Modelling the timing of major spring bloom events in the central Yellow Sea

Estuarine, Coastal and Shelf Science 113 (2012) 283e292

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Modelling the timing of major spring bloom events in the central Yellow Sea Ji-Liang Xuan a,1, Feng Zhou a, b,1, Daji Huang a, b, *, Xiao-Hua Zhu a, b, 2, Chuanxi Xing c, 3, Xiaopeng Fan a,1 a

State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, State Oceanic Administration, Bao-Chu-Bei-Lu Road 36, Hangzhou 310012, China Department of Ocean Science and Engineering, Zhejiang University, Hangzhou 310058, China c Institute for Hydrobiology and Fisheries Science, University of Hamburg, Groe Elbstrae 133, Hamburg 22767, Germany b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 6 March 2012 Accepted 14 August 2012 Available online 24 August 2012

Spring blooms observed in the Yellow Sea, which contribute the primary production in the local food chain, generally occur in the central part of the Yellow Sea (YS) in the early spring from March to April. In this paper, we use a 3-D physical ocean model and 1-D ecosystem model to explore the timing of the five major spring bloom events observed in the central YS in 2007. The results show that Sverdrup’s critical depth model can be applied to simulate the first four spring bloom events in March and April of 2007. Under the conditions when nutrients are sufficient, the timing of the spring bloom events appears to always be controlled by physical processes and the reduction of the wind speed in particular. The magnitude of the bloom events is affected by light and temperature in the euphotic layer. The correlation between the timing of the spring bloom and the reduction of the wind speed is investigated by reversal computations and linear regression, and a critical wind speed of less than 5.7 m s
1 was determined to trigger the bloom. Ó 2012 Elsevier Ltd. All rights reserved.

Keywords: algal blooms China Yellow Sea modelling physical properties hydrodynamics light

1. Introduction The Yellow Sea (YS), a shelf sea between the Chinese mainland and Korean Peninsula (Fig. 1), is one of the most productive areas in the North Pacific. Episodic spring phytoplankton blooms are often observed in the area (Hu et al., 2004; Sherman et al., 2009; Xuan et al., 2011; Zhao et al., 2011), and they contribute to the biological production in the pelagic and benthic food chains (Han et al., 2008). Some intensive algal blooms can have significant environmental and social impacts. For example, a large-scale bloom eventually aggregated over the coastal region of the YS during the spring of 2008, which significantly influenced the preparations of the 2008 Summer Olympic sailing games (Shi and Wang, 2009). Therefore, studying the algal blooms and their timing in particular in the YS and understanding the role of physical forces on the development of the bloom are greatly important.

* Corresponding author. State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, State Oceanic Administration, BaoChu-Bei-Lu Road 36, Hangzhou 310012, China. E-mail addresses: [email protected] (J.-L. Xuan), [email protected] (F. Zhou), [email protected] (D. Huang), [email protected] (C. Xing), [email protected] (X. Fan). 1 Tel.: þ86 571 81963089; fax: þ86 571 88839374. 2 Tel.: þ86 571 81963090; fax: þ86 571 88839374. 3 Tel.: þ49 0 40 428386652. 0272-7714/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ecss.2012.08.017

As the central YS is far from the coast, the influence of human activity on phytoplankton growth over the central YS is negligible. The study of the spring bloom events in this natural ocean environment mainly focuses on understanding the physical mechanisms by which the hydrodynamic environment regulates the timing and magnitude of the bloom events. The area studied in this paper is enclosed between two 70 isobaths of the central YS (Fig. 1). As the costal currents over the area flow along isobaths and are rather weak (Hsuch, 1988; Guo and Yanagi, 1998; Teague et al., 1998), land-based nutrients are rarely transported by the coastal currents to the study area, and human factors can be neglected in the central YS. Based on the analysis of numeric modelling data, field survey data and remote-sensing data, the occurrence of the spring blooms in the central YS can be identified according to the following three criteria. First, some biological models (Fasham, 1995; Obata et al., 1996; Zhao et al., 2005) show that the phytoplankton bloom occurs when the growth rate is greater than the respiration rate. Second, a critical value of the chlorophyll-a concentration in the field survey data, which is approximately 4 mg L
1, is used to identify the bloom occurrence (Zhu et al., 1993; Sun and Liu, 1999). During the cruise surveys in the YS in 2007 and 2009, five bloom sites (Fig. 1, triangles) where the chlorophyll-a concentration was higher than 4 mg L
1 were identified. The highest chlorophylla concentrations were mainly located over the central part of the YS, where the water depth is greater than 70 m. Third, a critical

