Abstract

Remote effects modulating the austral summer precipitation over southern Africa during El Niño/El Niño Modoki events are investigated by analyzing the observed events during December–February of the years from 1982/83 to 2010/11. Based on the composite analyses, it is found that southern Africa experiences significantly below normal precipitation during El Niño events compared to El Niño Modoki events. During these latter events, precipitation anomalies are not so significant although southern Africa as a whole receives below normal precipitations. The differences in the spatial distribution of precipitation over southern Africa are seen to be related to the sea surface temperature (SST) anomalies of the equatorial Pacific through atmospheric teleconnections.

The low-level (850 hPa) Matsuno–Gill response to anomalously high precipitation over the Pacific during El Niño events results in an anomalous anticyclone extending from the equatorial to the subtropical South Indian Ocean. These anomalous anticyclonic winds weaken the tropical moisture flow into the southern Africa landmass. Rossby wave activity flux analysis of the upper-level (300 hPa) circulation shows an anomalous tropospheric stationary wave from the Pacific propagating toward southern Africa and maintaining an anomalous anticyclone over southern Africa. The anomalous Matsuno–Gill response and the anomalous tropospheric stationary wave response are intense during El Niño events, causing drought over southern Africa. During El Niño Modoki events, these processes are weaker compared to El Niño events.

1. Introduction

Southern Africa (south of 15°S) receives much of its precipitation during the austral summer months from December to February (DJF). Variations in the summer precipitation over the southern African landmass have been attributed to the anomalous changes in the surrounding oceans (Hirst and Hastenrath 1983; Walker 1990; Reason and Mulenga 1999; Behera and Yamagata 2001; Reason 2001; Rouault et al. 2003; Reason et al. 2006; Morioka et al. 2011). For example, an anomalous increase in the sea surface temperature (SST) over the southwest South Indian Ocean, south of Madagascar, during the positive phase of the subtropical Indian Ocean dipole (Behera and Yamagata 2001) is known to cause increase in precipitation over the southern Africa landmass. In addition, variations in the Pacific Ocean SST due to El Niño–Southern Oscillation (ENSO) are known to exert a remote forcing to cause the variations of the precipitation over this region (Jury 1996; Nicholson and Kim 1997; Goddard and Graham 1999; Cook 2001; Mason and Goddard 2001; Camberlin et al. 2001; Rouault and Richard 2003; Jury et al. 2004; Reason and Jagadeesha 2005; Lyon and Mason 2007; Fauchereau et al. 2009; Crétat et al. 2010; Hart 2012).

Dry years over southern Africa are often associated with El Niño events in the Pacific. Richard et al. (2000) found the relation between southern African precipitation and ENSO events to be robust after 1970s and this relation is strong during December to March (Lyon and Mason 2007, and references there in). Earlier studies of Rocha and Simmonds (1997) showed that the dry conditions over southern Africa during El Niño years are associated with marked low-level anomalous cyclonic circulations over the central Indian Ocean, which divert the moisture from the landmass and result in reduction of precipitation. However, most of the studies (Lindesay 1988; Cook 2001; Reason and Jagadeesha 2005) associate the dry conditions over southern Africa with the northeastward shift of the South Indian convergence zone (SICZ; Cook 2000). The SICZ is a band of precipitation extending from tropical southern Africa to the subtropics through South Africa and is maintained by the interaction between the tropical easterlies and the midlatitude systems. The importance of this band of precipitation on the southern Africa climate is well documented in the literature (Harrison 1984; Tyson 1986; Lyons 1991). It was speculated that the SICZ moves northeastward from its climatological position because of the upper-level convergence over the equatorial Indian Ocean (Cook 2001).

An eastward shift of the climatological upper-level trough situated southward of South Africa (Tyson 1981) is also known to be a cause of droughts over southern Africa. Such a shift is known to affect the formation and location of tropical temperate troughs (TTTs; Lyon and Mason 2007), the main rain-bearing systems over southern Africa. The formation of TTTs over the southern Africa landmass tends to be low during El Niño years (Ratna et al. 2013; Hart 2012), thus causing droughts over southern Africa. Carrying out atmospheric general circulation model experiments by specifying SST anomalies over different locations of the Pacific, Hart et al. (2009) and Hart (2012) found the TTTs to be also affected by the location of the heating over the Pacific.

