Continental Shelf Research 143 (2017) 257–270

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

Research papers

Interannual variation of spring phytoplankton bloom and response toturbulent energy generated by atmospheric forcing in the central Southern
Yellow Sea of China: Satellite observations and numerical model study
Jie Shia,b, Yi Liuc,d, Xinyan Maoe, Xinyu Guoa,b,f, Hao Weig, Huiwang Gaoa,b,

MARK

⁎

a

Key laboratory of Marine Environment and Ecology, Ocean University of China, Ministry of Education, 238 Songling Road, Qingdao 266100, China
College of Environmental Science and Engineering, Ocean University of China, 238 Songling Road, Qingdao 266100, ChinacState Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, 164 West Xingang Road,
Guangzhou 510301, Chinad
University of Chinese Academy of Sciences, Beijing 100049, ChinaeCollege of Oceanic and Atmospheric Sciences, Ocean University of China, 238 Songling Road, Qingdao 266100, ChinafCenter for Marine Environmental Studies, Ehime University, 2-5 Bunkyo-Cho, Matsuyama 790-8577, Japang
School of Marine Science and Technology, Tianjin University, Tianjin 300072, Chinab

A R T I C L E I N F O

A BS T RAC T

Keywords:
Spring phytoplankton bloom
Start timing
Magnitude
Duration
Turbulent kinetic energy
the central Southern Yellow Sea

The interannual variations of the start timing, magnitude and duration of the spring phytoplankton bloom(SPB) in the central southern Yellow Sea (SYS) were studied using the satellite-derived surface chlorophyll-aconcentrations (Chl-a) from 2000 to 2014. The correlations between the characteristics of SPB and thegeneration rate of turbulent kinetic energy (TKERT) supplied from the atmosphere to the ocean were examined.
The start timing of SPB was delayed in years with high TKERT supplied to the ocean before SPB. The TKERT
during SPB had no relationship with the magnitude of SPB, but had positive correlation with the duration. A 1D physical-biological model was used to examine the inﬂuencing mechanisms of the TKERT on thecharacteristics of SPB quantitatively. The wind speeds and related TKERT before the start of SPB were stronger
in 2010 than in 2008. Comparison of the model results forced by winds in the two years suggested that theenhanced physical dilution of phytoplankton caused by the stronger TKERT in 2010 induced a later start timingof SPB. When increasing the winds during SPB period, more phytoplankton was taken downward from thesurface layer by the enhanced vertical mixing. Meanwhile, more nutrients were pumped upward to the surface
layer and supported more net growth of phytoplankton. These two contrary processes led to the independenceof the magnitude of SPB on the TKERT during the SPB period. However, larger TKERT along with stronger wind
resulted in a longer duration of SPB because of more nutrients supply by stronger vertical mixing.

1. Introduction
Oceanic phytoplankton is the basis of the oceanic food web which
supports the higher trophic production and ﬁsh recruitment(Townsend et al., 1994; Son et al., 2014). It is also regarded as a key
factor to modulate the climate change due to its ability of converting
carbon dioxide to oxygen through photosynthesis (Falkowski et al.,
1998; Brown et al., 2014). Phytoplankton bloom (PB) is deﬁned as thefast growth and accumulation of phytoplankton, which contributes
much to the global primary production (Wollast, 1991; Tilstone et al.,2014). Spring phytoplankton bloom (SPB), occurring in diﬀerent
geographical regions (subpolar, temperate and subtropical zones) andin diﬀerent waters (both deep oceans and shallow seas), was always⁎

described by three characteristics, i.e., the start timing (Tian et al.,2011), magnitude (Lips et al., 2014) and duration (Frajka-Williams
and Rhines, 2010). The start timing is important for the developmentof zooplankton and has implications for ﬁsh larvae and their stocks(Rey et al., 1987; Platt et al., 2003). The magnitude and durationaﬀected the amount of energy transferred to the higher trophic level(Sigler et al., 2014).
There are two theories explaining the start of SPB. A classicalparadigm is the Sverdrup Theory (Sverdrup, 1953), which is also called
to be the critical depth concept. At the critical depth, the depthintegrated photosynthesis is equal to the depth-integrated losses ofphytoplankton. Hence, SPB can develop only if the upper mixed layer isshallower than the critical depth. In spring of temperate zones, theCorresponding author at: College of Environmental Science and Engineering, Ocean University of China, 238 Songling Road, Qingdao 266100, China.
E-mail address: huiwanggao@126.com (H. Gao).

http://dx.doi.org/10.1016/j.csr.2016.06.008
Received 31 December 2015; Received in revised form 1 May 2016; Accepted 15 June 2016
Available online 16 June 20160278-4343/ © 2016 Elsevier Ltd. All rights reserved.

