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Diagnostic Analysis of the Response of Volume Transport through the Tsushima Strait
to the Eddy-Induced Variations in the Kuroshio Region
JUNYONG ZHENG,a,b,c XINYU GUO ,c,d YASUMASA MIYAZAWA,d HAIYAN YANG,e MIN YANG,e XINYAN MAO,a,bf
AND WENSHENG JIANG
aFrontier Science Center for Deep Ocean Multispheres and Earth System (FDOMES) and Physical Oceanography Laboratory,
Ocean University of China, Qingdao, Chinab
College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao, ChinacCenter for Marine Environmental Studies, Ehime University, Matsuyama, Japand
Application Laboratory, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japane
Graduate School of Science and Engineering, Ehime University, Matsuyama, Japanf
Key Laboratory of Marine Environment and Ecology, Ministry of Education, Ocean University of China, Qingdao, China(Manuscript received 2 August 2022, in ﬁnal form 18 August 2023, accepted 22 August 2023)
ABSTRACT: The Tsushima Strait (TS) is the sole passage for volume transport from the East China Sea to the Sea ofJapan. To date, the process underlying the interannual variability in volume transport remains unclear. In this study, 27-yr(1993–2019) reanalysis data from the Japan Coastal Ocean Predictability Experiment 2 (JCOPE2M) system and in situ/satellite observations were employed to understand this process. The results suggest that TS transport was generally high in
1999, 2003/04, and 2010 and low in 1995/96, 2005/06, 2008, and 2014/15. The sea level anomaly (SLA) outside the entranceof the TS, that is, the upstream TS forcing, dominates the interannual TS transport variation. A high SLA pumps morewater into the Sea of Japan via the TS, and vice versa. By synthesizing JCOPE2M reanalysis data and satellite observations, further analysis revealed that cyclonic mesoscale eddies from the subtropical countercurrent (STCC) could be responsible for this high SLA by reducing Kuroshio transport, enhancing Kuroshio intrusion across the shelf, and increasing
the SLA around the upstream TS region. The reverse was true for anticyclonic STCC eddies. Variability in the Kuroshio
intrusion southwest of Kyushu induces variations in the TS transport on an interannual time scale.
KEYWORDS: Boundary currents; Eddies; Transport; Interannual variability

1. Introduction
The Tsushima Strait (TS) is a shallow channel (;130-m depth)
between the Japanese Archipelago and the Korean Peninsula in
the northwest Paciﬁc Ocean that serves as an aortic valve for volume transport, heat, salt, nutrients, and sediments from the East
China Sea to the Sea of Japan (Fig. 1). The TS plays a major rolein air–sea interactions, upper oceanic circulation, deep ventilation,and sediment texture in the Sea of Japan (Hirose et al. 2009;
Isobe 2020; Yabe et al. 2021; Zou et al. 2021; Jung et al. 2022).
Over the past several decades, bottom- and ship-mounted
acoustic Doppler current proﬁlers (ADCPs), as well as submarine cables, have been used for in situ measurements of TS
transport (Teague et al. 2002; Lyu and Kim 2005; Takikawa
et al. 2005; Fukudome et al. 2010). Long-term observations indicate that for the mean state, approximately 2.3–2.7 Sv (1 Sv ;106 m3 s21) of water volume is carried into the Sea of Japan

Supplemental information related to this paper is available
at the Journals Online website: https://doi.org/10.1175/JPO-D-220164.s1.
M. Yang’s current affiliation: State Key Laboratory of Satellite
Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, China.
Corresponding author: Xinyu Guo, guo.xinyu.mz@ehime-u.ac.jp

across the TS (Table 1). This transport is generally fed by two
components: the Kuroshio onshore intrusion, which is always
triggered in northeast Taiwan and southwest Kyushu (Guo et al.
2006), and the Taiwan Strait transport (Fang et al. 1991). Afterbeing pumped into the Sea of Japan, almost the same amountof water ultimately treks out through two northeast straits ofthe sea: the Tsugaru Strait (1.5 Sv; Nishida et al. 2003) and theSoya Strait (0.91 Sv; Ohshima et al. 2017) (Fig. 1). This inﬂow–
outﬂow system is known as the Japan Sea Throughﬂow (Kidaet al. 2016, 2020). Moreover, substantial seasonal variation (amplitude of 0.8–1.4 Sv) in the TS transport has been addressed,featuring a minimum in winter (January) and double maxima
during late summer and early autumn (August and October;
Teague et al. 2002; Fukudome et al. 2010).
In addition to these observations, many efforts have been
made to explore the mechanisms driving transport throughthe TS. Minato and Kimura (1980) conducted a pioneering
study by using theoretical linear, steady, and barotropic models. It was suggested that sea level differences between thestraits (high in the TS and low in the Tsugaru and Soya Straits),
which were primarily forced by wind stress curl over the North
Paciﬁc, dominated TS transport, which dynamically connects local transport through two marginal seas with remote forcing in
the open ocean. This persistent interstrait sea level difference
was veriﬁed by Toba et al. (1982) using tidal gauge station records. Ohshima (1994) proposed that this sea level difference
was owing to a geopotential anomaly between the subtropicaland subpolar gyres. Motivated by the coastally trapped wave

DOI: 10.1175/JPO-D-22-0164.1
Ó 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding
reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).
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FIG. 1. Bathymetry of the northwest Paciﬁc (shading). Sevenlines indicate transects at the Taiwan Strait, northeast Taiwan, PN
Line, Tokara, Tsushima, Tsugaru, and Soya Straits for volume transport calculations. Three outlined boxes denote the areas outside theentrance of the TS and exits of the Tsugaru and Soya Straits forarea-averaged sea level estimations.

theory, Ohshima (1994) employed a numerical model with amore realistic coastal topography to illustrate how TS transportmatures under a prescribed sea level difference. Using a basinscale general circulation model incorporating realistic topography and wind forcing over the North Paciﬁc, Tsujino et al.
(2008) improved the study of Ohshima by determining the sea
level difference within the model itself, rather than using artiﬁcial speciﬁcations.
The mechanisms underlying the seasonal variability in TS
transport have also been extensively studied. Lyu and Kim(2005) suggested that seasonality is related to sea level variations outside the entrance of the TS and the exits of the
Tsugaru and Soya Straits. The local wind stress can change
above sea level through Ekman transport and may be important in the fall (Moon et al. 2009; Cho et al. 2013); however, it
contributes only a small part (;0.14 Sv) to the entire seasonal
amplitude, and the seasonality should therefore be ascribed toremote forcing from the open ocean (Seung et al. 2012; Kida
et al. 2016). Based on the around-island integral constraint in

