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Recurrent daily OLR patterns in the Southern Africa/SouthwestIndian Ocean region, implications for South African rainfalland teleconnections
Nicolas Fauchereau Æ B. Pohl Æ C. J. C. Reason ÆM. Rouault Æ Y. Richard
Received: 14 October 2007 / Accepted: 19 May 2008
� Springer-Verlag 2008
Abstract A cluster analysis of daily outgoing longwave
radiation (OLR) anomalies from 1979 to 2002 over the
Southern Africa/Southwest Indian Ocean (SWIO) region
for the November to February season reveals seven robust
and statistically well separated recurrent patterns of large-
scale organized convection. Among them are three regimes
indicative of well defined tropical–temperate interactions
linking the hinterland parts of Southern Africa to the mid-
latitudes of the SWIO. Preferred transitions show a ten-
dency for an eastward propagation of these systems.
Analysis of daily rainfall records for South Africa shows
that six of the OLR regimes are associated with spatially
coherent and significant patterns of enhanced or reduced
daily rainfall over the country. Atmospheric anomalies
from the NCEP/DOE II reanalysis dataset show that the
OLR regimes are associated with either regional or near-
global adjustments of the atmospheric circulation, the three
regimes representative of tropical–temperate interactions
being in particular related to a well-defined wave structure
encompassing the subtropical and temperate latitudes,
featuring strong vertical anomalies and strong poleward
export of momentum in the lee of the location of the cloud-
band. The time-series of OLR regimes seasonal frequency
are correlated to distinctive anomaly patterns in the global
sea-surface-temperature field, among which are shown to
be those corresponding to El Nino and La Nina conditions.
The spatial signature of El Nino Southern Oscillation’s
(ENSO) influence is related to the combination of an
increased/decreased frequency of these regimes. It is
shown in particular that the well-known ‘‘dipole’’ in con-
vection anomalies contrasting Southern Africa and the
SWIO during ENSO events arises as an effect of seasonal
averaging and is therefore not valid at the synoptic scale.
This study also provides a framework to better understand
the observed non-linearities between ENSO and the sea-
sonal convection and rainfall anomalies over the region.
Keywords Southern Africa and Southwest
Indian Ocean � Atmospheric convection � Cluster Analysis �Tropical-temperate-troughs � Rainfall variability �Scale interactions
1 Introduction
Southern Africa (‘‘SA’’, south of 10S) experiences its main
rainfall season during the austral summer half-year, except
for the Western Cape region where winter rainfall prevails.
Because of the predominance of rain-fed agriculture
(Mason and jury 1997; Jury 2002; Reason and Jagadheesha
2005), large departures in the seasonal rainfall amount
(either drought or floods) are liable to have particularly
detrimental effects on the economies and societies of the
region.
According to Jury (2002), an analysis of food and water
supplies and economic growth in South Africa emphasizes
the major role played by climate variability. Summer
rainfall in the period of 1980–1999 is closely associated
with year-to-year changes in the gross domestic product. It
is estimated that over U.S.$1 billion could be saved
annually with reliable long range seasonal forecasts. Such
predictions are however not easy to produce, as heavy
N. Fauchereau (&) � C. J. C. Reason � M. Rouault
Department of Oceanography, University of Cape Town,
Rondebosch, Cape Town 7701, South Africa
e-mail: [email protected]
B. Pohl � Y. Richard
Centre de Recherches de Climatologie, CNRS/Universite de
Bourgogne, Dijon, France
123
Clim Dyn
DOI 10.1007/s00382-008-0426-2
rainfall, impacting on the final seasonal amount, is often
recorded during relatively short-lived events.
It is for instance known that a significant amount of
summer rainfall over SA is attributed to the occurrence of
synoptic-scale tropical-temperate-troughs (TTTs, see Har-
rison 1984, 1986), extending over both the landmass and
the adjacent Southwest Indian Ocean (‘‘SWIO’’) region.
During TTT events, convection over the continent is linked
to the transients in the mid-latitudes. The most obvious
spatial signature of such tropical–temperate interactions is
the presence of a band of clouds, convection and rain,
elongated along a NW-SE direction. These TTTs are
related to the establishment of the so-called South-Indian
Convergence Zone (SICZ, Cook 2000). SA and the SWIO
is one of the three known preferred regions in the Southern
Hemisphere for the occurrences of such cloud bands
(Streten 1973). Unlike its counterparts, namely the South
Atlantic and South Pacific Convergence Zones, the SICZ is
however mainly restricted to the austral summer. Todd and
Washington (1999), Washington and Todd (1999) and
Todd et al (2004) investigated the variability of daily
rainfall over the region through an Empirical Orthogonal
Function (EOF) analysis of 8 years of daily satellite rainfall
estimates over land and ocean. The first two EOFs display
two contrasting bands positioned NW–SE extending from
eastern SA to the mid-latitudes of the SWIO, and were
interpreted as directly reflecting the changes in the pre-
ferred location of these TTT systems. The authors
estimated that such events could account for 30% (resp.
60%) of the overall rainfall amount over SA during the
October to December season (resp. January).
At longer timescales, the region also shows marked
fluctuations in the seasonal rainfall amount from one year
to another. A significant part of the interannual variability
over the area is related to the state of El Nino Southern
Oscillation (‘‘ENSO’’) in the Eastern Pacific basin (Dyer
1979; Lindesay 1988; Lindesay and Vogel 1990; Reason
et al. 2000). The relationship is significant particularly
since the 1970s (Richard et al. 2000, 2001) but its linearity
remains still questionable.
