PUBLICATIONSJournal of Geophysical Research: Oceans

Special Section:

Interannual variations of Kuroshio transport in the East China
Sea and its relation to the Pacific Decadal Oscillation andmesoscale eddies

Western Pacific Ocean Circulation and Climate

Endro Soeyanto1, Xinyu Guo1,2, Jun Ono3,4, and Yasumasa Miyazawa2

RESEARCH ARTICLE
10.1002/2013JC009529

1

Key Points:
 ECS-Kuroshio transport from 1993 to2012 is given by a reanalysis(JCOPE2)
 ECS-Kuroshio transport is associatedwith both PDO index and mesoscaleeddies Mesoscale eddies southeast of
Taiwan were intensiﬁed after 2003

Correspondence to:
X. Guo,guoxinyu@sci.ehime-u.ac.jp
Citation:
Soeyanto, E., X. Guo, J. Ono, and Y.
Miyazawa (2014), Interannual
variations of Kuroshio transport in the
East China Sea and its relation to the
Paciﬁc Decadal Oscillation andmesoscale eddies, J. Geophys. Res.
Oceans, 119, 3595–3616, doi:10.1002/
2013JC009529.
Received 21 OCT 2013
Accepted 21 MAY 2014
Accepted article online 24 MAY 2014
Published online 9 JUN 2014

Center for Marine Environmental Studies, Ehime University, Matsuyama, Japan, 2Research Institute for Global Change,
Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan, 3National Institute of Polar Research,
Tachikawa, Japan, 4Graduate School of Frontier Sciences, University of Tokyo, Kashiwa, Japan

Abstract Results of a data-assimilative ocean model (JCOPE2) from 1993 to 2012 were used to examine
the correlation between the Paciﬁc Decadal Oscillation (PDO) index and interannual variations of the Kuroshio transport in the East China Sea (ECS) and the inﬂuences of mesoscale eddies on this correlation. In aperiod from 1993 to 2002, the Kuroshio transport estimated from the JCOPE2 reanalysis has a positive correlation with the PDO index. This well-known correlation became weak or even disappeared when the analysis period was extended from 1993–2002 to 1993–2012. This occurs because the variation range of the PDO
index became small during enhanced mesoscale eddy activity southeast of Taiwan in years after 2002. The
eddies caused a larger variation in the Kuroshio transport in the years after 2002 than before 2002, andtherefore, changed the correlation between the PDO index and Kuroshio transport in the ECS. The inﬂuenceof mesoscale eddies on the Kuroshio transport has strong regional dependence: the Kuroshio transport
from the area east of Taiwan to the midway along the shelf break in the East China Sea depends mainly on
eddies arriving from southeast of Taiwan, while transport from the midway along the shelf break to theTokara Strait depends mainly on the eddies arriving from northeast of Okinawa Island. The combination of
PDO-related signals and eddy-related signals determines the interannual variations of the Kuroshio transport in the ECS and sufﬁcient attention must be paid to the spatial dependence of the Kuroshio transport in
the ECS on eddies.

1. Introduction
The sea surface height (SSH) and Kuroshio transport in the East China Sea (ECS) is known to respond well toa basin-scale interannual oscillation, i.e., the Paciﬁc Decadal Oscillation (PDO) [Mantua et al., 1997] that may
be used as a proxy for large-scale wind stresses curl forcing [Andres et al., 2009]. For example, Han and
Huang [2008] reported a negative correlation between the interannual variations of SSH inside the ECS andPDO index by analyzing 11 years (October 1992 to July 2002) of altimetry data and sea level data at tidal
gauges, and suggested a positive correlation between Kuroshio transport in the ECS and PDO index by following an idea given by Gordon and Giulivi [2004]. Andres et al. [2009] conﬁrmed this positive correlation
using the satellite altimetry-derived Kuroshio transport midway along the shelf break in the ECS.
On the other hand, mesoscale eddies also affect the Kuroshio transport in the ECS. Yang et al. [1999]
reported the arrival of cyclonic and anticyclonic mesoscale eddies southeast of Taiwan with an interval of
100 days and their signiﬁcant impacts on the Kuroshio transport east of Taiwan. Zhang et al. [2001] estimated the Kuroshio transport east of Taiwan from 20 months of mooring data and found a period of 100
days in the variations of Kuroshio transport due to arrival of mesoscale eddies from the interior ocean.
Hwang et al. [2004] found that most mesoscale eddies arriving from southeast of Taiwan had their origin in
the Subtropical Countercurrent (STCC) region and propagated westward along a zonal band near 22 N–
24 N. In addition, mesoscale eddies also affect the Kuroshio transport through the Tokara Strait. Ichikawa[2001] reported that an eddy-related signal existing south of Okinawa 60 days prior may move to the Kuroshio region in the ECS by passing through the Kerama Gap and ﬁnally arrive at the Tokara Strait.
With accumulation of altimetry data, the interannual variability of mesoscale eddies themselves was gradually realized. Using the 16 years (1993–2008) of altimetry data, Qiu and Chen [2010] reported enhanced
eddy activity in 1996–1998 and 2003–2008 and weakened eddy activity in 1993–1995 and 1999–2002 in

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the STCC region within 18 N–25 N in the western North Paciﬁc. Recently, Qiu and Chen [2013] again
reported the interannual modulation of mesoscale eddy activity and attributed it to the change in surface
heat ﬂux forcing. Since the mesoscale eddies in the STCC band eventually arrive at the Kuroshio region,
their interannual variability is expected to result in interannual variation in the Kuroshio transport. Actually,
Chang and Oey [2011] suggested that the eddy activity in the STCC region determines both the seasonaland interannual variations of the Kuroshio transport east of Taiwan.
As stated above, previous studies on the interannual variability in the Kuroshio transport in the ECS werebased on SSH data from altimetry and tide gauges or hydrographic data along one or two transects. The
inﬂuence of mesoscale eddies on the Kuroshio transport in the ECS has also been realized but the analysis
was carried out separately for the area east of Taiwan [Yang et al., 1999; Zhang et al., 2001; Chang and Oey,2011] and for the Tokara Strait [Ichikawa, 2001]. The length of the shelf break in the ECS is on the order of1000 km, which is much larger than the size of mesoscale eddies from the interior ocean [Yang et al., 2013].
The pathway of eddies moving from south of Okinawa to the Tokara Strait [Ichikawa, 2001] is far from thearea east of Taiwan, indicating that the Kuroshio transport east of Taiwan and Tokara Strait are probably
affected by different eddies. At present, we still do not have an understanding of the spatial variations in
the inﬂuences of eddies on the Kuroshio transport along its pathway inside the ECS.
In this study, we examine the interannual variations of Kuroshio transport in the ECS by using a reanalysis
product from 1993 to 2012 produced by a data-assimilative ocean model that uses all the available observation data and provides an optimum estimation for the Kuroshio region [Miyazawa et al., 2009]. We have two
speciﬁc purposes. The ﬁrst is to revisit the correlation between interannual variability of the SSH in the ECS
and PDO index reported by Han and Huang [2008] and the correlation between interannual variability ofKuroshio transport in the ECS and PDO index reported by Andres et al. [2009]. Our analysis shows that thepreviously reported correlations were not sustained throughout our analysis period (1993–2012). The second is to elucidate the inﬂuence of mesoscale eddies on the interannual variability of Kuroshio transport in
the ECS. Our analysis shows that the mesoscale eddies southeast of Taiwan strongly affect the Kuroshiotransport from east of Taiwan to midway along the shelf break (upstream region), while the mesoscaleeddies northeast of Okinawa strongly affect the Kuroshio transport from the midway along the shelf breakto the Tokara Strait (downstream region) in the ECS.
This paper is organized as follows. In section 2, the data-assimilative ocean model and analysis method aredescribed. Section 3 describes the comparison of model results with available observations of the Kuroshiotransport in the ECS and presents the correlation of Kuroshio transport in the ECS calculated from model
results with PDO index. Section 4 demonstrates the inﬂuence of mesoscale eddies on the Kuroshio transport
along its pathway in the ECS with special attention on the regional dependence of the Kuroshio transport
on eddies. Section 5 summarizes this study.

