1. Introduction
Weather and climate in the African Sahel are dominated by two major wind systems: the southwesterly West African monsoon (WAM) and the northeasterly (harmattan) trade winds emanating from the Sahara Desert. As the primary driver for precipitation, the WAM is a vital component of the socioeconomic environment of the region. In addition to the agricultural benefit of the rains, the public health sector is affected through the relationship between the onset of monsoon moisture and end of meningitis outbreaks (Molesworth et al. 2003). Meningitis outbreaks in West Africa are associated with very low humidity and dusty conditions, both of which occur with the harmattan (Besancenot et al. 1997; Molesworth et al. 2003). Low absolute humidity and dust may enhance meningococcal invasion by damaging the mucosal barrier directly or by inhibiting mucosal immune defenses (Molesworth et al. 2002). Various measures of critical meteorological conditions, based primarily on monthly data, have been correlated with meningitis epidemics. Besancenot et al. (1997) identified three variables associated with the harmattan regime and meningitis epidemics: minimum mean temperature of no more than 20°C, a mean relative humidity (RH) of no more than 40%, and the presence of at least 3 days of dust haze. Greenwood (1999) used absolute humidity and mean maximum temperature while Thomson et al. (2006) examined rainfall, satellite-derived dust loading, and vegetation indices. Sultan et al. (2005) found a strong correlation between the timing of the epidemic onset in Mali and the winter wind maximum but could not differentiate low and high incidences. Yaka et al. (2008) found that variations in surface winds can explain 25% of the year-to-year differences in meningitis outbreaks in Niger.
The most robust climate–meningitis relationship is the strong correlation between the start of the rainy season and the abrupt decline in the transmission of the disease (Molesworth et al. 2003). Recently, Dukic et al. (2012) linked meningitis incidence in Ghana to relative humidity, lagged by 2 weeks, and carbon monoxide (a proxy for biomass burning) using a general additive model (GAM). These studies show that knowledge of moisture distribution during the spring could aid the mitigation of meningitis by identifying areas where epidemics will end naturally, so that scarce vaccines can be moved elsewhere.
With the recognition of the critical role of high humidity in ending meningitis outbreaks, the next imperative is to understand what influences the onset of high humidity. Sultan and Janicot (2003) define the ‘‘preonset’’ of the summer monsoon as the start of the rainy season over the Sudano–Sahelian zone, based on the northward migration of the intertropical front (ITF), which delineates the northern limit of the southwesterly winds of the monsoon. The authors also describe how scattered rainfall events during mid-May are connected to the ITF crossing 15°N, and to the arrival of the monsoon winds, which advect moist air into Sahelian latitudes. The displacement of the intertropical convergence zone (ITCZ), usually identified by the band of precipitation south of the ITF, also modulates moisture variability over West Africa (Sultan and Janicot 2000; Le Barbé et al. 2002; Sultan and Janicot 2003). However, meteorological signals prior to the start of the WAM are weak at a regional scale because rainfall initiation over the Sudano–Sahelian zone is seldom abrupt and it is usually preceded by a succession of isolated precipitation systems (Omotosho et al. 2000; Ati et al. 2002). How the ITF is defined varies among recent studies. Lélé and Lamb (2010) used daily temperature, humidity, and rainfall data at 10-day (dekad) resolution to calculate concurrent monthly ITF–rainfall relations. Sultan and Janicot (2003) define the ITF by the 925-hPa zero isoline of the zonal wind (where the westerly monsoon winds begin). Another marker of the ITF is the 15°C dewpoint temperature (Pospichal et al. 2010). This study mainly uses the Sultan and Janicot (2003) definition of the ITF.
Moisture and precipitation over West Africa are associated with synoptic disturbances including African easterly waves (AEWs), the dominant synoptic system during the summer (Reed et al. 1977; Diedhiou et al. 1999; Kiladis et al. 2006). Westward-propagating convective systems south of the ITF region can propagate for long distances over the Sahel during the transition season (Flamant et al. 2007). Cold pool outflows generated by downdrafts in these convective systems can bring moisture north over the northern Sahel, effectively cooling the desert surface and aiding in the evolution of the WAM onset (Flamant et al. 2009). Subseasonal circulations such as convectively coupled equatorial waves (CCEWs) and the Madden–Julian oscillation (MJO; Madden and Julian 1994) also modulate convection. Equatorial Kelvin waves enhance easterly waves during the boreal summer (Mekonnen et al. 2008) and can affect the summer monsoon onset (Mounier et al. 2007). Since Kelvin waves are active during the spring, it is vital to understand how they relate to convection and moisture variability over West Africa. Equatorial Rossby (ER) waves have also been found to influence precipitation over West Africa by increasing cyclonic shear on the equatorward flank of the African easterly jet (AEJ), leading to enhanced transient convective activity (Matthews 2004).
Intense dry season rain events are a potentially predictable source of precipitation that could affect the progression of the ITF. In Knippertz and Fink (2008), a January 2004 precipitation event in West Africa, associated with a midlatitude disturbance, occurred in a year in which the ITF arrived at 15°N 23 days in advance of its climatological date. Intense synoptic time-scale precipitation events during the dry and transition seasons are usually associated with midlatitude upper-tropospheric troughs that, solely or in combination with the northward movement of the wintertime heat low, induce poleward transport of tropical moisture.
The present study aims to describe the variability and onset of surface moisture in West Africa using global model analyses, satellite observations, and station observations. Also covered are some dynamical characteristics of the spring in West Africa under certain critical synoptic and climatological states. The next section of this paper describes the data and methods used. Section 3 contains trajectory analyses of moisture sources and the evolution of large-scale circulations. Section 4 focuses on the variability of surface moisture in West Africa during spring 2009 and the associated interactions among CCEWs and forcing from the extratropics. Synthesis and concluding remarks on the implications of the results for meningitis mitigation are presented in section 5.
2. Data and methodology
a. Observations and reanalysis
The study domain is centered on the meningitis belt of West Africa and is large enough to encompass cross-equatorial and midlatitude influences that may affect regional moisture variability (Fig. 1). Data from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis products (NNRP; Kalnay et al. 1996) is used for the majority of the analysis in this study, including parcel trajectory analysis, general climatology, and fields related to CCEWs. The data are mapped onto a 2.5° × 2.5° grid at 0000, 0600, 1200, and 1800 UTC. The 1.0° × 1.0° gridded NCEP Global Forecast System (GFS) Final (FNL) analysis, also at 6-hourly synoptic times, is used to examine synoptic weather in greater detail for the spring months of 2000–09.
Study domain with gray shading to mark the meningitis belt [based on Molesworth et al. (2003) and references therein]; the black box outlines the region for which back-trajectory analysis was conducted. Large black circles are stations that have fewer gaps in observations, while small gray circles denote stations with a limited amount of data.
Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00175.1
Study domain with gray shading to mark the meningitis belt [based on Molesworth et al. (2003) and references therein]; the black box outlines the region for which back-trajectory analysis was conducted. Large black circles are stations that have fewer gaps in observations, while small gray circles denote stations with a limited amount of data.
Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00175.1
Study domain with gray shading to mark the meningitis belt [based on Molesworth et al. (2003) and references therein]; the black box outlines the region for which back-trajectory analysis was conducted. Large black circles are stations that have fewer gaps in observations, while small gray circles denote stations with a limited amount of data.
Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00175.1
The Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) was utilized to show the temporal progression of precipitation over the study region (Xie and Arkin 1997). CMAP merges observations from rain gauges with precipitation estimates from several satellite-based algorithms (infrared and microwave) blended with NNRP precipitation. The analyses are on a 2.5° × 2.5° grid and cover the 1979–2009 period.
Daily mean outgoing longwave radiation (OLR), produced by the National Oceanic and Atmospheric Administration (NOAA)/Earth System Research Laboratory/Physical Sciences Division, is used to study subseasonal circulations. This dataset, derived from polar orbital satellite observations, is on a 2.5° × 2.5° grid (Gruber and Krueger 1984). OLR minima represent large-scale deep tropical convection, and space–time filtering is applied to the OLR data in order to identify CCEWs and the MJO.
Standard meteorological station observations for the stations shown in Fig. 1 were obtained from the National Climatic Data Center (NCDC; http://www.ncdc.noaa.gov/) and the database of the African Monsoon Multidisciplinary Analysis (AMMA; http://amma-international.org/). The seven stations presented in this study are highlighted in Fig. 1.
b. Parcel trajectory analysis
A parcel back-trajectory analysis utilizing the horizontal and vertical wind components, as well as RH (%) from NNRP, is used to compute the sources of air parcels for the endpoints bounded by 10°–15°N and 10°W–10°E for the 2000–09 period. The matrix of endpoints is shown in Figs. 2b–d. Back trajectories are run over 5 days at 1-day intervals initiated each day. The surface endpoint is set at 925 hPa to circumvent noise generated in the reanalysis below 925 hPa. Three 19-day periods are chosen to represent surface moisture regimes before (P1, 27 January–15 February), during (P2, 15 April–4 May), and after (P3, 11–30 June) the passage of the ITF through the region. Averages for each of the trajectory endpoints are calculated for each period. Mean source points are computed by averaging the source of each endpoint over the 19-day iterations of the trajectories.
