INTERNATIONAL JOURNAL OF CLIMATOLOGY
Int. J. Climatol. 21: 973–1005 (2001)
DOI: 10.1002/joc.673
SEASONALITY AND ATMOSPHERIC DYNAMICS OF THE
TELECONNECTION BETWEEN AFRICAN RAINFALL AND
TROPICAL SEA-SURFACE TEMPERATURE: ATLANTIC VS. ENSO
P. CAMBERLINa,*, S. JANICOTb and I. POCCARDa
Centre de Recherches de Climatologie/CNRS UMR 5080, Uni6ersité de Bourgogne, Dijon, France
b
Laboratoire de Météorologie Dynamique du CNRS, Ecole Polytechnique, Palaiseau Cedex, France
a
Recei6ed 24 March 2000
Re6ised 14 March 2001
Accepted 16 March 2001
ABSTRACT
A 47-year record (1951–1997) of gridded data covering Africa south of the Sahara was used to document the spatial
and seasonal patterns of the correlation between precipitation and sea-surface temperatures (SST) in key tropical
areas, as depicted by the NIN0 O3, South Atlantic and North Atlantic indices. El Niño –Southern Oscillation (ENSO)
is confirmed as playing a dominant part in northeastern, eastern and southern Africa. However, its impact is also
found over the Sahel during the northern summer, and other parts of the Gulf of Guinea region outside this season,
a hitherto poorly documented feature. Over these two areas, ENSO and Atlantic SST (predominantly South Atlantic)
contribute to different parts of the rainfall variance. The correlation with South Atlantic SST appears as a
south – north dipole (positive/negative correlation) which shifts northward following the Inter-tropical Convergence
Zone (ITCZ) translation between the northern low-sun and high-sun periods. A typing of the seasonal correlation
patterns and a mapping of the multiple correlation coefficients are carried out in order to synthesize the space– time
impacts of the three SST indices. Decadal-scale changes affect the strength of the teleconnections with both Atlantic
and East Pacific SST, as reflected for instance by a small rise of the correlation with the NIN0 O3 index since
1970– 1975 in the Sahel and southern Africa, and additional shifts for the Atlantic Ocean, but the main patterns
remain generally apparent over the whole period.
The circulation anomalies associated with the teleconnections were assessed using National Center for
Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis data. A study of the
dataset accuracy in depicting long-term climatic variations revealed that a major shift, mainly artificial, is found in
1967– 1968 in the time-series of most of the variables. The rest of the work thus concentrated on the 1968–1997
period. A number of changes in east–west circulation patterns have been found to be associated to ENSO variations.
Over West Africa, El Niño events tend to result in enhanced northeasterlies/reduced monsoon flow, coupled to
weakened upper easterlies, and hence dry conditions over West Africa close to the surface position of the ITCZ, in
July –September, as well as January–March. Over the southwestern Indian Ocean, the positive equatorial
temperature/geopotential height anomalies, which at 200 hPa accompany El Niño events, are conducive to an
eastward shift of the mid-latitude upper troughs, thus being detrimental to summer rainfall over South Africa.
Abnormally wet ‘short rains’ in East Africa can be accounted for by an ENSO-forced weakening of the equatorial
Walker-type (east –west) cell which is found over the Indian Ocean during that season. By contrast, the impact of
South Atlantic warmings is mostly shown in low-level dynamics, as exemplified by the weakened trades and monsoon
flow which directly result in a southward shift of the ITCZ. The combination of ENSO and Atlantic SST anomalies
are found to give rise to complex wind flow changes in the near-equatorial Atlantic. In addition to large-scale
SST-forced atmospheric dynamics, a few regional atmospheric signals are found to explain residual parts of rainfall
variance. For instance, a strengthening of the African Easterly Jet, or northerly wind anomalies across the Sahara,
are shown to be related to drought conditions in the Sahel (July–September) and the Gulf of Guinea area
(January –March), once the remote effect of SST anomalies is removed. Copyright © 2001 Royal Meteorological
Society.
KEY WORDS:
African rainfall; atmospheric circulation; composite analysis; ENSO; interannual variability; NCEP/NCAR
reanalysis; seasonal patterns; tropical SST
* Correspondence to: Centre de Recherches de Climatologie/CNRS UMR 5080, Université de Bourgogne, Sciences Gabriel, 6 Bd
Gabriel, 21000 Dijon, France.
Copyright © 2001 Royal Meteorological Society
974
P. CAMBERLIN ET AL.
1. INTRODUCTION
In the last two decades, a large number of studies have shown that a significant part of interannual
rainfall variance over several African regions is induced by, or at least related to, large-scale sea-surface
temperature (SST) variability, especially in tropical areas. Given the development of the African coastline
facing the Atlantic Ocean, this oceanic basin was given prime attention. In the northern summer, a West
African meridional dipole showing negative rainfall anomalies in the Sahel, to the north, and positive
rainfall anomalies along the Guinean coast, to the south, was found to result from a warming in the south
equatorial Atlantic and/or cooling in the North Atlantic (Lamb, 1978a,b; Hastenrath, 1984; Lough, 1986;
Servain, 1991; Janicot, 1992; Fontaine and Janicot, 1996; Ward, 1998). Such a north–south SST dipole
is part of larger scale (global) interhemispheric SST gradients, which are shown to have a pronounced
impact on Sahel rainfall variations (Folland et al., 1986, 1991; Trzaska et al., 1996). A positive correlation
with South Atlantic SST, as for the Guinean coast of West Africa, was documented for March –April
rainfall in coastal Angola (Hirst and Hastenrath, 1983a; Hastenrath, 1984; Nicholson and Entekhabi,
1987). Away from the Atlantic coast, no such strong influence of Atlantic SST on rainfall variations can
be found (Hirst and Hastenrath, 1983b), though the inclusion of basin-wide SST indices were shown to
improve significantly statistical rainfall prediction models for areas as far as Ethiopia (Seleshi, 1995) and
eastern equatorial Africa (Mutai et al., 1998).
SST anomalies in the equatorial Pacific Ocean are an expression of the El Niño–Southern Oscillation
(ENSO) phenomenon. Though distant from Africa, they are significantly correlated with rainfall
variations over the eastern side of the African continent, but the signs of the correlations and their
phasing to the seasonal cycle vary from region to region. El Niño events are associated with droughts in
Ethiopia (Ininda et al., 1987; Tadesse, 1994; Camberlin, 1995; Seleshi and Demarée, 1995) and southern
Africa (Lindesay et al., 1986; Ropelewski and Halpert, 1987; van Heerden et al., 1988; Jury et al., 1994),
whereas, in between, the October – December ‘short rains’ in eastern equatorial Africa are abnormally
heavy (Farmer, 1988; Ogallo, 1988; Hutchinson, 1992; Hastenrath et al., 1993). Teleconnections have also
been demonstrated with Indian Ocean SST. The East African ‘short rains’, as well as the early part of the
rainy season in southern Africa, show significant correlations with an east–west SST dipole in the
equatorial Indian Ocean (Ogallo et al., 1988; Beltrando and Camberlin, 1993; Richard et al., 1998).
However, there is so far not much evidence of any such strong teleconnections with Indian Ocean SST for
other seasons or other regions in Africa.
Most of the above-quoted studies focus either on a given part of Africa, or on a given ocean basin, and
they do not show the continental pattern of the SST forcing on African rainfall. An exception to this is
the work of Barnston and Smith (1996), which considered the coupled patterns of global SST and African
rainfall (along with that of other continents). Other studies, carried out at the scale of Africa (Nicholson
and Entekhabi, 1986; Janowiak, 1988; Nicholson and Kim, 1997) or at a global scale (Ropelewski and
Halpert, 1987; Kiladis and Diaz, 1989), solely considered the ENSO teleconnection. Although they shed
light on the spatial patterns of the teleconnection between Africa rainfall and some aspects of global-scale
climatic variability, they did not provide a detailed picture of their phasing to the seasonal cycle. In this
study, we consider an alternative approach, by investigating both space and seasonal modulation of
African rainfall associated with recognized modes of SST variability in the tropics. We concentrate on two
major signals: ENSO signal, as depicted by eastern equatorial Pacific SST (NIN0 O3 index, which is well
correlated to other ENSO indicators), and south equatorial Atlantic signal. South Atlantic SST variability
has been shown to be spatially very consistent, and recurrently emerges as one of the leading empirical
orthogonal function (EOF) modes of global SST variability, after ENSO (Folland et al., 1991;
Kawamura, 1994; Moron et al., 1995). Tropical North Atlantic SST is also considered in the study (in
particular in view of its contribution, together with South Atlantic SST, in meridional temperature
gradient anomalies). However, we excluded Indian Ocean SST variations, for the following reasons: (i)
they never emerge as a separate coherent variability mode; (ii) though there exist a few warming events
(as in 1961) occurring outside El Niño years, Indian Ocean SST are closely associated with ENSO
Copyright © 2001 Royal Meteorological Society
Int. J. Climatol. 21: 973 – 1005 (2001)
AFRICAN RAINFALL—SST TELECONNECTIONS
975
variability, showing a distinct rise after the onset of El Niño episodes; (iii) studies focusing on the Indian
summer monsoon have revealed that Indian Ocean SST are much more clearly forced by monsoon
dynamics rather than the reverse. However, this by no means signifies that the Indian Ocean has no role
in the fluctuations of African rainfall, but the teleconnection is complex, often of a regional character, and
not easily separated from that arising from ENSO.
Another major goal of this study is to document the actual atmospheric dynamics through which
rainfall could be forced by the large-scale SST anomalies. Although again some results have been achieved
on a region-by-region basis for selected seasons, there is a lack of information regarding the separate and
joint impact of ENSO and Atlantic SST variability on upper-air patterns above Africa, at the various
stages of the seasonal cycle. This is an important issue if the statistical relationship between rainfall and
SST are used for prediction purposes. To this end, we used National Center for Environmental
Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis data recently made
available for the period 1958– 1997. The reliability of these data, when utilized to document long-term
interannual variability remains questionable, but we shall concentrate on those variables which are
strongly or significantly influenced by actual climate observations, rather than those solely from model
computation. However, it is still desirable to undertaken a thorough checking of possible inhomogeneities
in time series.
In Section 2 we shall present rainfall, SST and upper-air data used in the study, and provide
information on the validity of the reanalysis time-series. The methods adopted to investigate the
rainfall/SST teleconnection, and the associated atmospheric dynamics, will also be discussed in that
section. The seasonal and spatial patterns of the relationship between SST indices and rainfall variability
over Africa will be presented in Section 3. Through composite analyses, the global and regional
circulation anomalies accounting for the teleconnections will be studied, on a region-by-region basis, for
those seasons/regions which will be shown to exhibit a significant response to SST (Section 4).
Information about atmospheric dynamics related to drought/heavy rainfall in the absence of any
corresponding SST signal will also be provided. Section 5 summarizes the results and suggests future
research orientations.
2. DATA AND METHODS
2.1. Characteristics of the datasets
Three independent datasets have been used: the Centre de Recherches de Climatologie (CRC) dataset
for rainfall, a collection of regional SST indices and the NCEP/NCAR reanalysis to depict atmospheric
dynamics.
