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    The leading EOF of 700-hPa geopotential height poleward of 20°S for (a) austral spring and (b) austral summer.

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    (a),(b) The climatological, seasonal-averaged, zonal-mean zonal wind for (a) austral spring and (b) austral summer. (c)–(f) The composite zonal-mean zonal wind anomalies for all SAM events on the lag 0 day for (c) positive phase, austral spring; (d) negative phase, austral spring; (e) positive phase, austral summer; and (f) negative phase, austral summer. The vertical coordinate indicates pressure (hPa). The contour interval is 5.0 m s−1 in (a) and (b) and 1.0 m s−1 in (c)–(f). Shading corresponds to those values that exceed the 90% statistical significance level for a two-sided t test.

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    The temporal evolution of the difference composite zonal-mean wind anomalies at 60°S for (a) austral spring and (b) austral summer. The results are shown for positive phase minus negative phase SAM events. The vertical coordinate indicates pressure (hPa). The contour interval is 1.0 m s−1. Shading corresponds to those values that exceed the 90% statistical significance level for a two-sided t test.

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    (a),(b) The composite evolution of the anomalous, synoptic-scale, eddy momentum flux convergence for SAM events in the austral summer at 60°S: (a) positive phase, (b) negative phase. (c)–(f) Composites of synoptic-scale EP flux vectors and their divergence for SAM events at lag −1 day for (c) total vector field, positive phase; (d) total vector field, negative phase; (e) anomalous vector field, positive phase; and (f) anomalous vector field, negative phase. The vertical coordinate indicates pressure (hPa). The contour interval is 0.5 × 10−5 m s−2 in (a) and (b) and 1.0 × 10−5 m s−2 in (c)–(f). The scale of the EP-flux vectors is 1.0 × 1014 kg m s−1. Shading corresponds to those values that exceed the 90% statistical significance level for a two-sided t test. EP flux vectors are plotted only if at least one component has a value that exceeds the 90% statistical significance level for a two-sided t test.

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    Composite latitude–time evolution of the WBI for (a) positive SAM, austral summer; (b) negative SAM, austral summer; (c) positive minus negative SAM, austral summer; (d) positive SAM, austral spring; (e) negative SAM, austral spring; and (f) positive minus negative SAM, austral spring. The contour interval is 0.025 (2.5%). Shading corresponds to those values that exceed the 90% statistical significance level for a Monte Carlo test. The thick black curves represent critical latitudes, where the phase speed equals the zonal-mean zonal wind speed (i.e., c = U = 9, 12, and 15 m s−1 at the equatorward side of the eddy-driven jet; c = U = 4, 7, and 10 m s−1 at the poleward side of the eddy-driven jet).

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    (a),(b) The monthly variation of the (a) WBI and (b) ∂Q/∂y at 200 hPa. (c) Composite potential vorticity gradient values at 200 hPa for positive phase minus negative phase SAM events in the austral summer. The contour interval is 0.025 (2.5%) in (a), is 3 × 10−12 m−1 s−1 in (b), and is 2.5 × 10−12 m−1 s−1 in (c). The shaded regions denote WBI values over 0.2 in (a) and ∂Q/∂y values below 2 × 10−11 m−1 s−1 in (b). In (c) shading corresponds to those values that exceed the 90% statistical significance level for a Monte Carlo test. The thick black curves represent critical latitudes, where the phase speed equals the zonal-mean zonal wind speed (i.e., c = U = 9, 12, and 15 m s−1 at the equatorward side of the eddy-driven jet; c = U = 4, 7, and 10 m s−1 at the poleward side of the eddy-driven jet).

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    The monthly variation of the zonal-mean zonal wind difference composite (shown as La Niña minus El Niño) for (a) 30°S and (b) 60°S. The contour interval is 0.5 m s−1. Shading corresponds to those values that exceed the 90% statistical significance level for a two-sided t test.

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    The difference composite (La Niña − El Niño) for ∂Q/∂y: (a) the monthly variation at 200 hPa, (b) averaged from November through February for 100–400 hPa. The contour interval is 2.5 × 10−12 m−1 s−1. Shading corresponds to those values that exceed the 90% statistical significance level for a Monte Carlo test.

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    The monthly variation of the WBI difference composite (La Niña minus El Niño). The contour interval is 0.025 (2.5%). Shading corresponds to those values that exceed the 90% statistical significance level for a Monte Carlo test.

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    The PDFs and corresponding beta distribution curve fits (the curves) of the WBI for November–February. The PDFs are calculated using all grid points within the range 35°–50°S.

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    The temporal evolution of difference composites of the zonal-mean wind anomalies at 60°S for (a) SAM/ENSO events during the austral summer, (b) SAM/ENSO events during the austral spring, (c) SAM/Neutral events during the austral summer, and (d) SAM/Neutral events during the austral spring. The results are shown for positive phase minus negative phase SAM events. The contour interval is 1.0 m s−1. Shading corresponds to those values that exceed the 90% statistical significance level for a two-sided t test.

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    Composites of the latitude–time evolution of the WBI for positive phase minus negative phase SAM/ENSO events during (a) austral summer and (b) austral spring. The contour interval is 0.025 (2.5%). Shading corresponds to those values that exceed the 90% statistical significance level for a Monte Carlo test.

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    The e-folding time scale for the multilevel SAM index for the austral spring. The solid curve corresponds to All/SAM events, the dashed curve to SAM/ENSO events, and the dotted curve to SAM/Neutral events.

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The Impact of ENSO on Wave Breaking and Southern Annular Mode Events

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  • 1 College of Physical and Environmental Oceanography, Ocean University of China, Qingdao, China
  • | 2 Department of Meteorology, The Pennsylvania State University, University Park, Pennsylvania
  • | 3 College of Physical and Environmental Oceanography, Ocean University of China, Qingdao, China
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Abstract

This study examines the relationship between intraseasonal southern annular mode (SAM) events and the El Niño–Southern Oscillation (ENSO) using daily 40-yr ECMWF Re-Analysis (ERA-40) data. The data coverage spans the years 1979–2002, for the austral spring and summer seasons. The focus of this study is on the question of why positive SAM events dominate during La Niña and negative SAM events during El Niño. A composite analysis is performed on the zonal-mean zonal wind, Eliassen–Palm fluxes, and two diagnostic variables: the meridional potential vorticity gradient, a zonal-mean quantity that is used to estimate the likelihood of wave breaking, and the wave breaking index (WBI), which is used to evaluate the strength of the wave breaking. The results of this investigation suggest that the background zonal-mean flow associated with La Niña (El Niño) is preconditioned for strong (weak) anticyclonic wave breaking on the equatorward side of the eddy-driven jet, the type of wave breaking that is found to drive positive (negative) SAM events. A probability density function analysis of the WBI, for both La Niña and El Niño, indicates that strong anticyclonic wave breaking takes place much more frequently during La Niña and weak anticyclonic wave breaking during El Niño. It is suggested that these wave breaking characteristics, and their dependency on the background flow, can explain the strong preference for SAM events of one phase during ENSO. The analysis also shows that austral spring SAM events that coincide with ENSO are preceded by strong stratospheric SAM anomalies and then are followed by a prolonged period of wave breaking that lasts for approximately 30 days. These findings suggest that the ENSO background flow also plays a role in the excitation of stratospheric SAM anomalies and that the presence of these stratospheric SAM anomalies in turn excites and then maintains the tropospheric SAM anomalies via a positive eddy feedback.