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Fig. 1. Spatial distribution of the observed bloom sites and multi-annual mean sea surface chlorophyll-a in the spring in the YS. The triangles show the five bloom sites from the cruise survey data from 2007 to 2009. The colour map shows the multi-annual mean sea surface chlorophyll-a levels according to statistical analysis from the SeaWiFS data (Acker et al., 2002) from 1998 to 2009. The thin solid lines are the 30, 50 and 70 m isobaths.

value of the mean chlorophyll-a concentration in the remotesensing data, which is approximately 2 mg L
1, is used for the bloom identification (Xuan et al., 2011). The mean chlorophylla concentration in spring is calculated from multi-year (1998e 2009) remote-sensing data (colour map in Fig. 1) and shows the bloom also mainly occurs in the central YS. Various physical factors are related to the timing of the spring blooms. The surface wind is widely accepted to play an important role in the initiation of the spring bloom in shallow coastal areas, and the timing of blooms is found to have a close relationship with the reduction of the wind speed in many areas (Furuya et al., 1993; Nezlina and Li, 2003; Ueyama and Monger, 2005; Kim et al., 2007). Iriarte and Purdie (2004) showed that photosynthetic active radiation (PAR) is crucial to trigger the spring bloom in coastal areas and that the average light intensity in the water column is a fundamental factor in controlling phytoplankton growth. Furthermore, Sharples et al. (2006) suggested that the rapid growth of phytoplankton requires an environment that combines an appropriate water temperature and a degree of stratification. By using a 3-D physical-ecosystem coupled model, Hu et al. (2004) showed that the spring bloom in the YS begins when the vertical advection weakens and stratification begins to build up (in late March or early April), which indicates that water stability is crucial. Whether the phytoplankton can continuously stay in the euphotic layer is determined by water stability, and the phytoplankton growth rate can be affected by water temperature. Hence, both water stability and water temperature play an important role in the timing of the spring blooms. The relationship between primary production, water temperature and vertical stratification is described in Sverdrup’s (1953) critical depth model, which provides a basic understanding of

how the physical processes influence the phytoplankton blooms. Sverdrup’s critical depth model relies on two parameters: the upper-mixed layer depth DML and the critical depth DCR. The DML is the depth above which the water column is mixed homogeneously, and the DCR is the depth at which the net phytoplankton production equals zero. When the DML is shallower than the DCR, a bloom will occur, assuming that there are sufficient nutrients. However, many cruise surveys and numeric simulations show that the spring blooms also occur in areas with weaker vertical stratification or even no vertical stratification (Ryther and Hulburt, 1960; Ignatiades, 1979). The timing of the spring blooms may also be related to the weather conditions (i.e., wind and sunlight) and other factors, such as grazing in the upper-mixed layer (Radach and Moll, 1993; Waniek, 2003). In this paper, two significant issues associated with the spring blooms in 2007 were studied using numerical methods. The first issue addresses whether Sverdrup’s critical depth model can be applied to the central YS, and the second addresses the influence of potential causal factors, such as wind, light and temperature, on the timing of spring blooms. The objectives of the present study are to have a better understanding of the roles of the physical factors on the spring blooms in the central YS and to find an easy method to predict the timing of spring blooms just by using wind velocity. 2. Model description A 3-D model is used to simulate the general hydrodynamic regime in the central YS, including parameters needed by the biological model, such as the Richardson number and water temperature. These parameters are averaged horizontally to obtain the representative 1-D physical profiles required by the 1-D biological

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285

model and for the comparison with the observations in the central YS. This simplified structure is then transferred to the 1-D biological model to determine the net primary production and the variation of DCR. Finally, the timing of the major spring bloom events is determined by Sverdrup’s critical depth model using the outputs of the 3-D physical model and the 1-D biological model.

rate in spring 2007. Here, W is from QuikSCAT, Q is from OAFlux, and the values of other parameters are assigned to be the same as those of Simpson et al. (1978), i.e., c ¼ 4  103 J kg
1 C
1, d ¼ 0.023, ks ¼ 6.4  10
5 and a ¼ 2  10
4 C
1.

2.1. 3-D physical ocean model

The vertical 1-D biological model with a spatial resolution of 1 m per level is applied to a water column of a 70 m water depth. Together with Sverdrup’s critical depth model, the assumption of having sufficient nutrients is also made, and the model parameters used are listed in Table 1. The 1-D biological model is integrated from March 1 to May 30 in 2007 for a period of 91 d with a time step of 1 d. By definition, the DCR is the vertical depth at which the net phytoplankton production Pz from the surface to DCR equals zero (Sverdrup, 1953; Siegel et al., 2002). Pz is positively related to phytoplankton photosynthesis and negatively related to population respiration and grazing. The equation describing Pz is given below.

