Abstract

The Southern Hemisphere annular mode (SAM) is the dominant mode of climate variability in the extratropical Southern Hemisphere. Representing variations in pressure and the corresponding changes to the circumpolar zonal flow, it is typically thought of as an “annular” or ringlike structure. However, on seasonal time scales the zonal symmetry observed in the SAM in monthly or annual mean data is much less marked. This study further examines the seasonal changes in the SAM structure and explores temperature signals across the Southern Hemisphere that are strongly tied to the asymmetric SAM structure.

The SAM asymmetries are most marked in the Pacific sector and in austral winter and spring, related to changes in the jet entrance and exit regions poleward of 30°S. Depending on the season, the asymmetric SAM structure explains over 25% of the variance in the overall SAM structure and has strong connections with ENSO or zonal wavenumber 3. In austral summer and autumn the SAM has been becoming more zonally symmetric, especially after 1980, perhaps tied to changes in anthropogenic forcing. Across the Pacific sector, including the Antarctic Peninsula, temperature variations are strongly tied to the asymmetric SAM structure, while temperatures across East Antarctica are more strongly tied to the zonally symmetric SAM structure.

The results suggest that studies examining the climate impacts of the SAM across the Southern Hemisphere need to consider the seasonal variations in the SAM structure as well as varying impacts between its positive and negative polarity to adequately describe the underlying relationships.

1. Introduction

The southern annular mode (SAM) is the dominant mode of circulation variability in the Southern Hemisphere (SH) extratropics, representing ~35% of the total circulation variability from time scales of a few days (Baldwin 2001) to decades (Kidson 1999). As the dominant mode of variability, the SAM appears as the leading empirical orthogonal function (EOF) of pressure or geopotential height from sea level throughout the troposphere (Rogers and van Loon 1982; Kiladis and Mo 1998; Thompson and Wallace 2000). The spatial pattern of this leading EOF represents the strength of the meridional pressure gradient from the middle to high latitudes of the SH, with lower pressures observed over Antarctica and an associated increase in circumpolar westerly flow in its positive polarity; opposite conditions exist in its negative polarity.

The structure of the SAM is maintained by interactions of transient eddies with the mean flow in the latitudes surrounding the polar front jet. It is termed an “annular” mode, as it appears in its mean state as a near-zonally symmetric pattern across the high southern latitudes. This zonally symmetric structure exists primarily because the contributions of the eddy activity to the jet stream are roughly equal across all longitudes, as there are fewer continental barriers to direct or confine eddy activity in the high southern latitudes (Hall and Visbeck 2002). An obvious exception to this occurs near the Drake Passage, where the high terrain of both southern South America and the Antarctic Peninsula act to create a regional asymmetry over the Amundsen and Bellingshausen Seas (Lefebvre et al. 2004). Global climate models demonstrate a high degree of zonal symmetry in the monthly SAM structure, in both control simulations (Fyfe et al. 1999) and simulations forced with time-varying anthropogenic greenhouse gases (Miller et al. 2006; Karpechko et al. 2009).

However, recent studies have noted that various zonal asymmetries in the SAM structure are important to consider, especially when examining seasonal variations. Fan (2007) demonstrated that a portion of the austral winter SAM zonal asymmetry is due to variations in the Southern Oscillation. In turn, this impacts the distribution of precipitation across the SH (cf. their Fig. 4). Neff et al. (2008), using radiosonde observations across Antarctica, found asymmetric trends in geopotential height during December–May: in coastal East Antarctica, negative trends at 500 hPa prevail during 1957–2005 while the more central- to western-located stations of Halley, McMurdo, and South Pole all indicate near-zero or weakly positive 500-hPa-height trends. The conclusions of Neff et al. (2008) suggest a large nonzonally symmetric response to ozone depletion, which has been commonly linked to corresponding positive trends in the SAM (Thompson and Solomon 2002). Jones et al. (2009) discussed zonal asymmetries in the seasonal SAM patterns, especially how the varying seasonal SAM structure impacted their quantitative reconstruction skill of the twentieth-century SAM index. They noted the amplitude of the SAM pattern changed seasonally with respect to the locations of long-term station observations over the southern continents, giving rise to a nonstationary set of predictor data.

Other studies have also examined nonstationary impacts of the SAM, which may further suggest that there are notable deviations from the assumed zonally symmetric SAM structure. Silvestri and Vera (2009) found that there were nonstationary impacts of the SAM on South American climate, particularly in terms of moisture convergence and associated precipitation. They also noted changes in the sign of the correlation of the SAM with precipitation between the 1960s–70s and the 1980s–90s in Australia/New Zealand. Marshall et al. (2011) note a nonstationary aspect of the SAM in terms of the temperature relationship at Halley station in Antarctica. They related this nonstationary SAM–surface temperature relationship to the nonannular component of the SAM (i.e., its zonally asymmetric structure), and urge caution when using Antarctic isotope data as a proxy for the SAM since it appears to be more regionally influenced.

The goal of this paper is to build upon these recent studies by demonstrating that even for very strong SAM events, marked asymmetries exist on seasonal time scales. Our method is consistent with, but independent of, previous studies. Here we examine how the zonal symmetry changes based on composites, and provide insight on a few mechanisms that might lead to these spatial variations. We then investigate how these zonally asymmetric structural changes lead to inconsistent regional climate anomalies, in particular SAM–temperature relationships across the SH on seasonal time scales. The latter improves upon the study by Gillett et al. (2006), who demonstrate when all months are considered the impacts on temperature, precipitation, and pressure are nearly zonally symmetric across the middle and high latitudes of the Southern Hemisphere.

2. Data and methods

The study makes use of two mean sea level pressure (MSLP) datasets to investigate the SAM structure, the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40; Uppala et al. 2005) and the Hadley Centre gridded SLP dataset (HadSLP2; Allan and Ansell 2006). The comparison of both datasets aids in assessing the reliability of both products in the data sparse regions of the SH: where the data agree, higher confidence can be placed on the quantitative results depicted by these fields. While other reanalyses extend to present, ERA-40 has higher skill for many state variables during the modern satellite era across the Southern Hemisphere (Bromwich and Fogt 2004; Bromwich et al. 2007). To investigate how the zonal asymmetries in the SAM structure influence regional climate, we use monthly surface temperature observations from the Global Historical Climate Network (Peterson and Vose 1997; Peterson et al. 1998) and the Reference Antarctic Data for Environmental Research (READER) database (Turner et al. 2004).

