J Oceanogr (2015) 71:663–673
DOI 10.1007/s10872-015-0305-5

ORIGINAL ARTICLE

A long‑term volume transport time series estimated by combining
in situ observation and satellite altimeter data in the northern
South China Sea
Xiao‑Hua Zhu1,3 · Ruixiang Zhao1 · Xinyu Guo1,2 · Yu Long1 · Yun‑Long Ma1 ·Xiaopeng Fan1

Received: 9 March 2015 / Revised: 1 June 2015 / Accepted: 4 June 2015 / Published online: 26 June 2015
© The Oceanographic Society of Japan and Springer Japan 2015

Abstract We deployed five pressure-recording inverted
echo sounders (PIES) along a satellite altimeter trackacross the continental slope of the northern South China
Sea (NSCS) from October 2012 to July 2014, and obtained
a time series of volume transport (VTPIES) across the section from their records. Applying the empirical relationship between VTPIES and the satellite altimeter sea surface
height anomaly difference across the section, we obtained
a time series of volume transport (VTNSCS) over 22 years
from 1992 to 2014. The VTNSCS shows a small mean valueof −1.6 Sv (1 Sv = 106 m3 s−1) (i.e., toward the southwest), but a significant seasonal reversal and mesoscale
eddy induced fluctuations (−11.8 to 19.7 Sv). The monthly
mean of VTNSCS over 22 years shows a maximum (3.6 Sv)
in July and a minimum (−7.3 Sv) in December. This is
the first long time series of volume transport for the NSCS
based on in situ data.
Keywords Long-term time series of volume transport ·Pressure-recording inverted echo sounder · Gravest
empirical mode · Satellite altimeter · Seasonal variability ·South China sea* Xiao‑Hua Zhu
xhzhu@sio.org.cn
1

State Key Laboratory of Satellite Ocean Environment
Dynamics, Second Institute of Oceanography, State Oceanic
Administration, 36 Baochubei Road, Hangzhou 310012,
China

2

Center for Marine Environmental Study, Ehime University,
2‑5 Bunkyo‑cho, Matsuyama 790‑8577, Japan

3

Ocean College, Zhejiang University, Hangzhou 310058,
China

1 Introduction
The South China Sea (SCS) is located at the west side of
the North Pacific and is the largest semi-enclosed marginal sea of the North Pacific Ocean, with a wide continental shelf and slope along the mainland coast of southern
China. The upper layer large-scale circulation is predominantly seasonal, not only changing its magnitude, but also
reversing direction, mainly because of the Asian monsoon
seasonal cycle (Wyrtki 1961; Liu et al. 2001). The basin
scale circulation pattern of the SCS has been investigatedin many studies incorporating in situ measurements (e.g.,
Wyrtki 1961; Fang et al. 2002), satellite-based observations(Wu et al. 1998), as well as numerical simulations (Wanget al. 2006). During boreal winter, the basin scale cyclonicgyre forms a strong southwestward current known as the
SCS western boundary current, flowing along the southern
China continental slope and eastern Vietnam coast from
Dosha Island to the region south of Vietnam. During boreal
summer, the SCS western boundary current in the northern
SCS (NSCS) becomes weak and variable, and needs further
clarification (Fang et al. 2012).
On the other hand, deep, cold, and salty Philippine Sea
water enters the SCS through the Luzon Strait and thenflows out through the Taiwan, Karimata, and Mindoro
Straits as shallow warm currents. This current system, aheat and freshwater conveyor, is called the South China
Sea throughflow (Qu et al. 2006). During the past several decades, many investigations have produced widely
varying estimates of the net volume transport through theLuzon Strait, from 0.5 to 10 Sv (1 Sv = 106 m3 s−1) (e.g.,
Wyrtki 1961; Hsin et al. 2012). Mean net volume transport through the Taiwan Strait is about 1 Sv in winter and
2–3 Sv in summer (Fang et al. 2009; Hu et al. 2010). The
mean outflow transports through the Karimata and Mindoro

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25oN

102oE

108oE

114oE

China

108oE

110oE

112oE

X
MS

Hainan

00

P1
P3
P5

KS

(b)

Borneo

0
20
500

P2
P4

20oN

18oN

16oN

14oN
22oN

0

P1

500

Depth(m)