(a) Time series of relative humidity for the regions in West Africa for latitudes 10°N (dotted), 12°N (solid black), and 15°N (solid, gray). The 40% and 80% relative humidity thresholds are marked. Trajectory analysis endpoints (black circles), surface relative humidity <40% (gray shade, a threshold that is correlated with meningitis epidemics), and 925-hPa winds for (b) P1 (27 Jan–15 Feb), (c) P2 (15 Apr–4 May), and (d) P3 (11–30 Jun).
Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00175.1
(a) Time series of relative humidity for the regions in West Africa for latitudes 10°N (dotted), 12°N (solid black), and 15°N (solid, gray). The 40% and 80% relative humidity thresholds are marked. Trajectory analysis endpoints (black circles), surface relative humidity <40% (gray shade, a threshold that is correlated with meningitis epidemics), and 925-hPa winds for (b) P1 (27 Jan–15 Feb), (c) P2 (15 Apr–4 May), and (d) P3 (11–30 Jun).
Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00175.1
(a) Time series of relative humidity for the regions in West Africa for latitudes 10°N (dotted), 12°N (solid black), and 15°N (solid, gray). The 40% and 80% relative humidity thresholds are marked. Trajectory analysis endpoints (black circles), surface relative humidity <40% (gray shade, a threshold that is correlated with meningitis epidemics), and 925-hPa winds for (b) P1 (27 Jan–15 Feb), (c) P2 (15 Apr–4 May), and (d) P3 (11–30 Jun).
Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00175.1
c. Space–time filtering
The wavenumber–frequency filtering technique is applied to the NOAA OLR data following the method of Wheeler and Kiladis (1999) to obtain MJO, Kelvin, ER, and tropical depression (TD type) signals, where the latter identifies AEWs. This method has been successfully applied in other studies based on NOAA OLR (e.g., Straub and Kiladis 2002; Straub and Kiladis 2003a,b), satellite-observed brightness temperature (Mekonnen et al. 2008), and precipitable water (Roundy and Frank 2004). For this study, the wavenumber–frequency filtering is performed using all available OLR data for the 30-yr record (1979–2009) in order to retrieve the filtered time–longitude data for the full record for the waves of interest. As in Wheeler and Kiladis (1999), the ends of the 30-yr series are tapered to zero and the first three harmonics of the seasonal cycle are removed.
3. Synoptic climatology and conceptual models
a. Moisture during early, mid-, and late spring
Surface humidity values from NNRP are used to determine moisture conditions relevant to meningitis management in the region bounded by 10°–15°N and 10°W–10°E for 2000–09. Figure 2a shows the time series for the average RH (%) interpolated along three latitudinal strips: 9.5°–10.5°N for 10°N, 11.5°–12.5°N for 12°, and 14.5°–15.5°N for 15°N. These latitudes represent the southern, moister sector (10°N); a highly populated latitude (12°N); and the dry Sahara transition region (15°N). Table 1 shows the mean arrival date of the 40% relative humidity threshold (RH40 hereafter) currently used for meningitis mitigation (Dukic et al. 2012). The general position of the RH40 line remains at a quasi-stable position between 10° and 12°N from late March through mid-May. Most of the spatiotemporal variability of the RH40 line occurs during mid-April to mid-June, and this is consistent with the northward progression of the ITF to 15°N by the end of June, signaling the advent of the monsoon (Sultan and Janicot 2003).
Mean date that the 40% RH threshold crosses a given latitude inside the study area.
The passage of the ITF, derived from NNRP data, is depicted by surface winds and the RH40 line in Figs. 2b–d. The study domain is completely immersed in the dry Saharan air during P1 (Fig. 2b), partially within the moist environment of the WAM during P2 (Fig. 2c), and almost entirely outside of the desert air in P3 (Fig. 2d). Also note the strength and direction of the prevailing winds during these periods: strong northeasterlies during P1, northerly and northeasterlies for points north of the RH40 line and WAM southwesterlies to the south during P2, and southwesterlies during P3 even for points that remain below 40%.
b. Airmass sources from trajectory analysis
The 5-day back-trajectory analysis for February–June 2000–09 shows the horizontal and vertical patterns of evolution of air parcel sources over West Africa (Fig. 3). For P1 (February), the majority of the mean source points are tightly clustered north of 30°N in North Africa. For P2 (April), the spread of source points is much more scattered and extends from north of 30°N (44% in NW Sahara) to south of the equator (20% in the South Atlantic). In contrast with P1, the P3 (June) source points are clustered south of the equator (73%) and over the Gulf of Guinea (GOG; 20%). As shown in Fig. 3b, vertically, the P1 source points are tightly clustered in the midtroposphere (750–650 hPa). The P2 points during the beginning of the monsoon, in contrast, are scattered from 650 hPa at 30°N to lower than 850 hPa south of the equator. The source points during P3 are nearly all in the low troposphere (lower than 800 hPa) and south of the equator.
Distribution of air parcel source points in the (a) horizontal and (b) vertical planes for periods P1, P2, and P3.
Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00175.1
Distribution of air parcel source points in the (a) horizontal and (b) vertical planes for periods P1, P2, and P3.
Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00175.1
Distribution of air parcel source points in the (a) horizontal and (b) vertical planes for periods P1, P2, and P3.
Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00175.1
c. Circulation patterns affecting air parcel trajectories
A conceptual evolution of the major features of the spring WAM (Fig. 4) is shown to help explain the observed parcel trajectories and implications for moisture variability. Figure 4a depicts the major attributes of the early spring (15 March–15 April). The early spring is marked by predominantly northerly to northeasterly airflow that can be traced back to descending air from mid- to upper levels of the atmosphere over North Africa, Europe, and the North Atlantic (arrow starting at 300 hPa). Descending air from the midlatitudes and subtropics flows around and away from the surface anticyclones into the equatorial trough. The descending air undergoes adiabatic compression, increasing its temperature while maintaining its initial moisture content resulting in low relative humidity at the surface. Only a limited amount of southwesterly air flows from the Atlantic Ocean. The monsoon flow is weak and precipitation is confined to the GOG coast, which is characterized by warm sea surface temperatures (SSTs). The ITF is at its southernmost point, and the AEJ is located at about 3°N and near 700 hPa. Also present during this time of the year is an active subtropical jet (STJ).
Three-dimensional schematic of the large-scale circulations and major synoptic features during the (a) early, (b) mid-, and (c) late boreal spring periods.
Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00175.1
Three-dimensional schematic of the large-scale circulations and major synoptic features during the (a) early, (b) mid-, and (c) late boreal spring periods.
Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00175.1
Three-dimensional schematic of the large-scale circulations and major synoptic features during the (a) early, (b) mid-, and (c) late boreal spring periods.
Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00175.1
By the middle of spring (Fig. 4b), the waters of the GOG begin to cool with the relaxation of trade winds (Hagos and Cook 2009). At the same time, waters off West Africa in the northeast tropical Atlantic (NETA) begin to warm. With increased solar radiation, the Sahara Desert rapidly warms and induces a stronger SHL and equatorial trough, which in turn creates a pressure gradient between the higher pressure over the ocean and the lower pressure over the land. This results in a stronger monsoon flow and a waning harmattan. Air is still flowing from the northeast into parts of the Sahel but it is mostly from the mid- to lower levels of the atmosphere. The increased baroclinicity also induces a stronger AEJ and causes it to move north to 6°N and to 650 hPa. As a result of the stronger monsoonal flow, the ITF shifts northward. This also allows for transient, westward-propagating mesoscale convective systems and TD-type waves, which are recognized as AEWs during the monsoon (crescent shape in Fig. 4b).
During the development of the spring WAM and its transition to the summer monsoon, many of the important attributes of the summer monsoon are present (Fig. 4c). As inferred from Fig. 3a, the main airmass sources during this period are oceanic, from the equatorial Atlantic, South Atlantic, and the NETA off West Africa. The NETA has undergone modest warming and the Azores high has drifted farther north. The stronger monsoon flow also shifts the ITF farther north and prompts a dynamical response by the AEJ, which moves to 9°–10°N. The SHL has also moved north and west. Further, the Sahel is now increasingly affected by AEWs that propagate from their genesis locations in the Ethiopian Highlands and Darfur Mountains (Tetzlaff and Peters 1988; Laing and Fritsch 1993; Lin et al. 2005; Laing et al. 2008). The increasing precipitation at 10°N causes subsidence along the coast of the GOG, effectively suppressing precipitation in the region. Ultimately, the shift of the precipitation maxima leads to the characteristic monsoon “jump,” the sudden shift of the ITCZ to a new, quasi-stable position at 10°N (Eltahir and Gong 1996; Sultan and Janicot 2000; Le Barbé et al. 2002; Sultan and Janicot 2003; Hagos and Cook 2007; Okumura and Xie 2004; Ramel et al. 2006; Sijikumar et al. 2006).
d. Rainfall and large-scale circulations
During the midspring, isolated rain events can modify the moisture content of the environment (Knippertz and Fink 2009) and induce or be the precursor to increased humidity (Flamant et al. 2009) over the Sahel. The mean 2000–09 rainfall time series (mm day−1) during February–July is plotted in Fig. 5 using area averages of grid boxes bounded by 10°–12.5°N and 10°W–10°E as well as 12.5°–15°N. A noteworthy change occurs in the slope of the northern box on 10 May, where the slope during 10 May–15 June is 5 times as steep as during 30 March–10 May. This change in slope suggests that isolated events increase in frequency within the core of the Sahel during early May and then rapidly increase from late May to June. This finding is consistent with Sultan and Janicot (2003).
