMI index averaged over the Sahelian and Guinean zones (standardized units). Letters for months; the circles mark the period during which conv(MSE) is greater over Sahel than over Sudan between pentads #33 and #53. Pentad # 36 is the mean date for ODatm, i.e., when grad(MSE) > 0, WAMI > 0.1 and conv(MSE) occurs mainly over Sahel. b) Comparison between ODolr in Julian days, precipitation (circles) and atmospheric descriptors (squares) relative to the longitudes 10W-10E and the period 1979-2004.

Figure 1 :

 1 Figure 1: April-October latitudinal mean variations of OLR values in W/m2 averaged over five longitudinal windows: (a) 30°W-20°W, (b) 20°W-10°W, (c) 10°W-10°E, (d) 10°E-20°E, (e) 20°E-30°E windows. Period 1979-2005.

Figure 2 :

 2 Figure 2 : Comparison between selected April-October signals in OLR averaged in the 10°W-10°E window as a function of latitude: (a) OLR mean, (b) OLR minimum; (c,e) percentages of occurrences with OLR < 240 and < 180 W/m2, respectively; (d,f) percentages of occurrences for convection maximums. The superimposed red curve on the left panels marks the heart of ITCZ using OLR minimums (a) and convection maximums (c,e). Period 1979-2005.

Figure 3 :

 3 Figure 3: (a) Time series of the different onset dates: OD(cmap), OD(gpcp) and OD(olr); (b) mean field of OLR around OD(olr) in W/m2; (c) OLR tendency (mean field difference between OLR observed after OD(olr) minus OLR observed before each year (seeTable 1);(d) as (c) but for OD(pre). In c,d solid (dashed) lines point out negative (positive) OLR differences associated with increasing (decreasing) convection and shadings the significant differences at p= 0.05 regarding a Student t-test.Period 1979Period -2005. .

Figure 4 :

 4 Figure 4: Hovmöller diagrams averaged in the 10°W-10°E window for the years 1988, 1995 and 1998: OLR values (left) and maximums of convection (right). The superimposed '+' and '*' mark OD(pre) and OD(olr), respectively.

Figure 5 :

 5 Figure 5: Mean annual and tendencies (composite evolution before and after ODs) of selected indexes averaged in the 10°W-10°E window : (a,b), OLR at 10°N, 5°N and equator; (c,d) WAMI and (e,f) Conv(MSE) over central Sahel, central Sudan and central Guinea. Markers are superimposed when a local (5-day) tendency is greater than mean tendency plus 2 standard deviations in June-July.

Figure 6 :

 6 Figure 6: Time evolution of correlation coefficients between OD(olr) and selected indexes averaged in the 10°W-10°E window: a) OLR difference between 10°N and 5°N (dashed line) and between 5°N and equator (solid); b) WAMI differences between central Sahel and Guinea (dashed line) and Conv(MSE) difference between central Sudan and Guinea (solid line).

Figure 7 :

 7 Figure 7: Time evolution of atmospheric dynamics stratified against OD(olr) (lag time in days) for the Western (dashed line with a square), Central (bolded line) and Eastern Sahel (dash-dotted line with a triangle). The onset date (OD on x-axis) is also indicated by markers on the curves. (a) Relative vorticity at 925 hPa in s -1 (b) Velocity potential at 200 hPa in s -1 .

Figure 8 :

 8 Figure 8: a) Mean annual cycle of selected atmospheric parameters: the meridional MSE gradient between [10N-15N] and [05N-10N] vs WAMI over the Sahelian and Guinean zones (standardized units). Letters for months; the circles mark pentads when conv(MSE) is greater over Sahel than over Sudan between pentads #33 and #53. Pentad # 36 is the mean date for OD(atm), i.e., when grad(MSE) > 0, WAMI > 0.1 and conv(MSE) occurs mainly over Sahel. b) Comparison between ODs(olr) (in Julian days), precipitation (circles) and atmospheric descriptors (squares). Longitudes [10W-10E], period 1979-2004.

  Figure 1 displays OLR values along the meridional plane, for different longitudinal windows in West Africa for the period 1979-2005. If a West African onset can be observed between 20°W and 10°E it is mainly attested eastward to 10°W where the zonal extension of the Guinean coast enhances all meridional sea-land contrasts (Figure

	American	Meteorological	Society,	June	1996).	See

http://www.cdc.noaa.gov/cdc/data.interp_OLR.html for more details on satellites and day time crossing.

Table 1 :

 1 The results show that 60% of the OD variance is explained when the regression is performed in a leave-one-out cross-validation way: late ODs are preceded by a northward displacement of moist convection over the equatorial Atlantic associated and a northward displacement of the monsoon cell over West Africa. Early ODs are obviously linked to the reverse signals. Basic statistics for ODolr and ODpre in julian days over the period

	Tables

  2004

	ODcmap 172 177 172 167 167 172 182 192 177 187 167 167 187
	ODgpcp 172 172 172 167 167 157 182 192 177 182 172 167 172
	ODgsod 177 177 182 177 202 197 142 247 187 192 217 177 167

Table 2 :

 2 Dates of onset derived from the 3 rainfall datasets in julian days: ODcmap,

	ODgpcp, ODgsod.
		P#26	P#29	P#27	P#30
	5N-Eq. OLR grad.	5N-Eq. OLR grad	SAH-GUI WAMI diff	SAH-GUI WAMI diff
	Reg.Coeff	-0.41	-0.47	+0.25	+0.18
	R2=60%
	Corr. Coeff.	-0.45	-0.60	+0.48	+0.46
	VIF=1.1	1.05	1.08	1.12	1.17

Table 3 :

 3 Cross

	-validated hindcasts of ODolr using pentad (P) values of 4 predictors in May
	(pentads# 26-30) based on OLR and WAMI meridional gradients (line 1), along with
	explained variance (R2) and the regression coefficients obtained with standardized series (line
	2). The variance inflation factor (VIF) is displayed in line 4 following Chatterjee and Price

Acknowledgements:

The authors are very grateful to the NOAA-CIRES ESRL/PSD Climate Diagnostics branch, Boulder, Colorado, USA, for providing OLR data (at http://www.cdc.noaa.gov/). The study was supported by the Global change and Ecosystems programme (EU Integrated project:

African Monsoon Multidisciplinary Analysis (AMMA) and the French component of AMMA. Based on an French initiative, AMMA was built by an international scientific group and is currently funded by a large number of agencies, especially from France, UK, US and Africa. It has been the beneficiary of a major financial contribution from the European Community's Sixth Framework Research Programme. Detailed information on scientific coordination and funding is available on the AMMA International web site http://www.amma-international.org