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Yang (2007), the upstream TS forcing owing to variations in the
Kuroshio along the Japanese coast was emphasized by Ma et al.
(2012). This constraint suggests that as the Kuroshio transport
increases, the TS transport must increase to respond to the enhanced frictional torque, and vice versa. Conversely, Tsujino
et al. (2008) pointed out that the seasonal signal was excited byvariations in the alongshore wind stress in the subpolar region,
particularly over the Okhotsk Sea; that is, the downstream region was the origin of the seasonal signals. This downstream signal can penetrate the Soya Strait, travel through the Sea ofJapan to the TS, and regulate the TS transport. Recently, Kida
et al. (2016) used the above mechanism with a two-layer idealized model and highlighted the central roles of baroclinic-wave
ﬁltering in the Soya Strait and barotropic sea level adjustmentsin the Sea of Japan.
In comparison with previous studies on the mean state andseasonal variation of TS transport, interannual time scales
have received less attention. Although Lyu and Kim (2005)
proposed that the sea level outside the straits of the Sea ofJapan, especially outside the TS, is of great importance on aninterannual time scale, the underlying mechanism remains unknown and unexplored. Where did the interannual signal originate? How did this signal regulate the TS transport? Such
unsettled questions remind us that exploration of the mechanism behind the interannual ﬂuctuation in TS transport is stillin its initial phase (Kida et al. 2020). As stated above, the TS
is the only inﬂow valve intruding into the Sea of Japan, andits interannual transport variation could have an intense effect on local ocean hydrodynamics as well as regional climatechange. Therefore, further studies are required to clarify thisissue.
In this study, we investigated the interannual variations in TS
transport using 1993–2019 reanalysis data from the Japan
Coastal Ocean Predictability Experiment 2 (JCOPE2M) system,
focusing on its underlying dynamics. To evaluate the model performance of the JCOPE2M, 11-yr (1997–2007) observations ofTS transport reported by Fukudome et al. (2010) were adopted
in this study. In addition, 27-yr (1993–2019) sea level height(SSH) data obtained using satellite altimeters were also used toprovide observational support for this study.
The remainder of this study is organized as follows. Section 2
introduces the JCOPE2M reanalysis data, observation data,
and data preprocessing methods. Section 3 describes the interannual variation in TS transport from the JCOPE2M andcompares it with observations from Fukudome et al. (2010).
Section 4 interprets the possible mechanisms driving interannual TS transport variation. Section 5 provides further
discussion of the above mechanisms, and section 6 summarizes the study.

TABLE 1. Comparison of long-term averaged transport through the TS observed in previous studies and modeled by the JCOPE2M. NE
or SW respectively indicate that the transport was observed in the northeastern or southwestern transect inside the TS.
References

PeriodObs (Sv)

Model (Sv)

Teague et al. (2002)
Takikawa et al. (2005)
Fukudome et al. (2010)

May 1999–Mar 2000
Feb 1997–Aug 2002
Feb 1997–Feb 2007

2.3 (NE); 2.7 (SW)
2.64
2.65

2.57
2.63
2.67

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ZHENG ET AL.

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2. Data and methods
a. JCOPE2M reanalysis data
In this study, the original daily data were obtained from the reanalysis products of the data-assimilative ocean model JCOPE2M(Miyazawa et al. 2017), which is based on the Princeton
Ocean Model with a generalization of sigma coordinate(POMgcs; Mellor et al. 2002). This model covers the western North Paciﬁc (10.58–628N, 1088E–1808) with a horizontal resolution of (1/12)8 in both the zonal and meridional
directions and a vertical resolution of 46 sigma levels. The
model was forced by 6-hourly atmospheric forcing at thesea surface obtained from the National Centers for Environmental Prediction–National Center for Atmospheric Research(NCEP–NCAR) reanalysis data. Through the one-way nesting
method, the open boundary conditions were determined using anouter model that encompasses the whole North Paciﬁc (308S–
628N, 1008E–908W) with a coarser horizontal resolution of ;1=48
and 21 vertical levels. Speciﬁcally, remote sensing and in situ observation data (including sea surface height anomaly, sea surface
temperature, and in situ temperature/salinity proﬁle data) wereassimilated into the JCOPE2M model system using the multiscalevariational scheme method, which provides effective reproduction and resolution of oceanic variabilities in the western North
Paciﬁc (Miyazawa et al. 2017). Interannual time scale studies associated with variations in the Kuroshio and Oyashio have beensuccessfully conducted with these reanalysis results (e.g., Changet al. 2015; Hasegawa et al. 2021). Comprehensive informationon the JCOPE2M model settings and advances can be found in
Miyazawa et al. (2009, 2017, 2019).
The JCOPE2M model is operated by the Japan Agency for
Marine-Earth Science and Technology (JAMSTEC), and its
data are available upon request. The main variables usedwere sea surface elevation, zonal and meridional velocity
components, temperature, and salinity at 46 sigma levels, witha 1-day interval spanning from 1993 to 2019.

b. Observation data
1) IN SITU OBSERVATION OF VOLUME TRANSPORTTHROUGH THE TS
Fukudome et al. (2010) documented the values of themonthly averaged volume transport through the TS for 11 years
from 1997 to 2007 February (see their Table 4). This observation was measured using a 300-kHz ADCP mounted on a ferryboat, which was shuttled around both sides of the TS. The
time-sampling interval of this equipment was set to 24 s, andthe vertical resolution was 8 m from a depth of 14 m to thesea bottom. More details about how to handle the ADCP current dataset in TS transport are described in their study aswell as in Takikawa et al. (2005).
In this study, the aforementioned in situ observation data
were employed to assess the JCOPE2M model results on
long-term mean and interannual scales. The missing values(March–June 2004) for these observational data were ﬁlledwith climatological mean values. It is worth mentioning thatthese observational data were actually an independent dataset and were not assimilated into the JCOPE2M.

To examine the variability in the sea surface elevation illustrated by the JCOPE2M data, we used the SSH data processed by Ssalto/Duacs and distributed by the Copernicus
Marine and Environment Monitoring Service (CMEMS).
This dataset is available from 1993 to the present, with a daily
temporal interval and 1=48 horizontal resolution. The data period analyzed here was from 1993 to 2019, covering the same
period as that of the JCOPE2M reanalysis data mentioned
above.

c. Methods to extract interannual signals
The original JCOPE2M reanalysis and satellite observation
data are available in a daily format. We ﬁrst averaged thesevariables monthly to obtain provisional data at a 1-month resolution. In general, month-sampled data from 1993 to 2019combined the long-term mean ﬁeld, linear trend, seasonal variations, interannual variations, and other factors that could be
regarded as noise in this study. To derive pure interannual signals, the following conventional procedures were performed:
1) the long-term mean value was subtracted from provisional
monthly data; 2) the linear trend captured by the least squares
was erased; 3) the climatologically averaged seasonal variations from 1993 to 2019 were excluded; and 4) a 13-monthrunning mean for noise canceling was applied. Following theabove procedures, we obtained the interannual componentsof sea surface elevation, horizontal velocities, temperature,and salinity at 1-month intervals for the following analysis.

3. Interannual variability in the volume transport
through the TS
Volume transport through the TS was calculated using the
JCOPE2M reanalysis data by horizontal and vertical integrationof the velocity normal to a zonal transect (red line in Fig. 1).
During the reanalysis period (1993–2019), the monthly TS
transport from the JCOPE2M model had a time-averaged
value of 2.67 Sv and a standard deviation of 0.61 Sv (Fig. 2a).
Almost the same amount of transport ﬂowed out of the Sea ofJapan through the Tsugaru Strait (1.63 Sv) and Soya Strait(0.97 Sv) (blue lines in Fig. 1 denote transect locations).
When compared with the observations of Fukudome et al.
(2010) (red line in Fig. 2a), the modeled TS transport averaged over the observational period (1997–2007) was also
2.67 Sv, which was very close to their observed results (2.64 Sv).
The standard deviation of the model results (0.63 Sv) was
slightly higher than that of the observed results (0.41 Sv). The
root-mean-square error (RMSE) and Pearson correlation coefﬁcient (at 95% conﬁdence level, similarly hereinafter) were 0.39
Sv and 0.8, respectively, indicating that the JCOPE2M model
results are consistent with the observations. The other two observations were compared with the model transport through theTS, as shown in Table 1. The negligible discrepancy further conﬁrmed the reliability of the JCOPE2M model results. Figure 2cshows the power spectrum of the transport through the TS
based on the JCOPE2M modeling results shown in Fig. 2a. Two
considerable peaks were present at 6 months and 1 year,

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FIG. 2. (a) Time series of monthly volume transport through the TS from the JCOPE2M (blueline) and the observation of Fukudome et al. (2010) (red line). (b) As in (a), but for interannual
variation. Dark red or blue shadings respectively indicate values higher than 0.15 Sv or lower
than 20.15 Sv lasting at least 3 months. Light red or blue shadings also respectively indicate values higher than 0.15 Sv or lower than 20.15 Sv but lasting less than 3 months. (c) Spectrum ofmonthly TS transport [blue line in (a)]; PSD means power spectrum density. (d) As in (c), but
for interannual TS transport [blue line in (b)].