2. Reanalysis Product and Analysis Method
2.1. Data-Assimilative Ocean Model
The reanalysis product used in this study was obtained using a data-assimilative ocean model developed inJapan Coastal Ocean Predictability Experiments 2 (JCOPE2) [Miyazawa et al., 2009]. The JCOPE2 model isbased on the Princeton Ocean Model with generalized coordinate system (POMgcs) [Mellor et al., 2002]. It
includes two submodels that are connected by a one-way nesting method [Guo et al., 2003]. The innermodel (10.5 N–62 N, 108 E–180 E) has a horizontal grid interval of 1/12 in both meridional and zonal
directions and 46 levels in the vertical. The outer model (30 S–62 N, 100 E–90 W) has a horizontal gridinterval of 1/4 in both meridional and zonal directions and 21 levels in the vertical. The resolution of theinner model may directly resolve mesoscale eddies and has previously been used to study the interaction
between mesoscale eddies and the Kuroshio large meandering south of Japan [Miyazawa et al., 2004].
Data assimilation in the JCOPE2 model uses all available observational data: (1) satellite altimetry data consisting of the TOPEX/Poseidon, ERS-1, Jason-1, and Geosat Follow-On for the SSH anomaly; (2) sea surface
temperature obtained from the Advanced Very High Resolution Radiometer/Multi-Channel Sea Surface
Temperature (AVHRR/MCSST) products; and (3) vertical proﬁles of temperature and salinity obtained fromthe in situ data archive of the Global Temperature-Salinity Proﬁle Program (GTSPP). The details of the assimilation method and parameters used in it have been described by Miyazawa et al. [2009]. The present

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Figure 1. (a) Rainbow contours show mean sea surface height (SSH, m) of the JCOPE2 reanalysis from January 1993 to December 2012. Black thick lines A0, B0, C0, E0, F0, G0, and K1
denote transects through which the Kuroshio transport was calculated. Two blue triangles, CES and PAC, denote the grid points whose SSH are presented in Figures 2 and 5. Two graysquares (ET, EO) denote the area used for the area-averaged vorticity given in Figure 15 and eddy kinetic energy in Figures 16a and 16b. (b) Standard deviation (m) of monthly interannual component (MIC) of SSH over entire model domain for January 1993 to December 2012.

version of the reanalysis (FRA-JCOPE2) additionally assimilates the in situ data from the World Ocean Database (WOD) 2009 (http://www.nodc.noaa.gov/OC5/WOD09/pr_wod09.html) and an archive compiling thedata obtained around the Japan coasts provided by the Fishery Research Agency, Japan.
After a spin-up of 15 and 5 years for the outer low-resolution model and inner high-resolution model,
respectively, reanalysis calculations from November 1992 to June 2013 were carried out for both models
with the data assimilation and the external forcing including wind stress and heat ﬂux ﬁelds calculated
from the six hourly National Centers for Environmental Prediction/National Center for Atmospheric Research(NCEP/NCAR) reanalysis data [Kalnay et al., 1996] using bulk formulae [Kagimoto et al., 2008], and sea surface
salinity restored to the monthly mean climatology data [Conkright et al., 2002] with a time scale of 30 days.
As the product of this reanalysis calculation, we saved the resultant SSH, two horizontal velocity components, water temperature, and salinity at 46 sigma levels at a 1 day interval.
2.2. Data Analysis Method
The raw reanalysis data for this study are daily SSH and two horizontal velocity components from 4 November 1992 to 30 June 2013. The procedures for extracting interannual signals are the following: (1) removing
the mean ﬁeld and annual cycle from the daily data; and (2) applying an 81 day running mean to the daily
data after step 1. The annual cycle was represented by a cosine function with a period of 1 year. Its amplitude and phase were obtained by harmonic analysis of the daily raw data. After these two steps, we
obtained interannual components of SSH and velocity with 1 day interval from 1 January 1993 to 31
December 2012. This data set is referred to here as daily interannual components of SSH and velocity.
The SSHs at two grid points (Figure 1a) are presented in Figure 2 to demonstrate our data processing procedures. One grid point (Station CES in Figure 1a) is inside the ECS, i.e., on the shelf side of the Kuroshio while
the other grid point (station PAC in Figure 1a) is on the ocean side of the Kuroshio. The daily raw data of
SSH at station CES (gray line in Figure 2a) have a mean value of 20.12 m and demonstrate an annual cyclewith an amplitude of 0.11 m (Figure 2a). At this station, the annual variation is a dominant component. The
81 day running mean effectively removes the short-term variations (black line in Figure 2a) to produce thedaily interannual component (green line in Figure 2a). At station PAC, the mean SSH is 0.55 m and the

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Figure 2. (a) Daily sea surface height (SSH, gray line), composition of mean and annual cycle of SSH (dash line), daily SSH anomaly after
removing the mean and annual cycle (black thick line), and 81 day running mean of daily SSH anomaly (green line) at station CES. ‘‘Mean’’is mean value of SSH over entire period and ‘‘Amp’’ is amplitude of annual cycle. (b) The same as Figure 2a but for station PAC.

amplitude of the annual cycle is only 0.05 m (Figure 2b). The interannual signals are the dominant component in the variations of SSH there (green line in Figure 2b).
Following previous studies [Han and Huang, 2008; Andres et al., 2009], we also use the PDO index to represent interannual variations in basin-scale wind stress curl forcing. The monthly mean PDO index was
acquired from the website of the Joint Institute for the Study of the Atmosphere and Ocean (JISAO) at the
University of Washington (http://jisao.washington.edu/pdo/PDO.latest). For comparison with monthly mean
PDO index, we calculated the monthly mean from daily interannual components of SSH and velocity andhereafter refer to the month mean as the monthly interannual component (MIC). To separate the interannual variations that persist for less or more than 1 year, we applied a 13 month running mean to MIC of SSH
and velocity and obtained their long-term monthly interannual component (LMIC). The difference between
MIC and LMIC is deﬁned as the short-term monthly interannual component (SMIC).
The signiﬁcance of a correlation coefﬁcient is examined by a criterion based on effective degree of freedom[Emery and Thomson, 2001]. The criterion is denoted by r0 ðN; aÞ, where N is effective degree of freedom anda is signiﬁcance level.

