Acta Oceanol. Sin., 2014, Vol. 33, No. 11, P. 55–63
DOI: 10.1007/s13131-014-0555-x
http://www.hyxb.org.cn
E-mail: [email protected]
Warm water wake off northeast Vietnam in the South China Sea
YAN Yunwei1,2, CHEN Changlin2*, LING Zheng2
1
2
College of Physical and Environmental Oceanography, Ocean University of China, Qingdao 266100, China
State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, State
Oceanic Administration, Hangzhou 310012, China
Received 24 February 2014; accepted 13 June 2014
©The Chinese Society of Oceanography and Springer-Verlag Berlin Heidelberg 2014
Abstract
Due to orographic blockage, a weak wind wake occurs in summer off northeast Vietnam in the South China
Sea. Under the wind wake, warm water is observed from both high-resolution satellite data and hydrographic observations. The wake of warm water forms in June, continues to mature in July and August, starts to
decay in September, and disappears in October. The warm water wake also shows robust diurnal variation – it intensifies during the day and weakens in the night. Warm water wakes can be generated through
wind-induced mixing and thermal (latent heat flux) processes. In this paper, a mixed layer model is used to
evaluate the relative importance of the two processes on seasonal and diurnal timescales, respectively. The
results demonstrate that thermal processes make a greater contribution to the wake than wind-induced
mixing processes on a seasonal timescale, while the warm water wake is dominated by wind-induced mixing
processes on a diurnal timescale.
Key words: warm water wake, wind-induced mixing processes, thermal processes, seasonal timescale,
diurnal timescale, northeast Vietnam, South China Sea
Citation: Yan Yunwei, Chen Changlin, Ling Zheng. 2014. Warm water wake off northeast Vietnam in the South China Sea. Acta
Oceanologica Sinica, 33(11): 55–63, doi: 10.1007/s13131-014-0555-x
1 Introduction
The South China Sea (SCS) is the largest marginal sea in
Southeast Asia surrounded by the Indo-China Peninsula to the
west and South China to the north (Fig. 1a). On the east coast
of Indo-China Peninsula, mountain ranges, named Annam
Cordillera, lie in the north-south direction and can be divided
into three segments (north, middle, and south segments) according to the land topography with elevations greater than
800 m. Ngoc Linh, the highest mount in the middle segment,
is a 2 598 m high mountain. Under the control of the East Asian
monsoon, the SCS is dominated by the southwest monsoon in
summer (Wyrtki, 1961; Qu, 2000; Liu et al., 2001). In this study,
summer refers to June, July, and August. As the southwest summer monsoon impinges on Annam Cordillera, a strong wind
jet forms at the southern tip of the mountains (Xie et al., 2003).
Beyond that, we also notice that a weak wind wake appears in
the lee of the middle segment of Annam Cordillera off northeast
Vietnam (Fig. 1a).
Sea surface temperature (SST) in the SCS has been widely
documented in previous studies (e.g., Qu, 2001; Wang et al.,
2002; Xie et al., 2003; Liu et al, 2004; Wang et al., 2006). In summer, Xie et al. (2003) found that the cold water along the south
Vietnam coast, being induced by upwelling, flows into the open
SCS with a northeastward current, forming a cold filament and
causing a basin-scale cooling. Apart from the basin-scale summer cooling, a warm water trailing is found under the weak wind
wake (Fig. 1a), which has not been reported previously. The goal
of our paper is to investigate the features and formation mecha-
nisms of the warm water wake off northeast Vietnam.
Wakes of warm water have been observed off other islands,
and their formation mechanisms are different (Caldeira et al.,
2005; Li et al., 2012). Caldeira et al. (2005) argued that the high
SST leeward of Santa Catalina Island is a result of sheltering from
the wind with weaker wind mixing and shallower Ekman depth,
allowing for higher heat storage; while Li et al. (2012) concluded
that the reduced surface latent heat flux in the lee region plays
a major role in generating the warm pool off Hainan Island. So,
which mechanism is suitable for the warm water wake in summer (on a seasonal timescale) off northeast Vietnam?