Every warm ENSO year (‘‘El Nino’’) is indeed not
systematically dry over Southern Africa. A prime example
is the strong event of 1997/1998. While southern Zimba-
bwe and Namibia experienced drought during this summer,
most of Southern Africa had near average precipitation
amounts for the season despite a dry start to the summer
rainy season. More recently, the relatively weak El Nino
event of 2002/2003 was associated with rather strong and
persistent dry conditions over SA. Some observational
studies suggest that the ENSO signal neither very strong
nor direct in SA. The interannual variability in Southern
African precipitation could instead constitute a response to
Indian and/or southern Atlantic Ocean sea surface
temperatures (SST), which may not be causally connected
to ENSO (e.g. Mason 1995; Nicholson and Kim 1997).
Recent theories in climatology suggest that the inter-
annual fluctuations in the climate system may directly
depend on the cumulative influence of rain-causing events
recorded at very high frequencies, for instance the day-to-
day variability of the rains. Basically, the background
conditions of the climate system could influence each
individual event recorded during a given rainy season
through scale interaction mechanisms (Meehl et al. 2001).
In turn, individual events have a determinant impact on the
seasonal amounts, and thus finally on the rainfall fluctua-
tions that are recorded between successive years.
Over the SA region, similar scale interactions are
hypothesized to play a major role on the interannual vari-
ability of the rains. Cook (2000), Washington and Todd
(1999) and Todd and Washington (1999) suggest that the
latter could significantly relate to changes in the preferred
location and frequency of the synoptic-scale TTT systems.
The linkages between these two timescales, i.e. the day-to-
day changes in recurrent atmospheric patterns on the one
hand, and the year-to-year changes in rainfall amounts and
large-scale teleconnections on the other hand, have how-
ever not been fully established to date. This paper aims at
filling this gap.
The first objective of this study is to provide an objec-
tive characterization of recurrent outgoing longwave
radiation (OLR) patterns over the region, to investigate the
spatial response of the rainfall field and to gain knowledge
of the atmospheric anomalies conducive to such preferred
regimes. The second objective is to examine how the
variability observed at the daily timescale is linked to
interannual variability and large-scale teleconnections.
The paper is organized as follows: Section 2 presents
the data and methodology used for this work. Section 3
documents the results of the cluster analysis of OLR.
Section 4 focuses of the response of the rainfall field over
the South African country. Section 5 presents the atmo-
spheric dynamic anomalies associated with OLR regimes.
Section 6 investigates the interannual variability in regime
frequencies and the associated large-scale SST patterns,
while Section 7 focuses on the implications for the rela-
tionships between seasonal convection and the ENSO
phenomenon. The results are summarized in Section 8.
2 Data and methods
Tropical convection is estimated using the daily version of
the OLR dataset (Liebmann and Smith 1996). It is avail-
able on a 2.5� 9 2.5� regular grid from 1974, with a
10-month gap in 1978. The study period has been restricted
to 1979–2002 to match the NCEP2 reanalysis period).
N. Fauchereau et al.: Recurrent daily OLR patterns in SA/SWIO region
123
Daily rainfall amounts over the republic of South Africa
(of the largest of the 14 Southern Africa countries) are
provided by the rain-gauge records compiled in Water
Research Commission database by Lynch (2003). Seven
thousand six hundred and sixty-five stations (out of
11,000), presenting no missing values, are extracted on the
1979–1999 period; they document with a high resolution
the rainfall field over South Africa and the neighbouring
countries of Lesotho and Swaziland (Fig. 1). The use of
such a database makes it possible to relate daily OLR
variations to the actual precipitation field.
Atmospheric circulation is examined using the NCEP-
DOE AMIP-II (NCEP-2) reanalyses (Kanamitsu et al.
2002). This study makes use of the zonal (U) and meridi-
onal (V) components of the wind (m/s) at 700 hPa and
vertical velocity (omega) at 500 hPa. The 700 hPa level
has been selected because it is high enough to be above the
interior plateau of Southern Africa, but low enough (Pohl
et al. 2007) to be significant in carrying moisture over the
region. The 500 hPa level for omega represents the center
of mass for the troposphere and allows for an insight on
large-scale vertical movements in the whole troposphere.
Monthly SST are obtained from the HadISST dataset
(Rayner et al. 2003) on a 1� 9 1� regular grid, for the
1950-present period. Only NDJF seasonal means and
anomalies are used here.
In the present paper, we make use of the objective
classification scheme known as dynamical clustering (or
k-means clustering) on the daily OLR anomalies over SA
and SWIO. The methodology essentially follows that of
Cheng and Wallace (1993) and Michelangeli et al. (1995).
Given a previously fixed number of regimes, k, the aim of
the regime analysis algorithm is to obtain a partition, P, of
the observations (days) into k regimes that minimizes the
sum of the intra-regime variances, W. The Euclidian dis-
tance is used to measure the similarity between two
observations, X and Y. The overall minimum of the func-
tion W(P) corresponds then to the partition that best
separates the different points. When the classification is
applied to large samples, climatological series for example,
this overall minimum cannot be found in practice, because
of the huge number of different possibilities to explore.
The algorithm defines n iterative partitions, P(n), for which
W[P(n)] decreases with n and eventually converges to a
local minimum of the function, W(P). The overall mini-
mum of W(P) is surrounded by many local minima that
differ from it by only a few observations, exchanged from
one regime to another and essentially found at the
periphery of them. The latter may largely depend on the
analysed sample, the algorithm being initialized by a ran-
dom draw of the k regimes. The reproducibility of the
obtained partitions should therefore be tested.