3. Results
3.1. Comparison of Interannual Variations of the Kuroshio Transport in the ECS Between the Model
Results and Observations
The dense contours in mean SSH from January 1993 to December 2012 (Figure 1a) indicate that the JCOPE2
model reproduces a well-known path of the Kuroshio that starts from east of Luzon Island, passes the Luzon
Strait, east of Taiwan, the ECS Shelf break, and south of Japan, and ends east of Japan around 35 N. The
mean SSH is negative inside the ECS (20.2 to 20.1 m) but positive offshore of the Kuroshio (0.1–0.6 m).
Based on measured sea level difference between tide gauges of Keelung and Ishigaki (two ends of transect
A0 in Figure 1a), Chang and Oey [2011] reported the interannual variations of Kuroshio transport east of

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Figure 3. (a) Sum of annual cycle component and monthly interannual component of volume transport (Sv) through transect A0 (blueline), that of sea level difference (cm) across transect A0 (red line) from JCOPE2 reanalysis, 90 day low pass of sea level difference (cm)across the East Taiwan Channel (green line) [Chang and Oey, 2011]. (b) Long-term monthly interannual component of volume transport(Sv) through transect A0 (blue line), that of sea level difference (cm) across transect A0 (red line) from JCOPE2 reanalysis, 360 day low passof sea level difference (cm) across the East Taiwan Channel (green line) [Chang and Oey, 2011].

Taiwan. Following their idea, we extracted the sea level difference between Keelung and Ishigaki from thereanalysis data of JCOPE2 and calculated the Kuroshio transport through transect A0 that connects Keelung
and Ishigaki (Figure 1a). The correlation coefﬁcient between MIC of sea level difference between Keelung
and Ishigaki (red line in Figure 3a) and MIC of the Kuroshio transport through transect A0 (blue line in Figure3a) is 0.67 (>r0 ð144; 95%Þ50:16), close to the value (0.72–0.78) from observations [Johns et al., 2001]. The
correlation coefﬁcients between measured and modeled sea level differences between Keelung and Ishigaki for 1993–2008 are 0.42 (>r0 ð101; 95%Þ50:20) for the 90 day low-passed data (Figure 3a) and 0.55(>r0 ð18; 95%Þ50:46) for the 360 day low-passed data (Figure 3b).
Wei et al. [2013] reported the interannual variations of Kuroshio transport at PN line (equivalent to transect
E0 in Figure 1a) that was calculated from in situ hydrographic data collected from 1955 to 2010 with aninterval of 6 or 3 months by the Japan Meteorological Agency. Although they reported the Kuroshio transport based on season of every year, the exact observation date for each cruise can be found in a report of
the Japan Meteorological Agency. The direct comparison between the transport through transect E0 calculated from the reanalysis data of JCOPE2 at the exact cruise date (black dots in Figure 4a) and thosereported by Wei et al. [2013] (green dots in Figure 4a) gives a correlation coefﬁcient of 0.49(>r0 ð68; 95%Þ50:24). Assuming that the Kuroshio transport reported by Wei et al. [2013] represented a seasonal mean, its correlation coefﬁcient to the corresponding seasonal mean of model results (red dots in Figure 4a) is 0.35 (>r0 ð66; 95%Þ50:24). Apparently, the Kuroshio transport calculated from cruise data by Wei
et al. [2013] includes both seasonal mean and high-frequency variations. The high-frequency variations in
the Kuroshio transport, which are demonstrated by the daily Kuroshio transport calculated from the reanalysis data of JCOPE2 (gray line in Figure 4a), act as a noise to the seasonal mean in the cruise data. This problem becomes more serious for the case of yearly means (Figure 4b).
Based on the sea level difference across the Kuroshio and its relation with Kuroshio transport estimatedfrom in situ data [Andres et al., 2008], Andres et al. [2009] reported the yearly mean of Kuroshio transport calculated from satellite altimetry data with an interval of approximately 10 days. A comparison of their yearlymean (blue line in Figure 4b) and the yearly mean calculated from four or ﬁve data in the same year given
by Wei et al. [2013] (green line in Figure 4b) helps to understand the effects of high-frequency variations on

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Figure 4. (a) Volume transport reported by Wei et al. [2013] with exact observation dates from the Japan Meteorological Agency (greendots), daily anomaly of volume transport through transect E0 (gray line) from JCOPE2 reanalysis, and those corresponding to the exact
observation date from the Japan Meteorological Agency for the volume transport reported by Wei et al. [2013] (black dots), seasonal meancalculated by daily anomaly of volume transport through transect E0 from JCOPE2 reanalysis corresponding to the season used by Wei
et al. [2013] for their estimated volume transports (red dots). (b) Yearly anomaly calculated by the volume transport reported by Wei et al.
[2013] (green line), by the daily anomaly of JCOPE2 reanalysis on the days corresponding to the volume transport reported by Wei et al.
[2013] (black line), by the daily anomaly of JCOPE2 reanalysis every day in a year (red line), and those reported by Andres et al. [2009] (blueline).

the yearly mean of Kuroshio transport. In addition to these two data sets, we also calculated two yearlymeans from the reanalysis of JCOPE2: one calculated from four or ﬁve data as those in Wei et al. [2013](black line in Figure 4b, referred as pseudo yearly mean hereafter) and the other calculated from 365 or 366
daily data within 1 year (red line in Figure 4b).
There are some consistent variations among the four time series shown in Figure 4b. For example, all show
a positive anomaly from 1997 to 1999 and a negative anomaly from 2007 to 2008. Meanwhile, there arealso many inconsistent points. The yearly mean of Wei et al. [2013] (green line in Figure 4b) and the pseudo
yearly mean of JCOPE2 (black line in Figure 4b) presents a large interannual variation range throughout theentire period. However, the yearly mean of Andres et al. [2009] (blue line in Figure 4b) and that of JCOPE2(red line in Figure 4b) show a reduction in variation range after 2003. The large variation range after 2003
given by Wei et al. [2013] and pseudo yearly mean of JCOPE2 is likely caused by insufﬁcient sampling number and the presence of high-frequency variations in the Kuroshio transport. When the sampling interval isshortened to 10 days [Andres et al., 2009], the high-frequency variations in the Kuroshio transport are ﬁltered out in its yearly mean. Therefore, a reasonable yearly mean must be based on data with a sufﬁciently
short sampling interval.
3.2. Interannual Variations in SSH and Its Relation With the PDO Index
A general idea of the spatial variation in the interannual magnitude of SSH variations can be obtained fromthe distribution of standard deviation of its MIC (Figure 1b). Two well-deﬁned zonal bands appear south ofJapan to the longitude of the Kuroshio Extension region along 34 N and southeast of Taiwan along 22 N.
The northern band is related to the meandering of the Kuroshio path [e.g., Miyazawa et al., 2008] and the mesoscale eddies separated from the Kuroshio Extension [e.g., Ebuchi and Hanawa, 2001]. The southern band isassociated with the eddy activity in the STCC region [e.g., Qiu and Chen, 2010]. The standard deviation is small
inside the ECS. As shown in Figure 2a, the major components in the variation of sea level inside the ECS are
annual and short-term variations. However, both of these have been excluded in the MIC.
The expected negative correlation between the MIC of SSH at station CES and monthly PDO index is not signiﬁcant and the correlation coefﬁcient between the two time series for entire reanalysis period (1993–2012)

