JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 117, C01026, doi:10.1029/2011JC007292, 2012

The Kuroshio nutrient stream and its temporalvariation in the East China Sea
Xinyu Guo,1,2 Xiao-Hua Zhu,2,3 Qing-Song Wu,2 and Daji Huang2,3Received 14 May 2011; revised 10 November 2011; accepted 21 November 2011; published 31 January 2012.

[1] Using in situ data from 88 cruises from 1987 to 2009 in the East China Sea,
downstream nutrient flux (the product of velocity and nutrient concentration) and nutrienttransport (integration of flux over a section) by Kuroshio were examined. The presence of a
maximum nutrient flux core in the middle layer was confirmed. Seasonal variation in the
nutrient flux was not significant and was much smaller than interannual variations. Thechange in the Kuroshio speed and current structure were major causes for interannual
variations in the nutrient flux. The downstream nitrate transport by the Kuroshio in the East
China Sea had a mean value of 170.8 kmol s1 and a standard deviation of 41.6 kmol s1.
The mean seasonal nitrate transport ranged between about 161 and 177 kmol s1 and theabsolute interannual variation from about 100 to 280 kmol s1. The phosphate flux andphosphate transport can be approximately estimated by the ratio (13.64) of nitrateconcentration to phosphate concentration. The nitrate concentration in the middle andbottom layers across the Kuroshio in the East China Sea was found to increase significantly
over the 23 year period, and especially after 2004 but not at ratios with oxygen that suggest
increased remineralization of organic matter. The nutrient transport, however, did notincrease significantly because increases in the surface layer were offset by decreases in the
middle and bottom layers caused by reduction in velocity in the density ranges of 26.0 to
27.2 sq below the Kuroshio.
Citation: Guo, X., X.-H. Zhu, Q.-S. Wu, and D. Huang (2012), The Kuroshio nutrient stream and its temporal variation in the
East China Sea, J. Geophys. Res., 117, C01026, doi:10.1029/2011JC007292.

1. Introduction[2] The idea of a nutrient stream that transports nutrientsalong a west boundary current has been proposed for the
Gulf Stream for many years [Pelegrí and Csanady, 1991;
Pelegrí et al., 1996]. The Gulf Stream’s counterpart in the
North Pacific, the Kuroshio, is naturally expected to have thesame function as the Gulf Stream for transporting nutrientsalong it, from its origin area, east of Luzon Island where it
forms as a northward branch of the westward North Equatorial Current, to the Kuroshio Extension (Figure 1a).
Robbins and Bryden [1994] estimated the meridional nutrient transport in the North Pacific Ocean but did not pay
special attention to the role of Kuroshio. Chen et al. [1994,
1995a] first reported nutrient flux and nutrient transport by
the Kuroshio based on four cruises along a section extending
eastward from Taiwan.

1Center for Marine Environmental Studies, Ehime University,
Matsuyama, Japan.
2State Key Laboratory of Satellite Ocean Environment Dynamics,
Second Institute of Oceanography, State Oceanic Administration,
Hangzhou, China.
3
Department of Ocean Science and Engineering, Zhejiang University,
Hangzhou, China.

Copyright 2012 by the American Geophysical Union.
0148-0227/12/2011JC007292[3] The nutrient stream results from the strong westernboundary current and the high nutrient concentration within
the water that is carried by this boundary current. As a resultof reduced current speed and increased nutrient concentration with depth, the nutrient flux, defined as the product of
the current speed and nutrient concentration, usually has asubsurface high value. The current, which is faster at its
center than at its sides, further produces a subsurface corewith a maximum nutrient flux [Pelegrí and Csanady, 1991].
[4] Our first objective in this study is to present the vertical structure of mean nutrient flux based on long-term
repeat observations across the Kuroshio in the East China
Sea. On one hand, the vertical gradients of current speed andnutrient concentration in the Kuroshio region are probably
different from those in the Gulf Stream; the comparison ofnutrient flux in two western boundary currents is therefore
an interesting issue. On the other hand, reports by Chen et al.
[1994, 1995a] were based on four cruises across the Kuroshio and their results may be affected by some processes
with short time scales, e.g., the passage of mesoscale eddies.
By averaging long-term repeated observations, we expect topresent a robust document on the mean state of nutrient flux
by the Kuroshio in the East China Sea.
[5] Our second purpose in this study is to examine thetemporal variations in nutrient flux and nutrient transport by
the Kuroshio in the East China Sea. It has been demonstrated
that the nutrients carried by the Gulf Stream can reach theeuphotic layer and contribute to the primary production in

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Figure 1. (a) Study area and schematic image of Kuroshio path. ECS denotes East China Sea; TW
denotes Taiwan. (b) Position of hydrographic stations for water temperature and salinity (black dots)
and for nutrient concentrations (red plus signs). Line marked “200” denotes 200 m isobath.
the North Atlantic [Pelegrí et al., 2006; Williams et al.,2006]. In the North Pacific, high primary production has
been reported in the Kuroshio/Oyashio mixing region [e.g.,
Tadokoro et al., 2009]. Since the nutrient concentration ishigher in the Oyashio water than in the Kuroshio water, thenutrients carried by the Oyashio from subpolar region to thisregion likely contribute mostly to the high primary production there. However, the mixing nature of two water masses
indicates that the nutrients carried by the Kuroshio may also
contribute to the primary production in this region and will
have an impact in all waters being influenced by the subarctic current across the Pacific. For this reason, a good
documentation of the temporal variations in nutrient flux andnutrient transport by the Kuroshio will add new material forunderstanding the long-term variations in the nutrients andprimary production in the Kuroshio/Oyashio mixing region.
[6] In the long journey from its origin to the Kuroshio
Extension area, the Kuroshio has a relatively more stable
path along the shelf break of the East China Sea than in otherregions. This provides good conditions for monitoring its