If the distribution of the climatological dataset is uni-
form, the final partition is assumed to be largely dependent
on the initial randomly chosen seeds. In contrast, when the
dataset is distributed into well-defined regimes, two dif-
ferent initial draws should theoretically lead to roughly
similar final partitions. The dependence of the final result
on the initial random draw may thus be used as an indicator
of the degree of classifiability of the dataset into k regimes.
Following Michelangeli et al. (1995) and Moron and
Plaut (2003), we performed 50 different partitions of the
OLR anomaly patterns, each time initialized by a different
random draw. The most natural way to measure the
dependence of the final partition on the initial random
draw, and thus the classifiability of the original dataset,
consists of comparing several final partitions for a given
number of regimes k. We then retain the partition having
the highest mean similarity with the 49 other ones. A
classifiability index, c* (Cheng and Wallace 1993), is next
defined, which measures the average similarity within the
50 sets of regimes: its value would be exactly 1 if all the
partitions were identical. If the OLR anomaly patterns
gather into k regimes in a natural way, one would expect
the classifiability of the actual maps to be significantly
better than that of an ensemble of artificial datasets gene-
rated through a first-order Markov process having the same
covariance matrix as the true atmospheric data (Moron and
Plaut 2003). The red-noise test (applied to Markov-gene-
rated red-noise data) operates as follows: 100 samples of
the same length as the atmospheric dataset are generated,
providing 100 values of the classifiability index, which are
ranked to find the 10 and 90% confidence limits. The value
of c* for the atmospheric dataset is then compared with
these limits: a value above the 90% confidence limit
indicates, for the corresponding value of k, a classifiability
35°S
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location of the WRC rainfall stations
Fig. 1 Location of the 7,665 daily rainfall stations extracted from the
WRC dataset
N. Fauchereau et al.: Recurrent daily OLR patterns in SA/SWIO region
123
significantly higher than that of the red-noise model. The
operation is repeated for k varying from two to ten: in most
cases the best choice for the number of regimes appears
quite unambiguously (Michelangeli et al. 1995).
This method has been applied here to daily OLR
anomalies over the domain 10–40�S, 7.5–70�E (858 grid-
points) from November to February (leading to 120 daily
values for each season, the 29th of February in leap years
being removed). This domain encompasses both the SA
and the SWIO and is the same as in the Todd and Wash-
ington (1999) study (Fig. 2). In order to reduce the
dimensionality of the problem and ensure linear indepen-
dence between the input variables (Huth 1996), an EOF
analysis is first performed on the data correlation matrix
and the first 11 PCs, explaining 51.7% of the original
variance, are retained (note that the results are not depen-
dent on the percentage of variance retained). The clustering
algorithm is then performed on the subspace spanned by
the corresponding PCs. The corresponding results are
presented in the next section.
3 Recurrent OLR regimes over the SA and SWIO
Figure 3 presents the classifiability index c* as a function of
the number of clusters k along with the significance levels
computed from the first-order Markov process. It shows a
clear and significant (at the 95% level) peak for k = 7.
Larger numbers of regimes are also determined as pre-
senting a high degree of robustness among the regime
analysis based initiated with different random draws, but
hereafter the seven regimes partition is chosen because the
classifiability index is the largest and this partition is the one
that provides the best and compact summary of the infor-
mation among those that reach significance.
Figure 4 presents the results of an Analysis of Variance
(ANOVA) on the OLR field according to the regime cat-
egories. The ANOVA depicts the regions for which the
intra-regime variance is significantly lower than the inter-
regime variance. The classification (i.e. the respective
regime to which each day of the period is assigned) sig-
nificantly discriminates the day-to-day OLR fluctuations
Fig. 2 Mean OLR field over the November to February season
(W/m2), the values below 240 W/m2 are shaded in blue, interval
10 W/m2. The domain on which the cluster analysis is performed is
delineated in red. The labels ‘‘SA’’ and ‘‘SWIO’’ refer as to Southern
Africa and Southwest Indian Ocean respectively
Fig. 3 Classifiability index c* as a function of the number of regimes
k (solid line). The levels of significance (dashed and dashed-dottedlines) at 80, 90 and 95% are computed according to a first-order
Markov process
Fig. 4 Analysis of variance
between the OLR grid-points
and the results of the clustering
procedure for the seven regimes
partition. Shadings materialize
the areas that are significantly
discriminated by the cluster
analysis at the given confidence
level (in percentage). The
domain on which the cluster
analysis is performed is
delineated in red
N. Fauchereau et al.: Recurrent daily OLR patterns in SA/SWIO region
123
over the whole region located between the Equator and 50S
in latitude, and encompassing the eastern half of the
Atlantic Ocean and most of the Indian Ocean region.
Interestingly, large patches of significance are also noticed
over the tropical Pacific region, suggesting that OLR pat-
terns determined on SA and the SWIO region may be
linked with modulation of in the tropical Pacific, e.g.
through ENSO.
