PUBLICATIONSJournal of Geophysical Research: Biogeosciences
RESEARCH ARTICLE
10.1002/2016JG003331
Key Points:• Typhoon Damrey increased thephytoplankton biomass in both thesurface and subsurface layers• Phytoplankton biomass experienceddifferent changing processes in the
two layers• Contributions of physical andbiogeochemical processes weredifferent in the two layers

Correspondence to:J. Shi,shijie@ouc.edu.cn

Citation:
Pan, S., J. Shi, H. Gao, X. Guo, X. Yao, and
X. Gong (2017), Contributions of physical and biogeochemical processes tophytoplankton biomass enhancementin the surface and subsurface layers
during the passage of Typhoon Damrey,
J. Geophys. Res. Biogeosci., 122,
doi:10.1002/2016JG003331.
Received 11 JAN 2016
Accepted 4 JAN 2017
Accepted article online 9 JAN 2017

Contributions of physical and biogeochemical processes tophytoplankton biomass enhancement in the surface andsubsurface layers during the passage ofTyphoon Damrey
Shanshan Pan1,2, Jie Shi1,2
Xiang Gong1,2, Huiwang Gao1,2, Xinyu Guo1,2,3, Xiaohong Yao1,2

, and

1

Key Laboratory of Marine Environment and Ecology, Ministry of Education, Qingdao, China, 2College of Environmental
Science and Engineering, Ocean University of China, Qingdao, China, 3Center for Marine Environmental Studies, Ehime
University, Matsuyama, Japan

Abstract In this study, a one-dimensional physical-biogeochemical coupled model was established toinvestigate the responses of the upper ocean to Typhoon Damrey in the basin area of the South China
Sea. The surface chlorophyll a concentration (Chl a) increased rapidly from 0.07 to 0.17 mg m3 when thetyphoon arrived and then gradually reached a peak of 0.61 mg m3 after the typhoon’s passage. The
subsurface Chl a decreased from 0.34 to 0.17 mg m3 as the typhoon arrived and then increased gradually to
0.71 mg m3. Analyses of model results indicated that the initial rapid increase in the surface Chl a and thedecrease in the subsurface Chl a were caused mainly by physical process (vertical mixing), whereas thesubsequent gradual increases in the Chl a in both the surface and subsurface layers were due mainly tobiogeochemical processes (net growth of phytoplankton). The gradual increase in the Chl a lasted for longer
in the subsurface layer than in the surface layer. Typhoon Damrey yielded an integrated primary production(IPP) of 6.5 × 103 mg C m2 (~14% of the annual IPP in this region).

1. Introduction
Typhoons are intense atmospheric events with strong effects on the physical and biogeochemical processes
in oceans [Tang et al., 2014; Guan et al., 2014; Yang et al., 2015]. Many studies have suggested that typhoons
can enhance the chlorophyll a concentration (Chl a) in the upper water layer, as well as causing phytoplankton blooms via nutrient pumping to the surface [Zheng and Tang, 2007; Shan et al., 2014; Zhao et al., 2015].
Consequently, typhoons can promote marine primary production and associated carbon ﬁxation [Lin et al.,
2003; Sun et al., 2010]. Moreover, typhoons can strengthen ocean-atmosphere CO2 exchange and hence
year-to-year variability of CO2 ﬂuxes over the subtropical oceans [Bates et al., 1998]. However, due to therough sea conditions associated with typhoon events, only a few shipboard observations are available tostudy typhoons and their oceanic impacts [Zheng and Tang, 2007; Zhao et al., 2009; Ye et al., 2013]. In addition,observations collected by moorings during typhoon events can only be captured by chance [Dickey et al.,
1998; Zedler et al., 2002; Guan et al., 2014; Yang et al., 2015]. Thus, the responses of Chl a have been analyzed
mainly at the surface based on satellite data [Babin et al., 2004; Tang et al., 2004a; Walker et al., 2005; Zhao
et al., 2008; Zhao et al., 2013].

©2017. American Geophysical Union.
All Rights Reserved.

PAN ET AL.

Typhoon Kai-Tak passed over the South China Sea (SCS) and triggered a 30-fold increase in the surface Chl
a in July 2000 [Lin et al., 2003]. In the central Arabian Sea, the surface Chl a increased from ~0.4 mg m3 up
to 4 mg m3 over a large area due to a moderate tropical cyclone in December 1998 [Naik et al., 2008]. The
Chl a increased more than 20-fold after the passage of typhoon Hagibis in the middle of the SCS in
November 2007 [Sun et al., 2010]. Satellite data indicate that a signiﬁcant increase of Chl a by 0.8 mg m3
was associated with hurricane Igor over the adjacent Grand Banks of Newfoundland in September 2010
[Han et al., 2012]. The fast-moving and weak tropical storm Washi enhanced the surface Chl a by ~20% overthe continental shelf southeast of the SCS in July 2005 [Zhang et al., 2014]. The contributions of typhoons toocean primary production have also been estimated based on satellite data. Typhoon Kai-Tak contributed
2–4% of the annual primary production in the SCS [Lin et al., 2003]; Typhoon Hagibis contributed up to 30%[Sun et al., 2010].