To determine leading SAM events we make use of two SAM indices, the leading empirical orthogonal function (EOF) of sea level pressure south of 20°S (Thompson et al. 2000) and the observationally based index of Marshall (2003, hereafter M03). The EOF-based SAM index was defined as the first principal component (PC) of detrended area-weighted (by the square root of the cosine of latitude) seasonal mean ERA-40 MSLP (interpolated to a 5° by 5° resolution) for the domain 20°–80°S. Because of uncertainties in the early ERA-40 data across the high southern latitudes (Bromwich and Fogt 2004; Bromwich et al. 2007), the EOFs were calculated using the detrended MSLP data during the 1979–2001 period, and the PCs calculated by projecting the original (i.e., nondetrended) data anomalies for the full period on the seasonal EOFs. The M03 index approximates the Gong and Wang (1999) index using zonal means from six station pressure observations at approximately 40° and six at 65°S (twelve stations total). To place both indices on the same scale, they have both been standardized over the full period of overlap, 1958–2001.

Our intent is to investigate the seasonal zonal asymmetric SAM structure and its impacts on the regional SH climate. To avoid any (small) structural changes resulting from different methods of calculating the SAM index (i.e., Bromwich et al. 2007; Jones et al. 2009; Fogt et al. 2009), we only define a SAM event as seasons when both indices are outside the threshold of ±0.75 standard deviations. Using the 0.75 standard deviation threshold ensures that only strong SAM events are considered in the analysis, thereby reducing the influence of unrelated noise as much as possible while keeping the sample size sufficient. All seasons refer to the SH and are defined as follows: summer [December–February (DJF)], autumn [March–May (MAM)], winter [June–August (JJA)], and spring (September–November (SON)].

3. Results

a. The zonally asymmetric SAM structure

Figure 1 compares the two indices for each season, standardized to have zero mean and unit variance over 1958–2001. Examining the seasonal plots in Fig. 1 demonstrates that the two indices are remarkably similar after 1979 when the ERA-40 data are of higher quality (detrended correlations are >0.85 during this period; Jones et al. 2009). Some substantial differences do occur, due either to data quality issues or stemming from the SAM definitions themselves, most notably the low 1964 JJA value in M03 and the pre-1979 data in JJA and SON in general. Focusing on the similarities rather than the differences, Table 1 lists the years for positive and negative SAM events for each season, as defined previously. The comparably lower number of events listed in JJA is because the two indices agree the least in JJA, partly because of the greater interannual circulation variability in the SH during this season and the larger JJA uncertainties in both HadSLP2 and ERA-40 (Fogt and Bromwich 2006; Allan and Ansell 2006). Further, the strong negative event in 1964 in M03 (which is likely related to the Agung eruption; M03) makes the standardized values of the other years comparatively weaker. This in turn lowers the number of events outside of the 0.75 threshold in M03, and hence reduces the number of times both indices fall outside of the threshold. Sensitivity tests were conducted to examine the influence of how the leading SAM events were defined, including a percentile-based method, different thresholds, and different base periods used to standardize the data. While adjusting these methods does change the years included in subsequent analyses, the resulting spatial structures change little. Thus, our main conclusions presented herein remain intact, and our results are insensitive to the particular method employed.

Fig. 1.

Standardized seasonal SAM indices, 1958–2001.

Fig. 1.

Standardized seasonal SAM indices, 1958–2001.

Table 1.

Years used for seasonal composites and statistics.

Years used for seasonal composites and statistics.
Years used for seasonal composites and statistics.

To examine the seasonal spatial structure during the leading SAM events, anomaly composites from ERA-40 MSLP and HadSLP2 were constructed, with the anomalies calculated as differences from the 1979–2001 seasonal mean. The seasonal ERA-40 MSLP composites are displayed in Fig. 2; HadSLP2 composites are displayed in Fig. 3. The shading represents regions where composite anomaly means are significantly different from zero at the p < 0.05 level, based on a two-tailed Student’s t test. Although subtle differences are apparent, Fig. 2 and Fig. 3 are similar in terms of the locations of the statistically significant anomalies, implying data quality issues in pre-1979 ERA-40 are not significantly influencing the results. This is further justified by the fact that over 65% of the years listed in Table 1 are from 1979 or later; over 80% occur from 1971–onward, the time when ERA-40 begins to show marked improvement with the assimilation of the Vertical Temperature Profile Radiometer data (Bromwich and Fogt 2004). The 500-hPa composites (not shown) are similar to those presented in Figs. 2 and 3, therefore the reduction to sea level pressure, which is unreliable over the high interior of the Antarctic continent, is not influencing the pattern. The differences in magnitude between Figs. 2 and 3 result primarily from ERA-40 having a larger standard deviation (about 1.5 times larger, not shown) than HadSLP2.

Fig. 2.

Time anomaly composites based on ERA-40 MSLP for (left) SAM+ and (right) SAM− by season, based on the years listed in Table 1: (a) DJF SAM+, (b) DJF SAM−, (c) MAM SAM+, (d) MAM SAM−, (e) JJA SAM+, (f) JJA SAM−, (g) SON SAM+, and (h) SON SAM−. Contours are in hPa with a 1-hPa contour interval, negative contours dashed. Shading denotes regions where the MSLP anomalies are statistically different from zero at the p < 0.05 level.

Fig. 2.

Time anomaly composites based on ERA-40 MSLP for (left) SAM+ and (right) SAM− by season, based on the years listed in Table 1: (a) DJF SAM+, (b) DJF SAM−, (c) MAM SAM+, (d) MAM SAM−, (e) JJA SAM+, (f) JJA SAM−, (g) SON SAM+, and (h) SON SAM−. Contours are in hPa with a 1-hPa contour interval, negative contours dashed. Shading denotes regions where the MSLP anomalies are statistically different from zero at the p < 0.05 level.