Warm water wakes also show robust diurnal variation,
which have been identified from satellite images: they form
during the day and weaken or disappear in the night (Van Camp
et al., 1991). This is because daytime heating in the absence of
strong wind mixing leads to the formation of a thin warm stratified surface layer in the lee region, but in the exposed region the
effect of surface heating is masked by the strong wind-induced
mixing to produce a uniform surface layer (Barton et al., 1998,
2000; Caldeira et al., 2002). Caldeira and Marchesiello (2002) argued that the weaker latent heat flux in the wake region may
enhance the diurnal warming as well. A natural question is:
does the warm water wake off northeast Vietnam have robust
diurnal variability? If yes, what roles do the weak wind mixing
and the reduced latent heat flux play in the diurnally varying
warm water wake?
As mentioned above, on seasonal and diurnal timescales,
warm water wakes can be generated through wind-induced
Foundation item: The National Science Fund of China for Distinguished Young Scholars (NSFDYS) under contract No. 41125019; the National Basic
Research Program of China under contract Nos 2012CB955601 and 2013CB430301; the Basic Research Program of Second Institute of Oceanography, State Oceanic Administration of China under contract No. JT1301.
*Corresponding author, E-mail: [email protected]
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YAN Yunwei et al. Acta Oceanol. Sin., 2014, Vol. 33, No. 11, P. 55–63
110°
112°
114°
116° E
China
a
106°
30.0
22°
N
29.8
20°
6.0
ut
14°
12°
Indo−China
Peninsula
10 m/s
7.0
8.0
h
Ch
29.2
in
a
Se
a
116° E
0.50
0.45
18°
SST/°C
So
6.0
114°
6.0
29.4
5.0
4.5
112°
China
b
5.0
5.0
16°
110°
20°
29.6
18°
108°
29.0
4.5
16°
0.40
6.0
14°
12°
10°
28.8
10°
8°
28.6
8°
Indo−China
Peninsula
7.0
10 m/s
8.0
So
ut
h
Ch
DSST/°C
22°
N
108°
5.0
106°
in
a
Se
0.35
a
0.30
0.25
Fig.1. The average SCOW wind vector (arrows), wind speed (white lines, in unit of m/s), PFV50 SST (a), and SeaFlux-V1.0 diurnal
SST (DSST) (b) in summer. Annam Cordillera with elevations greater than 800 m is shaded in black. The highest mount in the middle
segment, Ngoc Linh (2 598 m), is marked with magenta triangle.
mixing and/or thermal (latent heat flux) processes, but the
roles they play in the warm water wake formation off northeast
Vietnam are still not clear as of now. In this study, we will first
investigate the features of the warm water wake and then evaluate the relative importance of wind-induced mixing and thermal processes on seasonal and diurnal timescales, respectively,
using a mixed layer model. Lastly, we will explain why the warm
water wake is dominated by wind-induced mixing processes on
diurnal timescale.
2 Data and model
2.1 Data
The Scatterometer Climatology of Ocean Winds (SCOW)
is estimated from the 10-year record (September 1999–October 2009) of wind measurements by the QuikSCAT scatterometer (Risien and Chelton, 2008). It provides the global wind,
wind stress, and wind stress derivative fields with a resolution 0.25°×0.25°. Compared with NCEP/NCAR reanalysis wind
fields, the SCOW atlas can capture small-scale features, especially in regions influenced by topography, SST gradient, and
ocean current.
The advanced very high resolution radiometer (AVHRR)
Pathfinder Version 5 SST Project (PFV50) (Casey et al., 2010) is a
reanalysis of the AVHRR data, which uses improved algorithms
and processing steps to produce twice-daily global SST at a resolution of approximately 4 km. Monthly SST climatology based
on 1982–2008 periods is used here.