22oN

2000

X

5oN
0o

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10

10oN

(a)

114oE

TS
200

ne
llipi
Phi

15oN

126oE

D LS

H

m
na
et
Vi

20oN

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Hainan

1000
1500

P4

P2

18oN

P5

16oN

2000

P3

(c)
2500
18.5

18

(d)
17.5

17

108oE

110oE

112oE

114oE

116oE

14oN

Latitude(oN)
Fig. 1  a The South China Sea (SCS) and its adjacent seas; 200 m
isobaths denotes the position of shelf slope along which the SCS
western boundary current flows, b detailed map of the observationalsite, c vertical cross-section view of bottom topography along themooring array, d positions of hydrocasts used in computing the GEM.
The TOPEX/POSEIDON and Jason-1/2 satellite altimeter track (Pass114) is indicated with blue dots in b. Red triangles (P1, P2, P3, P4,

and P5) indicate the locations of the five PIESs. The black dots andgreen squares indicate the positions of Argo profiles and CTD casts,
respectively. “H” and “X” in a and b indicate Hainan Island andXisha Island, respectively. “TS”, “LS”, “MS”, and “KS” in a indicate Taiwan Strait, Luzon Strait, Mindoro Strait, and Karimata Strait,
respectively. Isobath depths are given in meters (color figure online)

Straits are, respectively, given as 3.6 Sv in boreal winter(Fang et al. 2010) and 2.4 Sv during 2004–2007 (Qu andSong 2009). However, by comparison with these transportestimates through the straits around the SCS, transportestimates in the inner SCS are very scarce. To date, there
have been no volume transport time series based on in situ
observations in the SCS.
In this study, we carried out a 22-month mooring
deployment of five pressure-recording inverted echosounders (PIES) in the NSCS to obtain a 22-month-longrecord of volume transport across the mooring array.
We then estimated a 22-year-long volume transport timeseries by developing and using an empirical relationshipbetween the observed volume transport and satellite altimeter-determined sea surface height anomaly difference andinvestigated its mean and variability, including the seasonal
cycle and fluctuations induced by mesoscale eddies.

13

2 Data and methods
We deployed an array of five PIESs across the continental
slope in the NSCS, along a satellite track (Pass114) of the
TOPEX/POSEIDON and Jason-1/2 altimeters in October
2012, and maintained it until July 2014 (Fig. 1a, b). ThisPIES array crosses over the gap between Hainan Island andXisha Island (Fig. 1b, c). The PIES array is roughly perpendicular to the bathymetry contours of the continental slope,i.e., the western boundary current in the NSCS, assuming
the current flows along isobaths over the continental slope.

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A long-term volume transport time series estimated by combining in situ observation and…

Temperature GEM

Pressure(dbar)

0
200
400
600
800

−6

x 10
6

27

5.4

24

4.8

21

4.2

18

3.6

15

3

12

2.4

9

1.8

6

(a)
1000

δ GEM

30

1.2

(b)

3

Temperature GEM RMS error

0

0.6
0

δ GEM RMS error−7

Pressure(dbar)

0
200
400
600
800

x 10
10

1.8

9

1.6

8

1.4

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1.2

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5

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2

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1.324

1.325

1.326

1.327

1.323

1
1.324

1.325

τ1000(s)τ1000(s)

1.326

0

1.327

Fig. 2  Temperature (°C) and specific volume anomaly (m3 kg−1) GEM (upper panels) and their errors (bottom panels)

0
200
400
600

Pressure(dbar)

While the PIESs were making measurements, we performed a ship-mounted acoustic Doppler current profilers (ADCP, RDI-VM38, 38.4 kHz) survey along the PIES
array and took five conductance–temperature–depth (CTD)
casts at the PIES stations during 15–16 December 2012, toget data for comparison with the PIES mooring data.
The PIES measures the round-trip acoustic travel time (τ)
between sea surface and bottom and the near-bottom pressure (Pb). The τ measurements were used to estimate verticalprofiles of temperature and specific volume anomaly by thegravest empirical mode (GEM) method (Watts et al. 2001;
Zhu et al. 2003; Andres et al. 2008b) using 1355 profiles oftemperature and salinity obtained by CTD and Argo measurements (Fig. 1d). The GEM errors for temperature (specific volume anomaly) are 0.77 °C (2.41 × 10−7 m3 kg−1)
in the upper 300 m layer and 0.15 °C (6.51 × 10−8 m3 kg−1)
for a full-depth layer, respectively (Fig. 2). The GEMinferred temperatures on 16 December 2012 are in good
agreement with those independent data measured by the

P1

P2

10

20

P3

P4

P5

30

40

50

800
1000
1200
1400
1600
1800
2000
0

60

70

Temperature(°C)

Fig. 3  Temperature profiles obtained from CTD measurements(black) and the GEM at five PIES sites. The temperature profiles forP2–P5 stations have been offset rightward by 10 °C successively

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X. Zhu et al.

