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INTERNATIONAL JOURNAL OF CLIMATOLOGY, VOL. 17, 235–265 (1997)
INTERANNUAL VARIABILITY OF SOUTH-EASTERN AFRICAN
SUMMER RAINFALL. PART 1: RELATIONSHIPS WITH AIR–SEA
INTERACTION PROCESSES
ALFREDO ROCHA
Departamento de Fı́sica, Universidade de Aveiro, 3800 Aveiro, Portugal
email:arocha@zeus.ci.ua.pt
AND
IAN SIMMONDS
School of Earth Sciences, University of Melbourne, Parkville, Victoria, 3052, Australia
email: ihs@met.unimelb.edu.an
Received 29 September 1995
Revised 28 June 1996
Accepted 4 July 1996
ABSTRACT
This paper investigates the role that air–sea interaction processes may play in interannual variability of south-eastern African
summer rainfall. The principal spatial modes of south-eastern African summer rainfall are first identified using principal
component analysis. Four modes are retained. The most important mode of variability is found to represent rainfall variability
over 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. The
relationship is such that during the summer following the onset of an ENSO event, south-eastern Africa tends to experience
dry 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 Indian
Ocean correlates with rainfall better than the SOI. Strongest correlations are found when this index leads rainfall by about 1 to
3 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 Oceans
that are linked with rainfall changes over the subcontinent. The relationship is such that warm anomalies tend to be followed
by dry conditions over much of south-eastern Africa. Strongest relationships are found when SSTs lead the rainfall season by
about 1 to 3 months.
Well-defined atmospheric anomalies are identified during dry south-eastern African summers. These include, amongst
others, anomalously warm tropospheric temperatures and marked low-level cyclonic circulation anomalies over the central
Indian Ocean, which generate abnormally weak easterly winds along much of the south-eastern coast of Africa. These
perturbations 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 ENSO
effects have been removed, suggesting that the atmospheric circulation anomalies observed over south-eastern Africa during
dry summers, are linked mainly to SST anomalies over the Indian Ocean. This hypothesis will be tested in a companion paper
through a series of GCM simulations.
south-eastern Africa; principal component analysis; correlation; anomaly fields; air–sea interaction; summer rainfall; atmospheric
circulation; Brandon–Marion Index.
KEY WORDS:
1. INTRODUCTION
Extremes of interannual climate variability can often create stresses in many aspects of human life. Drought has
been recognized as a common occurrence in many parts of south-eastern Africa, and is characterized by a
recurrent distribution in time and spatial coherence over large areas (Nicholson, 1986a,b). These can be
exemplified 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
236
A. ROCHA AND I. SIMMONDS
south-eastern African countries. A large proportion of the subcontinent experiences a single rainy season centred
on 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 carried
out for limited areas, particularly South Africa. Some of those studies have concentrated on describing the
temporal behaviour of drought and the associated pressure anomalies that develop over the region and
surrounding 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 causes
of south-eastern African drought. Particular attention has been given to the influence of the El Niño–Southern
Oscillation (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 for
restricted areas or using coarse rainfall networks. Also, lag relationships between ENSO and summer rainfall
have 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 Heerden et 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) analysed
documentary 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 central
South 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 normal
conditions 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 equatorial
eastern Africa, which exhibits concurrent rainfall anomalies of opposite sign, that is, wet conditions during ENSO
events. 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). The
signs 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
SOUTH-EAST AFRICAN RAIN: PART 1
237
core areas of ENSO related interannual rainfall anomalies (reproduced from Ropelewski and Halpert, 1987) and
is 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 the
summer rainfall region of south-eastern Africa. The most important modes of interannual rainfall variability are
identified over the subcontinent using principal component analysis (PCA). Their association with an index of
ENSO is analysed and contrasted with the spatial signature of ENSO over the region. Lag relationships up to 12
months are detailed in an attempt to evaluate the potential of ENSO as a predictor of south-eastern African
summer rainfall. We will also examine the link between rainfall projected on to these modes of variability and a
measure of the regional circulation, in an attempt to determine factors that are associated with south-eastern
African 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-surface
temperature (SST) anomalies have the potential to generate imbalance in the heat-flux field, which in turn can set
up anomalous atmospheric circulation and rainfall patterns (Horel, 1982; Rasmusson and Carpenter, 1982). They
may persist for several months over large areas, and they appear as the most easily monitored potential perturbers
of the climatic system. However, it is still unclear to what extent the SST anomalies are a causal factor as
opposed to simply being a response to large-scale circulation changes that produce both SST and rainfall
anomalies.
Here we are concerned mainly with the identification of SST anomalies (not necessarily related to ENSO) that
are associated with interannual variations in climate, in particular large-scale rainfall, in the summer rainfall
regions of south-eastern Africa. We have carried out an observational study of the possible links between summer
rainfall and world-wide SST anomalies, and related large-scale atmospheric features. Verification of the
hypothesis 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 the
large-scale circulation changes generate rainfall anomalies. Rather, attention is focused upon the large-scale
circulation 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 it
will be useful for future forecasting techniques of summer rainfall in the region. Emphasis will thus be placed on
lead–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 a
number 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 space
of monthly rainfall totals, it proved to be difficult to check for irregularities in the data. However, a few simple
quality 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 displayed
in 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 and
seasonal data showed distributions far from normal and, therefore, several of the most common transformations
were 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, the
rainfall distribution of which assumed a near-normal form. This transformation has been applied elsewhere to
monthly rainfall amounts (e.g. Wright, 1974; Whetton, 1988).
Next, the seasonal cycle was removed from data by subtracting each monthly (and seasonal) value from its
respective monthly (and seasonal) average calculated for the period 1951–1989. These anomaly data were then
standardized by the respective monthly (and seasonal) standard deviation, resulting in time series of average zero
and unit standard deviation. This procedure facilitates the comparison of stations with different rainfall
variability.
