Recurrent daily OLR patterns in the Southern Africa/Southwest Indian Ocean region, implications for South African rainfall and teleconnections

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1 Clim Dyn DOI 1.17/s Recurrent daily OLR patterns in the Southern Africa/Southwest Indian Ocean region, implications for South African rainfall and teleconnections Nicolas Fauchereau Æ B. Pohl Æ C. J. C. Reason Æ M. Rouault Æ Y. Richard Received: 14 October 27 / Accepted: 19 May 28 Ó Springer-Verlag 28 Abstract A cluster analysis of daily outgoing longwave radiation (OLR) anomalies from 1979 to 22 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 largescale organized convection. Among them are three regimes indicative of well defined tropical temperate interactions linking the hinterland parts of Southern Africa to the midlatitudes of the SWIO. Preferred transitions show a tendency 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 nearglobal 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 cloudband. 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 N. Fauchereau (&) C. J. C. Reason M. Rouault Department of Oceanography, University of Cape Town, Rondebosch, Cape Town 771, South Africa Nicolas.Fauchereau@uct.ac.za B. Pohl Y. Richard Centre de Recherches de Climatologie, CNRS/Université de Bourgogne, Dijon, France (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 convection 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 seasonal 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 1S) 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 22; Reason and Jagadheesha 25), 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 (22), 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 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

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 Harrison 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 2). 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 (24) 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 preferred location of these TTT systems. The authors estimated that such events could account for 3% (resp. 6%) 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 199; Reason et al. 2). The relationship is significant particularly since the 197s (Richard et al. 2, 21) 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 Zimbabwe 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 22/23 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 interannual 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-today 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. 21). In turn, individual events have a determinant impact on the seasonal amounts, and thus finally on the rainfall fluctuations that are recorded between successive years. Over the SA region, similar scale interactions are hypothesized to play a major role on the interannual variability of the rains. Cook (2), 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-today 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 however not been fully established to date. This paper aims at filling this gap. The first objective of this study is to provide an objective 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 atmospheric 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 relationships 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 available on a regular grid from 1974, with a 1-month gap in The study period has been restricted to to match the NCEP2 reanalysis period).

3 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 (23). Seven thousand six hundred and sixty-five stations (out of 11,), presenting no missing values, are extracted on the 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. 22). This study makes use of the zonal (U) and meridional (V) components of the wind (m/s) at 7 hpa and vertical velocity (omega) at 5 hpa. The 7 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. 27) to be significant in carrying moisture over the region. The 5 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. 23) ona1 9 1 regular grid, for the 195-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 25 S 3 S location of the WRC rainfall stations 35 S 17 E 22 E 27 E 32 E Fig. 1 Location of the 7,665 daily rainfall stations extracted from the WRC dataset the observations (days) into k regimes that minimizes the sum of the intra-regime variances, W. The Euclidian distance is used to measure the similarity between two observations, X and Y. The overall minimum of the function 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 minimum 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 random draw of the k regimes. The reproducibility of the obtained partitions should therefore be tested. If the distribution of the climatological dataset is uniform, 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 different 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 (23), we performed 5 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 5 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 generated through a first-order Markov process having the same covariance matrix as the true atmospheric data (Moron and Plaut 23). The red-noise test (applied to Markov-generated red-noise data) operates as follows: 1 samples of the same length as the atmospheric dataset are generated, providing 1 values of the classifiability index, which are ranked to find the 1 and 9% confidence limits. The value of c* for the atmospheric dataset is then compared with these limits: a value above the 9% confidence limit indicates, for the corresponding value of k, a classifiability

4 Fig. 2 Mean OLR field over the November to February season (W/m 2 ), the values below 24 W/m 2 are shaded in blue, interval 1 W/m 2. 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 This method has been applied here to daily OLR anomalies over the domain 1 4 S, E (858 gridpoints) from November to February (leading to 12 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 Washington (1999) study (Fig. 2). In order to reduce the dimensionality of the problem and ensure linear independence 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 dependent 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 Fig. 3 Classifiability index c* as a function of the number of regimes k (solid line). The levels of significance (dashed and dashed-dotted lines) at 8, 9 and 95% are computed according to a first-order Markov process 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). 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 presenting 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 information among those that reach significance. Figure 4 presents the results of an Analysis of Variance (ANOVA) on the OLR field according to the regime categories. The ANOVA depicts the regions for which the intra-regime variance is significantly lower than the interregime variance. The classification (i.e. the respective regime to which each day of the period is assigned) significantly discriminates the day-to-day OLR fluctuations 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