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Figure 5. (a) Monthly interannual component (MIC) of sea surface height (SSH, green line) at station CES and monthly PDO index (blackline). ‘‘r(1993–2002)’’ and ‘‘r(1993–2012)’’ represent correlation coefﬁcient between the two time series for a period of 1993–2002 and a period of
1993–2012, respectively. (b) The same as Figure 5a but for station PAC.

is 20.16 (<r0 ð112; 95%Þ50:19; Figure 5a). The negative correlation has been reported for the period from1993 to 2002 [Han and Huang, 2008]. For a conﬁrmation of our analysis, we calculated the correlation coefﬁcient for the period from 1993 to 2002 and obtained a value of 20.24 (<r0 ð55; 95%Þ50:26; Figure 5a).
Therefore, it is likely that the negative correlation between the MIC of SSH at station CES and monthly PDO
index decreased for the entire time series period (1993–2012). At station PAC, the correlation coefﬁcient
between the MIC of SSH and monthly PDO index is 0.33 (>r0 ð53; 95%Þ50:27) for the entire period and 0.32(>r0 ð93; 95%Þ50:20) for the period of 1993–2002.
The spatial distribution of correlation between the MIC of SSH and monthly PDO index demonstrates anapparent change inside the ECS and Japan Sea from the period of 1993–2002 to that of 1993–2012 (Figure6). In the period of 1993–2002, there is signiﬁcant negative correlation in the two marginal seas except forseveral spots in the Japan Sea (Figure 6a). In the period of 1993–2012, the correlation coefﬁcient decreased
over a large area in the two marginal seas (Figure 6b). In the areas outside the two marginal seas, the correlation coefﬁcient is generally positive, independent of choice of period. The reduction of correlation coefﬁcient in the two marginal seas suggests that the relationship between the Kuroshio transport inside the ECS
and the PDO index probably changes after 2003, which is examined directly from volume transport of Kuroshio in the ECS in the next subsection.
3.3. Interannual Variations in the Kuroshio Transport and Its Relation With PDO Index
The Kuroshio transports calculated from the MIC of velocity at transects A0 to G0, which are located along
the pathway of the Kuroshio and are designed to span the Kuroshio (Figure 1a), present a range of the interannual variations larger than 10 Sv (black line in Figures 7a–7h). The standard deviation of the MIC of volume transport at these transects is within a range of 2.17–4.28 Sv, which is signiﬁcantly larger than theamplitudes of the annual cycle (Table 1).
To understand the cause of the interannual variation of Kuroshio transport in the ECS, we separate the MIC
of volume transport (black line in Figures 7a–7h) into two parts according to time scales of variation byusing a 13 month running mean. One is a relatively slow variation with a time scale longer than 1 year, i.e.,
LMIC of volume transport (blue line in Figures 7a–7h) and the other is relatively fast variation with a timescale shorter than 1 year, i.e., SMIC of volumes transport (Figure 8), which is the difference of MIC and LMICof volume transport.

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Figure 6. Distribution of correlation coefﬁcient between monthly interannual component of sea surface height and monthly PDO index for (a) a period of 1993–2002 and (b) a period of
1993–2012.

Figure 7. (a–h) Monthly interannual component of volume transport (black line) and its 13 month running mean (blue line) through transects A0-G0 whose positions are given in Figure 1a.
(i and j) Monthly PDO index (black line). Shaded red and green areas represent the time for composite of long-term monthly interannual component of surface velocity shown in Figure 10.

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Table 1. Mean and Amplitude of Annual Cycle of Volume Transport Through Transects A0-G0 for the Period From 1993 to 2012aTransect

Mean value (Sv)
Amplitude of annual cycle (Sv)
SD of MIC-VT(Sv)
1993–2002
1993–2012
SD of LMIC-VT(Sv)
1993–2002
1993–2012
SD of SMIC-VT(Sv)
1993–2002
1993–2012

A0

B0

C0

K1

D0

E0

F0

G0

25.88
1.45

24.99
1.42

27.31
1.21

20.18
0.48

31.49
0.52

33.89
0.47

31.86
0.90

30.11
1.19

3.21
3.35

3.26
3.36

3.38
3.49

4.28
4.19

3.62
3.10

3.34
2.90

2.49
2.39

2.48
2.41

2.00
1.58

2.09
1.62

2.11
1.65

2.30
2.04

2.03
1.77

1.95
1.75

1.37
1.33

1.38
1.34

2.29
2.91

2.24
2.92

2.40
3.12

3.29
3.61

2.62
2.29

2.43
2.12

1.97
1.85

2.03
1.91

a
Standard deviation (SD) of monthly interannual component of volume transport (MIC-VT), SD of long-term monthly interannualcomponent of volume transport (LMIC-VT), and SD of short-term monthly interannual component of volume transport (SMIC-VT)
through transects A0-G0 in the period of 1993–2002 and that of 1993–2012.

Transects A0, B0, and C0 are located along the upstream half of the Kuroshio in the ECS. The Kuroshio transport at these three transects presents a consistent variation in both LMIC (blue line in Figures 7a–7c) and
SMIC (Figures 8a–8c), this is also conﬁrmed by the high correlation between any two of them (Table 2). The
LMICs of volume transport at the three transects show a large peak in 1997, small peaks in 2001, 2003, and2008, and troughs in 1994, 2000, and 2002. The standard deviations of LMIC of volume transport at transects A0, B0, and C0 are 2.00, 2.09, and 2.11 Sv in the period of 1993–2002 but 1.58, 1.62, and 1.65 Sv in the

Figure 8. Short-term monthly interannual component of volume transport through transects A0-G0. Red and blue crosses in (a) and (h)represent the times when the short-term monthly interannual component of sea surface height and velocity are shown in Figures 11–14.

