RESEARCH ARTICLE
10.1029/2020JC016849
Key Points:• V ariations of the Kuroshio in theLuzon Strait (LS) were examinedby analyzing sectional watertemperature and sea-level anomalydata• Kuroshio-South China Sea waterexchange contributes to the opposite
seasonal variation in volumetransport from east of Luzon to
the LS• Westward propagated eddies along
21°–24°N and variations from theNorth Equatorial Current bifurcation
impact the Kuroshio in the LS
Supporting Information:
Supporting Information may be foundin the online version of this article.
Correspondence to:
X.-H. Zhu,
xhzhu@sio.org.cn
Citation:
Long, Y., Zhu, X.-H., Guo, X., Ji, F., &Li, Z. (2021). Variations of the Kuroshio
in the Luzon Strait revealed by EOF
analysis of repeated XBT data and sealevel anomalies. Journal of GeophysicalResearch: Oceans, 126, e2020JC016849.
https://doi.org/10.1029/2020JC016849
Received 6 OCT 2020
Accepted 10 JUN 2021

Variations of the Kuroshio in the Luzon Strait Revealedby EOF Analysis of Repeated XBT Data and Sea-Level
Anomalies
Yu Long1, Xiao-Hua Zhu1,2,3, Xinyu Guo1,4, Fei Ji1, and Zhiyuan Li5

1
State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of
Natural Resources, Hangzhou, China, 2Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai,
China, 3School of Oceanography, Shanghai Jiao Tong University, Shanghai, China, 4Center for Marine Environmental
Study, Ehime University, Matsuyama, Japan, 5Key Laboratory for Technology in Rural Water Management of Zhejiang,
Zhejiang University of Water Resources and Electric Power, Hangzhou, China

Abstract The Kuroshio in the Luzon Strait (KLS) has long been a focus of research for its complex
ocean dynamics; however, it is poorly understood owing to a lack of long-term in situ observations.
Empirical orthogonal function (EOF) analyses were applied to quarterly expendable bathythermographdata (XBT, with a spatial resolution of 10–20 km in the Kuroshio region and 33 km in the other regions)and sea-level anomaly (SLA) data, to examine the variations of the KLS. The first EOF modes of watertemperature and SLA represent seasonal variations. The volume transport of the KLS referring to a
700 m depth reaches its maximum in warm months (19.4 Sv, July–September) and its minimum in cold
months (17 Sv, January–March). This variation is similar to that east of Taiwan but is opposite to that eastof Luzon, although the Luzon Strait is only 2° distant from the area east of Luzon. The outflow (1.3 Sv)
from the South China Sea (SCS) to the Kuroshio in the warm months and the Kuroshio intrusion into
the SCS in the cold months are responsible for this seasonal contrast. The second EOF modes of watertemperature and SLA are related to the latitudinal movement of the Kuroshio recirculation on a timescale of 2–24 months. Lag correlation and wavelet analysis demonstrate that this mode is triggered by
the westward-propagating SLA along 21°–24°N and is affected by the downstream propagation of lowfrequency variation (mostly annual to biennial) from the North Equatorial Current bifurcation area.
Plain Language Summary Variations of the Kuroshio in the Luzon Strait (KLS) from 1999

to 2010 were examined by using sea-level anomaly (SLA) data and long-term repeated water temperature
data observed by expendable bathythermography (XBT, cast by automatic launchers equipped oncommercial ships). The decomposition (empirical orthogonal function, EOF) analysis of the two datasets
shows that the most dominant components represent seasonal variations. The volume transport of the
KLS reaches a maximum in warm months (July–September) and a minimum in cold months (January–
March), which is similar to the seasonal variation of the Kuroshio east of Taiwan but is opposite to thateast of Luzon. The Kuroshio-South China Sea (SCS) water exchange is the main cause of the reversed
seasonal variation in the Kuroshio transport between the area east of Luzon and the Luzon Strait. The
second dominant components represent the latitudinal movement of the Kuroshio recirculation and result
from the westward-propagating SLA along 21°–24°N and the downstream propagation of low-frequency
variation from the area where the North Equatorial Current bifurcates into the Kuroshio and Mindanao
Current. This work has implications for the Kuroshio-SCS water exchange and Kuroshio-eddy interactions
from east of Luzon to the east of Taiwan.

1. Introduction© 2021. American Geophysical Union.
All Rights Reserved.

LONG ET AL.

The Kuroshio transports tremendous amounts of heat, salt, and nutrients poleward and has a large impact
on adjacent marginal seas (Atkinson, 2010). Among these, the South China Sea (SCS) has been the focus ofoceanographers, owing to its frequent water exchange with the Kuroshio (e.g., Farris & Wimbush, 1996; Huet al., 2000; Liu et al., 2008; Nan et al., 2015; Shaw, 1991; Wu & Hsin, 2012). As the Luzon Strait (LS) is the
only passage connecting the Kuroshio and the SCS, the LS has received much attention.

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Previous studies conducted in the LS have involved a wide range of topics, including the Kuroshio-SCS
water exchange (Qu et al., 2000), Kuroshio-eddy interactions (Lien et al., 2014; Z. Sun et al., 2020; Zheng
et al., 2011), propagated and nonpropagated sea-level anomalies (Nan, Xue, et al., 2011; D. Yuan et al., 2006),
and variations in the Kuroshio axis (Nan, He, et al., 2011). Generally, these topics are interconnected. For
example, the eddy impinging on the Kuroshio may alter its axis, from a leaping path that flows directly
northward across the LS to a penetrating path that loops toward the SCS (D. Yuan et al., 2019). This Kuroshio-eddy interaction may induce an eddy-shedding event, which offers a route for water exchange between the Kuroshio and the SCS (Z. Zhang et al., 2017) and impacts upper layer circulation in the SCS (Linet al., 2016; Wang et al., 2019, 2020; Xu and Oey, 2014, 2015; Z. Zhang et al., 2013).
Local ocean dynamics are highly variable both spatially and temporally. For example, R. Sun et al. (2016)
reported that the Kuroshio in the LS bifurcates into two branches. The western branch varies on a seasonalbasis, and the eastern branch shows intraseasonal and interannual variability associated with mesoscaleeddies. Y. Yuan et al. (2014) demonstrated that the winter intrusion in 2008 could be attributed to monsoon
winds, and the summer intrusion in 2009 was affected by the El Niño-Southern Oscillation (ENSO). Hence,one- or two-year mooring observations at one or several stations (Andres et al., 2017; Liang et al., 2008; Lienet al., 2014; Y. Yuan et al., 2014; Z. Zhang et al., 2015) or snapshot ship-based surveys (Li et al., 1998; Janet al., 2015) showed different results and were difficult to compare.
Generally, the Kuroshio in the LS varies at different time scales owing to variations in the local wind,volume transport of the Kuroshio or its branches, subtropical gyre, or Kuroshio-eddy interactions. Nan
et al. (2015) demonstrated that the Kuroshio volume transport in the LS is large in summer but small in
winter. For the Kuroshio east of Luzon, Yaremchuk and Qu (2004) demonstrated that the annual variationin its volume transport at 18.5°N reached a maximum of ∼32 Sv in March–April and a minimum of ∼23 Sv
in October–November (Figure 14 in their paper). On the other hand, Lien et al. (2014) reported that the volume transport of the Kuroshio east of Luzon exhibited a positive anomaly in winter and a negative anomaly
in summer and autumn, based on their analysis of mooring and altimetry data. For the Kuroshio east of
Taiwan, Gilson and Roemmich (2002) demonstrated that its volume transport reaches a maximum in July
(25.6 Sv) and a minimum in April (17.2 Sv). These results show that the Kuroshio east of Luzon and east of
Taiwan exhibit different seasonal variations in their volume transport. However, the cause of this difference
is not clear.
Some studies have used sea-level anomaly (SLA) data to show long-term variations in the local eddy field(Yan et al., 2016) and Kuroshio intrusion (Nan, He, et al., 2011). Although SLA data have advantages in termsof spatial coverage and temporal resolution, they do not provide a vertical structure and have limitationsin terms of understanding the detailed dynamics. Hence, long-term repeated hydrographic observations
are a good complement to SLA data. Unfortunately, there is no repeated conductivity-temperature-depth(CTD) section in the LS. As a secondary choice, long-term repeated expendable bathythermograph (XBT)
observations conducted by commercial ships can be a good alternative for revealing the variations of theKuroshio in the LS.
Empirical orthogonal function (EOF) analysis is widely used to study the variations of the Kuroshio in the
LS and east of Taiwan. For example, Yan et al. (2016) applied EOF analysis within the region 121°–125°E
and 21°–25°N to understand how mesoscale eddies impact Kuroshio volume transport east of Taiwan. Sheu
et al. (2010) examined eddy propagation by applying EOF analysis to 10-year model output within the region 121°–128°E and 15°–28°N. Applying EOF analysis to the PCM1 mooring data set, D. Zhang et al. (2000)
demonstrated that ∼60% of the total subinertial variances in velocity and temperature in the Kuroshio eastof Taiwan were associated with volume transport and current axis variation. This study applied EOF analysis to SLA data spanning the LS region and water temperature data at the PX44 section collected by the
Scripps High Resolution XBT program from 1999 to 2010 to extract the dominant variations of the Kuroshio
in the LS, at the sea surface and at depth. Moreover, the flow structures under the modulation of the EOF
modes are shown by calculating the corresponding mean geostrophic velocity normal to the section.
In the next section, we provide a brief description of the data used in this study. Section 3 presents our results, and Sections 4 and 5 present our discussion and summary, respectively.

