Continental Shelf Research 143 (2017) 240–256

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Continental Shelf Research
journal homepage: www.elsevier.com/locate/csr

Research papers

The impact of monsoon winds and mesoscale eddies on thermohaline
structures and circulation patterns in the northern South China Sea

MARK

⁎

Ruixiang Zhaoa, Xiao-Hua Zhua,c, , Xinyu GuobaState Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, State Oceanic Administration, Hangzhou 310012,
Chinab
Center for Marine Environmental Study, Ehime University, 2-5 Bunkyo-cho, Matsuyama 790-8577, Japanc
Ocean College, Zhejiang University, Zhoushan 316021, China

A R T I C L E I N F O

A BS T RAC T

Keywords:
MonsoonMesoscale eddies
Thermohaline structure
Circulation
South China Sea
PIES

We deployed 5 pressure-recording inverted echo sounders (PIES) along a section in the northern South China
Sea (NSCS), and estimated well the distributions of temperature, salinity and velocity across the section.
Applying the empirical orthogonal function (EOF) method, we found that variability of the estimates isdominated by two modes: one named the seasonal mode aﬀecting strongly on the hydrographic distribution
with explained variability of temperature/salinity by 62.9/72.2%; the other named the eddy mode, corresponding to the arrival of mesoscale eddies, aﬀecting strongly on the circulation pattern with explained variability ofvelocity by 63.2%. Temporal variation of the seasonal mode is highly correlated with the monsoon winds
southeast of Vietnam, suggesting a nonlocal forcing mechanism. Case studies looking at the structures andevolutions of three captured eddies, whose impacts were well quantiﬁed by the eddy mode. The monsoon(eddies) signiﬁcantly aﬀects temperature, salinity and velocity shallower than 635 m (860 m), 160 m (150 m)
and 1055 m (920 m), respectively. The monsoon (eddies) can induce maximum temperature, salinity andvelocity anomalies up to −1.6 to 2.1 °C (−2.5 to 2.2 °C), −0.11 to 0.14 psu (−0.13 to 0.27 psu) and −0.31 to
0.46 m/s (−0.40 to 0.38 m/s), respectively. Mean volume transport (VT) across the section is 1.0 Sv(1 Sv=1 × 106 m3 s−1, positive to the northeast). Seasonal VT (with eddy impacts removed) is −4.6 Sv,
11.4 Sv, −5.1 Sv and −4.1 Sv for spring, summer, autumn and winter, respectively.

1. Introduction

generally accepted that the circulation in the upper layer is primarily
driven by the East Asian monsoon (Qu, 2000; Hu et al., 2000; Su, 2004;
Liu et al., 2008). The basinwide winds are northeasterly in winter andsouthwesterly in summer, and the transition periods are from April to
May and September to October, respectively (Wang et al., 2004). The
pioneering work of Wyrtki (1961) gives a schematic map of the SCS
surface circulation for boreal winter and boreal summer. In winter, acyclonic gyre exists basinwide in the SCS; in summer, a weak cyclonicgyre remains in the northern SCS and an anticyclonic gyre exists in thesouthern SCS. Based on historical hydrographic data from the SCS, Xu
et al. (1982) computed the dynamic height distribution and conﬁrmed
the above seasonal circulation patterns. They further pointed out thatthese circulation patterns can extend down deeper than 500 m. Liuet al. (2001) suggested that the seasonal circulation of the deep SCS
basin is forced by the basinwide wind stress curl, reaching a quasisteady upper ocean baroclinic Sverdrup balance. However, long term
mooring observations carried out by Zhu et al. (2015) suggested that

The South China Sea (SCS), located west of the northwest Paciﬁc
Ocean, is one of the world's largest marginal seas. It is semienclosed by
the China mainland to the north, Philippine Islands to the east,
Indonesia Islands to the south and the Indo-China Peninsula to thewest. It is connected to the Paciﬁc Ocean mainly through the Luzon
Strait with maximum sill depth of about 2400 m. It contains a broad
continental shelf shallower than 100 m situated in the northern South
China Sea (NSCS), south of the China mainland. The SCS features aunique circulation pattern and distinctive seawater properties, andabounds with mesoscale eddies, which have been extensively studied
with in situ hydrographic measurements (Wang et al., 2008; Chenet al., 2010; Hu et al., 2011, 2012), satellite observations (Hwang andChen, 2000; Wang et al., 2003; Yuan et al., 2007; Nan et al., 2011) andnumerical simulations (Wu and Chiang, 2007; Xiu et al., 2010).
The circulation of the SCS has strong seasonal variability and it is⁎Corresponding author at: State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, State Oceanic Administration, Hangzhou 310012,
China.
E-mail address: xhzhu@sio.org.cn (X.-H. Zhu).

http://dx.doi.org/10.1016/j.csr.2016.06.009
Received 29 December 2015; Received in revised form 3 May 2016; Accepted 15 June 2016
Available online 17 June 20160278-4343/ © 2016 Published by Elsevier Ltd.

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ﬁlter is adequate to retain the most of the signals from meso-scaleeddies without introducing extra noise.
In order to evaluate the quality of the PIES estimates, we performed
conductance–temperature-depth (CTD) casts at the PIES stations
aboard Research Vessel Shiyan 3 during 15–16 December 2012, a
period when the PIESs were deployed.
To construct the Gravest Empirical Mode (GEM) (see Section 2.2),
we used 1355 proﬁles of temperature and salinity in the SCS obtainedby historical CTD casts and Argo measurements. These CTD proﬁleswere collected since the year 2000. The Argo datasets were from China
Argo Real-time Data Center, downloaded from ftp://ftp.argo.org.cn/
pub/ARGO/global/.
The 10 m wind dataset from ECMWF (European Center for
Medium-Range Weather Forecasts) was used to study the monsoonal
impact. The zonal and meridional component time series of the winds
were smoothed with a 30-day-moving-average and subsampled at aninterval of 1 day, consistent with the smoothing of the PIES data.
The along track SLA data of satellite altimeter Jason 2 and thegridded MSLA (Merged Sea Level Anomaly) products from AVISO(Archiving, Validation, and Interpretation of Satellite Oceanographic)
during our observation period were used to identify eddies. The
gridded MSLA product has been recently updated to have one day
resolution in time, favoring our identiﬁcation of eddy centers andtrajectories.

the NSCS circulation does not exactly follow Sverdrup balance, and insummer the mean ﬂow is northeastward rather than southwestward as
in winter. Their volume transport time series is quite consistent withthe one derived from HYCOM model output (Bleck, 2002). Zhu et al.
(2015) attribute this discrepancy to the weak and complicated summer
circulation there. So, it is likely that the hydrographic data collected byformer researchers like Wyrtki (1961) and Xu et al. (1982) were not
suﬃcient to provide adequate spatial and temporal coverage, resulting
in the inferred NSCS circulation being vulnerable to mesoscale eddy
activity. The impacts of monsoon winds and eddies were rarely
separated quantitatively and assessed. So what is the pattern of themonsoon forced climatological circulation? How is the seasonal
circulation driven? How much impact can monsoon winds andmesoscale eddies exert on the hydrography and circulations in the
NSCS? What is their relative importance? We will address these issues
in this paper.
Although SCS mesoscale eddies have been observed and reported
often, most of these observations have been either measurements of theocean surface by satellite altimeter or infrared sensor, or derived from“snapshots” taken from hydrographic surveys. Satellite data have broadspans in space and time, but they can reveal little about hydrographic
distributions and variability in the ocean interior. Hydrographic casts
from research vessels can give snapshots of properties and structures ofthe mesoscale eddies captured, but it is diﬃcult to use them todemonstrate the eddies' evolution processes; moreover, strictly speaking, theses casts are not simultaneous. Using mooring observations is agood way for overcoming some of these shortcomings, but due to costs
and challenging operation conditions, mooring observations are quiterare in the SCS (Yuan et al., 2012; Zhang et al., 2013; Wang et al.,2015). Moreover, most mooring observations have not been lasted for aperiod over one year (needed to cover one annual cycle of themonsoon), and have not been capable of measuring the temperature,salinity and velocity distributions at the same time through the fullwater column. So one of the purposes of our paper is to fully
demonstrate the hydrographic structures and evolutions of eddies
passing through our observational region.
From October 2012 to July 2014, we deployed 5 pressure-recording
inverted echo sounders (PIES) along satellite altimeter track 114 in the
NSCS, and from the data obtained reliable estimates of temperature,salinity and velocity distributions across the deployment section.
Applying the EOF (empirical orthogonal function) method to theseestimates, we show that the ﬁrst two modes can represent well- andeven quantify the impacts of - the monsoon winds and mesoscaleeddies. In this paper, we utilize these results to address our questions
and study these impacts.