Continental Shelf Research 143 (2017) 257–270

J. Shi et al.

upper mixed layer becomes shallower due to the increasing heat ﬂux at
sea surface. The light conditions become favorable for photosynthesis.
So, the interannual variation of the start timing of SPB is closely related
to the development of seasonal stratiﬁcation, which has been proved
useful in many oceans (Kirk, 1994; Navarro et al., 2012; Xu et al.,2013; Zhou et al., 2013; Cherkasheva et al., 2014). However, thiscritical depth concept has been challenged by several observations.
Townsend et al. (1992) found that the start timing of SPB in the Gulf ofMarine preceded the onset of vertical water column stratiﬁcation.
Similar observed phenomenon also occurs in the central Yellow Sea(YS) of China (Han et al., 2011). In early spring, SPB usually starts
before the formation of the seasonal stratiﬁcation.
Huisman et al. (1999) provided another theoretical explanation forthese observations, i.e., the critical turbulence concept. Before SPB
occurs, nutrients in the water are always suﬃcient. Strong verticalmixing inhibits the accumulation of phytoplankton by taking it to lower
water layers where light is limited (Ueyama and Monger, 2005). SPB
develops only when the turbulent mixing rates are less than a criticalturbulence. Many studies linked the interannual variation of the starttiming of SPB with wind and surface heat ﬂux that could inﬂuence thevertical mixing of water. Kim et al. (2007) studied the direct relationship between the timing of SPB and wind speed using the satellite datain the East/Japan Sea, and found SPB began 6–15 days after the windstress weakened. Moreover, Stramska (2005) pointed out that thevariations of wind and heat ﬂux determined the start timing of SPB bychanging the turbulent kinetic energy supplied from atmosphere to theoceans in the north polar Atlantic. However, only using satellite data, it
is diﬃcult to specify the physical dilution and accumulation ofphytoplankton when the TKERT supplied to the ocean varied underdiﬀerent atmospheric forcing, which directly inﬂuences the start timingof SPB.
There are no ﬁxed relationships between the interannual variationsof the magnitude of SPB and the vertical mixing. By examining thecorrelation of the magnitude of SPB and vertical mixing during SPB,
Ueyama and Monger (2005) found that the large bloom-period wind
mixing could increase or reduce the magnitude in the North Atlantic
using the satellite observations. This result was proved in some otheroceans (Dutkiewicz et al., 2001; Follows and Dutkiewicz, 2001). During
the SPB period, phytoplankton grows fast and nutrients are gradually
exhausted. The vertical mixing induced by atmospheric forcing could
not only determine the physical transport of phytoplankton in thewater column, but also inﬂuence the nutrient supplies. The balance
between the physical and biological processes will determine themagnitude of SPB, which needs to be further studied based on bothobservations and models. The interannual variations of the duration of
SPB attracted less attention compared to those of the start timing andmagnitude, and the responses to the vertical mixing has rarely
reported.
In this study, the interannual variations of the start timing,
magnitude and duration of SPB in the central southern Yellow Sea(SYS) were studied from 2000 to 2014, using the satellite-derived
surface chlorophyll-a concentration (Chl-a). The relationships between
the interannual variation of the turbulent kinetic energy forced bywinds and heat ﬂuxes and SPB were examined. Moreover, a 1-D
physical-biological coupled model was carried out to quantify theinﬂuencing mechanism of turbulent kinetic energy on the interannual
variation of SPB.

Fig. 1. Bathymetric and geographic map of the YS. The red line is the isobath of 70 m indepth. The square is the study area for the central SYS. (For interpretation of thereferences to color in this ﬁgure legend, the reader is referred to the web version of thisarticle.)overestimated due to the presence of strong resuspension of sediments.
So, this study chooses the area between latitudes 34° and 36°N, andlongitudes 123° and 125°E in the central SYS, which is almost morethan 70 m in depth (square area in Fig. 1). Based on the historicalobservations, the nutrients from the rivers always inﬂuenced thewestern coastal areas of the SYS (Wei et al., 2011) and the southwestern SYS (32–33°N, 121–123°E) (Li et al., 2014) rather than the
study area. So, the terrestrial impacts on the SPB can be neglected. The
time series of the satellite surface Chl-a and the atmospheric forcing
averaged in this area are derived to investigate the interannual
variations and their relationship.
2.2. Data
From 2000 to 2014, the daily sea surface Chl-a data are retrieved in
the study area. For the period from 2000 to 2007, the surface Chl-a are
acquired from the SeaWiFS (Sea-viewing Wide Field-of-view Sensor)
Level 3 binned data. For the period from 2003 to 2014, similar data are
retrieved from MODIS-Aqua (Moderate Resolution Imaging
Spectroradiometer) Level 3 binned data. The spatial resolution is
9 km (http://oceancolor.gsfc.nasa.gov/cgi/I3).
The satellite surface Chl-a are always overestimated in winter due
to the resuspension of sediments in the coastal and even central SYS.
However, increases of satellite surface Chl-a observed in the central
SYS in spring are considered to be the occurrence of SPB (Yamaguchi
et al., 2013). In this study, we adopt the satellite surface Chl-a datafrom March 1 to August 31 in each year to study the interannual
variation of SPB process in the central SYS, avoiding the data obtainedin winter. The two time series are merged into one long time series
from 2000 to 2014.
There are diﬀerences between the Chl-a retrieved from SeaWiFS
and MODIS-Aqua (Morel et al., 2007). Before merging the twodatasets, the compatibility between the SeaWiFS data and the
MODIS-Aqua data is checked during the overlapping period from2003 to 2007 (excluding data in winter). The correlation between thetwo datasets is calculated by plotting the daily pixel value from
SeaWiFS against that from MODIS-Aqua at the same pixel within the

2. Methodology
2.1. Study area
The SYS, located between the mainland of China and the peninsulaof Korea, is a marginal sea of the northwest Paciﬁc Ocean, with anaverage depth of 44 m and an area of 3.8×105 km2. In the coastal
shallow area of the SYS, the satellite-derived surface Chl-a is greatly
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SPB is the day when the cumulative value rises to a speciﬁc percentageof that of the 243rd day. Among eight thresholds between 5 and 40%(5, 10, 15, 20, 25, 30, 35% and 40%), it is found that only when thethreshold is 30%, the start timing of SPB of all the 15 years falls
between tm-σ and tm. Therefore, the threshold of 30% is adopted. The
peak time is when the Chl-a reaches to its peak value. The end of SPB is
set to be symmetric with the start timing about the peak time. The
duration is the time period between the start timing and the end of
SPB. The magnitude is deﬁned as the peak value of the surface Chl-aduring the SPB process, which can be expressed as the maximum valueon the Gaussian curve.

2.4. The rate of generation of turbulent kinetic energy (TKERT)
2.4.1. The deﬁnition of TKERTThe vertical mixing of water aﬀects the SPB by inﬂuencing thestability of the water column and the nutrients transportation fromdeep sea water up to the euphotic layer. We use meteorological data toquantify the intensity of vertical mixing in spring in the study area
(Stramska, 2005) and test if there is a signiﬁcant correlation between
the vertical mixing and the three characteristics of SPB. According to
the bulk mixed layer theories (Kraus and Turner, 1967; Niiler andKraus, 1977), the vertical mixing is related to the rate of generation of
turbulent kinetic mixing (TKERT). The TKERT can be quantiﬁed interms of wind stirring and buoyancy forcing as follows:

Fig. 2. Comparison of daily surface Chl-a retrieved by SeaWiFS and MODIS-Aqua
during 2003–2007. The red solid line is the linear regression on the logarithmic scale,and the red dashed line is the 1:1 line. (For interpretation of the references to color inthis ﬁgure legend, the reader is referred to the web version of this article.)