The MITgcm (Marshall et al., 1997; Marotzke et al., 1999) is applied with a downscaling nested scheme to the Bohai Sea and YS for the region from 117 to 127 E in longitude and 32 N to 41 N in latitude, with a horizontal resolution of 1 /12  1 /12 and with 90 vertical levels (1 m per level). In this model, the initial temperature and salinity are taken from the Editorial Board for Marine Atlas (1992), and the initial velocity is set to zero. The sea surface atmospheric forces include the daily heat flux provided by the objectively analysed air-sea fluxes (OAFlux) (Yu and Weller, 2007) and the daily wind stress provided by NASA’s Quick Scatterometer (QuikSCAT) in the spring of 2007. The open boundary conditions of the model are provided by the numerical results in 2007 of a larger model consisting of the Bohai, Yellow and East China Seas with the same horizontal and vertical resolution and with a domain of 115e139 E in longitude and 15e41 N in latitude (Fan et al., 2006). The model tidal forces of the four major tidal constituents of M2, S2, K1 and O1 are included in the open boundary, and the tidal harmonic constants are taken from the numerical results of Shum et al. (1997). The model is integrated from March 1 to May 30 in 2007 for a duration of 91 d, with a time step of 1 h to obtain the hydrodynamic regime of the physical variables in spring 2007. The mixed-layer depth DML is the main parameter calculated by the 3-D physical model. Here, we assume that DML is the depth where the Richardson Number (RN) is less than 0.25 from the sea surface because a RN less than 0.25 means a well-mixed water column (Howard, 1961; Miles, 1961). The RN, the ratio of the density gradient to the square of the velocity gradient, is a measure of vertical stability in the water column (Large et al., 1994; Durski et al., 2004). The equation for the RN is given below.

i h ih RN ¼ ð
g=rÞ$ðvr=vzÞ ðvu=vzÞ2 þðvv=vzÞ2

(1)

where g is acceleration due to gravity, r is the density of sea water and u and v are the east and north velocity components, respectively. Here, g equals 9.8 m s
2, and the density and velocities are taken from the output of the 3-D physical model. The density gradient is related to the vertical structures of temperature and salinity. The velocity gradients near the sea surface and near the bottom are related to the wind and tides, respectively. The RN is calculated at every grid of the 3-D model and with a time step of 1 h. The wind mixing effect is quantified by the equivalent wind mix depth HW. Within HW, the available turbulent kinetic energy from the wind stress is balanced with the buoyancy flux input at the surface (Simpson et al., 1978). Here, we use HW to analyse the wind mixing effect on the variation of DML in the central YS. HW has a linear correlation with the cube of the wind speed and is calculated at a time step of 1 d. The equation describing HW is given below.

HW ¼ 2cdks rs W 3 =agQ

(2)

where c is the specific heat of sea water, d and ks are two coefficients of wind mixing, rs is the air density, a is the volume expansion coefficient, W is the surface wind speed, and Q is the mean heat flux

2.2. 1-D biological model

Z Pz ¼

z 0

ðs
R
GÞdz

(3)

The phytoplankton growth rate s gives a positive contribution to Pz, varies with diurnal fluctuations and depends on the vertical position in the water column (Eppley, 1972; Platt et al., 1980). s is given below, which is a function of temperature and PAR.



s ¼ gp emp T e
g1 I 1
e
g2 I



(4)

where T is the temperature, I is the PAR in the water column, gp is the maximum phytoplankton growth rate, mp is a temperaturecontrolled parameter, g1 is a light-controlled parameter, and g2 is initial slope of the ProductioneIrradiance (PeI) curve. The PAR is calculated in terms of surface short-wave radiation (SSR) from OAFlux (Yu and Weller, 2007) and cloud thickness from ISCCP (Schiffer and Rossow, 1983). The PAR in water column I is an exponential function of depth.

