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  • View in gallery

    Annual-mean salinity distribution in psu on the σθ = 25.0 surface, after Qu et al. (2000).

  • View in gallery

    Layer-3 flows in winter (December–February). They are averaged over model years 4–6 of our base experiment (experiment M3 in Table 1). A branch of the Kuroshio enters the SCS after it impinges upon Taiwan and then joins the interior SCS wind-driven circulation south of 20°N. Light shading indicates water depths shallower than 200 m, and medium shading indicates depths less than 100 m. The throughflow ports are marked by hatched lines, with denser ones indicating deeper ports.

  • View in gallery

    Transport estimates from data assimilations in solid curves. (a) Kuroshio transport at 18.5°N based on 3DVAR by Yaremchuk and Qu (2004). (b) Transports exiting the SCS via the TS, KS, and MS based on 4DVAR are shown by solid curves marked with Xs, open squares, and filled circles, respectively (M. Yaremchuk 2005, unpublished manuscript). The dashed curve is the shifted Taiwan Strait transport, to be used as MTS.

  • View in gallery

    Annual-mean distribution of layer-3 salinity. The shadings are for model year 7, and the red, black, and light blue contours indicate the location of the 34.6-psu contour for model years 4, 7, and 10, respectively. These four experiments are forced by different strengths of the SCS throughflow, as listed in Table 1. The S3 distribution of experiment M3 resembles the observation very closely.

  • View in gallery

    Annual-mean w23 field for experiments (a) M3, (b) M3KS0, (c) M3MS0, and (d) M3TS0 listed in Table 1. The shadings are for w23 < 0 cm day−1, indicating freshwater detrained from layer 2. (b)–(d) Each of the three experiments has only two open secondary straits. The dashed, solid, and dotted contours are the locations of S3 = 34.6 psu from model years 4, 7, and 10, respectively.

  • View in gallery

    (a), (b) Annual-mean w23 field for experiments M4 and W0 that are forced either by wind stress or by the SCS throughflow. The shadings are for w23 < 0 cm day−1. Superimposed are layer-3 flows in winter. The dashed and solid curves in (b) are the locations of S3 = 34.6 psu from model years 4 and 7, respectively.

  • View in gallery

    Annual-mean distribution of layer-3 salinity. Similar to Fig. 4, but for testing sensitivities to forcing fields and parameters related to freshwater, as defined in Table 1. The shadings are for S3 ≥ 34.6 psu of model year 7, and the dashed, solid, and dotted contours mark the locations of S3 = 34.6 psu from model years 4, 7, and 10, respectively.

  • View in gallery

    (a), (b) Annual-mean distribution of layer-3 salinity. Same as in Fig. 4, but for experiments W1 and W2 that test S3 sensitivity to wind stress. (c), (d) Wintertime layer-3 flows superimposed on wintertime S3. The shadings indicate the December–February mean of S3 averaged over model years 4–6, and the thick black curve marks the 34.6-psu contour.

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    Same as in Fig. 8, but for experiments V1 and V2 that test S3 sensitivity to horizontal mixing.

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Subsurface Salinity Balance in the South China Sea

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  • 1 International Pacific Research Center, University of Hawaii at Manoa, Honolulu, Hawaii
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Abstract

The South China Sea (SCS) is often treated as a semienclosed water body, with the Luzon Strait as its only connection to the Pacific Ocean. A branch of the Kuroshio flows northwestward across the Luzon Strait to enter the SCS, carrying North Pacific Tropical Water (NPTW) into the basin. Using the subsurface salinity maximum as a tracer for NPTW, the authors show how important three secondary straits—the Taiwan Strait to the north and the Karimata and Mindoro Straits to the south—are to the NPTW intrusion at the Luzon Strait. The authors demonstrate that the SCS cannot reach an equilibrium state that is consistent with the observed subsurface salinity distribution unless all of the following components are in place: the Kuroshio, transports through the three secondary straits, downward mixing of freshwater, horizontal mixing induced by mesoscale eddies, and forcing by the local monsoonal winds.

