Abstract

The Antarctic Oscillation (AAO) has been observed as a deep oscillation in the mid- and high southern latitudes. In the present study, the AAO pattern is defined as the leading mode of the empirical orthogonal function (EOF-1) obtained from daily 700-hPa geopotential height anomalies from 1979 to 2000. Here the objective is to identify daily positive and negative AAO phases and relationships with intraseasonal activity in the Tropics and phases of the El Niño–Southern Oscillation (ENSO) during the austral summer [December–January–February (DJF)]. Positive and negative AAO phases are defined when the daily EOF-1 time coefficient is above (or below) one standard deviation of the DJF mean. Composites of low-frequency sea surface temperature variation, 200-hPa zonal wind, and outgoing longwave radiation (OLR) indicate that negative (positive) phases of the AAO are dominant when patterns of SST, convection, and circulation anomalies resemble El Niño (La Niña) phases of ENSO. Enhanced intraseasonal activity from the Tropics to the extratropics of the Southern (Northern) Hemisphere is associated with negative (positive) phases of the AAO. In addition, there is indication that the onset of negative phases of the AAO is related to the propagation of the Madden–Julian oscillation (MJO). Suppression of intraseasonal convective activity over Indonesia is observed in positive AAO phases. It is hypothesized that deep convection in the central tropical Pacific, which is related to either El Niño or eastward-propagating MJO, or a combination of both phenomena, modulates the Southern Hemisphere circulation and favors negative AAO phases during DJF. The alternation of AAO phases seems to be linked to the latitudinal migration of the subtropical upper-level jet and variations in the intensity of the polar jet. This, in turn, affects extratropical cyclone properties, such as origin, minimum/maximum central pressure, and their equatorward propagation.

1. Introduction

The existence of an oscillation-like pattern in the pressure belt across Chile and Argentina in opposition to the Weddell Sea and the Bellingshausen Sea has long been noticed (Walker 1928). Some decades later, with a more suitable source of data, the oscillation in the middle and high southern latitudes was properly described (Kidson 1988; Yoden et al. 1987; Shiotani 1990; Hartmann and Lo 1998; Gong and Wang 1999; Thompson and Wallace 2000) and has been referred to as the Antarctic Oscillation (AAO). Gong and Wang (1999) defined an objective index for the AAO, based on empirical orthogonal function (EOF) analysis. For that purpose, they used monthly mean sea level pressure (SLP) anomalies (1958–97) from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis (Kalnay et al. 1996). Gong and Wang (1999) suggested that the AAO pattern is observed throughout the year, with the lowest variance explained by the EOF-1 in March (17.2%) and the highest in December (33.1%). The prominent feature they found in the AAO pattern was the strong negative relationship between 40° and 70°S.

Thompson and Wallace (2000) identified what they called “annular modes” in monthly anomalies of geopotential height in both hemispheres of the extratropical circulation. These modes of variability are characterized by deep zonally symmetric or “annular” structures with opposite geopotential height perturbations over the poles and centered approximately in a zonal ring with around 45° latitude. Therefore, the AAO has a Northern Hemisphere (NH) counterpart named the Artic Oscillation (AO). The annular modes indicate a zonally symmetric structure involving exchanges of mass between mid- and high latitudes. Interestingly enough, Thompson and Wallace (2000) found that both oscillations exist year-round in the troposphere, but they amplify with height upward into the stratosphere during certain times of the year or “active seasons.” The authors showed that the SH (NH) active season is late spring (midwinter) when the annular modes seem to modulate the strength of the Lagrangian mean circulation in the lower stratosphere, total column ozone, and tropopause height over mid- and high latitudes and the strength of the trade winds of their respective hemispheres. The seasonal variability of the annular modes and the connection from the troposphere to the lower stratosphere has further implications. Thompson et al. (2002) showed evidence that anomalous low (high) wintertime surface air temperatures and increased frequency of occurrence of extreme cold (warm) events over some continental areas in the NH seem to be linked to a pronounced weakening (strengthening) of the NH wintertime stratospheric polar vortex.

Although great insight has been gained with the identification of the annular modes and their relationships with the stratosphere, their implications for high to midlatitude climate and weather in the SH need to be examined in detail. The impacts of contrasting phases of the El Niño–Southern Oscillation (ENSO) phenomenon in the global circulation, in particular in the position and intensity of the high-level subtropical jets in both hemispheres, have been well documented (e.g., Karoly 1989; van Loon and Rogers 1981; Chen et al. 1996; Mo and Kousky 1993; Kiladis and Mo, 1998). Since the annular modes have a strong correspondence with the mean circulation in the upper troposphere and lower stratosphere (Thompson and Wallace 2000; Thompson et al. 2002), and considering the hypothesis that extratropical circulations can be amplified by eddy transports (O’Sullivan and Salby 1990), it is possible that ENSO phases play an important role in modulating AAO phases.

The Madden–Julian oscillation (MJO) has been recognized as the dominant mode of tropical intraseasonal variability on time scales between 10 and 90 days (Madden and Julian 1994). Some studies have also indicated that the interaction of the MJO with the extratropical regions can influence weather forecasts on medium and extended ranges (e.g., Ferranti et al. 1990). The MJO has also been related to conditions that affect the ENSO cycle. Kessler and Kleeman (2000), for instance, suggest that westerly wind bursts can induce SST anomalies in the Pacific basin that can generate further bursts and interact constructively with the ENSO cycle. The possible linkages between MJO and ENSO motivate a further investigation of the relationships between the MJO and AAO. Our rationale is that, even though the existence of the AAO is due to internal dynamical mechanisms in the mid- to high latitudes of the Southern Hemisphere, the MJO and ENSO are the most important modes of tropical variability on intraseasonal and interannual time scales and they can have important interactions with the AAO.

