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  • View in gallery

    Mean steric height (m) relative to 1800 m at (a) the sea surface, (b) 200, (c) 500, and (d) 1000 m derived from Argo data during 2004–13. Two white dashed lines in each panel indicate locations of 170° and 150°W.

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    Temporal mean zonal velocity (cm s−1) along (a) 170° and (b) 150°W during 2004–13 derived from Argo data.

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    Linear trend (cm yr−1) of altimetric SSH from October 1992 to December 2013. Black contours indicate the SSH climatology from satellite altimetry. The data were filtered with a 1-yr low-pass filter to remove the seasonality before calculating the trend. A Mann–Kendall significance test was used, and trend values below the 95% confidence level were masked out. Green contours show areas where the signal-to-noise ratio is larger than 1.

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    (a) Spatial pattern (cm) and (b) time series associated with the first trend-EOF mode of the SSH over the South Pacific. (c),(d) As in (a),(b), but for the steric height at the sea surface relative to 1800 m. The data were filtered with 1-yr low-pass filter before the trend-EOF analysis.

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    First trend-EOF mode of the steric height (cm) at (a) 1000 m relative to 1800 m and (b) its time series. Black contours in (a) indicate the mean steric height. The data were filtered with 1-yr low-pass filter before the trend-EOF analysis.

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    Linear trend (cm yr−1) of the steric height in different depth ranges during 2004–13: (a) 0/1800, (b) 0/500, (c) 500/1000, and (d) 1000/1800 m. Contours show the mean steric height in corresponding depth ranges. A Mann–Kendall significance test was used in the calculation of the trend, and values below the 95% confidence level were masked out. Green contours show areas where the signal-to-noise ratio is larger than 1.

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    Linear trend (cm yr−1) of (top) steric height, (middle) thermosteric height, and (bottom) halosteric height in the depth range of (left) 0/1800, (center) 0/1000, and (right) 1000/1800 m. Those trend values below the 95% confidence level have been masked out. Green contours show areas where the signal-to-noise ratio is larger than 1.

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    SEC transport (Sv, 0–1000 m, blue) along (a) 170°W and (b) 150°W calculated from monthly time series of Argo data. Red solid curves indicate the 1-yr filtered time series, and red dashed curves show the standard error of the filtered time series.

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    As in Fig. 8, but for the Deep SEC transport (1000–1800 m).

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    Changes of the zonal velocity (color, cm s−1) at (a) 150°W and (b) 170°W. The velocity change is estimated as the difference of mean zonal velocity between 2009–13 and 2004–08. Contours show the mean zonal velocity during 2004–13, with the solid contour for positive values and the dashed contour for negative values.

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    (a) Spatial pattern (10−8 N m−3) and (b) time series of the first trend-EOF mode of the wind stress curl, and (c) the linear trend of the wind stress curl (10−8 N m−3 yr−1) in the South Pacific during 1976–2013. The wind stress curl was filtered with a 1-yr low-pass filter before the EOF analysis. Contours in (a) show the mean wind stress curl during 1976–2014, with a solid contour for positive values and a dashed contour for negative values. The blue curve in (b) shows the 3-yr low-pass-filtered time series of the SAM index. Purple contours in (c) show areas where the signal-to-noise ratio is larger than 1. Trend values in (c) below the 95% confidence level have been masked out.

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    SSH anomalies (cm) along 40°S from (a) satellite altimetry and (b) the reduced gravity (RG) model. The result was smoothed through averaging in a 5° latitude range centered at 40°S. The seasonal cycle was removed in both panels.

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    (a) Mean SSS (psu) during 2004–13 from Argo. (b) SSS (psu, color) and velocity (cm s−1, vector) difference between 2009–13 and 2004–08 (2009–13 minus 2004–08). White contours in (b) indicate the SSS climatology from (a).

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Low-Frequency Variability of the South Pacific Subtropical Gyre as Seen from Satellite Altimetry and Argo

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  • 1 International Pacific Research Center, School of Ocean and Earth Science and Technology, University of Hawai‘i at Mānoa, Honolulu, Hawaii
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Abstract

Low-frequency variability of the South Pacific Subtropical Gyre is investigated using satellite altimeter and Argo data. In most of the region studied, both sea surface height and steric height exhibit a linearly increasing trend, with its largest amplitude in the western part of the basin. Analysis of the Argo data reveals that the steric height increase north of 30°S is primarily caused by variations in the upper 500 m, while the steric height increase south of 30°S is determined by variations in the whole depths from the sea surface to 1800 m, with contributions from below 1000 m accounting for about 50% of the total variance. Most of the steric height increase is due to thermal expansion, except below 1000 m where haline contraction is of comparable magnitude with thermal expansion. Correspondingly, the South Pacific Subtropical Gyre has strengthened in the past decade. Within the latitude range between 10° and 35°S, transport of the gyre circulation increased by 20%–30% in the upper 1000 m and by 10%–30% in the deeper layers from 2004 to 2013. Further analysis shows that these variations are closely related to the southern annular mode in the South Pacific.

School of Ocean and Earth Science and Technology Contribution Number 9512 and International Pacific Research Center Contribution Number IPRC-1153.

Current affiliation: Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China.

