Journal of Oceanography, Vol. 65, pp. 737 to 756, 2009

Water Mass Variability in the Western North Pacific Detected in a 15-Year Eddy Resolving Ocean Reanalysis
Y ASUMASA MIYAZAWA1*, RUOCHAO ZHANG1, XINYU GUO1,2, HITOSHI TAMURA1, DAISUKE AMBE3,
J OON-SOO L EE3†, AKIRA OKUNO3††, HIROSHI YOSHINARI3†††, TAKASHI SETOU3
and K OSEI KOMATSU3,4
1

Research Institute for Global Change, Japan Agency for Marine-Earth Science and Technology,
Yokohama, Kanagawa 236-0001, Japan
2
Center for Marine Environmental Studies, Ehime University, Matsuyama, Ehime 790-8577, Japan
3
National Research Institute of Fisheries Science, Fisheries Research Agency,
Yokohama, Kanagawa 236-8648, Japan
4
Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba 277-8561, Japan(Received 20 October 2008; in revised form 16 June 2009; accepted 26 June 2009)This paper describes a reanalysis of the variability of water mass properties and currents in the western North Pacific using an ocean forecast system, Japan Coastal Ocean
Predictability Experiment 2 (JCOPE2), to provide the basic description and information about the quality for data users. We have created the reanalysis data withhigh horizontal resolution of 1/12° to describe the oceanic variability associated with
the Kuroshio-Kuroshio Extension, the Oyashio, and the mesoscale eddies from 1993
to 2007. The products made by an eddy-resolving ocean model combined with thethree-dimensional variational data assimilation well reproduced the mean water massproperty in the western North Pacific and the interannual variations of the KuroshioKuroshio Extension and the Oyashio coastal branch. From the reanalysis data, wefound that both the mean kinetic energy of the Kuroshio Extension axis at the firstmeandering crest and southward intrusion of the Oyashio coastal branch were closely
related with the horizontal distribution of both the Oyashio Water and North PacificIntermediate Water within the appropriate interannual time scale. The reanalysis
data also indicated that the north-south migration of the Kuroshio Extension associated with its regime transitions affected the decadal modulation of the SubtropicalMode Water formation in the recirculation gyre of the Kuroshio Extension.

Keywords:⋅ Data assimilation,⋅ reanalysis,⋅ eddy-resolving
model,⋅ western North
Pacific,⋅ North PacificIntermediate Water(NPIW),⋅ mode water,⋅ Kuroshio,⋅ Oyashio,⋅ Mixed Water
Region.

merged using a data assimilation technique (Daley, 1991)
to prepare the initial state of the forecast. This merging
procedure is usually called “analysis”. Since the analysis
data (merged data) provides an optimum estimation of
the oceanic condition based on both the model and observation, the analysis can be used to diagnose sparsely
observed phenomena. To improve the forecast accuracy,
however, the operational ocean forecast systems often
require updates of parameters and numerical schemes included in the ocean models and data assimilation components. Thus the time sequences of the analyses sometimes
show unrealistic jumps in state variables between before
and after changes of the systems. To describe oceanic
conditions during particular period in a consistent manner, it is quite useful to reconstruct the oceanic conditions without any change of the model and data assimilation schemes using almost all available observation data.
This procedure is called “reanalysis”.

1. Introduction
Combining ocean models and observation data, operational numerical ocean forecast has allowed us to predict variations of the western boundary currents andmesoscale eddies with a timescale of an order of 10 days
(e.g., Ezer and Mellor, 1994; Kagimoto et al., 2008). In
such an operation, the model and observation data are* Corresponding author. E-mail: miyazawa@jamstec.go.jp†

Present address: Ocean Climate Lab., Ocean Research Team, National
Fisheries Research & Development Institute, Sirang-ri, Gijang-eup,
Gijang-gun, Busan 619-902, Korea.
††
Present address: Japan Sea National Fisheries Research Institute,Fisheries Research Agency, Niigata 951-8121, Japan.
†††
Present address: Hokkaido National Fisheries Research Institute,Fisheries Research Agency, Kushiro, Hokkaido 085-0802, Japan.
Copyright©The Oceanographic Society of Japan/TERRAPUB/Springer

737

In the last several years, a number of ocean reanalyses
over multi decades have become available and have beenused for analyses of water mass variability. However, mostof the available reanalyses are mapped on a horizontal
coarse grid of approximately 1° (e.g., Carton andSantorelli, 2008). Our study is the first attempt to examine the interannual water mass variability in the western
North Pacific using the ocean reanalysis data with a highhorizontal resolution of 1/12°.
One of the important applications of reanalysis isstatistical analysis to find the physical relationships between different physical variables, for example, between
currents and water masses. To check the suitability of ourdata for this sort of analysis, we examined how the watermass variability associated with North Pacific Intermediate Water (NPIW) and Subtropical Mode Water (SMW)
was statistically related to the ocean current variations
using our reanalysis data.
The western North Pacific has a complicated ocean
current system with significant water mass variability(e.g., Yasuda, 2003). The Kuroshio transports a large
amount of warm and saline water to the south of Japan.
The Kuroshio Extension formed by separation of theKuroshio from the coast of Japan and the Oyashio whichtransports cold, fresh water creates the mixed water region east of Japan. The Kuroshio Extension is quite powerful; it makes stationary meanders and sheds warm andcold eddies through their separations from the meandercrests and troughs. The Oyashio Water forms a front near
the Japanese coast as a part of the subarctic front. Twobranches of the Oyashio water, a coastal branch and an
offshore branch, intrude southward from the Oyashio front
and interact with the meander crests of the Kuroshio Extension and the warm eddies.
NPIW is characterized by a salinity minimum in intermediate layers of the subtropical region (e.g., Reid,
1965; Yoshinari et al., 2004). Hasunuma (1978) suggested
that the salinity minimum was not a sign of water mass
but a boundary between the lower layer water originatingfrom the subarctic region and the upper layer water originating from the subtropical region. Recent studies using
high horizontal resolution temperature and salinity observations and high-resolution numerical modeling suggested that NPIW could be formed by isopycnal diffusion of the Oyashio and Kuroshio waters (Talley, 1993;
Yasuda et al., 1996; Ishizaki and Ishikawa, 2004) and by
meridional overturning circulation with diapycnal andcross-gyre transports associated with water mass formation in the Okhotsk Sea (Yasuda, 1997; Tatebe and Yasuda,2004). There are a few studies investigating interannualand decadal variations of NPIW. For example, Nakano etal. (2005) revealed that interannual variation of NPIW
core size at 137°E was well correlated with that of therecirculation intensity associated with the intensity of the738