Time series of mean 2000–09 pentad CMAP rainfall fields (mm day−1) during February–July for grid boxes averaged over 10°–12.5°N (dashed line) and 12.5°–15°N (solid line).
Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00175.1
Time series of mean 2000–09 pentad CMAP rainfall fields (mm day−1) during February–July for grid boxes averaged over 10°–12.5°N (dashed line) and 12.5°–15°N (solid line).
Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00175.1
Time series of mean 2000–09 pentad CMAP rainfall fields (mm day−1) during February–July for grid boxes averaged over 10°–12.5°N (dashed line) and 12.5°–15°N (solid line).
Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00175.1
Precipitation patterns during the spring have been shown to be associated with subseasonal circulations such as the MJO, as described by Gu (2009), or with equatorial Kelvin waves, both of which move eastward along the equator (Nguyen and Duvel 2008). Kelvin waves that affect Africa are forced by warm SSTs in the equatorial Atlantic (Wheeler and Kiladis 1999) but can also form in the eastern Pacific or in association with the MJO (Mekonnen et al. 2008). Kelvin wave activity is at its peak during the boreal spring (Wheeler and Kiladis 1999) with a center of high variance along the GOG coast (Fig. 6a). The findings are consistent with Gu and Adler (2004), who found that shorter-period, eastward-propagating precipitation signals dominate the precipitation regime at 5°N during the April–June period.
(a) Kelvin wave–filtered and (b) TD-type-filtered OLR variances (W m−2)2 for 15 Mar–15 Jun averaged for 2000–09. (c) The 1 Apr–10 May 2009 climatology anomaly for Kelvin-filtered variance (contours) and TD-type variance (shaded). Only positive numbers are shaded for clarity.
Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00175.1
(a) Kelvin wave–filtered and (b) TD-type-filtered OLR variances (W m−2)2 for 15 Mar–15 Jun averaged for 2000–09. (c) The 1 Apr–10 May 2009 climatology anomaly for Kelvin-filtered variance (contours) and TD-type variance (shaded). Only positive numbers are shaded for clarity.
Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00175.1
(a) Kelvin wave–filtered and (b) TD-type-filtered OLR variances (W m−2)2 for 15 Mar–15 Jun averaged for 2000–09. (c) The 1 Apr–10 May 2009 climatology anomaly for Kelvin-filtered variance (contours) and TD-type variance (shaded). Only positive numbers are shaded for clarity.
Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00175.1
The southward penetration of westerly disturbances into West Africa and their effect on precipitation patterns have been documented in past studies (Flohn 1975; Thorncroft and Flocas 1997; Grist 2002). During the spring, the STJ mean core position is much closer to tropical Africa, at around 25°N, with mean westerly speeds up to 16–24 m s−1 at 200 hPa over 15°N (Grist 2002). In April–June 2009, the STJ was stronger than normal, south of its long-term mean position, and westerly anomalies dipped south of 10°N (not shown), a pattern that is conducive to rising motion and precipitation.
Westward-propagating convective systems in the ITF region that travel for long distances over the Sahel are another source of rainfall (Flamant et al. 2007). At the beginning of the monsoon, these convective systems produce conditions favorable to the monsoon progression toward the north by cooling and moistening the surface north of the ITF (Flamant et al. 2009). If these systems occur earlier in the spring, the surface changes incurred could influence moisture and provide a mechanism for the cessation of meningitis. Analysis of TD-type filtered systems reveals that activity is highest along the GOG coast (Fig. 6b); although the variance shows that these systems can occur as far north as 15°N. For example, as Fig. 6c shows, during April–early May 2009 TD-type systems were particularly active, relative to the 2000–09 period, north of 8°N and from 10°W to 10°E. In addition, Kelvin wave activity was also higher around 10°N east of 0°. This is especially noteworthy given the potential impact of these systems on areas experiencing meningitis outbreaks. The duration and spatial extent of these waves also factor into their effectiveness in changing the moisture regime in the Sahel, examples of which will be discussed in the next section.
4. Case study of a midspring moist event
A surge of moisture occurred over the western Sahel during early May 2009 and pushed the surface humidity far above the RH40 threshold for an extended period. In Fig. 7, a Hovmöller plot of total atmospheric column precipitable water (kg m−2) anomalies calculated from the NCEP FNL (chosen because of its higher resolution), for 2009 minus the 2000–09 period, reveals that the May moist event (MME hereafter) moves westward along latitudes 10°–15°N from 20°E on 1 May to 20°W on 13 May. Also plotted in Fig. 7 is the RH40 line at 13°N from NCEP FNL, showing that only the MME was able to raise the humidity at this particular latitude above the threshold. During this event, the RH40 line reached Niamey, Niger, on 9 May and Bamako, Mali, on 11 May; both cities reside close to 13°N.
Time–longitude Hovmöller plot of NCEP FNL precipitable water anomalies (kg m−2, shaded) averaged for 10°–15°N and spanning from 40°E to 40°W, and the RH40 line for all points at 13°N.
Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00175.1
Time–longitude Hovmöller plot of NCEP FNL precipitable water anomalies (kg m−2, shaded) averaged for 10°–15°N and spanning from 40°E to 40°W, and the RH40 line for all points at 13°N.
Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00175.1
Time–longitude Hovmöller plot of NCEP FNL precipitable water anomalies (kg m−2, shaded) averaged for 10°–15°N and spanning from 40°E to 40°W, and the RH40 line for all points at 13°N.
Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00175.1
The MME provided the first sustained influx of moisture for the spring of 2009 for locations within the northern half of the meningitis belt (Fig. 1) that reported incidences of meningitis (with the exception of Bamako). The scale of the MME can be better appreciated from Meteosat infrared satellite images (Fig. 8). Several major features can be identified: (i) clouds from the GOG to the southern Sahel with embedded mesoscale convection; (ii) a large area of deep convection over the GOG that moves eastward, possibly associated with equatorial circulations (e.g., Kelvin waves); and (iii) a swath of clouds that extends from West Africa into the midlatitudes over the Arabian Peninsula and beyond. An area of thick clouds moves westward along the southern boundary of the Sahel, coincident with the observed increase in surface relative humidity within the meningitis belt. The subsequent sections illustrate that the MME has components of varying spatial and temporal scales, associated with convectively coupled equatorial waves and midlatitude synoptic systems.
Meteosat IR images at 1800 UTC for the duration of the MME, 4–11 May 2009. Dates are marked on the images.
Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00175.1
Meteosat IR images at 1800 UTC for the duration of the MME, 4–11 May 2009. Dates are marked on the images.
Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00175.1
Meteosat IR images at 1800 UTC for the duration of the MME, 4–11 May 2009. Dates are marked on the images.
Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00175.1
a. Convectively coupled equatorial waves and the MJO
As discussed in section 3d, April–early May 2009 was characterized by a wide belt of Kelvin and TD-type wave activity that stretched from the GOG into southern portions of the western Sahel (Fig. 6c). In Fig. 9, analysis of 2009 OLR anomalies averaged over 10°–15°N, the meningitis domain of interest, displays various interesting features. First, unfiltered daily OLR anomalies (relative to the 2000–09 mean, shaded) show a sustained negative anomaly from 0° to 30°E from 30 April to 11 May associated with an ER wave. Also visible is an eastward-propagating feature, in association with this anomaly, which begins in late April as far west as 80°W. In addition, a westward-propagating negative anomaly that begins at 10°E on 7 May and travels to 20°W by 12 May is evident.
NCEP OLR anomaly with respect to 2000–09 (shaded) for 1 Apr–31 May 2009. Filtered waves are adjusted from twice-daily to daily scale in this figure. The TD-type (thick black, solid contour), Kelvin (thick, dashed gray contour), equatorial Rossby (thin black, solid contour), and MJO (thin, dotted gray contour) filtered OLR anomalies (W m−2) averaged for 10°–15°N from 80°W to 80°E. The TD1, K1, and ER1 are labeled.
Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00175.1
NCEP OLR anomaly with respect to 2000–09 (shaded) for 1 Apr–31 May 2009. Filtered waves are adjusted from twice-daily to daily scale in this figure. The TD-type (thick black, solid contour), Kelvin (thick, dashed gray contour), equatorial Rossby (thin black, solid contour), and MJO (thin, dotted gray contour) filtered OLR anomalies (W m−2) averaged for 10°–15°N from 80°W to 80°E. The TD1, K1, and ER1 are labeled.
Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00175.1
NCEP OLR anomaly with respect to 2000–09 (shaded) for 1 Apr–31 May 2009. Filtered waves are adjusted from twice-daily to daily scale in this figure. The TD-type (thick black, solid contour), Kelvin (thick, dashed gray contour), equatorial Rossby (thin black, solid contour), and MJO (thin, dotted gray contour) filtered OLR anomalies (W m−2) averaged for 10°–15°N from 80°W to 80°E. The TD1, K1, and ER1 are labeled.
Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00175.1
Overlaid on the unfiltered OLR anomalies are filtered analyses of three different types of CCEWs: Kelvin (thick, dashed gray contours), TD type (thick, solid black contours), ER (thin, solid black contours), and MJO (thin, dashed gray contours). The filtered data reveal that the eastward-propagating feature in early May is a strong Kelvin wave (K1 hereafter). This wave had a significant influence on convection during the MME and may have affected moisture transport farther north as it interacted with the westward-propagating portion of the MME. The westward component of the MME is determined to be a TD-type disturbance (TD1 hereafter) and it represents a potentially predictable aspect of West Africa moisture variability. Additionally, an ER wave (ER1 hereafter) is also shown to be present during the time of the MME. Notice that TD1 and K1 intersect at about 10°E on 9 May and that TD1 becomes visible at the −10 W m−2 threshold on the eastern side of ER1 also on 9 May. Early May 2009 is also marked by the development of a moderate MJO, although with a limited signal over West Africa. Its presence is nonetheless noteworthy since the MJO tends to have a stronger signal at higher frequencies over Africa (Pohl et al. 2007) and it has been shown that the incidence of the MJO along with convergence of ER and Kelvin waves explains the peak in intraseasonal convection during the monsoon (Matthews 2004; Gu 2009; Janicot et al. 2009).
Dynamical features used to identify CCEWs are analyzed in Fig. 10, where all Hovmöller diagrams are averaged over 2.5°–12.5°N. In Fig. 10a, potential vorticity (PV) is shown along with the filtered TD-type wave contoured only at −10 W m−2 for clarity. Positive PV is collocated with the TD-type features, with TD1 being the strongest of the period. Horizontal analysis of unfiltered 850-hPa-level streamlines, PV (contours), and TD-type filtered OLR anomalies (shaded) reveals a center of circulation associated with TD1 to the west of the negative OLR anomalies on 10 May (TD1 has its strongest signal in the OLR during 10–11 May). Figure 10b is similar to the dynamics of AEWs that track north of the AEJ, as found in Kiladis et al. (2006, Fig. 4c), where a secondary circulation center is east of the positive OLR anomalies and north of the AEJ.
(a) Hovmöller plot of TD-type (black contours) and PV (shaded) results at 850 hPa. (b) The 10 May 850-hPa-height streamlines (thin lines with arrowheads), PV (thick contours), and TD-type filtered OLR (shaded). The PV is plotted only at 0.01-PVU (potential vorticity unit) intervals (1 PVU = 10−6 K kg−1 m2 s−1) between 0.1 and 0.15 for clarity. (c) Hovmöller plot of Kelvin (black contours) and velocity potential (shaded) at 200 hPa. (d) The 9 May 200-hPa divergent wind (vectors), velocity potential (contours), and OLR anomalies (shaded). (e) Hovmöller plot of ER (black contours) and zonal wind (shaded) at 850 hPa. (f) The 7 May 850-hPa ER anomalies (shaded), geopotential height (contours), and winds (vectors). Filtered OLR anomalies are shown in dark gray for negative and light gray for positive numbers at 10 W m−2 intervals. Velocity potential is in 106 m2 s−2, winds in m s−1, and geopotential height in m. Hovmöller plots are for April–May 2009 for latitudinal averages 2.5°–12.5°N.
Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00175.1
(a) Hovmöller plot of TD-type (black contours) and PV (shaded) results at 850 hPa. (b) The 10 May 850-hPa-height streamlines (thin lines with arrowheads), PV (thick contours), and TD-type filtered OLR (shaded). The PV is plotted only at 0.01-PVU (potential vorticity unit) intervals (1 PVU = 10−6 K kg−1 m2 s−1) between 0.1 and 0.15 for clarity. (c) Hovmöller plot of Kelvin (black contours) and velocity potential (shaded) at 200 hPa. (d) The 9 May 200-hPa divergent wind (vectors), velocity potential (contours), and OLR anomalies (shaded). (e) Hovmöller plot of ER (black contours) and zonal wind (shaded) at 850 hPa. (f) The 7 May 850-hPa ER anomalies (shaded), geopotential height (contours), and winds (vectors). Filtered OLR anomalies are shown in dark gray for negative and light gray for positive numbers at 10 W m−2 intervals. Velocity potential is in 106 m2 s−2, winds in m s−1, and geopotential height in m. Hovmöller plots are for April–May 2009 for latitudinal averages 2.5°–12.5°N.
Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00175.1
(a) Hovmöller plot of TD-type (black contours) and PV (shaded) results at 850 hPa. (b) The 10 May 850-hPa-height streamlines (thin lines with arrowheads), PV (thick contours), and TD-type filtered OLR (shaded). The PV is plotted only at 0.01-PVU (potential vorticity unit) intervals (1 PVU = 10−6 K kg−1 m2 s−1) between 0.1 and 0.15 for clarity. (c) Hovmöller plot of Kelvin (black contours) and velocity potential (shaded) at 200 hPa. (d) The 9 May 200-hPa divergent wind (vectors), velocity potential (contours), and OLR anomalies (shaded). (e) Hovmöller plot of ER (black contours) and zonal wind (shaded) at 850 hPa. (f) The 7 May 850-hPa ER anomalies (shaded), geopotential height (contours), and winds (vectors). Filtered OLR anomalies are shown in dark gray for negative and light gray for positive numbers at 10 W m−2 intervals. Velocity potential is in 106 m2 s−2, winds in m s−1, and geopotential height in m. Hovmöller plots are for April–May 2009 for latitudinal averages 2.5°–12.5°N.
Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00175.1
Kelvin waves are analyzed in Figs. 10c,d for their possible role in the enhancement of the MME. Velocity potential at 200 hPa is plotted along with the filtered Kelvin waves in Fig. 10c. The highest velocity potential, and hence divergence aloft, during April–May 2009 is associated with the K1 wave. The scale of K1 can further be appreciated in Fig. 10d, the 9 May map of 200-hPa level velocity potential (contour), divergent winds (vectors), and the Kelvin-filtered OLR (shaded). The upper-level divergence associated with K1 extends from the equator to about 12°N, which is indicative of enhanced rising motion and deep convection, overlaps with TD1 during 8–10 May.
For ER waves, the 850-hPa zonal wind (m s−1) is plotted along with the filtered anomalies (Fig. 10e), showing westerly flow in association with ER1 at these latitudes. Although the effect of the heat low is prevalent in this figure, some of the stronger westerly flow during the MME lies within the envelope of the ER signal from 1 May at 20°E through 8 May at 5°W. Further, the westerly progression of the unfiltered OLR anomalies (Fig. 9), as well as precipitable water (Fig. 7), associated with the MME is 4 m s−1, close to the 4.5 m s−1 phase speed for the ER wave in Kiladis et al. (2009, Fig. 17). On 7 May, the 850-hPa-level wind vectors and geopotential height map (Fig. 10f) show their association with the ER-filtered OLR anomalies. The figure illustrates the potential impact of ER1 on TD1 and the early development of the MME. Here, the convective signal associated with ER1 is to the east of a circulation center at 20°N, consistent with findings of Kiladis et al. (2009), although ER1 is much farther north than their composite ER; the elongated pattern from southwest (around 10°N in West Africa) to northeast (25°N over Egypt) is likely due to the presence of an extratropical Rossby wave trough (cf. Fig. 8). A secondary low pressure system is located to the southeast of the main area of negative OLR and this is found to be a prominent feature for the period covering mid-April–mid-May in association with higher levels of humidity over the Sudan east of 20°E (not shown). The long meridional extent of the ER1 signal is related to an extratropical system, adding complexity to the structure of ER1. The possible role of the midlatitude disturbance will be discussed later.