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Subsurface chlorophyll a maximum (SCM) has been observed frequently in tropical and subtropical oceans
throughout the whole year, while it occurs during the warm season in temperate ocean zones. The subsurface layer has been reported to be the most productive layer among the whole water column in tropicaland subtropical oceans throughout the year and in temperate ocean zones during summer [Gong et al.,
2000; Liu et al., 2002; Ardyna et al., 2013; Gong et al., 2015]. However, satellite data can only indicate the
distribution of Chl a in the surface layer. Thus, little information has been documented about the
responses of the subsurface Chl a to typhoons and the mechanisms involved. Field observations conducted 10 days after typhoon. Observations conducted ten days after the typhoon Nuri in 2008 showedthat there was also a phytoplankton bloom in the subsurface layer following the surface bloom [Yeet al., 2013]. It is likely that the subsurface phytoplankton bloom induced by typhoons may be different
from that in the surface layer in terms of its duration and magnitude.
The mechanisms involved in the impacts of typhoons on phytoplankton blooms and primary production
may change with region and depth. Strong vertical mixing, upwelling, and even high rainfall are inducedby typhoons [Tang et al., 2014]. For example, Typhoon Damrey, which occurred in the SCS during
September 2005, triggered an offshore surface phytoplankton bloom due to increased nutrient levels asa result of vertical mixing and upwelling, and a nearshore bloom because of rainwater discharge and seaward advection by a typhoon-induced current [Zheng and Tang, 2007]. Zhao et al. [2015] found that the
low Chl a in the offshore area increased supported by the addition of nutrients by upwelling and verticalmixing. Usually, vertical mixing is suggested to be the primary mechanism for cooling the ocean below thetyphoons [Price, 1981; Knaff et al., 2013]. The vertical mixing is typically 10 times more effective at coolingthe upper ocean than the wind-driven upwelling [Jacob et al., 2000; Cione and Uhlhorn, 2003]. The lastingtime of upwelling is typically half a day, which is shorter than that of vertical mixing, whose effects last for
5–10 days [Price et al., 1994].
Satellite-based and shipboard data have demonstrated that typhoon-induced vertical mixing canenhance the surface Chl a. One cause of the rapid growth of surface phytoplankton is the biogeochemical response to the supply of nutrients from deep layers. Another cause is the physical entrainment ofphytoplankton from the subsurface layer where the SCM is formed. However, it is unclear whether theobserved increase in Chl a is due to the supply of nutrients or the physical entertainment of phytoplankton from the subsurface, or both processes [Walker et al., 2005; Ye et al., 2013]. Moreover, it is logistically
difﬁcult and expensive to quantify the contributions of these two processes to the change of surface Chl
a by only observations. Shibano et al. [2011] showed that without biogeochemical processes, the increasein Chl a in a numerical model was much smaller than observation, indicating that the biogeochemical
processes are essential factors related to surface phytoplankton increases. However, they did not quantify
the contributions of physical and biogeochemical processes to the increases in thesurface phytoplankton.
The SCS is the largest marginal sea in the western Paciﬁc (Figure 1), with an average depth of 1200 m anda total area of ~3.5 × 106 km2 [Wang et al., 2009; Yang et al., 2002]. The upper water is permanently stratiﬁed and oligotrophic in the basin area due to strong solar radiation throughout the year. In addition,a signiﬁcant phytoplankton biomass is generally observed in the subsurface, which is the most productive
layer [Gong et al., 2000; Liu et al., 2002; Gong et al., 2015]. The primary production depends mainly on the
supply of nutrients from deeper nutrient-rich water to the euphotic zone by turbulent vertical transport[Tang et al., 2004b]. The SCS is located in an area with a high frequency of tropical cyclones [Ha et al.,2014; Li and Zhou, 2014] and more than 10 typhoons pass through this region each year [Wanget al., 2007].
In order to quantify the contribution of vertical mixing strengthened by Typhoon Damrey in 2005 to theenhancement of phytoplankton biomass, a one-dimensional (1-D) vertically resolved physicalbiogeochemical coupled model was established in the stratiﬁed basin area of the SCS. After describing the
responses of the vertical proﬁles of temperature, nutrients, and Chl a, the contributions of physical and biogeochemical processes to the changes of phytoplankton biomass in different water layers were quantiﬁed.
Moreover, the contributions of Typhoon Damrey to the vertical transport of nutrients and ocean primary production were calculated and compared with other studies. The potential long-term impacts of typhoons werealso discussed.

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Figure 1. Topography of the northern South China Sea and the track of Typhoon Damrey. The locations of SEATS (18°N,
116°E), Station A (19.3°N, 114°E), and Station B (19°350 N, 112°E) are denoted by the star, triangle, and rectangle, respectively. The center position of the typhoon every 6 h is indicated by gray points. The dates beside the typhoon center were at
00:00 h on each day. All dates and times are in universal time coordinated.

2. Methods
2.1. Model Description
The 1-D model comprises a hydrodynamic module and a biogeochemical module. The hydrodynamic module provides the water temperature and diffusivity coefﬁcient to the biogeochemical module.
The hydrodynamic module is based on the 1-D Princeton Ocean Model [Blumberg and Mellor, 1987]. The
embedded level-2.5 Mellor-Yamada turbulent closure model is used to calculate the vertical viscosity and diffusivity. However, the calculated mixed layer is always warmer and shallower than observations [Martin,
1985]. To address this problem, turbulent mixing is ampliﬁed by considering the effects of waves [Zhang,2003; Hu et al., 2004; Huang and Qiao, 2010; Huang et al., 2011]. In our model, we use a simple parameterized
scheme based on the descriptions given by Hu et al. [2004] and Zhang [2003]. The surface wave-induced vertical kinematic viscosity (Kwm) and diffusivity (Kwh) are added to those calculated by the Mellor-Yamada turbulent closure model. Kwm and Kwh are given by
2k 2 3 3 gz
δβ W e 2 2 ;
g
β W

(1)

K wh ¼ PK wm ;(2)

K wm ¼

2where k = 0.4 is von Karman’s constant, g = 9.8 m s is the gravitational acceleration, δ = 0.1 is the wave
steepness, β ¼ Wc < 1:0 is the wave age, c is the wave velocity (m s1), W is the surface wind speed (m s1),
z is the vertical coordinate positive upward (m), and P is a dimensionless coefﬁcient related to the
Richardson number, where P = 0.1 [Hu et al., 2004; Zhang, 2003].
The biogeochemical module is simpliﬁed from the biomass-based European Regional Sea Ecosystem Model[Baretta et al., 1995; Vichi et al., 2004]. As shown in Figure 2, 10 state variables are included: three nutrient elements (dissolved inorganic nitrogen (DIN), dissolved inorganic phosphate (DIP), and dissolved silicate (DSi)),phytoplankton, bacterioplankton, three related types of zooplankton (mesozooplankton, microzooplankton,
and heterotrophic nanoﬂagellates), and organic matter (in dissolved form (DOM) and particle form (detritus)).
Phytoplankton absorbs nutrients for photosynthesis, and it is consumed by mesozooplankton and microzooplankton. Bacterioplankton feeds on organic matter (DOM and detritus), and it is grazed by heterotrophic
nanoﬂagellates. Mesozooplankton is at the highest trophic level in the model, and it feeds on phytoplankton
and microzooplankton. DOM is produced by the excretion of phytoplankton, bacterioplankton, microzooplankton, and heterotrophic nanoﬂagellates. Detritus is produced by the fecal pellets of zooplankton andthe dead remains of phytoplankton and zooplankton. DIP and DIN are recycled from DOM and detritus,