Corresponding author address: Dr. Tangdong Qu, International Pacific Research Center, School of Ocean and Earth Science and Technology, University of Hawai‘i at Mānoa, 1680 East–West Rd., Honolulu, HI 96822. E-mail: tangdong@hawaii.edu

Abstract

Low-frequency variability of the South Pacific Subtropical Gyre is investigated using satellite altimeter and Argo data. In most of the region studied, both sea surface height and steric height exhibit a linearly increasing trend, with its largest amplitude in the western part of the basin. Analysis of the Argo data reveals that the steric height increase north of 30°S is primarily caused by variations in the upper 500 m, while the steric height increase south of 30°S is determined by variations in the whole depths from the sea surface to 1800 m, with contributions from below 1000 m accounting for about 50% of the total variance. Most of the steric height increase is due to thermal expansion, except below 1000 m where haline contraction is of comparable magnitude with thermal expansion. Correspondingly, the South Pacific Subtropical Gyre has strengthened in the past decade. Within the latitude range between 10° and 35°S, transport of the gyre circulation increased by 20%–30% in the upper 1000 m and by 10%–30% in the deeper layers from 2004 to 2013. Further analysis shows that these variations are closely related to the southern annular mode in the South Pacific.

School of Ocean and Earth Science and Technology Contribution Number 9512 and International Pacific Research Center Contribution Number IPRC-1153.

Current affiliation: Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China.

Corresponding author address: Dr. Tangdong Qu, International Pacific Research Center, School of Ocean and Earth Science and Technology, University of Hawai‘i at Mānoa, 1680 East–West Rd., Honolulu, HI 96822. E-mail: tangdong@hawaii.edu

1. Introduction

The South Pacific Subtropical Gyre (SPSG) provides a major pathway for water in the subtropics to be transported to the equator and high latitudes (Ganachaud et al. 2014). Changes in the amount of subtropical water transported to the equator by the SPSG are believed to modulate the El Niño–Southern Oscillation (ENSO) cycle and thereby produce basin-scale climate feedbacks (e.g., Gu and Philander 1997; Luo and Yamagata 2001; McPhaden and Zhang 2002). Despite this importance, compared with its North Pacific counterpart (e.g., Qu 2002; Giglio et al. 2012; Qiu and Chen 2012), the basin-scale variability of the SPSG is much less documented due to the lack of observations (Qu and Lindstrom 2002; Ganachaud et al. 2007).

Sea surface height (SSH) measurements from satellite altimetry during the past two decades have provided a powerful means to study the ocean circulation variability. Prior studies have shown that the South Pacific has the most significant increasing trend of SSH in the global ocean, and the strongest signal is located in the southwestern Pacific (e.g., Church et al. 2004; Cazenave and Nerem 2004; Willis et al. 2004). Based on Ocean Topography Experiment (TOPEX)/Poseidon altimeter data and expendable bathythermograph (XBT) data from 1993 to 1998, McCarthy et al. (2000) examined SSH variations along 30°S in the South Pacific and noticed a spinup of the SPSG during that period. Qiu and Chen (2006) suggested that the SSH signals in the South Pacific are modulated decadally and have a spatially nonuniform pattern, which is tightly connected with wind stress curl variations on the basin scale. Based on analysis of model outputs, satellite altimetry, and tide gauge data, Sasaki et al. (2008) suggested that the sea level rise in the South Pacific in the late 1990s was part of a decadal oscillation associated with the ENSO decadal variation, and these decadal changes were accompanied by circulation changes at 1000 m. Studies of global ocean SSH from satellite altimetry also showed similar decadal variations in the South Pacific (e.g., Lee and McPhaden 2008).

Despite the progress noted above, satellites only measure sea surface signals, and as a consequence the ocean’s subsurface variability cannot be detected by altimetry data. A large number of Argo floats have been deployed in the past decade, providing temperature and salinity measurements from a typical top level of 5 m to about 2000-m depth. By combining these temperature and salinity measurements with satellite altimetry and repeat hydrographic data, Roemmich et al. (2007) showed that the decadal SSH variability in the SPSG detected by satellite altimetry data reflects a dynamic signal extending from the sea surface to a depth of at least 1800 m, which is associated with the spinup of the subtropical gyre at intermediate depths. They also suggested that changes in the midlatitude wind stress curl are the cause of this decadal variability, being closely related to the southern annular mode (SAM). Sutton and Roemmich (2011) investigated the variability in the Southern Ocean using data collected by Argo, WOCE, and satellite, and found that more than 50% of the SSH rise since 1992 is steric. Based on the recent Argo data (2004–12), Zilberman et al. (2014) estimated the meridional volume transport across 30°S in the South Pacific, and showed that the East Australian Current (EAC), as part of the SPSG, has strong interannual variability driven by the wind stress curl anomaly associated with SAM.

In general, after Qiu and Chen (2006) and Roemmich et al. (2007), few studies have specifically focused on large-scale variability of SSH in the subtropical South Pacific and its associated ocean circulation. With about 10 more years of altimeter and Argo data since these previous studies, the present analysis revisits this problem and provides a detailed description of the low-frequency variability of the SPSG. In particular, Argo temperature and salinity measurements from the sea surface to about 2000 m enable us to identify the relative contributions from different layers and different water properties (temperature, salinity, etc.) to the low-frequency SSH variability. The paper is organized as follows. The data and method are presented in section 2. The mean state of the SPSG is described in section 3. Section 4 shows the low-frequency variability of SSH and steric height in the subtropical South Pacific, along with estimates of the relative contributions from different layers and different water properties and their associated circulation variability. The relationship between SSH changes and basin-scale wind stress forcing is finally discussed in section 5.