Figures 5 and 6 respectively present the mean and
anomaly composite patterns according to the results of the
k-means clustering analysis on OLR. While the cluster
analysis has been performed on a restricted window (see
Sect. 2, Fig. 2), the composite fields are computed on a
larger domain to check for regional structures in which OLR
patterns could be embedded. Three regimes (Fig. 5e–g;
regimes #5, #6, #7) are characterized on average by a
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240g) regime # 7
e) regime # 5 f) regime # 6
c) regime # 3 d) regime # 4
a) regime # 1 b) regime # 2
20°E 40°E 60°E 80°E 100°E
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Fig. 5 Outgoing longwave radiation regimes for NDJF: composite means, values below 240 W/m2 are shaded
N. Fauchereau et al.: Recurrent daily OLR patterns in SA/SWIO region
123
well-defined pattern of maximum convection (OLR values
below 240 W/m2, blue shades in the figures) organized in a
NW/SE band extending from the Southern African sub-
continent or Madagascar at tropical latitudes to the mid-
latitudes of the SWIO (South of 30S). These bands are
rooted in Southern Africa respectively over Northeastern
South Africa, Mozambique and Madagascar for regimes #5,
#6, #7. At the southern boundary of the study domain, the
convection band ends at longitudes varying between
approximately 40E and 65E. The corresponding composite
anomalies (Fig. 6e–g) show that consistent strong negative
OLR anomalies are associated with the position of the mean
cloud band, this band of anomalously large convection
being surrounded to the east and to the west by decreased
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e) regime # 5 f) regime # 6
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Fig. 6 Outgoing longwave radiation regimes for NDJF: composite anomalies, contour interval is 5 W/m2. Only the grid-points for which the
anomalies are significant at the 95% confidence level according a Student’s t-test are displayed
N. Fauchereau et al.: Recurrent daily OLR patterns in SA/SWIO region
123
convection (positive OLR anomalies) extending similarly in
a NW–SE direction. The anomaly patterns are in good
accordance with the EOF loadings displayed in Todd and
Washington (1999). In the following, these three regimes
are thus chosen as representative of TTT systems, and they
thus mainly account for their variations in longitudinal
position.
The remaining four regimes are not obviously associated
with such tropical–temperate linkages (Fig. 5), even
though negative OLR anomalies exhibited by the regimes
#2 and #4 present a somewhat NW–SE structure. The
composite anomalies show that regime #1 (Fig. 6a) rep-
resents a pattern of overall decreased convection over the
regime analysis domain, with the exception of a small
region in Southern Angola and Northern Namibia. Outside
the domain analysis, convection is also increased within
and south of the mean ITCZ position (see Fig 2 for the
OLR mean field) over the central tropical Indian Ocean,
around 10S and 80E. The regime #2 indicates large
increased convective activity east of the east coast of
Madagascar, around 20–25S, while convection is reduced
over the southeastern Southern Africa (Fig. 6b). The con-
vection anomalies are mainly restricted to the tropics,
failing to reach the mid-latitudes. The regime #3 shows a
large region of increased convection over the continent
south of 10S as well as over Madagascar and immediately
east of it, while it is generally reduced over the oceanic
domain. It represents a general southward extension of the
continental ITCZ, while the ITCZ is more restricted to the
north over the oceanic domain. During occurrences of
regime #4 (Fig. 6b), convection is increased over the
continent south of 20S as well as over the oceanic region
immediately south of the tip of Africa. It represents a
northward extension of the region of low OLR values
associated with the midlatitude circulation, while
decreased convective activity occurs over Zimbabwe/
Mozambique and the SWIO region.
Table 1 presents the total number of days spent in each
regime (second column) as well as the percentage of those
days that are followed by days in the same and other
regimes (columns 3–9). These can be seen as the condi-
tional probabilities of regime transitions. The high
percentages observed on the diagonal give an indication on
the persistence of each regime. High percentages are also
observed between the TTT regimes, with a preferred
transition path from regime #5, then #6 and eventually #7,
indicating that TTT regimes have a tendency to propagate
from west (regime #5, located over the continent) to east
(regime #7, east of Madagascar). It is also interesting to
note that more than 30% of the days affiliated to regime #4
are followed by regime #5. Though regime #4 is not
characterized in average by a well-defined tropical–tem-
perate cloud band (Fig. 5d), it is related to negative OLR
anomalies (Fig. 6d) in Southwest Southern Africa extend-
ing over the midlatitudes, and can thus could be considered
as a precursor of TTT systems. The preferred transition
between regimes #4 and #5 is thus consistent with the
development and the general eastward propagation of these
systems.
4 Relationships to the daily rainfall field
in South Africa
In this section, composite daily rainfall anomalies are
computed according to the above classification (Fig. 7),
thus showing the daily anomalies associated with the
occurrences of the regimes. It is worth underlining that the
patterns displayed in Fig. 7 are generally spatially very
coherent, and that most stations experience highly signifi-
cant anomalies during the occurrences of the OLR regimes.
This confirms that the cluster analysis depicts synoptic-
scale features that are involved in a significant amount of
day-to-day rainfall variability over the region, in accor-
dance with the previous papers (Washington and Todd
1999; Todd and Washington 1999; Todd et al. 2004).
The regime #1 (Fig. 7a) is associated with overall dry
conditions over the whole South Africa, consistent with the
sign of the OLR anomalies in Fig. 6a. The regime #2
(Fig. 7b) is the only one not related to coherent and sig-
nificant rainfall anomalies, with very few and scattered
stations considered as significant. By contrast to the regime
#1, the regime #3 (Fig. 7c) is related to above normal
rainfall over most of the country except over the far
southwest. During the regime #4 occurrences (Fig. 7d) wet
conditions generally prevail, with contrasting dry condi-
tions experienced only over the northeastern South Africa.