Observed rainfall assembled in the form of a gridded dataset at CRC, has been preferred to reanalysis
rainfall data, which was found to be unreliable for the African continent (Poccard et al., 2000). The
dataset covers the period 1951 – 1997, though there are many gaps in the last 7 years. Details on the spatial
distribution of the rain gauges, sources of the data, can be found in Bigot et al. (1995, 1997). Original
station data have been amalgamated into 3.75°× 3.75° grid squares. A finer grid could not be adopted
since it would have resulted in too many spatial gaps, but this rather poor resolution is not a serious
drawback since we focus on large-scale rainfall variability. In a similar way, in an attempt to get stabilized
rainfall signals over time, we smoothed out intermonthly variability by using 3-month moving averages.
The same 3-month smoothing was applied to any other data (SST, reanalysis) used in the study.
Since our aim was to concentrate on the relationship between rainfall and recognized major SST
signals, ‘ready-made’ SST indices were obtained from the Climate Prediction Center [National Oceanic
and Atmospheric Administration (NOAA) US]. These are: the NIN0 O3 index (5°N–5°S, 150°– 90°W), a
widely used ENSO indicator, a south equatorial Atlantic index (‘SATL’, 0°– 20°S, 30°W– 10°E), and a
tropical north Atlantic index (‘NATL’, 5°–20°N, 60°–30°W). Data were available for the years
1951– 1997, as for rainfall data. The use of this index for the South Atlantic (restricted to the latitudes
Copyright © 2001 Royal Meteorological Society
Int. J. Climatol. 21: 973 – 1005 (2001)
976
P. CAMBERLIN ET AL.
0°– 20°S), was preferred to larger-scale indices, since during the southern summer (January–March), SST
at the northern margin of the basin (Gulf of Guinea, between 0°–5°N) does not correlate well with that
of the rest of the basin.
NCEP/NCAR reanalysis data (hereafter NCEP) consist of a comprehensive set of variables, available
at 17 standard pressure levels (Kalnay et al., 1996). The data were regridded on a 3.75° × 3.75° grid, and
three levels have been retained: 1000, 700 and 200 hPa, respectively. The 850 and 500 hPa levels were also
used in the preliminary analysis for quality control. Five variables have been selected: zonal and
meridional components of the wind; geopotential height; air temperature; and specific humidity. The first
four of these variables are strongly influenced by observed data, while the latter is partially affected by
the observations but also strongly influenced by the model characteristics.
2.2. Critical 6alidation of NCEP reanalysis data
Though the data assimilation process used in the NCEP/NCAR project is kept unchanged over the
whole period 1958– 1997 (Schubert et al., 1993), several changes occurred in the observing system
(discontinuities in station time-series, introduction of satellite data . . . ) in the course of these 40 years.
Since we are concerned with interannual climate variability over a sufficiently long period, it was essential
to check the data for artificial jumps resulting from these inevitable changes. A methodology identical to
that used in Poccard et al. (2000) was performed to detect abrupt shifts in the five variables selected
above. Each 40-year time-series was subjected to split moving-windows dissimilarity analysis— SMWDA
(see Cornelius and Reynolds, 1991; Kemp et al., 1994 for a detailed description). The SMWDA is a
multivariate method intended to date accurately climatic changes in chronological series. Potential
discontinuities are shown by peaks in the time-series of a dissimilarity index, which compares successive
samples within a given time-window. The nature of the change identified with this method depends upon
the selected window size. Small size windows allow the identification of atypical years and short-range
variability, whereas big size windows allow the detection of long-term changes. We have chosen four
window sizes (8, 12, 16 and 20 years), in accordance with the length of the series (40 years). All abrupt
shifts significant at the 99% threshold (Monte Carlo simulation) identified for each grid-point have been
classified, and the frequency of spatial occurrence over the whole of tropical Africa for each year of the
1958–1997 period has been plotted. A mapping is also provided of the grid-points where the main shifts
(20-year windows) are significant.
Our tests were performed on the five above-mentioned variables at five pressure levels (1000, 850, 500,
700 and 200 hPa). The following results focus on the 850, 500 and 200 hPa levels, which are characteristic
of the lower, middle and upper troposphere, respectively. The SMWDA time-series and spatial location
of the discontinuities are presented for five sample variables/levels (Figure 1). Six recurrent discontinuities
were found in the fields that were tested (Table I). Most variables showed shifts between the beginning of
the record and 1967 – 1968 (this latter year being that with the highest frequency of abrupt shifts). The
1967– 1968 discontinuity was also found in precipitation data (Poccard et al., 2000), but this variable is
entirely model-dependent, whereas those we use to depict upper-air dynamics strongly depend on
observed data. The shift can be found at any level, but it is more pronounced in the lower troposphere
because this level is strongly dependent on surface conditions defined in the model (Figure 1(a) and (b)).
Large parts of the continent are affected, but in the lower levels the signal is particularly strong over West
Africa. The numerous earlier signals are also widespread, though with stronger weight over localized areas
(e.g. northeast Africa for that of 1963– 1964). Significant shifts occur later in 1974–1975 and 1975–1976,
but it is not easy to determine whether there is a single discontinuity or two distinct ones. Shifts do not
always appear as sharp peaks in the frequency plots. However, the 1974 –1975 shift is restricted to
500-hPa specific humidity, and is strictly found over the ocean basins, mostly the Atlantic (not shown).
The 1975–1976 or 1976 – 1977 shift is also regional, but concerns Central Africa and/or East Africa, and
is best shown in dynamical fields (Figure 1(a), (b), and (e)). A discontinuity was found between 1976 and
1977 in a study of NCEP tropical surface air temperatures (Poccard and Janicot, 1998). However, it is not
clear whether the 1975– 1976 shift detected in African data pertains to the same signal. A sharp
Copyright © 2001 Royal Meteorological Society
Int. J. Climatol. 21: 973 – 1005 (2001)
AFRICAN RAINFALL—SST TELECONNECTIONS
977
Figure 1. SMWDA of NCEP reanalysis time-series: (a) 850-hPa geopotential heights; (b) 850-hPa zonal wind; (c) 850-hPa specific
humidity; (d) 200-hPa air temperature; (e) 200-hPa zonal wind. Left panels: spatial frequency of abrupt shifts (significant at the 99%
level, according to a Monte-Carlo simulation) for each year of the period 1958– 1997 using four different size windows (8, 12, 16
and 20-year, respectively Q8, Q12, Q16 and Q20); right panels: grid-points exhibiting a significant abrupt shift (Q20, 99% level) for
selected years. The dates correspond to the last year before the change
discontinuity is found slightly later (peaking in 1978 –1979) as a widespread signal covering almost the
whole of Africa, but restricted to the upper tropospheric temperature (Figure 1(d)). The 200-hPa
geopotential height also shows a widespread discontinuity in 1978–1979, but is difficult to separate from
Copyright © 2001 Royal Meteorological Society
Int. J. Climatol. 21: 973 – 1005 (2001)
978
P. CAMBERLIN ET AL.
Table I. Main discontinuities found in the time-series of the NCEP variables tested for the African region using
SMWDA
Years
Variables
affected
Levels
1967–1968 and before
All variables
1974–1975
1975–1976 (or 1976–1977)
H
Z (U)
1978–1979
1983–1984
1987–1988
T
H
(U) (V)
Nearly all levels, but more widespread for the
lower troposphere
500 hPa
All levels for Z, mid to upper troposphere for
the other variables
200 hPa
All levels
850 hPa
the antecedent one (1976– 1977). Minor discontinuities are found during the 1980s. That of 1983– 1984 is
limited to specific humidity, for grid-points clustered around Tanzania (850 hPa, Figure 1(c)) or South
Africa. That of 1987 – 1988 is found in some low-level fields, from the central South Atlantic Ocean to
southwestern Africa (Figure 1(b)).
Since most of the NCEP data used in our study are strongly influenced by the observation, and given
that most readily available data are already assimilated into the dataset, it was difficult to check reanalysis
time-series against independent, reliable upper-air data, as was done for rainfall in Poccard et al. (2000).
However, data for a few rawinsonde stations (Nairobi, Niamey and Dakar) from the monthly
Geophysical Fluids Dynamical Laboratory (GFDL) global rawinsonde dataset (Oort, 1978, 1983; Oort
and Liu, 1993) were compared with the reanalysis time-series for the nearest grid-square. The results
generally showed that the sharpest discontinuities (e.g., 1967– 1968 in most variables) were not readily
apparent in the rawinsonde data (not shown). Over West Africa, using a gridded version of the GFDL
dataset, Fontaine et al. (1995) found a shift in their monsoon index defined from 900 and 200 hPa winds,
but the magnitude of the shift in the NCEP time-series is highly exaggerated. Beyond 1968, it was more
often the raw upper-air time-series that exhibited spurious shifts, not reproduced in the NCEP data. Apart
from these shifts, and outside the 1958 – 1967 period, the NCEP time-series are in relatively good
agreement with the observed ones. Thus, no firm evidence could be found that the secondary shifts found
in the NCEP data (1975, 1983, 1987) were artificial.
Although it is difficult to assess to what extent these discontinuities affect the results of diagnostic
studies such as the one we propose to carry out, the origin of the strongest shifts is clearly found in
changes in the observing system. Sample Hovmöller plots of observation counts are produced in Figure
2. They reveal that the major shift detected after 1967 in most variables is due to a sudden increase in the
number of observations, especially land surface (Figure 2(a) and (b)) and ship reports (Figure 2(e) and
(f)). Other early jumps in observation numbers are found within the 1958 – 1967 period, for instance in
ship records. An encoding problem in data till 1967 was also reported on the Wesley/NOAA NCEP web
server. Wrongly encoded surface pressure records (below 1000 hPa) were rejected from the input data. The
exclusion of these records is likely to make the final product more dependent on the assimilation process,
but the problem mostly affects extratropical areas, which are not of interest here. The more regional shift
detected by SMWDA in the mid-1970s in equatorial Africa is also related to the beginning and
termination of a few surface and upper-air time-series in this area, though discontinued series (i.e. Entebbe
upper-air station in Uganda) are supplemented by neighbouring stations (e.g., Nairobi, Figure 2(d)).
Discontinuities at the end of the 1970s and 1980s (the latter of lesser importance) have more to do with
the introduction of satellite temperature estimates (1979) and satellite winds (1987), but in the southern
African region both land and ocean surface observations additionally exhibit strong variations (Figure
2(c) and (f)).
These overall results indicated that the pre-1968 data were unreliable, especially for a comparison with
later years. We therefore decided to exclude the 1958 –1967 records from all subsequent analyses. Other
Copyright © 2001 Royal Meteorological Society
Int. J. Climatol. 21: 973 – 1005 (2001)
AFRICAN RAINFALL—SST TELECONNECTIONS
979
Figure 2. Time ×longitude reanalysis monthly observation count at selected latitudes: land surface observations at (a) 7.5°N; (b)
0°N; (c) 25°S; (d) rawinsonde and pibal observations at 0°N; (e) ship observations at 20°N; and (f) 20°S. All data are smoothed using
a 5-month moving average
Copyright © 2001 Royal Meteorological Society
Int. J. Climatol. 21: 973 – 1005 (2001)
980
P. CAMBERLIN ET AL.
jumps were found to be more regional in character, and no procedure was attempted to eliminate
them from the records. The exception is the 1978 –1979 jump, which is likely to derive mostly from
the introduction of satellite temperature estimates. The two periods 1968–1978 and 1979–1997 were,
therefore, normalized separately in the rest of the analysis. Composite analyses carried out on the raw
and normalized data revealed that the results were virtually unchanged in the lower levels, but slightly
more at 200 hPa (where the shift was most pronounced), the consistency between temperature, wind
and geopotential variables becoming stronger.
