Journal of Oceanography

, Volume 68, Issue 1, pp 139–150 | Cite as

Interannual variations in low potential vorticity water and the subtropical countercurrent in an eddy-resolving OGCM

Special Section: Short Contribution New developments in mode-water research: Dynamic and climatic effects


Interannual-to-decadal variations in the subtropical countercurrent (STCC) and low potential vorticity (PV) water and their relations in the North Pacific Ocean are investigated on the basis of a 60-year-long hindcast integration of an eddy-resolving ocean general circulation model. Although vertically coherent variations are dominant for STCC interannual variability, a correlation analysis shows that an intensified STCC vertical shear accompanies lower PV than usual to the north on 25.5- to 26.1-σθ isopycnal surfaces, and intensified meridional density gradient in subsurface layers, consistent with Kubokawa’s theory (J Phys Oceanogr 29:1314–1333, 1999). The low-PV signals appear at least 2 years before peaks of STCC, propagating southwestward from the subduction region.


Subtropical countercurrent Low potential vorticity water North Pacific Interannual variability Eddy-resolving ocean general circulation model 

1 Introduction

In the late 1960s, a weak eastward current embedded in the broad westward current in the southern part of the North Pacific subtropical gyre was discovered (Uda and Hasunuma 1969). As its direction is opposite to that expected from the gyre circulation, it is named the subtropical countercurrent (STCC). Although its whole distribution had not been clarified for a long time because of high eddy activity in the region (Qiu 1999), the accumulation of observational data recently allowed its detailed distributions to be described (Kobashi and Kawamura 2001, 2002; Kobashi et al. 2006). Although STCC is not a strong current, the associated temperature front, the subtropical front (STF), can influence atmospheric fields aloft (Kobashi et al. 2008; Kobashi and Xie 2011). Coupled model results suggest positive coupled feedback associated with the atmospheric response (Xie et al. 2011). In coupled model projections, changes in STCC under global warming can significantly affect distributions of sea surface temperature (SST) changes in the North Pacific (Xie et al. 2010).

Formation mechanisms for STCC have been debated since its discovery. Yoshida and Kidokoro (1967) and Roden (1975) discuss the possible importance of detailed distributions of zonal wind stress, and meridional Ekman convergence, respectively. On the basis of ocean general circulation model (OGCM) experiments, Takeuchi (1984), however, shows that STCC can be formed without narrow scale structures in zonal wind and meridional Ekman convergence. In a theoretical study, Kubokawa (1999) proposes that STCC is associated with meridional density gradient caused by the accumulation of low potential vorticity (PV), thick water layers subducted to the north. A numerical simulation supports this hypothesis (Kubokawa and Inui 1999). Specifically, the mode water pushes the upper pycnocline upward. On the southern flank of the mode water, this creates a northward shoaling of the upper pycnocline, which sustains an eastward current shear by thermal wind. Observational data also suggest that relative distributions between STCC and low-PV water are consistent with Kubokawa’s (1999) hypothesis (Aoki et al. 2002). Furthermore, from detailed investigation of historical observed data, Kobashi et al. (2006) show that the northern and eastern branches of STCC locate at the southern edge of the low-PV regions corresponding to the North Pacific Subtropical Mode Water (NPSTMW, Masuzawa 1969; Hanawa and Talley 2001), and to the North Pacific Central Mode Water (NPCMW, Nakamura 1996; Suga et al. 1997; Oka and Suga 2005), respectively.

Although these observed climatological fields strongly support Kubokawa’s (1999) hypothesis, it is still not clear if interannual-to-decadal variations in STCC are caused by variations of low-PV waters. On decadal timescales, in a 300-year simulation of a coupled model, Xie et al. (2011) show that STCC variations are caused by changes in low-PV water subduction. Although the resolution of the coupled model is limited and low-PV water tends to be exaggerated in the model, on the basis on an eddy-resolving OGCM simulation Yamanaka et al. (2008) show that decadal differences in STCC are associated with those in low-PV waters, consistent with Kubokawa’s hypothesis. On interannual timescales, Qiu and Chen (2010) and Kobashi and Xie (2011) indicate the importance of Ekman convergence for STF and STCC, but the possibility of contribution of low-PV water variations has not been explored. The present study investigates if variations in low-PV water subduction can affect STCC on interannual timescales, based on an eddy-resolving OGCM simulation that is different from Yamanaka et al.’s.