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Figure 1. (a) Map and location of the expendable bathythermography (XBT) casts (red dots); the black rectangle indicates the study area shown in (c). (b) XBTcasts as a function of time and longitude; (c) mean absolute dynamic topography (ADT, color shading) and surface geostrophic velocity (black arrows), WB, EB
and RG represent the west branch and the east branch of the Kuroshio and Kuroshio recirculation gyre, respectively; and (d) mean geostrophic velocity normal
to the section and water temperature (purple lines) at the PX44 section. The black dots in (c and d) denote the data points used to calculate geostrophic velocity.

2. Data
The high-resolution XBT network supported by commercial ships, which cruise at a typical speed of 21
knots and cover the LS within 1–2 days (Figure 1a), offers us a quarterly eddy-resolving view of the upper ocean thermal structure (Sprintall & Roemmich, 1999). Gilson and Roemmich (2002) analyzed datacollected along the PX44 line (PX-44HK from California to Hong Kong and PX-44Ka from California to
Kao-Hsiung) from 1993 to 2001 and suggested that the mean geostrophic transport of the Kuroshio in theupper 800 m was 22 ± 1.5 Sv, and its variation is affected by the impingement of westward-propagating
mesoscale eddies. Since then, XBT observations at the PX-44HK line continued until 2010, and there havebeen 43 cruises (Figure 1b) that provide us with a quarterly sampling, high-spatial resolution (10–20 km in
the Kuroshio and 33 km in the other region) and long duration (1999–2010) data set, allowing an analysis
of long-term variations in the Kuroshio in the LS.
Salinity data were obtained from the “Grid Point Value of the Monthly Objective Analysis using the Argodata” data set (MOAA-GPV, Hosoda et al., 2008), which has a horizontal resolution of 1°. For the period
before February 2001 (6 cruises in all) when there were no salinity data to match the XBT data, we used themonthly mean salinity in the same month of XBT data. The error caused by uncertainties in salinity is lessthan 2 cm/s for the velocity and less than 0.5 Sv for the volume transport (additional details on the error
estimation are provided in the supplemental information).

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The geostrophic velocity was calculated along the section where XBT data (117.5°–125.0°E) and MOAAGPV data (118.5°–125.0°E) overlap (i.e., 118.5°–125.0°E). When computing the geostrophic velocity, we
resampled the original temperature data at 0.2° intervals (the water temperature was first horizontally interpolated with a 0.05° interval, then filtered with a cutoff of 0.2°, and finally interpolated with an interval of0.2°) to remove abrupt variations that may have resulted from small-scale dynamics and set the no-motion
level at 700 m depth. Because the Kuroshio was confined to the upper 700 m in the LS and east of Taiwan(Andres et al., 2017; Gilson & Roemmich, 2002; Jan et al., 2015), the calculated geostrophic velocity referring to a depth of 700 m can capture most variations in the Kuroshio.
We also used SLA and absolute dynamic topography (ADT) data to obtain sea-surface information. These
data were obtained from AVISO (Archiving, Validation, and Interpretation of Satellite Oceanographic data),
with a spatial resolution of 1/4° × 1/4°, covering the period from 1993 to 2017. To determine the surface
velocity field in the LS, we collected trajectory data of the Argos drifter (1979–2019) at a depth of 15 m fromthe World Ocean Circulation Experiment—Surface Velocity Programme (WOCE-SVP). In addition, temperature and salinity data from Argo floats were used to examine the variation in water properties from east of
Luzon to the east of Taiwan.
EOF analysis was conducted within the area 115°–127°E and 15°–25°N, both for XBT and SLA data at locations deeper than 200 m. Because the EOF results depend on the choice of spatial and temporal coverage ofthe data, a sensitivity test was also performed to ensure consistency between the EOF results of daily SLA
data and quarterly XBT data. By definition, the input data (X) have the following form:



 X11

 X21
X  Xij m n X m1 

X12  X1n 
X21  X2 n  ,X m 2  X mn where n is the number of data in space and m denotes the number of data in time.
The EOF result has the form of


m

Xij 
 Pik Tkj , i 
1,2, , m; j 
1,2,  , nk 1

where k is the number of modes, Pik is the orthogonal spatial pattern and Tkj are the corresponding principal
components whose magnitudes were normalized to 1.
Supposing there is a full data set (data A) with a sufficiently short temporal interval and another data set(data B) with a long temporal interval, the question between daily SLA data and quarterly XBT data becomes whether the EOF modes derived from data A can be recognized by the EOF results of data B. For
this purpose, we set data A as daily SLA and prepared data B by resampling daily SLA using the time ofquarterly XBT.
We present the spatial pattern and principal component of the first (Figures 2a–2c) and second (Figures 2d–2f) EOF modes of SLA from daily SLA and quarterly resampled SLA corresponding to 43 XBT cruises. The
spatial patterns show consistency in the positive area west of Luzon in the first EOF mode (Figures 2aand 2b) and in the negative area east of Taiwan in the second EOF mode (Figures 2d and 2e). In the area
where the variation is small, although the corresponding spatial patterns do not match well, it does not affect the conclusion of this study. Additionally, the corresponding principal components (Figures 2c and 2f)
show similar variations, confirming that these two modes can be recognized by the sparse sample at thetime of the XBT cruise.
The potential spicity (π, a function of temperature, salinity and pressure, R. X. Huang et al., 2018) was used
to distinguish water masses and calculate their mixing ratio between the end members at the same potential
density (σ). σ-π space has advantages over traditional T-S space on account of its orthogonal nature (R. X.
Huang et al., 2018).
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Figure 2. The comparison of the first (a–c) and second (d–f) SLA-EOF results using the entire datasets (black line) and SLA data during the expendable
bathythermography (XBT) casts (red line). (a, b, d, and e) are the spatial patterns (the numbers at the end of the title are its explanation, and green dots
represent the PX44 line), and (c and f) are the corresponding principal components.

