Deep-Sea Research II 119 (2015) 30–39

Contents lists available at ScienceDirect

Deep-Sea Research II
journal homepage: www.elsevier.com/locate/dsr2

Seasonal variability in coastal fronts and its inﬂuence on sea surface
wind in the Northern South China Sea
Rui Shi a,n, Xinyu Guo b, Dongxiao Wang a, LiLi Zeng a, Ju Chen aaState Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, 164 West Xingang Road,
Guangzhou 510301, Chinab
Center for Marine Environmental Studies, Ehime University, 2-5 Bunkyo-cho, Matsuyama 790-8577, Japan

art ic l e i nf o

a b s t r a c t

Available online 18 December 2013

High-resolution reanalysis data of Sea Surface Temperature (SST) show pronounced seasonal variationsin oceanic fronts in the coastal area of the Northern South China Sea (NSCS), which are accompanied by
the seasonality of monsoons. The NSCS oceanic fronts cover a wider area of the coastal sea in winter thanin summer as strong winter monsoons progress. Nonetheless, the average SST gradients of the frontal
area in both seasons are comparable. The response of surface wind to SST perturbations attributed tooceanic fronts in the NSCS coastal area has also been investigated by the observation data of satellite
borne scatterometers and the simulation data of the Weather Research and Forecasting (WRF) model.
Both the satellite observations and the simulations of the WRF model show apparent positive linear SSTwind coupling for most months in 2008, indicating the local inﬂuence of coastal SST fronts on the sea
surface wind in the NSCS. The SST-wind coupling coefﬁcients in the NSCS coastal sea are larger thanthose observed at mid-latitude oceans but smaller than those observed near equatorial oceans. It is also
found that the inﬂuence of topography on the sea surface wind could be more important than that of the
SST front at the southern end of the Taiwan Strait in winter. The transition of the monsoon could also
affect the SST-wind coupling in the NSCS.
& 2013 Elsevier Ltd. All rights reserved.

Keywords:
Oceanic front
SST
Seasonal variation
SST–wind coupling
WRF model

1. Introduction
For large scale ocean basins, Sea Surface Temperature (SST)
perturbations are found to be negatively correlated with surface
wind speed perturbations (Xie, 2004). Large-scale atmosphericcirculation patterns change surface ocean temperatures throughmodulation of surface heat ﬂuxes and surface mixed layer depth(Cayan, 1992). This is a one-way forcing of the ocean by theatmosphere. However, on smaller spatial scales between 100 and1000 km, such as the mesoscale, the oceans drive an atmospheric
response. In situ observations, satellite observations, and numerical simulations have consistently shown a positive correlation
between surface wind speed and mesoscale SST variations (Small
et al., 2008; Chelton and Xie, 2010).
An oceanic front is a typical mesoscale oceanic phenomenon
identiﬁed by a discontinuity in temperature, salinity, or nutrientand chlorophyll a content (Belkin et al., 2009). SST perturbations
associated with oceanic fronts can induce adjustment of the
Marine Atmospheric Boundary Layer (MABL), and result in perturbations of the surface wind with enhanced winds over warmnCorresponding author. Tel.: þ 86 208 902 4757.
E-mail address: shirui@scsio.ac.cn (R. Shi).

0967-0645/$ - see front matter & 2013 Elsevier Ltd. All rights reserved.
http://dx.doi.org/10.1016/j.dsr2.2013.12.018

water and reduced winds over cold water. The responses of sea
surface wind to SST perturbations over frontal areas are common
in equatorial and mid-latitude oceans, including the eastern
tropical Paciﬁc (Wallace et al., 1989; Hayes et al., 1989; Chelton
et al., 2001), the Kuroshio and its extension (Nonaka and Xie,
2003; Tokinaga et al., 2006), the Gulf Stream and its extension(Chelton et al., 2004; Song et al., 2006), the Brazil-Malvinas
conﬂuence in the South Atlantic (Tokinaga et al., 2005), and theAgulhas Current and Agulhas Return Current (O0 Neill et al., 2005).
Previous studies have proposed several hypotheses to explain themechanism of the surface wind response to oceanic SST fronts. One is
the generation of hydrostatic pressure gradients through adjustmentsof the MABL mass ﬁelds (Lindzen and Nigam, 1987; Small et al., 2003;
Song et al., 2006) and another is the stability-dependent modiﬁcationof vertical mixing of momentum from aloft to the surface (Wallace
et al., 1989; Hayes et al., 1989; Tokinaga et al., 2006). Tanimoto et al.
(2011) proposed that both of these two mechanisms are active in the
SST–wind interactions over a frontal area. Samelson et al. (2006)
proposed an approximately linear relationship between the surface
wind stress and the height of the MABL under a quasi-equilibrium
condition, in which the MABL approximately comes into equilibriumwith steady free-atmospheric forcing.
According to Yanagi and Koike (1987), oceanic fronts areclassiﬁed into coastal water fronts, shelf fronts, and open ocean