study area on the same day (Fig. 2). There are 42,194 pairs ofconcurrent data for the same pixel over the ﬁve year, excluding datathat diﬀer by more than 3 times the root-mean-square of the deviations. The correlation between the two datasets is represented by thefollowing regression equation:log(SeaWiFS ) = log(MODISa) × 0.7504 + 0.0063, (R = 0.87, p < 0.0001),

TKERT = m1u*3 + m 2

where SeaWiFS and MODISa are the SeaWiFS and MODIS-Aqua
derived surface Chl-a, respectively. Correlations were evaluated on
log-log scale due to the lognormal distribution of Chl-a (Campbell,
1995; Tan et al., 2011; Tan and Shi, 2012).
With this linear regression, the MODIS-Aqua data are converted tothose compatible with the SeaWiFS data (He et al., 2013). Then themerged surface Chl-a in the study area are averaged to form the timeseries for the further analyses.
The variation of satellite surface Chl-a from March 1 to August 31 is
ﬁtted to the Gaussian function of time to get a continuous temporalvariation of surface Chl-a during the SPB period (Yamada and Ishizaka,
2006; Jo et al., 2007). Gaussian curve is expressed by the equation:

αg MLD( − H0 ),ρcp 2

where TKERT is the turbulent kinetic energy, MLD is the mixed layer
depth, u* is the wind-induced friction velocity, ρ is the water density, cp
is the speciﬁc heat, g is the gravitational acceleration, ɑ is the coeﬃcientof logarithmic expansion of ρ as a function of water temperature, and
H0 is the net heat ﬂux. Several previous studies have assumed m1=1.25
and m2=1 for negative buoyancy forcing when ocean loses heat to theatmosphere and m2=0.2 for positive buoyancy forcing when ocean
gains heat from the atmosphere (Kraus et al., 1988). The ﬁrst term on
the right side of the equation (Peak ) indicating the rate of work by thewind is referred to as tm . The second term on the right side of theαg MLDequation (m 2 ρc 2 ( − H0 )) representing the rate of potential energyp⎡ (t − t )2 ⎤m⎥,
Chl (t ) = B0 + Peak × exp⎢ −⎢⎣2σ 2 ⎥⎦

change caused by heat ﬂuxes across sea surface is represented by
TKEH.
The wind always increases TKERT in the oceanic upper boundary
layer. The buoyancy forcing can either increase TKERT when the watercolumn loses heat to the atmosphere, or reduce TKERT when the watercolumn becomes stratiﬁed owing to the absorption of heat from theatmosphere.

where B0 is the background surface Chl-a, Peak and tm are the peakvalue and peak time of the surface Chl-a, t is the time expressed by
Julian day, and σ is the standard deviation of the Gaussian curve. Then,the surface Chl-a in January and February are determined by theGaussian curve. The following analyses are based on the Gaussian
curve of each year.
The net heat ﬂuxes and wind speed data from 2000 to 2014 aretaken from NCEP/NCAR daily reanalysis data (http://www.esrl.noaa.gov/psd/data/gridded/reanalysis/). The spatial resolution of thesedata is 1.875°. The data acquired in the study area every day are
averaged to serve as the value for the day.

2.4.2. The deﬁnition of TKERT weakening time
The SPB usually starts in late March or early April (Xuan et al.,2012). The daily TKERT is normalized by dividing the maximum TKERTin March before the SPB in these 15 years. The mean normalized
TKERT is hereby derived by averaging over all the daily normalized
TKERT in March of the 15 years. The daily anomalies of normalized
TKERT are the diﬀerences between the daily TKERT and the mean
normalized TKERT. Then the CUSUM stands for the cumulative valueof the daily anomalies of normalized TKERT. The TKERT weakening
time is deﬁned as the time when CUSUM curve begins to descend andthe decreasing tendency lasts for no less than one week to eliminate thepossibility of violent ﬂuctuation. The method has been used to describe
the weakening of wind stress in the sub-polar frontal area of the Japan/
East Sea (Kim et al., 2007).

2.3. Determination of SPB characteristics
The start timing, magnitude and duration are three importantcharacteristics of SPB. The threshold method based on the cumulative
Chl-a is widely used in ocean color data to identify the start timing of
SPB (Brody et al., 2013). Based on the Gaussian curve of Chl-a in eachyear, the daily Chl-a are cumulatively summed day by day from the 1stday (the beginning of January) to 243rd day (the end of August) of theyear to create a time series of cumulative variance. The start timing of259

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a and POM are set to be low values.
The numerical simulations consist of ﬁve cases, namely, CONTROL,
Exp1, Exp2, Exp3 and Exp4. In CONTROL, the climatological atmospheric forcing, including wind, heat ﬂuxes, and solar radiation, areprovided to simulate the seasonal cycles of the physical and biologicalvariables. The WOA2013 data in four seasons represented by February,
May, August and November are used to validate the modeled vertical
proﬁles of temperature, DIN concentrations. Validation data forseasonal variations of Chl-a proﬁles are from the in situ observationsin April, July, November of 2011 and January of 2012 from observations by the authors’ research groups.
Exp1 and Exp2 are designed to evaluate the inﬂuence of TKERT
before SPB (taking wind-driven TKE for example) on the start timing of
SPB. In Exp1, the sea surface forcing are set to be those in 2008. In
Exp2, the sea surface heat ﬂuxes and solar radiation are the same as
those in Exp1. The water temperature inﬂuencing the biologicalprocesses is also the model results of Exp1. However, the winds arederived from the values in 2010. So, the calculated vertical diﬀusivity
coeﬃcients are diﬀerent in Exp1 and Exp2 caused only by the diﬀerent
winds. Exp3 and Exp4 are designed to evaluate the inﬂuences of TKERT(taking wind-driven TKE for example) during the SPB period on themagnitude and duration of SPB. All the forcing conditions in Exp3 andExp4 are the same as those in CONTROL except the wind during the
SPB period. The 15-year mean wind speed during the SPB period is
6.0 m/s. The maximum mean wind speed during SPB period is 8.6 m/s
in 2011, while the minimum value is 4.0 m/s in 2014. Therefore, inExp3, the daily wind speeds adopted during SPB are set as 2.6 m/slarger than that in CONTROL, while in Exp4, the daily wind speeds are
set as 2.0 m/s lower.