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Table 2. Correlation Coefﬁcients Between a Pair of Transects for (a) Monthly Interannual Components of Volume Transport (MIC-VT), (b)Long-Term Monthly Interannual Components of Volume Transport (LMIC-VT), and (c) Short-Term Monthly Interannual Components ofVolume Transport (SMIC-VT) for the Period of 1993–2002 (Values Inside Left-Lower Triangle) and the Period of 1993–2012 (Values Inside
Right-Upper Triangle)a(a) MIC-VT
1993–2012

1993---2002

A0
A0
B0
C0
K1
D0
E0
F0
G0

0.99
0.81
0.48
0.27
0.30
0.29
0.29

B0

C0

K1

D0

E0

F0

G0

0.99

0.84
0.89

0.59
0.61
0.65

0.21
0.25
0.35
20.47

0.19
0.23
0.28
20.48
0.95

0.19
0.22
0.21
20.36
0.73
0.89

0.18
0.21
0.20
20.35
0.71
0.87
0.99

0.86
0.48
0.32
0.36
0.34
0.34

0.54
0.42
0.41
0.34
0.34

20.53
20.50
20.38
20.38
(b) LMIC-VT

0.97
0.78
0.77

0.89
0.88

0.99

1993–2012

1993---2002

A0
A0
B0
C0
K1
D0
E0
F0
G0

0.99
0.93
0.56
0.39
0.39
0.33
0.39

B0

C0

K1

D0

E0

F0

G0

0.99

0.91
0.95

0.56
0.53
0.54

0.26
0.33
0.39
20.56

0.24
0.31
0.35
20.56
0.97

0.20
0.25
0.28
20.50
0.84
0.92

0.24
0.29
0.31
20.48
0.84
0.92
0.99

0.95
0.53
0.45
0.45
0.39
0.44

0.55
0.51
0.50
0.45
0.49

20.43
20.42
20.37
20.33
(c) SMIC-VT

0.98
0.86
0.87

0.91
0.92

0.99

1993–2012

1993---2002

A0
A0
B0
C0
K1
D0
E0
F0
G0

0.98
0.67
0.43
0.13
0.15
0.17
0.17

B0

C0

K1

D0

E0

F0

G0

0.99

0.83
0.88

0.66
0.70
0.75

0.17
0.19
0.31
20.38

0.10
0.12
0.20
20.41
0.93

0.10
0.12
0.16
20.29
0.69
0.88

0.11
0.13
0.16
20.29
0.69
0.89
0.99

0.76
0.46
0.19
0.22
0.24
0.24

0.56
0.35
0.32
0.28
0.27

20.57
20.54
20.41
20.42

0.96
0.76
0.77

0.90
0.90

0.99

a

A0-G0 are transects whose positions are given in Figure 1a. In period of 1993–2002, the effective degree of freedom is 45–64 for
MIC-VT, 10–15 for LMIC-VT, and 38–87 for SMIC-VT; the corresponding criterion for signiﬁcance at the 95% level is 0.24–0.29 for MIC-VT,
0.52-0.61 for LMIC-VT, and 0.21-0.31 for SMIC-VT. In period of 1993–2012, the effective degree of freedom is 105–145 for MIC-VT, 23–30
for LMIC-VT, and 70–161 for SMIC-VT; the corresponding criterion for signiﬁcance at the 95% level is 0.16–0.19 for MIC-VT, 0.35–0.40 for
LMIC-VT, and 0.15–0.19 for SMIC-VT. The signiﬁcant correlation coefﬁcients are given in bold font.

period of 1993–2012 (Table 1). Apparently, their variation range becomes smaller after 2003 (Figures 7a–7c).
On the other hand, the SMIC of volume transport at three transects has a relatively large variation range
from 2003 to 2008 (Figures 8a–8c), resulting in an increase of standard deviations of SMIC of volume transport at transects A0, B0, and C0 for the period of 1993–2012 (Table 1).
Transects D0, E0, F0, and G0 are located along the downstream half of the Kuroshio in the ECS. The LMIC
(blue line in Figures 7e–7h) and SMIC (Figures 8e–8h) of volume transport are spatially consistent at thefour transects and the correlation coefﬁcient between any two of them is also high (Table 2). The reductionin the variation range in the upstream region that the LMIC of volume transport after 2003 is also apparent
at the four downstream transects (Figures 7e–7h), but the SMIC of volume transport in the downstream
region does not show any apparent change throughout the entire period (Figures 8e–8h).
Transect K1 is located at the Kerama gap (Figure 1a), through which the Kuroshio does not directly pass.
Both the LMIC and SMIC of volume transport through transect K1 (Figures 7d and 8d, positive value is in the
direction from the Kuroshio region to the interior ocean) shows a positive correlation with the

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Figure 9. (a) Correlation coefﬁcients between monthly interannual component of volume transport (MIC-VT) through transects A0-G0 andmonthly PDO index for a period of 1993–2002 and a period of 1993–2012. (b) The same as Figure 9a but for long-term monthly interannual component of volume transport (LMIC-VT) and monthly PDO index. A0-G0 refer to the transects whose positions are given in Figure
1a. In period of 1993–2002, the effective degree of freedom is 40–49 for MIC-VT and 19–23 for LMIC-VT; the corresponding criterion for signiﬁcance at the 95% level is 0.28–0.30 for MIC-VT and 0.41–0.44 for LMIC-VT. In period of 1993–2012, the effective degree of freedom is
78–128 for MIC-VT and 33–40 for LMIC-VT; the corresponding criterion for signiﬁcance at the 95% level is 0.17–0.22 for MIC-VT and 0.31–
0.34 for LMIC-VT. The signiﬁcant correlation coefﬁcients at the 95% level are denoted by red font.

corresponding component of volume transport through the transects at the upstream region and a negative correlation with the corresponding component of volume transport through the transects at the downstream region in the ECS (Table 2). Therefore, an increase of Kuroshio transport in the upstream region or areduction of Kuroshio transport in the downstream region is accompanied by an outﬂow of current fromthe ECS to the Paciﬁc.
As with SSH inside the ECS, the extension of data from the period of 1993–2002 to the period of 1993–2012
also reduces the correlation between the MIC of volume transport and the PDO index (Figure 9a). This feature is particularly apparent in the upstream region (transects A0-C0, Figure 9a), where the correlation coefﬁcients are above 0.20 in the period of 1993–2002, but decreases to the values less than 0.10 over theentire period (1993–2012). The LMICs of volume transport through transects A0-C0 in the upstream regions
give a little high correlation coefﬁcients with the PDO index in the period of 1993–2002 (Figure 9b). The
removal of SMIC from MIC efﬁciently improves the correlation between LMICs and PDO index in the
upstream region but not in the downstream region, suggesting a possibility that the SMIC behaves differently in the upstream region compared to the downstream region.
To examine the cause for the difference in correlation between the Kuroshio transport and PDO index in
the upstream and downstream regions, we made composite maps of LMIC of currents based on the PDO
index (Figures 7i and 7j). We selected the period when the PDO index was larger than its mean value plus
one standard deviation in the period of 1993–2002 and in the period of 1993–2012 (red area in Figures 7i
and 7j); and the period when the PDO index was smaller than its mean value minus one standard deviationin the period of 1993–2002 and in the period of 1993–2012, respectively (green area in Figures 7i and 7j). A