R. Shi et al. / Deep-Sea Research II 119 (2015) 30–39

fronts. A considerable number of studies on the relationshipbetween SST and sea surface wind have been conducted on thecontinental shelf and the open ocean fronts in the equatorial andmid-latitude oceans. However, studies on the modiﬁcation of sea
surface wind via coastal water fronts have rarely been reported.
The South China Sea (SCS) is an epi-continental marginal sea of
the western Paciﬁc Ocean and a Mediterranean-type basin dominated by the Asian monsoon, which blows northeasterly from
October to April and southwesterly from May to September in thenorthern regions of the SCS. The active coastal fronts observed in
the Northern South China Sea (NSCS) vary throughout the year due
to the combined effects of reversing monsoon winds, variations inbathymetry, and the tide (Wang et al., 2001; Hu et al., 2003; Liuet al., 2010). These shallow and narrow coastal fronts are accompanied by marked seasonal variations in strength and coverage.
Thus, SST–wind coupling over coastal fronts in the NSCS could
vary from those over open oceans.
In the present study, the seasonal variations in a NSCS coastal
front and its inﬂuence on the sea surface wind in the NSCS are
investigated. The observation data and the model conﬁguration
are described in Section 2. The seasonal variability of the NSCS
coastal front and the observed SST–wind coupling induced by thefront are demonstrated in Section 3.1. The veriﬁcation of our oneyear simulation is presented in Section 3.2, and the simulation
results are used to analyze the SST–wind coupling. The analysis
results are discussed in Section 4. Section 5 summarizes the study.

2. Data and model conﬁguration
2.1. SST and wind data
The Operational SST and Sea Ice Analysis (OSTIA; Stark et al.,2007) data are provided by the Group for High Resolution SST(GHRSST) pilot project, which was initiated by the Global Ocean
Data Assimilation Experiment in 2000. OSTIA uses several kinds ofsatellite data from the GHRSST project, as well as in-situ observations, to determine the SST for the global ocean. The combinationof multi-sources in the OSTIA data eliminates some abnormal
perturbations and generates a smooth SST map, which can beconveniently used by atmospheric models.
The OSTIA SST has a resolution of 0.051 at daily intervals. The
OSTIA dataset provides robust data and acceptable accuracy nearshores, which enables it to identify coastal fronts in the NSCS.
Xie et al. (2008) estimated ﬁve types of SST products of the
GHRSST project and found that the OSTIA SST yields the smallest
Root Mean Square Difference (RMSD) compared to the independent buoy and ship observations in the coastal and shelf seasaround China.
The sea surface wind data used in this study were observed by
Quick Scatterometer (QuikSCAT) and Advanced Scatterometer(ASCAT). Thus far, QuikSCAT provides a more extensive geographical and temporal coverage and higher spatial resolution of theocean vector winds compared to those obtained by other spaceborne sensors (Chelton and Wentz, 2005). After QuikSCAT ﬁnishedits mission in October 2009, ASCAT continues to provide global sea
surface wind data. These two kinds of wind data have been widelyused in previous studies on SST–wind coupling over open ocean
fronts (Chelton et al., 2001; Nonaka and Xie, 2003; Tokinaga et al.,2006; Song et al., 2006; O0 Neill et al., 2005, 2010; O0 Neill, 2012).
The QuikSCAT data from 1999 to 2009 are distributed by
Remote Sensing Systems (RSS). The ASCAT data from 2009 to2011 are available from the Asia-Paciﬁc Data Research Center.
Despite missing near-shore data, the existing data covering the
NSCS frontal area are sufﬁcient to support the present study.
Similar to previous studies, the analysis in this study that utilizes