2.2. Methods
The Gravest Empirical Mode (GEM) method is adopted to estimate
temperature, salinity and speciﬁc volume anomaly proﬁles at the PIES
sites during the deployment. The GEM method has been successfully
applied to the study of the North Atlantic Current (Meinen, et al.,2000), Antarctic Circumpolar Current (Sun and Watts, 2001), Ryukyu
Current (Zhu et al., 2003; Zhu et al., 2004; Zhu et al., 2008; Andres
et al., 2008), Japan/East Sea (Mitchell et al., 2004; Park et al., 2005),
the Kuroshio south of Japan (Book et al., 2002) and the Kuroshio
Extension (Donohue et al., 2010), etc. In this study, we constructed the
GEM for the SCS following Watts et al. (2001) rather than the multiindex GEM by Park et al. (2005). It is because the proﬁles in the SCS
are very limited, especially for those with depth deeper than 1500 m
and SST lower than 24 °C. Even though, our GEM performance (forfurther detail see Zhu et al., 2015) for temperature and salinity wascomparable to that in previous studies, and the estimations were
expected to be good. When the PIESs were making measurements, CTD
casts were conducted at the PIES stations during 15–16 December
2012. The temperature and salinity proﬁles estimated from the PIES
data using the GEM method turn out to be in good agreement withthose obtained from the CTD casts (Fig. 2). The RMSD (root meansquare diﬀerence) of full-depth temperature and salinity is 0.26 °C and0.03 psu, respectively. Although the maximum diﬀerence of temperature (salinity) can exceed 2 °C (0.2 psu) in the surface and subsurface
layers, this is of little importance to our study because these diﬀerences
only appear in certain regions of limited water depth likely to beundergoing small scale processes, which do not concern us in thispaper.
Shipboard ADCP current data, the bottom pressure record, and theestimated speciﬁc volume anomaly proﬁles are used together toestimate the velocity normal to the PIES section during our deployment, since no current meter was available to provide the reference
velocity at depth for calculating the absolute geostrophic velocity.
According to the agreement of velocity structures and volume transports from the PIES estimations, CTD derived geostrophic currents and
ADCP measurements (Zhu et al., 2015; Fig. 5), we believe that theresults were satisfactory. The procedure is presented in Zhu et al.
(2015).
The empirical orthogonal function (EOF) method is utilized toinvestigate the spatial distribution and temporal variation related to

2. Data and methods
2.1. Data
From October 2012 to July 2014, we deployed 5 PIESs along thesatellite altimeter (TOPEX/POSEIDON and Jason 1/2) track 114 in the
NSCS. This section is situated at the gap between Hainan Island andthe Xisha (Paracel) Islands, and is roughly perpendicular to thebathymetry contours of the continental shelf (Fig. 1). The PIES arrayis intended to capture the South China Sea western boundary current(Fang et al., 2012). Our PIESs were moored at the seaﬂoor (water
depth is 642 m, 1842 m, 2091 m 1348 m and 1030 m for stations P1,
P2, P3, P4 and P5, respectively), measuring the round-trip acoustic
travel time (τ) between the bottom and the sea surface at 10 min
intervals. Almost simultaneously they also measure the near bottompressure (Pbot). These records were windowed, detided (only for Pbot),
despiked, and dedrifted (only for Pbot) following the proceduredescribed by Kennelly et al. (2007). For the time scales of concern,
the processed records were smoothed with a 30-day-moving-averageand subsampled at an interval of 1 day. The 30-day-moving-average
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Fig. 1. Location map of the PIES stations. Red triangles indicate the individual PIES instruments and blue dots indicate the TOPEX/POSEIDON and Jason-1/2 satellite altimeter track(track 114). Bathymetry is contoured in meters. The location of the region is shown in the upper left corner. (For interpretation of the references to colour in this ﬁgure legend, the reader
is referred to the web version of this article.)

the SLA clearly increased (decreased), and simultaneously –τ1000 andthermocline depth observed by our PIES array increased (decreased).
Assuming the strength of an eddy is well represented by the absolutevalue of its SLA, the performance of our PIES observations leads us tobelieve that the PIES is highly competent for capturing mesoscale eddy
signals.
To check the baroclinicity of the eddies, we look at variations of thebottom pressure (Pbot). Pbot varied between −0.02 dbar and 0.04 bar(Fig. 3e) and does not have good correspondence to the mesoscaleeddies. Following Zhu et al. (2008), surface height change caused by
the mass-loading (Hp ) can be calculated from Pbot as Hp = Pbot / ρg , whereρ denotes the density, and g denotes the local gravity. So the Hp
associated with Pbot varied between −2 cm and 4 cm. But the total SLA
varied between −20 cm and 40 cm, which is about 10 times the Hp . This
result indicates that the eddies were dominated by baroclinicity.
Volume transport (VT) across the section is obtained from the PIES
estimations, and shows evident seasonal reversal, resembling thevariation of the monsoon (Fig. 3). However, we have also noticed that
the VT did not exactly match the local wind variation, which may berelated to mesoscale eddy activities or nonlocal wind forcing. The
question is left for later discussion.
Fig. 4 is a demonstration of the PIES estimated temperature (2ndrow), salinity (3rd row) and velocity (bottom row) distributions at
diﬀerent eddy-occupied times. Corresponding MSLA maps are shown

the seasonal monsoon and mesoscale eddies, respectively. The results
are presented in the next section.