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3. Results
The surface geostrophic velocities demonstrate that the Kuroshio in the LS zonally expands from 119.0°E
to 123.0°E (Figure 1c). The geostrophic current given by XBT data shows two branches: a west branch(118.8°–121.8°E) with large volume transport (16.1 ± 6.7 Sv, mean ± standard deviation) and an east branch(122.0°–123.0°E) with small volume transport (6.1 ± 6.2 Sv, Figure 1d). The negative (southward) velocity
east of 123.0°E is a part of the local recirculation gyre (123°–124.6°E, Figures 1c and 1d) and has a volumetransport of −2.6 ± 9.1 Sv. In addition, the negative velocity between the two Kuroshio branches is the island wake of the Kuroshio, and its maximum velocity exceeds 0.1 m/s. The volume transport integrated overthe entire Kuroshio region (118.8°–124.6°E) is 18.7 ± 7.2 Sv, in which the large standard deviation of 7.2 Svsuggests that the Kuroshio in the LS is highly variable.
The EOF analysis decomposes the standing variations in both the SLA data and water temperature into orthogonal modes. More than 40% of the total variance in the SLA (33.2% and 9.5%, respectively) and sectional
water temperature (27.5% and 14.6%, respectively) can be explained by the first two EOF modes.
The first EOF modes of water temperature and SLA represent seasonal variations (Figure 3). The large
positive value (3°C) in the spatial pattern of water temperature (Figure 3a) in the surface layer corresponds
to seasonal heating and cooling. In the subsurface layer, the spatial pattern is horizontally diverse, with positive values east of 121.0°E and negative values at a depth of ∼250 m between 120.0°E and 121.0°E. Further
to the west, the spatial pattern is vertically reversed, with positive values in the upper 600 m and negativevalues in the deeper layer. The spatial pattern of ADT (Figure 3b) exhibits an in-phase variation with amaximum value west of Luzon where the Luzon cold eddy was reported (Z. Huang et al., 2019). The corresponding principal component consists of seasonal variation and weak interannual variation (Figure 3c).
Both the climatological daily mean of the SLA principal component and the climatological monthly meanof the water temperature principal component (Figure 3d) indicate peaks in warm months (July, August,and September) and troughs in cold months (January, February, and March), confirming the nature ofseasonal variation.
To understand the first EOF mode, we prepared composite maps. The mean water temperature and geostrophic velocity for the period when the principal component of the first EOF mode of water temperature
was larger (smaller) than its positive (negative) standard deviation was calculated. The mean ADT for theperiod when the principal component of the first EOF mode of the SLA was larger (smaller) than its positive(negative) standard deviation was calculated. As shown in Figure 3d, the former occurs in warm months,
while the latter occurs in cold months. The isotherms in the Kuroshio region are steeper in warm months(Figure 4a) than in cold months (Figure 4d), indicating that the Kuroshio is stronger in warm months thanin cold months. The SLA shows that the Kuroshio in warm months flows northward and leaps over the LS,along with a strong Kuroshio recirculation east of the LS (Figure 4b). In cold months (Figure 4e), part of
the Kuroshio penetrates into the SCS, the LS cold eddies become apparent and the local Kuroshio recirculation gyre becomes weaker, in accordance with our general knowledge on the seasonal variation in local
circulation.
The composite geostrophic velocity shows that the currents normal to the section, both northward andsouthward, are stronger and narrower in warm months than in cold months (Figures 4c and 4f). Consequently, the two branches and local recirculation gyre exhibit a larger volume transport through the section
in warm months than in cold months (Table 1). In addition, the net volume transport over the entire Kuroshio region also attains its maximum in warm months and minimum in cold months. The large standard
deviation in the volume transport indicates that the currents in the LS are highly variable. The flow width
exhibits an opposite variation compared with the volume transport; that is, when the volume transport islarge, the flow width is small, and vice versa.
The second EOF mode of water temperature (Figure 5a) represents a longitudinally reversed variation. Its
subsurface maximum is located at a depth of ∼100 m, with a maximum of −2°C on the SCS side and a maximum of 4°C on the Pacific side. The second EOF mode of the SLA (Figure 5b) presents a large magnitude
on the Pacific side that has a latitudinal seesaw structure. The location of the PX44 line allows the XBT toobserve a negative phase on the SCS side and a positive phase on the Pacific side.

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Figure 3. (a) Spatial pattern of the first EOF mode of water temperature (contour interval: 0.2°C for negative values and 1°C for positive values; the thick blackline represents 0°C). (b) Spatial pattern of the first empirical orthogonal function (EOF) mode of SLA (contour interval: 0.05 m). Blue dots represent the PX44
line. (c) The principal components of the first modes of water temperature (red line) and sea-level anomaly (SLA, blue line). The dashed line represents positive
and negative standard deviations. (d) The seasonal cycle of two principal components in (c). The thick blue line (shading) represents the daily mean (standard
deviation) of the principal component of the first EOF mode of SLA. The thick red line (shading) represents the monthly mean (standard deviation) of theprincipal component of the first EOF mode of water temperature. The values around the thick red lines are the data numbers used for calculating the mean andstandard deviation.

We also prepared a composite map of water temperature and ADT for their second EOF mode (Figure 6).
The isotherms exhibit a larger slope in the Kuroshio region during the period when the principal component of the second EOF mode of water temperature is larger than its positive standard deviation (Figure 6a)compared with the period when the principal component of the second EOF mode of water temperature issmaller than its negative standard deviation (Figure 6d). When the principal component of the second EOF
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Figure 4. Composite maps. (a) Mean water temperature when the principal component of the first empirical orthogonal function (EOF) mode of watertemperature is larger than its standard deviation. (b) Mean absolute dynamic topography when the principal component of the first EOF mode of sea-levelanomaly (SLA) is larger than its standard deviation. (c) Mean velocity when the principal component of the first EOF mode of water temperature is larger thanits standard deviation. (d–f) are the same as (a–c) but for the period when the principal component of each EOF mode is smaller than minus one standard
deviation. The blue dots in (b and e) indicate the XBT stations. The horizontal thick red and blue lines at the upper edge in (c and f) denote the area for
calculating the volume transport of Kuroshio's two branches and of the Kuroshio recirculation.

mode of the SLA is larger (smaller) than its positive (negative) standard deviation (Figures 6b and 6e), theKuroshio recirculation is located to the east of LS (east of Taiwan) and intensifies the Kuroshio northeastof Luzon (east of Taiwan).
The composite map of geostrophic velocity normal to the section shows that the Kuroshio is strong (weak)in the period when the principal component of water temperature is larger (smaller) than the positive
Table 1
Volume Transport (VT), Flow Width (FW, Which is Projected to the Direction Normal to the Current Derived From theAngle Between the Section and the Current, According to Figures 4b and 4e) and the Longitudinal Range (RG) at Section
PX44 in the Warm and Cold months

Warm months

Cold months

VT (Sv)

West branch

East branch

Island wake

Recirculation gyre

Kuroshio region

20.3 ± 5.6

12.7 ± 10.5

−4.0 ± 13.0

−9.6 ± 11.8

19.4 ± 7.3

FW (km)

129

65

43

302

539

RG (°E)

120.4–121.8

122–122.8

121.8–122

122.8–125.6

120.4–125.6

VT (Sv)

13.6 ± 7.1

8.3 ± 10.3

−1.4 ± 8.3−3.6 ± 9.1

17.0 ± 8.3

FW (km)

236

172

43

238

711

RG (°E)

119.2–121.4

121.8–123.6

121.4–121.8

123.6–126

119.2–126

Note. Positive values denote a northward current.

