Remote forcing of East African rainfall and ... - Wiley Online Library

INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 23: 67–89 (2003) Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/joc.861

REMOTE FORCING OF EAST AFRICAN RAINFALL AND RELATIONSHIPS WITH FLUCTUATIONS IN LEVELS OF LAKE VICTORIA

b

VINAY V. MISTRYa and DECLAN CONWAYb, * a Unaffiliated School of Development Studies, University of East Anglia, Norwich, NR4 7TJ, UK Received 11 December 2000 Revised 22 August 2002 Accepted 22 August 2002

ABSTRACT This study investigates the climatological variables responsible for fluctuations in Lake Victoria levels, in particular the causal mechanism responsible for a major anomaly that occurred in 1961. A Lake Victoria rainfall series (LVRS) correlates significantly (5%) with Lake Victoria levels and is utilized for subsequent analysis of rainfall variability. Relationships between annual and seasonal (March–May and October–December (OND)) LVRS and a number of tropical and extratropical series were analyzed. The results illustrate the dominance of the El Niño–Southern Oscillation phenomenon in modulating LVRS. The greatest correlation is found between LVRSOND and the Southern Oscillation index (r = −0.39, significant at the 1% level), although the relationship is non-linear over the course of the century. Velocity potential χ is employed as the principal diagnostic variable. Seasonal composite maps and empirical orthogonal function (EOF) analysis of Indian Ocean region χ-fields are utilized to test the hypothesis that an Indian Ocean Walker cell is responsible for the anomalous 1961 rainfall episode. Subsequent analysis leads to the rejection of this hypothesis. EOF analysis of OND 200 hPa χ-fields reveals a number of modes of tropical variability. EOF analysis of the Indian Ocean basin illustrates the emergence of a meridional circulation directed over the Indian Ocean (EOF5). This time series correlates significantly with LVRSOND (r = −0.40, 1% level), although the spatial pattern only explains a small proportion (2.35%) of the total variance. χ analysis for OND over the broader tropical region reveals significant relationships (5%) between OND EOF3∗ and July–September Sahel rainfall for the preceding season and subsequent year. This relationship may assist future long-lead seasonal forecast schemes for the Sahel region. Copyright  2003 Royal Meteorological Society. KEY WORDS:

Lake Victoria; rainfall; NCAR–NCEP reanalysis; velocity potential; ENSO; EOF analysis; NAO; East Africa; tropical circulation

1. INTRODUCTION Climatic variations in East Africa over the course of the century, such as the intense and prolonged high rainfall between 1961 and 1964, have resulted in dramatic increases in the level of Lake Victoria, widespread flooding and an increased White Nile discharge (Flohn, 1987). The 1961–64 period resulted in an increase in the level of Lake Victoria of 2.25 m, from 1961 to its peak in 1964 (Conway and Hulme, 1993). The rainfall event of 1961–62 was unprecedented in intensity, duration, and extent during the 20th century in this region (Lamb, 1966; Conway, 2002). Numerous stations reported record seasonal or annual rainfall totals during 1961 (Rodhe and Virji, 1976). The rainfall anomalies around the lake were not as great as some of those experienced throughout East Africa, although they did fall between 300 and 500% of mean totals, and were sufficient to result in the lake’s greatest and most pronounced rise last century (Thompson and Mörth, 1965). The subsequent rains of 1963 and 1964, in part associated with the 1963 El Niño event, exacerbated the flooding problems. Additional anomalous rainfall events have occurred in other years, such as 1905–06 * Correspondence to: Declan Conway, School of Development Studies, University of East Anglia, Norwich, NR4 7TJ, UK; e-mail: [email protected]

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V. V. MISTRY AND D. CONWAY

and 1916–17, but without the prolonged impact on lake levels that occurred after the 1961 event (Conway, 2002). A similar event to 1961 occurred in 1997, when Lake Victoria levels rose by up to 1.6 m (Birkett et al., 1999) over a period of 7 months. Numerous studies have illustrated that gradually varying surface boundary forcing conditions, including sea surface temperature (SST) and vegetation cover, influence variability of tropical general circulation patterns on seasonal and longer time scales (Charney, 1975; Charney and Shukla, 1981). The role of global and regional SST anomalies (SSTAs) in tropical rainfall variability is widely recognized and has been the subject of intensive investigations in recent years. With regard to East Africa, empirical studies by Cadet and Beltrando (1987), Ogallo et al. (1988), Ininda (1994), Mutai et al. (1998), and Latif et al. (1999) have demonstrated the existence of an association between rainfall variability and SSTA patterns. Saji et al. (1999) have recently discussed the appearance of a tropical Indian Ocean dipole mode to assist in accounting for anomalous rainfall episodes. Camberlin (1995) and Mutai et al. (1998) have illustrated significant relationships between East African rainfall and atmospheric flow patterns over the region. It is the aim of this paper to examine those atmospheric climatological parameters responsible for the variations in East African rainfall, with particular reference to the 1961 rainfall event that had a profound effect upon Lake Victoria. This is primarily with regard to the analysis of velocity potential and wind fields, since SSTs have already been widely studied by numerous authors (e.g. Ogallo, 1988; Saji et al., 1999; Webster et al., 1999). A greater understanding of atmospheric phenomena allied to the SST analyses may assist future East African rainfall prediction schemes.

1.1. The temporal distribution of East African rainfall Rainfall variations over East Africa operate on a number of time scales, from diurnal (Johnson, 1962; Asnani and Kinuthia, 1979) to quasi-periodic fluctuations of time scales greater than a year (Rodhe and Virji, 1976; Nicholson, 1996). The movement of the inter-tropical convergence zone (ITCZ) results in a predominantly bi-modal rainfall pattern, with the principal ‘Long Rains’ season occurring in March–May (MAM) and a shorter secondary ‘Short Rains’ season occurring during October–December (OND). Despite the unique characteristics of Lake Victoria, with rainfall throughout the year, the bi-modal pattern is marked. Analysis, however, is complicated, since the two principal rainy seasons possess differing spatial organization, suggesting fundamental differences in the causes of rainfall distribution (Hills, 1978). Nicholson (1996) provides an extensive analysis of interannual rainfall variability, noting the very high correlation between October and November and annual rainfall departures in the region (r = 0.7), and noting the immense magnitude of the 1961 rainfall episode. In this study, the analysis will be restricted to the Short Rains season. Spectral analyses of the rainfall time series highlight non-random fluctuations that occur on time scales between 2 and 5 years (Rodhe and Virji, 1976; Ogallo, 1979; Nicholson and Entekhabi, 1986). The spectrum for both Long and Short Rains is dominated by a peak at 5–6 years, with additional significant peaks evident at approximately 3.5 and 2.3 years (Nicholson, 1996). The 5–6 year peak corresponds to the first principal component described by Nyenzi (1988), thereby suggesting the presence of a forcing mechanism of 5–6 year periodicity that is responsible for the majority of East African rainfall and its relative spatial coherence. This periodicity suggests the influence of El Niño–Southern Oscillation (ENSO), although arguments remain with regard to the relative importance of Indian Ocean versus Pacific Ocean forcing of East African rainfall (e.g. Latif et al., 1999). The 2.3 year periodicity may suggest some link to the quasi-biennial oscillation (QBO). Previous studies employing principal component analysis have illustrated the heterogeneity of East African rainfall (e.g. Ogallo, 1983; Bärring, 1988; Semazzi et al., 1996), and highlighted a distinctive Lake Victoria rainfall regime. Ogallo et al. (1988) refer to the Lake Victoria region as a discrete zone within East Africa as a whole. Indeje et al. (2000), utilizing empirical orthogonal function (EOF) analysis, have identified a homogeneous Lake Victoria region (their Zone VI). The Indeje et al. (2000) study has permitted the investigation of the relationship between numerous rainfall zones and the ENSO phenomenon. Further subregional variations in East Africa have been analysed by Basalirwa (1995). Copyright  2003 Royal Meteorological Society