2. Data and method

The satellite altimetric Absolute Dynamic Topography (ADT) data from Archiving, Validation and Interpretation of Satellites Oceanographic Data (AVISO) is used in this study, which is composed of a mean dynamic topography and sea level anomalies (SLAs). The mean dynamic topography is computed by combining the Gravity Recovery and Climate Experiment (GRACE) data, satellite altimeter measurements, and in situ observations with a method developed by Rio et al. (2011). The SLAs are estimated by merging recent satellite altimeter observations from TOPEX/Poseidon (T/P), Jason, and European Remote Sensing (ERS) with a mapping technique (Ducet et al. 2000). The data are mapped into weekly time series from October 1992 to the present, with a horizontal resolution of 0.25° between 82°S and 82°N.

The original temperature and salinity (TS) data at observed levels from the National Ocean Data Center (NODC; http://www.nodc.noaa.gov/OC5/WOD13; Boyer et al. 2013) are also used in this study. Because the data coverage in the South Pacific is relatively good only after 2004 when a large number of Argo floats started to be deployed, our analysis here is restricted to the Argo period from 2004 to 2013. As part of the data processing, quality controlled TS profiles (i.e., with flag equal to zero and with more than five samples) are interpolated onto standard depths as the World Ocean Database 2013 (WOD13; Boyer et al. 2013). Next, annual and seasonal standard deviation checks at each level and in each 2° × 2° grid box are applied, to remove those profiles where the biases are larger than 3 times the standard deviation. As a result of this procedure, we obtain 181 088 “reliable” casts reaching 1000-m depth in the region between 10° and 60°S and between 120°E and 70°W. Of these reliable casts, 134 037 reach 1800-m depth. These selected TS profiles are then averaged in 2° × 2° grid boxes for every month in the time period of interest. For grid boxes with less than five profiles, we extend the searching radius to include at least five profiles from which the mean and standard deviation are estimated. Those profiles deviating from the mean by a value 3 times larger than the standard deviation are removed, and then the mean and standard deviation are reestimated. After that, a Gaussian filter with e-folding scale of about 200 km is used to smooth the mean fields. Finally, the resulting monthly time series for the period 2004–13 are used in the following analysis.

A trend empirical orthogonal function (EOF) analysis was conducted to examine the variation of SSH, steric height, and wind stress curl over the South Pacific. This method was developed by Hannachi (2007) as a nonlinear transformation to extract trend patterns from a space–time dataset. It overcomes the limitations of conventional EOF analysis in capturing trend patterns by casting the trend in a single dominant mode rather than spreading it through different EOF modes. More details can be found in Hannachi (2007).

3. Mean state of the subtropical gyre

a. Horizontal distribution

The time mean dynamic height referenced to 1800 m (Fig. 1, from Argo data) shows a structure of the SPSG that is generally consistent with previous studies (e.g., Qu and Lindstrom 2002; Kessler and Cravatte 2013; Ganachaud et al. 2014). At the sea surface, the South Equatorial Current (SEC) is observed as a broad northwestward-flowing current crossing the equator. South of the SEC, there is an eastward flow between 20° and 40°S. The narrow western boundary currents are not well resolved due to the 2° averaging, but the southward-flowing EAC is still visible along the Australia coast, supplying water to the broad meandering eastward Tasman Front (TF; Ridgway and Dunn 2003). Another feature at the sea surface is a low dynamic height tongue at about 10°S between the western boundary and the date line, indicating a weak eastward current on its northern flank, which appears to be the South Equatorial Countercurrent (SECC).

Fig. 1.
Fig. 1.

Mean steric height (m) relative to 1800 m at (a) the sea surface, (b) 200, (c) 500, and (d) 1000 m derived from Argo data during 2004–13. Two white dashed lines in each panel indicate locations of 170° and 150°W.

Citation: Journal of Physical Oceanography 45, 12; 10.1175/JPO-D-15-0026.1

Below the surface layer at 200 m, the SEC becomes a nearly zonal flow between 5° and 20°S (Fig. 1b). Because of the complex topography, the westward-flowing SEC is divided into multiple jets as it flows around a number of islands east of Australia (Kessler and Gourdeau 2007; Ganachaud et al. 2008; Couvelard et al. 2008). After encountering the east coast of Australia, the flow bifurcates into the southward-flowing EAC and the northward-flowing North Queensland Current (NQC) at about 18°S (e.g., Qu and Lindstrom 2002; Ganachaud et al. 2008; Kessler and Cravatte 2013; Ganachaud et al. 2014). The NQC turns east in the northern Coral Sea, feeding the eastward Hiri Current (Burrage 1993). Recent studies show that the NQC and the Hiri Current are both part of a continuous western boundary current that flows around the Gulf of Papua, collectively called the Gulf of Papua Current (GPC; Ganachaud et al. 2014). The GPC then enters the Solomon Sea and finally flows northward through three narrow passages north of the Solomon Sea (e.g., Melet et al. 2010; Cravatte et al. 2011; Hristova and Kessler 2012). Most of the EAC leaves the coast of Australia and flows eastward at 30°–40°S, forming the TF, while the rest continues southward reaching the eastern coast of Tasmania. The TF flows across the Tasman Sea with part of it entering the Pacific and another part reaching east of New Zealand as the East Australia Undercurrent (EAUC) (e.g., Tilburg et al. 2001). The EAUC then merges with the subtropical front and turns eastward in the confluence area (Fernandez et al. 2014).