238
A. ROCHA AND I. SIMMONDS
Table I. ENSO and anti-ENSO events from
1950 to 1989 (Events up to 1985 were
obtained from van Loon and Shea (1985) and
those from 1986 to 1989 from Kiladis and
Diaz (1989))
Warm events
(ENSO)
Cold events
(anti-ENSO)
1951–1952
1953–1954
1954–1955
1957–1958
1963–1964
1964–1965
1965–1966
1966–1967
1969–1970
1970–1971
1972–1973
1973–1974
1976–1977
1978–1979
1982–1983
1986–1987
1988–1989
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 Climate
Analysis Center (1986). This SOI is the normalized difference between the normalized mean monthly mean sealevel pressures at Tahiti and Darwin. Monthly values of the SOI from 1950 to 1989 were obtained from the
monthly issues of the Climate Analysis Center (1983 and later years). Next, all data were filtered using a 3-month
moving average. As shown by Trenberth (1984), such a filter can increase the SOI signal-to-noise ratio (mostly
caused by small-scale disturbances of the circulation) from 1144, for monthly data, to 1197 without losing the
variance on the ENSO time-scales. However, one is aware that such a procedure reduces the effective number of
degrees of freedom as explained by Trenberth (1984). Table I displays ENSO and anti-ENSO events that have
occurred since 1950, as classified by van Loon and Shea (1985) (up to 1985) and Kiladis and Diaz (1989) (from
1986 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 anomaly
field over the region and neighbouring oceans (e.g. Tyson, 1981; Miron and Tyson, 1984; Matarira, 1990). Most
of these studies have obtained results that are consistent, in that, at interannual time-scales, during wet years
lower than normal pressure occurs over southern Africa, with the reverse in dry years. However, the structure of
the wet and dry composite pressure anomalies over the adjacent oceans is not so clear. Tyson (1981) and Miron
and Tyson (1984) have reported that during wet summers pressure is abnormally high to the south-west over the
South Atlantic, and to a lesser extent over the Indian Ocean to the south-east. Using summer 1000 hPa pressure
anomaly data, Matarira (1990) has shown that during dry south-eastern African years positive pressure anomalies
occur over the land, whereas the surrounding oceans experience lower than normal pressures, with the exception
of an area centred approximately at Marion Island (46 53 S, 37 52 E) where anomalies are strongly positive. This
seems to indicate that the mid-latitude trough associated with the standing waves 1 and 3 (Tyson, 1981), which is
normally located at about 30 E, shifts eastwards. The trough and its coupling with tropical easterly waves are
0
0
SOUTH-EAST AFRICAN RAIN: PART 1
239
related to major cloud bands, which usually form over the subcontinent in summer (Kuhnel, 1989). These cloud
bands 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 to
south-eastern African rainfall, a circulation index was constructed. Monthly mean sea-level pressure data for two
stations in the Indian Ocean were obtained for the period 1955–1988. These stations, Marion Island and St
Brandon (16 27 S, 59 37 E) (represented by the crosses in Figure 1), are located in two ocean areas of Matarira’s
pressure composite map corresponding to dry summers (his Figure 4(c)), which show high positive and negative
pressure anomalies, respectively. Monthly normalized pressure difference time series between St Brandon and
Marion Island were constructed in the same way as the SOI was using data for Tahiti and Darwin (monthly values
of the BMI are presented in the Appendix). This index, which we name the Brandon–Marion Index (BMI), can be
seen as a measure of changes in the position of the trough. The BMI will assume negative values when the trough
shifts eastwards. Note that Harrison (1983) has shown that during dry South African summers the cloud bands
associated with the trough are located more to the east towards the Indian Ocean. As for the SOI, this time series
was filtered using a 3-month moving average in order to eliminate high frequency oscillations.
0
0
2.4. Ocean surface data
Monthly averages of SST, mean sea-level pressure (MSLP), surface specific humidity and wind were obtained
from the Comprehensive Ocean–Atmosphere Data Set (COADS) (Slutz et al., 1985; Woodruff et al., 1987). The
data were obtained on a 2 2 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 and
Rocha (1991).
Sea-surface temperature trends were found in the data, particularly over the north and equatorial eastern Pacific
Ocean, with typical values up to ÿ015 C and 015 C per century respectively, and, because this paper is concerned
with 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 four
groups; December, January, and February (DJF), March, April, and May (MAM), June, July, and August (JJA),
and September, October, and Novemer (SON).
6
2.5. The Southern Hemisphere atmospheric data set
In this study the Southern Hemisphere data analyses prepared by the World Meteorological Centre (WMC) in
Melbourne, Australia, are also used. These data consist of daily analyses at 0000 UTC produced since 1972 and
are derived from station, buoy, ship and satellite data. In this paper we use MSLP, wind temperature, and water
vapour mixing ratio at various pressure levels. The data base and analysis scheme used are described by Le
Marshall et al. (1985) and Guymer (1986). Temporal and spatial inhomogneities of the data set have been
mentioned by Trenberth (1979) and Guymer and Le Marshall (1980), amongst others. A comparison between the
monthly climatologies of the WMC analyses and of a similar daily chart series developed by the South African
Weather Bureau (the NOTOS chart series) for the period 1951–1962 (Taljaard et al., 1969) is presented by Le
Marshall et al., (1985). The reliability of the monthly mean sea-level pressure of the WMC data set has been
assessed by Jones (1991).
The daily analyses from June 1972 to December 1984 were used to compute monthly means for every month
and 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 significane
assessed using Student’s t-test. In all cases a two-tailed, 5 per cent significance level is adopted unless indicated
otherwise. It should be mentioned that this significance test assumes normally distributed populations for both
variables. As described above, the frequencey distributions of all data sets were checked for normality. Partial
240
A. ROCHA AND I. SIMMONDS
correlation is also applied to some variables in order to evaluate how a relationship between two of them is
independent of a third (Spiegel, 1988).