Fig. 3. The Scheme of the biological module.

2.5. The 1-D physical-biological coupled model
2.5.1. Model description
The 1-D physical-biological coupled model comprises a hydrodynamic module and a biological module (Fig. 3). The hydrodynamic
module provides the water temperature and vertical mixing coeﬃcientto the biological module. The hydrodynamic module is based on the 1D Princeton Ocean Model (Blumberg and Mellor, 1987). Three statevariables are included in the biological module: dissolved inorganic
nitrogen (DIN), one phytoplankton, and particulate organic matter(POM). The biological processes included in the module are shown in
Fig. 3. Phytoplankton absorbs DIN for photosynthesis and releases DIN
through respiration. The mortalities of phytoplankton contribute to
POM, which returns to DIN through remineralization. Parameters usedin the model (Table 1) are based on those reported in literatures (Vichi
et al., 2004; Tian et al., 2005; Liu et al., 2007; Zhao and Guo, 2011) andmade a reasonable modiﬁcation when debugging and validating themodel.
2.5.2. Model conﬁguration and data sources
The water depth in the model is set to be 70 m in the central SYS.
There are 40 layers in the vertical direction. Both physical andbiological variables are calculated simultaneously at a time step of
108 s. The model is forced by the daily wind, heat ﬂuxes and sea surface
salinity, all of which are derived from NCEP reanalysis datasets (http://
www.esrl.noaa.gov/psd/data/gridded/reanalysis/).
The model is initialized with the World Ocean Atlas 2013
(WOA2013) that is the climatological monthly mean data with thespatial resolution of 1°(http://www.nodc.noaa.gov/OC5/woa13/
woa13data.html). The WOA2013 data in January in the study area
are averaged to provide the initial vertical proﬁles of water temperature, salinity and DIN concentration. The initial concentrations of Chl-

3. Results
3.1. Validation of satellite-derived Chl-a in the central SYS
The satellite-derived surface Chl-a were validated by observations
from the literatures and observations by the authors' research groups(Fig. 4). In situ Chl-a data were obtained in October and November of2000 from Deng et al. (2008), in March, April, and September of 2003
from Zhang et al. (2009), in October and November of 2005 from

Table 1
Parameters in the biological module and sensitivities of annual mean surface Chl-a to theparameters.
Parameters

Value

References

Variation of
Parameter

Sensitivities ofannual meansurface Chl-a

Maximum speciﬁc
photosynthetic rate(/day)
Characteristic Q10
coeﬃcient
Half saturation value
for N-limitation(mmol N/m3)
Basal speciﬁc
respiration rate(/day)
Activity respiration
fraction
Basic lysis rate (/day)

1.1

Zhao andGuo, 2011

+50%
−50%

0.28
0.40

2.0

Vichi et al.,2004
Zhao andGuo, 2011

+50%
−50%
+50%
−50%

0.06
0.30
0.34
0.80

0.05

Tian et al.,
2005

+50%
−50%

0.04
0.06

0.14

Tian et al.,2005
Liu et al.,2007
Vichi et al.,
2004
Tian et al.,
2005

+50%
−50%
+50%
−50%
+50%
−50%
+50%
−50%

0.12
0.16
0.60
1.34
0.58
0.22
1.00
1.00

Remineralization rate(/day)
Optimal C:Chl quota(mg C/mg Chl-a)

1.8

0.1
0.03
50.0

Fig. 4. Comparison of satellite-derived Chl-a and in situ Chl-a.

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Fig. 5. Spatial distribution of monthly mean surface Chl-a in the SYS calculated from all the satellite data in the same month from 2000 to 2014. The colors represent the surface Chl-a.
The solid lines are the isobaths of 50 m and 70 m. The square is the study area. (For interpretation of the references to color in this ﬁgure legend, the reader is referred to the web versionof this article.).

The 15-year averaged TKEH, TKEW and TKERT are presented inFig. 6 (yellow, purple and red curves in Fig. 6). In winter, before thestart of SPB, the TKEH is mostly positive, indicating the intensive lossof heat by the ocean. The TKEW is higher than that in other seasons dueto the intensive winter monsoon. Therefore, the TKERT of this period ishigh so that the ocean is well mixed, better pumping up the nutrients tothe euphotic layer. However, the strong vertical mixing is not good forthe physical accumulation of phytoplankton. Note that during this timeof a year, the TKEH contributes more to TKERT than TKEW. In early
spring when SPB starts, the TKEH is sometimes negative and sometimes positive, with the absolute values more than an order ofmagnitude smaller than the values in winter. The TKEW also reduces
due to the attenuation of the winter monsoon. Consequently, the watercolumn is less disturbed and becomes stable, favorable for theaccumulation of phytoplankton. During the period of SPB, the TKERT
remains low. The contribution of TKEW to TKERT becomes comparable
to those of TKEH. From the end of SPB to summer, the TKEH is mostly
negative due to the net heat gain by the ocean from the atmosphere.
The TKERT is thus reduced, resulting in the stratiﬁcation of the watercolumn. The upward transport of nutrients is hindered so that thesurface Chl-a is low.

