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INTERNATIONAL JOURNAL OF CLIMATOLOGY, VOL. 17, 235–265 (1997)

INTERANNUAL VARIABILITY OF SOUTH-EASTERN AFRICANSUMMER RAINFALL. PART 1: RELATIONSHIPS WITH AIR–SEA

INTERACTION PROCESSES

ALFREDO ROCHA

Departamento de Fı´sica, Universidade de Aveiro, 3800 Aveiro, Portugalemail:[email protected]

AND

IAN SIMMONDS

School of Earth Sciences, University of Melbourne, Parkville, Victoria, 3052, Australiaemail: [email protected]

Received 29 September 1995Revised 28 June 1996Accepted 4 July 1996

ABSTRACT

This paper investigates the role that air–sea interaction processes may play in interannual variability of south-eastern Africansummer rainfall. The principal spatial modes of south-eastern African summer rainfall are first identified using principalcomponent analysis. Four modes are retained. The most important mode of variability is found to represent rainfall variabilityover most of the domain, particularly in the regions to the south.

The influence of ENSO (as measured by the SOI) on summer rainfall is investigated in detail for different SOI leads. Therelationship is such that during the summer following the onset of an ENSO event, south-eastern Africa tends to experiencedry conditions. Strongest relationships are found with the SOI leading rainfall by about 3 to 6 months.

A second index, the Brandon–Marion Index (BMI) which is indicative of changes in the pressure field over the IndianOcean correlates with rainfall better than the SOI. Strongest correlations are found when this index leads rainfall by about 1 to3 months. More importantly, a partial correlation analysis reveals that the BMI influences rainfall independently of ENSO.Both the SOI and the BMI are potential predictors of summer rainfall.

An investigation of rainfall associations with global SST anomalies reveals areas in the tropical Indian and Pacific Oceansthat are linked with rainfall changes over the subcontinent. The relationship is such that warm anomalies tend to be followedby dry conditions over much of south-eastern Africa. Strongest relationships are found when SSTs lead the rainfall season byabout 1 to 3 months.

Well-defined atmospheric anomalies are identified during dry south-eastern African summers. These include, amongstothers, anomalously warm tropospheric temperatures and marked low-level cyclonic circulation anomalies over the centralIndian Ocean, which generate abnormally weak easterly winds along much of the south-eastern coast of Africa. Theseperturbations to the low-level flow divert moisture from the continent and result in precipitation decreases.

An important and related finding is the fact that the SST–rainfall link over the Indian Ocean remains strong after the ENSOeffects have been removed, suggesting that the atmospheric circulation anomalies observed over south-eastern Africa duringdry summers, are linked mainly to SST anomalies over the Indian Ocean. This hypothesis will be tested in a companion paperthrough a series of GCM simulations.

KEY WORDS:south-eastern Africa; principal component analysis; correlation; anomaly fields; air–sea interaction; summer rainfall; atmosphericcirculation; Brandon–Marion Index.

1. INTRODUCTION

Extremes of interannual climate variability can often create stresses in many aspects of human life. Drought hasbeen recognized as a common occurrence in many parts of south-eastern Africa, and is characterized by arecurrent distribution in time and spatial coherence over large areas (Nicholson, 1986a,b). These can beexemplified by the severe droughts of 1982–83 and 1991–92, which spread famine and distress amongst most

CCC 0899-8418/97/030235-31 $17.50# 1997 by the Royal Meteorological Society

south-eastern African countries. A large proportion of the subcontinent experiences a single rainy season centredon January, and its failure leaves the local population deprived of water for the rest of the year.

Most of the research on the year-to-year variability of summer rainfall in south-eastern Africa has been carriedout for limited areas, particularly South Africa. Some of those studies have concentrated on describing thetemporal behaviour of drought and the associated pressure anomalies that develop over the region andsurrounding oceans (e.g. Rubin, 1956; Miron and Tyson, 1984; Tyson, 1984).

During the last decade or so a considerable amount of research has been undertaken seeking non-local causesof south-eastern African drought. Particular attention has been given to the influence of the El Ninõ–SouthernOscillation (ENSO) phenomenon on summer rainfall (e.g. Dyer, 1979; Ropelewski and Halpert, 1987, 1989;Lindesay, 1988a; Kiladis and Diaz, 1989; Matarira, 1990). However, these studies have often been performed forrestricted areas or using coarse rainfall networks. Also, lag relationships between ENSO and summer rainfallhave rarely been investigated in detail.

Evidence of an association between ENSO and rainfall in the region has been documented in many studies(Pittock, 1983; Nicholson and Entekhabi, 1986; Schulze, 1986; Lindesay, 1988a; van Heerdenet al., 1988;Matarira, 1990). The relationship, which explains about 20 per cent of rainfall interannual variability (Lindesay,1988b), is such that dry conditions tend to occur during ENSO episodes. Lindesay and Vogel (1990) analyseddocumentary rainfall data since 1820 and show this relationship to be stable.

Using annual rainfall data for stations in the tropics, Stoeckenius (1981) has also identified an area in centralSouth Africa that correlates with an annual SO index. In a global-scale analysis of monthly rainfall variability,Ropelewski and Halpert (1987) and Kiladis and Diaz (1989) have reported a tendency for drier than normalconditions to occur over south-eastern Africa during the summer following an ENSO event. Janowiak (1988)confirms the ENSO–rainfall link for a region immediately to the north of the summer rainfall area, in equatorialeastern Africa, which exhibits concurrent rainfall anomalies of opposite sign, that is, wet conditions during ENSOevents. This dipole pattern between south-eastern and eastern equatorial Africa is shown in Figure 1 by the two

Figure 1. The two core regions represent ENSO related interannual rainfall anomalies as obtained by Repelewski and Halpert (1987). Thesigns represent those of rainfall anomalies during ENSO events. Stars represent rainfall stations used in this study. The crosses represent the

locations of St Brandon and Marion Island, used to derive an index in section 2

236 A. ROCHA AND I. SIMMONDS

core areas of ENSO related interannual rainfall anomalies (reproduced from Ropelewski and Halpert, 1987) andis also present in the study performed by Nicholson and Entekhabi (1986).

In this study the ENSO–rainfall link is investigated using a relatively fine rainfall network covering most of thesummer rainfall region of south-eastern Africa. The most important modes of interannual rainfall variability areidentified over the subcontinent using principal component analysis (PCA). Their association with an index ofENSO is analysed and contrasted with the spatial signature of ENSO over the region. Lag relationships up to 12months are detailed in an attempt to evaluate the potential of ENSO as a predictor of south-eastern Africansummer rainfall. We will also examine the link between rainfall projected on to these modes of variability and ameasure of the regional circulation, in an attempt to determine factors that are associated with south-easternAfrican rainfall, but which are independent of ENSO.

Because ENSO is a consequence of air–sea interaction processes, a physical understanding of the ENSO–rainfall link might be best achieved if changes at the air–sea interface are investigated. Large-scale sea-surfacetemperature (SST) anomalies have the potential to generate imbalance in the heat-flux field, which in turn can setup anomalous atmospheric circulation and rainfall patterns (Horel, 1982; Rasmusson and Carpenter, 1982). Theymay persist for several months over large areas, and they appear as the most easily monitored potential perturbersof the climatic system. However, it is still unclear to what extent the SST anomalies are a causal factor asopposed to simply being a response to large-scale circulation changes that produce both SST and rainfallanomalies.

Here we are concerned mainly with the identification of SST anomalies (not necessarily related to ENSO) thatare associated with interannual variations in climate, in particular large-scale rainfall, in the summer rainfallregions of south-eastern Africa. We have carried out an observational study of the possible links between summerrainfall and world-wide SST anomalies, and related large-scale atmospheric features. Verification of thehypothesis suggested by this paper will be achieved in a subsequent paper through a modelling approach.Meanwhile, one should mention that this study is not concerned directly with the synoptic systems whereby thelarge-scale circulation changes generate rainfall anomalies. Rather, attention is focused upon the large-scalecirculation adjustment to SST forcing and consequent rainfall changes.

The main motivation behind the research to be carried out here is the hope that knowledge emerging from itwill be useful for future forecasting techniques of summer rainfall in the region. Emphasis will thus be placed onlead–lag relationships.

2. DATA AND ANALYSIS TECHNIQUES

2.1. Rainfall data

Monthly rainfall data for the summer (November to March) for the period 1951–1989 were obtained from anumber of different sources (Lindesay, pers. comm.; Mulenga, pers. comm.; Instituto Nacional de Meteorologia(Mozambique); ESSA, 1967; NCDC, 1971 and later years). Owing to the highly variable nature in time and spaceof monthly rainfall totals, it proved to be difficult to check for irregularities in the data. However, a few simplequality controls were applied. The final set comprised 85 stations covering an approximate area of 2 800 000 km2,representing an average density of about 10 stations per 330 000 km2. The location of these stations is displayedin Figure 1. For each station, summer seasonal totals were subsequently computed.