Most of the above studies used the SST anomalies over the Niño-3.4 region to identify the ENSO events. However, the Niño-3.4 index mixes up the signals associated with both the ENSO and the ENSO Modoki phenomena (Weng et al. 2007). Analyzing the SST data from the recent decades (1979–2004), Ashok et al. (2007) found the preferred spatial distribution of the anomalies warming to be different from the conventional ENSO events that occur in the equatorial Pacific. It was named ENSO Modoki and an index was defined to identify those events. El Niño Modoki is also referred to as the central Pacific El Niño (Kao and Yu 2009; Yeh et al. 2009) and warm pool El Niño (Kug et al. 2009). The spatial distribution of the SST anomalies during a typical El Niño Modoki has warmer central Pacific anomalies with cooler SST anomalies in the eastern and western Pacific, whereas in a conventional El Niño the eastern Pacific exhibits warm SST. El Niño and El Niño Modoki have different impacts on the boreal winter climate of different countries (Ashok et al. 2007; Weng et al. 2009; Ashok and Yamagata 2009; Feng et al. 2010; Taschetto et al. 2010; Ratnam et al. 2011; Tadeschi et al. 2013) due to differences in the Walker circulation and the teleconnection patterns generated by the two types of El Niño.

In this study we distinguish the equatorial Pacific events as conventional ENSO events based on the Niño-3 index and ENSO Modoki based on the index defined by Ashok et al. (2007), and we try to understand the atmospheric teleconnections that could affect the precipitation variations over southern Africa by classifying the events into conventional ENSO and ENSO Modoki events. In the following section the data and methodology used for the study are described, followed by an analysis of the precipitation and wind anomalies.

2. Data and methodology

ENSO–ENSO Modoki events during austral summer (DJF) for the period 1982–2011 are identified based on the SST anomaly indices. ENSO events are identified by the Niño-3 index (area averaged SST anomaly over 5°N–5°S, 150°–90°W) and ENSO Modoki events are based on the ENSO Modoki index (EMI) defined as EMI = SSTAA − 0.5 × SSTAB − 0.5 × SSTAC, where SSTAA is the area averaged SST anomaly over 10°S–10°N, 165°E–140°W; SSTAB is the area averaged anomaly over the region 15°S–5°N, 110°–70°W; and SSTAC is the area averaged SST anomaly over 10°S–20°N, 125°–145°E (Ashok et al. 2007).

The SST anomalies used for identifying the events are derived from the National Oceanic and Atmospheric Administration (NOAA) Optimum Interpolation (OI) sea surface temperature (SST) V2 (OISSTV2) (Reynolds et al. 2002). The indices are normalized by their standard deviation and the years with standard deviation greater than 0.8 are considered as event years in this study.

Monthly precipitation data from the Global Precipitation Climatology Project (GPCP) version 2 (Adler et al. 2003) is used for analyzing the precipitation anomalies associated with ENSO–ENSO Modoki events.

Monthly meridional and zonal wind data along with temperature and specific humidity data at various levels were taken from the Interim European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-Interim; Dee et al. 2011). Monthly anomalies of SST and other variables are from the monthly climatology based on DJF 1982/83–DJF 2011/12. Significance (at 95%) of the composites of ENSO–ENSO Modoki events is tested using a t test.

Global teleconnections affecting the climate of southern Africa due to the variation in SST over the equatorial Pacific during ENSO–ENSO Modoki are analyzed in terms of stationary Rossby waves in the upper level (300 hPa). It was shown in the earlier studies of Hoskins and Ambrizzi (1993) that the Southern Hemisphere westerly jet during austral summer acts as a waveguide for the propagation of Rossby waves with a typical stationary wavenumber of 5–6. In this study the stationary wavenumber, which is a useful diagnostic for representing the background state (Nascimento and Ambrizzi 2002), is calculated for the basic flows representing the composite of ENSO–ENSO Modoki events. The stationary wavenumber over sphere on Mercator projection is given by

 
formula

where Ω is the angular speed of rotation of the earth, φ is the latitude, and , where is the mean zonal wind. The quantity Ks is the total wavenumber for which a barotropic Rossby wave is stationary at a particular location. The spatial distribution of Ks can be used to infer the location of critical lines and waveguides for stationary Rossby waves (Nascimento and Ambrizzi 2002).

A stationary Rossby wave flux as defined by Schubert and Park (1991) is used to diagnose the stationary wave propagation during ENSO–ENSO Modoki events. The horizontal components of the wave activity flux are expressed as

 
formula
 
formula

where ψ′ is the anomalous eddy streamfunction (deviation of the streamfunction anomaly from zonal mean), φ is latitude, λ is longitude, a is the radius of the earth, and p is pressure. These equations are similar to the equations originally proposed by Plumb (1985). The anomalous convergence (divergence) of the horizontal wave fluxes shows the regions of accumulation (dissipation) of anomalous wave energy.