temporal variations. In fact, the Japan Meteorological
Agency has maintained a section across the Kuroshio in the
East China Sea for more than 40 years. Using the hydrographical and nutrient data from such long-term observations, we examined the spatial structure of nutrient flux andthe temporal variations of nutrient flux and nutrient transport
by the Kuroshio in the East China Sea.
[7] In section 2, we give a brief description of the observations and the data processing procedures, such as theinverse method being used to calculate the velocity normal
to the section from hydrographic data, and the linear interpolation of nutrient data to the velocity grid points. In
section 3, we present the spatial distribution of mean nutrient
flux and examine temporal variations in the nutrient flux byusing empirical orthogonal function (EOF) analysis and bydirectly decomposing the variance of nutrient flux into several terms that depend on the variances of velocity andnutrient concentration. The temporal variations of nutrienttransport, with attention to the differences before 2004 andafter 2004, are also given in section 3. In section 4, we give

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Figure 2. Scatterplot of nitrate concentration (CNO3) versus phosphate concentration (CPO4). The correlation coefficient between CNO3 and CPO4 is 0.99. The linear regression of CNO3 to CPO4 gives a ratio of
13.64 and a cutoff of 0.36 mmol m3 of CNO3.
an analysis on possible causes for the differences in velocity
and nutrient concentrations before 2004 and after 2004 andcompare spatial structures of nutrient flux with those presented by Chen et al. [1994, 1995a] and those reported in the
Gulf Stream region [Pelegrí and Csanady, 1991]. A briefsummary is given in section 5.

2. Data and Processing Methods[8] Hydrographic, nutrient, and dissolved oxygen data
along two sections (PN and TK) in the East China Sea(Figure 1b) from 1965 to 2009 were obtained from Japan
Meteorological Agency Web site (http://www.data.kishou.
go.jp/kaiyou/db/vessel_obs/data-report/html/ship/ship.php).
Since the data before 1987 were not sufficient for calculating
nutrient flux, we used only the data from the 88 cruises after
1987 in this study. The hydrographic data were collected by
CTD (conductivity-temperature-depth profiler); the nutrientand dissolved oxygen data were from water samples collected at standard depths and analyzed using routine methods(see Aoyama et al. [2008, Table 1] for analysis method
details).
[9] Usually, there were four cruises every year and eachcruise roughly corresponded to one season. The seasonalmean was obtained by averaging all the data from March to
May for spring, from June to August for summer, from
September to November for autumn, and from December to
February for winter. To obtain a time series with even timeintervals for the EOF analysis, we defined four fixed dates ineach year (0.25, 0.50, 0.75, and 1.00) and obtained the value
at a date by linearly interpolating the data before and after
the given date.

[10] Hydrographic data along both section PN and section
TK were used in the inverse calculation of the velocities.
The inverse method [Wunsch, 1978] was applied to the area
enclosed by section PN, section TK, and the line connecting
two north stations of the sections (Figure 1b). The waterexchange through two gaps between the islands at thesouthwestward extension of section TK was neglected.
Following Zhu et al. [2006], the water column was verticallyseparated into five layers by sea surface, four isopycnals(24.5, 26.0, 26.5, and 27.2 sq), and sea bottom. Mass conservation was assumed within each of five layers, while salt
conservation was assumed within the lower four layers. With
these conditions, the velocities at the reference level, which
was set as the depth of deepest hydrographic data between
each pair of stations, were obtained [Zhu et al., 2006].
Applying the thermal winds relation to a pair of hydrographic stations, we then obtained the velocity normal to theline connecting two stations at 1 m interval in the vertical.
[11] The reference velocity is different for each cruise.
The mean value calculated from 88 cruises at section PN
varies from 0.05 to 0.1 m s1 for stations west of 127°E
and approaches to 0 m s1 for stations east of 127°E (seebottom velocity in Figure 3b). The standard deviation isaround 0.1 m s1 for stations west of 127°E and decreases
to 0.02 m s1 for stations east of 127°E along section PN.
[12] Only data along section PN were used to calculate thenutrient flux because section TK was not sufficiently sampled. The nutrients in the Japan Meteorological Agency data
set have nitrate and phosphate but not silicate. Since nitrate
and phosphate concentrations have a good correlation(Figure 2), we will mainly present the results of nitrate inthis study and describe those of phosphate if necessary. The

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Figure 3. Average values from 88 cruises along section PN. (a) Potential density (sq). (b) Velocity(m s1). (c) Nitrate concentration (mmol m3). (d) Phosphate concentration (mmol m3). (e) Nitrate flux
(mmol m2 s1). (f) Phosphate flux (mmol m2 s1). The inverse triangles denote hydrographic stations
where water temperature, salinity, and nutrient concentrations are available.
nitrate and phosphate concentrations at standard levels werefirst linearly interpolated to depth at one meter intervals at
the hydrographic stations along section PN (Figure 1b). For
calculating nutrient flux, the nutrient concentration was linearly interpolated horizontally to the grid points where current speed was determined at the same depth, and whichwere defined at the middle of two hydrographical stations.
The nutrient transport was obtained by integrating thenutrient flux over the section.
[13] In addition to water temperature, salinity, and nutrient
data, the dissolved oxygen data were used to discuss thepossible causes for the temporal variations of water property
in the middle and bottom layers in section 4.