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EAST AFRICAN RAINFALL AND LAKE VICTORIA

1.2. Aims of the investigation The principal objective of this investigation is to examine the climatological conditions associated with anomalous rainfall events over the Lake Victoria region. Unlike East African coastal zone rainfall, which exhibits some degree of seasonal predictability (Farmer, 1988; Mutai et al., 1998), the highland area is not so predictable. It is hoped that some understanding of atmospheric conditions in this region will contribute to potential predictability of rainfall in this region. The 1961–64 extreme rainfall event is well documented (Mörth, 1967; Flohn, 1987; Conway, 2002), and many studies into the 1997 event have been and are in progress. This investigation will attempt to ascertain the wider climatological parameters and circulation patterns associated with extreme rainfall events, principally through the analysis of composite maps and EOF analysis. The principal aims of the study are as follows: • Test the hypothesis that the position and strength of a local Indian Ocean Walker circulation is responsible for the anomalous 1961 rainfall episode. This will be tested by analysing velocity potential, an indicator for convergence and divergence, at a variety of atmospheric levels. • Gauge the importance and applicability of utilizing velocity potential as an indicator of the tropical circulation.

2. DATA AND METHODOLOGY 2.1. Lake Victoria level data Monthly observations of Lake Victoria level, measured at Jinja Gauge (0.5 ° N, 33 ° E) are utilized over the period January 1900 to August 1998 (Figures 1 and 11). The lake-level series was detrended for the purposes of correlation analysis. Residuals were calculated from a sixth-order polynomial line placed through the lake-level series. The time series was detrended in order to reduce autocorrelation, an intrinsic element of lake-level data, since one value is contingent upon the previous month’s value. The resulting residuals, without autocorrelation, were utilized in correlation analysis. 2.2. Lake Victoria rainfall series (LVRS) The principal rainfall data utilized is the historical monthly rainfall dataset for global land areas between 1900 and 1996 gridded at 2.5° × 3.75° latitude/longitude resolution from Hulme (1994, updated). An areaaveraged index, illustrated in Figure 1, relating to monthly Lake Victoria catchment rainfall was constructed from the data set over the domain 2.5 ° N–5 ° S, 30–37.5 ° E. Given the spatial complexity of East African rainfall, Basalirwa et al. (1999) contend that there is no simple scheme that can be utilized for the determination of the spatial extent of homogeneous rainfall regions. 2.3. National Centers for Environment Prediction (NCEP)–National Center for Atmospheric Research (NCAR) reanalysis data Circulation data utilized in this study are products of the NCEP–NCAR reanalysis project (Kalnay et al., 1996; hereafter referred to as NCEP). Data are utilized at a monthly resolution, gridded at 2.5° latitude/longitude, over the 1961–98 period for velocity potential and wind fields, and the 1948–98 period for rainfall. The principal diagnostic variable employed in this study is velocity potential χ. This represents the irrotational component of winds. These data are utilized at 200, and 850 hPa, and sigma levels 0.2101 and 0.995. Zonal u and meridional v winds at 200 and 850 hPa are also analysed. Comparisons of the NCEP data with rainfall derived from the Microwave Sounding Unit (MSU) over oceans and station data (Schemm et al., 1992) over 1982–94 reveal a 20% deficit in the NCEP rainfall, and seasonal cycles are not well represented (Rao et al., 1998). An abrupt shift in NCEP rainfall occurs in 1967 over large parts of tropical Africa, and discontinuities occur over east-central Africa in 1975 and 1983 that do Copyright  2003 Royal Meteorological Society

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Figure 1. Annual LVRS anomalies (bars) with superimposed Lake Victoria level anomalies (line) (both series 1900–96)

Figure 2. NCEP rainfall (dashed) and LVRS rainfall (continuous) anomalies over the period 1948–96

not occur in the observations (Poccard et al., 2000). Comparisons between LVRS and a standardized NCEP rainfall series over Lake Victoria (4.7 ° S–4.7 ° N, 30 ° E–37.5 ° E) illustrate marked differences (Figure 2). An examination of running correlations, chosen arbitrarily at 5 and 9 years, reveals distinct differences over the 48 year record, with predominantly negative correlations between 1960 and 1990 (Figure 3). Copyright  2003 Royal Meteorological Society

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Figure 3. Running-mean correlations between NCEP and LVRS for 5 and 9 year periods. Thin dashed line represents the 5% significance level for 9 year periods

2.4. Climatological time series The following time series of climatological phenomena are also employed in this study: • • • • •

Southern Oscillation Index (SOI), 1900–96, derived from NCEP data; all-India summer monsoon rainfall (AISMR), 1900–96 (Parthasarathy et al., 1991, 1995); QBO, a zonal wind index at 30 hPa for the period 1953–96 (Marquardt and Naujokat, 1997, updated); North Atlantic oscillation (NAO), 1961–96 (Jones et al., 1997); Sahel rainfall (June–September 10–20 ° N, 15 ° W–30 ° E), based upon observed station data for the period 1961–95 (Hulme, 1994, updated).