The focus of the present paper is on the observational investigation of the variability of the AAO during the austral summer [December–January–February (DJF)] with the objective to address the following questions: 1) Are distinct AAO phases related to the variability of convection and circulation in the Tropics caused by interannual variation phenomena such as El Niño/La Niña? 2) On intraseasonal time scales are there variations of circulation in the Tropics and subtropics related to distinct phases of the AAO? 3) Can tropical anomalies such as the MJO play a role in modulating phases of the AAO? 4) How does the extratropical cyclone properties in the SH respond to modifications in the circulation associated with distinct phases of the AAO?

This article is composed of seven sections. Section 2 discusses the dataset used and methods to obtain daily AAO index. Section 3 discusses phases of the AAO and interannual variations in the Tropics such as ENSO. The role of tropical and subtropical intraseasonal anomalies modulating AAO phases is examined in section 4. Patterns of teleconnections related to distinct phases of the AAO are investigated in section 5. Section 6 shows some properties of extratropical cyclones in the SH that change in opposite AAO phases. Conclusions are presented in section 7.

2. Data and method of analysis

a. The AAO daily index

The Climate Prediction Center (CPC) at NCEP computes the AAO daily index by projecting the 700-hPa geopotential height anomaly onto the leading mode (EOF-1) derived from monthly mean 700-hPa height anomalies (from 20° to 90°S) during 1979–2000 (more information available online at http://www.cpc.ncep.noaa.gov/products/). In the current study, the NCEP daily geopotential height anomalies at 700 hPa (H700) from the NCEP–NCAR reanalysis (Kalnay et al. 1996) were used to define the AAO index (1979–2000). Daily anomalies were determined by removing the annual and semiannual cycle computed as the first two harmonics adjusted to the mean annual cycle of the 700-hPa geopotential height at each grid point poleward of 20°S. Prior to EOF calculation, each time series of H700 was scaled by the square root of the cosine of the gridpoint latitude. The leading mode explained about 26% of the total variance and is well correlated (linear correlation ∼0.95) with the CPC/NCEP AAO index. The EOF-1 pattern (Fig. 1) shows negative loadings over Antarctica and positive loadings in midlatitudes, indicating the presence of the annular mode discussed in Thompson and Wallace (2000). Except for large magnitudes of the EOF-1 loading near southern Africa and South America, the EOF-1 pattern in Fig. 1 is very similar to the one obtained by the CPC/NCEP procedure. For this reason, the EOF-1 time coefficients will be hereafter referred to as AAO. Therefore, the pattern depicted in Fig. 1 represents typical positive AAO index whereas the opposite (i.e., positive loadings over Antarctica and negative loadings in midlatitudes) are typical of negative values of the AAO index.

Fig. 1.

Regression of the 700-hPa geopotential height daily anomalies on EOF-1

Fig. 1.

Regression of the 700-hPa geopotential height daily anomalies on EOF-1

b. AAO phases during DJF

Our analysis is focused on DJF because this is the wet monsoon season in tropical South America and Africa (e.g.,Karoly and Vincent 1998 and references therein). Moreover, this is also the period when ENSO phases reach their mature stage. In addition, tropical/subtropical intraseasonal anomalies that propagate eastward are quite active in the austral summer (Wang and Rui 1990; Madden and Julian 1994; Jones et al. 2004). Both phenomena can modulate the circulation in the upper and lower troposphere, with further implications for variations in the monsoon regime in tropical South America (Carvalho et al. 2004; Jones and Carvalho 2002; Kousky et al. 1984; Grimm 2003, Ropelewski and Halpert 1987). Thus, understanding variations in phases of the AAO during DJF and respective links with ENSO and intraseasonal anomalies from the Tropics to midlatitudes can be especially important for the SH cyclone activity and also for the variability of the SH convergence zones. We recall that, according to Thompson and Wallace (2000) the active season of the SH annular mode is observed in November and, therefore, with a lag relation to the period of our analysis.

In the current paper, positive (negative) AAO phases were determined when the daily DJF AAO index (i.e., the time coefficient of the EOF-1) was above (below) one (minus one) standard deviation from the mean DJF AAO index. Persistence was defined as the number of consecutive days in each positive or negative AAO phase. We consider as independent events all occurrences that were two or more days apart. With this criterion, we identified 349 days with positive AAO separated in 70 events and 326 days with negative AAO separated in 40 events. The frequency distribution of persistence for both phases of the AAO is shown inFig. 2. Negative (positive) AAO phases show median persistence equal to 5 (3) days, upper quartile equal to 13 (7) days, and long (short) maximum. These characteristics of the distribution clearly indicate that negative events tend to persist more than positive events. This behavior of the distributions holds even if one considers as independent events those sequence of days in a given phase that are separated from each other by a time interval longer than 2 days (not shown). In the following sections we investigate the large-scale mechanisms associated with positive and negative AAO phases.

Fig. 2.

Box-plot diagram of statistical properties of the persistence of AAO phases. Outliers are data point values ≥ 1.5 times the interquartile range. Extremes are data point values ≥ 3.0 times the interquartile range. The size of box represents the interquartile range

Fig. 2.

Box-plot diagram of statistical properties of the persistence of AAO phases. Outliers are data point values ≥ 1.5 times the interquartile range. Extremes are data point values ≥ 3.0 times the interquartile range. The size of box represents the interquartile range

3. AAO phases and interannual variations in the Tropics

To understand how daily variations of AAO can be linked to ENSO, we started our investigation by performing composites of low-frequency sea surface temperature (hereafter SSTLF) and 200-hPa zonal wind (hereafter U200LF). The time series of SSTLF and U200LF were obtained by filtering sea surface temperature and 200-hPa zonal wind in frequency domain with a fast Fourier transform (FFT). Only periods longer than 365 days were retained. Composites were done for the two samples of daily positive and negative AAO phases. The statistical significance of our composites was assessed by assuming that the number of degrees of freedom is equal to 19 (21) for negative (positive) AAO, which corresponds to the number of distinct DJF seasons in each sample. The difference in the number of DJF seasons in each phase occurs because negative (positive) AAO phases, as defined in the present study, were not observed in 3 (1) out of 22 DJF seasons considered here. The SSTLF and U200LF time series have the same length and temporal resolution as the AAO time series (i.e., 90 daily values per DJF season) and were considered more appropriate to perform composites than monthly averages.