Fig. 3.

As in Fig. 2, but based on HadSLP2.

Fig. 3.

As in Fig. 2, but based on HadSLP2.

Figures 2 and 3 highlight marked seasonal variations of the SAM structure and also reveal nonlinearities (i.e., differences between positive and negative polarities) of the SAM. While the composites or regression patterns based on annual or monthly mean data are predominantly zonally symmetric, the zonal symmetry is not as apparent in the seasonal composites, especially during the austral winter and spring. The regions of significant differences in the midlatitudes vary slightly during each season or SAM phase, and no broad region equatorward of 50°S shows a persistent significant response during all SAM phases and seasons. Moreover, the significant differences are not always continuous over Antarctica, as austral winter SAM+ (Figs. 2e and 3e) shows regions of strong, but statistically insignificant, pressure anomalies poleward of 60°S. The nonzonally symmetric structure apparent in all panels of Figs. 2 and 3 also suggests that using difference composites (i.e., positive – negative events or vice versa) would mask the underlying nonzonally symmetric structure and the nonlinearity readily apparent when considering the SAM phase separately.

b. The transient asymmetric SAM component

To further highlight the zonally asymmetric SAM structure, the zonal mean was removed from Fig. 2, and the resulting field is displayed in Fig. 4 for ERA-40; qualitatively similar results are obtained using the HadSLP2 data (not shown). Following Peixoto and Oort (1992), the resulting term after decomposing the circulation by removing both the time and zonal means (p′*, with the prime and asterisk denoting departures from the time and zonal mean, respectively) is called hereafter the transient asymmetric (eddy) portion of the circulation. The plots in Fig. 4 thus detail the composite average transient asymmetric component of the SAM structure in the sea level pressure field, , where the overbar represents the composite average. Shading denotes anomalies significantly different from zero at the p < 0.05 level, which can be thought of as regions where the transient asymmetric component of the SAM structure p′* tends to remain constant across the SAM events identified in Table 1. It therefore highlights temporally consistent zonal asymmetries in the SAM structure (defined as the composite average of the temporal anomalies in Figs. 2 and 3).

Fig. 4.

The ERA-40 composite mean transient asymmetric portion of the SAM structure . The pattern can be constructed by removing the zonal mean from Fig. 2. Contour conventions as in Fig. 2. Shading denotes regions where the MSLP anomalies are statistically different from zero at the p < 0.05 level.

Fig. 4.

The ERA-40 composite mean transient asymmetric portion of the SAM structure . The pattern can be constructed by removing the zonal mean from Fig. 2. Contour conventions as in Fig. 2. Shading denotes regions where the MSLP anomalies are statistically different from zero at the p < 0.05 level.

Overall, the transient asymmetric component provides an important contribution to the SAM structure, especially for SAM+ in JJA, and SAM− in SON, readily seen when comparing the structure in Fig. 2e (Fig. 2h) and Fig. 4e (Fig. 4h). Further, across all seasons the anomaly patterns in Fig. 4 reach their greatest amplitude in the Pacific sector. This structure in the Pacific resembles a wave train pattern of anomalies extending from New Zealand, next to the coast of West Antarctica, and then into the Weddell Sea/Atlantic Ocean sector, similar to the Pacific–South American Pattern (PSA; Karoly 1989; Mo and Higgins 1998; Mo and Paegle 2001), a common teleconnection pattern of anomaly propagation from the central Pacific to the high southern Pacific Ocean. This result, and the discussion in the following section (3b), suggest that tropical forcing plays a significant role in modulating the variability of the transient asymmetric component of the SAM, and of the SAM pattern itself (L’Heureux and Thompson 2006; Fogt et al. 2011). Another notable feature is the similarities between positive and negative SAM phases in SON (Figs. 4g,h; spatial correlation r = −0.79); there are many fewer similarities between the positive and negative SAM phases in DJF and JJA, again highlighting a nonlinear feature of the SAM in these seasons.

A time series of transient asymmetric SAM component was created for each SAM phase by projecting the patterns in Fig. 4 on the full field using the ERA-40 data; this technique can be thought of as an area-weighted spatial regression of the patterns in Fig. 4 onto the p′* field for each year. Indices created using the HadSLP2 data are similar (not shown). Slight differences in the location and magnitude of the centers of action in Fig. 4 in the HadSLP2 give rise to slightly different indices, with correlations r > 0.65 for positive SAM phases and r > 0.70 for negative SAM phases between an ERA-40 based index and a HadSLP2 based index. Nonetheless, the significant relationships described below are observed using both HadSLP2 and ERA-40 data, and are thus insensitive to the data employed.

Figure 5 displays this transient asymmetric SAM index. A positive value of this index implies a pattern in the transient pressure field that resembles the matching pattern in Fig. 4. Immediately apparent are the high values prior to 1980 in DJF (for both SAM phases) and the high values of the SAM− series in MAM. While these may somewhat be influenced by the less reliable ERA-40 data, there are several reasons to believe at least the SAM− indices in these seasons are a robust signature of variability in the transient asymmetric SAM component:

  1. Table 1 shows that together the majority of SAM− events in DJF and MAM occurred before 1980.

  2. An index based on HadSLP2 data (calculated as those based on ERA-40 in Fig. 5), which is not influenced by the introduction of satellite data after 1979, also has mainly positive values for the SAM− indices in DJF and MAM (not shown) prior to 1980.

  3. Shifts in other Southern Hemisphere circulation indices, such as zonal wavenumber 1 (van Loon et al. 1993; Raphael 2003) and 3 (Raphael 2004), were also seen around 1979. There is also evidence that such shifts are not specific to the Southern Hemisphere (Trenberth and Hurrell 1994).

  4. The quality of ERA-40 before 1979 is much better in austral summer than in austral winter (Bromwich and Fogt 2004), suggesting that concerns on the reliability of the early ERA-40 data likely have little impact in the time series in Fig. 5a.

It is therefore believed that these shifts represent a true change from a predominantly asymmetric SAM structure in early austral summer and autumn to a much more zonally symmetric structure during the last decades. The extent to which the more zonal structure may be due to external forcing mechanisms such as greenhouse gases or stratospheric polar ozone depletion remains to be seen and is planned for future work.