Corresponding author address: Tingting Gong, Department of Meteorology, 407 Walker Building, The Pennsylvania State University, University Park, PA 16802. Email: tug5@psu.edu

Abstract

This study examines the relationship between intraseasonal southern annular mode (SAM) events and the El Niño–Southern Oscillation (ENSO) using daily 40-yr ECMWF Re-Analysis (ERA-40) data. The data coverage spans the years 1979–2002, for the austral spring and summer seasons. The focus of this study is on the question of why positive SAM events dominate during La Niña and negative SAM events during El Niño. A composite analysis is performed on the zonal-mean zonal wind, Eliassen–Palm fluxes, and two diagnostic variables: the meridional potential vorticity gradient, a zonal-mean quantity that is used to estimate the likelihood of wave breaking, and the wave breaking index (WBI), which is used to evaluate the strength of the wave breaking. The results of this investigation suggest that the background zonal-mean flow associated with La Niña (El Niño) is preconditioned for strong (weak) anticyclonic wave breaking on the equatorward side of the eddy-driven jet, the type of wave breaking that is found to drive positive (negative) SAM events. A probability density function analysis of the WBI, for both La Niña and El Niño, indicates that strong anticyclonic wave breaking takes place much more frequently during La Niña and weak anticyclonic wave breaking during El Niño. It is suggested that these wave breaking characteristics, and their dependency on the background flow, can explain the strong preference for SAM events of one phase during ENSO. The analysis also shows that austral spring SAM events that coincide with ENSO are preceded by strong stratospheric SAM anomalies and then are followed by a prolonged period of wave breaking that lasts for approximately 30 days. These findings suggest that the ENSO background flow also plays a role in the excitation of stratospheric SAM anomalies and that the presence of these stratospheric SAM anomalies in turn excites and then maintains the tropospheric SAM anomalies via a positive eddy feedback.

Corresponding author address: Tingting Gong, Department of Meteorology, 407 Walker Building, The Pennsylvania State University, University Park, PA 16802. Email: tug5@psu.edu

1. Introduction

The southern annular mode (SAM) is the dominant pattern of large-scale variability in the extratropical Southern Hemisphere (Thompson and Wallace 2000). The SAM is characterized by a nearly zonally symmetric north–south vacillation of the midlatitude westerly jet (Kidson 1988; Karoly 1990; Thompson and Wallace 2000). The positive phase of the SAM is defined by a poleward displacement of the midlatitude jet. Analogously, the negative phase is defined by an equatorward movement of the midlatitude jet.

Many studies have shown that the annular modes in both hemispheres [the SAM and the corresponding northern annular mode (NAM)] are internal patterns of variability with an intrinsic time scale of approximately 10 days (e.g., Feldstein and Lee 1998; Feldstein 2000; Lorenz and Hartmann 2001, 2003). Within the atmospheric science community, it is now accepted that annular modes in both hemispheres are excited and maintained by the interaction between transient eddies and the zonal-mean flow. Support for this perspective has been obtained in both observational and modeling studies (e.g., Robinson 1991, 1996, 2000; Yu and Hartmann 1993; Feldstein and Lee 1996; Hartmann and Lo 1998; Lee and Feldstein 1996; Feldstein and Lee 1998; Lorenz and Hartmann 2001, 2003; Luo et al. 2007).

Studies on the NAM, and the very similar North Atlantic Oscillation (NAO), have shown that annular mode patterns arise from wave breaking (Abatzoglou and Magnusdottir 2006a; Benedict et al. 2004; Feldstein and Franzke 2006; Franzke et al. 2004; Martius et al. 2007; Rivière and Orlanski 2007; Strong and Magnusdottir 2008; Woollings et al. 2008; Woollings and Hoskins 2008). Typically, the positive phase of the NAM is associated with anticyclonic wave breaking, and the negative phase with cyclonic wave breaking (e.g., Thorncroft et al. 1993). In the early stages of the wave breaking, as the meridional tilt of the eddies is increasing, it is the corresponding enhancement in the eddy momentum flux convergence/divergence that drives the annular modes. Because of the dynamical and morphological similarities between the NAM/NAO and the SAM, we anticipate that the SAM is also associated with wave breaking.

In the troposphere, the SAM is observed to be active throughout the year (Thompson and Wallace 2000). The SAM is also observed to extend into the stratosphere (Thompson et al. 2005). However, in the stratosphere, the SAM is active only during the austral spring season (Thompson and Wallace 2000).

On interannual time scales, much longer than the approximate 10-day intrinsic time scale of the SAM, a close link has been observed between the SAM and the El Niño–Southern Oscillation (ENSO)—the phenomenon characterized by interannual variation of sea surface temperature across the tropical Pacific Ocean. For example, L’Heureux and Thompson (2006) found a linear correlation of −0.52 between the cold tongue index (CTI; a measure of ENSO) and the SAM index (a measure of the amplitude and sign of the SAM), averaged over the months of November–February. This finding implies that the SAM index, averaged over the above four months, tends to be positive during La Niña and negative during El Niño. A similar relationship between the SAM and ENSO was not observed during other months in the Southern Hemisphere. Although not focusing on the SAM, other studies that have investigated the midlatitude response to ENSO also find a zonally symmetric component that appears to have a large projection onto the SAM spatial pattern (Karoly 1989; Seager et al. 2003). Similar dipole zonal-mean zonal wind anomalies were also attained in the two-level model study of Robinson (2002), in response to an anomalous zonally symmetric tropical heating anomaly.

Motivated by the above relationship between the four-month-averaged ENSO and SAM (L’Heureux and Thompson 2006), we examined whether ENSO is also associated with a shift in the frequency of occurrence of individual SAM events, which, as discussed above, tend to have a time scale of approximately 10 days (see Table 1). As can be seen, in contrast to neutral ENSO years (the threshold criteria for ENSO and SAM events are defined in section 2), when both phases of SAM occur at a similar frequency, we find that positive SAM events dominate during La Niña and negative SAM events during El Niño. The dominance of each phase of SAM is quite striking, as positive SAM events are observed to occur 4 times as often as negative SAM events during La Niña, while negative SAM events take place almost twice as often as positive SAM events during El Niño. To examine the extent to which this strong preference for positive SAM events during La Niña and negative SAM events during El Niño does not arise through random chance, we examine the statistical significance of the values in Table 1 with the aid of the binomial distribution. Using all 112 SAM events to determine the probability of positive and negative SAM events, we find that the probability of there being 12 or more negative SAM events occurring randomly out of a total of 19 SAM events (for the El Niño years) is 0.12, a value that indicates statistical significance above the 88% confidence level. If this analysis is limited to the months of November–February, those months when the Southern Hemisphere midlatitude response to ENSO is largest, the level of statistical significance increases slightly to 90%. An analogous calculation for positive SAM events (for the La Niña years) yields a probability of 0.03 (i.e., at the 97% confidence level).