Corresponding author address: Dr. Zuojun Yu, IPRC/SOEST, University of Hawaii at Manoa, 1680 East–West Road, POST #413A, Honolulu, HI 96822. Email: zuojun@hawaii.edu

Abstract

The South China Sea (SCS) is often treated as a semienclosed water body, with the Luzon Strait as its only connection to the Pacific Ocean. A branch of the Kuroshio flows northwestward across the Luzon Strait to enter the SCS, carrying North Pacific Tropical Water (NPTW) into the basin. Using the subsurface salinity maximum as a tracer for NPTW, the authors show how important three secondary straits—the Taiwan Strait to the north and the Karimata and Mindoro Straits to the south—are to the NPTW intrusion at the Luzon Strait. The authors demonstrate that the SCS cannot reach an equilibrium state that is consistent with the observed subsurface salinity distribution unless all of the following components are in place: the Kuroshio, transports through the three secondary straits, downward mixing of freshwater, horizontal mixing induced by mesoscale eddies, and forcing by the local monsoonal winds.

Corresponding author address: Dr. Zuojun Yu, IPRC/SOEST, University of Hawaii at Manoa, 1680 East–West Road, POST #413A, Honolulu, HI 96822. Email: zuojun@hawaii.edu

1. Introduction

In recent years, estimated values of the Luzon Strait transport (LST) into the South China Sea (SCS) have converged toward an annual average of 3–4 Sv (1 Sv = 106 m3 s−1), with a maximum in January and a minimum in July (e.g., Metzger and Hurlburt 1996; Lebedev and Yaremchuk 2000; Fang et al. 2005; Cai et al. 2005; Qu et al. 2006). This potential loss of more than 10% of the Kuroshio transport to the SCS represents a significant reduction in the heat transport from low to high latitudes in the ocean, because most of the LST goes west through the northern SCS to exit the basin in the south via the Karimata (KS) and Mindoro Straits (MS). As such, its interannual variability could impact Indian Ocean (Qu et al. 2005) as well as North Pacific Ocean climate.

Qu et al. (2000) carefully examined water-mass distributions in the SCS using all hydrographic data in the National Oceanographic Data Center archives before April 1993. They focused on two well-defined North Pacific water masses: high-salinity North Pacific Tropical Water (NPTW) and low-salinity North Pacific Intermediate Water. Figure 1 is a reproduction of their annual-mean salinity map on the σθ = 25.0 surface, which lies near the high-salinity core of NPTW. At the mouth of the Luzon Strait (nearly parallel to 122°E), salinity is 34.76 psu. The salinity gradually decreases toward the southwest. Near 17°N, the 34.6-psu contour orients more or less in an east–west direction. South of 15°N, the salinity change is more gradual with contours oriented in a southwest–northeast direction. The southern SCS is occupied by fresher water with a salinity of about 34.55 psu.

In this study, we seek to identify the key processes that are responsible for the structure of the NPTW-induced, subsurface salinity maximum in the SCS basin. Our approach is to use an eddy-resolving regional ocean model, which is given next.

2. Ocean model

The model is nearly identical to the one described in Yu and Potemra (2006). It consists of four active layers, each having a thickness hi, velocity vi, salinity Si, and temperature Ti (i = 1, 2, 3, or 4 is a layer index), overlying a deep, inert ocean where the pressure gradient vanishes (a 4½-layer system). Each of the layers represents water generated primarily by a specific process, and hence corresponds mostly to a single water-mass type: layer 1 is the surface mixed layer, layer 2 is the seasonal thermocline, and layers 3 and 4 represent lower-thermocline (NPTW) and upper-intermediate waters, respectively.

