Continental Shelf Research 152 (2018) 14–26

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Continental Shelf Research
journal homepage: www.elsevier.com/locate/csr

Air-sea heat ﬂux control on the Yellow Sea Cold Water Mass intensity andimplications for its prediction

T⁎

Junying Zhua,b, Jie Shia,b, , Xinyu Guoa,c, Huiwang Gaoa,b, Xiaohong Yaoa,baKey laboratory of Marine Environment and Ecology, Ocean University of China, Ministry of Education, 238 Songling Road, Qingdao 266100, China
Laboratory for Marine Ecology and Environmental Sciences, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, ChinacCenter for Marine Environmental Studies, Ehime University, 2-5 Bunkyo-Cho, Matsuyama 790-8577, Japanb

A R T I C L E I N F O

A B S T R A C T

Keywords:
Yellow Sea Cold Water Mass (YSCWM)
Intensity
Air-sea heat ﬂux
Hydrodynamic model
Prediction
Interannual variation

The Yellow Sea Cold Water Mass (YSCWM), which occurs during summer in the central Yellow Sea, plays animportant role in the hydrodynamic ﬁeld, nutrient cycle and biological species. Based on water temperatureobservations during the summer from 1978 to 1998 in the western Yellow Sea, ﬁve speciﬁc YSCWM years wereidentiﬁed, including two strong years (1984 and 1985), two weak years (1989 and 1995) and one normal year
(1992). Using a three-dimensional hydrodynamic model, the YSCWM formation processes in these ﬁve years
were simulated and compared with observations. In general, the YSCWM began forming in spring, matured insummer and gradually disappeared in autumn of every year. The 8 °C isotherm was used to indicate the YSCWM
boundary. The modelled YSCWM areas in the two strong years were approximately two times larger than thosein the two weak years. Based on the simulations in the weak year of 1995, ten numerical experiments were
performed to quantify the key factors inﬂuencing the YSCWM intensity by changing the initial water condition in
the previous autumn, air-sea heat ﬂux, wind, evaporation, precipitation and sea level pressure to those in thestrong year of 1984, respectively. The results showed that the air-sea heat ﬂux was the dominant factor inﬂuencing the YSCWM intensity, which contributed about 80% of the diﬀerences of the YSCWM average watertemperature at a depth of 50 m. In addition, the air-sea heat ﬂux in the previous winter had a determining eﬀect,
contributing more than 50% of the diﬀerences between the strong and weak YSCWM years. Finally, a simple
formula for predicting the YSCWM intensity was established by using the key inﬂuencing factors, i.e., the sea
surface temperature before the cooling season and the air-sea heat ﬂux during the cooling season from theprevious December to the current February. With this formula, instead of a complicated numerical model, wewere able to roughly predict the YSCWM intensity for the following summer by using the data available online inwinter.

1. Introduction
Cold water masses, also known as “cold pools”, have been widely
observed in shelf seas (Hill, 1996; Wyllie-Echeverria and Wooster,
1998; Brown et al., 1999; Horsburgh et al., 2000). They feature lower
temperatures compared to their surrounding waters. However, theformation mechanisms of these cold water masses are not exactly thesame. Many cold water masses are formed locally. Cold waters from theprevious winter are locally trapped in a topographic depression after
the onset of seasonal stratiﬁcation, for example, the cold water masses
in the Irish Sea (Hill et al., 1994), Middle Atlantic Bight (Houghton
et al., 1982), North Sea (Warrach, 1998; Luyten et al., 2003), Yellow
Sea (YS) (He et al., 1959; Hur et al., 1999; Wei et al., 2010; Yuan et al.,2013) and Bering Sea (Azumaya and Ohtani, 1995). Other cold water⁎

masses occur due to cold water intrusion from adjacent seas. For example, the Korea Strait Bottom Cold Water (KSBCW) is formed by the
intrusion of near-bottom cold water from the Japan/East Sea (Johnson
and Teague, 2002; Kim et al., 2006).
There are strong temperature gradients between cold water masses
and surrounding waters, which generate special hydrodynamic processes, such as cyclonic circulation above the cold water mass (Hill,
1996; Horsburgh et al., 2000; Yu et al., 2016). Additionally, cold water
masses play important roles in the distribution of nutrients and biological species. Brown et al. (1999) suggested that several species of ﬁsh
and shellﬁsh of commercial value (e.g., herring, plaice, turbot andNephrops norvegicus) spawn in the North Sea cold pool. The cold watermass in the western Irish Sea generated a gyre that provided a physicalretention of the planktonic larvae of Norway lobster and pelagic

Correspondence to: College of Environmental Science and Engineering Ocean University of China, 238 Songling Road, Qingdao, Shandong 266100, China.
E-mail address: shijie@ouc.edu.cn (J. Shi).

https://doi.org/10.1016/j.csr.2017.10.006
Received 26 April 2017; Received in revised form 7 October 2017; Accepted 13 October 2017
Available online 14 October 20170278-4343/ © 2017 Published by Elsevier Ltd.