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whereas DSi is released only fromdetritus. The parameters used in
the model are based on thosereported in previous studies, andthey are listed in Tables 1 and 2.
2.2. Model Conﬁguration andData Sources
The water depth in the model is set
to 1200 m, which is the average
depth of the SCS, thereby representing the permanent stratiﬁedbasin area. There are 40 layers in
the vertical direction. In the basin
Figure 2. Schematic view of the biogeochemical module.
area of the SCS, the seasonal variation and responses to typhoons
occur mainly in the upper layers [Tseng et al., 2005]. Thus, there are 35 layers in the upper 150 m depth
and ﬁve layers below 150 m depth in our model. Both the physical and biological variables are calculated
simultaneously at a time step of 216 s.
The model is forced by wind and heat ﬂuxes. The air-sea net heat ﬂux has four components. Net longwave
radiation ﬂux and net short wave radiation ﬂux were derived from National Centers for Environmental
Prediction (NCEP) reanalysis data sets (http://www.esrl.noaa.gov/psd/data/gridded/reanalysis/). Sensible
heat ﬂux and latent heat ﬂux are calculated by using the COARE 2.0 bulk ﬂux algorithm [Fairall et al., 1996]
based on the air temperature, sea surface temperature (SST), relative humidity, air pressure, and wind speed.
The sea surface salinity (SSS) was also derived from NCEP reanalysis data sets.
The numerical simulations comprised two cases, which we refer to as CONTROL and TYPHOON. In CONTROL,
we simulated climatological seasonal cycles in the ecosystem at the South East Asia Time Series Study (SEATS)
station (18°N, 116°E) (Figure 1). Wind forcing, SSS, net longwave radiation ﬂux, and net short wave radiation
ﬂux were obtained from NCEP monthly long-term mean data collected between 1981 and 2010 at the SEATS
Table 1. General Parameters in the Model and Their Sensitivities

PAN ET AL.

Parameter

SymbolValue

Sensitivity

Fraction of photosynthetically available radiation ()1Optical extinction coefﬁcient for pure water (m )1
3 1
Optical extinction coefﬁcient for phytoplankton (m (mg C m ) )1
Settling velocity of particulate detritus (m d )Parameters of Phytoplankton
Maximum speciﬁc photosynthetic rate (per day)
Characteristic Q10 coefﬁcient3
Half saturation value for N limitation (mmol N m )3Half saturation value for P limitation (mmol P m )3Half saturation value for Si limitation (mmol Si m )Basal speciﬁc respiration rate (per day)Activity respiration fraction ()Excreted fraction of primary production ()Maximum speciﬁc lysis rate (per day)1Standard Si:C ratio in diatoms (mmol Si mg C )1Optimal C:chl quota (mg C mg chl )Parameters for Bacterioplankton
Potential speciﬁc growth rate (per day)Basal speciﬁc respiration rate (per day)
Assimilation efﬁciency ()Speciﬁc mortality rate (per day)(1)
Speciﬁc potential R uptake (per day)(6)
Speciﬁc potential R uptake (per day)Speciﬁc rate of uptake or remineralization (per day)

εPAR
λW
λphyt
v sed
R6

0.40
0.06
3
0.5 × 10
1.00

0.00
0.22

r0p
Q10p
hnp
hpp
hsp
bp
γp
βp
d0p
sopt
pð1Þ
θ0chl

2.60
2.00
0.50
0.05
0.30
0.01
0.10
0.05
0.50
0.01
25.00

0.93
0.94
0.85
0.27
0.00
0.44
0.04
0.62
0.19
0.49
0.00

r0B
bB
ηB
d 0B
v 1B
v 6B
v nB ¼ v pB

8.38
0.01
0.40
0.00
0.30
0.01
1.00

0.00
0.09
0.41
0.09
0.32
0.22
0.14

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Table 2. Parameters for the Zooplankton Groups and Their Sensitivities
Mesozooplankton

Microzooplankton

Heterotrophic Nanoﬂagellates

Parameter

SymbolValue

Sensitivity

Value

Sensitivity

Value

Sensitivity

Characteristic Q10 coefﬁcient (per day)3
Michaelis constant for total food ingestion (mg C m )3Feeding threshold (mg C m )Potential speciﬁc growth rate (per day)Basal speciﬁc respiration rate (per day)
Assimilation efﬁciency ()Excreted fraction of uptake ()Partition between dissolved and particulate excretion of C ()Partition between dissolved and particulate excretion of N ()Partition between dissolved and particulate excretion of P ()Speciﬁc rate of nutrients and carbon excretion (per day)Speciﬁc mortality rate (per day)3
1 1
Density-dependent speciﬁc mortality rate (m mg C d )Exponent for density dependent mortality ()

Q10z
hZF
μz
r0z
bz
ηz
βz
εcZ
εnZ
εpZ
vz
d0z
d dns
z
γz

3.00
80.00
0.00
2.00
0.02
0.60
0.55
0.00
0.00
0.00
1.00
0.02
0.02
2.50

0.06
0.09

2.00
20.00
20.00
2.00
0.02
0.60
0.40
0.50
0.84
0.96
1.00
0.05
0.00

0.96
0.32
0.14
0.83
0.12
0.75
0.14
0.37
0.24
0.04
0.05
0.03

2.00
20.00
20.00
10.00
0.02
0.50
0.30
1.00
1.00
1.00
1.00
0.05
0.00

0.16
0.06
0.04
0.08
0.07
0.24
0.45
0.09
0.10
0.03
0.02
0.01

0.00
0.03
0.00
0.02

0.01
0.00
0.01
0.04

station. The model was initialized with the multiyear averaged vertical proﬁles of the water temperature, salinity, nutrient concentrations, and Chl a observed at the SEATS station in winter. The initial concentrations ofbacterioplankton, zooplankton, and organic matter (DOM and detritus) were set to zero. The coupled model
was integrated for 3 years under the same surface climatological forcing to obtain a quasi-steady state. The
results of the third year were used to compare with observations collected at the SEATS station and theWorld Ocean Atlas 2013 (WOA2013) statistical mean data (http://www.nodc.noaa.gov/OC5/woa13/woa13data.html) interpolated to the SEATS station in four seasons. The observed temperature, salinity, and Chl aat the SEATS station were reported by Chen [2004] and Liu et al. [2007], and the observed temperature, salinity, Chl a, and nutrients were the unpublished data provided by Prof. Minhan Dai from Xiamen University of
China (personal communication). The speciﬁc information of the observations used for model validations waslisted in Table 3. The observations at the SEATS station as reported by Liu et al. [2013] were also used for comparison with the modeled surface Chl a.
In TYPHOON, the model was initialized with the results obtained by CONTROL for 20 September, which was
the day before the arrival of Typhoon Damrey. Surface forcing data, including the wind and heat ﬂux during
typhoon period from 20 to 30 September 2005, were obtained from NCEP data collected at 6 h intervals near
Station A (19.3°N, 114°E) (Figure 1). The exact wind speed was not available at Station A, and thus, the windspeed determined from the NCEP reanalysis was used for forcing. The coupled 1-D model was integrated
from 20 September until the end of the year. The simulated vertical proﬁle of the water temperature before
the arrival of Typhoon Damrey on 20 September was also compared with shipboard observations collected at
Station A [Zheng and Tang, 2007]. Typhoon Damrey happened to pass by a mooring station (19°350 N, 112°E)(Station B in Figure 1) on the continental shelf of the northwestern SCS. The observed variation of temperature caused by the Typhoon Damrey [Yang et al., 2015] was used to compare with and validate the model
results. The daily SST and sea surface Chl a time series during the passage of the typhoon were validated
by using daily Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) SST data (http://www.
remss.com/) and Chl a values derived from the Moderate Resolution Imaging Spectroradiometer (MODIS)

