Elevated Mixing in the Periphery of Mesoscale Eddies in the South China Sea
QINGXUAN YANG AND WEI ZHAO
Physical Oceanography Laboratory/CIMST, Ocean University of China, and Qingdao National Laboratory for
Marine Science and Technology, Qingdao, China
XINFENG LIANG
College of Marine Science, University of South Florida, St. Petersburg, Florida
JIHAI DONG
Marine Science College, Nanjing University of Information Science and Technology, Nanjing, China
JIWEI TIAN
Physical Oceanography Laboratory/CIMST, Ocean University of China, and Qingdao National Laboratory for
Marine Science and Technology, Qingdao, China
(Manuscript received 16 November 2016, in final form 9 February 2017)
ABSTRACT
Direct microstructure observations across three warm mesoscale eddies were conducted in the northern
South China Sea during the field experiments in July 2007, December 2013, and January 2014, respectively,
along with finestructure measurements. An important finding was that turbulent mixing in the mixed layer
was considerably elevated in the periphery of each of these eddies, with a mixing level 5–7 times higher than
that in the eddy center. To explore themechanismbehind the highmixing level, this study carried out analyses
of the horizontal wavenumber spectrum of velocities and spectral fluxes of kinetic energy. Spectral slopes
showed a power law of k22 in the eddy periphery and of k23 in the eddy center, consistent with the result that
the kinetic energy of submesoscale motion in the eddy periphery was more greatly energized than that in the
center. Spectral fluxes of kinetic energy also revealed a forward energy cascade toward smaller scales at the
wavelength of kilometers in the eddy periphery. This study illustrated a possible route for energy cascading
from balanced mesoscale dynamics to unbalanced submesoscale behavior, which eventually furnished tur-
bulent mixing in the upper ocean.
1. Introduction
The South China Sea (SCS) is a large marginal sea
located west of the tropical Pacific Ocean. There is sig-
nificant, high-level turbulent mixing compared with its
neighboring Pacific Ocean; hence, the SCS has a re-
markable effect on modifications of water mass and cir-
culation both locally and remotely in the Pacific Ocean
(Tian et al. 2009; Zhao et al. 2014; Yang et al. 2016).Many
studies were carried out to explore the ocean processes
that are responsible for this intensifiedmixing in the SCS.
Various processes are known to provide energy for
mixing in the SCS from shallow shelf to deep water.
Convection caused by solar radiation is mainly re-
sponsible for diurnal variability of mixing in the mixed
layer (e.g., Yang et al. 2014b). There are many in-
ternal solitary waves in the northern SCS (e.g., Zhao
et al. 2004; Klymak et al. 2006; Lien et al. 2014), which
contribute to mixing in the shallow continental shelf
of the SCS. In the deep water, internal solitary waves
do not break; so, they do not directly provide energy
for mixing. When propagating westward to the shelf of
the SCS, these solitary waves become dissipative, ex-
hibit strong shear, and generate vigorous turbulence
due to shear instability (e.g., St. Laurent et al. 2011;
Xu et al. 2012). St. Laurent et al. (2011) reported that
the turbulent kinetic energy (TKE) dissipation rate
was elevated to 13 1024Wkg21 in the trailing edge ofCorresponding author e-mail: Jiwei Tian, [email protected]
APRIL 2017 YANG ET AL . 895
DOI: 10.1175/JPO-D-16-0256.1
� 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS CopyrightPolicy (www.ametsoc.org/PUBSReuseLicenses).
Unauthenticated | Downloaded 11/12/21 07:05 PM UTC
an internal solitary wave captured by their direct mi-
croscale observations.
At depth, internal tides play a key role in furnishing
high-level mixing in the SCS (Jan et al. 2008; Tian et al.
2009). In the northern SCS, internal tide mostly comes
from the generation site of the Luzon Strait (Zhao
2014). The energy of these tides was estimated at
;10GW based on field experiments (Tian et al. 2009)
and numerical simulations (Niwa and Hibiya 2004; Jan
et al. 2007; Jan et al. 2008). In addition, lee waves, gen-
erated by the interaction between barotropic tidal cur-
rent (or geostrophic current) and unique complex
bathymetry, were considered to play an important role
in supporting high-level mixing in the deep water of the
SCS (Buijsman et al. 2012).
In contrast to the processes mentioned above, we know
much less about mesoscale eddies and their effects on
mixing, although mesoscale eddies are abundant in the
SCS (Wang et al. 2008). Yang et al. (2014a) examined
influences of both warm and cold eddies on mixing in the
SCS. They found that warm eddies generated a negative
relative vorticity and resulted in a lower effective Coriolis
frequency, which reinforced downward-propagating, near-
inertial waves and promoted the occurrence of strong
mixing. By comparison, cold eddies reduced the down-
ward propagation and thus inhibited strongmixing. Their
results were based on comparison of parameterized
diffusivity values, and the authors did not study the mech-
anisms at work. Using McLane Moored Profiler obser-
vations in the northern SCS, Sun et al. (2016) suggested
that the generation of near-bottom, near-inertial waves
through the interaction of mesoscale eddies and unique
bottom topography was a main cause for the intense
turbulent mixing in the region.