3. Results
3.1. Spatial Structure of Mean Nutrient Flux
[14] The mean density, current speed, nitrate concentration, phosphate concentration, nitrate flux, and phosphate
flux at section PN (Figure 3) were obtained by averaging
88 data sets from 1987 to 2009. The Kuroshio axis having
maximum surface current speed can be identified over theslope (Figure 3b). The current speed decreases from theKuroshio axis to its two sides and to the lower layer. As
expected, vertical and horizontal variations in current speed
are similar to the geostrophic velocity calculated with a nomotion level at 700 dbar [Oka and Kawabe, 1998; Guo et al.,2003, Figure 9]. The total volume transport given by thecurrent speed in Figure 3b is 22.5 Sv (1 Sv = 106 m3 s1),which is close to the traditional estimate of the volumetransport trough section PN [e.g., Hinata, 1996].
[15] The nitrate (Figure 3c) and phosphate concentrations(Figure 3d) generally increase with depth but the ratedepends on water depth. The increasing rate of nutrient
concentration with depth is lowest from the sea surface to200 m depth, is highest from 200 m depth to 700 m
depth (e.g., 5 mmol m3 per 100 m for nitrate), andhas an intermediate value from 700 m depth to sea bottom(>900 m). The product of nitrate or phosphate concentration
with current speed gives a maximum nutrient flux at 400 m
depth. There, the nitrate flux reaches 8 mmol m2 s1 asa product of 20 mmol m3 of nitrate concentration and
0.4 m s1 of current speed, and the phosphate flux reaches
0.6 mmol m2 s1 as a product of 1.5 mmol m3 ofphosphate concentration and 0.4 m s1 of current speed.

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Figure 4. (a) Horizontally averaged velocity (m s1), nitrate concentration (mmol m3), and nitrate
flux (mmol m2 s1) along section PN. (b) Vertically averaged velocity (m s1), nitrate concentration(mmol m3), and nitrate flux (mmol m2 s1) from sea bottom to sea surface.
[16] To identify the geographical position where thenutrient flux is large, the nutrient flux was averaged horizontally or vertically (Figure 4). The vertical profile of horizontally averaged nutrient flux (Figure 4a) gives amaximum value at 400 m depth and shows a reduction ofnearly the same rate toward both sea surface and sea bottom.
Such a profile is reasonable if we observe the vertical profile
of horizontally averaged velocity and that of nutrient concentration in the same figure (Figure 4a). Horizontally, themaximum nutrient flux approximately coincides with thestrongest vertically averaged velocity and the largest vertically averaged nutrient concentration (Figure 4b).
[17] To associate nutrient flux with water mass, we calculated volume and nutrient transports per unit width
through five isopycnal layers (Figures 5a and 5b). The
lightest layer, with density less than 24.5 sq, has the largest
volume transport but a small nutrient transport; the heaviest
layer, with density larger than 27.2 sq, has little volumetransport and nutrient transport; the three layers with middle
density (24.5–27.2 sq) have different volume transports butvery similar nutrient transports. Apparently, the nutrienttransport is mainly from the three layers with middle density(24.5–27.2 sq). The maximum nutrient flux is located at
26 sq (Figure 5c). When plotting the nutrient flux, potential
temperature, and salinity on a T-S diagram (Figure 6), thewaters with relatively large nutrient flux are distributedroughly along a line connected by two water masses, theKuroshio Intermediate Water and the Kuroshio Tropical
Water (see Chen et al. [1995b, Table 1] for definition of two
water masses). This suggests the important role of mixing
between two water masses in the formation of the water witha large nutrient flux. Apparently, the Kuroshio Intermediate
Water (IW) contributes more than the Kuroshio Tropical
Water (TW) to the water with a large nutrient flux. Since theKuroshio Intermediate Water has close relation to the North
Pacific Intermediate Water (NPIW), we will discuss theconnection between the NPIW and the water propertychanges at section PN in section 4.
3.2. Temporal Variations of Nutrient Flux[18] The seasonal variation in nutrient flux is small(Figure 7). The position of the maximum nitrate flux core
stays at 400 m throughout the year. The areas with positive
nutrient flux are slightly larger in winter and smaller inautumn, while the area with the nitrate flux larger than
8 mmol m2 s1 is slightly larger in winter and smaller insummer.
[19] An EOF analysis was applied to nitrate concentration,
velocity, and nitrate flux (Figure 8). The first five EOF
modes explains 47.5%, 10.6%, 10.2%, 5.2%, 4.4% of thetotal variance in nitrate concentration, respectively; 20.0%,
14.6%, 13.8%, 9.5%, 7.1% of the total variance in velocity,respectively; and 21.7%, 16.4%, 10.5%, 9.4%, 5.8% of thetotal variance in nitrate flux. The sum of first EOF mode

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Figure 5. (a) Vertically integrated velocity (= volume transport per unit width, m2 s1) within five isopycnal layers. (b) Vertically integrated nitrate flux (= nitrate transport per unit width, mmol m1 s1)
within five isopycnal layers. Circles in Figures 5a and 5b denote data grid points. (c) Distribution of nitrate
flux (mmol m2 s1) as a function of potential density and longitude. The potential temperature is givenon the right ordinate as a reference.

