Tropical cyclone enhanced vertical transport in the northwestern South China Sea I: Mooring observation analysis for Washi (2005)

Journal Pre-proof Tropical cyclone enhanced vertical transport in the northwestern South China Sea I: Mooring observation analysis for Washi (2005) Liju Wang, Lingling Xie, Quanan Zheng, Junyi Li, Mingming Li, Yijun Hou PII:

S0272-7714(19)30689-4

DOI:

https://doi.org/10.1016/j.ecss.2020.106599

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YECSS 106599

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Estuarine, Coastal and Shelf Science

Received Date: 25 July 2019 Revised Date:

8 January 2020

Accepted Date: 10 January 2020

Please cite this article as: Wang, L., Xie, L., Zheng, Q., Li, J., Li, M., Hou, Y., Tropical cyclone enhanced vertical transport in the northwestern South China Sea I: Mooring observation analysis for Washi (2005), Estuarine, Coastal and Shelf Science (2020), doi: https://doi.org/10.1016/j.ecss.2020.106599. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Ltd.

1

Tropical cyclone enhanced vertical transport in the northwestern

2

South China Sea I: Mooring observation analysis for Washi (2005)

3

Liju Wanga, Lingling Xiea, b∗, Quanan Zhengc, Junyi Lia,b, Mingming Lia,b, Yijun Houd

4

a

5

and Meteorology, Guangdong Ocean University, Zhanjiang 524088, China

6

b

7

Guangdong Laboratory (Zhanjiang), Zhanjiang 524025, China

8

c

9

20742, USA

Guangdong Key Laboratory of Coastal Ocean Variation and Disaster Prediction, College of Ocean

Marine Resources Big Data Center of South China Sea, Southern Marine Science and Engineering

Department of Atmospheric and Oceanic Science, University of Maryland, College Park, Maryland

10

d

11

Highlights

12

Storm-induced vertical velocity and transport are diagnosed from mooring

13

observations

14

Geostrophic and unsteady components dominate variation of the vertical velocity

15

Upward velocity was enhanced to O(1×10-3) m s-1 as storm Washi (2005) passed

16

Diagnosed vertical velocity is one order larger and one day later than Ekman pumping

17

Vertical advection transport of O(1×10-5) kg s-1 m-3 is greater than mixing diffusion

18

flux

Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China

∗

Corresponding author at: College of Ocean and Meteorology, Guangdong Ocean

University, Zhanjiang 524088, China. Tel.: +86 759 2396037; fax: +86 759 2396055. E-mail address: [email protected] 1

19

Abstract

20

Using mooring-observed horizontal velocities and temperature from July 28 to

21

August 2, 2005, this study analyzes the temporal-vertical variation of the diagnostic

22

vertical velocity and mass transport during passage of tropical storm Washi (2005)

23

over the northwestern continental shelf of the South China Sea (SCS). The results

24

show that the total vertical velocity is of the order of O(1×10-4) m s-1 in the mixed

25

layer above 25 m, and of O(1×10-5) m s-1 in the lower layer. Dynamically, the

26

geostrophic advection and unsteady behavior of density induced by near-inertial

27

oscillation are dominant factors in the upper and lower layers, respectively. As

28

tropical storm Washi (2005) passed by from July 29 to 31, 2005, the upward vertical

29

velocity was dominant and significantly enhanced to the order of O(1×10-3) m s-1.

30

The diagnosed vertical velocity in the upper layer is one order greater than the

31

averaged Ekman pumping velocity, which occurred one day earlier. The vertical

32

advection transport calculated from the diagnosed vertical velocity reaches O(1×10-5)

33

kg s-1m-3, one order greater than that induced by turbulent mixing. Time-averaged

34

transports by vertical advection and mixing are both upward in the layer above the

35

thermocline during the storm passage.

36

Keywords: vertical velocity; turbulent mixing; tropical cyclone; mooring observation;

37

South China Sea.

2

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1. Introduction

39

Tropical cyclone-induced ocean vertical transport is of great importance for

40

coastal bio-geo-physical research (Williams et al., 2007; Li et al., 2010; Barceló-Llull

41

et al., 2016; Wu et al., 2016; Zhan et al., 2018; Aoki et al., 2019; Kashem et al., 2019).