Fig. 5.

Seasonal plots of a transient asymmetric SAM index based on ERA-40 MSLP, defined separately for positive (SAM+) and negative (SAM−) SAM phases. The units of the index are hPa hPa−1. See text for details.

Fig. 5.

Seasonal plots of a transient asymmetric SAM index based on ERA-40 MSLP, defined separately for positive (SAM+) and negative (SAM−) SAM phases. The units of the index are hPa hPa−1. See text for details.

Also readily apparent in Fig. 5 is the anticorrelation between the two indices, most marked in SON (r = −0.91). As described earlier, this reflects the fact that the centers in Figs. 4g,h between the SAM+ and SAM− composites occur roughly in the same locations, but with opposite signs (i.e., the SAM structure is quite linear between positive and negative phases in SON). In addition to the negative trends in Figs. 5a and 5b, there is also a negative trend of the transient asymmetric index for SAM+ cases in JJA (Fig. 5c).

Table 2 displays the correlations of the indices in Fig. 5 with the M03 SAM index, the Niño-3.4 sea surface temperature anomalies [an El Niño–Southern Oscillation (ENSO) index], and a zonal wavenumber-3 (ZW3) index calculated from the 500-hPa geopotential height zonal anomaly field using the equation of Raphael (2004; the average of normalized height anomalies at 49°S and longitudes 50°E, 166°E, 76°W). With 42 degrees of freedom, |correlations| > 0.39 are statistically different from zero at p < 0.01; and |correlations| > 0.30 are statistically different from zero at p < 0.05. Since the index monitors the transient asymmetric SAM component, it is not surprising that it has statistically significant correlation with the SAM. The magnitude of the correlations indicates that this component explains as little as 2% of the temporal SAM variance in DJF to over 25% in other seasons. Although the SAM is the leading climate mode of the Southern Hemisphere extratropical circulation, it only explains ~35% of the temporal climate variability across the Southern Hemisphere on various time scales (Baldwin 2001; Kidson 1999), so the percentages of explained variance based on the correlations in Table 2 are quite remarkable. They suggest that a nonnegligible portion of the SAM structure and its variability with time is manifested in this transient asymmetric component. Using data from Table 2, the maximal percentage of total seasonal Southern Hemisphere extratropical circulation variance accounted for by the transient asymmetric component of the SAM (up to ~9%) is consistent with the amount of circulation variance associated with the PSA pattern (Mo and Higgins 1998).

Table 2.

Correlations (1958–2001) of the ERA-40 transient asymmetric SAM index with various Southern Hemisphere climate parameters.

Correlations (1958–2001) of the ERA-40 transient asymmetric SAM index with various Southern Hemisphere climate parameters.
Correlations (1958–2001) of the ERA-40 transient asymmetric SAM index with various Southern Hemisphere climate parameters.

As discussed earlier, the transient asymmetric component may be related to tropical activity given the fact that in many cases it resembles the PSA. This is reinforced by the fact that there are significant correlations of the transient asymmetric SAM index with the Niño-3.4 sea surface temperatures in Table 2. Consistent with Fogt et al. (2011), the ENSO–SAM relationship, and also the relationship of ENSO with the asymmetric portion of the SAM, is most marked during positive SAM phases, but is seen in both phases in spring–summer. Impressively, for SAM+ in austral summer, the Niño-3.4 index correlates with the transient asymmetric SAM index from HadSLP2 at r = −0.72 (not shown), implying a very strong relationship between the zonal asymmetries in the SAM with ENSO in austral summer (based on ERA-40 the correlation is −0.49, still significant at p < 0.01). This result has been observed in other studies, which note in particular a strong ENSO–SAM relationship confined to the austral summer season (Carvalho et al. 2005; L’Heureux and Thompson 2006; Fogt and Bromwich 2006). This relationship is in part due to the fact that both ENSO and SAM have a pronounced center of action in the southeast Pacific (Simmonds and King 2004). It also extends the work of Fogt et al. (2011), who note that the SAM structure is more zonally symmetric when SAM events occur without a simultaneous ENSO event. Their study finds that in-phase combinations of ENSO and SAM (i.e., a La Niña occurring with a positive SAM or an El Niño occurring with a negative SAM), which contribute to the zonal asymmetric SAM structure, are most common in November–February. The correlations of the transient asymmetric SAM index and the Niño-3.4 SST anomalies peaking in SON and DJF (Table 2) are therefore consistent with the role tropical forcing plays in creating zonal asymmetries in the SAM structure. The relationship with tropical activity also suggests that the SON correlation between the SAM index and the Southern Oscillation index in Silvestri and Vera (2003) is strongly tied to the zonally asymmetric component of the SAM structure apparent in this season. Given the significant negative correlation during positive SAM phases in austral winter in Table 2, there is also support for the argument that the winter SAM zonal asymmetries addressed by Fan (2007) are similarly tied to tropical activity.

Correlations with the ZW3 index of Raphael (2004) based on ERA-40 500-hPa-height data are shown in the last column of Table 2. Overall, significant correlations are observed only in JJA and for SAM+ phases in SON, indicating at these times the transient asymmetric pattern in Fig. 4 aligns with the locations of the persistent ridges in ZW3 identified in Raphael (2004). Although the pattern in MAM in Figs. 4c and 4d also resembles a ZW3 pattern, the location of the ridges and troughs do not correspond precisely to the three grid points used to define the Raphael (2004) index. Thus, this wave-3 structure in fall is an uncommon circulation configuration (see also Fig. 9 of Renwick 2005).