31

wind data is based on 10-m equivalent neutral winds. Equivalent
neutral wind is the wind that would exist for an idealized
condition at a given height if the atmospheric boundary layer
was neutrally stratiﬁed.
2.2. Model conﬁguration
The Weather Research and Forecasting (WRF) model with theAdvanced Research WRF (ARW) dynamic solver (Skamarock et al.,2005) is used to conﬁrm the response of sea surface winds tomesoscale SST perturbations over the NSCS coastal fronts. The
WRF model has been used in previous studies to examine air–sea
coupling processes over oceanic fronts (Song et al., 2009; O0 Neillet al., 2010).
The WRF model in this study has two nested domains, D1 andD2, with horizontal resolutions of 18 and 6 km, respectively. The
outer domain (D1) covers the NSCS while the inner domain (D2)
focuses on the southern coastal area of China (Fig. 4A). Two-way
nesting is applied to allow interactions between the two domains.
This study used the simulation results of D2.
Two domains were initialized with the National Centers forEnvironmental Prediction (NCEP) operational analysis data. The
lateral boundary conditions for D1 were updated by NCEP analysis
data every 6 h to allow large-scale synoptic weather systems
outside of the domain to propagate through the domain. Daily
OSTIA SST data, which were updated every 24 h during thesimulation periods, were used as the bottom boundary condition.
The initial and boundary conditions were designed to obtain arealistic simulation result and to maintain consistency in theanalysis of the observation and simulation results.
The simulation was performed using 43 vertical sigma levels
including 25 levels below 1000 m. A ﬁne vertical resolution was
speciﬁed in the boundary layer to accurately simulate the verticalturbulent momentum exchange in the layer. The lowest level
extends from the surface to a height of 12 m, whereas the highest
level almost reaches a height of 20 km.
An improved Mellor–Yamada–Nakanishi–Niino (MYNN) level3 scheme is employed to simulate the marine atmosphericboundary layer. This scheme is based on the original Mellor–
Yamada level 2.5 turbulence closure model (Mellor and Yamada,1982) and imposes additional restrictions to assure its reliability
and numerical stability (Nakanishi and Niino, 2006). The Noahland surface model is also adopted in the simulation. The microphysical parameterizations include explicitly resolved water vapor,cloud, and precipitation processes. A modiﬁed version of the Kain–Fritsch scheme is used to represent the subgrid-scale effects of
convection and shallow clouds.
The model was run for an integration period of one month,
starting at 0000 UTC on the ﬁrst day of the month and ending at
2400 UTC on the last day of the month. This process was
conducted for the 12 months of 2008 to investigate the inﬂuenceof fronts on sea surface winds during different seasons.

3. Results
3.1. Observation
3.1.1. Seasonal variability of the SST front
The monthly mean magnitude of the SST gradient was calculated using the OSTIA data from 2006 to 2011. The magnitude of
the SST gradient can be regarded as an index of the strength of thecoastal fronts. Therefore, maps showing the magnitude of the SST
gradient were used to investigate the spatial distribution andseasonal variation of the SST front.

R. Shi et al. / Deep-Sea Research II 119 (2015) 30–39

Visual examination suggests that coastal fronts mainly occur inthree areas in the NSCS where the magnitude of the SST gradient isgreater than 0.005 1C km  1 (Fig. 2). The three areas are located in

Ta

iw
an

China Mainland
20

20

Ta
iw
an

50

24°N

it

Beibu Bay, the southeastern coastal area of mainland China (SCC),
and off the northwest coast of Luzon Island (see Fig. 1 for thegeographic locations). Fronts in the SCC area appear regardless of

St
ra

32

22°N
20
50

Beibu Bay

n

am

tn
Vie

16°N
105°E

2000

100

a
ain

50

18°N

Luzon Strait

20

H

200
500
2000

100

108°E

111°E

ea
aS
hin
C
uth
So

114°E

117°E

Luzon

20°N

120°E

Fig. 1. Map of the northern South China Sea with bathymetry contours (m). The star denotes the Pearl River estuary.

Fig. 2. Monthly mean SST (black contours, 1C) and the magnitude of the SST gradient (shaded gray area, 1C km  1) calculated by using the OSTIA data from January 2006 to
December 2011.

R. Shi et al. / Deep-Sea Research II 119 (2015) 30–39

which is the minimum value of coverage for the entire year
(Fig. 3A).
The magnitudes of the SST gradient at each grid point wereaveraged in the frontal area mentioned above. The mean magnitude of the SST gradient does not change as signiﬁcantly as thecoverage of the front does in different months (Fig. 3B). In general,the mean magnitude of the SST gradient associated with a welldeveloped front is stronger in winter than in summer, decreasing
by approximately 0.004 1C km  1 from 0.021 1C km  1 in January to
0.017 1C km  1 in July (Fig. 3B). The mean magnitude of the SST
gradient does not decrease as abruptly as the coverage of the front
does in May. Instead, the mean magnitude decreases graduallyfrom January to June, slightly increases in July, and decreases again
until October (Fig. 3B).
The seasonal variability of a front in terms of spatial distribution and strength could induce different responses of sea surface
wind. The potential effects are discussed in the subsequent
section.