The data were tested for normality by computing the skewness and kurtosis quantities. Both the monthly andseasonal data showed distributions far from normal and, therefore, several of the most common transformationswere applied to these data, namely square-root, cube-root, logarithmic, and natural logarithmic transformations.After a careful examination of the transformed data sets we decided to adopt the square-root transformation, therainfall distribution of which assumed a near-normal form. This transformation has been applied elsewhere tomonthly rainfall amounts (e.g. Wright, 1974; Whetton, 1988).

Next, the seasonal cycle was removed from data by subtracting each monthly (and seasonal) value from itsrespective monthly (and seasonal) average calculated for the period 1951–1989. These anomaly data were thenstandardized by the respective monthly (and seasonal) standard deviation, resulting in time series of average zeroand unit standard deviation. This procedure facilitates the comparison of stations with different rainfallvariability.

SOUTH-EAST AFRICAN RAIN: PART 1 237

2.2. The Southern Oscillation Index

As an index of ENSO we use in this study the Southern Oscillation Index (SOI) as derived by the ClimateAnalysis Center (1986). This SOI is the normalized difference between the normalized mean monthly mean sea-level pressures at Tahiti and Darwin. Monthly values of the SOI from 1950 to 1989 were obtained from themonthly issues of the Climate Analysis Center (1983 and later years). Next, all data were filtered using a 3-monthmoving average. As shown by Trenberth (1984), such a filter can increase the SOI signal-to-noise ratio (mostlycaused by small-scale disturbances of the circulation) from 1�44, for monthly data, to 1�97 without losing thevariance on the ENSO time-scales. However, one is aware that such a procedure reduces the effective number ofdegrees of freedom as explained by Trenberth (1984). Table I displays ENSO and anti-ENSO events that haveoccurred since 1950, as classified by van Loon and Shea (1985) (up to 1985) and Kiladis and Diaz (1989) (from1986 to 1989).

2.3. A circulation index over the Indian Ocean

A number of studies have related dry and wet conditions in southern Africa with the surface pressure anomalyfield over the region and neighbouring oceans (e.g. Tyson, 1981; Miron and Tyson, 1984; Matarira, 1990). Mostof these studies have obtained results that are consistent, in that, at interannual time-scales, during wet yearslower than normal pressure occurs over southern Africa, with the reverse in dry years. However, the structure ofthe wet and dry composite pressure anomalies over the adjacent oceans is not so clear. Tyson (1981) and Mironand Tyson (1984) have reported that during wet summers pressure is abnormally high to the south-west over theSouth Atlantic, and to a lesser extent over the Indian Ocean to the south-east. Using summer 1000 hPa pressureanomaly data, Matarira (1990) has shown that during dry south-eastern African years positive pressure anomaliesoccur over the land, whereas the surrounding oceans experience lower than normal pressures, with the exceptionof an area centred approximately at Marion Island (46�530S, 37�520E) where anomalies are strongly positive. Thisseems to indicate that the mid-latitude trough associated with the standing waves 1 and 3 (Tyson, 1981), which isnormally located at about 30�E, shifts eastwards. The trough and its coupling with tropical easterly waves are

Table I. ENSO and anti-ENSO events from1950 to 1989 (Events up to 1985 wereobtained from van Loon and Shea (1985) andthose from 1986 to 1989 from Kiladis and

Diaz (1989))

Warm events Cold events(ENSO) (anti-ENSO)

1951–19521953–1954

1954–19551957–19581963–1964

1964–19651965–1966

1966–19671969–1970

1970–19711972–1973

1973–19741976–1977

1978–19791982–19831986–1987

1988–1989


related to major cloud bands, which usually form over the subcontinent in summer (Kuhnel, 1989). These cloudbands are one of the most important rain-bringing systems in southern Africa (Harrison, 1986).

To gain an insight into the importance of pressure changes over the adjacent oceans and how they relate tosouth-eastern African rainfall, a circulation index was constructed. Monthly mean sea-level pressure data for twostations in the Indian Ocean were obtained for the period 1955–1988. These stations, Marion Island and StBrandon (16�270S, 59�370E) (represented by the crosses in Figure 1), are located in two ocean areas of Matarira’spressure composite map corresponding to dry summers (his Figure 4(c)), which show high positive and negativepressure anomalies, respectively. Monthly normalized pressure difference time series between St Brandon andMarion Island were constructed in the same way as the SOI was using data for Tahiti and Darwin (monthly valuesof the BMI are presented in the Appendix). This index, which we name the Brandon–Marion Index (BMI), can beseen as a measure of changes in the position of the trough. The BMI will assume negative values when the troughshifts eastwards. Note that Harrison (1983) has shown that during dry South African summers the cloud bandsassociated with the trough are located more to the east towards the Indian Ocean. As for the SOI, this time serieswas filtered using a 3-month moving average in order to eliminate high frequency oscillations.

2.4. Ocean surface data

Monthly averages of SST, mean sea-level pressure (MSLP), surface specific humidity and wind were obtainedfrom theComprehensive Ocean–Atmosphere Data Set(COADS) (Slutzet al., 1985; Woodruffet al., 1987). Thedata were obtained on a 2�

62� grid over the world oceans for the period 1946–1987.Here we use the ‘trimmed’ subset of COADS and no extra quality control has been performed on these data. To

improve their spatial coverage the data were subject to the same preprocessing described by Simmonds andRocha (1991).

Sea-surface temperature trends were found in the data, particularly over the north and equatorial eastern PacificOcean, with typical values up toÿ0�5�C and 0�5�C per century respectively, and, because this paper is concernedwith interannual time-scales, the SST anomaly data set was detrended.

In addition, a seasonal subset was created by averaging, for every grid-point, the monthly values into fourgroups; December, January, and February (DJF), March, April, and May (MAM), June, July, and August (JJA),and September, October, and Novemer (SON).

2.5. The Southern Hemisphere atmospheric data set

In this study the Southern Hemisphere data analyses prepared by the World Meteorological Centre (WMC) inMelbourne, Australia, are also used. These data consist of daily analyses at 0000 UTC produced since 1972 andare derived from station, buoy, ship and satellite data. In this paper we use MSLP, wind temperature, and watervapour mixing ratio at various pressure levels. The data base and analysis scheme used are described by LeMarshall et al. (1985) and Guymer (1986). Temporal and spatial inhomogneities of the data set have beenmentioned by Trenberth (1979) and Guymer and Le Marshall (1980), amongst others. A comparison between themonthly climatologies of the WMC analyses and of a similar daily chart series developed by the South AfricanWeather Bureau (the NOTOS chart series) for the period 1951–1962 (Taljaardet al., 1969) is presented by LeMarshallet al., (1985). The reliability of the monthly mean sea-level pressure of the WMC data set has beenassessed by Jones (1991).

The daily analyses from June 1972 to December 1984 were used to compute monthly means for every monthand year. Owing to its relatively short coverage (12 years) it proved difficult to inspect the data for trends.

2.6. Correlation analysis

In this study we use the Pearson’s correlation coefficient (r) as defined by Spiegel (1988), with significaneassessed using Student’st-test. In all cases a two-tailed, 5 per cent significance level is adopted unless indicatedotherwise. It should be mentioned that this significance test assumes normally distributed populations for bothvariables. As described above, the frequencey distributions of all data sets were checked for normality. Partial


correlation is also applied to some variables in order to evaluate how a relationship between two of them isindependent of a third (Spiegel, 1988).

The reduction of the number of degrees of freedom due to autocorrelation can be a problem in correlationanalysis. How this is determined is not entirely clear, particularly with relatively short data records. Greenhut(1979) presents a method (following Davis, 1976) to calculate the effective number of degrees of freedom. Itshould be emphasized that only whenboth time series display an autoregressive structure, is there a reduction ofthe degrees of freedom (see equation (2) of Greenhut). Rainfall is a variable in all correlations computed in thispaper. We have computed the power spectrum of summer rainfall in the regions. All peaks were not significantlydifferent fromwhite noiseprocesses. So, even if other variables possess autoregressive structure, and it is knownthat the SOI does (Trenberth and Shea, 1987), the persistence of rainfall anomalies are sufficiently weak to not beof great concern in our analysis. We emphasize that in this paper, data of all time series used to calculatecorrelations are separated by 12 months, so memory in sequential months does not affect the degrees of freedom.