In Sect. 2, we introduce the model and describe simulated low-PV waters. Simulated STCC is described in Sect. 3, and the relationship between interannual-to-decadal variations in STCC and low-PV waters is investigated in Sect. 4. Section 5 gives a summary and discussion.

2 Model

2.1 OFES

We used the Modular Ocean Model 3 (MOM3) OGCM (Pacanowski and Griffies, 2000) with substantial modification for the vector-parallel hardware system of Japan’s Earth Simulator. Our ocean model for the Earth Simulator (OFES; Masumoto et al. 2004) covers a near-global domain of 75°N–75°S, with a horizontal resolution of 0.1°. The model has 54 vertical levels with resolutions of 5 m at the surface, and the maximum depth is 6065 m. For horizontal mixing of momentum and tracers, we adopted scale-selective damping with a bi-harmonic operator (Smith et al. 2000). The nonlocal K-profile parameterization (KPP) boundary layer mixing scheme (Large et al. 1994) was adopted for the vertical mixing.

The surface heat flux and evaporation were calculated by the bulk formula with atmospheric variables based on National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis data (Kalnay et al. 1996) and the simulated SST field. The freshwater flux was evaluated from the evaporation field and daily precipitation rate data, under the constraint that sea surface salinity (SSS) is restored to the observed monthly climatology at a timescale of 6 days. Wind stress is also obtained from the NCEP/NCAR reanalysis. For details, see Masumoto et al. (2004) and Sasaki et al. (2008).

Following a 50-year integration with climatological monthly-mean forcing from the annual-mean temperature and salinity climatological fields without motion, we conducted a 60-year hindcast integration with daily mean atmospheric fields of the NCEP/NCAR reanalysis data from 1950 to 2009. This hindcast simulation successfully captures variability with intraseasonal-to-decadal timescales (Sasaki et al. 2008) and has been used to investigate interannual-to-decadal variability in the western North Pacific region (Nonaka et al. 2006, 2008; Taguchi et al. 2007, 2010). In the following analyses, the resolution of our model output is reduced to 0.5° for analytical convenience by selecting the data at every five grid points both in the zonal and meridional directions.

2.2 Simulated low-PV water distributions

In the present study, we investigate the relationship between interannual variations of low-PV waters and STCC on the basis of the OFES hindcast integration. Then, first we describe simulated summertime (July–September) low-PV water distributions based on meridional and zonal sections of PV (Fig. 1). In the western part of the North Pacific basin, at 150°E, low-PV water corresponding to the NPSTMW is found around 25.2–25.6 σθ (Fig. 1d), and its distribution is well represented in the model (Fig. 1a). In the central part of the basin in the 170°E meridional transect, low-PV water with density around 25.5 σθ develops more strongly in the model (Fig. 1b) than in observations (Fig. 1e), although there is a PV minimum in the observation around 30°N. In the zonal section of PV at 28°N, it is confirmed that the simulated low-PV water on 26.0 σθ is displaced eastward compared to observations (Fig. 1c, f). Also, in the central to the eastern part of the basin, the simulated low-PV water is isolated from the lower low-PV layer by relatively high-PV water of around 26.5 σθ. Although the simulated low-PV water develops more strongly than the counterpart in the observation in the central to eastern part of the basin, PV distributions are well represented to the west of the international dateline. On isopycnal surfaces (figures not shown but suggested in Fig. 5), it is confirmed that the simulated PV minimum exists about 10° longitude eastward of the observed one in layers of 25.6 σθ or higher density (e.g., Fig. 9 of Kobashi et al. 2006), although it is well represented in the shallower layers.
Fig. 1