3.1.2. Observed SST–wind coupling
The monthly averaged SST and wind ﬁeld are shown in Fig. 4B
for December 2008, which is a representative time period to study
the inﬂuence of a SST front on the sea surface wind in the NSCS.
During this period, the entire domain was covered by the strong
northeasterly monsoon, and an apparent front parallel to thecoastline can be identiﬁed. Along this front, the wind speed washigh over the warm water side and low over the cold water side of
the front, which is an indication of the positive correlation of SST–
wind coupling (Chelton and Xie, 2010). This positive correlation ispresented more clearly in the meandering of the front around

OSTIA Sea Surface Temperature

30
o

24 N
o

25

22 N

°C

the season, whereas fronts in the Beibu Bay and off the northwest
coast of Luzon Island occur only in winter and disappear insummer. Therefore, the following analysis was mainly performedin the SCC area (111–1201E, 19–24.51N).
During winter, a SST front expands across most of the SCC. This
front starts to develop in November (Fig. 2l), enters its mature
stage in December (Fig. 2A), and gradually weakens in March(Fig. 2D). In winter, the SST gradient is generally weaker in thewestern portion of the SCC than in the eastern area near the
Taiwan Strait. The eastern SST gradient of the SCC is stronger bothin January and February than in December, and the western SST
gradient varies slightly in winter (Fig. 2A–C).
The summer front begins to form in June (Fig. 2G), develops inJuly and August (Fig. 2H and I), and weakens in September (Fig. 2J).
In summer, apparent fronts appear only in the eastern part of the
SCC. Thus, the area covered by the front is smaller in summer thanin winter. Compared to the winter front, the summer front isrestricted to a shallower sea that is close to the coastline. Notably,
fronts at the southern end of the Taiwan Strait are observed during
all seasons and apparently weaken in summer. A maximum valueof over 0.07 1C km  1 for the SST gradient is observed in this area inJanuary and this value decreases to approximately 0.06 1C km  1
in July.
For a quantitative analysis of the seasonal variation in coastal
fronts in the SCC, grid points were speciﬁed to cover the frontal
area. The contour of the 23.5 1C isotherm in the SCC in December(Fig. 2A) was chosen as the center line of the area covered by grid
points. Thirty-seven grid points were ﬁrst given at every quarter
degree along the contour of the 23.5 1C isotherm. From each grid
point along the line, four grids were then speciﬁed in intervals of
0.251 along both sides normal to the line. Finally, an array of 37  9grids covering an area of 91  21 in the SCC was obtained (see dots
in Fig. 4A). The coverage and the mean strength of the front were
calculated based on the area covered by these grid points.
The coverage of the front was deﬁned by the percentage of grid
points with a SST gradient magnitude greater than 0.005 1C km  1.
The SST front covers 80% of the SCC area in winter and does notchange signiﬁcantly from January to April (Fig. 3A). However, anabrupt reduction is observed in the covering rate from 80% to 40%
in May, and a continuous reduction to about 30% in June. The
coverage increases slightly in July and then decreases again in
August. The front covers only 20% of the SCC area in September,

33

o

20 N
20
o

18 N
o

16 N
105oE

Front coverage (%)

100

15
108oE

111oE

114oE

117oE

120oE

80

QuikSCAT Wind & SST

60

15

40

24oN

20

12
o

0

SST gradient (°C km−1)

9

20oN

0.04

6

18oN

0.03
0.02

16oN
o
105 E

0.01
0
Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Fig. 3. Two statistical parameters of the SST fronts at the grid points shown in
Fig. 4a: (A) seasonal variations in the coverage rate of the fronts (black triangles),and (B) mean values (black dots, 1C km  1) and 7 1 standard deviation (verticalbars) of the magnitudes of the SST gradient for each month. The grid points where
the SST gradient was smaller than 0.005 1C km  1 were not included in (A) and (B).

ms

−1

22 N

3
o

108 E

o

111 E

o

114 E

o

117 E

o

120 E

Fig. 4. (A) Monthly mean SST obtained from the OSTIA data for December 2008,
and (B) the QuikSCAT equivalent neutral winds at 10 m height (black arrows) andwind speeds (shaded tone, m s  1) overlaid by the OSTIA SST contours from 18 1C to
28 1C at an interval of 1 1C. The original OSTIA SST with a horizontal resolution of
0.051 was interpolated to the QuikSCAT grid points. The area enclosed by the blackbox in (A) is the inner nest domain of the WRF model. The black dots in (A) are thegrid points covering the most active frontal area in the NSCS and are deﬁned in
Section 3.1.