The subtropical gyre continues to shift southward with increasing depth, and its core moves from about 18°S at the sea surface to about 45°S at the 500–1000-m depths (Figs. 1c,d), consistent with previous studies (e.g., Qu and Lindstrom 2002). As part of the SPSG, the SEC also shifts southward with increasing depth, and no westward flow can be identified north of 15°S at 500 m or below. Instead, a very weak eastward flow appears at 500 m along 10°S east of Solomon archipelago (Fig. 1c), which seems to be the southern branch of the Southern Subsurface Countercurrent (SSCC; Tsuchiya 1975).

b. Vertical structure

Zonal velocity sections along 170° and 150°W are presented to show the vertical structure of the gyre circulation (Fig. 2). In general, our results show similar features to those reported by previous studies (e.g., Kessler and Taft 1987; Reid 1997; Qu and Lindstrom 2002). At 170°W, the westward-flowing SEC consists of two branches that are separated by the weak eastward-flowing SECC in the upper 100 m at about 10°S. In the following we focus our discussion on the southern branch, which represents the northern boundary of the SPSG. The core of this branch is located near 14°S with a mean velocity of about 4 cm s−1 at depths of 100–200 m. At the sea surface, the zonal current is relatively weak, agreeing well with previous studies (Qu and Lindstrom 2002; Kessler and Gourdeau 2007). Synoptic glider observations in the Coral Sea show a conspicuous surface signature in the westward-flowing jets (Gourdeau et al. 2008), implying significant variations of the surface currents. The SEC velocity core shifts southward with increasing depth, reaching 23°S at depths around 800 m, where the velocity drops to about 2 cm s−1. South of the SEC, there is a broad eastward flow in the upper 300 m between 20° and 35°S, as already shown in Fig. 1. This flow also exhibits multibranch structures, and is first mentioned as the “South Tropical Countercurrent” (Merle et al. 1969). However, later studies show that it is merely the southern component of the subtropical gyre rather than a countercurrent (e.g., Tsuchiya 1982; Qu and Lindstrom 2002). Below the SEC between about 7° and 20°S, there is the weak eastward-flowing SSCC with its core at about 800 m. Similar structures are also found at 150°W, except that the SECC does not seem to exist there (Fig. 2b).

Fig. 2.
Fig. 2.

Temporal mean zonal velocity (cm s−1) along (a) 170° and (b) 150°W during 2004–13 derived from Argo data.

Citation: Journal of Physical Oceanography 45, 12; 10.1175/JPO-D-15-0026.1

4. Low-frequency variability of the subtropical gyre

a. Sea surface height from satellite altimetry

An important feature of the large scale, low-frequency variability of the SPSG is its linear trend in SSH (Fig. 3). During the past 21 years, SSH has increased in most of the South Pacific, with the exception of only the northeastern corner of the basin and a small region south of 50°S (where SSH has decreased). This result is consistent with previous studies (e.g., Qiu and Chen 2006; Sasaki et al. 2008), but some differences exist. The most significant difference is that the largest increasing trend of SSH from 1992 to 2013 takes place near the Solomon archipelago in the tropical Pacific, with its magnitude exceeding 1 cm yr−1, while most of the previous studies suggested a maximum SSH trend east of New Zealand in the subtropical Pacific based on relatively shorter time series from satellite altimetry (e.g., Qiu and Chen 2006; Sasaki et al. 2008). This difference implies that the low-frequency variations of SSH in the tropical and subtropical South Pacific are different and probably controlled by different processes. We will return to this point in section 6.

Fig. 3.
Fig. 3.

Linear trend (cm yr−1) of altimetric SSH from October 1992 to December 2013. Black contours indicate the SSH climatology from satellite altimetry. The data were filtered with a 1-yr low-pass filter to remove the seasonality before calculating the trend. A Mann–Kendall significance test was used, and trend values below the 95% confidence level were masked out. Green contours show areas where the signal-to-noise ratio is larger than 1.

Citation: Journal of Physical Oceanography 45, 12; 10.1175/JPO-D-15-0026.1

To further illustrate the variability of the SPSG, we conducted a trend-EOF analysis of SSH. The first trend-EOF mode is a single dominant mode, which explains 28% of the total variance, much stronger than the second (3%) and other modes. We therefore only show the spatial pattern and time series of the first mode in Fig. 4. Its time series is dominated by an increasing trend, and its spatial pattern resembles closely the linear SSH trend pattern shown in Fig. 3. This trend-EOF mode is consistent with Qiu and Chen (2006), who did a conventional EOF analysis for the period October 1992–February 2005. Besides the increasing SSH trend in the first decade, Qiu and Chen (2006) also noticed a reversal of the SSH trend since 2002 and suggested the reversal is connected to the spindown of the SPSG after a decade of strengthening (Qiu and Chen 2006; Roemmich et al. 2007). Here, we note that the reversal reported by previous studies during 2002–04 was merely an interannual perturbation, and after that the SSH in the South Pacific rebounded, leading to a spinup of the SPSG throughout the entire observation period (Fig. 4b).

Fig. 4.
Fig. 4.