Composites of negative AAO phases (Fig. 3, top) clearly show a relationship with positive SSTLF in the central Pacific (El Niño), whereas positive AAO phases (Fig. 3, bottom) indicate a relationship with negative SSTLF (La Niña). Positive (negative) SSTLF during negative (positive) AAO phases are above (below) +0.8° (−0.6°) in the central Pacific near the equator.

Fig. 3.

Composites of SST low-frequency anomalies for (top) negative and (bottom) positive AAO phases. Only regions with statistical significance at the 95% confidence level are shown

Fig. 3.

Composites of SST low-frequency anomalies for (top) negative and (bottom) positive AAO phases. Only regions with statistical significance at the 95% confidence level are shown

To understand if similar relationships are observed when one considers low-frequency variations in the AAO, we first computed H700 anomalies from the annual cycle and then applied FFT filtering such that only periods longer than 365 days were retained (H700LF). Next, an EOF analysis of H700LF was performed in the domain from 20°S poleward. The first mode, which explains 35% of the total variance, was used as an indicator of low-frequency variations in the Southern Hemisphere annular mode (AAOLF). The AAOLF shows an approximately bimodal frequency distribution; therefore, composites of negative (positive) AAOLF phases were done by considering the upper and lower quartiles of the distribution (Fig. 4). The largest differences between Fig. 3 (top) and Fig. 4 (top) are observed next to Antarctica where an area with warm SSTLF is observed in negative AAO phases in the extratropical Southern Pacific. It is important to recall that the composites of Figs. 3 and 4 are based on the AAO daily index. Therefore, the SST composites essentially show that persistence of AAO extreme phases can be favored during ENSO phases. Also important to note is that the signals shown in the results above cannot be detected when one uses monthly or seasonal data (i.e., smoothed time series) and especially if extreme phases of the AAO are not taken into account (e.g., Renwick 2002).

Fig. 4.

As in Fig. 3 but for AAOLF phases (see text for details): (top) negative AAOLF phases; (bottom) positive AAOLF phases. Only regions with statistical significance at the 95% confidence level are shown

Fig. 4.

As in Fig. 3 but for AAOLF phases (see text for details): (top) negative AAOLF phases; (bottom) positive AAOLF phases. Only regions with statistical significance at the 95% confidence level are shown

The composites of U200LF (Fig. 5) are consistent with the patterns of SSTLF observed in distinct phases of the AAO. Negative (Fig. 5 top) and positive (Fig. 5, bottom) phases of the AAO are associated with opposite features of U200LF over the Tropics and midlatitudes, which are almost symmetric relative to the equator. The symmetry of features around the equator for negative AAO phases (Fig. 5, top) is consistent with the pattern of upper-tropospheric height anomalies that have been observed during mature stages of El Niño (Karoly 1989). The patterns of upper-level geopotential height and wind are likely caused by wave train circulation anomalies that are a response to the anomalous convective activity over the central Pacific (Karoly 1989) is noticeable. The strengthening (weakening) of the subtropical high-level jet is noticeable in the composites of negative (positive) AAO phases in both hemispheres and also the weakening (strengthening) of the high-level polar jet, particularly in the SH, during negative (positive) AAO events (Fig. 5). This observation is consistent with some other related studies (e.g., Chen et al. 1996). The reversal in subseasonal anomalies of the 500-hPa geopotential height during opposite phases of ENSO has also been identified in Compo et al. (2001). Thus, the tropical forcing resulting from anomalous convective activity over the equatorial central Pacific affects the Hadley circulation (Gray et al. 1992) and seems to be an important mechanism modulating the amplitude and sign of exchange of mass and momentum between high and midlatitudes and, therefore, the sign of the AAO.

Fig. 5.

Composites of 200-hPa zonal wind low-frequency anomalies for (top) negative and (bottom) positive AAO phases. Only regions with statistical significance at the 95% confidence level are shown

Fig. 5.

Composites of 200-hPa zonal wind low-frequency anomalies for (top) negative and (bottom) positive AAO phases. Only regions with statistical significance at the 95% confidence level are shown

Figure 6 shows the percent of days in each AAO phase observed during warm, cold, or neutral seasons as compiled by the CPC/NCEP based on the assessment of reanalyzed SST along the equator from 150°W to the date line. The number of warm (weak and strong) episodes is equal to 9, whereas cold (weak and strong) is equal to 6. This figure indicates that the number of days in the negative AAO phase during warm ENSO episodes is approximately twice as large during cold and neutral ENSO phases. The trend is reversed for positive AAO phases. However, this figure also suggests that not all variability of the AAO phases is modulated by ENSO phases, and other mechanisms can be involved in daily variations of the AAO. Moreover, intradecadal variability seems to exist in the trend of the number of days in each AAO phase. Table 1 indicates that in the first 15 yr, an average of one moderate to strong warm ENSO episode in five DJF seasons resulted in similar total of days observed in the negative AAO phase. However, in the last five seasons considered (1994/95–1997/98) two moderate to strong warm events occurred (including the 1997/98 strong episode), but the number of days with negative AAO was only about 34% of the respective total observed previously (e.g., 1984/85–1988/89 five-season period). On the other hand, the number of days in the positive AAO phase significantly increased if one compares the two 5-yr periods 1984/85–1988/89 and 1994/95–1998/99, both periods with two strong to moderate La Niña episodes. It is interesting to notice that the La Niña season of 1999/2000 alone presented 52 days with positive AAO and only 1 day with negative AAO. Inter-ENSO variations may account for variations in the number of days observed in each phase of the AAO. A possible reason is that teleconnection patterns are sensitive to the spatial pattern of tropical Pacific SST anomalies, which in turn is quite variable from event to event (Hoerling and Kumar 2000). Moreover, Ambrizzi and Souza (2003) showed evidence that the Hadley and Walker circulations during the 1980 El Niños were stronger than the respective circulations during the 1990s. It is important to mention that the AAO index was computed from detrended H700 and shows no statistically significant trend in the 22 yr considered in this analysis.