The reduction of the number of degrees of freedom due to autocorrelation can be a problem in correlation
analysis. 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. It
should be emphasized that only when both time series display an autoregressive structure, is there a reduction of
the degrees of freedom (see equation (2) of Greenhut). Rainfall is a variable in all correlations computed in this
paper. We have computed the power spectrum of summer rainfall in the regions. All peaks were not significantly
different from white noise processes. So, even if other variables possess autoregressive structure, and it is known
that the SOI does (Trenberth and Shea, 1987), the persistence of rainfall anomalies are sufficiently weak to not be
of great concern in our analysis. We emphasize that in this paper, data of all time series used to calculate
correlations 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 patterns
in 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, the
principal 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 performed
using the correlation matrix of the standardized rainfall data. In order to obtain the PCs that best represent the
rainfall data, Varimax rotation was applied and the resulting PCs compared with those before the rotation. We
also 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 oceanic
variables 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 the
eigenvalue series was adopted. By using this method, all eigenvalues smaller than unity were discarded. The
same rule has been applied in numerous meteorological studies (e.g. Horel, 1981; ). By applying this truncation
rule, the first 21 PCs are retained. These PCs were then rotated using the Varimax and Oblimin methods. The
latter 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 unrotated
and rotated PCs. Figure 2 shows the sampling error for each of the first 15 eigenvalues of the unrotated solution as
calculated using the method of North et 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 spacings
comparable to their sampling errors. All the remaining eigenvalues form degenerate multiplets and can be
discarded according to this criterion. One should note the differences between cut-off rules. Whereas Gutman’s
truncation criterion discards eigenvalues beyond 21, the North et 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 a
great 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 highest
loadings located in one particular region. Principal component 1 represents a large-scale pattern of rainfall
anomalies located just south of the mean ITCZ summer position. It coincides broadly with the first PC of the PCA
performed by Janowiak (1988) on African rainfall for the DJFM season, and with the south-eastern African
rainfall anomaly type 4 reported by Nicholson (1986a). Principal component 2 lies mostly on the northern parts of
241
SOUTH-EAST AFRICAN RAIN: PART 1
Table II. The percentage of total variance
associated with eigenvalues of each of the
first 21 unrotated and rotated PCs
PC
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
Sum
Unrotated
Rotated
2417
919
511
319
313
217
214
212
211
119
118
117
117
116
115
114
114
114
114
112
112
7416
1616
612
510
418
416
312
312
219
219
219
218
215
213
212
210
119
118
118
118
116
116
7416
the Drakensberg Ranges and displays a similar pattern to that of Janowiak’s PC 3, south of 15 S. Principal
component 3 is located in regions with elevations between 500 and 1000 m. Principal component 4 occupies the
gap 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 method
suggested by Wigley et al. (1984) and used by Richman and Lamb (1985) was adopted. For each PC, the station
with the highest loading is identified. Point-correlations between rainfall time series (same data as used to
compute the correlation matrix) at this and all the remaining stations were performed. The resultant spatial
correlation field was mapped and compared with the loadings of the PC. This procedure was carried out for all
unrotated 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 the congruence coefficient (Richman and Lamb, 1985). The difference between the
correlation 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 magnitude
similarity. In sharp contrast with the unrotated PCs, almost all rotated PCs yield high congruence coefficients
with a mean value of 0179, against 0109 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 represent
the clusters present in the data, partly occurring due the convective nature of rainfall in the region. On the other
hand, 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 regions
for the following reasons. The PC scores are a measure of the strength of PC spatial patterns (i.e. loadings) in time
and represent an average (over the whole area) of standardized station rainfall weighted by the respective station
loading (this latter normalized by the respective eigenvalue). Although they are dominated by stations with high
loadings, in a particular year, if these stations have low rainfall and low-loading stations have high rainfall, the
respective PC score is not as dominated by the stations with high loadings as it otherwise would be. For this
reason, regional rainfall time series were constructed based solely on the stations with the highest loadings of
242
A. ROCHA AND I. SIMMONDS
Figure 2. The percentage of the total variance associated with each of the first 15 eigenvalues (unrotated solution) of south-eastern African
summer rainfall. Bars denote the eigenvalue sampling error calculated using the method of North et al., (1982)
each PC. These time series were obtained by considering only the area inside the 015 isopleth of every PC. The
015 isopleth level was chosen for two reasons. Firstly, loadings are correlations and can, therefore, be subject to
statistical significance testing usually performed on the correlation coefficient. The critical correlation coefficient
significantly different from zero at the 1 per cent significance level is 0118, according to Student’s t-test. The 015
loading isopleth is, thus, highly significant. Secondly, by using this isopleth level little overlapping occurs
amongst the PCs, which means that regionalization can be accomplished without ambiguity. This method has
been applied by many authors (e.g. Walsh et al., 1982; Ehrendorfer, 1987; White et al., 1991). Examples of
alternative 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 stations
within the region. The PC spatial pattern within each region was taken into account by weighting each station
data by the respective PC loading.
The interregion correlations of summer rainfall were computed and are displayed in Table III. Region 1
correlates positively with the other three regions but most highly with region 4. This may, despite the convective
nature of rainfall in the area, indicate the importance of the large-scale in determining summer rainfall. Regions 2
and 4 correlate negatively with region 3. This north–south dipole is consistent with the south-eastern African
anomaly types 5 and 6 reported by Nicholson (1986a) and with PC 1 and PC 2 of Janowiak (1988).
243
SOUTH-EAST AFRICAN RAIN: PART 1
Figure 3. Spatial loadings of the first four rotated PCs of south-eastern African summer rainfall. Isopleth levels are ÿ017, ÿ015, ÿ012, 010, 012,
017. Areas with loadings greater than 015 are stippled
Table III. Cross-correlations of summer rainfall between the four
most important rainfall regions. Italic numbers represent correlations different from zero at the 1 per cent significance level
Region 2
Region 1
Region 2
Region 3
0144
Region 3
0139
0 34
ÿ 1
Region 4
0177
0168
ÿ0103
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 cases
where the indices lead rainfall by up to 12 months and, for convenience, leads and lags are referred relative to
January rainfall (the rainfall season’s middle month).
244
A. ROCHA AND I. SIMMONDS
4.1. SOI–rainfall associations
Figure 4 displays the standardized rainfall anomaly time series for regions 1 to 4. Year refers to the calendar
year at the end of the rainy season (e.g. 1983 means the rainy season starting in November 1982 and ending in
March 1983). The ENSO and anti-ENSO years are denoted by ‘W’ and ‘C’, respectively. Inspection of these time
series reflects what has been reported previously by some studies (e.g. Lindesay, 1988b; van Heerden et 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 ENSO
years. Indeed, the driest summer in regions 1 and 4 since 1950, namely 1968, was not associated with ENSO. The
same applies for wet summers and anti-ENSO years. The general impression from Figure 4 is that, although
rainfall 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 index
leading 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 feature
of note is that correlations are strongest for region 2. For this region the correlation steadily increases with SOI
lead decreasing, peaks at 4 months lead (r ˆ 0150 for September SOI), and decays thereafter. Correlations
are significant (1 per cent for SOI leads from 3 to 6 months. There are no significant correlations for regions
1, 3, and 4.