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positive correlation between the Kuroshio transport in the ECS and PDO index means a strong Kuroshio during the period with positive PDO index but a weak Kuroshio during the period with negative PDO index. In
the anomaly ﬁeld of currents, i.e., MIC and LMIC, a strong Kuroshio is shown by a current in the same direction as the Kuroshio, while a weak Kuroshio is shown as a current in the opposite direction to the Kuroshio.
Figures 10a and 10c are composites for positive PDO index in the periods of 1993–2002 and 1993–2012,
respectively. Both show a current in the same direction as the Kuroshio. The magnitude of current is weakerin Figure 10c than in Figure 10a. Figures 10b and 10d are composites for negative PDO index in the periodsof 1993–2002 and 1993–2012, respectively. The current in the opposite direction to the Kuroshio is not consistent over the entire shelf break in both ﬁgures: it is strong in the upstream region but weak in the downstream region; it also becomes weak when the analysis period is extended to 20 years.
The extension of the analysis period from 1993–2002 to 1993–2012 likely weakens the correlation of Kuroshio transport in the ECS to the PDO index. This weakening is more apparent in the upstream region thanin the downstream region. Since only the SMIC of volume transport in the upstream region is stronger inyears after 2002 than before 2002 (Figure 8), it is possible that the regional difference in the correlation of
MIC or LMIC of volume transport with the PDO index is caused by the processes responsible for the SMIC ofvolume transport. In section 4, we demonstrate that the mesoscale eddies are responsible for the SMIC of
Kuroshio transport in the ECS and their inﬂuences on the Kuroshio volume transport have a strong regional
dependence.

4. Influence of Mesoscale Eddies on the Kuroshio Transport and Its Regional
Dependence
4.1. Eddy-Related Variations in the Kuroshio Transport in the ECSThe correlation between SMICs of volume transport among each pair of transects (Table 2) suggests a spatially consistent variation occurring in the upstream region and downstream region. To understand thecauses for the SMIC of volume transport in these two regions, we choose two maximum (December 1995;
July 2008) and two minimum (June 1994; October 2008) SMICs of volume transport through transect A0(Figure 8a), as well as two maximum (June 1996; July 2011) and two minimum (October 1994; February2011) SMICs of volume transport through transect G0 (Figure 8h), and present horizontal and vertical distributions of the corresponding currents (Figures 11–14).
The maximum (minimum) SMIC of volume transport through transect A0 is closely associated with the presence of an anticyclonic (cyclonic) eddy southeast of Taiwan (Figure 11). The presence of an anticycloniceddy induces a northeastward current along the inner side of the Kuroshio at transect A0 (Figures 11a and11b), while the presence of cyclonic eddy southeast of Taiwan induces a southwestward current along theinner side of the Kuroshio (Figures 11c and 11d). For the same anticyclonic eddies (Figures 11a and 11b) or
the same cyclonic eddies (Figures 11c and 11d), a stronger eddy (Figure 11b or 11d) induces a stronger
eddy-related current than the corresponding weak one (Figure 11a or 11c). Such an eddy-related current isevident in the upstream region of the Kuroshio, i.e., from east of Taiwan to the midway along the shelf
break. Consequently, the Kuroshio in the upstream region is intensiﬁed (weakened) by the presence of an
anticyclonic (cyclonic) eddy southeast of Taiwan. The downstream region of the Kuroshio in the ECS, i.e.,from the midway along the shelf break to the Tokara Strait, does not show a consistent response to theeddies southeast of Taiwan in the four cases shown in Figure 11.
The eddy-related current occupies entire depth of transect A0 and is strong in the upper layer and weak in
the lower layer (Figure 12). The arrival of a stronger eddy (Figure 12b or 12d), regardless of whether theeddy is cyclonic or anticyclonic, induces a stronger SMIC of velocity over the entire transect than that of a
weak one (Figure 12a or 12c). This in turn increases the SMIC of volume transport through the transect.
Therefore, the larger variation range in the SMIC of volume transport in the upstream region of the Kuroshio
in the ECS in the years from 2003 to 2008 (Figures 8a–8c) is likely caused by the intensiﬁcation of mesoscaleeddies southeast of Taiwan.
The maximum (minimum) SMIC of volume transport through transect G0 is associated with the presence ofan anticyclonic (cyclonic) eddy northeast of Okinawa Island (Figure 13). As an anticyclonic eddy is foundnortheast of Okinawa Island, a northeastward current is also found along the Kuroshio pathway fromSOEYANTO ET AL.

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Figure 10. (a) Composite of long-term monthly interannual component of surface current for the time when the PDO index is larger than its mean plus one standard deviation in aperiod of 1993–2002. (b) The same as Figure 10a but for the time when the PDO index is less than its mean minus one standard deviation in a period of 1993–2002. (c) The same as Figure 10a but for a period of 1993–2012. (d) The same as Figure 10b but for a period of 1993–2012. The exact time for the composite is given in Figures 7i and 7j.

transects D0 to G0 (Figures 13a and 13b); as a cyclonic eddy is formed northeast of Okinawa Island, a southwestward current is found along the Kuroshio pathway from transect G0 to D0 (Figures 13c and 13d). Sucheddy-related currents are similar to those in the upstream region of the Kuroshio. However, an essential difference in the eddy-related currents between the upstream region and the downstream region is also apparent. In the upstream region, a part of the eddy generally merges into the Kuroshio and actually becomes apart of the Kuroshio (Figure 11); in the downstream region, the eddy is far from the Kuroshio and therefore
cannot merge into the Kuroshio main stream (Figure 13). The island chain from Okinawa Island to theTokara Strait is likely a barrier for the eddy northeast of Okinawa Island to approach the Kuroshio mainstream.
The eddy-related current is also strong in the upper layer and weak in the lower layer at transect G0 (Figure14). However, the eddy-related current at transect G0 is not as sensitive as that at transect A0 to the eddy
strength. For example, the anticyclonic eddy is stronger in June 1996 (Figure 13a) than in July 2011 (Figure13b) but their corresponding currents at transect G0 are very similar (Figures 14a and 14b). This is also true
for the case of two cyclonic eddies shown in Figure 13.

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Figure 11. (a and b) Short-term monthly interannual component (SMIC) of surface velocity (arrows) and sea surface height (m, color tone) in the presence of an anticyclonic eddy east of
Taiwan. (c and d) The same as Figures 11a and 11b but for the presence of a cyclonic eddy east of Taiwan. The corresponding SMIC of volume transport through transects A0-G0 are presented in Figure 8.