The influence of K1 and ER1 can be further explored using twice-daily OLR and NNRP analyses. The filtered wave types are tracked on a series of maps (Fig. 11), along with the divergent wind at 200 hPa in a manner similar to Mounier et al. (2007). Only values greater than one standard deviation are plotted for the filtered OLR. ER waves are marked only by the −10 W m−2 contour. Only negative OLR anomalies are displayed for all wave types for the sake of clarity. TD1 remains far to the south at 0600 UTC on 8 May as it propagates along ~7°N over southern Chad (20°E). Meanwhile, K1 maintains its strength with strong divergence aloft over most of the GOG coast and as far north as 15°N. ER1 is represented as a diagonal structure from 5°N at 20°W to northeast Africa. TD1 begins to strengthen at 1800 UTC on 8 May and 0600 UTC on 9 May as it approaches K1 and its related divergence at 200 hPa. Also notice that at 1800 UTC on 8 May, a secondary TD-type feature is just to the north of TD1, within the ER1 envelope, and it merges with TD1 at 0600 UTC 9 May. Thus, TD1 grows in its latitudinal range and spans from the GOG (5°N) to northern Niger in the vicinity of Agadez (17°N, 8°E), which recorded an increase in humidity.
Time sequence of OLR anomalies filtered for Kelvin waves (gray, shaded), TD type (red, dash contours), and ER (light blue, solid contours, only at −10 W m−2) from 1800 UTC 8 May to 0600 UTC 10 May. Kelvin and TD type are contoured from −10 W m−2 and only negatives are shown for clarity. The K1, TD1, and ER1 are labeled and vectors represent divergent wind.
Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00175.1
Time sequence of OLR anomalies filtered for Kelvin waves (gray, shaded), TD type (red, dash contours), and ER (light blue, solid contours, only at −10 W m−2) from 1800 UTC 8 May to 0600 UTC 10 May. Kelvin and TD type are contoured from −10 W m−2 and only negatives are shown for clarity. The K1, TD1, and ER1 are labeled and vectors represent divergent wind.
Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00175.1
Time sequence of OLR anomalies filtered for Kelvin waves (gray, shaded), TD type (red, dash contours), and ER (light blue, solid contours, only at −10 W m−2) from 1800 UTC 8 May to 0600 UTC 10 May. Kelvin and TD type are contoured from −10 W m−2 and only negatives are shown for clarity. The K1, TD1, and ER1 are labeled and vectors represent divergent wind.
Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00175.1
The possible interaction between K1 and TD1 occurs at 1800 UTC 9 May. Here, the northern edge of K1 intersects the TD-type system over central Nigeria, close to the Jos Plateau (10°N, 8°E). Notice that a pocket of <−20 W m−2 has formed within the TD1 envelope as the system gains in strength, presumably, through its interaction with the Kelvin wave. The result of this interface is a stronger TD1 on 10 May as it moves westward with a significant northern edge that traverses most of Niger and Mali until it reaches the west coast of Africa over Sierra Leone on 11 May (not shown).
The point at which K1 and TD1 meet was analyzed for the possibility of coupling of their negative phases over central Nigeria. An area average (9°–11°N, 7°–9°E) of both fields found that both wave signals reach their lowest values in the filtered OLR nearly simultaneously, indicating phase coupling and its possible role in the strengthening of TD1.
In addition to its interaction with K1, TD1 remains within the envelope of negative anomalies from ER1 throughout the period analyzed. ER waves have contributed to some instances of tropical cyclogenesis, which is an intensification of TD-type systems (Frank and Roundy 2006; Bessafi and Wheeler 2006; Molinari et al. 2007). Those cyclogenesis events tend to cluster to the east of the ER wave trough (Kiladis et al. 2009) and it is possible that intensification of TD1 could have occurred during this period.
b. Midlatitude forcing
An extratropical trough may have had a role in the development of convection over northern Chad, in association with ER1 during the early stages of the MME and the northward growth of TD1. The satellite image in Fig. 8 shows a prevalent feature alluded to in section 3d: a long swath of clouds that connects the MME over Sahelian Africa to a midlatitude system in the eastern Mediterranean. This type of feature, termed a tropical plume, has been observed in the southwesterly flow ahead of midlatitude troughs, extending from the ITCZ into the midlatitudes (McGuirk et al. 1988; Knippertz and Fink 2008, 2009). While dry season precipitation studies have focused on occurrences in winter, their connection with midlatitude systems can occur during the spring as well (A. Fink 2013, personal communication). In Nicholson (1981), winter and spring rainfall events were referred to as Soudano–Sahelian depressions and the author suggested there was an interaction of diagonal troughs with low-level AEWs.
Extratropical systems can also couple with Kelvin waves (Straub and Kiladis 2002) and ER waves (Kiladis and Wheeler 1995; Kiladis 1998; Hoskins and Yang 2000; Yang et al. 2007). In fact, the northeastward extension of the ER1 signal suggests this particular wave could have interacted with the midlatitude trough inferred from the cloud pattern in Fig. 8.
Isotach and streamline analysis at 200 hPa overlaid on the OLR on 7 May depicts a typical midlatitude trough with a tropical plume and jet streak along the STJ east of the trough (Fig. 12a), similar to those described by Knippertz and Fink (2008). Upper-level divergence in the right-entrance region of the jet streak will enhance rising motion and low-level convergence near 20°E, where convection associated with ER1 is observed at the early development of TD1 (Fig. 8). Previous studies (Kiladis and Wheeler 1995; Kiladis 1998; Hoskins and Yang 2000; Yang et al. 2007) have shown that the ER signal can be preceded by positively tilted extratropical Rossby wave trains propagating equatorward, as may be the case with the trough in Fig. 12a, supporting the evidence that ER1 may have been forced by wave energy from the midlatitude system.
(a) Horizontal view of 7 May 2009 OLR (W m−2, shaded), and 200-hPa streamlines (yellow lines with arrowheads) and isotachs (m s−1, black contours). Thick red contours delineate isotachs > 55 m s−1. The blue line at 20°E represents the cross section depicted in the panel below. (b) Vertical cross section along 20°E of vertical velocity, ω (shaded), PV anomaly (contours) of 7 May 2009 relative to average of May 2000–09, and the mass circulation shown by the pseudovectors for 7 May 2009 taken. Contour interval for PV is at 0.25 PVU between −1 and 1 and 0.5 PVU otherwise; and the yellow contour denotes the 0 PVU isoline for PV. The contour interval for ω is 1 × 10−2 Pa s−1 and the pseudovectors are scaled such that a 1 m s−1 meridional divergent wind is equal to a −5 × 10−2 Pa s−1 vertical motion.
Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00175.1
(a) Horizontal view of 7 May 2009 OLR (W m−2, shaded), and 200-hPa streamlines (yellow lines with arrowheads) and isotachs (m s−1, black contours). Thick red contours delineate isotachs > 55 m s−1. The blue line at 20°E represents the cross section depicted in the panel below. (b) Vertical cross section along 20°E of vertical velocity, ω (shaded), PV anomaly (contours) of 7 May 2009 relative to average of May 2000–09, and the mass circulation shown by the pseudovectors for 7 May 2009 taken. Contour interval for PV is at 0.25 PVU between −1 and 1 and 0.5 PVU otherwise; and the yellow contour denotes the 0 PVU isoline for PV. The contour interval for ω is 1 × 10−2 Pa s−1 and the pseudovectors are scaled such that a 1 m s−1 meridional divergent wind is equal to a −5 × 10−2 Pa s−1 vertical motion.
Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00175.1
(a) Horizontal view of 7 May 2009 OLR (W m−2, shaded), and 200-hPa streamlines (yellow lines with arrowheads) and isotachs (m s−1, black contours). Thick red contours delineate isotachs > 55 m s−1. The blue line at 20°E represents the cross section depicted in the panel below. (b) Vertical cross section along 20°E of vertical velocity, ω (shaded), PV anomaly (contours) of 7 May 2009 relative to average of May 2000–09, and the mass circulation shown by the pseudovectors for 7 May 2009 taken. Contour interval for PV is at 0.25 PVU between −1 and 1 and 0.5 PVU otherwise; and the yellow contour denotes the 0 PVU isoline for PV. The contour interval for ω is 1 × 10−2 Pa s−1 and the pseudovectors are scaled such that a 1 m s−1 meridional divergent wind is equal to a −5 × 10−2 Pa s−1 vertical motion.
Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00175.1
A vertical cross section of the tropical–extratropical interaction on 7 May is taken at 20°E (Fig. 12b), which allows for a view of mass transport following a procedure used by Kiladis (1998). The mass circulation pattern (vectors) shows the greatest upward transport extending from near 10°N in the lower troposphere to near 25°N in the upper troposphere. Also present is a positive PV anomaly in the upper troposphere, computed for 7 May relative to the 2000–09 May mean, centered at 30°N and extending to 25°N, which in Kiladis (1998) represented the influence from the midlatitudes in the development of an idealized OLR anomaly in the Pacific Ocean. The same kind of mechanism could be occurring over northern, tropical Africa, further providing energy and instability to the tropical waves and the development of the MME.
c. Synoptic analysis
Forcing from the extratropics and the various CCEWs involved in the formation and westward progression of the MME allowed for an appreciable increase in surface moisture across the western Sahel. The spatial extent of the surface moisture and related dynamical features can elucidate other structural components of the MME and relate it to occurrences of similar systems.