In this study, we address the question of why the frequency of occurrence of intraseasonal SAM events is strongly skewed toward one particular phase during ENSO events. A possible answer to our question is that the tropospheric background flow associated with La Niña (El Niño) favors the type of wave breaking and associated eddy momentum flux convergence/divergence that drives the positive (negative) SAM phase. Such a relationship between ENSO and wave breaking has been examined primarily in the Northern Hemisphere (Waugh and Polvani 2000; Shapiro et al. 2001; Scott and Cammas 2002; Abatzoglou and Magnusdottir 2006b; Martius et al. 2007). The general picture presented by these studies is that over the northeastern Pacific the frequency of anticyclonic wave breaking is much greater during La Niña than it is during El Niño, with opposite characteristics being observed for cyclonic wave breaking. Scott and Cammas (2002) and Abatzoglou and Magnusdottir (2006b) suggest that it is the large (small) meridional potential vorticity gradient over the northeastern Pacific during El Niño (La Niña) that accounts for these wave breaking properties. Consistently, Postel and Hitchman (1999) show that the strongest wave breaking tends to coincide with local minima in the meridional potential vorticity gradient. These results lead to the question of whether it is the background meridional potential vorticity gradient associated with ENSO that determines this relationship with SAM events.

This paper is outlined as follows. In section 2, we describe the methodology and datasets. The results, from composite analyses of the anomalous zonal-mean flow, Eliassen–Palm (EP) fluxes, and various wave breaking diagnostics, are presented in section 3, followed by a discussion of the impact of ENSO on the SAM in sections 4 and 5. The conclusions are presented in section 6.

2. Data and methodology

The primary dataset used in this study is the daily averaged 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40; Uppala et al. 2005). We use data for the period from 1 January 1979 to 31 August 2002. Data prior to 1979 were not included because of the marked improvement in Southern Hemisphere data quality that began in 1979 with the assimilation of satellite data (Uppala et al. 2005). Daily data are evaluated on 23 pressure levels, extending from 1000 to 1 hPa on a 2.5° latitude × 2.5° longitude grid. In addition to standard variables, we also examine the daily potential temperature θ field on the dynamic tropopause [the 2–potential-vorticity-unit (PVU) surface; 1 PVU = 106 m2 s−1 K kg−1]. This variable is generated from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis dataset. (It is to be expected that potential inconsistencies between the ERA-40 and NCEP–NCAR Reanalysis datasets are smallest after 1979 because of the incorporation of satellite data.) The 2-PVU potential temperature, which is a conserved quantity in the absence of diabatic heating, is used to detect the occurrence of wave breaking.

ENSO events are identified with the monthly mean Niño 3.4 index (defined as the sea surface temperature averaged over the region 5°S–5°N, 170°–120°W) from the Climate Prediction Center (CPC; data available online at http://www.cpc.ncep.noaa.gov/data/indices/sstoi.indices; Reynolds et al. 2002). As discussed in the introduction, because a strong relationship between ENSO and SAM is observed only during the austral spring and summer seasons, our investigation will be confined to these two seasons. For this study, we will define the austral spring as corresponding to the months of September–December and the austral summer as the months of January–April. Furthermore, because the stratospheric SAM is active primarily during the austral spring, our diagnostic analysis for the austral spring and summer seasons will be performed separately. In this study, anomalies are defined as departures from the seasonal cycle, where the seasonal cycle is determined by applying a 20-day low-pass digital filter to the daily calendar mean values from the entire 24-yr record.

In this study, the SAM is defined as the leading empirical orthogonal function (EOF) of the daily unfiltered 700-hPa geopotential height field, poleward of 20°S. The EOF analysis is performed separately for the austral spring and summer (Fig. 1). As can be seen, the EOF1 spatial patterns show a strong zonally symmetric component, with one extremum over the South Pole and the other in midlatitudes. These two EOF patterns exhibit a close similarity with the Antarctic Oscillation (AAO) pattern of the CPC (see www.cpc.noaa.gov/products/precip/CWlink/daily_ao_index/aao/aao.shtml) based on the NCEP–NCAR reanalysis. A calculation of the linear correlation between our daily EOF1 principal component time series and that provided by CPC has a value of 0.97 for the austral spring and 0.92 for the austral summer (both exceed the 99% statistical significance level; note that the effective degrees of freedom are Ndof = 76 and 134, respectively). In this study, for both the austral spring and summer, we use our EOF1 principal component time series as the daily SAM index. The daily SAM index is also standardized separately for each season. We also use the daily, multilevel SAM index from Baldwin and Dunkerton (2001; available online at http://www.nwra.com/resumes/baldwin/nam.php), which is defined as the projection of the daily geopotential height field onto first EOF of monthly mean geopotential height poleward of 20°S at 17 different pressure levels from 1000 to 10 hPa.

For this study, a SAM event is defined to have taken place if the magnitude of the SAM index exceeds 1.0 standard deviation for five or more consecutive days. If two SAM events occur within 10 days of each other, then the event with the smaller amplitude is discarded (because of the 10-day e-folding time scale of the SAM). Within a SAM event, the particular day when the SAM index reaches its extreme value is called the lag 0 day. To investigate why SAM events exhibit a phase preference during the two phases of ENSO, we divide all SAM events into two categories, i.e., SAM/ENSO and SAM/Neutral events, and compare their different characteristics. Specifically, if the amplitude of the austral spring or summer seasonal mean Niño 3.4 index exceeds 1.0 standard deviation, then that season is chosen as an ENSO season. During those seasons, all positive SAM events that coincide with La Niña, and similarly all negative SAM events that take place during El Niño, are designated as SAM/ENSO events. Note that for each of the SAM/ENSO events, the SAM and ENSO indices take on the opposite sign. We limit our analysis of the SAM/ENSO relationship to events with these properties because, as can be seen from Table 1, there are only a small number of SAM events for which the SAM and ENSO indices have the same sign. The other category, SAM/Neutral, includes all SAM events that occur when the magnitude of the seasonal mean Niño 3.4 index is less than 1.0 standard deviation. The number of events in each of these categories, along with the total number of SAM events, is shown in Table 2.