Zheng et al. (2006), in June and July of 2006 from Wang et al. (2009),
in July and August of 2006 from Fu et al. (2009), in April, October, andNovember of 2006 and March and August of 2007 from Liu et al.
(2015), in April of 2007 from Xuan et al. (2012), in October andNovember of 2007 from Wei et al. (2013), in April of 2009 from Xuanet al. (2011), in April of 2011 from Wen et al. (2012), in August of 2011
from Liu et al. (2014), and in March, June, and August of 2009, Julyand October of 2011, October of 2012, June, July, and September of2013 from observations by the authors’ research groups. These data
were averaged in the study area. The satellite data were correlated
strongly to the in situ Chl-a, with a correlation coeﬃcient of 0.86,
relationship y=1.29 ×0.71. Correlations were evaluated on log-log scales
due to the lognormal distribution of Chl-a (Campbell, 1995).
3.2. Climatological variations of SPB process and TKERT in thecentral SYS
The distribution of the surface Chl-a are analyzed based on themulti-annual monthly average data (Fig. 5). March is the incubation
period of the SPB in the central SYS (Xuan et al., 2011). The surface
Chl-a in the area with the depth more than 50 m is generally low. In
April, the obvious SPB occurs. The surface Chl-a is high in the study
area, and April is always regarded as the occurrence period. The
vanishing period of the SPB in the central SYS is May. However, theChl-a in near-shore areas is generally high throughout the year. The
overestimation by the satellite data usually occurs there due to theresuspension of sediments in these shallow regions. The other reason isthat nutrients from the river always support vigorous growth ofphytoplankton. So, the SPB occurs primarily in deep regions of thecentral SYS (the study area).
Using the space-average method to process the daily surface Chl-afrom 2000 to 2014 in the study area (Fig. 1), the climatological SPB
process is analyzed (black curve in Fig. 6). The surface Chl-a remains
low, less than 1.0 mg/m3, in January and February when the watertemperature is low and solar radiation is weak. In March, the surface
Chl-a increases sharply as the physical environment becomes favorable
for photosynthesis. The SPB starts on the 89th day of the year (around
March 30), when the cumulative sum (CUSUM) of the surface Chl-a(presented by blue curve in Fig. 6) reaches 30% of the CUSUM on the240th day (the end of August), as deﬁned in Section 2.3. Then, thesurface Chl-a ascends to its peak value of 2.5 mg/m3 on the 102nd dayof the year (around April 12). The end timing of SPB is the 115th day(around April 25), which is symmetrical with the start timing of SPB
about the peak time. The SPB lasts for 26 days, with the magnitude of
2.5 mg/m3. Then the surface Chl-a decreases due to the depletion ofnutrients in the euphotic layer and remains low in summer.

3.3. Interannual variations of SPB and TKERT in the central SYS
3.3.1. Interannual variation of SPB
The variations of the daily surface Chl-a from January to June in
the 15 years were well described by the Gaussian function (Fig. 7). The
15-year averaged correlation coeﬃcient between the Gaussian curve
and the satellite-derived Chl-a was 0.76. Three parameters, i.e., Peak ,tm and σ , were estimated for each year. The averaged values of Peak , tm
and σ in the 15 years were 3 mg/m3, the 102nd day and 18 days,
respectively.
The start timing, magnitude and duration of SPB showed obvious
interannual variations in the central SYS (Table 2). The characteristics
of SPB in 2006 were greatly diﬀerent from those of the other 14 years.
It started the latest on April 9, lasted for the shortest period of only 8days and had the largest magnitude of 9.82 mg/m3. For the other 14
years, the earliest start timing was found in 2008, on March 22 and thelatest in 2013, on April 8. The maximum value of the magnitude of SPB
was 3.40 mg/m3 in 2008, nearly doubling the lowest value of 1.73 mg/
m3 in 2009. The 14-year averaged magnitude was 2.64 mg/m3. The
duration of SPB in 2003 and 2008 was shortest, which is 18 days, about
half of the longest duration of 38 days found in 2010. The average
duration for the 14 years was 26 days. Tan and Shi (2012) attributed
the signiﬁcantly high surface Chl-a and primary production in thecentral SYS in 2006 to the Asian dust rather than the vertical mixing of261

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J. Shi et al.

Fig. 6. The climatological variations of TKEW (purple curve), TKEH (yellow curve), TKERT (red curve), daily satellite-derived surface Chl-a (gray and green columns). The black line is
the Gaussian curve ﬁtting the variation of surface Chl-a from March to August (green columns), and the blue curve represents the CUSUM of the surface Chl-a. The dark gray area
represents the SPB period. (For interpretation of the references to color in this ﬁgure legend, the reader is referred to the web version of this article.)

small, the water column was stable for the accumulation of phytoplankton and led to the start of SPB.
The TKERT weakening time and the start timing of SPB exhibited asigniﬁcantly positive correlation with a coeﬃcient of 0.9 (Fig. 9d). It
was suggested that the earlier TKERT weakened, the earlier SPB started.
There was also a time lag between the TKERT weakening time and thestart timing of SPB. The SPB started 5–20 days after the weakening of
TKERT. The minimum time lag is in the year 2011, which is 5 days,
while the maximum is in 2013, 20 days (Table 2).
In early spring before the SPB started, the large TKERT caused
strong vertical mixing, which cannot maintain a stable water column
for the accumulation of phytoplankton. Thus, the larger TKERT before
the SPB, the later the SPB started. Once the TKERT weakened, the SPB
started. However, they did not occur simultaneously. It took some days
from the weakening of TKERT to the start of SPB. The time lag
indicated that the water column needed some time to be stable enoughafter the weakening of TKERT.
The variation of TKERT was caused by diﬀerent wind and surface
heat ﬂuxes. Fig. 10 showed the correlations between the anomolies of
TKEH, TKEW and TKERT and one week before the SPB. The interannual
variation of TKERT signiﬁcantly related to that of TKEW, indicated by apositive coeﬃcient of 0.87 (Fig. 10a). However, the variation of TKERT
was not relevant to the TKEH indicated by a large p-value more than
0.1 (Fig. 10b). It was suggested that the interannual variation of wind
determined the start timing of SPB by inﬂuencing the stability of thewater column.

water. So, the year of 2006 was excluded when analyzing the correlation between SPB and TKERT in the following sections.
3.3.2. Interannual variation of TKERTThe earliest start timing was the 81st day found in 2008. The
average values of TKERT in one week, two weeks and one month before
the 81st day were calculated in each year, namely as TKERT-1w, TKERT2w and TKERT-1m. The interannual variations of these three values wereanalyzed (Fig. 8a). In most years, the TKERT-1m was the largest with the
15-year averaged value of 1.9×10−6 m3/s3, while the TKERT-1w was thesmallest with the average value of 1.1×10−6 m3/s. The TKERT-2w was in
between. In winter, strong winter monsoon and atmospheric coolingincreased the TKERT. Consequently, longer averaged time period before
the SPB led to stronger TKERT. However, it is not the case with theyears of 2003, 2004, 2010 and 2011, which were probably caused byepisodic strong wind event or cold air outbreak in the transition timefrom winter to spring.
The interannual changing trends of TKERT-2w and TKERT-1m were
almost the same. The values were low in 2002, 2008 and 2013, while
relatively higher values occurred in 2001, 2005, 2011 and 2012. The
peak values could reach about 3.0×10−6 m3/s3, while the low values
were around zero. The interannual variation of TKERT-1w before the
SPB was similar to TKERT-2w and TKERT-1m except the years of 2001
and 2005.
The TKERT during the SPB remained low in most of the years except
2010 with the value being 2.2×10−6 m3/s3 (Fig. 8b). The lowest value
was −1.2×10−7 m3/s3, which in 2008. The mean value was
4.8×10−7 m3/s3, one order smaller than TKERT before SPB.
In addition to the values of TKERT, the interannual variation of
TKERT weakening time was also examined (Table 2). The average
weakening time was the 78th day (March 19). The earliest weakening
time was the 65th day (March 6), found in 2002, which was almost one
month earlier than the latest weakening time of the 90th day (March
31) as in 2006.