The SeaFlux Turbulent Flux Dataset Version 1.0 (SeaFluxV1.0) provides diurnal SST (DSST - the difference between the
maximum and minimum SST due to diurnal warming over a
24-hour period defined from 0000Z to 0000Z of the next day),
which is parameterized using the methodology of Bogdanoff
and Clayson (2013, unpublished manuscript), an update of the
Clayon and Curry (1996) parameterization. The product with
a (1/4)° equal-angle grid is currently available from January
1, 1998 to December 31, 2007. A comparison of DSST in 1998
summer derived from SeaFlux-V1.0 with three ATLAS buoys in
the SCS deployed during SCSMEX (Lau et al., 2000) is listed in
Table 1. It is found that the mean differences of DSST at three
stations are 0.042, 0.037, and 0.002°C, respectively, with root
mean square errors (RMSE) of 0.27, 0.31, and 0.20°C and correlation coefficients of 0.65, 0.71, and 0.89, which illustrates that
the product is capable of reproducing the diurnal amplitude in
the summer SCS.
Table 1. Comparison of summer DSST in 1998 derived from
SeaFlux-V1.0 with three ATLAS buoys in the SCS deployed during SCSMEX
18.1°N, 115.6°E 15.3°N, 115.0°E 13.0°N, 114.4°E
Mean (ATLAS)/°C
0.499
0.436
0.481
Mean (SeaFlux)/°C
0.457
0.473
0.479
RMSE/°C
0.268
0.315
0.198
Correlation
0.651
0.709
0.893
ERA-Interim is the latest global atmospheric reanalysis
produced by the European Centre for Medium-Range Weather
Forecasts (ECMWF). Comparisons between ERA-Interim and
ERA-40 show that ERA-Interim makes substantial improvements in many aspects (Dee et al., 2011). Three-hourly surface
forecast momentum and heat fluxes fields (wind stress, shortwave radiation, longwave radiation, latent heat flux, sensible
heat flux) derived from daily full resolution ERA-Interim are
used here.
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YAN Yunwei et al. Acta Oceanol. Sin., 2014, Vol. 33, No. 11, P. 55–63
2.2 Model
The mixed layer model developed by Price et al. (1986) (PWP
model) only considers atmospheric forcing and vertical mixing,
ignoring advection; and the temperature, salinity, and momentum equations are (Price et al., 1978):
∂Tl 1  Q
∂h 
=
+ δT  ,


∂t h  ρ r C p
∂t 
(1)
∂Sl 1 
∂h 
=
 Sl ( E − P) + δS  ,
∂t h 
∂t 
(2)
∂Vl
1 τ
∂h 
= − f × Vl +  + δV
,
h  ρr
∂t
∂t 
(3)
where Tl , Sl , Vl are the mixed layer temperature, salinity, and
horizontal velocity. δT, δS , δV are the differences across the
base of the mixed layer; Q is net heat flux; E − P is evaporation
minus precipitation; τ is wind stress vector; h is the mixed lay-
∂h
= − we (we is entrainment velocity); ρ r is the
∂t
reference density; C p is the heat capacity of seawater; and f is
the Coriolis parameter.
Previous studies have verified that the PWP model is able to
simulate the mixed layer temperature and its diurnal variation
(Price et al., 1986; Anderson et al., 1996; Li et al., 2012). In this
study, a control run is carried out to reproduce the warm water
wake on seasonal and diurnal timescales. In the control run, the
vertical resolution is set to be 1 m, the Generalized Digital Environment Model Version 3.0 dataset (GDEM3) (Carnes, 2009) T-S
profiles in May are set as the initial condition, and the surface
forcing is the 3-hourly ERA-Interim momentum and buoyancy
fluxes in June. The model is integrated for 1 month.
er depth (MLD);
22°
N
106°
108°
110°
112° E
3 Results
3.1 Wind and warm water wake on seasonal timescale
Figure 1a shows the average summer wind speed and SST
climatology in the northwestern SCS. Due to orographic blockage, a weak wind wake forms in the lee of the middle segment of
Annam Cordillera off northeast Vietnam. The wind wake ranges
from about 6 m/s at the southeast boundary (where wind speed
begins to decrease sharply from southeast to northwest off
northeast Vietnam) to 4.5 m/s at the center. Concurrently, warm
water is found under the wind wake, and its temperature at the
center is about 30°C, which is around 0.8°C higher than that at
the southeast boundary. Hydrographic observations along the
coast of northeast Vietnam from June 10 to June 24 in 1995 also
capture the features of the warm water wake (Fig. 2a): a hightemperature water mass appears off northeast Vietnam with the
maximum temperature exceeding 30°C. Compared to satellite
data (Fig. 2b), the field observations only cover the northern
part of the wake.