In order to investigate the temporal structure of the SOI signal, the same lag correlations stratified by rainfall
month 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 month
correlation 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 the
rainfall–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 12
months. The BMI–rainfall relationship is strongest in regions 1 and 4 and weakest in region 3. The correlations
steadily increase with decreasing BMI lead, peak when the index leads rainfall by 2 to 4 months (September to
November BMI) and decay at 1 month lead. Region 2 displays a similar correlation curve when BMI leads, but
values are lower than in regions 1 and 4, and only marginally significant (1 per cent) for BMI leads from 2 to 4
months. Summer rainfall in region 3 appears to have no relationship with the BMI at any of the considered leads
and 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 leads
up to 12 months. These are strongest for regions 1 (Figure 8), 2, and 4, but unlike with the SOI, highest values
occur 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 index
leading rainfall by up to 12 months. One of the strongest of these correlation patterns, that between October BMI
and rainfall, is displayed in Figure 9. Most of the area with correlations greater than 014 (significant at the 1 per
cent significance level) fall within regions 1 and 4, in close agreement with the correlations between the BMI and
seasonal rainfall in each of these regions. As defined, the BMI is negative when the pressure is abnormally low
over the Indian Ocean, just east of Madagascar, and above normal at Marion Island where the mid-latitude trough
is preferentially located in summer. Positive BMI–rainfall correlations indicate reduced precipitation over the
subcontinent 2 to 4 months after the negative index anomalies take place or increased precipitation for reversed
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245
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-ENSO
events are denoted by ‘W’ and ‘C’, respectively)
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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
pressure anomalies (i.e. abnormally strong trough). The first scenario seems to be consistent with an eastward
shift of the preferred cloud band mean position towards the Indian Ocean during dry summers, as reported by
Harrison (1986).
Next, the BMI–rainfall associations were investigated with the effects of ENSO removed. A partial correlation
analysis was performed between summer rainfall and the BMI with the effects of the SOI removed (the SOI
simultaneous with the BMI). Figure 10 displays these partial correlations for regions 1 to 4 (the index leading
rainfall by up to 12 months). Figure 10 is comparable with Figure 7. Monthly stratified partial correlations for
region 1 are shown in Figure 11 which can also be contrasted with Figure 8. It is evident that Figures 7 and 8 are
almost identical to Figures 10 and 11, respectively. One can therefore conclude that the BMI–rainfall association
is independent of the SOI. We have computed lag correlations between monthly SOI and BMI,
Figure 6. Lag correlations between monthly summer rainfall and monthly SOI for region 2. SOI months refer to the year in which the rainfall
season 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 014 are different from zero at the 1 per cent
significance level
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247
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 014 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
for lags from ÿ72 months (SOI leading) to ‡ 72 months (SOI lagging). Correlations were all below 0115 for
lags between ÿ12 and ‡ 12 months. Strongest correlations (0125) were found for the SOI leading the BMI by
50 months, but even these were barely significant if the reduction of the degrees of freedom due to memory in
both 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 southeastern Africa. As with the atmospheric indices in section 4, our interest is twofold. Firstly, we hope to find SST
anomalies 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 of
dry years, will be used to suggest a SST–rainfall physical link.
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Figure 9. Lag correlations between station summer rainfall and the BMI for October. The BMI leads rainfall. Isopleth interval is 012. Areas
with correlations greater than 014 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 (simultaneous
with the BMI) removed. The BMI leads rainfall by up to 12 months. Correlations greater than 014 are different from zero at the 1 per cent
significance level
5.1. Global correlation analysis
6
The SST–rainfall link is investigated by correlating summer rainfall in regions 1 to 4 with world-wide SST
anomalies in every 2 2 grid box. The seasonal (DJF, MAM, JJA, and SON) SST anomaly data sets are used
here. 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
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Figure 11. Lag partial correlations between monthly summer rainfall and monthly BMI for Region 1, with the effect of the SOI (simultaneous
with the BMI) removed. BMI months refer to the year in which the rainfall season starts. (The BMI leads rainfall except for November rainfall
and 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 014 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 only
in 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 SST
anomalies indicate that during JJA preceding dry summers (not shown), ocean waters tend to be abnormally
warm in most of the tropical Indian and Pacific Oceans. The SSTs are above normal in the Atlantic Ocean and a
core area of significant correlations is located in its south-eastern parts. This global correlation pattern is very
similar to the SST anomalies during ENSO. Correlations are, however, not significantly different from zero over
large areas of the ocean. Three months later, in SON (Figure 12), the correlation patterns are better defined and a
large area of negative significant correlations (positive SST anomalies in dry years), with magnitudes in excess of
015, are observed in the central Indian Ocean. To the south and south-east of Madagascar, the surface of the ocean
Figure 12. Lag correlations between summer rainfall in region 1 and SST anomalies in SON (three months lead). The isopleth interval is 012.
Stippling indicates correlations significant at the 5 per cent significance level
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tends to be cooler than normal during dry years (positive correlations). In the Pacific scattered pools of significant
associations are also evident. The SSTs in the Atlantic are weakly correlated with rainfall. In DJF (the peak of the
rainy season) (not shown), significant correlations cover a smaller area than in SON, particularly over the central
Indian Ocean but the general patterns present in SON remains unchanged. An area of relatively high positive
correlations (anomalous cold waters in dry summers) appears in the Indian Ocean just off the South African east
coast.