I ¼ I0 ð1
g3 $CÞe
kz

(5)

where I0 is the SSR, g3 is the sea surface reflectivity, C is the cloud thickness, and k is the light attenuation parameter. Using the cruise survey data from 2007 to 2009, we revised the parameter k using an exponential curve fitting method, and the results show that k ¼ 0.2308 m
1 in the central YS. Values of g3 and C are given in Table 1. Table 1 Model parameter values used for simulating the spring bloom in the central YS. Parameter

Symbol Units

Maximum phytoplankton gp growth rate Temperature-controlled mp parameter Light limited parameter g1

d
1 
1

C

2


1

2


1

m W

References

1.45

Eppley, 1972

0.063

Eppley, 1972

d
1

Platt et al., 1980 0.0018 Platt et al., 1980 0.08 Sheng et al., 2003 0.005 Ivlev, 1945

d
1

0.62

Initial slope of PeI curve

g2

m W

Sea surface reflectivity

g3

m
1

Maximum phytoplankton mp respiration rate Zooplankton max gz grazing rate Zooplankton grazing 3 parameter

Values

0.054

mmol
2 m
6 d
1 3.0

Fasham, 1995 Fasham, 1995

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The phytoplankton respiration rate R gives a negative effect of community respiration to Pz and is given as an exponential function of temperature (Ivlev, 1945).

R ¼ mp emp T

3.1. Water stability and temperature

(6)

where mp is the maximum phytoplankton respiration rate and mp is a temperature-related parameter. Values of mp and mp are given in Table 1. The grazing rate G gives a negative effect of grazing on Pz and has a specific correlation with the phytoplankton resources (Fasham, 1995).

  G ¼ gz 3 rz2 = gz þ 3 rz2

(7)

where gz is the maximum grazing rate, 3 is the grazing parameter and rz is the resource that is related to s with the empirical equation (8) (Fasham, 1995). The values of the grazing parameter in equation (7) are listed in Table 1.

rz ¼ 0:45s
0:03

the timing of the spring bloom according to Sverdrup’s critical depth model.

(8)

2.3. Model coupling The 3-D physical model and 1-D biological model run separately with a one-way coupling of data. The hydrodynamic parameters needed by the 1-D biological model with a time step of 1 d, such as water temperature and Richardson Number, are provided by the 3D physical model. We divided this coupling into three processes: data average, data transfer and data application. The 3-D physical model outputs and 2-D remote-sensing data are averaged horizontally for the needs of the 1-D biological model. The averaged 3-D physical model outputs include RN and temperature, and the averaged remote-sensing data include wind, SSR and cloud thickness. To obtain an area of representative values in the central YS suitable for the 1-D biological model, the data are first regionally averaged over the area of the central YS, which is between the two 70 isobaths in the region of 120e126 E, 33e 37.5 N (Fig. 1), and then, the 1 h interval data are further temporally averaged over 1 d to have daily vertical column data. Some of the averaged data are used to force the 1-D biological model, such as the temperature, SSR and cloud thickness, which are transferred according to the model computing processes. First, the SSR and cloud thickness data are transferred according to equation (5) to compute the biological parameter PAR. The output data of the PAR are vertically layered by 90 levels (1 m per level), which are the same levels as those in the biological model grid. Second, the temperature and PAR data are transferred according to equation (4) to compute the phytoplankton growth rate s. Third, the temperature data are also transferred according to equation (6) as the major parameter for computing the phytoplankton respiration rate R. The 1-D biological model outputs and the averaged 3-D model data are finally applied to Sverdrup’s critical depth model. This application is implemented in two steps: (1) the timing of the spring bloom events is determined by the condition where DML is less than DCR; and (2) the role of the physical parameters (i.e., wind, light and temperature) on the spring blooms in the central YS are analysed. 3. Results The bloom-related simulation outputs in the spring of 2007 include the water stability and temperature from the 3-D physical model, the DCR from the 1-D biological model, and the prediction of