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juvenile ﬁsh (Dickey-Collas et al., 1997). Arctic species (e.g., Arcticcod) prefer to live in the cold water mass of the Bering Sea (WyllieEcheverria and Wooster, 1998). Therefore, intensity variations of cold
water masses have signiﬁcant impacts on the marine ecosystem(Dickey-Collas et al., 1997; Wyllie-Echeverria and Wooster, 1998;
Stabeno et al., 2007; Na et al., 2010). The cold pool warming trend in
the Bering Sea from 1988 to 2005 led to an ecosystem reorganizationthat favored pelagic over benthic predators (Grebmeier et al., 2006).
The YS is a semi-closed marginal sea in the northwest Paciﬁc Ocean.
A cold water mass called the Yellow Sea Cold Water Mass (YSCWM)
occurs in the bottom layers in the central part of the YS (Hur et al.,
1999; Zhang et al., 2008; Wang et al., 2014a, 2014b). It occupies ~30%of the total water volume in the YS during summer (Su and Weng,
1994). The YSCWM is also regarded as a nutrient-rich pool that contributes to the subsurface chlorophyll maximum phenomenon during
summer and therefore aﬀects primary production (Li et al., 2006; Wanget al., 2014a, 2014b; Liu et al., 2015; Xin et al., 2015). The key speciesof zooplankton (Calanus sinicus) is favored to survive the hot summer in
the YSCWM (Wang et al., 2003). Consequently, the demersal ﬁsh stocks
are closely related to the YSCWM intensity (Cho, 1982; Du et al., 1996).
The YSCWM intensity changes annually (Weng et al., 1989; Jiang
et al., 2007; Park et al., 2011; Li et al., 2015, 2016a). The inﬂuencing
factors reported by previous studies always fall into two aspects: remotefactors and local atmospheric forcings. Park et al. (2011) suggested thatthe eastern YSCWM intensity is negatively correlated to the sea surface
temperature and surface air temperature in the tropical to subtropicalnorthwest Paciﬁc Ocean by analyzing the data during 1967–2008 fromthe Korea Oceanographic Data Center (KODC) dataset. The intensity ofthe entire YSCWM simulated by Song et al. (2009) has a variation
period of 5-years, which can be attributed to the El Niño-Southern
Oscillation (ENSO) event. The Kuroshio Meander always induces a
warmer YSCWM in the following year (Li, 1991; Jiang et al., 2007).
Through statistical analyses, the YSCWM intensity is also associatedwith other climatic events, such as Arctic Oscillation (AO), Paciﬁc
Decadal Oscillation (PDO) and so on (Park et al., 2011; Li et al., 2015).
These studies always focus on the response of the regional sea to climate change via teleconnection. However, it is diﬃcult to explain theinﬂuencing mechanisms of remote factors on the YSCWM in detail.
Compared to remote factors, local forcing conditions have more
direct eﬀects on the YSCWM intensity. Several local atmospheric forcings related to the YSCWM intensity have been examined using correlation analyses. Based on the China Coastal Waters and Adjacent Seas
Reanalysis (CORA) data from 1958 to 2008, Li et al. (2015) demonstrated that the local heat ﬂux and wind stress in winter aﬀected theYSCWM intensity (Song et al., 2009), while the local heat ﬂux and windstress in summer had a little impact. Jiang et al. (2007) found that theintensity of the northern YSCWM was primarily inﬂuenced by the airtemperature in winter. Yang et al. (2014) stated that the lower the sea
surface temperature was in February, the further south the boundary of
the YSCWM was located. Additionally, the intensiﬁed thermocline from
June to August blocks downward heat transport and then causes a cold
event in the YSCWM (Park et al., 2011). Previous studies suggest thatmany important local factors determine the YSCWM intensity, but thecontributions of each factor have not been estimated quantitatively.
Since the YSCWM intensity has a close relationship with the nutrient
concentrations, primary production, and aggregation and migration ofﬁsh, it would be helpful to know the YSCWM intensity for the approaching summer in advance. Although some complicated numericalmodels can be used to achieve this goal, a simple and eﬀective formulafor prediction would be more convenient for those who are not familiar
with models.
In this study, variations of the YSCWM intensity were examined andrepresentative years of strong and weak YSCWM were identiﬁed basedon observations from 1978 to 1999. In these representative years, athree-dimensional hydrodynamic model was used to reproduce theformation process of the YSCWM. Subsequently, a set of numericalFig. 1. Map of the Yellow Sea with topography. Grey solid contours denote bathymetry(in m), and the 50 m contour is bold. The observation stations are represented by redstars. The 36°N section is marked by a blue dashed line. The green solid line is the section
from Dalian to Chengshan Jiao. (For interpretation of the references to color in this ﬁgurelegend, the reader is referred to the web version of this article.)experiments was performed to estimate the contributions of several
local factors to the YSCWM intensity. Finally, a simple formula wasproposed to predict the YSCWM intensity based on analyses of the inﬂuencing mechanism.
2. Materials and methods
2.1. Observation data
The observational water temperatures at standard depths (0, 10, 20,
30 and 50 m) in the YS (Fig. 1) in February and August from 1978 to1998 were obtained from the archives at the State Oceanic Administration (SOA) of China. The observed area covered the western area of
the YS. Over the entire analysis period (1978–1998), there were no data
for February for the years 1984, 1996, 1997, and August 1993
(Table 1). Following the previous studies (Guan, 1963; Li, 1991; Liet al., 2015), we used an isotherm of 8 °C as the boundary of theYSCWM.
The YSCWM can be easily identiﬁed from the horizontal distributionof water temperatures observed at a depth of 50 m (Fig. 2a) and thevertical distribution of temperatures along the 36°N section (Fig. 2b).
The YSCWM was isolated from ambient warmer waters by strong
temperature fronts (Fig. 2a) and thermoclines (Fig. 2b).
To quantify the YSCWM intensity during summer, we deﬁned four
indices, namely, the average water temperature inside the YSCWM andthe YSCWM area at a depth of 50 m as well as those along the 36°N
section in August. The observation stations in August were primarily
located in the western region of the YS, with slight diﬀerences in eachyear. To maintain a consistent observation area throughout the entire
analysis period, we deﬁned a ﬁxed area from 35°N to 38°N and from
122.2°E to 124.0°E (the orange rectangle in Fig. 2a) and identiﬁed theYSCWM using the 8 °C isotherm in the area. Along the 36°N section, theobserved area covered as far as 124.0°E in the east for most years
Table 1The observation years.