In the present study, we found mixing was consider-
ably elevated in the periphery of warmmesoscale eddies
based on measurements from three cruises in the SCS,
each capturing a warm eddy. We also investigated po-
tential mechanisms behind the high-level mixing. In the
text that follows, the field observations, including the
instruments used and their setups, are described in
section 2. Themain results are presented in section 3. A
detailed discussion and summary are given in section 4.
2. Field observations
Aimed at understanding the effects of mesoscale eddies
on turbulent mixing in the SCS, three cruises (Fig. 1) set
out in August 2007, December 2013, and January 2014,
respectively, after careful inspection of satellite images.
During 31 July and 5August 2007, 35 stations located west
of Luzon Island were sampled. The spacing of these
stations was planned to be half a degree; however,
measurements at a few stations were not obtained suc-
cessfully. During this period, a prominent warm eddy ex-
isted west of Luzon Island, which spanned 28 in both
meridional (168–188N) and zonal (1188–1208E) directions.This warm eddy remained in this area from 10 July to
10 September based on satellite images, with its location
and size almost unchanged. The maximum sea level
anomaly (SLA) of this warm eddy exceeded 30cm at its
center. There was also a weak warm eddy to the west of
this strongwarm eddy. The stations were designed only for
FIG. 1. Sea level anomaly in the South China Sea during three
field experiments. The data were produced by the SSALTO/
DUACS and distributed by the AVISO, with support from
the CNES. The black dots in each panel indicate station loca-
tions, where both finescale and microscale measurements were
carried out.
896 JOURNAL OF PHYS ICAL OCEANOGRAPHY VOLUME 47
Unauthenticated | Downloaded 11/12/21 07:05 PM UTC
studying the strongwarmeddy,which cut through the eddy
in both zonal and meridional directions.
During 4–5 December 2013, only a zonal section was
surveyed, which cut through a warm eddy from the east
to the west. There were 11 stations in all, with a spacing
of;25 km. This strong warm eddy occurred west of the
Luzon Strait and had an elliptical shape. The SLA at
the eddy center reached 35 cm. Unlike the stationary
eddy in August 2007, this eddy moved westward con-
tinuously since its generation on 10 November based
on satellite images.
During 14–16 January 2014, a high spatial resolution
section was surveyed, which cut through a warm eddy
meridionally from 18.58 to 21.58N. There were 28 micro-
structure stations and 85 temperature stations along the
300-km-long section, resulting in an average spacing be-
tween two adjacent stations being as short as;3.5km. This
warmeddy had a diameter of;28 andwas, in fact, the same
eddy as that observed in December 2013 based on satellite
images, which propagated westward to this new location.
At all stations, hydrographic measurements were sam-
pled by deploying both conductivity–temperature–depth
system (CTD 911plus, Sea-Bird Electronics) and acoustic
Doppler current profiler (300-kHz ADCP, Teledyne RD
Instruments) to the seafloor. Microscale velocity shear
was obtained using two microstructure probes named
Turbulence Ocean Microstructure Acquisition Profiler
(TurboMAP-L, JFE Advantech Co. Ltd.) and MSS-90L
(Sea and Sun Technology). TurboMAP and MSS were
loosely tethered, free-falling instruments ballasted to
fall at a speed of 0.5–0.7m s21, and their depth ranges
were limited to 500m from the surface. The sampling
frequencies were 256 and 512Hz for TurboMAP and
MSS, respectively. The microstructure data were ana-
lyzed, and the TKE dissipation rate was estimated
following Wolk et al. (2002). A shipboard broadband
ADCP (75 kHz, Teledyne RD Instruments) worked all
the time during the three cruises, providing velocity
information for the water column in the upper 500m
(Figs. 2a–c). The bin size of the shipboard ADCP was
set to 16m, and the sampling interval was 1min; so we
can get one ensemble per minute. This resulted in ve-
locity measurements taken across each eddy center
with a horizontal resolution of;300m, since the average
ship speed was 9–10 kt (1 kt 5 0.51m s21; Figs. 2d–f).
This spatial resolution is high enough to explore sub-
mesoscale processes, which have a scale on the order of
kilometers (Callies et al. 2015).
3. Results
a. TKE dissipation rates along five sections
Figure 3 shows the distributions of TKE dissipation
rate « along the five sections of the three cruises. The
dissipation levels along the three sections in August
2007 were mostly on the order of 1029Wkg21. Some
elevated values of « ranging from 1028 to 1027Wkg21
occurred in the upper 100m of the water column. The
distributions of TKE dissipation rate along the two
sections in December 2013 and January 2014 were
significantly different from those in August 2007,
FIG. 2. (top) Cruise tracks of research vessels (blue) superimposed on sea level anomaly (cm; color). (bottom) Corresponding ship speeds.