To enlarge the sample size, and because the anomalies associated with positive and negative SAM/ENSO events tend to have opposite characteristics, we construct SAM/ENSO composites by subtracting negative SAM/ENSO from positive SAM/ENSO composites. Similarly, as the La Niña and El Niño time-mean flows also exhibit opposite spatial patterns, we construct ENSO difference composites—that is, the La Niña composite minus the El Niño composite—based on the four strong El Niño events and three strong La Niña events that occurred between 1980 and 2002. Furthermore, to minimize the influence of SAM events on the ENSO difference composites, those days when SAM events are active are excluded from the calculation. These tests of statistical significance will be evaluated at the 90% confidence level rather than the more customary 95% confidence level. Our willingness to accept the 90% confidence level is motivated both by the small sample size of the data (i.e., the limited number of ENSO years and SAM/ENSO events) and also dynamical justification. With regard to the latter, as we will see, the results at the 90% confidence level are supported by dynamical reasoning, which enhances the legitimacy of the statistical relationships.

In this study, a Monte Carlo test is used for testing the statistical significance of the wave breaking index (WBI) and the meridional potential vorticity gradient composites associated with all SAM events and SAM/ENSO events. The null hypothesis being tested here is that the composite values are the same as the sample mean. The sample sizes for these composites are listed in Table 1. The composite calculations are performed 1000 times with randomly sampled subsets from the 24-yr dataset, and the null hypothesis is rejected if the observed composite values lie within the top or bottom 5% of the Monte Carlo generated distribution. The same Monte Carlo approach is used for testing the statistical significance of the ENSO difference composites. For these calculations, the null hypothesis is that the composite values are equal to zero. The sample size for the difference composites is seven, four El Niño years and three La Niña years. The difference composite calculations are performed 1000 times with randomly sampled subsets from the 24-yr dataset. Again, we are examining statistical significance at the 90% confidence level. The above calculations are applied to each grid point.

3. General characteristics of SAM

To better understand the relationship between the SAM and ENSO, we will first explore the general characteristics of SAM events. The approach used here is to compute composites for all SAM events, including both SAM/ENSO and SAM/Neutral events. Based on the daily SAM index, we will investigate the composite evolution of the zonal-mean zonal wind, EP flux, and wave breaking parameters associated with the SAM.

a. Zonal-mean flow

As the SAM spatial pattern is dominated by its zonally symmetric component (Fig. 1), it is to be expected that the background zonal-mean flow structure plays an important role in the excitation of the SAM. Therefore, we begin by examining the climatological, seasonal-mean, zonal-mean zonal wind (Figs. 2a,b). As can be seen, the climatological zonal-mean zonal wind for the austral spring shows a double-jet structure in the troposphere, whereas for the austral summer the subtropical jet (centered at 30°S; 200–300 hPa) is rather weak, with a single midlatitude eddy-driven jet dominating the flow.

We next examine the lag-0 composite zonal-mean zonal wind anomalies associated with the SAM during both the austral spring and summer (Figs. 2c–f). As has been shown in many previous studies (e.g., Lorenz and Hartmann 2001, 2003; Thompson and Wallace 2000) for both seasons and SAM phases, the zonal-mean zonal wind anomalies exhibit an equivalent barotropic dipole structure in the extratropical troposphere with centers of opposite polarity located near 40° and 60°S. Figures 2c and 2d also show that during the austral spring the high-latitude anomalies extend farther upward into the stratosphere.

Lag–pressure diagrams of the composite zonal-mean zonal wind anomalies at 60°S associated with the SAM (positive minus negative phase) are shown in Fig. 3. For both seasons, within the troposphere it can be seen that westerly anomalies at 60°S first emerge at approximately lag −10 days. The primary difference between the two seasons appears to be the descent of the zonal-mean zonal wind anomalies from the upper stratosphere into the troposphere during the austral spring prior to the excitation of the SAM in the troposphere.

b. EP flux analysis

For both phases of the SAM, we examine the composite evolution of the anomalous, synoptic-scale (zonal wavenumbers 3–8) eddy momentum flux convergence at 60°S (Figs. 4a,b). These wavenumbers were isolated with the aid of a Fourier filter. Our analysis focuses on the synoptic-scale eddies, as it is these eddies that are found to dominate the driving of the SAM in the troposphere (e.g., Feldstein and Lee 1998; Lorenz and Hartmann 2001). Since this quantity exhibits similar features in both seasons, we only show the results for the austral summer. As can be seen, for both phases, small values are present from approximately lag −15 to lag −5 days. This is followed by a rapid increase, with peak values occurring at lag −1 day, and then a rapid decline after lag 0.

We next examine the synoptic-scale (zonal wavenumbers 3–8) EP flux vectors (Andrews and McIntyre 1976; Edmon et al. 1980). In addition to illustrating wave activity propagation features, those regions with a large equatorward (poleward) component of the EP flux vector point to the occurrence of strong anticyclonic (cyclonic) wave breaking. This is because the horizontal tilt of breaking waves indicates the direction of the EP flux vector (it should be noted that waves that are not breaking can still have nonzero EP flux vectors). The EP flux vectors (in log-pressure coordinates) are defined in Dunkerton et al. (1981) as
i1520-0469-67-9-2854-eq1
where u′, υ′, and θ′ are the eddy (deviation from the zonal mean) zonal wind, meridional wind, and potential temperature, respectively. Also, ρ is the density, a is the radius of the earth, φ is latitude, f is the Coriolis parameter, and θz is the vertical gradient of the zonal-mean potential temperature.

Composites of the synoptic-scale EP-flux vectors at lag −1 day are shown in Figs. 4c and 4d for the positive and negative phases of SAM. It can be seen that positive SAM excitation is associated with upward and equatorward wave activity propagation throughout midlatitudes. On the poleward side of the eddy-driven jet, there is weak poleward wave activity propagation. In contrast, for negative SAM, the equatorward wave activity propagation in midlatitudes is much weaker, and the poleward wave activity propagation at high latitudes is slightly stronger. For both SAM phases, the impact of the eddies on the zonal-mean flow is more easily visualized with the anomalous EP flux vectors (Figs. 4e,f). As can be seen, the synoptic-scale eddies drive both phases of the SAM.

The planetary-scale (zonal wavenumbers 1 and 2) EP fluxes associated with SAM events are also examined. The primary difference is found to involve the vertical component, centered near 60°S, in the upper troposphere and lower stratosphere. These EP fluxes were found to be slightly stronger for negative SAM events than for positive SAM events (not shown).

c. Wave breaking index

To quantify the wave breaking characteristics associated with the SAM, we construct the WBI with the potential temperature field on the 2-PVU surface. The occurrence of wave breaking is identified by the sign reversal of the normally positive meridional potential temperature gradient. At each latitude, for our calculation of the WBI, we select those grid points for which the anomalous 300-hPa eddy streamfunction amplitude exceeds the 1.0-standard-deviation value for that latitude. The 1.0-standard-deviation criterion is adopted to identify those grid points where the waves a have large amplitude because it is these waves that are more likely to break. All grid points are considered since we are assuming that the wave breaking can occur at all longitudes because of the approximate zonal symmetry of the SAM spatial patterns (Fig. 1). Next, for those grid points that exceed this threshold, we calculate the fraction of grid points for which the meridional potential temperature gradient is negative. Thus, the WBI can be seen as a simple measure of the longitudinal extent of the wave breaking. We use this quantity to measure the strength of the wave breaking at each latitude.