5 1 S 2 S 3 S a) regime # 1 b) regime # S S 2 E 4 E 6 E 8 E 1 E E 4 E 6 E 8 E 1 E S c) regime # 3 d) regime # S S S S 2 E 4 E 6 E 8 E 1 E E 4 E 6 E 8 E 1 E e) regime # 5 f) regime # S S S S 21 5 S 2 E 4 E 6 E 8 E 1 E E 4 E 6 E 8 E 1 E g) regime # S S S 2 4 S 5 S E 4 E 6 E 8 E 1 E Fig. 5 Outgoing longwave radiation regimes for NDJF: composite means, values below 24 W/m 2 are shaded over the whole region located between the Equator and 5S 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 patterns 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

6 1 S 2 S 3 S 4 S a) regime # 1 b) regime # S -3 2 E 4 E 6 E 8 E 1 E 2 E 4 E 6 E 8 E 1 E c) regime # 3 d) regime # S 2 S 3 S 4 S S -3 2 E 4 E 6 E 8 E 1 E 2 E 4 E 6 E 8 E 1 E e) regime # 5 f) regime # S 2 S 3 S 4 S S -3 2 E 4 E 6 E 8 E 1 E 2 E 4 E 6 E 8 E 1 E -3 g) regime # S 2 S 3 S 4 S 5 S 2 E 4 E 6 E 8 E 1 E Fig. 6 Outgoing longwave radiation regimes for NDJF: composite anomalies, contour interval is 5 W/m 2. Only the grid-points for which the anomalies are significant at the 95% confidence level according a Student s t-test are displayed well-defined pattern of maximum convection (OLR values below 24 W/m 2, blue shades in the figures) organized in a NW/SE band extending from the Southern African subcontinent or Madagascar at tropical latitudes to the midlatitudes of the SWIO (South of 3S). 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 4E 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

7 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) represents 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 1S and 8E. The regime #2 indicates large increased convective activity east of the east coast of Madagascar, around 2 25S, while convection is reduced over the southeastern Southern Africa (Fig. 6b). The convection 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 1S 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 2S 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 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 Percentages above 3% are indicated in bold 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 conditional 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 3% 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 temperate cloud band (Fig. 5d), it is related to negative OLR anomalies (Fig. 6d) in Southwest Southern Africa extending 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 significant anomalies during the occurrences of the OLR regimes. This confirms that the cluster analysis depicts synopticscale features that are involved in a significant amount of day-to-day rainfall variability over the region, in accordance with the previous papers (Washington and Todd 1999; Todd and Washington 1999; Todd et al. 24). 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 significant 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 conditions 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

8 a) regime # 1 23 S 4 3 b) regime # S S S 31 S S S c) regime # 3 23 S -4 mm/day 4 3 d) regime # 4-4 mm/day S S S 31 S S S e) regime # 5 23 S -4 mm/day 4 3 f) regime # 6-4 mm/day S S S 31 S S S -4 mm/day g) regime # 7 h) ANOVA 4 23 S 3 25 S 2 27 S 1 29 S S S S 17 E E 21 E 23 E 25 E 27 E 29 E 31 E mm/day 17 E 19 E 21 E 23 E 25 E 27 E 29 E 31 E -4 mm/day % signif.

9 b 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 9% according to a twotailed 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) 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 discriminate 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 atmospheric structures. These circulation features are discussed in the next section. 5 Associated atmospheric dynamic anomalies Figures 8 and 9 presents respectively the wind at 7 hpa level and the 5 hpa vertical velocity anomalies associated 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) a) regime # 1 b) regime # 2 1 N 1 N 1 S 1 S 2 S 2 S 3 S 3 S 4 S 4 S 6 W 4 W 2 W 2 E 4 E 6 E 8 E 1 E 6 W 4 W 2 W 2 E 4 E 6 E 8 E 1 E.2 m/s.2 m/s c) regime # 3 d) regime # 4 1 N 1 N 1 S 1 S 2 S 2 S 3 S 3 S 4 S 4 S 6 W 4 W 2 W 2 E 4 E 6 E 8 E 1 E 6 W 4 W 2 W 2 E 4 E 6 E 8 E 1 E.2 m/s.2 m/s e) regime # 5 f) regime # 6 1 N 1 N 1 S 1 S 2 S 2 S 3 S 3 S 4 S 4 S 6 W 4 W 2 W 2 E 4 E 6 E 8 E 1 E 6 W 4 W 2 W 2 E 4 E 6 E 8 E 1 E.2 m/s.2 m/s g) regime # 7 1 N 1 S 2 S 3 S 4 S 6 W 4 W 2 W 2 E 4 E 6 E 8 E 1 E.2 m/s Fig. 8 Circulation anomalies at 7 hpa associated with the seven OLR regimes. vectors are only plotted where absolute wind speed anomalies are over.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