indicating that the intraseasonal and seasonal signals for the TS
transport series were prominent.
After excluding the mean ﬁeld, linear trend, and seasonal variations, we applied a 13-month running mean to the monthly reanalysis data and captured distinct variations in the TS transport
on an interannual time scale (blue line in Fig. 2b). The power
spectrum of this interannual TS transport is shown in Fig. 2d. The
disappearance of the 6-month and 1-yr peaks indicates that themethods used to extract the interannual signal are suitable for
the TS transport series. The interannual TS transport ranges from20.30 to 0.48 Sv with a standard deviation of 0.15 Sv. Speciﬁcally,
three relatively higher value events (.one deviation, lasting atleast 3 months) occurred in 1999, 2003/04, and 2010 (dark red
shading) and four relatively lower value events (,minus one deviation, lasting at least 3 months) occurred in 1995/96, 2005/06, 2008,
and 2014/15 (dark blue shading). We also extracted the interannual variation in TS transport from observational data (red line in
Fig. 2b). The standard deviation of the model results (0.17 Sv)

over the observation period was similar to that of the observations(0.14 Sv). The RMSE and correlation coefﬁcient were 0.14 Sv and
0.63, respectively, suggesting that the JCOPE2M reanalysis results
overestimated or underestimated the observations. Consideringthat the main objective of this study was to reveal the dynamic
mechanisms of interannual variation, the above discrepancy was
acceptable, and we will continue additional analyses using the
JCOPE2M reanalysis data.

4. Dynamical mechanisms for the interannual variability
a. Variation of interstrait sea level anomaly differences
Figure 3a shows the spatial distribution of the annual meansea level from the JCOPE2M model averaged over the entire
reanalysis period (1993–2019). A distinct sea level difference
was found between the East China Sea (from 20.2 to 0.1 m)
and the region east of the Tsugaru and Soya Straits (fromBrought to you by China Ocean University | Unauthenticated | Downloaded 11/06/23 02:44 PM UTCNOVEMBER 2023

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FIG. 3. (a) Annual mean (1993–2019) sea level of the northwest Paciﬁc from the JCOPE2M
data. (b) Interannual variations of TS transport (blue line), SLA difference of the Tsushima–
Tsugaru (red dashed line), and Tsushima–Soya (red solid line) boxes. The r means correlation
coefﬁcient with TS transport. (c) The SLA of the Tsushima (black solid line) and Soya (black
dashed line) boxes. The r means correlation coefﬁcient with the SLA difference of the
Tsushima–Soya box [red solid line in (b)]. (d) Linear regression of interannual TS transport andTsushima–Soya SLA difference.

20.5 to 20.3 m). The Sea of Japan (from 20.4 to 20.2 m)
connects the sea level of these two regions, serving as abridge. We selected three box areas outside the TS, Tsugaru,
and Soya Straits (Fig. 1) to quantify the sea level differencesamong these three straits. The annual mean sea level of thebox in front of the TS was 20.06 m, and those behind the
Tsugaru and Soya Straits were 20.38 and 20.44 m, respectively. Correspondingly, the sea level difference in the
Tsushima–Tsugaru box and Tsushima–Soya box was 0.32
and 0.38 m, respectively. As mentioned in section 1, thedifferences in the interstrait sea level forced a net inﬂow
from the East China Sea to the Sea of Japan, that is, volume transport through the TS.
Following this idea, we examined whether these differenceswere still controlling TS transport on an interannual timescale. Figure 3b shows the time series of sea level anomaly(SLA) difference of Tsushima–Tsugaru and Tsushima–Soyaboxes (dashed and solid red lines, respectively). The ranges ofthese two SLA difference series were 0.13 and 0.09 m, respectively (see right red vertical axis in Fig. 3b). The correlation coefﬁcient between the Tsushima–Tsugaru SLA difference andTS transport (blue line in Fig. 3b) was 0.35. This weak correlation was not surprising because the Tsugaru Strait is located between the northern (Hokkaido) and southern (Honshu) islands,
and multisource signals propagating along these two islands
blended and tended to blur the sea level of the Tsugaru box(Seung et al. 2012; Kida et al. 2016). In contrast, the correlation
coefﬁcient for the Tsushima–Soya SLA difference with TS
transport reached 0.72, which suggests that on the interannual
time scale, the Tsushima–Soya SLA difference has a more dynamic fetter with transport through the TS. Linear regression
analysis indicated that an increase of 1 cm in the Tsushima–

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Soya SLA difference resulted in an increase of 0.06 Sv for TS
transport (Fig. 3d).
To identify which region dominates the Tsushima–Soya
SLA difference, we examined the individual SLA time seriesof the Tsushima and Soya boxes (solid and dashed lines inFig. 3c, respectively). SLA series ranges in the Tsushima andSoya boxes were 0.08 and 0.02 m, respectively. Intriguingly,
the Tsushima SLA series was quite close to the series of the
Tsushima–Soya SLA difference (solid red line in Fig. 3b),with a correlation coefﬁcient of 0.74. In contrast, the Soya
SLA series was out of phase, with a correlation coefﬁcient of
20.17. Moreover, the standard deviation of the SLA series waslarger in the Tsushima (1.71 3 1022 m) than that in the Soya
Strait (0.49 3 1022 m), suggesting that the SLA in the Tsushimabox made the greatest contribution to the standard deviation of
the Tsushima–Soya SLA difference (1.88 3 1022 m). These results indicate that the interannual signal for TS transport variation originated from the southern region, that is, the upstream
TS region. Lyu and Kim (2005) also highlighted the dominantrole of SLA changes around the TS in the interannual variability of TS transport.

b. Variation of the SLA in the upstream TS region
As discussed in section 4a, the SLA of the Tsushima box is
an important factor controlling TS transport on an interannual scale. To detect the potential agent that changes the
SLA of the Tsushima box and TS transport, we ﬁrst examined
the lag correlation between the SLA of the Tsushima box andthat of the other areas. Figures 4a–f show the correlation coefﬁcient distributions with the SLA of the Tsushima box lagging
from 25 to 0 (same time) months. From 25 months to thesame time, the positive correlation inside the East China Sea
gradually became strong, which demonstrated that the SLAof the Tsushima box synchronously responded to that in the
East China Sea. Two regions with negative coefﬁcients were
found in the South Japanese Archipelago and the South
Ryukyu Islands (approximately 238N, 1278E). As the temporal lags decreased, the latter gradually ripped northward
into the positive correlation area and was linked to the former negative correlation area.
We then repeated the lag correlation analysis, replacing the
SLA of the Tsushima box with TS transport (Figs. 4g–l). As
expected, a strong positive correlation was observed in the
East China Sea. With the reduction of temporal lags, the highcorrelation gradually concentrates to the area close to the TS,
indicating a strong dependence of TS transport on the SLA.
A zonal negative correlation coefﬁcient band appeared 208–
258N, east of Taiwan (Fig. 4g). With a decrease in lag time,
this negative correlation band could move westward, intrude
across the gaps and channels along the Ryukyu Islands, stretch
into a long band, and propagate northward across the Tokara
Strait to south of the Shikoku and Honshu Islands. Figure 5
shows the results obtained by conducting lag correlation analysis
on the satellite SLA data. Here, TS transport was also quantiﬁedusing JCOPE2M data instead of satellite data because the lattercannot output the vertical structure of the horizontal velocities.
We obtained a similar correlation coefﬁcient distribution to that

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shown in Fig. 4, which provides observational support for the
JCOPE2M results.
The positive correlation region in Figs. 4 and 5 suggests thatthe interannual variation in the SLA in the East China Sea, especially in the Tsushima box, was substantially related to the TS
transport. Despite this strong positive correlation, local forces(such as wind stress) over this region appear ineffective in sustaining SLA variations on an interannual time scale. The evolution of the negative correlation band in Figs. 4 and 5 provides
some insight into how remote forcing affects the interannual
variation in SLA and TS transport. The process is described in
the following sections.

c. Eddy effects on Kuroshio in the upstream TS region
1) EDDY EFFECTS FOR INDIVIDUAL EVENTS
To illustrate the potential effect of the above negativecorrelation band in detail, we chose three relatively higher value
events of TS transport (1999, 2003/04, and 2010) as a composition and presented the corresponding snapshots of SLA variations. Figures 6a–f show the composited SLA variations at 5, 4,
3, 2, 1, and 0 (peak value) months before the TS transport
reaching its peak value. The mean-state (1993–2019) Kuroshio
axis (yellow line) is also shown as reference for the following description. Traditionally, the Kuroshio axis is deﬁned as where
the surface velocity of the Kuroshio is maximum, and we determined the Kuroshio axis using the detection method proposedby Ambe et al. (2004). When the TS transport had a high-value
event, the SLA around the upstream TS region was always positive. The SLA was higher along the Japanese Archipelago thanthat along the Korean Peninsula, which is consistent with thelarger volume transport through the TS. Speciﬁcally, 5 months
earlier, a cyclonic mesoscale eddy with a negative SLA appeared south of Ryukyu Islands (approximately 258N, 127.58E;
Fig. 6a). Over time, this SLA signal propagated westward,
passed through the gaps and channels along the Ryukyu Islands,
impinged on the eastern ﬂank of the Kuroshio, elongated into asouthwest–northeast band, and moved downstream along thesouthern side of the Kuroshio path to the south of the Tokara
Strait (Figs. 6b–f). This eddy-related signal trajectory has also
been reported in previous studies by Ichikawa (2001) and Zhu
et al. (2004), which focused on the eddy-induced transport variation of the downstream Kuroshio. Moreover, Na et al. (2014)
and Yu et al. (2015) suggested that the effects of eddies could
pass through the Kerama Gap (a narrow and deep gap in the
Ryukyu Islands; see Fig. 1 for location) and affect the downstream Kuroshio transport. The eddy itself did not pass throughthe gaps and channels along the Ryukyu Islands. Eddy-related
signals enter the area east of the Kuroshio through the gaps andchannels of the Ryukyu Islands (Andres and Cenedese 2013;
Zhou et al. 2018). Viewed at the PN Line (a repeat hydrographic line crossing the Kuroshio) and Tokara Strait (see Fig. 1
for locations), the presence of a cyclonic eddy could induce awestward (or southwestward) current, which is opposite to theﬂow direction of the Kuroshio and tends to decrease the Kuroshio transport there (Andres and Cenedese 2013; Soeyanto
et al. 2014). We estimated the Kuroshio transport through the
PN Line and Tokara Strait (blue lines in Fig. 1 denote transect

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FIG. 4. Spatial distributions of lag correlation coefﬁcient: (a)–(f) SLA of the Tsushima box lags that in the other areaat 25, 24, 23, 22, 21, and 0 (same time) months. (g)–(l) As in (a)–(f), but for TS transport lagging.

locations) by horizontally and vertically integrating the normal
velocity from the sea surface to a depth of 700 m. Table 2 showsthat the composited TS transport was enhanced by 0.29 Sv, andthe composited Kuroshio transport through the PN Line andTokara Strait were decreased by 1.89 and 1.61 Sv, respectively. The upstream Kuroshio region, that is, the coastal region of northeast Taiwan, was almost unaffected by eddy
evolution. Further discussion of this phenomenon is provided
in section 5a.
Furthermore, we chose four relatively lower value eventsof TS transport (1995/96, 2005/06, 2008, and 2014/15) as acomposition and provided the composited SLA variations at
5, 4, 3, 2, 1, and 0 (trough value) months before the TS transport reaching its trough value (Figs. 6g–l). However, this situation is reversed. The SLA around the upstream TS region
was negative, and a lower SLA along the Japanese Archipelago than that along the Korean Peninsula indicated weak TS
transport (a reduction of 0.24 Sv, Table 2). Similar to the cyclonic eddy, an evident anticyclonic eddy-related signal characterized as a high SLA center also underwent gap-passing,
impinging, elongating, and moving from its birthplace to theTokara Strait. The downstream Kuroshio transport at the PN

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FIG. 5. As in Fig. 4, but using satellite SLA data.

Line and Tokara Strait were both enhanced by 1.50 Sv (Table 2).
Therefore, it is likely that, as the negative-correlation band in
Figs. 4 and 5 indicates, when TS transport is in a strong phase,the downstream Kuroshio transport through the PN Line andTokara Strait is weak and when it is in a weak phase, the downstream Kuroshio transport through the PN Line and Tokara
Strait is strong.
Eddy-related signals along the eastern side of the Kuroshio
always appear in the form of a long SLA band. The formationof this band can be understood by the following analogy: if ahigher sea level signal stays outside a stream, the pressure gradient will inject the water of this signal into the stream. A

positive SLA then appeared and propagated along thestream. As long as the higher sea level signal remains there,
the injection is continuous, and we can see a long positive
SLA band along the stream. Similarly, a persistently lower
sea level can excite a long SLA band, but with a negative
SLA owing to water leakage. The length of the long SLA
band is the product of the stream speed and the time of thecontinuous injection/leak. Considering that the water at theouter edge of the Kuroshio has a speed of O(1021) m s21, 1month stay of an eddy outside the Ryukyu Islands can form along SLA band with a length of 200–300 km. Because theeddy remains outside the Ryukyu Islands for several months,

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FIG. 6. Spatial distributions of composited SLA: composited SLA at (a) 5, (b) 4, (c) 3, (d) 2, (e) 1, and (f) 0 (peakvalue) months before TS transport reaching its peak value (the yellow line is the mean Kuroshio axis over
1993–2019). (g)–(l) As in (a)–(f), but before reaching its trough value.

it is long enough for a long SLA band (approximately 500 km
extending from the PN Line to the Tokara Strait) to form
along the east side of the Kuroshio.
To avoid potential confusion owing to the above composition
method, Figs. S1a–f in the online supplemental material present
the SLA variations from May to October 1999, when the ﬁrst
relatively higher value event occurred and supplemental

Figs. S1g–l present the SLA variations from October 1995 toMarch 1996, when the ﬁrst relatively lower value event occurred. The mesoscale eddy-related signal was performed
again, as mentioned above; that is, westward-propagating,northward-veering, downstream-moving, and ﬁnally affecting
the downstream Kuroshio transport. In addition, we veriﬁedthat the SLA evolution shown in Fig. 6 could be mirrored

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TABLE 2. Variation of transport (Sv) through the TS, northeast Taiwan, PN Line, and Tokara Strait for composited higher- or
lower-value events, as well as eddy-rich- or eddy-poor-year events. Positive or negative values respectively indicate transportenhancement or reduction.
Variation of transport

TS

Northeast Taiwan

PN LineTokara Strait

Higher-value event
Lower-value event
Eddy-rich-year event
Eddy-poor-year event

0.29
20.24
20.02
0.03

0.19
1.22
0.88
21.12

21.89
1.50
0.76
20.70

21.61
1.50
0.89
20.61

using satellite data (ﬁgure not shown here). The above results
further conﬁrm the negative correlation band shown in Figs. 4
and 5.