Y. Miyazawa et al.

subtropical gyre.
SMW is also one of the typical water masses in thenorthwestern subtropical region (Masuzawa, 1969). It ischaracterized by a pycnostad between the seasonal andmain pycnoclines, and is formed by regional wintertime
convective cooling (e.g., Suga and Hanawa, 1995b). Although the thickness of SMW in the western North Pacific is basically governed by the intensity of the wintermonsoon, it is also influenced by the dynamic states of
the Kuroshio and Kuroshio Extension (Suga and Hanawa,
1995a; Qiu and Chen, 2006; Qiu et al., 2007).
As suggested by previous observational studies, thedynamic states of the Kuroshio, Kuroshio Extension, andOyashio affect the water mass variability in the western
North Pacific. In the mixed water region, because
mesoscale eddy activities were very high, it has been difficult to investigate detailed relationships between theoceanic conditions and the variability of water mass properties owing to lack of long term hydrographic data withhigh horizontal resolution. By combining satellite
altimetry data and dense observation by profiling floats,Qiu et al. (2007) recently showed that the mesoscale eddies actually weakened SMW’s thickness in therecirculation region of the Kuroshio Extension within aperiod of less than 7 months.
In this paper we describe the 15-year eddy-resolving ocean reanalysis data from 1993 to 2007. Continuous
operation of satellite altimetries after the launch of
TOPEX/Poseidon in 1992 allowed us to reproduce theoceanic variability including the Kuroshio, its Extension
paths and mesoscale eddies over the past 15 years (e.g.,
Uchida and Imawaki, 2003; Ambe et al., 2004). We havereconstructed the oceanic states in the western North Pacific using a high-resolution ocean general circulation
model together with the assimilation of both the satellite
and in-situ data. Since the reanalysis data provide uniformly gridded oceanic states over particular period, it isuseful for various kinds of studies. Some researchers havealready used our data for their studies. The purpose ofthis study is to provide users with a basic description andinformation about the skills of our reanalysis data.
Previous version of the ocean forecast system(JCOPE1; Miyazawa et al., 2004, 2005, 2008a; Kagimoto
et al., 2008) was used to analyze the Kuroshio path variation south of Japan. The water mass properties reproduced by JCOPE1 had significant biases in the western
North Pacific despite data assimilation (Yoshinari et al.,2008). We modified both the model and data assimilation
schemes for better representation of the water mass properties in the western North Pacific, especially in the mixed
water region. Our operational forecast system (http://
www.jamstec.go.jp/frcgc/jcope/) has been updated to
JCOPE2 from JCOPE1 in November 2006.
This paper is organized as follows. In Section 2, wedescribe the configuration of the JCOPE2 ocean forecast
system. In Section 3, we present climatological meanstates of the 15-year data. In Section 4, we describe skillsof the 15-year data for variability of the Kuroshio,
Kuroshio Extension, and Oyashio coastal branch. Then
we discuss the relationships between the interannual variation of the oceanic conditions and that of both NPIW
and SMW as found in the 15-year data. Section 5 contains a final summary and discussion.
2. The JCOPE2 Ocean Forecast System
2.1 Ocean model
The ocean model in the JCOPE2 system is based on
the Princeton Ocean Model with a generalized coordinate of sigma (POMgcs; Mellor et al., 2002). A high-resolution regional model with a spatial grid of 1/12° and 46
vertical levels was embedded in a low-resolution modelwith a spatial grid of approximately 1/4° and 21 sigma
levels, covering the almost entire Pacific region (30°S–
62°N, 100°E–90°W). The bottom topography of the lowresolution model was created from the 1/12° global height
data, GETECH DTM5. The lateral boundary conditionsof the low-resolution model are the monthly
climatological temperature and salinity (Conkright et al.,2002) with zero velocity.
The inner high-resolution model domain covers thewestern North Pacific (10.5°–62°N, 108°–180°E); and its
lateral boundary conditions are determined from the basin-wide model using a one-way nesting method with theflow relaxation scheme (Oey and Chen, 1992; Guo et al.,2003). The bottom topography of the high-resolution
model was also created from the 1/12° data, DTM5. In
addition, the 1/108° data, JTOPO30, was embedded in
the coastal sea around Japan and the 1/12° bottom topography data used by Uehara et al. (2002) was embedded in
the East China Sea.
Because the western North Pacific has a wide rangeof variability in both the bottom topography and watermass properties, some errors in the baroclinic pressure
gradient generated in the sigma coordinate systems witha steep bottom slope may contaminate the calculation(Mellor et al., 1994). Thus, the bottom topography in boththe low- and high-resolution models has been smoothed
to reduce the pressure gradient error (Mellor et al., 1994).
The maximum bottom slope between two adjacent grid
points was constrained by the inequality, |H1 – H2|/|H1 +H2| ≤ 0.15. In addition, we have modified the POMgcs to
reduce the errors by replacing the second order schemeof baroclinic pressure gradient with the fourth order(McCalpin, 1994).
To exclude small scale noises in temperature andsalinity distributions, we replaced the central advection
and harmonic diffusion schemes for temperature and sa-

linity with the flux-corrected transport (Boris and Book,
1973) and bi-harmonic schemes (Griffies and Hallberg,
2000), respectively. The horizontal viscosity was calculated by the Smagorinsky-type formulation with a coefficient of 0.2. The horizontal diffusivity coefficient was
assumed to be one-half of the viscosity coefficient.
The model was driven by wind stresses, and heat andsalt fluxes. The wind stress and heat flux fields were calculated from the 6-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). Instead of the observed sea surface temperature with horizontal coarse resolution (2°) included in the NCEP/NCAR
data, the high-resolution model used its output of sea surface temperatures for the calculation of the bulk formulae. This improved the low (high) surface temperature bias
in the marginal seas in winter (summer). Salinity at theocean surface is restored to the monthly mean climatology data (Conkright et al., 2002) with a time scale of 30days. In both the Okhotsk and Bering Seas, temperatureand salinity are relaxed to the monthly climatology(Conkright et al., 2002) with a restoring time scale of 30days to improve water mass property in the Mixed Water
Region (Mitsudera et al., 2004).
The low-resolution model is spun-up for 15 years by
the monthly mean surface forcing from an initial condition of no motion with the annual-mean temperature andsalinity fields created from the climatology (Conkrightet al., 2002). The high-resolution model is also run fromthe same initial condition. The lateral boundary condition of the 5-year spin-up run of the high-resolution modelis calculated using the spin-up results of the low-resolution model over the last 5 years. Then both the low- andhigh-resolution models are driven by the 6-hourly surface forcing during the period from October 1992 to January 2008.
2.2 Data assimilation
The following observation data were assimilated into
the model: along-track sea surface height anomaly(SSHA) obtained from the TOPEX/Poseidon and ERS-1,
2 satellites during the period December 1992 to January
2002 and from the Jason-1 and Geosat Follow-On during
the period January 2002 to January 2008; sea surface temperature (SST) obtained from the Advanced Very High
Resolution Radiometer/Multi-Channel Sea Surface Temperature (AVHRR/MCSST) products; and vertical profilesof temperature and salinity obtained from the data archiveof the Global Temperature-Salinity Profile Program(GTSPP; see http://www.nodc.noaa.gov/GTSPP/
index.html). We used the delayed-mode archive of GTSPP(“Best Copy Data Sets”), which were assembled to provide the most complete data sets without duplication.

Water Mass Variability in the Western North Pacific739

Notice that the GTSPP archive includes no salinity profiles from 1995 to 1999 in the western North Pacific,
which slightly affected the accuracy of the reanalysis data
(see Section 3).
The data assimilation procedure in our system comprises two steps: the creation of temperature-salinity
analysis data on a three-dimensional uniform grid by thevariational assimilation method (Fujii and Kamachi,
2003), and incremental analysis update (IAU) (Bloom etal., 1996) of the analysis data for smooth initialization.
The variational assimilation method minimizes a costfunction that represents the sum of the square distances
between the analysis and the observations. The grid of
the analysis temperature and salinity has horizontal resolution of 1/4° and 24 vertical levels on the z-coordinate
from a depth of 0 m to 1500 m. The cost function J iswritten in the form of a function of the analysis temperature and salinity (X),

(

)

(

t

)

J ( X ) = X − X f B −1 X − X f() ()+( y − H X ) R ( y − H X )
+( y
−H
−H
( X )) R ( y+( y − H X ) R ( y − H X ),t