APRIL 2017 YANG ET AL . 897
Unauthenticated | Downloaded 11/12/21 07:05 PM UTC
with higher dissipation values between 1027 and
1026W kg21, muchmore pronounced in the upper 200m.
The difference could come from two factors. The first
factor is latitudinal difference. The two sections sur-
veyed in December 2013 and January 2014 were lo-
cated north of 188N, and the three sections surveyed in
August 2007 were south of 188N. Turbulence in the
SCS generally becomes weaker from the north to the
south (Liu and Lozovatsky 2012; Yang et al. 2016),
since its energy source mainly comes from internal
tides, which are generated in the Luzon Strait and
propagate southwestward into the central SCS. The
energy flux of internal tides becomes weaker from the
north to the south along the propagating path. For
example, Zhao (2014) pointed out that the M2 internal
tide was hardly detected south of 168N.Our results here
show a consistent spatial pattern, which was about one
order weaker from north of 188N to south of 188N. The
second factor is seasonal variability, caused by seasonal
variations of wind and internal tide. Wind is generally
stronger in winter than in summer in the SCS and so is
internal tide. Liu et al. (2015) observed enhanced in-
ternal tides in winter in the northern SCS. Although
the observation periods in this study were short, we
FIG. 3. Spatial distributions of TKE dissipation rate (Wkg21; logarithmic scale) along the sections surveyed in
(a)–(c) August 2007 (18.58, 18.08, and 17.58N), (d) December 2013, and (e) January 2014.
898 JOURNAL OF PHYS ICAL OCEANOGRAPHY VOLUME 47
Unauthenticated | Downloaded 11/12/21 07:05 PM UTC
could infer that the turbulence in winter was more
active than that in summer in the SCS (Sun et al. 2016).
Based on the Osborn (1980) model, eddy diffusivity is
calculated by taking mixing efficiency as a constant.
Here, we use 0.2, as in some studies of frontal regions
(e.g., St. Laurent et al. 2012; Sheen et al. 2013). The
associated diffusivity values with these TKE dissipa-
tion rates ranged from 1024 to 1023m2 s21, consistent
with previous studies (e.g., Liu and Lozovatsky 2012;
Yang et al. 2014b).
b. TKE dissipation rate in the mixed layer
Here, we focus on spatial variation of TKE dissipa-
tion rate; specifically, we compare the rate in the eddy
center with that in the eddy periphery. Based on the
velocity data measured by the shipboard ADCP, we
defined the center and periphery for each eddy first. We
examined the velocity component perpendicular to the
section. For example, we examined the u component
for the meridional section in January 2014 and found
the velocity across the eddy increased from zero at
20.008N to maximum values of 21.0m s21 at 19.508Nand 0.9m s21 at 20.458N (Fig. 4a). For the eddy in De-
cember 2013, the y component of velocity increased
from zero at 118.508E to maximum values of21.5m s21
at 117.88E and 21.4m s21 at 119.308E (Fig. 4b). Al-
though the velocity across the eddy in August 2007 was
weak, its variation trend was the same as those in De-
cember 2013 and January 2014 (Fig. 4c). We defined
the eddy center as the area where the velocity increased
outward until it reached its maximum. So, the enclosed
SLA contour passing through the two locations with
maximum velocities was the outer edge of the eddy
center. We defined the range from the maximum ve-
locity to the outermost enclosed SLA contour as the
eddy periphery. These definitions are consistent with
the features in the cross-track gradient of potential
density based on high spatial resolution sampling (Fig. 4a).
Sharp potential density gradient corresponded to the
locations of velocity maxima well. This density gra-
dient was not examined for the eddies surveyed in
December 2013 and August 2007 because we did not
have as high-resolution measurements as those in
January 2014.
The center and periphery for the three eddies are
shown in Figs. 4d–f, respectively. With this definition,
there were 15 profiles in the eddy periphery, and 17
profiles in the eddy center. For each eddy, the dissi-
pation profiles were averaged over the eddy periphery
and the eddy center, respectively (Fig. 5). The results
show that the mean values of TKE dissipation rate
in the upper layer were obviously elevated in the
eddy periphery for all three eddies, being 5–7 times
larger than those in the eddy center. Thicknesses of
these layers with high TKE dissipation rate ranged
from 70 to 80m for these three eddies, which were
consistent with the mixed layer depths based on the
vertical profiles of temperature and potential density
(Fig. 6). Here, we defined the bottom of the surface
mixed layer using the difference from the surface den-
sity greater than 0.125 kgm23 (e.g., Rimac et al. 2016).
We conclude that the turbulent mixing in the mixed
layer was elevated in the periphery of each warm me-
soscale eddy surveyed.
FIG. 4. (a)–(c) Horizontal velocity component along the cruise track shown in Fig. 2 and (d)–(f) the defined eddy center and periphery.
