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    Long term–mean (1950–2011) surface velocity field (vector) simulated by the OFES and geographic location of 1) the Luzon Strait at 120.5°E, 2) the Taiwan Strait at 25°N, 3) the Mindoro Strait at 11°N, and 4) the Karimata Strait at 1.5°N. Areas with water depth shallower than 200 m are shaded in dark gray.

  • View in gallery

    Comparison of (a) seasonal and (b) interannual variability of the SSHA differences across the Luzon Strait from the model (blue dashed line) and satellite (red dashed line). The upper-400-m LST from the model (blue solid line) and observations [red solid line; after Qu (2000)] are included.

  • View in gallery

    Upper-layer (0–745 m) LST (a) monthly mean time series, (b) power spectrum, and (c) interannual (thin) and decadal (thick) variability. The mean seasonal cycle has been removed before the power spectrum analysis. The dashed line in (b) indicates the 95% confidence level.

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    Decadal variability of the upper-layer (0–745 m) LST (thick solid line), MST (thick dashed line), TST (thin dashed line), KST (gray dashed line), and the sum of the three outflows (thin solid line).

  • View in gallery

    (a) Correlation between the upper-layer (0–745 m) LST and zonal wind stress anomalies (contour) on the decadal time scale, and (b) time series of LST from the model (blue) and the island rule (red) smoothed by a 13-month (thin) and 84-month low-pass (thick) filter. The shading in (a) indicates the area satisfying the 95% significance level by a Student's t test. See the text for more information about the path ABCD.

  • View in gallery

    (a) Time series of the upper-layer (0–745 m) LST (solid) and PDO index (dashed), (b) long term–mean (1950–2011) wind stress, and (c) the difference between the strong (1959, 1967, 1986, 1995, and 2004) and weak (1955, 1963, 1972, 1991, and 2000) PDO years as marked by the black circles in (a). A 7-yr low-pass filter has been applied before plotting.

  • View in gallery

    Normalized decadal variability of the upper-layer (0–745 m) LST (thick solid line) and KT (dashed line) compared with the NECB latitude (thin solid line). Positive values indicate westward transport in the LST, southward transport in the KT, and northward movement of the NECB.

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    (a) Long term–mean (1950–2011) vertically integrated (0–745 m) circulation and (b) the difference between the strong (1959, 1967, 1986, 1995, and 2004) and weak (1955, 1963, 1972, 1991, and 2000) PDO years simulated by the OFES. A 7-yr low-pass filter has been applied before plotting.

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Imprint of the Pacific Decadal Oscillation on the South China Sea Throughflow Variability

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  • 1 College of Physical and Environmental Oceanography, Ocean University of China, Qingdao, China, and International Pacific Research Center, SOEST, University of Hawai‘i at Mānoa, Honolulu, Hawaii
  • 2 International Pacific Research Center, SOEST, University of Hawai‘i at Mānoa, Honolulu, Hawaii
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Abstract

Analysis of the 62-yr hindcast outputs from an eddy-resolving ocean general circulation model reveals a prominent decadal variability in the upper-layer (0–745 m) Luzon Strait transport (LST), a key component of the South China Sea throughflow. This variability is in phase with the basin-scale wind stress anomalies associated with the Pacific decadal oscillation (PDO). A composite analysis shows that during the positive phase of the PDO, the Aleutian low and its related positive wind stress curl anomalies intrude southward, reducing the trade winds and enhancing the westerly wind anomalies in the tropical North Pacific. In response, the North Equatorial Current bifurcation shifts northward, resulting in a weaker Kuroshio east of Luzon and consequently a stronger South China Sea throughflow in the upper 745 m.

Corresponding author address: Tangdong Qu, International Pacific Research Center, SOEST, University of Hawai‘i at Mānoa, 1680 East-West Road, Honolulu, HI 96822. E-mail: tangdong@hawaii.edu

School of Ocean and Earth Science and Technology Contribution Number 8967 and International Pacific Research Center Contribution Number IPRC-995.

Abstract

Analysis of the 62-yr hindcast outputs from an eddy-resolving ocean general circulation model reveals a prominent decadal variability in the upper-layer (0–745 m) Luzon Strait transport (LST), a key component of the South China Sea throughflow. This variability is in phase with the basin-scale wind stress anomalies associated with the Pacific decadal oscillation (PDO). A composite analysis shows that during the positive phase of the PDO, the Aleutian low and its related positive wind stress curl anomalies intrude southward, reducing the trade winds and enhancing the westerly wind anomalies in the tropical North Pacific. In response, the North Equatorial Current bifurcation shifts northward, resulting in a weaker Kuroshio east of Luzon and consequently a stronger South China Sea throughflow in the upper 745 m.

