The length of the analyzed time series is 68 years (Fig. 3, left) covering more than 4 full 16-year periods determined in the ESP temperature (Fig. 4a). An unusually stable oscillation is statistically significant (Figs. 2b and 5a and 5b) and requires the existence of equally long-acting sources.
As described in Sect. 2, the SW16 index was introduced to quantitatively represent the 16-year periodicity in the ESP time series (Fig. 2). Based on the SW16 index, one can verify whether a source of this periodicity exists in the SH atmosphere variability.
Figures 6a–6d display anomalies in the spatial distribution of linear correlation coefficient r between SW16 and several SH atmospheric variables: sea level pressure SLP and surface air temperature and wind components. It is seen that the largest area of significant correlation anomalies is in the SLP response (Fig. 6a). Correlation anomalies in the SH subtropical and middle latitudes demonstrate zonal wave-3 pattern, represented by three positive correlation anomalies (Fig. 6a) alternating with negative ones (most pronounced in correlation with surface temperature in Fig. 6b). This quasi-stationary pattern in the SH atmosphere is determined by the alternation of three continents and three ocean basins (Raphael 2004) and three subtropical anticyclones (van Loon and Jenne 1972; Carleton 2003; al Fahad et al. 2020) in the longitudinal direction.
Less pronounced wave 3 patterns are observed in the correlations with the wind components (Fig. 6c and 6d). Correlation anomalies significant at the 95% confidence level exist over and in the vicinity of the AP region (Fig. 6b–6d). The intense positive anomalies (r = 0.3–0.6) related to zonal wave 3 are seen in the New Zealand (NZ) region (Fig. 6a and 6b). They indicate a 16-year periodicity in the sea level pressure and surface temperature over a fairly large area. Indeed, even the annual mean temperature observed in New Zealand (Fig. 7c) shows that the spectral band of 13.3–16.0 (12.7–17.2) years is statistically significant at the 99% (95%) confidence level (Fig. 7d). The peak in the wavelet power spectrum is at 14.6 years. This result is based on the NZ temperature records in 1952–2009 averaged over 11 stations (Fig. 7c) and available at the National Institute of Water and Atmospheric Research (NIWA) web site https://niwa.co.nz/climate/information-and-resources/nz-temperature-record.
The SW16 index is coupled with the SLP anomalies of the anticyclone type: an increase in SW16 corresponds to an increase in the regional SLP anomaly. A strong positive anomaly in correlation ‘SW16 vs SLP’ located in the southwestern Atlantic (SWA, Fig. 6a) adjacent to the AP region indicates the close relationship between the anomalies in the SWA SLP and the ESP temperature on the interdecadal time scale.
The area of the maximal SLP response to SW16 limited by the coordinates of 40–50°S, 30–50°W (the dashed rectangle in Fig. 6a) was used to verify the correlations. The time series of area-averaged SLP anomalies inside this domain (Fig. 7a) was considered as the regional 16-year SLP variability index, SLP16. It is seen from Fig. 7b that the 16.6-year period clearly stands out in the spectrum of SLP16 (the solid vertical line) and spectral band of about 15–19 years is significant at the 99% confidence level (the shaded rectangle).
As noted above, the wave 3 ridges in Fig. 6a are located mainly in the SH subtropics and middle latitudes and their phase correlation with SW16 suggests the presence of close to 16-year period throughout this zone. The zonal mean SLP anomalies for 30–50°S (characterized by the index SLP30–50S), demonstrate a close period of 15.5 years statistically significant at the 99% confidence level (Fig. 7f). The existence of this variability in the mid-latitude zone shows the important role of a periodicity close to 16 years as one of the most powerful modes in the SH climate change. Moreover, the correlation anomalies in Fig. 6 highlight certain areas where changes in SLP, surface temperature and wind components can affect the climate on an interdecadal time scale.
The coupling of the SLP16 index with the SH sea level pressure is shown in Fig. 6e. Correlation anomalies display zonal wave 3 (the blue dotted curve at zero correlation) and SAM (opposite anomalies in middle and high SH latitudes) patterns. It should be pointed out that the correlations coefficient distribution for both SW16 (Fig. 6a) and SLP16 (Fig. 6e) does not reveal statistically significant anomalies along the equatorial Pacific, characteristic of the El Niño phenomenon. Positive anomaly in correlation ‘SLP16 vs T-surface’ in the western tropical Pacific is evident (Fig. 6f), however, this is not a typical El Niño region located usually in the central and eastern Pacific (Rasmussen and Carpenter 1982; Trenberth et al. 2002). In addition, SW16 and SLP16 are weakly coupled not only with tropical surface air temperature (Fig. 6b and 6f), but also with the tropical sea surface temperature (SST; Fig. S2). Moreover, in the case of SST, no clear anomalies that resemble SAM or wave 3 patterns are evident (Fig. S2). Such relatively weak presence of the 16-year periodicity in the SST variability can indicate that this spectral component is more associated with atmospheric than with oceanic processes.
We note that the SLP16 index correlates with T, U-wind and V-wind much stronger (rmax = 0.6; Fig. 6f–6h) than the SW16 index (rmax = 0.4; Fig. 6b–6d, respectively). Statistically significant correlation anomalies cover the northern part of AP, but do not reach its southern part. For U-wind and V-wind (Fig. 6g and 6h, respectively), they are statistically significant at ESP and ORC and not significant at FV (the stations’ locations are marked with the red, green and yellow circles respectively). The SLP16 effect in the surface temperature has a similar difference in significance but FV is located at the edge of the area outlined by the 95% significance contour (Fig. 6f). The influence of the SLP16 anomaly (Fig. 6a) on the AP region is, therefore, spatially limited and Fig. 6f–6h clarifies why the 16-year oscillation can be more reliably recorded in the northern AP but not in the southern AP.