Sea level anomaly (cm) is shown in color in the bottom panels, from left to right, the dates are January 2014, December 2013, and August
2007. The light blue curve in (a) is the horizontal gradient of potential density along the cruise track.
APRIL 2017 YANG ET AL . 899
Unauthenticated | Downloaded 11/12/21 07:05 PM UTC
4. Discussion and summary
a. Wind and buoyancy flux
It is important to understand what kind of dynamic
process in the mixed layer could drive such elevated
turbulent mixing in the periphery of these mesoscale
eddies. The two most obvious candidates are wind and
buoyancy flux, which are the basic driving force of
mixing in the mixed layer. Hence, we examined obser-
vation dates and times and wind and buoyancy flux for
dissipation profiles in the eddy center and periphery,
respectively (Table 1). Visually, nearly all profiles in the
eddy periphery were collected in the daytime, except for
one profile sampled at an early morning [0559 local time
(LT) on 15 January 2014]. In contrast, half of the profiles
FIG. 5. Mean dissipation profiles in the eddy periphery (black)
and in the eddy center (red) for the three warm eddies in
(a) August 2007, (b) December 2013, and (c) January 2014.
FIG. 6. Selected (a) temperature and (b) potential density pro-
files in the periphery of the three eddies observed in August 2007,
December 2013, and January 2014. The various colors indicate
profiles collected at different dates/times.
900 JOURNAL OF PHYS ICAL OCEANOGRAPHY VOLUME 47
Unauthenticated | Downloaded 11/12/21 07:05 PM UTC
in the eddy center were collected at night. The wind
during each observation period varied only slightly. To
quantify the contributions of wind and buoyancy flux to
the TKE dissipation rate in the mixed layer, we esti-
mated wind energy flux E10 and buoyancy flux J0b. Here,
E10 is given by
E105 r
aC
DU3
10 ,
where ra is air density (1.2 kgm23), drag coefficient
CD5 1.143 1023 (Large and Pond 1981), andU10 is the
wind speed at 10-m height above the sea surface. The
procedure for calculating J0b can be found in Shay and
Gregg (1986), which requires a total of 15 parameters.
Among them, seven parameters were constants, five
parameters were taken from the shipboard meteoro-
logical measurements, and the rest (three parameters)
were obtained from the daily ERA-Interim dataset, at a
time interval of 6 h. We assumed a fraction of 1%, as in
Oakey and Elliott (1982), for the wind energy flux that
entered the mixed layer to support turbulent dissipation.
Based on this assumption, the mean dissipation level
caused by bothwind and buoyancy flux during the survey
periods of the profiles shown in Table 1 was about
1.4 3 1027Wkg21. This value was comparable to the
mean TKE dissipation rate of these three eddies in the
eddy center of 2.13 1027Wkg21, but only about 16%of
the mean dissipation value of 8.7 3 1027Wkg21 in the
eddy periphery. This large difference indicates that other
process contributed to the active turbulence in the pe-
riphery of the three mesoscale eddies.
b. Submesoscale motion
Both numerical simulations and satellite observations
suggest that submesoscale motions are frequently cre-
ated from mesoscale eddies and can be detected clearly
in the eddy periphery (e.g., Capet et al. 2008a; Gaultier
et al. 2014). Submesoscale motion provides a dynamic
conduit for energy transfer toward microscale dissi-
pation and diapycnal mixing (McWilliams 2016). We
TABLE 1. Information on the profiles collected in the center and periphery for three eddies in January 2014, December 2013, and
August 2007.
Station Date Time (LT) Wind (m s21) Buoyancy flux (3 1027Wkg21)
January 2014 Center 01 15 Jan 2014 1817 9.1 2.3
Center 02 15 Jan 2014 2111 9.8 1.5
Center 03 16 Jan 2014 1211 8.8 0.0
Center 04 16 Jan 2014 0310 9.8 1.7
Center 05 16 Jan 2014 0430 9.4 1.9
Center 06 16 Jan 2014 0708 9.3 1.6
Center 07 16 Jan 2014 0918 9.0 1.1
Periphery 01 15 Jan 2014 0559 9.5 0.9
Periphery 02 15 Jan 2014 0843 10.5 0.7
Periphery 03 15 Jan 2014 1239 10.1 21.1
Periphery 04 15 Jan 2014 1527 9.4 20.9
Periphery 05 16 Jan 2014 1130 8.8 0.0
Periphery 06 16 Jan 2014 1331 8.0 20.7
Periphery 07 16 Jan 2014 1524 7.9 20.6
Periphery 08 16 Jan 2014 1719 7.9 20.1
December 2013 Center 01 4 Dec 2013 1615 7.3 20.7
Center 02 4 Dec 2013 2021 7.6 1.1
Center 03 5 Dec 2013 0252 7.6 1.0
Center 04 5 Dec 2013 0746 7.0 0.7
Periphery 01 4 Dec 2013 1001 6.8 21.4
Periphery 02 5 Dec 2013 1313 6.3 20.7
Periphery 03 5 Dec 2013 1456 6.4 20.8
Periphery 04 5 Dec 2013 1647 6.5 1.3
August 2007 Center 01 3 Aug 2007 0718 6.3 0.0
Center 02 3 Aug 2007 1602 6.3 20.7
Center 03 3 Aug 2007 2036 6.1 0.2
Center 04 5 Aug 2007 1122 6.5 0.0
Center 05 5 Aug 2007 1521 5.4 0.8
Center 06 7 Aug 2007 0120 6.1 0.9
Periphery 01 3 Aug 2007 1024 6.4 20.8