The latitudinal–temporal evolution of the WBI is illustrated for both phases of SAM in Fig. 5. For the positive SAM phase, for both seasons, on the equatorward side of the eddy-driven jet (between 40°S and 50°S), the largest anomalies in the WBI tend to occur at about the same latitude and over a similar time interval as the extrema in the anomalous eddy momentum flux convergence (Figs. 4a,b). At 45°S, over the time interval from lag −3 to lag +1 days, the average WBI value during the summer is approximately 0.3 and during the spring is approximately 0.375. On the poleward side of the eddy-driven jet, for both seasons, the excitation of SAM is associated with a very slight decline in the WBI. In contrast, for the negative SAM phase, on the equatorward side of the eddy-driven jet, much smaller values of the WBI are observed during the lag −3 to lag +1 day interval. The time-averaged values at 45°S are 0.125 and 0.165 for the austral summer and spring, respectively. On the poleward side of the jet, the WBI values undergo a small increase during both the austral summer and spring. To identify the type of wave breaking, we calculated the zonal mean of the horizontal tilt of the θ contours on the 2-PVU surface at those grid points for which the anomalous 300-hPa eddy streamfunction amplitude exceeds the 1.0-standard-deviation value for that latitude. For each grid point, the horizontal tilt is defined as arctan[−(∂θ/∂x)/(∂θ/∂y)]. For the Southern Hemisphere, at those grid points where anticyclonic wave breaking occurs, the isentropes tilt northwest–southeast and the horizontal tilt must be negative; for cyclonic wave breaking, the isentropes tilt northeast–southwest and the horizontal tilt must be positive. We have calculated lagged composites of arctan[−(∂θ/∂x)/(∂θ/∂y)] for the SAM events that took place during the austral spring and summer seasons (not shown). It is found that the horizontal tilt on the equatorward side of the eddy-driven jet is negative at most lags, indicating anticyclonic wave breaking. Similarly, on the poleward side of the eddy-driven jet, the horizontal tilt is positive, indicating cyclonic wave breaking. Therefore, we conclude that the wave breaking on the equatorward (poleward) side of the jet is primarily anticyclonic (cyclonic).

These differences between the two phases of SAM are summarized with WBI difference composites (Figs. 5c,f). These composites show dipole structures centered at lag 0 days, with much larger values on the equatorward side of the eddy-driven jet. These features suggest that on the equatorward side of the eddy-driven jet positive SAM is associated with strong anticyclonic wave breaking and negative SAM with weak anticyclonic wave breaking. On the poleward side of the eddy-driven jet, these results suggest that there is very little wave breaking associated with positive SAM and weak wave breaking with negative SAM. These results also show that in terms of wave breaking the primary difference between the two SAM phases involves the strength of the anticyclonic wave breaking on the equatorward side of the eddy-driven jet. Furthermore, because of the overlap between the extrema in the anomalous eddy momentum flux convergence (Figs. 4a,b) and the WBI at negative lags, these results also suggest that it is the eddy driving associated with the wave breaking that drives the SAM. These wave breaking characteristics contrast with those of the NAM/NAO, for which the positive phase is associated with anticyclonic wave breaking and the negative phase cyclonic wave breaking.

Another important feature indicated by the WBI is that most of the wave breaking associated with both SAM phases does not take place at critical latitudes, where the wave phase speed equals the zonal-mean zonal wind speed (Fig. 5). (The critical latitudes are indicated by the thick solid lines in Fig. 5, three at low latitudes and three at high latitudes. There are no critical latitudes displayed at low latitudes for the austral spring because they occur equatorward of 20°S.) To determine the location of the critical latitudes, we follow the findings of Kim and Lee (2004), who performed a cospectrum analysis of the Southern Hemisphere eddy momentum flux convergence (see their Fig. 2a). They found an eddy momentum flux convergence maximum with phase speeds ranging from 5 to 15 m s−1 on the equatorward side of the eddy-driven jet and 4 to 10 m s−1 on the poleward side of the eddy-driven jet. Based on their results, we choose three phase speeds: 9, 12, and 15 m s−1 on the equatorward side of the jet and 4, 7, and 10 m s−1 on the poleward side of the jet. One can see that on the equatorward side of the jet the wave breaking occurs more than 10° poleward of the critical latitudes (also see Kim and Lee 2004), and on the poleward side of the jet the wave breaking occurs at least 10° equatorward of the critical latitudes. Therefore, the wave breaking associated with the SAM does not coincide with critical latitudes.

d. Meridional potential vorticity gradient

For both SAM phases, the timing of the largest anomalies in the tropospheric zonal-mean flow (Fig. 3) and the WBI (Fig. 5) are observed to overlap. This leads to the question of whether the above wave breaking characteristics of the SAM can be accounted for by particular features of the zonal-mean background flow. Studies such as Postel and Hitchman (1999) find that the strongest wave breaking tends to coincide with local minima in the meridional potential vorticity gradient ∂Q/∂y. As they discuss, at those locations where ∂Q/∂y is weak, meridional particle displacements are large (Pedlosky 1992). In the presence of horizontal shear, wave breaking can more readily take place.

To examine the relationship between the WBI and the zonal-mean ∂Q/∂y, we first show the seasonal cycle of both quantities (Figs. 6a,b). (In the expression for ∂Q/∂y, we use its barotropic form: ∂Q/∂y = βUyy, where β is the meridional gradient of the Coriolis parameter and Uyy is the horizontal curvature of the zonal-mean zonal wind). As can be seen, during most months of the year, there is a tendency for the WBI maxima (Fig. 6a) and the zonal-mean ∂Q/∂y minima (Fig. 6b) to coincide.

The difference between the zonal-mean ∂Q/∂y for the two SAM phases is shown in Fig. 6c. As can be seen, throughout negative lags, beginning at approximately lag −8 days, there is a dipole structure with large negative values equatorward of the eddy-driven jet and large positive values poleward of the eddy-driven jet. These values are consistent with the much stronger anticyclonic (cyclonic) wave breaking characteristics of the positive (negative) SAM phase. Furthermore, during the developing stage of the SAM events, the statistically significant ∂Q/∂y composite values lead those of the WBI composite by approximately 5 days (cf. Figs. 5c and 6c). As the SAM anomalies continue to grow, and the wave breaking intensifies, the composite ∂Q/∂y becomes smaller on the equatorward side of the eddy-driven jet. This result suggests that the PV gradient is capable of not only predicting the impact of the background flow change on wave breaking before the establishment of SAM events, but it also captures the wave breaking (mixing) after SAM achieves its maximum amplitude.