The SAM can be thought of physically as meridional shifts in the position of the jet streams across the Southern Hemisphere, and the transient asymmetric SAM structure is also sensitive to these fluctuations. Figure 6 displays the climatological mean zonal wind at 300 hPa over the 1979–2001 period (shaded). The outlined regions denote significant correlations (p < 0.05) of the ERA-40 transient asymmetric SAM indices in Fig. 5 with the 300-hPa zonal wind (over the same period), with solid contours indicating positive correlations (i.e., transient asymmetric structures in the SAM which resemble Fig. 4). Negative correlations (dashed in Fig. 6) display a less asymmetric structure or conditions nearly opposite those seen in Fig. 4. For positive SAM cases (left panels in Fig. 6), all seasons display positive correlations with the 300-hPa zonal winds on the poleward flanks of the polar front jet core in the Indian Ocean sector, suggesting that as the jet strengthens and contracts (as it does during a positive SAM event; Fyfe and Lorenz 2005), the asymmetric structures in Fig. 4 arise. Conversely, during positive SAM cases negative correlations are found on the equatorward flank of the polar front jet throughout much of the Atlantic and Indian Ocean sectors, indicating that the equatorward displacement of the jet, common in negative SAM events, would either weaken the asymmetric structure seen in Fig. 4 or create opposite conditions. For negative SAM cases (right panels), the signs of the correlations are reversed (negative correlations on the poleward flank of the jet, positive on the equatorward flank of the jet), and by similar arguments, again reinforce the sensitivity of not only the SAM phase to the meridional movement of the jets, but also its asymmetric seasonally varying structure (see also Barnes and Hartmann 2010). In general, however, these correlations on the poleward and equatorward flanks of the jet cover small areas and are consistent with expected behavior as the SAM changes phase. Thus, the correlations could arise from the fact that the transient asymmetric SAM indices are significantly correlated with the SAM index (Table 2).

Fig. 6.

ERA-40 300-hPa average zonal wind, 1979–2001 (shaded) by season. The contours denote regions where the correlation of the ERA-40 transient asymmetric SAM indices (during 1979–2001) with the 300-hPa zonal wind is statistically different from zero at the p < 0.05 level.

Fig. 6.

ERA-40 300-hPa average zonal wind, 1979–2001 (shaded) by season. The contours denote regions where the correlation of the ERA-40 transient asymmetric SAM indices (during 1979–2001) with the 300-hPa zonal wind is statistically different from zero at the p < 0.05 level.

Perhaps more telling are the strong correlations in the Pacific sector in Fig. 6. In all seasons, significant correlations between the 300-hPa zonal wind and the transient asymmetric SAM indices are observed in the Pacific sector, in a region where the zonal wind is at a relative minimum (poleward of 30°S). While this may not be surprising given the strong pressure anomalies noted in this region in Fig. 4, it does provide further insight that variations in the jet entrance (eastern Pacific) and jet exit (western Pacific) regions are very important mechanisms associated with the asymmetric SAM structure. As the jets themselves arise from the interaction of the transient eddies (both heat and momentum induced from individual cyclones) with the mean flow associated with the meridional circulation cells (i.e., Hadley, Ferrel, and Polar Cells), variations in these transient eddies can give rise to marked changes in the asymmetric SAM structure. This is evidenced in part in Fig. 7, which is similar to Fig. 6, but based on the 300-hPa total meridional momentum flux, (in the Southern Hemisphere, the total meridional momentum flux is dominated by influences from the transient eddies, Oort and Peixoto 1983, and is positive for equatorward momentum fluxes). The 300-hPa level was chosen as the meridional momentum fluxes reach their maximum amplitude here, corresponding to the approximate level of the jet cores in the Southern Hemisphere (Oort and Peixoto 1983). Note that daily data were used in calculating the total momentum flux; therefore averaging it over a season depicts the net effect due to cyclonic activity.

Fig. 7.

As in Fig. 6, but for 300 hPa .

Fig. 7.

As in Fig. 6, but for 300 hPa .

In the Pacific sector around 40°S, significant correlations are found with the upper-tropospheric momentum flux in both the positive and negative phases. This suggests variations in cyclonic activity (linked with changes in the zonal flow; Fig. 6) in this region give rise to local asymmetries in the atmospheric circulation, manifested in the transient asymmetric component of the SAM in Fig. 4. The correlations cover the largest area from austral fall–spring, the time when the South Pacific SAM asymmetric structure is the strongest. Moreover, the regionalization of the correlations of the transient eddies with the SAM (i.e., the fact that they are maximized in the Pacific sector) is in contrast with how transient eddies influence the total SAM structure—previous studies (Limpasuvan and Hartmann 1999; Hall and Visbeck 2002; Rashid and Simmonds 2004) find that the zonal structure commonly attributed to the SAM is due to roughly equal contribution of transient eddies across all longitudes. While ENSO is especially known to influence these transient eddies across the Pacific sector (L’Heureux and Thompson 2006; Fogt et al. 2011), other more regional influences, including low-level heat content (of both the atmosphere and ocean) and the position of tropical convection anomalies (Lachlan-Cope and Connolley 2006), also likely play an important role in generating the asymmetric SAM structure and are the subject for future work. Thus while both the total SAM structure and its transient asymmetric component are at least somewhat influenced by meridional shifts in the jet (Fig. 6), the transient asymmetric component is particularly sensitive to variations in the jet entrance/exit regions in the Pacific (Fig. 6) associated with the contribution from transient eddies there (Fig. 7).

c. The influence of the SAM structure on surface temperatures

In this section we investigate the influence of asymmetries in the SAM structure on surface temperatures. We do not consider precipitation, as reliable precipitation observations are not widely available for the Antarctic. Correlations calculated in all four seasons between the ERA-40 SAM index and surface temperatures (for the period 1958–2001, both time series were detrended prior to calculation) are shown in Fig. 8. To distinguish between insignificant correlations and missing data, the former are shown as small black dots, and the latter as black stars, in Figs. 811. Displaying the seasonal variations in SAM–temperature correlations expands on previous work that has either considered the four seasons regionally (e.g., Marshall 2007 for the Antarctic; Kidston et al. 2009 for New Zealand), or hemispherically for individual seasons (e.g., Gillett et al. 2006 annually; Silvestri and Vera 2009 for SON). To investigate whether the SAM–temperature signal may differ between (strong) positive and (strong) negative SAM phases compared to the linear signal identified in the correlations in Fig. 8, positive and negative composites of surface temperature anomalies for the years identified in Table 1 are displayed in Fig. 9.

Fig. 8.