(a) Spatial pattern (cm) and (b) time series associated with the first trend-EOF mode of the SSH over the South Pacific. (c),(d) As in (a),(b), but for the steric height at the sea surface relative to 1800 m. The data were filtered with 1-yr low-pass filter before the trend-EOF analysis.

Citation: Journal of Physical Oceanography 45, 12; 10.1175/JPO-D-15-0026.1

b. Steric height from Argo

Based on the recent Argo data from 2004 to 2013, we calculated the trend-EOF mode of the sea surface steric height relative to 1800 m (Figs. 4c,d). Regardless of the shorter time span, the trend-EOF mode from the Argo data agrees well with that from satellite altimetry in both space and time, except that the amplitude of the Argo steric height is relatively small compared with that of the altimetry SSH. Here, we smoothed the altimetry data in both space and time to be consistent with the Argo gridding before the EOF analysis. Therefore, the difference in amplitude is not due to the spatial/temporal resolution difference of the two datasets. Given that the mass-induced sea level change is nearly negligible in this area (Cheng et al. 2013), one may speculate that such a difference is due to variations below 1800 m.

To investigate the depth dependence of the SPSG variability, we also applied trend-EOF analysis to the steric height at 1000 m relative to 1800 m (Fig. 5). The time series of the first mode exhibits a prominent increasing trend over the observation period. Differing from that at the sea surface, the increasing trend at 1000 m is weak in the tropics. Most of the variability occurs in the region south of 30°S and east of New Zealand. From 2004 to 2013, the steric height east of New Zealand increased by about 2 cm, as can be seen from the trend-EOF mode and its time series in Fig. 5. This maximum variability coincides with the SPSG center at 1000-m depth (Fig. 1d), indicating a strengthening deep subtropical gyre. By analyzing the WOCE hydrographic data during 1991–96 and Argo float data during 2003–05, Roemmich et al. (2007) noted that the deep subtropical gyre at 1000-m depth spun up by at least 20% during the 1990s. Our analysis suggests that the spinup of the deep subtropical gyre continued in the recent decade.

Fig. 5.
Fig. 5.

First trend-EOF mode of the steric height (cm) at (a) 1000 m relative to 1800 m and (b) its time series. Black contours in (a) indicate the mean steric height. The data were filtered with 1-yr low-pass filter before the trend-EOF analysis.

Citation: Journal of Physical Oceanography 45, 12; 10.1175/JPO-D-15-0026.1

The above trend-EOF analysis shows that the steric height in the subtropical South Pacific has increased in the past two decades both at the sea surface and 1000-m depth (Figs. 4 and 5).Yet the spatial pattern of this increasing trend is different at the two depths. To better understand these differences, we calculate the linear trends of steric height in different depth ranges. The water column is divided into three layers: 0–500, 500–1000, and 1000–1800 m, called the upper, intermediate, and deeper layer, respectively. The steric height in each layer is estimated as the vertical integral of the specific volume anomaly, and its linear trend is discussed along with that of the steric height at the sea surface (0/1800 m). The linear trend of steric height at the sea surface (0/1800 m, Fig. 6a) is consistent with the spatial pattern of its first trend-EOF mode (Fig. 4c). Also, variability in the upper 500 m captures most of this linear trend, especially north of 35°S (Fig. 6b). The increasing trend south of 35°S is, instead, much smaller in the upper ocean, and this difference can be largely explained by the variability in the deeper layer (Figs. 6b,d). Within the latitude band between 35° and 60°S, the increasing trend of steric height in the deeper layer is of comparable or higher magnitude than that in the upper layer. Moreover, contribution from the deeper layer increases gradually toward the south (Fig. 6d), accounting for as much as 65% of the total trend near 50°S, while contribution from the intermediate layer is nearly negligible (Fig. 6c). Recently, Sutton and Roemmich (2011) also noticed that the deep ocean variability contributes significantly to the total steric height trend in the Southern Ocean south of 35°S, with contributions from below 1500 m accounting for about 35% of the total variance. Our results are in good agreement with this earlier study.

Fig. 6.
Fig. 6.

Linear trend (cm yr−1) of the steric height in different depth ranges during 2004–13: (a) 0/1800, (b) 0/500, (c) 500/1000, and (d) 1000/1800 m. Contours show the mean steric height in corresponding depth ranges. A Mann–Kendall significance test was used in the calculation of the trend, and values below the 95% confidence level were masked out. Green contours show areas where the signal-to-noise ratio is larger than 1.

Citation: Journal of Physical Oceanography 45, 12; 10.1175/JPO-D-15-0026.1

It must be noted that the linear trend of the 0/1800-m steric height from Argo is larger than the SSH trend from satellite altimetry (Figs. 3 and 6a), which seems to be contrary to the trend-EOF analysis (Fig. 4). In fact, the time periods of Argo and altimetry are different, and in the same period of 2004–13, the SSH trend is still larger than the steric height trend (not shown), agreeing well with Fig. 4. These features are primarily related to the acceleration of the SSH increasing after 2004, which could be seen in the time series of the first trend-EOF mode of the SSH (Fig. 4b).

c. Thermosteric and halosteric heights

We examine the relative importance of thermosteric and halosteric contributions to steric height variability, calculating their linear trend separately, during the Argo period (Fig. 7). For the whole water column from the sea surface to 1800 m (Figs. 7a–c), the thermosteric trend is dominant, capturing 70%–80% of the total trend of steric height, while the halosteric trend is much weaker. The halosteric trend appears to have an opposite sign to the thermosteric trend, and this partly compensates the increasing trend of steric height caused by thermal expansion (Figs. 7a–c).