Fig. 6.

Percentage of days in negative and positive AAO phases that were observed during El Niño, La Niña, and neutral ENSO episodes

Fig. 6.

Percentage of days in negative and positive AAO phases that were observed during El Niño, La Niña, and neutral ENSO episodes

Table 1.

Intradecadal variability (summers from 1979/80 to 1998/99) of phases of the AAO. Period corresponds to five consecutive summers, initiating in Dec and finishing in Feb of the next year. Number of days in negative (positive) AAO phases in the period is labeled as N AAO− (N AAO+). The warm/cold ENSO episodes classified by season according to CPC/NCEP in each period are also shown. Moderate to strong warm/cold episodes are underlined

Intradecadal variability (summers from 1979/80 to 1998/99) of phases of the AAO. Period corresponds to five consecutive summers, initiating in Dec and finishing in Feb of the next year. Number of days in negative (positive) AAO phases in the period is labeled as N AAO− (N AAO+). The warm/cold ENSO episodes classified by season according to CPC/NCEP in each period are also shown. Moderate to strong warm/cold episodes are underlined
Intradecadal variability (summers from 1979/80 to 1998/99) of phases of the AAO. Period corresponds to five consecutive summers, initiating in Dec and finishing in Feb of the next year. Number of days in negative (positive) AAO phases in the period is labeled as N AAO− (N AAO+). The warm/cold ENSO episodes classified by season according to CPC/NCEP in each period are also shown. Moderate to strong warm/cold episodes are underlined

To examine the DJF low-frequency pattern of convection over the Tropics that is associated with the SH annular mode in mid- and high latitudes, we performed a combined EOF analysis. The input data in this case was H700 (as described in section 2a), 200-hPa daily zonal wind anomalies southward of 20°S, (hereafter U200), and low-frequency OLR anomalies from 30°N to 40°S (hereafter OLRLF). The OLRLF were obtained with the same procedure described before, that is, using FFT and retaining periods longer than 365 days. Only DJF was considered in this analysis.

The resulting leading mode of the DJF combined EOF analysis (henceforth EOFc-1) explains about 22% of the total variance. The regression (equivalent to correlation) of H700, U200, and OLRLF on the EOFc-1 are shown in Fig. 7. The annular pattern identified in Fig. 1 as the AAO is clearly reproduced in the EOFc-1 patterns (Fig. 7, top left). The largest difference is observed in the central Pacific east of the date line. Nonetheless, the correlation between the EOF-1 DJF daily time coefficients and the respective EOFc-1 time coefficients is ∼0.97. The regression of U200 on the EOFc-1 (Fig. 7, top right) presents some similarities to those obtained for the regression of the zonal monthly mean geostrophic wind on the annular modes shown in Thompson and Wallace (2000). They show the dominance of zonally symmetric features with maxima centered ∼60°S anticorrelated to another region of (weaker) maxima centered ∼35°S. Nevertheless, the zonal symmetry of the latter is disrupted between 180° and 90°W in the central–eastern Pacific, where the maximum shifted equatorward (Fig. 7, top right).

Fig. 7.

Regression of the DJF (top left) 700-hPa geopotential height anomalies, (top right) 200-hPa zonal wind anomalies, and (bottom) low-frequency OLR anomalies on the leading mode of combined EOF (EOFc-1). See text for more details

Fig. 7.

Regression of the DJF (top left) 700-hPa geopotential height anomalies, (top right) 200-hPa zonal wind anomalies, and (bottom) low-frequency OLR anomalies on the leading mode of combined EOF (EOFc-1). See text for more details

The combined features of Fig. 7 characterize the north–south seesaws of atmospheric exchange of mass between high and midlatitudes (Thompson and Wallace 2000). The regression of OLRLF on EOFc-1 (Fig. 7, bottom) indicates the east–west seesaw of convection in low latitudes or the Pacific dipole pattern (Hoerling and Kumar 2000), which is dominated by ENSO. Similarities do exist between the regression of Fig. 7 (bottom) and DJF OLR-derived rainfall anomalies that are linearly related to EOF-1 of tropical Pacific SST variations (see Fig. 2.3 of Hoerling and Kumar 2000). In summary, the combined EOF analysis indicates that positive (negative) DJF AAO phases (Fig. 7, top right) are related to the strengthening (weakening) of the upper-level zonal wind in high latitudes and weakening (strengthening) in the subtropics and in the central–eastern tropical Pacific (Fig. 7, top left).

If ENSO phases play a role in modulating AAO phases, it is reasonable to think that the leading mode of variability of geopotential height anomalies exhibit distinct characteristics during warm and cold episodes. To show these differences, we first separated the data record of H700 and H700LF anomalies in El Niño (DJF seasons: 1982/83; 1986/87; 1987/88; 1991/92; 1994/95; 1997/98) and La Niña (DJF seasons: 1983/84; 1984/85; 1988/89; 1995/96; 1998/99; 1999/2000). Next, we performed EOF analysis on each sample of H700 and H700LF anomalies. Figure 8 shows remarkable differences in the characteristics of the leading modes for both H700 (Fig. 8, left panels) and H700LF (Fig. 8, right panels) in opposite ENSO phases. The EOF-1 explains about 14% of the total variance of H700 for both ENSO phases (Fig. 8 top panels), whereas the explained variance increases to 37% (44%) during El Niño (La Niña) episodes when H700LF is considered (Fig. 8, bottom panels). During El Niño years there is a clear break and weakening of the midlatitude annular feature (Fig. 8 top panels) when compared to EOF-1 obtained for the entire period [Figs. 1 and 7 (top left)]. On the other hand, there is a relative strengthening of the annular features in midlatitudes around 45°S, particularly over the Atlantic and Indian Oceans, during La Niña years (Fig. 8 bottom panels) in both H700 (Fig. 8 top right) and H700LF patterns (Fig. 8 bottom right). This feature is more prominent for H700LF (cf. Fig. 8 left and bottom right bottom panels). In summary, the EOF patterns in Fig. 8 indicate that during El Niño (La Niña) DJF seasons the weakening (strengthening) of the annular mode and therefore the seesaw, along with an increase (decrease) of geopotential anomalies over the Antarctica, is consistent with dominant negative (positive) phases of the AAO as observed before.