Figure 6. Nitrate flux (mmol m2 s1) distribution in a T-S diagram. Colors denote nitrate flux. The
contour lines with number denote potential density. TW denotes the Kuroshio Tropical Water, while
IW denotes the Kuroshio Intermediate Water defined by Chen et al. [1995b].
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Figure 7. Average nitrate flux (mmol m2 s1) in four seasons. See section 2 for definition of eachseason.

Figure 8. The first empirical orthogonal function (EOF) of (a) nitrate concentration, (b) velocity, and(c) nitrate flux. The second EOF of (d) nitrate concentration, (e) velocity, and (f) nitrate flux. The unitsof Figures 8a and 8d are mmol m3, those of Figures 8b and 8e are m s1, and those of Figures 8c
and 8f are mmol m2 s1. The numbers in brackets denote the percentage of variance explained by thecorresponding EOF mode.
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Figure 9. The time series of the first EOF principal component of (a) nitrate concentration, (b) velocity,and (c) nitrate flux and (d) their power spectral density (cpy = cycles per year). The two lines in
Figures 9a–9c show mean plus and minus one standard deviation. The units in Figures 9a–9c are
nondimensional.
(EOF1) and second EOF mode (EOF2) can explain morethan half variability of nitrate concentration and 40%
variability of velocity and nitrate flux. The relatively low
percentage explained by EOF1 and EOF2 indicate thecomplex temporal variations in the velocity field of thisregion.
[20] The EOF1 spatial pattern suggests an in-phase temporal variation in the nitrate concentration (Figure 8a) and ahorizontally reversed temporal variation in the velocity(Figure 8b) and the nitrate flux (Figure 8c) over the section.
The largest variances are between 400 and 600 m depth forthe nitrate concentration, in the surface layer for the velocity,
and around 400 m depth for the nitrate flux. The time seriesof the EOF1 principal component suggest that the nitrate
flux (Figure 9c) has temporal variations more similar to
those of velocity (Figure 9b) than to those of the nitrateconcentration (Figure 9a). A period of 5 years is found in the
power spectral density of the EOF1 principal component ofvelocity and nitrate flux (Figure 9d).
[21] To understand the mechanism responsible for the
EOF1, we made a composite map of nitrate concentration,
velocity, and nitrate flux (Figure 10) calculated from thecruises when the EOF1 principal component of nitrate flux isabove the mean plus one standard deviation (Figure 9c), andfrom the cruises when the EOF1 principal component of
nitrate flux is below the mean minus one standard deviation(Figure 9c), respectively. According to Figure 10, the highnitrate flux (Figure 10c) is accompanied by strong velocity(Figure 10b) while the low nitrate flux (Figure 10f) is
accompanied by weak velocity (Figure 10e). The nitrateconcentration has no apparent difference between thesetwo cases (Figures 10a and 10d). The section-averaged
nitrate concentration is 10.2 mmol m3 for Figure 10a and10.3 mmol m3 for Figure 10d; the volume transport is22.4 Sv for Figure 10b and 19.4 Sv for Figure 10e; thenitrate transport is 188.6 kmol s1 for Figure 10c and151.2 kmol s1 for Figure 10f. All these results indicate that
the EOF1 of nitrate flux can be attributed to the change inthe Kuroshio speed.
[22] The EOF2 spatial pattern suggests a verticallyreversed temporal variation in the nitrate concentration(Figure 8d), a horizontally fine structure of temporal variation in the velocity (Figure 8e), and a horizontally reversed
temporal variation in the nitrate flux (Figure 8f). Although

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Figure 10. Composite of (a) nitrate concentration, (b) velocity, and (c) nitrate flux for the period when
the first EOF principal component of nitrate flux was larger than mean plus one standard deviation. Composite of (d) nitrate concentration, (e) velocity, and (f) nitrate flux for the period when the first EOF principal component of nitrate flux was smaller than mean minus one standard deviation. The plus symbol ineach plot indicates the position of maximum nitrate flux in Figure 10c or 10f.
we can observe a horizontally reversed structure in both
EOF1 and EOF2 of nitrate flux, the magnitudes of positive
and negative values are of different orders in the EOF1
pattern (Figure 8c) but of the same order in the EOF2 pattern(Figure 8f). The time series of the EOF2 principal component of nitrate concentration, velocity, and nitrate flux do nothave a good correspondence between any two of threevariables (Figures 11a–11c). A period of 1.25 year appears
in the power spectral density of the EOF2 principal component of velocity and nitrate flux (Figure 11d).
[23] Following the same method as Figure 10 for EOF1,
we made a composite map of nitrate concentration, velocity,and nitrate flux for the EOF2 (Figure 12). Again, the nitrateconcentration does not show a significant difference at thetimes with high and low EOF2 principal components,
respectively (Figures 12a and 12d). The section-averaged
nitrate concentration is 10.2 mmol m3 for Figure 12a and10.5 mmol m3 for Figure 12d. However, the difference inthe nitrate flux is apparent. The differences in the maximum
values and in horizontal structure of the nitrate flux can be
identified in Figure 12c and Figure 12f. These differences
are associated with the change in current structure, i.e., theKuroshio became wide during the period with a high EOF2
principal component for nitrate flux (Figure 12b) whilebecame narrow during the period with a low EOF2 principal