Table 3. Information of Observations at the SEATS Station Used for Model Validations
Observations
Variables

WinterSpringSummerAutumnData Source

Temperature, salinity, and Chl a

Jan 2003
Jan 2010
Feb
Jan 2010

May 2000
May 2011
May
May 2011

Jul 2000
Aug 2009 and 2012
Aug
Aug 2009 and 2012

Nov 1999
Nov 2006 and 2010
Nov
Nov 2006 and 2010

Observations1bObservations2
WOA2013
Observations2

NutrientsaaObservations1 was the data from Chen [2004] and Liu et al. [2007].
b

Observations2 was the unpublished data from Prof. Minhan Dai (personal communication).

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onboard Aqua (http://oceandata.
sci.gsfc.nasa.gov/MODISa/Mapped/Daily/9 km/) and Sea-viewing
Wide
Field-of-view
Sensor(SeaWiFS)(http://oceandata.sci.
gsfc.nasa.gov/SeaWiFS/Mapped/
Daily/9km/), respectively.
Typhoon tracking data are issued
by the Japan Meteorological
Agency, and they were obtained
from the Typhoon Database of the
National Institute of Informatics ofJapan(http://agora.ex.nii.ac.jp),
including the center location, central pressure, and maximum
10 min sustained wind speeds
every 6 h. The satellite-derived
images of SST and sea surface Chla before, during, and after
Typhoon Damrey were from thedaily TMI data with spatial resolution of 0.25° and MODIS standard
level2 products with spatial resolution of 1 km, respectively.

3. Results
Figure 3. (a) Time series of wind speed (m/s) from NCEP for the CONTROL(dash line) and TYPHOON (solid line) simulations. For CONTROL simulation,
the wind speed was obtained from NCEP monthly long-term mean (1981 to2010) data collected at the South East Asia Time Series Study (SEATS) station(18°N, 116°E). (b) For TYPHOON simulation, the wind speed from 20 to 30
September 2005 comprised 6 h interval data from Station A (19.3°N, 114°E),
which were obtained from NCEP.

3.1. Modeled Climatological
Seasonal Trends of the Upper
Ocean Ecosystem

The seasonal variations in physicaland biological variables in theupper layer of the SCS are controlled primarily by the East Asianmonsoon [Wong et al., 2007; Liuet al., 2013]. The NCEP reanalysis data sets showed that the wind speed in our study area was approximately
5 m s1 in the summer monsoon season (from June to September), before increasing up to approximately
10 m s1 in the winter monsoon season from November to March, and it was approximately 4 m s1 during
spring and fall (Figure 3a).
The modeled results in February, May, August, and November represented results in winter, spring, summer,
and autumn. The modeled vertical proﬁles of physical variables were in good agreement with the observations collected at the SEATS station (Figure 4). The East Asian monsoon also affected the vertical structureof the water temperature and salinity in the study area by wind forcing (Figures 4a and 4b). According to
the modeled results, the SST reached its highest value of 29.5°C in summer and a lowest value of 23.6°C in
winter. SSS varied from 33.09 to 34.10 with two lowest salinity periods in May and October. In this study,
we deﬁned the mixed layer depth (MLD) as the depth where the water temperature is 1°C lower than themean value of the surface layer in the 10 m depth [Wagner, 1997] (Figure 4a). The calculated MLD was over
90 m in the winter monsoon season, which was the largest among the four seasons because of the high windspeed and strong water surface cooling. In spring, the calculated MLD became shallower due to enhanced
solar radiation and weakening of the wind speed. As the solar radiation increased further in the summer monsoon season, the calculated MLD was approximately 20 m. The calculated MLD gradually deepened during
fall as the winter monsoon formed. The calculated vertical diffusivity coefﬁcient in the upper ocean(Figure 4c) varied seasonally, and it was closely related to the wind speed. In the winter monsoon season,

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Figure 4. Depth-time plots of the modeled (a) water temperature (°C), (b) salinity, and (c) logarithmic distributions of the2 1vertical diffusivity coefﬁcient (m s ) in the CONTROL simulation, where the thick solid line in Figure 4a represents themixed layer depth (MLD, unit: meter). Modeled vertical proﬁles of the water temperature (a-1 to a-4) and salinity (b-1 to b-4)
in four seasons are plotted to compare with the observations acquired at the SEATS station. Observations1 (denoted byblue dots) is reported by Chen [2004] and Liu et al. [2007]. Observations2 (denoted by orange dots) is the unpublished data
provided by Prof. Minhan Dai from Xiamen University of China.