Periphery 02 7 Aug 2007 0715 5.5 1.0
Periphery 03 5 Aug 2007 0754 6.5 1.4
APRIL 2017 YANG ET AL . 901
Unauthenticated | Downloaded 11/12/21 07:05 PM UTC
suspect the submesoscale process existed in the pe-
riphery of these eddies, which can explain the elevated
TKE dissipation rates observed. To verify this hypoth-
esis, we examined the horizontal wavenumber spectra of
shipboard ADCP-measured velocities in the eddy pe-
riphery and eddy center, respectively (Fig. 7). When
performing spectral calculation, the ADCP data were
horizontally interpolated to a unified spacing of 500m
from its original resolution of ;300m. The horizontal
wavenumber spectra were then obtained by applying
fast Fourier transform to the interpolated ADCP-
measured velocities for each individual layer and fi-
nally averaged over the mixed layer depth range. The
spectra in the eddy periphery and center exhibited
a marked slope difference (Figs. 7d–i). In the eddy
center, the spectrum followed a power law of k23 in
small wavenumbers; in the eddy periphery, however,
the spectrum followed a power law of k22 in larger
wavenumbers (with a wavelength of kilometers). This
difference indicates that submesoscale motion was more
energetic in the eddy periphery than in the eddy center
(Callies and Ferrari. 2013). Large potential energy
stored in the mesoscale eddy can be released easily to
this submesoscale motion (Capet et al. 2008b). Another
piece of supporting evidence came from the Rossby
number Ro, the ratio of relative vorticity z to ambient
vorticity f based on the uniformly interpolated ADCP-
measured velocities (Fig. 8). The results show a clear
pattern that the small Rossby number existed in the
eddy center, but they were significantly elevatedwithRo
values reaching 1 in the eddy periphery. The bands with
high Ro values in the eddy periphery were indicators for
active submesoscale motions. In addition, the positive
Ro values dominated over the negative Ro values in the
eddy periphery. This asymmetry was mainly caused by
the inertial instability, as reported by Capet et al.
(2008a). In the eddy periphery, the ageostrophic balance
could be in the form of vortex filaments with a scale of
FIG. 7. (top) Selected track segments. The cyan line indicates the eddy periphery, and the black line indicates the eddy center. Hori-
zontal wavenumber spectra of velocities along the track across the (middle) eddy periphery (cyan, averaged over the two segments in the
periphery) and (bottom) eddy center (black). From left to right the dates are January 2014, December 2013, and August 2007. Power
spectra of k22 and k23 are indicated.
902 JOURNAL OF PHYS ICAL OCEANOGRAPHY VOLUME 47
Unauthenticated | Downloaded 11/12/21 07:05 PM UTC
kilometers, which could be detected from remote sens-
ing images. A composite sea surface temperature map
over 3–5 December 2013 from the MODIS level-2
product revealed this kind of filaments around the
eddy periphery (Fig. 9).
We also investigated the kinetic energy distribution of
both mesoscale and submesoscale motions along the
transection using our cruise data. The mesoscale and
submesoscale velocities were obtained by applying
20-km low-pass and high-pass filters to the shipboard
ADCP-measured velocities, respectively. The kinetic
energy was defined as KE5 (1/2)Ð 02MLD
r(u2 1 y2) dz,
where MLD is mixed layer depth. The results suggest
that submesoscale motion was significant in the eddy
periphery but relatively weak in the eddy center. The
kinetic energy of submesoscale motion accounted for up
to 74% in the eddy periphery and was only about one-
quarter in the eddy center in January 2014 (Fig. 10).
Although the high-pass-filtered data may include the
FIG. 8. Variation of Rossby number with depth along the cruise track. The dates are
(a) January 2014, (b) December 2013, and (c) August 2007. The dashed lines in each panel
separate the eddy center and the periphery.
FIG. 9. Map of composite sea surface temperature with a hor-
izontal resolution of 1 km. The data are from the MODIS level-2
product and are averaged between 3 and 5 December 2013.
Filaments around the eddy periphery are indicated by two
black arrows.
APRIL 2017 YANG ET AL . 903
Unauthenticated | Downloaded 11/12/21 07:05 PM UTC
information of internal waves and tides, the energy
variation at the order of kilometers caused by these
waves and tides is likely not significant. Similar results
were obtained for the other two eddies. For the eddy
observed in January 2014, the submesoscale motion was
greatly energized in the northern part of the eddy pe-
riphery. This was likely related to the presence of
Dongsha Island there, since the island favors the gen-
eration of submesoscale motion (Zheng et al. 2008).