+ yTo − HT X RT−1 yTo − HT X
o
S

t

S

−1
S

S

S

t

o
SSHA

SSHA

o
SST

SST

t

−1
SSHA

o
SSHA

o
SST

SST

−1
SST

SSHA

( X ))(1)

where B is the background error covariance matrix forthe first guess; yo is a column vector whose elements are
the observed variables; H denotes an operator for a projection from the model variables to the observed variables. H T, HS and H SST are bilinear interpolation operators. HSSHA is a nonlinear operator for calculation of thesea surface dynamic height; R denotes an observation error covariance matrix. At the time of the analysis, by minimizing the cost function (1) with the use of a conjugate
gradient method, we obtained the analysis temperatureand salinity. We created the analysis with an interval of 7days. The time windows of observed SSHA, SST, andvertical profiles of temperature and salinity used in thecost function (1) are 10, 10, and 20 days, respectively.
By using temperature and salinity coupling EOF
modes (ETi, ESi), the analysis temperature (T) and salinity (S) are represented as follows:

(

)

M

X = (T , S ) = T f , S f + ∑ α i λi ( DTz ETi , DSz ESi ),
i =1

(2 )

where Xf = (Tf, Sf) are first guess values; αi are ampli740

Y. Miyazawa et al.

tudes of the EOF mode; DT(S)z are standard deviations oftemperature (salinity) variations at z levels calculated
from the historical data archives; and λi are the singularvalues of the EOF mode. The control variables of theanalysis temperature and salinity for the cost function (1)are amplitudes of the EOF modes, αi. We calculated the
EOF modes from the historical data archives of temperature and salinity profiles (World Ocean Database 2001)
and Marine Information Research Center Ocean Data Set
2001) in six regions: subarctic region (39.5°–55°N, 140°–
180°E); mixed water region (37°–45°N, 140°–180°E);
Kuroshio–Kuroshio Extension region (22°–33°N, 120°–
180°E); subtropical region (12°–27.5°N, 120°–180°E);
the southern part of the Japan Sea (35°–39°N); and thenorthern part of the Japan Sea (41°–53°N). Because thefirst twelve modes can represent over 95% of the observed
variability in all regions, we used the first twelve EOF
modes for calculation of the analysis, i.e., M = 12 in (2).
Although the three-dimensional variational assimilation method using the temperature-salinity coupling
EOF modes has already been used in the other type of theoperational ocean forecasting model (Usui et al., 2006),
it was quite important to customize the data assimilation
method for an individual ocean model by investigating
sensitivities of the parameters. We found that relatively
sensitive parameters for the estimation of the analysis
temperature and salinity using the minimization of thecost function (1) were the EOF modes (ETi, ESi), error
covariance matrices B and R, first guess Xf, and the meansea surface dynamic height (SSDH) field that is necessary for calculation of the model SSHA.
We assumed that the components of the backgrounderror covariance matrix B have horizontal correlation thatis supposed to be a Gaussian function:()

exp − x 2 / Sx2 − y 2 / Sy2 ,(3)

where x and y are distances between the grid points along
eastward and northward directions, respectively; Sx and
Sy are horizontal scales that were assumed to be constantsin the regions except for the Kuroshio-Kuroshio Extension region (see Appendix) and determined according to
the estimates obtained by the statistical analysis of satellite altimeters (Kuragano and Kamachi, 1999).
The observation covariance R was supposed to be a
diagonal matrix; the observation data have no correlation with each other. We assumed that the observation
error of the satellite SST was 1.0°C and that of SSHA
was 0.05 m except for the Kuroshio-Kuroshio Extension
region (see Appendix). The observation error of in-situ
temperature and salinity was specified as follows:ooTerr= (1 − σ )STz , Serr
= (1 − σ )SSz (σ = 0.7 − 0.9).

( 4)

(b)

(a)

Fig. 1. (a) Volume transport stream function of mean flow calculated from the reanalysis data within 1993 to 2007. Contour
interval is 10 Sv. Regions of the stream function upper than 20 Sv are shaded. (b) As in (a) except for the magnified view
showing the Japanese coastal ocean. Lines indicate sections along which the mean volume transports were calculated (Table
1).

The prescribed error of first guess temperature and salinity was related with the observation error.
ff
Terr= σTTz , Serr
= σSSz (σ = 0.7 − 0.9).

(5)The parameter σ provides the relative weight of the first
guess temperature and salinity compared to the observation.
We used both the climatology data Xclim and model
variables Xmodel for the first guess:
X f = Wclim Xclim + (1 − Wclim ) Xmodel
Wclim = min( max(W , 0), 1), W =Gradh − Gradhmax
Gradhmin − Gradhmax, (6 )

where Gradh is the horizontal gradient of temperature at
200 m depth; | | indicates the two-dimensional vector
norm. Scheme (6) increases the weight of the model (climatology) for the first guess value at the grid point where
the horizontal temperature gradient is large (small). Useof scheme (6) significantly reduced biases involved in
the water mass property represented by the assimilation
product.
The mean SSDH is necessary to calculate the model
SSHA because not SSDH but SSHA of the satellite altimeter is available from the satellite altimeters. We calculated the mean SSDH from the annual mean climatol-ogy (Conkright et al., 2002) and modified the obtained
mean SSDH in the Kuroshio region with that from a previous version of our ocean forecast system (JCOPE1;
Kagimoto et al., 2008; Miyazawa et al., 2008a). This isbecause that the Kuroshio path southeast of the Kii Peninsula appearing in the SSDH calculated from the annual mean climatology was similar to the large meander
path, but the observed mean Kuroshio path during theassimilation period from 1993 to 2007 was the non-large
meander path. Thus only for the Kuroshio region, we used
the mean SSDH from the JCOPE1 product over the period from November 2002 to October 2006 during which
the mean Kuroshio path was the non-large meander path
(Miyazawa et al., 2008a).
Using the analysis temperature and salinity, we conducted the IAU procedure for data from December 1992
to January 2008 and stored the daily mean data, referred
to as the reanalysis data. The reanalysis data from January 1993 to December 2007 were used for the present
analysis.
3.

Mean State and Water Mass Properties Reproduced by the Reanalysis Data from 1993 to 2007
Figure 1(a) shows the volume transport stream function calculated from the mean velocity field of the 15year reanalysis data. The subtropical gyre, which consists of the North Equatorial Current, Kuroshio, andKuroshio Extension, is well represented. The Ryukyu
Current east of the Kuroshio main stream in the East China
Sea (Zhu et al., 2003) and the Kuroshio recirculation south
Water Mass Variability in the Western North Pacific741

Table 1. Comparison of volume transports (Sv) between the reanalysis and observation data. The transports of the reanalysis data
were calculated from surface to bottom along the several sections across the main currents in the Japanese coastal oceans. The
names of the sections (see Fig. 1(b)) are shown in the parentheses.
Currents
Taiwan Strait Current (TW)Kuroshio in the East China Sea (PN)
Ryukyu Current (OK)Ryukyu Current (AM)
Kuroshio south of Japan (ASUKA)
Tsushima Warm Current (TS)Tsugaru Warm Current (TG)Soya Warm Current (SY)Oyashio (OICE, southwestward)of the Shikoku Island (Imawaki et al., 2001) can also be
identified. The mean Kuroshio path southeast of the Kii
Peninsula during the reanalysis period from 1993 to 2007is the non-large meander path (Miyazawa et al., 2008a).
The reanalysis produces a strong flow of the Kuroshio
Extension with the two crests at 143°E and 148°E. The
intensity of the Kuroshio Extension weakens east of
155°E, indicating its bifurcation around the Shatsky Rise.
These features of the Kuroshio Extension are consistent
with observation (Mizuno and White, 1983). The East
Kamchatka current originates from the Bering Sea andflows southwestward along the continental slope. The
cyclonic circulation accompanied with the Oyashio
coastal intrusion along the Honshu Island intensifies theKuroshio Extension. The return flow of the Oyashio together with the strong recirculation flow forms the coastaland offshore branches of the Oyashio. We compared mean
volume transports along some sections (Fig. 1(b)) acrossthe main currents with the observed data (Table 1). The
reanalysis transports were of the same order as those reported by previous studies.
To check the accuracy of the reanalysis data for themean water mass properties of the western North Pacific,
we compared the reproduced temperature and salinity
profiles with the observed profiles in detail. Since thereanalysis combines the information of remote and insitu observations by assimilating SSHA, SST, and in-situ
profiles obtained from the GTSPP archive, we examined
the fit of the reanalysis data for the GTSPP profiles tocheck whether the assimilation worked well or not. We
compared temperature-salinity diagrams of the reanalysis
data with diagrams of in-situ observed profiles averaged
over the period from 1993 to 2007 (Fig. 2). The diagramsof the reanalysis data correspond fairly closely with theobservations in the subarctic, mixed water, subtropicalregions, respectively (see Fig. 3 for locations of the regions). The results suggest that the system has assimi742