With regime #5 occurrences (Fig. 7e), wet conditions are
experienced over the eastern half of the country and along
the south coast while generally dry conditions occur over
Table 1 Number of occurences of each regime (column 2) and
percentage of days followed by the same or another regime (columns
3–9)
Cluster No. of
days
1 2 3 4 5 6 7
1 501 59.88 7.63 3.59 10.39 6.86 7.22 15.55
2 262 3.19 56.49 6.28 4.3 3.68 1.03 7.77
3 446 11.78 8.02 52.92 8.6 5.39 8.76 9.66
4 279 8.98 6.87 10.31 37.99 3.19 3.61 7.35
5 408 6.99 6.49 15.92 30.11 39.46 5.67 3.36
6 388 3.19 3.44 4.26 3.58 36.03 42.78 3.99
7 476 5.39 10.31 5.83 4.66 4.66 30.16 50.84
Percentages above 30% are indicated in bold
N. Fauchereau et al.: Recurrent daily OLR patterns in SA/SWIO region
123
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mm/day
d) regime # 4
mm/day
e) regime # 5
mm/day
f) regime # 6
mm/day
g) regime # 7
mm/day
h) ANOVA
% signif.19°E 21°E 23°E 25°E 27°E 29°E 31°E 17°E 19°E 21°E 23°E 25°E 27°E 29°E 31°E
N. Fauchereau et al.: Recurrent daily OLR patterns in SA/SWIO region
123
the western half, though more marginally. The occurrences
of regime #6 (Fig. 7f) are associated with dry conditions
over the country with the exception of the far northeastern
part (Limpopo province region), while the regime #7
(Fig. 7g) is related to large negative rainfall anomalies
prevailing over the whole country. The OLR regimes dis-
criminate significantly the daily variations of the rainfall
amounts over the overall republic of South Africa
(Fig. 7h): they thus provide the link between day-to-day
rainfall anomalies in South Africa and large-scale atmo-
spheric structures. These circulation features are discussed
in the next section.
5 Associated atmospheric dynamic anomalies
Figures 8 and 9 presents respectively the wind at 700 hPa
level and the 500 hPa vertical velocity anomalies associ-
ated with the regime occurrences. If one looks first at the
TTT regimes (regimes #5, #6, #7), one notices large
similarities in the circulation anomaly patterns, with a clear
wave structure evident and a strong anticyclonic (cyclonic)
0.2 m/s
40°S
30°S
20°S
10°S
0°
10°N
0.2 m/s
40°S
30°S
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10°S
0°
10°N
0.2 m/s
40°S
30°S
20°S
10°S
0°
10°N
0.2 m/s
40°S
30°S
20°S
10°S
0°
10°N
0.2 m/s
40°S
30°S
20°S
10°S
0°
10°N
0.2 m/s
40°S
30°S
20°S
10°S
0°
10°N
0.2 m/s
40°S
30°S
20°S
10°S
0°
10°N
60°W
g) regime # 7
e) regime # 5 f) regime # 6
c) regime # 3 d) regime # 4
a) regime # 1 b) regime # 2
40°W 20°W 0° 20°E 40°E 60°E 80°E 100°E
60°W 40°W 20°W 0° 20°E 40°E 60°E 80°E 100°E 60°W 40°W 20°W 0° 20°E 40°E 60°E 80°E 100°E
60°W 40°W 20°W 0° 20°E 40°E 60°E 80°E 100°E 60°W 40°W 20°W 0° 20°E 40°E 60°E 80°E 100°E
60°W 40°W 20°W 0° 20°E 40°E 60°E 80°E 100°E 60°W 40°W 20°W 0° 20°E 40°E 60°E 80°E 100°E
Fig. 8 Circulation anomalies at 700 hPa associated with the seven
OLR regimes. vectors are only plotted where absolute wind speed
anomalies are over 0.2 m/s. Shaded areas denote grid-points for
which wind anomalies are significant at the 95% significance level
according to a two-tailed Hotelling test
Fig. 7 Station rainfall anomalies associated with the seven OLR
regimes. Negative (positive) anomalies in red (blue). Only the rainfall
station where anomalies are significant at the 90% according to a two-
tailed Student’s t-test are represented. The panel f provides the results
of an analysis of variance between the station rainfall and seven
regimes, the values indicates the stations that are significantly
discriminated by the cluster analysis at the given confidence level
(in percentage)
b
N. Fauchereau et al.: Recurrent daily OLR patterns in SA/SWIO region
123
circulation anomaly present immediately west (east) of the
cloud band location between 15S and 40S. The cloud band
position is thus related to a strong poleward transport
anomaly and upward motion in the mid-troposphere as
depicted by Fig. 9. The whole system is accordingly shif-
ted in longitude between the regimes, with the poleward
transport anomaly located from 40 to 70E between regimes
#5 and #7. This similarity is in good accordance with the
propagative properties of the TTT regimes depicted in
Table 1. The TTT regimes are thus associated with either a
standing or a transient wave in the whole troposphere.
Interestingly, a relatively similar, though shifted westward,
pattern is recorded during regime #4 occurrences, which
has been shown to be frequently a precursor to TTT sys-
tems. The most prominent feature is a cyclonic anomaly
located southwest off South Africa and centered at 35–40S
(Fig. 8d). This significant anomaly pattern is surrounded by
anticyclonic anomalies at similar latitudes located imme-
diately west and east of it, though these features are not
statistically significant. These features are however clearly
60°S
50°S
40°S
30°S
20°S
10°S
0°
10°N
-8
-8
-8
0
00 0
0
0
0
00
000
0
88
88 8
8
16
-75-60-45-30-150153045
-30
-15
0 0
0
0
0
00 0
0
0
0
0
0 00
0
15
15
-75-60-45-30-150153045
60°S
50°S
40°S
30°S
20°S
10°S
0°
10°N
-10
0
0
0
000
0
0
0
0
0
00
0
0
10
10
20
-75-60-45-30-150153045
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-15
0
0
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0
000
0
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0
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0
0
0
15
1515
-75-60-45-30-150153045
60°S
50°S
40°S
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0
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0 0 0
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00
0
0
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20
-75-60-45-30-150153045
60°S
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10°S
0°
10°N
60°W
-15
00 0
0
0
0
00
0
0
0
0
0
0
1530
-75-60-45-30-150153045
x 1000
a) regime # 1
x 1000
b) regime # 2
x 1000
c) regime # 3
x 1000
d) regime # 4
x 1000
e) regime # 5
x 1000
f) regime # 6
x 1000
g) regime # 7
40°W 20°W 0° 20°E 40°E 60°E 80°E 100°E
60°W 40°W 20°W 0° 20°E 40°E 60°E 80°E 100°E 60°W 40°W 20°W 0° 20°E 40°E 60°E 80°E 100°E
60°W 40°W 20°W 0° 20°E 40°E 60°E 80°E 100°E 60°W 40°W 20°W 0° 20°E 40°E 60°E 80°E 100°E
60°W 40°W 20°W 0° 20°E 40°E 60°E 80°E 100°E 60°W 40°W 20°W 0° 20°E 40°E 60°E 80°E 100°E
Fig. 9 Vertical velocity anomalies at 500 hPa associated with the
seven OLR regimes. Blue (red) areas denote grid-points where omega
anomalies are significant at the 95% significance level according to a
two-tailed Student’s t-test. Negative values mean uplift anomalies.