3. VARIABILITY OF LAKE VICTORIA RAINFALL AND LEVELS 3.1. The rainfall series LVRS does not exhibit any marked trends across the century, although rainfall totals reveal an irregular harmonic pattern of quasi-periodic fluctuations on seasonal and annual time scales. This facet of the time series has been recognized in previous time series analysis (e.g. Rodhe and Virji, 1976). LVRS was Kalman-filtered to examine its low- (LF) and high-frequency (HF) characteristics. The data were filtered, with 50% cut-off, at 5-, 7-, and 10-year points. HF components are similar with all filter-lengths, and reveal little information. This is to be expected due to the strength of the seasonal cycle in East Africa rainfall. LF components suggest that an oscillation of 5–7 years is the predominant periodicity over the century. No decadal-scale fluctuations are evident. This LF oscillation would tend to suggest an ENSO influence on LVRS. In order to test this hypothesis, both the LVRS and a time series of the SOI series were subjected to spectral analysis (not shown) in order to investigate the dominant periodicities in both records. The results confirm the periods of oscillations discussed by authors including Rodhe and Virji (1976), Ogallo (1979), and Nicholson and Entekhabi (1986). Peaks between 5 and 6.5 years further suggest an ENSO influence, both accounting for similar amounts of variance in each record (in excess of 5%). Correlation coefficients (CCs) between ENSO and LVRS are presented in Table I. It is noticeable that LVRS correlates significantly with most variables, although those with MAM and OND are to be expected. The relationship is not as great as one may expect given the evidence for the influence of an ENSO-type Copyright  2003 Royal Meteorological Society

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Table I. Correlation coefficients between AISMR (Parthasarathy et al., 1991, 1995), SOI (taken from NCEP data), and LVRS (divided into Long Rains, MAM; Short Rains, OND; and annual periods) 1900–96. Significance levels: ∗ 5%, ∗∗ 1%

AIMSR SOI LVRSMAM LVRSOND LVRSAnnual

AISMR

SOI

LVRSMAM

LVRSOND

LVRSAnnual

1

0.35∗∗ 1

0.06 −0.01 1

−0.11 −0.39∗∗ −0.02 1

0.26∗ −0.18 0.55∗∗ 0.54∗∗ 1

fluctuation (r = −0.18). However, given the general strength of global SOI–rainfall relationships, the CCs do not appear to represent a statistical artefact. Individual high-rainfall years do not correlate linearly with El Niño events, evident by the 1961 and 1977–78 rainfall anomalies, which were not associated with ENSO years. However, some excessive rainfall events have been (in)directly associated with specific El Niño events, such as the recent 1997–98 episode, due to suppression of convection in regions of normally high rainfall resulting in a reversal of the prevailing wind regime across the Indian Ocean (Kousky et al., 1998; Birkett et al., 1999). One cannot discount the influence of the Southwest Asian monsoon (SAM) on LVRS, since the SAM dominates the atmosphere overlying the Indian Ocean. Annual correlations between AISMR and LVRS are significant at the 1% level (r = 0.26); however, correlations at the seasonal scale are not significant and, therefore, are not included in the remainder of the study. 3.2. The nature of the LVRS and impacts upon lake levels Following Mörth (1967) and Kite (1981), it is assumed that rainfall variability governs lake-level variability. This is evident on the annual scale. Figure 4 shows the mean monthly rainfall, and lake levels, extracted from the 1965–94 normal period. The rainfall pattern is bi-modal, exhibiting both Long (MAM) and Short (OND) Rains. The Long Rains are more abundant than the Short Rains, although the deviations from the mean are greater in OND (24.4 mm per season) than MAM (19.2 mm per season). It is apparent that a lag, of undetermined period, obscures the relationship between lake level and rainfall. Theoretically, lake levels might be expected to be directly affected by rainfall changes. Correlations between monthly rainfall and detrended lake-level data at zero and minus 1 month lag are very high (r > 0.9). However, the one to two degrees of freedom are not statistically sufficient to warrant any great merit in these results. Owing to different amplitudes in the changes of both variables the two series may become out of phase, and hence blur the causal relationship between them. The LVRS with Lake Victoria levels superimposed is shown in Figure 1. The impact of rainfall is most evident with specific rainfall episodes that are followed 1 to 2 years later by lake-level peaks, notably those during 1961–64, 1977–78 and, more recently, in 1997–98 (see Figure 11). Correlations performed over the century at a number of lags, at both monthly and annual resolution are presented in Table II. Mörth (1967) notes very high CCs between lake levels and Lake Victoria rainfall between 1958 and 1964 (r = 0.96), although it should be noted that Mörth utilized seasonal rainfall total residuals, exhibiting a high degree of autocorrelation, which may account for the high coefficient (Beresford, 1982). Since LVRS is founded upon land-based observation, and given Lake Victoria’s area in proportion to its catchment (roughly one quarter), it is conceivable that some of the rainfall variability contributing to lake-level variability is missed. Using satellite analysis of rainfall over Lake Victoria, Ba and Nicholson (1998) found that, although the frequency of cold cloud duration is roughly 25–30% greater over the lake than over land, the estimated lake rainfall is strongly correlated with lake basin rainfall. It is unlikely, therefore, that much of the unexplained lake-level variability originates from over-lake rainfall alone. The seasonal pattern of changes noted above on the annual scale appears to be superimposed as minor short-term fluctuations or oscillations upon a broader pattern of roughly cyclic behaviour. Annual correlations Copyright  2003 Royal Meteorological Society

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Figure 4. Monthly mean Lake Victoria catchment rainfall (bars) and lake level (line) (both series 1965–94)

Table II. Correlation coefficients between LVRS and the level of Lake Victoria over the course of the century and three sub-periods. Rainfall is lagged 1 to 7 years with respect to lake level over the century and 1 to 5 years for sub-periods. Mean annual lake level is utilized. Thus a 1 year lag represents rainfall during the preceding 12 months. Significance levels: ∗∗∗ 0.1%, ∗∗ 1%, ∗ 5% Lag of lake level (years)

1900–96

0

1

2

0.17

0.41∗∗∗

0.31∗∗

0.10 0.03 −0.19

0.44∗∗ 0.67∗∗∗ 0.33

0.13 0.17 0.49∗∗

3 0.23∗

4

5

6

7

0.14

0.14

0.16

0.21∗

— — —

— — —

Sub-periods 1900–30 1931–60 1961–96

0.10 −0.19 0.40

0.20 −0.44∗ 0.15

−0.13 0.02 0.10

reveal most significance at a 1 year lag, accounting for 17.1% of the variance in lake levels across the century (45.7% between 1931 and 1960). However, of potentially greater interest are the lags evident between 7 and 2–3 years. Coefficients between LVRS and lake levels decrease markedly after 7 years over the century, and after 5 years for the sub-periods.