3. Results
3.1. Time series
Fig. 3 shows the time series of PIES measurements and estimations,as well as simultaneous local wind and SLA. The local wind was
obtained by bilinear interpolation from the ECMWF wind dataset. It is
obvious that the local wind shows signiﬁcant seasonal reversals(Fig. 3a).
Fig. 3b is a Hovmöller diagram of the along track (track 114) SLA
where the PIESs were deployed. The diagram shows some highs andlows, corresponding to mesoscale eddies (also identiﬁable in the MSLA
maps) which aﬀected the PIES measurements during our observations.
There were 8 eddies in total; 5 of them were warm eddies (anticycloniceddies) marked as W1 to W5, and 3 were cold eddies (cyclonic eddies)marked as C1 to C3. The inﬂuence of mesoscale eddies can beeﬀectively traced by our PIES array, as demonstrated by the variationof −τ1000 (Fig. 3c; τ1000 denotes the measured round trip travel timeleveled to 1000 dbar; for ease of interpretation, negative values arehere adopted) and thermocline depth (Fig. 3d; thermocline depth being
deﬁned as the 20 °C isotherm depth): when a warm (cold) eddy arrived,
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0

P1

P2

P3

P4

P5

30 P1

25
20

100

P3

P4

P5

30 P1

25
20

25

200

1

25

20

10

400

P3

5

0

2000
0

34
34.5

100

1
0.8
0.6
0.4

5

(b)
0

0.2

(c)

0
0.2

35

35
34

34.8

34.5

1.8

1.4

10

5

(a)

2

1.6

15

10

P5
1

1.2

15
5

P4
1

2

20

10

500
1000

0.2
0.1

34.8

0.18

34.6

0.16

34.4

34.4

0.14

400

34.2

34.2

0.12

500

34

34

0.1

200
Depth (m)

P2

2

1

300
Depth (m)

P2

34.5

300

34.5

1000

(d)
2000

34.6

34.5

33.8

0.08

33.6

33.6

0.06

33.4

33.4

0.04

33.8

33.2

34.5

33.2

(e)

33

33

(f)

0.02
0

Fig. 2. The temperature/salinity distribution obtained from CTD casts (left panel (a)/(d)) and PIES estimates (middle panel (b)/(e)). Absolute values of their diﬀerences are alsopresented (right panel, (c)/(f)). For clarity, the top 500 m layer is three times exaggerated.

3.2.1. The seasonal mode
The spatial distribution for temperature (Fig. 5a) shows two coresin the near surface (20 m) and subsurface (100 m) layers with opposite
signs. The extreme values for these cores are 0.93 °C and −0.86 °C,
located at P5 (30 m depth) and P1 (70 m depth), respectively. Between
P1 and P3, although there is an area of negative values lying beneath,slightly positive values appear in the top 30 m.
The spatial pattern for salinity (Fig. 5b) can be classiﬁed into threelayers: the top layer which is shallower than 200 m, the middle layer
which is between 200 m and 500 m, and the bottom layer which is
deeper than 500 m. The top layer shows areas of positive and negativevalues in the north (near P1) and south (near P5), respectively. In thesouth, there are two negative cores (−0.07 and −0.02 psu) at P5 withdepths 0 m and 60 m, respectively. In the north, there is one positive
core (0.09 psu) at P1 with depth 55 m. We also note that the negative
core in the south extends to the upper 30 m between P2 and P3, andthe positive core in the north intrudes to P4 at 50 m to 80 m depth. It
seems that the upper layer (about above 300 m), warmer (colder) wateris always accompanied with lower (higher) salinity, which also holds forthe eddy mode as shown later. In the middle layer, the spatial patternreverses from the top layer: the positive region occupies the south(between P3 and P5) and the negative region occupies the north (fromP1 to P3). The spatial distribution of the bottom layer is nearly uniformwith pattern opposite to that of the middle layer: the negative region
occupying the south and the positive region occupying the north.
The spatial distribution for velocity is all positive (Fig. 5c). The
maximum velocity core exists at the surface between P2 and P3,

in the top row. When a warm (cold) eddy arrived, the temperaturesalinity distribution showed clear positive (negative)/negative (positive) anomalies in the top 500/200 m, accompanied by large current
cores situated at the surface. The temperature/salinity/velocity structures varied, depending on the eddy's strength, the distance from theeddy's center, background ﬂow pattern, etc.
Overall, we see that the PIES capably captures the signals from boththe monsoon and the mesoscale eddies. The estimated VT time seriesshows clear seasonal reversals which appear to be related to themonsoon. The estimated temperature/salinity/velocity time series havereproduced the basic structures of the mesoscale eddies, together with
their individual characteristics.
3.2. The EOF result
The empirical orthogonal function (EOF) method is applied to theabove estimates. The time series we obtained are all found to be
dominated by the ﬁrst two modes. The ﬁrst mode of temperature/salinity/velocity explains 62.9/72.2/63.2% and the second mode 33.0/
21.7/24.1% of the total variances. The cumulative variance percentages
explained by the ﬁrst two modes are 95.9/93.9/87.3%, respectively.
Further checks indicate that the two modes have clear physicalmeanings. We refer to the second mode of temperature/salinity and theﬁrst mode of velocity as seasonal modes, because their temporalvariations show strong seasonality. We refer to the ﬁrst mode oftemperature/salinity and the second mode of velocity as eddy modes,
because they essentially demonstrate eddy impacts as shown later.
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Fig. 3. Time series of (a) local wind, (b) along track SLA, (c) −τ1000, (d) thermocline depth, (e) bottom pressure and (f) estimated volume transport across the section. Warm eddies (coldeddies) detected by the SLA are marked as W1 to W5 (C1 to C3) in panel (b). Positive values in (f) indicate transport towards the northeast.

wind, for times when the temporal variation of the seasonal mode fortemperature or salinity or velocity is above the sum of mean and one
standard deviation (Fig. 5d-f, above the dashed line), which is 154-dayaveraged, and for times when the temporal variation of temperature or
salinity or velocity is below the mean minus one standard deviation(Fig. 5d-f, below the dashed line), which is 87-day-averaged. Note thatin this paper, in order to improve the spatial pattern, the temperature/salinity/velocity composites for seasonal mode and eddy mode are
made by adding the time-mean state into anomaly, which is calculated
from EOF results.
The upper (lower) panels of Fig. 6 show composite distributions
when temporal variation of the seasonal mode is higher (lower) thanthe mean plus (minus) one standard deviation. In this ﬁgure we see thefollowing patterns: a subsurface cold (warm) core exists at P1 (depth

reaching 0.18 m/s. The velocity decreases dramatically with depth. It is
0.15 m/s on average at the surface, and is only 0.05 m/s on average forthe layer at 300 m.
It is found that the temporal variations of the seasonal mode fortemperature, salinity and velocity are highly coherent with strong
seasonal variability (Fig. 5d-f). From May to September, the time series
are positive with the highest values occurring in July/August/July,reaching 2.51/2.13/2.57; From October to April, the time series arenegative with the lowest value occurring in December/April/April,reaching −1.18/−1.28/−1.74. From December to March, slight secondary high values occur, which can be traced to the VT variation(Fig. 3f).
To further understand the underlying physical processes, we madecomposite maps (Fig. 6) of temperature, salinity, velocity, MSLA and244