The simulation parameter RN calculated with equation (1) is used to describe the water stability. Fig. 2a shows the temporal variations of the vertical structure of RN in the central YS in the spring of 2007. Setting the RN to 0.25 as the dividing line, the water stability has a 3-layer vertical structure: an upper-mixed layer, a lower-mixed layer and an intermediate stable layer. The upper-mixed layer is above 30 m, where the RN is less than 0.25, and the bottom line (Fig. 2a, the black thick line above 30 m) indicates the mixed-layer depth DML. Within the upper-mixed layer, the phytoplankton is vertically well mixed. This uppermixed layer is not influenced by tides, as the RN is related to the vertical shear of velocity (equation (1)), but the vertical shear of velocity caused by tides is small in this layer. The lower-mixed layer is below 60 m, where the RN is also less than 0.25. This layer is mainly induced by tidal mixing with a mechanism explained by Simpson and Hunter (1974). The intermediate stable layer is between the DML and the lower-mixed layer, where the RN is greater than 0.25. The DML, which is the bottom depth of the upper-mixed layer, plays an important role in the critical depth model. In March and April of 2007, there were four periods during which the DML became evidently shallow and then deepened: March 4e15, March 16e27, April 2e12, and April 22e30. During these four periods, DML varied, first shallowing rapidly from nearly 20 m to 10 m and then deepening rapidly to 20 m. During the two periods in March, the minimal value of DML was approximately 10 m, which occurred on March 13 and 19. During the two periods in April, the minimal value of DML was approximately 5 m, which occurred on April 4 and 27. In May, the DML is generally shallow (<10 m) and becomes increasingly shallower until the end of May. Water temperature is another important parameter for phytoplankton growth. Fig. 2b shows the temporal variation of the vertical water temperature in the spring in the central YS. The results indicate that the temperature ranges from 8 to 11  C in March and April. Warm temperatures occur from April 5 to 12 (approximately 10  C) and from April 26 to 29 (>10  C), when the DML is shallow. The temperature increases constantly in May, ranging from 12 to 15  C. In addition, the vertical structure of the temperature shows that the deep water is warmer than the surface water during MarcheApril and that the situation reverses in May, which reflects the transition of the temperature structure from winter to summer. A comparison between the thermocline depth (Fig. 2b the dashed line) and DML shows the differences between the mixed layer and mixing layer in the central YS. The differences between the mixing layer (DML) and mixed layer (thermocline depth) have been long recognised (Brainerd and Gregg, 1995), but both terms are very usually confounded in the usage of the critical depth model (Haren et al., 1998; Lucas et al., 1998; Siegel et al., 2002; Waniek, 2003). Here, the thermocline depth is the range from the sea surface to the depth of the maximum temperature gradient in the water column, but no thermocline condition occurs when the maximum temperature gradient is less than 0.2  C m
1. The results show that thermocline depth is deeper than the DML from March 5 to 27 and from May 7 to 30 and that no thermocline exists during the other days of spring 2007, indicating that the thermocline depth was unable to describe the mixing depth in the spring in the central YS. To determine the main physical factors that cause the time transitions in DML, the variations in DML, the wind mixing depth HW

J.-L. Xuan et al. / Estuarine, Coastal and Shelf Science 113 (2012) 283e292

287

Fig. 2. Time-depth contours of the RN (a) and temperature (b) in the spring in the central YS. The arrows indicate the four shoaling events of DML: March 4e15, March 16e27, April 2e12, and April 22e30. The dashed line indicates thermocline depth.

and wind speed (Fig. 3) were compared. DML and HW have the same pattern in March and April, which indicates that wind mixing is also a main factor affecting the variation of DML in March and April, as it worked on the HW. Further comparison between the DML and wind speed in March and April shows that the wind speed is approximately lower than 5.7 m s
1 in the four time periods when the DML has dome-shape variations. However, the variation of DML is very different from that of HW in May. DML varied little at the depth of 4e7 m, whereas HW varied greatly at the depth of 3e20 m. This difference in May indicated that wind mixing alone is not a major factor affecting the DML but rather that the constant warming of the sea water (Fig. 2a) probably could affect the DML.

1−Mar 0

3.2. The phytoplankton DCR DCR is calculated using equation (3), which describes the critical water thickness where the integrated net phytoplankton production from the surface is zero. Fig. 4a shows the vertical distribution of DCR in spring in the central YS. DCR ranges from 8 to 20 m, and the temporal variation of DCR is rather different within the three months. In March, DCR increases first from 8 to 15 m and then remains between 13 and 15 m, with relatively small changes. In April, DCR has two apparent peak values, which are approximately 20 m on April 6 and 17 m on April 28. In May, DCR ranges between 9 and 12 m and shows relatively small changes.

11−Mar 21−Mar 31−Mar 10−Apr 20−Apr 30−Apr 10−May 20−May 30−May

Depth (m)

10 20 30 H

W

40

DML

50

a

10

−1

Wind (m s )

8 6 −1

4

5.7 m s

2 0 1−Mar

b 11−Mar 21−Mar 31−Mar 10−Apr 20−Apr 30−Apr 10−May 20−May 30−May

Fig. 3. Variations in DML (solid line in a), H (dashed line in a) and wind speed (b) in the spring in the central YS.