a. Entrainment and detrainment

To simulate the processes of upwelling, subduction, and diapycnal mixing, fluid is allowed to transfer across the interfaces between adjacent layers, conserving mass, heat, and salt as it does. There are three primary parameterizations of mass exchange across the interfaces. The first type is based on the Kraus and Turner (1967) mixed layer model. It describes the exchange between layers 1 and 2, and involves parameters for mixing by wind stirring m and cooling efficiency n (Hood et al. 2003). To prevent the mixed layer from becoming too deep in winter, we set m = exp(−h1/hm), where hm = 300 m, and n = 0. The second type specifies entrainment from below wherever layer thicknesses become smaller than critical values of h1min = 10 m, h2min = 10 m, and h3min = 50 m. The third type is a restoring term that detrains water from layer 2 into layer 3:
i1520-0485-38-2-527-e1
where Hd = 65 m, td = 180 days, and θ(x) is a step function (θ = 1 for x ≥ 0 and is zero otherwise). Equation (1) allows a gradual shoaling of the seasonal thermocline (layer 2) after the surface mixed layer (layer 1) starts to thin during spring. Clearly, the amount of vertical mixing induced by wd23 can be very different depending on the values of Hd and td. Our choice of Hd = 65 m is based on the mean thermocline depth in the SCS during wintertime (e.g., Qu et al. 2007). The time scale of one-half year is a sensible choice for a monsoonal regime; changing td by a factor of 2 will not affect our main conclusions. In section 3b, we will discuss model sensitivity to wd23, as well as its impact in terms of estimated vertical mixing coefficient.

b. Model basin, initial conditions, and spinup

The model setup is similar to that of Shaw and Chao (1994). The basin includes the SCS and a small part of the Pacific Ocean east of the Luzon Strait to allow for the Kuroshio (Fig. 2). In essence then, the influence of the Pacific Ocean is condensed to the transport carried by the Kuroshio. To determine basin boundaries, model grid points are taken to be land wherever the ocean depth is shallower than 200 m in the Earth Topography 5-minute data (ETOPO5; National Geophysical Data Center of the National Oceanographic and Atmospheric Administration), except for the model’s Karimata Strait, which is shaped more like the 100-m isobath. We specify the monthly mean transports through three secondary straits, namely, the Taiwan (TS), Karimata, and Mindoro Straits, as well as the Kuroshio transports at 17° and 25°N (see section 2d).

The model grid is in spherical coordinates with Δx = Δy = 0.1°, and the integration time step is Δt = 10 min. In layers 1 and 2, initial temperature and salinity fields are based on mean climatological fields taken from the World Ocean Atlas 1998 (Conkright et al. 1998) at depths of 20 and 50 m, respectively. In layer 3 (layer 4), they are set to 17°C and 34.55 psu (8.8°C and 34.44 psu), representing typical values in the salinity maximum (minimum) layer according to Qu et al. (2000). The model is spun up from a state of rest for 3 yr, by which time the two upper layers of the model have very nearly adjusted to their equilibrium states. The fields used for analyses are based on 5-day sampling of the output from model years 4–10, mostly from layer 3. As we shall see, the equilibrium state of layer 3 is the result of a subtle balance among various forcing fields, which may not be achieved (or may not resemble the observations) if one of the key processes is missing.

c. Surface forcing

Because the model is thermodynamically active, surface boundary conditions include forcing by heat, freshwater, and momentum fluxes. Climatological monthly mean shortwave radiation Qsw, longwave radiation, air temperature, and specific humidity, together with the model’s T1 field, are used in the calculation of sensible and latent heat fluxes, as in McCreary and Kundu (1989) and McCreary et al. (1993). Forcings for Qsw, longwave radiation, and precipitation are taken from the Comprehensive Ocean–Atmosphere Data Set prepared at the University of Wisconsin—Milwaukee, constrained as recommended by the authors based on a global budget obtained from an oceanic general circulation model (da Silva et al. 1994); specifically, we use 93% of the Qsw and 112% of the precipitation. Monthly mean climatologies of wind stress, wind speed, and cubic wind speed are determined using 6-hourly surface winds from the European Centre for Medium-Range Weather Forecasts (ECMWF) for 1998–2004, as in Yu et al. (2007), and the drag coefficient is assumed to be CD = 1.3 × 10−3. In addition, a minimum wind speed of 8 m s−1 is used for the latent heat calculation; without this minimum the model’s T1 is 1°–2°C warmer than Franz’s (2000) nighttime sea surface temperature in summer (not shown).