2.3. Methodology
With the aim to detect the rainfall variance explained by the SST indices at each phase of the
annual cycle, correlation coefficients were systematically computed between 3-month moving averages
of rainfall (at each grid-point) and SST. The result was a series of 12 correlation coefficients for each
of the 130 grid-points. In order to compare the seasonality of the teleconnections, a hierarchical
clustering analysis (HCA) was performed, using Ward’s criterion, to identify groups of grid-points
exhibiting similar seasonal teleconnection (correlation) patterns with each of the SST indices. This
procedure enabled us to define homogenous areas, for which regional rainfall indices (as the average
of the interannual rainfall anomalies over all the available grid-points) were later calculated. Further
teleconnection analyses were carried out using these regional indices. In particular, a separation of
low-frequency (LF) and high-frequency (HF) variability was undertaken, since SST variations (e.g. in
the Atlantic Ocean) and some rainfall series (e.g., in the Sudano– Sahelian belt) are known to exhibit
quasi-decadal signals. Low-pass and high-pass Butterworth recursive filters, designed to retain
fluctuations above 8 years (LF) and between 2 and 8 years (HF), respectively, were therefore used to
assess whether the rainfall/SST teleconnections were merely a reflection of common long-term
oscillations, or year-to-year climate variability.
A second step was the definition, for each grid-point and each regional index, of multiple regression
models between rainfall (dependent variable) and the SST indices (predictors). We used stepwise
regression, with a significance threshold of 90% for a predictor to enter the model, and stored both
the predicted rainfall values and the residuals. From those values, different combinations were selected
in order to show the atmospheric dynamics associated with teleconnection patterns (i.e. those found in
‘fit’ years), as well as those found when the rainfall anomaly was not of the expected sign (i.e. in
‘non-fit’ years). ‘Fit years’ were defined as those for which the observed rainfall departure was in
agreement with the expected rainfall, as obtained from the (simple or multiple) linear regression with
the SST predictors. Years showing residuals not exceeding − 0.5 or + 0.5 standard deviations were
classified as such. Sample A (B) was then defined as those of the selected years showing observed
rainfall greater (lower) than + 0.5 (− 0.5) standard deviations. The final composite for ‘fit years’ was
the difference between the two samples. Where possible, an analysis of ‘non-fit years’ was also
performed. These were selected as having observed rainfall anomalies strongly departing from the
expected ones, that is when the residual was larger than 0.5 standard deviations, and the sign of the
expected anomaly differed from that of the observed one. In a similar way, the final composite for
these ‘non-fit years’ was defined as the difference between the ‘unexpectedly high rainfall’ and
‘unexpectedly low rainfall’ patterns.
Another procedure was used to confirm the actual atmospheric dynamics of the rainfall/SST
teleconnection. Extreme years for a given SST index were identified, and ‘warm minus cold’
composites of the various NCEP fields were computed. Similarly, for each regional rainfall index, ‘wet
minus dry’ (or ‘dry minus wet’) composites were defined. The SST-based and rainfall-based composites
were then compared. Similarities between both composite maps then show the probable way SST
anomalies may force rainfall variations. ‘Wet’ and ‘dry’ composites have also been computed in order
to assess the linearity of the relationships; they generally displayed opposite patterns and thus are not
presented below, except for the few cases of non-linearity.
Copyright © 2001 Royal Meteorological Society
Int. J. Climatol. 21: 973 – 1005 (2001)
AFRICAN RAINFALL—SST TELECONNECTIONS
981
3. TELECONNECTION BETWEEN AFRICAN RAINFALL AND SST
3.1. Relationship with Atlantic SST
The correlations between 3-month rainfall and 3-month SATL SST have been systematically computed.
We considered the full 1951 – 1997 period, but the stationarity of the signals will be discussed in Section
3.3. The typing of rainfall responses to concurrent SATL anomalies, obtained from a HCA (Figure 3),
indicate that most of southern and eastern Africa is virtually free from significant correlations (see extent
of type 1 in these areas).
Figure 3. Typing of the seasonal rainfall response to South Atlantic SST, from an ascending hierarchical clustering performed on
3-month correlation coefficients between rainfall and SST (1951– 1997). Top: spatial patterns; bottom: seasonal correlation
distribution for each type. Bold solid line: average of the correlation values; thin solid lines: variance envelopes (20% and 80% of
the grid-points pertaining to the type); dashed line with stars: correlation between the mean spatial rainfall of a given type and the
SST index. Horizontal lines correspond to the 90% and 95% significance thresholds
Copyright © 2001 Royal Meteorological Society
Int. J. Climatol. 21: 973 – 1005 (2001)
982
P. CAMBERLIN ET AL.
West Africa is undoubtedly the region where the signal is strongest. Note that the average correlation
(bold line) is not necessarily high, because local scale variability is not smoothed out in a grid-point
approach. However, the validity and coherence of the seasonal correlation patterns is demonstrated by the
narrow variance envelope, and by the much higher correlations obtained when we use the average rainfall
for all grid-points of a given type (dashed line). Over the Sudano– Sahelian belt, a mix of type 2 and type
3 seasonal patterns is found. Both indicate a reduction (increase) of July – September rainfall when the
South Atlantic is abnormally warm (cool). Type 3 shows a continuous east –west extent from 20°E to the
Atlantic, with stronger and more persistent (June –October at the 90% level) negative correlations. Type
2 is more evident in the east, and exhibits slightly less significant correlations. Notice than even a few
grid-points as far east as Ethiopia or northwestern Somalia show a significant negative correlation during
their main rainy season. To the south of 10°N, type 4 grid-points exhibit a transition pattern with type
5, the July–September correlation becoming positive. On the shores of the Gulf of Guinea (type 5), the
pattern is now totally reversed, with ensemble correlation with South Atlantic SST reaching +0.7 for
June–August. A vanishing of the so-called ‘little dry season’ is experienced when warm events occur. It
is worthwhile to note that this pattern is restricted to the central part of the Guinean West African coast
(that showing marked dryness at this time), since to the west of 8°W, in the Republic of Guinea, the
correlations (as for the mean seasonal rainfall) are more like those of the Sahel. South of the equator, in
Gabon and coastal Congo, the pattern is again slightly different (type 6): the positive correlations with
South Atlantic SST occur a little earlier, and more importantly the northern winter does not exhibit any
more negative correlations with SST (see below).
In addition to the well-known north– south West African dipole in July–September, a few other
teleconnection patterns are shown which have attracted much less attention so far. First is the tendency
for western equatorial Africa to exhibit positi6e correlations with SATL. The impact of warm (cold) SST
on coastal rainfall enhancement (reduction) between 5 –15°S was documented by Hirst and Hastenrath
(1983a) and Nicholson and Entekhabi (1987), but the latter only considered SST in the Benguela current
area. Further north, in the coastal strip of Gabon and at Sao Tome island (0°23%N), abnormally heavy
rains have been reported between June and October 1984, in coincidence with a warm anomaly in the east
equatorial Atlantic (Buisson, 1985; Philander, 1986). Maps of rainfall/SST correlations feature a
south-north shift of maximum positive correlations (near 5°–10°S in March –May, 0°–5°N in
June–August). The significant correlations, restricted to the coastal areas, arise from LF and HF
variations (not shown).
Another worthy feature is the tendency for large parts of West Africa and nearby equatorial Africa to
experience significant negati6e correlations at the time of the main dry season (which is far from being
totally rainless in the Guinean region). As demonstrated by the time-series for southwestern Cameroon
(Figure 5(a)), a reduction (increase) of rainfall amounts often accompanies the occurrence of warm (cool)
SST in the south equatorial Atlantic, as was the case in 1973– 1974 and 1983–1985 (respectively, 1951,
1956 and 1978). However, the relationship is most evident on low-frequency filtered time-series
(r= − 0.87). Janicot and Fontaine (1997) revealed that this negative correlation for Guinean West Africa
was partly explained by the marked downward rainfall trend between the 1950s and 1990s. In most cases,
the dry season was much less pronounced in the 1950s than in the 1980s, in conjunction with SST
warming. A negative correlation could also be identified between South Atlantic SST and March–May
simulated rainfall in West Africa, using the ECHAM4 atmospheric circulation model forced with
observed SST (Moron et al., 1998, see their figure 17(b)).
Many of the above results, at least for West Africa, can be interpreted in terms of variations in the
latitudinal location of the ITCZ. For instance, large-scale warm anomalies in the South Atlantic mean a
reduced temperature gradient towards the overheated continent, and therefore a reduced northward
excursion of the ITCZ in the northern summer (and hence, the heavy rains along the Gulf of Guinea), as
well as, in the northern winter, an abnormally southerly shift of the ITCZ, that is close to the equator
over the ocean, rather than over land. A marked southward position of the ITCZ in January –May 1984
was, for instance, noted by Citeau et al. (1985).
Copyright © 2001 Royal Meteorological Society
Int. J. Climatol. 21: 973 – 1005 (2001)
983
AFRICAN RAINFALL—SST TELECONNECTIONS
In some parts of Africa, rainfall was found in previous studies to respond to interhemispheric SST
gradients in the Atlantic, rather than solely to SST conditions in the South Atlantic. A comprehensive
study of the teleconnections between African rainfall and a ‘dipole index’ (South minus North Atlantic
SST), representative of the SST gradient between the two basins, was carried out but yielded results which
did not differ much from those obtained for the South Atlantic index. The dominant features are as
follows:
(i) A belt of negative significant correlation shifts from south (January –March) to north
(July– September), and then southward again (October–December). The highest correlations are
systematically found on northwest – southeast orientated coasts (from Senegal to Liberia, and in the
Bay of Bonny), i.e. at right angles to the southwesterly monsoon flow.
(ii) Along the shores of the Gulf of Guinea, a pole of positive correlations moves from Angola
(March –May) to Gabon and then to Guinean West Africa (July–September). Although this is
apparently only a HF teleconnection, the removal of the linear trend in the series results in a much
better agreement between the HF and the LF correlations (see the example for coastal Angola/Congo
in Table II). This suggests that there might remain instrumental biases in SST data, not fully removed
by the pre-treatment procedures.
(iii) A less expected feature is the occurrence of (weak) positive HF correlations in East Africa during the
‘short rains’ (October – December). Cooling in the South Atlantic Ocean was found to result in poor
rainfall in that region (Nicholson and Entekhabi, 1987; Mutai et al., 1998), though this is not the
major forcing of East African rains.
Whereas the clearest response of African rainfall to south equatorial Atlantic SST variations occurs
along the Atlantic shores or in West Africa, weaker signals are found further east (Ethiopia) and south,
in the LF range only. In inland southern Africa (Figure 5(b)), a negative correlation is evident ( −0.70 in
December –February over 1951 – 1997), but the reduced number of degrees of freedom calls for care in the
interpretation. A low frequency (about 18 – 20 year) modulation of southern Africa rainfall has been
found in earlier studies (Tyson et al., 1975; Kruger, 1999) but its origin has never been identified.