Fig. 7.
Fig. 7.

Linear trend (cm yr−1) of (top) steric height, (middle) thermosteric height, and (bottom) halosteric height in the depth range of (left) 0/1800, (center) 0/1000, and (right) 1000/1800 m. Those trend values below the 95% confidence level have been masked out. Green contours show areas where the signal-to-noise ratio is larger than 1.

Citation: Journal of Physical Oceanography 45, 12; 10.1175/JPO-D-15-0026.1

The linear trends of thermosteric and halosteric height at 0–1000 and 1000–1800 m are also calculated to identify their contributions to the steric height variability in different depth ranges. At 0–1000 m (Figs. 7d–f), both trends are similar to those of the whole water column (0–1800 m), with the thermosteric component being dominant over the halosteric component. At 1000–1800 m (Figs. 7g–i), the thermosteric and halosteric components are of equal importance, suggesting that salinity becomes a more important parameter in the deep ocean.

Differing from the upper (0–1000 m) ocean, where salinity changes partly compensate the thermal expansion, variation of the halosteric height below 1000-m depth is “in phase” with that of the thermosteric height, which significantly strengthens the increasing trend of steric height at 1000 m (Figs. 7g–i). In addition, the contribution from the deeper (1000/1800 m) halosteric height increases toward the south (Fig. 7i). Part of this southward increase may be related to changes in salinity having a greater impact on density in colder waters compared to warmer waters.

By analyzing the basin-wide zonally averaged linear trends of the thermosteric and halosteric components of steric height integrated through 3000-m depth for the (1955–59)–(1994–98) period, Levitus et al. (2005) also noticed density-compensating changes in temperature and salinity in the South Pacific between 22° and 38°S. Our analysis here suggests that such density-compensating features mainly appear in the upper ocean above 1000 m. Below 1000 m, temperature and salinity act in concert to change the steric height. This phenomenon is of importance for understanding the physics of steric height variability in the South Pacific.

d. South Equatorial Current transport

Given the SSH and steric height variability mentioned above, the question that may arise immediately is how the subtropical gyre circulation varies in response to this variability. In the following we focus our discussion on the SEC, a major component of the SPSG. This current is relatively stable and can be well resolved by the Argo data. Figure 8 shows the SEC transport along 170° and 150°W during the Argo period. Here, the SEC transport is calculated as an integration of all westward currents in the 0–1000-m depth range between 10° and 35°S, and the reference level used to compute geostrophic currents is 1800 m. For the Argo period the SEC transport exhibits a significant increasing trend. At 170°W, it increased by about 7 Sverdrups (Sv; 1 Sv ≡ 106 m3 s−1) from 2004 to 2013, with a linear trend of about 0.77 Sv yr−1 (Fig. 7a). Similarly, the SEC transport at 150°W also significantly increased during the past decade (Fig. 7b).

Fig. 8.
Fig. 8.

SEC transport (Sv, 0–1000 m, blue) along (a) 170°W and (b) 150°W calculated from monthly time series of Argo data. Red solid curves indicate the 1-yr filtered time series, and red dashed curves show the standard error of the filtered time series.

Citation: Journal of Physical Oceanography 45, 12; 10.1175/JPO-D-15-0026.1

The SEC transport in the deep SPSG is also estimated as an integration of all westward currents in the 1000–1800-m depth range between 20° and 50°S at 170° and 150°W (Fig. 9). Its time series exhibits obvious increasing trends at both locations in the past decade. At 150°W, the SEC transport increased by nearly 30%, indicating a spinup of the deep SPSG from 2004 to 2013 (Fig. 9b). Based on the WOCE hydrographic and Argo data, Roemmich et al. (2007) observed the spinup of the deep SPSG at 1000 m in the 1990s. Analysis of these data also led them to a speculation that, after a decade of strengthening, the deep SPSG has experienced a spindown after 2002. With more Argo data becoming available, here we show that the SPSG continued to spin up from 2004 to 2013. Analysis of satellite altimetry data in section 4a indicates that the downward change around 2002 is just an interannual perturbation.

Fig. 9.
Fig. 9.

As in Fig. 8, but for the Deep SEC transport (1000–1800 m).

Citation: Journal of Physical Oceanography 45, 12; 10.1175/JPO-D-15-0026.1

Moreover, some indirect evidence of the SPSG spinup can be found in previous studies. For example, long-term near-surface temperature and salinity measurements show that the EAC extension has moved southward by approximately 350 km over the past 60 years, implying the strengthening of the EAC and its related subtropical gyre (Ridgway 2007; Hill et al. 2008). Using an Island Rule model forced with wind stress curl data, Cai (2006) also described the strengthening of the subtropical gyre between the late 1970s and early 2000s. This strengthening is believed to continue during the twenty-first century, in response to the wind stress curl increase over the subtropical South Pacific (Oliver and Holbrook 2014).