component for nitrate flux (Figure 12e). The volume transport is 22.1 Sv for Figure 12b and 20.2 Sv for Figure 12e,
while the nitrate transport is 184.0 kmol s1 for Figure 12cand 166.6 kmol s1 for Figure 12f.
3.3. Factors Affecting the Temporal Varianceof Nutrient Flux[24] Based on the EOF analysis, we expected that thetemporal change in velocity would determine most temporalchanges in the nutrient flux. To confirm this idea, we directly
decomposed the variance of nutrient flux into 6 terms thatcan be easily explained.
[25] The velocity (Vi) and nutrient concentration (Ci) froma cruise can be expressed as sum of the mean values (V , C)over entire analysis period (1987–2009) and the anomalies(Vi′, Ci′). HereN
N
X
1X
 Vi′ ¼ Vi  V ;
¼ 1
Ci ; V ¼
Vi ; Ci′ ¼ Ci  C;
C
N i¼1
N i¼1

in which N is total data number and i is time index for thedata. The nutrient flux (Fi = ViCi) can be then expressed as:

9 of 17 þ V Ci′ þ Vi′C þ Vi′Ci′:
Fi ¼ V C

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Figure 11. The same as Figure 9 but for the second EOF principal components.
 becomes
The mean value of nutrient flux (F)NX
þ1 ¼ V C
Vi′Ci′:
F
N i¼1

Here
ð2Þ

After introducing a new symbol Gi = Vi′Ci′ and substituting
NX
¼ 1
Vi′Ci′
G
N i¼1

into equation (2), we have þ G:
 ¼ V CF

ð3Þ

The variance of nutrient flux (s2F) is defined as
s2F ¼

N
1X
 2:
ðFi  FÞ
N i¼1

ð4Þ

Substituting equations (1) and (3) into equation (4), we canobtain s2 þ V s2 þ 2C
 V s2 þ 2Cs
 2 þ 2V s2 þ s2 : ð5Þ
s2F ¼ C
V
C
CV
VG
CG
G
2

2

N
N
1X
1X
 2;
ðVi  V Þ2 ; s2C ¼
ðCi  CÞ
N i¼1
N i¼1
N
1X
 ðVi  V Þ;ðCi  CÞs2CV ¼
N i¼1
N
N
X
1X
 Þ; s2 ¼ 1
 ðG i  G Þ;ðVi  V ÞðGi  GðCi  CÞs2VG ¼
CG
N i¼1
N i¼1
N
1X
 2:
ðGi  GÞ
s2G ¼
N i¼1

s2V ¼

According to equation (5), the variance of nutrient flux
 2s2V arises froms2F is composed of six terms, in which C
mean nutrient concentration and variance of velocity;V 2s2C from mean velocity and variance of nutrient concen V s2CV from mean velocity, mean nutrient concentration; 2C
tration and covariance of velocity and nutrient concentration;
 s2VG from mean nutrient concentration and covariance2C 2CG from mean velocity and covarianceof velocity and Gi; 2Vsof nutrient concentration and Gi; s2G is variance of Gi.
[26] The left-hand side of equation (5) is presented in
Figure 13a, while the six terms from the right-hand side of

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Figure 12. The same as Figure 10 but for the second EOF principal component.

Figure 13. Total variance of nitrate (a) flux and (b–g) the six terms decomposed following equation (5).
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Figure 14. Time series of (a) section-averaged nitrate concentration, (b) section-integrated velocity(= volume transport), and (c) section-integrated nitrate flux (= nitrate transport). The red line in eachplot denotes linear regression versus time (year).
equation (5) are in Figures 13b–13g, respectively. It is 2s2V (Figure 13b) is the major
apparent that the term of C
component in variance of the nutrient flux (s2F). The terms of V s2CV are smaller than the term of C 2s2V but
V 2s2C and 2C
they are not small enough to be negligible. These results
suggest that the temporal variation in the current velocity
causes most temporal variation in the nutrient flux, while
the temporal variation in the nutrient concentration and thecovariance of velocity and nutrient concentration also contribute to the temporal variation in the nutrient flux.
3.4. Temporal Variations of Nutrient Transport[27] The mean nutrient transport through section PN
during the period from 1987 to 2009 is 170.8 kmol s1
for nitrate (Figure 3e) and 12.7 kmol s1 for phosphate(Figure 3f), respectively. The seasonal mean nitrate (phosphate) transport, which was calculated from Figure 7 or corresponding figure for phosphate (not shown here), is 172.7(12.5) kmol s1, 177.1 (13.6) kmol s1, 161.5 (12.6) kmol s1,
and 166.7 (12.8) kmol s1, for spring, summer, autumn,
and winter, respectively.
[28] In the past two decades, the section-averaged
nitrate concentration varied within a range between 8 and
14 mmol m3 (Figure 14a) and had a standard deviation of
1.1 mmol m3. An increasing trend can be identified in
Figure 14a. In particular, the section-averaged nitrate concentration was at a high level (>12 mmol m3) after 2004. The
same feature may also be found in time series of the EOF1
principal component of nitrate concentration (Figure 9a).
[29] The volume transport through section PN varied
between 15 and 30 Sv (Figure 14b) and had a standard
deviation of 4.1 Sv. It is difficult to identify a significant
increasing or decreasing trend in the volume transport,