Figure 3a shows seasonal variation of the wind wake. It forms
in May with the onset of the SCS summer monsoon and then
matures in July and August when the SCS summer monsoon
has fully established. After that, the wind wake starts to decay in
September and disappears in October with the reversion of the
monsoon. Due to the occurrence of the weak wind wake, a wake
of reduced latent heat flux forms off northeast Vietnam, and its
seasonal evolution is consistent with that of the wind wake except the beginning time (Fig. 3b), probably because the wind
wake is too weak to generate the latent heat flux wake in May.
Under the combined influence of the weak wind and the reduced latent heat flux, the warm water wake shows similar seasonal variation: it forms in June, attains its maximum in July and
August, and weakens from September (Fig. 3c). A comparison
of the beginning time of wakes of warm water, wind, and latent
heat flux indicates that thermal processes may play a more important role than wind-induced mixing processes in the warm
22°
N
106°
30
108°
110°
112° E
30
20°
20°
29
29
16°
28
14°
16°
28
14°
a
12°
SST/°C
18°
SST/°C
18°
27
bb
27
12°
Fig.2. SST during the period from June 10, 1995 to June 24, 1995 derived from the World Ocean Database cruise SU016777 (a) and
the average AVHRR SST in June 1995 (b).
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YAN Yunwei et al. Acta Oceanol. Sin., 2014, Vol. 33, No. 11, P. 55–63
a
108°
b
112° E
108°
c
112° E
108°
d
112° E
108°
112° E
16°
N
12°
May
May
May
May
Jun.
Jun.
Jun.
Jun.
Jul.
Jul.
Jul.
Jul.
Aug.
Aug.
Aug.
Aug.
Sep.
Sep.
Sep.
Sep.
Oct.
Oct.
Oct.
Oct.
16°
N
12°
16°
N
12°
16°
N
12°
16°
N
12°
16°
N
12°
3
6
WS/m∙s−1
9
60
105
LHF/W∙m−2
150
27.5
29.25
SST/°C
31
0.18
0.36
DSST/°C
0.54
Fig.3. Seasonal variation of SCOW wind speed (WS) (a), ERA-Interim latent heat flux (LHF) (b), PFV50 SST (c) and SeaFlux-V1.0
DSST (d).
59
YAN Yunwei et al. Acta Oceanol. Sin., 2014, Vol. 33, No. 11, P. 55–63
water wake. The relative importance of the two processes will be
discussed in more detail below.
3.2 Roles of wind-induced mixing and thermal processes on
seasonal timescale
Due to the presence of cold advection induced by a northeastward current, a basin-scale cooling occurs in the SCS in
summer (Xie et al., 2003). The SST pattern shows that the cold
advection mainly happens off southeast Vietnam in June (Fig.
3c), which has little effect on warm water. That means advection is not important in the wake region. Moreover, the wind-
106°
108°
110° E
induced mixing and thermal processes mentioned above are
1D vertical processes. Thus the PWP model is employed here
to evaluate their roles in the warm water wake. To quantify the
effects of the two processes on the warm water wake, the intensity of the wake on seasonal/diurnal timescale is calculated
by subtracting SST/DSST at the wake’s southeast boundary. As
expected, a wake of warm water forms off northeast Vietnam in
the control run simulation, and its intensity is consistent with
the observation on a seasonal timescale (Fig. 4), suggesting that
the PWP model can reproduce the warm water wake very well.