River runoff is also substantial in the SCS. Since accurate runoff data are not available, we parameterize its effects by nudging layer-1 salinity (S1) to observed sea surface salinity (SSS) at basin boundaries whenever S1 > SSS (Yu and McCreary 2004). Note that this approach is similar to the usual relaxation of model surface salinity to observations, except that it is highly conditional; that is, it only acts to freshen surface salinity adjacent to coasts.

d. Inflow and outflow

Closed, no-slip conditions, ui = υi = 0, are imposed on all basin boundaries, except at the five inflow/outflow ports indicated in Fig. 2. The transport for each layer i across the Karimata, Mindoro, and Taiwan Straits and for the Kuroshio across 17° and 25°N is set according to
i1520-0485-38-2-527-e2
where Mstr is the vertically integrated transport through a particular port (str), with positive values for transports out of the SCS via the three secondary straits and for the northward Kuroshio, and the nondimensional factor ϕi(str) specifies the vertical partitioning of the transport in each layer and its sum Σiϕi(str) = 1.

Figure 3 plots the seasonally varying, vertically integrated transports that are imposed at basin ports. The solid curves are all dynamically constrained estimates obtained through assimilations of available observations into ocean models. Specifically, the curve in Fig. 3a is used as M17N, which is based on three-dimensional variational data assimilation (3DVAR; Yaremchuk and Qu 2004) and is similar to the Kuroshio transport used in Shaw and Chao (1994). The three solid curves in Fig. 3b are based on 4DVAR (M. Yaremchuk 2005, unpublished manuscript); their sum in wintertime (summertime) is similar to (much smaller than) that used by Shaw and Chao (1994), which is based on an early estimate by Wyrtki (1961). We use the curves for the Karimata and Mindoro Straits as MKS and MMS, respectively. Although the magnitude of the annual-mean Taiwan Strait transport from the 4DVAR agrees well with the available data, its phase differs from observation-based estimates, which suggest a maximum in summer and minimum in winter (e.g., Fang et al. 1991). For this reason, we use a synthetic curve for MTS, which has the same annual mean as the 4DVAR curve, but its minimum is shifted to December (dashed curve in Fig. 3b). So, the strength of the SCS throughflow (SCSTF) is MTF = MKS + MMS + MTS. Clearly, the MTF is the net loss of the total Kuroshio water to the SCS across the Luzon Strait. By referring to the LST as the SCSTF, we stress its importance in connecting the Pacific Ocean and the Indonesian Sea, as well as in the dynamic balance of the entire SCS. To complete our specification of boundary transports, we set M25N = M17NMTF, ensuring that mass is conserved in the model domain.

To make sensible choices of ϕi(str), we take into consideration port depths and observed Kuroshio structures. Since the Taiwan and Karimata Straits are both shallower than 100 m, we set ϕ1(str) + ϕ2(str) = 1 and ϕ3(str) = ϕ4(str) = 0, thereby allowing outflow only in layers 1 and 2. The Mindoro Strait is about 400 m deep, so we set ϕ1(MS) + ϕ2(MS) = ϕ3(MS) = ½, and ϕ4(MS) = 0. For the Kuroshio, unless stated otherwise we distribute the transports M17N and M25N uniformly among layers (1+2), 3, and 4 by setting ϕ1(lat) + ϕ2(lat) = ϕ3(lat) = ϕ4(lat) = ⅓, where “lat” is 17° or 25°N. Since the thicknesses of layers (1+2), 3, and 4 are on the order of 100, 200, and 400 m, respectively, the vertical partition thus gives a much swifter Kuroshio in the upper 300 m, as in many observational and modeling results (e.g., Gilson and Roemmich 2002; Shaw and Chao 1994). Last, to determine ϕ1 and ϕ2 separately, we apply the ratio ϕ1/ϕ2 = h1/h2 at each port, which is consistent with the property that the upper two layers are tightly connected by mixed layer dynamics.

Our choices for the partitioning are sensible ones, but they are not the only possible choices. We discuss the sensitivity of solutions to transport strength and partitioning in sections 3a and 3f. A particularly noteworthy property of our choices is that the default partitioning for M25N and MTF requires that there is an upward mass transfer from layers 3 and 4 into layers above somewhere in the basin, since more transport exits than enters the basin in the two upper layers because of the shallowness of the outflow ports. As we shall see, this MTF-induced upwelling is one of the essential processes in determining the model’s S3 distribution.