Long-term mean summertime (July–September) potential vorticity (shades as indicated at the bottom of panels in 10−12 cm−1 s−1) and density (contours, with intervals of 0.1 σθ) distributions in OFES (ac) and observational data [df; World Ocean Atlas 2001 (Stephens et al. 2002; Boyer et al. 2002)]. a, d Latitude-depth section of 145°–155°E mean. b, e The same as a, d, but for 165°–175°E mean. c, f Longitude-depth section of 27°–29°N mean

Distributions of the simulated late winter (February–March) mixed layer (ML) depth and sea surface density (Fig. 2) indicate that the deep ML develops in the western and central part of the basin to form a sharp frontal structure of ML depth (MLD). The ML depth front is somewhat sharper than in the observations (Suga et al. 2004). The sharp ML front intersects outcrop lines of around 25.5-σθ layers, and induces subduction of the aforementioned lower-PV water in the model, as the intersection of the ML front and outcrop line is a source of low-PV water on the isopycnal layer, and a deeper ML at the intersection can induce lower PV (Inui et al. 1999; Kubokawa and Inui 1999; Xie et al. 2000).
Fig. 2

Long-term mean late winter (February–March) mixed layer depth (MLD; shades in m) and sea surface density (white contours for 24.5, 25.0, 25.5, and 26.0 σθ) fields simulated in OFES. The color scale is given at the bottom of the panel. MLD is defined as the depth where density difference from the sea surface exceeds 0.125 σθ

3 Simulated STCC

3.1 Mean STCC

Figure 3 shows the long-term mean summertime subsurface zonal current velocity field as it has a maximum in summer (not shown), consistent with observations in the eastern part of STCC (White et al. 1978; Kobashi and Kawamura 2002). STCC appears as a weak eastward flow extending in a southwest–northeast direction from 25°N, 150°E to 30°N, 170°W. At the northeastern end, STCC merges with a broad eastward flow corresponding to the northern part of the anticyclonic subtropical gyre. There is another strong eastward current at 19°N west of Hawaii, the Hawaiian Lee Counter Current (HLCC, Qiu et al. 1997; Xie et al. 2001), but the aforementioned STCC clearly separates from it. The simulated STCC is limited to the east of 150°E, and the northern STCC that extends in the western basin in observations (Kobashi et al. 2006) is not represented in the model. Also, the simulated STCC is weaker than in observations (Kobashi et al. 2006). These discrepancies may be due to strong dissipation in the model. The latitude-depth section of zonal velocity around the international dateline (Fig. 3b) indicates that STCC extends from the surface to less than 200-m depth as in observations (Fig. 3 of Kobashi and Kawamura 2002). Whereas the observed geostrophic STCC has the maximum eastward velocity at the sea surface, the simulated STCC has the vertical maximum just below the surface mixed layer.
Fig. 3

Long-term mean summertime (July–September) zonal current velocity (shades as indicated to the right of b in cm s−1) a at 54-m depth and b in 175°E–175°W mean latitude-depth section in OFES. Contours in a are the corresponding sea surface height field (contour intervals are 10 cm)

3.2 Relation between low-PV water and STCC in long-term mean and interdecadal variations

We next investigate summertime STCC and low-PV water around 170°E (Fig. 4), focusing on the eastern STCC as the western northern STCC is not represented in the model. In the latitude-depth section of long-term mean summertime PV field, there is low-PV water in the northern part of the subtropical gyre, extending southward along isopycnal surfaces around 25.3–25.7 σθ. Above the southern edge of the low-PV water (Fig. 4a), the upper pycnocline shoals northward, and STCC forms just below the sea surface (Fig. 4b). The relative position of STCC to the low-PV water is consistent with observational data (e.g., Kobashi et al. 2006), and Kubokawa’s (1999) theory. To the south of STCC, there is the broad and deep westward North Equatorial Current (NEC), the southern branch of the subtropical gyre. Surrounded by the westward current of NEC, HLCC exists just to the south of 20°N. While it is also trapped in the upper layer as STCC, no low-PV water is found in the lower layer to the north of HLCC.
Fig. 4