42

It is this vertical motion that transports the subsurface cold water rich in dissolved

43

matters up to the euphotic layer, determining the abundance of phytoplankton (Allen

44

et al., 2005; Lévy et al., 2005), and affecting fishery production in continental shelf

45

seas (Hong et al., 2011; Nowald et al., 2015; Zhang et al., 2018).

46

Ocean vertical velocities are generally smaller than 1×10-3 m s-1 and hard to

47

measure directly using the state-of-the-art cruise observation technologies (Liang et

48

al., 2017; Xie et al., 2017b). As a consequence, dynamic diagnosis methods have been

49

developed to calculate the vertical velocity from hydrographic observations (Hoskin

50

et al., 1978; Bryden, 1980; Halpern et al., 1989; Lindstorm and Watts, 1994). For

51

example, Poulian (1993) obtained an upwelling velocity of 1.5 – 2×10-4 m s-1 at a

52

depth of 50 m in the equatorial Pacific from drifter current measurements. Lindstorm

53

and Watts (1994) derived a vertical velocity of about 1×10-3 m s-1 in the Gulf Stream

54

from float pressure measurements. Using a generalized omega equation, a steady

55

vertical velocity of the order of 5×10-4 m s-1 was derived from 3D observations of the

56

horizontal velocity and the density in the Alboran Sea (Pallàs-Sanz and Viúdez, 2005).

57

Recently, Sévellec et al. (2015) derived a new equation to calculate the variable

58

vertical velocity profiles based on single mooring observations of the horizontal 3

59

velocity and the density. This diagnostic method provides better estimates for the time

60

evolutionary vertical velocity without the requirement for fine-spatial-resolution

61

observations.

62

The northwestern continental shelf of the SCS is an ideal fishery ground due to

63

its high primary productivity (Xie et al., 2012a; Jing et al., 2015; Zheng et al., 2018).

64

In summer, the southwesterly monsoon prevails and the northeastward flow

65

dominates on the shelf (Qu, 2000; Su, 2004). Meanwhile, strong upwelling appears

66

along the coast accompanied by cross-shelf circulation (Jing et al., 2009; Xie et al.,

67

2012b; Hu and Wang, 2016). Previous investigators have estimated the coastal

68

upwelling velocity from observed isopycnal uplift, salinity flux or numerical models

69

under general weather conditions (Deng et al., 1995; Guo et al., 1998; Su and

70

Pohlmann, 2009). Using the generalized omega equation and high-spatial-resolution

71

cruise observations, Xie et al. (2017b) obtained the steady 3D vertical circulation in

72

the upwelling and front zones east of Hainan Island during the normal summer

73

monsoon period. They found the maximum downward velocity reached –5×10-5 m s-1,

74

accompanied by a maximum upward velocity of 7×10-5 m s-1 on two sides.

75

The northwestern continental shelf of the SCS is also a frequent path of tropical

76

cyclones from the Pacific and the SCS (Liu et al., 2011; Shi et al., 2019). On average,

77

there are 9 – 14 tropical cyclones passing the northwestern SCS every year (Wang et

78

al., 2007; Chen et al., 2012). Most of the tropical cyclones occur in summer months

79

from June to August, and are within intensity categories of tropical storm and severe 4

80

tropical storm (Yang, 2005; Zheng et al., 2015; Xie et al., 2017a). As the tropical

81

storm passes by, chlorophyll-a (Chl-a) concentration is significantly increased by

82

typhoon-induced upwelling and mixing (Gierach and Subrahmanyam, 2008; Lin,

83

2012; Ye et al., 2013; Zhang et al., 2014). Of the vertical velocity induced by tropical

84

cyclones, previous investigators usually calculated the Ekman pumping velocity (EPV)

85

in the upper layer using the wind stress curl (Chang et al., 2008; Sun et al., 2010;

86

Zhao et al., 2017). However, the vertical structure and temporal variation of the

87

cyclone-induced vertical velocity are seldom investigated.

88

This study aims to obtain the time-evolutionary profiles of vertical velocity

89

during tropical cyclone passage over the northwestern SCS, and makes an analysis of

90

the effects of the vertical advection and mixing on the vertical mass transport. In the

91

first part of the study, a diagnostic method is used with mooring-observed temperature

92

and horizontal velocities from July 28 to August 2, 2005 during the passage of

93

tropical storm Washi (2005), a representative case of tropical cyclones over the

94

northwestern SCS. The remainder of the paper is organized as follows. The next

95

section describes the cruise observation program, data processing, and diagnostic

96

equations. Section 3 analyzes the vertical structure of the vertical velocity and the

97

temporal variation during storm passage. The mass transports induced by vertical

98

advection and turbulent mixing are compared in section 4. Sections 5 and 6 contain

99

the discussion and conclusions.

100

2. Data and methodology 5

101

2.1. Data and data processing

102

A mooring observation system was deployed at 19°35′ N, 112° E in the

103

northwestern SCS (red dot in Figure 1a) for 6 days from July 28 to August 2, 2005 by

104

the Institute of Oceanology, Chinese Academy of Sciences. The depth of the mooring

105

station is 117 m, and about 100 km away from the east coast of Hainan Island. During

106

the observational period, the tropical storm Washi (2005) passed over the shelf 130

107

km south of the mooring station from July 29 to 31 as shown in Fig. 1a.

108

Affected by the storm Washi (2005), the wind direction turned anticlockwise

109

with an averaged southeasterly wind at the mooring station as shown in Fig. 1b. The

110

time series of wind speed at the mooring station are shown in Fig. 1c. One can see

111

that the wind speed was within a range of 4 – 8 m s−1 on July 28 before the storm and

112

August 1 – 2 after the storm, but reached a maximum value of 17 m s−1 on July 30

113

during the storm passage. The wind speed decreased on July 29 as the storm center

114

moved closer to the mooring station, and the wind speed was lower inside the

115

maximum wind speed radius of the storm.

116

A mooring system consisting of a downward-looking 190 kHz acoustic Doppler

117

current profiler (ADCP) and a temperature sensor chain was used to measure the

118

horizontal velocity and the temperature, respectively. The measurement range of the

119

ADCP is from 14 m to 114 m with a vertical interval of 2 m and a sampling rate of 10

120

minutes. The accuracy of the derived velocity is 1×10-2 m s-1. The observed vertical

121

velocity component cannot be used as the real ocean vertical velocity due to the 6

122

limited measurement accuracy and the measurement errors as the ADCP swung with

123

the mooring line. Temperature profiles were collected every 10 minutes from 4 m to

124

75 m with a vertical interval of 1 m. With the pre/post-cruise calibration, the accuracy

125

of temperature sensors reached about 0.01 oC (Xu et al., 2011).

126

Figs. 1d – f show the time series of horizontal velocity components

,

and

profiles derived from the mooring observations. One can see that on

127

temperature

128

July 28 before the storm, the horizontal velocities were dominated by the westward

129

flow with a maximum value of −0.4 m s−1 in the upper 40 m. The temperature was

130

higher than 29 ℃. After July 29, the horizontal velocities increased to ±0.5 m s−1 due

131

to passage of tropical storm Washi (2005). Moreover, the velocity directions were

132

opposite in the two layers above 20 m and below 45 m. The temperature in the upper

133

layer decreased to 28 ℃ as the layer depth increased. The isothermals oscillated

134

periodically as negative and positive velocities alternatively appeared.

135

The power spectra of the velocities and temperature are derived from the

136

periodogram method (not shown). We find that most of the energy is distributed at

137

frequencies lower than the inertial frequency (= 4.9 × 10-5 s-1). In the whole water

138

body, the sub-inertial (

139

in the mixed layer above 30 m.

≤ ) energy counts for 65% of the total energy, and up to 82%

140

In order to apply the diagnosis method to the main dynamic processes, we

141

low-pass the mooring observed velocities and temperature time series data using a

142

second-order Butterworth filter. The cutoff frequency is chosen to be 1.25 7

to retain

143

the local inertial frequency and remove high frequency components (Zhang et al.,

144

2014). The low-passed results are shown in Figs. 1g – i. One can see that sub-inertial

145

velocities (

146

reached 29 ℃ before the storm passage. During the passage of Washi (2005) on July

147

30, the sub-inertial velocities increased and the temperature dropped. The velocities

148

and isothermals exhibit a pronounced oscillation with a period of about 1.5 days,

149

which is the main component of the sub-inertial flow.

、 ) were dominated by the westward flow and the temperature ( )

150 151

Fig. 1. a) Study area with mooring station (red dot) and ground tracks of tropical

152

storm Washi (2005). Blue contours are isobaths in m. b) Time averaged wind field

153

during observation. c) Time series of wind speed at the mooring station during the 8

154

observation period. d-f) 2D (z – t) distributions of mooring observed zonal and

155

meridional velocity components u, v and temperature T. g –i) Sub-inertial signal

156

profiles derived from low-pass filtering of (d – f). Black dashed lines in panel (i)

157

represent the thermocline.

158

The climatological mean salinity profile for July and August obtained from

159

World

Ocean

160

https://www.nodc.noaa.gov/cgi-bin/OC5/woa13/woa13.pl?parameter=s) is used to

161

represent the background salinity at the mooring station. Then, 2 m grids of density

162

profiles and squared Brunt-Väisälä frequency

163

temperature, depth and WOA13 salinity data with the International Thermodynamic

164

Equation

165

https://www.io-warnemuende.de/teos-10-2284.html).

of

Seawater

Atlas

2010

(TEOS-10)

2013

(WOA13,

are calculated from the observed

(McDougall

and

Barker

2011,

166

The typhoon track data were downloaded from the Unisys Weather website

167

(http://weather.unisys.com/hurricanes/2005/west-pacific/wash), which is based on the

168

best hurricane track data issued by the Joint Typhoon Warning Center (JTWC). The

169

data include maximum sustained surface wind speeds and the storm center locations

170

every 6 h. The wind data at 10 m above the sea level were downloaded from the

171

European Meteorological Center database (http://apps.ecmwf.int/datasets/), which is a

172

reanalysis product with spatial resolution of 0.125° by 0.125° and temporal resolution

173

of 6 h. The ECWMF wind product has been used in previous studies for storms over

174

the shelf sea (Yang et al., 2005). Compared with the wind from the South China Sea 9

175

Ocean Data Base (SCSDB) (http://www.ocdb.csdb.cn/), the variations of the ECWMF

176

and the SCSDB winds are consistent during the observational period with a maximum

177

difference of less than 2 m s-1, indicating the reliability of the ECWMF wind data.

178

Bottom

179

(http://maps.ngdc.noaa.gov/viewers/wcs-client/).

180

2.2. Dynamic analysis

topographical

data

are

derived

from

ETOPO1

181

According to Sèvellec et al. (2015), for motions with low viscosity (Re =

182

>>1), small inertial term (Ro=

<<1), and low diffusion (Pe =

>>1), i.e.,

183

the viscosity, advection and diffusion terms are negligible, and satisfying the

184

Boussinesq approximation, the vertical velocity

185

thermodynamic equations and momentum equations as follows

186

=−

/

187

"

=−

∅

188

#

=−

∅ ∅

189

$

=−

190 191

where

∅

−

−

∅ ∅

+

can be derived from the

(1a) (1b)

+

(1c)

/

(1d)

is the total vertical velocity, % is time, & is the vertical coordinate, (

= −' * )

is the squared Brunt-Väisälä frequency, +(+- ) is the (reference)

is the Coriolis parameter, g is the gravitational acceleration, / is the

192

density,

193

partial derivative, 0 and ∅ are the amplitude and direction angle of horizontal

194

velocity. We have

195

east. The total vertical velocity

= 0cos∅,

= 0sin∅ as ∅ measured anticlockwise from the can be decomposed into three components: 10

",

196

#

and

$.

As shown in Eq. (1b),

"

mainly depends on the turning of the

197

horizontal velocities with the depth, as that derived from thermal wind relation in

198

geostrophic balance.

199

vertical and temporal variations of the horizontal velocities.

200

unsteady behavior of the density associated with the internal waves (van Aken et al.,

201

2007). These equations indicate that the vertical velocity can be estimated from time

202

series measurements of the density and the horizontal velocities at multiple depths

203

from a single mooring.

#

is the ageostrophic component, which depends on the $

is linked to the

204

In the study case as shown in Fig. 1, the characteristic scale of the sub-inertial

205

velocity is U = O(0.2) m s-1 at the mooring station, the Coriolis parameter f = 4.9×10-5

206

s-1, the depth D = 117 m, the water molecular viscosity coefficient ν = O(1×10-6) m2

207

s-1, then the Reynolds number Re =

208

term is negligible. For the typhoon process, the characteristic scale of vertical velocity

209

W = O(1×10-3) m s-1 (Sun et al., 2010; Tseng et al., 2010), the turbulent eddy

210

diffusivity 9 = O(1×10-3) m2 s-1 (Zhang et al., 2014), therefore, the Rossby number

211

Ro =

212

1.2×102≫1, implying that all dynamic parameters satisfy requirements of the above

213

dynamic diagnosis. For the turbulent friction, the eddy viscosity 9

214

O(1×10-3) m2 s-1 as 9 , and is three orders larger than ν. Then, the ratio of the

215

turbulent friction to the advective term is

= 2.3×107 ≫1, thus, the molecular viscosity

= 0.2≪1, the ratio of advective term to diffusive term Pe =

11

;

=

may reach

= 1 × 10> ≪ 1, and the ratio to the

;

= 2×10-4 ≪1. The turbulent friction is also relatively weak

216

Coriolis force is

217

compared to the other dynamic terms and negligible in the diagnostic equation.

218

3. Diagnostic results of vertical velocity

219

3.1. Vertical distribution

220

From filtered time series of the horizontal velocities and the temperature, 2D (z –

221

t) distribution of the total vertical velocity

can be calculated from Eq. (1a). The

222

results are shown in Fig. 2a, and the probability distribution of the vertical velocity is

223

shown in Fig. 2b. One can see that the value of

224

of the magnitude from 1×10-5 to 1×10-4 m s-1 during the observation period. It is

225

larger in the upper mixed layer above 25 m, where 50-70% of

226

1×10-4 m s-1, and the extreme velocities reach ±2 ×10-3 m s-1. In layers deeper than

227

25 m, the probability on the order of 1×10-4 m s-1 decreases to 20-40%, and the most

228

of

229

upper mixed layer. Similar to the temporal variation of the horizontal velocities, the

230

vertical velocity direction also changes positively and negatively with time. The

231

upward vertical velocity in the mixed layer above 25 m increased significantly as the

232

tropical storm arrived on July 30.

varies within a range of one order

is of the order of

are on the order of 1×10-5 m s-1, one order of magnitude smaller than that in the

233

Furthermore, three components of

234

results of geostrophic vertical velocity

235

in Figs. 2c – d. One can see that

236

value of O(1×10-4) m s-1 is mainly distributed at depths above 25 m, and generally

"

can be calculated from Eqs. (1b – d). The "

and its magnitude distribution are shown

homomorphically varies with

12

. The larger

237

smaller than 1×10-4 m s-1 at depths deeper than 25 m. The probability distribution of

238

the magnitude is the same as

239

O(1×10-4) m s-1 and O(1×10-5) m s-1 in the upper and deeper layers, respectively. The

240

ageostrophic vertical velocity

241

2e-f. The magnitude of

242

40% reaches 1×10-4 m s-1 in the upper 25 m. In the lower layer, 70% is of O(1×10-5)

243

m s-1 at 25 – 40 m, and O(1×10-6) m s-1 below 40 m. The profiles of

244

magnitude distribution are shown in Figs. 2g – h. One can see that 70 – 80% of

245

of the order of 1×10-5 m s-1 at 20 – 35 m, and reaches a larger value of 1×10-4 m s-1

246

below 70 m due to the weak stratification. In general, all the vertical velocity

247

components increased significantly in the upper layer on July 30, 2005, when the

248

tropical storm Washi (2005) passed over.