Temperatures and salinities of the Kuroshio inflow at 17°N are externally specified to be (T1, T2, T3, T4) = (T1obs, 26, 18.3, 8.4)°C and (S1, S2, S3, S4) = (S1obs, 34.6, 34.78, 34.4) psu, according to Boyer et al. (2002) and Qu et al. (2000), where T1obs and S1obs are climatological monthly mean observations. By selecting S3 = 34.78 psu at 17°N, we ensure that layer 3 represents NPTW. Boundary values at the other (outflow) ports are determined internally by specifying zero normal derivatives.

3. Results

In this section, we report the sensitivity of the layer-3 salinity field S3 to various forcings and mixing. (Recall that S3 represents the subsurface salinity maximum associated with the intrusion of NPTW.) The sensitivity is large, thereby demonstrating the importance of various processes, in particular the strength and structure of MTF.

a. Sensitivity to throughflow transports

When the full strength of the MTF is used to force the model (experiment M1 in Table 1), S3 does not reach an equilibrium state during model years 4–10 (Fig. 4a). Instead, the 34.6-psu contour continues to advance southward, passing 6°N in the western part of the basin at the end of the 10-yr integration (light blue contour). When MTF is reduced to 75% of its full strength (experiment M2), the 34.6-psu contour is more or less confined north of 11°N (Fig. 4b). The S3 distribution forced by 50% of the MTF (experiment M3) resembles the observations quite well (Fig. 4c): the three 34.6-psu contours for model years 4, 7, and 10 are located mostly north of 15°N and the southern SCS is occupied by rather uniform fresher water. We therefore consider experiment M3 to be our base experiment, although there is no significant change to our conclusions if experiment M2 is used instead. When the secondary straits are all closed (MTF = 0, experiment M4), the model SCS basin is filled with freshwater (S3 < 34.5 psu in Fig. 4d). Experiment M4 demonstrates clearly that the SCS cannot be treated as a semienclosed sea with the Luzon Strait as the only opening.

Since the secondary straits differ in their depths, geographic locations, and transport magnitudes, we examined the contribution of each individual strait by obtaining a test run with the strait closed (experiments M3KS0, M3MS0, and M3TS0 in Table 1; note that the SCSTF is weaker in these three test cases than that in experiment M3 because of the closure of one port). The 34.6-psu contours for model years 4, 7, and 10 for the three tests are all located farther to the north (Figs. 5b–d), closer to 20°N, than those from experiment M3 (Fig. 5a). These sensitivity experiments indicate that the model Karimata Strait has more influence on S3 than the other two secondary straits, which could be due to the relatively large MKM from the 4DVAR.

The MTF of Wyrtki (1961) was used by Shaw and Chao (1994), which has transports similar to those in Fig. 3b during winter but much larger reversed transports during summer. As a result, the net annual-mean MTF of Wyrtki (1961) is only 0.25 Sv, much smaller than the net 1.37 Sv used in experiment M3. In a final test run for model sensitivity to MTF, we set MTF to Wyrtki’s monthly mean values, and the solution (not shown) looked more like that of Fig. 4d. Therefore, we conclude that the magnitude of the net annual-mean MTF is essential for obtaining a realistic S3 equilibrium state.

b. Roles of detrainment and MTF-induced upwelling

One way to understand the S3 sensitivity to MTF is through a comparison of the layer-3 circulations in two extreme solutions, in which the secondary straits are all closed (experiment M4) or wind stress is set to zero (experiment W0). Figure 6 shows the layer-3 flows from these two cases. In experiment M4, the flows north of 20°N consist of an anticyclonic eddy near Dongsha Island (located near 20.5°N, 116.5°E) and a cyclonic eddy just west of the Luzon Strait; south of 20°N, the winter cyclonic gyre dominates. In experiment W0, the circulation is driven mainly by the SCSTF, with southward flow along the western boundary and an eastward branch leading toward the Mindoro Strait along 12°N; east of Dongsha Island, there exists a cyclone. When the model is forced by both the wind and the SCSTF, the flows are a combination of the two patterns seen in Fig. 6, as is the case for experiment M3 (Fig. 2).