The relative position of positive and negative anomalies in the response of wind components to SLP16 exactly corresponds to anticyclonic (counterclockwise) circulation in South Atlantic and Weddell Sea (solid curved arrows in Fig. 6g and 6h). The positive correlation anomaly in the U-wind (V-wind) at the southern (eastern) edge of the SLP16 region in Fig. 6g (Fig. 6h) can be interpreted as a westerly (southerly) enhancement in response to the SLP16 index increase. This pattern is consistent with the structure of the anticyclonic wind (solid curved arrows in Figs. 6g and 6h) in the SH atmospheric circulation. In turn, the westerly enhancement (Fig. 6g) means greater transport of warm oceanic air to the northern AP. The latter has a 16-year component of variability due to periodic enhancement and weakening of the anticyclonic anomaly in the SLP16 region (Fig. 6a). A similar warming effect can be expected from the negative correlation anomaly in V-wind in the AP region (Fig. 6h) due to the cold southerly anomaly decrease (or, in terms of anticyclonic circulation, the warm northerly anomaly increase). Spatially, as noted above, ESP and ORC appear to be more sensitive to the 16-year periodicity (Fig. 6e–6h) originating in the SLP16 region (Fig. 6a).
There is also a large-scale response to SLP16 containing the wave-3 and SAM patterns (Fig. 6e–6h). Spectral properties of the wave-3 pattern are presented for the three regions: (i) the SWA region (peak at 16.6 years from the winter SLP16 index in Fig. 7b), (ii) the northern AP (16.2 and 15.6 years in the ESP and ORC winter temperature in Fig. 3b and 3d, respectively) and (iii) New Zealand (14.6 years in the annual mean temperature in Fig. 7d). Spectrum of winter SAM index shows a peak of spectral power at the 17.5-year period (Fig. 7h). Additionally, large-scale variability with strong period 15.5 years exists in 30–50°S zonal mean SLP (Fig. 7f). Figure S3 shows that the zonal wave-3 and SAM patterns are even more evident in correlations with the index SLP30–50S than in correlations with the index SLP16 (Fig. 6f–6h).
In general, at least 5 indices of variability (SW16, SLP16, SLP30–50S, SAM, and NZ) demonstrate a noticeable prevalence of oscillation close to the 16-year period associated with quasi-stationary regional anomalies in the SH circulation. Surface temperature, SLP, and wind components in these anomalies undergo periodic changes on the interdecadal time scale (Fig. 6) and contribute to regional climate changes, which become both cyclical and synchronized. The effects of the 16-periodicity in the regional couplings are the subject to study in a separate work. The main coupled regions are located at the wave 3 ridges in the SLP anomalies, which strongly correlate with the SW16 and SLP16 indices (Fig. 6a and 6e) and are associated with the zonal mean SLP variability (SLP30–50S index, Fig. 7f) and the annular mode pattern (SAM index, Fig. 7h).
A multiple linear regression model was analyzed using Esperanza’s temperature as the dependent variable and the four indices (SW16, SLP16, SLP30–50S and NZ) treated as independent variables (Fig. S6). From the regression model, the square of the correlation coefficient R2 = 0.33 is significant at the 95% confidence level. This means that the near 16-year interdecadal periodicity provides a 33% contribution to the ESP temperature variability giving notable change in long-term trend.
We note that the east–west polarity of the correlation anomalies in the region of the southern Australia and New Zealand is opposite to the polarity in the southwestern Atlantic–Antarctic Peninsula region (Fig. 6f). This pattern suggests a cyclonic regional anomaly (dashed curved arrow in Fig. 6h). According to the generalized results, the periodicities between about 13 and 21 years centered close to 16-year period are statistically significant at the 95% confidence level (Fig. 3b; shaded rectangles in Fig. 7, right).
We also examined the spectral features of surface temperature variations in the AP region (Fig. S5) using the NCEP–NCAR reanalysis data. Four domains were chosen in: northeastern (N–E) AP, southwestern (S–W) AP, Orcadas area (latitude ⋅ longitude = 5° ⋅ 10°) and South Pacific region (10° ⋅ 10°), as shown by the shaded rectangles in Fig. S5e. The degree of consistency of interdecadal spectral peaks with station data was determined. The spectra were compared in pairs: N–E AP vs ESP, Orcadas area vs ORC, and S–W AP vs FV. Table 1 shows that the reanalysis reliably reproduces even small spatial differences in spectral peaks observed from the station data. In South Pacific domain further west of the peninsula, there is no periodicity close to 16 years (Table 1 and Fig. S5a).
Table 1 Comparison of interdecadal periods in the spectra of surface temperature time series from the station data in the AP region (Esperanza, Orcadas and Faraday/Vernadsky) and from the NCEP–NCAR reanalysis (NNR) for the three sub-regions (N–E AP, Orcadas area and S–W AP, respectively), and additionally for the South Pacific sub-region. The spectra are compared in Fig. S5a–S5d, and the boundaries of the sub-regions are shown in Fig. S5e
Finally, the wavelet spectra show that the interdecadal temperature oscillation in 2000s to 2010s exhibits transition from the negative to positive phases at Esperanza and Orcadas (Fig. 4a and 4b) and from positive to negative phases at Faraday/Vernadsky (Fig. 4c). This result is consistent with the trends in the temperature time series observed recently: warming (Fig. 3a and 3c) and cooling (or no warming, Fig. 3e), respectively (Oliva et al. 2017; Evtushevsky et al. 2020).