4. Background flow properties associated with ENSO

To begin addressing the question of why there is a strong preference for positive SAM events during La Niña and negative SAM events during El Niño, as discussed in the introduction, we examine ENSO difference composites of the zonal-mean zonal wind, the zonal-mean ∂Q/∂y, and the WBI. The motivation for calculating these particular quantities is based on our hypothesis that it is the background zonal-mean flow and its impact on Rossby wave breaking that determines the relative frequency of the positive and negative SAM events during La Niña and El Niño events.

a. ENSO zonal-mean flow

Figure 7 shows the monthly variation of the ENSO difference composite for the zonal-mean zonal wind at 30° and 60°S. As can be seen in Fig. 7a, the subtropical jet in the upper troposphere is stronger during El Niño (Arkin 1982; Seager et al. 2003; L’Heureux and Thompson 2006). This relative strengthening of the El Niño subtropical jet can be seen to persist throughout the year. In contrast, in the extratropics, for the austral spring and summer, the difference composite indicates that the eddy-driven jet is stronger during La Niña (Fig. 7b). The largest values of the difference composite occur primarily for the months of November–February (Fig. 7b), the time period when ENSO reaches its mature stage.

b. Wave breaking associated with ENSO

We next examine the ENSO difference composites for the zonal-mean ∂Q/∂y (Fig. 8). In midlatitudes, where the wave breaking is strongest, the temporal evolution of the ∂Q/∂y ENSO difference composite (Fig. 8a) shows its largest negative values on the equatorward side of the eddy-driven jet, and positive values poleward of the jet maximum, just as was found for the difference between positive and negative SAM events (Fig. 6c). Furthermore, this midlatitude dipole in ∂Q/∂y is strongest for the months of November–February, which corresponds to the mature stage of ENSO, and is weaker for most of the remainder of the year. An examination of the vertical structure of ∂Q/∂y (Fig. 8b), averaged from November through February, shows that ∂Q/∂y maintains this spatial structure throughout the upper troposphere and lower stratosphere, those levels where wave breaking is most prominent.

The temporal evolution of the WBI ENSO difference composite is illustrated in Fig. 9. Consistent with the ∂Q/∂y difference composite (Fig. 8a) for the months of November–February, the WBI difference composite shows an enhanced frequency of wave breaking on the equatorward side of the eddy-driven jet and a slightly lowered frequency on the poleward side of the eddy-driven jet (November–early January).

Another conventional method often used to study wave propagation features is the quasigeostrophic (QG) refractive index (Matsuno 1970; Harnik and Lindzen 2001; Seager et al. 2003). In this study we also calculated the QG refractive index for the El Niño and La Niña background flows. The largest difference composite values were found to be located in the subtropics, near the critical latitudes (not shown). However, from the above WBI analysis (Figs. 5 and 6a), it was shown that the frequency of wave breaking at the critical latitudes is relatively low. These findings suggest that refractive index diagnostics may not be very helpful for trying to understand the dynamical processes that drive SAM events.

c. Daily PDFs of the WBI and EP flux

The above WBI difference composites present monthly time averages over La Niña and El Niño events (Fig. 9). However, even though these difference composites are consistent with our hypothesis that the La Niña and El Niño background flows have wave breaking characteristics that preferentially excite one phase of SAM (i.e., that positive phase SAM events take place primarily during La Niña and negative phase SAM events during El Niño), there must also be a change in the frequency of occurrence of small and large WBI values. That is, on the equatorward side of the eddy-driven jet, it is to be expected that the frequency of occurrence of large WBI values should be much greater during La Niña. Likewise, the frequency of occurrence of small WBI values should be much higher during El Niño.

To assess whether this is indeed the case, we examine probability density functions (PDFs) of the WBI for all grid points between 35° and 50°S for the months of November–February (Fig. 10). This analysis is performed separately for La Niña and El Niño. In these PDFs, we focus on the WBI values shown in Fig. 5, averaged over the lag −3 to lag +1 day interval, at 45°S. These particular WBI values are regarded as representing the strength of the wave breaking associated with both phases of the SAM. Referring to Fig. 5, and as discussed in section 3, for the positive SAM phase the average WBI value for the summer is 0.3 and for the spring is 0.375. For the negative SAM phase, the average WBI values are 0.125 and 0.165 for the austral summer and spring, respectively. Inspection of Fig. 10 shows that the frequency of occurrence during La Niña is approximately 1.5 times greater than that during El Niño for a WBI value of 0.3 and approximately 2.0 times greater for a WBI value of 0.375. Similarly, the frequency of occurrence during El Niño is approximately 2.0 times greater than that for La Niña for a WBI value of 0.125 and approximately 1.5 times greater for a WBI value of 0.165. These differences in the PDFs show a very clear preference for strong, positive-SAM type anticyclonic wave breaking during La Niña, and weak, negative-SAM type anticyclonic wave breaking during El Niño.

A PDF analysis of the austral spring planetary-scale EP-flux vectors was also performed. The vertical components of the vectors were averaged over 55°–75°S and from 10 hPa to 100 hPa. The results of this calculation indicated that El Niño exhibits a slightly greater frequency of days with strong upward wave activity propagation, and La Niña shows a slightly higher frequency of days with weak vertical wave activity propagation (not shown). A corresponding calculation of the WBI PDF was also performed for the austral spring over the same range of latitudes. The results indicate that larger WBI values occur more frequently during El Niño and smaller WBI values during La Niña (not shown). These results imply that poleward of the eddy-driven jet, El Niño coincides with slightly more frequent cyclonic wave breaking and stronger vertical wave activity propagation than does La Niña. One plausible explanation that may link the wave breaking and vertical wave activity propagation is that a greater frequency of breaking synoptic-scale waves during El Niño can generate more planetary wave activity via the upscale energy cascade. These planetary waves can then readily penetrate into the stratosphere (Charney and Drazin 1961). With regard to SAM events, as was discussed in section 3, negative SAM events coincide with stronger cyclonic wave breaking and vertical wave activity propagation than positive SAM events. Thus, the above PDFs suggest that the properties of the tropospheric background flow may also contribute toward the dominance of positive stratospheric SAM events during La Niña and negative stratospheric SAM events during El Niño.