The shading in Fig. 6a maps out the regions where there is annual-mean detrainment from layer 2 into layer 3 in experiment M4. As noted in section 2a, detrainment wd23 occurs primarily during the spring after the wintertime winds weaken and the mixed layer thins, thereby allowing (h1+h2) to relax back to Hd. Note that there is essentially no detrainment inside the SCS in Fig. 6b, because experiment W0 has no wind forcing and, hence, lacks this process; as a result, nearly the entire SCS is occupied by high-salinity water of S3 > 34.6 psu by year 7 (the thick solid curve).

To confirm that it is this detrainment process that freshens the NPTW intrusion, we set wd23 = 0 in experiment D1 (the experiment would otherwise be identical to experiment M3): without freshwater from above, layer 3 is taken over by the NPTW quickly (Fig. 7a). One could interpret the effect of wd23 in terms of vertical diffusion κ, following Hu (1996). The difference map of κ based on experiments M3 and D1 is similar to the shaded regions in Fig. 5a for w23 < 0, and is on the order of 1 cm2 s−1, a sizeable but still reasonable value for κ.

As mentioned in section 2d, our default vertical partition forces upwelling from layer 3 into layer 2. The impact of this MTF-induced upwelling can be seen by comparing Fig. 6a with Fig. 5a. Detrainment in experiment M3 (Fig. 5a) is weaker (less shaded area) than that in experiment M4, because experiment M3 has MTF-induced upwelling that weakens the wd23 term.

After seeing the horizontal and vertical flow fields, it is clear that the S3 distribution in Fig. 4 basically results from the NPTW intrusion and the freshwater subduction from layer 2. When the SCSTF is too large, layer 3 is occupied by the high-salinity NPTW (experiment M1), and when there is no SCSTF, layer 3 is dominated by freshwater from above (experiment M4).

c. Sensitivity to freshwater flux

Since the freshwater that balances the high-salinity NPTW in layer 3 all arises from subduction through wd23, it follows that any process that affects surface salinity, S1, will also impact S3. Figures 7b–d explore the influences of river runoff and precipitation on S3. In experiment R1 (Fig. 7b), which neglects all runoff, the 34.6-psu contour advances southward at nearly 1° per year. Similarly, a change in precipitation rate, say by ±20%, is significant to tilt the S3 balance (Figs. 7c,d). As noted by Yu and McCreary (2004), any two sets of precipitation products can easily differ by 20% in magnitude, even when their spatial patterns are very similar. Of course, there is also the evaporation, which is calculated in the model using bulk formulas (section 2b). If it were taken from a product as part of the evaporation minus precipitation, its impact on S3 would be as significant as that of precipitation.

d. Sensitivity to wind strength

Here we investigate the S3 sensitivity to the magnitude of the wind stress, when modified by ±20%. Figure 8 shows the annual-mean S3 distribution during model years 4–10 of experiments W1 and W2 (Table 1). With a 20% change in wind stress, the solutions are not too different from that of experiment M3 (Fig. 4c). However, the three 34.6-psu contours for years 4, 7, and 10 are located somewhat to the south (north) when the stress is weaker (stronger). During winter, the stronger cyclonic circulation between 14° and 19°N in experiment W2 (Fig. 8d) advects more fresher water westward along 19°N and pushes the 34.6-psu contour (thick black curve) much closer to the western boundary than in the weaker wind case (Fig. 8c).

e. Role of eddy-induced horizontal mixing

Most global ocean models have horizontal resolutions coarser than 0.1° and use constant eddy viscosity and diffusivity. What would happen if the eddy viscosity (κ2) and diffusivity (ν2) in our model were set to those constants commonly used by non-eddy-resolving (coarser grid) models? Instead of using the Smagorinsky (1963, 1993) scheme and minimum diffusivity (ν2) (listed as standard options in Table 1), we carry out two experiments to see the model sensitivity to eddy-induced mixing: in experiment V1 we still use the Smagorinsky scheme to maintain numerical stability but set minimum ν2 to 5 × 106 cm2 s−1 and constant κ2 = 5 × 106 cm2 s−1; in experiment V2 we replace the Smagorinsky scheme with constant ν2 = κ2 = 2 × 107 cm2 s−1.