3.4.2. Correlations between the magnitude and duration of SPB and
TKE
When calculating the correlation coeﬃcient between the interannual variation of the SPB and the TKERT during the SPB, the p-value waslarger than 0.1, which indicated that there was no obvious correlation
between the two items (Fig. 11a). Strong vertical mixing tended tomake the Chl-a vertically diluted. Consequently, large TKERT was notgood for the accumulation of phytoplankton in the euphotic layer anddid not support a large magnitude of SPB. In contrast, small TKERT
failed to bring suﬃcient nutrients from nutrient-rich lower water up to
the euphotic layer to support the growth of phytoplankton, although
weak vertical mixing was good for the physical accumulation ofphytoplankton. Therefore, there were no signiﬁcantly negative or
positive correlation between the TKERT and the magnitude of SPB.
The positive correlation coeﬃcient (r=0.66) between the interannual variation of the duration of SPB and TKERT during the SPB period(Fig. 11b) indicated that stronger TKERT during the SPB period could
support a longer duration of SPB. As mentioned in the previous
section, stronger TKERT did not necessarily support a higher magnitude of SPB, especially when the physical dilution of phytoplankton
overwhelmed the biological promotion. However, stronger TKERT

3.4. Relationship between three characteristics of SPB and TKE
3.4.1. Correlations between the start timing of SPB and TKE
The relationships between the start timing of SPB and TKERT-1w,
TKERT-2w and TKERT-1m were studied (Fig. 9). The start timing of SPB
was most related to the TKERT-1w with the correlation coeﬃcient of0.63 (Fig. 9a). The positive coeﬃcient showed that larger TKERT before
the SPB induced later start timing of SPB. The smaller correlationcoeﬃcient of 0.57 and a larger p-value indicated a weaker relationshipbetween the start timing of SPB and TKERT-2w (Fig. 9b). However, thep-value larger than 0.1 suggested that the start timing of SPB was not
related to TKERT-1m (Fig. 9c). Hence, the start timing of SPB depended
on the value of TKERT shortly before the SPB. Once TKERT became
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Table 2The interannual variations of the start timing, magnitude, duration of SPB, theweakening time of TKERT and the lag time from weakening time of TKERT to the starttiming of SPB.
Year

Start
timing(Julianday)

Magnitude(mg/m3)

Duration(day)

Weakening timeof TKERT (Julian
day)

Lag
time
(day)

2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014

87
82
82
87
89
94
99
83
81
86
96
90
96
98
89

3.17
2.02
2.32
3.10
2.42
3.40
9.82
2.42
3.40
1.73
2.32
1.92
3.06
3.26
2.44

24
26
20
18
28
22
8
22
18
34
38
30
24
22
34

67
74
65
67
78
88
90
76
66
74
85
85
85
86
80

20
8
17
20
11
6
9
7
15
12
11
5
11
12
9

successively pumped up nutrients to support the growth of phytoplankton, delayed the end of SPB and thus maintained a longer SPB.
In order to evaluate the contribution of wind and heat ﬂuxes to thevariation of TKERT during the SPB, the correlation coeﬃcients between
TKERT and TKEH and TKEW were calculated (Fig. 12). The correlationcoeﬃcient between TKERT and TKEW reached up to 0.97 (Fig. 12a),while the variation of TKERT was not relevant to that of TKEH
(Fig. 12b). It was suggested that the interannual variation of winds
during SPB, rather than heat ﬂuxes, determined the magnitude andduration of the SPB by inﬂuencing the stability of the water column andnutrients supply.
4. Discussion
It was found that the TKERT had some relationship with the threecharacteristics of SPB in the previous section. The TKERT inﬂuenced
the SPB by changing the vertical mixing in the water. The variation of
Chl-a in the surface layer was caused partly by the vertical mixing ofphytoplankton, which was referred as the physical process. In addition,
nutrients pumped up to the euphotic layer by the vertical mixing
promoted the growth of phytoplankton, which was referred as the
biological process. It was diﬃcult to evaluate the contributions of thetwo processes caused by TKERT to the variations of SPB process only by
calculating the correlation coeﬃcients between them. So, in thissection, a 1-D physical-biological coupled model was used to study
the inﬂuences of TKERT on the interannual variation of the start timing,
magnitude and duration of SPB. The winds were changed to represent
the variations of TKERT in the numerical experiments, and the
responses of the three characteristics of SPB were estimated. The
detail deﬁnitions of the numerical runs were in the section of
methodology. Moreover, the variations of the surface Chl-a caused byvertical mixing (i.e., the contribution of the physical process) and net
growth of phytoplankton (i.e., the contribution of the biologicalprocess) were also calculated in each numerical experiments andcompared to discuss the inﬂuencing mechanism of TKERT on the threecharacteristics of SPB.
4.1. Model validations
Fig. 7. The variations of surface Chl-a (green curve), CUSUM of surface Chl-a (blue curve)
and CUSUM of TKERT anomalies (red curve) from 2000 to 2014. The gray and green
columns are the daily satellite-derived surface Chl-a. The green curve is the Gaussian curve
ﬁtting the variations of surface Chl-a from March to August. The black dashed line is thestart timing of SPB, and the red dashed line represents the weakening timing of TKERT. The
diﬀerence between the two timing is the lag time. (For interpretation of the references tocolor in this ﬁgure legend, the reader is referred to the web version of this article.)