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Fig. 4. MSLA (cm) distributions (top row) from satellite altimeter, and corresponding temperature, salinity and velocity sections (lower three rows) estimated from PIES measurements
at times when altimeter track 114 was crossed by eddies C1, W3, W4 and C3 (see Fig. 3) as indicated. In the MSLA panels, the PIES stations are shown as black stars, and the bold greylines are the zero contours. Temperature, salinity and velocity anomalies are shown as colours, with labeled grey contour lines showing raw values. For clarity, the top 500 m is threetimes exaggerated in the temperature, salinity and velocity panels. (For interpretation of the references to colour in this ﬁgure legend, the reader is referred to the web version of thisarticle.)the north (between P2 and P3), with strongest velocities of −0.19 m/s
and 0.05 m/s, respectively, forming an anticyclonic (cyclonic) eddy
when the temporal variation is positive (negative). Overall it is notablethat the spatial distributions of temperature, salinity and velocity of theeddy mode all show the evident structure of mesoscale eddies.
Fig. 7d-f shows the temporal variation of the eddy modes fortemperature, salinity and velocity. They are highly coherent, but they
display deseasoned signals. The extreme high and low values correspond well to the occupation of mesoscale eddies identiﬁed from thesatellite altimeter (Fig. 3b): 5 warm eddies inﬂuencing the PIES
stations, with the highest SLAs occurring early November and late
December in 2012, from late June to July and early November in 2013,
and June in 2014. Correspondingly, the temporal variation of the eddy
modes we obtained all show 5 clear high peaks at almost the sametimes. Meanwhile, the values of these peaks are also good indicators forthe strength of these eddies: W4 is among the strongest while W5 is theweakest, which can be checked in the temperature, salinity and velocity
proﬁles we obtained. Likewise, there were 3 cold eddies inﬂuencing the
PIES stations, with the lowest SLAs occurring April and September in
2013, and April in 2014. Correspondingly, the temporal variation of theeddy modes we obtained all show 3 clear low troughs at the same times.
Meanwhile, the values of these lows indicate well the strength of theseeddies: C1 and C3 are strong while C2 is weak according to the SLA
distribution (Fig. 3b), or the temperature, salinity and velocity proﬁles90 m) and warm (cold) waters occupies the surface layer at the south
end of the section; relatively fresh (saline) waters are at the surface andsubsurface south of P2; a strong velocity core exists near P3 withvelocity exceeding 0.4 m/s (−0.2 m/s), which indicates a swift northeastward (southwestward) ﬂow; the general SSH pattern is high (low)at the south end of the section but low (high) at the north end; theprevailing northward summer (southwestward winter) monsoon has
the strongest wind appearing southeast of Vietnam (southeast ofVietnam and west of Luzon Strait).
3.2.2. The eddy mode
The spatial distribution for temperature (Fig. 7a) shows a typicalcone-shaped eddy structure having a strong positive subsurface core
located at P4 (60 m depth) with core temperature reaching 1.09 °C.
The whole section is all positive.
The spatial distribution for salinity (Fig. 7b) shows a strong
negative subsurface core located at P4 (40 m depth) with core salinity
reaching −0.14 psu. As in the case of the seasonal mode, the salinity
distribution can be divided into three layers: the top layer which is all
negative located above 200 m, the middle layer which is all positive
located from 200 m to 500 m, and the bottom layer which is all
negative located from 500 m down to the bottom.
The spatial distribution for velocity (Fig. 7c) shows two distinct
cores, one located in the south (between P4 and P5) and the other in245

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R. Zhao et al.

North

Depth (m)

P1

P2

South
P3

P4

P5
1

0
100
200
300
400
500

2
0.5
1
0

1000

0
−0.5

(d)

Depth (m)(a) 33.0%

−1

2000

−1

0
100
200
300
400
500

0.1

2

0.05

1.5
1

0
0.5
−0.05

0

1000
−0.1

−0.5

(e)

(b) 21.7%−1Depth (m)

2000
0
100
200
300
400
500

0.2
2
0.1
1
0
0

1000
−0.1
−1

(c) 63.2%
2000

(f)
−0.2

12

−1

2

13

−0

2

13

−0

4

13

−0

6

13

−0

8

13

−1

0

13

−1

2

14

−0

2

14

−0

4

14

−0

6

Fig. 5. Spatial distribution (left panels) and temporal variation (right panels) of the seasonal mode. The upper/middle/lower panels correspond to temperature (°C), salinity (psu) andvelocity (m/s), respectively. The percentages of variance explained are shown in panels (a)/(b)/(c), respectively. In the left panels the bold grey lines indicate the zero contour line, andtopography of the PIES section is shaded grey. For clarity, the upper 500 m layer is three times exaggerated. Dashed lines in the right panels denote mean ± one standard deviation foreach variable. Note that for easier comparison between the seasonal mode and the eddy mode, the EOF time series is normalized with its standard deviation, and EOF spatial distribution
is multiplied with the standard deviation.

4. Discussion

we obtained (Fig. 4, C2 is not shown). The above analysis suggests thatthe eddy mode essentially represents the signals of deseasoned
mesoscale eddy activities.
To further understand the underlying physical processes, we madecomposite maps (Fig. 8) of temperature, salinity, velocity, MSLA andwind, for times when the temporal variation of the eddy mode fortemperature or salinity or velocity is above the sum of mean and one
standard deviation (Fig. 8d-f, above the dashed line), which is 153-dayaveraged, and when the temporal variation of temperature or salinity
or velocity is below the mean minus one standard deviation (Fig. 8d-f,
below the dashed line), which is 194-day-averaged.
The upper (lower) panels of Fig. 8 show the composite distributions
when the temporal variation of the eddy mode is higher (lower) thanthe mean plus (minus) one deviation. In this ﬁgure we see the following
patterns: a subsurface warm (cold) core exists at P4 (depth 90 m);
relatively fresh (saline) water occupies the surface and subsurface ofthe section; a strong negative velocity core with speed exceeding 0.5 m/
s (0.1 m/s) at the surface and a weak positive core with speed no larger
than 0.1 m/s (0.1 m/s) were located in the south and north (north andsouth), respectively, forming an anticyclonic (cyclonic) eddy; the SSH
pattern suggests the propagating trace of a warm (cold) eddy; theprevailing wind is strong southwestward (weak northward), which
probably suggests favorable conditions for eddy propagation.

4.1. The monsoon driven seasonal mode
It is shown in the previous section that the seasonal mode variedwith the annual monsoon cycle. It is obvious that the temporalvariation of the seasonal mode is primarily monsoon driven.
However, we have also noticed that it does not quite match the local
wind variation. So nonlocal eﬀects must be taken into consideration.
Shaw and Chao (1994) used a numerical model to demonstrate that theSouth China Sea western boundary current, which ﬂows northward andseparates from the coast between 11°N and 14°N in summer, transitions into a southward undercurrent in September, because of remote
forcing by the northeast monsoon in the northern reaches of the South
China Sea. Liu et al. (2001) pointed out that the large scale circulationin the SCS is primarily driven by wind stress curl following the
Sverdrup relationship. However, the model used by Liu et al. (2001)
is too simple to capture the total variance of the circulation pattern in
the NSCS, leading to quantitative and even qualitative discrepancies
from the observational results (Zhu et al., 2015). In this section, we
utilize SCS wind data derived from the ECMWF dataset to seek a highly
correlated area which may drive this variability remotely. The eastward
and northward wind components, u and v, at each grid point of thedataset were smoothed with a 30-day-moving-average to eliminate
weather scale signals.
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Fig. 6. Composite distributions for times when seasonal mode temporal variation is above the sum of mean plus one standard deviation (upper panels) or below the mean minus one
standard deviation (lower panels). In the panels for temperature (°C), salinity (psu) and velocity (m/s), the colours indicate the anomaly, while the raw values are shown by grey contour
lines. For clarity, the top 500 m is three times exaggerated. MSLA is shown in cm in panels (g) and (h) with contour interval 5 cm. Wind direction is shown with white arrows in panels (i)and (j), while wind speed (m/s) is contoured in colour. (For interpretation of the references to colour in this ﬁgure legend, the reader is referred to the web version of this article.)

North

Depth (m)

P1

P2

South
P3

P4

0
100
200
300
400
500

P5
1

0.5

1

0

0

−0.5

−1

1000

Depth (m)(a) 62.9%
2000

−1

0
100
200
300
400
500

0.1

(d)

−2

1
0.05
0
0
−1

−0.05
1000
−0.1

(b) 72.2%−2(e)Depth (m)

2000
0
100
200
300
400
500

0.2

2

0.1

1

0

0

1000
−1

−0.1

(f)

(c) 24.1%
2000

−0.2

12

−1

2

13

−0

2

13

−0

4

13

−0

6

Fig. 7. Same as Fig. 5, but for the eddy mode.