To quantify the relationship between the mesoscale eddies and Kuroshio transport in the ECS, we calculated an area-averaged vorticity from SMIC of surface velocity within two ﬁxed areas, southeast of Taiwan(Zone ET, Figure 1a) and northeast of Okinawa Island (Zone EO, Figure 1a). The vorticity averaged in the twoareas well represents the strength of eddies southeast of Taiwan and northeast of Okinawa Island. For
example, the two eddies southeast of Taiwan in December 1995 (Figure 11a) and July 2008 (Figure 11b)
give two negative peaks of vorticity in Figure 15a. The vorticity magnitude of eddies is larger in July 2008
than in December 1995, consistent with the current ﬁelds presented in Figures 11a and 11b. The eddies
shown in Figures 11c and 11d, as well as those in Figure 13, are also easily identiﬁed in Figures 15a and 15c.
The correlation between the mean vorticity within Zone ET and the Kuroshio transport in the ECS is as highas 20.70 (>r0 ð88; 95%Þ50:21) at transect A0 but gradually decreases in magnitude from transect A0 totransect C0 and abruptly becomes insigniﬁcant from transect D0 to G0. Apparently, the impact of the mesoscale eddies southeast of Taiwan on the Kuroshio transport is limited to the upstream region of the Kuroshio in the ECS. The signiﬁcant negative correlation coefﬁcient at transect K1 hints that the mesoscale

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Figure 12. (a and b) Short-term monthly interannual component (SMIC) of velocity (color tone) normal to transect A0 in the presence of an anticyclonic eddy east of Taiwan. (c and d)
The same as Figures 12a and 12b but for the presence of a cyclonic eddy east of Taiwan. Positive direction for the velocity normal to transect A0 is northward. Black contours show sumof SMIC and temporal mean of velocity normal to transect A0 over the entire period from 1993 to 2012. The SMIC of volume transport (SMIC-VT) is given inside each plot.

eddy southeast of Taiwan is not completely absorbed by the Kuroshio main stream and the Kerama Gap
probably acts as a leakage pathway of the eddy from the ECS. This is probably why the impact of eddies
southeast of Taiwan on the Kuroshio transport cannot be found in downstream region of the Kuroshio in
the ECS.
There is no signiﬁcant change throughout the entire analysis period in the correlation between the meanvorticity within Zone ET and the Kuroshio transport at transects A0-C0 in the upstream region (Figure 15b).
Therefore, the relation between the Kuroshio transport at transects A0-C0 and the mesoscale eddy southeast of Taiwan must be independent of the choice of period and the larger variation range of SMIC of volume transport in the upstream region in from 2003 to 2008 (Figures 8a–8c) is surely caused byintensiﬁcation of the eddies (Figure 11).

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Figure 13. The same as Figure 11 but for peaks and troughs of volume transport through transect G0 corresponding to the presence of (a and b) an anticyclonic eddy and (c and d) a
cyclonic eddy northeast of Okinawa Island.

The correlation between the area-averaged vorticity within Zone EO and the Kuroshio transport in the ECS
is high at transects G0 and F0 and gradually decreases from the downstream region to the upstream region.
The SMIC of volume transport in the downstream region of the Kuroshio in the ECS is affected more by theeddies northeast of Okinawa Island than by those southeast of Taiwan. This conclusion also does notdepend on choice of period.
4.2. Change of Intensity of Mesoscale Eddies
As shown in Figure 8, the SMIC of Kuroshio transport in the upstream region has a larger variation range inthe years from 2003 to 2008. According to the linear correlation between vorticity of mesoscale eddies andKuroshio transport (Figure 15), it is expected that the mesoscale eddies must be stronger during this period.
We useeddy kinetic
 energy (EKE) to elucidate the variation in eddy intensity. The EKE is deﬁned as
02
02
EKE5 12 u 1v , where u’ and v’ are SMIC of surface velocity at a grid point.
The area-averaged EKE within Zone ET shows an apparent interannual variation, being relatively large in
1993, 1995, 1998, 2000, 2004, 2006, 2008, and 2010 (Figure 16a). The largest value of EKE in Zone ET in 2006

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Figure 14. The same as Figure 12 but for the velocity normal to transect G0 corresponding to the four cases shown in Figure 13.

and 2008 is consistent with the largest variation range of SMIC of Kuroshio transport through transects A0,
B0, C0, and K0 in the same year (Figure 8). Based on the EKE, we deﬁned years with strong eddies when the
EKE is larger than the sum of temporal mean and standard deviation of EKE over entire period (1993–2012).
The temporal mean of the area-averaged EKE in Zone ET is 1:5431022 m2 s22 for the years with strong
eddies and 0:5531022 m2 s22 for the other years, while the standard deviation of the area-averaged EKE in
Zone ET is 0:2931022 m2 s22 for the years with strong eddies and 0:2331022 m2 s22 for the other years.
Apparently, the EKE southeast of Taiwan has not only a higher mean state but also a higher variability inthe years with strong eddies than in other years.
The area-averaged EKE within Zone EO also shows a large interannual variation, being large in 1995, 1996,
2001, 2004, and 2006. Some consistent variations in EKE between Zone ET and Zone EO can be found in theyears such as 2004 and 2006; however, they are not always consistent. The correlation coefﬁcient between
the two area-averaged EKEs shown in Figures 16a and 16b is 0.23 (>r0 ð130; 95%Þ50:17) for the entire
period.

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Figure 15. (a) Spatially averaged vorticity in Zone ET (122 E–126 E, 21 N–25 N, Figure 1a). The vorticity at each grid point within Zone ET was calculated from SMIC of surface velocity.
(b) Correlation coefﬁcient between the spatially averaged vorticity in Zone ET and SMIC of volume transport through transects A0-G0 in a period of 1993–2002 and a period of 1993–
2012. (c) The same as Figure 15a but for Zone EO (128 E–132 E, 26 N–29 N, Figure 1a). (d) The same as Figure 15b but for the spatially averaged vorticity in Zone EO.

Since the minimum EKEs in Zones ET and EO did not change greatly throughout the entire period, theincrease of EKE in the years with strong eddies appears in both its temporal mean and standard deviation.
We therefore present only the difference of temporal mean of EKE between the years with strong eddies
and the other years to observe where the eddy intensity increased (Figure 16c).
The enhancement of EKE at Zone ET in the years with strong eddies occurs over a large area southeast of
Taiwan outside the ECS and over the Kuroshio upstream region inside the ECS from northeast of Taiwan to
the midway along the shelf break (Figure 16c). The enhancement of EKE at Zone ET in the years with strong
eddies is not found in the Kuroshio downstream region inside the ECS. The change of EKE in the years withstrong eddies is therefore different between upstream and downstream regions of the Kuroshio in the ECS.
This difference is consistent with the SMIC of Kuroshio transport that shows an increase of variation range
in the years with strong eddies in the upstream region but almost the same variation range throughout theentire period in the downstream region (Figure 8). On the other hand, the enhancement of EKE at Zone EO
in the years with strong eddies occurs southeast of Tokara Strait outside the ECS (Figure 16d). There is noapparent increase of EKE inside the Tokara Strait, suggesting again that the response of Kuroshio transport
in the downstream region to the mesoscale eddies has a different mechanism from the upstream region
and is not as sensitive as that in the upstream region (Figures 11 and 13).
When enlarging the domain for Figure 16c, we found that the enhancement of EKE at Zone ET in the years
with strong eddies reaches to the eastern boundary of the model domain, forming a zonal band within
16 N–21 N from the central Paciﬁc to the Kuroshio region covering a meridional band from east of Luzon
Island to east of Taiwan (ﬁgure not shown here). This is consistent with the report that most mesoscaleeddies arriving southeast of Taiwan originate in the STCC region [Hwang et al., 2004].
Qiu and Chen [2010] examined the interannual variation of EKE in the STCC band using satellite altimeterdata from 1993 to 2008 and found high eddy activity in some years (1998–1999 and 2003–2008) and low
eddy activity in other years (1993–1995 and 1999–2002). Qiu and Chen [2013] reported concurrent