Before quantitatively analyzing the model results, the model
sensitivity to parameters uncertainties was estimated. The sensitivityof a predicted state variable to selected parameter was quantiﬁed as a
ΔA / Afactor s = ΔB / B , where A was the annual mean surface Chl-a and B was
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Fig. 8. The variations of TKERT-1w (black line), TKERT-2w (red line), TKERT-1m (blue line) before SPB, the weakening time of TKERT (a), and the variation of average TKERT during SPB(b). (For interpretation of the references to color in this ﬁgure legend, the reader is referred to the web version of this article.)model results were robust if these parameters were changed moderately. The model results were more sensitive to the basic lysis rate andthe half saturation value of DIN. The sensitivity of the value of C/Chl-a
was a constant (s=1). The C/Chl-a was not a parameter using in themodel calculation, but a coeﬃcient to convert the unit of the resultsfrom C to Chl-a. This ratio showed variable due to changes in light,

the value of one model parameter. ΔA was the variation of A when one
parameter varied at ΔB . In this study, the value of any selectedparameter was increased and decreased by 50% from the standard
value listed in Table 1. Sixteen experiments were carried out tocalculate the sensitivities (Table 1). Only in one case, the sensitivity
was bigger than 1. More than 60% of the sensitivities are ≦0.5. So, theFig. 9. Correlations between TKERT-1w (a), TKERT-2w (b), TKERT-1m (c), the weakening time of TKERT (d) and the start timing of SPB.

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Fig. 10. Correlations between TKEW anomalies (a), TKEH anomalies (b) and TKERT anomalies in one week before SPB.

Fig. 11. Correlations between magnitudes (a), durations of SPB (b) and TKERT during SPB.

Fig. 12. Correlations between TKEW anomalies (a), TKEH anomalies (b) and TKERT anomalies during SPB.

The modeled results for water temperature, DIN concentration and
Chl-a were in good agreement with the observations (Fig. 13). In
winter, all physical and biochemical elements were mixed well by thestrong winter monsoon. The water temperature remained lowest

nutrients conditions and phytoplankton species (Geider et al., 1997).
The value was set to be 50 according to some previous model studies in
East China Seas (Tian et al., 2005; Liu et al., 2007; Zhao and Guo,2011).
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Fig. 13. Comparison of modeled seasonal variations of the vertical proﬁles of temperature, DIN concentrations and Chl-a (solid line in the ﬁgures) with the observations (circles in theﬁgures). The observations of temperature and DIN concentrations are from WOA2013 data in February, May, August and November. Observations of Chl-a are the in-situ observationsin April, July, November of 2011 and January of 2012.

4.2. The inﬂuence of TKERT on the start timing of SPB

throughout the year due to the loss of heat to the atmosphere. DIN was
suﬃcient in the water column, however the growth of phytoplankton
was limited by the low temperature and weak solar radiation, as aresult, Chl-a was very low. In spring, the environment is favorable forthe growth of phytoplankton, so Chl-a fast increased in the upper layer
which was SPB phenomenon. The growth of phytoplankton was still
limited by light under the euphotic layer, and Chl-a remained low
there. The vertical proﬁle of DIN concentration showed an inverse
shape comparing with that of Chl-a. In summer, the water was
stratiﬁed indicated by the proﬁle of temperature. DIN was exhausted
by phytoplankton in surface layer. Under the inﬂuences of bothnutrient and light, the subsurface chlorophyll maximum formed which
was indicated by the proﬁle of Chl-a. In fall, the water column tended
to be well mixed again due to the surface cooling and strengthening ofwind.

After model validations, two numerical experiments (Exp1 andExp2) were carried out to evaluate the inﬂuence of TKERT on the starttiming of SPB. In Exp1, the annual cycles of vertical proﬁles of watertemperature, vertical mixing coeﬃcient, DIN concentration and Chl-ain 2008 were simulated. Compared with the atmospheric forcing in
Exp1, only the winds were replaced by that of 2010 in Exp2. However,
the water temperatures used in the biological module were set to be thesame as those in Exp1. So, the only diﬀerence between Exp1 and Exp2
was the vertical mixing coeﬃcient forced by diﬀerent TKERT.
SPB in the central SYS always started in late March to early April.
Before the start timing of SPB, the average wind speed in March of2008 was 5.5 m/s, which was much smaller than that in 2010 (9.0 m/s)
(Fig.14 (a)). As a result, the variations of TKERT in Exp1 and Exp2 were266

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Fig. 14. The variations of wind speed (a), TKERT (b), modeled surface Chl-a (c) and the contributing percentages of the vertical mixing to the variation of modeled surface Chl-a. The
black and red solid lines represent the variables in Exp1 and Exp2, respectively. The black and red dashed lines in (c) represent the start timing of SPB in Exp1 and Exp2, respectively.
(For interpretation of the references to color in this ﬁgure legend, the reader is referred to the web version of this article.)

Fig. 15. The variations of TKERT (a) and modeled surface Chl-a (b) in three numerical experiments. The black, red and blue solid lines represent the variables in CONTROL, Exp3 andExp4, respectively. (For interpretation of the references to color in this ﬁgure legend, the reader is referred to the web version of this article.)