15

Month

Years with observation

February
August1978–1983, 1985–1995, 1998
1978–1992, 1994–1998

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Fig. 2. The distribution of multi-year average water temperature (°C) in August from 1978 to 1998 at the depth of 50 m (a) and along the 36°N section (b). The orange rectangle is thecalculating area from 35°N to 38°N and from 122.2°E to 124.0°E. (For interpretation of the references to color in this ﬁgure legend, the reader is referred to the web version of thisarticle.)indices for one year were marked with a ‘-’ and the two area indiceswere marked with a “+”, we identiﬁed the year as a strong YSCWM
year (Table 2). On the contrary, if the two indices for the water temperature were marked with a “+” and the two indices for the area witha “-” for a certain year, we deﬁned the year to be a weak YSCWM year.
If all four indices for one year had the symbol “O”, it was a normal
YSCWM year. For the years not marked as strong, weak, or normal
years, we considered them to be atypical years for the YSCWM intensity.

except for 1995. Therefore, we set the eastern boundary of calculatingarea as 124.0°E and used a linear extrapolation to horizontally extend
the observation area from 123.5°E to 124.0°E in 1995.
The YSCWM intensity was deﬁned by comparing the anomaly of thefour indices in each year with their mean ranges of changes over theentire analysis period (Zhang and He, 1981). X was set to be any of thefour indices, and X was the mean value of X from 1978 to 1998. ∆Xi
was anomaly of X at the year of i:(1)∆Xi = Xi − X

Its mean range of change over entire study period is denoted bymean value of the absolute value of ∆Xi :

∆X =

n
1
∑i =1 ∆Xin

2.2. Model description and conﬁguration
The three-dimensional hydrodynamic model used in our study was
based on the Princeton Ocean Model (Blumberg and Mellor, 1987;
Mellor, 2003) and was conﬁgured with a nesting method to obtain ahigh horizontal resolution (1/18°) for the Kuroshio simulation (Guoet al., 2003). There were 21 sigma layers in the vertical direction. Basedon Guo et al. (2003), Wang et al. (2008) used the same model for asmaller model domain covering the Bohai Sea, YS and East China Sea,
but added the tidal forcing (M2, S2, K1 and O1 tides) along the lateralboundary and freshwater input from the sea surface and rivers.
Our model domain was 117.5–131.5°E and 24.0–41.0°N. The external and internal time steps were 6 s and 360 s, respectively. The
model was driven by the monthly mean river runoﬀ, monthly sea surface salinity, daily air-sea heat ﬂux, daily wind stress, daily surface levelpressure, daily precipitation and evaporation rates. The daily air-sea
heat ﬂux in the model (Qnet_model ) was the net sea surface heat ﬂux fromthe NCEP reanalysis dataset (Qnet_NCEP ) plus an adjustment term proportional to the diﬀerence between the sea surface temperature fromthe NCEP reanalysis (TNCEP ) and simulated sea surface temperature(Tmodel ) as follows:(2)

where n was total number of years. The comparison for the four indiceswere presented in Table 2, in which the symbol “-” was marked when1
the anomaly of an index in a year (∆Xi ) was smaller than − 2 ∆X ; “+”1

was marked when ∆Xi was larger than 2 ∆X ; “O” was marked when1
∆Xi was between − 2

1
and 2

∆X
∆X .
A strong YSCWM year was characterized by low temperatures andlarge areas both horizontally and vertically, while a weak YSCWM year
by high water temperatures and small areas. If the two temperature
Table 2The YSCWM intensity in August.
Year

1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1994
1995
1996
1997
1998

The 50 m depth

The 36°N section

Grade

Average temperature

Area

Average temperature

Area

O
O
＋
＋
＋
＋
－
－
O
－
－
＋
O
O
O
＋
＋
O
O
＋

O
－
O
＋
＋
－
＋
＋
＋
＋
＋
－
－
－
O
－
－
＋
O
O

－
＋
－
＋
O
O
－
－
O
O
O
＋
－
＋
O
O
＋
＋
－
O

O
－
－
－
O
－
＋
＋
O
＋
－
－
＋
－
O
－
－
＋
－
＋

Qnet _model = Qnet _NCEP +∂Q(Tmodel − TNCEP )∂T(3)

where the coeﬃcient ∂Q is − 50 W/m2 K− 1 (Ezer and Mellor, 1992).
∂T
The Qnet_NCEP was the sum of shortwave radiation ﬂux, longwave ra-strongstrongdiation ﬂux, latent heat ﬂux and sensible heat ﬂux. The daily atmospheric forcing was determined by averaging the original 6 hourly
products from the NCEP/DOE reanalysis (https://www.esrl.noaa.gov/
psd/data/gridded/data.ncep.reanalysis.html). The initial conditions inJanuary were from a nested ocean model with the same resolution(NEST3 in Guo et al., 2003) and monthly open boundary conditions
were from a large domain model with a coarse resolution (NEST2 in
Guo et al., 2003). The river runoﬀs were obtained from the Marine Atlasof the Bohai Sea, Yellow Sea, and East China Sea: Hydrology (Chen,
1992).
Before simulating the formation process of the YSCWM in a speciﬁc

weak

normalweak

16

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J. Zhu et al.