Latitude-depth sections of summertime (July–September) a potential vorticity (shades), b zonal velocity (shades), and density (black contours in a and b; intervals are 0.1 σθ). All variables are based on OFES simulation, and are long-term mean and zonally averaged in 160°–180°E. White (blue) contours in a (b) are for zonal velocity of 0 cm s−1 (potential vorticity of 1.5 × 10−12 cm−1 s−1). Shades are indicated as shown at the bottom of each panel

Horizontal distributions of layer thickness (Fig. 5) show that the maximum thickness corresponding to low-PV water appears in the western part of the basin in upper layers (Fig. 5a, b) and in the eastern/northeastern region in deeper layers (Fig. 5c–e). The thick, low-PV water extends from its maximum southwestward or southeastward and then southwestward, but directions of low-PV water ventilation are different among the layers as indicated by the streamlines. As a result, thick waters accumulate to the north of STCC (Fig. 5f), as suggested theoretically by Kubokawa (1999), and the meridional gradient of the layer thickness from 25.1 to 26.1 σθ shows a local maximum almost along STCC around 27°N (Fig. 5g). In contrast, in the shallower layers (from 24.0 to 25.1 σθ), the meridional thickness gradient is small (Fig. 5h) below STCC, indicating that the northward shoaling isopycnal surfaces in the upper layer and the corresponding eastward flow (STCC) is associated with the meridional thickness gradient of the layer from 25.1 to 26.1 σθ. It should be also noted that there is a local maximum of the meridional thickness gradient of the 25.1- to 26.1-σθ layer below the eastward current to the west of 160°E around 20°N, which may correspond to the southern STCC (Kobashi et al. 2006).
Fig. 5

Horizontal distributions of summertime (July–September) layer thickness (shades as indicated to the right of ef) for a 25.1–25.3 σθ, b 25.3–25.5 σθ, c 25.5–25.7 σθ, d 25.7–25.9 σθ, e 25.9–26.1 σθ, g 25.1–26.1 σθ, and h 24.0–25.1 σθ. f The same as a but for thickness = 60 m for each layer in ae. Shades in gh show the meridional gradient of the corresponding thickness (m latitude−1 as indicated to the right of each panel), and white contours show zonal velocity = 0, and 2 cm s−1 at 54-m depth. In each panel of ae, streamlines on the isopycnal surface at the center of each layer are superimposed. All are the long-term mean simulated fields

To study interdecadal variations, we plot the same zonal mean fields for the periods of 1975–1979 and 1990–1994 (Fig. 6). STCC is stronger and more organized in structure at 25°–27°N in the 1970s than in the 1990s, consistent with Yamanaka et al. (2008). Low-PV water is confined mostly to the STMW density range of 25.2–25.6 σθ in the 1990s but its production increases markedly in the CMW range of 25.5–26.0 σθ in the 1970s. The low-PV water intrudes much more southward in the 1970s than 1990s. These changes in low-PV water ventilation are consistent with the STCC change from the thermal wind relationship. Our results show that the association between the STCC and PV distribution holds in both the climatology and interdecadal variability in OFES, consistent with observations and the previous study. This encouraged us to investigate interannual variations in STCC and its relationship to low-PV water as discussed in the next section.
Fig. 6

The same as Fig. 4a, but for the fields averaged in summer time in a 1975–1979 and b 1990–1994. White contours for the zonal current field show 0 and 2 cm s−1