Figure 13 presents synoptic surface maps during 5–11 May showing precipitable water, surface winds, and the RH40 isoline. On 5 May (Fig. 13a), the RH40 isoline reaches far north into the Sahel east of 15°E, covering much of southern Chad and all of Nigeria. On 6 May (Fig. 13b), the RH40 line east of 15°E shifts farther south but the precipitable water remains above 30 kg m−2 across a zonal swath north of about 12°N. The RH40 line has moved slightly northward (~1° latitude) but remains south of major cities like Ouagadougou, Burkina Faso; Bamako; and Niamey (see locations in Fig. 1). Moisture in northern Chad and Sudan increases once again on 7 May (Fig. 13c) with high surface humidity over northeast Chad. Strong cross-isobar northwesterly flow is also present in the northeast corner of the domain. On 8 May, increased southerly flow into eastern Niger forces the RH40 line to its northernmost point of the period, north of 15°N (Fig. 13d).
Synoptic evolution over tropical West Africa on (a) 5, (b) 6, (c) 7, (d) 8, (e) 9, (f) 10, and (g) 11 May 2009. Solid lines show the mean sea level pressure contoured every 1 hPa and shading depicts total atmospheric column precipitable water (kg m−2). Vectors represent the 10-m winds. The RH40 line is marked by a thick dash–dotted line.
Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00175.1
Synoptic evolution over tropical West Africa on (a) 5, (b) 6, (c) 7, (d) 8, (e) 9, (f) 10, and (g) 11 May 2009. Solid lines show the mean sea level pressure contoured every 1 hPa and shading depicts total atmospheric column precipitable water (kg m−2). Vectors represent the 10-m winds. The RH40 line is marked by a thick dash–dotted line.
Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00175.1
Synoptic evolution over tropical West Africa on (a) 5, (b) 6, (c) 7, (d) 8, (e) 9, (f) 10, and (g) 11 May 2009. Solid lines show the mean sea level pressure contoured every 1 hPa and shading depicts total atmospheric column precipitable water (kg m−2). Vectors represent the 10-m winds. The RH40 line is marked by a thick dash–dotted line.
Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00175.1
The westward advancement of the MME is clearly visible on 9 May (Fig. 13e) as the eastern edge (east of 15°E) of the event becomes drier with stronger harmattan winds and the central portion of the domain (5°–10°E) becomes moister. Areas west of 5°E also become much drier, especially in places like Bamako (<10 kg m−2 precipitable water). This figure also shows stronger monsoonal flow in south-central Niger and a corresponding northward shift in the RH40 line. As the MME continues its westward track (10 May; Fig. 13f), in its wake are stronger winds and drier air over Chad, Sudan, and eastern Niger. The moist pulse is over western Niger and most of Burkina Faso. The RH40 has crossed over Niamey, consistent with surface observations (see next section). Finally, on 11 May (Fig. 13g), the MME reaches the western Sahel and induces an increase in humidity over southern Mali. The low-level circulation is strongest during 10–11 May and this is consistent with findings related to AEWs by Reed et al. (1977), where the waves reach their maximum intensity between the prime meridian and 15°W. Similar northward bursts of moisture, associated with the AEWs, enhance southerly flow and affect the more northern arid region of the Hoggar Massif in southern Algeria during the summer (Cuesta et al. 2010).
d. Station observations
The MME resulted in a substantial change in surface moisture that was capable of affecting meningitis epidemics on the northern side of the meningitis belt. Thus, the use of observations to validate the reanalysis and CCEW results adds value to the findings presented thus far. Figure 14 provides time series of observed relative humidity for 2009, as well as average relative humidities for 2000–09 (except Kano, Nigeria, where observations were not consistent before 2009), and area averages of the 2009 OLR anomalies at Bamako, Niamey, Ouagadougou, and Kano. Note that Kano and Ouagadougou break the RH40 line with negative OLR anomalies, which are determined to be the TD-type systems within the filtered data (not shown). For locations such as Niamey and Bamako, the RH40 threshold is not reached until the arrival of the MME and the associated TD1 and the RH during the MME exceed the 2000–09 averages.
Observed 1 Apr –20 May, 2000–2009 average and 2009 RH (%); and area averages of OLR anomalies (W m−2) at: (a) Bamako, (b) Niamey, (c) Kano, and (d) Ouagadougou. Small arrows denote significant TDs found in Fig. 9. TD1 and the MME are labeled.
Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00175.1
Observed 1 Apr –20 May, 2000–2009 average and 2009 RH (%); and area averages of OLR anomalies (W m−2) at: (a) Bamako, (b) Niamey, (c) Kano, and (d) Ouagadougou. Small arrows denote significant TDs found in Fig. 9. TD1 and the MME are labeled.
Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00175.1
Observed 1 Apr –20 May, 2000–2009 average and 2009 RH (%); and area averages of OLR anomalies (W m−2) at: (a) Bamako, (b) Niamey, (c) Kano, and (d) Ouagadougou. Small arrows denote significant TDs found in Fig. 9. TD1 and the MME are labeled.
Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00175.1
5. Synthesis and concluding remarks
This study examined the variability of atmospheric moisture over West Africa during spring in order to aid mitigation efforts for the climate-sensitive meningitis disease. A three-dimensional conceptual model of the spring, separated into early, middle, and late stages, shows the main circulation patterns responsible for airmass origin over the Sahel and the small-scale features that affect moisture variability. The surface moisture is highly dependent on the position of the ITF, which varies meridionally and temporally with the seasonal cycle. The timing of the arrival of moisture varies on interannual scales and is highly influenced by subseasonal phenomena such as Kelvin waves, ER waves, and TD-type disturbances.
The moist event of May 2009 was highlighted because it created a temporally significant change in the moisture regime for highly populated areas north of 12°N. This event provided the first influx of moisture for spring 2009 that was sufficient to break the RH40 threshold, one of the proxies for the cessation of meningitis epidemics, across nearly all of Sahelian West Africa. The MME was investigated at multiple scales and with different datasets. What began as anomalous moist conditions in the eastern Sahel in the synoptic analysis (Fig. 13), soon evolved into a feature present in station observations that altered surface humidity and brought significant rainfall. The temporal range of the MME, however, suggests longer-scale phenomena were also at play.
TD-type systems that are coherent enough to influence moisture in the region were particularly active from April to early May 2009, relative to the 2000–09 period. Note, however, that for 2009 the majority occurred in April and were limited in spatial range with only the MME covering the breadth of the study area. Understanding factors that might affect the frequency and range of TD-type waves, such as the state of the MJO, ER waves, or Kelvin wave activity, could aid in predicting peaks in surface moisture that are caused by TD-type waves and help guide priorities for local meningitis mitigation.
The case study presented in section 4 provides a potential framework for the predictability of these types of occurrences. The meningitis outbreak of 2009, the largest since 1996–97, provided a wealth of high-resolution information related to disease incidence for Nigeria, which, along with collocated meteorological observations at Kano, allowed for a more robust analysis. The succession of strong moist pulses over the city of Kano during April and May of 2009 (Fig. 15a), which shifted the moisture regime from harmattan to monsoon conditions, appears to have contributed to the rapid decline of meningitis cases within the state of Kano (Fig. 15b). However, the highly populated areas north and west of Kano were drier than the 2000–09 average (Fig. 14) and did not reach the RH40 threshold until the onset of the MME. One of the major factors present during the MME was a westward-propagating TD-type system that traversed the length of the meningitis belt. Such systems could be a major source of moisture for the region during the spring. The question then arises: How predictable are systems like TD1?
(a) Time series of observed RH (%) for Kano during March–May 2009 (solid line) and the 7-day moving average (dotted line). (b) Number of districts reporting epidemics in the state of Kano during March and April 2009 (solid line) and concurrent weekly mean RH (bars, %) in the city of Kano.
Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00175.1
(a) Time series of observed RH (%) for Kano during March–May 2009 (solid line) and the 7-day moving average (dotted line). (b) Number of districts reporting epidemics in the state of Kano during March and April 2009 (solid line) and concurrent weekly mean RH (bars, %) in the city of Kano.
Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00175.1
(a) Time series of observed RH (%) for Kano during March–May 2009 (solid line) and the 7-day moving average (dotted line). (b) Number of districts reporting epidemics in the state of Kano during March and April 2009 (solid line) and concurrent weekly mean RH (bars, %) in the city of Kano.
Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00175.1
Regional climate models have shown the capability to simulate AEWs or TD-type systems during summer (e.g., Berry and Thorncroft 2012), suggesting that TD-type systems during the spring can also be predicted. Additionally, dry season precipitation and moist events over West Africa that are linked with extratropical systems can be simulated by global models (Knippertz and Fink 2009). Extratropical forcing also aids in the development of CCEWs (e.g., Straub and Kiladis 2003a), which, in turn, initiate and/or strengthen TD-type waves as demonstrated in this study. Also, Mera et al. (2010) conducted real-time simulations of 2009 winter and spring precipitation in West Africa using the Weather Research and Forecasting (WRF) model. Those simulations yielded positive results and, for systems forced partially by the extratropics, the model showed moderate skill. The moist event in May was forecasted 10 days in advance, although the model predicted the event to cross the Sahel a day earlier than observed.