4. ATMOSPHERIC CIRCULATION PATTERNS ASSOCIATED WITH LAKE VICTORIA RAINFALL 4.1. Velocity potential χ The principal aim of this section is to test the hypothesis that a local Indian Ocean Walker circulation cell was responsible for the anomalous OND 1961 rainfall episode. Velocity potential χ is utilized as the Copyright  2003 Royal Meteorological Society

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principal indicator of the atmospheric circulation, since it is easy to see the vertical motion components of the Walker circulation (Newell et al., 1996). This field has been utilized to examine aspects of the Madden–Julian oscillation and ENSO (Slingo et al., 1999). Within the East African context this variable provides a better indicator of circulation changes than sea-level pressure, for instance. One disadvantage of the variable is that it does not isolate horizontal linkages, and to overcome this we also use zonal winds at 200 and 850 hPa. 4.2. Global correlations with LVRS Hills (1979) contends that the circulation systems surrounding the principal rain-bearing seasons are distinct. In order to investigate this claim, global χ-fields at 200 and 850 hPa were correlated with seasonal rainfall extracted from LVRS over the 1961–96 period (LVRSOND ). The correlation between LVRSOND and global χ-fields at 850 hPa reveals two significant areas of CCs (5% level, not shown). Positive CCs are located in a broad longitudinal band encompassing Europe, Africa, and the Indian sub-continent. A zone of negative CCs extends northward from Antarctica to the Kamchatka Peninsula encompassing the zone of tropical convective maximum (Soman and Slingo, 1997). The situation at 200 hPa reveals a contrasting structure from that at 850 hPa. Four distinct zones of significant CCs are arranged throughout the tropics (Figure 5) in contrast to a hemispheric pattern illustrated by MAM correlations (not shown). Negative coefficients are located in the Indian Ocean area, including East Africa, and the central Pacific. Positive CCs are located in the tropical Atlantic and Brazilian Nordeste, and the western Pacific, including Australia. This pattern of CCs appears representative of branches of the circumglobal Walker circulation pattern. 4.3. Velocity potential anomalies during OND 1961 Monthly anomaly χ-fields, from the 1965–94 normal period, were plotted for the domain 40 ° N–40 ° S 0–180° . Results and analysis are limited to the 1961 OND season at σ0.2101 and σ0.995 . Anomalous upperatmosphere χ-field composites provide a good insight into the atmospheric circulation pattern surrounding

Figure 5. Velocity potential versus LVRS at 200 hPa. OND 1961–96. Statistically significant correlations at the 5% level (positive CCs, line; negative CCs, dashed) Copyright  2003 Royal Meteorological Society

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the anomalous rainfall event (Figure 6(a)). However, one must note that the patterns described below are predominantly manifest during November–December. October exhibited weak anomalies in the Indian Ocean. Divergence is marginally increased (−4 × 106 m s−1 ) in the western Pacific suggesting increased convection. The area of divergence over the Bay of Bengal and Yellow Sea is decreased, suggesting reduced convection in this region (Figure 6(a)). The western Indian Ocean and much of eastern and central Africa, including the Lake Victoria region, exhibit negative χ-anomalies (predominantly −2 × 106 to −4 × 106 m s−1 ). The region of maximum negative anomalies is located over the Horn of Africa, suggesting that there is increased divergence in this area, although the general pattern is still one of weak divergence. The area of convergence in the South Atlantic also possesses negative anomalies, thereby suppressing convective activity.

(a)

(b)

Figure 6. Composite anomaly velocity potential map (m2 s−1 ). OND 1961 at (a) σ0.2101 and (b) σ0.995 . Source: NOAA–CIRES Climate Diagnostics Center, Boulder CO; http://www.cdc.noaa.gov/ Copyright  2003 Royal Meteorological Society

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Surface χ-anomalies support the general picture presented at σ0.2101 . During OND, much of the eastern coast and North Africa exhibit positive χ-anomalies (∼1 × 106 m s−1 over Lake Victoria) that conform to increased convergence (Figure 6(b)). Areas at σ0.2101 with increased convergence correspond to regions of decreased convergence at σ0.995 , suggesting a suppression of convection in these regions. The zone of enhanced convergence over East Africa is displaced northward, relative to OND. Significant correlations (5%) between LVRSOND and χ at 200 and 850 hPa (not shown) do not illustrate any coherent patterns. This is to be expected, given the single degree of statistical freedom. However, lagged correlations at −1 month (LVRSOND versus χSON ) show coherent regions of CCs exhibiting a Northern Hemisphere–Southern Hemisphere (SH) difference in sign. In this case, a region of positive CCs reside over western Europe and the eastern Atlantic, with a sinuous band of negative CCs extending from southern Africa into the southern Indian Ocean. Given the lack of significant correlations, a t-test was performed between the normal period (1965–94) and OND 1961 velocity potential. Three significant regions exist. The largest region, positive mean differences, is located over South America, the South Atlantic, the eastern Pacific, and the USA. The largest differences are in the SH. Negative differences are located within the northern and central Pacific basin. Globally, this represents enhanced divergence over the Pacific and greater convergence over the broader South American region. A small area of positive mean differences in the Bay of Bengal corresponds to a reduction in divergence. This information suggests a distortion of the normal Walker circulation regime in the Indian Ocean described by Newell et al. (1974). A similar physical mechanism is proposed by Kousky et al. (1998) for the anomalous rainfall event in October–November 1997. Suppression of convection in a region of normally high rainfall probably contributed to a reversal in the east–west direct circulation cell that covers the Indian Ocean–Indonesia region, thereby contributing to enhanced rainfall over East Africa. 4.4. Wind anomalies during OND 1961 To validate this claim further, meridional v and zonal u wind data components were examined for OND 1961 to test the hypothesis that there was a reversal of the prevailing westerly wind regime in accordance with this Indian Ocean Walker cell. The 850 hPa u wind anomalies (not shown) reveal a general pattern of anomalous easterlies. These winds are of varying strength, depending upon location. The strongest easterly winds are located in the southern Indian Ocean and the Arabian Peninsula (6 m s−1 ), although winds in the western Indian Ocean are 4 m s−1 . Winds over the Lake Victoria catchment are principally weak westerlies on the northern shore with relatively stronger easterlies around the southern shore. The 200 hPa u winds, however, also reveal a predominantly easterly pattern. This is not to be expected if divergence is taking place. In view of this evidence it is not possible to accept the hypothesis that a local Walker circulation cell is operative within OND 1961. Further evidence is required to substantiate this claim. Height–longitude and height–latitude χ plots are utilized to provide an indication of broader circulation patterns over the Indian Ocean region. Figure 7(a), depicting the longitudinal cross-section (30 ° N–30 ° S, 0–180° ) of χ for OND 1961, illustrates that a Walker circulation cell is indeed not operative in the Indian Ocean sector. This region is overlain by positive χ values, suggesting a broad zone of convergence, although the anomaly plot (Figure 7(b)) depicts enhanced convergence over East Africa, whereas the eastern Indian Ocean shows weak positive χ anomalies at the surface and upper levels. The v wind exhibits a strong southerly component through the Mozambique Channel and along much of the East African coast. Okoola (1999) has observed a similar phenomenon in his study of the Long Rains as an important determinant of wet or dry seasons. The premise of this association is that a burst of cool air from the southern Indian Ocean may accentuate rainfall anomalies by inducing atmospheric instability and potentially creating convectional instability of the second kind (CISK). 5. PRINCIPAL PATTERNS IN VELOCITY POTENTIAL FIELDS 5.1. Indian Ocean circulation EOF analysis has previously been employed in East African rainfall variability studies (e.g. Ogallo, 1988), but not for χ-fields. From the preceding analysis of χ-fields it is apparent that the clearest signals appear Copyright  2003 Royal Meteorological Society