Fig. 8.

Regression of the 700-hPa geopotential height DJF daily anomalies on EOF-1 in opposite phases of ENSO (clockwise): (top left) high-frequency (H700) during El Niño; (top right) high-frequency (H700) La Niña; (bottom left) low-frequency (H700LF) El Niño; (bottom right) low-frequency (H700LF) La Niña. Shading indicates the 5% significance level (degrees of freedom = 6 for H700LF and 12 for H700). EOF-1 explained variance is indicated at the top of each frame. The El Niño/La Niña DJF seasons used in the EOF analysis are indicated in the text

Fig. 8.

Regression of the 700-hPa geopotential height DJF daily anomalies on EOF-1 in opposite phases of ENSO (clockwise): (top left) high-frequency (H700) during El Niño; (top right) high-frequency (H700) La Niña; (bottom left) low-frequency (H700LF) El Niño; (bottom right) low-frequency (H700LF) La Niña. Shading indicates the 5% significance level (degrees of freedom = 6 for H700LF and 12 for H700). EOF-1 explained variance is indicated at the top of each frame. The El Niño/La Niña DJF seasons used in the EOF analysis are indicated in the text

The features shown in Figs. 6, 7 and 8 suggest that a plausible teleconnection mechanism to explain the dominant signal of the daily AAO during DJF seasons seems to be the response of the atmospheric circulation to the anomalous convective activity over the Pacific warm pool. Positive (negative) AAO phases are favored by the enhancement (weakening) of convection over Indonesia and weakening (enhancement) over the central Pacific and a westward (eastward) shifting of the South Pacific convergence zone (SPCZ; Fig. 7, bottom), and consequent weakening (enhancement) of the Hadley circulation (Gray et al. 1992). A companion enhancement/weakening of convective activity in northern and southeastern South America is also an important feature that appears to be related to the AAO patterns (Fig. 7, bottom). Nonetheless, other forcing mechanisms may exist on other time scales. We discuss these issues in more detail in the following sections.

4. AAO phases and intraseasonal activity

The relationships between AAO and intraseasonal activity are examined here with respect to circulation and convection from the Tropics to the extratropics. Global U200 intraseasonal anomalies were used to investigate variations in the upper-troposphere circulation, whereas OLR intraseasonal anomalies from 60°S to 60°N were used as a proxy to study relationships between tropical convective activity and AAO phases on intraseasonal time scales.

a. Variations in circulation on intraseasonal time scales

To examine variations in the circulation of the upper troposphere on intraseasonal time scales, we computed an intraseasonal index (hereafter referred to as ISI); (e.g., Slingo et al. 1999; Jones 2000). The ISI was obtained by filtering global 200-hPa zonal wind (U200) in the frequency domain with a FFT and cutoff periods between 10 and 90 days. The filtered U200 were then squared and smoothed with a 101-day moving average window (e.g., Jones 2000). The ISI characterizes the local amplitudes of intraseasonal events. In addition, the ISI index shows both seasonal and interannual variability at all latitudes. The existence of seasonal variability of intraseasonal anomalies in the Tropics is well documented. One example is the increase in frequency and intensity of tropical intraseasonal anomalies during the austral summer (e.g., Jones et al. 2004). In midlatitudes, on the other hand, the maximum in the intraseasonal activity is observed during wintertime in both hemispheres (not shown). To eliminate the seasonal effects, the annual and semiannual cycles were removed from the ISI time series. For the sake of simplicity, the anomalies from the annual and semiannual cycle of the intraseasonal index will henceforth also be referred to as ISI.

The hypothesis we want to investigate is whether AAO phases are related to the enhancement/weakening of intraseasonal activity from the Tropics to the extratropics. Furthermore, if this pattern of variability does exist and is related to AAO phases, our objective is to verify its latitudinal dependence. To examine these issues, ISI composites were performed in distinct phases of the AAO. The statistical significance was then assessed by considering the number of seasons observed in distinct phases of the AAO (i.e., 19 independent events for negative AAO and 21 for positive AAO) as degrees of freedom. To verify the latitudinal variation of intraseasonal activity, zonal averages of ISI were performed by considering only grid points that passed the local t test. The resulting zonal averages were then scaled by the square root of cosine(latitude).

Figure 9 shows the zonal average (top panel) and difference (middle panel) observed for distinct AAO phases. It clearly indicates that, despites latitudinal fluctuations, negative (positive) AAO phases prevail during periods of increased (decreased) intraseasonal activity in the SH from the Tropics to the extratropics. Moreover, Fig. 9 shows that positive (negative) AAO phases are related to increased (decreased) intraseasonal activity in the NH, particularly near 45°N. ISI also shows interannual variability at all latitudes. Similar results can be obtained when composites are done with low-frequency ISI anomalies (periods longer than 365 days), except that larger values of ISI in the SH are observed equatorward of 30°S for negative AAO phases (Fig. 9 bottom).

Fig. 9.

(top) Composites of zonal average ISI for positive and negative AAO phases; differences in the composites of zonal average (middle) ISI (negative − positive AAO phases) and (bottom) ISILF

Fig. 9.