10 N. Fauchereau et al.: Recurrent daily OLR patterns in SA/SWIO region a) regime # 1 b) regime # N 3 1 S 2 S - 3 S -3 4 S S S 6 W 4 W 2 W 2 E 4 E 6 E 8 E 1 E x 1 6 W 4 W 2 W 2 E 4 E 6 E 8 E 1 E c) regime # 3 d) regime # N 3 1 S 2 S - 3 S -3 4 S S S 6 W 4 W 2 W 2 E 4 E 6 E 8 E 1 E x 1 6 W 4 W 2 W 2 E 4 E 6 E 8 E 1 E e) regime # N 3 1 S 2 S - 3 S -3 4 S S S 6 W 4 W 2 W 2 E 4 E 6 E 8 E 1 E x 1 6 W 4 W 2 W 2 E 4 E 6 E 8 E 1 E f) regime # x x x 1 1 N 1 S 2 S 3 S 4 S g) regime # 7 5 S 6 S 6 W 4 W 2 W 2 E 4 E 6 E 8 E 1 E x 1 Fig. 9 Vertical velocity anomalies at 5 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 circulation anomaly present immediately west (east) of the cloud band location between S and 4S. 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 shifted in longitude between the regimes, with the poleward transport anomaly located from 4 to 7E 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 two-tailed Student s t-test. Negative values mean uplift anomalies. The values are multiplied by 1, for readability 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 systems. The most prominent feature is a cyclonic anomaly located southwest off South Africa and centered at 35 4S (Fig. 8d). This significant anomaly pattern is surrounded by anticyclonic anomalies at similar latitudes located immediately west and east of it, though these features are not statistically significant. These features are however clearly

11 evident and significant if one considers the vertical velocity anomalies at 5 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 3% 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/2E, 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. 26). 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 2S (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 3E (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 2S/E. The low-level circulation pattern is related to upward anomalies at 5 hpa over the continent west of 3E and downward anomalies over the SWIO (Fig. 9c). 6 Interannual variability and teleconnections with the SST field The Fig. 1 presents the time-series of regimes frequency for each season from NDJF 1979/198 to NDJF 21/22. 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 positive (wet) departures of this indice are depicted by respectively red and blue stars in Fig. 1. 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 context of this study and for the Central SA, a near-average year can be the result of either equally enhanced probabilities 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 generally related to above normal number of occurrences of wet regimes such as #3 and #5. We now investigate the SST conditions that are associated 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 considered as a constant forcing over a season. Linear correlations are computed between the number of occurrences of each regime for each season (time-series shown in Fig. 1) 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 teleconnections 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

12 Fig. 1 Time-series of the number of days spent in each regimes during each season from NDJF 1979/8 to NDJF 21/2. The red line indicates the long-term mean. The red and blue stars denote receptively the four driest and four wettest years according to the Central SA indice presented in Fig. 11 nb. of days (a) regime # 1 (b) regime # 2 (c) regime # 3 nb. of days (d) regime # 4 nb. of days nb. of days (e) regime # 5 (f) regime # nb. of days nb. of days (g) regime # 7 nb. of days Indian Ocean similar to the negative polarity of the subtropical dipole presented in Behera and Yamagata (21) and Reason (21). On the opposite, the regime #6, which is associated with TTT systems located over the Mozambique Channel and negative rainfall anomalies over South Africa (with the exception of the northeastern part) is related to warm (cold) anomalies in the southwest (northeast) South Indian Ocean, corresponding to the positive phase of the subtropical Indian Ocean dipole. Note that the associated convective anomalies (see Fig. 6f) are consistent 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 Fauchereau et al. (23) and Hermes and Reason (25) 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

13 25 S 3 S Central SA indice 35 S 17 E 22 E 27 E 32 E Fig. 11 Domain over which the Central SA index is computed along with the location of the rainfall stations from the WRC dataset 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 (199) 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 anomalies 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 probability 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) respectively 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 synoptic 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 schematically 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 Madagascar: the strong negative OLR anomalies (up to 3 W/m 2 ) 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 convective 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. 2; Colberg et al 24). 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 century) was not associated with as large rainfall anomalies over SA as the weaker 1991/1992 or 1986/1987 events (Reason and Jagadheesha 25). From Fig. 1a 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. 1d, 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 4 days out of 12.

14 Fig. 12 Correlations between seasonal regime frequency and SST anomalies. Outlined areas denote correlations significant at the 95% confidence level 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 specifically 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 perturbations), the variability in the number of occurrences from year to year is shown to be modulated by distinctive

15 Fig. 13 Composite November to February OLR anomalies for the five largest El Nino (a) and La Nina (b) years over the 1979/ 198 to 21/22 period according to the Nino3.4 timeseries. Thick contour indicates anomalies significant at the 95% level according a Student s t-test (a) (b) 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 seasons 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 seasonal 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 framework 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,

16 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 teleconnection 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 physical 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 Oscillation or the Antarctic Oscillation) remain to be established. 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