2.7. Principal component analysis

Principal component analysis is a statistical technique that has the objective of identifying variability patternsin a data set. It has been applied in meteorology to fluctuations in mean sea-level pressure (e.g. Kutzbach, 1967;Trenberth and Paolino, 1981), geopotential height (e.g. Craddock and Flood, 1969; Horel, 1981), temperature(Diaz and Fulbright, 1981), and precipitation (Dyer, 1975), amongst other variables. In most studies, as here, theprincipal component (PC) patterns are spatial fields and their coefficient series represent their amplitude in time.This usually is referred to as an S-mode PCA (Richman, 1986). Principal component analysis was performedusing the correlation matrix of the standardized rainfall data. In order to obtain the PCs that best represent therainfall data, Varimax rotation was applied and the resulting PCs compared with those before the rotation. Wealso performed Oblimin rotation but its spatial patterns were similar to those of Varimax.

3. PRINCIPAL COMPONENTS OF SUMMER RAINFALL

In order to investigate relationships between large-scale rainfall changes and other atmospheric and oceanicvariables it is important that the most important modes of rainfall variability be identified.

To achieve this we carried out PCA on the monthly summer rainfall data. Gutman’s cut-off criterion of theeigenvalue series was adopted. By using this method, all eigenvalues smaller than unity were discarded. Thesame rule has been applied in numerous meteorological studies (e.g. Horel, 1981; ). By applying this truncationrule, the first 21 PCs are retained. These PCs were then rotated using the Varimax and Oblimin methods. Thelatter solution was not considered further because it gave results similar to the former.

Table II displays the percentage of the total variance associated with eigenvalues of each of the 21 unrotatedand rotated PCs. Figure 2 shows the sampling error for each of the first 15 eigenvalues of the unrotated solution ascalculated using the method of Northet al., (1982). The first four eigenvalues are well separated from each other.Eigenvalue pair four and five and pair five and six, although passing this cut-off criterion, have spacingscomparable to their sampling errors. All the remaining eigenvalues formdegenerate multipletsand can bediscarded according to this criterion. One should note the differences between cut-off rules. Whereas Gutman’struncation criterion discards eigenvalues beyond 21, the Northet al., rule retains only the first four eigenvalues.We also performed rotation only on the first four PCs, but the resulting spatial patterns (not shown) were to agreat extent present on the 21 PC rotation solution.

3.1. Principal component spatial patterns

The spatial loadings of the first four rotated PCs are displayed in Figure 3. Each of these PCs have their highestloadings located in one particular region. Principal component 1 represents a large-scale pattern of rainfallanomalies located just south of the mean ITCZ summer position. It coincides broadly with the first PC of the PCAperformed by Janowiak (1988) on African rainfall for the DJFM season, and with the south-eastern Africanrainfall anomaly type 4 reported by Nicholson (1986a). Principal component 2 lies mostly on the northern parts of


the Drakensberg Ranges and displays a similar pattern to that of Janowiak’s PC 3, south of 15�S. Principalcomponent 3 is located in regions with elevations between 500 and 1000 m. Principal component 4 occupies thegap between PC 1 and PC 2, slightly overlapping the latter.

In order to verify how well the PCs represent the underlying interstation correlation fields, the methodsuggested by Wigleyet al. (1984) and used by Richman and Lamb (1985) was adopted. For each PC, the stationwith the highest loading is identified. Point-correlations between rainfall time series (same data as used tocompute the correlation matrix) at this and all the remaining stations were performed. The resultant spatialcorrelation field was mapped and compared with the loadings of the PC. This procedure was carried out for allunrotated and rotated PC pairs.

A quantitative measure of the match between these two types of maps, the loadings and the point-correlations,is obtained by calculating thecongruence coefficient(Richman and Lamb, 1985). The difference between thecorrelation and the congruence coefficients is that the latter does not remove the means of the two variables.Therefore, it is not only a measure of pattern similarity, such as the correlation coefficient, but also of magnitudesimilarity. In sharp contrast with the unrotated PCs, almost all rotated PCs yield highcongruence coefficientswith a mean value of 0�79, against 0�09 for the unrotated modes over all 21 PCs.

It is believed that the spatial orthogonality constraint of the unrotated solution does not allow PCs to representthe clusters present in the data, partly occurring due the convective nature of rainfall in the region. On the otherhand, rotated PCs are able to isolate these clusters successfully. Hereafter only rotated PCs will be considered.

The main purpose here is to associate a region with each of the four PCs, and construct a respective time series.Therefore, we shall not use the time series of amplitudes (i.e. scores) to represent rainfall changes in these regionsfor the following reasons. The PC scores are a measure of the strength of PC spatial patterns (i.e. loadings) in timeand represent an average (over the whole area) of standardized station rainfall weighted by the respective stationloading (this latter normalized by the respective eigenvalue). Although they are dominated by stations with highloadings, in a particular year, if these stations have low rainfall and low-loading stations have high rainfall, therespective PC score is not as dominated by the stations with high loadings as it otherwise would be. For thisreason, regional rainfall time series were constructed based solely on the stations with the highest loadings of

Table II. The percentage of total varianceassociated with eigenvalues of each of the

first 21 unrotated and rotated PCs

PC Unrotated Rotated

1 24�7 16�62 9�9 6�23 5�1 5�04 3�9 4�85 3�3 4�66 2�7 3�27 2�4 3�28 2�2 2�99 2�1 2�9

10 1�9 2�911 1�8 2�812 1�7 2�513 1�7 2�314 1�6 2�215 1�5 2�016 1�4 1�917 1�4 1�818 1�4 1�819 1�4 1�820 1�2 1�621 1�2 1�6

Sum 74�6 74�6


each PC. These time series were obtained by considering only the area inside the 0�5 isopleth of every PC. The0�5 isopleth level was chosen for two reasons. Firstly, loadings are correlations and can, therefore, be subject tostatistical significance testing usually performed on the correlation coefficient. The critical correlation coefficientsignificantly different from zero at the 1 per cent significance level is 0�18, according to Student’st-test. The 0�5loading isopleth is, thus, highly significant. Secondly, by using this isopleth level little overlapping occursamongst the PCs, which means that regionalization can be accomplished without ambiguity. This method hasbeen applied by many authors (e.g. Walshet al., 1982; Ehrendorfer, 1987; Whiteet al., 1991). Examples ofalternative methods would be to assign each station to the PC to which they best relate (e.g. Karl and Koscielny,1982) or to consider a given isopleth level of the point-correlation maps (e.g. Mallants and Feyen, 1990).

Next, rainfall time series were obtained for each of the four regions by averaging the data from all stationswithin the region. The PC spatial pattern within each region was taken into account by weighting each stationdata by the respective PC loading.

The interregion correlations of summer rainfall were computed and are displayed in Table III. Region 1correlates positively with the other three regions but most highly with region 4. This may, despite the convectivenature of rainfall in the area, indicate the importance of the large-scale in determining summer rainfall. Regions 2and 4 correlate negatively with region 3. This north–south dipole is consistent with the south-eastern Africananomaly types 5 and 6 reported by Nicholson (1986a) and with PC 1 and PC 2 of Janowiak (1988).

Figure 2. The percentage of the total variance associated with each of the first 15 eigenvalues (unrotated solution) of south-eastern Africansummer rainfall. Bars denote the eigenvalue sampling error calculated using the method of Northet al., (1982)


4. RELATIONSHIPS BETWEEN ATMOSPHERIC INDICES AND RAINFALL

In this section we examine the associations between the SOI and the BMI, and rainfall. We consider the caseswhere the indices lead rainfall by up to 12 months and, for convenience, leads and lags are referred relative toJanuary rainfall (the rainfall season’s middle month).

Figure 3. Spatial loadings of the first four rotated PCs of south-eastern African summer rainfall. Isopleth levels areÿ0�7,ÿ0�5,ÿ0�2, 0�0, 0�2,0�7. Areas with loadings greater than 0�5 are stippled

Table III. Cross-correlations of summer rainfall between the fourmost important rainfall regions. Italic numbers represent correla-

tions different from zero at the 1 per cent significance level

Region 2 Region 3 Region 4

Region 1 0�44 0�39 0�77Region 2 ÿ0�34 0�68Region 3 ÿ0�03


4.1. SOI–rainfall associations

Figure 4 displays the standardized rainfall anomaly time series for regions 1 to 4. Year refers to the calendaryear at the end of the rainy season (e.g. 1983 means the rainy season starting in November 1982 and ending inMarch 1983). The ENSO and anti-ENSO years are denoted by ‘W’ and ‘C’, respectively. Inspection of these timeseries reflects what has been reported previously by some studies (e.g. Lindesay, 1988b; van Heerdenet al., 1988;Matarira, 1990) in that most dry summers tend to coincide with ENSO and wet summers with anti-ENSO events,particularly for regions 1, 2, and 4. However, it is also evident that some dry summers did not occur during ENSOyears. Indeed, the driest summer in regions 1 and 4 since 1950, namely 1968, was not associated with ENSO. Thesame applies for wet summers and anti-ENSO years. The general impression from Figure 4 is that, althoughrainfall and ENSO are related, the link is not strong.