3. Results

a. Spatial distribution of SST anomalies

Based on EMI and Niño-3 indices (Fig. 1a), austral summers of four years, DJF 1982/83, 1986/87, 1991/92, and 1997/98, are identified as El Niño events and five austral summers, DJF 1990/91, 1994/95, 2002/03, 2004/05, and 2009/10, are identified as El Niño Modoki events. The standardized anomalies of DJF 1986/87 have similar value for both the EMI and Niño-3 index. However, based on the spatial pattern of the DJF 1986/87 SST anomalies (not shown), DJF 1986/87 is classified as an El Niño event. Figures 1b and 1c show composite of significant SST anomalies during El Niño–El Niño Modoki events. During El Niño (Fig. 1b), warm SST anomalies extend from the equatorial eastern Pacific to the date line and beyond with cooler SST anomalies over equatorial western Pacific, whereas during El Niño Modoki events (Fig. 1c) warm SST anomalies are confined to the equatorial central Pacific with cool SST anomalies over the southeast and western Pacific, similar to the SST anomalies distribution shown in Ashok et al. (2007) and Weng et al. (2009). During El Niño Modoki events significant warm SST anomalies are observed over the central South Indian Ocean (Fig. 1c).

Fig. 1.

(a) Standardized DJF SST anomalies based on the Niño-3 index (blue) and El Niño Modoki index (purple). The year corresponds to the year to which December belongs. Also shown are observed DJF SST anomalies (°C) significant (at 95% using a t test) during (b) El Niño and (c) El Niño Modoki events.

Fig. 1.

(a) Standardized DJF SST anomalies based on the Niño-3 index (blue) and El Niño Modoki index (purple). The year corresponds to the year to which December belongs. Also shown are observed DJF SST anomalies (°C) significant (at 95% using a t test) during (b) El Niño and (c) El Niño Modoki events.

The differences in the distribution of SST anomalies during El Niño and El Niño Modoki events would have caused differences in precipitation anomalies over southern Africa. We analyze this in the next section.

b. Spatial distribution of precipitation anomalies

Because of the uncertainties present in the observations over southern Africa (Nikulin et al. 2012; Sylla et al. 2013), we checked the consistency of the GPCP estimated precipitation with those estimated by the Global Precipitation Climate Center (GPCC) and the Climate Anomaly Monitoring System (CAMS) and the outgoing longwave radiation (OLR) Precipitation Index (OPI)-derived precipitation datasets, in estimating the phase and amplitude of precipitation anomalies during the ENSO events over the austral summers from 1982 to 2011. Area-averaged normalized (by their standard deviation) GPCP-derived precipitation anomalies over southern Africa landmass are compared with the normalized anomalies of the high-resolution (0.5° × 0.5°) GPCC full data reanalysis version 6.0 (Schneider et al. 2011) and the CAMS and OPI (hereafter CAMS OPI) data (Janowiak and Xie 1999). The CAMS OPI data are a real-time dataset and available up to the latest month. Precipitation anomalies of all the datasets are derived from the climatology based on the DJF 1982/83 to DJF 2009/10 period and the area average index over the domain 15°–35°S, 15°–33°E is considered here, consistent with the area considered by Richard et al. (2000, 2001) for the southern African rainfall index (SARI). Figure 2a shows that, except for a few differences in the amplitudes, all the datasets have similar phase in all event years, from 1982 to 2010. Figure 2a also shows that the GPCP dataset used in this study provides a realistic estimate of precipitation anomalies over southern Africa and is comparable to the high-resolution reanalysis products such as GPCC and the real-time CAMS OPI estimates. From Fig. 2a, it can be clearly seen that the El Niño events are associated with severe droughts over southern Africa compared to the El Niño Modoki events. Of the four El Niño events during the study period, three events have amplitudes above one standard deviation. The El Niño of DJF 1997/98 was less severe due to the effects of the Indian Ocean dipole (Lyon and Mason 2007).

Fig. 2.

(a) Area-averaged precipitation anomalies (mm day−1), averaged over southern Africa, 15°–35°S, 15°–33°E) normalized (by their standard deviation) from CAMS OPI (blue), GPCC (purple), and GPCP (gray) datasets during austral summer (DJF). The year corresponds to the year to which December belongs. (b) Spatial distribution of these precipitation anomalies significant (at 95% using a t test) during El Niño events. (c) As in (b), but for El Niño Modoki events.

Fig. 2.