4 Interannual variations in STCC and low-PV water

4.1 Interannual variations in simulated STCC

For investigation of interannual variations in STCC, we plot area-averaged summertime mean zonal velocity at several depths in Fig. 7a. As represented by the speed at 54-m depth, STCC has strong interannual variations with a weak mean eastward current. Also, it is clear that the interannual variations are very similar to those at 404- and 604-m depths. Indeed, the vertical profile of correlation with the area-averaged 54-m zonal velocity (Fig. 7c) shows a deep structure, with correlation coefficient, r, higher than 0.9 (0.7) in the upper 600 (1000) m.
Fig. 7

a Time series of area mean simulated summertime (July–September) zonal current velocity at (26°–28°N, 160°–180°E) at 54-m (black), 404-m (red), and 604-m (green) depths. b Time series of the index of STCC vertical shear that is defined in the text. c Vertical profile of correlation coefficients between the area mean simulated zonal current velocity at (26°–28°N, 160°–180°E) at 54-m depth and those at each vertical level

Lagged correlation maps (Fig. 8) between 404-m-depth summertime zonal current velocity and its area-mean at (26°–28°N, 160°–180°E) indicate that narrow bands of the vertically coherent interannual variations propagate southwestward with a few degrees in meridional width and more than 20° in longitudinal length. These properties resemble those of zonal jets or striations examined by Richards et al. (2006), likely not wind-driven but oceanic internally induced variability. Indeed, similar lagged correlation maps of Ekman pumping do not show coherent forcing for the zonal current signals indicated in Fig. 8 (figures not shown). Mechanisms for the formation of the zonal jets/striations are still under debate (Melnichenko et al. 2010), and beyond the scope of the present study.
Fig. 8

Lagged correlation (shades) maps of summertime (July–September) simulated 404-m-depth zonal current onto the same field averaged at (26°–28°N, 160°–180°E) with lags of −4 to +3 years from the top to bottom. Blackcontours show the simultaneous correlation = 0.4

These vertically coherent interannual variations are not related to variations in low-PV water. As the purpose of the present paper is to investigate the possible influence of low-PV variations on STCC, in the following analyses, we focus on interannual variations that deviate from the vertically uniform structure. To extract upper layer variations independent of deeper layer variations, we compute the linear regression of the time series of 54-m zonal velocity upon velocity at 404-m depth, and then subtract the regression from the original 54-m zonal velocity time series (Fig. 7b). In the following analyses, we use this new time series averaged at (26°–28°N, 160°–180°E) (Fig. 7b) to represent interannual variations in STCC that is trapped to the near-surface layer and independent of the coherent variations in the upper 1000 m. Selection of the depth for the regression, 404 m, is arbitrary, but the resultant time series does not strongly depend on this choice of depth; almost the same results are obtained if regression upon time series at a different depth, say 604 m, is removed. This STCC shear index is a measure of current shear and a good representation of the surface-trapped current in climatology.

Simultaneous correlation map between the STCC shear index and summertime 54-m-depth zonal velocity (Fig. 9) indicates that the peak of the correlation is about 0.5, and the index explains about 20% of interannual variance in zonal velocity. In other words, major parts of interannual variations in the zonal velocity near the surface are vertically coherent as suggested in Fig. 7. The correlation map shows a dipole pattern with positive and negative correlation zones to the south and north of 27.5°N, respectively, around 160°–180°E, suggesting meridional shift of STCC on interannual timescales.
Fig. 9

Simultaneous correlation (contours for ± 0.3, 0.5) and regression (shades as indicated at the bottom of the panel) coefficients between summertime (July–September) 54-m-depth zonal current velocity and the STCC shear index (Fig. 7b)

4.2 Correlation between STCC and low-PV water

To investigate if the STCC interannual variations are related to variations in low-PV water below, we conduct a correlation/regression analysis between the STCC shear index defined above and subsurface fields. Simultaneous correlation maps for PV on a meridional-density section (Fig. 10a) indicate that negative correlations appear to the north of STCC around 25.5–26.1 σθ, whereas positive correlations are found in shallower layers (shallower than about 25.3 σθ). Those negative correlations, representing the intrusion of lower-PV water to the north of STCC, are accompanied by a steeper northward shoaling of isopycnals (Fig. 10b, contours). The stronger northward gradient of density corresponds to stronger vertical shear of eastward geostrophic current, consistent with the stronger STCC (shades in Fig. 10b).
Fig. 10

a Correlation of 160°–180°E mean PV (shades) and regression map of 160°–180°E mean zonal velocity (white contours with 1 cm s−1 intervals) onto the STCC shear index as a function of latitude and density. b The same as the top panel, but for the meridional-depth regression maps for zonal velocity (shades) and density (red contours with climatology added). The regression maps correspond to 1 cm s−1 anomaly of the STCC index. Black contours in a and b are long-term mean isopycnal depth (100–500 m, with 100-m intervals) and density (24.8–26.2 σθ, with 0.2-σθ intervals). All variables are summertime (July–September) simulated fields