Corresponding author address: Tangdong Qu, International Pacific Research Center, SOEST, University of Hawai‘i at Mānoa, 1680 East-West Road, Honolulu, HI 96822. E-mail: tangdong@hawaii.edu

School of Ocean and Earth Science and Technology Contribution Number 8967 and International Pacific Research Center Contribution Number IPRC-995.

1. Introduction

The South China Sea (SCS) throughflow (SCSTF) involves the inflow through the Luzon Strait and the outflows through the Karimata, Mindoro, and Taiwan Straits (Fig. 1). Luzon Strait is the only deep channel connecting the SCS and the Pacific Ocean. Water entering the SCS through the Luzon Strait not only affects the circulation and water characteristics in the SCS (Shaw 1991; Qu et al. 2000), but also conveys the impacts of El Niño–Southern Oscillation (ENSO) and probably other large-scale climate modes into the SCS (Qu et al. 2004; Gordon et al. 2012). Besides, a potential loss of over 10% of the Kuroshio water to the SCS represents a significant reduction in the poleward heat transport in the North Pacific (Yu et al. 2007). On the other hand, water exiting the SCS in the south via the shallow Karimata and Mindoro Straits may play a role in the heat and freshwater transport from the Pacific to the Indian Ocean (Qu et al. 2006; Tozuka et al. 2007).

Fig. 1.
Fig. 1.

Long term–mean (1950–2011) surface velocity field (vector) simulated by the OFES and geographic location of 1) the Luzon Strait at 120.5°E, 2) the Taiwan Strait at 25°N, 3) the Mindoro Strait at 11°N, and 4) the Karimata Strait at 1.5°N. Areas with water depth shallower than 200 m are shaded in dark gray.

Citation: Journal of Climate 26, 24; 10.1175/JCLI-D-12-00785.1

As a key component of the SCSTF, the volume of water that enters the SCS through the Luzon Strait, referred to as the Luzon Strait transport (LST) hereinafter, has been extensively studied by earlier investigators (e.g., Wyrtki 1961; Metzger and Hurlburt 1996; Qu 2000; Qu et al. 2004, 2006; Wang et al. 2006). The surface water pileup due to monsoonal wind was considered to be the primary forcing mechanism for the LST seasonal variability (Metzger and Hurlburt 1996), while its interannual variability was attributed to the meridional migration of the North Equatorial Current bifurcation (NECB) (Qu et al. 2004; Kim et al. 2004). During El Niño years, the northward shift of the NECB results in a weaker Kuroshio east of the Philippines, and the meridional advection of potential vorticity is not strong enough to overcome the β effect, allowing more Kuroshio water to penetrate through the Luzon Strait, referred to as the “teapot effect” (Sheremet 2001). The situation during La Niña years is reversed.

Both seasonal and interannual variabilities of the LST have been related to the basin-scale wind stress anomalies in the Pacific. But, until this time, the decadal variability of the LST has been poorly explored, due to the lack of long-term observations. Based on results from a high-resolution ocean general circulation model (OGCM), this study reveals the existence of a decadal variability in the upper-layer (0–745 m) LST. Taking advantage of the dynamically consistent model outputs, the connection of this variability with basin-scale wind stress anomalies and, in particular, with the Pacific decadal oscillation (PDO) is examined.

2. Model description and validation

The model used for this study is an eddy-resolving OGCM for the Earth Simulator (OFES). Here, we briefly describe the model configuration. See Sasaki et al. (2004, 2007) for more details. The OFES was based on the Modular Ocean Model, version 3 (MOM3). It has a horizontal resolution of 0.1° both in longitude and latitude, and the model domain covers from 75°S to 75°N. The model has 54 levels; the vertical grid spacing varies from 5 m near the surface to ~100 m at 1000 m with a maximum depth of 6065 m. The ° bathymetry dataset was used to construct the model topography.