5.1.2. Region 2. For region 2 these spatial lag correlation patterns are similar but the values and significance
of the correlations are higher. During JJA (not shown) only in the Pacific Ocean are correlations significant over
considerable areas. Three months later, during SON (Figure 13), there is a strong association between rainfall and
SST anomalies in most of the equatorial Indian and Pacific Oceans, as evident from the large areas covered by
stippling. Correlations in some of those areas reach 017. Warm oceans tend to be observed in the southern parts of
the Indian Ocean, as in the correlations with region 1. The Atlantic SSTs show at this time no relation with
summer rainfall. During DJF (not shown) the SST–rainfall link weakens in the Indian and Pacific but a zone of
significant correlations appears in the eastern Atlantic. Also, the band of positive correlations in the south Indian
Ocean during SON expands eastwards, even becoming significant in its core. Walker (1989) has investigated the
association between Indian Ocean SSTs and rainfall in the south African summer region (its northern parts
coincide with our region 2). As in this study, Walker found that, over the Indian Ocean, dry summers are related
with 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 shape
of all patterns resembles that of an ENSO SST anomaly composite but correlations are in general weak and not
significant. 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 rainfall
regions, the SST–rainfall link is weaker for SST leads greater than 6 months (i.e. JJA SSTs) as well as for SSTs
lagging 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 six
driest (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. In
general, 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 012.
Stippling indicates correlations significant at the 5 per cent significance level
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251
5.2 Partial correlation analysis: sea-surface temperature and rainfall associations in the absence
of ENSO effects
It has been shown above that summer rainfall in regions 1 and 2 correlates significantly with SST anomalies in
vast areas of the tropical Indian and Pacific Oceans. Strongest associations were found with the SSTs leading
rainfall by approximately 3 months (SON SSTs). One may question how much of that relationship is ENSOrelated 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 after
removing the effect of the SOI (taken synchronously with SST). Only regions 1 and 2 were considered here
because rainfall in region 3 does not seem to be related in any coherent way with SSTs. The correlation spatial
patterns obtained when SSTs lead rainfall by 3 months (SON SSTs) are shown in Figure 14 (a) for region 1 and
Figure 14 (b) for region 2. For both regions, the area of significant correlations over the Indian Ocean was
reduced 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 after
the ENSO effects are removed. These findings are consistent with the weak associations between the SOI and
summer rainfall in region 2. Over the Pacific, the influence of ENSO on the SST–rainfall total correlation fields
is, as expected, considerably stronger. The strong negative associations present in the total correlations (see
Figures 12 and 13) virtually disappear with the removal of ENSO effects. Similar changes occur in both oceans if
rainfall 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 SSTs
over 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 dry
summers will now be identified. Some studies have reported on the atmospheric changes taking place over the
subcontinent and nearby oceans that occur during dry south-eastern African summers. It has been shown
in the previous sections that the SOI, BMI, and SST associations with rainfall in region 1 are representative of
those in regions 2 and 4. Therefore, only atmospheric anomalies typical of dry conditions in region 1 will be
identified.
The COADS contains only data for the ocean surface and, therefore, the Australian Southern Hemisphere data
set described above is used mainly in the following correlation analysis. However, due to the shorter period of the
Southern 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 gridpoint, with pressure leading rainfall by about 3 months (SON) and at zero lag (DJF). These two correlation fields
are displayed in Figure 15. Three months before the rainy season, pressure changes over the land and the adjacent
oceans in a coherent manner over large areas. Correlations indicate that before dry summers a band of positive
pressure anomalies extends from the central and eastern south-eastern Africa to the south and south-western
Indian Ocean. At the same time, negative pressure anomalies occur in the central Indian ocean, east of
Madagascar. Most of the South Atlantic is dominated by abnormally low pressures. Correlations are barely
significant at 3 months lead. At zero lag (DJF), the pressure anomaly pattern of SON becomes stronger. The
spatial structure of the correlations stays the same only over region 1 and to the east and south of Madagascar. In
other 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, are
denoted by crosses. The correlations shown here reflect the strong relationship obtained between rainfall in region
1 and the BMI (with Marion Island in phase and St Brandon out of phase with pressure changes over the
subcontinent), thereby justifying the usage of the BMI as an indicator of rainfall changes over region 1. This
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Figure 14. Partial correlations between summer rainfall and SST anomalies in SON with the SOI (simultaneous with the SSTs) being kept
constant, for (a) region 1 and (b) region 2. The isopleth interval is 012. Stippling indicates correlations significant at a 5 per cent significance
level
pressure anomaly dipole (with centres near Madagascar and to its south) may also be related with fluctuations of
the preferred position of the ridge associated with the standing waves 1 and 3 which are normally located over the
South 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 midlatitude trough associated with wave number 3 (Streten, 1973). The spatial correlation patterns of Figure 15
agree, when interpreted in terms of what they represent during day conditions, with the pressure composites
constructed by Matarira (1990) for dry minus normal south-eastern African years (his figure 4(c). When
superimposed on the climatological MSLP field, these anomalies reflect a weakening of the South Atlantic and
Indian Oceans high-pressure cells (not so clear during SON) and, consequently, a reduction of the surface
pressure gradient directed from the oceans to the subcontinent.
Clearly, large-scale pressure changes take place 3 months before abnormally dry rainy seasons and intensify at
zero lag with rainfall. Only during DJF are these anomalies significantly correlated with rainfall. Global analysis
of atmospheric changes during ENSO years have shown that over southern Africa pressures tend to be
abnormally high (e.g. van Loon and Madden, 1982; van Loon and Shea, 1987). An important feature of the low
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Figure 15. Correlations between summer rainfall in region 1 and MSLP in (a) SON and (b) DJF. The isopleth interval is 011. Stippling
indicates correlations significant at a 5 per cent significance level. The crosses represent the locations of St Brandon and Marion Island
pressure anomaly east of Madagascar is that it is located just south of the positive SST anomalies indicated by the
negative correlations of Figure 12. The surface pressure composite analysis performed by Cadet (1985) for the
Indian Ocean shows that during SON of El Niño years, an area of positive anomalies is observed north-east of
Madagascar, broadly coincident with the zone of positive correlations (negative anomalies in dry summers) of
Figure 15. This may indicate an ENSO signal in the correlation fields shown here. As for the SSTs, MSLP
composites were constructed. They agree well with the correlation maps but are not shown here.
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6.2. Winds
As with pressure, correlations were computed between summer rainfall in region 1 and the zonal wind
component 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 for
SON 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 and
north-easterly over south-western Africa. At zero lag (DJF), the spatial pattern of the meridional wind anomalies
changes little but the zonal winds are now anomalous westerly in the southern parts. To the north, a zone of high
positive significant correlations (easterly anomalies during dry summers) is located over central south-eastern
Africa. When superimposed on the mean vector field, these correlations represent, particularly for DJF, an
anomalous anticyclonic circulation which acts to weaken the thermal low pressure cell characteristic of the region
during summer.