J.-L. Xuan et al. / Estuarine, Coastal and Shelf Science 113 (2012) 283e292 0

15

5 10 15 20

a

25

5

16

0.5

14

0.4

12 10

b

0.3 0.2

c

8

d

0.1

0.35

0.06

−1

G (d )

0.3

R (d−1)

10

0

σ (d−1)

Temperature (°C)

Light (W m−2)

Critical Depth (m)

288

0.25 0.2

e

0.15 1−Mar 11−Mar 21−Mar 31−Mar 10−Apr 20−Apr 30−Apr 10−May 20−May 30−May

0.04 0.02

f

0 1−Mar 11−Mar 21−Mar 31−Mar 10−Apr 20−Apr 30−Apr 10−May 20−May 30−May

Fig. 4. Variations of DCR (a), light (b), temperature (c), phytoplankton growth rate (d), respiration rate (e) and grazing rate (f) in the water column of the central YS.

The physical mechanism that affects DCR is investigated in the following analysis. According to equations (4)e(8), both light and temperature are the most significant physical factors. The average temporal variations of light, temperature, phytoplankton growth rate (s), respiration rate (R), and grazing rate (G) in the water column are shown in Fig. 4. Comparisons between DCR and the above five parameters show the following: (1) In March, the DCR rapidly deepens with an increase in the light intensity. Moreover, the low light (PAR < 10 W m
2) and temperature (<10  C) restricted the DCR from going below 15 m. (2) In April, the DCR increases to two peak values on April 6 and April 28 (20 m and 17 m, respectively) with a higher light intensity (SSR > 10 W m
2) and temperature (z10  C). On April 6 and April 28, the phytoplankton grows rapidly, but respiration and grazing are not significant, which cause the deepening of DCR. (3) In May, the DCR is maintained at the depth of approximately 12 m. The increase in the light and temperature leads to a high phytoplankton growth rate, but respiration and grazing are also greatly increased, which limits the DCR at the depth of approximately 12 m. 3.3. Timing of the spring bloom According to Sverdrup’s critical depth model, the time when the DML (solid line, Fig. 5) is shallower than the DCR (dotted line, Fig. 5)

Depth (m)

1−Mar 0

11−Mar

21−Mar

31−Mar

10−Apr

can be considered to coincide with the timing of the spring bloom events (grey part, Fig. 5). The results show that there are five time segments for the spring bloom occurrence, i.e., March 11e14, March 18e24, April 4e12, April 25e29 and May 1e30. Furthermore, the magnitude of the five bloom events is quantitatively described by the average phytoplankton growth rate (s). Based on the fact that phytoplankton is mainly distributed within the DML and is vertically well mixed, the calculation of the average s is only from the surface to the DML. In general, the average s will grow exponentially when illumination increases are accompanied by an increase in temperature. However, a decrease in DML would cause the phytoplankton to grow mainly at the sea surface, where the production is higher because the light intensity is greater, which would make the average s greater. Further analysis indicates that the magnitude of the bloom events is affected by the light and temperature exposure in the upper layer. The influence of temperature on the average s is not obvious when the DML is set as the mean depth of 15 m in the spring (Fig. 6 Left), and in particular, the temperature ranging from 8 to 10  C in March and April causes a small change in the average s. However, the DML greatly influences the growth rate when the temperature is set to the mean value of 10  C in the spring (Fig. 6 Right). When the blooms occur in March (time segments A and B), a DML of 12 m and a surface light intensity less than 300 W m
2 cause the average s to be only slightly higher than 1 d
1. However, although the DML in April and May (time segments CeE) is approximately 7 m, the light intensity is less than 300 W m
2, and the phytoplankton growth rate reaches a maximum of approximately 1.45 d
1. 20−Apr

30−Apr

10−May

20−May

30−May

10 20 30

DML

A

B

C

D

E

DCR DML< DCR

40 Fig. 5. Application of Sverdrup’s critical depth model, and the timing of the spring bloom events. The solid line is the DML, the dashed line is DCR, and the grey parts of AeE represent the five time segments of the spring bloom.

J.-L. Xuan et al. / Estuarine, Coastal and Shelf Science 113 (2012) 283e292

1.5

1.5

C 16 ° 14 °C °C 12

C 10 °

C

5m

8 °C

0.5

1

A

B

E

15 m 20 m

0.5

T=10 °C

Dm=15 m 0

D

0m

1

σ (d−1)

σ (d−1)

1

0m

289

0

100

200

300

400

0

0

100

I (W m−2)

200

300

400

I (W m−2)

0

0

Fig. 6. The relationship between the light intensity and growth rate for various temperatures (left) and mixed-layer depths (right).