although we present the linear regression equation in
Figure 14b.
[30] The nitrate transport through section PN (Figure 14c)
varied within a range between 100 and 280 kmol s1 andhad a standard deviation of 41.6 kmol s1. This standard
deviation actually represents an interannual variability
because the standard deviation in each season, which was
calculated from values in the same season but in different
years, changes little: 48.7 kmol s1 in spring, 44.1 kmol s1
in summer, 42.3 kmol s1 in autumn, and 52.2 kmol s1 inwinter.
[31] From Figure 14, again, we can understand the more
important role of current velocity or the volume transport of
the Kuroshio compared to the nitrate concentration in thetemporal variation of nitrate transport. The correlation
coefficient between volume transport (Figure 14b) andnitrate transport (Figure 14c) is 0.61, while that between
section-averaged nitrate concentration (Figure 14a) andnitrate transport (Figure 14c) is 0.12.
[32] The result that an apparent increasing trend in sectionaveraged nitrate concentration (Figure 14a) does not induce
an apparent increasing trend in the nitrate transport(Figure 14c) is an interesting issue. Using the linear
increasing rate of three variables in Figure 14, we knowthat the section-averaged nitrate concentration increased by
2.2 mmol m3, the volume transport by 1.84 Sv, and thenitrate transport by 17.6 kmol s1 in past 20 years. The
ratio of these numbers to their corresponding values at 1985
is 0.23, 0.086, and 0.11, respectively.
[33] To understand why there is no apparent response innitrate transport to the increasing trend in section-averaged
nitrate concentration, we averaged all the data before 2004
and after 2004. We present the mean before 2004, the mean

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Figure 15. Velocity, nitrate concentration, and nitrate flux averaged from all the data before 2004, after
2004, and the difference (after 2004 – before 2004). The red lines with red numbers in the plots at rightdenote isopycnal layers.
after 2004 and their difference (before 2004 – after 2004) inFigure 15. The difference in velocity field is the intensification of the surface current and weakening of the middle
and deep currents in the Kuroshio mainstream, indicating an
increase in the vertical shear of current velocity, i.e., theintensification of Kuroshio baroclinicity. The increase innitrate concentration occurred mostly in the places deeper
than 200 m, with one maximum value (2.5 mmol m3)
between 400 m to 600 m depth where the water density is
26.0  26.5 sq and another one (3.0 mmol m3) at 800 m
depth where the water density is around 27.0 sq. The nitrate
flux shows a positive difference above 500 m depth but anegative difference below it. Since the positive and negative
nitrate flux differences canceled each other, the increase innitrate transport through the section is small.

4. Discussions
4.1. Causes for Differences in Velocity and Nutrients
Before 2004 and After 2004[34] The variations in velocity field (Figure 15) have close
relation to the difference in water temperature and salinity
before 2004 and after 2004 (Figure 16). In the surface layer,
the water temperature increased while the salinity decreased.
Both of them induced a reduction in surface density. An
important point is that the magnitude of difference in surface
water temperature and salinity varied horizontally. The
water temperature (salinity) increased (decreased) more at
offshore side of Kuroshio than at its mainstream. Consequently, the horizontal gradient of surface density increased.
The increased horizontal density gradient then intensified

vertical shear of the velocity according to thermal wind
relation of a geostrophic current.
[35] The causes for nutrient increases in the middle layer(26.0–26.5 sq) and bottom layer (27.0 sq) (Figure 15) are
more complex. The nitrate increase in the middle layer was
accompanied by apparent reduction in water temperature,salinity, and dissolved oxygen (Figure 16). However, thenitrate increase in the bottom layer was accompanied by anincrease in water temperature, salinity and dissolved oxygen(Figure 16). Therefore, it is reasonable to separate the causes
for nutrient increase in two layers.
[36] The change in water mass property of the centralmode water with a core density of 26.2 sq [Nakano et al.,2007; Kouketsu et al., 2009] is the first possible cause
responsible for the nitrate increase at section PN after 2004,
in particular for that in the middle layer (26.0–26.5 sq).
Nakano et al. [2007] reported freshening at mid-depth(between the main thermocline and salinity minimum layerof NPIW) in the North Pacific subtropical gyre and attributed it to the isopycnal surface deepening due to subsurface
water warming, and to westward shifts of the salinity minimum tongue due to strengthening of the subtropical gyre.
Kouketsu et al. [2009] confirmed the freshening and warming effects and additionally reported increase in apparent
oxygen utilization and silicate around the density of centralmode water. Although we do not have silicate data, we canidentify declines in water temperature, salinity, dissolved
oxygen and increase in nitrate for the water with density
26.0  26.5 sq after 2004 in Figure 16. All these features are
consistent with those reported by Nakano et al. [2007] andKouketsu et al. [2009].