247

13

−0

8

13

−1

0

13

−1

2

14

−0

2

14

−0

4

−2

14

−0

6

Continental Shelf Research 143 (2017) 240–256

R. Zhao et al.

Fig. 8. Same as Fig. 6, but for the eddy mode.

of Central Vietnam) toward the northwest (W3), from west of thePhilippines toward the west (W4), and along the NSCS continental
shelf (C3). An eddy center is deﬁned as the point of maximum MSLA
absolute value.

Fig. 9 shows correlation maps of the u and v components with the
EOF seasonal mode temporal variations. For the u component, the areaof highest correlation with the temporal variations of temperature/salinity/velocity appears in the region southeast of southeastern
Vietnam, reaching values of 0.85/0.86/0.74 respectively, with almost
no lag in each case. In contrast, the correlation with the local wind islower, reaching only 0.55/0.53/0.48 respectively, with no time lag once
again. For the v component, the area of highest correlation with thetemporal variation of temperature/salinity/velocity also appears in theregion southeast of southeastern Vietnam, reaching values of 0.76/
0.77/0.59 respectively, with no time lag. In contrast, the correlation of
the local wind is lower, reaching only 0.48/0.42/0.37 respectively, withalmost no lag. So temporal variation of the seasonal mode is not highly
correlated with the local wind variation, but is highly correlated withthe region southeast of Vietnam. From the composite maps shown in
Fig. 6, it is evident that this region happens to be an area with strong
wind stress forcing. Although the above analysis indicates that the
response time to the remote forcing is quite rapid, and the process bywhich the remote winds aﬀect the seasonality of our section is unclear,
the facts that the region is strongly wind forced and has highcorrelations with the seasonal mode implies that nonlocal eﬀects fromthe wind of this region, rather than the local wind, may play a crucialrole in driving the seasonality.

4.2.1. W3
W3 was a strong anticyclonic eddy identiﬁed from satellite altimeter(Fig. 2b), whose inﬂuence on our stations lasted from June to July
2013, and was strongest on June 20th according to the eddy mode. Its
origin can be traced back from MSLA maps to be the region east ofVietnam (Fig. 10b). It stayed almost still for about 40 days, thenquickly moved northeastward and became stronger. Next it gradually
moved northwestward parallel to the Vietnam coast and inﬂuenced the
PIES stations from the south. From the MSLA map (Fig. 10b), we canimmediately identify a giant warm eddy which was moving northward
and aﬀecting the region south of the PIES stations.
Although our PIES array only captured the northern margin of thiseddy, evident eddy signals are clearly reﬂected in the eddy mode. The
temperature, salinity, velocity anomaly distributions show that with thearrival (before June 20th) and departure of the eddy (after June 20th),
the surface and subsurface temperature signiﬁcantly increased anddecreased at the south end of the section with fresher waters occupying
the surface; the maximum temperature anomaly reached 1.5 °C.
Meanwhile, the swift northeastward ﬂow with its core located between
P2 and P3 signiﬁcantly strengthened and weakened; the maximum
speed of the current core exceeded 0.70 m/s. It is also noted that asouthwestward current appeared between P4 and P5, constituting ananticyclonic eddy with northeastward ﬂow in the north.
W3 was a Xisha warm eddy which has been frequently observed insummer (Chu et al., 2014; Wang et al., 2015). Chu et al. (2014)
observed the strongest Xisha warm eddy with temperature/salinity
anomaly of 7.7 °C/−0.78 psu. That exceptionally strong warm eddy was
related to the strong monsoon anomaly. In comparison, W3 seems to
be a weaker eddy, because the monsoonal pattern was close to theclimatological mean (ﬁgure not shown).

4.2. Case studies of three captured mesoscale eddies
During our observations, 5 warm (anticyclonic) eddies and 3 cold
(cyclonic) eddies inﬂuenced the records from our PIES stations, and wehave demonstrated that the eddy mode indicates well the activities ofthese mesoscale eddies. In this section, we choose two warm eddies,
namely W3 and W4, and one cold eddy, namely C3, to carry out casestudies. We focus on scenarios of how the eddies evolved and aﬀected
our observational region, in combination with the temporal variation of
the eddy mode (we have chosen the one for temperature, for ease of
discussion) and daily MSLA maps from AVISO. The three eddies
originated in three diﬀerent regions, and propagated along diﬀerent
paths as Wang et al. (2015) mentioned, i.e., from the central basin (east248Continental Shelf Research 143 (2017) 240–256

R. Zhao et al.

Fig. 9. Correlation maps of u and v ECMWF wind components with the EOF seasonal mode temporal variation for temperature, salinity and velocity. Time periods from −30 days to 30
days indicated on the left represent lag times of the wind time series relative to the seasonal mode. The correlations are contoured in colours with interval 0.1. (For interpretation of thereferences to colour in this ﬁgure legend, the reader is referred to the web version of this article.)

4.2.2. W4W4 was a strong warm eddy (Fig. 2b), whose origin can be traced
back from MSLA maps to the region just west of Luzon Island(Fig. 11b). After a short excursion to the east before November 10th,
it moved directly westward at a mean speed of about 7 cm/s, with its
strength gradually decreasing. The eddy inﬂuenced the PIES section
249

Continental Shelf Research 143 (2017) 240–256

R. Zhao et al.

110 E

20

115 E

120 E

22 N
20 N

0

18 N

−20

(a)

−40
12/11

16 N
13/01

13/03

13/05

13/07

13/09

13/11

14/01

14/03

14/05

14 N

14/07

(b)
12 N

Time (yy/mm)

2013−06−10

2013−05−31

2013−06−30

2013−06−20

2013−07−10

Latitude

20oN
20

18oN

0

16oN

−20

Pressure(dbar)

Pressure(dbar)

Pressure(dbar)

14oN
0
200

25
20
15

25
20
15

25
20
15

25
20
15

25
20
15

400

10

10

10

10

10

2
0

600
800

5

0

5

34
34.5
34.5

200

5

34
34.5
34.5

34
34.5
34.5

400

−2

5

5

34
34.5
34.5

34
34.5
34.5

0.2
0

600

−0.2

800

34.5

34.5

0
200

0.1

0.3
0.2

0

0.1

400

34.5

0.4
0.3
0.2

0

34.5

34.5

0.4
0.3
0.2

0

0.1

0.2
0.1

0

0.2
0

0.1

600

−0.2

800
111.6

111.8

112

111.6

111.8

112

111.6

111.8

112

111.6

111.8

112

111.6

111.8

112

Longitude(oE)Fig. 10. The W3 case study. Panel (a) shows temporal variation of the eddy mode for temperature as a black line, with red dots superimposed at 10-day intervals indicating dates within
20 days before or after the date (DW3, June 20) when the temporal variation reached its highest value. Panel (b) is the trajectory of eddy centers identiﬁed from MSLA at 10-day
intervals, the corresponding dates of which are within 60 days before and 20 days after DW3. The green star denote the origin. Triangles indicate the PIES stations. Panels of the lower
rows are snapshots of MSLA/temperature/salinity/velocity distributions corresponding to the times of the red dots in panel (a). For panels of temperature, salinity and velocity, theanomalies are contoured with colour, zero anomalies are shown with bold black lines, and absolute values are shown by grey contour lines. (For interpretation of the references to colourin this ﬁgure legend, the reader is referred to the web version of this article.)near P1, which may be due to a background current since a strong
southwestward South China Sea western boundary current has beenfrequently reported during boreal winter in previous investigations(Fang et al., 2012). This western boundary current can also explain theasymmetry of the eddy velocity structure: the southwestward ﬂow wasenhanced in the south and the northeastward ﬂow was weakened in thenorth. After November 10th, the temperature/salinity/velocity in theupper layer gradually decreased/increased/decreased, indicating attenuation of the eddy's impact.
From its origin and propagation path, W4 is recognized from MSLA
as a Luzon Warm Eddy (LWE), so named by Yuan et al. (2007). Nan
et al. (2011) captured two of them from CTD casts taken west of Luzon,
and showed they had maximum temperatures exceeding 29 °C at thesurface and minimum salinities lower than 33.7 psu. Our observed
eddy temperature is a little lower and salinity a little higher, probably
because our observed eddy being located at Xisha was much weakened