2) EDDY EFFECTS FOR INTERANNUAL TIMESCALE EVENTS

According to the above analysis, mesoscale eddies travel tothe Kuroshio area and modulate downstream Kuroshio transport, accompanied by individually enhanced and decreased TS
transport events. An “inverse” relationship between the TS
transport and the downstream Kuroshio transport is likely.
As shown in Figs. 6a and 6g, these eddies emanate from 258
to 278N east of Taiwan, where the unstable North Paciﬁc Subtropical Countercurrent (STCC) ﬂows eastward. Although the
STCC was relatively weak (;5 cm s21) and shallow (;100 m),
it was occupied by energetic eddies caused by the baroclinic instability between the STCC and the westward-ﬂowing North
Equatorial Current (Chang et al. 2015; Qiu and Chen 2021).
The lifetimes of such mesoscale eddies, not just those originating from the STCC region, have a scale of only O(100) days. In
contrast, the interannual signal, as the interannual variability in
the TS transport, can survive for several years. The failure of
the analysis to emphasize short-period individual eddy events is
exposed when we refer to long-period interannual TS transportvariation. Therefore, the analysis of eddy evolution in eddy-richand eddy-poor years is indispensable to sufﬁciently conﬁrm theeddy effects on interannual time scales, as well as the inverse
relationship.
Figure 7a presents the annual-mean eddy kinetic energy(EKE) distribution over the northwest Paciﬁc. The EKE isdeﬁned as
EKE 5 (u 2 1 y  2 )/2,

differences for eddy-rich and eddy-poor years, the spatial distributions of the composited sea level differences were calculated as the composited sea level of the eddy-rich year minusthat of the eddy-poor year (Fig. 7c). The occurrence of a positive sea level difference in the STCC region suggests that
more anticyclonic eddies spawned in eddy-rich years than in
eddy-poor years, which is consistent with the higher abundance of anticyclonic STCC eddies than that of cyclonic eddies over interannual and longer periods (Hwang et al. 2004;
Chang and Oey 2011; Yang et al. 2013).
To demonstrate how the STCC eddies modify the Kuroshiotransport on an interannual time scale, the composited SLA distributions for the eddy-rich and eddy-poor years are plotted inFig. 8. For the composited SLA in eddy-rich years (Fig. 8a), thecomposited SLA around the upstream TS region was negative.
The anticyclonic eddy-related signals associated with a positive
SLA occupied the eastern ﬂank of the Kuroshio, whereas the
SLA for the western ﬂank of the Kuroshio was negative. Such(1)

where u and y  are the perturbations of the zonal and meridional velocity components, respectively, after removing thelong-term (1993–2019) mean values. Two high-EKE bands
appeared from the south of Japan to the Kuroshio Extension
region and eastward from eastern Taiwan, that is, the STCC
band. In Fig. 7b, the interannual EKE anomaly series of the
STCC band (averaged over 178–278N, 1308–1758E) is presented, and the eddy-rich (positive EKE anomaly lasting forat least 1 year) and eddy-poor (negative EKE anomaly lasting
for at least 1 year) years are marked with red and blue shadings, respectively. Eddy-rich events occurred in 1996–98,
2003–08, 2015/16, and 2017–19 and eddy-poor events occurred
during 1999–2002 and 2009–14. To show the eddy character

FIG. 7. (a) Spatial distributions of annual mean (1993–2019) EKE
from JCOPE2M reanalysis data. (b) Time series of interannual
EKE anomaly for STCC region (area-averaged over 178–278N,
1308–1758E). Red and blue shadings indicate eddy-rich and eddypoor years, respectively. (c) Spatial distributions of composited sea
level difference (composited sea level in eddy-rich years minus thatin eddy-poor years).

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Fig. S2b shows the SLA patterns for the ﬁrst eddy-pooryear event in 1999–2002, which further conﬁrm the effectof STCC eddies on interannual Kuroshio transport in thedownstream region.
Overall, the effects of STCC eddies on the downstream
Kuroshio transport are illustrated not only for individual relatively higher or lower value events of TS transport (discussed
in the above subsection) but also for interannual time scale
eddy-rich or eddy-poor years (discussed in this subsection).
An inverse relationship between the TS transport and downstream Kuroshio transport is emerging from these results.
This suggests that when TS transport is strong, a high sea levelis always present around the upstream TS region and, when
TS transport is weak, a low sea level is always present aroundthe upstream TS region. Simultaneously, an anticyclonic mesoscale eddy from the STCC enhanced the volume transportof the downstream Kuroshio or a cyclonic mesoscale eddy
from the STCC decreased the volume transport of the downstream Kuroshio. Further discussion in the following section
will form a junction for the above two phenomena despite being seemingly independent.

d. Bonding role of Kuroshio intrusion

FIG. 8. (a) Spatial distributions of composited SLA for eddy-richyears. The yellow line is the mean Kuroshio axis over 1993–2019.
(b) As in (a), but for eddy-poor years.

seesaw SLA structures indicate enhanced Kuroshio transport
through the PN Line and Tokara Strait during eddy-richyears. In addition, the composited SLA along the JapaneseArchipelago is lower than that along the Korean Peninsula, and a decrease in TS transport is expected. Table 2
suggests that, during the eddy-rich year, the compositedtransports through the PN Line and the Tokara Straits
were increased by 0.76 and 0.89 Sv, respectively, whereasthat through the TS was decreased by 0.02 Sv. In the eddypoor years, the SLA around the upstream TS region and
SLA seesaw structures along the Kuroshio and TS were reversed (Fig. 8b). Instigated by the presence of cyclonic
eddy-related signals, the Kuroshio transport was decreased
by 0.70 Sv for the PN Line and 0.61 Sv for the Tokara
Strait, whereas TS transport was enhanced by 0.03 Sv(Table 2). The inverse relationship between TS transportand Kuroshio transport reappeared. Figure S2a in the online supplemental material shows the SLA patterns for theﬁrst eddy-rich-year event in 1996–98 and supplemental

We detected an inverse relationship between TS transport(forced by SLA changes around the upstream TS region) anddownstream Kuroshio transport (regulated by STCC eddies),
as reported by Gordon and Giulivi (2004). Why does such an
inverse relationship exist? Under the ﬁrst approximation, thelarge-scale current is constrained to the geostrophic balanceand ﬂows along the f/H contours (f and H are the Coriolis parameter and water depth, respectively) when the relative vorticity is substantially smaller than the planetary vorticity. In
the East China Sea, the Kuroshio ﬂows northeastward along
the continental slope and veers eastward southwest of Kyushu(yellow line in Fig. 8), with deﬂection from the f/H contours.
Substantial offshore or onshore intrusions perpendicular to
the f/H contours are induced. This Kuroshio intrusion partially feeds TS transport (Guo et al. 2003), and its ﬂuctuation
potentially serves to establish an inverse relationship between
the TS transport and Kuroshio transport. To conﬁrm this hypothesis, we ﬁrst examined the Kuroshio intrusion into theEast China Sea across the shelf break.
The Kuroshio intrusion in the East China Sea is excited byplanetary or topographic beta effects associated with variations in shelf topography (Liu et al. 2021). As in previous
studies (e.g., Zhang et al. 2017), transport across the 200-m
isobath (black line in Fig. 9a; 187 JCOPE2M grid points) was
selected to represent and quantify the Kuroshio intrusion. For
the annual mean, the spatially integrated ﬂux starting from
northeastern Taiwan (the ﬁrst grid point) to a given grid pointis shown in Fig. 9b. The entire intrusion (integrated to the lastpoints) was 1.30 Sv, which agrees well with the previous estimation (1.4 Sv; Isobe 2008) based on several observations.
Furthermore, it is worth noting that the ﬁrst 39 grid points(northeast of Taiwan; intrusion I) and grid points 133–187(southwest of Kyushu; intrusion III) were the two major areasof onshore ﬂux (3.06 and 1.34 Sv, respectively) to the East

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FIG. 9. (a) Bathymetry (shading) with the 200-m isobath (black line). A total of 187 JCOPE2M
model grid points form the black line, and the 1st, 39th, 132nd, and 187th (last) grid points arescattered (red dots). The red-outlined box indicates the location shown in Fig. 11a). (b) Spatiallyintegrated annual-mean Kuroshio intrusion from Taiwan (ﬁrst grid point) to a given grid pointof the 200-m isobath. Three sections are divided depending on whether the major intrusion is onshore (sections I and III) or offshore (section II). (c) Time series of interannual TS transport(blue line) and the entire Kuroshio intrusion (black line). (d) Time series of interannual
Kuroshio intrusion through sections I, II, and III (green dashed, black dashed, and purple solidlines, respectively). The blue line is the same as that in (c).