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Figure 16. Potential temperature, salinity, and dissolved oxygen averaged from all the data before 2004,
after 2004, and the difference (after 2004 – before 2004). The red lines with red numbers in each plot atright denote isopycnal layers.
[37] The second possible cause responsible for the nitrateincrease at section PN after 2004 is the temporal changes information regions of NPIW [Ono et al., 2001; Watanabe
et al., 2001; Nakanowatari et al., 2007; Watanabe et al.,2008]. As estimated by You et al. [2003], the westward
transport of NPIW across a section at 130°E is 3.9 Sv, a partof which is expected to enter the South China Sea via theLuzon Strait [You, 2003], a strait between Taiwan and Luzon
Island (Figure 1a), and eventually return to the Kuroshio
region by the outflow of South China Sea intermediate water[Chen, 2005]. The change in NPIW should affect more thenitrate increase in the bottom layer than in the middle layer
after 2004 because the middle layer is above the salinity
minimum at section PN (Figure 16). The fact that the minimum salinity is higher at section PN than in the NPIW in thesubtropical gyre indicates that the mixing of NPIW withother water masses may weaken the interannual signals of
NPIW in its formation regions as they reach at section PN.
[38] The third possible cause responsible for the nitrateincrease at section PN after 2004 is the outflow of nutrientrich South China Sea intermediate water to the Kuroshio
region through the Luzon Strait [Chen and Huang, 1996;
Chen and Wang, 1998; Chen, 2005; Chou et al., 2007]. Thechanges in the volume transport or in the water property ofSouth China Sea intermediate water can induce an increaseor decrease in nutrient concentrations at section PN, in particular at western side of Kuroshio mainstream. The fourth
possible cause is the intensification of Kuroshio baroclinicity since the interaction between bathymetry andKuroshio baroclinicity affects the water movement acrossthe shelf break [Guo et al., 2006].

[39] In Figure 17, we present the time series of watertemperature, salinity, nitrate, dissolved oxygen, velocity,and nitrate flux in three isopycnal layers (24.5–26.0 sq, 26–26.5 sq, 26.5–27.2 sq) for understanding the realizations
behind the “state” change after 2004 (Figures 15 and 16).
We neglect the surface and bottom layers because they
contribute little to nutrient transport through the section(Figure 6b).
[40] The temporal variations in Figure 17 can be divided
into two parts: linear trend and interannual variations.
According to the linear regression equations in each panel of
Figure 17, the trends of water temperature, salinity, velocity,and nitrate flux in three layers are not as apparent as nitrateconcentration and dissolved oxygen. Qualitatively, the two
apparent trends of nitrate concentration and dissolved oxygen have an inverse relation in three isopycnal layers,
indicating important role of microbial respiration. Quantitatively, the ratio of increasing rate of nitrate concentrationand decreasing rate of dissolved oxygen changes largely inthree layers. The increasing rates of nitrate concentration inthree layers are of the same order, but the decreasing rates
of dissolved oxygen in three layers are of different order.
Therefore, in addition to the microbial respiration, the water
property changes in mode waters [Nakano et al., 2007;
Kouketsu et al., 2009] and the linear trends in the NPIW
[Ono et al., 2001; Watanabe et al., 2001; Nakanowatari
et al., 2007; Watanabe et al., 2008] are also important factors affecting the linear trend. Additional evidence for supporting this deduction is that there is no apparent trend of
weakening in the velocity in three layers.
[41] The interannual variations, as presented by one year
running mean in Figure 17, have a time scale of several

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Figure 17. Anomalies (thin lines with dots) and their 1 year running means (bold lines) of (a) potential
temperature, (b) salinity, (c) nitrate concentration, (d) dissolved oxygen, (e) current velocity, and (f) nitrate
flux averaged in three isopycnal layers (24.5–26.0 sq, 26.0–26.5 sq, and 26.5–27.2 sq). The anomalies
were obtained by removing mean value over the entire observation period from raw data. The linear
regression equation was calculated from raw data. In all plots, black color denotes variables from layer
2 (24.5–26.0 sq), red color from layer 3 (26.0–26.5 sq), and blue color from layer 4 (26.5–27.2 sq).
years. Due to short length of time series, we cannot identify
a significant period for interannual variations by using
energy spectra analysis and wavelet analysis. Nevertheless,the realizations of high nitrate concentration level after 2004
becomes clear. The overlap of a positive phase of interannual variations and the linear increasing trend is its direct
reason. The interannual variations induced not only theperiod with high level of nutrient concentration (1997–1999;
2004–2006) but also that with low level of nutrient concentration (2000–2002). Among three layers, the layer of
26.5–27.2 has the largest range of interannual variations in

nitrate concentration (Figure 17c) but smallest range ofinterannual variations in dissolved oxygen and in velocity(Figure 17d).
[42] The interannual variations of nitrate concentrationand dissolved oxygen do not match in phase (Figure 17).
The peak (trough) of dissolved oxygen is generally later than
the trough (peak) of nitrate concentration by several years.
This mismatch of phase suggests again that microbial respiration is not the sole process controlling the interannual
variations of two variables at section PN.