3.2. Relationships with ENSO
The seasonality of the ENSO/African rainfall teleconnection is displayed in Figure 4, which shows that
during the 1951– 1997 period, most parts of the continent recorded significant correlations. For each
region, the largest values are generally found at the peak of the rainy season. The strongest correlations
are found during the second half of the year (July – September and October – December), at the mean time
of the largest Southern Oscillation Index (SOI) or NIN0 O3 anomalies, whereas the April–June season only
shows tenuous relationships with ENSO, whose phase shift actually often occurs at this time of the year.
Summer precipitation areas characteristic of the tropical regimes of the Northern (Sudano–Sahelian
belt, and northeast Africa west of the Rift Valley) and the Southern Hemisphere (a large southern Africa,
apart from the western strip) all show negative correlations with NIN0 O3 SST (types 4 and 5 in the
clustering analysis). The occurrence of droughts in southern Africa in El Niño episodes is a well-known
feature (e.g. Lindesay et al., 1986; Janowiak, 1988; Kiladis and Diaz, 1989) although some years fail to
exhibit the expected low rainfall, as in 1997–1998. Figure 5(c) confirms these observations for both
high-pass and low-pass filtered data.
Table II. Correlation between March–May rainfall along the Atlantic coast from Angola to Gabon and the
March–May South Atlantic SST index (1951–1990)
Raw data
Detrended data (rainfall and SST)
Copyright © 2001 Royal Meteorological Society
Unfiltered
series
High-pass filtered
(2–8 year) series
Low-pass filtered
(\ 8 year) series
−0.35
−0.54
−0.63
−0.64
+0.04
−0.55
Int. J. Climatol. 21: 973 – 1005 (2001)
984
P. CAMBERLIN ET AL.
Figure 4. Same as Figure 3, for the rainfall response to NIN0 O3 SST
In tropical northern Africa, the impact of ENSO events is still debated. However, several studies tend
to show that, after discarding the LF signal (wet conditions during the 1950s and 1960s, dry conditions
thereafter), El Niño events result in dry conditions over the Sahel (Rowell et al., 1995; Janicot et al., 1996;
Ward, 1998). This is particularly so for the last three decades, suggesting that some form of LF
modulation exists in the Sahel – ENSO teleconnection, possibly caused by different ‘background’ SST in
the global ocean (Trzaska et al., 1996). To the east, in the Ethiopian region, the ENSO/rainfall correlation
is stronger and not restricted to the recent decades (Camberlin, 1995, 1997; Seleshi and Demarée, 1995).
The present results, with data back to the 1950s, confirm the correlation with ENSO, peaking in
July–September. The correlation coefficient for high-pass filtered data reaches − 0.68 for a Sudan– Sahel
index west of 20°E. A few grid-squares showing non-significant correlations (type 1) are intertwined with
Copyright © 2001 Royal Meteorological Society
Int. J. Climatol. 21: 973 – 1005 (2001)
AFRICAN RAINFALL—SST TELECONNECTIONS
985
Figure 5. Standardized anomalies of 3-month average rainfall (bars) and 3-month average SST (dashed), for selected regions (see
insert maps) and seasons: (a) Bight of Bonny rainfall and South Atlantic SST in January– March; (b) Southcentral Africa rainfall
and South Atlantic SST in December– February; (c) South Africa rainfall and NIN0 O3 SST in January – March; (d) Uganda rainfall
and NIN0 O3 SST in July–September; (e) Western Equatorial Africa rainfall and NIN0 O3 SST in March – May; (f) and Guinean West
Africa rainfall and NIN0 O3 SST in January –March. Bold lines are low-pass filtered (\8 years) series, for rainfall (solid) and SST
(dashed). The correlation coefficients between the rainfall and SST time-series are shown below the plots
type 4 areas, but they exhibit the same general seasonality in the correlations. The largest values are found
in the central Sahel (Niger, Chad), and to the east in Ethiopia and Uganda (Figure 5(d)). A large part of
this signal reflects a robust teleconnection between the Indian summer monsoon and rainfall in this part
of Africa (Camberlin, 1997; Ward, 1998).
Copyright © 2001 Royal Meteorological Society
Int. J. Climatol. 21: 973 – 1005 (2001)
986
P. CAMBERLIN ET AL.
In the equatorial belt, a strong ENSO/rainfall correlation is found in East Africa (type 5), with a phasing
onto the ‘short rains’ season (October – December). Contrary to the tropical regions, the correlation is
positive, indicating wet (dry) conditions in El Niño (La Niña) years. In East Africa, El Niño years fairly
nicely fit above-normal rainfall records, except for a few occasions where rainfall is unexpectedly low
(1976, 1987), and others which displayed record high rainfall in the absence of El Niño (1961, 1967).
Seasonal and spatial patterns of this teleconnection were documented by Ogallo (1988), Beltrando (1990),
Hutchinson (1992) and Richard et al. (1998). Nicholson and Kim (1997), using harmonic analysis, also
reported this teleconnection, but the method failed to identify the sharp reversal in the sign of the
correlation with ENSO which occurs in the East African highlands west of the Rift Valley between
July–September (dry in El Niño years) and October –December (wet), as depicted by type 4.
A more unexpected feature is the ‘type 2’ seasonal pattern, with negative correlation between NIN0 O3
SST and rainfall in March– June and August– November, in western equatorial Africa, a region which had
never been shown before to exhibit significant correlations with ENSO. This indicates that in El Niño
years, the northern summer dry spell which separates the two rainy seasons tends to get longer, through
a lower frequency of both rainy seasons (see Figure 5(e) for March–May). Composite maps drawn by
Kiladis and Diaz (1989) do show a dry anomaly in the Gulf of Guinea area in El Niño years from
September, but rather wet conditions in March –May (though the method and period are very different).
Moron et al. (1995) also found a weak negative correlation between an EOF mode representative of ENSO
variability in SST, and Gabon rainfall, but in September – October only. Similarly, note that ‘type 3’
teleconnection (maximum negative correlation in December–February) is also found along the southern
coast of West Africa (see out-of-phase variations on Figure 5(f), especially for high-pass filtered series).
3.3. Synthesis
In order to synthesize the relationship between African rainfall and the above two indicators of SST
variability, plus the tropical North Atlantic index (NATL, not shown), a stepwise multiple regression has
been computed for each month and each grid-square across Africa (Figure 6). The dependent variable is
the 3-month average rainfall for the corresponding grid-square, and the predictors are the three SST
indices. The significance level for a predictor to enter the model was fixed at 90%. This is a rather low
threshold, but only weak correlation values are to be expected on a grid-square basis, and we concentrate
on their spatial coherence. The predictors included in the models are shown on Figure 6 (left panels). The
right panels display the multiple r 2 values for each grid-point. Rainfall series not reaching a multiyear
average of 10 mm (sum over 3 months) have been excluded from the analysis.
The ENSO forcing (circles) is virtually undisputed in the regions bordering the Indian Ocean from
equatorial Africa to South Africa, with negative correlations with NIN0 O3 in the northern and southern
summers, and positive correlations in the transition seasons in eastern equatorial Africa (esp.
October–December). A marginal incidence of the NATL predictor (squares) is found over these areas for
rather scattered months and grid-points, but the variance explained is low.
In West Africa and parts of western equatorial Africa, there is more evidence of combined impacts of
the three SST signals. The SATL index (pluses) is included in the models almost all year round; the sign
of the regression coefficient is negative (dry when warm) along the northern edge of the rainbelt. It switches
to positive along the southern edge of the rainbelt (April–June in western equatorial Africa,
July–September along the Gulf of Guinea coast). NATL also partly contributes to rainfall variability, with
an opposite sign to that of South Atlantic SST, but at the northern edge of the rainbelt only, and with
lower partial correlation coefficients. In some areas/seasons, the NIN0 O3 index is also found to have a
partial contribution (dry when warm, esp. in January –March at 0°–10°N and July – September at
10° –20°N). Few regions do show a simultaneous impact of the three SST indices. Some parts of the
continent (Central Africa, northeast Africa during the March –May rainy season) even fail to exhibit
consistent relationships with any of the SST indices, suggesting that rainfall variability responds to other
oceanic patterns, or only to local/regional atmospheric dynamics.
Copyright © 2001 Royal Meteorological Society
Int. J. Climatol. 21: 973 – 1005 (2001)
Figure 6. Results of stepwise multiple regression of seasonal rainfall variations for 1951– 1997. Top panels: SST indexes entered in the regression defined for each grid-point
(significance threshold: 90%). Thin (bold) symbols indicate positive (negative) coefficients. Bottom panels: multiple r 2
AFRICAN RAINFALL—SST TELECONNECTIONS
Copyright © 2001 Royal Meteorological Society
987
Int. J. Climatol. 21: 973 – 1005 (2001)
988
P. CAMBERLIN ET AL.
Over some African regions, there have been decadal-scale changes in the teleconnections with global
SST patterns. For the Sahel for instance, Janicot et al. (1996) found that the correlation with south
equatorial Atlantic SST (ENSO) has weakened (resp. increased) since 1970. A moderate increase in the
South Africa–ENSO teleconnection has also been noticed since that date (Richard et al., 2000). We thus
computed 17-year moving correlations between each of the three SST indices and rainfall in the regions
that have just been shown to exhibit consistent teleconnection signals. On the whole, the correlations
remain fairly constant (Figure 7), e.g. for the NIN0 O3 index in Guinean West Africa in January– March.
However, ENSO variations have become slightly more apparent in southern Africa and Sahel rainfall
series since the mid-1970s. In East Africa (October –December), the correlation also shows sizeable
changes, becoming non-significant in the 1960s and mid-1980s. While in the Sahel, the South Atlantic
index has been undergoing a dramatic decrease in its correlation with rainfall, it has maintained high,
significant correlations with Guinean rainfall in July –September (Figure 7(d)). However, consistently low,
or decreasing correlations, do not mean that a given SST signal plays no role in rainfall variability, as it
may be obscured by other teleconnections. Ward (1998) showed that despite the marked changes in the
Figure 7. The 17-year moving correlation between SST indices and regional rainfall indices: (a) South Africa in January– March; (b)
Guinean West Africa in January–March; (c) Sudano-Sahelian belt in July– September; (d) Guinean West Africa in July– September;
and (e) East Africa in October–December. The upper and lower horizontal dashed lines correspond to the 95% confidence level. The
year on the bottom scale is that of the middle of the 17-year period
Copyright © 2001 Royal Meteorological Society
Int. J. Climatol. 21: 973 – 1005 (2001)
AFRICAN RAINFALL—SST TELECONNECTIONS
989
correlation with ENSO and South Atlantic SST modes from pre- to post-1970, both modes continued to
explain useful fractions of Sahelian rainfall variance in both epochs. Therefore, whilst results hereafter refer
to the complete 1968– 1997 period, due to the unreliability of NCEP data over Africa prior to 1968, they
should be seen as relevant to the full period, with some caution for the regions/seasons underlined in this
section.