The occurrence of TD-type systems, and the associated onset of high humidity, can vary substantially from year to year. They intensify through interactions with the convective phases of convectively coupled Kelvin and ER waves, larger-scale circulations that are monitored in real time by operational centers. The properties of these systems and their interactions during the spring should be studied in more detail, as they could contain inherent predictability that can be exploited by forecasting agencies and decision makers in areas affected by meningitis. Finally, this type of multiscale interaction between convectively coupled equatorial waves and midlatitude intrusions does not only help explain the humidity peaks, but may also be an important mechanism for the amplification of the westward-propagating synoptic disturbances.
Acknowledgments
Support for this project was provided in part by Google.org. The authors thank Anatha Aiyyer for advice on the trajectory analysis. Thanks to George Kiladis and Stefan Tulich for helpful discussions on tropical waves. Meteosat data were made available by EUMETSAT Archives (http://www.eumetsat.int/). Daily mean OLR and NCEP–NCAR reanalyses winds were provided by the NOAA/ESRL/Physical Sciences Division, Boulder, Colorado.
REFERENCES
Ati, O. F., C. J. Stigter, and E. O. Oladipo, 2002: A comparison of methods to determine the onset of the growing season in northern Nigeria. Int. J. Climatol., 22, 731–742, doi:10.1002/joc.712.
Berry, G. J., and C. D. Thorncroft, 2012: African easterly wave dynamics in a mesoscale numerical model: The upscale role of convection. J. Atmos. Sci., 69, 1267–1283, doi:10.1175/JAS-D-11-099.1.
Besancenot, J. P., M. Boko, and P. C. Oke, 1997: Weather conditions and cerebrospinal meningitis in Benin (Gulf of Guinea, West Africa). Eur. J. Epidemiol., 13, 807–815, doi:10.1023/A:1007365919013.
Bessafi, M., and M. C. Wheeler, 2006: Modulation of south Indian Ocean tropical cyclones by the Madden–Julian oscillation and convectively coupled equatorial waves. Mon. Wea. Rev., 134, 638–656, doi:10.1175/MWR3087.1.
Cuesta, J., C. Lavaysse, C. Flamant, M. Mimouni, and P. Knippertz, 2010: Northward bursts of the West African monsoon leading to rainfall over the Hoggar Massif, Algeria. Quart. J. Roy. Meteor. Soc., 136, 174–189, doi:10.1002/qj.439.
Diedhiou, A., S. Janicot, A. Viltard, P. de F’elice, and H. Laurent, 1999: Easterly wave regimes and associated convection over West Africa and the tropical Atlantic: Results from the NCEP/NCAR and ECMWF reanalyses. Climate Dyn., 15, 795–822, doi:10.1007/s003820050316.
Dukic, V., and Coauthors, 2012: The role of weather in meningitis outbreaks in Navrango, Ghana: A generalized additive modeling approach. J. Agric. Biol. Environ. Stat., 17, 442–460, doi:10.1007/s13253-012-0095-9.
Eltahir, E. A. B., and C. Gong, 1996: Dynamics of wet and dry years in the West Africa. J. Climate, 9, 1030–1042, doi:10.1175/1520-0442(1996)009<1030:DOWADY>2.0.CO;2.
Flamant, C., J. P. Chaboureau, D. P. Parker, C. M. Taylor, J. P. Cammas, O. Bock, F. Timouk, and J. Pelon, 2007: Airborne observations of the impact of a convective system on the planetary boundary layer thermodynamics and aerosol distribution in the intertropical discontinuity region of the West African monsoon. Quart. J. Roy. Meteor. Soc., 133, 1175–1189, doi:10.1002/qj.97.
Flamant, C., P. Knippertz, D. J. Parker, J.-P. Chaboureau, C. Lavaysse, A. Agusti-Panareda, and L. Kergoat, 2009: The impact of a mesoscale convective system cold pool on the northward propagation of the intertropical discontinuity over West Africa. Quart. J. Roy. Meteor. Soc., 135, 139–159, doi:10.1002/qj.357.
Flohn, H., 1975: Tropische Zirkulationsformen im Lichte der Satellitenaufnahmen (Tropical Circulation Types as Reflected in Satellite Imagery). Bonner Meteorologische Abhandlungen, No. 21, Westdeutscher Verlag, 82 pp.
Frank, W. M., and P. E. Roundy, 2006: The role of tropical waves in tropical cyclogenesis. Mon. Wea. Rev., 134, 2397–2417, doi:10.1175/MWR3204.1.
Greenwood, B., 1999: Meningococcal meningitis in Africa. Trans. Roy. Soc. Trop. Med. Hyg., 93, 341–353, doi:10.1016/S0035-9203(99)90106-2.
Grist, J., 2002: Easterly waves over Africa. Part I: The seasonal cycle and contrasts between wet and dry years. Mon. Wea. Rev., 130, 197–211, doi:10.1175/1520-0493(2002)130<0197:EWOAPI>2.0.CO;2.
Gruber, A., and A. F. Krueger, 1984: The status of the NOAA outgoing longwave radiation data set. Bull. Amer. Meteor. Soc., 65, 958–962, doi:10.1175/1520-0477(1984)065<0958:TSOTNO>2.0.CO;2.
Gu, G., 2009: Intraseasonal variability in the equatorial Atlantic–West Africa during March–June. Climate Dyn., 32, 457–471, doi:10.1007/s00382-008-0428-0.
Gu, G., and R. F. Adler, 2004: Seasonal evolution and variability associated with the West African monsoon system. J. Climate, 17, 3364–3377, doi:10.1175/1520-0442(2004)017<3364:SEAVAW>2.0.CO;2.
Hagos, S. M., and K. H. Cook, 2007: Dynamics of the West African monsoon jump. J. Climate, 20, 5264–5284, doi:10.1175/2007JCLI1533.1.
Hagos, S. M., and K. H. Cook, 2009: Development of a coupled regional model and its application to the study of interactions between the West African monsoon and the eastern tropical Atlantic Ocean. J. Climate,22, 2591–2604, doi:10.1175/2008JCLI2466.1.
Hoskins, B. J., and G.-Y. Yang, 2000: The equatorial response to higher-latitude forcing. J. Atmos. Sci., 57, 1197–1213, doi:10.1175/1520-0469(2000)057<1197:TERTHL>2.0.CO;2.
Janicot, S., F. Mounier, N. M. J. Hall, S. Leroux, B. Sultan, and G. N. Kiladis, 2009: Dynamics of the West African monsoon. Part IV: Analysis of 25–90-day variability of convection and the role of the Indian monsoon. J. Climate, 22, 1541–1565, doi:10.1175/2008JCLI2314.1.
Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437–471, doi:10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.
Kiladis, G. N., 1998: Observations of Rossby waves linked to convection over the eastern tropical Pacific. J. Atmos. Sci., 55, 321–339, doi:10.1175/1520-0469(1998)055<0321:OORWLT>2.0.CO;2.
Kiladis, G. N., and M. Wheeler, 1995: Horizontal and vertical structure of observed tropospheric equatorial Rossby waves. J. Geophys. Res., 100, 22 981–22 997, doi:10.1029/95JD02415.
Kiladis, G. N., C. D. Thorncroft, and N. M. J. Hall, 2006: Three-dimensional structure and dynamics of African easterly waves. Part I: Observations. J. Atmos. Sci., 63, 2212–2230, doi:10.1175/JAS3741.1.
Kiladis, G. N., M. C. Wheeler, P. T. Haertel, and P. E. Roundy, 2009: Convectively coupled equatorial waves. Rev. Geophys., 47, RG2003, doi:10.1029/2008RG000266.
Knippertz, P., and A. H. Fink, 2008: Dry-season precipitation in tropical West Africa and its relation to forcing from the extratropics. Mon. Wea. Rev., 136, 3579–3596, doi:10.1175/2008MWR2295.1.
Knippertz, P., and A. H. Fink, 2009: Prediction of dry-season precipitation in tropical West Africa and its relation to forcing from the extratropics. Wea. Forecasting, 24, 1064–1084, doi:10.1175/2009WAF2222221.1.
Laing, A. G., and J. M. Fritsch, 1993: Mesoscale convective complexes in Africa. Mon. Wea. Rev., 121, 2254–2263, doi:10.1175/1520-0493(1993)121<2254:MCCIA>2.0.CO;2.
Laing, A. G., R. E. Carbone, V. Levizzani, and J. D. Tuttle, 2008: The propagation and diurnal cycles of deep convection in northern tropical Africa. Quart. J. Roy. Meteor. Soc., 134, 93–109, doi:10.1002/qj.194.
Le Barbé, L., T. Lebel, and D. Tapsoba, 2002: Rainfall variability in West Africa during the years 1950–90. J. Climate, 15, 187–202, doi:10.1175/1520-0442(2002)015<0187:RVIWAD>2.0.CO;2.