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Figure 5. (a) Spatial pattern of the second empirical orthogonal function (EOF) mode of temperature (contour interval: 0.5°C, the thick black line represents
0°C). (b) Spatial pattern of the second EOF mode of sea-level anomaly (SLA) (contour interval: 0.1 m). Blue dots represent the PX44 line. (c) The principal
components of the second modes of water temperature (red line) and SLA (blue line). The dashed line represents positive and negative standard deviations.

(negative) standard deviation (Figures 6c and 6f). The volume transport (Table 2) exhibits the same variation except that the Kuroshio recirculation shows an opposite variation to the two Kuroshio branches. Thisis likely caused by the differences in Kuroshio recirculation and its intersection with the PX44 line during the periods corresponding to the two composite maps; that is, when the Kuroshio recirculation moves
southward (northward) and the PX44 line passes through its center (edge), the intersection areas of both
positive and negative velocities are large (small), and the volume transport at the section becomes larger(smaller).

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Figure 6. Composite maps, the same as Figure 4 but for their second empirical orthogonal function (EOF) mode.

4. Discussion
4.1. Definition of the Kuroshio and Uncertainties in the Volume Transport
To guarantee the continuity of the Kuroshio volume transport from east of Luzon to the LS and from LS tothe east of Taiwan, it is necessary to discuss the definition of the Kuroshio. Andres et al. (2017) demonstrated that the northward increase in the net volume transport (∼6 Sv) of the Kuroshio in the upper 1000 mfrom east of Luzon (14 Sv) to the east of Taiwan (21 Sv) is consistent with the value given by Sverdruptransport theory (∼8 Sv east of Luzon and ∼14 Sv east of Taiwan). The Kuroshio volume transport in theLS estimated in this study is 18.7 Sv, which is within the range of the above two volume transports given by
Andres et al. (2017) for east of Luzon and east of Taiwan.
Table 2
Volume Transport (VT), Flow Width (FW, Which is Projected to the Direction Normal to the Current Derived From the Angle Between the Section and Current,
According to Figures 6b and 6e) and the Longitudinal Range (RG) at Section PX44 in the Warm and Cold months

Positive standard deviation

Negative standard deviation

VT (Sv)

West branch

East branch

Island wake

Recirculation gyre

Kuroshio region

18.6 ± 4.5

8.0 ± 6.8

−3.1 ± 8.4−1.8 ± 10.5

21.8 ± 5.9

FW (km)

258

65

65

432

819

RG (°E)

119–121.6

122–122.8

121.6–122

122.8–123.8

119–123.8

VT (Sv)

18.4 ± 9.4

3.9 ± 2.4

−2.2 ± 8.0−11.6 ± 7.8

8.5 ± 7.1

FW (km)

279

86

43

173

668

RG (°E)

119–121.8

122–123

121.8–122

123–125.4

119–125.4

Note. Positive values denote a northward current.

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However, the volume transport of the Kuroshio is sensitive to the choice of the level of no motion. Althougha 700 m depth is the historical choice (Gilson & Roemmich, 2002) and has been validated, a thorough investigation is still needed. In the real ocean, the velocity at the level of no motion is not zero. While the flow
structure changes little if this velocity is added to the entire water column, volume transport may not. This
could be the largest uncertainty in the estimation of the volume transport. Hence, it is essential to examine
the extent to which this uncertainty affects the net volume transport of the Kuroshio in the LS.
Some long-term mooring array observations provide information on the mean velocity of the entire water column. Z. Zhang et al. (2015) showed that the mean velocities at 700 m at their LS2 (121°13.78’E
20°42.46’N) and LS3 (121°11.65’E 20°22.16’N) mooring stations were ∼0.02 and 0.04 m/s, respectively (Figure 5 in their study; these two sites are near the PX44 section). Considering the orientation of the PX44
section, their normal components to the section do not exceed 0.02 m/s. It is also confirmed that the velocity
normal to the section in the depth range of 500–700 m does not exceed 0.02 m/s. On the other hand, thehorizontal change in the isotherm (Figures 1d, 4a, 4d, 6a, and 6d) is the smallest at a 700 m depth. Hence,
considering the mean state and composite maps, a 700 m depth is a proper choice for the reference level at
the PX44 section. For this choice, the uncertainty in the volume transport of the west branch is 0.02 m/s ×
900 m × 200 km = 3.6 Sv and that of the east branch is 0.02 m/s × 900 m × 100 km = 1.8 Sv. Since both ofthem are smaller than their standard deviation, the variations explained by EOF analysis are robust signals.
4.2. Seasonal Variation of the Kuroshio Volume Transport From Upstream to Downstream
Regions
In this study, seasonal variations of the Kuroshio at the PX44 line were examined by analyzing the first EOF
mode of the SLA and sectional water temperature. Here, we extend this examination by comparing our
results with seasonal variations in the upstream Kuroshio east of Luzon, the downstream Kuroshio east of
Taiwan, and the Kuroshio-SCS water exchange.
From a climatological view, the volume transport of the Kuroshio east of Taiwan reaches its maximumin July (25.6 Sv) and minimum in April (17.2 Sv, 0–800 m, Gilson & Roemmich, 2002), whereas that eastof Luzon is maximal (∼32 Sv) in March–April and minimal (∼23 Sv) in October–November (entire watercolumn, Yaremchuk & Qu, 2004). To understand the seasonal variations in the volume transport of theKuroshio from east of Luzon to the east of Taiwan, we examined the surface velocity derived from the Argos
drifter data (Figure 7). As drifter data reflect the current at a depth of 15 m, the Ekman component is lessthan 10 cm/s (Centurioni et al., 2004). The surface velocity from drifters is generally consistent with thecomposite maps of geostrophic current (Figures 4b and 4e) but reveals some additional detailed features.
In warm months (Figure 7a), the Kuroshio recirculation gyre is located slightly south, intersects more with
the PX44 line, and thus has a larger northward and southward volume transport normal to the PX44 line
than those in cold months (Table 1). In addition, there is a northward current near northwestern Luzon
in warm months (Figure 7a), which was also reported by a model study (Shaw & Chao, 1994) and in situ
observations from a single cruise (Q. Yang et al., 2010). Before passing through the PX44 line, it enters theKuroshio (Figure 7a). The trajectory of the drifters that entered the LS also confirmed this northward flow
(Figure 7b).
In cold months (Figure 7c), the Kuroshio recirculation gyre is located slightly north and has little intersection with the PX44 line. Part of the Kuroshio flows westward before passing through the PX44 line and is
known as the Kuroshio intrusion into the SCS.
Therefore, the Kuroshio intrusion into the SCS and the northerly location of the Kuroshio recirculation incold months and the northward current from the northwest of Luzon into the Kuroshio and the southerly
location of the Kuroshio recirculation in warm months qualitatively explain the seasonal variation in thevolume transport normal to the PX44 line.
Because the volume transport of the northward current from the SCS into the Kuroshio in the LS and east of
Taiwan in warm months has not been reported in terms of the mean state, we analyze the water properties
to provide a general estimation of its contribution. The water temperature and salinity profiles observedby the Argo floats in the region east of Taiwan and east of Luzon and in the SCS (Figure 8) were used to
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Figure 7. Mean velocity (arrows) derived from the trajectory data of the Argos drifter in a grid with a bin size of 0.25 × 0.25° in (a) warm months (July, August,and September) and (c) cold months (January, February, and March). Colored scatter denotes the magnitude of velocity. The width of the arrows increases withthe data number in each grid that was classified as <10, 10–40 and >40. The magenta line denotes the PX44 section. (b and d) Trajectory of the Argos drifterthat passed through the Luzon Strait (LS, 120°–122°E, 18°–23°N) in (b) warm months and (d) cold months. The red line in (b) denotes the trajectory of Argos
drifters that originated in the South China Sea (SCS) and entered the Kuroshio in the LS and east of Taiwan.