calculating area. Because of the absence of the YSCWM in these four
years, we highlighted the lowest water temperature along the section in
Fig. 3b with red stars. A signiﬁcantly negative correlation (− 0.69) was
shown between the average water temperature and the YSCWM area
along the section (Fig. 3b).
The negative correlations between the water temperature and theYSCWM area at a depth of 50 m and along the 36°N section indicated astronger YSCWM with a lower water temperature and larger area at thesame time. In addition, the YSCWM variations in the horizontal andvertical directions were generally consistent with each other. The correlation coeﬃcient between the average water temperatures of theYSCWM at a depth of 50 m and along the 36°N section was 0.48, andthe correlation coeﬃcient between the YSCWM areas was 0.56.
The four indices for the YSCWM intensity from 1978 to 1998, asdeﬁned in Section 2.1, were given in Table 2. The YSCWM experienced
a colder (stronger) period from 1984 to 1988 and a warmer (weaker)
period from 1989 to 1995. According to the consistency among the four
indices shown in Table 2, 1992 was deﬁned as a normal YSCWM year,
1984 and 1985 as strong YSCWM years, and 1989 and 1995 as weakYSCWM years. In the following sections, the controlling factors on theYSCWM intensity were investigated by comparing the simulated formation processes of the YSCWM in the strong years (1984 and 1985),
weak years (1989 and 1995) and normal year (1992).
3.2. The formation process of the YSCWM
The model results in the normal year of 1992 were used to examine
the formation process of the YSCWM. The modelled water temperatures
were consistent with the observations in 1992 (Fig. 4d), with a correlation coeﬃcient of 0.98 in February and 0.93 in August. Compared tothe observations, 80% of the model results fell into the error range of
10% and 91% were within the error range of 20%. Since the modelledwater temperatures were averaged monthly, while the observations
were ﬁxed-point measurements at a speciﬁc time, some discrepancies
were expected. Comparing the modelled and observed horizontal andvertical distributions of water temperature, it was indicated that themodel reasonably captured the major hydrological features in the YS(Fig. 5 and Fig. 6). Both model results and observations showed thehorizontal domination of the Yellow Sea Warm Current in winter(Fig. 5b and Fig. 6b) and the YSCWM in summer (Fig. 5d and Fig. 6d),and vertically well mixing (Fig. 5f and Fig. 6f) in winter and strong
stratiﬁcation in summer (Fig. 5h and Fig. 6h). The thermocline began toform in spring (Fig. 5g and Fig. 6g) and gradually disappeared in autumn (Fig. 5e and Fig. 6e). Besides, observed multi-year average temperatures in August (Fig. 2) were similar with those in the normal of
1992 (Fig. 6d, h), which indicated the model capacity to reproduce theaveraged YSCWM. Consequently, we used the model results (Fig. 5) toanalyze the processes from disappearance of the YSCWM in last autumn
to maturity of this YSCWM in current summer.
In autumn (November) of the previous year (1991), the stratiﬁcation became weak with the increase of wind speed and decrease of solar
radiation. Consequently, the YSCWM almost vanished (Fig. 5a, e). The
area surrounded by the 8 °C isotherm shrunk to a very small area at a
depth of 50 m. Along the 36°N section, water with a temperature lessthan 8 °C was found below the depth of 50 m and occurred only between 124°E and 125°E. In winter, the water temperature was vertically homogeneous (Fig. 5f) due to the strong vertical mixing caused by
the strong winter monsoon and surface cooling. At a depth of 50 m, anorthward warm tongue was found along the YS central trough (Fig. 5f)
and the 8 °C isotherm extended northward to 35–36°N (Fig. 5b), indicating the presence of the Yellow Sea Warm Current. In spring, thewater temperature in the upper layer rose (Fig. 5g) with the increase ofsolar radiation and decrease of wind speed since winter. The thermocline formed at a depth of ~ 15 m. Cold water remained in the deep
layer of the YS trough. The Yellow Sea Warm Current gradually weakened, and the YSCWM began to form (Fig. 5c, g). There were three low

Fig. 3. Time series of the average water temperature (°C) and the YSCWM area (km2) at a
depth of 50 m (a) and along the 36°N section (b) in August. The solid and dashed redlines in (a) are the linear trends of water temperature and area. The years when theYSCWM is not observed are marked with red stars in (b). (For interpretation of the references to color in this ﬁgure legend, the reader is referred to the web version of thisarticle.)year, we started the calculation in January with a climatological initialcondition and adopted a spin-up mode by running the model for three
years with the air-sea ﬂux and other inputs from the year before thespeciﬁed year. Then, the sea surface forcing was changed to those of thefollowing speciﬁed year to simulate the water conditions of that year.
The model results from September of the third year to August of thefollowing speciﬁed year were used to examine the formation of theYSCWM.

3. Results
3.1. Deﬁnition of the YSCWM intensity using observation data
The YSCWM showed signiﬁcant interannual variations in terms of
the average water temperature and area (Fig. 3). At a depth of 50 m, theaverage water temperature inside the YSCWM varied from 6.83 °C(1984) to 7.71 °C (1983) from 1978 to 1998, with an average watertemperature of 7.35 °C (Fig. 3a). A slightly increasing trend of
0.0023 °C/y was determined using a linear regression. The YSCWM
area had its maximum value of 3.9 × 104 km2 in 1984, which was
almost 50 times the minimum value of 700 km2 in 1989. The YSCWM
area at a depth of 50 m had a multi-year average value of 1.8 ×
104 km2 and a declining trend at a rate of 452 km2/y from 1978 to
1998. The correlation coeﬃcient between the average water temperature and the YSCWM area was − 0.73 (Fig. 3a).
Along the 36°N section, the average water temperature inside theYSCWM was the lowest in 1990 (7.35 °C) and highest in 1994 (7.98 °C)(solid line in Fig. 3b). The YSCWM area along the section changed fromzero to 6 km2 (dashed line in Fig. 3b). The zero area of the YSCWM
appeared in 1979, 1989, 1991 and 1995 when there was no water witha temperature lower than 8.0 °C in August along this section in the17

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J. Zhu et al.

Fig. 4. The comparison between the simulated and observed water temperatures. The blue solid line denotes the 1:1 line. (For interpretation of the references to color in this ﬁgurelegend, the reader is referred to the web version of this article.)whole YS between strong and weak YSCWM years.
From modelled distribution of water temperature at a depth of 50 m
(Fig. 7a-d), the YSCWM was located in the central trough of the YS. The
northern boundary of the YSCWM reached approximately 39°N in boththe strong years and weak years. However, the southern boundary of
the YSCWM changed annually, from 33.5°N in the strong years of 1984
and 1985 to approximately 35°N in the weak years of 1989 and 1995.
Consequently, the YSCWM areas at a depth of 50 m in the strong years(1.15 × 105 km2 in 1984 and 1.05 × 105 km2 in 1985) were muchlarger than those in the weak years (4.73 × 104 km2 in 1989 and 4.31
× 104 km2 in 1995).
Along the 36°N section (Fig. 7e–h), the deep cold water was isolated
from the surface warm water by a strong thermocline with a temperature gradient of 2–3 °C/m. The YSCWM within the 8 °C isotherm
usually had two cold cores, i.e., the eastern core and western core. In
the strong years of 1984 and 1985, the two cold cores were relatively
stable. The lowest water temperature of the western core was 3.44 °C,
while that of the eastern core was 4.86 °C. In the weak years of 1989
and 1995, the eastern cold cores also occurred, and the lowest watertemperatures were 7.21 °C and 7.20 °C in 1989 and 1995, respectively.
However, the western cold core was not stable, i.e., it occurred in 1989,
with the lowest water temperature of 7.57 °C, but did not occur in 1995.
The period from the previous autumn to the summer of a speciﬁc
year was the formation period of the YSCWM. During this period, theaverage water temperature, both at the sea surface and a depth of 50 m,
showed similar seasonal variations in all ﬁve years (Fig. 8a, b). However, the water temperatures at the sea surface and a depth of 50 m
exhibited diﬀerent variations. In the previous autumn (September),when the previous YSCWM began to disappear, the sea surface temperature decreased due to atmospheric cooling, while the temperaturetemperature cores at a depth of 50 m (Fig. 5c). One lied in the north YS,
and the other two cores were located at the eastern and western part ofthe south YS. In summer, the sea surface temperature reached up to
26 °C, while the temperature of the deeper water was below 8 °C
(Fig. 5h). The three cold cores at a depth of 50 m in spring merged toform the isolated YSCWM (Fig. 5d).
The simulated water temperature in other years showed similarseasonal variations compared to the normal year of 1992. In general,
the YSCWM began forming in spring, matured in summer and graduallydisappeared in autumn every year. Besides, the observations also indicated that the YSCWM intensity had signiﬁcant interannual variations(Fig. 3).
3.3. Strong and weak years of the YSCWM
The years 1984 and 1985 were strong years for the YSCWM, while
1989 and 1995 were weak years for the YSCWM (Table 2). The model
simulated these four years with the same procedures performed for thenormal year of 1992. Fig. 4 showed the comparisons of the watertemperatures between the model results and observations in Februaryand August for these four years. The correlation coeﬃcients between
the simulations and observations were 0.95, 0.95, 0.98 and 0.86 inAugust of 1984, 1985, 1989 and 1995, respectively. On average, morethan 90% of the simulated water temperatures fell into an error range of
20%. Although the modelled average temperature within the YSCWM
was a little lower than the corresponding observed value in the same
year, the model reproduced the lower temperature of the YSCWM in thestrong years (1984 and 1985) and higher temperature of the YSCWM in
the weak years (1989 and 1995) (Table 3). Consequently, we were able
to use the model results to analyze the diﬀerences of the YSCWM in the18