(top) Composites of zonal average ISI for positive and negative AAO phases; differences in the composites of zonal average (middle) ISI (negative − positive AAO phases) and (bottom) ISILF

b. Intraseasonal variations of tropical convective activity and MJO

The relationships between AAO and intraseasonal variations in convective activity from the Tropics to the extratropics are examined here by computing OLR anomalies with a Lanczos bandpass filter with cutoff periods of 20 and 70 days and 151 weights (hereafter OLR20_70) (Duchon 1979). Lag composites were performed for all AAO negative and positive events and are shown in Fig. 10. To emphasize the importance of the intraseasonal anomalies in modulating persistent AAO phases, only events with persistence ≥ 4 days were considered in the composites. To assess the statistical significance of our composites, we considered independent events those that are at least 7 days apart from each other. This yielded 18 independent events in each positive and negative AAO composite, which is a reasonable assumption if one considers an average of one major MJO event per DJF season (Jones et al. 2004). An important characteristic of the MJO that must be noticed for the sake of interpretation of the magnitudes of the anomalies in Fig. 10 is its high degree of case-to-case variability as well as its seasonal to interannual variations (Madden and Julian 1994; Jones et al. 2004 and references therein).

Fig. 10.

Lag composites of OLR intraseasonal anomalies (20–70 days) for (left column) negative AAO and (right column) positive AAO. Lags are indicated on the top of each frame. Only regions with statistical significance at the 95% confidence level are shown

Fig. 10.

Lag composites of OLR intraseasonal anomalies (20–70 days) for (left column) negative AAO and (right column) positive AAO. Lags are indicated on the top of each frame. Only regions with statistical significance at the 95% confidence level are shown

The lag composites of negative AAO events (Fig. 10, left column) indicate that the onset of these events are likely related to the buildup and eastward propagation of tropical intraseasonal convective anomalies, which characterizes MJO events. From lag 0 to +5 days, negative OLR20_70 are enhanced over the equatorial Indian Ocean, whereas positive OLR20_70 are observed in the central Pacific near the date line and over southeastern South America. The latter feature has been systematically observed in association with the MJO (e.g., Carvalho et al. 2004; Wang and Rui, 1990) when convection is intense over the Maritime Continent. From lag +10 to +15 days, convection moves toward the central Pacific and from lag 20 to 25 days there is an intensification of the SPCZ. Concomitantly, positive OLR20_70 intensify over the equatorial Indian Ocean and negative OLR20_70 is observed over eastern South America. Again, this tropical teleconnection pattern has been observed during the phase of the MJO when convection is enhanced over the central Pacific (Carvalho et al. 2004). Conversely, the lag composites for positive AAO events (Fig. 10 right column) clearly show opposite features. From lag −5 to +5 days, there is an intensification of positive OLR20_70 over the Maritime Continent as negative OLR20_70 are observed near the date line. From lag +10 to +25 days, positive OLR20_70 intensify southward of the equator from 150°E to 180° as convection decreases east of this region and is enhanced over Australia and eastern South America. Although the positive and negative lag composites of Fig. 10 suggest that opposite phases of the MJO may interact with the AAO phases, it is likely that one possible ingredient for the maintenance of negative AAO phases is the propagation of intense convection toward the eastern Pacific and SPCZ. On the other hand, positive AAO phases seem to be favored by intense positive OLR20_70 over the Indian Ocean with moderate negative OLR20_70 over the Pacific (cf. lag 0 of positive AAO with lags +20 to +25 of negative AAO). The increased (decreased) ISI activity in the SH in association with negative (positive) phases of the AAO is consistent with the MJO activity shown in the lag composites of Fig. 10. It is interesting to notice that the total hemispheric ISI observed during each DJF season does not show any evident correspondence with the intensity of ENSO (not shown).

5. Patterns of teleconnections and the AAO

The objective of the present analysis is to examine teleconnection patterns in 200-hPa daily zonal wind anomalies (U200) that are exclusively related to opposite AAO phases. Teleconnection patterns in monthly mean anomalies of sea level pressure and 500-hPa geopotential height in the SH have been documented in Mo and White (1985). The reason for choosing U200 is that strong westerly jets can act as Rossby waveguides and teleconnection features can provide a further interpretation of preferred propagation patterns from the Tropics to midlatitudes (Hoskins and Ambrizzi 1993). With this goal in mind, we applied the technique described in detail in Wallace and Gutzler (1981) and summarized as follows. Positive and negative AAO events were analyzed separately. The correlation matrix 𝗖 was first determined by calculating the temporal (simultaneous) correlation coefficients between anomalies at any given point ui (from 90° to 40°S and all longitudes, where the annular modes have large amplitudes) and anomalies at every grid point uj (from 90°S to 90°N and all longitudes). The element cij of the matrix is the correlation of anomalies at a grid point ui with anomalies at the grid point uj. The teleconnectivity Ti of the grid point ui is defined as the strongest negative element cij of the matrix 𝗖, for all grid points uj:

 
formula

Because no restriction was imposed with respect to persistence of the AAO, we assumed that our sample with negative (positive) AAO cases corresponds to 40 (70) independent events as discussed in section 3a. In this case, a 95% level of statistical significance requires maximum correlation coefficients of ∼ −0.26 for negative AAO phase and −0.20 for positive AAO phase. Figure 11 shows the teleconnectivity for each AAO phase as well as the difference between them. Teleconnections, as defined here, are significant only in the Southern Hemisphere and are stronger poleward of ∼30°S. The near zonally symmetric pattern of teleconnections observed in the subtropics (high latitudes) with negative maximum approximately at 45°S (60°S) are likely related to the SH subtropical (polar) jet. For negative AAO phases (Fig. 11, top), the subtropical features are displaced more equatorward than positive AAO, whereas the high-latitude features are weaker; except in two regions: one centered ∼180° and the other centered ∼100°W. Another remarkable difference is observed near the southeastern coast of South America (Fig. 11, bottom), which together with these two regions of maxima discussed before, suggests the propagation path of midlatitude wave trains observed in DJF (Liebmann et al. 1999). Oftentimes, these wave trains modulate convection in the South Atlantic convergence zone (SACZ; Liebmann et al. 1999; Carvalho et al. 2004). Positive AAO phases (Fig. 11, middle) are clearly associated with a poleward shift of the subtropical feature and an intensification of the high-latitude features.