Y. Miyazawa et al.

Reanalysis
1.4
32.7
6.9
6.0
49.0
2.5
2.1
0.4
41.0

Observations
1.8 (Wang et al., 2003)
25.4 (Hinada, 1996)
6.1 (Zhu et al., 2003)
16 (Ichikawa et al., 2004)
42 (Imawaki et al., 2001)
2.64 (Takikawa et al., 2005)
1.4 (Shikama, 1994)
0.7 (Ebuchi et al., 2006)
31 (Uehara et al., 2004)

Fig. 2. Temperature-salinity diagram of mean profiles fromthe reanalysis (open circles) and the in-situ observation data
included in the GTSPP archive (crosses). Left, middle, andright profiles were means of the data in 45°–60°N, 155°–
175°E (Subarctic Region; SAR), 35°–45°N, 142°–172°E(Mixed Water Region; MWR), and 20°–35°N, 130°–150°E(Subtropical Region; STR), respectively. Contour lines denote iso-σθ contours.

lated the in-situ observation profiles with only small biases.
To examine spatial differences in the reproduction
skills, we plotted Root Mean Square Error (RMSE), meanerror (bias), and correlation between the reanalysis andobserved profiles from the surface to a maximum depthof 1500 m (Fig. 3). The mixed water region shows RMSE

Fig. 3. Root Mean Square Error (RMSE; upper panels), mean error (bias; middle panels), and correlation of the temperature (leftpanels) and salinity (right panels) profiles of the reanalysis data by those of the in-situ observation obtained from the GTSPP
archive. Rectangles in the panels indicate the subarctic, mixed water, and subtropical regions used for the calculations of
metrics shown in Figs. 2 and 4.

Water Mass Variability in the Western North Pacific

743

(a)

(c)

2
1
0
-1

93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08(year)RMSE

BIAS(deg.C)

3

4
Cor.

(deg.C)

4

5
3
2
1
0
-1

COR

93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08(year)RMSE

SAR (155E175E45N60N)

0.1
093 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08

0.4
0.3
(psu)

0.2

1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0

Cor.

(psu)

0.3

0.2
0.1
0
-0.1

93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08(year)BIAS

RMSE(year)
COR

RMSE

(b)

(deg.C)4
3
2
1
0
93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08

1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0

Cor.

MWR (142E172E35N45N)

5

-1

1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0

COR

STR (130E150E20N35N)

0.4

-0.1

BIAS

1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0

Cor.

STR (130E150E20N35N)

1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0

Cor.

SAR (155E175E45N60N)

5

BIAS

COR

Fig. 4. Time sequences of Root Mean Square Error (RMSE;
thick solid curves), mean error (bias; dashed curves) of thetemperature (upper panels) and salinity (lower panels) profiles of the reanalysis data for those of the GTSPP data.
Correlation and minimum correlation values required forstatistical significance at the 95% confidence level are also
indicated by thin solid curves with closed circles and dotted curves with open squares, respectively. (a), (b), and (c)show averaged values in 45°–60°N, 155°–175°E (thesubarctic region), 35°–45°N, 142°–172°E (the mixed water
region), and 20°–35°N, 30°–150°E (the subtropical region),
respectively.

(year)
BIAS

RMSE

COR

(psu)

0.3
0.2
0.1
0
-0.1

93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08(year)RMSE

744

Y. Miyazawa et al.

BIAS

COR

1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0

Cor.

MWR (142E172E35N45N)

0.4

higher than 2°C and 0.15 psu east of Japan (upper panelsof Fig. 3) because of the large water mass variability there.
Since RMSE is smaller than the standard deviation of theobserved profiles (not shown), the model has passableaccuracy for the assimilation of the in-situ profiles there.
The Japan Sea shows RMSE higher than 1.5°C and 0.1
psu but it is also smaller than the observed standard deviations. In the shelf region of the East China Sea, RMSE
is comparably large compared to the observed standard
deviation owing to there being no assimilation of in-situ
profiles there. In the Pacific, regions of positive temperature bias higher than 0.5°C are shown around 35°N and
145°–155°E (middle panels of Fig. 3). This is because
the mean Kuroshio Extension path is more north than theone observed there (see Subsection 4.1). The shelf region

(a)

0
-100

(m)

-200
-300
-400
-500
-600
-700
0

2

4

6

8

34.2
(psu)

34.4

10

(psu)

(b)

0
-100

(m)

-200
-300
-400
-500
-600
-700
33.6

33.8

34

34.6

Fig. 5. (a) Temperature profiles of the observation (solid curves
with closed circles) and reanalysis data (dashed curves)
sampled from 40°–62°N and 140°–180°E in February 2001.
The profiles that have correlation values lower than 0.3 areselected. (b) Same as in (a) except for salinity profiles sampled from 35°–45°N and 140°–180°E in November 2001.

of the East China Sea shows positive temperature and low
salinity biases. Correlation coefficients for temperatureand salinity are relatively low in this region (lower panels of Fig. 3). In the subarctic regions, correlation is low
for temperature but high for salinity. The lower right panelof Fig. 3 indicates slightly low correlation for salinity inthe mixed water region, shelf of the East China Sea, Japan Sea, and near the southern boundary for salinity. In
summary, the reanalysis data is reasonably accurate forwater mass representation in the western North Pacific.
Figure 4 plots time sequences of RMSE, bias, andcorrelations of the reanalysis data averaged in thesubarctic (Fig. 4(a)), mixed water (Fig. 4(b)), and subtropical regions (Fig. 4(c)). Since no salinity profile isarchived in GTSPP from 1995 to 1999 for all the three
regions, no precise information for salinity is shown during this period. The effectiveness for the subtropical region is stable throughout the reanalysis period except forsalinity in 1993 and 2001.
In the subarctic regions, the correlation for temperature varies with the seasonal time scale; the fit is worse

from winter to spring than from summer to autumn. It
was found that the reanalysis data sometimes failed torepresent the surface cooling associated with themesothermal structure (intermediate temperature maximum; Uda, 1963) in the subarctic and mixed water regions. Figure 5(a) is an example indicating that surface
temperature of the reanalysis data in the subarctic regions
had warmer bias in winter.
In the mixed water region, the correlation for salinity shows a temporal variation (lower panel of Fig. 4(b)).
Also, the correlation value averaged over the reanalysis
period was relatively low in this region compared to thatin the other regions (lower right panel of Fig. 3). Figure5(b) depicts comparison of some salinity profiles in November 2001, which showed the lowest correlation valueof monthly average (0.66) during the reanalysis period(lower panel of Fig. 4(b)). The observed profiles had more
complicated features than the reanalysis profiles. In particular, the reanalysis data did not reproduce low surface
salinity because the surface salinity in the reanalysis data
was basically relaxed to the monthly climatology throughthe surface boundary condition and the assimilation at
this time did not effectively correct the temporal and spatial variations in surface salinity. The mixed water region
is included in the storm truck region of the western North
Pacific involving the large variability in precipitation.
Then enough amounts of the salinity observation and/or
more reliable surface boundary condition of salinity are
required to reproduce well the variations of surface salinity in this region.
Note that the fluctuation of the correlation for bothtemperature and salinity was moderated from 2002 to2007 due to the increase of the number of assimilated insitu profiles after the launch of the Argo floats
deployments (Feder, 2000).
4.