According to the above arguments, small magnitude bloom events are likely to occur in the first two periods from March 11 to 14 and March 18 to 24, strong bloom events will occur in the latter two periods from April 4 to 12 and April 25 to 29, and the conditions of the bloom are satisfied during all of May. 4. Discussion 4.1. The applicability of Sverdrup’s critical depth model

−1

Chlorophyll a (μg L )

Sverdrup’s critical depth model has been a useful tool for interpreting phytoplankton population responses to changing physical dynamics in the upper ocean, such as in the North Atlantic and western North Pacific Oceans (Obata et al., 1996). However, some physical mechanisms, such as a strong tide and low-salinity plume, might cause Sverdrup’s critical depth model to become invalid in shallow coastal waters (Simpson et al., 1990, 1991). Behrenfeld (2010) also pointed out that this model was not suitable for understanding bloom dynamics in the subarctic Atlantic in the absence of spring mixed-layer shoaling. To discuss the applicability of Sverdrup’s critical depth model in the central YS, a comparison between the model results and observational results is performed. Using Sverdrup’s critical depth model, five bloom events are identified in the central YS in the spring of 2007, including two small bloom events from March 11 to 14 and March 18 to 24, two strong bloom events from April 4 to 12 and April 25 to 29, and a continuous bloom during all of May. The timing and magnitude of the bloom events in the spring of 2007 are also inferred from the in situ and remote-sensing chlorophyll-a data. Fig. 7 shows the variation in chlorophyll-a in the spring of 2007 in the central YS from the observational data. The satellite observation is strongly affected by the cloud cover, and the remote-sensing chlorophyll-a data are not always effective because data are missing over the central YS. When the missing spatial data

are less than 40% of the total data in the central YS, we regard these data as effective remote-sensing data (Fig. 7, blue bar). Otherwise, referential remote-sensing data (Fig. 7, red bar) is defined when the missing data are more than 40% of the total data. In the same areas, field survey data (Fig. 7, *) were obtained from April 1 to 23 in 2007. Fig. 7 shows that the magnitude of the chlorophyll-a levels is generally low in March; however, the chlorophyll-a levels are relatively higher in the two periods from March 11 to 13 and March 18 to 19. In April, the results of the in situ and remote-sensing chlorophyll-a data show that the average chlorophyll-a reached 6 mg L
1 during April 4e6. Moreover, the observed chlorophylla levels by the remote-sensing data are greater than 2 mg L
1 on April 28. In May, intermittent high values of chlorophyll-a are observed by the remote-sensing data on May 8, 13, 19 and 30: 1.0, 1.7, 1.3 and 0.7 mg L
1, respectively. However, these values are smaller than those observed in April. Based on the comparison between the simulation and the observed results, Sverdrup’s critical depth model could be applied to simulate the first four bloom events in March and April of 2007 but failed in the application to the fifth bloom in May 2007. The timing and magnitude of the bloom events simulated in March and April of 2007 are in accordance with the observed bloom events, indicating that the influence of the hydrodynamic conditions is more important than other factors at these times. However, in May, the timing of the bloom is incompatible with the observation data. The simulation result shows that DML is less than DCR during all of May, indicating that the bloom would continually recur, but no continuous bloom event is observed. Kelly-Gerreyn et al. (2004) noted that the seasonal stratification in the offshore area obstructed the supply of nutrients from the lower layer to the upper water. We believe that this restriction of nutrients plays an important role in preventing a continuous bloom during May 2007. However, the biological model result might be different from the reality of the coastal system due to the extreme 1-D

6 Effective RS Data Referential RS Data FS Data

5 4 3 2 1 0 1−Mar

11−Mar

21−Mar

31−Mar

10−Apr

20−Apr

30−Apr

10−May

20−May

30−May

Fig. 7. Variation of chlorophyll-a in the spring of 2007 in the central YS. Bar and * indicate the data from the remote-sensing and field surveys, respectively. The blue bars indicate the effective remote-sensing data for which the spatial data missing in the central YS are less than 40%, and the red bars indicate the referential remote-sensing data for which the spatial data missing in the central YS are greater than 40%.

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3

Chlorophyll=−0.0048*W +2.6633 CorCoef=−0.84, 76 Data Points

5 4 3 2 1 0

6 3

Chlorophyll=−0.0079*W +3.6426 CorCoef=−0.80, 79 Data Points

5

at 95% significant level

−1

Chlorophyll a (μg L )

b

6

Chlorophyll a (μg L−1)

a

at 95% significant level

4 3 2 1

0

100

200

300

400

500

0

600

0

3 −3

Chlorophyll=−0.0011*W3+1.6050 CorCoef=−0.48, 88 Data Points

4 3 2 1 0

300

400

500

600

6 Chlorophyll=−0.0065*W3+3.2096 CorCoef=−0.81, 155 Data Points

5

at 95% significant level

−1

Chlorophyll a (μg L )

d

6 5

200

Third−power wind Speed (m s )