(a) Area-averaged precipitation anomalies (mm day−1), averaged over southern Africa, 15°–35°S, 15°–33°E) normalized (by their standard deviation) from CAMS OPI (blue), GPCC (purple), and GPCP (gray) datasets during austral summer (DJF). The year corresponds to the year to which December belongs. (b) Spatial distribution of these precipitation anomalies significant (at 95% using a t test) during El Niño events. (c) As in (b), but for El Niño Modoki events.

Consistent with the SST anomalies, the spatial distribution of composite precipitation anomalies during El Niño (Fig. 2b) shows significant positive anomalies over the equatorial central Pacific and negative anomalies over the Indonesian region, equatorial South America, and equatorial West Africa (Ashok et al. 2007; Weng et al. 2009). Negative precipitation anomalies are also seen to the east of Australia and over some parts of northwest Australia. Significant negative anomalies are seen over all of southern Africa (Fig. 2b).

During El Niño Modoki events, positive precipitation anomalies are seen extending from 150° to 190°E along the equator with negative precipitation anomalies over the west and east Pacific equatorial regions (Fig. 2c), in agreement with the SST anomalies distribution (Fig. 1b). Interestingly, during the El Niño Modoki events (Fig. 2c) spatial distribution of precipitation over southern Africa shows significant suppression of precipitation confined to the Mozambique region.

The above analysis shows that ENSO events have a prominent effect on precipitation anomalies over southern Africa with El Niño suppressing precipitation anomalies. However, El Niño Modoki events do not show a significant effect on precipitation anomalies over southern Africa. These results are better understood if one looks at the area-averaged normalized precipitation anomalies over southern Africa during the austral summer seasons (Fig. 2a) from DJF 1982/83 to DJF 2011/12. During the El Niño events from DJF 1982/83 to 2011/12, one can see suppressed precipitation anomalies over southern Africa. However, during two of the five El Niño Modoki events (DJF of 1990/91 and 2009/10) precipitation anomalies are positive or near neutral. Also, during the El Niño Modoki event of 2004/05 negative precipitation anomalies are less prominent.

We investigate the physical processes responsible for the differences in the spatial distribution of the precipitation anomalies over southern Africa during El Niño–El Niño Modoki events in the next section.

c. The role of the Matsuno–Gill response in modifying southern Africa precipitation anomalies

As was shown by Matsuno (1966) and demonstrated by Gill (1980) using a shallow water equation model, an anomalous heat source at the equator produces a damped Kelvin wave to the east of the heat source along the equator and a damped Rossby to the northwest and southeast of the heat source. This response of the atmosphere to the equatorial heating is widely known as the Matsuno–Gill response and is used for explaining the heat-induced circulations in the tropics. The Matsuno–Gill response is baroclinic with phase reversal in the lower and upper troposphere. In this section we analyze the streamfunction anomalies at 850, 500, and 300 hPa to see if the Matsuno–Gill response to the equatorial heating/cooling over the Pacific during the El Niño–El Niño Modoki events was responsible for modification of precipitation anomalies over southern Africa.

Climatological 850-hPa eddy streamfunction (zonal mean removed; Fig. 3a) shows cyclonic circulation over southern Africa with anticyclonic circulation over the subtropical South Indian Ocean and subtropical South Atlantic Ocean. Cyclonic circulation is also seen over Australia and the near-equatorial Indonesian region. The anticyclone in the subtropical South Indian Ocean plays an important role in the transfer of moisture into southern Africa and hence in the spatial distribution of the precipitation. At 500 (Fig. 3b) and 300 hPa (Fig. 3c) anticyclonic circulation is seen over southern Africa.

Fig. 3.

Climatological (mean of DJF 1982/83 to DJF 2010/11) eddy streamfunction (×106 kg s−1) at (a) 850, (b) 500, and (c) 300 hPa. Eddy streamfunction anomalies (contours; ×106 kg s−1) and significant winds (vectors; m s−1) during El Niño and El Niño Modoki events at (d),(e) 850, (f),(g) 500, and (h),(i) 300 hPa, respectively, with anomalies significant at 95% using a t test shaded.

Fig. 3.

Climatological (mean of DJF 1982/83 to DJF 2010/11) eddy streamfunction (×106 kg s−1) at (a) 850, (b) 500, and (c) 300 hPa. Eddy streamfunction anomalies (contours; ×106 kg s−1) and significant winds (vectors; m s−1) during El Niño and El Niño Modoki events at (d),(e) 850, (f),(g) 500, and (h),(i) 300 hPa, respectively, with anomalies significant at 95% using a t test shaded.