To investigate the roles of wind-induced mixing and ther-
106°
0.8
108°
110° E
0.2
0.6
0
16°
−0.2
WWWI/°C
0.2
0.1
18°
N
0
16°
−0.4
control run
−0.1
Exp. TAU
−0.6
14°
14°
−0.8
106°
108°
110° E
WWWI/°C
0.4
18°
N
−0.2
106°
0.5
108°
110° E
0.2
0.4
0.3
0
16°
−0.1
WWWI/°C
0.1
0.1
18°
N
0.2
0
16°
−0.2
−0.1
−0.3
Exp. LHF
14°
Exp. SHF
14°
−0.4
−0.5
106°
108°
110° E
WWWI/°C
18°
N
−0.2
106°
0.5
108°
110° E
0.2
0.4
0.3
0
16°
−0.1
WWWI/°C
0.1
0.1
18°
N
0.2
0
16°
−0.2
Exp. SWR
14°
−0.1
−0.3
−0.4
−0.5
WWWI/°C
18°
N
Exp. LWR
14°
−0.2
Fig.4. The intensity of warm water wake (WWWI) on seasonal timescale in the control run, Exp. TAU, Exp. LHF, Exp. SHF, Exp. SWR,
and Exp. LWR.
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YAN Yunwei et al. Acta Oceanol. Sin., 2014, Vol. 33, No. 11, P. 55–63
mal processes in the warm water wake, five additional experiments (Table 2) are set up to evaluate the contributions of wind
mixing (TAU), latent heat flux (LHF), sensible heat flux (SHF),
shortwave radiation (SWR), and long wave radiation (LWR) to
the warm water wake. In each experiment, only one forcing
field is the same as that in the control run, and others are set
to values at the wake’s southeast boundary. The results show
that a warm water wake forms off northeast Vietnam in Exp
TAU (Fig. 4) due to weak wind-induced shallow MLD (Fig. 5)
and in Exp LHF (Fig. 4) due to reduced latent heat flux (Fig. 5),
which means that the two processes both work here. Compared
with wind-induced mixing processes (0.2°C), thermal processes
(0.5°C) make a greater contribution to the warm water wake on
seasonal timescale (Fig. 4).
3.3 Warm water wake and roles of wind-induced mixing and
thermal processes on diurnal timescale
Figure 1b shows the average DSST of SeaFlux-V1.0 in summer. Large diurnal amplitude occurs in the wake region with
the maximum value exceeding 0.45°C, suggesting that the warm
water wake has robust diurnal variation. The diurnally varying
warm water wake can also be observed from AVHRR satellite images: a strong warm water wake appears off northeast Vietnam
in the average summer daytime image, and it weakens sharply
in the night image (figure not shown). The diurnal amplitude of
the warm water wake shows obvious seasonal variation: it begins to increase in May, attains its maximum in summer and
then decreases from September (Fig. 3d), which is consistent
with the seasonal variation of the wind wake.
It has been verified that diurnal amplitude depends primarily on solar radiation and wind speed (e.g., Gentemann et al.,
2003; Kawai and Wada, 2007). Stronger insolation causes larger
diurnal amplitude due to the absorption of radiation near the
surface, while stronger wind results in smaller diurnal ampli-
tude through inducing stronger turbulent mixing and drawing
more heat from the ocean. As sufficient heating from solar radiation is absorbed, the location and amplitude of diurnal warming appears to be primarily determined by wind patterns (Gentemann et al., 2008). The peak solar radiation shows that strong
insolation occurs over the whole area (> 680 W/m2) (Fig. 5); thus
the weak wind speed in the wake region is the main factor for
the large diurnal amplitude. However, the relative importance
of the weak wind-induced mixing and reduced latent heat flux
are still unclear.
To examine the roles of the weak wind-induced mixing and
reduced latent heat flux quantitatively, the intensity of the wake
on a diurnal timescale is estimated in the PWP model experiments. The results show that a large diurnal amplitude appears
under the wind wake off northeast Vietnam in the control run
(Fig. 6), demonstrating that the PWP model is also able to reproduce the diurnal variation of the warm water wake. A comparison of patterns of diurnal warming, solar radiation and wind
confirms that the location and amplitude of diurnal warming is
determined by the weak wind wake. Sensitive experiments suggest that both the wind-induced weak mixing and reduced latent heat flux can generate relatively large diurnal amplitude off
northeast Vietnam, but the wind mixing (0.25°C) makes a much
greater contribution than the latent heat flux (0.02°C) (Fig. 6),
indicating the formation of the diurnally varying warm water
wake here is dominated by wind-induced mixing processes.