4. ATMOSPHERIC DYNAMICS
The atmospheric dynamics associated with African rainfall anomalies are studied using NCEP reanalysis
data for the years 1968– 1997, on which composite analyses are performed. Only those regions/seasons
showing the most significant teleconnections with the above three SST indices are selected. The aim is to
identify the atmospheric dynamics accounting for, first the teleconnection between rainfall and SST (i.e.
circulation anomalies in ‘fit years’), second the departures from those teleconnections (i.e. ‘non-fit years’
with respect to the SST forcing; see definition in Section 2.3). The dynamics involved in the SST/rainfall
teleconnection were deduced from visual comparison between the ‘wet minus dry’ (or ‘dry minus wet’)
composite and the ‘warm minus cold’ (or ‘cold minus warm’) composite for each of the SST indices
involved in the forcing. As a confirmation, a separate composite for ‘fit years’ was computed. A partial
analysis of ‘non-fit years’ was also carried out.
4.1. January– March
4.1.1. Gulf of Guinea region. In multiple regressions, the low rainfall season along the Gulf of Guinea
was shown to be significantly related to all three SST indices. A simple ‘dry minus wet’ composite for that
region shows two low-level mid-latitude troughs are located in the southern subtropics. Over the
continent, positive pressure anomalies are found close to the surface. West Africa exhibits stronger than
usual northeasterlies (‘harmattan’), resulting in a moisture deficit at 10°–15°N. Wind anomalies close to
the equator are not significant. Comparing these patterns to the ‘warm minus cold’ South Atlantic and
NIN0 O3 composites, it is evident that both SST signals intervene (Figures 8 and 9). A warmer than usual
South Atlantic ocean results in a weakened St Helena high, weakened trades and a weakened monsoon
flow across the Gulf of Guinea and neighbouring land areas. A strong moisture deficit is found at both
the 700 and 1000 hPa levels over West Africa (as well as over East Africa). El Niño conditions also give
rise to enhanced northeasterlies over West Africa (Figure 9), though these now seem to result from
warmer than usual SST in the near-equatorial trough area between Africa and South America (not
shown). It is noticeable that over the Atlantic Ocean, with the exception of the Gulf of Guinea, the wind
anomalies are almost the reverse of those exhibited in the ‘warm minus cold’ South Atlantic composite.
Interestingly, these contrasting patterns, which are indicative of an abnormal southward (northward) shift
of the oceanic arm of the ITCZ in the South Atlantic (NIN0 O3) composites, respectively, have similar
consequences along the Gulf of Guinea shores, i.e. a weakened monsoon and enhanced dryness. The
misleading absence of any low-level wind signal over the equatorial Atlantic in the ‘dry minus wet’
composite (Figure 10) indeed reveals the superimposition of two totally different dynamics,
accounting for the observed West African dryness.
All together more than 41% of the rainfall variance is explained by the three SST predictors, when the
rainfall area close to the Bay of Bonny (South Cameroon, Northern Gabon) is taken as a base region to
those shown above, low-level dynamics partly similar to those shown above are obtained (Figure 11(a)).
Enhanced northeasterlies in the ‘dry minus wet’ composite result from a cold (north)/warm (south)
anomaly dipole over the Atlantic Ocean, or a forcing by ENSO conditions. But the composite additionally
shows strong continental anomalies across the Northern Hemisphere subtropics, with enhanced pressure,
resulting in sustained northeasterlies, from North to West Africa and the Indian Ocean, a cooling over
the same regions (not shown), and reduced specific humidity, especially in the ITCZ area. When
considering only ‘non-fit years’ (i.e. with rainfall anomalies not in accordance with SST-based
Copyright © 2001 Royal Meteorological Society
Int. J. Climatol. 21: 973 – 1005 (2001)
990
P. CAMBERLIN ET AL.
Figure 8. Composite maps of (a) 200, (b) 700 and (c) 1000 hPa NCEP fields, for ‘warm minus cold’ SST in January – March over
the South Atlantic Ocean. Only shown are the significant (95% level) anomalies, according to Student’s t-test. Arrows: wind
anomalies (length is function of t); shaded areas: geopotential height anomalies (dark shading: positive, light shading: negative);
thick lines: specific humidity anomalies (solid: positive, dashed: negative)
predictions), the importance of cold air advection across the eastern Sahara is evident (Figure 11(b)).
Besides the basic role of SST patterns onto rainfall variability along the Gulf of Guinea shores, that of
a durable, purely atmospheric forcing (possibly related to land surface conditions) is thus suggested.
4.1.2. South Africa. The ‘dry minus wet’ composite (Figure 12) shows significant atmospheric anomalies
in both the upper and lower troposphere. At 200 hPa, most noticeable is a southwest/northeast
trough/ridge pattern over southeastern Africa and the nearby Indian Ocean. At 700 and 1000 hPa, above
normal geopotential heights are found over large parts of the Tropics. Over southern Africa, a
particularly strong anticyclonic anomaly overlies the Kalahari Desert, in association with positive air
temperature anomalies and a decrease in specific humidity. Southerly wind anomalies are found around
Copyright © 2001 Royal Meteorological Society
Int. J. Climatol. 21: 973 – 1005 (2001)
AFRICAN RAINFALL—SST TELECONNECTIONS
991
Figure 9. Same as Figure 8, for ‘warm minus cold’ SST in January – March over the NIN0 O3 area
the Mozambique Channel and Madagascar, while the Indian Ocean northeast of Madagascar is warmer
than usual (not shown), in agreement with weakened trades, a weakened Mascarene high, a southward
shift of the ITCZ and a reduced moisture flux towards South Africa. These features are in agreement with
Tyson (1987) and Jury (1996). Some discrepancies with respect to Rocha and Simmonds (1997a) are
found for upper-air patterns, though the upper westerly anomalies are conveniently reproduced, but their
analyses focused on southeast Africa rainfall, that is slightly northeast of the area presently considered.
However, surface wind and moisture flux patterns over the Indian Ocean are remarkably similar.
Stepwise multiple regression of southern Africa rainfall retained NIN0 O3 SST as the only significant
predictor. A comparison between the ‘dry minus wet’ and the ‘warm minus cold’ NIN0 O3 composites
(Figure 9) provides clues on the dynamics involved in the forcing. Two upper troughs over southeast
Africa and West Africa appear as key features, which are best displayed when only the years where the
rainfall anomalies are in agreement with the ENSO forcing (i.e. dry when warm, and wet when cold) are
Copyright © 2001 Royal Meteorological Society
Int. J. Climatol. 21: 973 – 1005 (2001)
992
P. CAMBERLIN ET AL.
Figure 10. Composite maps of (a) 200 and (b) 1000 hPa NCEP fields for ‘dry minus wet’ January – March conditions in Guinean
West Africa. Legend as in Figure 8
considered (not shown). The role of equatorial Atlantic upper wind in transmitting the Pacific El Niño
signal to southern Africa has been emphasized by Jury et al. (1994) and Jury (1996). Interactions between
El Niño-induced tropical anomalies and the extratropical circulation (with enhanced meridional
exchanges, but shifted eastward to the southwest Indian Ocean) are also put forward on these figures. The
importance of such changes in the upper tropospheric circulation has been partly documented by Tyson
(1987). Low-level anomalies are not as apparent, although the anticyclonic circulation anomalies, reduced
moisture flux, and increased temperatures are still evident over southern Africa. Over the Indian Ocean,
there are some differences between the NIN0 O3 and the South Africa rainfall composites. Whereas the
former shows a uniformly warm ocean, and mostly zonal wind anomalies (especially at 200 hPa), the
latter indicates that meridional dynamics, associated with the Indian Ocean monsoon circulation, play a
greater role in South Africa rainfall variability. A specific analysis of South Africa drought years which
occurred outside warm NIN0 O3 events (1968, 1971, 1984, 1986), exhibit at 1000 and 700 hPa a symmetric
pattern of cyclonic anomalies over the western Indian Ocean, as well as a drier/cooler eastern and
southcentral Africa (not shown). It is speculated that the reduced moisture advection from the north
along with the setting-up of an anticyclonic circulation over South Africa, drawing dry and stable air from
the southeast Atlantic region, are instrumental in the development of drought conditions outside El Niño
years.
4.2. July–September: Sahel – Sudan
The ‘dry minus wet’ composites of July –September rainfall in the West African Sahel exhibit
well-known upper-air anomalies (Figure 13):
(i) At 200 hPa, a weakened Tropical Easterly Jet (TEJ), though over West Africa only.
Copyright © 2001 Royal Meteorological Society
Int. J. Climatol. 21: 973 – 1005 (2001)
AFRICAN RAINFALL—SST TELECONNECTIONS
993
Figure 11. Composite map of NCEP fields, for ‘dry minus wet’ January – March conditions in the Bight of Bonny region: (a)
observed rainfall; (b) residuals to estimated rainfall from the SST indices. Legend as in Figure 8
(ii) At 700 hPa, an anticyclonic circulation off Senegal, resulting in enhanced easterlies between
Cameroon and Guyana, which indicates a strengthening/southward shift of the African Easterly Jet,
a regional-scale mid-tropospheric (ca. 600 hPa) jet.
(iii) Close to the surface, enhanced northeasterlies over the tropical North Atlantic, and to a lesser extent
along the southern fringe of the Sahara, the latter being associated with an east –west belt of higher
land temperature (not shown) and decreased specific humidity. Note that surface patterns for ‘wet’
and ‘dry’ composites are not always a negative image of each other over West Africa and the North
Atlantic Ocean, suggesting that not all the teleconnections are linear.
Other previously less documented features are as follows:
(i) A cyclonic anomaly at 200 hPa over the Mozambique Channel, and anomalous upper southwesterlies
along the western margin of the Indian Ocean.
(ii) Extensive positive geopotential height anomalies at 700 hPa over the whole Indo-African monsoon
area, and a partly reduced Indian monsoon flow.
(iii) Weakened South Atlantic southeasterly trades, but to the south of 10 –15°S only; the absence of any
definite surface circulation anomalies over the Gulf of Guinea is intriguing.
A comparison with the ‘warm minus cold’ NIN0 O3 composite for the same season (Figure 14) reveals
quite similar upper and lower tropospheric North Atlantic/West African circulation anomalies, suggesting
a direct control of ENSO on Sahel rainfall. This teleconnection operates mostly via the North
Atlantic/Americas, since the strong anomalies found over the Indian Ocean for the NIN0 O3 composite are
Copyright © 2001 Royal Meteorological Society
Int. J. Climatol. 21: 973 – 1005 (2001)
994
P. CAMBERLIN ET AL.
Figure 12. Same as Figure 8, for ‘dry minus wet’ January – March conditions in South Africa
not reproduced in the Sahel rainfall composite. Janicot et al. (2001) found, through three-dimensional
particle trajectory analysis, that reduced Sahel rainfall was primarily related to eastern Pacific SST
through changes in the low level circulation over the tropical Atlantic/West Africa.