Dots are correlations between the ERA-40 SAM index and station temperatures, 1958–2001. Correlations that are significant at p < 0.05 are shown as colored dots, black dots are insignificant correlations, and black stars stations with >17 yr of data missing for (a) DJF, (b) MAM, (c) JJA, and (d) SON. Both time series were detrended prior to calculation. Contour lines are a covariance map between ERA-40 MSLP anomalies and the ERA-40 SAM index, calculated over the period 1958–2001.

Fig. 8.

Dots are correlations between the ERA-40 SAM index and station temperatures, 1958–2001. Correlations that are significant at p < 0.05 are shown as colored dots, black dots are insignificant correlations, and black stars stations with >17 yr of data missing for (a) DJF, (b) MAM, (c) JJA, and (d) SON. Both time series were detrended prior to calculation. Contour lines are a covariance map between ERA-40 MSLP anomalies and the ERA-40 SAM index, calculated over the period 1958–2001.

Fig. 9.

Sea level pressure (contour lines, hPa) and temperature (shaded circles, °C) composites for the events listed in Table 1, as anomalies relative to the 1958–2001 mean: (a) DJF SAM+, (b) DJF SAM−, (c) MAM SAM+, (d) MAM SAM−, (e) JJA SAM+, (f) JJA SAM−, (g) SON SAM+, and (h) SON SAM−. Only stations with significant anomalies are shown; stars mark stations with missing data.

Fig. 9.

Sea level pressure (contour lines, hPa) and temperature (shaded circles, °C) composites for the events listed in Table 1, as anomalies relative to the 1958–2001 mean: (a) DJF SAM+, (b) DJF SAM−, (c) MAM SAM+, (d) MAM SAM−, (e) JJA SAM+, (f) JJA SAM−, (g) SON SAM+, and (h) SON SAM−. Only stations with significant anomalies are shown; stars mark stations with missing data.

Fig. 10.

Sea level pressure (contour lines, hPa) and temperature (shaded circles, °C) composite differences (positive − negative) for the events listed in Table 1, as anomalies relative to the 1958–2001 mean: (a) DJF, (b) MAM, (c) JJA, and (d) SON. Only stations with significant anomalies are shown, stars mark stations with missing data. Note the different scale to Fig. 9.

Fig. 10.

Sea level pressure (contour lines, hPa) and temperature (shaded circles, °C) composite differences (positive − negative) for the events listed in Table 1, as anomalies relative to the 1958–2001 mean: (a) DJF, (b) MAM, (c) JJA, and (d) SON. Only stations with significant anomalies are shown, stars mark stations with missing data. Note the different scale to Fig. 9.

Fig. 11.

Correlations (colored dots) between the transient asymmetric SAM indices, (as calculated in section 3b) and station temperatures. Correlations calculated over the period 1958–2001. Both time series detrended prior to calculation. Contours are the transient asymmetric SAM pattern in Fig. 4: (a) DJF SAM+, (b) DJF SAM−, (c) MAM SAM+, (d) MAM SAM−, (e) JJA SAM+, (f) JJA SAM−, (g) SON SAM+, and (h) SON SAM−. Stars mark stations with missing data.

Fig. 11.

Correlations (colored dots) between the transient asymmetric SAM indices, (as calculated in section 3b) and station temperatures. Correlations calculated over the period 1958–2001. Both time series detrended prior to calculation. Contours are the transient asymmetric SAM pattern in Fig. 4: (a) DJF SAM+, (b) DJF SAM−, (c) MAM SAM+, (d) MAM SAM−, (e) JJA SAM+, (f) JJA SAM−, (g) SON SAM+, and (h) SON SAM−. Stars mark stations with missing data.

In all seasons, correlations based on the full SAM index are significant over Antarctica, with the fewest significant correlations in SON (Fig. 8). In the midlatitudes, New Zealand has the strongest and most consistent correlations across all seasons, with significant positive correlations over both islands in summer and winter. The composites based on the events in Table 1 show more regions and stations with a significant midlatitude SAM response (Fig. 9), but are in agreement with the finding in section 3a that no broad region equatorward of 50°S has a significant response in all seasons with the SAM: midlatitude regions show a variable SAM–temperature signal, and differences between positive and negative phases in some regions, perhaps explaining the insignificant correlations in Fig. 8. For example, in Australia for SAM+ in DJF (Fig. 9a), there are numerous significant temperature anomalies, but fewer for SAM−. Similar changes in the sign and/or significance in the SAM–temperature relationship are found across the seasons and between SAM phases in the midlatitudes, highlighting the need to consider midlatitude SAM impacts seasonally. We note however that insignificant correlations may result from a changing linear SAM–temperature relationship within the time period considered (e.g., Silvestri and Vera 2009 for South America and New Zealand).

In MAM, there is a more widespread and stronger Antarctic temperature response in the SAM+ composite than the SAM− composite (Figs. 9c,d), as more stations have significant anomalies in the former compared to the latter. In the MAM SAM+ composite (Fig. 9c), very strong temperature anomalies over the Antarctic Peninsula as large as +2.6°C are noted, indicating an especially strong, consistent temperature response between SAM+ events in this region. In contrast, the SAM–temperature signal weakens over the Antarctic continent during negative phases in MAM, while it increases over Australia (Fig. 9d), reflected in significant positive temperature anomalies throughout eastern Australia and negative across Western Australia. Differences in the temperature response between the positive and negative SAM also occur in SON (Figs. 9g,h), with a larger number of significant anomalies for SAM− over Antarctica and the midlatitudes (especially Australia), compared to the SAM+ composite. This change in SON is in part consistent with the stronger (in magnitude and area) significant SLP anomalies for negative SAM events (Fig. 2h). The SAM–temperature relationships are most symmetric in DJF and JJA (Figs. 9a,b,e,f, respectively).