5. Characteristics of SAM events during ENSO

In this section, we contrast the characteristics of SAM/Neutral and SAM/ENSO events with difference composites.

a. Zonal-mean flow

The composite evolution of SAM/ENSO events differs markedly from that for SAM/Neutral events. As can be seen, for the austral spring (Fig. 11b), up to 60 days prior to the establishment of SAM/ENSO anomalies in the troposphere, there are anomalies of the same sign present in the stratosphere. These stratospheric SAM anomalies gradually descend toward the troposphere. In contrast, for the SAM/Neutral events during the austral spring (Fig. 11d), although stratospheric anomalies are present at negative lags near lag −60 days, between lag −20 and lag −5 days the anomalies are much smaller and exhibit a weaker connection with the stratosphere.

The SAM/ENSO and SAM/Neutral events for the austral summer appear somewhat different (Figs. 11a,c). As is shown, the SAM/ENSO events during this season exhibit weak zonal-mean zonal wind anomalies in the troposphere and in the lower half of the stratosphere that extend to very early lags. In contrast, the SAM/Neutral anomalies first appear at lag −10 days. However, an examination of individual cases finds that the SAM/ENSO anomalies at very early negative lags are not part of the summer season and are instead due to the impact of the SAM anomalies during the previous season. This indicates that the zonal-wind anomalies associated with SAM/ENSO and SAM/Neutral events during the austral summer do closely resemble each other. After the lag 0 day, the composite SAM/ENSO anomalies persist in the troposphere for approximately 25 days for the summer, and 60 days for spring, much longer than the 10 days observed for SAM/Neutral events.

b. WBI

We next address whether the prolonged persistence associated with SAM/ENSO events is associated with wave breaking. For this purpose, we calculate the composite time evolution of the difference between WBI values for positive and negative SAM/ENSO events (Fig. 12). As can be seen, for both seasons, there is a period of prolonged persistence of either enhanced or weakened wave breaking. As in Fig. 11a, the prolonged wave breaking shown in Fig. 12a at negative lags is due to the impact of the SAM events during the previous season.

c. Time scales of SAM events

The time scales of SAM/ENSO and SAM/Neutral events are evaluated with a composite analysis of the daily multilevel SAM index from Baldwin and Dunkerton (2001). For this purpose, we calculate the e-folding time scale of the composite index for all 17 pressure levels (Fig. 13). Our main interest lies in the time scale of tropospheric SAM events. As can be seen, it is during the austral spring ENSO that tropospheric SAM anomalies persist for an extended period of time, lasting for close to 30 days. For other time periods, including the austral summer ENSO, SAM events in the troposphere last for only 10 days (not shown). As shown above (Figs. 11 and 12), in the troposphere the austral spring SAM/ENSO anomalies coincide with a period of prolonged wave breaking. This suggests that the long time scale in the troposphere for SAM/ENSO events involves a positive feedback between the stratospheric SAM and wave breaking in the troposphere. Such a connection between the lower stratospheric zonal-mean flow and a positive eddy feedback has been found in idealized model calculations of baroclinic life cycles (Wittman et al. 2007).

6. Conclusions

An observational study with ERA-40 data was performed to investigate the relationship between the occurrence of southern annular mode (SAM) events and ENSO during the austral spring and summer seasons. The main question that this study addressed is why positive SAM events dominate during La Niña and negative SAM events during El Niño.

Two diagnostic variables are examined to address this question. These are the zonal-mean potential vorticity gradient ∂Q/∂y, which appears to be a good measure of the likelihood of wave breaking, and the wave breaking index (WBI), which measures the intensity of the wave breaking. The results from an examination of these variables, along with other zonal-mean flow quantities and EP fluxes, suggest that the zonal-mean background flow associated with La Niña is preconditioned for very strong anticyclonic wave breaking on the equatorward side of the eddy-driven jet. It is this type of wave breaking that is shown to generate positive SAM events. Similarly, the zonal-mean background flow associated with El Niño has the structure that favors weak anticyclonic wave breaking on the equatorward side of the eddy-driven jet, which is found to excite negative SAM events. Consistently, a PDF analysis showed that strong (weak) anticyclonic wave breaking occurs at a much higher frequency during La Niña (El Niño). It is suggested that these wave breaking characteristics, and their dependency on the background flow, can account for the SAM events being so strongly skewed toward one particular phase during ENSO.

The results of this study lead to interesting and important questions on the fundamental dynamical processes that drive the SAM. For example, our findings indicate that the wave breaking associated with the SAM occurs away from critical latitudes. This suggests that a deeper understanding of SAM dynamics requires a much better understanding of the nature of wave breaking, particularly away from critical latitudes. It was also observed that austral spring SAM events that coincide with ENSO persist for as much as 3 times longer than SAM events during other time periods. These SAM/ENSO events were found to be preceded by strong stratospheric SAM anomalies and then followed by a prolonged episode of wave breaking. This finding of strong stratospheric SAM anomalies during ENSO suggests that perhaps variation in the structure of the tropospheric background flow can modulate the intensity of cyclonic wave breaking on the poleward side of the eddy-driven jet, and that the subsequent fluctuation in the upscale energy cascade and vertical wave activity propagation may excite the stratospheric SAM. This extended period of wave breaking during ENSO points to the possibility that the stratospheric SAM anomalies excite and then maintain the tropospheric SAM anomalies through a positive eddy feedback. In future research, we plan to investigate each of these processes with the aid of idealized modeling experiments.

Acknowledgments

The authors offer their gratitude for support through the Chinese Scholarship Council, the Office of Science (BER) and the National Science Foundation, Grant ATM-0649512. In addition, we thank Drs. Sukyoung Lee, Michelle L’Heureux, Mu Mu, Zenghao Qin, and Lixin Wu and two anonymous reviewers for offering helpful suggestions that improved the quality of this work. We also thank the European Centre for Medium-Range Weather Forecasts for providing us with the ERA-40 reanalysis data and the Climate Analysis Branch of the NOAA Earth System Research Laboratory/Physical Sciences Division for providing us with the NCEP–NCAR Reanalysis data. In addition, we acknowledge the NOAA Climate Prediction Center and the Climate Analysis Center of NCAR for providing us with the climate indices.

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Fig. 1.
Fig. 1.

The leading EOF of 700-hPa geopotential height poleward of 20°S for (a) austral spring and (b) austral summer.

Citation: Journal of the Atmospheric Sciences 67, 9; 10.1175/2010JAS3311.1

Fig. 2.
Fig. 2.

(a),(b) The climatological, seasonal-averaged, zonal-mean zonal wind for (a) austral spring and (b) austral summer. (c)–(f) The composite zonal-mean zonal wind anomalies for all SAM events on the lag 0 day for (c) positive phase, austral spring; (d) negative phase, austral spring; (e) positive phase, austral summer; and (f) negative phase, austral summer. The vertical coordinate indicates pressure (hPa). The contour interval is 5.0 m s−1 in (a) and (b) and 1.0 m s−1 in (c)–(f). Shading corresponds to those values that exceed the 90% statistical significance level for a two-sided t test.