Figure 11 shows lagged correlation maps between the STCC shear index and PV on the 25.6-σθ isopycnal surface, on which strongest correlations are found in Fig. 10. Negative PV correlations appear at least 2 years before the peak of STCC, develop and shift south and southwestward. In association with this southwestward shift of negative correlations [which indicate the southwestward development of lower-PV water (black contours)], positive correlations of zonal velocity also shift southward and are found to the south of the negative PV correlation region (white contours in Fig. 11), consistent with the meridional shift suggested in Fig. 8. These results strongly suggest that the STCC variations are induced through a subduction process of low-PV water that develops more than usual.1
Fig. 11

Lagged correlation (shades) and regression (black contours) maps of PV on the 25.6-σθ surface onto the STCC shear index with lags of a −3, b −2, c −1, d 0, e 1, and f 2 years. White contours show the corresponding correlation (0.2, and 0.4) map of 54-m-depth zonal velocity onto the STCC shear index. The regression maps correspond to 1 cm s−1 anomaly of the index with climatology added. All variables are summertime (July–September) simulated fields

The association between STCC and PV anomalies is further examined in a latitude-time section of zonally averaged (in 160°–180°E) layer thickness in 25.5- to 26.1-σθ layers (Fig. 12). The layer thickness (contours in Fig. 12a) shows significant interannual variations especially in its subduction region (around 32°–36°N), from which the thick layer and its interannual variations extend southward. At the southern edge of a thicker layer, stronger meridional gradients of the thickness (shades in Fig. 12a) induce stronger meridional density gradient and vertical zonal current shear in the upper layer. Indeed, zonal current vertical shear (shades in Fig. 12b) is intensified above the strong meridional gradient of the thickness in 25.5- to 26.1-σθ layers (contours in Fig. 12b), and together they propagate southward coherently. For example, at 26°–28°N, the meridional gradient of the thickness and vertical shear of zonal current, the STCC shear index, has high correlation r = 0.77 (Fig. 12c). These results confirm the lagged correlation analysis (Fig. 11).
Fig. 12

a Latitude-time section of 25.5- to 26.1-σθ layer thickness (contours; intervals are 25 m) and its meridional gradient (shades; in m latitude−1). b The same as a, but for vertical shear of zonal velocity (shades). Contours are the same field as that shown in shades in a. c 26°–28°N mean vertical shear of zonal velocity (black, top axis), which is identical to the STCC shear index (Fig. 7b), and meridional gradient of thickness shown in a, b (grey, bottom axis). All variables are 160°–180°E mean of summertime (July–September) simulated fields. The vertical shear in b is defined in the same way as the STCC shear index (Fig. 7b)