Vby assuming the temporal variation of deep pressure issmall (p2′ ≈ p22′) (see Fig. 4 for definition of p22′). p1′ andp22′ can be obtained from the PIES pressure measurements
at the two stations.
The velocity measured by the ship-mounted ADCP ata deep layer (800 m) and averaged between two stations,
including both barotropic and baroclinic components,
should be the same as that estimated by the PIES data:

VADCP = V bt + Vbt′ (tADCP ) + Vbc (tADCP )where tADCP denotes the time of ADCP measurement.
Therefore, the temporal-mean barotropic component V bt
can be estimated as follows:

PIES1
PIES2

′

V bt = VADCP − Vbt (tADCP ) − Vbc (tADCP )Fig. 4  Diagram (not to scale) of terms used in Eqs. (1)–(4)CTD casts at the five PIES stations during our cruise. Rootmean-square differences (RMSDs) between the two measurements are 0.76 °C in the upper 300 m layer and 0.29 °C
for a full-depth layer, respectively (Fig. 3). This indicatesthe measurements of τ provide good proxy for estimationof the vertical profiles of temperature by the GEM method.
Each Pb record is processed to remove the tide and sensor drift for estimating the barotropic velocity between
station pairs as follows (Donohue et al. 2010). Absolute
geostrophic velocity (V) relative to a deep reference level
includes both barotropic component (Vbt) and baroclinic
component (Vbc) (Fig. 4):(1)

V = Vbc + Vbt

The Vbc can be calculated from the PIES-measured τdata using the GEM-derived specific volume anomalies.
The Vbt at level Z can be calculated from the pressure gradient between two stations:
Vbt = (p1 − p2 ) (�xf ρ)(2)where p1 and p2 are pressures at level Z1 from two stations,
ρ is density, f is the Coriolis parameter (5.0 × 10−5 s−1),
and Δx is the distance between two stations (Fig. 4).
Since p1 and p2 are not always measured at the same depth,
we need to use pressure records at different depth to calculatebarotropic component. For this purpose, Vbt is divided into
the temporal mean (V bt) and its deviation (V′bt(t)):′

Vbt (t) = V bt + Vbt (t)(3)

The V′bt(t) can be rewritten as:
Vbt′ (t) =

 ′

 ′

p1 (t) − p′2 (t) p1 (t) − p′22 (t) ≈(�xf ρ)(�xf ρ)

(4)

13

(5)

(6)

V′bt(tADCP) can be estimated by applying Eq. (4) to Pb
while Vbc (tADCP ) by using the GEM-derived hydrographic
data during the period of the ship-mounted ADCP measurement, respectively. The resulting V bt at the four station
pairs was within a range between −1.0 and 1.5 cm s−1.
Substituting V bt given by Eq. (6) into Eq. (3) and estimating V′bt(t) from records of Pb and Vbc from records of τ, we
obtained the absolute geostrophic velocity and corresponding volume transport through the mooring section.

3 Results
3.1 Velocity structure
The vertical cross-section of the current velocity obtainedby the ship-mounted ADCP measurements on 16 December
2012 shows a southwestward current with a subsurface maximum velocity core of −44.0 cm s−1 (Fig. 5a). The absolute geostrophic velocity sections, estimated from the CTD
data at five PIES stations by referring to the ship-mounted
ADCP velocity at 800 m (Fig. 5b) and from the GEMderived hydrographic data and PIES pressure data as shownin Eqs. (1)–(6) (Fig. 5c), are similar to each other: both ofthem show a southwestward current with a strong surface
velocity. Since the barotropic component of current is at anorder of several cm/s, it is smaller than the baroclinic component (Fig. 5). The volume transports integrated over the section for Fig. 5a–c are −8.4, −8.0, and −8.1 Sv, respectively.
Agreement of velocity structures and volume transports forFig. 5a–c indicates that our volume transports estimatedusing the PIES-measured Pb and τ are likely reasonable.
During 22 months of PIES measurement, the velocity
structure and associated volume transport derived from
PIES-measured Pb and τ have a large variability. As two
examples, the velocity was northeastward with a maximum
velocity of 104.1 cm s−1 (volume transport 17.4 Sv) on 1