In order to quantify the relationship, summer rainfall in the four regions was correlated with the SOI, the indexleading rainfall by up to 12 months. Figure 5 displays these lag correlations. Almost all correlations are positive,meaning that during ENSO years (negative SOI) the region tends to experience below normal rainfall. A featureof note is that correlations are strongest for region 2. For this region the correlation steadily increases with SOIlead decreasing, peaks at 4 months lead (r � 0�50 for September SOI), and decays thereafter. Correlationsare significant (1 per cent for SOI leads from 3 to 6 months. There are no significant correlations for regions1, 3, and 4.

In order to investigate the temporal structure of the SOI signal, the same lag correlations stratified by rainfallmonth were computed. Once more, highest correlations are observed for region 2 (Figure 6). For this region,December rainfall correlates more highly with the SOI than rainfall in the other months. For this rainfall monthcorrelation peaks for August and September SOI as it does for summer rainfall (see Figure 5). Despite the small,non-significant (1 per cent) correlations in regions 1, 3, and 4 (not shown), some features are worth mentioning.In general, the SOI correlates best with November, December, and March rainfall (the exception is for region 3,where January and February rainfall appear to dominate the relationship with the SOI). For region 3 (not shown)much of the correlations are negative. This might mean a change of signal, from positive to negative, in therainfall–SOI relationship from the south to the north.

4.2. BMI–rainfall associations

In this subsection, lag correlations between the BMI and rainfall are computed in the same way as for the SOI.Figure 7 presents the lag correlations between the BMI and summer rainfall, the index leading rainfall by up to 12months. The BMI–rainfall relationship is strongest in regions 1 and 4 and weakest in region 3. The correlationssteadily increase with decreasing BMI lead, peak when the index leads rainfall by 2 to 4 months (September toNovember BMI) and decay at 1 month lead. Region 2 displays a similar correlation curve when BMI leads, butvalues are lower than in regions 1 and 4, and only marginally significant (1 per cent) for BMI leads from 2 to 4months. Summer rainfall in region 3 appears to have no relationship with the BMI at any of the considered leadsand correlations are, therefore, not shown. A comparison with similar correlations with the SOI (see Figure 5)clearly highlights the greater rainfall forecast potential of the BMI for regions 1 to 4.

As for the SOI, we computed lag correlations between monthly stratified rainfall and the BMI, for BMI leadsup to 12 months. These are strongest for regions 1 (Figure 8), 2, and 4, but unlike with the SOI, highest valuesoccur during the mid-summer months of December, January, and February and for BMI leads of 1 to 4 months.For region 3 correlations are weak and not significant.

To unveil the spatial signature of the BMI, we correlated station summer rainfall with the BMI, the indexleading rainfall by up to 12 months. One of the strongest of these correlation patterns, that between October BMIand rainfall, is displayed in Figure 9. Most of the area with correlations greater than 0�4 (significant at the 1 percent significance level) fall within regions 1 and 4, in close agreement with the correlations between the BMI andseasonal rainfall in each of these regions. As defined, the BMI is negative when the pressure is abnormally lowover the Indian Ocean, just east of Madagascar, and above normal at Marion Island where the mid-latitude troughis preferentially located in summer. Positive BMI–rainfall correlations indicate reduced precipitation over thesubcontinent 2 to 4 months after the negative index anomalies take place or increased precipitation for reversed


Figure 4. Time series of summer rainfal index for regions 1, 2, 3, and 4. The SOI leads rainfall by up to 12 months. (ENSO and anti-ENSOevents are denoted by ‘W’ and ‘C’, respectively)


pressure anomalies (i.e. abnormally strong trough). The first scenario seems to be consistent with an eastwardshift of the preferred cloud band mean position towards the Indian Ocean during dry summers, as reported byHarrison (1986).

Next, the BMI–rainfall associations were investigated with the effects of ENSO removed. A partial correlationanalysis was performed between summer rainfall and the BMI with the effects of the SOI removed (the SOIsimultaneous with the BMI). Figure 10 displays these partial correlations for regions 1 to 4 (the index leadingrainfall by up to 12 months). Figure 10 is comparable with Figure 7. Monthly stratified partial correlations forregion 1 are shown in Figure 11 which can also be contrasted with Figure 8. It is evident that Figures 7 and 8 arealmost identical to Figures 10 and 11, respectively. One can therefore conclude that the BMI–rainfall associationis independent of the SOI. We have computed lag correlations between monthly SOI and BMI,

Figure 5. Lag correlations between the SOI and summer rainfall for regions 1, 2, 3, and 4. The SOI leads rainfall by up to 12 months.Correlations greater than 0.4 are different from zero at the 1 per cent significance level

Figure 6. Lag correlations between monthly summer rainfall and monthly SOI for region 2. SOI months refer to the year in which the rainfallseason starts. (The SOI leads rainfall except for November rainfall, and November and December SOI (zero lag and SOI lags by one month,respectively), and for December rainfall and December SOI (zero lag)). Correlations greater than 0�4 are different from zero at the 1 per cent

significance level


for lags fromÿ72 months (SOI leading) to� 72 months (SOI lagging). Correlations were all below 0�15 forlags betweenÿ12 and� 12 months. Strongest correlations (0�25) were found for the SOI leading the BMI by50 months, but even these were barely significant if the reduction of the degrees of freedom due to memory inboth time series is taken into account.

5. RELATIONSHIPS BETWEEN SEA–SURFACE TEMPERATURES AND RAINFALL

The main objective here is to identify large-scale ocean areas that are related with summer rainfall in south-eastern Africa. As with the atmospheric indices in section 4, our interest is twofold. Firstly, we hope to find SSTanomalies and rainfall lag associations, with the SSTs leading rainfall, which have some forecast potential.Secondly, these observed relationships, together with observed atmospheric circulation changes characteristic ofdry years, will be used to suggest a SST–rainfall physical link.

Figure 7. Lag correlations between the BMI and summer rainfall for regions 1, 2, 3, and 4. The BMI leads rainfall by up to 12 months.Correlations greater than 0�4 are different from zero at the 1 per cent significance level

Figure 8. As in Figure 6 but for the BMI and for region 1


5.1. Global correlation analysis

The SST–rainfall link is investigated by correlating summer rainfall in regions 1 to 4 with world-wide SSTanomalies in every 2�62� grid box. The seasonal (DJF, MAM, JJA, and SON) SST anomaly data sets are usedhere. Leads and lags are labelled, for convenience, with respect to the middle month of the rainfall season,January. We have correlated summer rainfall with SST anomalies leading by 12 months (DJF SSTs), 9 months(MAM SSTs), 6 months (JJA SSTs) 3 months (SON SSTs), at zero lag (DJF SSTs) and lagging by 3 months(MAM SSTs). Only a selection of the most important spatial correlation patterns obtained are presented here.Correlations were computed only if the SST grid-point in question had at least 10 common values (years) with

Figure 9. Lag correlations between station summer rainfall and the BMI for October. The BMI leads rainfall. Isopleth interval is 0�2. Areaswith correlations greater than 0�4 are stippled

Figure 10. Lag partial correlations between the BMI and summer rainfall for regions 1, 2, 3, and 4, with the effect of the SOI (simultaneouswith the BMI) removed. The BMI leads rainfall by up to 12 months. Correlations greater than 0�4 are different from zero at the 1 per cent

significance level


rainfall. For clarity in the subsequent analysis, all spatial correlation patterns may occasionally be discussed onlyin terms of what they represent with respect to dry conditions.

5.1.1. Region 1.The spatial patterns of correlations between summer rainfall in region 1 and grid-point SSTanomalies indicate that during JJA preceding dry summers (not shown), ocean waters tend to be abnormallywarm in most of the tropical Indian and Pacific Oceans. The SSTs are above normal in the Atlantic Ocean and acore area of significant correlations is located in its south-eastern parts. This global correlation pattern is verysimilar to the SST anomalies during ENSO. Correlations are, however, not significantly different from zero overlarge areas of the ocean. Three months later, in SON (Figure 12), the correlation patterns are better defined and alarge area of negative significant correlations (positive SST anomalies in dry years), with magnitudes in excess of0�5, are observed in the central Indian Ocean. To the south and south-east of Madagascar, the surface of the ocean

Figure 11. Lag partial correlations between monthly summer rainfall and monthly BMI for Region 1, with the effect of the SOI (simultaneouswith the BMI) removed. BMI months refer to the year in which the rainfall season starts. (The BMI leads rainfall except for November rainfalland November and December BMI (zero lag and the BMI lags by 1 month, respectively), and for December rainfall and December BMI (zero

lag)). Correlations greater than 0�4 are different from zero at the 1 per cent significance level

Figure 12. Lag correlations between summer rainfall in region 1 and SST anomalies in SON (three months lead). The isopleth interval is 0�2.Stippling indicates correlations significant at the 5 per cent significance level


tends to be cooler than normal during dry years (positive correlations). In the Pacific scattered pools of significantassociations are also evident. The SSTs in the Atlantic are weakly correlated with rainfall. In DJF (the peak of therainy season) (not shown), significant correlations cover a smaller area than in SON, particularly over the centralIndian Ocean but the general patterns present in SON remains unchanged. An area of relatively high positivecorrelations (anomalous cold waters in dry summers) appears in the Indian Ocean just off the South African eastcoast.