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(a)

(b)

Figure 7. Composite height–longitude velocity potential plot. Observed values (m2 s−1 ) for October–December 1961. Averaged between 30 ° N and 30 ° S. Source: NOAA–CIRES Climate Diagnostics Center, Boulder CO; http://www.cdc.noaa.gov/. (b) Same as (a), but anomaly values for same domain

in the upper troposphere. Thus χ data at 200 hPa is initially subjected to EOF analysis over the domain 20 ° N–20 ° S 20–110 ° E, again for OND over the period 1961–96. The purpose of EOF analysis in this context is to ascertain if there are any specific patterns in the atmosphere related to the anomalous rainfall episode in 1961. Given the presence of the ENSO signals during the 1963 and 1997 rainfall episodes, the 1961 event is of particular pertinence. The first six EOF patterns for OND were retained for analysis (Table III). The resultant time series for each pattern were correlated with a variety of climatological series, including LVRS, SOI, NAO and Sahel rainfall, to ascertain which phenomena were associated with each pattern (Table IV). OND EOF1 essentially describes the dominant El Niño mode with an east–west dipole pattern (not shown). This claim is substantiated by the significant correlations between EOF1 time series and both LVRS and SOI at the 1% level, although the directions of the CCs are opposite. The strongest positive loadings (>0.9) are located in the western Indian Ocean, suggesting a movement of the usual centre of convergence over coastal and oceanic areas. The time series of OND EOF2 resembles that of Sahel rainfall (not shown). However, correlations between this series and Sahel rainfall (1961–95) prove insignificant. In contrast, EOF3 correlates significantly (5% level) with NAOOND , SOI, and AISMR. This is an interesting result, given that this EOF accounts for Copyright  2003 Royal Meteorological Society

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Table III. Proportion of variance accounted for by first six EOF patterns for OND 1961–96 at 200 hPa EOF series

Proportion of variance (%)

1 2 3 4 5 6

50.45 29.17 9.10 3.06 2.35 1.68

Table IV. Correlations between EOF time series and climatological time series for OND 1961–96 at 200 hPa. ∗ Significant at 5% level. ∗∗ Significant at 1% level

LVRSOND AISMR SOI SAHEL NAOOND

EOF1

EOF2

EOF3

EOF4

EOF5

EOF6

−0.64∗∗ 0.02 0.55∗∗ −0.20 −0.20

0.23 −0.31 0.03 0.25 0.13

0.04 −0.34∗ −0.35∗ −0.47∗∗ 0.35∗

−0.10 0.05 −0.046 −0.10 0.17

−0.40∗∗ −0.20 −0.15 −0.16 0.27

0.11 0.06 0.06 0.11 0.01

only 9% of variance. Could the relationship with the NAO simply be a statistical artefact or is it a real physical mechanism? Correlations between LVRS and χ-fields (previously mentioned) substantiate a possible mechanism. SOI is again important (also correlating significantly with NAO r = 0.52 between 1961 and 1996). This is also the only EOF to correlate significantly with AISMR. Of greatest interest within the scope of study is EOF5. This series distinguishes the 1961 event and correlates significantly (1%) with LVRSOND (r = −0.4) and accounts for 15.8% of the variance in LVRSOND (Figure 8(a)). This series distinguishes the 1961 event. The pattern of this mode of variability in tropical χ-fields is essentially tri-polar in nature (Figure 8(b)). Small negative loadings (0.09–0.31) are located over continental Africa, except the Horn of Africa. This suggests a modest weakening of divergence in this area. However, of greater interest is the development of a north–south axis over the tropical Indian Ocean. Positive loadings prevail through the Indian Ocean, with a region of negative loadings over peninsular India, the Arabian Sea, and Bay of Bengal. The corollary of this result is of great importance within the context of this paper. The original aim of this section was to ascertain whether an Indian Ocean branch of the Walker circulation was responsible for rainfall variations, particularly OND 1961. EOF5, however, suggests the opposite scenario. Indeed, this spatial pattern of loadings, although weak, represents a meridional rather than zonal circulation regime in the Indian Ocean. In the southern Indian Ocean the zone of upper-tropospheric convergence is accentuated with an increase in divergence over peninsular India. This may be associated with a stronger monsoonal outflow, although winds at this time exhibit a more easterly component toward the convergence zone over East Africa. Caution is necessary to avoid over interpretation of the importance of the EOF5 pattern, which only explains 2.35% of the variance. Further analysis of the north–south axis over the tropical Indian Ocean is required to strengthen this interpretation of EOF5. 5.2. Greater tropical region Given the small spatial domain of the above analysis, rotated EOF analysis would not elucidate any clearer patterns. In order to examine whether the EOF5 pattern observed above is manifest on a larger tropical scale, Copyright  2003 Royal Meteorological Society

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(a)

(b)

Figure 8. Velocity potential OND EOF5 at 200 hPa. For period 1961–96 and domain 20 ° N–20 ° S 25–105 ° E. (a) Time series of EOF5; (b) spatial loadings