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4.2. Comparisons With Nutrient Flux and Nutrient
Transport by the Kuroshio East of Taiwan and by the
Gulf Stream[43] A comparison of nutrient flux at section PN(Figure 3) to previously reported results at different places is
helpful to understand the factors controlling spatial structureof nutrient flux. Chen et al. [1994] reported a maximum coreat 500 m depth in the phosphate flux (there is no nitrate fluxin this paper) through a section east of Taiwan, which islocated upstream of the Kuroshio relative to section PN.
Comparing this with our results (Figure 3f), we know thatthe depth of maximum nutrient flux was lifted upward by
100 m as the Kuroshio moves from the section east of
Taiwan to section PN. The maximum value of the phosphate
flux reported by Chen et al. [1994] is 0.3 mmol m2 s1, or
half of our maximum value 0.6 mmol m2 s1. This ismainly due to the difference in velocity at the depth of
maximum core of nutrient flux: 0.2 m s1 in the work by
Chen et al. [1994] but 0.4 m s1 in this study.
[44] Although the maximum value of the phosphate flux issmaller at the section east of Taiwan than at section PN, thephosphate transport is larger at the section east of Taiwan(22.1 kmol s1) than at section PN (12.7 kmol s1). The
current structure is likely the main cause for such difference.
Two cores of strong northward current can be found at thesection east of Taiwan of Chen et al. [1994] but the offshorecore is not a part of the Kuroshio. As reported by Chen et al.
[1995a] and also in our results (Figure 14), the large temporal variability in nutrient transport makes it difficult tocompare our mean state results with the results from a single“snapshot” cruise [Chen et al., 1994, 1995a]. Nevertheless,the consistent spatial structure of the nutrient flux and thesame order of nutrient transport suggest the presence of anutrient stream in the Kuroshio region in the East China Sea.
[45] The nutrient flux of the Gulf Stream through a section
near 36°N [Pelegrí and Csanady, 1991] has a maximum coreat 500 m depth and a maximum value of 15 mmol m2 s1for nitrate and of 0.8 mmol m2 s1 for phosphate,respectively. The nutrient transport was 860 kmol s1 fornitrate and 55 kmol s1 for phosphate, both of which are
4  5 times larger than the values obtained for the Kuroshio
in the East China Sea. The large nutrient flux and nutrienttransport resulted from the strong currents and large volumetransport in the Gulf Stream. The current speed of the Gulf
Stream at the section near 36°N of Pelegrí and Csanady[1991] was 1.4 m s1 at the surface, 0.8 m s1 at 500 m
depth, and 0.2 m s1 at 900 m depth. The corresponding
current speed of the Kuroshio at section PN is 0.8 m s1,
0.4 m s1 and 0 m s1, respectively. Clearly, the Gulf
Stream is stronger than the Kuroshio and must transport
more water and nutrients. However, this is not the solecause. The volume transport between the Gulf Stream near
36°N and the Kuroshio at section PN is 3 times greater, but
the nutrient transport is 4–5 times greater. Therefore, thehigher nutrient concentration carried by the Gulf Stream
compared to the Kuroshio is another important cause. For
example, the nitrate concentration around the maximum
nitrate flux at 500 m depth in the Gulf Stream ranged from
20 to 25 mmol m3, but the concentration around the maximum nitrate flux at 400 m depth in the Kuroshio ranged
from 10 to 20 mmol m3. Since the nutrient concentration

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generally increases with depth in both the Gulf Stream andKuroshio, the deeper current structure of the Gulf Stream
compared to the Kuroshio could be the direct cause of thelarger nutrient flux in the Gulf Stream.

5. Conclusions[46] In this study, we not only presented the mean state ofnutrient flux by the Kuroshio calculated from 88 data setsfrom 1987 to 2009 in the East China Sea, but also examined
its temporal variations. The seasonal variation in the nutrient
flux is slight and much smaller than the interannual variation. EOF analysis of the nitrate concentration, velocity
field, and nutrient flux suggested that the changes in the
Kuroshio speed and current structure are the major causes
for interannual variation in the nutrient flux. This conclusion
is further confirmed by directly decomposing the variance ofnutrient flux into terms depending on the variances ofnutrient concentration and velocity.
[47] The downstream nitrate transport by the Kuroshio hasa mean value of 170.8 kmol s1 and a standard deviationof 41.6 kmol s1. Its mean seasonal variation ranges from161.5 to 177.1 kmol s1 and its absolute interannual variation ranges from about 100 to 280 kmol s1.The mean
onshore nutrient transport from the Kuroshio to the continental shelf of the East China Sea was estimated to be
9.4 kmol s1 for the nitrate and 0.7 kmol s1 for the phosphate [Zhao and Guo, 2011], both of which are 1/18 of thedownstream nutrient transport by the Kuroshio. Consequently, the nutrients within the nutrient stream of the Kuroshio are essentially transported downstream toward thesouth of Japan and Kuroshio Extension area.
[48] The nitrate concentration at section PN, especially inthe middle and deep layers, increased significantly after
2004, but the nutrient transport did not increase significantly. The small change in nutrient transport can be
explained by the fact that the velocity increased in the surface layer but decreased in the middle and bottom layers
after 2004. The direct cause for intensification of vertical
shear in the Kuroshio is the warming and freshening of
surface layer after 2004 that had a larger magnitude at theoffshore side. The direct cause for nutrient increase in the
middle and bottom layers is the overlap of linear increasing
trend and positive phase of interannual variations of nutrient
concentration. Four possible causes are (1) water propertychanges of subtropical mode waters in the North Pacific,(2) changes in formation of NPIW, (3) changes in outflow ofSouth China Sea Intermediate Water toward the Kuroshio
region, and (4) dynamic response of water movement acrossthe shelf break of the East China Sea to the intensification of
Kuroshio baroclinicity, were suggested to be responsible forthe observed variations in nutrient concentrations in the
middle and bottom layers.
[49] Acknowledgments. This study is supported by JSPS KAKENHI(21310012) and by multiple sponsors on the Chinese side: (1) the National
Natural Science Foundation of China (41176021, 40776021), (2) the National
Basic Research Program of China (2011CB409803, 2011CB403503), (3) the
Young Ocean Foundation of the SOA (2010229), (4) Overseas, Hong Kongand Macao Scholars Collaborated Researching Fund from National Science
Foundation of China (41028006), and (5) the Open Fund of Key Laboratoryof Marine Ecosystem and Biogeochemistry, SOA (LMEB201107). Comments from J. L. Pelegrí and two anonymous reviewers were helpful inimproving the original manuscript.

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