Differences between the regional temperature response between positive and negative SAM phases indicate that analyses that consider the difference between high and low index polarity (e.g., Hendon et al. 2007) may not uncover these differences. To test this, the difference between the SAM+ and SAM− composites were calculated (Fig. 10). In all four seasons over the Antarctic, and particularly the Peninsula, there are significant difference anomalies for stations that have significant composite anomalies in one SAM phase but not the other in Fig. 9. For example in MAM, stations on the Dronning Maud Land coast of Antarctica have significant negative temperature anomalies for SAM+ (Fig. 9c), but not for SAM− (Fig. 9d), but show [with the exception of Belgrano (78.0°S, 38.8°W)] significant negative values in the positive minus negative composites (Fig. 10b). Similarly in SON, significant positive temperature anomalies only for SAM− (Fig. 9h) result in significant negative values in the positive minus negative composite at many continental Antarctic stations (Fig. 10d). Another region influenced by this is eastern Australia, for example, in MAM significant positive temperature anomalies at most stations for SAM− (Fig. 9d) result in significant negative values in the positive minus negative composite (Fig. 10b).

While the temperature composites show interesting regional changes in the SAM–temperature relationship in the Southern Hemisphere, they include influences from both the zonally symmetric and zonally asymmetric features of the SAM. Given that there have been notable changes in the asymmetric structure of the SAM in many seasons (Fig. 5), the strength of the SAM–temperature relationship will have also changed in regions where the influence from the asymmetric structure of the SAM dominates over the zonally symmetric portion. First, we investigate how the asymmetric portion of the SAM influences temperature anomalies over the Southern Hemisphere, based on correlations between the transient asymmetric SAM indices with surface temperatures, over the period 1958–2001 (Fig. 11). Not surprisingly, given the greatest amplitude of asymmetric SAM structure in the Pacific sector (Fig. 4, contours in Fig. 11), temperature observations in the New Zealand region and the Antarctic Peninsula (except during DJF) are significantly correlated with the transient asymmetric SAM indices (Fig. 11). Southeastern Australia is also significantly correlated with the transient asymmetric SAM index for both SAM+ and SAM− in SON, with SAM+ in DJF, and SAM− in MAM. The correlations are largely of opposite sign (linear) between the SAM+ and SAM− phases, in agreement with the results shown in section 3b. An exception is New Zealand in MAM, where the correlations of the transient asymmetric SAM indices are significant on the North Island in SAM+ correlations, but not for SAM− (Fig. 4c shows significant anomalies in the New Zealand region, whereas Fig. 4d does not).

These results suggest that the transient asymmetric component of the SAM may alter the linear SAM–temperature signal in regions influenced by the ZW3 pattern in the Pacific sector. To investigate these potential influences, we consider the temperature and pressure anomalies for examples of individual events that project strongly onto the asymmetric SAM index.

Where the sign of the transient asymmetric SAM index agrees with the SAM index, for some events the SAM pressure and hence temperature response is strengthened. As an example, JJA 1995 is a SAM− year (Fig. 1, Table 1), which also is marked with a strong asymmetric component (Fig. 5c). It can be seen from Fig. 12a that this year has particularly strong positive SLP anomalies in the eastern Pacific (anomalies as large as 16 hPa) negative anomalies over New Zealand and in the western South Atlantic, fitting with the zonally asymmetric SAM structure in this season (Fig. 4f). By assuming geostrophy, the Pacific anomalies increase cold air advection across the Antarctic Peninsula, thereby strengthening the SAM response and generating strong temperature anomalies (−3.3°C; Fig. 12a). These temperature anomalies can be contrasted to the composite based on the leading events (i.e., Fig. 9f), where the maximum negative significant anomaly on the Antarctic Peninsula is −2.7°C (Marambio) with significant anomalies only along the northern Antarctic Peninsula. Similarly in the SAM− event in MAM 1990 (Table 1, Fig. 1, Fig. 12b), the strong asymmetric SAM component (Fig. 5b) results in a strong positive SLP anomaly in the southeastern Pacific, (again projecting strongly on the transient asymmetric SAM, Fig. 4d), giving enhanced southerly flow anomalies to the Antarctic Peninsula, with temperature anomalies as low as −5.4°C (at Marambio). A similar event happens in MAM 1958 (not shown).

Fig. 12.

Sea level pressure (contour lines, hPa) and temperature (shaded circles, °C) for (a) JJA 1995, (b) MAM 1958, and (c) DJF 1970, as anomalies relative to the 1958–2001 mean. Only stations with significant anomalies are shown; stars mark stations with missing data. Note the different scale to Fig. 9.

Fig. 12.

Sea level pressure (contour lines, hPa) and temperature (shaded circles, °C) for (a) JJA 1995, (b) MAM 1958, and (c) DJF 1970, as anomalies relative to the 1958–2001 mean. Only stations with significant anomalies are shown; stars mark stations with missing data. Note the different scale to Fig. 9.

However, the asymmetric component of the SAM can also weaken the SAM–temperature signals in Figs. 8 and 9, particularly when the SAM index and transient asymmetric SAM index are of opposite sign, although this does not occur frequently. DJF 1970 has a moderately strong negative SAM index (Fig. 1); however, this season projects strongly onto the positive SAM asymmetric index (Fig. 5). Figure 12c shows that negative SLP anomalies in the central Pacific and positive SLP anomalies over New Zealand project strongly onto the positive transient asymmetric SAM (Fig. 4a), although the high-latitude SLP anomalies are positive as for a negative SAM. The positive SLP anomalies over New Zealand therefore result in positive temperature anomalies (fitting with the positive transient asymmetric SAM), instead of negative temperature anomalies that may be expected under a positive SAM in this region (Fig. 8a).

d. Connection to previous work

Marshall et al. (2011) find that changes in the wavenumber-3 pattern result in a reversal in the SAM–temperature relationship at Halley in MAM. However our transient asymmetric SAM index is not significantly correlated to temperatures at this station and is also not significantly correlated with the zonal wavenumber-3 index (Table 2) in this season. Thus, the connection between the asymmetric SAM structure we describe here and the Halley temperatures is not straightforward. The lack of a strong connection likely is due to subtle differences between the location of our pressure anomalies in Figs. 4c and 4d compared to the pressure anomalies Marshall et al. (2011) find related to Halley temperatures (their Fig. 6). Namely the pressure anomaly centers to the east and west of the Antarctic Peninsula are farther south than in our Figs. 4c and 4d.