34

R. Shi et al. / Deep-Sea Research II 119 (2015) 30–39

201N, 1161E (Fig. 4B). However, no evidence was observed for asimilar positive SST–wind coupling at the south end of the Taiwan
Strait and off the northwest coast of Luzon Island.
Previous studies show that the removal of large-scale ﬁelds byspatial high-pass ﬁltering provides an intuitive presentation of thecoupling of perturbation ﬁelds (Chelton et al., 2001; Nonaka andXie, 2003). The same method was slightly modiﬁed and applied in
the present study. A spatial ﬁlter of nine grids in the direction
across the front and three grids in the direction along the front
was constructed using a Gaussian smoothing function. Spatiallyhigh-pass-ﬁltered ﬁelds at the grid points were obtained byremoving spatially low-pass-ﬁltered ﬁelds with the Gaussiansmoothing function. Spatially high-pass-ﬁltered ﬁelds are hereinafter referred to as perturbation ﬁelds.
In winter, the perturbations of SST and sea surface wind havethe same sign over the frontal area west of 1171E (Fig. 5A, B, and L),
indicating a positive correlation of SST–wind coupling. There wasan asymmetric perturbation of the SST on the cold and warm sidesof the SST front, which also corresponds to an asymmetricperturbation of surface wind. The negative SST perturbation varies
from  0.05 to 1.5 1C, whereas the positive SST perturbation
varies from 0.05 to 0.5 1C. The positive perturbation of sea surface

wind is about 0.05 to 0.5 m s  1 over warm water and  0.05 to 0.75 m s  1 over cold water (Fig. 5A, B, and L). Thus, theperturbations of surface wind are larger over cold water than overwarm water.
However, the positive SST–wind correlation in winter could beoverlaid by the inﬂuence of terrain in the area east of 1171E, where
the perturbed SST and wind have opposite signs (Fig. 5A, B and L).
The narrowing effect of the Taiwan Strait intensiﬁes the sea
surface wind speed inside the Taiwan Strait and weakens it off
the south end of the Taiwan Strait (Fig. 4B). Meanwhile, theblocking effect of Taiwan Island reduced the sea surface windspeed along the southwest of Taiwan Island (Fig. 4B). The joint
effect of these two topographic factors was more signiﬁcant than
the inﬂuence of the SST front in this area. Therefore, there was anegative SST perturbation overlaid by a positive wind speed
perturbation in the Taiwan Strait and a positive SST perturbation
overlaid by a negative wind speed perturbation along the southwest coast of Taiwan Island.
On the other hand, the positive SST–wind correlation can also
be observed in the area east of 1171E in June, July, and August(Fig. 5F–H) when the SST front has a small but well-developed
range (Fig. 2G–I). During this season, the wind direction reverses

Fig. 5. Spatially high-pass-ﬁltered monthly mean values of the OSTIA SST (1C) overlaid as contours on the spatially high-pass-ﬁltered monthly mean values of the QuikSCATequivalent neutral wind speeds (m s  1) at 10 m height from January 2008 to December 2008. Positive and negative high-pass-ﬁltered SSTs are denoted by solid and dashedlines, respectively, at an interval of 0.25 1C. Gray lines denote a zero high-pass-ﬁltered SST. Description of the high-pass ﬁlter is given in Section 3.1.

R. Shi et al. / Deep-Sea Research II 119 (2015) 30–39

A quantitative evaluation of the surface wind simulated by themodel for each month in 2008 was performed. Two statisticalparameters, the RMSD of the wind speed at 10 m height and themean absolute error (MEAN) of the wind direction at 10 m height,
were calculated by using the following equations:!
N∑ ðX imod X iobs Þ2 =N

RMSD ¼ sqrt

ð1Þ

i¼1

N

MEAN ¼ ∑ j X imod  X iobs j =N

ð2Þ

i¼1

3.2.1. Validation of simulation results
A one-year realistic simulation using the WRF model was
carried out to reproduce the observed SST–wind coupling process
in the NSCS in 2008. The simulation is considered a one-way
coupling of SST and wind, which simpliﬁes the complex ocean
dynamical processes to be represented by updating the daily SST.
The performance of the WRF model was evaluated before further
analysis. The monthly mean wind speed and direction at a heightof 10 m were simulated by the WRF model and interpolated to thespacing of the grid points at 25 km. These were then compared to
the QuikSCAT data of the same domain.
The model successfully simulated the spatial pattern of the sea
surface wind in the NSCS in December 2008 (Fig. 6B). The vector
mean of the simulated wind direction was highly consistent withthe observations. However, the mean of the simulated wind speed
was about 1 m s  1 lower than that of the observations. The
underestimation of wind speed by the model could be considerednegligible because the observed mean wind speed was generally
greater than 10 m s  1 in this area.