The Matsuno–Gill response to the anomalous Pacific heating during El Niño events results in equatorially symmetric anticyclonic circulation anomalies from the Indian Ocean to the west Pacific in the low levels (850 hPa) (Fig. 3d). The anticyclonic response in the South Indian Ocean is significant and extends to the subtropical regions. Because of the anomalous Matsuno–Gill response, the anomalous low-level winds (Fig. 3d) are such as to weaken the climatological tropical flow into southern Africa. The anomalous weakening of the tropical flow has consequences for the moisture fluxes into southern Africa and hence the precipitation. This will be discussed in detail in a later section. The cyclonic circulation over southern Africa in the climatological field (Fig. 3a) is weakened and a significant anomalous anticyclonic circulation is seen over southern Africa during the El Niño events (Fig. 3d). The south Atlantic subtropical high is also seen extending into the southern African landmass (Fig. 3d).

At 500 hPa, the anomalous Matsuno–Gill response to the El Niño heating is seen with the same phase as at 850 hPa and is seen contributing to the anomalous reduction of tropical winds into southern Africa. However, over southern Africa, the anticyclonic circulation seen in the climatology (Fig. 3b) is intensified (Fig. 3f). The Matsuno–Gill response at 300 hPa (Fig. 3h) is seen away from the equatorial region in both the Northern and Southern Hemispheres, with the response in the Northern Hemisphere being stronger. In the Southern Hemisphere the response is significant to the west of Australia and of opposite phase to that at 850 hPa. The anticyclone over the southern Africa is also intensified at 300 hPa (Fig. 3h).

Because of weaker SST anomalies over Pacific during El Niño Modoki events compared to El Niño events, the Matsuno–Gill response is weaker over the South Indian Ocean. However, similar to El Niño events, the low-level anomalous winds show anticyclonic circulation (Fig. 3e) due to the Matsuno–Gill response although their effect on the tropical winds into southern Africa is not seen. Similar to El Niño events, anticyclonic circulation is seen over southern Africa during El Niño Modoki events, although weaker in magnitude. Anomalous extension of the subtropical South Atlantic high into southern Africa is also absent during the El Niño Modoki events (Fig. 3e). Intensification of anticyclone at 500 (Fig. 3g) and 300 hPa (Fig. 3i) is observed over southern Africa during the El Niño Modoki events similar to the El Niño events, though weaker in magnitude.

Analysis of the circulation anomalies at various levels reveals an anomalous Rossby wave response over the South Indian Ocean in response to the anomalous changes to diabatic heating over the Pacific. During El Niño, the anomalous Rossby wave response is anticyclonic, extending from low levels to 500 hPa over the South Indian Ocean. The Rossby wave response is intense in the low levels compared to higher levels. The anomalous anticyclonic response anomalously weakens the tropical winds, which may lead to unfavorable conditions for the transport of moisture into southern Africa and hence may lead to drier conditions during El Niño. The anomalous anticyclonic response over the South Indian Ocean is much weaker during El Niño Modoki events.

Modification of the sources of the moisture fluxes leads to variations in the precipitation over southern Africa (D’Abreton and Lindesay 1993; D’Abreton and Tyson 1995). In the following section, we analyze anomalous moisture fluxes during El Niño–El Niño Modoki events to better understand the role of the Matsuno–Gill response in the modification of precipitation anomalies over southern Africa.

d. Moisture supply

Vertically integrated moisture flux anomalies have been calculated using the relation , where g is the acceleration due to gravity, q is the specific humidity anomaly, Ps is the surface pressure, P300 is the pressure at the top (300 hPa) of the troposphere, and V is the wind vector anomaly (Chen 1985; D’Abreton and Tyson 1995; Behera et al. 1999; Todd et al. 2004; Vigaud et al. 2007). The vertically integrated moisture flux is separated into divergent and nondivergent components following the method adopted by Chen (1985). This separation helps in identifying the sources and sinks of moisture fluxes and their transports.

The divergent component of the vertically integrated mean climatological moisture flux (Fig. 4a) shows the South Atlantic as a major source of moisture that is transported to the tropical southern Africa. Moisture flux diverging from the equatorial east Atlantic is seen converging over the tropical southern Africa (Todd et al. 2004). The anticyclonic rotational component of the mean climatological moisture flux (Fig. 4b) over the South Indian Ocean is seen extending eastward to continental subtropical southern Africa and it is instrumental in transporting moisture flux into subtropical southern Africa from the Indian Ocean side.

Fig. 4.