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200 hPa OND χ-fields over the domain 35 ° N–37.5 ° S 0–180° were subjected to EOF analysis. Given the greater spatial domain, it is expected that EOF5 will be reduced in importance. The results are presented below. In order to avoid confusion, EOFs for this larger region will be asterisked (e.g. EOF1∗ ). A similar analysis between EOF∗ s and climatological series was undertaken. Those time series with significant correlations are presented for OND. OND EOF∗ s suggest a number of different modes of variability with further associations with LVRS, NAO, and monsoonal and Sahelian rainfall (illustrated by Table V). OND EOF1∗ (accounting for 44.95% variance) correlates significantly (1%) with LVRSOND , SOI, and the preceding NAOMAM . The spatial pattern of loadings reveals a dipole structure with opposing areas of loadings with values greater than 0.8: positive loadings over eastern and southeastern Africa, and negative loadings over the western Pacific. ENSO appears most dominant. However, although EOF1∗ correlates significantly with LVRS, the time series does not identify the 1961 anomaly, where one would expect to observe marked negative eigenvalues (not shown). OND EOF3∗ (12.5% variance) correlates significantly with LVRSOND and Sahelian rainfall. This time series clearly identifies an anomalous event in the early 1960s, although 1962 appears greater than 1961 (Figure 9(a)). There is a marked change in the time series at the end of the 1960s. Loadings in this pattern are principally positive, apart from two negative regions (Figure 9(b)). The first region in the South Atlantic corresponds to enhanced divergence, as does the region in the eastern Indian Ocean. This results in increased convergence over East Africa, resulting in the negative correlation with LVRSOND . There is also increased convergence over the Sahelian area. Of great interest in OND EOF3∗ is the negative correlation between a mode of variability in tropical χ-fields at 200 hPa and Sahelian rainfall. This is a consequence of the decline in rainfall experienced from the 1960s that signifies the uniqueness of the Sahel region. The pattern of loadings would be reversed in the early 1960s. Significant correlation (1%) between OND EOF3∗ and the following June–September rainfall in the Sahel provides an interesting result. A greater understanding of this variability may aid predictive skill of Sahel seasonal rainfall in future. OND EOF4∗ (7.73% variance) also correlates significantly with LVRSOND . This time series clearly distinguishes the 1961 event and other positive rainfall years (e.g. 1977, Figure 10(a)). This pattern describes a mode of variability with decreased convergence over Africa and increased divergence over Indonesia and Australia (Figure 10(b)). There is enhanced convergence over the Indian Ocean and decreased convergence in the South Atlantic. 6. DISCUSSION 6.1. Rainfall series Lake Victoria catchment rainfall, represented by LVRS, does impact upon lake levels. This association is surprisingly low on monthly time scales (r = 0.17) given Mörth’s (1967) results using seasonal residuals Table V. Table of correlations between OND EOF∗ s for broader tropical region and climatological phenomena. MAM refers to correlations with preceding season in same year. (+1) represents correlation with following year. ∗ Significant at 5% level. ∗∗ Significant at 1% level EOF∗ LVRSOND LVRSMAM SOI SAHEL SAHEL (+1) AISMR AISMR (+1) NAOOND NAOMAM

1

2

3

4

10

−0.47∗∗ 0.07 0.61∗∗ −0.08 0.04 0.11 −0.12 −0.23 −0.44∗∗

0.26 0.22 −0.15 0.16 0.08 −0.43∗∗ 0.17 0.22 0.27

−0.39∗∗ −0.12 −0.14 −0.49∗∗ −0.43∗∗ −0.30 0.04 0.30 −0.23

−0.43∗∗ 0.15 0.29 0.15 0.14 0.04 0.05 −0.27 −0.18

0.08 −0.11 −0.03 −0.06 0.03 0.03 0.13 −0.36∗∗ 0.12


13 0.07 −0.34∗ 0.10 0.06 0.02 −0.09 −0.06 −0.07 −0.04

14 −0.13 0.21 0.07 −0.06 0.14 0.13 0.41∗∗ −0.09 −0.24

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(a)

(b)

Figure 9. Velocity potential OND EOF3* at 200 hPa. For period 1961–96 and domain 35 ° N–37.5 ° S 0–180 ° E. (a) Time series of EOF3*; (b) spatial loadings

(r = 0.96), but better at annual time scales with up to 45% of lake-level variance explained by annual rainfall fluctuations during 1931–60. The annual LVRS record is dominated by ENSO, a feature common to most East African lakes (Nicholson, 1997, 1999). However, seasonal associations reveal a greater association between SOI and OND (r = −0.39, significant at the 1% level) than MAM (r = −0.01). Mean seasonal composite correlations between LVRS (MAM and OND) and global 200 hPa χ-fields illustrate the different structure of atmospheric patterns pertaining to each season. OND exhibits a distribution of CCs resembling branches of the tropical Walker circulation in contrast to the MAM hemispheric pattern. Numerous factors may account for Copyright  2003 Royal Meteorological Society

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V. V. MISTRY AND D. CONWAY (a)

(b)

Figure 10. Velocity potential OND EOF4* at 200 hPa. For period 1961–96 and domain 35 ° N–37.5 ° S 0–180 ° E. (a) Time series of EOF4*; (b) spatial loadings

this phenomenon. Phase changes in the southern oscillation frequently occur during March–April, impeding global ENSO-related predictability, thereby disrupting linear relationships influencing the region (Meehl, 1987). ENSO is manifest primarily between December and February. Thus, ENSO influence is likely to be more evident in the preceding months (namely OND). Despite the significant relationship exhibited by SOI and LVRSOND , the association is non-linear. El Niño years correspond to both wet and dry East African rainfall seasons. This may be a consequence of the decadal variability of ENSO activity. In addition, the long-term occurrence frequency of ENSO events has not been Copyright  2003 Royal Meteorological Society

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resolved on centennial or millennial time scales (Solow, 1995; Rajagopalan et al., 1997). Kane (1999) notes that greatest associations between El Niño events and SAM are evident during ‘unambiguous ENSOW’ years (El Niño years in which SOI minima and Pacific SST maxima occurred in the middle of the calendar year). This may be applicable in the East African context and may enhance prediction schemes founded upon the SOI (e.g. Farmer, 1988). 6.2. The early 1960s anomalous rainfall episode Analysis of the early 1960s rainfall data reveals two distinct events contributing to elevated Lake Victoria levels. These events had a profound effect upon the hydrodynamic behaviour of Lake Victoria, and high levels were maintained at pre-1960 levels for over 20 years (Flohn, 1987). The initial effect was a consequence of the 1961 Short Rains. These rains were augmented by subsequent heavy rainfall seasons, particularly in 1963. This accounts for the prolonged elevation of levels in contrast to the impacts of other events. After the extreme rainfall event in OND 1997 the lake level exhibits a steady decline from its peak level in May 1998 to return to its pre-1997 level by the end of 2000 (Figure 11) in line with unexceptional rainfall conditions during 1998–2000 (Conway, 2002). Such large lake-level fluctuations over short periods, particularly the prolonged rise after 1961, present a major challenge to the design of additional hydropower installations on the White Nile downstream of Lake Victoria (Waterbury, 2002).