where Xmod denotes the variable from the model results, Xobs is thecorresponding variable of the satellite observations, N is the data
number, and i is the grid number. The RMSDs of the wind speed
between the model results and the observation data were 1 m s  1 during most months and less than 1 m s  1 during thesummer (Table 1). Meanwhile, the MEANs of the wind direction
were less than 101 during most months (Table 1). Given that all the
Table 1Comparison between the winds simulated by the WRF model at 10 m height and
the QuikSCAT observed equivalent neutral winds at 10 m height from January 2008
to December 2008. See the text for the deﬁnitions of RMSD and MEAN.
RMSD of wind speed (m s  1)
1.09 1.15
0.72
0.92
0.62 0.52 0.52
MEAN of wind direction (deg)6.05 5.95 10.24 21.5
46.41

0.77

7.58 6.56 12.99 18.3

24o N
23 o N15.25

22 o No−1−.7215.5 −.715−1− −00.5
−
5
0
2
.
−0
25
0.

23 N
21 o N

ms

21 N

1.5

5

1
0.5
0

5

−0.5

0.
5

20 o N5
5.2−1.7 −1.25−00.25 5−15−1.5 −01 0.7−00.5.752
5.
1−0.225 0.5
0.
0.
1
75
0.5

0.2

0.

− −0 −1
0 0.5.75

−1

22 N
10

5

.2

−1

o

o

2

−1

0.7

QSCAT Wind and SSTo8.38 3.93 4.23

0. 0
5

−1
.5

24 N

0.73 1.26 1.03 0.89

53.2. Model simulation

−1

0.5

−1.5

19 o N

−2

110oE

20oN

m s−1

and the topographic effects are little. Thus, the positive SST–windcorrelation becomes most apparent at the southern end of the
Taiwan Strait, where the SST gradient is much stronger than theother areas in the NSCS in summer. However, this relationship israrely observed in the months when the monsoon season begins,such as April, May, and September (Fig. 5D, E, and I), since thefrequent changes in wind direction caused by the unstableprevailing monsoon make it difﬁcult to identify the inﬂuence of
the SST perturbation on the sea surface wind.

35

112oE

114oE

116oE

118oE

120oE

o

19 N

5
112oE

o

114 E

o

116 E

o

118 E

2.5

o

QSCAT
WRF

120 E

WRF Wind and SST

o

15

24 N
23 oN

10
o

21 N

ms

−1

22 oN

20 oN

Perturbation 10−m neutral
equivalent wind speed (m s−1)

2
1.5
1
0.5
0
−0.5
−1
−1.5
−2

o

19 N

5
o

112 E

o

114 E

o

116 E

o

118 E

o

120 E

Fig. 6. (A) Monthly mean values of the QuikSCAT equivalent neutral winds (blackarrows) and their speeds (m s  1, color tone) at 10 m height in December 2008.
(B) The same as (A) but for the winds simulated by the WRF model. (A) and (B) areoverlaid by the monthly mean SST contours (black lines) from 18 1C to 28 1C at aninterval of 1 1C.

−2.5
−2.5

−2

−1.5

−1

−0.5

0

0.5

1

1.5

2

2.5

OSTIA perturbation SST (°C)

Fig. 7. (A) Spatially high-pass-ﬁltered OSTIA SSTs (contours, 1C) and spatially highpass-ﬁltered wind speeds (color tone, m s  1) at 10 m height as simulated by the
WRF model in December 2008. (B) Scatter plot of the spatially high-pass-ﬁlteredwind speeds versus the spatially high-pass-ﬁltered OSTIA SSTs for the data shownin panel (A) (crosses) and for those shown in Fig. 5L (triangles).

36

R. Shi et al. / Deep-Sea Research II 119 (2015) 30–39

a higher concentration in the ﬁrst quadrant than in the third
quadrant because the positive perturbation of the SST varies over asmall range (Fig. 7A). Meanwhile, the plots in the second andfourth quadrants illustrate the opposite signs of the perturbationsof the SST and surface winds in the area east of 1171E (Fig. 7A).
In Section 3.1.2, the observed SST and sea surface wind
perturbations showed that the positive SST–wind coupling canbe observed in the different frontal areas accompanied by thechanges in season (Fig. 5). Therefore, the monthly scatter plots of
the SST and sea surface wind perturbations were carried out in theapparent coupling area to investigate the seasonal variations of thecoupling (Fig. 8).
Both the results of the WRF model and the observation show
apparent positive linear SST–wind coupling during most months
with variations in the coupling coefﬁcient, which is deﬁned as theslope of the least-square regression line (Fig. 8). In general, thecoupling coefﬁcients obtained by the simulation and observation
range from about 0.27 to 0.5 (Table 2), which is larger than the
coupling coefﬁcients observed over the Gulf Stream but smaller
than those observed over the equatorial Paciﬁc (O0 Neill, 2012).
The SST–wind coupling coefﬁcients are a little larger in November
and December than in June and July (Table 2), despite the signiﬁcantdifference in their spatial size. However, the coupling coefﬁcients are

simulations were conducted without data assimilation and thesimpliﬁcation of oceanic processes, the performance of the model
was acceptable for the subsequent analysis.