Climatological (mean of DJF 1982/83 to DJF 2010/11): (a) velocity potential (shaded; ×106 kg s−1) and divergent component of vertically integrated moisture flux (vectors; kg m−1 s−1) during austral summer; (b) climatological streamfunction (shaded; ×106 kg s−1) and rotational component of vertically integrated moisture flux (vectors; kg m−1 s−1). (c) As in (a), but for moisture flux anomalous during El Niño events; contours show the velocity potential anomalies with anomalies significant at 95% level shaded. (d) As in (c), but for moisture flux anomalies during El Niño Modoki events. (e) As in (b), but for moisture flux anomalies during El Niño; streamlines show the moisture flow and vectors show the significant moisture flow with shaded region showing the significant vertically integrated moisture streamfunction anomalies. (f) As in (e), but during El Niño Modoki events.

Fig. 4.

Climatological (mean of DJF 1982/83 to DJF 2010/11): (a) velocity potential (shaded; ×106 kg s−1) and divergent component of vertically integrated moisture flux (vectors; kg m−1 s−1) during austral summer; (b) climatological streamfunction (shaded; ×106 kg s−1) and rotational component of vertically integrated moisture flux (vectors; kg m−1 s−1). (c) As in (a), but for moisture flux anomalous during El Niño events; contours show the velocity potential anomalies with anomalies significant at 95% level shaded. (d) As in (c), but for moisture flux anomalies during El Niño Modoki events. (e) As in (b), but for moisture flux anomalies during El Niño; streamlines show the moisture flow and vectors show the significant moisture flow with shaded region showing the significant vertically integrated moisture streamfunction anomalies. (f) As in (e), but during El Niño Modoki events.

During El Niño (Fig. 4c) the divergent component of the vertically integrated anomalous moisture flux shows significant moisture diverging from the southern Africa landmass. During El Niño events, the nondivergent component of the vertically integrated anomalous moisture flux (Fig. 4e) clearly shows the anomalous anticyclonic circulation due to the anomalous Matsuno–Gill response transporting the tropical moisture away from the southern Africa landmass, drastically reducing the moisture inflow from the tropical regions to southern Africa. The moisture is also seen being transported anticyclonically out of subtropical southern Africa (Fig. 4e).

During El Niño Modoki events, the divergent component of the vertically integrated anomalous moisture shows a weak transport of anomalous moisture out of southern Africa (Fig. 4d). The nondivergent component of the vertically integrated anomalous moisture flux (Fig. 4f), similar to El Niño events, shows an anticyclonic circulation transporting moisture away from most of the southern Africa, although it is weaker than El Niño events. Nevertheless, it is generating anomalous negative precipitation anomalies over southern Africa (Fig. 2c).

The transport of vertically integrated moisture explains the dissimilarity in precipitation distribution over southern Africa among El Niño and El Niño Modoki events. During El Niño the anomalous divergent and rotational components of the moisture flux are more intense compared to El Niño Modoki events. The anomalous rotational component of the moisture flux is seen transporting more moisture away from southern Africa during El Niño events compared to the El Niño Modoki events, thus creating a severe drought over southern Africa during El Niño events. The above analysis shows that the Matsuno–Gill response can explain the reduction of the tropical moisture into southern Africa landmass during the El Niño events. However, this phenomenon does not explain the moisture transport out of southern Africa in the subtropical regions as seen in Fig. 4e. In the next section we look at the role of the atmospheric teleconnections from the extratropical latitudes in contributing to the transport of moisture out of subtropical southern Africa during El Niño events.

e. Extratropical atmospheric teleconnections

The Matsuno–Gill response to the suppression–enhancement of anomalous precipitation over the Pacific during El Niño–El Niño Modoki events explains partly the observed low-level anomalous winds and the vertically integrated anomalous moisture fluxes. In this section we analyze how the extratropical atmosphere during El Niño–El Niño Modoki contributes to the maintenance of the effects of the Matsuno–Gill response on the southern African precipitation anomalies.

Earlier studies of Karoly (1989) showed an equivalent-barotropic stationary wave response in the Southern Hemisphere troposphere to ENSO. The westerly jet in the Southern Hemisphere can act as waveguide for the propagation of these waves (Hoskins and Ambrizzi 1993) and affect the climate at a remote place. The Rossby wave activity flux (Plumb 1985) is a diagnostic to analyze the propagation of these waves. Convergence and divergence of the Rossby wave activity flux shows the source and sinks of the Rossby waves. Lyon and Mason (2007) showed in their analysis that these anomalous tropospheric stationary waves can affect the climate of southern Africa and Ratna et al. (2013) showed their roles in the formation of tropical temperate troughs. We analyze these stationary Rossby waves during the El Niño–El Niño Modoki events to understand their effect on the southern Africa climate.