The values are multiplied by 1,000 for readability
N. Fauchereau et al.: Recurrent daily OLR patterns in SA/SWIO region
123
evident and significant if one considers the vertical velocity
anomalies at 500 hPa (Fig. 9d). A wave structure thus
develops in the mid-latitudes of the Southern Hemisphere.
The regime #4 is thus associated with mainly extra-tropical
processes as no significant anomalies are recorded in the
tropics. Table 1 above shows that 30% of the regimes #4
occurrences however are followed by the establishment of
a link between those anomalies and anomalous convection
in the tropics, leading to a TTT system (regime #5) located
over the continent and SWIO.
A strong anticyclonic anomaly is associated with regime
#1 with center located around 25S/20E, over the Angolan
region (Fig. 8a). It is thus related to a weakening of the
Angolan thermal low which normally develops during the
summer half of the year (Reason et al. 2006). Accordingly,
the vertical velocity in the mid-layers of the troposphere is
largely reduced (Fig. 9a).
The regime #2 is related to a strong cyclonic anomaly
centered immediately off the east coast of Madagascar, at
approximately 20S (Fig. 8b). Large upward anomalies are
located off the east coast of Madagascar, above some
minor positive omega values south of it (Fig. 9b).
The regime #3 is associated with a strong anticyclonic
anomaly developing off the southern tip of Africa, located
near 30E (Fig. 8c). This anticyclonic feature is connected
to a strong westerly anomaly from the tropical southeast
Atlantic, that feeds into a cyclonic anomaly of limited
extent centered on 20S/15E. The low-level circulation
pattern is related to upward anomalies at 500 hPa over the
continent west of 30E and downward anomalies over the
SWIO (Fig. 9c).
6 Interannual variability and teleconnections
with the SST field
The Fig. 10 presents the time-series of regimes frequency
for each season from NDJF 1979/1980 to NDJF 2001/2002.
The number of days during which the regimes are recorded
varies greatly from year to year. Each season is then
characterized by the combination of various number of
regime occurrences. To assess how the variations in
regimes frequency project onto rainfall at the seasonal
time-scale, an indice of seasonal (NDJF) rainfall anomalies
from the WRC dataset for the central interior of South
Africa (‘‘Central SA’’, see Fig. 11) is computed. The years
corresponding to the four largest negative (dry) and posi-
tive (wet) departures of this indice are depicted by
respectively red and blue stars in Fig. 10. One must keep in
mind that the relationship between the regimes frequency
and the seasonal rainfall amounts is however not expected
to be straightforward and linear. For example, in the con-
text of this study and for the Central SA, a near-average
year can be the result of either equally enhanced proba-
bilities of wet (e.g. #3) and dry (e.g. #4) regimes, then
canceling their effects at the seasonal scale, or the effect of
a large increase in the occurrence of regime #2, which is
not related to significant rainfall anomalies. In addition, the
regimes are not orthogonal to each other and any region
can be under the influence of several regimes. It is however
expected that very dry or wet seasonal rainfall amounts are
related to a large number of occurrences of respectively
‘‘dry’’ and ‘‘wet’’ regimes. One indeed note that three of the
worst dry years over the period for the Central SA indice
are related to larges increases in the occurrences of the
regime #1, which is indeed related to large negative rainfall
anomalies at the intra-seasonal time-scale. These dry years
correspond to relative decrease in the frequency of the
regime #3. On the other hand, the wettest years are gene-
rally related to above normal number of occurrences of
‘‘wet’’ regimes such as #3 and #5.
We now investigate the SST conditions that are asso-
ciated with variability in the regimes frequency. The SST is
considered here as a good indicator of the background
climate state and given its persistence SST can be con-
sidered as a constant forcing over a season. Linear
correlations are computed between the number of occur-
rences of each regime for each season (time-series shown
in Fig. 10) and the mean seasonal SST values. The results
are shown in Fig. 12.
Four regimes present a pattern in the tropical Pacific
clearly reminiscent of either El Nino or La Nina conditions.
The regimes #1 and #2 occur more often during El Nino
events (Fig. 12a, b): an increased (decreased) number of
occurrences of regime #1 is expected during El Nino (La
Nina) conditions, along with phases of warming (cooling)
in the tropical Indian Ocean. Similar to regime #1, #2 is
associated with an ENSO pattern in the tropical Pacific. In
the latter case however, the SST maximum anomalies are
located more off the South American coast compared to
Fig. 12a. On the contrary, regime #3 and more strongly #5
are more frequent during La Nina events (Fig. 12c, e).