4.3 Causes of thickness variations

As interannual variations in the thickness of the 25.5- to 26.1-σθ layer can induce interannual variations in STCC, we further investigate what causes the thickness anomalies in the subduction region, which corresponds to 160°–180°E, 32°–34°N in the climatology. In this subduction region, the interannual variations in the thickness highly correlate with the local SST (r = −0.80) (Fig. 13a), and MLD to the north (34°–36°N) (r = 0.65) in the late winter. MLD in the subduction region of 32°–34°N, however, has lower correlation (Fig. 13e), and the correlation between SST and thickness there is not caused by the deepening of the winter mixed layer. To see the relation between SST and thickness variations, we compare latitude-depth sections of wintertime and summertime density between a cold (1995–1997) and a warm (2000–2002) SST period (Fig. 13b, c). When SST is cold (Fig. 13b), the outcrop line of 25.5-σθ layer shifts southward, and the 25.5- to 26.1-σθ layer is exposed to the sea surface at 32°–34°N. Although the 25.5-σθ isopycnal surface submerges in summer (blue curve), the 25.5- to 26.1-σθ layer remains thick. In the warm years (Fig. 13c), the outcrop of the 25.5-σθ surface shifts northward and the 25.5- to 26.1-σθ layer does not outcrop in the region of 32°–34°N, is kept thin in winter, and remains thin in summer. SST anomalies in the source region (32°–34°N, 160°–180°E) significantly correlate with eastward wind stress (r = −0.52) and are likely influenced by mixing, Ekman cooling, and wind-induced evaporation (but do not significantly correlate with the net surface heat flux probably due to cancellation among influences of atmospheric and SST anomalies on the net flux). Additionally, correlation maps of SST and MLD with the thickness in the source region (Fig. 13d, e) indicate that thicker layer tends to be associated with cooler SST anomalies to the south of the Kuroshio Extension Current (KEC) and deeper MLD near the KEC axis, around which meridional local minimum of MLD forms (Fig. 13e). These anomaly patterns in SST and MLD resemble those with weakened KEC (Nonaka et al. 2011, manuscript in preparation). Indeed, zonally averaged KEC speed (green curve in Fig. 13a) negatively correlates with the thickness (r = −0.64). The relation among variations in KEC, low-PV water subduction, and STCC is suggested by Xie et al. (2011) in their analysis of climate models, and this is supported by the results from this eddy-resolving OGCM hindcast that can represent interannual-to-decadal variations of KEC realistically (Nonaka et al. 2006; Taguchi et al. 2007).
Fig. 13

a Time series of 25.5- to 26.1-σθ layer thickness (black; left axis) in summertime (July–September), SST (red; right axis) in March both averaged at (32°–34°N, 160°–180°E), and the surface Kuroshio Extension Current (KEC) speed (green; right-most axis in cm s−1) in March averaged at 140°–180°E. The KEC speed is detected at each zonal grid point as a maximum zonal current speed at 30°–40°N. b, c Meridional-depth sections of 160°–180°E mean density in March (black contours with 0.1-σθ intervals and thickened for 25.5 and 26.1 σθ), and in summertime (blue for 25.5 and 26.1 σθ). b Average fields in 1995–97 and c 2000–2002. d, e Correlation map of SST (d) and MLD (e) in March onto the time series of layer thickness shown in a. Black contours indicate the long-term mean of the corresponding field, and white contours show sea surface density (25.5 and 26.1 σθ). All variables are simulated fields

5 Summary and discussion

On the basis of a 60-year-long hindcast integration of an eddy-resolving OGCM, OFES, we have investigated interannual-to-decadal variations in STCC and low-PV water and their relations in the North Pacific Ocean. In the model climatology, STCC is found on the south edge of low-PV water as predicted by Kubokawa’s (1999) theory and consistent with observations. This close relationship holds for interdecadal variations in STCC and low-PV water in the model. The specific purpose of the present study is to investigate if the relationship between STCC and low-PV water also holds in interannual variations.

In the OFES hindcast, STCC appears from 150°E to around the international dateline, and is trapped to near the surface. As it peaks in summer near the dateline, we examine interannual variations in summertime STCC and low-PV water. The STCC variability on interannual timescales is dominated by a vertically deep structure (>1000 m) that seems to relate to narrow bands of zonal currents or striations. We then define an STCC shear by removing vertically coherent variations from variations in the near-surface layer zonal velocity, to represent the surface-trapped structure of STCC.

A correlation analysis shows that intensified STCC is accompanied by lower PV than usual to the north on 25.5- to 26.1-σθ isopycnal surfaces, and by intensified meridional density gradient in subsurface layers (Fig. 10). The low-PV signals appear at least 2 years before peaks of STCC, develop and shift southwestward (Fig. 11). These low-PV signals can be traced back to subduction processes. The results of the correlation analysis are further confirmed by latitude-time sections (Fig. 12).