Lélé, M. I., and P. J. Lamb, 2010: Variability of the intertropical front (ITF) and rainfall over the West African Sudan–Sahel zone. J. Climate, 23, 3984–4004, doi:10.1175/2010JCLI3277.1.
Lin, Y.-L., K. E. Robertson, and C. M. Hill, 2005: Origin and propagation of a disturbance associated with an African easterly wave as a precursor of Hurricane Alberto (2000). Mon. Wea. Rev., 133, 3276–3298, doi:10.1175/MWR3035.1.
Madden, R. A., and P. R. Julian, 1994: Observations of the 40–50-day tropical oscillation—A review. Mon. Wea. Rev., 122, 814–837, doi:10.1175/1520-0493(1994)122<0814:OOTDTO>2.0.CO;2.
Matthews, M., 2004: Intraseasonal variability over tropical Africa during northern summer. J. Climate, 17, 2427–2440, doi:10.1175/1520-0442(2004)017<2427:IVOTAD>2.0.CO;2.
McGuirk, J. P., A. H. Thompson, and J. R. Schaefer, 1988: An eastern Pacific tropical plume. Mon. Wea. Rev., 116, 2505–2521, doi:10.1175/1520-0493(1988)116<2505:AEPTP>2.0.CO;2.
Mekonnen, A., C. D. Thorncroft, A. R. Aiyyer, and G. N. Kiladis, 2008: Convectively coupled Kelvin waves over tropical Africa during the boreal summer: Structure and variability. J. Climate, 21, 6649–6667, doi:10.1175/2008JCLI2008.1.
Mera, R., A. Laing, and F. H. M. Semazzi, 2010: Variability of atmospheric moisture during the boreal spring in West Africa. 29th Conf. on Hurricanes and Tropical Meteorology, Tucson, AZ, Amer. Meteor. Soc., 6D.4. [Available online at https://ams.confex.com/ams/pdfpapers/168968.pdf.]
Molesworth, A. M., L. E. Cuevas, A. P. Morse, J. R. Herman, and M. C. Thomson, 2002: Dust clouds and spread of infection. Lancet, 359, 81–82, doi:10.1016/S0140-6736(02)07304-X.
Molesworth, A. M., L. E. Cuevas, S. J. Connor, A. P. Morse, and M. C. Thomson, 2003: Environmental risk and meningitis epidemics in Africa. Emerging Infect. Dis., 9, 1287–1293, doi:10.3201/eid0910.030182.
Molinari, J., K. Lombardo, and D. Vollaro, 2007: Tropical cyclogenesis within an equatorial Rossby wave packet. J. Atmos. Sci., 64, 1301–1317, doi:10.1175/JAS3902.1.
Mounier, F., G. N. Kiladis, and S. Janicot, 2007: Analysis of the dominant mode of convectively coupled Kelvin waves in the West African monsoon. J. Climate, 20, 1487–1503, doi:10.1175/JCLI4059.1.
Nguyen, H., and J.-P. Duvel, 2008: Synoptic wave perturbations and convective systems over equatorial Africa. J. Climate, 21, 6372–6388, doi:10.1175/2008JCLI2409.1.
Nicholson, S. E., 1981: Rainfall and atmospheric circulation during drought periods and wetter years in West Africa. Mon. Wea. Rev., 109, 2191–2208, doi:10.1175/1520-0493(1981)109<2191:RAACDD>2.0.CO;2.
Okumura, Y., and S. Xie, 2004: Interaction of the Atlantic equatorial cold tongue and the African monsoon. J. Climate, 17, 3589–3601, doi:10.1175/1520-0442(2004)017<3589:IOTAEC>2.0.CO;2.
Omotosho, B., A. A. Balogun, and K. Ogunjobi, 2000: Predicting monthly and seasonal rainfall, onset, and cessation of the rainy season in West Africa using only surface data. Int. J. Climatol., 20, 865–880, doi:10.1002/1097-0088(20000630)20:8<865::AID-JOC505>3.0.CO;2-R.
Pohl, B., Y. Richard, and N. Fauchereau, 2007: Influence of the Madden–Julian oscillation on southern African summer rainfall. J. Climate, 20, 4227–4242, doi:10.1175/JCLI4231.1.
Pospichal, B., D. B. Karam, S. Crewell, C. Flamant, A. Hünerbein, O. Bock, and F. Saıd, 2010: Diurnal cycle of the intertropical discontinuity over West Africa analysed by remote sensing and mesoscale modeling. Quart. J. Roy. Meteor. Soc., 136, 92–106, doi:10.1002/qj.435.
Ramel, R., H. Gallée, and C. Messager, 2006: On the northward shift of the West African monsoon. Climate Dyn., 26, 429–440, doi:10.1007/s00382-005-0093-5.
Reed, R. J., D. C. Norquist, and E. E. Recker, 1977: The structure and properties of African wave disturbances as observed during phase III of GATE. Mon. Wea. Rev., 105, 317–333, doi:10.1175/1520-0493(1977)105<0317:TSAPOA>2.0.CO;2.
Roundy, P. E., and W. M. Frank, 2004: A climatology of waves in equatorial region. J. Atmos. Sci., 61, 2105–2132, doi:10.1175/1520-0469(2004)061<2105:ACOWIT>2.0.CO;2.
Sijikumar, S., P. Roucou, and B. Fontaine, 2006: Monsoon onset over Sudan–Sahel: Simulation by the regional scale model MM5. Geophys. Res. Lett., 33, L03814, doi:10.1029/2005GL024819.
Straub, K. H., and G. N. Kiladis, 2002: Observations of a convectively coupled Kelvin wave in the eastern Pacific ITCZ. J. Atmos. Sci., 59, 30–53, doi:10.1175/1520-0469(2002)059<0030:OOACCK>2.0.CO;2.
Straub, K. H., and G. N. Kiladis, 2003a: Extratropical forcing of convectively coupled Kelvin waves during austral summer. J. Atmos. Sci., 60, 526–543, doi:10.1175/1520-0469(2003)060<0526:EFOCCK>2.0.CO;2.
Straub, K. H., and G. N. Kiladis, 2003b: Interaction between the boreal summer intraseasonal oscillation and higher-frequency tropical wave activity. Mon. Wea. Rev., 131, 945–960, doi:10.1175/1520-0493(2003)131<0945:IBTBSI>2.0.CO;2.
Sultan, B., and S. Janicot, 2000: Abrupt shift of the ITCZ over West Africa and intra-seasonal variability. Geophys. Res. Lett., 27, 3353–3356, doi:10.1029/1999GL011285.
Sultan, B., and S. Janicot, 2003: The West African monsoon dynamics. Part II: The preonset and onset of the summer monsoon. J. Climate, 16, 3407–3427, doi:10.1175/1520-0442(2003)016<3407:TWAMDP>2.0.CO;2.
Sultan, B., K. Labadi, J. F. Guegan, and S. Janicot, 2005: Climate drives the meningitis epidemics onset in West Africa. PLoS Med., 2, e6, doi:10.1371/journal.pmed.0020006.
Tetzlaff, G., and M. Peters, 1988: A composite study of early summer squall lines and their environment over West Africa. Meteor. Atmos. Phys., 38, 153–163, doi:10.1007/BF01029779.
Thomson, M. C., A. M. Molesworth, M. H. Djingarey, K. R. Yameogo, F. Belanger, and L. E. Cuevas, 2006: Potential of environmental models to predict meningitis epidemics in Africa. Trop. Med. Int. Health, 11, 781–788, doi:10.1111/j.1365-3156.2006.01630.x.
Thorncroft, C. D., and H. A. Flocas, 1997: A case study of Saharan cyclogenesis. Mon. Wea. Rev., 125, 1147–1165, doi:10.1175/1520-0493(1997)125<1147:ACSOSC>2.0.CO;2.
Wheeler, M., and G. N. Kiladis, 1999: Convectively coupled equatorial waves: Analysis of clouds and temperature in the wavenumber–frequency domain. J. Atmos. Sci., 56, 374–399, doi:10.1175/1520-0469(1999)056<0374:CCEWAO>2.0.CO;2.
Xie, P., and P. A. Arkin, 1997: Global precipitation: A 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull. Amer. Meteor. Soc., 78, 2539–2558, doi:10.1175/1520-0477(1997)078<2539:GPAYMA>2.0.CO;2.
Yaka, P., B. Sultan, H. Broutin, S. Janicot, S. Philippon, and N. Fourquet, 2008: Relationships between climate and year-to-year variability in meningitis outbreaks: A case study in Burkina Faso and Niger. Int. J. Health Geogr., 7, 34, doi:10.1186/1476-072X-7-34.
Yang, G.-Y., B. Hoskins, and J. Slingo, 2007: Convectively coupled equatorial waves. Part III: Synthesis structures and their forcing and evolution. J. Atmos. Sci., 64, 3438–3451, doi:10.1175/JAS4019.1.