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J. Zhu et al.

Fig. 5. The seasonal distribution of the simulated water temperatures (°C) in the normal year of 1992 at a depth of 50 m (a)–(d) and along the 36°N section (e)–(h). The dashed line
denotes the 50 m isobath.

temperature gradually increased both at the sea surface and at a depthof 50 m. The increasing rate was much faster at the sea surface than thatat a depth of 50 m. A similar seasonal variation of the YSCWM was also
observed at a station near the YSCWM boundary by Li et al. (2016b).
Using the normal year of 1992 as a reference, the temperature

at a depth of 50 m increased since the thermocline was graduallybroken and the heat ﬂux was transported downward to the deep waterlayer. In the previous winter, the water temperature was verticallyhomogeneous and the water temperature of the entire water column
decreased. Subsequently, in the following spring and summer, the water
19

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Fig. 6. As Fig. 5, but for observation. The black dots are observation stations.

20

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3.4. Key factors controlling the YSCWM intensity

Table 3Comparison of the simulated and observed water temperatures (°C) for the YSCWM in
August during the representative years of the YSCWM.
Year

1984
1985
1992
1989
1995

Average temperature at a depth of
50 m

Average temperature along the 36°N
sectionModel

Observation

Model

Observation

6.06
6.19
7.63
7.71
7.67

6.83
6.97
7.42
7.50
7.56

5.24
6.10
7.73
7.85
7.84

7.39
7.37
7.55
> 8.0
> 8.0

To quantitatively investigate the eﬀects of several factors on theYSCWM intensity, several numerical experiments were designed basedon the reference years of 1984 (a strong YSCWM year) and 1995 (aweak YSCWM year), and the details of each simulation are listed inTable 4. The simulation of 1995 was set to be the control run. The
forcing conditions, including the initial conditions, wind, evaporation,
precipitation, sea level pressure and air-sea heat ﬂux, were changed tothose of 1984 in the experiments labelled as INITIAL, WIND, EVA, PRE,
SLP and HEAT, respectively. In these experiments, except for the factors
in Table 4, the other input data for model were the same as those in thesimulation for 1995. To quantify the contributions of these factors to
the diﬀerences between the two reference years (1984 and 1995) interms of the water temperature and area of the YSCWM, the contribution rate (α ) was calculated using the following formula:anomalies between the other four years and 1992 were determined atboth the sea surface (Fig. 8c) and at a depth of 50 m (Fig. 8d). From theprevious September to December, the water temperatures in the strong
years of the YSCWM were not necessarily lower than those in the weakyears either at the sea surface or at a depth of 50 m. The sea surface
temperature in the strong years (1984 and 1985) almost always showed
positive temperature anomalies, while the sea surface temperature in
the weak years (1989 and 1995) showed positive and negative
anomalies alternatively. At a depth of 50 m, the water temperatures inthe strong year of 1984 and weak year of 1989 were similar to those in
the normal year of 1992, as indicated by their small temperature
anomalies. Therefore, the period from September to November was not
the key time that determined the YSCWM intensity for the following
summer.
During the cooling season from December to February, the variations of the water temperature anomalies at the sea surface were similar
with those at a depth of 50 m (Fig. 8c, d). The water temperature had
negative anomalies in the strong years of the YSCWM. Although thetemperature anomalies were not always positive in the weak years,
their values were much higher than those in the strong YSCWM years.
From spring to summer, the variations of the water temperature
anomalies at the sea surface and at a depth of 50 m were signiﬁcantly
diﬀerent due to the strong stratiﬁcation. At the sea surface, the diﬀerences of the water temperature anomalies among these ﬁve years were
not as signiﬁcant as those in winter. However, the water temperature
anomalies at a depth of 50 m remained approximately the same as thosein the previous cooling season. The water temperature anomalies in thestrong years of 1984 and 1985 were almost 2 °C lower than those in the
weak years of 1989 and 1995. In the deep layer of the central YS(Fig. 8d), the consistency of water temperature anomalies from theprevious winter to the following summer indicated that the coolingprocess in the previous winter was critical for determining the YSCWM
intensity in summer.
The area where water temperature was lower than 8 °C at a depth of
50 m decreased in the previous autumn, sharply increased in the previous winter and then slightly decreased in the following spring andsummer during these ﬁve years (Fig. 9). In the previous autumn, thearea was largest in the strong year of 1985 and smallest in the weak
year of 1995. However, the area in the strong year of 1984 was approximately the same as that in the weak year of 1989, both of whichwere larger than that in the normal year of 1992. Subsequently, during
the cooling season, the areas in all ﬁve years increased rapidly due tostrong vertical mixing and enhanced heat loss at the sea surface. At theend of the cooling season, the areas in the two strong years were signiﬁcantly larger than those in the two weak years, and the area in thenormal year of 1992 was between the areas in the strong years andweak years, which was the case until the formation of the YSCWM insummer. In 1984 (the strongest year), the cold water area with temperatures less than 8 °C was larger than that in 1995 (the weakest year),
4.8 × 104 km2 larger in February and 7.0 × 104 km2 larger in August,showing that 69% of the diﬀerence was preserved from the previous
winter to summer.

α=

MExp − Mweak
Mstrong − Mweak

× 100%(4)

where MExp is the average water temperature inside the YSCWM or theYSCWM area in August in each numerical experiment. Mstrong is thecorresponding value in 1984, and Mweak is the corresponding value in
1995. The contribution rates obtained from those sensitivity experiments were showed in Table 5.
The results of the experiments indicated that the air-sea heat ﬂux
was the dominant factor inﬂuencing the YSCWM intensity, which
contributed about 80% and 74% to the diﬀerences of the average watertemperature and the YSCWM area at a depth of 50 m, respectively, and
49% and 91% to those along the 36°N section, respectively. The secondary factor was the initial water conditions in the previous autumn
when the previous YSCWM tended to fade, which contributed around
10% of the diﬀerences in the water temperature of the YSCWM in thefollowing year. The inﬂuence of wind in the experiment WIND includedhydrodynamic processes, such as mixing and convection. Wind had anegative inﬂuence (approximately − 6% to temperature at a depth of50 m) on the YSCWM intensity. The average meridional wind speed in
the previous winter of 1984 as well as for WIND was 4.9 m/s, which
was larger than the value of 4.2 m/s in 1995. Under the stronger wintermonsoon, the Yellow Sea Warm Current intruded more northward
along the YS trough (Lie et al., 2001; Oh et al., 2013). Consequently,
water with higher temperatures remained to form the warmer YSCWM.
Additionally, other sea surface forcing, including evaporation, precipitation and sea level pressure, had only a little impact on the YSCWM
intensity.
Since the air-sea heat ﬂux plays the most important role in theYSCWM intensity, four additional numerical experiments were performed to know which season of the air-sea heat ﬂux was the determining factor. The air-sea heat ﬂux from the previous autumn (fromprevious September to November), previous winter (from previous
December to this February), this spring (from March to May) and thissummer (from June to August) were changed from 1995 to 1984 in theexperiment Autumn-HEAT, Winter-HEAT, Spring-HEAT and SummerHEAT, respectively. It was found that the air-sea heat ﬂux in the previous winter was the primary factor, contributing 64% and 58%, respectively, of the diﬀerences of the average water temperature and areaof the YSCWM at a depth of 50 m, while along the 36°N section, thecontributions were 40% and 76%, respectively. The secondary factor
was the air-sea heat ﬂux in spring, followed by that in the previous
autumn. The contribution of the summer air-sea heat ﬂux to the difference of the YSCWM intensity in two years was nearly zero.
The results of the numerical experiments can also be interpreted
based on the time series of the air-sea heat ﬂux in diﬀerent years(Fig. 10). The air-sea heat ﬂux of diﬀerent years varied more signiﬁcantly in the previous winter than the other three seasons. The airsea heat ﬂux in the previous winters of 1984 and 1985 (the strong
21

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J. Zhu et al.

Fig. 7. The distribution of modelled water temperatures (°C) at a depth of 50 m (a)–(d) and along the 36°N section (e)–(h) in two strong YSCWM years (1984,1985) and weak YSCWM
years (1989, 1995).

years) were − 187 W/m2 and − 178 W/m2, respectively, while thosein the previous winters of 1989 and 1995 (the weak years) were −124 W/m2 and − 162 W/m2, respectively (Table 6). The ocean lost
more heat to the atmosphere in the previous winter of a strong YSCWM

year and thus led to a cooler YSCWM in summer.
The ﬁve components of the air-sea heat ﬂux used in the model wereshortwave radiation, longwave radiation, sensible heat ﬂux, latent heatﬂux and the adjustment term (Table 6). It was found that the ocean did
22

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J. Zhu et al.

Fig. 8. Time series of the average water temperature (a, b) and temperature anomalies (c, d) at the sea surface and at a depth of 50 m in the Yellow Sea in the ﬁve YSCWM representative
years.

Fig. 9. Time series of the simulated cold water area with temperatures less than 8 °C at a
depth of 50 m in the ﬁve YSCWM representative years.

Fig. 10. Air-sea heat ﬂux over the Yellow Sea in the ﬁve YSCWM representative years.

not necessarily gain less solar radiation in the previous winter of thestrong YSCWM years (1984 and 1985). The sea surface obtained
135 W/m2 and 121 W/m2 of heat in the strong YSCWM year of 1984
and 1985, respectively, which were larger than that in the weakYSCWM year of 1989 (111 W/m2). However, the ocean was characterized by losing more heat in the previous winter of the strongYSCWM years than weak YSCWM years. Among these components, thesensible heat was the key factor. Using a strong year (1984) and weak
year (1995) as examples, the diﬀerences of the sensible heat contributed 66% of the total diﬀerence of air-sea heat ﬂux between theprevious winter of 1984 and 1995. This could be attributed to that the
air-sea temperature diﬀerence in strong years (6.90 °C in 1984, 6.96 °C
in 1985) was larger than these in weak years (5.34 °C in 1989, 6.18 °C
in 1995), and the wind speed was also larger in strong years (6.70 m/s
in 1984, 6.82 m/s in 1985) than weak years (6.58 m/s in 1989, 6.61 m/
s in 1995) (Table 6) (Friehe and Schmitt, 1976).

Table 4
Forcing conditions for the sensitivity experiments (based on the simulation of 1995).
Experiment

Forcing conditions

INITIAL
HEAT
WIND
EVA
PRE
SLP

Water conditions in September of 1983
Air-sea heat ﬂux from September in 1983 to September in 1984
Wind from September in 1983 to September in 1984
Evaporation from September in 1983 to September in 1984
Precipitation from September in 1983 to September in 1984
Sea level pressure from September in 1983 to September in
1984
Air-sea heat ﬂux from September in 1983 to November in 1983
Air-sea heat ﬂux from December in 1983 to February in 1984
Air-sea heat ﬂux from March in 1984 to May in 1984
Air-sea heat ﬂux from June in 1984 to August in 1984

Autumn-HEAT
Winter-HEAT
Spring-HEAT
Summer-HEAT

Table 5The contribution rate (α ) of forcings to the diﬀerences of the average water temperature
and area of the YSCWM between the strong (1984) and weak (1995) YSCWM years.
Experiment

INITIAL
HEAT
WIND
EVA
PRE
SLP
Autumn-HEAT
Winter-HEAT
Spring-HEAT
Summer-HEAT

Temperature

4. Discussion

Area

The 50 m
depth

The 36°N
sectionThe 50 m
depth

The 36°N
section

11%
80%
− 6%
2%
2%
0.1%
3.2%
64%
18%
0.5%

9%
49%
− 5%
4%
6%
− 1%
2.7%
40%
13%
− 0.4%

26%
74%
− 13%
3%
1%
− 0.1%
7.5%
58%
32%
0.2%

40%
91%
− 26%
− 1%
− 1%
− 2%
7.1%
76%
28%
2.1%

4.1. Relationship between the YSCWM and air-sea heat ﬂux in the previous
winterAs demonstrated in Section 3, the air-sea heat ﬂux in the previous
winter was a key factor that controlled the YSCWM intensity in strong
and weak YSCWM years. In order to verify whether the air-sea heat ﬂux
in the previous winter was dominant factor to the YSCWM intensity
over an interannual scale, rather than just occasionally for the twoyears (1984 and 1995), a set of numerical experiments was performed
from 1981 to 1998 when the daily air-sea heat ﬂux was available online. In these experiments, we used the air-sea heat ﬂuxes in previous
winter (from previous December to this February) in a speciﬁc year
from 1981 to 1998 to replace the corresponding ones in the simulationof 1992. The initial conditions, the other air-sea conditions (such as23

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J. Zhu et al.

YSCWM intensity were consistent with those in the western part of thesouth YS.
In addition, the observed and modelled YSCWM intensities in thisstudy were consistent with those from previous studies. Park et al.
(2011) suggested the existence of three cold periods (1967–1971,
1983–1988 and 1996–2000) and two warm periods (1972–1980 and
1990–1995) for the YSCWM based on analyses of observed temperatures oﬀshore from the Korea Peninsula from 1967 to 2008. Zhang andYang (1996) reported that 1984 was the strongest year for the northern
YSCWM, according to the observation data along a section between
Dalian and Chengshan Jiao (green solid line in Fig. 1) of 42 years
among 1928–1990.

Table 6The variables related to the air-sea heat ﬂux (W/m2) in the previous winter of the strong
and weak years of the YSCWM.
Variables from the previous December tothis February

1984

1985

1989

1995

Air-sea heat ﬂux (W/m2)
Shortwave (W/m2)
Long wave (W/m2)
Sensible heat (W/m2)
Latent heat (W/m2)
Adjustment (W/m2)
Air-sea temperature diﬀerence (°C)
Wind speed（m/s）

− 187
115
− 97.9− 99.2
− 125
20.1
6.90
6.70

− 178
108
− 85.8− 96.3
− 117
13.1
6.96
6.82

− 124
103
− 77.9− 61.8− 95.2
7.90
5.34
6.58

− 162
113
− 93.4− 82.7
− 128
29.1
6.18
6.61

Note: The air-sea heat ﬂux is positive when the sea obtains heat.

4.2. Implication to prediction of the YSCWM intensity

wind stress, surface level pressure, precipitation, and evaporation
rates), and the open boundary conditions were kept the same as those in
the simulation of 1992. The modelled interannual variations of theYSCWM were compared to the observations.
T50w and S50w were used to represent the modelled average watertemperature and the YSCWM area at a depth of 50 m in the western partof the south YS (orange rectangle in Fig. 2) where observations wereavailable; T50 and S50 were used to represent those for the entire YSCWM
at a depth of 50 m. The correlation coeﬃcient was 0.64 for the simulated and observed temperatures in the western YSCWM (Fig. 11a) and
was 0.73 for the simulated and observed area of the YSCWM in thewestern YSCWM (Fig. 11b), both of which has a signiﬁcant level over
99%. The signiﬁcant positive correlations indicated that the interannual
variations of the YSCWM can be reproduced by changing the air-sea
heat ﬂux in the previous winter because other boundary conditions forthe simulations over those years were the same as those of the normal
year of 1992. In addition, T50 and S50 were apparently related to T50w and
S50w with correlation coeﬃcients of 0.99 and 0.97, respectively(Fig. 11), suggesting that the interannual variations of the entire

Based on the analysis above, we found that the interannual variation of the YSCWM intensity in summer was mainly controlled by airsea heat ﬂux in the previous winter. This encouraged us to infer theYSCWM intensity by establishing a simple equation using air-sea heatﬂux in the previous winter. This way should be helpful to people whowant to roughly predict the YSCWM intensity for the following summer
but do not want to run a three-dimensional prediction model.
According to the results of the numerical experiments in Section 3.4,
the air-sea heat ﬂux in the previous winter was the principal factor thatinﬂuenced the interannual variation of the YSCWM intensity. Whenestablishing the regression equation, the satellite-derived average net
sea surface heat ﬂux (the sum of the shortwave radiation, longwave
radiation, sensible heat ﬂux, latent heat ﬂux denoted by HFwinter , W/m2)
and the mean sea surface temperature (denoted by SSTwinter , °C) from
NCEP over the YS in the previous cooling season (from the previous
December to this February) were taken into consideration. Since theinitial water condition in the previous autumn was also expected toaﬀect the YSCWM intensity, the mean sea surface temperature (SSTinit ,°C) over the YS before the cooling season (i.e., on December 1) was also
included in the regression equation.
Using all the available observational data for 1978–1998, following
linear relationship between the YSCWM intensity, represented by theaverage water temperature in the western YSCWM (T50w ) and SSTinit ,HFwinter , SSTwinter was obtained.

HF
×S×T
T50w = 1.2 × 10−2∙SSTinit + ⎜⎛9.30 × 10−2∙ winter+2.06
Cp × S × d × ρ⎝× 10−1∙SSTwinter ⎞⎟ + 6.13⎠

(5)

Where S is the area of the entire YS (3.8 × 1011 m2 ), T is the cooling timein the previous winter (79488000 s), Cp is the speciﬁc heat capacity ofseawater (4096 J∙kg−1∙℃−1), d is the average water deep of the YS(44 m), ρ is the seawater density (1025 kg∙m−3 ). After unifying the unitof each term in Eq. (5) to degree for temperature, we obtained following formula:

T50w = 1.2 × 10−2∙SSTinit + (4.00 × 10−3∙HFwinter +2.06 × 10−1∙SSTwinter )+ 6.13(6)

The items in the bracket within Eq. (6) represent the eﬀect of the
air-sea heat ﬂux during the cooling season that was decided by the
downloaded heat ﬂux and the modulation of SST as given in Eq. (3).
The physical meaning of prediction equation was that the YSCWM intensity in summer depended slightly on the water temperature before
the cooling (SSTinit ), strongly on the cooling process during the previous
winter (HFwinter , SSTwinter , Fig. 8 and Fig. 9). Therefore, a good predictionmeans that the water property at the end of winter is well preserved innext summer.
The time series of T50w from 1981 to 1998 (circles in Fig. 12) calculated using Eq. (6) were compared with T50w observed (dots in

Fig. 11. Time series of the average water temperature (T50w, T50 ) (a) and area (S50w, S50 )(b) of the YSCWM in August at 50 m depth from 1981 to 1998. T50w and S50w are those forwestern part of YSCWM, T50 and S50 are those for entire YSCWM.

24

Continental Shelf Research 152 (2018) 14–26

J. Zhu et al.

temperature diﬀerence in August between the strong and weak YSCWM
years. Among components of heat ﬂuxes, sensible heat is the main
contributor to the diﬀerence in the previous winter between strong andweak YSCWM years.
Following above results, the YSCWM intensity in summer is reconstructed using sea surface temperature in the previous early
December and the air-sea heat ﬂux in the previous winter over theentire YS. The interannual variations of reconstructed YSCWM intensity
show a good correlation with the observed one. In the future, not only
local factors but also remote factors should be considered in the reconstructing of YSCWM intensity in summer.

Fig. 12. Reconstructed YSCWM average water temperature (T50w ) and some observed
water temperatures. The blue triangles mark the average water temperature below 30 m
along the 36°N section in the western part of the Yellow Sea (Li et al., 2016). The orange
squares highlight the spatially averaged temperature at a depth of 50 m (33–37°N,
124.4–126°E) in the eastern part of the Yellow Sea using the Korea Oceanographic Data
Center (KODC) dataset (Park et al., 2011). (For interpretation of the references to color inthis ﬁgure legend, the reader is referred to the web version of this article.)

Acknowledgement
The forcing data of the model were obtained from NCEP Reanalysis
data provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA(http://www.esrl.noaa.gov/psd/). The other data for this paper isproperly cited and referred to in the reference list. We thank Hao Wei
from the Tianjin University of China for providing unpublished observations in the Yellow Sea. We thank the support from the NSFCShandong Joint Fund for Marine Ecology and Environmental Sciences
under the contact of U1606404, the National Natural Science
Foundation of China under the contracts of 41576010 and 41106007.
X, and the Leading Academia in Marine and Environment Pollution
Research (LaMer) of Ehime University. Guo thanks support from theFundamental Research Funds for Central Universities of the Ministry of
Education of China (201512004). J. Shi thanks the support from theStrategic Priority Research Program of the Chinese Academy of Science
under the contract of XDA11010101.

Fig. 12). The correlation coeﬃcient was 0.55, with a signiﬁcant level of
95%. It was found that the prediction was not applicable in 1986 and1987 compared with observation (Fig. 12). When removing the twoyears, the correlation coeﬃcient raised to 0.76 with a signiﬁcant levelof 99%. Therefore, not only heat ﬂux but also some other factors likely
inﬂuenced the YSCWM intensity in 1986 and 1987. The horizontal
lateral transport of heat, the phase of PDO and western Paciﬁc Kuroshio
heat transfer (Wei et al., 2013; Shi et al., 2014) are possible candidates.
Using this simple formula, we reconstructed the time series of T50w
from 1999 to 2016 (circles in Fig. 12). Since no observational data forthis period were available, we collected relevant observational datafrom the literature to validate Eq. (6). Li et al. (2016a) calculated theaverage water temperature below 30 m along the 36°N section in thewestern area of the YS from 1999 to 2006 (triangles in Fig. 12). Although these values were generally higher than T50w given by Eq. (6),the two time series showed a consistent variation with a correlationcoeﬃcient of 0.68. Park et al. (2011) reported the spatially averaged
temperature at a depth of 50 m in the eastern part of the YS (33–37°N,
124.4–126°E) from 1981 to 2008 using data from the KODC (squares inFig. 12). The correlation coeﬃcient between the data given by Parket al. (2011) and those determined from Eq. (6) was 0.48, with a signiﬁcant level of 95%. Some episodic observations of temperatures at a
depth of 50 m inside the eastern YSCWM in August of 2010 (Oh et al.,2013) and those in the calculating area (Fig. 2a) in August of 2011 and2013 (unpublished data provided by Prof. Wei Hao from Tianjin University of China) also conformed to the estimations from Eq. (6)(Fig. 12). The agreement between the observations and calculations
from Eq. (6) also indicated the crucial role of the air-sea heat ﬂux on theYSCWM intensity. Only using the data available online (for example,
the NCEP/NCAR reanalysis data, the European Center for MediumRange Weather Forecasts), instead of running the complicated numerical model, we can roughly predict the YSCWM intensity for the following summer.

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