At 200 hPa, the zonal wind correlates negatively with rainfall in SON (westerly anomalies in dry years) over
most of south-eastern Africa (not shown). Strongest significant associations exist at about 15 S over northern
Zambia, Mozambique, and Madagascar. At zero lag (DJF), correlations indicate that during dry years westerly
anomalies persist over most of the domain south of 15–20 S. Westerly wind anomalies have been reported to
occur over southern Africa during ENSO years (Arkin, 1982; Lindesay, 1988b) but the easterly wind anomalies
implied here by the positive correlations at low latitudes, particularly over the Atlantic during DJF, are not
present in the analyses of Arkin and Lindesay. Weaker easterlies at 200 hPa have been related to a lower
frequency of easterly wave and cloud band formation over the subcontinent, resulting in below average rainfall
over the summer rainfall region of South Africa (Harrison, 1983, 1986), the northern parts of which fall within
our region 2.
Westerly surface wind anomalies across much of the eastern coast of southern Africa have been reported by
Pan and Oort (1983) to occur when SSTs are abnormally warm in the central equatorial Pacific (i.e. ENSO
events). 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 wind
anomalies over the western Indian Ocean during ENSO.
Figure 16. As in Figure 15 but for DJF zonal wind at 850 hPa
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Figure 17. As in Figure 15 but for DJF meridional 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 be
abnormally warm over much of the tropics of our domain. Similar but weaker patterns were obtained for SON
T850 and T500 (not shown). As with the parameters analysed before, the rainfall–temperature association is
stronger during DJF, when significant correlations cover a larger area. This is particular evident for T850 during
DJF, when significant correlations occur over most of south-eastern Africa. Warm low-level temperatures during
dry 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 and
Diaz (1989). Indeed, the studies of Horel and Wallace (1981) and Pan and Oort (1983) have shown that most of
the 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 the
850 and 500 hPa levels. Correlation fields between DJF relative humidity and summer rainfall are shown in
Figure 19 for (a) 850 hPa and (b) 500 hPa. They indicate that during dry years, relative humidity at 500 hPa is
lower than normal (positive correlations) over region 1. At zero lag (DJF), correlations are strongly positive and
significant 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 seems
to be a spatially coherent relationship between the pressure and 500 hPa relative humidity anomalies over the
continent and Indian Ocean during dry region 1 summers. Areas of positive MSLP anomalies (negative
correlations in Figure 15(b) tend to coincide broadly with areas of reduced relative humidity (positive
correlations). This is particularly evident during DJF when an alternating three pole pattern of positive and
negative anomalies can be observed over the eastern Atlantic and south-eastern Africa (positive correlations) and
western Indian Ocean east of Madagascar (negative correlations).
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Figure 18. As in Figure 15 but for DJF temperature at (a) 850 hPa and (b) 500 hPa
The following scenario is suggested to explain the observed relative humidity anomalies. During dry summers
in region 1, pressure increases over the land weakening the thermal low. Weaker vertical motion associated with
these pressure anomalies would not favour convection, resulting in decreased moisture condensation in the
middle troposphere and, hence, reduced rainfall. Reversed conditions occur over south-western Africa and the
western Indian Ocean. This scenario is consistent with the eastward shift of the southern African cloud band from
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257
Figure 19. As in Figure 18 but for relative humidity
the subcontinent to the western Indian Ocean, described by Harrison (1983) as occurring during dry South
African summers. Anomalous features at 850 hPa are very similar to those at 500 hPa. Significance is only
achieved in the mid-latitudes, particularly over the south-east Atlantic Ocean during DJF where correlations
represent reduced relative humidity during region 1 dry summers. Inspection of low-level horizontal moisture
flux changes during region 1 summers using the Southern Hemisphere data (not shown) indicates that maximum
anomalous equatorward moisture divergence takes place around 40 S parallel near the Greenwich meridian
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(where correlations imply a maximum reduction in the relative humidity, as shown in Figure 19(a)). The positive
anomalies in relative humidity over south-west Africa are at least partly due to anomalous moisture convergence
from 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 stationary
component of the surface moisture flux was computed. These were calculated using monthly values of the surface
winds and moisture mixing ratio obtained from the COADS. Although COADS provides a poor spatial coverage
south of 40 S, the major moisture sources of the summer rainfall in the region originate in the tropical oceans, in
particular over the Indian Ocean.
Here we present the structure of the horizontal surface moisture fluxes calculated from the seasonal mean
surface wind and moisture. As calculated, this represents only the ‘stationary’ part of the moisture flux. Moisture
fluxes affected by the transient components may be important, particularly for southern coastal areas where
frontal 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 associated
with the large-scale circulation controls in the tropics (Chen, 1985). It is assumed here also that surface moisture
transport is an appropriate representation of the moisture flux in the lower troposphere where most moisture is
concentrated (Chen, 1985).
To analyse how this moisture flux is related to rainfall, composites were constructed for the six driest minus the
six wettest summers of region 1. As a reference for the subsequent analysis, the climatological stationary
moisture flux is presented in Figure 20 for SON (a) and DJF (b). Figure 21 displays the moisture flux anomaly
composites for SON (a) and DJF (b). Three months before the rainy season (SON) when SSTs are anomalously
warm over most of the tropical Indian Ocean, a strong easterly moisture flux anomaly is observed emanating from
the western flank of the Indian Ocean high-pressure cell towards the warm waters. Along the African eastern
coast, 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) and
20(b)). Note that in DJF the ITCZ is well defined by the confluence zone of the north-east monsoonal winds and
the south-east Trades at about 10 S. During spring (SON), the north-east monsoon is not yet established and the
circulation near the east African coast is south-easterly. One should also notice that over the Indian Ocean the
ITCZ encompasses a large area where the horizontal advection is rather weak, and therefore likely to allow
heating anomalies associated with warm SSTs to propagate relatively fast in the vertical, as suggested by the
theory of Webster (1981). The composite for DJF shows that a large and well organized cyclonic anomaly in the
moisture flux field develops at the surface east of Madagascar, resulting in a reduction of moisture advection into
the continent across the eastern coast. Along the Mozambique Channel, wind anomalies have a southerly
component, which agrees with the correlation analysis performed earlier (see Figure 17) and with the wind
anomalies 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 is
modulated by ENSO (no relationships were found for the northern regions of our domain). Strongest relationships
with the SOI were found with region 2, which encompasses most of northern South Africa. This confirms the link
between rainfall in the summer rainfall region of South Africa (which coincides broadly with region 2 here) and
the 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 southern
Mozambique 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 yields
the highest correlations with the SOI.