Chlorophyll a (μg L−1)

c

100

3 −3

Third−power wind Speed (m s )

at 95% significant level

4 3 2 1

0

100

200

300

400

500

600

3 −3

Third−power wind Speed (m s )

0

0

100

200

300

400

500

600

3 −3

Third−power wind Speed (m s )

Fig. 8. Linear regression between the chlorophyll-a and cube of the wind speed in different time segments during March 2007 (a), April 2007 (b), May 2007 (c), and March and April 2007 (d).

simplification, although the model result is compatible with the observational data in March and April of 2007 in the central YS. In the 1-D approach, the phytoplankton dynamics that could be caused by advections and upwelling is not considered. This approach would probably miss some small-scale blooms in the central YS, such as the phytoplankton patchiness in the 3-D model of Hu et al. (2004). The 1-D biological model could be easily used in the coastal areas, especially when we lacked some basic data for the nutrients and light in the sea water. 4.2. The role of wind on the timing of the spring bloom A critical value of 5.7 m s
1 for wind speed is determined in the numerical study of the spring bloom events in 2007, which shows that a wind speed of less than 5.7 m s
1 is a necessary condition to affect the timing of the spring bloom. The wind speed at the beginning and end of the first four blooms in March and April of 2007 are analysed. The wind speed at the beginning of the first four blooms was reduced to a threshold of 5.82 m s
1, 5.85 m s
1, 5.98 m s
1, and 4.68 m s
1, whereas the wind speed at the end of the four blooms increased to a threshold of 5.79 m s
1, 5.70 m s
1, 6.10 m s
1, and 5.72 m s
1. These wind thresholds were close, and their mean value of 5.7 m s
1 was used to predict the timing of the spring blooms. Two physical mechanisms indicated that weak wind is the driving force to the timing of the spring blooms: (1) Wind speed controls the variation of DML, and a weak wind reduces the DML. (2) The changes of DML affected the timing of the blooms more than that of DCR because the variation in DML is much larger than that of DCR over the entire spring season. However, the

lack of variability in the computed DCR might be due to the simplification that we did not consider the phytoplankton shelf-shading coefficient, which is an important coefficient in terms of the light attenuation (Fasham, 1995; Azumaya et al., 2001). The correlation between the spring bloom and wind is further analysed by the remote-sensing data of chlorophyll-a and the wind. Here, we used linear regression between chlorophyll-a and the cube of wind speed according to equation (2) and chose the effective chlorophyll-a data that covered more than 60% of the central YS. The following results are observed: (1) in March (Fig. 8a) and April (Fig. 8b), there is a high negative correlation between the spring bloom and wind, and the correlation coefficient was less than
0.8 at 95% significance level; (2) in May (Fig. 8c), a low correlation between the spring bloom and wind speed indicates that the wind restriction is not the main factor affecting the occurrence of the spring bloom; (3) the combined regression of March and April (Fig. 8d) shows a correlation between chlorophylla and wind speed, as shown in equation (9). Using the critical value of 2 mg L
1 for chlorophyll-a in the remote-sensing data, we calculated the wind speed in equation (9), which is close to the simulated value of 5.7 m s
1.

Chlorophyll ¼
0:0065W 3 þ 3:2096

(9)

5. Conclusions Using the 3-D physical ocean model MITgcm and a simplified 1D biological model, Sverdrup’s critical depth model can be applied for the first four spring bloom events in the central YS in 2007. The

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results suggest that Sverdrup’s critical depth model may be applied to the first spring bloom events annually in the central YS. We determined that the initial time of the spring bloom in 2007 started on March 11 and that only a small amount (s was slightly larger than 1 d
1) of phytoplankton production occurred at this time. The simulated timing and magnitude of the first spring bloom event were consistent with the results obtained from the in situ and remote-sensing chlorophyll-a data. Sverdrup’s critical depth model may also be useful in predicting the recurrence and even the third and fourth spring bloom events, and these events were confirmed by comparing the spring bloom events in 2007 from the simulated and observed results. Therefore, the timing of the spring bloom events appears to be controlled by physical processes when nutrients are sufficient in the upper waters or when they are supplemented from the bottom water. We observed a simple correlation between the timing of the spring bloom and a reduction in the mean wind speed. DML, which is mainly affected by changes in the wind speed, has a more important role on the timing of the spring bloom than DCR because the variation in DCR is much smaller than in DML over the entire spring season in the extremely simplified condition of our biological model. The model result and remote-sensing data in March and April of 2007 both showed that when the wind speed is less than 5.7 m s
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