By contrast, the ‘dry minus wet’ Sahel rainfall composite does not apparently share with the ‘warm
minus cold’ South Atlantic SST composite (Figure 15) many common circulation patterns, despite the fact
that the latter significantly contribute to the multiple linear regression for Sahel rainfall modelling. Part
of the weakened surface flow originating from the St Helena high (southeasterlies and monsoon winds)
found in the rainfall composite can be ascribed to a South Atlantic warming. The other atmospheric
dynamics (strong African Easterly Jet, weak TEJ) are apparently unrelated to it. These disappointing
results may arise from interactions between ENSO and the South Atlantic. At surface level (Figure 14),
ENSO conditions lead to enhanced convergence in the Atlantic ITCZ region, with strengthened easterlies
both to the north and south of the ITCZ (e.g. off West Africa at 15 and 5°N). As a result, the equatorial
Copyright © 2001 Royal Meteorological Society
Int. J. Climatol. 21: 973 – 1005 (2001)
AFRICAN RAINFALL—SST TELECONNECTIONS
995
Figure 13. Same as Figure 8, for ‘dry minus wet’ July – September conditions in the Sudano– Sahelian belt
upwelling gets more intense, resulting in negative temperature anomalies at 0–5°S/20°W–5°E (not
shown). In the near-equatorial region, these conditions are opposite to those associated with South
Atlantic warmings, as depicted in Figure 13, where there is a marked weakening of the monsoon flow
between the equator and 15°N. These contradictory impacts of ENSO and South Atlantic SST near the
West African coast account for the hitherto surprising absence of significant low level circulation
anomalies south of the Sahel region in the dry minus wet composite (Figure 15).
To summarize, Sahel droughts are mostly related to ENSO anomalies via zonal dynamics, involving
both the upper (weakened TEJ) and the lower troposphere, and to South Atlantic SST via meridional
dynamics, in the lower troposphere only (weakened monsoon flow). North Atlantic SST anomalies appear
to have little direct impact on circulation patterns over West Africa. Their importance lies mainly in the
enhancement (weakening) of the south – north SST gradient (so-called ‘dipole’ structure) across the
Atlantic.
Copyright © 2001 Royal Meteorological Society
Int. J. Climatol. 21: 973 – 1005 (2001)
996
P. CAMBERLIN ET AL.
Figure 14. Same as Figure 8, for ‘warm minus cold’ July – September over the NIN0 O3 area
Though 44% of the 1951 – 1997 rainfall variance is explained by the three SST indices, the
corresponding regression model remains imperfect:
(i) It does not fully account for the strong downward rainfall trend, shown to be associated with
extratropical SST, or global interhemispheric SST gradients (Folland et al., 1986; Shinoda and
Kawamura, 1994; Trzaska et al., 1996).
(ii) Some years depart from the expected pattern: large positive residuals are found in 1969, 1974, 1989
and 1994, and negative residuals in 1973, 1983, 1984, 1990 and 1992.
Compositing these two latter samples of years, three main features are exhibited (Figure 16), though not
simultaneously in all years:
Copyright © 2001 Royal Meteorological Society
Int. J. Climatol. 21: 973 – 1005 (2001)
AFRICAN RAINFALL—SST TELECONNECTIONS
997
Figure 15. Same as Figure 8, for ‘warm minus cold’ July – September over the South Atlantic Ocean
(i) Over the Gulf of Guinea, in dry years, a stronger than usual and southward shifted African Easterly
Jet accompanies two anticyclonic cells anchored off Senegal and Angola. The northerly anomaly over
the Sahel results in reduced moisture content, a thinner monsoon layer, and enhanced low-level
shear. By far, this is the most robust signal, and it was present for instance in 1973, 1983, 1984,
whereas opposite anomalies were found in 1974 and 1994.
(ii) Northeasterly surface anomalies across the Sahara and the Sahel, in conjunction with significant
positive pressure anomalies over the Central Sahara, and reduced atmospheric moisture at its
southern margins due to a lower frequency of the wet southwesterly monsoon flow. This feature
occurred in 1973, 1990 and 1992, and the reverse in 1994. It points out the part played by regional
scale surface anomalies, shown to improve significantly statistical seasonal rainfall forecasts
(Fontaine et al., 1999b). A significant partial correlation of − 0.57 (as compared to − 0.54 for total
correlation) is found between Sahel rainfall and the intensity of the Saharan heat low (surface
Copyright © 2001 Royal Meteorological Society
Int. J. Climatol. 21: 973 – 1005 (2001)
998
P. CAMBERLIN ET AL.
Figure 16. Composite map of NCEP fields, for ‘dry minus wet’ July– September conditions in the Sudano– Sahelian belt: ‘dry minus
wet’ residuals to estimated rainfall from the SST indices. Legend as in Figure 8
pressure averaged over the 20°– 25°N/5°– 20°E area, 1968–1997), when the SST signals are removed.
The Saharan low was particularly deep in August–September 1969, 1974, 1989 and 1994, all years of
relatively abundant rainfall in the Sahel which cannot be adequately explained by global-scale SST
anomalies. Note that simulations only based on SST anomalies failed to reproduce the heavy 1994
Sahel rains.
(iii) The 200-hPa circulation anomalies at the interface of the African and Indian monsoon systems,
particularly around the Arabian peninsula. This suggests that an increase of TEJ velocity between
India and Lake Chad, in agreement with a ridge over the Near East, is sometimes associated with dry
conditions in the West African Sahel. Although these results are puzzling at first sight (a TEJ
weakening is usually found to be associated with Sahel droughts), they do not directly apply to the
circulation over West Africa, but rather to the interface between the Indian and African monsoon
Copyright © 2001 Royal Meteorological Society
Int. J. Climatol. 21: 973 – 1005 (2001)
AFRICAN RAINFALL—SST TELECONNECTIONS
999
systems. A stronger TEJ to the east of the Sahel implies a sharp decrease in TEJ velocity over the
Sahel, and therefore a velocity convergence that could hinder deep convection over that region.
However, Ward (1998) found a positive correlation between India and Sahel rainfall ( + 0.55 on
high-pass filtered 1949 – 1988 series). This mostly expresses a common response of both monsoon
systems to ENSO, especially in the West African Sahel. Cross-correlations between Sahel rainfall and
both India rainfall and a TEJ index taken at 10°N/50°E around Cape Guardafui (Table III), for
1968–1994, confirm the reality of the Sahel/India teleconnection, but suggests that it has nothing to
do with the upper tropospheric circulation, as evident from the strong partial correlation when the
TEJ index is removed. It also confirms that, when the effects of the global-scale SST or Indian
monsoon activity on TEJ velocity are removed, a stronger TEJ over East Africa is associated with
drier than expected conditions across the Sahel (partial r= − 0.40 and − 0.48).
4.3. October–December: East Africa
The October –December rainy season in East Africa, also called ‘short rains’, was shown to be strongly
influenced by ENSO. The atmospheric dynamics associated with East Africa rainfall (‘wet minus dry’
composite, Figure 17) and NIN0 O3 SST (‘warm minus cold’ composite, Figure 18) bear remarkable
similarities, as previously noted in Mutai and Ward (2000):
(i) Consistent easterly anomalies are found in the lower levels (1000 and 700 hPa) in the eastern Indian
Ocean along the equator.
(ii) Over the same region, westerly anomalies are found at 200 hPa, indicative of the barotropic nature
of circulation anomaly patterns in the area. Upper westerly anomalies are also shown to the north
and south of East Africa near 20° latitude in both hemispheres. Notice the symmetric ‘heart-shaped’
pattern over the western Indian Ocean, related to the enhanced deep convection, release of latent heat
in the upper troposphere, and upper divergence.
(iii) An east –west surface dipole, more or less clearly shown depending on the atmospheric variable, is
found across the Indian Ocean, for SLP, temperature and specific humidity.
(iv) Over East Africa itself, anomalous westerlies (1000 hPa) and easterlies (200 hPa) occur in association
with an east– west geopotential height gradient between the westernmost part of the Indian Ocean
and the equatorial Atlantic/Congo Basin area.
These features confirm that an ENSO-forced weakening of the equatorial Walker-type cell, which is
found over the Indian Ocean during that season, is primarily responsible for abnormally wet ‘short rains’
in East Africa. The role of a secondary east– west cell across equatorial Africa is also suggested. The two
cells share anomalous low-level convergence and upper-level divergence near the East African coast.
A few years do not conform with this general scheme; for instance, wet conditions were experienced in
the absence of Pacific El Niño conditions, in 1961, 1967 and 1978. Such situations are rare, although
rainfall can be exceptionally heavy, as in 1961. The 1961 floods were shown to result from a complete
reversal of the zonal (Walker) circulation across the whole Indian Ocean, as shown by SST, surface wind
and divergence (Reverdin et al., 1986; Beltrando and Cadet, 1990; Kapala et al., 1994). This indicates that
Table III. July–September 1968–1994 correlations (r) between rainfall in the West African Sudan–Sahel region
(10°–20°N, 20°W–20°E) and all-India monsoon rainfall/TEJ velocity over northeast Africa (10°N, 50°E)a
Total r
Partial r,
Partial r,
Partial r,
Partial r,
a
NIN0 O3 SST removed
all three SST indices removed
India rainfall removed
TEJ velocity removed
All-India monsoon rainfall
TEJ velocity (10°N, 50°E)
0.48
0.17
−0.04
–
0.59
0.05
−0.27
−0.48
−0.40
–
Bold (underlined) figures are significant at the 95% (99%) confidence level.
Copyright © 2001 Royal Meteorological Society
Int. J. Climatol. 21: 973 – 1005 (2001)
1000
P. CAMBERLIN ET AL.
Figure 17. Same as Figure 8, for ‘wet minus dry’ October – December conditions in East Africa
strong charges in the Walker type circulation across the Indian Ocean, are likely to occur even outside El
Niño years. We composited other positive and negative rainfall anomaly years (all with a small departure
from normal of the NIN0 O3 index) for the period 1968–1997. However, the composite patterns seldom
reach the 95% significance level, according to the Student’s t-test. This is likely to indicate that different
causes of dryness/wetness prevail in each individual year of the composite. A few abnormally dry years
(1987, 1993), for which ENSO was in a phase favourable to wet conditions in East Africa, show westerly
wind anomalies in the Indian Ocean, but restricted to the west equatorial sector. We suggest that in such
years smaller-scale circulation cells develop over the equatorial Indian Ocean, instead of a large, single
east–west Walker cell. 1978 also shows this type of pattern, but for an abnormally wet year. Other years
fail to exhibit recurrent circulation anomalies. Some dry years (like 1987) show 700-hPa easterly anomalies
over the Congo Basin, which could mean a reduced moisture advection from the west, but this pattern is
not systematic.
Copyright © 2001 Royal Meteorological Society
Int. J. Climatol. 21: 973 – 1005 (2001)
AFRICAN RAINFALL—SST TELECONNECTIONS
1001
Figure 18. Same as Figure 8, for ‘warm minus cold’ October – December over the NIN0 O3 area
5. SUMMARY AND CONCLUSIONS
The teleconnection between tropical Africa rainfall and SST variations in the eastern equatorial Pacific
(representative of ENSO) and the tropical Atlantic Ocean is investigated with a view to document its
spatial and seasonal patterns. NCEP reanalysis is used to assess the atmospheric circulation anomalies
associated with the teleconnections, after carrying out a thorough investigation of the dataset accuracy in
depicting long-term interannual variations. The inhomogeneities found in most of the NCEP series across
Africa at the beginning of the period made it necessary to restrict the second part of the study to the years
1968–1997, and to normalize separately the data over two subperiods (1968 – 1978 and 1979– 1997).