Correlations between the number of occurrences of these
regimes and the seasonal mean Multivariate ENSO index
(Wolter and Timlin 1993) (not shown) support these
results.
Besides the obvious EL Nino (La Nina) patterns in the
Pacific ocean related to regimes #1 and #2 (regimes #3 and
#5), regional SST anomalies associated with several
regimes are related to well-known modes of variability that
have been extracted by e.g. multivariate analyses and
depicted elsewhere in the literature. The sign of these
relationships is also in good accordance with the telecon-
nections diagnosed at the seasonal scale in previous
studies. The regime #2 is for example related as well to
cold (warm) anomalies in the Southwest (Northeast) South
N. Fauchereau et al.: Recurrent daily OLR patterns in SA/SWIO region
123
Indian Ocean similar to the negative polarity of the sub-
tropical dipole presented in Behera and Yamagata (2001)
and Reason (2001). On the opposite, the regime #6, which
is associated with TTT systems located over the Mozam-
bique Channel and negative rainfall anomalies over South
Africa (with the exception of the northeastern part) is
related to warm (cold) anomalies in the southwest (north-
east) South Indian Ocean, corresponding to the positive
phase of the subtropical Indian Ocean dipole. Note that the
associated convective anomalies (see Fig. 6f) are consis-
tent with enhanced rainfall over tropical Southern Africa
noticed by these authors. Positive correlations are also
noticed at the subtropical latitudes of the Southwest
Atlantic, while negative correlations are present in the
northeastern part northeastern part, corresponding to the
EOF pattern described in Venegas et al. (1997). These
large-scale anomalies in the Southern Hemisphere are
reminiscent of the mode of variability described in Fau-
chereau et al. (2003) and Hermes and Reason (2005) with
in-phase subtropical SST dipoles throughout the Southern
Hemisphere Oceans during austral summers. Strong warm
anomalies in the SWIO, south of Madagascar and in the
southern part of the Mozambique channel are favourable
for an increased probability of regime #5, which is related
1980 1985 1990 1995 20000
10
20
30
40
nb. o
f day
s
1980 1985 1990 1995 20000
10
20
30
40
nb. o
f day
s
1980 1985 1990 1995 20000
10
20
30
40
nb. o
f day
s
1980 1985 1990 1995 20000
10
20
30
40
nb. o
f day
s
1980 1985 1990 1995 20000
10
20
30
40
nb. o
f day
s
1980 1985 1990 1995 20000
10
20
30
40
nb. o
f day
s
1980 1985 1990 1995 20000
10
20
30
40
nb. o
f day
s
(a) (b)
(d)
(f)
regime # 1 regime # 2
regime # 4
regime # 6
regime # 7
regime # 5
regime # 3(c)
(e)
(g)
Fig. 10 Time-series of the
number of days spent in each
regimes during each season
from NDJF 1979/80 to NDJF
2001/02. The red line indicates
the long-term mean. The redand blue stars denote
receptively the four driest and
four wettest years according to
the Central SA indice presented
in Fig. 11
N. Fauchereau et al.: Recurrent daily OLR patterns in SA/SWIO region
123
to TTTs located over the continent (Fig. 5e) and positive
rainfall anomalies in northeastern South Africa (Fig. 7e).
These anomalies are consistent with the regional SST mode
and the relationships to seasonal rainfall described in
Walker (1990) and Mason (1995). The relationships
between the regimes frequency and these SST modes
provides the links between the synoptic convective activity
and the seasonal teleconnections diagnosed at the seasonal
scale. This aspect is investigated in more details in the
following section in the ENSO case.
7 Implications for the ENSO impact over Southern
Africa and the SWIO at the seasonal scale
The Fig. 13 presents the composite seasonal OLR anoma-
lies related respectively with the five largest El Nino
(Fig. 13a) and La Nina (Fig. 13b) events according to the
November to February anomalies of the NINO3.4 indice.
The spatial pattern presents the well-known ‘‘dipole’’
structure contrasting, e.g. decreased (increased) seasonal
convection over Southern Africa (the SWIO) related to El
Nino, that has been depicted in numerous studies before
(see e.g. Jury 1992, 1997; Mason and Jury 1997; Mutai
et al. 1998).
Based on our typology, two regimes out of seven are
favoured during El Nino years (Fig. 12), i.e. their proba-
bility is enhanced when warm conditions prevail in the
Eastern Pacific. The OLR anomalies presented by these
two classes (regimes #1 and #2, see Fig. 6a, b) respec-
tively depict decreased convective activity over Southern
Africa (regime #1) and increased convection over the
SWIO (regime #2). These two distinct patterns clearly
merge, at the seasonal time-scale, to form the well-known
dipole shown on Fig. 13a. Our analysis suggests therefore
that this pattern is in fact constituted by two independent
poles, that correspond to two distinct regimes at the syn-
optic timescale, which do not occur simultaneously.
Instead of a dipole, we suggest therefore the existence of
two distinct cores that are independent at the subseasonal
time-scale.
During La Nina years, the reverse situation is sche-
matically observed (Fig. 13b), though the contrast between
the ocean and the hinterland parts of Southern Africa is
less clear and the pattern presents less of a ‘‘dipole’’
structure. Once again, an explanation can be furnished by
the synoptic-scale convective regimes. The regimes #3 and
#5 are favoured during cold events in the Pacific
(Fig. 12c, e), both of them showing increased convection
and positive rainfall anomalies over SA in agreement with
the above-average rainfall that tend to be recorded there
during these years. Over the Southwest Indian region
however, these two regimes show anomalies of opposite
signs and contrasting patterns south and west of Mada-
gascar: the strong negative OLR anomalies (up to
30 W/m2) related to regime #5 are partly compensated by
the positive anomalies associated with the regime #3,
hence the weak OLR anomalies noted in Fig. 13b during
La Nina events. For these reasons, the anomaly pattern
observed during La Nina is not exactly the opposite to the
one recorded during El Nino; the amplitude of the con-
vective anomalies over the SWIO region remains also
weaker. Such asymmetry between El Nino and La Nina
impacts on rainfall and circulation in the South Atlantic
and South Indian Ocean regions is typical (Reason et al.