from late October to early December in 2013, while its strength
decreased dramatically. As indicated by a maximum value of the eddy
mode temporal variation, the inﬂuence of this eddy was strongest on
November 10th (Fig. 11a), when the center of the eddy was locatedvery near station P3. After passing through our section, the eddy nearly
disappeared.
With the arrival of this eddy, temperature/salinity in the upper
layer gradually increased/decreased; a southwestward current core
emerged in the south at ﬁrst, then it strengthened, accompanied by anortheastward current in the north, together constituting an anticyclonic eddy. On November 10th, warm and fresh waters occupied thesurface and subsurface layers, the maximum/minimum anomaly fortemperature/salinity was 2.2 °C/−0.19 psu, respectively. The southwestward ﬂow in the south and the northeastward ﬂow in the northcontinued to strengthen, with the core velocities reaching −0.65 m/s
and 0.24 m/s, respectively. A southwestward ﬂow was also identiﬁed
250

Continental Shelf Research 143 (2017) 240–256

R. Zhao et al.

Fig. 11. Same as Fig. 10, but for W4.

southwestward current core also slightly increased. The inﬂuence of theeddy peaked in April 20th, as inferred from the temporal variation of
the eddy mode; at that time, the surface temperature/salinity anomalyof −2.8 °C/0.25 psu was found at P4, and the speed of the southwestward ﬂow reached 0.51 m/s. When the eddy is leaving, thetemperature/salinity of the upper layer gradually increased/decreased
a little, and the speed of the northern southwestward current core also
decreased slightly. The northeastward ﬂow in the south also increased alittle, probably because a large warm eddy in the south was about toaﬀect the section.
From its origin and propagation path, C3 is recognized as a
Dongsha Cyclonic Eddy (DCE), so named by Chow et al. (2008).
From the collected historical drifter and satellite data (Chow et al.,2008), the DCE seems to be a seasonal eddy formed in winter or spring,with its center moving at a mean speed of 9 cm/s (8 cm/s for C3
observed by us). The eddy has mean tangential speeds of 12–16 cm/sin late spring and summer, and 16–31 cm/s in winter and early spring.
In comparison, our eddy appears stronger with maximum speed of
51 cm/s.

after its long propagation distance.
4.2.3. C3
C3 was a cold eddy which originated in the region west of Luzon
Strait, just north of the origin of W4 (Fig. 12). The trajectory of thiseddy resembled that of W4, but initially located somewhat to the north,with its northern margin constrained by the 1000 m isobath. The mean
propagation speed of this eddy was 8 cm/s. Satellite altimeter measurements suggest the eddy inﬂuenced our PIES section from lateMarch to early May in 2014, but our PIES observations indicate thatthe time span may have been much longer. Its inﬂuence, quantiﬁed by
the temporal variation of the eddy mode, reached its highest value on
April 20th, 2014, when the center of the eddy was located very near the
P4 station. After passing through our section, the eddy almost
disappeared, as in the case of W4.
From the temperature and salinity distributions (Fig. 4), we can seethat at the end of March, a cold, saline water mass had alreadyoccupied the surface and subsurface layers of the section, and acyclonic ﬂow structure had already been established. With the arrivalof the eddy, the temperature/salinity of the upper layer gradually
decreased/increased, though not greatly, and the speed of the northern
251Continental Shelf Research 143 (2017) 240–256

R. Zhao et al.

110 E

20

115 E

120 E

22 N
20 N

0

18 N

−20

(a)

−40
12/11

16 N
13/01

13/03

13/05

13/07

13/09

13/11

14/01

14/03

14/05

14 N

14/07

(b)
12 N

Time (yy/mm)

2014−04−10

2014−03−31

2014−04−30

2014−04−20

2014−05−10

Latitude

20 N
20

18 N

0

16 N

−20

Pressure(dbar)

Pressure(dbar)

Pressure(dbar)

14 N
0
200
400

25
20
15

25
20
15

25
20
15

25
20
15

25
20
15

10

10

10

10

10

2
0

600
800

5

5

0

34
34.5
34.5

34
34.5
34.5

200

5

5

34
34.5
34.5

−2

5

34
34.5
34.5

34
34.5
34.5

0.2

400

0

600
800

34.5

34.5

−0.2

34.5

34.5

34.5

0
200

0

−0.1

0

0

−0.2

0

−0.1

400

0

0

−0.1

0

0

−0.1

−0.1

0

0.2
0

600

−0.2

800
111.6

111.8

112

111.6

111.8

112

111.6

111.8

112

111.6

111.8

112

111.6

111.8

112

o

Longitude( E)Fig. 12. Same as Fig. 10, but for C3.

strong and less contaminated by signals from eddies. So the range ofimpacts is estimated as follows:

4.3. Quantiﬁed impacts of monsoon winds and eddies
In Section 3, we have demonstrated the spatial patterns andtemporal variations of the seasonal and eddy modes, which areconsidered to be quantiﬁcations of the impacts of monsoon winds
and mesoscale eddies. Now, Let us return to the questions proposed in
the Introduction section: What is the pattern of the monsoon forced
climatological circulation? How much impact can monsoon winds andmesoscale eddies exert on the hydrography and circulations in the
NSCS? What is their relative importance?
As demonstrated before, the seasonal mode explains 33.0/21.7% of
the variability for temperature/salinity, while the eddy mode explains
62.9/72.2%, respectively; while for velocity, the seasonal mode explains 63.2% and the eddy mode explains 24.1%. The results are also
supported by the rough estimation of impacts of monsoon winds andeddies as follows: take temperature for the seasonal mode as an
example: we ﬁrstly make composites of temperature when its temporalvariation is above/below the mean plus/minus one standard deviation(the composites are marked as T+ and T− respectively). These
composites are made because the impacts of monsoon winds are

Tseasonal = < T+ − T − >where |T+−T−| is the absolute value of T+−T−, the outer bracket is thespatial mean of the transect. Using this method, we estimate that the
impact of monsoon winds on temperature/salinity/velocity is 0.29 °C,
0.01 psu and 0.11 m/s, respectively, and the impact of eddies ontemperature/salinity/velocity is 0.50 °C, 0.03 psu and 0.05 m/s, respectively.
It is well known that the response of SCS to monsoon is in a basin
scale. The change in basin scale current ﬁeld does not induce a largechange in the ﬁeld of water temperature and salinity because of thepresence of stratiﬁcation. On the other hand, eddies has a local scaleand can induce a strong vertical motion of water. The vertical motion ofwater with passage of eddies then induces a large change in the ﬁeld ofwater temperature and salinity because of again the presence ofstratiﬁcation. This is why for the full water depth, the monsoon winds
have a greater impact on the velocity structure, while the eddies’ impact
is greater on the thermohaline distribution.
252