calculate the potential spicity. The potential spicity-potential density relation shows that in warm months,
the potential spicity of the Kuroshio east of Taiwan (blue line in Figure 8b) is away from that east of Luzon(red line in Figure 8b) compared with those in cold months (Figure 8e).
Assuming that the seawater of the Kuroshio east of Luzon and the SCS are the two end members, the mixing
ratio of the SCS water to the Kuroshio east of Taiwan is 20% in the cold months and 30% in the warm months
within the layer lighter than 25.1 (additional details for this estimation can be found in the supplemental
information). This implies that the portion of the upstream Kuroshio water in the Kuroshio east of Taiwan
is 10% larger in warm months than in cold months. If we consider the conditions in the cold months as areference level of water mixing (the flow pattern shows that the Kuroshio directly intrudes the SCS in thecold months), the volume transport of the SCS water in the Kuroshio east of Taiwan should be ∼1.3 Sv,
which is 10% of the net volume transport (12.8 Sv) within layers lighter than 25.1 in the Kuroshio region.
Hence, to what extent the Kuroshio-SCS water exchange contributes to the difference of the Kuroshio volume transport east of Luzon and in the LS can be considered. We obtain 18.1 Sv (19.4–1.3 Sv) by subtracting
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Figure 8. (a and d) Temperature-salinity relationships; (b and e) potential spicity-potential density relationships. The thin line represents the mean potential
spicity; (c and f) spatial distribution of Argo profiles (black dot) and its trajectory (gray line). Blue/red/black dots in (a, d) and (b, e) and blue/red/black lines in(b and e) correspond to the profiles within the blue/red/black rectangle in (c). (a–c) are for the warm months. (d–f) are for the cold months.

the SCS outflow in the warm months, and 20–23.5 Sv (17 Sv + 3–6.5 Sv) by adding the volume transport of
the Kuroshio intrusion into the SCS in the cold months. With this exclusion of the impacts of Kuroshio-SCS
water exchange, the resultant seasonal variation of the Kuroshio transport east of Taiwan is consistent withthat east of Luzon.
On the other hand, the mesoscale eddy is reported to have seasonality in this region (Chang & Oey, 2011) because of the instability of the Subtropical Counter Current - North Equatorial Current system (STCC-NEC).
Qiu et al. (2014) suggested that the eddy kinetic energy within the STCC band (18°–28°N and 135°–160°E)
reaches a maximum in May and April. Considering the necessary time of mesoscale eddies propagatingfrom the STCC band to LS (8.4 months, calculated by the distance between 120° and 135°E and the mean
propagating speed of 7.2 cm/s, G. Yang et al., 2013), the eddies within the STCC band in May will impact
the Kuroshio in the LS in the cold months. G. Yang et al. (2013) demonstrated that the latitudinal band of
the LS is dominated by cyclonic eddies, which tend to decrease Kuroshio volume transport as it approaches
the Kuroshio. Hence, mesoscale eddies could be responsible for the decrease in Kuroshio volume transport
in the LS in cold months. Second, the wind work is also reported to impact the Kuroshio path in the LS.
Recently, Z. Sun et al. (2020) demonstrated that the local wind contributes 66% to the Kuroshio loop current(the transition state between the leaping path and leaking path when the mesoscale eddies interact with the
Kuroshio). However, how the local wind affects the preference of the Kuroshio path in the LS is not quantitatively discussed. Hence, it is necessary to quantify their contribution in the future.
The Kuroshio favors the leaping (leaking) path in the warm (cold) months when the Kuroshio volumetransport is small (large) east of Luzon but large (small) in the LS. These results imply that the “teapot
effect” (Sheremet, 2001, when the upstream volume transport of a western boundary current increased, it
tends to leap over the gap, and vice versa) cannot explain the seasonal variation of the Kuroshio in the LS.
Hence, the mesoscale eddy and local wind work should be considered.
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Figure 9. Lag correlation coefficients. (a–i) SLA leads the second empirical orthogonal function (EOF) mode of sea-level anomaly (SLA) by 405, 360, …., 45,
and 0 days. The time interval between two neighboring panels is 45 days.

4.3. Cause of the Second EOF Mode
To determine the cause of the second EOF mode, we calculated the lag correlation coefficient between theprincipal component of the second EOF mode of the SLA and the SLA over an area extending to 170°E(Figure 9). It is likely that the SLA propagating at the latitude band (21°–25°N) where Taiwan is located(Figure 9b), rather than that at the latitude band (18°–21°N) where the LS is located, is responsible for thesecond EOF mode of the SLA. The SLA is first recognized at ∼150°E (Figures 9b and 9c) and propagates
westward to ∼130°E (Figures 9h and 9i), where another SLA with an opposite sign appears to its south. The
two SLAs then continue their westward propagation and increase their intensity. Once they reach the areas
east of Taiwan and east of LS (Figure 9j), the southern one combines with the local recirculation gyre east of
the LS and therefore affects its strength. A positive correlation indicates that an anticyclonic eddy intensifies
the recirculation gyre east of the LS and that a cyclonic eddy weakens it. The origin of the SLA at ∼150°E is
located at the Subtropical Counter Current eddy zone (18°–25°N, 135°–170°E), where intense eddy activity
is caused by variations in the baroclinic instability growth rate under different wind stress curl conditions(Qiu & Chen, 2010).
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Figure 10. (a) Wavelet power (color shading) of the second empirical orthogonal function (EOF) mode of sea-level anomaly (SLA) during 1993–2017. (b)Wavelet coherence (color shading) between latitudes of the North Equatorial Current Bifurcation (obtained from the SLA in the region of 12°–14°N, 127°–
130°E, calculated by Yp(t) = 11.9 − 0.13 × SLA(t) following Qiu and Chen (2010)) and the second EOF mode of SLA during 1993–2017. The downward arrow
indicates that the NECB leads the second EOF mode of SLA by 90° in phase. Leftward arrow indicates antiphase. The 5% significance level against red noise isshown as a thick contour, and the cone of influence (COI) where edge effects might distort the picture is shown as a lighter shade in (a) and (b). (c and d) are
the bandpass filtered SLA-EOF2 and NECBL. (c) shows the 1-year variation, and SLA-EOF2 is shifted to the left by a duration of 110 days; (d) shows the 1.5–2year variation, and SLA-EOF2 is shifted to the left by a duration of 240 days.