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A long-term volume transport time series estimated by combining in situ observation and…

cm/s

0

60

500
40
1000

20

Pressure (dbar)

1500−8.4 Sv
16 Dec. 2012

(a)

2000

−8.0 Sv
16 Dec. 2012(b)

−8.1 Sv
16 Dec. 2012

(c)

0

0

500
−20
1000
−40

1500

17.4 Sv
01 Jul. 2013

(d)

2000
0

50

100

150

−7.8 Sv
09 Apr. 2014

(e)
0

50

100

150

1.0 Sv
Mean

(f)
0

50

100

150

−60

Distance (km)Fig. 5  Vertical distribution of velocity normal to the section forthe mooring array: a measured by ship-mounted ADCP; b absolute
geostrophic velocity estimated using the CTD data by referring to
the ship-mounted ADCP velocity at 800 m; and PIES-derived geostrophic velocities estimated by using Eqs. (1)–(6) on c 16 December 2012, d 1 July 2013 when a warm-eddy arrived, e 9 April 2014

when a cold-eddy arrived; and f mean PIES-derived geostrophic
velocity for the whole mooring period. Positive (negative) values
indicate northeastward (southwestward) currents. Contour interval
is 10 cm s−1. Thick line shows zero velocity. Triangles on the top ofeach panel indicate the locations of the PIESs

July 2013 (Fig. 5d) and southwestward (volume transport−7.8 Sv) on 9 April 2014 (Fig. 5e). Combined with satellite altimeter data, we know the former case (Fig. 5d) corresponds to the arrival of a warm-eddy to the section while
the latter case (Fig. 5e) to that of a cold eddy, that is, thevelocity at the shelf slope of NSCS is strongly influenced
by arrivals of mesoscale eddies. The 22-month mean velocity shows a weak northeastward current whose volumetransport is 1.0 Sv (Fig. 5f).

to July 2014 across the section. However, this 22-monthtime series is not long enough to examine long-term variations and their statistical characters because the current
during 22 months is strongly influenced by mesoscaleeddies and the seasonal monsoon. To obtain a long-term
volume transport time series, we established an empirical
relationship between this 22-month volume transport timeseries and simultaneous satellite altimeter data following
the methods that have been used previously for the Kuroshio and Ryukyu Current (Imawaki et al. 2001; Zhu et al.
2004; Andres et al. 2008a).
The satellite track of Pass 114 (blue dots in Fig. 1b) is
a descending track passing five PIES sites. The satellitemeasured sea surface height anomaly (SSHA) data along
the track were first lowpass-filtered with a five-point filter

3.2 Estimation of a 22‑year volume transport timeseriesUsing the mooring array data, we can estimate a 22-monthvolume transport time series (VTPIES) from October 2012

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SSHAD(cm)20Transport(Sv)

15
10

−10

0

10

20

SSHAD(cm)
30

−10

0

r=0.93r=0.80(a)(b)

r=0.90r=0.99

(c)

(d)

10

20

30

5
0
−5
−10
−15
−20
20

Transport(Sv)

15
10
5
0
−5
−10
−15
−20

−10

0

10

20

30

SSHAD(cm)