One obvious effect of constant (larger) mixing is a much smoother S3 field that spreads the NPTW farther southward (Figs. 9a,b). As the constant mixing value increases, the wintertime cyclonic circulation south of 19°N is weakened (Figs. 9c,d). As a result, less fresher water is brought in from the south west of Luzon, and less fresher water is advected westward northwest of Luzon. A high-salinity tongue can be seen off the coast near 115°E in winter in experiment V2 (Fig. 9d).

f. Sensitivity to ϕi(25N)

The S3 sensitivity to the SCSTF strength shown in Fig. 4 is to some extent affected by the vertical partition of ϕi(25N). We designed some experiments to test this effect in a systematic way; one such way was to set ϕ3(25N) = ϕ4(25N) = 1/N, so that ϕ1(25N) + ϕ2(25N) = 1 − (2/N). As we increased N from 3 (the default value used in all experiments listed in Table 1) to 4 and then to 5, the annual-mean locations of 34.6-psu contours for model years 4–10 retreated northward gradually, and layer 3 kept on getting thicker (e.g., its mean value during model year 7 increased from 158 to 166 m and then to 180 m as N increased). Clearly, this is due to the weakened MTF-induced upwelling (thanks to the new partitions), which is no longer capable of limiting the detrainment of freshwater.

Alternatively, one could also let ϕ4(25N) vary monthly but set its annual-mean value to zero to mimic seasonally reversed Luzon Strait intrusion below the NPTW with little net transport (You et al. 2005), or allow water mass to accumulate inside the SCS (so that instantaneous mass conservation is not required). We are currently using a data assimilation approach to refine the structure of ϕi(str) as well as uncertainties in other forcing fields and parameters to advance our understanding of the SCS dynamics.

4. Summary and discussion

We have shown that the most important processes behind the observed subsurface salinity maximum distribution are the SCSTF and detrainment of freshwater. The SCSTF induces upwelling that counters the freshwater flux from above, as well as brings in high-salinity NPTW. The S3 sensitivity to freshwater discharge and the monsoonal circulation makes it a challenge to determine the proper strength and vertical structure of the MTF, because large uncertainties still exist in these forcing fields.

Note that in this study we did not include Rossby waves and eddies that may propagate into the Luzon Strait region from the east. Some observations suggest that Pacific water enters the SCS in the form of warm-core eddies (Li et al. 1997, 1998). A natural extension of our study is to include such eddies and to understand their interactions with the mean Kuroshio near the Luzon Strait. Equally important is the inclusion of the interannual variability of the Kuroshio transport, as well as that of the SCSTF. A global ocean model, such as the high-resolution Ocean General Circulation Model for the Earth Simulator (OFES; Masumoto et al. 2004), is needed to identify the key factors that control the strength of the SCSTF.

Acknowledgments

This study was supported by NASA through Grants NAG 5-10045 and NNX07AG53G and by the Japan Agency for Marine–Earth Science and Technology (JAMSTEC) through its sponsorship of the International Pacific Research Center (IPRC). The assistance of the Ferret Group at NOAA/PMEL and of Sharon DeCarlo and Jan Hafner is greatly appreciated. Discussions with Guohong Fang, Xuhua Chen, Yan Du, Tangdong Qu, Jianping Xu, Dongxiao Wang, and Guihua Wang were helpful. Critical comments from two anonymous reviewers helped to improve the presentation. Author ZY thanks the South China Sea Institute of Oceanology in China for its partial support to attend the 2005 Guilin Meeting on the South China Sea.

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Fig. 1.
Fig. 1.

Annual-mean salinity distribution in psu on the σθ = 25.0 surface, after Qu et al. (2000).

Citation: Journal of Physical Oceanography 38, 2; 10.1175/2007JPO3661.1

Fig. 2.
Fig. 2.

Layer-3 flows in winter (December–February). They are averaged over model years 4–6 of our base experiment (experiment M3 in Table 1). A branch of the Kuroshio enters the SCS after it impinges upon Taiwan and then joins the interior SCS wind-driven circulation south of 20°N. Light shading indicates water depths shallower than 200 m, and medium shading indicates depths less than 100 m. The throughflow ports are marked by hatched lines, with denser ones indicating deeper ports.