Kim and Yin (2004) showed that the North Equatorial Current bifurcation latitude (NECBL) influences theKuroshio volume transport east of Luzon, and Qu et al. (2004) showed that it further influences transport
through the LS. Hence, the intensification of the positive correlation from 130°E (Figure 9i) to the area eastof Luzon (Figure 9j) probably also depends on the variation in the NECBL.
To confirm this hypothesis, we extend the temporal range of SLA data to a period from 1993 to 2017 andapplied EOF analysis to obtain more information from its principal component (and to reduce edge effects
in the wavelet analysis to the 1999–2010 SLA data) by wavelet analysis. The results of EOF analysis for thislonger data set are similar to those presented before for the SLA data from 1999 to 2010 (Figure 2f). Wavelet
analysis of the principal component of the second EOF mode of the SLA during 1993–2017 demonstrated adominant time scale of 2–24 months (Figure 10a).
The cross wavelet to the NECBL (Figure 10b) shows that annual (1995–1998, 2003–2007, and 2009–2010)
and biennial (1996–2000) variations in the second EOF mode of SLA are correlated with the variations in the
NECBL. These periods are consistent with the wavelet results of NECBL (Figure 8a of Qiu & Chen, 2010),
which is driven by the wind stress curl in the area of 12°–14°N and 140°–170°E. The annual (biennial) variation in the NECBL leads the second EOF mode of SLA by 90°–135° (135°) in phase, implying that there
is a 3–4.5 (8) month phase difference between the second EOF modes of the SLA and NECBL (Figures 10c
and 10d). Using the phase difference as the propagation duration from the area of the NECBL to the LS,that is, from 13°N, 128.5°E to 20°N, 122°E, the phase speed is 9 (4.5 month)–13 (3 month) cm/s. Although
this speed is larger than the propagation speed of mesoscale eddies at this latitude, considering that the
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background flow is northwestward, it is reasonable to believe that the phase difference (3–4.5 months) is apossible propagation duration.
Chang and Oey (2012) suggested that the Philippines-Taiwan Oscillation (PTO, the difference of areal mean
wind stress curl between the Taiwan pole, 22°–27°N, 155°–180°E, and Philippines pole, 8°–13°N, 130°–
155°E) plays an important role in the thermocline variation east of Taiwan and east of Philippines. By definition, the variation in the wind stress curl over each pole generates a series of mesoscale eddies throughSverdrup convergence and consequently modulates the Kuroshio intrusion into the SCS and circulation and
SLAs in the SCS. However, the maximal correlation coefficient is −0.38, for which the PTO leads SLA-EOF2
by 8 months (figure not shown). Indeed, the eddy activity at the latitudinal band of Taiwan is affected bywinds over the Taiwan pole, and the NECBL is affected by winds over the Philippines pole. The time for themesoscale eddies propagating from their origin to the east of Taiwan (∼1 year) and the time for the variationfrom NECBL to the east of Luzon (3–4.5 months) are different. The phase difference explains the small lag
correlation between the PTO and SLA-EOF2. Since the spatial pattern of the second EOF mode of SLA issimilar to that of the PTO, this mode can be considered the local ocean response to the PTO.
For our case, a plausible explanation is as follows. When the anticyclonic eddy arrives east of Taiwan (Figures 11a–11e), it strengthens the Kuroshio recirculation gyre east of Taiwan. As the Kuroshio recirculation
gyre is driven by the low-potential vorticity water from the low-latitude region, the low-potential vorticity
water from the NECBL region is also favorable for the strengthening of the Kuroshio recirculation. Whenthe cyclonic eddy impinges on the Kuroshio east of Taiwan (Figures 11f–11j), the downstream advectionof the low-potential vorticity water is blocked, and part of it accumulates and forms a positive SLA east of
Luzon. Consequently, a strong Kuroshio recirculation gyre forms there. Therefore, the westward-propagating SLAs at the latitude band (21°–23°N) are the main cause of the second EOF mode of the SLA, and thelatitudinal variation of the NECB in the upstream region is also a responsible factor.

5. Summary
High-resolution XBT observations collected quarterly during 1999–2010 and daily satellite altimetry data
were examined using EOF analyses to investigate the variations of the Kuroshio in the LS. The first EOF
modes of water temperature and SLA correspond to seasonal variations. The net volume transport of thetwo branches of the Kuroshio and Kuroshio recirculation and that over the entire Kuroshio region (theintegration of the two branches, island wake, and Kuroshio recirculation) is maximal in warm months(19.4 ± 7.3 Sv) and minimal in cold months (17.0 ± 8.3 Sv). This variation is in contrast to the Kuroshio eastof Luzon but similar to that east of Taiwan. Although the upstream Kuroshio east of Luzon shows different
seasonal variations, the outflow from the SCS (∼1.3 Sv in the layer lighter than 25.1 ) to the Kuroshio in
the LS contributes to the seasonal maximum in the Kuroshio volume transport in the warm months, andthe Kuroshio intrusion into the SCS in the southern area of the LS contributes to the seasonal minimum inthe cold months.
The second EOF modes represent the latitudinal movement of the Kuroshio recirculation. This mode is
directly modulated by the westward-propagating SLAs at the latitude band of Taiwan, and the variation in
NECBL acts as a secondary factor. The anticyclonic (cyclonic) eddy impinging on the Kuroshio east of Taiwan results in the strengthening (weakening) of the Kuroshio and Kuroshio recirculation. For the strengthening case east of Taiwan, the advection of low-potential vorticity water from the North Equatorial Current
bifurcation region also positively contributes, while for the weakening case, the low-PV water accumulates
to the east of LS, and thus, the Kuroshio recirculation there is enhanced. In addition, the wavelet analysis
suggested that this mode is also probably modulated by low-frequency variations from the NECBL.
Although the XBT and SLA data were used to show dominant variations in the LS, the data have limitationsin showing the time series of the Kuroshio volume transport. We examined four-dimensional variational
ocean reanalysis (FORA) data (Usui et al., 2017) and found offshore (eastward) movement of the Kuroshio
during February 2004 September 2004 and November 2009 (more details are provided in the supporting
information). Since the conclusions of this study are through analyzing observations, future studies basedon FORA output are expected.

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Figure 11. Lag composites of positive (left panels, a–e) and negative (right panels, f–j) events. For any two adjacentpanels, the upper panel leads the lower by 30 days. The bottom one is the composite without a time lag.

Data Availability Statement
The data used in this study are available from the Scripps High Resolution XBT program (www-hrx.ucsd.
edu), AVISO (http://marine.copernicus.eu), JAMSTEC (http://www.jamstec.go.jp/ARGO/argo_web/argo/?page_id=83&lang=en), NOAA (https://www.aoml.noaa.gov/phod/gdp/index.php) and FORA
(http://synthesis.jamstec.go.jp/FORA/).

