Recurrent daily OLR patterns in the Southern Africa ...blyon/REFERENCES/P67.pdf · Department of Oceanography, University of Cape Town, Rondebosch, Cape Town 7701, South Africa e-mail:

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

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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


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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


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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


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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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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


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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


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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


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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

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

0.2 m/s

40°S

30°S

20°S

10°S

0°

10°N

60°W

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



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


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

-30-15

-15

0

0

0

0

000

0

0

0

0

0

0

0

15

1515

-75-60-45-30-150153045

60°S

50°S

40°S

30°S

20°S

10°S

0°

10°N

-40-20

0

0

0

0

0

0 0 0

0

0

0

0

0

0

0 0

20

20

-75-60-45-30-150153045

-40

-200

0

0

0

0

0

0

00

0

00

0

0

0

20

-75-60-45-30-150153045

60°S

50°S

40°S

30°S

20°S

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




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


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


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


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


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


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


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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Recurrent daily OLR patterns in the Southern Africa ...blyon/REFERENCES/P67.pdf · Department of Oceanography, University of Cape Town, Rondebosch, Cape Town 7701, South Africa e-mail: