PUBLICATIONSJournal of Geophysical Research: Oceans
RESEARCH ARTICLE
10.1002/2016JC011748
Key Points:
 Climatology and linear trend of
seasonal water temperature in acoastal sea connected to the
Kuroshio are investigated Air-sea heat ﬂux plays a dominantrole in warming of winter-time watertemperature Oceanic heat transports are
responsible for enhanced cooling in
springCorrespondence to:
E. Tsutsumi,
tsutsumi@riam.kyushu-u.ac.jp
Citation:
Tsutsumi, E., and X. Guo (2016),
Climatology and linear trends ofseasonal water temperature and heatbudget in a semienclosed sea
connected to the Kuroshio region,
J. Geophys. Res. Oceans, 121, 4649–
4669, doi:10.1002/2016JC011748.
Received 23 FEB 2016
Accepted 1 JUN 2016
Accepted article online 6 JUN 2016
Published online 2 JUL 2016

Climatology and linear trends of seasonal water temperatureand heat budget in a semienclosed sea connected to the
Kuroshio region
Eisuke Tsutsumi1,2 and Xinyu Guo2
1

Research Institute of Applied Mechanics, Kyushu University, Fukuoka, Japan, 2Center for Marine Environmental Studies,
Ehime University, Matsuyama, Japan

Abstract The climatology and linear trend of seasonal water temperature and heat budget in asemienclosed sea connected to the Kuroshio region (Seto Inland Sea: SIS) are investigated by constructing
and analyzing a gridded data set of water temperature, salinity, and air-sea heat ﬂuxes. In the SIS,
winter-time water temperature and ocean heat content (OHC) showed a signiﬁcant increasing trend while
those in summer-time exhibited no signiﬁcant change. By analyzing the heat budget between the tendencyof ocean heat content @OHC/@t and air-sea net heat ﬂux Qnet, the driving factor of the winter-time warming
trend in the SIS was identiﬁed as autumn Qnet, while the unchanged summer-time OHC is mainly due todecreased spring-time oceanic heat transport to the SIS from the Kuroshio region. In addition, we showedthat the decreased spring-time heat transport could have been induced by upwelling due to wind curl andwind speed in the shelf-slope region, heat transport by the Kuroshio south of Shikoku Island, and freshwater
input to the SIS from rivers. The importance of both oceanic and atmospheric forcing mechanisms for
explaining variation in the water temperature in coastal areas is demonstrated.

1. Introduction
The upper ocean temperature, especially sea surface temperature (SST), impacts climate variability both inthe ocean and atmosphere through the air-sea exchange of heat, fresh water, carbon dioxide, and so on.
The upper ocean warmed globally over the period 1971–2010, with an average warming rate in the upper
75 m of 10.118C per decade [Rhein et al., 2013], but the rate of change exhibits temporal and spatial
variability.
Rapid warming (0.3–0.58C per decade) in semienclosed and shelf seas over the world as well as in the Kuroshio Current during a period of 1982–2006 has been reported by Belkin [2009], who investigated SSTchanges since 1951 in all coastal areas in the world ocean. Similarly, many warming phenomena in SSTs
over the past 30 years have been detected in the world-wide coastal area (0.188C per decade, on average)
by Lima and Wethey [2012]. They indicated the warming rate has been heterogeneous in both space andseasonality, and the seasonality of SST warming is extremely important for coastal marine ecosystems.
Mechanisms causing the seasonality in the warming rate however remains a challenge in each area.
High warming rates for SSTs have also been found over the global western boundary currents [Wu et al.,2012]. Over the Kuroshio in the northwestern Paciﬁc, mean SST warming rate over the period 1900–2008(1950–2008) is 1.29 (1.10)8C per century, which is signiﬁcantly higher than the global mean, 0.62 (0.71)8C
per century [Wu et al., 2012].

C 2016. American Geophysical Union.
V

All Rights Reserved.

TSUTSUMI AND GUO

In the inner (onshore) region of the Kuroshio, the warming rate of the water temperature has been
observed to be higher in winter than in summer. In the East China Sea (Bohai and Yellow Seas), the wintertime SST trend was 0.38 (0.27)8C per decade over the period 1962–2011, while the summer-time one was
0.18 (0.12)8C per decade [Bao and Ren, 2014]. In Taiwan Strait, Belkin and Lee [2014] reported that the longterm SST warming during 1957–2011 is strongest in winter (0.78C per decade) and weakest in summer(0.28C per decade). In the Tsushima Warm Current, which is a branch of the Kuroshio, the winter-time watertemperature increased 0.4–1.38C over the period 1971–2000, while the change in the summer-time watertemperature was insigniﬁcant [Senjyu et al., 2010]. In a semienclosed bay near the Tsushima Warm Current,

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Takashige et al. [2013] showed that the winter(summer)-time SST trend was about 0.03(20.02)8C yr21 overthe period 1955–1995. Kondo et al. [2005] found that the winter(summer)-time SST changed by 0.4–1.3(21.2 to 20.7)8C over the period 1975–2001 in a bay connected to the Tsushima Warm Current region. In
the Seto Inland Sea (SIS), which is a semienclosed bay connected to the Kuroshio region, Takahashi and Seiki[2004] showed that an average increase of approximately 18C was detected in winter in the water temperature at 10 m depth over the period 1972–2001 over the whole of the SIS, compared to insigniﬁcant changes
in the summer-time water temperature.
In the aforementioned studies, several mechanisms have been proposed to explain the trends in the watertemperature. Bao and Ren [2014] showed winter monsoon intensity was well correlated with the wintertime SST in the China marginal seas while summer monsoon had less effect on the summer-time one. Belkinand Lee [2014] discussed relations between the winter ampliﬁcation of long-term SST warming in the Taiwan Strait and intensiﬁcation of the winter monsoon. Senjyu et al. [2010] suggested a relationship between
inter-decadal temperature variation and the Paciﬁc Decadal Oscillation. Takashige et al. [2013] indicated thesummer-time cooling trend was due to atmospheric forcing while Kondo et al. [2005] concluded thesummer-time cooling trend was explained by oceanic water exchange with the Tsushima Warm Current
region. Although these studies showed seasonality in the linear trend of the water temperature and their
possible driving mechanisms, none of them considered both atmospheric and oceanic effects in a quantitative way.
The heat budget has also been examined to determine the drivers for temperature trends. In the East China
Sea, a signiﬁcant contribution to oceanic heat transport by the Kuroshio has been reported [Kondo, 1976;
Liu et al., 2010]. In the SIS, Tawara [1986] and Murakami et al. [1989] investigated the seasonal variation of
the heat budget using in situ data and a numerical model, respectively. Both studies concluded that the surface heat ﬂux was dominant in the heat budget in the SIS in summer and autumn but warm water transport
from the open ocean was also signiﬁcant in winter and spring. However, in their studies, there were uncertainties in their estimations of the surface heat ﬂux and the long-term variation of the heat budget was notinvestigated.
Here we present an investigation of the linear trend of water temperature and heat budget in the SIS and
its connecting channels by constructing and analyzing data sets of water temperature and air-sea heat
ﬂuxes. The SIS is a typical coastal sea and its hydrographic condition is considered to be highly affected by
the Kuroshio, and it therefore presents a good case study for understanding the mechanisms controlling
water temperatures in coastal seas. Special emphasis is placed on the seasonality in linear trends of watertemperature and both atmospheric and oceanic forcing.
The SIS is surrounded by the islands of Shikoku, Honshu and Kyushu and consists of several subbasins andstraits (Nada and Seto in Japanese, respectively), as shown in Figure 1. We deﬁne the area including IyoNada, Suo-Nada, Aki-Nada, Hiuchi-Nada, Bisan-Seto, Harima-Nada, and Osaka Bay as the SIS. The SIS connects to the open ocean through western and eastern channels; the Bungo and Kii channels, respectively.
We also deﬁne the 200 m depth isobaths south of the SIS as the location of the shelf edge (white line in Figure 1) and refer to the area south of the shelf edge as the Kuroshio region. Speciﬁcally, we refer to the area
between the average northern and southern edges of the Kuroshio (dotted lines in Figure 1) and the area
between the shelf edge and the northern edge of the Kuroshio as the Kuroshio and shelf-slope region,
respectively (for the deﬁnition of the average Kuroshio edges, see Appendix A).

2. Data
2.1. Data Sources
For the analysis of sea water temperature and salinity (T-S) in the SIS, we used in situ hydrographic data
constructed by Takahashi and Seiki [2004, hereinafter TS04], as well as archived data from the Japan Oceanographic Data Center (JODC; http://www.jodc.go.jp) and in situ data from the Japan Meteorological Agency(hereinafter, JMAs; http://www.data.jma.go.jp/kaiyou/db/vessel_obs/data-report/html/ship/eﬁle_NoS2.
html). The TS04 data set consists of monthly regular samplings conducted by the Prefectural Fisheries
Experiment Stations in the SIS (Figure 2a; blue ﬁlled circles). The JODC data are a compilation of in situ
measurements made around the Japanese islands by various organizations including universities and thePrefectural Fisheries Experiment Stations (Figure 2a; gray ﬁlled circle), but not the JMA. Therefore, the JODCTSUTSUMI AND GUO

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Figure 1. (left) Bathymetric map of the Seto Inland Sea (color shade) and the boundaries between basins (Nada in Japanese), straits (Seto),bays, and channels (white dashed line). Bathymetry is based on the 500 m grid depth data from the Japan Oceanographic Data Center.
The thick white line denotes the shelf edge at 200 m depth isobaths south of the channels. Dot-dashed and dot gray curves show the climatological mean Kuroshio path and the northern and southern edges over the period 1964–2001. (right) Map showing the locations of
the Seto Inland Sea (square) and the average Kuroshio path (gray curve).

data set includes many observations identical to the TS04. We extracted these duplicate observations fromthe JODC data set, and utilizing them, cross checked and corrected the TS04 data. The JMA data set is verysmall compared to the other two sources, but its regularly sampled, seasonal data may provide useful information about the Kuroshio region (Figure 2a). Most of these data were sampled around the reference

Figure 2. (a) Map showing the position of the in situ data of Takahashi and Seiki [2004] (TS04; blue dot), the Japan Oceanographic Data Center (JODC; gray dot), and the Japan Meteorological Agency ship (JMA ship; magenta dot). Positions of the JMA meteorological observatories are also shown with open circles. Monthly time series of the cumulative number of temperature and salinity proﬁles are shown in Figures 2b and 2c, respectively.

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depths: 0, 10, 20, 30, 50, 75, 100, 150, 200, 300, 500, 1000, 2000, 3000, 4000, 5000 m, and near the seabed.
We used the JODC and JMA data within 1308E–1368E, 308N–358N and all of the TS04 data to construct a
gridded data set of temperature and salinity for objective analysis as described below. We selected 1964–
2001 for our analysis period, as this provided the best coverage from the T-S data (Figures 2b and 2c).
For the calculation of air-sea heat ﬂuxes, we used daily-average global solar radiation and the rate of cloud
cover observed at the observation stations of the JMA shown in Figure 2a (open circles) as well as surface
air temperature (SAT), relative humidity, sea level pressure, and surface wind from two reanalysis productsby the JMA; Japanese 55 year reanalysis (JRA-55) [Kobayashi et al., 2015] and mesoscale-model grid pointvalues (MSM-GPV). Because the MSM-GPV has a ﬁne spatial resolution (5 km horizontally) but short temporal coverage (2006–2013), while the JRA-55 has a long coverage period (1955–2013) but relatively coarse
resolution (1/28 horizontally), we reconstructed daily-average atmospheric data over the period 1964–
2001 with linear regression of the JRA-55 data on the MSM-GPV data for the overlapping period of the twodata sets. Correlation coefﬁcients between the reconstructed data set and the MSM-GPV for the period
2006–2013 are generally high (r > 0.8). The reconstructed data are linearly interpolated onto the ocean grid,
while the cloud cover rate is interpolated using an exponential ﬁlter whose parameters are determinedfrom the covariance of the data.
2.2. Gridding Method for Seawater Temperature and Salinity
To obtain the three-dimensional distribution (longitude, latitude, and depth below the sea surface) of themonthly average T-S, an objective analysis method was applied to the in situ observation data noted in section 2.1. The bathymetry in the SIS and the Kuroshio region are gridded at 0.028 intervals over 308N–358N
and 1308E–1368E using 500 m mesh depth data produced by the JODC (shown in Figure 1), and then grid
point values of T-S are obtained with the following method.
Analysis data of monthly temperature and salinity were obtained with a standard optimal interpolation
method [Bretherton et al., 1976]; analysis xa is calculated from observation yo and the ﬁrst guess xb by
xa 5 xb1K(yo – Hxb), where K and H are a weight for optimal interpolation and an operator which functions
as the bilinear interpolation from the observation points onto the grid points, respectively. We use
persistence-based prediction for xb; xbn 5 xclimn 1 a(xan–1 – xclim
n–1 ), where superscript ‘‘clim’’ and subscript ‘‘n’’
represent monthly climatology and calendar month, respectively [Smith, 1995]. The autocorrelation coefﬁcients for a 1 month lag, a, are set to 0.5 based on the evaluation using the in situ data. The weight K isdeﬁned as K 5 Pgo(Poo 1 R) using covariance matrices Pgo and Poo where the covariance between two
points k, l is modeled as a function of distance dkl by Pkl 5 P1exp(–dkl/L) 1 P0 at each depth. Parameters L, P0,
and P1 are determined by ﬁtting the model to the covariance of the in situ data. The observation error R
was given by the variability of T-S in each month. In general, most of the in situ data are sampled in the ﬁrst
week of each month so that we simply set the time of the respective monthly analyses to the ﬁrst day of amonth.
2.3. Air-Sea Heat Fluxes
The net air-sea heat ﬂux Qnet is deﬁned as
Qnet 5Qs 1Qb 1Qh 1Qe(1)where Qs, Qb, Qh, and Qe are shortwave radiation, longwave back radiation, and sensible and latent heat
ﬂuxes, respectively. All ﬂuxes are deﬁned to be positive if they are seaward. Shortwave and longwave radiative ﬂuxes are estimated using a standard and Clark et al. [1974] formulae, respectively:
Qs 5ð12aÞIg

(2)

2
3
Qb 52erTs 4 ð0:3920:05e1=2
a Þð12dnc Þ24erTs ðTs 2Ta Þ(3)where Ig is the global solar radiation in W m22, a is the sea surface albedo that is assumed to be highest in
winter and lowest in summer (5 0.06–0.1) [Tawara, 1986], e is the emissivity of sea surface (5 0.97), r is the
Stefan-Boltzmann constant (5 5.67 3 1028 W m22 K24), ea is vapor pressure in hPa, d is the cloud coefﬁcient (5 0.65), nc is the cloud cover rate (as a decimal proportion of one) and Ts and Ta are SST and SAT in K,
respectively.

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Sensible and latent heat ﬂuxes are calculated using the following bulk formulae:
Qh 5qa ca ch ðTa 2Ts ÞjVj(4)

Qe 5qa Lce ðqa 2qs ÞjVj(5)where qa is the air density in kg m23, ca is the speciﬁc heat capacity of the air in J kg21 K21, L is the latent
heat of evaporation in J kg21, qs and qa are the vapor pressure at the sea surface and the surface air in hPa,
respectively, and V is the surface wind vector in m s21. The bulk coefﬁcients ch and ce in equations (4) and(5), respectively, are determined according to Kondo [1975].
Daily values of these ﬂuxes were calculated ﬁrst from the daily atmospheric data and daily SST that wereobtained by linear interpolation from the monthly SST, and their monthly average was deﬁned at the middle day of a month. In this study, shortwave radiation is assumed to be spatially uniform and calculatedusing the global solar radiation data over 18–40 (varies in time) observation stations located in westernJapan.

3. Climatology
We ﬁrst show climatological features of water temperature and air-sea heat ﬂuxes and conﬁrm the mechanisms for heat ﬂuxes to the SIS in each season. Hereinafter, we refer to December, January, and February as
winter, March, April, and May as spring, June, July, and August as summer, and September, October, andNovember as autumn. We choose these ‘‘terrestrial’’ seasons because, as we will show in section 3.3, seasonal heat budget in the SIS is strongly affected by net air-sea heat ﬂux and its seasonal variation is close toterrestrial one rather than to ‘‘oceanic’’ one (i.e., January, February, and March as winter, . . .). Also, we use
the terrestrial seasons because seasonal contrast of linear trends of terrestrial-seasonally averaged watertemperature and heat budget is more signiﬁcant than that with the oceanic seasons.
3.1. Water Temperature and Air-Sea Temperature Difference
Figures 3a–3d show the seasonal average SST. In the SIS and the channels, water temperature is generally
lowest in March and highest in August or September throughout the water column. In winter and spring,thermal fronts are formed between the cold (<158C), shallow inland sea and the warm (20–218C), deep Kuroshio water. In summer, the temperature of the SIS and the channels is lower than in the Kuroshio region,
partly due to strong tidal mixing in the narrow straits. This is evident from the frequent formation of tidal
fronts in the SIS and the Bungo Channel [Yanagi, 1987; Takeoka et al., 1993; Takeoka et al., 1997]. In autumn,
these frontal structures are relatively relaxed. The seasonally varying SST gradient in the SIS is consistent
with that found in the latest regional climatology of the East Asian Seas [Johnson and Boyer, 2015]. The
water temperature in the SIS is thus generally lower than the surface temperature in the Kuroshio region
throughout the year.
The air-sea surface temperature difference, which plays a key role in air-sea heat exchange, is affected
mainly by the SST structure (Figures 3e–3h). In spring and summer (autumn and winter), the SST in the SIS
is higher (lower) by 1–38C than the surface air temperature (SAT) at the same position. In the Kuroshio
region, the SST is generally higher than the SAT throughout the year because of the large amount of meridional heat transport by the Kuroshio Current.
In the SIS and its channels, the water column is vertically homogeneous in winter and autumn and stratiﬁedin spring and summer. The temperature at the bottom in the SIS and the channels is signiﬁcantly lower insummer (2–58C) than the SST but not so different in the other seasons (< 18C) (Figures 3i–3l). A striking feature of the water temperature at 200 m depth can be seen in the shelf-slope region: it is signiﬁcantly colder(<158C) than at the bottom of the channels and at 200 m depth south of the Kuroshio throughout the year
(Figures 3i–3l). It forms a band structure along the Kuroshio.
Such subsurface cold water exists along the shelf-slope throughout the year. This is evident from the thermal structure across the Kuroshio (lines A and B in Figure 4). In both sections, isotherms are located at ashallower depth on the inshore side of the Kuroshio than on the offshore side. This is mainly due to dynamical isotherm uplift caused by geostrophic adjustment related to the Kuroshio, which has been commonly
observed in the western boundary current systems [e.g., Shen et al., 2011]. In addition, the horizontal

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Figure 3. Seasonal climatological map of the (a–d) sea surface temperature, (e–h) the air-sea surface temperature difference, and (i–l) the seabed or 200 m depth water temperature.
Solid and dashed white lines in the bottom plots show the 200 m depth isobaths and the average Kuroshio edges, respectively.

gradient of subsurface (around 200 m depth) temperature across the northern edge of the Kuroshio isrelaxed in spring and strengthened in summer or autumn (Figure 4). This fact is dynamically consistent withthe seasonal variation of the Kuroshio transport south of Shikoku reported by Kakinoki et al. [2008], which is

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Figure 4. Climatology (solid line) and linear trend (color shade) of seasonal water temperature in 8C at vertical sections along the lines (a–d) A and (e–h) B that are shown in the bottomright plot. In Figures 4a–4h, black ﬁlled triangles indicate the location of the Kuroshio edges shown in the bottom-right plot by gray solid line.

at its minimum in March and maximum in September. It should be noted that SST gradient across the Kuroshio front becomes weak in summer to autumn south of the SIS (Figures 3a–3d), which is consistent withthe reported seasonal variation of the Kuroshio front in the East China Sea [e.g., Hickox et al., 2000].

3.2. Heat Fluxes
Figures 5a and 5b shows the monthly climatology of the net air-sea heat ﬂux and its components averaged
over the SIS and the two channels, respectively. The maximal heating of 176 W m22 in the SIS and 187 W
m22 in the channels occurs in July and the maximal cooling of 2149 W m22 in the SIS and 2213 W m22 in
the channels is in December. The turbulent heat ﬂux, Qh 1 Qe, in the SIS and the channels in summer isquite small because of weak winds and small air/sea vapor pressure difference, qa–qs (ﬁgure not shown).
The difference in the heat ﬂux between the SIS and the channels is not so large, but the turbulent heat ﬂux
during late autumn to winter is larger in the channels by 40–90 W m22 than in the SIS, which is due to thelarge SST-SAT difference (Figure 3e) and stronger northerly winds (ﬁgure not shown) in the channels.
To assess our heat ﬂux calculations, a comparison with previously reported heat ﬂuxes is made in Table 1.
Tawara [1986] estimated the heat ﬂuxes using in situ SST and meteorological data over the SIS and thechannels, Murakami et al. [1989] estimated the heat ﬂuxes in the SIS and the channels using in situ meteorological data and calculated the SST with a simple numerical model, and Chang et al. [2009] estimated theheat ﬂux using in situ atmospheric and oceanic data at a central station in the SIS. Because there are no signiﬁcant differences among the shortwave radiation of individual studies including ours, we compared thenet ﬂuxes and the other components (Qb, Qe and Qh).

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Figure 5. Monthly climatological (top) air-sea heat ﬂuxes and (bottom) heat budget averaged over (a, c) the Seto Inland Sea (SIS) and (b, d) sum of the Bungo Channel and the Kii Channel (Channels). @OHC/@t is the tendency of ocean heat content and Qs, Qb, Qh, Qe, and Qnet are shortwave radiation, longwave radiation, sensible heat ﬂux, latent heat ﬂux, and net heat
ﬂux, respectively, where Qnet 5 Qs 1 Qb 1 Qh 1 Qe. Error bars in the bottom plots show one standard deviation.

Using the estimates from the present study as a reference, the root-mean-square error (RMSE) and meanerror (ME) for the reported heat ﬂuxes are calculated (Table 1). The net heat ﬂux of Tawara [1986] and Murakami et al. [1989] appeared to be much lower than that of the present study both in the SIS and in the channels (ME 5 2144.6 to 268.9 W m22). The comparison with the heat ﬂuxes of Chang et al. [2009] is relatively
similar (ME 5 230.3 in Qnet). The difference between our calculation and that of Tawara [1986] and Murakami et al. [1989] could arise from the bulk coefﬁcient for the latent ﬂux and the monthly values of Ts, Ta
and V used in both Tawara [1986] and Murakami et al. [1989]. In these studies, they used an empirical constant of bulk coefﬁcient for the latent heat ﬂux (ce 5 2.0 3 1023) proposed by Ishizaki and Saito [1978]. It islarger, especially in summer, than the typical value of Kondo’s bulk coefﬁcient that would fully account for
the effect of surface air stability. In addition, usage of monthly averaged SST, SAT and wind in Tawara[1986] and Murakami et al. [1989] could introduce errors by ignoring covariance between wind and SST or
SAT [Hirose et al., 1999].

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Table 1. Heat Fluxes in the SIS and Channels. RSME and ME are Root Mean Squared Error and Mean Error, Respectively

Tawara 1986
(SIS)aTawara 1986
(Channels)a

Murakami et al. 1989
(SIS1Channels)b
Chang et al. 2009
(SIS)cPresent study(SIS)

Present study(Channels)

Present study(SIS1Channels)

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

unit:
RMSE

Wm22
ME

Qnet
Qb
Qe
Qh
Qnet
Qb
Qe
Qh
Qnet

2237.2276.42155.7281.82477.2282.72339.72159.62165

2133.1271.7
2125.4256.0
2313.4278.1
2250.62111.2
2103

253.5
265.6
2112.2230.8
2197.0269.72214.7
267.4
252

45.3
245.6
271.0
2.8
220.8
247.5
2126.7
29.2
41

86.5
240.0
272.8
10.9
37.6
238.4
2117.0
6.1
93

80.5
229.7
270.9
7.2
83.3
225.5
273.0
12.5
114

116.6
218.8
264.5
17.0
167.3
213.8
233.4
29.9
145

71.3
222.7
2112.6
12.7
72.7
220.6
2111.7
20.0
103

255.6
239.9
2153.7
214.1
258.4
235.7
2160.929.7
2212161.6265.1
2191.5241.1
2220.9260.32239.8249.5
2145

2210.9276.4
2181.0257.72315.0277.82262.5280.2
2248

2253.8277.72180.3278.12423.1281.5
2303.82126.7
2237

98.3
10.6
73.5
25.8
163.2
8.1
121.4
44.9
73.5

294.1
28.4
272.7
215.6
2144.625.5
2113.9
27.1
268.9

Qnet
Qb
Qe
Qh
Qnet
Qb
Qe
Qh
Qnet
Qb
Qe
Qh
Qnet

2100
260
2110
220
287.5
265.1
274.7
232.1
2181.6276.4
2136.0263.72106.6

235
250
290
210
2.5
254.0
241.7
210.6
285.6
266.2
297.3
239.6
215.4

45
240
265
0
74.5
244.8
224.3
3.8
5.9
254.6
268.3
214.6
60.5

95
240
255
5
124.3
238.3
215.0
6.8
82.0
244.2
242.9
21.0
115.7

115
235
245
5
151.5
234.3
211.9
5.4
128.2
236.8
226.0
2.9
146.8

115
230
240
5
149.7
223.7
24.0
4.7
145.6
223.6
24.7
5.9
148.9

140
230
240
5
175.6
219.5
0.4
6.2
187.4
218.9
7.5
8.6
178.0

90
235
280
0
146.9
224.5
223.3
3.8
166.3
222.5
210.4
7.5
150.8

225
245
2125
210
25.9
234.7
277.9
28.2
53.3
230.2
262.1
21.9
31.4

295
270
2135
215
260.3
251.9
2113.7
220.6
257.1
248.2
2117.8
217.8
259.6

2140
275
2135
220
2130.9265.1
2123.7235.92160.2265.3
2151.6242.2
2136.8

2145
275
2125
225
2149.3272.5
2109.8245.62213.3278.62156.9264.7
2162.3

34.4
9.5
37.7
8.6

230.3
24.7
235.4
3.5

a

RSME and ME are root-mean-squared error and mean error, respectively.
From Tawara [1986, Table 8].
From Murakami et al. [1989, Figure 6a].
d
From Chang et al. [2009, Figure 4].
bcFigure 6. Linear trend for annual and seasonally averaged water temperature anomaly (a–e) at the sea surface and (f–j) at the seabed or 200 m depth. Solid and dashed white lines are
the same as in Figures 3i–3l. Dark shading is overlaid on the region where the trend is not signiﬁcant at the 95% signiﬁcance level.

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In fact, the winter-time turbulent heat ﬂux of 2400 W m22 and the summer-time ﬂux of 2100 W m22
approach or exceed typical values above the Kuroshio [e.g., Kida et al., 2015], and therefore are likely toolarge for the values in the channels and the SIS which are distant from the Kuroshio. Relatively good correspondence with the heat ﬂuxes by Chang et al. [2009], who use Kondo’s [1975] bulk coefﬁcients with daily
SST, SAT and wind, implies a reproducibility of our ﬂux estimation. Although there are many uncertainties inour ﬂux estimation, such as uncertainties in the reanalysis data, in the reconstruction method, from notaccounting for hourly covariance between SST and wind, and so on, the above studies do not contradict
the results of our calculations.
3.3. Heat Budget
In the SIS and the channels, the rate of change of the OHC is assumed to be controlled by air-sea heat ﬂuxes
Qnet and the divergence of horizontal transport of OHC by ocean currents Qv, that is,@OHC
5Qnet 1Qv
@t

(6)

Ð0
where OHC5 2H qs cp hdz, qs and h are the potential density of water and the potential temperature of seawater, respectively, cp is the speciﬁc heat capacity of seawater (5 4020 J kg21 K21), and H is the water depth[e.g., Stewart, 2008]. Note that H is set to 200 m in the area in which the water depth is greater than 200 m
such as the Kuroshio region. The tendency of OHC in a calendar month n is calculated as (OHC(n11) –OHC(n))/Dt(n), where Dt(n) is the time in terms of month n. All terms in equation (6) are deﬁned at the middle of the month.
In the SIS, @OHC/@t, which ranges from 2200 W m22 in winter to 130 W m22 in summer, is dominated by
Qnet in winter and spring, while the inﬂuence of horizontal heat transport Qv (5 @OHC/@t – Qnet) becomes
greater (2100 to 250 W m22) from summer to autumn (Figure 5c). This means that water temperature in
the SIS is reduced by the outer seawater in summer to autumn. In the channels, spring-time positive heattransport (warming) by ocean currents is also signiﬁcant (100 W m22) as well as summer-autumn-time
cooling (–100 W m22), exhibiting an annual sinusoidal like variation. It results in a signiﬁcant peak in Aprilin the tendency of the OHC (Figure 5d).
The summer-time cooling by ocean currents is consistent with many studies on the intrusion of oceanic
cold water into the channel and the SIS [e.g., Takeoka et al., 2005; Sugimatsu and Isobe, 2010]. The springtime warming heat ﬂux by the oceanic transport in the channels has not yet been conﬁrmed with observations, but is strongly suggested in numerical simulations by Chang et al. [2009], who showed a warm, northward surface current extending from the channels to the SIS with a southward counter current at thebottom in spring.

4. Linear Trend
In section 3, we conﬁrmed that our T-S and air-sea heat ﬂux data sets are reasonably consistent with paststudies. In this section, we investigate the linear trend of the water temperature and OHC. In this study, we
assessed the signiﬁcance of linear trends by calculating the 95% signiﬁcance levels using a Student’s t test.
Due to the relatively short length of the data set (38 years), we must consider that the linear trends presented below may be a result of inter-decadal and longer variabilities, rather than long-term changes in the
climate.
4.1. Water Temperature
Figure 6 shows the linear trend of annual and seasonally averaged water temperature anomalies at the sea
surface (a–e) and bottom or 200 m depth (f–j). The linear trend of the annual averaged SST is 0.01–0.028C
yr21 in the SIS, the channels and the Kuroshio region, all of which are judged as statistically signiﬁcant at
the 95% signiﬁcance level. This is roughly consistent with the reported value above the Kuroshio Current by
Wu et al. [2012] of 1.10 6 0.318C per century over the period 1950–2008.
The linear trend of SST shows obvious seasonality as reported by many studies: in autumn and winter, mostof the rates of warming for the SST are relatively high at 0.02–0.038C yr21 and statistically signiﬁcant over
the SIS, the channels and the Kuroshio region. On the other hand, in spring and summer, SST trends inthese areas are generally small and most of them are statistically insigniﬁcant. For the bottom temperature

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Figure 7. Linear trend (solid line) and the 95% conﬁdence limits (dotted lines) of (a, b) ocean heat content (OHC) and (c, d) its time derivative (@OHC/@t) in the SIS and the Kuroshio region. OHC is deﬁned as heat content in the upper 200 m in the Kuroshio region for the wholewater column in the shallower region.

in the SIS and the channel, linear trends are slightly smaller (<0.0058C yr21, Figures 6f–6j) and less signiﬁcant compared to those of the SST.
In the Kuroshio region, most of the linear trends of water temperature at the 200 m depth are insigniﬁcantbecause of its large variability, presumably due to ﬂuctuations in the Kuroshio path. However, the Kuroshiotransport and mesoscale eddy activity [e.g., Qiu and Miao, 2000; Book et al., 2002; Miyazawa et al., 2004; Sugimoto and Hanawa, 2014] show a coherent pattern: water temperature in the shelf-slope region consistently
decreases at a rate of 20.04 to 20.018C yr21, especially in spring and summer. Locations where such cooling
trends appear are roughly in agreement with the areas where isotherms are uplifted, as seen in Figure 4.
The linear trend of water temperature in the Kuroshio region also shows notable seasonality. A coherent
and strong warming trend can be found in the upper 100 m (Figures 4a and 4e) in winter and in the regionof the Kuroshio in spring (Figures 4b and 4f), implying warming in winter and spring could be related toatmospheric forcing and Kuroshio variability, respectively. Detailed relationships between these factors andtheir effect on the SIS will be described in section 5.
4.2. Ocean Heat Content and Its Tendency
The linear trend of ocean heat content in the SIS displays a similar seasonality to that of the water temperature (Figure 7a). OHC in the SIS signiﬁcantly increases in winter (3.6 3 106 J m22 yr21) but slightly decreased

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10.1002/2016JC011748(not signiﬁcant) in summer (20.6 3 106 J m22 yr21). The spring-time and autumn-time OHC also increasedby 1.7 3 106 and 2.1 3 106 J m22 yr21, respectively. In the Kuroshio region, seasonality in the linear trendof the upper 200 m OHC is different from that of the SIS (Figure 7b): the linear trend is high in winter andspring (21.4 3 107 J m22 yr21) and also positive (but not signiﬁcant) in summer and autumn (20.5 3 107 J
m22 yr21). In the channels, seasonality in the OHC trend seems to be a mixture of those in the SIS and theKuroshio region (ﬁgure not shown).
The linear trend of @OHC/@t in the SIS signiﬁcantly increases in autumn and decreases in spring, asshown in Figures 8a–8d. This is because the winter-time OHC markedly increases, while that insummer-time is nearly unchanged (Figures 7a and 7c). In spring, the SIS is getting warmer (positive@OHC/@t; Figure 5c) and therefore the negative spring-time @OHC/@t trend (20.34 W m2 yr21: Figure7c) means a reduction in the rate of this heating. In autumn, the SIS is cooling (negative @OHC/@t), so
the positive autumn-time @OHC/@t trend (0.43 W m2 yr21: Figure 7c) indicates a reduction in the rateof cooling.
In the Kuroshio region, although linear trends of winter-time, summer-time, and autumn-time @OHC/@t are not signiﬁcant, the results are similar to those for the SIS in that the spring-time trend is significantly negative (21.9 W m 2 yr 21 : Figure 7d). The positive winter-time trend of @OHC/@t in the
Kuroshio region may be a source of the difference in the OHC trends for the SIS and the Kuroshio
region.
4.3. Air-Sea Heat Fluxes and Heat Budget
Air-sea heat ﬂux could be a major driver of the long-term trends in the water temperature. A large linear
trend of the net heat ﬂux is seen in the summer-time anomalies for the SIS and the channels (20.8 W m2
yr21) as shown in Figures 8e–8h. Speciﬁcally, this positive trend in summer-time Qnet in the SIS is due to theenhancement of heating by shortwave and longwave radiation and increased turbulent heat transfer fromthe air to the sea, Qe1Qh (Table 2). In spring and autumn, the situation is similar (0.4–0.5 W m2 yr21 in theSIS) but the contribution of Qs to the increased net heat ﬂux is negligible. The trend of winter-time Qnet isnearly zero.
Based on the heat balance in equation (6), we can estimate the contribution of the air-sea heat ﬂux tothe tendency of OHC quantitatively by Qv 5 @OHC/@t – Qnet. The pattern in linear trends of Qv (Figures 8i–8l) is generally similar to that of @OHC/@t (Figures 8a–8d) in the Kuroshio region because of its large
heat content. In the SIS, a signiﬁcant negative trend of Qv can be found in spring-time and summer-time(Figures 8j and 8k).
A quantitative comparison between @OHC/@t and Qnet in the SIS is summarized in Table 3. The 7 year running mean autumn-time @OHC/@t anomaly is well correlated (r 5 0.82) with that of Qnet and their linear
trends are almost identical, suggesting the long-term weakening of autumn-time cooling is mainly due toincreasing air-sea heat ﬂux. More speciﬁcally, it is mainly due to longwave radiation and turbulent heat ﬂux
Qe1Qh (Table 2).
On the other hand, the correlation of the spring-time @OHC/@t with Qnet anomalies is not signiﬁcant(r 5 20.07) and the linear trends have opposite sign to the winter-time trends (positive Qnet trend against
negative @OHC/@t trend: Table 3). This suggests that the weakened heating trend in the spring-time SIS
could be due to decreased heat transport by ocean currents, Qv whose trend is estimated as 20.74 W m22
yr21. In a climatological sense, Qv in the SIS is nearly zero (Figure 5c), so it is not clear which mechanism is
responsible for the decreased Qv.
The linear trend of summer-time @OHC/@t (0.10 W m22 yr21) is not signiﬁcant but that of Qnet is (0.76 W
m22 yr21), indicating a negative linear trend of Qv (20.66 W m22 yr21). In summer, because climatologicaloceanic heat transport is negative (Figures 5c and 5d), a negative trend of Qv means stronger cold watertransport to the channel and the SIS from the Kuroshio region became stronger. In winter, linear trends of@OHC/@t and Qv are both insigniﬁcant.
As a brief summary so far, winter-time water temperature and ocean heat content in the SIS and the channels have signiﬁcantly increased due to weakened cooling by the air-sea heat ﬂux in autumn, whilesummer-time temperature had not signiﬁcantly changed presumably due to decreased spring-time oceanic
heat transport may have canceled increased heat storage during autumn. In addition, an increasing trend

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Figure 8. Linear trends of seasonal anomalies of the tendency of the (a–d) ocean heat content @OHC/@t, (e–h) net heat ﬂux Qnet, and (i–l) heat transport by ocean currents, Qv 5 @OHC/@t–Qnet. Shading is overlaid where the trend is statistically insigniﬁcant. Solid and dashed white lines in each plot are the same as those in Figures 3i–3l.

of summer-time air-sea heat ﬂux is estimated to have been nearly balanced with the decreasing trend of
the summer-time oceanic heat transport. In the following discussion, we focus our scope to the decreased
oceanic heat transport in spring and investigate their mechanism.

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Table 2. Linear Trend and the 95% Conﬁdence Limits of Seasonal Anomalies of Shortwave Radiation Qs, Longwave Radiation Qb, Latent
Heat Flux Qe, and Sensible Heat Flux Qh in the SIS (Unit: W m22 yr21)

WinterSpringSummerAutumn

Qs

Qb

Qe

Qh

20.10 6 0.22
0.05 6 0.31
0.30 6 0.49
20.04 6 0.22

0.08 6 0.11
0.20 6 0.09
0.23 6 0.09
0.19 6 0.07

20.14 6 0.23
0.09 6 0.13
0.17 6 0.13
0.14 6 0.27

0.17 6 0.17
0.06 6 0.05
0.06 6 0.03
0.19 6 0.09

5. Discussion
5.1. Possible Mechanism Driving Oceanic Heat Transport Into the Spring-Time SIS
Oceanic heat transport to the SIS is largely due to water exchange with the Kuroshio region because the SIS
is semi-enclosed and connected primarily to the Kuroshio region. Oceanic heat transport Qv in the SIS andthe channels correlated relatively well with that in the shelf-slope region, as shown in Figure 9. Correlation
with the Kuroshio region is higher in spring than other seasons (r > 0.5), suggesting that the decreased
spring-time oceanic heat transport in the SIS originates from variability in the Kuroshio region.
In spring, water from the shelf-slope region is warmer than that in the channels and the SIS except for the
deep layer (Figures 3b, 3j, 4b, and 4f) and it may be transported into the channels and the SIS by densitydriven circulations. The change in the horizontal distribution of depth-averaged water density in spring is
the generation mechanism for such density currents (Figure 10a): there are strong positive meridional density gradients between the SIS and the slope region (estuarine sense) and negative (inverse-estuarine sense)in the east and west channels, respectively [Valle-Levinson, 2010], both of which can drive inshore (offshore)transport of warm (cold) water. Positive air-sea net heat ﬂux in spring (Figures 5a and 5b) also assists
density-driven circulations because it reduces vertical eddy diffusivity in the ocean, which plays a central
role in maintaining horizontal density gradients [Valle-Levinson, 2010]. The existence of such density-driven
circulations was conﬁrmed in the numerical simulations by Chang et al. [2009].
The density differences between the western and eastern regions of the SIS were mainly due to differencesin freshwater input from rivers: river discharge rate into the SIS (obtained from Rainfall/Discharge Rate Database 1938–2003 by the Japan River Association) was generally larger in the eastern part of the SIS than thewestern part as shown in Figure 11a.
The linear trend of depth-averaged water density shows the meridional density gradient was relaxed in theeastern part of the SIS (Figure 10b), which suggests the spring-time meridional circulation there was weakened. In the Kuroshio region, water density decreased due to increased water temperature but the density
decrease was relatively moderate in the shelf-slope region, implying dense (cold) water supplement to theshelf-slope region. Based on the above mechanism, colder water temperature in the shelf-slope region
would result in weaker warm water transport to the channel and the SIS because of a decrease in the meridional temperature gradient.
Here we consider some possible mechanisms that can reduce the water temperature in the shelf-slope
region and cause the long-term decreasing trend of oceanic heat transport.
5.2. Local Wind Effects
It is well known that surface winds can lower the sea temperature not only by air-sea turbulent heat ﬂux
but also by driving upwelling [e.g., Tomczak and Godfrey, 1994]. Here the effect of the surface ﬂux is estimated so that the latter effect can be considered. Upwelling is induced by divergence of the surface Ekman

Table 3. Linear Trend and the 95% Conﬁdence Limits of Seasonal Anomalies of @OHC/@t and Net Heat Fluxes Qnet in the SIS (Unit:
W m22 yr21) and Correlation Between 7 Year Running Averaged Seasonal Anomalies of @OHC/@t and Qneta

WinterSpringSummerAutumna

TSUTSUMI AND GUO@OHC/@t Trend

Qnet Trend

Correlation

20.13 6 0.31
20.34 6 0.26
0.10 6 0.30
0.43 6 0.30

0.01 6 0.46
0.40 6 0.35
0.76 6 0.41
0.48 6 0.40

0.53
20.07
0.56
0.82

The signiﬁcant correlation coefﬁcients are given in bold font.

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Figure 9. Correlation map of oceanic heat transport Qv averaged over the whole SIS and the local value in each season. Correlations are calculated for the 7 year running mean timeseries. Shading is overlaid where correlation is statistically insigniﬁcant. Solid and dashed white lines in each plot are the same as those in Figures 3i–3l.

transport and therefore its generation strongly depends on the wind (stress) curl [e.g., Tomczak and Godfrey,
1994]. Hereinafter, we refer to such an upwelling as wind curl-driven upwelling.
Figure 12 shows the linear trend of the spring-time wind velocity (vector) and of wind speed magnitude(color shade). It is obvious that westerly wind velocity increased above the Kuroshio (0.02 m s21 yr21),
while that immediately south of Shikoku Island and east of Kyushu Island was relatively small. In addition,
north-westerly wind velocity increased (0.01–0.02 m s21 yr21) in the western SIS and the channels (IyoNada and the Bungo Channel).

Figure 10. (a) Climatology and (b) linear trend of the spring-time depth-average potential density. In the Kuroshio region, the depth-average was taken over the upper 200 m depth.
Shade, solid, and dashed white lines in the right plot are the same as in Figures 3i–3l.

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Figure 11. (a) Annual mean discharge rate of the rivers in the SIS and the channels. The area of the circles depicted at each river mouth
indicate the discharge rate. (b) Interannual variation (thick line) and linear trend (thin line) of spring-time total discharge of the 10 rivers
numbered in Figure 11a.

The pattern of the long-term variation in wind velocity in the Kuroshio region resulted in a systematic variation of the wind curl ﬁeld: a linear trend of spring-time wind curl indicates increasing wind curl over the
shelf-slope region east of 1338E (Figure 13). The linear trend of the wind curl in the shelf-slope region was
typically 1–3 3 1027 s21 yr21, which is favorable for an increase in the rate of wind curl-driven upwelling. In
fact, long-term (7 year running mean) variations of the spring-time wind curl averaged over the shelf-slope
region east of 1338E and the spring-time Qv in the SIS show signiﬁcant negative and positive linear trends,
respectively (Figure 13b). In addition, they have a good correlation (r 5 20.60), suggesting stronger windcurl-driven upwelling in the eastern shelf-slope region relates to weaker oceanic heat transport to the SIS.
In addition to the wind curl-driven upwelling, it would be expected that coastal upwelling had also been
intensiﬁed by the enhancement of westerly wind speed whose role can be understood as a spatially constant wind stress over the shelf slope region (Figure 12a). Upwelling velocities due to wind curl-driven andcoastal upwelling may be scaled by we 5 curls/(q0f) and wc 5 sy/(q0fL)exp(–x/L) respectively, where sy is thealong-coast component of wind stress s, f is Coriolis frequency, x is the offshore distance from the coast,and L is internal Rossby radius of deformation [Gill, 1982]. To estimate the relative importance of wc to we, arough estimation on linear trend of the upwelling velocities is presented here. Using spring-time windstress, f 5 8 3 1025 s21 and L 5 10 km based on our data, we calculated spring-time we and wc from thecoast or 100 m isobaths to the northern edge of the Kuroshio and obtained their linear trend of 1.8 3 1026
and 1.7 3 1026 m s21 yr21, respectively. Thus, coastal upwelling could have played the same role in increasing upwelling in the shelf-slope region as wind curl-driven upwelling.

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Figure 12. Linear trend of the spring-time surface wind. Color shade and quiver denote the trend of wind speed magnitude and wind vector, respectively.

The increased north-westerly wind in the western SIS and channel also could have reduced water exchange
between the SIS and the shelf-slope water (Figure 12). Because spring-time oceanic heat transport in thewestern SIS was likely driven by the inverse-estuarine circulation (northward surface current with a counterbottom ﬂow) as noted in section 5.1, the increased north-westerly wind should have counteracted the circulation by creating southward surface Ekman transport.
We therefore conclude the changes in the local wind ﬁeld are one of the major causes for the decrease in
the spring-time oceanic heat transport to the SIS.

5.3. Effect of the Kuroshio Transports
As shown in Figure 8j, a negative linear trend of the spring-time oceanic heat transport Qv can be found not
only in the SIS but also in the Kuroshio region, especially in the western part. The negative trend of thespring-time Qv in the Kuroshio suggests that the spring-time Kuroshio heat transport has decreased. The
positive correlation between the spring-time Qv averaged over the SIS and that over the Kuroshio region

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Figure 13. (a) Linear trend (color shade) and climatology (line contour in 31025 s21) of spring-time wind curl. (b) The 7 year runningmean variation in the spring-time wind curl averaged over shelf-slope region east of 1338E (blue solid line) and spring-time Qv in the SIS(red solid line). Blue and red dashed lines in the bottom plot are linear ﬁts for the wind curl and Qv, respectively.

(r 5 0.56) indicates the Kuroshio heat transport could be related to the decreased spring-time oceanic heattransport to the SIS.
The Kuroshio can contribute to the Qv in the SIS through dynamical isotherm uplift, as noted in section 3.1.
Because the high spring-time linear trend in water temperature in the Kuroshio (Figures 4b and 4f) indicatesan increase of the Kuroshio front and therefore of the Kuroshio geostrophic (volume) transport, the associated isotherm uplift could have enhanced cold water transport to the SIS. However, high positive correlation between long-term Qv in the SIS and in the Kuroshio region (Figure 9b) does not support thesigniﬁcance of this process in spring, because if the isotherm uplift played a central role in the heat transport to the SIS, a positive correlation would appear only in the shelf-slope region, not in the Kuroshio in Figure 9b. We conclude that the decrease in the spring-time Kuroshio heat transport was another major
contributor to the decrease in the spring-time heat transport in the SIS.