China Sea, and grid points 40–132 (intrusion II) provided theoffshore ﬂux (3.10 Sv).
For interannual variability, the standard deviation of theentire Kuroshio intrusion time series is 0.17 Sv (Fig. 9c). The
time series of TS transport (blue line in Fig. 9c) is remarkably
consistent with that of the Kuroshio intrusion (black line),
both in magnitude and variation. A correlation coefﬁcient of
0.89 between the interannual TS transport and Kuroshio intrusion transport further conﬁrmed this close relationship.
The interannual variations in the Kuroshio intrusions I, II,
and III are shown in Fig. 9d. Their standard deviations were0.24, 0.27, and 0.16 Sv, respectively. We further compared thecorrelation between the Kuroshio intrusions I, II, and III and
TS transport. Intrusion III was more consistent with TS transport (correlation coefﬁcient of 0.80) than intrusions I (20.02)
and II (0.10), indicating that the onshore ﬂux southwest of
Kyushu strongly affected the interannual variation in TS transport. Additionally, as another feeding source for TS transport,interannual transport through the Taiwan Strait (not shownhere) exhibited a smaller standard deviation (0.08 Sv) and aweak correlation coefﬁcient (20.13) with TS transport. Thus,
we conclude that the Kuroshio intrusion southwest of Kyushu
caused interannual variation in TS transport.
We performed a lag correlation analysis of the Kuroshio intrusion III with the SLA in the other area from 25 to 0 (sametime) months (Fig. 10). As expected, the evolution of the correlation features shown in Fig. 10 is similar to that shown in
Figs. 4g–l and 5g–l. Occupation of the positive correlation coefﬁcient around the upstream TS region suggested that when
the Kuroshio intrusion III was enhanced, the SLA over thisregion would increase, implying stronger TS transport. The
reverse occurs as Kuroshio intrusion III decreases. This is the
dynamic relationship between TS transport and Kuroshio intrusion III. The eastern and western sides of the Kuroshio axis
are covered by negative and positive correlation coefﬁcients,
respectively. This indicates that, accompanied by enhanced
Kuroshio intrusion III, the SLA decreases east of the Kuroshio
axis and increases to the west, suggesting a reduction in downstream Kuroshio transport. This condition was reversed forthe weakened Kuroshio intrusion III. The modulation of the

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FIG. 10. Spatial distributions of lag correlation coefﬁcient: Kuroshio intrusion III lags the SLA at (a) 25, (b) 24,(c) 23, (d) 22, (e) 21, and (f) 0 (same time) months. The yellow line is the mean Kuroshio axis over 1993–2019.

Kuroshio intrusion southwest of Kyushu by changes in Kuroshio
intensity has been explained using the “teapot effect” theory(Sheremet 2001; Andres et al. 2009; Shin et al. 2022). Those who
regularly drink tea are familiar with the following phenomena. Ifthey tilt the teapot slowly, the tea slowly ﬂows out from the
spout and gradually trickles down along the outer surface of theteapot. However, when the teapot is tilted rapidly, the tea
quickly and directly ﬂows out into the cup without touching theouter surface of the teapot. This is the so-called teapot effect. A
similar phenomenon occurs when a western boundary current(such as the Kuroshio) encounters a boundary gap. When thevolume transport of the Kuroshio decreases, the Kuroshio is topographically controlled, and more water tends to loop into thegap between the shelf break before 1288E and that southwest of
Kyushu (in analogy with the situation of tilting the teapot
slowly); as a result, more onshore transport occurs during theloop pathway to the head of the gap and induces an enhanced
Kuroshio onshore intrusion. In contrast, if the volume transportof the Kuroshio increases, it passes more directly across the gap
between the shelf break before 1288E and that southwest of
Kyushu (analogous to the situation of rapidly tilting the teapot);
as a result, less onshore transport occurs. The Kuroshio and its
intrusion III, therefore, are conﬁned to the teapot effect. Considering the dynamic relationship between TS transport andKuroshio intrusion III mentioned earlier, we can conclude
that Kuroshio intrusion III induces the opposite relationshipbetween TS transport and Kuroshio transport.
To more clearly observe the bonding role of the Kuroshio
intrusion III, the spatial distributions of composited transport
southwest of Kyushu are shown in Fig. 11a. This compositedtransport was obtained by vertically integrating the horizontal

velocities when the TS transport reached its peak (red arrows) ortrough (blue arrows) value from the sea surface to the bottom.
Enveloped by the Kuroshio, the water was transported northeastward along the shelf break and turned eastward southwest of
Kyushu. The red arrows are generally shorter than the blue onesat the PN line and the Tokara Strait, suggesting weak downstream Kuroshio transport accompanied by strong TS transportor strong downstream Kuroshio transport accompanied by weakTS transport, which is the inverse relationship between them.
Figure 11b shows a closer view of the Kuroshio intrusion III area.
When the TS transport was strong, the composited Kuroshiotransport bifurcated into two pathways (pink arrows): one was
the typical main stream veering eastward at ;308N; the second
was an intruding branch that continued to ﬂow northeastward until ;318N, then veered westward intruding the 200-m isobath,and eventually ﬂowed farther northward to the TS. The change
in the potential density anomaly ﬁelds along the 200-m isobathsouthwest of Kyushu (Fig. 12a) also suggests a general onshore
intrusion (Fig. 12c). Figures 13a–d show the spatial distributionsof the composited sea level and surface velocity at 3, 2, 1, and 0(peak value) months before the TS transport reaching its peakvalue. Those 5 and 4 months prior were omitted because theeddy effect on TS transport was still weak (Figs. 6a,b). As the
eddy-related signal propagated northward to the area southwestof Kyushu (Figs. 6c–f), the sea level over this region gradually increased (Figs. 13a–d). Concomitantly, the current intruding the
200-m isobath ﬂowed northward; that is, the intruding branch inFig. 11b was enhanced, which eventually resulted in intensiﬁed
TS transport.
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Ryukyu Islands, veering northward, encountering the Kuroshio,
and continuing downstream in the Kuroshio region. When thedominant eddies were cyclonic, the downstream Kuroshio transport decreased because of the eddy-related currents. The weak
downstream Kuroshio, conﬁned by the teapot effect, led morewater to intrude onshore southwest of Kyushu, which excited ahigh SLA around the upstream TS region. Consequently, thiswater was pumped into the Sea of Japan through the TS andsupplied strong TS transport. The reverse occurred when the
anticyclonic eddies were in the stage. The anticyclonic eddyrelated current induced intensiﬁed downstream Kuroshio transport, which forced more water to intrude offshore and ﬂow outof the Tokara Strait to comply with the teapot effect, leaving a
low SLA around the upstream TS region. As a result, TS transport was decreased with insufﬁcient Kuroshio intrusion.
This dynamic mechanism for interannual variation in TS
transport highlighted the following two aspects: 1) remote forcing for interannual TS transport variation came from the upstream TS region rather than the downstream region of the
Tsugaru and Soya Straits, and 2) the STCC eddies regulated TS
transport by modulating the Kuroshio transport, regulating Kuroshio intrusion in the East China Sea, and further adjusting the
SLA around the upstream TS region.