3.2.2. Simulated SST–wind coupling
In Fig. 7A, the perturbation of the OSTIA SST is overlaid ascontours on the perturbation of the surface wind speed simulatedby the WRF model for December 2008. The model generated asimilar spatial map of the observed SST–wind coupling shown in
Fig. 5L. The perturbations of the SST and the sea surface wind bear
the same sign in the area west of 1171E and opposite signs insouthern end of the Taiwan Strait. The sea surface wind perturbation is also larger over cold water than over warm water (Fig. 7A),
indicating that the reduction in surface wind speed over cold
water is more signiﬁcant than the increase in surface wind speed
over warm water.
The SST-induced change in the surface wind speed in December 2008 can be statistically analyzed in the scatter plot of thesurface wind perturbations as a function of the SST perturbations(Fig. 7B). Both the simulation results and the observation results
show a similar positive linear relationship between the perturbations of SST and sea surface wind (Fig. 7B). The scatter plot exhibits

Perturbation 10−m neutral
equivalent wind speed (m s )

2.5
2
1.5
1
0.5
0
−0.5
−1
−1.5
−2
−2.5

Perturbation 10−m neutral
equivalent wind speed (m s )

2.5
2
1.5
1
0.5
0
−0.5
−1
−1.5
−2
−2.5

Perturbation 10−m neutral
equivalent wind speed (m s )

2.5
2
1.5
1
0.5
0
−0.5
−1
−1.5
−2
−2.5
−2.5 −2 −1.5 −1 −0.5 0

0.5

1

1.5

2

2.5

−2.5 −2 −1.5 −1 −0.5 0

OSTIA perturbation SST (°C)

0.5

1

1.5

2

2.5

OSTIA perturbation SST (°C)

−2.5 −2 −1.5 −1 −0.5 0

0.5

1

1.5

2

2.5

OSTIA perturbation SST (°C)

−2.5 −2 −1.5 −1 −0.5 0

0.5

1

1.5

2

2.5

OSTIA perturbation SST (°C)

Fig. 8. Scatter plot for the spatially high-pass-ﬁltered monthly OSTIA SST (1C) and surface wind speed (m s  1) in the area east of 1171E for June, July, August, and September(F, G, H, I), and in the area west of 1171E for the other months. Gray crosses and black triangles denote the WRF simulation results and the QuikSCAT observation data,
respectively. Gray and black lines represent the linear least-squares ﬁtting for the scatter plot of the WRF model results and QuikSCAT observations, respectively.

Table 2
Coupling coefﬁcients from the scatter plots for the SST and sea surface wind speed perturbations in the frontal areas from January 2008 to December 2008 (Fig. 8). The
symbol “–” denotes the coupling coefﬁcients that did not pass the conﬁdence test.
Coupling coefﬁcient
WRF
QuikSCAT

0.27
0.39

0.29
0.45

0.35
0.30

–
–

–
–

0.41
0.50

0.48
0.32

0.37
0.25

0.28
0.07

0.30
0.48

0.52
0.55

0.52
0.46

R. Shi et al. / Deep-Sea Research II 119 (2015) 30–39

smaller in January and February than in June and July. The SST–windcoupling is weak or difﬁcult to identify during the transition periods
to the monsoon season in April, May, and September.

4. Discussion
The seasonal variability in the coastal front in the NSCS issigniﬁcant, particularly along the southeastern coastline of China.
A simple explanation is presented by considering the effects ofreversing monsoons, variations in bathymetry, and river discharge.
During winter, the northeasterly monsoon causes strong cooling and stirring in the shallow coastal sea and induces a southward
intrusion of northern cold water along the coast. Consequently, thewater temperature is lower in the shallow water near the shore(water depth o30 m) than in the open water (water depth
450 m) (Fig. 1). Therefore, the SST front covering the shallow
coastal area formed between the 20 m and 50 m isobaths, parallelto the coastline of southeastern China (Fig. 2).
During summer, the prevailing southwesterly monsoon causes
offshore transport of coastal water, favoring the formation of upwelling systems in the NSCS (Xie et al., 2003, Wang et al., 2012) and thenortheastward movement of a fresh water plume from the Pearl Riverestuary (Gan et al., 2009). In relation to these two processes, summer
fronts appear in June, mature in July and August, and weaken in
September because of the retreat of the summer monsoon.
The differences between the sea surface wind observed by
QuikSCAT and simulated by the WRF model may be caused by
the overestimation or underestimation of the QuikSCAT equivalent37