A 50-yr climatological spinup was first executed from the annual-mean temperature and salinity fields of the World Ocean Atlas 1998 (WOA98) with no motion. Then, a hindcast integration from 1950 to 2011 was conducted. The surface fluxes were specified from the National Centers for Environmental Prediction (NCEP) reanalysis, with a monthly mean value for the spinup run and a daily mean value for the hindcast run, in addition to a surface salinity restoring to the climatological value of WOA98. To suppress grid-scale noises, a scale-selective damping of the biharmonic operator was adopted for horizontal mixing, and the K-profile parameterization (KPP) scheme was employed for the vertical mixing. Results from the 62-yr hindcast run are used for this study.

As already noted by many earlier studies, the OFES was able to reproduce most, if not all, of the detailed phenomena observed over the global ocean (Masumoto et al. 2004). Here, we provide some examples to validate the LST simulated by OFES. Up to now, given the lack of long-term observations, only the seasonal variability of the LST has been available from observations. Figure 2a compares the model result with the observed LST seasonal variability in the upper 400 m (Qu 2000). Both the model and observations show a maximum LST in winter and a minimum LST in summer, with comparable amplitudes of variability. According to Qu (2000), the dynamic height difference across the Luzon Strait is a key process determining the LST. For this reason, Fig. 2 also includes a comparison between the LST and the satellite-based, as well as the model-simulated, sea surface height anomaly (SSHA) differences. Here, the satellite-based monthly mean SSHA data, with a horizontal resolution of ⅓°, are from the Segment Sol Multimissions d'Altimétrie, d'Orbitographie et de Localisation Précise (SSALTO)/Data Unification and Altimeter Combination System (DUACS) multimission altimeter products distributed by the Archiving, Validation, and Interpretation of Satellite Oceanographic data (AVISO) group. As one can see from Fig. 2a, the LST shows essentially the same seasonal cycle as the SSHA differences between the northernmost (22.3°N, 120.5°E) and the southernmost (18.4°N, 120.5°E) point of Luzon Strait both from the satellite and the model.

Fig. 2.
Fig. 2.

Comparison of (a) seasonal and (b) interannual variability of the SSHA differences across the Luzon Strait from the model (blue dashed line) and satellite (red dashed line). The upper-400-m LST from the model (blue solid line) and observations [red solid line; after Qu (2000)] are included.

Citation: Journal of Climate 26, 24; 10.1175/JCLI-D-12-00785.1

As for the interannual variability (Fig. 2b), the satellite-based SSHA difference across the Luzon Strait is highly correlated with the simulated result. The correlation coefficient between them reaches 0.79. In addition, the model-simulated upper-400-m LST also corresponds well with the SSHA difference across the Luzon Strait on an interannual time scale. Its correlation with the model-simulated and satellite-based SSHA difference reaches as high as 0.88 and 0.66, respectively. All these results seem to suggest that the OFES does a reasonably good job in simulating the LST variability and the results presented in this study are mostly reliable.

3. Decadal variability of the SCSTF

As the only inflow from the Pacific into the SCS, the LST is a key component of the SCSTF. Figure 3a shows the raw time series of the upper-layer LST simulated by the OFES. As one would expect, the seasonal cycle is the most significant component of the LST variability in the upper 745 m. With a 6–13-month bandpass filter, this variability accounts for about 68% of the total variation. After the mean seasonal cycle is removed, the power spectrum (Fig. 3b) shows two predominant periods (above the 95% confidence level). One is around 14.2 yr and the other is near 3.3 yr, representing the decadal and interannual variability, respectively. Variabilities on these two time scales, smoothed by an 84-month and a 14–83-month bandpass filter, respectively, are of comparable strength (Fig. 3c), each accounting for ~11% of the total variation. Intraseasonal variability smoothed by a 5-month high-pass filter is also evident (not shown), accounting for ~10% of the total variance. Because intraseasonal-to-interannual variability has been extensively studied by earlier investigators, our discussion below will be focused on the decadal variability alone.

Fig. 3.
Fig. 3.

Upper-layer (0–745 m) LST (a) monthly mean time series, (b) power spectrum, and (c) interannual (thin) and decadal (thick) variability. The mean seasonal cycle has been removed before the power spectrum analysis. The dashed line in (b) indicates the 95% confidence level.

Citation: Journal of Climate 26, 24; 10.1175/JCLI-D-12-00785.1

After checking the decadal variability of the three outflows in the SCS (Fig. 4), one can see that their total transport is almost identical with the upper-layer LST. This suggests that the lower-layer LST variability is weak relative to the upper layer on the decadal time scale. In addition, the mean volume transports of these three outflows are of comparable strength, each accounting for about one-third of the upper-layer LST. One obvious difference is that the variability of the Mindoro Strait transport (MST) is much larger than the other two outflows. Approximately 70% of the SCSTF outflow variability occurs in the MST. The Taiwan Strait transport (TST) and Karimata Strait transport (KST) each account for about 15%. The correlation between the MST and upper-layer LST reaches 0.95.