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Figure 16. (a) Spatially averaged EKE for Zone ET (black line) calculated from short-term monthly interannual component (SMIC) of surface velocity. Shaded area represents the time forstrong eddies when the EKE is larger than the sum of its mean and standard deviation over entire period (1993–2012). (b) The same as Figure 16a but for Zone EO. (c) Difference of EKE
between its temporal mean over the time with strong eddies with shaded area in Figure 16a and that over the other time without shaded area in Figure 16a. (d) The same as Figure 16c
but for Zone EO. The squares enclosed by gray lines in Figures 16c and 16d are for spatially averaged EKE shown in Figures 16a and 16b, respectively.

occurrence of interannual variations of EKE over a broad band (18 N–28 N) and related this to the variations
in surface heat ﬂux forcing. If we chose the same areas as those used by Qiu and Chen [2010, 2013], we canobtain the same interannual variations in the EKE from JCOPE2 reanalysis (ﬁgures not shown here) as thosereported by Qiu and Chen [2010, 2013]. This is not surprising because the data assimilation in the JCOPE2
included the satellite altimeter data used by Qiu and Chen [2010, 2013].
4.3. Different Origin of Eddies Southeast of Taiwan and Northeast of Okinawa Island and Different
Mechanism for Them to Affect the Kuroshio in the ECS
A lag correlation between the daily interannual component of volume transport through transect A0 andthat of SSH over the model domain provides the trajectory of eddies that strongly affect the Kuroshio transport east of Taiwan (Figure 17a). As time goes back, i.e., with a negative lag in Figure 17a, the area of highcorrelation moves eastward from the area east of Taiwan along 22 N–24 N. This means that eddies propagating westward along 22 N–24 N eventually arrive east of Taiwan and affect the Kuroshio transport eastof Taiwan. The propagation speed of this region of high lagged correlation is approximately 0.11 m s21. The
features on the eddy propagation are very similar to the analysis by Hwang et al. [2004].
In contrast, the lag correlation between the daily interannual component of volume transport throughtransect G0 and that of SSH over the model domain does not suggests an apparent propagation pathwayof eddies found northeast of Okinawa Island (Figure 17b). The correlation coefﬁcient is high locally northeast of Okinawa Island and is sustained for only 1 to 2 months. Ichikawa [2001] also reported a short lag

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Figure 17. (a) Correlation coefﬁcient between daily interannual component of volume transport through transect A0 and that of SSH at all the grid points ‘‘lag’’ days ago (lag 5 0–150).
(b) The same as Figure 17a but for daily interannual component of volume transport through transect G0.

time (1–2 months) with a signiﬁcant correlation coefﬁcient between the volume transport through theTokara Strait and SSH northeast of Okinawa Island. The locally high correlation coefﬁcient, as well as a shortlife time, suggests a local intensiﬁcation mechanism of eddy activity northeast of Okinawa Island. The animation of SSH in this area actually shows combination of two or three eddies from different directions. Apparently, the eddies northeast of Okinawa Island have a different origin from the eddies southeast of Taiwan.
As shown in Figures 11 and 13, the physical processes involving the inﬂuence of mesoscale eddies southeast of Taiwan and that of the mesoscale eddies northeast of Okinawa Island on the Kuroshio are likely different. The eddies southeast of Taiwan may partly merge into the Kuroshio main stream but the eddies
northeast of Okinawa Island may not. Andres and Cenedese [2013] proposed a mechanism for the eddies
northeast of Okinawa to remotely affect the Kuroshio main stream in the ECS through interaction between
an eddy and an island. The relationship between the Kuroshio transport in its downstream region in the
ECS and eddies northeast of Okinawa Island given by this study is essentially consistent with the mechanism given by Andres and Cenedese [2013].

5. Summary
Using the JCOPE2 reanalysis from the past two decades, we demonstrated that both the PDO index andmesoscale eddies are closely related to the interannual variation of Kuroshio transport in the ECS. The

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positive correlation between the PDO index and the interannual variation of Kuroshio transport in the ECS
is conﬁrmed in a period from 1993 to 2002. However, this relation became weak as we extended the analysis to a period from 1993 to 2012.
Eddies southeast of Taiwan strongly affect the Kuroshio transport in the upstream region in the ECS andeddies northeast of Okinawa Island strongly affect the Kuroshio transport in the downstream region in the
ECS. The Kuroshio transport in the upstream region in the ECS is sensitive to the strength of the eddy southeast of Taiwan, while that in the downstream region is not sensitive to the strength of the eddy northeastof Okinawa Island. The merging of a part of the eddy east of Taiwan with the Kuroshio is the cause for a sensitive response of Kuroshio transport in the upstream region to the eddy strength southeast of Taiwan. The
merging of the eddy with the Kuroshio cannot occur in the downstream region because of the presence ofislands between Okinawa Island and Tokara Strait. The remote impact of the eddy on the Kuroshio transport
in the downstream region is the cause of insensitive response of Kuroshio transport in the downstream
region to the eddy strength northeast of Okinawa Island.
The PDO index showed a larger variation range in the period before 2002 (>2.0) than after 2002 (<2.0, Figure 7a). Consequently, the PDO-related signal in the interannual variation of Kuroshio transport is stronger
in the period before 2002 than after 2002. The eddies southeast of Taiwan and those northeast of Okinawa
Island were stronger in the period of 2003–2008 than in other years. As a result of the sensitive response to
the eddies southeast of Taiwan, the eddy-related variation of Kuroshio transport in the upstream region in
the ECS was larger during years of 2003–2008 than in other years; as a result of insensitive response to theeddies northeast of Okinawa Island, the eddy-related variation range of Kuroshio transport in the downstream region in the ECS was approximately the same throughout the entire period. The intensiﬁcation of
the eddies southeast of Taiwan and the lower PDO index variability are the reasons for the reduction of correlation values between the Kuroshio transport and the PDO index in the entire period (Figure 9a).
The intensiﬁcation of eddies in the STCC region can also be conﬁrmed in the satellite altimeter data [Qiuand Chen, 2010]. Due to assimilation of satellite altimeter data in the JCOPE2 reanalysis, we cannot further
analyze the dynamics responsible for the intensiﬁcation of the eddies in the STCC region [Qiu and Chen,
2013]. However, the reanalysis of JCOPE2 provides new insight on the response of the Kuroshio transport in
the ECS to the intensiﬁcation of eddies in the STCC region and how the response modiﬁes the relation
between the Kuroshio transport and PDO index. We conclude that the combination of PDO-related signals
and eddy-related signals determines the interannual variations of the Kuroshio transport in the ECS. In the
future, sufﬁcient attention must be paid to the spatial dependence of Kuroshio transport in the ECS on themesoscale eddies.

Acknowledgments
This work was supported by JSPS
KAKENHI (22106002) and the National
Basic Research Program of China(2012CB956004). We thank L.-Y. Oey
for providing the digital data of sea
level difference between the tidegauges of Keelung and Ishigaki.
Comments from two anonymous
reviewers were helpful in improving
the original manuscript.

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