Silvestri and Vera (2009) also found a reversal in the SAM–temperature relationship in austral spring (which they define as October, November, December) over the Antarctic Peninsula and Australia between 1958–79 and 1983–2004. Although our results show a distinct influence of the transient asymmetric SAM on the SAM index in these regions in SON, the transient asymmetric SAM usually acts to reinforce the SAM temperature signal, not to change its sign, as it is rare that the SAM and transient asymmetric SAM indices oppose each other (the DJF 1970 case shown in Fig. 12c is a rare example). Also there is no systematic difference in mean or trend in our index between these two periods that could explain the changes, and it is unlikely that the trend observed in DJF (Fig. 5a) is due to December alone.

Our results also show some similarities and differences with Marshall (2007), which considers the stationarity of SAM–temperature relationships for Amundsen–Scott, Mawson and Esperanza in JJA and DJF. Of these stations, we find only Esperanza (and other northern Antarctic Peninsula stations) in JJA to be significantly correlated with the transient asymmetric SAM index (Fig. 11e,f). The lack of significant summer relationships with the transient asymmetric SAM index supports his finding that the strengthening SAM–temperature relationship at Esperanza in DJF is a result of a strengthening summer SAM (and perhaps a weakening asymmetric SAM, Fig. 5a). The variations in the strength of the positive JJA SAM–temperature correlations at this station may however be potentially linked to variations in the asymmetric SAM.

4. Summary and conclusions

While indices of the SAM are constructed using purely statistical techniques (i.e., the leading EOF of the sea level pressure field or difference in zonal mean pressures), the indices have strong physical meaning through meridional displacements of the midlatitude jet and variations in its intensity (Fyfe and Lorenz 2005). Further, regardless of the method used to create a SAM index, the spatial pattern of the SAM created by regressing the index onto the sea level pressure anomalies shows a primarily zonally symmetric structure, especially when based on monthly or annual mean data (Thompson and Wallace 2000). Yet, embedded within this structure are varying degrees of asymmetry; this paper has explored the structure of these asymmetries by season in greater detail than before, including how these asymmetries are related to temperature anomalies across the Southern Hemisphere. Our key findings are as follows:

  1. The asymmetric SAM structure, often masked in conventional difference plots or correlation maps, is most marked in austral winter and spring. In summer and autumn there are notable negative trends in the asymmetric structure (i.e., the SAM has been becoming increasingly more zonally symmetric in these seasons).

  2. The asymmetric SAM structure explains as much as 25% of the variability in the overall SAM structure, especially in negative SAM phases.

  3. The asymmetric structure certainly has ties with ENSO variations, especially in spring and summer. In winter, it is more strongly tied to the zonal wavenumber-3 pattern (Raphael 2004) of the Southern Hemisphere circulation. In all seasons, the amplitude of the SAM zonal asymmetries is greatest in the Pacific sector.

  4. The asymmetric structure is associated with not only meridional displacements of the jet streams in the Southern Hemisphere (as is the SAM; Fyfe and Lorenz 2005), but also variations in the jet entrance and exit regions in the Pacific. Further, in contrast to the total SAM structure, which is influenced fairly evenly by transient eddies across all longitudes (Hall and Visbeck 2002), the asymmetric structure shows stronger forcing from the transient eddies in the Pacific sector.

  5. While the total SAM structure has hemispheric-wide impacts on temperature, the asymmetric structure has notable regional impacts, which not only vary by season, but by SAM phase. However, we do not find a simple relationship between our results and previous studies, which noted nonstationary SAM impacts of the SAM on Southern Hemisphere climate (i.e., Silvestri and Vera 2009; Marshall 2007; Marshall et al. 2011). In a few cases, changes in the SAM–temperature relationships across the Southern Hemisphere may be due to influences from the asymmetric SAM structure, as we note here. However, many SAM–temperature changes are due to other factors, some of which may stem from nonlinearities noted between positive and negative SAM in this study.

Results from the study suggest that caution is warranted when using the SAM index to understand climate variations across the Southern Hemisphere. Indeed, there are well-known seasonal variations in the Southern Hemisphere zonal flow (i.e., Bals-Elsholz et al. 2001, and references therein; Fig. 6), and since the SAM is essentially an index which monitors the zonal flow in the Southern Hemisphere, it is not surprising that it also shows strong seasonal variations. The underlying seasonal variations show structures that deviate significantly from zonal symmetry, as well as unexpected changes between positive and negative SAM phases. The seasonal changes in the SAM structure and the resulting changing climate impacts arising from the zonal and asymmetric components must therefore be considered, else any investigations might mask important variations that appear to be stationary using monthly and annual means but in actuality are not. These results also have implications for the use of proxy data from locations with nonstationary SAM–climate relationships for reconstructions of the SAM and other modes of Southern Hemisphere atmospheric circulation, as noted by Marshall et al. (2011).

We hypothesize that changes in anthropogenic forcing (i.e., ozone depletion and greenhouse gas increases), which have in part generated the positive SAM trends in summer, may also be changing the SAM structure to a more pronounced zonal signature. Marshall et al. (2011) note that variations in the SAM–temperature relationship in parts of East Antarctica are tied to natural SAM variability rather than any anthropogenic forcing. Consistent with our results here, this suggests that if in fact anthropogenic forcing leads to a more pronounced zonal SAM structure, the SAM–temperature impacts across the Southern Hemisphere in the future will be less regionally defined and more stationary with time. An exception is in the western side of the Antarctic Peninsula, where the topography and its blocking effect on low-level zonal flow will always induce an asymmetric component of the SAM in the Amundsen–Bellingshausen Seas (Lefebvre et al. 2004).

Acknowledgments

The Hadley Centre is thanked for providing HADSLP2 data, and the European Centre for Medium-Range Weather Forecasts is acknowledged for provision of the ERA-40 data. RLF acknowledges support from NSF OPP Grant ANT-0944168. JMJ thanks Tricia Muldoon and Graham Simpkins for help with collating the station data. JR is supported through contract C01 × 0701 with the NZ Ministry for Research and Innovation. All authors acknowledge the thorough comments from three anonymous reviewers that helped to tighten the text in many places and clarify our main points.

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