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Figure 5. Depth-time plots of the modeled (a) dissolved inorganic nitrogen (DIN, unit: mmol m ), (b) dissolved inorganic phosphate (DIP, unit: mmol m ), (c) dis3
3
3
3
solved silicate (DSi, unit: mmol m ), (d) Chl a (mg m ), (e) bacterioplankton (mg C m ), and (f) zooplankton (mg C m ) in the CONTROL simulation. Modeledvertical proﬁles of the DIP (a-1 to a-4), DIN (b-1 to b-4), DSi (c-1 to c-4), and Chl a (d-1 to d-4) in four seasons are plotted to compare with observations at SEATS station.
Observations1 (denoted by blue dots) is reported by Chen [2004] and Liu et al. [2007]. Observations2 (denoted by orange dots) is the unpublished data provided by
Prof. Minhan Dai from Xiamen University of China. The green dots are from WOA2013. The black dots in Figure 5d-5 are from Liu et al. [2013].

the calculated vertical diffusivity coefﬁcient was 101 m2 s1 at the surface and it decreased to 104 m2 s1 at
the 80 m depth, thereby corresponding to the strong winter monsoon. In the summer monsoon season, thecalculated vertical diffusivity coefﬁcient was 102 m2 s1 at the surface and less than 104 m2 s1 below the
30 m depth. The vertical diffusivity coefﬁcient decreased downward throughout the year.
Changes in physical variables such as the wind and water temperature can inﬂuence the vertical proﬁles of
biological elements and their seasonal variations. The modeled DIP, DIN, and DSi values were approximately
within the ranges of multiyear observations at the SEATS station, and the vertical structures were in good
agreement with observations (Figures 5a–5c). The calculated nutrient elements (DIN, DIP, and DSi) exhibited
only slight seasonal variations. In this study, the depth of the nitracline was deﬁned as the depth where thenitrate concentration was 1 mmol m3 [Cullen and Eppley, 1981; Raimbault et al., 1993]. According to thisthreshold, the depth of the nitracline was located at a depth of about 50 m, which was deeper than the
MLD throughout the whole year, except in the winter. Physical stratiﬁcation acts as a physical barrier that prevents the upward transport of nutrients from deeper nutrient-rich water below the depth of the nutricline. As
a result, the nutrient concentrations are low in the upper 50 m depth in the warm season, which are deﬁcientfor the growth of phytoplankton [Fisher, 1992]. In the winter monsoon season, the modeled DIN, DIP, and DSi
concentrations at the surface reached maximum values of 1.4 mmol m3, 0.11 mmol m3, and 1.4 mmol m3,
respectively, because of the enhanced vertical mixing.
The model reproduced the seasonal variations of Chl a observed at the SEATS station (Figure 5d). Modeled
results showed that the sea surface Chl a varied from 0.06 to 0.32 mg m3 with a prominent peak in winter.

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Figure 6. (a–c) Sea surface cooling and (d and e) Chl a increase related to Typhoon Damrey. The daily SST and sea surface
Chl a on 20, 25, and 30 September 2005 are retrieved from TMI and MODIS-Aqua, respectively.

Excluding the winter monsoon season, the calculated SCM was identiﬁed at a depth of approximately 60 m,
which was located immediately below the depth of the nutricline and above the euphotic layer depth (deﬁned
as the depth with 1% of the surface solar radiation, which was approximately 90 m throughout the whole year).
The strong stratiﬁcation made the bell-shaped SCM more obvious in summer (Figure 5d-3). The SCM began todisappear during the intense winter monsoon reﬂected by both model and observation (Figure 5d-1).
The modeled seasonal variations in the bacterioplankton and zooplankton concentrations had the same patterns as those of the phytoplankton (Figures 5e and 5f). For example, the surface maximum concentrations ofbacterioplankton and zooplankton were 14 mg C m3 and 27 mg C m3 in winter, respectively, and their subsurface maximums of 12 mg C m3 and 25 mg C m3 were located at a depth of about 60 m during
warm seasons.
3.2. Responses of the Upper Ocean Ecosystem to Typhoon Damrey
We also examined the modeled physical and biogeochemical responses of the upper ocean ecosystem to
Typhoon Damrey. Typhoon Damrey (0518, Category 2, where the category is based on the Safﬁr-Simpson
scale) caused obvious phytoplankton bloom in the SCS and enormous economic loss to Hainan province[Zheng and Tang, 2007]. It developed as a tropical depression over the Paciﬁc on 21 September 2005 andmoved northwest. On 22 September, it entered the northern part of the SCS, before intensifying to typhoon
category and passing through Station A at 12:00 on 24 September. On 25 September, it made landfall oneastern Hainan Island at 18:00 and dissipated over northern Vietnam 3 days later (Figure 1). From the satellite
images of SST and surface Chl a during the passage of Typhoon Damrey (Figure 6), the typhoon induced anapparent reduction of SST and occurrence of phytoplankton bloom around Station A.
The TYPHOON calculation was performed for Station A, which was under the typhoon’s track. The NCEP data
showed that the wind speed at Station A started to increase on 22 September, reached a maximum of
20 m s1 on 24 September, and gradually decreased subsequently (Figure 3b).