−20

−10

0

10

20

Transport(Sv)Fig. 6  Scatter plots of volume transport in a the upper 200 dbar, b
the deep layer below 200 dbar and c the full-water-column transport
against the sea surface height anomaly difference (SSHAD, unit:cm). d Scatter plots of volume transport in upper 200 dbar against
the full-water-column transport. The correlation coefficient and theroot-mean-square difference from the regression line are shown in theupper-left corner of each panel. The volume transport was estimatedfrom PIES data and SSHAD was estimated from satellite altimeterdata

corresponding to a spatial scale of about 29 km. The SSHAat each PIES site is highly correlated with τ measured by
the PIES. The highest correlation coefficients were 0.91
and 0.92 at P1 and P5, respectively. The SSHA difference
between P1 and P5 (ΔSSHA) is well correlated with the
40-day lowpass-filtered volume transport in the upper 200
dbar (correlation coefficient is 0.93) (Fig. 6a), and is also
correlated with similarly filtered volume transport in the
deep layer below 200 dbar (correlation coefficient is 0.80)(Fig. 6b). Because the volume transport in the upper 200
dbar is well correlated with the full-water-column transport(Fig. 6d), the ΔSSHA is, therefore, well correlated with thefull-water-column transport (correlation coefficient is 0.90)(Fig. 6c). Using this empirical relationship, we then produce a 22-year time series of volume transport from 1992

to 2014. We refer to this volume transport time series as
VTNSCS (volume transport in the NSCS).

13

3.3 Variability of the VTNSCSThe VTNSCS time series is in good agreement with the
VTPIES time series (Fig. 7a). Their RMSD is 3.5 Sv, muchsmaller than the peak-to-peak range of about 25 Sv. The
VTNSCS time series is also in good agreement with the volume transport (VTHYCOM) estimated from the 1/12º global
Hybrid Coordinate Ocean Model (HYCOM) results (Bleck,2002) (blue line in Fig. 7b) with a RMSD of 3.0 Sv. Consequently, our estimated VTNSCS is likely reasonable.
During the 22 years, VTNSCS fluctuates largely from
−11.8 to 19.7 Sv, with a standard deviation of 5.3 Sv

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20

Transport (Sv)

15
10
5
0
−5
−10

(a)

−15
12−11

13−01

13−03

13−05

13−07

13−09

13−11

14−01

14−03

14−05

14−07

2009

2011

2013

2015

Time(Year−month)

Transport (Sv)

20
10
0
−10

(b)

−20
1993

1995

1997

1999

2001

2003

2005

Year

2007

Fig. 7  a Time series of VTPIES (red line), VTNSCS (dotted black line), and VTHYCOM (blue line) for the mooring period (October 2012–July2014). b Time series of VTNSCS (dotted black line) and VTHYCOM (blue line) from January 1993 to December 2014 (color figure online)
12
11
10

Power x Frequency(Sv 2)(Fig. 7b). The 22-year mean VTNSCS is −1.6 Sv, indicatinga southwestward current. Maximum VTNSCS occurred inAugust 2010, which was due to an exceptional anticycloniceddy around Xisha Island (Chu et al. 2014). VTNSCS typically has a positive value in the warm season and a negativevalue in the cold season, which indicates a monsoon cycle
response of the SCS circulation.
The power spectrum of VTNSCS shows the most prominent energy at the annual period (Fig. 8). Two peaks at
about 605 and 875-day are between 90 % (thin broken line
in Fig. 8) and 95 % (thick broken line in Fig. 8) confidence
levels, suggesting the quasi-biennial oscillation occurredin the SCS, but were weaker than that in the North Pacific(Zhu et al. 2004). The power spectra of VTNSCS show thatthe peak around 100-day period is not remarkable. This
indicates that the mesoscale eddies with 100-day period,
originating from the North Pacific Subtropical Countercurrent region (Qiu 1999) propagated westward (Yang et al.
1999; Zhu et al. 2004) may not arrive at the PIES sites or
have been greatly weakened, possibly due to blocking by
the northward-flowing Kuroshio as the eddies pass throughthe Luzon Strait.
Since the power spectrum of VTNSCS shows the mostprominent energy peak at the annual period, we average

9
8
7
6
5
4
3
2
1
−2

−3

10

cpd

10

Fig. 8  Variance-preserving power spectrum of volume transports
through the mooring array. Thick and thin broken lines indicate 95
and 90 % confidence levels based on the best-fit, first-order autoregressive model spectrum, respectively

and plot the 22-year VTNSCS time series by month and byseason to examine the annual cycle and seasonal variation. The monthly averaged VTNSCS changes not only in13

X. Zhu et al.

Transport (Sv)