3.4 Why warm water wake is dominated by wind-induced
mixing processes on diurnal timescale
During the process of daytime heating, Eq. (1) can be rewritten as:
∂Tl / ∂t =Q / (hρ r C p ) ,
(4)
Table 2. The PWP model experiments
Experiment
control run
Exp. TAU
Exp. LHF
Exp. SHF
Exp. SWR
Exp. LWR
Wind stress
√
√
B
B
B
B
Latent heat flux
√
B
√
B
B
B
Sensible heat flux
√
B
B
√
B
B
Shortwave radiation
√
B
B
B
√
B
Longwave radiation
√
B
B
B
B
√
Notes: √ means the forcing field in the experiment is realistic, and B the forcing field is set to value at the southeast boundary of the
warm water wake.
110° E
106°
108°
12
0.05
11
0.04 3
0.0
10
9
0.02
14°
0.0
5
0.03
0.04
16°
a Exp. TAU
8
108°
−100
16°
−110
810
18°
N
790
770
750
16°
730
−120
14°
b Exp. LHF
−130
110° E
830
−90
18°
N
7
6
106°
−80
MLD/m
18°
N
110° E
14°
710
c Exp. SWR
690
Fig.5. Wind stress (white lines, in unit of N/m2) and mixed layer depth (MLD) in Exp. TAU (a), latent heat flux in Exp. LHF (b) and
the peak solar radiation (PSWR) in Exp. SWR (c).
PSWR/W∙m−2
108°
LHF/W∙m−2
106°
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YAN Yunwei et al. Acta Oceanol. Sin., 2014, Vol. 33, No. 11, P. 55–63
106°
108°
110° E
106°
0.3
108°
110° E
0.2
0.2
18°
N
0
16°
−0.1
control run
WWWI/°C
0.1
0
16°
−0.1
Exp. TAU
−0.2
14°
0.1
WWWI/°C
18°
N
14°
−0.2
−0.3
106°
108°
110° E
106°
0.02
108°
110° E
0.008
WWWI/°C
0
16°
18°
N
0.004
0
16°
WWWI/°C
0.01
18°
N
−0.004
−0.01
Exp. LHF
Exp. SHF
14°
14°
−0.008
−0.02
106°
108°
110° E
106°
0.04
108°
110° E
0.008
WWWI/°C
0
16°
18°
N
0.004
0
16°
WWWI/°C
0.02
18°
N
−0.004
−0.02
Exp. SWR
Exp. LWR
14°
14°
−0.008
−0.04
Fig.6. The intensity of warm water wake on diurnal timescale in the control run, Exp. TAU, Exp. LHF, Exp. SHF, Exp. SWR, and Exp.
LWR.
and the mixed layer depth is determined by a balance between
mechanical production and buoyancy production, the MoninObukhov length:
h ~τ
3/2
/Q .
(5)
Substituting Eq. (5) into Eq. (4), we can get:
∂Tl / ∂t ~ Q 2 / τ 3/2 .
(6)
So, the ratio of the SST tendency at the wake center to that at the
wake’s southeast boundary can be written as:
∆SSTc / ∆SSTb ~ (Qc / Qb ) 2 ⋅ (τ b / τ c )3/2
= (1 + ∆Qcb / Qb ) 2 ⋅ (1 + ∆τ bc / τ c )3/2 ,
(7)
where ∆SSTc ( ∆SSTb), Qc ( Qb ) and τ c (τ b) are SST tendency, net
heat flux and wind stress at the wake center (southeast boundary), ∆Qcb = Qc − Qb and ∆τ bc =τ b − τ c are the net heat flux and
wind stress differences between the center and the southeast
boundary of the wake. Since the sum of shortwave radiation,
longwave radiation, and sensible heat flux at the wake center is
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YAN Yunwei et al. Acta Oceanol. Sin., 2014, Vol. 33, No. 11, P. 55–63
the net heat flux difference can be replaced with the latent heat
flux difference. So Eq. (7) becomes:
∆SSTc / ∆SSTb ~ (1 + ∆QLcb / Qb ) 2 ⋅ (1 + ∆τ bc / τ c )3/2 ,
(8)
where ∆QLcb = QLc − QLb is the latent heat flux difference. Equa-
Heat flux/W∙m−2
00:30
800
03:30
06:30
09:30
tion (8) shows that the diurnally varying warm water wake is
determined by ∆QLcb / Qb and ∆τ bc / τ c . During the daytime,
the latent heat flux difference is far smaller than the net heat
flux at the southeast boundary ( O(∆QLcb / Qb ) << 1 ) but the wind
stress difference is larger than the wind stress at the center
( O(∆τ bc / τ c ) > 1) (Fig. 7), thus the warm water wake here is dominated by wind-induced mixing processes on a diurnal timescale.