The climatological DJF 300-hPa zonal wind (Fig. 5a) shows a westerly jet with its core south of South Africa around 45°S, which can act as a waveguide for the stationary Rossby waves. The climatological DJF eddy streamfunction (zonal mean removed from the streamfunction) at 300 hPa (Fig. 5b) shows a cyclonic circulation to the south of South Africa and an anticyclonic circulation over southern Africa and the adjacent oceans. The pattern over South America is similar to the one discussed in Vera et al. (2004). Wave activity flux associated with the DJF climatological eddy streamfunction shows wave propagation from the midlatitudes to southern Africa and converge to the southwest of Madagascar (Fig. 5b), the climatological position of SICZ.

Fig. 5.

Climatological (mean from DJF 1982/83 to DJF 2010/11) (a) zonal wind (m s−1) and (b) 300-hPa eddy streamfunction contours (×106 m2 s−1) and wave activity flux vectors (m2 s−2) based on the eddy streamfunction. Shaded region in (b) is the convergence of the wave activity flux. Zonal wind (m s−1) at 300 hPa during (c) El Niño and (d) El Niño Modoki. Stationary wavenumber Ks in Mercator coordinates derived from ERA-Interim estimated mean zonal wind at 300 hPa during (e) El Niño and (f) El Niño Modoki.

Fig. 5.

Climatological (mean from DJF 1982/83 to DJF 2010/11) (a) zonal wind (m s−1) and (b) 300-hPa eddy streamfunction contours (×106 m2 s−1) and wave activity flux vectors (m2 s−2) based on the eddy streamfunction. Shaded region in (b) is the convergence of the wave activity flux. Zonal wind (m s−1) at 300 hPa during (c) El Niño and (d) El Niño Modoki. Stationary wavenumber Ks in Mercator coordinates derived from ERA-Interim estimated mean zonal wind at 300 hPa during (e) El Niño and (f) El Niño Modoki.

During El Niño events, mean zonal westerly winds of strengths greater than 25 m s−1 are seen extending from 100°W to 160°E in the latitude band of 60° to 30°S with a subtropical jet core to the south of South Africa (Fig. 5c). The westerly jet core to the south of South Africa is weaker during El Niño Modoki events (Fig. 5d) compared to El Niño events. However, winds greater than 25 m s−1 are seen throughout the Southern Hemisphere in the same latitude band during El Niño Modoki events. The meridional gradient of zonal westerly winds in Mercator coordinates shows maxima in the jet with values decreasing equatorward and poleward of the jet during both the El Niño and El Niño Modoki events (not shown). The meridional gradient is small nearer the south of South Africa. Stationary wavenumber Ks based on the mean zonal winds during the events shows a waveguide in the Southern Hemisphere westerly jet with a stationary wavenumber varying from 4 to 6 during El Niño (Fig. 5e) and El Niño Modoki (Fig. 5f). Stationary wavenumber plot (Figs. 5e,f) also indicates wave propagation along the south Atlantic convergence zone (SACZ) from southern America around 30°S to the midlatitude westerly jet during the events. The above analysis shows the structure and amplitude differences of the westerly jet between the events and it also shows that the jet can act as waveguides for the propagation of waves (Hoskins and Ambrizzi 1993) during El Niño–El Niño Modoki events. The differences in the structure of the jet can affect the amplitude and phase of the stationary waves along them (Hoerling et al. 1995; Ting et al. 1996).

The anomalous Rossby wave activity flux at 300 hPa during El Niño shows the Pacific–South American (PSA; Mo 2000) pattern with fluxes from the central Pacific to the Southern Hemisphere midlatitudes where the anomalous stationary wave is reflected toward South America. It is interesting to see anomalous wave activity flux from South America into the Southern Hemispheric westerly jet (Fig. 6a), which is seen converging over South Africa. The anomalous wave activity from the westerly jet helps to generate and maintain an anomalous anticyclonic circulation over South Africa. The anomalous anticyclone over South Africa is equivalent-barotropic and can be seen even in the 500-hPa flow (Fig. 3f). A similar process is seen during El Niño Modoki events for the generation and maintenance of an anomalous anticyclonic circulation over eastern South Africa (Fig. 6b). To see how the anomalous anticyclone generated as a response to the anomalous Pacific heating, which would have generated negative precipitation anomalies over southern Africa, we plot the latitude–height anomalous velocity averaged over 12° to 40°E. During El Niño events (Fig. 6c), the velocity plot shows anomalous sinking over southern Africa from about 30°S, the latitude of the anomalous anticyclone generated by the anomalous wave activity. During El Niño Modoki events (Fig. 6d), the anomalous sinking motion is weaker compared to the El Niño events.