Fig. 4.
Fig. 4.

Decadal variability of the upper-layer (0–745 m) LST (thick solid line), MST (thick dashed line), TST (thin dashed line), KST (gray dashed line), and the sum of the three outflows (thin solid line).

Citation: Journal of Climate 26, 24; 10.1175/JCLI-D-12-00785.1

4. Role of basin-scale winds

Several hypotheses have been raised concerning the mechanism that govern the LST (e.g., Metzger and Hurlburt 1996; Qu et al. 2000). Based on results from a high-resolution OGCM, Qu et al. (2005) suggested that, as part of the circulation around the Philippines and Borneo, the LST is primarily forced by basin-scale winds over the Pacific. On the interannual time scale, they applied the “island rule” from Godfrey (1989) to the LST and found that the estimate from this simple theory has a good correlation with that simulated by the model. In a follow-up analysis, Wang et al. (2006) further noted that wind stress in the equatorial Pacific is the key factor regulating the LST interannual variability, whereas the effect of local wind stress in the vicinity of the Luzon Strait is secondary.

As a highly simplified theory, the so-called island rule can be used to depict the wind-induced variability of the MST, TST, and KST around the Philippines, Taiwan, and the Philippine/Borneo islands, respectively. For mass continuity, the LST is the sum of these three outflow transports. The effects of friction and nonlinear processes are excluded from this calculation. Because of the narrowness and shallowness of these straits, however, the effects of friction and nonlinear processes are not negligible. For this reason, the island rule cannot yield quantitative estimates of the LST, but it provides possible explanations of the LST variability through the basin-scale ocean adjustment to changes in wind stress. Previously, Qu et al. (2005) estimated the LST through the Karimata Strait, while Wang et al. (2006) made a similar calculation through the Mindoro Strait. Here, we apply the island rule to the LST, with the Mindoro Strait being the only outflow of the SCSTF. Because the MST captures most of the decadal variability in the upper-layer LST (Fig. 4), we feel that the results presented below should have a reasonable representation of the entire SCSTF variability. It is also worthwhile to note that although the effects of friction are not negligible, they may have little influence on the variability of the LST (Qu et al. 2005; Wang et al. 2006).

The monthly mean wind stress from NCEP reanalysis, with a horizontal grid spacing of 0.1°, is used to calculate the LST by applying the island rule. We integrate the wind stress along the path ABCD as shown in Fig. 5a. The segments AB lies at 18.7°N and CD lies at 4.75°N. The climatological-mean transport calculated from the island rule is 13.3 Sv (1 Sv ≡ 106 m3 s−1), about an order larger than the model-simulated MST. The discrepancy is largely due to the friction associated with the complex topography in the Mindoro and Luzon Straits (Qu et al. 2000). Interestingly, the LST calculated from the island rule shows an excellent correspondence with the result derived from the model (Fig. 5b). The island rule is not expected to be applicable to the seasonal cycle as the time taken for a baroclinic Rossby wave to cross the Pacific is more than a season at the latitudes of our interest (Qu et al. 2000). To remove the seasonal cycle, a 13-month low-pass filter is applied to the original time series of the LST. For the interannual variability, the zero-lag correlation between the island rule–derived and model-simulated LST is 0.41, and it increases to 0.59 when the former leads the latter by 4 months. This time difference represents the baroclinic Rossby wave adjustment time scale of the tropical Pacific. As for the decadal variability, the zero-lag correlation between the two time series reaches 0.87 (over 99.9% by a Student's t test). These results clearly demonstrate that the basin-scale winds of the Pacific are the primary control of the LST variability, especially for the decadal time scale, and the application of the island rule, although highly simplified, has provided a theoretical basis for understanding this variability. Among the four segments of the along-path wind stress integral, contributions from segments AB and CD are most significant, and their correlations with the model-simulated LST reach 0.24 and 0.87, respectively. This seems to suggest that the equatorial Pacific winds dominate the LST decadal variability, consistent with the earlier studies for the LST interannual variability (Wang et al. 2006; Liu et al. 2006).