Figure 11. Lake Victoria levels: in situ observations from January 1993 to August 1998; satellite-derived observations from September 1992 to December 2000 (Birkett, personal communication) Copyright  2003 Royal Meteorological Society

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6.3. An Indian Ocean Walker circulation cell? Kousky et al.’s (1998) mechanism for the 1997 event is plausible as a causal explanation for the 1961 event. χ-fields (at σ0.2101 and σ0.995 ) indicate the presence of suppressed convection in the Indonesian region. The u wind plots indicate the reversal of prevailing Indian Ocean westerlies. Initial evidence suggests the presence of an Indian Ocean Walker circulation cell. Subsequent analysis, however, leads to the rejection of this hypothesis in favour of another mechanism to describe the rainfall anomaly. Height–longitude plots reveal a lack of divergence in the eastern Indian Ocean. Height–latitude plots for both OND 1961 and 1977, another very wet year (Figures 12 and 13), illustrate a meridional circulation pattern over the Indian Ocean (20 ° N–40 ° S, 45–90 ° E). This pattern corresponds well with OND EOF5. This EOF time series correlates significantly with LVRSOND at the 1% level (r = −0.40). A potential diagnostic mechanism to describe factors responsible for the anomalous rainfall events based upon wind (u and v) and χ evidence is presented in Figure 14. In this model, convection in the Indonesian region is suppressed and the south Indian Ocean high is strengthened, resulting in a reversal to easterly winds across the Indian Ocean. These winds entrain moisture from the anomalously warm western Indian Ocean and penetrate into the East African highlands. This creates convectional instability in the Lake Victoria region and supplies latent heat energy to drive the enhanced convection. Instability is enhanced by a burst of cool air from the Mozambique Channel originating from the southern Indian Ocean. Moisture is advected from the Congo basin due to an increase in surface divergence in this region. Nakamura (1968) and Camberlin and Wairoto (1997) note the importance of westerly and diurnal flow contributing to enhanced rainfall. Hills (1979) notes the difficulty in interpretation of short-period rainfall patterns in relation to the mean anomaly flows and divergence patterns, largely because it must be demonstrated whether such patterns are related to the mean monthly conditions or result from perturbations therein. This tropical mode of variability is observed in 1961 and 1977. The question remains as to why this fluctuation occurs. Reverdin et al. (1986) contend that rainfall anomalies are responsible for locally forced circulation changes. Barnett (1984) has documented an association between anomalous surface circulation patterns and ENSO. In this relationship, one of the preferred spatial patterns of variability associates enhancement of Indian Ocean convergence with a simultaneous reduction over the central-eastern Pacific. This association is intrinsically related to SSTAs and latent heat release. SSTAs are the most feasible potential perturbers of the climatic system. Southern Indian Ocean SSTAs may have caused a southward displacement of the ITCZ, thereby permitting the meridional circulation and supplying latent heat energy.

Figure 12. Composite height–latitude plot between 20 ° N and 45 ° S, averaged over 45–90 ° E, for October–December 1961. Source: NOAA–CIRES Climate Diagnostics Center, Boulder, CO; http://www.cdc.noaa.gov/ Copyright  2003 Royal Meteorological Society

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Figure 13. Same as Figure 12, but for period October–December 1977

Easterly wind regime

Enhanced convection +ve and rainfall SSTAs

Suppressed convection and mass outflow 0° Pacific Ocean

Moist Congo air

Indian Ocean

Cool air 30°W

30°S

HIGH

30°E

0°

Africa

60°E

90°E

120°E

Strengthened Indian Ocean High

150°E

180°

Australia

Figure 14. Schematic diagram describing causal factors responsible for 1961 anomalous rainfall event in East Africa

6.4. A tropical mode of variability? Initial EOF analysis over the Indian Ocean domain revealed the importance of OND EOF5 to anomalous East African rainfall events. However, EOF∗ analysis of χMAM and χOND fields at 200 hPa over the period 1961–96 and the larger domain 35 ° N–37.5 ° S 0–180° also yielded notable results. These are discussed below. The most interesting result yielded by the OND EOF∗ analysis is the association between EOF patterns in the Indian Ocean region and Sahelian rainfall. The broader area patterns depict areas of increased convergence over the Sahelian region, suggesting an area of subsiding air descending over the Sahel region resulting in conditions conducive to drought. The OND EOF3∗ time series resembles the Sahel rainfall series (r = −0.49) and correlates with the following LVRSOND (r = −0.39, both correlations significant at the 5% level). This EOF series exhibits a marked transition in eigenvalues from the late 1970s. A longer time series would provide further evidence as to whether a real association exists between LVRSOND and June–September Sahel rainfall. OND EOF3∗ is also notable for the correlation between LVRSOND and Sahel rainfall in the following year (r = −0.43, significant at the 5% level). This association does not persist into the MAM Copyright  2003 Royal Meteorological Society

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EOF∗ s. A greater understanding of this pattern related to LVRS variability may prove useful in future longlead seasonal forecasting studies. The association between OND EOF∗ time series and the NAO requires further investigation. The correlations observed may perhaps indicate the NAO influence in northwest Africa (Lamb and Peppler, 1987, 1991). Ward (1994) describes a tropic-wide mode (TM) that associates Sahelian and Indian rainfall with tropical and extra-tropical variability. This operates in July–September. OND EOF5 is a distinct mode of variability and resembles Ward’s schematic representing a WET SAHEL. However, the feasibility of attempting to incorporate OND EOF5 into such a scheme would involve much conjecture, since EOF5 accounts for a small proportion of the total variance described by the EOF patterns and the Indian Ocean is subject to transition between southwest and northeast monsoon seasons. Nonetheless, a heuristic of this kind may prove to be a useful explanatory tool for East African rainfall variability. 6.5. The use of χ in tropical analysis This paper has illustrated one potential application of χ in a tropical context. Further usage has been illustrated by Kanumitsu and Krishnamurti (1978), Newell et al. (1996), and Slingo et al. (1999). χ does indeed hold advantages over various other climatological parameters, particularly with regard to identifying areas of convergence and divergence. The signal is clearer than divergence fields since the element of vorticity is removed. However, the field does not identify horizontal linkages (Newell et al., 1996). These results also illustrate the need to investigate χ-fields at a number of atmospheric levels. It would have been easy to accept the Indian Ocean Walker cell hypothesis on the basis of χ-data at σ0.2101 and σ0.995 alone. However, height–longitude/latitude plots reveal otherwise. Thus, one must consider using other variables in concert with χ to assess the validity of any results. The concept of cause and effect is called into question in relation to seasonal rainfall patterns. This is related to the resolution of analysis and the complexity of dynamic structure in the vicinity of the ITCZ. However, reliance on χ alone limits the degree of interpretability of data.