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Modeled results showed that Typhoon Damrey greatly enhanced vertical mixing in the upper ocean (Figure 7a).
The modeled surface diffusivity coefﬁcient reached a maximum value of 101 m2 s1, and it was greater than
104 m2 s1 in the whole upper 150 m depth on 24 September when the wind speed increased to 20 m s1.
The water was not disturbed by Typhoon Damrey below the 150 m depth.
Modeled vertical proﬁle of the water temperature at Station A on 20 September immediately before the arrival of Typhoon Damrey was in good agreement with the observations reported by Zheng and Tang [2007](Figure 7b). The calculated MLD was approximately 36 m on that day, below which the water was stratiﬁed(Figure 7d). Both the modeled and satellite-derived SST data indicated a remarkable decrease with a maximum decrease of ~4.0°C on 24 September (Figure 7e). Figure 7d shows the response of the water temperature to Typhoon Damrey in the upper 150 m depth. The enhanced vertical mixing redistributed the thermal
content in the water column. As a consequence, cooling occurred in the upper 50 m and warming occurredfrom depths of 50 m to 150 m (Figures 7c and 7d). For example, the water temperature at 100 m increased bymore than 1.0°C on 24 September (Figure 7e). The MLD deepened remarkably from 36 m to 86 m, but quickly
recovered to its original depth, and then gradually deepened following its seasonal variation in October(Figure 7d). Observed vertical proﬁles of the water temperature during Typhoon Damrey at Station A were
not available. The temperature proﬁles observed in a mooring station on the continental shelf of the northwestern SCS (Station B in Figure 1) indicated that Typhoon Damrey caused a cooling of 4.5°C in the surface
layer and a warming of 1.2°C in the subsurface layer (75 m depth) [Yang et al., 2015], which were comparable
to our results in which the surface temperature decreased by 4.0°C while the water temperature at 75 m
depth increased by 1.0°C (Figure 7c).
We also calculated several general nondimensional numbers [Black and Dickey, 2008] to make it easier tocompare the characteristics of Typhoon Damrey and the responses of physical variables with other studies.
The hurricane hazard index was 0.32; the nondimensional storm speed (S) was 1.1; the Burger number (B),indicating the degree of pressure coupling between the mixed layer currents and the thermocline current,
was 0.07; the Rossby number for mixed layer current (Q) was 0.05.
Under the inﬂuence of Typhoon Damrey, the MLD deepened and exceeded the depth of the nutricline (50 m),
thereby resulting in nutrients being enriched in the upper layer. For example, the modeled concentrations of
DIN, DIP, and DSi increased sharply at the surface from 0.63, 0.07, and 0.75 mmol m3 to 2.58, 0.17, and1.81 mmol m3, respectively, on 24 September. The nutrients in the upper layer then gradually decreased
from the maximum because of their consumption by phytoplankton, and their concentrations graduallyreached the original values before the arrival of the typhoon.
The simulated surface nutrient and Chl a concentrations were low on 20 September before the arrival of
Typhoon Damrey (Figures 8 and 9a and 9b). The modeled vertical proﬁle of Chl a exhibited the SCM phenomenon with a peak concentration at a depth of ~60 m (Figure 9b). This phenomenon was common in the study
area during this season when no episodic disturbances occurred [Chen, 2004]. The Chl a was distributed uniformly, and the SCM disappeared (Figure 9b) on 24 September. About 1 week after the typhoon, the Chl a in
the whole euphotic zone increased rapidly in response to the nutrients supplied from the deeper water. As
shown in Figures 9a and 9b, the Chl a at the surface, subsurface (~60 m), and the depth below the euphotic layer(e.g., 100 m) exhibited different responses to Typhoon Damrey. The modeled surface maximum for the Chl a of
0.61 mg m3 appeared on 1 October, which was consistent with the average value of 0.60 mg m3 derived from
MODIS and SeaWiFS. In addition to the sea surface, the subsurface (60 m) also exhibited a remarkable Chl aincrease with a maximum Chl a of 0.71 mg m3 on 4 October. At a depth of 100 m, there was also an increasein Chl a with a maximum of 0.08 mg m3 on 24 September (Figure 9a). The nutrients in the upper layer were largely consumed after the phytoplankton bloom. The deﬁciency of nutrients (Figure 8) and grazing by zooplankton (Figure 9d) reduced the Chl a (Figures 9a and 9b). After 10 October, the effects of the typhoon were minor.
The nutricline and SCM reformed, and they followed their seasonal variations (Figure 9b).
The modeled bacterioplankton and zooplankton (Figures 9c and 9d) exhibited similar responses to the phytoplankton. The subsurface maximums for the bacterioplankton and zooplankton disappeared rapidly on 24
September and then increased, with maxima of 22 mg C m3 for bacterioplankton and 63 mg C m3 for zooplankton in the surface and subsurface layers. The bacterioplankton peaked at the same time as the phytoplankton, but the zooplankton lagged by 3 days.

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2 1

Figure 7. (a) Depth-time plots of the modeled logarithmic distributions of the vertical diffusivity coefﬁcient (m s ) from
20 to 30 September in the TYPHOON simulation. (b) Vertical proﬁles of the modeled water temperature (°C) pretyphoon (20September, solid line) and posttyphoon (25 September, dashed line) in the TYPHOON simulation, and observations
reported by Zheng and Tang [2007] for before the typhoon (20 September, dark circles). (c) The corresponding temperature
variation. (d) Depth-time plot of the water temperature (°C) from 20 September to 30 October in the TYPHOON simulation,
where the thick solid line represents the mixed layer depth (MLD; unit = m). (e) Time evolution of the water temperature(°C) at depths of 0, 50, 60, 100, 120, and 150 m in the TYPHOON simulation and the satellite sea surface temperature (SST)data acquired by from the TRMM Microwave Imager (TMI, dark circles) from 20 September to 30 October.

3.3. Contributions of Physical and Biogeochemical Processes to the Phytoplankton Biomass in
Different Water Layers
Based on satellite and shipboard data, several studies have shown that typhoon-induced vertical mixing canincrease the surface Chl a [Zhao et al., 2009; Ye et al., 2013; Zhao et al., 2013; Tang et al., 2014]. Given the

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3

Figure 8. Time series of (a) DIN, (c) DIP, and (e) DSi (mmol m ) in the surface layer from 20 September to 30 October in the CONTROL (blue line) and TYPHOON
3(black line) simulations. Depth-time plots of the modeled (b) DIN, (d) DIP, and (f) DSi (mmol m ) from 20 September to 30 October in the TYPHOON simulation.

existence of the SCM, the increased Chl a at the surface is caused partly by mixing subsurface chlorophyll a up
to the surface, which is referred to as physical process. Strong vertical mixing can also pump nutrients fromthe deeper water up to the surface layer and promote the growth of phytoplankton, which is referred to as
biogeochemical processes. However, it is logistically difﬁcult and expensive to quantify the contributions ofthese two processes to the increased surface Chl a during typhoon events only using observational data.

3

Figure 9. (a) Time series of Chl a (mg m ) in the surface, subsurface, and deep layers from 20 September to 30 October according to the CONTROL (dash line) and
TYPHOON (solid line) simulations, and satellite sea surface Chl a data acquired from MODIS (dark triangles) and SeaWiFS (dark circles). Depth-time plots of modeled
33(b) Chl a, (c) bacterioplankton (mg C m ), and (d) zooplankton (mg C m ) from 20 September to 30 October in the TYPHOON simulation.

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Table 4. Contributions of Physical and Biogeochemical Processes to Phytoplankton Biomass Enhancement in Different
Water Layers
Water Layer

Stage

Surface (0 m)

Stage I
Stage II
Stage I
Stage II

Subsurface (60 m)
100 m

Physical Process (mg C m

3

)

3

Biogeochemical Processes (mg C m

1.50
12.68
6.42
16.78
2.46

)

0.27
24.54
2.12
30.46
0.41

Therefore, we examined our model results to separate the contributions of physical and biogeochemical processes to the phytoplankton biomass in the surface, subsurface, and deeper water layers during the different
stages (Table 4). The variation in the phytoplankton biomass is given by the following equation:
∂P ∂P
∂P
¼
þ
;
∂t
∂t phys ∂t bio(3)where the total time rate of change is given by the algebraic sum of a source term due solely to a physicalprocess and by a source/sink term determined by biogeochemical processes. The physical transport term
is usually made explicit by using primitive equations of motion in the following form.