These results strongly suggest that interannual-to-decadal variations in STCC are associated with variations of low-PV water ventilation. In other words, the relation between STCC and low-PV water suggested by Kubokawa (1999) and found in the observed climatology (Kobashi et al. 2006) holds in interannual-to-decadal variations at least in this particular model. This mechanism is at work for decadal changes in STCC (Yamanaka et al. 2008), and our study demonstrates that it works also on interannual timescales in addition to the influence of Ekman flow variations shown by Qiu and Chen (2010) and Kobashi and Xie (2011). Sasaki et al. (2011) suggest a similar influence of low-PV water ventilation on interannual variations of HLCC. The influence of Ekman flow on STCC is, however, not strong in this model. This may be due to the difference in the region of analyses: the studies of Qiu and Chen (2010) and Kobashi and Xie (2011) focus upon the western northern STCC, whereas the eastern STCC is investigated in this study. Also, the weaker meridional temperature gradient in this model than in the observations can weaken the impact of the Ekman flow.2

Low-PV water distributions in OFES are slightly different from observations as discussed in Sect. 2.2. Indeed, the low-PV waters in the layers around 25.6 σθ that correlate with STCC tend to develop and extend more eastward than in observations. This suggests that the relationships between variations in the low-PV water and STCC found in the model may be stronger than in the real ocean. Recently, subsurface observations by Argo profilers (Argo Science Team 2001; Hosoda et al. 2010) have accumulated rapidly, and it becomes possible to investigate subsurface interannual variations. Indeed, on the basis of Argo observations, Oka (2009) shows that interannual temperature anomalies in NPSTMW can be advected southward by at least 2° latitude with a half-year lag. The low-PV water related to STCC variability in the present study has higher density and subducts in higher latitudes than NPSTMW. Also, as shown in Fig. 12, thickness variability extends southward from the source region (32°–34°N) to STCC (26°–28°N) with a few years lag. Further accumulation of Argo observations will make it possible in the near future to investigate the propagation of PV anomalies in relation to STCC variability in the real ocean.


  1. 1.

    Around 160°–140°W, positive PV correlations are found to the north of positive zonal velocity anomalies. In contrast to that found in Fig. 10a, these are not accompanied by negative PV anomalies below. The positive PV anomalies are probably due to advection by westward current anomalies found there (not shown) on a background of strong zonal PV gradients.

  2. 2.

    Indeed, simulated STF in early spring also show weak correlation (r ~ 0.4 at the maximum) between the Ekman convergence and meridional temperature gradient to about 100-m depth. This is consistent with the result of Qiu and Chen (2010) but the correlation is weaker and spatially incoherent (not shown).



The OFES simulations were conducted on the Earth Simulator under the support of JAMSTEC. We thank the members of the OFES group, including Drs. H. Sakuma, Y. Masumoto, and T. Yamagata, for their efforts and support in the model development. Our thanks are extended to Drs. A. Kubokawa, F. Kobashi, and E. Oka for useful discussions. This study is partially supported by Grand-In-Aid for Scientific Research defrayed by the Ministry of Education, Culture, Sports, Science and Technology of Japan (22106006, 23340139). IPRC/SOEST publication #790/8196.


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Copyright information

© The Oceanographic Society of Japan and Springer 2011

Authors and Affiliations

  • Masami Nonaka
    • 1
  • Shang-Ping Xie
    • 2
  • Hideharu Sasaki
    • 3
  1. 1.Research Institute for Global Change, Japan Agency for Marine-Earth Science and Technology (JAMSTEC)YokohamaJapan
  2. 2.International Pacific Research Center (IPRC) and Department of Meteorology, School of Ocean and Earth Science and Technology (SOEST)University of Hawaii at ManoaHonoluluUSA
  3. 3.Earth Simulator Center, JAMSTECYokohamaJapan

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