5.1.2. Region 2.For region 2 these spatial lag correlation patterns are similar but the values and significanceof the correlations are higher. During JJA (not shown) only in the Pacific Ocean are correlations significant overconsiderable areas. Three months later, during SON (Figure 13), there is a strong association between rainfall andSST anomalies in most of the equatorial Indian and Pacific Oceans, as evident from the large areas covered bystippling. Correlations in some of those areas reach 0�7. Warm oceans tend to be observed in the southern parts ofthe Indian Ocean, as in the correlations with region 1. The Atlantic SSTs show at this time no relation withsummer rainfall. During DJF (not shown) the SST–rainfall link weakens in the Indian and Pacific but a zone ofsignificant correlations appears in the eastern Atlantic. Also, the band of positive correlations in the south IndianOcean during SON expands eastwards, even becoming significant in its core. Walker (1989) has investigated theassociation between Indian Ocean SSTs and rainfall in the south African summer region (its northern partscoincide with our region 2). As in this study, Walker found that, over the Indian Ocean, dry summers are relatedwith warm SSTs north of about 20�S whereas to the south ocean waters tend to be cooler.

5.1.3. Regions 3 and 4.For region 3 the rainfall–SST link is much weaker than for regions 1 and 2. The shapeof all patterns resembles that of an ENSO SST anomaly composite but correlations are in general weak and notsignificant. Rainfall in region 3 does not seem to be related to SST anomalies in any coherent way and are,therefore not shown.

Results for region 4 are not shown also because they fall between those of regions 1 and 2. For all four rainfallregions, the SST–rainfall link is weaker for SST leads greater than 6 months (i.e. JJA SSTs) as well as for SSTslagging rainfall by 3 months (MAM SSTs after the rainy season).

Confirmation of the correlations obtained above was sought by constructing composite SST fields for the sixdriest (1967–1968, 1972–1973, 1982–1983, 1963–1964, 1969–1970, and 1959–1960) minus the six wettest(1981–1980, 1962–1963, 1952–1953, 1977–1978, 1954–1955 and 1973–1974) summers in regions 1 to 4. Ingeneral, these are consistent with the correlation analysis and are not shown here.

Figure 13. Lag correlations between summer rainfall in region 2 and SST anomalies in SON (three months lead). The isopleth interval is 0�2.Stippling indicates correlations significant at the 5 per cent significance level


5.2 Partial correlation analysis: sea-surface temperature and rainfall associations in the absenceof ENSO effects

It has been shown above that summer rainfall in regions 1 and 2 correlates significantly with SST anomalies invast areas of the tropical Indian and Pacific Oceans. Strongest associations were found with the SSTs leadingrainfall by approximately 3 months (SON SSTs). One may question how much of that relationship is ENSO-related because it is well known that ENSO encompasses most of the tropical oceans.

A partial correlation analysis was performed between summer rainfall and grid-point SST anomalies afterremoving the effect of the SOI (taken synchronously with SST). Only regions 1 and 2 were considered herebecause rainfall in region 3 does not seem to be related in any coherent way with SSTs. The correlation spatialpatterns obtained when SSTs lead rainfall by 3 months (SON SSTs) are shown in Figure 14 (a) for region 1 andFigure 14 (b) for region 2. For both regions, the area of significant correlations over the Indian Ocean wasreduced when compared with that of the total correlation fields (see Figures 12 and 13 for regions 1 and 2,respectively). Differences are greatest for region 2. However, much of the SST–rainfall link is still present afterthe ENSO effects are removed. These findings are consistent with the weak associations between the SOI andsummer rainfall in region 2. Over the Pacific, the influence of ENSO on the SST–rainfall total correlation fieldsis, as expected, considerably stronger. The strong negative associations present in the total correlations (seeFigures 12 and 13) virtually disappear with the removal of ENSO effects. Similar changes occur in both oceans ifrainfall is correlated with SSTs at zero lag (DJF SSTs) (not shown). The relative independence of the SST–rainfall association from ENSO over the Indian Ocean is also evident in the work of Walker (1989). In that study,after the removal of ENSO effects, South African summer rainfall still showed significant correlations with SSTsover vast coherent areas of the Indian Ocean.

6. RELATIONSHIPS BETWEEN ATMOSPHERIC VARIABLES AND RAINFALL

In order to investigate the nature of the SST–rainfall link, atmospheric and circulation conditions typical of drysummers will now be identified. Some studies have reported on the atmospheric changes taking place over thesubcontinent and nearby oceans that occur during dry south-eastern African summers. It has been shownin the previous sections that the SOI, BMI, and SST associations with rainfall in region 1 are representative ofthose in regions 2 and 4. Therefore, only atmospheric anomalies typical of dry conditions in region 1 will beidentified.

The COADS contains only data for the ocean surface and, therefore, the Australian Southern Hemisphere dataset described above is used mainly in the following correlation analysis. However, due to the shorter period of theSouthern Hemisphere data set compared with that of the COADS, compositing is based on the latter.

6.1. Mean sea-level pressure

Correlations were performed between summer rainfall in region 1 and mean sea-level pressure at each grid-point, with pressure leading rainfall by about 3 months (SON) and at zero lag (DJF). These two correlation fieldsare displayed in Figure 15. Three months before the rainy season, pressure changes over the land and the adjacentoceans in a coherent manner over large areas. Correlations indicate that before dry summers a band of positivepressure anomalies extends from the central and eastern south-eastern Africa to the south and south-westernIndian Ocean. At the same time, negative pressure anomalies occur in the central Indian ocean, east ofMadagascar. Most of the South Atlantic is dominated by abnormally low pressures. Correlations are barelysignificant at 3 months lead. At zero lag (DJF), the pressure anomaly pattern of SON becomes stronger. Thespatial structure of the correlations stays the same only over region 1 and to the east and south of Madagascar. Inother regions the pattern changes and the sign of correlations even reverse.

In Figure 15, Marion Island and St Brandon, the stations used to derived the BMI index in section 2, aredenoted by crosses. The correlations shown here reflect the strong relationship obtained between rainfall in region1 and the BMI (with Marion Island in phase and St Brandon out of phase with pressure changes over thesubcontinent), thereby justifying the usage of the BMI as an indicator of rainfall changes over region 1. This


pressure anomaly dipole (with centres near Madagascar and to its south) may also be related with fluctuations ofthe preferred position of the ridge associated with the standing waves 1 and 3 which are normally located over theSouth Atlantic and south-western Indian Oceans, respectively (Streten, 1973). The southern African cloud band(one of the most important rain-bringing systems in southern Africa during summer) forms ahead of the mid-latitude trough associated with wave number 3 (Streten, 1973). The spatial correlation patterns of Figure 15agree, when interpreted in terms of what they represent during day conditions, with the pressure compositesconstructed by Matarira (1990) for dry minus normal south-eastern African years (his figure 4(c). Whensuperimposed on the climatological MSLP field, these anomalies reflect a weakening of the South Atlantic andIndian Oceans high-pressure cells (not so clear during SON) and, consequently, a reduction of the surfacepressure gradient directed from the oceans to the subcontinent.

Clearly, large-scale pressure changes take place 3 months before abnormally dry rainy seasons and intensify atzero lag with rainfall. Only during DJF are these anomalies significantly correlated with rainfall. Global analysisof atmospheric changes during ENSO years have shown that over southern Africa pressures tend to beabnormally high (e.g. van Loon and Madden, 1982; van Loon and Shea, 1987). An important feature of the low

Figure 14. Partial correlations between summer rainfall and SST anomalies in SON with the SOI (simultaneous with the SSTs) being keptconstant, for (a) region 1 and (b) region 2. The isopleth interval is 0�2. Stippling indicates correlations significant at a 5 per cent significance

level


pressure anomaly east of Madagascar is that it is located just south of the positive SST anomalies indicated by thenegative correlations of Figure 12. The surface pressure composite analysis performed by Cadet (1985) for theIndian Ocean shows that during SON of El Ninõ years, an area of positive anomalies is observed north-east ofMadagascar, broadly coincident with the zone of positive correlations (negative anomalies in dry summers) ofFigure 15. This may indicate an ENSO signal in the correlation fields shown here. As for the SSTs, MSLPcomposites were constructed. They agree well with the correlation maps but are not shown here.