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Acknowledgments
This study was supported by the
National Natural Science Foundation ofChina (grant numbers 41920104006 and
41776107); China Postdoctoral Science
Foundation (grant 2020M681969);
Scientific Research Fund of SIO, MNR(JZ2001, JB2105); Project of State Key
Laboratory of Satellite Ocean Environment Dynamics, SIO (SOEDZZ2106);and grants from the Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT) to the project on
Joint Usage/Research Center—Leading
Academia in Marine and Environmental Research (LaMer). X. Guo thanks
support by JSPS KAKENHI grant number (17H02959). The authors appreciate
three anonymous reviewers for theirhelpful comments and Dr. L. Oey for his
suggestion on revision. Y. Long thanks
Dr R.B. Ding for his suggestions in therevision work.

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References
Andres, M., Mensah, V., Jan, S., Chang, M.-H., Yang, Y.-J., Lee, C. M., et al. (2017). Downstream evolution of the Kuroshio's time-varying
transport and velocity structure. Journal of Geophysical Research: Oceans, 122, 3519–3542. https://doi.org/10.1002/2016JC012519
Atkinson, L. P. (2010). Western Boundary Currents. In K. K. Liu, L. Atkinson, R. Quiñones, & L. Talaue-McManus. (Eds.), Carbon
and nutrient fluxes in continental margins. Global change—The IGBP series (pp. 121–169). Berlin, Heidelberg: Springer. https://doi.
org/10.1007/978-3-540-92735-8_3
Centurioni, L., Niiler, P. P., & Lee, D. (2004). Observations of inflow of Philippine Sea surface water into the South China Sea through theLuzon Strait. Journal of Physical Oceanography, 34(1), 113–121. https://doi.org/10.1175/1520-0485(2004)034<0113:OOIOPS>2.0.CO;2
Chang, Y.-L., & Oey, L.-Y. (2011). Interannual and seasonal variations of Kuroshio transport east of Taiwan inferred from 29 Years of TideGauge Data. Geophysical Research Letters, 38(8), L08603. https://doi.org/10.1029/2011GL047062
Chang, Y.-L., & Oey, L.-Y. (2012). The Philippines-Taiwan Oscillation: Monsoon-like interannual oscillation of the subtropical-tropical western North Pacific wind system and its impact on the ocean. Journal of Climate, 25(5), 1597–1618. https://doi.org/10.1175/
JCLI-D-11-00158.1
Farris, A., & Wimbush, M. (1996). Wind-induced Kuroshio intrusion into the South China Sea. Journal of Oceanography, 52, 771–784.
https://doi.org/10.1007/BF02239465
Gilson, J., & Roemmich, D. (2002). Mean and temporal variability in Kuroshio geostrophic transport south of Taiwan (1993–2001). Journalof Oceanography, 58, 183–195. https://doi.org/10.1023/A:1015841120927
Hosoda, S., Ohira, T., & Nakamura, T. (2008). A monthly mean dataset of global oceanic temperature and salinity derived from Argo float
observations. JAMSTEC Report of Research and Development, 8, 47–59. https://doi.org/10.5918/jamstecr.8.47
Hu, J., Kawamura, H., Hongi, H., & Qi, Y. (2000). A review on the currents in the South China Sea: Seasonal circulation, South China Sea
Warm Current and Kuroshio intrusion. Journal of Oceanography, 56, 607–624. https://doi.org/10.1023/A:1011117531252
Huang, R. X., Yu, L.-S., & Zhou, S.-Q. (2018). New definition of potential spicity by the least square method. Journal of Geophysical Research: Oceans, 123, 7351–7365. https://doi.org/10.1029/2018JC014306
Huang, Z., Zhuang, W., Hu, J., & Huang, B. (2019). Observations of the Luzon Cold Eddy in the northeastern South China Sea in May 2017.
Journal of Oceanography, 75(5), 415–422. https://doi.org/10.1007/s10872-019-00510-z
Jan, S., Yang, Y. J., Wang, J., Mensah, V., Kuo, T.-H., Chiou, M.-D., et al. (2015). Large variability of the Kuroshio at 23.75°N east of Taiwan.
Journal of Geophysical Research: Oceans, 120, 1825–1840. https://doi.org/10.1002/2014JC010614
Kim, & Yin, Y. (2004). Seasonal and interannual variations of the north equatorial current bifurcation in a high-resolution OGCM. Journalof Geophysical Research, 109(C3), C03040. https://doi.org/10.1029/2003JC00201310.1029/2003jc002013
Li, L., Nowlin, W. D., Jr, & Su, J. L. (1998). Anticyclonic rings from the Kuroshio in the South China Sea. Deep-Sea Research Part I, 45,
1469–1482. https://doi.org/10.1016/S0967-0637(98)00026-0
Liang, W. D., Yang, Y. J., Tang, T. Y., & Chuang, W. S. (2008). Kuroshio in the Luzon Strait. Journal of Geophysical Research, 113, C08048.
https://doi.org/10.1029/2007JC004609
Lien, R.-C., Ma, B., Cheng, Y.-H., Ho, C.-R., Qiu, B., Lee, C. M., & Chang, M.-H. (2014). Modulation of Kuroshio transport by mesoscaleeddies at the Luzon Strait entrance. Journal of Geophysical Research: Oceans, 119, 2129–2142. https://doi.org/10.1002/2013JC009548
Lin, Y.-C., Oey, L.-Y., Wang, J., & Liu, K. K. (2016). Rossby waves and eddies observed at a temperature mooring in northern South China
Sea. Journal of Physical Oceanography, 46(2), 517–535. https://doi.org/10.1175/JPO-D-15-0094.1
Liu, Q. Y., Arata, K., & Su, J. L. (2008). Recent progress in studies of the South China Sea circulation. Journal of Oceanography, 64, 753–762.
https://doi.org/10.1007/s10872-008-0063-8
Nan, F., He, Z., Zhou, H., & Wang, D. (2011). Three long-lived anticyclonic eddies in the Northern South China Sea. Journal of GeophysicalResearch, 116, C05002. https://doi.org/10.1029/2010JC006790
Nan, F., Xue, H., Chai, F., Shi, L., Shi, M., & Guo, P. (2011). Identification of different types of Kuroshio intrusion into the South China Sea.
Ocean Dynamics, 61(9), 1291–1304. https://doi.org/10.1007/s10236-011-0426-3
Nan, F., Xue, H., & Yu, F. (2015). Kuroshio intrusion into the South China Sea: A review. Progress in Oceanography, 137, 314–333. https://doi.org/10.1016/j.pocean.2014.05.012
Qiu, B., & Chen, S. (2010). Interannual-to-decadal variability in the bifurcation of the north equatorial current off the Philippines. Journalof Physical Oceanography, 40(11), 2525–2538. https://doi.org/10.1175/2010JPO4462.1
Qiu, B., Chen, S., Klein, P., Sasaki, H., & Sasai, Y. (2014). Seasonal mesoscale and submesoscale eddy variability along the north Pacificsubtropical countercurrent. Journal of Physical Oceanography, 44(12), 3079–3098. https://doi.org/10.1175/JPO-D-14-0071.1
Qu, T., Kim, Y. Y., Yaremchuk, M., Tozuka, T., Ishida, A., & Yamagata, T. (2004). Can the Luzon Strait transport play a rolein​​conveying the impact of ENSO to the South China Sea? Journal of Climate, 17, 3644–3657. https://doi.org/10.1175/​
1520-0442(2004)0172.0.CO10.1175/1520-0442(2004)017<3644:clstpa>2.0.co;2
Qu, T., Mitsudera, H., & Yamagata, T. (2000). Intrusion of the North Pacific waters into the South China Sea. Journal of Geophysical Research, 105, 6415–6424. https://doi.org/10.1029/1999JC900323
Shaw, P.-T. (1991). The seasonal variation of the intrusion of the Philippine Sea Water into the South China Sea. Journal of GeophysicalResearch, 96(C1), 821–827. https://doi.org/10.1029/90JC02367
Shaw, P. T., & Chao, S. Y. (1994). Surface circulation in the South China Sea. Deep-Sea Research, 41(11–12), 1663–1683. https://doi.
org/10.1016/0967-0637(94)90067-1
Sheremet, V. A. (2001). Hysteresis of a Western Boundary Current Leaping across a Gap. Journal of Physical Oceanography, 31, 1247–1259.
https://doi.org/10.1175/1520-0485(2001)031<1247:HOAWBC>2.0.CO;2
Sheu, W. J., Wu, C. R., & Oey, L. (2010). Blocking and westward passage of eddies in the Luzon Strait. Deep-sea Research Part II-topical
Studies in Oceanography, 57(19), 1783–1791. https://doi.org/10.1016/j.dsr2.2010.04.004
Sprintall, J., & Roemmich, D. (1999). Characterizing the structure of the surface layer in the Pacific Ocean. Journal of Geophysical Research,
104, 23297–23311. https://doi.org/10.1029/1999JC900179
Sun, R., Wang, G., & Chen, C. (2016). The Kuroshio bifurcation associated with islands at the Luzon Strait. Geophysical Research Letters,
43, 5768–5774. https://doi.org/10.1002/2016GL069652
Sun, Z., Zhang, Z., Qiu, B., Zhang, X., Zhou, C., Huang, X., et al. (2020). Three-Dimensional structure and interannual variability of the Kuroshio loop current in the northeastern South China Sea. Journal of Physical Oceanography, 50, 2437–2455.
https://doi.org/10.1175/jpo-d-20-0058.1