Continental Shelf Research 143 (2017) 240–256

R. Zhao et al.

Seasonal Mode
Temperature
Depth (m)

0

0.6
0.4

100

Eddy Mode
0.4
0.8

0.2
0.40.6

0.4

200

1

0.2

300
400

0.8

0.2

500

0.6

635 m

860 m

1000

(b)

(a)
2000

0

0

0.5

0
0.1
0.05

0.2

(d)

1

160 m

200

0.4

(c)

0.05

100

Salinity
Depth (m)

1.2

0.6

1
0.8

0.6

0.5

1

0.1
150 m

300

0.1

400
500

0.05

1000

(e)

(f)

2000

0

0
200

0.05

(g)

P4

0.1
920 m
0.05

(i)
P5

0.15

1055 m

P3

P2

(k)

(j)

2000

P1

0

0.05

0
0.2

0.02
0.02

1000

0.1

0.04

0.04

500

0.05

0.1
0.08
0.06

300
400

(h)
0

0.1

0.16
0.14
0.12

0.1
0.08
0.06

100

Velocity
Depth (m)

0

0.1

0.15

0.2

P5

P4

P3

P2

(l)

P1

0

0.05

0.1

0.15

0.2

0

Fig. 13. The vertical structure of the standard deviation induced by the seasonal mode (left panels) and eddy mode (right panels) for temperature (top panels, °C), salinity (middle
panels, psu) and velocity (bottom panels, m/s), the top 500 m is three times exaggerated. Panels (a), (c), (e), (g), (i), (k) are standard deviation distributions of the section. Panels (b), (d),(f), (h), (j), (l) are proﬁles of the mean standard deviation for every pressure level, respectively. Red dots denote points at which the standard deviation is only 10% of the largest value inthe proﬁle, with its corresponding depth marked to the right. (For interpretation of the references to colour in this ﬁgure legend, the reader is referred to the web version of this article.)waters down to 500 m in the South China Sea (Xu et al., 1982; Su,
2004). For salinity the maximum variance appears at the surface or in
the subsurface layer at 50 m depth, reaching 0.08 psu in each case, withsigniﬁcant depth of 160 m, which is relatively shallow. The maximum
variance for velocity appears at the surface, reaching 0.15 m/s. In theupper 200 m, the mean variance decreases sharply with depth, while
below 200 m, it shows a relatively moderate rate of decrease. The
signiﬁcant depth for velocity is 1055 m, much deeper than those fortemperature and salinity.
For the eddy mode, the maximum temperature/salinity varianceappears only in the subsurface layer at 80/50 m depth, reaching 0.8 °C/
0.1 psu, respectively. Their signiﬁcant depths are 860 m and 150 m,
respectively, which is consistent with observations by Chu et al. (2014),
showing an eddy resulting in strong temperature variability in the

Further study looks at the vertical structure of the inﬂuence for eachmode. For EOF decomposition, the time series of the variable at eachspatial point is decomposed as follows:
N mod eF (x , y , t ) =∑ Xk (x, y)Tk (t )k =1where F(x,y,t) denotes the time series, Xk(x,y) is the spatial pattern for
the k-th mode, Tk(t) is the corresponding temporal variation, x, y, t andNmode represent the location, depth, time and total number of EOF
modes, respectively. The standard deviation map for each mode is
obtained as the standard deviation of Xk(x,y)Tk(t) in time.
Fig. 13 depicts the standard deviation distribution associated witheach mode. The distribution of standard deviation resembles thespatial distribution of the corresponding mode, because the standard
deviation is essentially the mode's spatial distribution times thestandard deviation of the corresponding time series.
The proﬁles of the mean standard deviation for every pressure level
are also presented. For the seasonal mode, the maximum variance fortemperature appears at the surface or in the subsurface layer at 100 m
depth, reaching 0.51 °C and 0.50 °C, respectively. Below the subsurface
layer, the impact of the seasonal mode decreases dramatically: at thedepth of 635 m, it is only 10% of the maximum variance (we deﬁne thisas the signiﬁcant depth hereafter). This is consistent with previous
studies which reported signiﬁcant seasonal pattern is reﬂected inTable 1
Maximum ranges of impact which the monsoon and the eddies can exert on temperature,salinity and velocity during the observations.

The monsoon
The eddies

253

Temperature (°C)

Salinity (psu)

Velocity (m/s)−1.6 to 2.1−2.5 to 2.2−0.11 to 0.14
−0.13 to 0.27

−0.31 to 0.46
−0.40 to 0.38

Continental Shelf Research 143 (2017) 240–256

R. Zhao et al.

strength of the eddies seems much weakened; (3) the PIES isinsensitive to the ﬁne structure of the water column (Watts andRossby, 1977), which may ﬁlter out extreme high values.
In previous studies, the seasonal variation of VT across ourobservational section was obtained by Zhu et al. (2015) utilizing a
22-year-long estimated time series. Eddy impacts and interannual
variations were ﬁltered out, so the obtained seasonal circulation
pattern was a wind-driven climatological result. By analyzing theseasonal mode, we obtained a 22-month-long VT time series which
eliminated the impact of occasional eddies and is mainly monsoon
forced. The mean VT of the 22-month time series is 1.0 Sv(1 Sv=1 × 106 m3 s−1). The seasonal VT caused by the seasonal mode(with the mean VT added) is calculated to be −4.6 Sv, 11.4 Sv, −5.1 Svand −4.1 Sv for spring (April–May), summer (June–September),
autumn (October to early November) and winter (November–March),
respectively. Although these seasonal mean VT values do not match
quantitatively with the previous study (0.8 Sv, 2.3 Sv, −5.0 Sv, and−5.1 Sv), this may be attributed to the relatively short period of ourestimations and the interannual variation of winds; and our analysis
does show qualitative agreement: the circulation varies with strong
seasonal reversal, i.e., a current pattern which is southwestward in
winter and northeastward in summer.
In addition, analogous to the volume transport, we have also
calculated the heat and salt transport across the section caused by
the seasonal mode. The mean heat transport is −0.19 PW(1 PW=1015 W), i.e., towards the southwest, with standard deviationof 0.52 PW. It is interesting to note that the calculated mean transport
agrees quite well with the heat transport by the South China Sea
throughﬂow (0.2 PW from the South China Sea to the Indonesian
maritime continent) obtained by Qu et al. (2006) based on a highresolution general circulation model. The seasonal mean heat transport
for spring, summer, autumn and winter is −0.47, 0.50, −0.47 and−0.44 PW, respectively. The salt transport is −94.78 Gg/s (1 Gg/
s=109 g/s), with standard deviation of 301.24 Gg/s. The seasonalmean salt transport for spring, summer, autumn and winter is−255.44, 305.86, −255.30 and −239.26 Gg/s, respectively.