To show the vertical structure of the SPSG strengthening, we compared the temporal mean zonal velocity during the first (2004–08) and second (2009–13) halves of the Argo period. Velocity differences between the two periods represent the change of the subtropical gyre (Fig. 10). In most parts of the SEC between 5° and 25°S along the 150°W section (Fig. 10a), the velocity differences are dominated by negative values, indicating a strengthening of the westward-flowing SEC. These velocity differences seem to be vertically coherent. In the deep gyre centered at about 48°S along the 150°W section, the strengthening is also observed (Fig. 10a), which is consistent with the spinup of deep subtropical gyre described above. Similar features are also visible at 170°W (Fig. 10b).

Fig. 10.
Fig. 10.

Changes of the zonal velocity (color, cm s−1) at (a) 150°W and (b) 170°W. The velocity change is estimated as the difference of mean zonal velocity between 2009–13 and 2004–08. Contours show the mean zonal velocity during 2004–13, with the solid contour for positive values and the dashed contour for negative values.

Citation: Journal of Physical Oceanography 45, 12; 10.1175/JPO-D-15-0026.1

5. Atmospheric forcing

To understand the large-scale SSH and steric height variability described above, we examined the wind products from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis. Figure 11 shows the first trend-EOF mode of monthly wind stress curl in the South Pacific during 1976–2013. Strong wind stress curl variability is mostly seen south of 40°S, along the path of the westerly wind. The time series of the first trend-EOF mode shows an obvious increasing trend accompanied by some interannual fluctuations (Fig. 11b). This linear trend is linked with enhanced downward Ekman pumping in the latitude band between 40° and 55°S and upward Ekman pumping poleward of 55°S. The total linear trend of wind stress curl is also presented (Fig. 11c), showing essentially the same pattern as the first trend-EOF mode (Fig. 11a).

Fig. 11.
Fig. 11.

(a) Spatial pattern (10−8 N m−3) and (b) time series of the first trend-EOF mode of the wind stress curl, and (c) the linear trend of the wind stress curl (10−8 N m−3 yr−1) in the South Pacific during 1976–2013. The wind stress curl was filtered with a 1-yr low-pass filter before the EOF analysis. Contours in (a) show the mean wind stress curl during 1976–2014, with a solid contour for positive values and a dashed contour for negative values. The blue curve in (b) shows the 3-yr low-pass-filtered time series of the SAM index. Purple contours in (c) show areas where the signal-to-noise ratio is larger than 1. Trend values in (c) below the 95% confidence level have been masked out.

Citation: Journal of Physical Oceanography 45, 12; 10.1175/JPO-D-15-0026.1

As we already knew, SAM is a major climate mode in the Southern Hemisphere, characterized by a zonally symmetric dipole structure with opposite geopotential height anomalies in the Antarctic region and at the southern midlatitudes (Thompson et al. 2000). During the past few decades, SAM has experienced a significant increasing trend (Thompson et al. 2000). In this study, we calculate the SAM index with the NCEP–NCAR reanalysis as a normalized difference of monthly zonal mean sea level pressure between 40° and 70°S (Fig. 11b). This index is tightly correlated with the time series of the first EOF mode of wind stress curl (Fig. 11b), and their correlation reaches 0.71. This result suggests that the linear trend of the wind stress curl in the South Pacific is an important aspect of SAM. Another phenomenon that should be noted is that the magnitude of the increasing trend in SAM seems to decrease after 2000, which probably contributes to the warming “hiatus” that is discussed recently (e.g., Meehl et al. 2011).

To relate these variations of the wind stress curl to the SSH and steric height changes, we use a 1.5-layer reduced gravity model to identify the baroclinic ocean response to the wind forcing. This model has been used by many previous studies to investigate the SSH and thermocline variability in the subtropical Pacific (e.g., Holbrook and Bindoff 1999; Qiu and Chen 2006). The detailed description of the model can be found in Qiu and Chen (2006). Monthly wind stress data from the NCEP–NCAR reanalysis are used to force the model, and the reduced gravity value g′ is set to be 0.05. The g′ value represents the strength of stratification in the reduced gravity model. It does not influence the SSH pattern, but controls the magnitude of the SSH variation. We chose 0.05 here so that the modeled SSH field had the same variance as the satellite observation. Following Qiu and Chen (2006), we use the long baroclinic Rossby wave speed estimated by Chelton et al. (1998) with a latitude-dependent amplification factor, and use (6 yr)−1 as the Newtonian dissipation coefficient.

The model-produced SSH along 40°S is compared with that from the satellite altimetry in Fig. 12. Despite some slight differences, the SSH anomalies from the model and satellite altimetry generally agree with each other. Both show negative SSH anomalies before 1997 and positive anomalies after 2007, consistent with the increasing trend of SSH. Westward propagation of SSH anomalies is shown in both the model and satellite altimetry after initiating at about 110°W. This result seems to suggest that the observed SSH variability is mainly caused by basin-scale wind stress curl through baroclinic Rossby wave adjustment, agreeing well with Qiu and Chen (2006) and Holbrook et al. (2011). However, the model-produced SSH anomalies between 20° and 30°S do not agree with the altimeter measurements as well as at 40°S (figure not shown), which is probably related to the strong eddy activity in that area. Qiu et al. (2015) shows that significant SSH trend and variability in the North Pacific can be generated by eddy momentum flux forcing, and a similar phenomenon might also exist in the South Pacific.

Fig. 12.
Fig. 12.

SSH anomalies (cm) along 40°S from (a) satellite altimetry and (b) the reduced gravity (RG) model. The result was smoothed through averaging in a 5° latitude range centered at 40°S. The seasonal cycle was removed in both panels.