Water Mass Variability Detected in the Reanalysis
Data
The Kuroshio-Kuroshio Extension, Oyashio southward intrusion, and mesoscale eddies play important roles
in water mass variability of the western North Pacific(e.g., Yasuda, 2003). We investigated the skill of thereanalysis data for the path variations of the KuroshioKuroshio Extension, the eddy kinetic energy in the mixed
water region, and the southern end latitude of the Oyashio
coastal branch. Then using the reanalysis data, we examined relations between the variations of the oceanic conditions described above and the water mass variability of
NPIW and SMW.
4.1 Variability of the Kuroshio Extension
It is well known that the Kuroshio south of Japan
has two stable paths: a large meander path and a nonlarge meander path (Yoshida, 1964). During the reanalysis

Water Mass Variability in the Western North Pacific745Fig. 6. The Kuroshio-Kuroshio Extension paths estimated at weekly interval in each year from 1993 to 2007. Left: estimatedfrom the surface layer velocities data calculated using drifting buoy and satellite altimetries (Ambe08). Right: estimated fromthe velocities of the reanalysis data at 100 m depth.
746

Y. Miyazawa et al.

Fig. 6. (continued).

period from 1993 to 2007, the Kuroshio almost always
took the non-large meander path except during the 1-year
period of the large meander, August 2004 to August 2005
(Miyazawa et al., 2008a). Using the satellite altimetrydata, recent observational studies (e.g., Qiu et al., 2007)
have discovered that the Kuroshio Extension has two
dynamic regimes: the stable and unstable states. Qiu etal. (2007) showed that the eddy kinetic energy associated with the Kuroshio Extension path variation was low
from October 1992 to mid-1996 and from the beginningof 2002 to the end of 2004, and high from mid-1996 to
the end of 2001 and from the beginning of 2005 to mid2006.
To examine the skill of the reanalysis data for thepath variations of the Kuroshio and Kuroshio Extension,
we compared path latitudes of the Kuroshio-Kuroshio
Extension in the reanalysis data with the observed latitudes estimated from the gridded surface geostrophic current velocity data with a horizontal resolution of 1/4°
(Ambe, D., K. Komatsu, M. Shimizu, and A. Okuno, 2009,
unpublished manuscript; hereafter Ambe08). Ambe08 is
an updated and extended version of the velocity data madeby combining the historical data archive of drifting buoys and satellite SSHA data (Uchida and Imawaki, 2003;
hereafter UI2003). UI2003 is quite useful to effectively

estimate the Kuroshio path (Ambe et al., 2004).
We estimated the position of the Kuroshio-Kuroshio
Extension path by tracking the grid points of maximum
kinetic energy for velocities at 100 m depth in thereanalysis data and those for the velocities in Ambe08.
The reason we chose 100 m depth is to avoid the contamination of ageostrophic components. Figure 6 compares the reanalysis and observed weekly KuroshioKuroshio Extension path from 1993 to 2007. The
reanalysis data likely reproduces both the large (August
2004 to July 2005) and non-large meander (January 1993–
July 2004 and August 2005–December 2007) states of theKuroshio south of Japan, and both the unstable (1996–
2001 and 2005–2007) and stable (1993–1995 and 2002–
2004) states of the Kuroshio Extension.
The Kuroshio path variation south of Japan is relatively stable compared with that of the Kuroshio Extension. As shown in Fig. 7(a), west of 140°E, RMSE of thepath latitude of the reanalysis data for that of Ambe08 iswithin approximately 50 km but increases to 150 km eastof 140°E. The absolute value of the mean error (bias) of
the path position indicated by a dashed curve is within 30
km except for 50 km between 145° and 152°E. We also
compared the mean kinetic energy measured along theKuroshio-Kuroshio Extension path for the reanalysis data
Water Mass Variability in the Western North Pacific

747

(a)

eddy kinetic energy

200
150
100
0

2 2

(m /s )

(km)

50
-50
-100
-150

RMSE
BIAS

-200
125 130 135 140 145 150 155 160 165
Longitude

0.4
stable
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0

stableunstable

unstable

93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08(year)(b)1

RA.100m
Obs

(m/s)

2

0.8
0.6
0.4
0.2
0
125 130 135 140 145 150 155 160 165
Longitude

Fig. 7. (a) Root Mean Square Error (RMSE; solid curve) andmean error (bias; dashed curve) of the latitudes of thereanalysis Kuroshio-Kuroshio Extension paths (right panels of Fig. 6) by the Ambe08 paths (left panels of Fig. 6).
(b) Mean kinetic energy distributions along the KuroshioKuroshio Extension paths calculated from the reanalysis(solid curve) and Ambe08 data (dashed curve).

and Ambe08 (Fig. 7(b)). The reanalysis data basically
reproduced distribution of the mean kinetic energy on the
Kuroshio-Kuroshio Extension path.
In the previous version of the JCOPE model(JCOPE1), the Kuroshio Extension path exhibited a strong
southward bias, with a maximum deviation of 100 km.
The mean kinetic energy of the Kuroshio Extension in
JCOPE1 was approximately half the magnitude observed(Miyazawa, 2007). The most significant difference between JCOPE1 (Miyazawa et al., 2008a) and JCOPE2 systems is the data assimilation component; JCOPE1(JCOPE2) adopted the multivariate optimum interpolation (the three-dimensional variational assimilation). The
variational assimilation used the temperature and salinity coupling EOF modes obtained from the in-situ observation data to describe vertical relationships between thesurface (e.g., SSHA or SST) and the subsurface (e.g., temperature and salinity) variables, which were quite important for the representation of water mass properties. Regression coefficients obtained from the model simulation,
which were used in the multivariate optimum interpolation of JCOPE1, caused biases of the water mass properties and the Kuroshio Extension path (Yoshinari et al.,2008).
748

Y. Miyazawa et al.

Fig. 8. Time sequences of surface eddy kinetic energy of thereanalysis data averaged in 32°–38°N and 141°–153°E. The
eddy kinetic energy was calculated from SSHA using
geostrophic flow approximation. Solid (dashed) curve indicates daily mean values (band-pass-filtered values within
periods from 1 year to 10 years). Thick lines denote stableand unstable years of the Kuroshio Extension. Dash dottedline indicates the temporal average magnitude, 0.157
m2s –2.