Consequently, the vertical mixing in water in Exp1 was weaker thanthat in Exp2 in March before the SPB started.
The modeled surface Chl-a in the two experiments exhibited
diﬀerent variations (Fig.14 (c)). SPB started on the 85th day in Exp1,
while on the 105th day in Exp2. On the 64th day, the surface Chl-a inquite diﬀerent. The TKERT in the two experiments were both high in
winter (January and February). In March, the TKERT in Exp2 was stillhigh due to the strong winds (red curve in Fig.14 (b)), while the TKERTin Exp1 decreased to be around zero (black curve in Fig.14(b)),
indicating less energy input to the water from the atmosphere.
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CONTROL, the contributions of the physical (vertical mixing) andbiological (net growth) processes to the variations of Chl-a were
estimated in the three experiments (Table 3). The variations of surface
Chl-a caused by the physical process were −7.0, −7.6 and −6.1 mg/m3
in CONTROL, Exp3 and Exp4, respectively. The negative contributionsof the physical process in the three experiments suggested that thevertical mixing took phytoplankton downward to reduce the Chl-a inthe surface layer. The absolute values of the physical contributions in
the three experiments were consistent with the magnitude of TKERT.
The largest TKERT in Exp3 led to the largest physical contribution to
the variations of surface Chl-a. In comparison, in Exp4, the verticalmixing took the least phytoplankton downward from the surface layer
due to the smallest TKERT. However, the contributions of the biologicalprocess to the variations of surface Chl-a in the three experimentschanged in a contrary tendency. The net growth of phytoplankton in
CONTROL, Exp3 and Exp4 were 8.8, 9.2 and 7.8 mg/m3, respectively.
The modeled amounts of DIN transported up to the surface layer fromthe start timing (the 104th day) to the peak time (the 113th day) of SPB
in CONTROL, Exp3 and Exp4 were 48.3, 56.0 and 43.5 mmol/m2,
respectively (Table 3). The consistent tendency of the net growth andthe nutrient transport in the three experiments indicated that the larger
vertical mixing took more nutrients up to the surface layer to support
more net growth of phytoplankton.
The durations of SPB in CONTROL, Exp3 and Exp4 were 18, 24
and 12 days, respectively, which was consistent with the magnitude of
TKERT during the SPB. The variations of DIN transported up to thesurface layer in the three experiments were calculated (Fig. 16), and theaverage values during the SPB (from the 104th to the 122nd day) were
3.9, 5.4 and 2.9 mmol/(m2 day) in CONTROL, Exp3 and Exp4,
respectively. Larger TKERT in Exp3 brought more DIN to the surface
layer through stronger vertical mixing, resulting in a longer SPB, andvice versa in Exp4.

Table 3The contributions of the physical and biological process to the variation of surface Chl-aand DIN ﬂux integrated from the start timing to the peak time of CONTROL.

Biological process (mg/m3)
Physical process (mg/m3)
DIN ﬂux (mmol/m2)

CONTROL

Exp3

Exp4

8.8
−7.0
48.3

9.2
−7.6
56.0

7.8
−6.1
43.5

Fig. 16. The variations of DIN transported upward to the surface layer in CONTROL(black line), Exp3 (red line) and Exp4 (blue line). (For interpretation of the references tocolor in this ﬁgure legend, the reader is referred to the web version of this article.)

5. Conclusions
Exp1 started to exceed that in Exp2, and the diﬀerence gradually
increased, leading to the earlier start timing in Exp1. In order toevaluate the contribution of the vertical mixing to the start of SPB, thecontributing rate of the surface Chl-a variation caused by verticalmixing to the total surface Chl-a variation were calculated (Fig.14(d)).
From the 64th day to the start timing in Exp1 (the 85th day), thecontributing percentages in both experiments maintained negative,
indicating downward physical transport of phytoplankton from thesurface layer. In addition, the absolute values of the contributing rate inExp2 were always larger than those in Exp1. It was suggested thatlarger vertical mixing in Exp2, which was caused by larger TKERT was
not favorable for the accumulation of phytoplankton and thus led to alater start timing of SPB.

The SPB has great contribution to the primary production in thelocal food chain in the central SYS. It always occurs in late March toearly April, when the strong winter monsoon begins to attenuate (Xuanet al., 2011; Yuan et al., 2014). The satellite-derived daily surface Chl-ain spring were ﬁtted by the Gaussian function from 2000 to 2014, andthe three characteristics of SPB were determined by the Gaussian
curve. The start timing, magnitude and duration of SPB all experienced
obvious interannual variations. After TKERT has weakened for sometime, SPB started. The start timing of SPB was positively related to
TKERT in a short period before SPB. The relationship between themagnitude of SPB and the TKERT during SPB was comparablycomplicated. Larger TKERT during the SPB did not deﬁnitely induce
a larger magnitude of SPB, but can prolong the duration of SPB. It wasnoted that the TKEW, instead of the TKEH, contributed to TKERT wheninﬂuencing the start timing and duration of SPB.
In addition, a 1-D physical-biological coupled model was carriedout to quantify how TKERT inﬂuenced SPB. Larger TKERT before SPB
delayed the start timing of SPB by transporting more phytoplankton
downward from the surface layer, i.e., the water column was unstable
for the accumulation of phytoplankton. When increasing/decreasing
the wind during the SPB period, more/less phytoplankton was taken
downward from the surface layer by the physical process. Meanwhile,
more/less nutrients were pumped up to the surface layer by theenhanced/weakened vertical mixing and supported more/less net
growth of phytoplankton. These two contrary processes led to the
independence of the magnitude of SPB on the TKERT during SPB.
However, larger TKERT resulted in a longer duration of SPB because of
more adequate nutrients supply by stronger vertical mixing.
The ﬁndings in this study indicate that the mechanism controllingthe interannual variations of SPB in continental shelf seas, such as theYS, is the critical turbulence concept (Huisman et al., 1999), which is

4.3. The inﬂuence of TKERT on the magnitude and duration of SPB
Three numerical experiments (CONTROL, Exp3 and Exp4) werecarried out to examine the inﬂuences of TKERT on the magnitude andduration of SPB. The model conﬁgurations of these experiments wereshown in Section 2.5.2. Based on the modeled variation of surface Chla in CONTROL, the start and end timing of SPB were calculated to be
the 104th day and the 122nd day. The average wind speeds during SPB
were increased to reach the maximum average value in the 15 years in
Exp3 and decreased to be the minimum average value in Exp4. As aresult, The TKERT in the three experiments varied with the change ofwinds (Fig. 15 (a)). The SPB processes in the three experiments wereconsequently diﬀerent in terms of magnitude and duration.
The magnitudes of SPB were 4.2 mg/m3, 3.6 mg/m3 and 4.1 mg/m3
in CONTROL, Exp3 and Exp4, respectively (Fig. 15 (b)). The magnitude of SPB seemed to be not related to the TKERT, which was alsosuggested by the data analyses in the result section. From the starttiming (the 104th day) to the peak time (the 113th day) of SPB of
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Acknowledgments
We thank NASA for providing the satellite-derived Chl-a datasetsand the atmospheric forcing data. This research is funded in part by the
National Natural Science Foundation of China under the contract of
41106007, the NSFC-Shandong Joint Fund for Marine Science
Research Centers under the contract of U1406403, the China
Postdoctoral Science Foundation under the contract of 2014M560575
and the National Basic Research Program (“973” program) of China
under the contract of 2011CB403606. Two anonymous reviewers andeditor provided detailed and constructive comments that strengthened
the manuscript.
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