Figure 15. Correlations between summer rainfall in region 1 and MSLP in (a) SON and (b) DJF. The isopleth interval is 0�1. Stipplingindicates correlations significant at a 5 per cent significance level. The crosses represent the locations of St Brandon and Marion Island


6.2. Winds

As with pressure, correlations were computed between summer rainfall in region 1 and the zonal windcomponent at 850 hPa and 200 hPa (U850, U200) and the meridional wind component at 850 hPa (V850).Figures 16 and 17 display the spatial correlation fields for U850 and V850, respectively, during DJF. Maps forSON are similar but correlations are weaker (not displayed). They indicate that 3 months before dry summers,low tropospheric wind anomalies are south-easterly over much of south-eastern Africa south of about 20�S andnorth-easterly over south-western Africa. At zero lag (DJF), the spatial pattern of the meridional wind anomalieschanges little but the zonal winds are now anomalous westerly in the southern parts. To the north, a zone of highpositive significant correlations (easterly anomalies during dry summers) is located over central south-easternAfrica. When superimposed on the mean vector field, these correlations represent, particularly for DJF, ananomalous anticyclonic circulation which acts to weaken the thermal low pressure cell characteristic of the regionduring summer.

At 200 hPa, the zonal wind correlates negatively with rainfall in SON (westerly anomalies in dry years) overmost of south-eastern Africa (not shown). Strongest significant associations exist at about 15�S over northernZambia, Mozambique, and Madagascar. At zero lag (DJF), correlations indicate that during dry years westerlyanomalies persist over most of the domain south of 15–20�S. Westerly wind anomalies have been reported tooccur over southern Africa during ENSO years (Arkin, 1982; Lindesay, 1988b) but the easterly wind anomaliesimplied here by the positive correlations at low latitudes, particularly over the Atlantic during DJF, are notpresent in the analyses of Arkin and Lindesay. Weaker easterlies at 200 hPa have been related to a lowerfrequency of easterly wave and cloud band formation over the subcontinent, resulting in below average rainfallover the summer rainfall region of South Africa (Harrison, 1983, 1986), the northern parts of which fall withinour region 2.

Westerly surface wind anomalies across much of the eastern coast of southern Africa have been reported byPan and Oort (1983) to occur when SSTs are abnormally warm in the central equatorial Pacific (i.e. ENSOevents). At the same time, westerly wind anomalies are observed at 200 hPa over most of the tropical Atlantic,Indian, and eastern Pacific Oceans. The composite analysis of Cadet (1985) also depicts surface westerly windanomalies over the western Indian Ocean during ENSO.

Figure 16. As in Figure 15 but for DJF zonal wind at 850 hPa


6.3. Temperature

Figure 18 displays the correlation fields between rainfall in region 1 and DJF temperature at (a) 850 (T850) and(b) 500 hPa (T500). They indicate that during dry summers the low and middle troposphere tends to beabnormally warm over much of the tropics of our domain. Similar but weaker patterns were obtained for SONT850 and T500 (not shown). As with the parameters analysed before, the rainfall–temperature association isstronger during DJF, when significant correlations cover a larger area. This is particular evident for T850 duringDJF, when significant correlations occur over most of south-eastern Africa. Warm low-level temperatures duringdry summers may be a result of reduced cloud cover and a consequent increase in insolation. Lindesay (1988b)has also reported widespread low and middle tropospheric warming over southern Africa during ENSO years.Her results are consistent with the global studies performed by van Loon and Madden (1981) and Kiladis andDiaz (1989). Indeed, the studies of Horel and Wallace (1981) and Pan and Oort (1983) have shown that most ofthe troposphere is anomalously warm during ENSO, particularly during DJF.

6.4. Relative humidity

A monthly relative humidity subset was calculated using monthly mixing ratio and temperature data for the850 and 500 hPa levels. Correlation fields between DJF relative humidity and summer rainfall are shown inFigure 19 for (a) 850 hPa and (b) 500 hPa. They indicate that during dry years, relative humidity at 500 hPa islower than normal (positive correlations) over region 1. At zero lag (DJF), correlations are strongly positive andsignificant over region 1 and strongly negative and significant over south-western Africa. Over the Indian Ocean,east of Madagascar, the relationship is negative (increased relative humidity during dry summers). There seemsto be a spatially coherent relationship between the pressure and 500 hPa relative humidity anomalies over thecontinent and Indian Ocean during dry region 1 summers. Areas of positive MSLP anomalies (negativecorrelations in Figure 15(b) tend to coincide broadly with areas of reduced relative humidity (positivecorrelations). This is particularly evident during DJF when an alternating three pole pattern of positive andnegative anomalies can be observed over the eastern Atlantic and south-eastern Africa (positive correlations) andwestern Indian Ocean east of Madagascar (negative correlations).

Figure 17. As in Figure 15 but for DJF meridional wind at 850 hPa


The following scenario is suggested to explain the observed relative humidity anomalies. During dry summersin region 1, pressure increases over the land weakening the thermal low. Weaker vertical motion associated withthese pressure anomalies would not favour convection, resulting in decreased moisture condensation in themiddle troposphere and, hence, reduced rainfall. Reversed conditions occur over south-western Africa and thewestern Indian Ocean. This scenario is consistent with the eastward shift of the southern African cloud band from

Figure 18. As in Figure 15 but for DJF temperature at (a) 850 hPa and (b) 500 hPa


the subcontinent to the western Indian Ocean, described by Harrison (1983) as occurring during dry SouthAfrican summers. Anomalous features at 850 hPa are very similar to those at 500 hPa. Significance is onlyachieved in the mid-latitudes, particularly over the south-east Atlantic Ocean during DJF where correlationsrepresent reduced relative humidity during region 1 dry summers. Inspection of low-level horizontal moistureflux changes during region 1 summers using the Southern Hemisphere data (not shown) indicates that maximumanomalous equatorward moisture divergence takes place around 40�S parallel near the Greenwich meridian

Figure 19. As in Figure 18 but for relative humidity


(where correlations imply a maximum reduction in the relative humidity, as shown in Figure 19(a)). The positiveanomalies in relative humidity over south-west Africa are at least partly due to anomalous moisture convergencefrom the equatorial regions there.

6.5. Surface horizontal moisture flux

To investigate the anomalous transport of moisture from the oceans to south-eastern Africa, the stationarycomponent of the surface moisture flux was computed. These were calculated using monthly values of the surfacewinds and moisture mixing ratio obtained from the COADS. Although COADS provides a poor spatial coveragesouth of 40�S, the major moisture sources of the summer rainfall in the region originate in the tropical oceans, inparticular over the Indian Ocean.

Here we present the structure of the horizontal surface moisture fluxes calculated from the seasonal meansurface wind and moisture. As calculated, this represents only the ‘stationary’ part of the moisture flux. Moisturefluxes affected by the transient components may be important, particularly for southern coastal areas wherefrontal systems in the mid-latitude westerly circulation contribute to summer precipitation totals (Tyson, 1986).One believes, however, that the steady component is appropriate to represent the moisture changes associatedwith the large-scale circulation controls in the tropics (Chen, 1985). It is assumed here also that surface moisturetransport is an appropriate representation of the moisture flux in the lower troposphere where most moisture isconcentrated (Chen, 1985).

To analyse how this moisture flux is related to rainfall, composites were constructed for the six driest minus thesix wettest summers of region 1. As a reference for the subsequent analysis, the climatological stationarymoisture flux is presented in Figure 20 for SON (a) and DJF (b). Figure 21 displays the moisture flux anomalycomposites for SON (a) and DJF (b). Three months before the rainy season (SON) when SSTs are anomalouslywarm over most of the tropical Indian Ocean, a strong easterly moisture flux anomaly is observed emanating fromthe western flank of the Indian Ocean high-pressure cell towards the warm waters. Along the African easterncoast, a reduction of moisture transport inland is generally evident by the westerly anomalies (although weak).During DJF the basic flow over the Indian Ocean is quite different from that in SON (compare Figures 20(a) and20(b)). Note that in DJF the ITCZ is well defined by the confluence zone of the north-east monsoonal winds andthe south-east Trades at about 10�S. During spring (SON), the north-east monsoon is not yet established and thecirculation near the east African coast is south-easterly. One should also notice that over the Indian Ocean theITCZ encompasses a large area where the horizontal advection is rather weak, and therefore likely to allowheating anomalies associated with warm SSTs to propagate relatively fast in the vertical, as suggested by thetheory of Webster (1981). The composite for DJF shows that a large and well organized cyclonic anomaly in themoisture flux field develops at the surface east of Madagascar, resulting in a reduction of moisture advection intothe continent across the eastern coast. Along the Mozambique Channel, wind anomalies have a southerlycomponent, which agrees with the correlation analysis performed earlier (see Figure 17) and with the windanomalies typical of ENSO years reported by Lindesay (1988b).