upper 600 m but strong salinity variability conﬁned to the upper
150 m. From the proﬁles of variance, it is noted that the eddy mode
has an impact on temperature and salinity much greater (about double)
that of the seasonal mode. The maximum variance for velocity appears
at the surface, reaching 0.07 m/s, and the signiﬁcant depth is 920 m.
The result matches an observation by Nan et al. (2011), showing an
eddy-induced strong current reaching ~900 m. The eddy mode has an
impact on velocity much weaker (about half) that of the seasonal mode.
One interesting phenomenon is that the signiﬁcant depths oftemperature and velocity are deeper than that of salinity for bothseasonal and eddy mode. We further check all T/S proﬁlescollected in the SCS and ﬁnd that salinity is uniform below 150 m (at159 m depth, its variance decrease to 10% of the maximum in thevertical), but temperature remains considerable variability inthe deep layer (at 773 m depth, its variance reaches 10% of the
maximum). It is also true for velocity whose variability depends moreon temperature in waters deeper than 150 m. We think the generaltemperature/salinity variability in the SCS causes such diﬀerent impact
depths.
To summarize: the eddy mode's impact is strongest on the hydrographic distribution (temperature and salinity), while the seasonal
mode's impact is strongest on the circulation pattern (velocity); theseasonal mode inﬂuences the hydrography mainly at the surface andthe subsurface layers, with two extreme maximum values (one in eachlayer), while the eddy mode mainly inﬂuences the subsurface layer;
signiﬁcant depths for the impact of the seasonal mode and the eddy
mode almost match, being about 700 m, 150 m and 1000 m fortemperature, salinity and velocity, respectively.
To investigate the maximum range of impacts which the monsoon/
eddies can exert, extreme cases of each mode are analyzed. For
example, the maximum/minimum values of the temperature spatial
distribution for the eddy mode reach 0.12/−0.09 °C, while the maximum/minimum values of the temperature temporal variation are 18/−21, so their products give the maximum ranges of −2.5 to 2.2 °C. The
overall results are listed in Table1. It seems that the seasonal mode andthe eddy mode can impact temperature, salinity and velocity in ranges
that are nearly equal.
Compared with previous observations, our estimated eddy impacts
are generally similar. There are many eddies in the SCS that have beenprobed by CTD casts: Nan et al. (2011) reported anticyclonic eddies
west of Luzon with mean temperature (0–1500 m) 0.65 °C higher than
the surrounding area, and mean salinity 0.02 psu lower; Hu et al.
(2011) constructed the three dimensional structure of a cold eddy inthe southwestern SCS with cold core temperature 8 °C lower than thatof the eastern surrounding water; Chen et al. (2011) found, from 763
Argo temperature proﬁles aﬀected by the eddies, that anticycloniceddies can raise the temperature 2–5 °C, while cyclonic eddies canlower the temperature by 0–3 °C; Chen et al. (2012) presented thestructures of two anticyclonic eddies west of Luzon with maximum
absolute temperature/salinity/velocity anomalies up to 3 °C/0.2 psu/
30 cm/s; Chu et al. (2014) reported the strongest anticyclonic eddy
ever recorded in Xisha waters with maximum temperature/salinity
anomalies reaching 7.7 °C and −0.78 psu. Mooring observations ofeddy activities in the SCS are rare. Zhang et al. (2013) reconstructed
the structure of a pair of mesoscale eddies southwest of Taiwan, using
long-term mooring observations augmented with satellite data andreanalysis data. It is shown that anticyclonic (cyclonic) eddies can
induce velocity anomalies up to 100 cm/s (50 cm/s) and temperature
anomalies up to 7.5 °C (−3.0 °C). Compared to the results of Hu et al.
(2011), Chu et al. (2014), and Zhang et al. (2013), our captured warm
eddies seem to be weaker. The plausible explanations may be: (1)eddies captured by Hu et al. (2011) and Zhang et al. (2013) were in wellknown areas of strong eddy kinetic energy (Cheng and Qi, 2010; Chenet al., 2011), while our captured eddies were in their decaying phasehaving propagated for long distances; (2) in our EOF analysis, theeddies’ impacts represented by the eddy mode are deseasoned, so the

5. Summary
In this study, we estimated the distribution of temperature, salinityand velocity across a section in the NSCS, using the GEM method ondata from PIESs deployed along the section. The estimates are in good
agreement with simultaneous CTD measurements, suggesting highaccuracy of the estimates. The EOF method was then applied to the 22month-long time series of estimates. It turns out that the total varianceis almost perfectly explained by the ﬁrst two modes. The second modeof temperature and salinity, and the ﬁrst mode of velocity, whose
spatial distributions can be well explained by monsoonal inﬂuence,
have strong seasonal variability, and is called the seasonal mode; theﬁrst mode of temperature and salinity, and the second mode of velocity,
whose spatial distributions display prominent eddy structure andcharacteristics, with temporal variation corresponding well with theoccupation of mesoscale eddies, is called the eddy mode. The EOF
results suggest that hydrography and circulation in the NSCS at a timescale longer than 20 days is mainly inﬂuenced by the seasonal monsoon
and mesoscale eddies.
However, the temporal variations of the seasonal mode do notexactly match the local wind variation. Further analysis indicates that it
is most highly correlated (reaching 0.86 at highest) with wind in theregion southeast of the southeastern Vietnam coast with nearly zerotime lag. The highly correlated region is also a region of high windspeed. These facts imply that this region may play an important role indriving the circulation of the NSCS.
The estimated eddy structures for three cases were examined anddiscussed in combination with the temporal variation of the eddy mode
for temperature. The chosen eddies originated in three diﬀerent
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regions, propagating along three separate paths as described inprevious papers. The deseasoned impact of the eddies was wellquantiﬁed by the eddy mode, for within the occupation period of one
eddy, when the temporal variation reached its peak the inﬂuence of theeddy was strongest, which can be seen in temperature/salinity/velocity
distributions. Also, when we compare the strengths of the eddies, thetemporal variation is a good indicator. The structures of the eddies’
temperature, salinity and velocity distributions are always asymmetric,
because of the strong background ﬂow (or SCS western boundary
current).
Finally, a comparison of the impacts of the monsoon winds and theeddies was presented. The seasonal mode (or monsoon impact)
explains 33.0/21.7/63.2% of the variability of temperature, salinityand velocity, while the eddy mode (or deseasoned eddy impact)
explains 62.9/72.2/24.1%, respectively. From analysis of the verticalstandard deviation structure, it is concluded that the eddy mode
impacts more the hydrographic distribution (temperature and salinity),
while the seasonal mode impacts more the circulation pattern (velocity); the seasonal mode inﬂuences the hydrography mainly at thesurface and the subsurface, while the eddy mode inﬂuences mainly thesubsurface; signiﬁcant depths for the impact of the seasonal mode andthe eddy mode almost match, and are about 700 m, 150 m and 1000 m
for temperature, salinity and velocity, respectively. Analysis of extreme
cases indicates that the seasonal mode can cause maximum temperature/salinity/velocity variations up to −1.6 to 2.1 °C, −0.11 to 0.14 psu
and −0.31 to 0.46 m/s, respectively. While the eddy mode caused maximum variations up to −2.5 to 2.2 °C, −0.13 to 0.27 psu and −0.40 to
0.38 m/s, respectively. Thus the seasonal mode and the eddy mode can
impact the temperature, salinity and velocity in ranges that areapproximately equal.
The mean VT of our 22-month time series is 1.0 Sv. Seasonalvariation caused by the seasonal mode (with the mean added) is
calculated to be −4.6 Sv, 11.4 Sv, −5.1 Sv and −4.1 Sv for spring,summer, autumn and winter, respectively. Our analysis is in qualitative
agreement with seasonality of the 22-year long term time series
obtained by Zhu et al. (2015): the circulation here varies with strong
seasonal reversal, i.e., the current pattern is southwestward in winterand northeastward in summer.
Acknowledgement
This study is supported by the National Natural Science Foundationof China (41576001, 41276095, 41476020 and 41321004); the projects of the State Key Laboratory of Satellite Ocean Environment
Dynamics, Second Institute of Oceanography (SOEDZZ1501 and
SOEDZZ1403) and the Global Change and Air-Sea interaction (GASI03-01-01-02) and the Scientiﬁc Research Fund of SIO under Grant
JT1402.
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