To identify the direction of energy cascade, we esti-
mated the spectral flux of kinetic energy P, which is
defined as the integral of the local horizontal advective
term in the kinetic energy equation from wavenumber k
to the largest wavenumber ks, corresponding to the grid
size:
P(k)5
ðksk
2Re[uh* � (u
h� c=
huh)](k) dk ,
where uh 5 (u, y) denotes the horizontal velocity vector
measured from the shipboard ADCP, and =h is the
horizontal gradient operator. The structure � represents
FIG. 10. Distributions of kinetic energy of submesoscale and mesoscale motions along the
section in (a) January 2014, (b) December 2013, and (c) August 2007. Kinetic energy is nor-
malized by its maximum value. The dashed lines in each panel separate the eddy center and the
periphery, and the percentage of kinetic energy in each area is also shown.
904 JOURNAL OF PHYS ICAL OCEANOGRAPHY VOLUME 47
Unauthenticated | Downloaded 11/12/21 07:05 PM UTC
the horizontal spectral transform, and ( )* stands for
complex conjugate, with Re( ) indicating the real
part. Computationally, [uh* � (uh � c=huh)] is estimated as
fft(u)*fft(udu/dx1 ydu/dy)1 fft(y)*fft(udy/dx1 ydy/dy),
where fft represents fast fourier transform. For a
meridional section observed in January 2014
or August 2007, [uh* � (uh � c=huh)] is simplified as
fft(u)*fft(ydu/dy)1 fft(y)*fft(ydy/dy). For a zonal sec-
tion observed in December 2013, [uh* � (uh � c=huh)] is
simplified as fft(u)*fft(udu/dx)1 fft(y)*fft(udy/dx).
The spectral flux was calculated individually for each
layer within the mixed layer, and depth-averaged flux
was used to examine the direction of flux transport.
From the definition of spectral flux of kinetic energy,
for a given wavenumber a positive (negative) flux value
indicates a forward (inverse) kinetic energy cascade.
The spectral fluxes of kinetic energy in the eddy center
and periphery for these three eddies are shown in
Fig. 11. The positive values around larger wavenumbers
(at wavelength of kilometers) occurred in the eddy
periphery, indicating a forward energy cascade toward
smaller scales. This suggests that submesoscale motions
in the eddy periphery could be a key factor leading to
the enhanced mixing. In contrast, the energy flux in the
eddy center was transferred backward toward large
scales. However, submesoscale motion cannot directly
provide energy for turbulent mixing because its char-
acteristic length is several orders larger than that for
turbulent dissipation to occur. Submesoscale motion
acts as a bridge, which decreases the scale gap between
mesoscale and microscale processes, and expedites the
occurrence of turbulent mixing.
c. Summary
To summarize, direct microstructure observations
across three warm mesoscale eddies were obtained in
the northern SCS during field experiments, along with
finestructure measurements. An important finding is
that the turbulent mixing in the mixed layer was con-
siderably elevated in the periphery of the three eddies,
where the mixing level was 5–7 times larger than that
in the eddy center. To explore the mechanism behind
the elevated mixing, we carried out analyses of the
horizontal wavenumber spectrum and spectral fluxes
of kinetic energy. The spectral slope showed a power
law of k22 in the eddy periphery and of k23 in the
center, which was consistent with the results that the
kinetic energy of submesoscale motion in the warm
eddy periphery was more greatly energized than that
in the eddy center. Spectral fluxes of kinetic energy
also revealed a forward energy cascade toward smaller
scales at wavelength of kilometers in the eddy pe-
riphery. These results illustrate a route for energy
cascading from balanced mesoscale dynamics to un-
balanced submesoscale behavior, which eventually
furnishes turbulent mixing. However, the eddies stud-
ied in this study were all warm ones. Generation of
submesoscale motion depends on mixed layer depth.
Warm (cold) eddies can deepen (shallow) the mixed
layer depth, which is beneficial (detrimental) for de-
veloping submesoscale motion (Callies et al. 2015).
Therefore, the submesoscale process is more likely to
FIG. 11. Spectral fluxes of kinetic energy in the eddy periphery
(cyan) and eddy center (black) for the three eddies observed in
(a) January 2014, (b) December 2013, and (c) August 2007.
APRIL 2017 YANG ET AL . 905
Unauthenticated | Downloaded 11/12/21 07:05 PM UTC
be generated when encountering a warm eddy than a
cold eddy. As a result, high-level mixing is expected to
occur in the periphery of a warm eddy. In the coming
days, systematic observations involving multiscale
processes are planned to examine the impacts of warm
and cold eddies on turbulent mixing in detail and to
reveal their associated energy cascade routes.