Citation: Journal of Physical Oceanography 45, 12; 10.1175/JPO-D-15-0026.1

6. Summary and discussion

Low-frequency variability of SSH, steric height, and their associated ocean circulation in the South Pacific is investigated using satellite altimetry and Argo data. Analysis of satellite data shows that SSH has increased in the past 21 years in a large part of the South Pacific, with its maximum amplitude appearing in the western part of the basin. This result is supported by recent in situ observations from Argo. Correspondingly, the South Pacific Subtropical Gyre has strengthened, with the SEC transport having increased by 20%–30% from 2004 to 2013. The Argo data also enable us to investigate the subsurface variability of the South Pacific Subtropical Gyre. The results show that the steric height increase at the sea surface north of 30°S is primarily caused by variability in the upper layer (0–500 m), while that south of 30°S is by variability of the whole water column (0–1800 m), with variability below 1000 m accounting for 50% of the total variance. Most of the steric height increase in the upper ocean is due to thermal expansion, but the effect of salinity becomes increasingly important with depth, reaching comparable strength with that of temperature at depths below 1000 m.

Previous studies have shown that the SSH variability in the subtropical South Pacific is determined by basin-scale wind stress curl variability (Qiu and Chen 2006; Sasaki et al. 2008), but its connection with large-scale climate modes is not well understood. Cai (2006) and Roemmich et al. (2007) suggested that wind stress curl variability and its associated subtropical gyre spinup in the subtropical South Pacific are closely related to SAM. Based on the model outputs, Sasaki et al. (2008) noticed that both SSH and wind stress curl show a decadal variability rather than a linear trend east of New Zealand. They therefore suggested that this variability is not forced by SAM, but by the decadal variability of ENSO or Pacific decadal oscillation (PDO). A detrended sea level time series from the tide gauge at Fort Denison in Sydney Harbor also showed strong decadal variations that were tightly correlated with the PDO (Holbrook et al. 2011). Actually, both the SAM and PDO have contributed to the sea level rise in the western South Pacific during the past two decades through surface wind stress curl. Because of the lack of long-term observations, no previous studies have distinguished the influences from these two climate modes, despite their importance in understanding and predicting the sea level changes in the South Pacific (Sasaki et al. 2008).

Our results have shown that the linearly increasing trend of SSH and its associated subtropical gyre spinup in the South Pacific are forced by basin-scale wind stress curl variability, consistent with the modeling study of Holbrook et al. (2011). It must be noted, that the time series of the data used for the present study is still not long enough to separate the influence from the two modes (i.e., SAM and PDO). However, preliminary analysis of a 50-yr-long tide gauge time series near New Zealand shows a long-term increasing trend accompanied by a strong decadal variability after removing the global mean sea level rise (not shown). Long-term records of temperature and salinity from Maria Island over the past 60 years also exhibit similar features [see Fig. 2 in Ridgway (2007) and Fig. 3 in Hill et al. (2008)]. Here, the long-term trend appears to be related to the SAM, and the decadal variability is more related to the PDO. This result seems different from what has been reported for the tropical region, where regional sea level variation is dominated by the PDO through its modulation of trade winds (e.g., Feng et al. 2004, 2010; Merrifield et al. 2012; Moon et al. 2013). Details of this difference need to be investigated further by research.

Along the path of the SEC, there is a sea surface salinity (SSS) maximum in the subtropical South Pacific, forming a contrast with a freshwater pool in the tropics (Fig. 13a). The SEC provides a significant pathway for the high salinity subtropical water to flow toward the tropical region. With the strengthening of the SEC, we speculate that more salty water may have been advected to the freshwater pool in recent years. Figure 13b shows the SSS variability between the second and first half of the Argo period. A dominant feature of this variability is the SSS increase in a tilting band of the South Pacific, which lies to the east of the Solomon Archipelago and extends southeastward to the central South Pacific.

Fig. 13.
Fig. 13.

(a) Mean SSS (psu) during 2004–13 from Argo. (b) SSS (psu, color) and velocity (cm s−1, vector) difference between 2009–13 and 2004–08 (2009–13 minus 2004–08). White contours in (b) indicate the SSS climatology from (a).

Citation: Journal of Physical Oceanography 45, 12; 10.1175/JPO-D-15-0026.1

Freshwater flux variability associated with the PDO largely determines the SSS changes in the Pacific (Delcroix et al. 2007). During the Argo period, the PDO shifted from a positive to a negative phase, which may have contributed to the increasing SSS north of the South Pacific convergence zone (Zhang and Qu 2014). The SSS variability shown in Fig. 13b largely resembles the negative PDO pattern in Zhang and Qu (2014), except for the location of the maximum SSS anomaly. The maximum SSS anomaly associated with the PDO is seen near the equator, while the maximum SSS anomaly shown in Fig. 13b occurs at 10°–15°S. A strong salinity front at these latitudes implies strong influence of horizontal advection. Because of the strengthening SEC, more westward salt advection may occur across the SSS front, which possibly explains the anomalously high SSS between 10° and 15°S. Details of how the spinup of South Pacific Subtropical Gyre influences the salinity budget of the western Pacific requires further investigation.

Acknowledgments

This study was supported by the National Science Foundation through Grant OCE11-30050 and by NASA as part of the Aquarius Science Team investigation through Grant NNX12AG02G.

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