∂P
∂
∂P
Kh
:
¼
∂t phys ∂z∂z(4)

The description of the processes involved in the biogeochemical dynamics of phytoplankton biomass has thefollowing form.
∂P¼ Assimilation  Excretion  Respiration  Grazing:
∂t 2

(5)

The phytoplankton biomass (P in Figure 10) and its rate of variation caused by physical process ( ∂P∂t phys in
Figure 10) and biogeochemical processes ( ∂P∂t bio in Figure 10) were determined for the surface, subsurface(60 m), and the depth under the euphotic layer, e.g., 100 m.
Figure 10a shows that the typhoon resulted in a maximum increase in the phytoplankton biomass of
13.63 mg C m3 at the surface. The phytoplankton biomass increased gradually after 23 September andreached a maximum of 15.23 mg C m3 on 1 October. The 9 day increasing period was apparently divided
into two stages with totally different increases in their slopes, i.e., Stage I from 23 to 24 September with arapidly increasing slope and Stage II from 24 September to 1 October with a slowly increasing slope. In
Stage I, the phytoplankton biomass increased by 1.77 mg C m3 on 24 September. Physical process contributed 1.50 mg C m3, whereas the contribution of biogeochemical processes was only 0.27 mg C m3.
However, in Stage II, physical process yielded a negative contribution of 12.68 mg C m3. The contributionof net growth was 24.54 mg C m3, thereby indicating that the increase in the surface phytoplankton biomass was determined mainly by net growth in Stage II.
Figure 10b shows that the typhoon resulted in a maximum increase in the phytoplankton biomass of
9.38 mg C m3 at a depth of 60 m. The phytoplankton biomass decreased rapidly by 4.32 mg C m3 from23 to 24 September and then increased to a maximum of 18.0 mg C m3 on 4 October. During the decreasingstage (Stage I), the contributions of physical and biogeochemical processes were 6.42 mg C m3 and2.12 mg C m3, respectively. The physical process overwhelmed the biogeochemical processes and caused
the disappearance of the SCM. During the increasing stage (Stage II), the phytoplankton biomass exhibiteda long-term increase from 24 September to 4 October. The contribution of the physical process remained
negative at 16.78 mg C m3. The biogeochemical contribution of 30.46 mg C m3 led to a net increase in
the phytoplankton biomass.
At the depth from the bottom of the euphotic layer to ~150 m (i.e., 100 m in Figure 10c), there was also a small
phytoplankton biomass peak on 24 September. The variations due to physical and biogeochemical processes
were 2.46 mg C m3 and 0.41 mg C m3, respectively. The weak increase was caused by downward
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Figure 10. Changing rate of phytoplankton biomass induced by physical (black solid line) and biogeochemical (black
dotted line) processes, and the phytoplankton biomass (blue solid line) at depths of (a) 0 m, (b) 60 m, and (c) 100 m from20 September to 10 October.

entrainment of phytoplankton from the euphotic layer. Subsequently, the phytoplankton biomass decreased
due to the domination of death relative to growth.

4. Discussion
4.1. Sensitivity Experiments to Parameters
Sensitivity experiments were carried out to examine how the predicted variables vary with the change of theparameters used in the model (Tables 1 and 2). The predicted state variable in this sensitivity analysis waschosen to be the monthly mean phytoplankton biomass during and 1 month after the passage of the
Typhoon Damrey (from 20 September to 20 October). Taking the parameters listed in Tables 1 and 2 as
the control run, the value of each selected parameter was increased and decreased by 50% and the model
was run for 1 year in each sensitivity experiment. The sensitivity of the predicted state variable to the selected
ΔF=F jparameter was quantiﬁed by a factor S ¼ jjΔa=a
j , where F was the mean phytoplankton biomass from 20

September to 20 October and α was the value of a parameter used in the model. Parameter ΔF was the variation of F corresponding the change (Δα) of the parameter α.
Fifty-four parameters were tested in this sensitivity analysis (Tables 1 and 2). The sensitivity factor S was smaller than 1.0 for all the parameters and actually less than 0.5 for more than 87% parameters. The predicted variable (mean phytoplankton biomass from 20 September to 20 October) was relatively sensitive to thephytoplankton related parameters, such as the maximum speciﬁc photosynthetic rate, half-saturation coefﬁcient for N limitation, excreted fraction of primary production and the characteristic Q10 coefﬁcient, and theparameters related to microzooplankton, such as the characteristic Q10 coefﬁcient, growth rate, andPAN ET AL.