Acknowledgments. This work is jointly supported
by the Natural Science Foundation of China (Grant
41576009), the National Key Basic Research Program
of China (Grant 2014CB745003), State Key Laboratory
of Tropical Oceanography, South China Sea Institute
of Oceanology, Chinese Academy of Science (Project
LTO1601), the NSFC-Shandong Joint Fund for Ma-
rine Science Research Centers (Grant U1406401), the
Foundation for Innovative Research Groups of the
National Natural Science Foundation of China (Grant
41521091), Global Change and Air-Sea Interaction Project
(Grants GASI-IPOVAI-01-03, GASI-03-01-01-03, and
GASI-IPOVAI-01-02), and the National High Tech-
nology Research and Development Program of China
(Grants 2013AA09A501 and 2013AA09A502). The
altimeter products are available from AVISO (www.
aviso.oceanobs.com/), the ERAdata are available through
the ECMWF (http://apps.ecmwf.int/datasets/data/interim-
full-daily/levtype5sfc/), and the MODIS products are
available through the GSFC (https://modis.gsfc.nasa.gov/
data/).
REFERENCES
Buijsman, M., S. Legg, and J. Klymak, 2012: Double-ridge in-
ternal tide interference and its effect on dissipation in
Luzon Strait. J. Phys. Oceanogr., 42, 1337–1356, doi:10.1175/
JPO-D-11-0210.1.
Callies, J., and R. Ferrari, 2013: Interpreting energy and tracer
spectra of upper-ocean turbulence in the submesoscale range
(1–200 km). J. Phys. Oceanogr., 43, 2456–2474, doi:10.1175/
JPO-D-13-063.1.
——, ——, J. M. Klymak, and J. Gula, 2015: Seasonality in sub-
mesoscale turbulence. Nat. Commun., 6, 6862, doi:10.1038/
ncomms7862.
Capet, X., J. C. McWilliams, M. J. Molemaker, and A. F.
Shchepetkin, 2008a: Mesoscale to submesoscale transition in
the California Current System. Part I: Flow structure, eddy
flux, and observational tests. J. Phys. Oceanogr., 38, 29–43,
doi:10.1175/2007JPO3671.1.
——, ——, ——, and ——, 2008b: Mesoscale to submesoscale
transition in the California Current System. Part III: Energy
balance and flux. J. Phys. Oceanogr., 38, 2256–2269, doi:10.1175/
2008JPO3810.1.
Gaultier, L., B. Djath, J. Verron, J. M. Brankart, P. Brasseur, and
A. Melet, 2014: Inversion of submesoscale patterns from a
high-resolution Solomon Sea model: Feasibility assessment.
J. Geophys. Res. Oceans, 119, 4520–4541, doi:10.1002/
2013JC009660.
Jan, S., C.-S. Chern, J. Wang, and S.-Y. Chao, 2007: Generation of
diurnal K1 internal tide in the Luzon Strait and its influence on
surface tide in the South China Sea. J. Geophys. Res., 112,
C06019, doi:10.1029/2006JC004003.
——, R.-C. Lien, and C.-H. Ting, 2008: Numerical study of
baroclinic tides in Luzon Strait. J. Oceanogr., 64, 789–802,
doi:10.1007/s10872-008-0066-5.
Klymak, J., R. Pinkel, C.-T. Liu, A. Liu, and L. David, 2006: Pro-
totypical solitons in the South China Sea.Geophys. Res. Lett.,
33, L11607, doi:10.1029/2006GL025932.
Large, W., and S. Pond, 1981: Open ocean momentum flux
measurements in moderate to strong winds. J. Phys. Oce-
anogr., 11, 324–336, doi:10.1175/1520-0485(1981)011,0324:
OOMFMI.2.0.CO;2.
Lien, R.-C., F. Henyey, B.Ma, andY.Yang, 2014: Large-amplitude
internal solitary waves observed in the northern South China
Sea: Properties and energetics. J. Phys. Oceanogr., 44, 1095–
1115, doi:10.1175/JPO-D-13-088.1.
Liu, J., Y. He, D.Wang, T. Liu, and S. Cai, 2015: Observed enhanced
internal tides in winter near the Luzon Strait. J. Geophys. Res.
Oceans, 120, 6637–6652, doi:10.1002/2015JC011131.
Liu, Z., and I. Lozovatsky, 2012: Upper pycnocline turbulence in
the northern South China Sea. Chin. Sci. Bull., 57, 2302–2306,
doi:10.1007/s11434-012-5137-8.
McWilliams, J. C., 2016: Submesoscale currents in the ocean. Proc.
Roy. Soc., A472, 20160117, doi:10.1098/rspa.2016.0117.Niwa, Y., and T. Hibiya, 2004: Three-dimensional numerical sim-
ulation of M2 internal tides in the East China Sea. J. Geophys.
Res., 109, C04027, doi:10.1029/2003JC001923.
Oakey, N. S., and J. A. Elliott, 1982: Dissipation within the surface
mixed layer. J. Phys. Oceanogr., 12, 171–185, doi:10.1175/
1520-0485(1982)012,0171:DWTSML.2.0.CO;2.