670
20

4

15

2

10

0

5

−2

0

−4

−5
−10
−15

−6

(a)
1

2

3

4

5

6

7

8

9

−8

10 11 12(b)
SpringSummerFall

WinterMonth
Fig. 9  a Monthly averaged VTNSCS (black lines) and VTHYCOM (bluelines) for the 22 individual years (thin lines) and averaged over all
22 years (thick line); the two dashed lines represent ±one standard

deviation of VTNSCS and VTHYCOM for each month. b Seasonal means
for VTNSCS (black) and VTHYCOM (blue) (color figure online)

30

o

24 N

20

o

22 N

10

o

20 N

0

o

18 N

−10

o

16 N

−20

o

14 N (a)

(b)
o

108 E

o

111 E

o

114 E

o

117 E

o

120 E

108oE

111oE

114oE

117oE

120oE

−30

Fig. 10  SSHA (cm) distribution when the volume transports takes the minimum value and the maximum value in a 7 April 2013 and b 24 June
2014, respectively. The triangles in each panel indicate the PIES positions. The regions with depth shallower than 200 m are masked

magnitude but also in direction (Fig. 9a). The monthly
averaged VTNSCS has a maximum (3.6 Sv, northeastward) in July and a minimum (−7.3 Sv, southwestward)
in December with a standard deviation of 3.6 Sv. The
annual range (peak-to-peak difference) reaches 10.9 Sv(Fig. 9a). This annual range is larger than those of theKuroshio in the East China Sea (Andres et al. 2008a)and south of Japan (Imawaki et al. 2001) and the Ryukyu
Current southeast of Okinawa (Zhu et al. 2004), although
its mean (−1.6 Sv) is much smaller than the means ofKuroshio and Ryukyu Current. The mean VTNSCS values for spring (April–May), summer (June–September),fall (October to early November), and winter (November–March) are 0.8, 2.3, −5.0, and −5.1 Sv, respectively(Fig. 9b) (the season is defined following Fang et al.
2002).

13

The reversal date of mean VTNSCS in spring when
VTNSCS changes from southwestward to northeastward is 8
April (thick black line in Fig. 9a), about 28 days after thereversal of wind direction over the SCS (figure not shown),
while the reversal date of mean VTNSCS in fall (from northeastward to southwestward) is 13 September, about 15 days
after the reversal of wind direction in fall.

4 Summary and discussion
We successfully carried out a 22-month mooring deployment of five PIESs along a satellite altimeter track acrossthe continental slope in the NSCS. Using the round-trip
acoustic travel time data with the GEM method, we estimated the temperature profile time series at the five PIES

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A long-term volume transport time series estimated by combining in situ observation and…

24oN

30

22oN

20
10

20oN

0

18oN

−10

16oN
14oN

−20

(a)

(b)

−30

24 oN

1200

22 oN

1000
800

20 oN

600

18 oN

400

16 oN
o

14 N

200

(c)

(d)
108oE

111oE

114oE

117oE

120oE

108oE

111oE

114oE

117oE

120oE

0

Fig. 11  SSHA distribution (cm) for a summer and b winter, and eddy kinetic energy (cm2 s−2) distribution for c summer and d winter. The triangles in each panel indicate the PIES positions. The regions with depth shallower than 200 m are masked

stations. The GEM-inferred temperatures were in good
agreement with those measured independently by the CTD
casts at the five PIES stations during mooring period. Then,
we calculated the absolute geostrophic velocity normal to
the section and obtained a 22-month-long record of volumetransports by combining the GEM-derived hydrographic
data, PIES pressure data, and the ship-mounted ADCP
velocity data.
By using this 22-month-long record of volume transport
data with satellite altimeter data, we estimated a 22-year
VTNSCS in the NSCS and determined its mean and variability including the seasonal cycle and influence of mesoscaleeddies. VTNSCS showed a significant seasonal reversal withan annual range of 10.9 Sv, which is much larger thanits mean (−1.6 Sv). Mesoscale eddies greatly impacted
VTNSCS with an upper range of about 20 Sv (Fig. 7b; inAugust 2010).
Currents near our mooring site are known to flow typically approximately along the isobaths of the SCS continental slope (Wang et al. 2013), but no previous studies
have obtained time series of volume transport over the
SCS. Our winter mean VTNSCS (−5.1 Sv) is slightly larger