8. DISCUSSION

It has been shown above that summer rainfall in the central and south-eastern parts of southern Africa ismodulated by ENSO (no relationships were found for the northern regions of our domain). Strongest relationshipswith the SOI were found with region 2, which encompasses most of northern South Africa. This confirms the linkbetween rainfall in the summer rainfall region of South Africa (which coincides broadly with region 2 here) andthe SOI reported in a number of studies (e.g. Schulze, 1986; Nicholson and Entekhabi, 1886; Ismail, 1987;Lindesay, 1988b; Matarira, 1990) and the moderate ENSO signal on rainfall in an area extending from southernMozambique into Zimbabwe, noted by Matarira (1990). When monthly instead of seasonal rainfall is considered,the association is practically non-existent for January rainfall (at any SOI lead) whereas December rainfall yieldsthe highest correlations with the SOI.

A number of factors can be suggested for the instability of these correlations within the rainy season. January isthe peak summer month and, by this time, the heat low and ITCZ are well established over the subcontinent and


embedded in a highly barotropic atmosphere (Lindesay, 1988b). During January, rainfall is predominantly of aconvective nature and rainfall-bringing baroclinic systems play a less important role than at the beginning andend of summer. It is therefore possible that the higher spatial and temporal variability of rainfall during January,when compared with the other summer months (at most stations of region 2, highest rainfall interannualvariability is found for January) may contribute to weaken the correlations with the SOI. It may also happen that

Figue 20. Climatological stationary surface moisture flux for (a) SON and (b) DJF. The longest vector corresponds to 0�14 (Kg Kgÿ1)m sÿ1


ENSO modulates summer rainfall through a predominance of certain types of atmospheric systems embedded inthe tropical(Lindesay, 1988b) or extratropical (van Heerdenet al., 1988) regimes.

The BMI correlates strongly with rainfall over the central regions of the summer rainfall area (regions 1 and 4of this study). The relationship is such that steeper than normal MSLP gradients between the western and the

Figure 21. Composite of the stationary surface moisture for the six driest minus the six wettest region 1 summers for (a) SON and (b) DJF. Thelongest vector corresponds to 0�8 (Kg Kgÿ1)m71 in (a) and 0�06 (Kg Kgÿ1)m sÿ1 in (b)


southwestern Indian Ocean tend to be followed by positive rainfall anomalies, or less precipitation for weakergradients. The spatial signature of the association is broadly coincident with region 1. It is orientated in the north-west–south-east direction which happens also to coincide with the preferred orientation of the southern Africancloud band (Kuhnel, 1989). This cloud band is one of the most important rain-bringing systems in southern Africa(Harrison 1984a,b; Smith, 1985). The BMI could, thus, be considered in any long-range forecast scheme ofsummer rainfall over the central areas of the summer rainfall region. It has been shown by the partial correlationbetween rainfall and the BMI with the effects of the SOI removed that both indices are, to a great extent,independent. Because both indices correlate well with summer rainfall in regions 2 and 4, the potentialpredictability of rainfall is increased if both are used as predictors. The low correlations found between the BMIand the SOI for different lags reinforces this potential.

Interannual rainfall variability over the summer rainfall region of south-eastern Africa south of about 16�S hasbeen shown to be related to global SST patterns. Strongest associations are observed for SST leads of about 3months and decay at zero lag with rainfall. The spatial pattern and the temporal evolution of these anomalies arereminiscent of a typical ENSO episode. However, a partial correlation analysis has revealed that, although theanomalies in the Pacific are ENSO related, over the central Indian Ocean warming is to a great extentindependent of ENSO. In fact, a careful inspection of monthly global SST anomalies and region 1 rainfallinterannual variability since 1950 shows that four (1959–1960, 1967–68, 1981–82 and 1983–84) of the eightdriest summers not associated with ENSO events coincided with warming over the central Indian Ocean. Therelative independence of the SST–rainfall association from ENSO over the Indian Ocean is also evident in thework of Walker (1989). In that study, after the removal of ENSO effects, South African summer rainfall stillshowed significant correlations with SSTs over vast coherent areas of the Indian Ocean. Similar positive SSTanomalies were reported by Reverdinet al., (1986) as having occurred in the Indian Ocean during non-ENSOyears.

During dry region 1 summers, the atmosphere over the subcontinent and adjacent oceans undergoes majorchanges. A broad zone of positive MSLP anomalies are observed over the central and south-eastern parts ofsouthern Africa extending south-eastwards into the Indian Ocean. At the same time, over much of the tropicalsouth-eastern Atlantic and north-east of Madagascar, pressure is anomalously low. In the lower troposphere,meridional wind anomalies are predominantly southerly along the eastern coast but zonal wind changes display amore complex spatial structure, with westerly anomalies located south of 25�S and easterly anomalies to the north(see Figure 16). Over the Indian Ocean anomalies of the opposite sign are observed. The net effect of thesechanges over the land (an anticyclonic circulation anomaly), which represents a weakening of the low pressurecell, is consistent with the readjustment of the MSLP field.

A spatial correlation analysis performed by Lindesay (1988) between simultaneous SOI and winds at 700 hPAduring OND and JFM displays some of the features shown in Figures 16 and 17. This may indicate that part of thewind anomalies reported here are related to ENSO, despite the fact that rainfall data and season classification onwhich correlations were based are different in both studies. During dry years, temperatures throughout the lowerand middle troposphere are abnormally high over the subcontinent and neighbouring oceans, a situation usuallylinked to ENSO, as a result of the westward expansion of the warm SSTs in the tropical Pacific and IndianOceans. In the relative humidity field at 500 hPa, a stationary synoptic-scale wave is observed over south-easternAfrica such that an area of moisture deficit is located above region 1 and positive anomalies are observed to thewest, over south-western Africa, and to the east over Madagascar and the western Indian Ocean. These anomaliesare consistent with reduced condensation at 500 hPA over south-eastern Africa, and can be associated with a shiftin the preferred cloud band position from the continent towards the eastern Indian Ocean, as reported by Harrison(1983). Coincident with the negative MSLP anomalies east of Madagascar, a stationary cyclonic circulationanomaly is observed (Figure 21). As a result, part of the moisture carried by the south-east Trades and north-eastmonsoon is diverted from its usual trajectory, and instead of entering the continent through the eastern coast,recurves towards the anomalous low pressure area. Over the Atlantic, minor changes take place in the low levelcirculation despite the relatively large SST anomalies observed during DJF.

Circulation changes during two very dry region 1 summers (those of 1967–1968 and 1982–1983) were alsoinvestigated. Three months before and during the summers of both years, SST anomalies were positive over theIndian Ocean, but in the equatorial central and eastern Pacific Ocean SSTs were very different. In 1967–1968,


surface waters were abnormally cool, whereas during 1982–1983 positive SST anomalies occupied most of theregion. During both summers, enhanced surface level moisture convergence took place over the central IndianOcean, in close agreement with the moisture flux composites for dry minus wet summers. However, it wassuggested by Harrison (1986b) and Lindesay (1988) that westerly wind anomalies at 200 hPa over southernAfrica, as a result of changes in the Walker Circulation during ENSO events, contributes to summer rainfalldeficits in the region. Indeed, during January 1983 westerly wind anomalies at 200hPa of the order of 10 m sÿ1

were observed over south-eastern Africa.

9. CONCLUDING REMARKS

No work to date has considered interannual variability of summer rainfall over south-eastern Africa in detail, andfor such a large area. Therefore, identification of its principal modes of variability has been accomplished in orderto investigate the spatial signature of precipitation variability. This has been achieved through PCA, and a carefulselection of the modes (unrotated versus Varimax rotated) that best represented the underlying variability of thedata. It has been shown that the most important mode of rainfall variability (region 1) covers much of central andeastern parts of the summer rainfall region of southern Africa.

The influence of ENSO (as measured by the SOI) on summer rainfall has been found to be important only overthe southern parts of our domain, but even there the relationship is not strong. The association is such that duringENSO (negative SOI) dry conditions tend to prevail. To the north, the SOI influence weakens considerably, withthe correlations changing sign over the northernmost areas. Moreover, strong associations are found when theSOI leads rainfall by about 4–5 months.

An index of the Indian Ocean atmospheric circulation, the BMI, has been devised in this study which isindependent of the SOI. The association of rainfall with the BMI has been found to be much stronger than withthe SOI, and is such that weak mean sea-level pressure gradients between St Brandon (east of Madagascar) andMarion Island (south-east of Africa) tend to be followed by dry conditions over south-eastern Africa. Unlike theSOI, the BMI correlates with summer rainfall over a large area of the dominion, but strongest values are found forthe central eastern regions. The relationship peaks when the BMI leads rainfall by approximately 2 to 4 months,and decays for shorter leads. The BMI–rainfall association remains unaltered when the SOI effects are removed,suggesting that over the western and south-western Indian Ocean, the atmospheric circulation is at least partlyindependent of ENSO.