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assimilation efﬁciency (Tables 1 and 2: the sensitivity factor S was larger than 0.5 for these parameters). The
sensitivities to half-saturation coefﬁcient of DIN and DIP were larger than that of DSi, suggesting that thephotosynthesis was limited mostly by DIN and occasionally by DIP but never by DSi in our calculations.
Besides, the predicted variable was more sensitive to parameters related to microzooplankton rather than
to the other two kinds of zooplankton because phytoplankton was mainly grazed by microzooplankton. Bychanging the parameters within a given range (i.e., 50%), we conﬁrmed that the variations of phytoplankton
biomass and the situation of limitation nutrient were relatively stable in our model calculations. Therefore,
the model results were likely robust to the model parameters.
4.2. Contributions of Typhoon Damrey to the Supply of Nutrients and the AssociatedPrimary Production
Typhoon Damrey brought cold nutrient-rich deeper water up to the euphotic layer and enhanced primary
production. We calculated the net nutrient ﬂux across the euphotic layer depth induced by vertical mixing.
The upward transport of DIN, DIP, and DSi increased sharply to maximum values of 182.27 mmol m2 d1,
8.84 mmol m2 d1, and 35.85 mmol m2 d1, respectively, on 24 September, which were nearly 300 times
the levels in the CONTROL (0.53 mmol m2 d1, 0.025 mmol m2 d1, and 0.69 mmol m2 d1 for DIN, DIP,
and Dsi, respectively). The total concentrations supplied during the period from 20 to 30 September were
86.98 mmol m2, 4.22 mmol m2, and 67.72 mmol m2 for DIN, DIP, and DSi, respectively, which were morethan 100 times those in the CONTROL (5.35 mmol m2, 0.26 mmol m2, and 6.82 mmol m2 for DIN, DIP,
and DSi, respectively). Among the total annual amounts supplied, these short-term increases caused by
Typhoon Damrey contributed 24% of the DIP, 25% of the DIN, and 20% of the DSi. In the southern East
China Sea, Hung et al. [2013] reported that the DIP and DIN input related to typhoon Morakot (7 to 9
August 2009) accounted for more than 80% of their amounts during the summer; however, no related estimates are available for the SCS.
In our study, we calculated the IPP by integrating the phytoplankton net growth production over the euphotic zone. Based on our CONTROL simulation results and previous observations [Tseng et al., 2005], the phytoplankton biomass and primary production in the northern SCS basin were extremely low throughout thewhole year. According to the CONTROL simulation, the annual mean value of IPP was 128 mg C m2 d1.
The highest value was 228 mg C m2 d1 in January, and the lowest value was 94 mg C m2 d1 in
October. Tseng et al. [2005] reported that the IPP at the SEATS station increased to 300 mg C m2 d1 during
the winter, but it remained at a low value of ~110 mg C m2 d1 for the rest of the year. Liu and Chai [2009]
suggested that the mean IPP in the SCS was 196 mg C m2 d1, with a highest value of 386 mg C m2 d1 inwinter and a lowest value of 156 mg C m2 d1 in summer. Thus, our mode results are comparable to thoseobtained in previous studies.
According to the TYPHOON simulation, the IPP increased to a maximum value of 623 mg C m2 d1 on 1
October, which was more than 6 times than that obtained in the CONTROL simulation(97.78 mg C m2 d1). The time-integrated IPP from 20 September to 20 October was 9.5 × 103 mg C m2,
which was 2 times higher than the modeled climatological IPP for the same period, where it contributed
14% of the annual-integrated IPP. Naik et al. [2008] estimated that a tropical cyclone contributed 5% of
the annual total in the Arabian Sea. In the East China Sea, Typhoon Meari induced a threefold IPP increase,which contributed 3.8% of the annual amount [Siswanto et al., 2008]. Lin et al. [2003] stated that 14 tropical
cyclones accounted for 20%–30% of the IPP every year in the SCS. In 2007, Typhoon Hagibis caused a signiﬁcant phytoplankton bloom, which accounted for 30% of the annual Chl a in the middle of the SCS [Sunet al., 2010]. A recent study showed that the contribution of typhoons to the annual new production was 5–
15% by using the actual typhoons passing through the SCS and considering the intensity of each typhoon
during 2003 to 2012 [Chen et al., 2015]. The modeled contribution of Typhoon Damrey in our study wascomparably high but within the reported range. In fact, our estimation was based on modeled vertical proﬁles rather than on the satellite-derived values at sea surface, and therefore, it also reﬂected the incrementof production in the subsurface layer. Another factor was that most of the previous studies estimated thecontributions of typhoons to the production over the whole SCS or a subregion of SCS, while our study
focus on one site (Station A) along the passage of Typhoon Damrey, where the inﬂuence of the typhoon
was apparently strong.

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4.3. Potential Long-Term Inﬂuence of Typhoons
Excluding the short-term response, it is not known whether typhoons have any long-term effects. According
to the TYPHOON calculation, the mean values of the DIN, DIP, and DSi concentrations in the upper 50 m
depth from 1 November until the end of the year (30 December) were 2.0 mmol m3, 0.13 mmol m3, and
1.7 mmol m3, respectively. These values were slightly higher compared with those obtained by the
CONTROL simulation (1.6 mmol m3, 0.12 mmol m3, and 1.2 mmol m3, respectively). As a result, thetime-integrated IPP from 1 November until the end of the year (30 December) was 1.1 × 104 mg C m2, i.e.,
32% more than that in the CONTROL simulation. Therefore, it may be assumed that typhoons have long-term
effects on ocean ecosystems rather than causing a short-term response for several weeks. These ﬁndings andhypotheses need to be examined in future studies based on more observations and numerical experiments.

5. Conclusions
In this study, using a 1-D physical-biogeochemical coupled model and previously published observations, we
systematically investigated the responses of the upper ocean ecosystem in the basin area of the SCS to
Typhoon Damrey. The model reproduced the climatological seasonal variations in the physical and biogeochemical elements based on comparisons of the results obtained by the model and observations collectedfrom WOA2013 at the SEATS station. Observations and our model results demonstrated a remarkable coolingof the SST by ~4.0°C and a maximum increase in the sea surface Chl a of ~0.6 mg m3 at Station A, which wasunder the typhoon track.
The surface Chl a increased rapidly from 0.07 to 0.17 mg m3 as the typhoon arrived, and it gradually reacheda peak of 0.61 mg m3 after the typhoon’s passage. In addition to the surface layer, we considered the
responses in the subsurface layer, which is difﬁcult to observe from satellites. The subsurface Chl a decreased
from 0.34 to 0.17 mg m3 as the typhoon arrived, and it then gradually increased to 0.71 mg m3. Our analysis of the model results indicated that the initial rapid increase in the surface Chl a and the decrease in thesubsurface Chl a were caused mainly by physical process (vertical mixing), whereas the subsequent gradualincreases in Chl a in both the surface and subsurface layers were due mainly to biogeochemical processes(net growth of phytoplankton). The gradual increase in the Chl a lasted for longer in the subsurface layer than
the surface layer.

Acknowledgments
This research is funded in part by the
Chinese Ministry of Science andTechnology under contract
2014CB953702, the National Nature
Science Foundation of China undercontracts 41506013 and 41406010, andthe “Strategic Priority Research
Program” of the Chinese Academy of
Sciences under contract XDA11010101.
X. Guo thanks support from JSPS
KAKENHI (15H05821). The forcing dataof the model were obtained from NCEP
reanalysis data sets. The model was
validated with the WOA2013 statisticalmean data. The daily SST was retrieved
from TMI SST datat, and the daily surface Chl a were from MODIS-Aqua and
SeaWiFS. The other data for this paper isproperly cited and referred to in thereference list. We thank Minhan Dai,
Zhiyu Liu, and Chuanjun Du from
Xiamen University for providing
unpublished observations at SEATS station. We thank the Associate Editor andreviewers who provided important andinsightful comments that signiﬁcantly
improved the presentation.

PAN ET AL.

During the period from 20 September to 20 October, Typhoon Damrey yielded an IPP of 6.5 × 103 mg C m2
accounting for 14% of the annual IPP. This study suggested that typhoon can enhance primary production
during its passage and may have potential long-term inﬂuence in oligotrophic oceans.
It is widely known that cold SST in the wake of typhoons are caused by the joint effects of upwelling (Ekman
pumping) and entrainment (vertical mixing). In this study, we considered entrainment but neglected upwelling because the former is much important than the latter to reduction of SST [Price, 1981; Price et al., 1994].
Apparently, a three-dimensional model is a better tool to involve more processes associated with typhoons(e.g., wind-driven upwelling). In the near future, we will extend our study to a three-dimensional model andclarify the role of horizontal processes in the response of upper ocean ecosystem to the passage of a typhoon.

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