5. Discussiona. Interannual variation of downstream Kuroshiotransport in the East China Sea
1) MODIFICATION OF WIND-DRIVEN SVERDRUP
FIG. 11. (a) Spatial distributions of composited transport southwest of Kyushu when TS transport reached its peak (red arrows)
and trough (blue arrows) value. Only the transport in the area
deeper than 2100 m is shown. Contours indicate the 100-, 125-,
200-, and 500-m isobaths from west to east. Black dots are JCOPE2M model grid points of Kuroshio intrusion III. The blue lines
indicate transects at the PN Line and Tokara Strait. (b) A closeup
image of the magenta-outlined box in (a). The pink arrows illustrate the general structure of the composited transport ﬁeld.

intruding branch was substantially weakened. A general offshore intrusion (Fig. 12d) was indicated by a change in the potential density anomaly (Fig. 12b). Figures 13e–h suggest a
dropping sea level southwest of Kyushu and a weaker ﬂow intruding the 200-m isobath, with the STCC-eddy-related signal
approaching (Figs. 6i–l). TS transport was decreased as a resultof insufﬁcient supplementation from the Kuroshio intrusion.
This analysis indicates that the Kuroshio intrusion southwest of
Kyushu serves as an inverted link between transport via the TS
and downstream Kuroshio.

e. Dynamical mechanisms
Based on the above discussion, a plausible explanation for
the interannual variation in the TS transport is proposed.
Baroclinic instability induces active anticyclonic or cycloniceddies in the STCC band (Qiu and Chen 2021). The effects ofthese eddies can trek westward in the form of Rossby waves,partially passing through the gaps and channels along the

TRANSPORT ON DOWNSTREAM

KUROSHIO TRANSPORT
A previous analysis revealed that the interannual TS transport was remarkably regulated by the downstream Kuroshiotransport variation through an inverse relationship, where themodiﬁcation role of mesoscale eddies from the STCC region
was emphasized. Although there is much evidence for sucheddy-induced Kuroshio variation (e.g., Jan et al. 2017), the classical western boundary current theory suggests that Kuroshiotransport is forced by large-scale wind stress curl over the
Paciﬁc interior through Sverdrup dynamics (e.g., Kagimoto
and Yamagata 1997). Therefore, the modiﬁcation of the interior wind stress on downstream Kuroshio transport requires
further examination.
The Sverdrup transport (MSv) is estimated as follows:

1 xW
MSv 5
= 3 t dx,(2)
rb xE H
where r denotes the seawater density, b is the meridional gradient of the Coriolis parameter, t is wind stress, and xE (eastern boundary of the Paciﬁc) and xW are the east and west
integral limits, respectively. The annual mean Sverdrup transport calculated from the NCEP–NCAR wind ﬁeld data is shownin Fig. 14a. The interannual variation in Sverdrup transport at
248N (red dashed line in Fig. 14a) was obtained and comparedwith that of the Kuroshio transport at the PN Line and Tokara
Strait (Fig. 14b). The Sverdrup transport is weakly correlated

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FIG. 12. Vertical distributions of composited (a) potential density anomaly and (c) velocity
anomaly along the 200-m isobath southwest of Kyushu from a depth of 100 to 200 m when the
TS transport reached its peak value. Positive and negative values in (c) respectively indicate onshore and offshore ﬂow. In (a), on the left side of the black line, an increase of density anomaly
from left to right along the isobaths suggests an offshore ﬂow, which agrees well with thenegative velocity anomaly in (c). (b),(d) As in (a) and (c), but for TS transport reaching its
trough value.

(correlation coefﬁcient # 0.2) with the Kuroshio transport at
the PN Line or at the Tokara Strait at the interannual timescale, suggesting that the interannual variation of downstream
Kuroshio transport in the East China Sea was not directly controlled by wind stresses over the basin, and the eddy effects, as
an important controlling factor, should not be overlooked(Chang and Oey 2011).

2) REGIONAL DEPENDENCE OF EDDY MODIFICATIONON UPSTREAM AND DOWNSTREAM

KUROSHIO TRANSPORT
As mentioned in section 4c, the cyclonic eddies for individual
higher TS transport events tend to leak their SLA signals
through the gaps and channels along the Ryukyu Islands andmove farther northward without affecting the Kuroshio region
northeast of Taiwan (Fig. 6). For a more quantitative analysis,
the Kuroshio transport northeast of Taiwan was calculated byhorizontally and vertically integrating the normal velocity fromthe sea surface to a depth of 700 m (Table 2). For individual
higher TS transport events, the composited Kuroshio transport
at the PN Line and Tokara Strait was decreased by 1.89 and1.61 Sv, respectively, whereas that northeast of Taiwan wasslightly enhanced by 0.19 Sv. This raises the question why thedownstream Kuroshio transport dramatically responds to these
individual eddies, whereas the upstream transport does not. On
the other hand, for individual lower TS transport events, thecomposited Kuroshio transport at the PN Line and Tokara
Strait were both enhanced by 1.50 Sv. Moreover, that ofnortheast Taiwan was enhanced by 1.22 Sv (Table 2). Why havethe upstream and downstream Kuroshio transport changed intensely now?
Soeyanto et al. (2014) reported that eddies that held sway upstream and downstream of the Kuroshio area had substantial regional dependence because of the leakage role of the Kerama
Gap. Speciﬁcally, some of the STCC eddies could move westward, impinge on the northeastern coast of Taiwan, affect the
upstream Kuroshio transport, and ﬁnally die out. The other eddies also propagate westward, but then veer northward after
passing through the gaps and channels along the Ryukyu Islands
and further modulate the downstream Kuroshio transport. The
different eddy trajectories suggest that for eddy responses, there
is a lack of connection or consistency between the upstream anddownstream Kuroshio transport.
To clearly identify the regional dependence of the eddy
effects, the lag correlation was examined for the SLA of the
upstream and downstream Kuroshio regions (bottom andtop boxes in Fig. 15a, respectively) in the East China Sea.
Figures 15a–f and 15g–l show the correlation coefﬁcient distributions with the SLA of the downstream and upstream Kuroshio
region lagging from 25 to 0 (same time) months, respectively. As
shown in Figs. 15a–f, the positive correlation area in the STCC region can propagate westward, intrude into the gaps and channels
along the Ryukyu Islands, then veer northward to the downstream Kuroshio (veering trajectory). As shown in Figs. 15g–l,
the westward-propagating positive correlation area continued tomove westward to the area east of Taiwan and locally affected

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FIG. 13. Spatial distributions of composited sea level (shading) and surface velocity (arrows) for (a)3, (b) 2, (c) 1, and (d) 0 (peak value)
months before TS transport reaching its peak value. The arrows denote the current direction, and their colors denote the magnitude. Only
the velocity in the region deeper than 2100 m is plotted. Black dots are model grid points of Kuroshio intrusion III along the 200-m isobath. The yellow line is the mean Kuroshio axis over 1993–2019. (e)–(h) As in (a)–(d), but before reaching its trough value.

the upstream Kuroshio (nonveering trajectory). These two expansions of the positively correlated area suggested that STCC
eddies can affect both the upstream and downstream Kuroshio
regions through nonveering and veering eddy trajectories,

FIG. 14. (a) Annual mean (1993–2019) Sverdrup transport fromthe NCEP–NCAR wind ﬁeld data; negative and positive values represent clockwise and anticlockwise circulation, respectively. The reddashed line denotes the integral path for calculating the Sverdruptransport at 248N to 1238E. (b) Interannual variation of Sverdruptransport at 248N (red line), and volume transport through the PN
Line (black line) and Tokara Strait (blue line).

respectively. The upstream Kuroshio is insensitive to veering
eddies and only responds to nonveering eddies. However, thedownstream Kuroshio has the opposite response. This is theregional dependence of the eddy modiﬁcation.
What about the region-dependent eddy effects for eddy-richand eddy-poor-year events? The transport variation in the
Kuroshio northeast of Taiwan was almost consistent with that at
the PN Line and Tokara Strait (Table 2). A closer look at Fig. 8
indicates that this consistency is owing to the occurrence of twoparallel but independent eddy-related signals in the upstream
and downstream Kuroshio regions. The regional dependence ofeddy modiﬁcation also exists for eddy-rich- and eddy-poor-year
events.
In short, the modiﬁcation of STCC eddies has a strong regional dependence on the upstream and downstream Kuroshiotransport. Nonveering STCC eddies can only affect the upstream Kuroshio transport northeast of Taiwan. However, theveering STCC eddies could move northward and modulate
the downstream Kuroshio transport through the PN Line andTokara Strait. This phenomenon further shapes the evolution of
the negative correlation band, as shown in Figs. 4 and 5.

b. Potential connections between this study andprevious studies
In this study, the interlocking Tsushima–SLA–Kuroshio(and intrusion)–eddy mechanism provides a new perspective

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ZHENG ET AL.