ENSO was confirmed as having a dominant role in eastern Africa (during both the July–September
rainy season in Ethiopia, and the October– December rains in east equatorial areas) and in southern
Africa, especially during the second part of its rainy season. Conversely, tropical Atlantic SSTs,
Copyright © 2001 Royal Meteorological Society
Int. J. Climatol. 21: 973 – 1005 (2001)
1002
P. CAMBERLIN ET AL.
particularly in the Southern Hemisphere, were reaffirmed as showing strong teleconnections with western
Africa and west equatorial Africa rainfall. However, ENSO’s impact extends far beyond the regions
bordering the Indian Ocean, though the variance explained is somewhat lower. Sahel rainfall is negatively
correlated with the NIN0 O3 index, as for the other tropical climate areas of northeastern and southern
Africa. Parts of the Gulf of Guinea region in western Africa also exhibit a negative correlation, outside
the northern summer season. These two findings had been either poorly documented before, or subject to
scepticism. However, in the 47-year period 1951 – 1997, the teleconnection with ENSO was found to
contribute significantly to the variance of both Sahel and Guinea rainfall (the latter during the first half
of the year), when considered in multiple regression models together with Atlantic SST. The analysis of
upper-air circulation associated with warm/cold events in the Pacific, additionally reveals a consistent
ENSO impact on West Africa atmospheric dynamics, via the Atlantic/American sector rather than the
Asian/Indian monsoon system. Warm events result in weakened upper easterlies, and, including during
the northern winter, northeasterly low-level wind anomalies, both are detrimental to abundant rainfall in
the ITCZ region of West Africa. The role of upper-air dynamics is found to be dominant in transmitting
the ENSO signal to the other regions/seasons: over the southwestern Indian Ocean in January–March,
the positive equatorial temperature/geopotential height anomalies, which at 200 hPa accompany El Niño
events, result in an eastward shift of the mid-latitude upper troughs which develop over the region and
are normally beneficial to summer rainfall in South Africa. For this latter country, our results make us
agree with Jury (1996) that upper kinematic processes may play an equivalent role to that of surface
Indian Ocean SST forcing on interannual rainfall variability. The fact that Indian Ocean circulation
anomalies are not all associated with ENSO may explain that some of the South African drought/wet
years are not ENSO-related. These results confirm those obtained by Rocha and Simmonds (1997b) in
global climate model (GCM) experiments.
Regarding tropical Atlantic SST, the impact of the southern basin and upper-air dynamics is found to
outweigh that of the northern basin. The strongest response of African rainfall encompasses the
10°S –20°N latitudinal belt, west of 20°E, though weaker signals are found farther east, for instance in
Ethiopia. A warm South Atlantic is suggested to lower the thermal gradient with the African continent,
resulting in a southward shift of the ITCZ. Whereas an abundant literature has already documented its
role in the setting up of some of the drought years in the Sahel, which lies at the mean latitude of the
ITCZ in the lower troposphere in July– August, evidence is given of a similar though weaker impact
further south near the Gulf of Guinea shores during the rest of the year. Rainfall not directly associated
with ITCZ activity, and falling in areas bordering the South Atlantic Basin, is by contrast positively
correlated with SST (high rainfall in years of warm South Atlantic waters); that is the case from Angola
to Gabon in April – May, and then further north as the season proceeds.
In this study, we did not specifically consider the delayed impact of ENSO on SST anomalies in the
Atlantic Ocean, especially north of the equator, which may in turn have an incidence on regional
atmospheric dynamics. This question has been addressed in Curtis and Hastenrath (1995), Enfield and
Mayer (1997), Nicholson (1997) and Fontaine et al. (1999a). But more widely, the relative impact of each
of the large-scale forcings on African rainfall dynamics is not always straightforward. For instance, we
found that in January – March, severe dryness in the Gulf of Guinea was associated with warm SST in
both the eastern Pacific and the South Atlantic. However, whereas ENSO events (warm East Pacific) tend
to result in strengthened trade winds over the equatorial Atlantic, warm South Atlantic conditions tend to
induce weakened trade winds in the same region. These contrasting responses of trade winds in the two
situations account for the surprising absence of any wind signal in that area when compositing all Gulf
of Guinea dry years. This does not really show the general behaviour of the regional atmospheric
circulation anomalies, but rather the origin of these anomalies (especially the spatial pattern of boundary
forcings, such as large-scale SST) is to be taken into account when investigating teleconnections, for
locally (in the above example along the Gulf of Guinea shores) the wind field may exhibit a response
different from that of the large-scale wind systems.
For a few regions, including the Gulf of Guinea region in January –March, but additionally the Sahel,
both ENSO and Atlantic SST signals contribute to a significant portion of the rainfall variance. Sahel
Copyright © 2001 Royal Meteorological Society
Int. J. Climatol. 21: 973 – 1005 (2001)
AFRICAN RAINFALL—SST TELECONNECTIONS
1003
droughts are related to ENSO anomalies via both upper and lower tropospheric dynamics (weakened TEJ
and enhanced northeasterlies), and to South Atlantic SST via lower tropospheric dynamics (weakened
monsoon flow). Tropical North Atlantic SST anomalies appear to have no strong influence on
atmospheric circulation over Africa, except for an additional weakening of the 200-hPa easterlies over the
Atlantic in abnormally cold years, a pattern similar to what happens in El Niño years. Whereas South
Atlantic SST anomalies may have a direct, independent impact on Africa rainfall, their North Atlantic
counterparts may only contribute, when sufficiently large, to an enhancement of the south–north thermal
gradient across the Atlantic (‘dipole’ pattern, the southern pole remaining the driving one as far as
African rainfall variations are concerned).
Though ENSO-related and Atlantic SST play a key part in interannual rainfall accross much of Africa,
they fail to exhibit significant teleconnections with a few regions, such as the Congo Basin, or with specific
rainy seasons in others, like for the East African ‘long rains’ (March–May). In addition, the rainfall
variance explained by large-scale SST anomalies seldom reaches 50%, and other regional- or large-scale
features have to be considered. For instance, purely regional circulation anomalies have been suggested to
contribute in a significant way to Sahel rainfall interannual variability, as shown in recent studies
considering energy gradients associated with local land surface conditions (Eltahir and Gong, 1996; Zheng
and Eltahir, 1998; Fontaine et al., 1999b). Such regional anomalies may be reflected in the African
Easterly Jet strengthening found in years where Sahel drought could not be adequately explained by
large-scale SST patterns. Similarly, persistent northerly wind anomalies across the Sahara have been
shown to be associated with abnormally dry conditions in January–March in the Gulf of Guinea area,
once the remote effect of SST anomalies (ENSO and Atlantic) is removed.
ACKNOWLEDGEMENTS
This work is part of the Etudes Climatiques de l’Atlantique Tropical (ECLAT) project, which is a
French contribution to the CLIVAR international programme. The authors wish to thank the
NCEP/NCAR for making the reanalysis dataset available. We are grateful to Wesley Ebisuzaki
(Climate Prediction Center, NCEP) for making the observation count integrated in the NCEP reanalysis
available via the web site: http://wesley.wwb.noaa.gov/cgi-bin:disp-m-obscnt.sh. We are thankful to John
R. Lanzante for providing the individual radiosoundings of the GFDL Atmospheric Circulation Tape
Library 1958– 1989.
REFERENCES
Barnston AG, Smith TM. 1996. Specification and prediction of global surface temperature and precipitation from global SST using
CCA. Journal of Climate 9: 2660 –2697.
Beltrando G. 1990. Space-time variability of rainfall in April and October– November over East Africa during the period 1932– 83.
International Journal of Climatology 10: 691– 702.
Beltrando G, Cadet DL. 1990. Variabilité interannuelle de la petite saison des pluies en Afrique Orientale: relations avec la
circulation atmosphérique générale. Veille Climatique Satellitaire 33: 19 – 36.
Beltrando G, Camberlin P. 1993. Interannual variability of rainfall in the Eastern Horn of Africa and indicators of atmospheric
circulation. International Journal of Climatology 13: 533 – 546.
Bigot S, Camberlin P, Moron V, Richard Y, Roucou P. 1995. Modes of rainfall variability in tropical Africa and their stability
through time [preprints, 21st Conference on Hurricanes and Tropical Meteorology], Miami, Florida, 24– 28 April 1995; 448– 449.
Bigot S, Camberlin P, Moron V, Richard Y. 1997. Structures spatiales de la variabilité des précipitations en Afrique: une transition
climatique à la fin des années 1960? Comptes Rendus de l’Académie des Sciences, série II, Sciences de la Terre et des Planètes 324:
181 – 188.
Buisson A. 1985. Une anomalie climatique au Gabon en 1984. La Météorologie, VIIe série 8: 36 – 45.
Camberlin P. 1995. June–September rainfall in North-Eastern Africa and atmospheric signals over the tropics: A zonal perspective.
International Journal of Climatology 15: 773– 783.
Camberlin P. 1997. Rainfall anomalies in the source region of the Nile and their connection with the Indian summer monsoon.
Journal of Climate 10: 1380–1392.
Citeau J, Cammas JP, Gouriou Y. 1985. Position de la zone intertropicale de convergence et températures de surface de l’océan.
Veille Climatique Satellitaire 5: 2 –5.
Cornelius J, Reynolds J. 1991. On determining the statistical significance of discontinuities within ecological data. Ecology 72:
2057 – 2070.
Copyright © 2001 Royal Meteorological Society
Int. J. Climatol. 21: 973 – 1005 (2001)
1004
P. CAMBERLIN ET AL.
Curtis S, Hastenrath S. 1995. Forcing of anomalous sea-surface temperature evolution in the tropical Atlantic during Pacific warm
events. Journal of Geophysics Research 100: 15835– 15847.
Eltahir EAB, Gong C. 1996. Dynamics of wet and dry years in West Africa. Journal of Climate 9: 1030 – 1042.
Enfield DB, Mayer DA. 1997. Tropical Atlantic sea surface temperature variability and its relation to El Niño– Southern Oscillation.
Journal of Geophysics Research 102: 929– 945.
Farmer G. 1988. Seasonal forecasting of the Kenya Coast short rains 1901– 84. International Journal of Climatology 8: 489 – 497.
Folland CK, Palmer TN, Parker DE. 1986. Sahel rainfall and worldwide sea temperatures (1901– 1985). Nature 320: 602 – 607.
Folland CK, Owen J, Ward N, Colman A. 1991. Prediction of seasonal rainfall in the Sahel region using empirical and dynamical
methods. Journal of Forecasting 10: 21 –56.
Fontaine B, Janicot S, Moron V. 1995. Rainfall anomaly patterns and wind field signals over West Africa in August (1958– 1989).
Journal of Climate 8: 1503–1510.
Fontaine B, Janicot S. 1996. Sea surface temperature fields associated with West African rainfall anomaly types. Journal of Climate
9: 2935 – 2940.
Fontaine B, Janicot S, Roucou P. 1999a. Coupled ocean-atmosphere surface variability and its climate impacts in the tropical
Atlantic region. Climate Dynamics 15: 451 – 473.
Fontaine B, Philippon N, Camberlin P. 1999b. An improvement of June– September rainfall forecasting in the Sahel based upon
region April–May moist static energy content (1968– 1997). Geophysics Research Letters 26: 2041 – 2044.