Fig. 5.
Fig. 5.

(a) Correlation between the upper-layer (0–745 m) LST and zonal wind stress anomalies (contour) on the decadal time scale, and (b) time series of LST from the model (blue) and the island rule (red) smoothed by a 13-month (thin) and 84-month low-pass (thick) filter. The shading in (a) indicates the area satisfying the 95% significance level by a Student's t test. See the text for more information about the path ABCD.

Citation: Journal of Climate 26, 24; 10.1175/JCLI-D-12-00785.1

To further identify the key areas where wind stress has the most contribution to the LST decadal variability, the zero-lag correlation between the LST and zonal wind stress anomalies is calculated (Fig. 5a). The time lag between the LST and wind stress anomalies is negligible on the decadal time scale because it only takes a few months for wind stress anomalies to reach the Luzon Strait from the central Pacific. We see high negative correlations (<−0.6) in the central equatorial Pacific, where the segment CD pass through. It seems clear that the upper-layer LST increases when westerly wind anomalies are enhanced in the equatorial Pacific, and vice versa. The wind stress anomalies in the Kuroshio Extension region also show a high negative correlation with the upper-layer LST. Two relatively low positive correlations are visible at 15°–30°N and east of New Guinea, roughly between 140° and 160°E. These high correlations reflect the influence of basin-scale winds both in the tropical and subtropical Pacific.

5. Link to the PDO

The PDO, defined by the leading principal component of the North Pacific sea surface temperature variability poleward of 20°N (Mantua et al. 1997), is closely related to the decadal variability of the Aleutian low (AL) and surface wind stress in the North Pacific (Mantua and Hare 2002; Qiu 2003). As basin-scale winds are the primary forcing mechanism of the LST, we suspect that the PDO also plays a role in the LST decadal variability through modulating the North Pacific subtropical gyre (NPSG).

Here, we find that the zero-lag correlation between the LST and PDO index exceeds 0.6 (over 95% by a Student's t test) on decadal time scale (Fig. 6a). This result suggests that the upper-layer LST tends to increase during the positive phase of PDO, and vice versa. A remarkable regime shift around 1977 was previously reported in the PDO index (Mantua et al. 1997; Zhang et al. 1997). This regime shift is also visible in the LST decadal variability. Peaks of the two time series show a good correspondence, except during the period from 1980 to 1993, when they appeared to be out of phase. The causes of these exceptions are unclear. We will return to this point in section 6.

Fig. 6.
Fig. 6.

(a) Time series of the upper-layer (0–745 m) LST (solid) and PDO index (dashed), (b) long term–mean (1950–2011) wind stress, and (c) the difference between the strong (1959, 1967, 1986, 1995, and 2004) and weak (1955, 1963, 1972, 1991, and 2000) PDO years as marked by the black circles in (a). A 7-yr low-pass filter has been applied before plotting.

Citation: Journal of Climate 26, 24; 10.1175/JCLI-D-12-00785.1

To better understand the wind stress anomalies during the positive phase of the PDO, a composite event representing the differences between the strong (1959, 1967, 1986, 1995, and 2004) and weak (1955, 1963, 1972, 1991, and 2000) PDO years (Fig. 6a) is analyzed. A 7-yr low-pass filter is applied before the analysis. In the mean, the northeasterly trades predominantly prevail at the latitude band between 5° and 25°N, under the control of the North Pacific high (Fig. 6b). As an anomalous AL develops in the composite event (Fig. 6c), a positive wind stress curl anomaly is seen near 40°N in the northeastern Pacific, as already noted by earlier investigators (e.g., Trenberth and Hurrell 1994; Qiu 2003). In response, the northeasterly trades in the eastern tropical Pacific weaken, leading to westerly wind anomalies over a large part of the tropical North Pacific. Similar phenomena can also be seen in the Southern Hemisphere, reflecting the interhemispheric symmetry of decadal variability in the PDO (Dettinger et al. 2000). Furthermore, westerly wind anomalies are visible in the Kuroshio Extension region, and southeasterly wind anomalies appear east of New Guinea. These results (Figs. 5a and 6c) suggest that the PDO has a significant imprint in the surface wind stress both in the tropical and subtropical Pacific, consistent with earlier studies (Mantua et al. 1997).