Fig. 11.

Patterns of teleconnection obtained for the 200-hPa zonal wind anomalies during (top) negative, (middle) positive AAO events, and (bottom) differences between the two fields

Fig. 11.

Patterns of teleconnection obtained for the 200-hPa zonal wind anomalies during (top) negative, (middle) positive AAO events, and (bottom) differences between the two fields

Hoskins and Ambrizzi (1993), based on the response of a barotropic model to localized forcing, suggested the existence of an important SH waveguide between 0° and 120°E and approximately at 45°S that is consistent with the strong signal of teleconnectivity observed in that area (cf. Fig. 11 with their Fig. 13). In the same study, the authors have shown that there are preferred propagation paths toward and away from this waveguide for forcing centered in the equatorial central Pacific, approximately between 120° and 90°W. To concatenate this observation with previous discussions, it is important to recall that deep tropical convective activity in the equatorial Pacific forced by ENSO and/or MJO has a direct response in the position and intensity of the subtropical jet (e.g., Karoly 1989; Hendon and Salby 1994). The teleconnection patterns in Fig. 11 suggest these modifications and Rossby waves may thus propagate poleward following the waveguides and wave paths described in Hoskins and Ambrizzi (1993). The seesaw represented by the strengthening and drifting equatorward of the subtropical jet and weakening of the polar jet reverses the annular wind feature shown in the combined EOF analysis (Fig. 7, top right). This mechanism implies a shift of the AAO toward its negative phase (Fig. 7, top left). Conversely, as convection is inhibited over large areas of the equatorial Pacific, the subtropical jet moves poleward and the polar jet intensifies, which is the pattern indicated in Fig. 7 (top right and top left panels) for positive AAO.

6. Properties of extratropical cyclones in the SH and AAO phases

Weather in the subtropics and polar regions is systematically affected by propagating extratropical cyclones. For instance, the rainfall regime over southeastern South America during summer can be influenced by these systems as they modulate the SACZ activity over the subtropical Atlantic Ocean (Carvalho et al. 2004). In this regard, the previous discussion raised some important questions: can contrasting AAO phases during DJF modulate properties of extratropical cyclones in the SH? If so, what properties are most sensitive to these changes? To objectively address these issues we used an automated procedure developed by Murray and Simmonds (1991), hereafter referred to as MS). A thorough discussion on the MS method can be found in Murray and Simmonds (1991). Past and recent applications of the MS method for the SH are reviewed in Pezza and Ambrizzi (2003). In summary, the MS automatic scheme finds and tracks low pressure centers at the surface by searching grid points in which the Laplacian of the surface pressure is greater than any of the eight surrounding points and is greater than a specific threshold. For open depressions, in which no point of minimum pressure exists, the method finds the point of minimum pressure gradient. To assign a midlatitude storm, the method requires a minimum average value of the pressure Laplacian over a specified radius of the cyclone center. The system is then tracked from the time of its appearance to its dissipation. To determine the trajectory of the cyclone, the MS method first estimates the new position and then calculates the probability of associations between the predicted and realized positions. The final match is found with the highest overall probability. Several parameters compose the MS scheme and will not be discussed here. The set of parameters used in this study are based on several tests performed by Pezza and Ambrizzi (2003). In addition, to be classified as a cyclone the central pressure of the system needed to be less or equal to 1020 hPa, the same threshold used in Pezza and Ambrizzi (2003).

With these assumptions the MS method was applied to search for cyclones trajectories and properties during the DJF season (1979–99). Sea level pressure from NCEP–NCAR reanalysis every 12 h was used for this purpose. Only those cyclones with origin poleward of 40°S and life cycle longer than 24 h were examined. The following properties of the cyclones provided by the MS scheme were investigated in opposite phases of the AAO: latitude of origin, longitude of origin, maximum latitude, longitude when the cyclone reaches its maximum latitude, minimum and maximum pressure during the cyclone life cycle, and life cycle duration. These properties were analyzed according to the phase of the AAO when the cyclone was first observed. A total of 429 (298) cyclones were analyzed in 317 (305) days observed in negative (positive) AAO phases. The results are summarized in Fig. 12. Distinct AAO phases, along with their respective patterns of atmospheric circulation, modulate the initial latitude of cyclones (Fig. 12a) and their maximum equatorward displacement (Fig. 12b). This is certainly a function of the displacement of the subtropical jet toward the equator during negative AAO events, which favors the cyclones to initiate and dissipate in lower latitudes (the median, 25th, and 75th percentiles indicate cyclone equatorward displacements of ∼10° during negative AAO phases). Modifications of the initial longitude (Fig. 12c) and longitude of the maximum equatorward displacement (Fig. 12d) are less sensitive to AAO phases and differences are not statistically significant. The cyclone minimum and maximum pressure during the life cycle are two other properties that significantly vary from one AAO phase to another. Cyclones during negative AAO phases have minimum and maximum pressure in their center ∼10 hPa higher considering the median, 25th, and 75th percentiles. Cyclones formed relatively closer to the Antarctica during positive AAO phases have central pressures lower than those that evolve far from that continent. This is likely caused by stronger baroclinicity near Antarctica. No statistically significant differences were observed in the life cycle duration for cyclones in distinct phases of the AAO (not shown).

Fig. 12.

Statistical analysis of properties of extratropical cyclones obtained with the MS method during negative and positive AAO phases: (a) initial latitude; (b) latitude of the largest equatorward displacement of the cyclone during its life cycle; (c) initial longitude of the cyclone; (d) longitude of the largest equatorward displacement of the cyclone during its life cycle; (e) cyclone minimum pressure during its life cycle; and (f) cyclone maximum pressure during its life cycle. Box plots, outliers, and extremes have the same meaning as in Fig. 2 

Fig. 12.