Citation: Journal of the Atmospheric Sciences 67, 9; 10.1175/2010JAS3311.1

Fig. 3.
Fig. 3.

The temporal evolution of the difference composite zonal-mean wind anomalies at 60°S for (a) austral spring and (b) austral summer. The results are shown for positive phase minus negative phase SAM events. The vertical coordinate indicates pressure (hPa). The contour interval is 1.0 m s−1. Shading corresponds to those values that exceed the 90% statistical significance level for a two-sided t test.

Citation: Journal of the Atmospheric Sciences 67, 9; 10.1175/2010JAS3311.1

Fig. 4.
Fig. 4.

(a),(b) The composite evolution of the anomalous, synoptic-scale, eddy momentum flux convergence for SAM events in the austral summer at 60°S: (a) positive phase, (b) negative phase. (c)–(f) Composites of synoptic-scale EP flux vectors and their divergence for SAM events at lag −1 day for (c) total vector field, positive phase; (d) total vector field, negative phase; (e) anomalous vector field, positive phase; and (f) anomalous vector field, negative phase. The vertical coordinate indicates pressure (hPa). The contour interval is 0.5 × 10−5 m s−2 in (a) and (b) and 1.0 × 10−5 m s−2 in (c)–(f). The scale of the EP-flux vectors is 1.0 × 1014 kg m s−1. Shading corresponds to those values that exceed the 90% statistical significance level for a two-sided t test. EP flux vectors are plotted only if at least one component has a value that exceeds the 90% statistical significance level for a two-sided t test.

Citation: Journal of the Atmospheric Sciences 67, 9; 10.1175/2010JAS3311.1

Fig. 5.
Fig. 5.

Composite latitude–time evolution of the WBI for (a) positive SAM, austral summer; (b) negative SAM, austral summer; (c) positive minus negative SAM, austral summer; (d) positive SAM, austral spring; (e) negative SAM, austral spring; and (f) positive minus negative SAM, austral spring. The contour interval is 0.025 (2.5%). Shading corresponds to those values that exceed the 90% statistical significance level for a Monte Carlo test. The thick black curves represent critical latitudes, where the phase speed equals the zonal-mean zonal wind speed (i.e., c = U = 9, 12, and 15 m s−1 at the equatorward side of the eddy-driven jet; c = U = 4, 7, and 10 m s−1 at the poleward side of the eddy-driven jet).

Citation: Journal of the Atmospheric Sciences 67, 9; 10.1175/2010JAS3311.1

Fig. 6.
Fig. 6.

(a),(b) The monthly variation of the (a) WBI and (b) ∂Q/∂y at 200 hPa. (c) Composite potential vorticity gradient values at 200 hPa for positive phase minus negative phase SAM events in the austral summer. The contour interval is 0.025 (2.5%) in (a), is 3 × 10−12 m−1 s−1 in (b), and is 2.5 × 10−12 m−1 s−1 in (c). The shaded regions denote WBI values over 0.2 in (a) and ∂Q/∂y values below 2 × 10−11 m−1 s−1 in (b). In (c) shading corresponds to those values that exceed the 90% statistical significance level for a Monte Carlo test. The thick black curves represent critical latitudes, where the phase speed equals the zonal-mean zonal wind speed (i.e., c = U = 9, 12, and 15 m s−1 at the equatorward side of the eddy-driven jet; c = U = 4, 7, and 10 m s−1 at the poleward side of the eddy-driven jet).

Citation: Journal of the Atmospheric Sciences 67, 9; 10.1175/2010JAS3311.1

Fig. 7.
Fig. 7.

The monthly variation of the zonal-mean zonal wind difference composite (shown as La Niña minus El Niño) for (a) 30°S and (b) 60°S. The contour interval is 0.5 m s−1. Shading corresponds to those values that exceed the 90% statistical significance level for a two-sided t test.

Citation: Journal of the Atmospheric Sciences 67, 9; 10.1175/2010JAS3311.1

Fig. 8.
Fig. 8.

The difference composite (La Niña − El Niño) for ∂Q/∂y: (a) the monthly variation at 200 hPa, (b) averaged from November through February for 100–400 hPa. The contour interval is 2.5 × 10−12 m−1 s−1. Shading corresponds to those values that exceed the 90% statistical significance level for a Monte Carlo test.

Citation: Journal of the Atmospheric Sciences 67, 9; 10.1175/2010JAS3311.1

Fig. 9.
Fig. 9.

The monthly variation of the WBI difference composite (La Niña minus El Niño). The contour interval is 0.025 (2.5%). Shading corresponds to those values that exceed the 90% statistical significance level for a Monte Carlo test.

Citation: Journal of the Atmospheric Sciences 67, 9; 10.1175/2010JAS3311.1

Fig. 10.
Fig. 10.

The PDFs and corresponding beta distribution curve fits (the curves) of the WBI for November–February. The PDFs are calculated using all grid points within the range 35°–50°S.

Citation: Journal of the Atmospheric Sciences 67, 9; 10.1175/2010JAS3311.1

Fig. 11.
Fig. 11.

The temporal evolution of difference composites of the zonal-mean wind anomalies at 60°S for (a) SAM/ENSO events during the austral summer, (b) SAM/ENSO events during the austral spring, (c) SAM/Neutral events during the austral summer, and (d) SAM/Neutral events during the austral spring. The results are shown for positive phase minus negative phase SAM events. The contour interval is 1.0 m s−1. Shading corresponds to those values that exceed the 90% statistical significance level for a two-sided t test.

Citation: Journal of the Atmospheric Sciences 67, 9; 10.1175/2010JAS3311.1

Fig. 12.
Fig. 12.

Composites of the latitude–time evolution of the WBI for positive phase minus negative phase SAM/ENSO events during (a) austral summer and (b) austral spring. The contour interval is 0.025 (2.5%). Shading corresponds to those values that exceed the 90% statistical significance level for a Monte Carlo test.

Citation: Journal of the Atmospheric Sciences 67, 9; 10.1175/2010JAS3311.1

Fig. 13.
Fig. 13.

The e-folding time scale for the multilevel SAM index for the austral spring. The solid curve corresponds to All/SAM events, the dashed curve to SAM/ENSO events, and the dotted curve to SAM/Neutral events.

Citation: Journal of the Atmospheric Sciences 67, 9; 10.1175/2010JAS3311.1

Table 1.

The distribution of SAM events during the austral spring and the austral summer from the January 1979–August 2002 time period. The number of SAM events and its corresponding frequency of occurrence—that is, the mean number of events per year (within the parentheses)—are shown for El Niño, La Niña, and neutral years.

Table 1.
Table 2.

The number of All/SAM, SAM/ENSO, and SAM/Neutral events for the austral summer and the austral spring. (The number of All/SAM events includes SAM/ENSO events of the same sign.) The letter p (n) indicates a positive (negative) SAM event.

Table 2.
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