2000; Colberg et al 2004).
The combination of several regimes favoured during
ENSO events also makes possible to explain part of the
non-linearity observed between ENSO and the seasonal
convection and rainfall anomalies. It is for instance known
that the 1997/1998 El Nino (the largest event of the cen-
tury) was not associated with as large rainfall anomalies
over SA as the weaker 1991/1992 or 1986/1987 events
(Reason and Jagadheesha 2005). From Fig. 10a it appears
that (contrarily to the average behaviour during El Nino
years) the frequency of regime #1 (associated with general
dry conditions over SA, see Fig. 7a) was indeed reduced
compared to the long-term mean, while the regime #2
(Fig. 10d, related to barely significant rainfall anomalies)
was largely favoured, thus helping to understand why the
South African region did not experience greatly reduced
rainfall during this year. On the other hand, the 1982/1983
and 1991/1992 events were related to devastating droughts
over the region at the seasonal scale, and the occurrences of
the regime #1 were nearly twice as the long-term mean, at
approximately 40 days out of 120.
35°S
30°S
25°S
17°E 22°E 27°E 32°E
Central SA indice
Fig. 11 Domain over which the Central SA index is computed along
with the location of the rainfall stations from the WRC dataset
N. Fauchereau et al.: Recurrent daily OLR patterns in SA/SWIO region
123
8 Summary and discussion
This paper constitutes the first objective attempt to classify
the large-scale convective anomaly patterns over the
Southern Africa–SWIO region at the daily timescale. The
spatial configurations of the OLR field were clustered into
seven well-individualized recurrent regimes of large-scale
convective anomalies. Among these, three regimes spe-
cifically presented the well-known signature of tropical–
temperate interactions, known to be of major importance in
the regional subseasonal variability of the summer rainfall
over Southern Africa. Six of the seven regimes were
nonetheless seen to be associated with significant and
spatially consistent dry or wet conditions over Southern
Africa, demonstrating their importance for the day-to-day
rainfall variability.
Though the regimes basically describe high-frequency
signals in the climate system (mostly synoptic-scale per-
turbations), the variability in the number of occurrences
from year to year is shown to be modulated by distinctive
Fig. 12 Correlations between seasonal regime frequency and SST anomalies. Outlined areas denote correlations significant at the 95%
confidence level
N. Fauchereau et al.: Recurrent daily OLR patterns in SA/SWIO region
123
seasonal SST anomaly patterns, which makes it possible to
focus on the interactions with the interannual time-scales.
The fluctuations noted between the successive rainy sea-
sons over SA can thus be interpreted here as differences in
the probability of occurrence of the different regimes, in
linkage with the different background conditions in the
climate system at the global or regional scale.
Of particular interest is the modulation of the frequency
of four regimes by the ENSO phenomenon. This link
provides a useful tool to clarify the impact of ENSO on
Southern African atmospheric convection at the seasonal
scale and point out its unexpected complexity. It is
demonstrated in particular that the ‘‘dipole’’ structure
exhibited by the seasonal convective anomalies related to
ENSO arises as an effect of averaging two different
regimes, each one accounting for one pole of the ‘‘dipole’’,
and is therefore not valid at the synoptic scale. The
asymmetry between the El Nino and La Nina-related sea-
sonal patterns over the region is also interpreted in the
context of enhanced probability of different regimes with
contrasted spatial configurations.
Furthermore, this study provides an interesting frame-
work to understand the non-linearities noted between the
state of El Nino and the seasonal rainfall amounts over the
region. Of the two regimes favoured during El Nino event,
only one is related to widespread dry conditions over South
Africa, and the non-linearities between the magnitude of
the ENSO and the response of the convection and rainfall
field can be related to variations in the frequency of these
two clusters. As an example, the year 1997/1998,
(a)
(b)
Fig. 13 Composite November
to February OLR anomalies for
the five largest El Nino (a) and
La Nina (b) years over the 1979/
1980 to 2001/2002 period
according to the Nino3.4 time-
series. Thick contour indicates
anomalies significant at the 95%
level according a Student’s
t-test
N. Fauchereau et al.: Recurrent daily OLR patterns in SA/SWIO region
123
characterized by a strong ENSO signal but a weak regional
response, was only related to increased occurrences of the
convection regime that is not associated with significant
rainfall anomalies. The relative weakness of the telecon-
nection between interannual rainfall variability over the
region and most ENSO indicators suggests indeed that El
Nino could have only an indirect and complex influence
over the region: this study provides a support as well as a
tool to investigate this problem.
Though beyond the scope of this paper, these results
could provide a useful framework to investigate the phys-
ical mechanisms by which the SST anomalies influence the
convection and rainfall at the seasonal scale. In addition to
ENSO, the interactions between these convection regimes
and other modes of atmospheric variability likely influence
Southern African rainfall (e.g. the Madden–Julian Oscil-
lation or the Antarctic Oscillation) remain to be
established. We plan to investigate these different aspects
in future works.
Acknowledgements Nicolas Fauchereau would like to thank UCT
for funding his post-doctoral fellowship. This study is part of the
Water Research Commission project K5/1747/1. The authors thanks
the anonymous reviewers for their useful comments and suggestions.
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