A number of factors can be suggested for the instability of these correlations within the rainy season. January is
the peak summer month and, by this time, the heat low and ITCZ are well established over the subcontinent and
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Figue 20. Climatological stationary surface moisture flux for (a) SON and (b) DJF. The longest vector corresponds to 0114 (Kg Kg 1)m s
ÿ
ÿ
1
embedded in a highly barotropic atmosphere (Lindesay, 1988b). During January, rainfall is predominantly of a
convective nature and rainfall-bringing baroclinic systems play a less important role than at the beginning and
end 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 interannual
variability is found for January) may contribute to weaken the correlations with the SOI. It may also happen that
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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. The
longest vector corresponds to 018 (Kg Kg 1)m71 in (a) and 0106 (Kg Kg 1)m s 1 in (b)
ÿ
ÿ
ÿ
ENSO modulates summer rainfall through a predominance of certain types of atmospheric systems embedded in
the tropical(Lindesay, 1988b) or extratropical (van Heerden et al., 1988) regimes.
The BMI correlates strongly with rainfall over the central regions of the summer rainfall area (regions 1 and 4
of this study). The relationship is such that steeper than normal MSLP gradients between the western and the
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southwestern Indian Ocean tend to be followed by positive rainfall anomalies, or less precipitation for weaker
gradients. The spatial signature of the association is broadly coincident with region 1. It is orientated in the northwest–south-east direction which happens also to coincide with the preferred orientation of the southern African
cloud 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 of
summer rainfall over the central areas of the summer rainfall region. It has been shown by the partial correlation
between 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 potential
predictability of rainfall is increased if both are used as predictors. The low correlations found between the BMI
and 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 has
been shown to be related to global SST patterns. Strongest associations are observed for SST leads of about 3
months and decay at zero lag with rainfall. The spatial pattern and the temporal evolution of these anomalies are
reminiscent of a typical ENSO episode. However, a partial correlation analysis has revealed that, although the
anomalies in the Pacific are ENSO related, over the central Indian Ocean warming is to a great extent
independent of ENSO. In fact, a careful inspection of monthly global SST anomalies and region 1 rainfall
interannual variability since 1950 shows that four (1959–1960, 1967–68, 1981–82 and 1983–84) of the eight
driest summers not associated with ENSO events coincided with warming over the central Indian Ocean. 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 SSTs over vast coherent areas of the Indian Ocean. Similar positive SST
anomalies were reported by Reverdin et al., (1986) as having occurred in the Indian Ocean during non-ENSO
years.
During dry region 1 summers, the atmosphere over the subcontinent and adjacent oceans undergoes major
changes. A broad zone of positive MSLP anomalies are observed over the central and south-eastern parts of
southern Africa extending south-eastwards into the Indian Ocean. At the same time, over much of the tropical
south-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 a
more 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 these
changes over the land (an anticyclonic circulation anomaly), which represents a weakening of the low pressure
cell, is consistent with the readjustment of the MSLP field.
A spatial correlation analysis performed by Lindesay (1988) between simultaneous SOI and winds at 700 hPA
during OND and JFM displays some of the features shown in Figures 16 and 17. This may indicate that part of the
wind anomalies reported here are related to ENSO, despite the fact that rainfall data and season classification on
which correlations were based are different in both studies. During dry years, temperatures throughout the lower
and middle troposphere are abnormally high over the subcontinent and neighbouring oceans, a situation usually
linked to ENSO, as a result of the westward expansion of the warm SSTs in the tropical Pacific and Indian
Oceans. In the relative humidity field at 500 hPa, a stationary synoptic-scale wave is observed over south-eastern
Africa such that an area of moisture deficit is located above region 1 and positive anomalies are observed to the
west, over south-western Africa, and to the east over Madagascar and the western Indian Ocean. These anomalies
are consistent with reduced condensation at 500 hPA over south-eastern Africa, and can be associated with a shift
in 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 circulation
anomaly is observed (Figure 21). As a result, part of the moisture carried by the south-east Trades and north-east
monsoon 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 level
circulation 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 also
investigated. Three months before and during the summers of both years, SST anomalies were positive over the
Indian Ocean, but in the equatorial central and eastern Pacific Ocean SSTs were very different. In 1967–1968,
262
A. ROCHA AND I. SIMMONDS
surface waters were abnormally cool, whereas during 1982–1983 positive SST anomalies occupied most of the
region. During both summers, enhanced surface level moisture convergence took place over the central Indian
Ocean, in close agreement with the moisture flux composites for dry minus wet summers. However, it was
suggested by Harrison (1986b) and Lindesay (1988) that westerly wind anomalies at 200 hPa over southern
Africa, as a result of changes in the Walker Circulation during ENSO events, contributes to summer rainfall
deficits in the region. Indeed, during January 1983 westerly wind anomalies at 200hPa of the order of 10 m s 1
were observed over south-eastern Africa.
ÿ
9. CONCLUDING REMARKS
No work to date has considered interannual variability of summer rainfall over south-eastern Africa in detail, and
for such a large area. Therefore, identification of its principal modes of variability has been accomplished in order
to investigate the spatial signature of precipitation variability. This has been achieved through PCA, and a careful
selection of the modes (unrotated versus Varimax rotated) that best represented the underlying variability of the
data. It has been shown that the most important mode of rainfall variability (region 1) covers much of central and
eastern 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 over
the southern parts of our domain, but even there the relationship is not strong. The association is such that during
ENSO (negative SOI) dry conditions tend to prevail. To the north, the SOI influence weakens considerably, with
the correlations changing sign over the northernmost areas. Moreover, strong associations are found when the
SOI leads rainfall by about 4–5 months.