The SSTs over large areas of the tropical Indian and Pacific Oceans correlate significantly with rainfall inregions 1, 2, and 4, such that anomalously warm waters in those oceanic areas tend to coincide with dryconditions. Correlations for region 3 are weak and spatially ill-defined, as are, in general, those over the AtlanticOcean. Strongest correlations have been found for regions 2 and 4 but their spatial structures are very similar tothat for region 1, and are strongly reminiscent of a typical ENSO SST anomaly pattern. The association peakswhen SSTs lead rainfall by about 3 months and decays thereafter. A partial correlation analysis where the ENSOeffects have been removed has revealed that, whereas much of the relationship disappears over the Pacific, strongsignificant correlations are still present in the Indian Ocean. This further supports the finding of the BMI–rainfall,that air–sea processes over the Indian Ocean are, in fact, partly independent of ENSO.

We have identified here anomalous atmospheric features that tend to occur during dry region 1 summers (infact during most dry south-eastern African summers). These are detailed below for the peak of summer (DJF):

(i) decreased MSLP takes place east of Madagascar whereas over south-east Africa positive MSLP anomaliesare observed;

(ii) north-westerly low-level wind anomalies prevail along much of the east coast of Africa;(iii) at 200 hPa the wind is anomalously westerly over south-eastern Africa;(iv) anomalously warm tropospheric temperatures occur over the subcontinent and adjacent tropical oceans;(v) relative humidity at 850 and 500 hPa decreases over south-east Africa but positive anomalies are present

over south-west Africa and the South Atlantic Ocean;(vi) low-level moisture flux convergence weakens over south-eastern Africa but intensifies over the tropical

Indian Ocean, particularly to the east of Madagascar.


The hypothesis we have formed is that SST anomaly patterns identified as typical of dry south-eastern Africasummers generate dry conditions through the above-mentioned atmospheric anomalous features. This hypothesiswill be tested through a series of modelling experiments in which the model’s atmosphere has been forced withspatial- and time-evolving SST anomalies characteristic of dry summers over south-eastern Africa. The results ofthese experiments are reported in Rocha and Simmonds (1997).

APPENDIX

Monthly values of the BMI from 1955 to 1988. Missing values are denoted byÿ99�9

Year January February March April May June July August September October November December

1955 ÿ0�3 ÿ1�4 ÿ1�3 0�1 ÿ0�2 ÿ0�3 ÿ0�5 ÿ1�6 ÿ1�0 ÿ0�3 ÿ0�2 0�01956 ÿ1�7 0�5 0�0 1�5 2�0 1�3 0�2 1�1 ÿ0�2 0�2 ÿ99�9 0�51957 ÿ0�6 1�0 ÿ0�2 ÿ0�1 ÿ0�5 1�1 1�0 0�7 1�0 1�5 1�6 ÿ0�11958 ÿ0�2 0�8 ÿ0�1 ÿ0�6 0�9 1�4 ÿ2�4 ÿ1�2 ÿ0�6 0�0 0�2 0�31959 ÿ0�9 ÿ0�1 ÿ1�0 ÿ1�4 ÿ1�7 1�6 0�4 ÿ0�4 ÿ2�2 ÿ0�6 ÿ1�5 0�11960 ÿ1�1 ÿ0�8 ÿ1�5 0�7 ÿ0�6 1�2 ÿ0�1 2�0 0�0 1�2 ÿ0�8 0�71961 0�3 1�1 2�2 0�8 ÿ0�4 ÿ0�4 ÿ1�0 ÿ0�8 0�5 0�9 ÿ0�6 ÿ2�01962 ÿ1�3 0�4 ÿ0�8 ÿ99�9 ÿ0�2 ÿ0�7 0�5 0�3 0�7 0�2 ÿ0�4 0�31963 ÿ1�4 ÿ0�6 ÿ1�3 ÿ0�3 ÿ0�4 ÿ0�6 0�1 ÿ0�2 0�0 ÿ1�5 ÿ0�8 ÿ0�61964 0�1 ÿ0�9 ÿ0�2 ÿ1�4 ÿ0�9 0�2 ÿ1�3 0�9 0�2 ÿ0�2 2�0 0�51965 ÿ0�4 0�0 0�6 ÿ0�6 0�7 ÿ0�8 ÿ0�3 ÿ0�4 0�6 ÿ0�6 0�3 1�11966 1�7 2�1 ÿ0�4 ÿ0�8 1�2 ÿ99�9 0�4 ÿ0�5 0�3 0�0 ÿ0�8 ÿ1�21967 0�8 1�0 0�2 ÿ1�7 ÿ1�5 ÿ0�2 ÿ1�0 ÿ0�7 ÿ0�5 ÿ0�6 ÿ0�4 ÿ1�61968 0�0 0�4 ÿ0�9 0�9 1�0 0�4 2�2 1�4 1�3 1�4 ÿ0�3 1�31969 2�1 1�9 0�6 1�4 ÿ0�1 ÿ0�2 0�5 ÿ0�9 ÿ1�2 ÿ0�4 0�0 0�91970 0�1 ÿ1�0 ÿ1�2 1�2 ÿ0�1 ÿ0�6 1�7 0�2 ÿ0�2 ÿ2�8 0�6 ÿ0�11971 ÿ0�8 ÿ1�9 ÿ0�5 1�7 0�3 0�2 ÿ1�2 ÿ0�2 0�6 ÿ0�2 ÿ0�5 1�21972 ÿ99�9 ÿ0�8 0�4 1�9 0�4 ÿ3�0 1�4 ÿ1�8 0�4 0�6 ÿ1�6 ÿ0�21973 ÿ0�1 ÿ1�1 ÿ0�1 ÿ1�1 0�1 ÿ0�4 ÿ1�5 ÿ0�3 0�7 1�3 1�2 ÿ0�21974 ÿ99�9 ÿ99�9 ÿ99�9 ÿ99�9 ÿ99�9 ÿ99�9 ÿ0�5 0�8 0�2 1�4 1�8 1�41975 1�8 0�4 ÿ0�4 0�5 0�2 ÿ0�3 0�3 1�2 ÿ1�0 ÿ0�2 1�0 0�31976 0�6 0�6 1�6 0�3 ÿ0�2 0�1 1�2 1�0 ÿ1�1 0�3 0�2 1�01977 1�0 0�2 2�3 ÿ0�6 0�4 1�3 0�0 0�1 1�1 0�4 1�1 0�71978 0�6 1�4 0�7 0�7 2�1 1�1 ÿ0�8 1�9 0�9 ÿ99�9 2�0 0�41979 0�9 ÿ0�6 0�0 ÿ0�1 0�6 0�8 0�9 ÿ1�3 2�2 0�5 0�1 0�31980 ÿ1�2 ÿ0�1 ÿ1�6 1�1 0�3 0�8 0�9 0�5 1�2 0�8 0�9 0�41981 1�5 ÿ0�4 1�4 ÿ1�1 ÿ3�0 ÿ1�2 ÿ1�6 ÿ0�5 ÿ0�4 ÿ0�1 ÿ1�0 ÿ0�61982 ÿ0�2 ÿ0�1 ÿ0�2 ÿ1�1 ÿ0�7 0�1 ÿ0�8 ÿ0�6 ÿ2�1 ÿ1�2 ÿ1�1 ÿ1�31983 0�5 ÿ0�6 0�6 ÿ0�2 ÿ0�6 ÿ1�1 ÿ0�2 ÿ99�9 0�2 ÿ1�0 ÿ0�6 ÿ2�21984 ÿ1�6 ÿ99�9 0�1 ÿ0�4 0�6 0�6 0�4 0�6 ÿ1�0 ÿ0�6 ÿ0�5 0�81985 ÿ0�1 ÿ2�2 1�1 ÿ0�4 ÿ0�8 ÿ0�3 0�2 ÿ0�1 2 0�8 1�7 ÿ99�9 ÿ2�41986 ÿ0�9 0�2 ÿ99�9 0�7 0�9 ÿ1�7 0�4 ÿ1�6 ÿ99�9 ÿ0�6 ÿ0�5 0�11987 0�3 0�6 ÿ0�1 ÿ1�5 ÿ0�1 ÿ0�3 0�0 0�6 ÿ0�3 ÿ1�6 ÿ0�5 0�31988 0�2 ÿ0�1 ÿ99�9 0�0 0�2 0�0 0�4 ÿ99�9 ÿ1�2 0�1 ÿ1�2 ÿ99�9

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