12:30
15:30
18:30
21:30
00:30
0.05
600
0.04
400
0.03
200
0.02
0
0.01
−200
Wind stress/N∙m−2
almost equal to that at the wake’s southeast boundary (Fig. 7),
0
Fig.7. Diurnal variation of net heat flux (black line), latent heat flux (blue line); sum of shortwave radiation, longwave radiation and
sensible heat flux (red line); and wind stress (green line) at the center (solid lines) and the southeast boundary (dash-dot lines) of
the warm water wake.
4 Summary
As the SCS summer monsoon impinges on Annam Cordillera, a weak wind wake occurs off northeast Vietnam in the
SCS due to orographic blockage. Concurrently, warm water is
observed under the wind wake from satellite and hydrographic
data. The warm water wake shows obvious seasonal variability:
it forms in June, matures in July and August, and starts to decay in September. Sensitive experiments using the PWP model
demonstrate that wind-induced mixing and thermal processes
both can generate a warm water wake off northeast Vietnam,
but thermal processes (0.5°C) make a greater contribution to
the wake than wind-induced mixing processes (0.2°C).
The warm water wake also shows robust diurnal variation,
and its diurnal amplitude is determined by ∆QLcb / Qb and
∆τ bc / τ c . During the daytime, the latent heat flux difference is
far smaller than the net heat flux at the southeast boundary
(O(∆QLcb / Qb ) << 1), but the wind stress difference is larger than
the wind stress at the center ( O(∆τ bc / τ c ) > 1), thus the diurnally
varying warm water wake is dominated by wind-induced mixing processes. The conclusion is confirmed by the PWP model
experiments.
The study evaluates the relative importance of wind-induced
mixing and thermal processes on seasonal and diurnal timescales using a mixed layer model, advancing our understanding
of the physical processes of warm water wakes. However, finer
scale structures of warm water wakes are still not clear due to
the restriction of the resolution of the datasets (Pullen et al.,
2007; Signell et al., 2010). Further work with higher spatial-temporal resolution observation and/or air-sea coupled model is
required to address these finer scale features.
References
Anderson S P, Weller R A, Lukas R B. 1996. Surface buoyancy forcing and
the mixed layer of the western Pacific warm pool: Observations
and 1D model results. J Climate, 9(12): 3056–3086
Barton E D, Aŕistegui J, Tett P, et al. 1998. The transition zone of the Canary Current upwelling region. Prog Oceanogr, 41(4): 455–504
Barton E D, Basterretxea G, Flament P, et al. 2000. Lee region of Gran
Canaria. J Geophys Res, 105(C7): 17173–17193
Caldeira R M A, Marchesiello P. 2002. Ocean response to wind sheltering in the Southern California Bight. Geophys Res Lett, 29(13),
doi: 10.1029/2001GL014563
Caldeira R M A, Marchesiello P, Nezlin N P, et al. 2005. Island wakes in
the Southern California Bight. J Geophys Res, 110: C11012
Caldeira R M A, Groom S, Miller P, et al. 2002. Sea-surface signatures
of the island mass effect phenomena around Madeira Island,
Northeast Atlantic. Remote Sensing of Environment, 80(2): 336–
360
Carnes M R. 2009. Description and evaluation of GDEM-V3.0, NRLRep.