Citation: Journal of Physical Oceanography 38, 2; 10.1175/2007JPO3661.1

Fig. 3.
Fig. 3.

Transport estimates from data assimilations in solid curves. (a) Kuroshio transport at 18.5°N based on 3DVAR by Yaremchuk and Qu (2004). (b) Transports exiting the SCS via the TS, KS, and MS based on 4DVAR are shown by solid curves marked with Xs, open squares, and filled circles, respectively (M. Yaremchuk 2005, unpublished manuscript). The dashed curve is the shifted Taiwan Strait transport, to be used as MTS.

Citation: Journal of Physical Oceanography 38, 2; 10.1175/2007JPO3661.1

Fig. 4.
Fig. 4.

Annual-mean distribution of layer-3 salinity. The shadings are for model year 7, and the red, black, and light blue contours indicate the location of the 34.6-psu contour for model years 4, 7, and 10, respectively. These four experiments are forced by different strengths of the SCS throughflow, as listed in Table 1. The S3 distribution of experiment M3 resembles the observation very closely.

Citation: Journal of Physical Oceanography 38, 2; 10.1175/2007JPO3661.1

Fig. 5.
Fig. 5.

Annual-mean w23 field for experiments (a) M3, (b) M3KS0, (c) M3MS0, and (d) M3TS0 listed in Table 1. The shadings are for w23 < 0 cm day−1, indicating freshwater detrained from layer 2. (b)–(d) Each of the three experiments has only two open secondary straits. The dashed, solid, and dotted contours are the locations of S3 = 34.6 psu from model years 4, 7, and 10, respectively.

Citation: Journal of Physical Oceanography 38, 2; 10.1175/2007JPO3661.1

Fig. 6.
Fig. 6.

(a), (b) Annual-mean w23 field for experiments M4 and W0 that are forced either by wind stress or by the SCS throughflow. The shadings are for w23 < 0 cm day−1. Superimposed are layer-3 flows in winter. The dashed and solid curves in (b) are the locations of S3 = 34.6 psu from model years 4 and 7, respectively.

Citation: Journal of Physical Oceanography 38, 2; 10.1175/2007JPO3661.1

Fig. 7.
Fig. 7.

Annual-mean distribution of layer-3 salinity. Similar to Fig. 4, but for testing sensitivities to forcing fields and parameters related to freshwater, as defined in Table 1. The shadings are for S3 ≥ 34.6 psu of model year 7, and the dashed, solid, and dotted contours mark the locations of S3 = 34.6 psu from model years 4, 7, and 10, respectively.

Citation: Journal of Physical Oceanography 38, 2; 10.1175/2007JPO3661.1

Fig. 8.
Fig. 8.

(a), (b) Annual-mean distribution of layer-3 salinity. Same as in Fig. 4, but for experiments W1 and W2 that test S3 sensitivity to wind stress. (c), (d) Wintertime layer-3 flows superimposed on wintertime S3. The shadings indicate the December–February mean of S3 averaged over model years 4–6, and the thick black curve marks the 34.6-psu contour.

Citation: Journal of Physical Oceanography 38, 2; 10.1175/2007JPO3661.1

Fig. 9.
Fig. 9.

Same as in Fig. 8, but for experiments V1 and V2 that test S3 sensitivity to horizontal mixing.

Citation: Journal of Physical Oceanography 38, 2; 10.1175/2007JPO3661.1

Table 1.

List of experiments. The standard (std) wind stress is the climatological, monthly mean ECMWF value from 1998 to 2004. The standard horizontal mixing is the Smagorinsky (1963, 1993 scheme for eddy viscosity and κ2 = 104 cm2 s−1 for temperature and salinity. The standard subduction and virtual runoff use 180 and 30 days as their restoring time scales, respectively. The standard precipitation is 112% of da Silva et al. (1994). Experiment M3 (boldface) is the base experiment.

Table 1.

* International Pacific Research Center Contribution Number 460 and School of Ocean and Earth Science and Technology Contribution Number 7133.

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