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The dynamical isotherm uplift might have contributed to the decreased summer-time Qv in the SIS, discussed in section 4.3, because the long-term Qv in the SIS does not correlate with that in the Kuroshio, butinstead with that in the shelf-slope region (Figure 9c). This is consistent with the fact that summer-time Qv
in the SIS is due to oceanic transport of cold water from the shelf-slope region.
Mechanisms controlling the Kuroshio transport south of Shikoku are complicated. The Kuroshio volumetransport is much larger south of Shikoku (60 Sv 5 106 m3 s21) than in the East China Sea (25 Sv), which
is mainly due to the Kuroshio recirculation that is created by an anticyclonic gyre south of Japan [Guo et al.,2013]. In addition, Kuroshio transport south of Shikoku is also affected by westward-propagating, cyclonicmesoscale eddies and/or variation of the Kuroshio path [Tsujino et al., 2013]. These factors possibly resulted
in the locality of the oceanic heat transport in the Kuroshio region south of Shikoku as well as its seasonality.
In fact, the decreasing trend in the Kuroshio heat transport in the East China Sea reported by Zhang et al.
[2012] was not found in this study. Investigation of the relation of Qv to such factors is beyond the scope of
this investigation because they are not fully resolved in our data sets.
5.4. Salinity Change in the SIS
As noted in section 5.1, spring-time water density in the eastern part of the SIS likely increased (Figure 10b).
This also suggests a weakening of the meridional circulation in the eastern SIS. Because the spring-timewater temperature in the SIS did not decrease (Figures 6c and 6h), the increase in water density is likely due
to an increase in salinity (ﬁgure not shown).
The decrease in salinity in the SIS could have been caused by the decrease in freshwater input from rivers:
the spring-time total discharge rate of the 10 rivers numbered in Figure 11a signiﬁcantly decreased
between 1964 and 2001 (23.96 6 3.54 m3 s21 yr21: Figure 11b). These 10 rivers were selected because data
for these rivers were available for the full period considered in this investigation. The discharge rate presented in Figure 11b was fairly representative of the long-term variation for all of the rivers.
The relation of the decreased river discharge with the local atmospheric forcing (i.e., precipitation) is unclear
because the spring-time total precipitation measured at the observation stations (Figure 2a) showed a large
inter-annual variability and therefore insigniﬁcant linear trend (20.4 6 34.7 mm month21 yr21). It also could
have depended on other factors such as winter-time snowfall and the timing of the melting of the snow,
effects of groundwater and the construction of dams.

6. Summary
Using in situ hydrographic data over the period 1964–2001, we investigated the climatology and linear
trend of seasonal water temperature and heat budget in the SIS. Winter-time water temperature and ocean
heat content showed signiﬁcant increasing trends, while the summer-time values exhibited no signiﬁcantchange. By analyzing heat budget between the tendency of ocean heat content @OHC/@t and air-sea net
heat ﬂux Qnet, the driving factor of the winter-time warming trend in the SIS was identiﬁed as autumn-time
Qnet, while the summer-time OHC was unchanged mainly due to decreased spring-time oceanic heat transport to the SIS from the Kuroshio region, Qv 5 @OHC/@t – Qnet.
We then proposed possible mechanisms for the long-term decreasing trend of spring-time heat transport.
First is the increased upwelling due to divergence of surface Ekman transport induced by enhanced windcurl and wind speed in the shelf-slope region. The reduction in surface temperature in the Kuroshio region
can weaken density-driven circulation in the channels responsible for the heat transport from the shelfslope region to the SIS. Second is the heat transport by the Kuroshio south of Shikoku Island. The springtime Kuroshio heat transport south of Shikoku Island signiﬁcantly decreased and this can contribute to theoceanic heat transport in the SIS. Third is the decreased freshwater input by rivers to the SIS. This can also
weaken water exchange between the SIS and the Kuroshio region.
Water temperature in the SIS is thus highly affected by both oceanic and atmospheric forcing which relates
to seasonal and long-term variations as well as various spatial scales (e.g., basin scale of the Kuroshio,
regional scale of wind curl and mesoscale of the recirculation gyre). Importantly, these forcings are dependent on each other. For example, summer-time low SST in the SIS affects surface wind speed [Iwasaki et al.,2015], and it can change coastal sea circulation such as estuarine circulation and therefore oceanic heat

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transport. The mechanisms controlling water temperature in the SIS are therefore complex and careful consideration is necessary in predictions of the oceanic responses to future climate change on aspects ofhydrographic condition, nutrient supplement, coastal ecosystem, and so on.

Appendix A: Definition of the Path and Edges of the Kuroshio
For identiﬁcation of the location of the Kuroshio, we used the Kuroshio Current Axis Dataset 1955–2006 by
the Marine Information Research Center in Japan Hydrographic Association. The data set generally provides
weekly or biweekly longitude and latitude of the path of the Kuroshio south of Japan from 1955 to 2006. In
the data set, the path of the Kuroshio is determined as follows. The northern edge of the Kuroshio is ﬁrst
estimated from horizontal distributions of the in situ sea surface temperature, 200 m depth temperatureand surface current velocity, location of a speciﬁc cold eddy and satellite altimetry data (if available). Then,the path and southern edge of the Kuroshio are determined as locations at distances of 24.6 and 73.9 km
from the northern edge to cross-stream direction, respectively.

Acknowledgments
This study is supported by the Ministryof Agriculture, Forestry and Fisheries,
Japan, through the research project‘‘Development of technologies for
mitigation and adaptation to climatechange in Agriculture, Forestry and
Fisheries’’ and MEXT KAKENHI grant
15H05821. The authors are grateful to
S. Takahashi for providing in situ
hydrographic data set and to Y.
Kubota for his help in data collection.
In situ temperature and salinity data
were collected by JODC and areavailable by J-DOSS (http://www.jodc.
go.jp/index.html). In situ hydrographic
and meteorological data werecollected by JMA and are available at
http://www.data.jma.go.jp/kaiyou/db/
vessel_obs/data-report/html/ship/
eﬁle_NoS2.html and http://www.data.
jma.go.jp/gmd/risk/obsdl/index.php,
respectively. The MSM-GPV data andthe JRA-55 data are produced by JMA
and provided by the website http://
opendap.rish.kyoto-u.ac.jp/opendap/
hyrax/jmadata/gpv/netcdf/and the
NCEP Research Data Archive (http://
rda.ucar.edu/datasets/ds628.0), respectively. The Kuroshio Axis Data Set was
produced by Marine Information
Research Center, Japan Hydrographical
Association (http://www.mirc.jha.or.jp/
en/index.html). The river discharge
data set was produced by Japan River
Association (http://www.japanriver.or.
jp/). The authors may be contacted forany further data requests (E. Tsutsumi:
tsutsumi@riam.kyushu-u.ac.jp, X. Guo:guo.xinyu.mz@ehime-u.ac.jp). The
comments from I. M. Belkin and R. D.
Hetland are helpful in improving theoriginal manuscript.

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