Fig. 6.

Eddy streamfunction anomaly (×106 m2 s−1; contours) and anomalous wave activity flux (vectors) derived from the eddy streamfunction anomaly (vectors; m2 s−2) and their convergence (shaded) during (a) El Niño and (b) El Niño Modoki. Black colored vectors are significant at 95% using the t test. Latitude–height anomalous vertical motion streamlines (averaged over 12°–40°E) and vectors (m s−1) during (c) El Niño and (d) El Niño Modoki; all vectors are significant at the 95% using the t test. Vertical velocity anomalies (shaded) and Q vectors (kPa m−1 s−1) at 500 hPa during (e) El Niño and (f) El Niño Modoki events.

Fig. 6.

Eddy streamfunction anomaly (×106 m2 s−1; contours) and anomalous wave activity flux (vectors) derived from the eddy streamfunction anomaly (vectors; m2 s−2) and their convergence (shaded) during (a) El Niño and (b) El Niño Modoki. Black colored vectors are significant at 95% using the t test. Latitude–height anomalous vertical motion streamlines (averaged over 12°–40°E) and vectors (m s−1) during (c) El Niño and (d) El Niño Modoki; all vectors are significant at the 95% using the t test. Vertical velocity anomalies (shaded) and Q vectors (kPa m−1 s−1) at 500 hPa during (e) El Niño and (f) El Niño Modoki events.

To show that the anomalous anticyclone, generated due to the remote teleconnections over southern Africa was the cause of the anomalous sinking motion seen in Fig. 6c, we plot the Q vector (Hoskins et al. 1978) anomalies. The Q vector diagnoses vertical motion due to adiabatic flow in the omega equation, under quasigeostrophic theory, and can be computed from temperature and height fields. The divergence–convergence of the Q vector indicates dynamically forced descent–ascent by a geostrophic motion. Figure 6e shows the anomalous vertical velocity and the Q vector anomalies at 500 hPa during El Niño events. From the figure, it can be clearly seen that the divergence of Q vector anomalies corresponds well with the downward vertical velocity anomalies (shaded), showing that the anomalous anticyclone generated due the anomalous stationary wave activity induces the anomalous downdraft over the region. However, during El Niño Modoki, the anomalous anticyclone is over eastern South Africa (Fig. 6b) and is not seen suppressing the precipitation over much of southern Africa (Fig. 6f).

4. Conclusions

In this study the role of teleconnections, generated due to El Niño–El Niño Modoki events, in modifying the precipitation over southern Africa is analyzed using ERA-Interim reanalysis data. During El Niño events the precipitation over southern Africa is seen to be modified by two teleconnections. The first teleconnection arises due to the Matsuno–Gill response to the anomalously enhanced precipitation over the Pacific. The anomalous Matsuno–Gill response that is stronger in the lower levels, reduces the moisture flux into southern Africa. The second teleconnection, the anomalous tropospheric stationary wave due to the Pacific heating, shows anomalous wave activity flux converging over southern Africa, maintaining an anomalous anticyclone over the southern Africa landmass. Also, Q vector analysis shows that the anticyclone forces sinking motion over southern Africa landmass and hence aids in the suppression of precipitation over southern Africa.

During El Niño Modoki events the process of generation of negative precipitation anomalies is similar to that of El Niño. However, because of the differences in the location and amplitude of Pacific heating anomalies between the two events, the Matsuno–Gill response and the tropospheric stationary wave response are different. The vertically integrated anomalous fluxes, although smaller in magnitude transport moisture out of southern Africa, creating a region of negative precipitation anomalies over southern Africa that however are not significant.

Because of the increase in the Modoki events in the recent decades (Ashok and Yamagata 2009; Behera and Yamagata 2010), the robust relation between the ENSO and southern Africa precipitation seems to have changed due to the changes in the teleconnections patterns. It is essential to verify the forecast models in their ability to reproduce the teleconnections in order to have better seasonal forecasts; this will be carried out in future study.

Acknowledgments

The authors thank two anonymous reviewers for their constructive comments and suggestions, which helped to substantially improve the quality of this paper. The authors are also thankful to the Physical Sciences Division, Earth System Research Laboratory, NOAA and ECMWF for making available the datasets used in this research.

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