Table 2. Standard deviations of the indices associated with the
dynamical regimes of the Kuroshio Extension indicating(a) the northern latitude of the first meandering crest (indegrees; NLFCKE) and (b) its mean kinetic energy (inm2s–1; MKE). Standard deviations of the daily mean data
were calculated for the band-pass-filtered values. Numbersin the parenthese denote the mean values.
Ambe08

Reanalysis data(a) NLFCKE
Stable (1993–1997)
Unstable (1996–2001)
Stable (2002–2004)
Unstable (2005–2007)

0.80 (35.8)
0.84 (36.5)
0.37 (36.0)
0.70 (35.9)

0.58 (36.0)
0.92 (36.4)
0.37 (36.0)
0.61 (36.0)(b) MKE
Stable (1993–1997)
Unstable (1996–2001)
Stable (2002–2004)
Unstable (2005–2007)

0.09 (0.52)
0.10 (0.47)
0.09 (0.66)
0.10 (0.55)

0.11 (0.43)
0.14 (0.43)
0.11 (0.65)
0.14 (0.50)

The eddy kinetic energy calculated from thereanalysis SSHA (Fig. 8) shows the transitions between
stable and unstable states similar to those reported by theprevious studies (e.g., see figure 4b in Qiu and Chen,
2006). From Fig. 8, we can define the stable (unstable)
years as being 1993 to 1995 (1996 to 2001) and 2002 to2004 (2005 to 2007) comparing the eddy kinetic energy
with its average magnitude, 0.157 m2s–2. We calculated
the following indices from both the reanalysis data andAmbe08: the northernmost latitude and mean kinetic en-(a)Ambe08RAnorthern latitide of K.E. 142E-148E

northern latitide of K.E. 142E-148E

40

40stablestablestable

39

38

38

37

37

(deg.)(deg.)

39

36
35

stable36
35

34

unstable34

unstable

33

unstable

unstable33

93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08

93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08(year)(year)(b)
Ambe08RA

kinetic energy of K.E. 142E-148E

kinetic energy of K.E. 142E-148E

1 stablestable

0.8

0.8

0.6

0.6

2 2

(m /s )

2 2

(m /s )

1 stable

0.4
0.2

unstable

unstable0

93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08(year)

stable0.4
0.2

unstable

unstable0

93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08(year)

Fig. 9. (a) Time sequence of northernmost latitudes of the first meandering crest of the Kuroshio Extension (FCKE) in 142°–
148°E. Left: Ambe08 data. Right: the reanalysis data. (b) As in (a) except for mean kinetic energy along the Kuroshio Extension path between 142° and 148°E. Solid lines denote stable and unstable years of the Kuroshio Extension.

ergy of the first meandering crest of the Kuroshio Extension (hereafter FCKE) in 142°–148°E (Fig. 9). Correlations of the interannual variations of the indices between
the reanalysis data and Ambe08 for the northernmost latitude and mean kinetic energy are 0.90 and 0.84, respectively. To quantitatively examine relationships between
the dynamical regimes of the Kuroshio Extension and thetwo indices shown in Fig. 9, we calculated standard deviations for the band-pass-filtered values and temporal
averages during the unstable and stable periods (Table2). Both the reanalysis data and Ambe08 indicate smaller
standard deviations for the stable period than for the unstable period. This suggests that the variations on thenorthernmost latitude and mean kinetic energy of FCKE
were stable (unstable) during the stable (unstable) periodof the Kuroshio Extension. In addition, during the unstable periods, the mean position of FCKE almost tended tomove northward and its mean kinetic energy was lower
than when compared with the energy during the stable
periods.
4.2 Variability of the Oyashio coastal branch
The Oyashio intrusion along the Japanese east coast(Oyashio coastal branch; hereafter OY1) varies with seasonal, interannual, and decadal time scales (Yasuda,2003). We calculated the daily mean sequence of thesouthern end latitude of the OY1 position (Fig. 10(a)) thatis defined as the southern end latitude of grid points witha temperature of 5°C at 100 m depth. The seasonal migration of the southern end latitude of OY1 in thereanalysis data is significant; the climatological monthly
mean of reanalysis data (not shown) shows that OY1
reaches to 37.9°N in May and retreats to 41.1°N in December. The monthly mean southern end latitude of OY1
estimated from the observation from 1960 to 2001 moves
to 38.8°N in March and April and retreat to 41.2°N in
November (Yasuda, 2003). Since the observed southernend latitude of OY1 has the largest interannual variability in April (Yasuda, 2003), we checked the interannual
variation of the latitude averaged from March to May.
Figure 10(b) compares the averaged southern end latitude with observed data estimated from the in-situ temperature obtained by Japan Meteorological Agency(JMA). Although the reanalysis data showed southwardbias of approximately 1° compared with the observation,
they reproduced the interannual variation; the correlation
Water Mass Variability in the Western North Pacific

749

(a)

(a)
Size of Oyashio water region

43
42
41
40
39
38
37
36
35

25
20
15

4

2

(10 km )(lat.)

southern limit of Oyashio coastal branch

105

93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08

0

93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08(year)(year)(b)Southern Limit of Oyashio Coastal Branch (March-May)

42(b)
Area of Oyashio water (March-May)

RA
JMA

25

RA
JMA

41

20

39

(104km2)

Lat.

40

38
37

15
10
5

36

93 94 95 96 97 98 99 00 01 02 03 04 05 06 07
Year

0

93 94 95 96 97 98 99 00 01 02 03 04 05 06 07
Year

Fig. 10. (a) Time sequences of the southern end latitude of the
OY1 estimated from the reanalysis data. The latitude wasdefined as the southern end latitude of grid points with atemperature of 5°C at 100 m depth in 36°–43°N and 140°–
145°E. (b) As in (a) except for mean values from March to
May in each year (thick curves with closed squares). Dashedcurve with crosses indicates the observation obtained by
the Japan Meteorological Agency.

between the reanalysis and observation data is 0.71. Boththe reanalysis and observation data indicate that OY1 did
not move southward in 1999 as usual but intruded southward in 2004.
We calculated another index: the size of the Oyashio
water region (Fig. 11(a)) that is defined as an area of watertemperature lower than 5°C at 100 m depth in 36°–43°N
and 140°–148°E (JMA, 2006). The size of Oyashio water
varies seasonally in a similar manner to the southern end
latitude of OY1 and also shows interannual variations.
We also compared the size of Oyashio water averaged
from March to May for the reanalysis data with the JMA
observation data (Fig. 11(b)); the correlation is 0.65. The
data well represented two peaks of 1998 and 2006.
4.3 Variability of the North Pacific Intermediate Water(NPIW)
Yasuda et al. (1996) indicated that the mixture of
low salinity Oyashio water and relatively saline Kuroshio
water (old NPIW) along the Kuroshio Extension just off
the east coast of Japan can produce new NPIW. The mean
750

Y. Miyazawa et al.

Fig. 11. As in Fig. 10 except for the size of the Oyashio water
region that was defined as regions with temperature lower
than 5°C at 100 m depth in 36°–43°N and 140°–148°E.

salinity of the reanalysis data at 26.7σθ surface (Fig. 12)shows that the salinity with intermediate value (33.9 <S < 34.1) corresponding to the new NPIW is distributed
along the Kuroshio Extension between the lower (S <33.9) and higher (S > 34.1; old NPIW) salinity waters.
Because spatial variability of salinity at 26.7σ θ surface in 36°–43°N and 140°–148°E was large (Fig. 12),
we investigated the time sequence of salinity averaged inthis region (Fig. 13). The areal mean salinity shows a significant seasonal variation, corresponding well to the seasonal southward intrusion of OY1 (Fig. 10(a)). Theinterannual variability indicates high (1993–2001 and2007) and low (2002–2006) salinity periods. The longterm in-situ observation at a section along 41.5°N (JMA,2006) also showed high (1996–2001) and low salinity
periods (2002–2004).
Both FCKE and OY1 highly affect the water mass
variability in the mixed water region (Yasuda, 2003).
Thus, as shown in Table 3, we checked correlations between the low frequency component of the four indicesdescribing the conditions of FCKE and OY1 (see Subsections 4.1 and 4.2) and that of the mean salinity. The
four indices likely have meaningful correlations with theinterannual variation of the salinity. Correlations among
the four indices (Table 4) indicate that the southern end