Osborn, T. R., 1980: Estimates of the local rate of vertical diffusion
from dissipation measurements. J. Phys. Oceanogr., 10, 83–89,
doi:10.1175/1520-0485(1980)010,0083:EOTLRO.2.0.CO;2.
Rimac, A., J. Storch, and C. Eden, 2016: The total energy flux
leaving the ocean’s mixed layer. J. Phys. Oceanogr., 46, 1885–
1900, doi:10.1175/JPO-D-15-0115.1.
Shay, T., and M. Gregg, 1986: Convectively driven turbulent mix-
ing in the upper ocean. J. Phys. Oceanogr., 16, 1777–1798,
doi:10.1175/1520-0485(1986)016,1777:CDTMIT.2.0.CO;2.
Sheen, K. L., and Coauthors, 2013: Rates and mechanisms of tur-
bulent dissipation and mixing in the Southern Ocean: Results
from the Diapycnal and Isopycnal Mixing Experiment in the
Southern Ocean (DIMES). J. Geophys. Res. Oceans, 118,
2774–2792, doi:10.1002/jgrc.20217.
Laurent, L., H. Simmons, T. Tang, and Y. Wang, 2011: Turbulent
properties of internal waves in the South China Sea. Ocean-
ography, 24, 78–87, doi:10.5670/oceanog.2011.96.——,A. NaveiraGarabato, J. Ledwell, A. Thurnherr, J. Toole, and
A. Watson, 2012: Turbulence and diapycnal mixing in Drake
Passage. J. Phys. Oceanogr., 42, 2143–2152, doi:10.1175/
JPO-D-12-027.1.
Sun, H., Q. Yang, W. Zhao, X. Liang, and J. Tian, 2016: Temporal
variability of diapycnal mixing in the northern South China
Sea. J. Geophys. Res. Oceans, 121, 8840–8848, doi:10.1002/
2016JC012044.
Tian, J., Q. Yang, and W. Zhao, 2009: Enhanced diapycnal mixing
in the South China Sea. J. Phys. Oceanogr., 39, 3191–3203,
doi:10.1175/2009JPO3899.1.
Wang, G., D. Chen, and J. Su, 2008: Winter eddy genesis in the
eastern South China Sea due to orographic wind jets. J. Phys.
Oceanogr., 38, 726–732, doi:10.1175/2007JPO3868.1.
906 JOURNAL OF PHYS ICAL OCEANOGRAPHY VOLUME 47
Unauthenticated | Downloaded 11/12/21 07:05 PM UTC
Wolk, F., H. Yamazaki, L. Seuront, and R. G. Lueck, 2002: A new
free-fall profiler for measuring biophysical microstructure.
J. Atmos. Oceanic Technol., 19, 780–793, doi:10.1175/
1520-0426(2002)019,0780:ANFFPF.2.0.CO;2.
Xu, J., J. Xie, Z. Chen, S. Cai, and X. Long, 2012: Enhanced
mixing induced by internal solitary waves in the South
China Sea. Cont. Shelf Res., 49, 34–43, doi:10.1016/
j.csr.2012.09.010.
Yang, Q., L. Zhou, J. Tian, and W. Zhao, 2014a: The roles of
Kuroshio intrusion and mesoscale eddy in upper mixing in the
northern South China Sea. J. Coastal Res., 30, 192–198,
doi:10.2112/JCOASTRES-D-13-00012.1.
——, J. Tian, W. Zhao, X. Liang, and L. Zhou, 2014b: Obser-
vations of turbulence on the shelf and slope of northern
South China Sea. Deep-Sea Res. I, 87, 43–52, doi:10.1016/j.dsr.2014.02.006.
——, W. Zhao, X. Liang, and J. Tian, 2016: Three-dimensional
distribution of turbulent mixing in the South China Sea.
J. Phys.Oceanogr., 46, 769–788, doi:10.1175/JPO-D-14-0220.1.
Zhao, W., C. Zhou, J. Tian, Q. Yang, B. Wang, L. Xie, and T. Qu,
2014: Deep water circulation in the Luzon Strait. J. Geophys.
Res. Oceans, 119, 790–804, doi:10.1002/2013JC009587.
Zhao, Z., 2014: Internal tide radiation from the Luzon Strait.
J. Geophys. Res. Oceans, 119, 5434–5448, doi:10.1002/
2014JC010014.
——, V. Klemas, Q. Zheng, and X.-H. Yan, 2004: Remote sensing
evidence for baroclinic tide origin of internal solitary waves in
the northeastern South China Sea. Geophys. Res. Lett., 31,L06302, doi:10.1029/2003GL019077.
Zheng, Q., H. Lin, J. Meng, X. Hu, Y. T. Song, Y. Zhang, and C. Li,
2008: Sub-mesoscale ocean vortex trains in the Luzon Strait.
J. Geophys. Res., 113, C04032, doi:10.1029/2007JC004362.
APRIL 2017 YANG ET AL . 907
Unauthenticated | Downloaded 11/12/21 07:05 PM UTC