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FIG. 15. Spatial distributions of lag correlation coefﬁcient, where SLA of the PN–Tokara box [top black-outlined
box in (a)] lags that in the other area at (a) 25, (b) 24, (c) 23, (d) 22, (e) 21, and (f) 0 (same time) months.
(g)–(l) As in (a)–(f), but for the SLA of the northeast Taiwan box [bottom black-outlined box in (a)] lagging.

for understanding TS transport variations on an interannual
time scale. As mentioned in the introduction, previous studies
mainly focused on the mean state and seasonal time scale andalso referred to the SLA and Kuroshio when discussing TStransport. Despite the different time scales, we argue here thepotential dynamic connections between the mechanisms proposed in this and previous studies.
The role of interstrait sea level differences as the main force
for mean-state TS transport was originally revealed by Minato
and Kimura (1980). Our perspective suggests that interstrait
SLA differences still play a vital role in determining TStransport on an interannual time scale. Furthermore, we foundthat the interstrait SLA difference was unlikely to be dominated by the SLA in the downstream region of the Tsugaru
and Soya Straits but by the SLA in the upstream TS region,
which was also noted by Lyu and Kim (2005).
Our proposed mechanism emphasizes the inverse relationship between the downstream Kuroshio transport in the East
China Sea and TS transport on an interannual time scale.
Gordon and Giulivi (2004), Kida et al. (2020), and Shin et al.
(2022) also reported an inverse relationship. Speciﬁcally, Kida
et al. (2020) proposed that a northward shift in the Kuroshio

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JOURNAL OF PHYSICAL OCEANOGRAPHY

FIG. 16. Kuroshio axes from JCOPE2M reanalysis data. The blue
shading indicates areas shallower than 200 m. The yellow line is themean Kuroshio axis over 1993–2019. The orange lines donate
Kuroshio axes when the interannual TS transport reached its peakvalue in October 1999 (H1), September 2004 (H2), and August2010 (H3), corresponding to three relatively higher value events deﬁned in section 3. The blue lines denote Kuroshio axes when the interannual TS transport reached its trough value in March 1996 (L1),
September 2005 (L2), June 2008 (L3), and April 2015 (L4), corresponding to four relatively lower value events deﬁned in section 3.

axis south of Japan can shape an increasing trend in TS transport by modulating the sea level. We also illustrate the Kuroshio
axes when the interannual TS transport reached its peak value
for the three relatively higher value events and trough value for

VOLUME 53

the four relatively lower value events as deﬁned in section 3(Fig. 16). For example, H1 is the Kuroshio axis in October 1999,
when the interannual TS transport reached its peak value forthe ﬁrst relatively higher value events in 1999. The mean-state(1993–2019) Kuroshio axis is shown as reference. In the East
China Sea, the Kuroshio axes, constrained to ﬂow northeastward along the shelf, were all similar to the mean state with minor ﬂuctuations. However, the Kuroshio axes south of Japan
exhibit substantial spatial variations: nearshore and offshore
non-large-meander paths for relatively higher and lower value
events, respectively. In other words, the enhanced TS transportand northward shift of the Kuroshio axes south of Japan occurred on an interannual time scale. This result, despite the axis
variation, again conﬁrmed that the Kuroshio modulates TStransport and dynamically corresponded to that in Kida et al.
(2020).
Overall, this study extends previous studies on interannual
time scales. The important roles of the SLA and Kuroshio
highlighted in previous studies were also emphasized in ourstudy.

6. Summary
Based on the JCOPE2M reanalysis and observational data,
we investigated the interannual variation in the volume transport through the TS from 1993 to 2019 and its dynamic

FIG. 17. Schematic diagram showing the dynamic response of TS transport to the remote forcing
from the STCC region.

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ZHENG ET AL.

mechanisms. Substantial interannual variations were addressed, with generally stronger TS transport in 1999, 2003/04,
and 2010 and weaker TS transport in 1995/96, 2005/06, 2008,
and 2014/15. The change in interannual TS transport wasforced by the SLA signal in the upstream TS region, rather
than in the downstream region of the Tsugaru and Soya
Straits. For a high SLA phase in the upstream TS region,
more water is transported into the Sea of Japan through theTS, and vice versa.
The eddy–Kuroshio interaction, bonding role of the Kuroshio
intrusion, and SLA response explain the variation in interannual TS transport with the SLA signal in the upstream TS region (Fig. 17). The westward-propagating cyclonic eddies in the
STCC band can pass its effect through the gaps and channels
along the Ryukyu Islands, which then veer northward to
decrease the Kuroshio transport through the eddy–Kuroshio interaction. Weak Kuroshio transport triggered more water intrusion southwest of Kyushu, which excited high SLA over thisregion. Consequently, this water is pumped into the Sea ofJapan through the TS and forms strong TS transport (Fig. 17a).
Conversely, the westward-propagating anticyclonic eddies in the
STCC band can pass its effect through the gaps and channels
along the Ryukyu Islands, which then veer northward to enhance
the Kuroshio transport through the eddy–Kuroshio interaction.
Strong Kuroshio transport triggered less water intrusion southwest of Kyushu, which excited low SLA over this region. Consequently, this water is pumped into the Sea of Japan through the
TS and forms weak TS transport (Fig. 17b).
Further discussion suggests a poor correlation between the
Sverdrup transport and downstream Kuroshio transport on aninterannual time scale. Alternatively, the importance of theeddy–Kuroshio interaction process for Kuroshio transport in
the downstream region of the East China Sea is proposed. In
this process, the eddies held sway upstream and downstream
Kuroshio region has a strong regional dependence. STCC eddies can affect the downstream or upstream Kuroshio transport
through a veering or nonveering eddy trajectory, respectively.
The leading role of the Kuroshio intrusion southwest of
Kyushu in TS transport has received less attention in previous
studies. This ﬁnding is consistent with the conclusions of previous studies on the modulation of the SLA and Kuroshio because the Kuroshio intrusion southwest of Kyushu is the key
pathway for Kuroshio water ﬂowing into the TS. Further observations and research in this region are anticipated.
Acknowledgments. This study was supported by a Grantin-Aid for Scientiﬁc Research (MEXT KAKENHI; Grant
22H05206). Author Junyong Zheng also is thankful for support from the China Scholarship Council (CSC, 202006330055)
for his research in Matsuyama, Japan.
Data availability statement. The JCOPE2M model data
used in this study are available upon request from JAMSTEC(https://www.jamstec.go.jp/jcope/htdocs/e/distribution/index.
html#conditions%20of%20use). Satellite SSH data can be accessed
through CMEMS (https://data.marine.copernicus.eu/product/
SEALEVEL_GLO_PHY_L4_MY_008_047/description).

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