neutral winds in the different stable conditions. For stable conditions, the equivalent neutral wind speeds are smaller than the realwind speeds at 10 m height. For unstable conditions, such as thecold northeasterly winds blowing over the warm water in the
NSCS in winter, the equivalent neutral wind speeds are greaterthan the real wind speeds at 10 m depth (Perlin et al., 2004).
The coupling coefﬁcients calculated by the simulated 10-m realwinds of the WRF model are smaller than those calculated by theobserved 10-m equivalent neutral winds of QuikSCAT (Table 2).
This discrepancy was also discussed in previous studies using
numerical models (Song et al., 2009) and buoy observations(O0 Neill, 2012). Such a difference was found to occur due to thedifference between the 10-m height real winds and equivalent
neutral winds under different stable conditions. In addition, thecoupling coefﬁcients observed by QuikSCAT reﬂect a two-way
coupling of SST and wind, although QuikSCAT has a low sampling
frequency of twice in one day. However, the simulation of the WRF
model is a one-way coupling simulation that does not include theresponse of the oceanic processes to the change of sea surface
wind. Thus, the simpliﬁcation in our simulation for a fullycoupling of SST and wind could also be responsible for differencesin the coupling coefﬁcients.
Using the consecutive wind data of QuikSCAT and ASCAT from2006 to 2011, the same SST–wind couplings at the frontal area of
the NSCS are also observed in other years (Fig. 9), but the coupling
coefﬁcients differ from year to year (Table 3). The SST–windcoupling coefﬁcient was more notable in 2008 than in other years.
This is why we chose 2008 as the representative year to study theinﬂuence of a front on surface winds and its seasonal variation in

Fig. 9. Same as Fig. 5 but for the monthly mean values of the QuikSCAT (A, B, C) and ASCAT (D, E, F) equivalent neutral wind speeds (m s  1) at 10 m height from December
2006 to December 2011.

38

R. Shi et al. / Deep-Sea Research II 119 (2015) 30–39

this paper. The cause of this yearly variation in the SST–windcoupling is a topic that is currently being discussed and will be
investigated in a future study.

Natural Science Foundation of China (nos. 41106028 and 41206011),
the Knowledge Innovation Project for Distinguished Young Scholars of
the Chinese Academy of Sciences (no. KZCX2-EWQN203), and theKnowledge Innovation Program of the Chinese Academy of Sciences(no. 54-SQ201104-1), Project on the Integration of Industry, Education
and Research of Guangdong Province (no. 20110908). This study was
also partly supported by Grants-in-Aid for Scientiﬁc Research(22106002) from the Ministry of Education, Culture, Sports, Scienceand Technology, Japan. The numerical simulation is supported by thehigh-performance computing division and Ms. Dandan Sui of theSouth China Sea Institute of Oceanology.

5. Summary and conclusion

References

The OSTIA SST with high resolution was used to investigate theseasonal variations of the oceanic front in the coastal area of the
NSCS. The climatological mean of the SST gradient showed that thecoastal fronts primarily formed in Beibu Bay, off the northwest
coast of Luzon Island, and along the SCC (Figs. 1 and 2).
The SST front can be observed in the SCC throughout the year. Its
coverage is much smaller during the summer than in winter, with
an abrupt reduction in the covering rate from 80% to 40% in May.
The annual minimum covering rate was 20% in September. The
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The local inﬂuence of the coastal SST front on the sea surface
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The SST–wind coupling discussed in this paper only reﬂects theinﬂuence of the ocean on the atmosphere. The feedback of theocean to the change in the atmosphere is taken into account. The
response of the sea surface wind to the SST front may signiﬁcantly
inﬂuence the underlying fronts. We will examine this issue in a
subsequent study by using an air-sea coupled model.

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Table 3
Coupling coefﬁcients from the scatter plots of the SST and sea surface wind
perturbations over the frontal areas in Decembers from 2006 to 2011 (Fig. 8).
Year

2006

2007

2008

2009

2010

2011

Coupling coefﬁcients

0.45

0.34

0.46

0.10

0.20

0.39

Acknowledgments
This study was supported by the National Natural Basic research
Program of China (973 Program, no. 2011CB403501), the National

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