Hastenrath S. 1984. Interannual variability and annual cycle: Mechanisms of circulation and climate in the Tropical Atlantic sector.
Monthly Weather Re6iew 112: 1097–1107.
Hastenrath S, Nicklis A, Greischar L. 1993. Atmospheric-hydrospheric mechanisms of climate anomalies in the western equatorial
Indian Ocean. Journal of Geophysics Research 98: 20219 – 20235.
Hirst AC, Hastenrath S. 1983a. Atmosphere-ocean mechanisms of climate anomalies in the Angola– Tropical Atlantic sector.
Journal of Physical Oceanography 13: 1146 –1157.
Hirst AC, Hastenrath S. 1983b. Diagnostics of hydrometeorological anomalies in the Zaire (Congo) basin. Quarterly Journal of the
Royal Meteorological Society 109: 881– 892.
Hutchinson P. 1992. The Southern Oscillation and prediction of ‘Der’ season rainfall in Somalia. Journal of Climate 5: 525 – 531.
Ininda J, Desalegne B, Befekadu A. 1987. The characteristics of rainfall in Ethiopia and its relationship to El Niño – Southern
Oscillation. In Proceedings of the First Technical Conference on Meteorology Research in Eastern and Southern Africa, Kenya
Meteorology Department, Nairobi, 6– 9 January 1987; 133– 135.
Janicot S. 1992. Spatiotemporal variability of West African rainfall. Part 2: Associated surface and airmass characteristics. Journal
of Climate 5: 499 – 511.
Janicot S, Moron V, Fontaine B. 1996. Sahel droughts and ENSO dynamics. Geophysics Research Letters 23: 515 – 518.
Janicot S, Fontaine B. 1997. Seasonal evolution of correlations between rainfall in Guinean Africa and sea-surface temperatures
(1945 – 1994). Comptes Rendus de l’Académie des Sciences, série IIa 324: 785 – 792.
Janicot S, Trzaska S, Poccard I. 2001. Summer Sahel-ENSO teleconnections and decadal time scale SST variations. Climate
Dynamics (in press).
Janowiak JE. 1988. An investigation of interannual rainfall variability in Africa. Journal of Climate 1: 240 – 255.
Jury MR, Mc Queen C, Levey K. 1994. SOI and QBO signals in the African region. Theoretical Applied Climatology 50: 103 – 115.
Jury MR. 1996. Regional teleconnection patterns associated with summer rainfall over South Africa, Namibia and Zimbabwe.
International Journal of Climatology 16: 135–153.
Kalnay E, and co-authors. 1996. The NCEP/NCAR 40-year Reanalysis Project. Bulletin of the American Meteorological Society 77:
437 – 471.
Kapala A, Born K, Flohn H. 1994. Monsoon anomaly or an El Niño event at the equatorial Indian Ocean? Catastrophic rains
1961/62 in East Africa and their teleconnections. In Proceedings of the International Conference on Monsoon Variability and
Prediction, Trieste, Italy, 9–13 May 1994. WMO/TD no. 619; 119– 126.
Kawamura R. 1994. A rotated EOF analysis of global sea surface temperature variability with interannual and interdecadal scales.
Journal of Physical Oceanography 24: 707 –715.
Kemp P, Cornelius J, Reynolds J. 1994. Temporal discontinuities in precipitation in the central North American Prairie.
International Journal of Climatology 14: 539–557.
Kiladis GN, Diaz HF. 1989. Global climatic anomalies associated with extremes in the Southern Oscillation. Journal of Climate 2:
1069 –1090.
Kruger AC. 1999. The influence of the decadal-scale variability of summer rainfall on the impact of El Niño and La Niña events
in South Africa. International Journal of Climatology 19: 59 – 68.
Lamb PJ. 1978a. Large-scale Tropical Atlantic surface circulation patterns associated with sub-saharan weather anomalies. Tellus
A 30: 240 – 251.
Lamb PJ. 1978b. Case-studies of tropical Atlantic surface circulation pattern during recent sub-saharan weather anomalies: 1967 and
1968. Monthly Weather Re6iew 106: 482–491.
Lindesay JA, Harrison M, Haffner T. 1986. The Southern Oscillation and South African rainfall. South African Journal of Science
82: 196 –198.
Lough J. 1986. Tropical Atlantic sea surface temperatures and rainfall variations in Sub-saharan Africa. Monthly Weather Re6iew
114: 561 –570.
Moron V, Bigot S, Roucou P. 1995. Rainfall variability in Sub-equatorial America and Africa and relationships with the main
sea-surface temperature modes (1951–1990). International Journal of Climatology 15: 1297 – 1322.
Moron V, Navarra A, Ward N, Roeckner E. 1998. Skill and reproducibility of seasonal rainfall patterns in the tropics in ECHAM-4
GCM simulations with prescribed SST. Climate Dynamics 14: 83 – 100.
Mutai CC, Ward MN, Colman AW. 1998. Towards the prediction of the East Africa short rains based on sea-surface
temperature-atmosphere coupling. International Journal of Climatology 18: 975 – 998.
Mutai CC, Ward MN. 2000. East African rainfall and the tropical circulation/convection on intraseasonal to interannual timescales.
Journal of Climate 13: 3915–3939.
Copyright © 2001 Royal Meteorological Society
Int. J. Climatol. 21: 973 – 1005 (2001)
AFRICAN RAINFALL—SST TELECONNECTIONS
1005
Nicholson SE. 1997. An analysis of the ENSO signal in the tropical Atlantic and western Indian oceans. International Journal of
Climatology 17: 345–375.
Nicholson SE, Entekhabi D. 1986. The quasi-peiodic behavior of rainfall variability in Africa and its relationship to the Southern
Oscillation. Archi6 für Meteorologie, Geophysick, Bioklimatologie, Série A 34: 311 – 348.
Nicholson SE, Entekhabi D. 1987. The nature of rainfall variability in equatorial and southern Africa: Relationships with SST along
the southwestern coast of Africa. Journal of Climate and Applied Meteorology 26: 561 – 578.
Nicholson SE, Kim J. 1997. The relationship of the El Niño – Southern Oscillation to African rainfall. International Journal of
Climatology 17: 117–135.
Ogallo LJ. 1988. Relationships between seasonal rainfall in East Africa and the Southern Oscillation. Journal of Climatology 8:
31 – 43.
Ogallo LJ, Janowiak JE, Halpert MS. 1988. Teleconnection between seasonal rainfall over East Africa and global sea-surface
temperature anomalies. Journal of the Meteorological Society of Japan 66: 807 – 822.
Oort AH. 1978. Adequacy of the rawinsonde network for the global circulation studies tested through numerical model output.
Monthly Weather Re6iew 106: 174– 195.
Oort AH. 1983. Global atmospheric circulation statistics: 1958– 73. NOAA Prof. Paper 14, US Government Printing Office:
Washington, DC, 180 pp.
Oort AH, Liu H. 1993. Upper-air temperature trends over the globe, 1958– 1989. Journal of Climate 6: 292 – 307.
Philander SGH. 1986. Unusual conditions in the tropical Atlantic Ocean in 1984. Nature 322: 236 – 238.
Poccard I, Janicot S. 1998. Interannual variability of SST-tropical circulation relationships on the period 1968– 1997: Focus on West
Africa. In Annales Geophysicae, Part II, vol. 16; C689.
Poccard I, Janicot S, Camberlin P. 2000. Comparison of rainfall structures between NCEP/NCAR reanalysis and observed data over
Tropical Africa. Climate Dynamics 16: 897– 915.
Reverdin G, Cadet DL, Gutzler D. 1986. Interannual displacements of convection and surface circulation over the Equatorial Indian
Ocean. Quarterly Journal of the Royal Meteorological Society 112: 46 – 47.
Richard Y, Camberlin P, Beltrando G. 1998. Recherche de structures spatio-temporelles en climatologie: l’exemple de la variabilité
interannuelle des précipitations en Afrique orientale. L’espace Géographique 27: 31 – 40.
Richard Y, Trzaska S, Roucou P, Rouault M. 2000. Modification of the Southern African rainfall variability/El Niño– Southern
Oscillation relationship. Climate Dynamics 16: 883 –895.
Rocha A, Simmonds I. 1997a. Interannual variability of south-eastern African summer rainfall. Part I: Relationships with air-sea
interaction processes. International Journal of Climatology 17: 235 – 266.
Rocha A, Simmonds I. 1997b. Interannual variability of south-eastern African summer rainfall. Part II: Modelling the impact of
sea-surface temperatures on rainfall and circulation. International Journal of Climatology 17: 267 – 290.
Ropelewski CF, Halpert MS. 1987. Global and regional scale precipitation and temperature patterns associated with El
Niño/Southern Oscillation. Monthly Weather Re6iew 115: 1606 – 1626.
Rowell DP, Folland CK, Maskell K, Ward MN. 1995. Variability of summer rainfall over tropical North Africa. Quarterly Journal
of the Royal Meteorological Society 121: 669–704.
Schubert SD, Rood R, Pfaendtner J. 1993. An assimilated dataset for Earth Science applications. Bulletin of the American
Meteorological Society 74: 2331–2342.
Seleshi Y. 1995. Stochastic prediction of summer rainfall amounts over the northeast African highlands and over India. PhD thesis,
Free University of Brussels.
Seleshi Y, Demarée GR. 1995. Rainfall variability in the Ethiopian and Eritrean highlands and its links with the Southern
Oscillation Index. Journal of Biogeography 22: 945–952.
Servain J. 1991. Simple climatic indices for the tropical Atlantic ocean and some applications. Journal of Geophysics Research 96:
15 137 – 15 146.
Shinoda M, Kawamura R. 1994. Tropical rainbelt, circulation and sea-surface temperatures associated with the Sahelian rainfall
trend. Journal of the Meteorological Society of Japan 72: 341 – 357.
Tadesse T. 1994. Summer monsoon seasonal rainfall of Ethiopia in ENSO episodic years. In Proceedings of the International
Conference on Monsoon Variability and Prediction, Trieste, Italy, 9– 13 May 1994, WMO/TD no. 619; 48 – 55.
Trzaska S, Moron V, Fontaine B. 1996. Global atmospheric response to specific linear combinations of the main SST modes. Part
I: Numerical experiments and preliminary results. Annal Geophysicae 14: 1066 – 1077.
Tyson PD. 1987. Climatic Change and Variability in Southern Africa. Oxford University Press: Capetown.
Tyson PD, Dyer TGJ, Mametse MN. 1975. Secular changes in South African rainfall: 1880 to 1972. Quarterly Journal of the Royal
Meteorological Society 101: 817 –833.
van Heerden J, Terblanche DE, Schulze GC. 1988. The Southern Oscillation and South African summer rainfall. Journal of
Climatology 8: 577 –597.
Ward MN. 1998. Diagnosis and short-lead time prediction of summer rainfall in tropical north Africa at interannual and
multidecadal timescales. Journal of Climate 11: 3167–3191.
Zheng X, Eltahir EAB. 1998. The role of vegetation in the dynamics of West African monsoons. Journal of Climate 11: 2078 – 2096.
.
Copyright © 2001 Royal Meteorological Society
Int. J. Climatol. 21: 973 – 1005 (2001)