An index of the Indian Ocean atmospheric circulation, the BMI, has been devised in this study which is
independent of the SOI. The association of rainfall with the BMI has been found to be much stronger than with
the SOI, and is such that weak mean sea-level pressure gradients between St Brandon (east of Madagascar) and
Marion Island (south-east of Africa) tend to be followed by dry conditions over south-eastern Africa. Unlike the
SOI, the BMI correlates with summer rainfall over a large area of the dominion, but strongest values are found for
the 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 partly
independent of ENSO.
The SSTs over large areas of the tropical Indian and Pacific Oceans correlate significantly with rainfall in
regions 1, 2, and 4, such that anomalously warm waters in those oceanic areas tend to coincide with dry
conditions. Correlations for region 3 are weak and spatially ill-defined, as are, in general, those over the Atlantic
Ocean. Strongest correlations have been found for regions 2 and 4 but their spatial structures are very similar to
that for region 1, and are strongly reminiscent of a typical ENSO SST anomaly pattern. The association peaks
when SSTs lead rainfall by about 3 months and decays thereafter. A partial correlation analysis where the ENSO
effects have been removed has revealed that, whereas much of the relationship disappears over the Pacific, strong
significant 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 (in
fact 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 anomalies
are 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.
263
SOUTH-EAST AFRICAN RAIN: PART 1
The hypothesis we have formed is that SST anomaly patterns identified as typical of dry south-eastern Africa
summers generate dry conditions through the above-mentioned atmospheric anomalous features. This hypothesis
will be tested through a series of modelling experiments in which the model’s atmosphere has been forced with
spatial- and time-evolving SST anomalies characteristic of dry summers over south-eastern Africa. The results of
these experiments are reported in Rocha and Simmonds (1997).
APPENDIX
Monthly values of the BMI from 1955 to 1988. Missing values are denoted by
Year January February March
1955
1956
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
03
17
ÿ016
ÿ012
ÿ019
ÿ111
013
ÿ113
ÿ114
011
ÿ014
117
018
010
211
011
ÿ018
ÿ9919
ÿ011
ÿ9919
118
016
110
016
019
ÿ112
115
ÿ012
015
ÿ116
ÿ011
ÿ019
013
012
ÿ 1
ÿ 1
14
015
110
018
ÿ011
ÿ018
111
014
ÿ016
ÿ019
010
211
110
014
119
ÿ110
ÿ119
ÿ018
ÿ111
ÿ9919
014
016
012
114
ÿ016
ÿ011
ÿ014
ÿ011
ÿ016
ÿ9919
ÿ212
012
016
ÿ011
ÿ 1
13
010
ÿ012
ÿ011
ÿ110
ÿ115
212
ÿ018
ÿ113
ÿ012
016
ÿ014
012
ÿ019
016
ÿ112
ÿ015
014
ÿ011
ÿ9919
ÿ014
116
213
017
010
ÿ116
114
ÿ012
016
011
111
ÿ9919
ÿ011
ÿ9919
ÿ 1
April
May
June
011
115
ÿ011
ÿ016
ÿ114
017
018
ÿ9919
ÿ013
ÿ114
ÿ016
ÿ018
ÿ117
019
114
112
117
119
ÿ111
ÿ9919
015
013
ÿ016
017
ÿ011
111
ÿ111
ÿ111
ÿ012
ÿ014
ÿ014
017
ÿ115
010
02
210
ÿ015
019
ÿ117
ÿ016
ÿ014
ÿ012
ÿ014
ÿ019
017
112
ÿ115
110
ÿ011
ÿ011
013
014
011
ÿ9919
012
ÿ012
014
211
016
013
ÿ310
ÿ017
ÿ016
016
ÿ018
019
ÿ011
012
03
113
111
114
116
112
ÿ014
ÿ017
ÿ016
012
ÿ018
ÿ9919
ÿ012
014
ÿ012
ÿ016
012
ÿ310
ÿ014
ÿ9919
ÿ013
011
113
111
018
018
ÿ112
011
ÿ111
016
ÿ013
ÿ117
ÿ013
010
ÿ 1
ÿ 1
ÿ
9919
July August September October November December
05
012
110
ÿ214
014
ÿ011
ÿ110
015
011
ÿ113
ÿ013
014
ÿ110
212
015
117
ÿ112
114
ÿ115
ÿ015
013
112
010
ÿ018
019
019
ÿ116
ÿ018
ÿ012
014
012
014
010
014
ÿ 1
16
111
017
ÿ112
ÿ014
210
ÿ018
013
ÿ012
019
ÿ014
ÿ015
ÿ017
114
ÿ019
012
ÿ012
ÿ118
ÿ013
018
112
110
011
119
ÿ113
015
ÿ015
ÿ016
ÿ9919
016
ÿ011
ÿ116
016
ÿ9919
ÿ 1
10
02
110
ÿ016
ÿ212
010
015
017
010
012
016
013
ÿ015
113
ÿ112
ÿ012
016
014
017
012
ÿ110
ÿ111
111
019
212
112
ÿ014
ÿ211
012
ÿ110
2 018
ÿ9919
ÿ013
ÿ112
ÿ 1
ÿ 1
03
012
115
010
ÿ016
112
019
012
ÿ115
ÿ012
ÿ016
010
ÿ016
114
ÿ014
ÿ218
ÿ012
016
113
114
ÿ012
013
014
ÿ9919
015
018
ÿ011
ÿ112
ÿ110
ÿ016
117
ÿ016
ÿ116
011
ÿ 1
02
9919
116
012
ÿ115
ÿ018
ÿ016
ÿ014
ÿ018
210
013
ÿ018
ÿ014
ÿ013
010
016
ÿ015
ÿ116
112
118
110
012
111
210
011
019
ÿ110
ÿ111
ÿ016
ÿ015
ÿ9919
ÿ015
ÿ015
ÿ112
ÿ 1
ÿ
010
015
ÿ011
013
011
017
ÿ210
013
ÿ016
015
111
ÿ112
ÿ116
113
019
ÿ011
112
ÿ012
ÿ012
114
013
110
017
014
013
014
ÿ016
ÿ113
ÿ212
018
ÿ214
011
013
ÿ9919
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