latitude of OY1, the eddy kinetic energy in the mixed
water region, and the northernmost latitude of FCKE havepositive correlations with each other. This is consistent
with the dynamics of the mixed water region; the warm
core rings sometimes shed from the northward migrating
FCKE, and the eddy activity due to the variations of FCKE
affects the southward intrusion of OY1. In contrast, themean kinetic energy of FCKE does not have so meaningful a correlation with the other indices.
To check the statistical relations among the indicesin more detail, we calculated the partial correlation coefficients (e.g., Storch and Zwiers, 1999), which describe
the regression between two random variables, with theeffect of removing a set of controlling random variables.
We chose two control variables: the mean kinetic energy
of FCKE and the southern end latitude of OY. Both theeddy kinetic energy and the northernmost latitude of
FCKE show an increase of positive correlation coefficients controlling the mean kinetic energy of FCKE (thirdcolumn of Table 3), but show a decrease of positive correlation coefficients controlling the southern end latitudeof OY1 (fourth column of Table 3). These results suggestthat the eddy kinetic energy and the northernmost latitude of FCKE were more related to the southern end latitude of OY1 rather than to the mean kinetic energy of
FCKE with regard to the effects on the interannual variations of the mean salinity.
To characterize the oceanic conditions of the mixed
water region in terms of the interannual variations of themean salinity at the 26.7σθ surface, we employed multiple regression analysis. We chose the mean kinetic energy of FCKE and the southern end latitude of OY1 as
the explanatory variables according to the estimate of thepartial correlation coefficients described above. For 15
values in the middle of June with an interval of 365 days
sampled from the low frequency component of the meansalinity, we constructed a regression equation as follows:

Sest = −0.16 EK + 0.02O1 + 32.9,(7)where Sest, EK, and O1 are the mean salinity at the 26.7σθ
surface, mean kinetic energy of FCKE, and southern end
latitude of OY1, respectively. The coefficient of multiple
determinations in Eq. (7) was 0.80; the t-statistic values

Oyashio Region 26.7

34
33.95
33.9
(psu)

33.85
33.8
33.75
33.7
33.65
33.6
93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08(year)

Fig. 12. Mean salinity at 26.7 σ θ surface calculated thereanalysis data from 1993 to 2007. Contour interval is 0.1.

Fig. 13. Time sequences of salinity of the reanalysis data at
26.7 σ θ surface averaged over 36°–43°N and 140°–148°E.
Solid (dashed) curve indicates daily mean values (bandpass-filtered values with periods from 1 year to 10 years).
Closed circles indicate the estimation based on the regressive equation (7).

Table 3. Correlation coefficients between the indices associated with the oceanic conditions in the mixed water region and thespatial mean salinity at 26.7σ θ surface. Parentheses in the first column indicate symbols of the indices. Third and fourthcolumns indicate partial correlation coefficients controlling the southern end latitude of the Oyashio coastal branch (OY1)
and mean kinetic energy of the first meandering crest of Kuroshio Extension (FCKE), respectively.

Mean kinetic energy of FCKE (MKE)Southern end latitude of OY1 (OY1)
Eddy kinetic energy in the mixed water region (EKE)
Northernmost latitude of FCKE (NLFCKE)

Correlation

Partial correlation

Partial correlation

–0.70
0.64
0.56
0.45

—
0.62
0.70
0.61

–0.69
—
0.42
0.34

Water Mass Variability in the Western North Pacific751

Table 4. Correlation coefficients among four indices: mean
kinetic energy of FCKE (MKE), southern end latitude of
OY1 (OY1), eddy kinetic energy in the mixed water region(EKE), northernmost latitude of FCKE (NLFCKE).

MKE
OY1
EKE
NLFCKE

MKE

OY1

EKE

NLFCKE

—
–0.31
–0.09
–0.02

—
—
0.42
0.31

—
—
—
0.66

—
—
—
—

for the coefficients of EK and O1 were –3.1 and 2.8, respectively; the Durbin Watson statistic value of (7) was
1.7. The salinity values estimated with Eq. (7) were plotted in Fig. 13. The highest (lowest) value of Sest in the
weak (intense) Oyashio water year 1999 (2004) is characterized by low (high) mean kinetic energy of FCKE andweak (strong) southward intrusion of OY1 (Fig. 14). The
new NPIW regions in the weak and intense Oyashio water years are narrow and broad, respectively.
4.4 Variability of the Subtropical Mode Water (SMW)
Although the thickness of SMW in the western North
Pacific is basically governed by intensity of the wintermonsoon, it is also influenced by the dynamic state of
the Kuroshio Extension (Qiu and Chen, 2006; Qiu et al.,2007). In particular, Qiu and Chen (2006) suggested thatthe dynamic state of the Kuroshio Extension affected thedecadal variation of SMW’s thickness in the recirculation
region of the Kuroshio Extension more strongly than theoverlying atmospheric conditions for the period from 1993
to 2004.
The reanalysis data reproduced both the stable andunstable dynamic states of the Kuroshio Extension (Subsection 4.1). With it, we investigated relationships between the interannual variability of SMW and that of theKuroshio Extension. Figure 15 displays the mean state of
the potential vorticity (PV), (f/ρ0)(∂ρ/∂z), (z is the positive value of depth; ρ is the density; ρ0 is equal to 1025
kg·m –3; f is the Coriolis parameter) at the 25.4σθ surface.
A closed region south of the Kuroshio Extension withlower PV indicates the SMW region represented in thereanalysis data. Comparison of the reanalysis data withthe climatology from in-situ observations (e.g., Suga and
Hanawa, 1995a) indicates that the SMW in the reanalysis
data shows similar spatial patterns to that observed but
its PV value has a positive bias of 1.5–2.0 × 10 –10 m–1s–1,
suggesting that the assimilation process still needs to be
improved by adjusting some parameters such as the EOF
modes and/or standard deviations of in-situ temperatureand salinity included in Eq. (2).
The 15-year variation of PV inside the Kuroshio
Extension recirculation gyre in the reanalysis data (Fig.
752

Y. Miyazawa et al.

(a) 1999(b) 2004

Fig. 14. As in Fig. 12 except for annual mean. (a) in 1999.
(b) in 2004.

16) shows that low PV at 300 m depth was intensified in
winter during the stable periods (1993 to 1995 and 2002
to 2004) compared with that in the unstable periods (1996
to 2001 and 2005 to 2007). The mixed layer depth in thestable periods tended to be deeper than in the unstable
periods. These results are consistent with the analysis
using the historical archive of temperature profiles obtained during the period from 1993 to 2005 reported by
Qiu and Chen (2006). PV at the 25.4σθ surface shows thesimilar interannual variation but it also shows an upward
movement of the iso-sigma surface due to heating in thesummer season. The correlation coefficients of the time
sequence of PV at 300 m depth (Fig. 17) with the northernmost latitude of FCKE (right panel of Fig. 9(a)) andeddy kinetic energy in the mixed water region (Fig. 8)

Fig. 15. PV (in 10–11 m–1s–1) in the reanalysis data at 25.4 σθsurface averaged over the period from 1993 to 2007. The
contour interval is 5 × 10 –11 m–1s–1. A region of PV lower
than 60 × 10–11 m –1s–1 is shaded.

potential vorticity 300m

70

stablestable

-1 -1

m s )

60

-11

5. Summary and Discussion
By using an eddy-resolving ocean forecast system,
JCOPE2, we have created 15-year ocean data of the western North Pacific with a horizontal resolution of 1/12°
and 46 vertical levels for the period from 1993 to 2007. A
combination of a state-of-the-art eddy-resolving ocean
general circulation model and the remote and in-situ observation data allowed us to produce realistic high-resolution data (the reanalysis data) of the sea surface height,
velocity, temperature, and salinity on the uniform grid
over the 15 years period. The reanalysis data well reproduced the mean water mass property included in the insitu observation in the western North Pacific. It also well
reproduced the mean state and interannual variation of
the Kuroshio-Kuroshio Extension path and the southernend latitude of OY1.
By analyzing the reanalysis data, we investigated therelationship between the mean salinity in the mixed water region and the behavior of FCKE and of the southernlatitude of OY1. Multiple regression analysis suggested
that the mean salinity in the mixed water region variedwith the interannual time scale responding to both themean kinetic energy of FCKE and the southern end lati-(10

were 0.77 and 0.61, respectively. This suggests that thereanalysis data well reproduced the relationship between
the dynamic state of the Kuroshio Extension and SMW’s
interannual variability. In contrast, the correlation coefficients with the southern latitude of OY1 (Fig. 10(a))
and mean kinetic energy of FCKE (right panel of Fig.
9(b)) were 0.34 and –0.21, respectively.