18 of 19

Journal of Geophysical Research: Oceans

10.1029/2020JC016849

Usui, N., Wakamatsu, T., Tanaka, Y., Hirose, N., Toyoda, T., Nishikawa, S., et al. (2017). Four-dimensional variational ocean reanalysis:
A 30-year high-resolution dataset in the western north pacific (FORA-WNP30). Journal of Oceanography, 73(2), 205–233. https://doi.
org/10.1007/s10872-016-0398-5
Wang, Q., Zeng, L., Chen, J., He, Y., Zhou, W., & Wang, D. (2020). The linkage of Kuroshio intrusion and mesoscale eddy variability in thenorthern South China Sea: Subsurface speed maximum. Geophysical Research Letters, 47(11), e2020GL087034. https://doi.org/10.1029/2020GL08703410.1029/2020gl087034
Wang, Q., Zeng, L., Shu, Y., Liu, Q., & Wang, D. (2019). Interannual variability of South China Sea winter circulation: Response to Luzon strait transport and El Nino wind. Climate Dynamics, 54(1/2), 1145–1159. https://doi.org/10.1007/s00382-019-05050-210.1007/
s00382-019-05050-2
Wu, C.-R., & Hsin, Y.-C. (2012). The forcing mechanism leading to the Kuroshio intrusion into the South China Sea. Journal of GeophysicalResearch, 117, C07015. https://doi.org/10.1029/2012jc007968
Xu, F.-H., & Oey, L.-Y. (2014). State analysis using the local ensemble transform Kalman filter (LETKF) and the three-layer circulation
structure of the Luzon Strait and the South China Sea. Ocean Dynamics, 64(6), 905–923. https://doi.org/10.1007/s10236-014-0720-y
Xu, F.-H., & Oey, L.-Y., (2015). Seasonal SSH Variability of the Northern South China Sea. Journal of Physical Oceanography, 45(6), 1595–
1609. https://doi.org/10.1175/JPO-D-14-0193.1
Yan, X., Zhu, X.-H., Pang, C., & Zhang, L. (2016). Effects of mesoscale eddies on the volume transport and branch pattern of the Kuroshio
east of Taiwan. Journal of Geophysical Research: Oceans, 121, 7683–7700. https://doi.org/10.1002/2016JC01203810.1002/2016jc012038
Yang, G., Wang, F., Li, Y., & Lin, P. (2013). Mesoscale eddies in the northwestern subtropical Pacific Ocean: Statistical characteristics andthree-dimensional structures. Journal of Geophysical Research: Oceans, 118, 1906–1925. https://doi.org/10.1002/jgrc.20164
Yang, Q., Tian, J., & Zhao, W. (2010). Observation of Luzon strait transport in summer 2007. Deep Sea Research Part I: Oceanographic Research Papers, 57, 670–676. https://doi.org/10.1016/j.dsr.2010.02.004
Yaremchuk, M., & Qu, T. (2004). Seasonal variability of the large-scale currents near the coast of the Philippines. Journal of Physical Oceanography, 34(4), 844–855. https://doi.org/10.1175/1520-0485(2004)034<0844:svotlc>2.0.co;2
Yuan, D., Han, W., & Hu, D. (2006). Surface Kuroshio path in the Luzon Strait area derived from satellite remote sensing data. Journal ofGeophysical Research, 111, C11007. https://doi.org/10.1029/2005JC003412
Yuan, D., Song, X., Yang, Y., & Dewar, W. K. (2019). Dynamics of mesoscale eddies interacting with a western boundary current flowing by
a gap. Journal of Geophysical Research: Oceans, 124, 4117–4132. https://doi.org/10.1029/2019JC014949
Yuan, Y., Liao, G., Yang, C., Liu, Z., Chen, H., & Wang, Z. (2014). Summer Kuroshio Intrusion through the Luzon Strait confirmed from observations and a diagnostic model in summer 2009. Progress in Oceanography, 121, 44–59. https://doi.org/10.1016/j.pocean.2013.10.003
Zhang, D., Lee, T. N., Johns, W. E., Liu, C., & Zantopp, R. J. (2000). The Kuroshio East of Taiwan: Modes of variability and relationship to Interior ocean mesoscale eddies. Journal of Physical Oceanography, 31(4), 1054–1074. https://doi.org/10.1175/
1520-0485(2001)031<1054:TKEOTM>2.0.CO;2
Zhang, Z., Zhao, W., Qiu, B., & Tian, J. (2017). Anticyclonic eddy sheddings from Kuroshio loop and the accompanying cyclonic eddy in thenortheastern South China Sea. Journal of Physical Oceanography, 47, 1243–1259. https://doi.org/10.1175/JPO-D-16-0185.1
Zhang, Z., Zhao, W., Tian, J., & Liang, X. (2013). A mesoscale eddy pair southwest of Taiwan and its influence on deep circulation. Journalof Geophysical Research: Oceans, 118, 6479–6494. https://doi.org/10.1002/2013JC008994
Zhang, Z., Zhao, W., Tian, J., Yang, Q., & Qu, T. (2015). Spatial structure and temporal variability of the zonal flow in the Luzon Strait.
Journal of Geophysical Research: Oceans, 120, 759–776. https://doi.org/10.1002/2014JC010308
Zheng, Q., Tai, C., Hu, J., Lin, H., Zhang, R., Su, F., & Yang, X. (2011). Satellite altimeter observations of nonlinear Rossby eddy-Kuroshio
interaction at the Luzon Strait. Journal of Oceanography, 67(4), 365–376. https://doi.org/10.1007/s10872-011-0035-2

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