than that (about −4 Sv) predicted by Liu et al. (2001) from
Sverdrup theory using the monthly mean Comprehensive
Ocean–Atmosphere Data Set (COADS) wind-stress curlover the SCS, while our summer mean VTNSCS (2.3 Sv)
is quite different from their prediction (about −3.5 Sv).
This suggests that the seasonal variation of VTNSCS in the
NSCS may not exactly follow Sverdrup balance, because
the SCS summer circulation is weak and more complicated than that in winter (Fang et al., 2012). Nevertheless,our summer VTNSCS is the first long-term measurement
evidence supporting the schematic circulation pattern in
the NSCS, hypothesized by Fang et al. (2012) (see Fig. 3
in their paper). The mean VTNSCS through fall and winteris about −5 Sv, larger in magnitude than the outflow transport through the Karimata Strait (3.6 Sv) in winter (Fanget al. 2010), which indicates that part of the VTNSCS may
flow out the SCS as a portion of the SCS throughflow,
while some of the VTNSCS joins the inner SCS circulationin the SCS.
The mesoscale eddies near the Xisha Island are mainly
from the south, propagated from the east of Vietnam, andalso from the east, propagated from the region near the

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Luzon Strait (Wang et al. 2003, 2015). To examine how
mesoscale eddies influence the VTNSCS, we plot the SSHA
distributions when the VTNSCS reaches the minimum on 7
April 2013 (Fig. 10a) and the maximum on 24 June 2014
(Fig. 10b). When the VTNSCS took the minimum (maximum) value, a cyclonic (anticyclonic) eddy extended tothe southeast side of observation line, making the VTNSCS
southwestward (northeastward).
From the 22-year time series of VTNSCS, it is known
that Sverdrup balance does not fully explain the seasonality of the transport in summer. To examine the possible
mechanism of the seasonal variability of VTNSCS, we plot
the mean SSHA and eddy kinetic energy distributions calculated using surface geostrophic velocity from SSHA data
for summer and winter (Fig. 11). The SSHA distributions
show a seasonal reversal structure corresponding to thesouthwestward and northeastward VTNSCS in summer andwinter, respectively. However, the eddy kinetic energy near
our mooring area is relatively small and almost the samein the two seasons, indicating that the mesoscale eddies
do not exhibit clearly seasonal variability. This means that
effects of mesoscale eddies can be removed by averaging
the 22-year-long data, and also implies that the mesoscaleeddies may not be an important factor of seasonality of the
VTNSCS. The seasonal variability of the Kuroshio transport
in the East China Sea also does not follow the Sverdrup
theory, whose mechanism was explained as joint effect ofbaroclinicity and bottom relief (JEBAR; Kagimoto andYamagata 1997). It is suggested that similar work, such as
high-resolution numerical simulation, should be carried out
to examine the possible mechanism of the seasonal variability in the SCS.
The methodology, using PIES near-bottom pressure
data combined with ship-mounted ADCP data to estimate
barotropic velocity, is applied here for the first time in acase lacking current meter measurements such as those
provided by the current pressure-recording inverted echosounder (Andres et al. 2008b; Donohue et al. 2010). The
present method has its limitations due to formula approximations. The temporal-mean barotropic component velocity V̄bt, estimated in our geostrophic calculation using
Eq. (6), were obtained from only single time ship-mounted
ADCP measurement. The error of V̄bt is expected to besmaller than the error of the ship-mounted ADCP (about
3 cm s−1). We also note that the volume transport presented in this study is not the total volume transport of the
SCS western boundary current, because our mooring arraydoes not cover the area shallower than the water depth of
P1 site (about 500 m, Fig. 1c). Nevertheless, the present
study provides a useful long-term volume transport timeseries based on in situ observation data, which will be
opened to the community in a near future (http://www.scsdata.icoc.cc/).

13

X. Zhu et al.
Acknowledgments This study is supported by the National Basic
Research Program of China (2011CB403503), the National Natural Science Foundation of China (41276095, 41476020, 41276028,
and 41321004), the Scientific Research Fund of SIO under grants
JT1402 and JT1207, and the projects of State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography(SOEDZZ1403), and Global Change and Air-Sea Interaction (GASI-0301-01-02). The Argo data are provided by China Argo Real-time Data
Center (http://www.argo.gov.cn/argo-eng/index.asp). The HYCOM data
are downloaded from http://hycom.org/. We thank the crew and participants of the R/V Shiyan-3 for their support during deployment cruise.

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