Washington D C: Nav Res Lab, NRL/MR/7330-09-9165
Casey K S, Brandon T B, Cornillon P, et al. 2010. The past, present and
future of the AVHRR Pathfinder SST program. In: Barale V, Gower
J F R, Alberotanza L, eds. Oceanography from Space. Netherlands: Springer, 273–287
Clayson C A, Curry J A. 1996. Determination of surface turbulent fluxes
YAN Yunwei et al. Acta Oceanol. Sin., 2014, Vol. 33, No. 11, P. 55–63
for the tropical ocean-global atmosphere coupled ocean-atmosphere response experiment: comparison of satellite retrievals
and in situ measurements. J Geophys Res, 101(C12): 28515–28528
Dee D P, Uppala S M, Simmons A J, et al. 2011. The ERA-Interim reanalysis: configuration and performance of the data assimilation
system. Quart J Roy Meteor Soc, 137(656): 553–597
Gentemann C L, Donlon C J, Stuart-Menteth A, et al. 2003. Diurnal
signals in satellite sea surface temperature measurements. Geophys Res Lett, 30, doi: 10.1029/2002GL016291
Gentemann C L, Minnett P J, Le Borgne P, et al. 2008. Multi-satellite
measurements of large diurnal warming events. Geophys Res
Lett, 35: L22602
Kawai Y, Wada A, 2007. Diurnal sea surface temperature variation and
its impact on the atmosphere and ocean: A review. Journal of
Oceanography, 63(5): 721–744
Lau K M, Ding Yihui, Wang Jough-Tai, et al. 2000. A report of the field
operations and early results of the South China Sea monsoon experiment (SCSMEX). Bull Am Meteor Soc, 81(6): 1261–1270
Li Jiaxun, Wang Guihua, Xie Shangping, et al. 2012. A winter warm pool
southwest of Hainan Island due to the orographic wind wake. J
Geophys Res, 117: C08036
Liu Qinyu, Jiang Xia, Xie Shangping, et al. 2004. A gap in the Indo-Pacific warm pool over the South China Sea in boreal winter: Seasonal development and interannual variability. J Geophys Res,
109: C07012
Liu Zhengyu, Yang Haijun, Liu Qinyu. 2001. Regional dynamics of seasonal variability in the South China Sea. J Phys Oceanogr, 31(1):
272–284
Price J F, Mooers C N K, van Leer J C. 1978. Observation and simulation
of storm-induced mixed-layer deepening. J Phys Oceanogr, 8(4):
63
582–599
Price J F, Weller R A, Pinkel R. 1986. Diurnal cycling: Observations and
models of the upper ocean response to diurnal heating, cooling,
and wind mixing. J Geophys Res, 91(C7): 8411–8427
Pullen J, Doyle J D, Haack T, et al. 2007. Bora event variability and the
role of air-sea feedback. J Geophys Res, 112: C03S18
Qu Tangdong. 2000. Upper-layer circulation in the South China Sea. J
Phys Oceanogr, 30(6): 1450–1460
Qu Tangdong. 2001. Role of ocean dynamics in determining the mean
seasonal cycle of the South China Sea surface temperature. J
Geophys Res, 106(C4): 6943–6955
Risien C M, Chelton D B. 2008. A global climatology of surface wind
and wind stress fields from eight Years of QuikSCAT scatterometer data. J Phys Oceanogr, 38(11): 2379–2413
Signell R P, Chiggiato J, Horstmann J, et al. 2010. High-resolution mapping of Bora winds in the northern Adriatic Sea using synthetic
aperture radar. J Geophys Res, 115: C04020
Van Camp L, Nykjaer L, Mittelstaedt E, et al. 1991. Upwelling and
boundary circulation off northwest Africa as depicted by infrared
and visible satellite observations. Prog Oceanogr, 26(4): 357–402
Wang Chunzai, Wang Weiqiang, Wang Dongxiao, et al. 2006. Interannual variability of the South China Sea associated with El Niño. J
Geophys Res, 111: C03023
Wang Dongxiao, Xie Qiang, Du Yan, et al. 2002. The 1997-1998 warm
event in the South China Sea. Chin Sci Bull, 47(14): 1221–1227
Wyrtki K. 1961. Physical oceanography of the southeast Asian waters.
San Diego, La Jolla: Univ. of Calif, 195
Xie Shangping, Xie Qiang, Wang Dongxiao, et al. 2003. Summer upwelling in the South China Sea and its role in regional climate variations. J Geophys Res, 108(C8), doi: 10.1029/2003JC001867