7. CONCLUSIONS Based upon the analysis of a number of climatological variables and testing of hypotheses, a number of conclusions have been reached in accounting for East African rainfall variations that have resulted in Lake Victoria level fluctuations, particularly in 1961. 1. Rainfall variability, expressed through the LVRS, has a demonstrable and significant (5% level) relationship with Lake Victoria levels. NCEP rainfall is found to be poor in representing the 1961 anomaly in East Africa. Running correlations indicate poor and mainly negative associations between NCEP rainfall and the observed LVRS. 2. ENSO is the predominant factor responsible for long-term variability of Lake Victoria rainfall, as revealed through spectral analysis of the LVRS and analysis of OND and MAM EOF(∗ ) series. 3. Indian Ocean SSTAs are more influential in determining moisture fluxes and supplying latent heat, particularly in the 1961 (and the subsequent MAM 1962) rainfall episode, than the Pacific Ocean (following Reverdin et al. (1986)). 4. The anomalous OND 1961 rainfall episode that contributed to the greatest measured rise in Lake Victoria level was not a consequence of an Indian Ocean cell in the circumglobal Walker circulation. 5. OND EOF5 (Figure 8) illustrates that a stronger meridional, rather than zonal, circulation develops over the Indian Ocean. This mode of variability correlates significantly with LVRS at the 5% level and may be associated with other anomalous high rainfall events (e.g. OND 1977) than OND 1961 alone. However, given that EOF5 explains only 2.35% of the variance, further work is necessary to explore the importance of this meridional interpretation. 6. The 1961 rainfall anomaly is found to be the consequence of a reversal in the prevailing wind regime in the Indian Ocean, positive SSTAs in the western Indian Ocean, and a burst of cool air from the southern Copyright  2003 Royal Meteorological Society

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Indian Ocean. These factors combine to create instability in the Lake Victoria region. The anomalous regime was maintained by a localized Indian Ocean El Niño-type phenomenon in 1961. This is similar in appearance to the event in 1997 (Saji et al., 1999; Webster et al., 1999). 7. Although the 1961 OND anomaly event describes a mode of tropical variability and fits Ward’s (1994) WET SAHEL scenario, it is not possible to connect this directly with the TM described for July–September. 8. An interesting association between OND χ EOF3∗ and lagged Sahelian rainfall is discovered. The significant relationship may provide an additional basis for enhancing long-lead seasonal forecasting schemes. 9. Velocity potential has the potential for being of great benefit in tropical climate analysis. However, there are limits to the interpretation of χ. χ should be utilized in conjunction with other variables, such as wind and sea-level pressure. ACKNOWLEDGEMENTS

The authors would like to acknowledge the assistance of Dr Mike Hulme and Dr Mick Kelly at the Climatic Research Unit, University of East Anglia, Norwich, Dr Richard Washington, at the School of Geography, University of Oxford, throughout the course of the research, and comments from an anonymous referee. We also acknowledge the use of the Hulme precipitation data set under the Department for the Environment, Transport, and the Regions contract no. EPG 1/1/85. In addition, the authors acknowledge the use of images from the NOAA-CIRES Climate Diagnostics Center, Boulder, CO, from their Web site at http://www.cdc.noaa.gov/. REFERENCES Asnani GC, Kinuthia JH. 1979. Diurnal variation of precipitation over East Africa. East African Meteorological Department Research Report No. 8/79. Ba MB, Nicholson SE. 1998. Analysis of convective activity and its relationship to the rainfall over the Rift Valley lakes of East Africa during 1983–90 using the Metoesat infrared channel. Journal of Applied Meteorology 37: 1250–1264. Barnett TP. 1984. Interaction of the monsoon and Pacific trade wind system at interannual time scales. Part II: the tropical band. Monthly Weather Review 112: 2380–2387. Bärring L. 1988. Regionalization of daily rainfall in Kenya by means of common factor analysis. Journal of Climatology 8: 371–389. Basalirwa CPK. 1995. Delineation of Uganda into climatological rainfall zones using the method of principal component analysis. International Journal of Climatology 15: 1161–1177. Basalirwa CPK, Odiyo JO, Mngodo RJ, Mpeta EJ. 1999. The climatological regions of Tanzania based on the rainfall characteristics. International Journal of Climatology 19: 69–80. Beresford AKC. 1982. Recent climatic changes in East Africa: lake levels, rainfall and upper airflow. PhD thesis, School of Environmental Sciences, University of East Anglia, UK. Birkett C, Murtugudde R, Allan T. 1999. Indian Ocean climate event brings floods to East Africa’s lakes and the Sudd Marsh. Geophysical Research Letters 26: 1031–1034. Cadet DL, Beltrando G. 1987. Relationship between surface fields over the Indian Ocean and rainfall over East Africa. Report of the Third Session of the SCOR–IOC/CCCO Indian Ocean Climate Studies Panel UNESCO/ICSU; 13–17. Camberlin P. 1995. June–September rainfall in north-eastern Africa and atmospheric signals over the tropics: a zonal perspective. International Journal of Climatology 15: 773–783. Camberlin P, Wairoto JG. 1997. Intraseasonal wind anomalies related to wet and dry spells during the “long” and “short” rainy seasons in Kenya. Theoretical and Applied Climatology 58: 57–69. Charney JG. 1975. Dynamics of deserts and drought in Sahel. Quarterly Journal of the Royal Meteorological Society 101: 193–202. Charney JG, Shukla J. 1981. Predictability of monsoons. In Monsoon Dynamics, Lighthill MJ, Pearce RP (eds). Cambridge University Press: Cambridge, UK. Conway D. 2002. Extreme rainfall events and lake level changes in East Africa: recent events and historical precedents. In The East African Great Lakes: Limnology, Palaeolimnology and Biodiversity, Odada EO, Olago DO (eds). Advances in Global Change Research, vol 12. Kluwer: Dordrecht. Conway D, Hulme M. 1993. Recent fluctuations in precipitation and runoff over the Nile sub-basins and their impact on main Nile discharge. Climatic Change 25: 127–151. Farmer G. 1988. Seasonal forecasting of the Kenya coast Short Rains 1901–84. Journal of Climatology 8: 489–497. Flohn H. 1987. East African rains of 1961/62 and the abrupt change of the White Nile discharge. Paleoecology of Africa 18: 3–18. Hills RC. 1978. The organisation of rainfall in East Africa. Journal of Tropical Geography 47: 40–50. Hills RC. 1979. The structure of the inter-tropical convergence zone in equatorial Africa and its relationship to East African rainfall. Transactions of the Institute of British Geographers 14: 329–352. Hulme M. 1994. Validation of large-scale precipitation fields in general circulation models. In Global Precipitations and Climate Change, Desbois M, Desalmand F (eds). NATO ASI Series. Springer-Verlag: Berlin, Germany. Copyright  2003 Royal Meteorological Society

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