Statistical analysis of properties of extratropical cyclones obtained with the MS method during negative and positive AAO phases: (a) initial latitude; (b) latitude of the largest equatorward displacement of the cyclone during its life cycle; (c) initial longitude of the cyclone; (d) longitude of the largest equatorward displacement of the cyclone during its life cycle; (e) cyclone minimum pressure during its life cycle; and (f) cyclone maximum pressure during its life cycle. Box plots, outliers, and extremes have the same meaning as in Fig. 2 

7. Conclusions

In the observational study presented in this article, we investigated some key elements relating tropical activity and phases of the AAO during austral summer. This mode of variability of the SH extratropics is characterized by deep and zonally symmetric or, annular, structures that represent exchanges of mass between mid and high latitudes. The novel aspect of this study is the focus on daily variability of the index and how it can be modulated by intraseasonal to interannual activity in the Tropics.

One of the primary tropical forcings of teleconnections responsible for variations in the AAO seems to be the low-frequency variability in SST modulating tropical convective patterns. Composites of SSTLF revealed that negative (positive) AAO phases were dominant when warm (cold) SSTLF was observed over the central–eastern Pacific. Patterns of low-frequency winds are consistent with these observations. Combined EOF analysis indicated that the annular mode in H700 observed during DJF has also a relationship with a seesaw in the upper-level tropospheric wind anomalies. These two extratropical patterns of geopotential and circulation are related to the east–west seesaw of convection in low latitudes or the Pacific dipole pattern (Hoerling and Kumar 2000), which is dominated by ENSO. EOF analysis of high- and low-frequency geopotential height in opposite ENSO phases are consistent with these observations and indicate the weakening (strengthening) of the annular mode along with positive (negative) geopotential anomalies over Antarctic during warm (cold) ENSO episodes. Nonetheless, the likely nonlinear nature of interactions between the Tropics and extratropics implies that extreme ENSO phases may not always be related to extreme opposite phases of the AAO. Inter-ENSO variations and corresponding teleconnections can differ considerably from event to event, even considering similar intensities of the phenomena (Hoerling and Kumar 2000; Ambrizzi and Souza 2003). Moreover, internal variability of the annular mode not explained by Tropics–extratropics interaction may also account for part of the variability of the AAO during summer.

On intraseasonal time scales, convection in the Tropics and circulation from the Tropics to midlatitudes in both hemispheres seem to have an important role in determining the variability of AAO phases. We showed that increased (weak) intraseasonal activity in the circulation (more specifically U200) in the SH from the Tropics to midlatitudes are related to negative (positive) AAO phases, whereas an enhancement (weakening) of intraseasonal activity in midlatitudes of the NH (∼45°–50°N) is linked to positive (negative) AAO phases. We also investigated the possible links with the MJO due to its known importance in modulating the circulation in the Tropics and subtropics. Negative phases of the AAO are related to the eastward propagation of intraseasonal anomalies that are able to enhance convection over the central Pacific and SPCZ. Conversely, persistent positive phases of the AAO are favored in opposite conditions, that is, when convection is suppressed near the date line and over the SPCZ. Jones et al. (2004) verified that, for the period 1979–2002, more MJO events occurred during El Niño and the neutral DJF season than during the La Niña DJF season; however, differences are not statistically significant. Therefore, the occurrence of MJO during La Niña or neutral years can represent an important tropical forcing for changes in the phase of the AAO.

Teleconnection patterns in the 200-hPa zonal wind daily anomalies show two zonally symmetric features at approximately 45° and 60°S, which seem to be related to the SH subtropical and polar jets, respectively. In negative AAO phases, the subtropical features are displaced equatorward, whereas the high-latitude features are weaker, except in two regions centered ∼180° and 100°W. Positive AAO phases are clearly associated with the poleward shift of the subtropical feature and an intensification of the high-latitude features. The SH waveguide between 0° and 120°E suggested by Hoskins and Ambrizzi (1993) is consistent with the strong signal of teleconnectivity observed in that region.

The importance of variations in the AAO can be observed in the extratropical cyclone properties (with the origin poleward of 40°S). Cyclones tend to form and to move to lower latitudes during negative phases of the AAO as well as have higher central pressures when compared to positive AAO phases. These relationships between AAO and baroclinic instability and, therefore, the extratropical dynamics indirectly examined here from the properties of extratropical cyclones, are consistent with the fact that AAO is an internal mode of mid to high latitudes, which in turn is affected by the variability in convection and circulation from the Tropics to midlatitudes and from intraseasonal to interannual time scales.

Besides reinforcing the significance of convective activity in the Tropics in modulating the extratropics, this observational study shows the need for global climate models that realistically represent interactions between the Tropics and extratropics at different time scales in order to properly simulate the current climate and predict future global changes.

Acknowledgments

The authors thank Dr. Ross Murray and Dr. Ian Simmonds for kindly providing the automatic cyclone-tracking scheme. The authors acknowledge the data support of the Climate Prediction Center (NOAA/NWS/NCEP). The NCEP–NCAR reanalysis data were provided by the NOAA–CIRES Climate Diagnostics Center. This study was supported by the following grants: L.M.V. Carvalho—FAPESP (proc. 01/13154-9) and CNPq (proc: 302203/02-8); C. Jones and L. M. V. Carvalho—NOAA (NA16GP1019 and NA16GP1020); L.M.V Carvalho and T. Ambrizzi—CNPq/PROANTAR (550363/02-5); T. Ambrizzi—FAPESP (proc 01/13816-1), CNPq (Proc: 302459/02-2). T. Ambrizzi was partially supported by IAI (CRN-055).

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Footnotes

Corresponding author address: Dr. Leila M. V. Carvalho, Institute of Astronomy, Geophysics and Atmospheric Sciences, University of São Paulo, Cidade Universitária, R. do Matão, 1226, São Paulo, SP 05508-900, Brazil. Email: leila@model.iag.usp.br