Fig. 16. Time sequence of PV in the reanalysis data inside of
the recirculation gyre where SSH subtracted its latitudinalmean was larger than 0.2 m in 30°–38°N and 140°–155°E.
A thick contour denotes the mixed layer depth, defined as
the depth at which σθ increases by 0.125 kg·m–3. A thincontour indicates iso-sigma depth of 25.4 σθ. Solid (dotted)
horizontal lines at 300 m depth denote stable (unstable)
periods of the Kuroshio Extension (cf. Fig. 6).

50
40
30

unstable

unstable20

93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08(year)

Fig. 17. As in Fig. 8 except for PV at 300 m depth from Fig.
16.

tude of OY1. The horizontal pattern of “new NPIW” inannual mean salinity distribution at 26.7σθ presented asignificant difference between the most saline year of1999 and the most fresh water year of 2004.
The reanalysis data confirmed the decadal variationof SMW in the Kuroshio Extension recirculation gyrereported by Qiu and Chen (2006). The positive correlaWater Mass Variability in the Western North Pacific753

tion between the interannual migration of the northernmost latitude of FCKE and the PV averaged inside therecirculation gyre at 300 m depth was significant. Because the northernmost latitude of FCKE is closely related with the eddy kinetic energy in the mixed water region, it is suggested that the relationship between the
dynamic state of Kuroshio Extension and SMW’s thickness in the recirculation region was well reproduced in
the reanalysis data.
The regressive equation (7) suggests that not only
the southward intrusion of OY1 but also the intensity of
FCKE were related to the salinity at 26.7σθ in the mixed
water region. This is consistent with the important rolesof the Kuroshio Extension in the formation of the salinity minimum in intermediate layers suggested by previous studies. Using the hydrographic data obtained at repeated observation sections from 1996–2001, Miyao andIshikawa (2003) indicated that the Oyashio transport of
5.0 Sv mixed with the Kuroshio transport of 10.9 Sv
within the layer of 26.6–27.4σθ along the Kuroshio Extension at 144°E. Shimizu et al. (2004) demonstrated that
the Oyashio waters were entrained in the Kuroshio Extension using the data of isopycnal-tracking floats. An
OGCM simulation showed the occurrence of isopycnal
mixing of the Oyashio and Kuroshio waters along theKuroshio Extension (Ishizaki and Ishikawa, 2004).
Since it is difficult to use the reanalysis data to investigate the dynamics related to the above relationships,
we carried out an additional simulation for the same 15
years but without the data assimilation. The interannualcomponent of intensity of FCKE maintained a meaningful negative correlation (–0.65) with that of the salinity
at 26.7 σθ in the 15-year simulation results like that in thereanalysis data. This encourages us to investigate the relevant physical mechanisms included in the simulation
results in the near future.
The in-situ data archive used in the present reanalysis(GTSPP) did not include most of the data obtained oncoastal repeated hydrographic observation lines operationally conducted by prefectural fisheries research agencies(hereafter, FR-DATA). Preliminary assimilation experiments with and without FR-DATA performed by the
JCOPE2 system showed that an inclusion of FR-DATA
did not affect the large scale features of the oceanic conditions in the mixed water region discussed in the present
study but yielded a certain impact on the representationof the local oceanic condition near the Japanese east coastin the mixed water region (Miyazawa et al., 2008b). We
also found that very few salinity profiles in the western
North Pacific from 1994 to 1999 were archived in thepresent version of GTSPP (Fig. 4). In the near future, wewill create another version of the reanalysis data including more additional in-situ data obtained from othersources such as FR-DATA and updated versions of the754

Y. Miyazawa et al.

World Ocean Database (e.g., Boyer et al., 2006), and further investigate more local water mass variability usingit.
Some noticeable biases and RMSEs for in-situ temperature and salinity data were detected in the shelf region of the East China and Okhotsk Seas (Fig. 3). We
found that the mixed layer modeling of the JCOPE2 inshallow regions of the marginal seas needed further improvements. Also, the present version of JCOPE2
reanalysis does not assimilate the in-situ data there. The
next version of the JCOPE2 reanalysis will use a better
mixed layer model and assimilate the in-situ data in themarginal seas.
The JCOPE2 reanalysis data and their updated versions are available from the authors by request. The
JCOPE web site (http://www.jamstec.go.jp/frcgc/jcope/)
provides information about downloading and visualizing
the reanalysis data for users.
Acknowledgements
This work is part of the Japan Coastal Ocean Predictability Experiment (JCOPE) promoted by the Japan
Agency for Marine-Earth Science and Technology(JAMSTEC). It is also supported by a joint research program (FRA-JCOPE) between the Fisheries Research
Agency of Japan (FRA) and JAMSTEC. Dr. T. Kagimoto
kindly provided us with FORTRAN codes describing theflux-corrected transport and bi-harmonic schemes fortracer calculations. Dr. K. Uehara also kindly provided
us with the bottom topography data of the East China Sea.
An anonymous reviewer and the editor Prof. H. Mitsudera
provided valuable comments to improve the earlier versions of the manuscript.
Appendix: Adaptive Estimates of the Error
Covariance for the Kuroshio-Kuroshio
Extension Region
For the Kuroshio-Kuroshio Extension region, wechanged the horizontal scales S of the horizontal correlation (3) of the EOF mode adaptively in the assimilation
process:(

S = S min + S max − S min(( RMSE ( x, y, t ) − RMSE )) ( RMSE − RMSE )
SSHA

min
SSHA

)

S( x, y, t ) = min max( S, Smin ), Smax ,

max
SSHA

max
SSHA

(A1)

where Smin and Smax are the minimum and maximum horizontal scales, respectively; RMSE min SSHA andRMSE maxSSHA are the critical values of Root Mean Square
Error for SSHA observation. Scheme (A1) specifies small
horizontal scales at the grid point where RMSE is large.
It allowed clear presentation of the sharp fronts of theKuroshio, Kuroshio Extension and mesoscale eddies.
In addition, for the Kuroshio-Kuroshio Extension
region, we specified the error of SSHA observation
adaptively,

(

o
min
max
min
SSHAerr= SSHAerr+ SSHAerr− SSHAerr))( RMSE ( x, y, t ) − RMSE
( RMSE − RMSE )SSHA ( x, y, t ) = min( max( SSHA , SSHA ), SSHA ),
⋅

o
err

max
SSHA

SSHA

min
SSHA

max
SSHA

o
err

min
err

max
err

(A2)

where SSHAminerr and SSHAmaxerr are the lower and upper
limits of the observation error, respectively. Scheme (A2)
was also useful for the sharp presentation of the oceanic
fronts.
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