The positive wind stress curl anomalies in the North Pacific may shift the zero zonally integrated wind stress curl line farther northward during the positive PDO years. This zero line, lying around 15°N in the mean, defines the boundary between the tropical and subtropical gyres in the ocean and is closely related to meridional migration of the NECB (Qiu and Lukas 1996; Kim et al. 2004). When this zero line shifts northward, the NECB will occur at a higher latitude, thereby resulting in a weaker-than-normal Kuroshio transport (KT) east of Luzon. As a result of the teapot effect (Sheremet 2001), the weakening of the KT east of Luzon will in turn enhance the LST and consequently the SCSTF, in the same way as discussed for the interannual variability (Qu et al. 2004, 2005; Wang et al. 2006).

The upper-layer LST simulated by the OFES shows a high correlation (0.72) with the NECB latitude, while its correlation with the model-simulated upper-layer KT east of Luzon can reach as high as −0.84 on the decadal time scale (Fig. 7). The composite analysis mentioned above is also applied to the model-simulated upper-layer circulation. Relative to the mean field (Fig. 8a), an anomalous westward (eastward) flow is seen on the northern (southern) side of the NEC, indicative of a northward shift of the current, during the positive PDO years (Fig. 8b). A southward anomalous flow is also evident in the KT east of Luzon, corresponding with a stronger than normal LST and consequently an intensified current along the western boundary of the SCS (Fig. 8b).

Fig. 7.
Fig. 7.

Normalized decadal variability of the upper-layer (0–745 m) LST (thick solid line) and KT (dashed line) compared with the NECB latitude (thin solid line). Positive values indicate westward transport in the LST, southward transport in the KT, and northward movement of the NECB.

Citation: Journal of Climate 26, 24; 10.1175/JCLI-D-12-00785.1

Fig. 8.
Fig. 8.

(a) Long term–mean (1950–2011) vertically integrated (0–745 m) circulation and (b) the difference between the strong (1959, 1967, 1986, 1995, and 2004) and weak (1955, 1963, 1972, 1991, and 2000) PDO years simulated by the OFES. A 7-yr low-pass filter has been applied before plotting.

Citation: Journal of Climate 26, 24; 10.1175/JCLI-D-12-00785.1

6. Discussion

The present analysis has revealed the existence of a notable decadal variability of the upper-layer LST. The simple island rule can explain this variability reasonably well. High correlations between the upper-layer LST and wind stress anomalies are identified both in the tropical and subtropical North Pacific, indicative of an atmospheric teleconnection between the positive wind stress curl anomaly in the subtropical and westerly wind anomalies in the tropical North Pacific, through which the PDO signals can be conveyed to the tropics. In general, the model results suggest that the upper-layer LST increases during the positive PDO years and decreases during the negative PDO years. There are intriguing exceptions to this trend, though. During the period between 1980 and 1993, the upper-layer LST was actually out of phase with the PDO. Careful examination of wind stress anomalies indicates that the center of the AL during this period was located farther northward (~55°N) than during the other periods (~40°N), probably due to a poleward transfer of the control region of the AL decadal variability (Overland et al. 1999). This poleward transfer may invalidate the atmospheric teleconnection and prevent the PDO signals from transferring into the tropical North Pacific. In other words, the westerly wind anomalies in the tropics will not accompany the positive wind stress curl anomaly in the subtropics. So, there was no positive correlation between the upper-layer LST and PDO at that time. The details require further investigation.

In addition to the atmospheric teleconnection noted above, ocean advection can also play a role in generating the LST decadal variability. Gu and Philander (1997) noted that the subduction and propagation of an anomalous subtropical signal can affect the equatorial thermocline via basin-scale circulation. Liu and Hu (2007) found that the low–potential vorticity water formed in the central North Pacific can reach east of Taiwan along the isopycnal surfaces around 200-m depth after approximately 12 years. In addition, both the decadal spiciness (Sasaki et al. 2010) and temperature anomalies (Chen et al. 2010) in the eastern subtropical North Pacific were found to migrate southwestward toward the tropical western Pacific. Here we find a high correlation (0.46) between the upper-layer LST and the PDO at a 7-yr lag, which can probably be used as evidence to suggest that the PDO signal can affect the LST through the circulation of subtropical mode waters. The details need to be investigated further by research.

Acknowledgments

This research was supported by the China Scholarship Council (File 2011633040) and by NSF OCE10-29704. The authors thank D. Wang and Q. Y. Liu for useful communications on the topic. The OFES outputs were provided by JAMSTEC. The PDO index was downloaded from http://jisao.washington.edu/pdo.

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