#

. One can see that the most of

#

"

are of the orders of

and its magnitude distribution are shown in Figs.

is mostly within a range from 1×10-6 to 1×10-5 m s-1, but

13

$

and its $

is

249 14

250 251

Fig. 2. 2D (z – t) distribution and probability distribution of the total vertical velocity

252

(a, b), the geostrophic component

(e, f) and the unsteady density component

#

253

254

"

(c, d), the ageostrophic component

$

(g, h). Black dashed lines represent

the thermocline.

3.2. Dynamic term analysis

255

We define the thermocline as a temperature gradient larger than 0.15 ℃/m,

256

which is at depths of about 25 – 45 m as shown by dashed lines in Fig. 1i (State

257

Bureau of Technical Supervision, 1992). The layers with relatively-uniform

258

temperature above and below the thermocline are defined as the mixed layer and the

259

deep layer, respectively. For the layer average, we choose the typical depths available,

260

which are (z1, 14 – 25 m) for the mixed layer, (z2, 30 – 40 m) for the thermocline and

261

(z3, 50 – 70 m) for the deep layer to gain insight into the relations of the total vertical

262

velocity to three components at different depths. The results are shown in Figs. 3a – c.

263

One can see that in the upper mixed layer (Fig. 3a), the average total vertical velocity

264

@

265

storm passed over on July 29, all vertical velocities began to increase, and reached the

266

maximum upward value greater than 1 ×10-3 m s-1 on July 30. In particular, the

267

geostrophic component BBBB"

268

velocity @

269

velocity @

270

near-inertial oscillation. The geostrophic component BBBB"

A

was within a range of ±2×10-4 m s-1 before Washi (2005) arrived. When the

A

A

became the main component of the total vertical

in the mixed layer. In the thermocline (Fig. 3b), the total vertical was mainly controlled by the unsteady component BBBB$

15

behaving like

was dominant with a

271

maximum value of 1 ×10-4 m s-1 on July 30, and the ageostrophic component BBBB#

272

increased to ±1×10-4 m s-1 after August 1. In the deeper layer (Fig. 3c), the total

273

vertical velocity @

274

the same magnitude and variable trend, which began to increase after July 29, and

275

reached the maximum value on August 2.

C

significantly depended on the unsteady component BBBB$

C

with

276

In the comparison of the three layers, the layer-averaged total vertical velocity @

277

is the largest in the mixed layer with the maximum value as 1×10-3 m s-1, and the

278

minimum value smaller than ±2 × 10-4 m s-1 appears in the deep layer. The

279

geostrophic component BBBB" reaches the maximum value of 7×10-4 m s-1 in the mixed

280

layer, and the minimum value of ±1 ×10-5 m s-1 below the thermocline. The

281

ageostrophic component BBBB# has the largest values of ±3.5×10-4 m s-1 in the upper

282

mixed layer, which is seven times of the value in the thermocline, and one order of the

283

magnitude larger than that in the deeper layer. The magnitudes of the unsteady

284

component BBBB$ in the three layers do not change much, and fluctuate within ±2×10-4

285

m s-1.

16

286 287

Fig. 3. Temporal variation of depth-mean vertical velocities in different layers. a)

288

mixed layer (14 – 25 m), b) thermocline (30 – 40 m) and c) deep layer (50 – 70 m).

289

Red, black, blue and green lines represent the total vertical velocity, the geostrophic,

290

ageostrophic and unsteady components, respectively. Magenta dotted line in panel (a)

17

291

represents the averaged Ekman pumping velocity in the upper layer. Note that the

292

y-axis for EPV is one order smaller than that for the diagnostic results.

293

3.3. Typhoon effects

294

Figure 4a shows the time-averaged profiles of vertical velocity during the entire

295

observation period. One can see that the averaged total velocity @ is upwelling at

296

depths above 35 m. Specifically, @ follows the time-averaged ageostrophic vertical

297

velocity BBBB# at depths above 15 m, and reaches the maximum value of 1.5 ×10-4 m

298

s-1 in the surface layer, and rapidly decreases with the depth. In the layer of 15 – 25 m,

299

@ decreases from 1×10-4 m s-1 to 1×10-5 m s-1. Its vertical decreasing trend is

300

similar to that of the geostrophic component BBBB" . Meanwhile, the unsteady

301

component BBBB$ decreases from 5×10-5 m s-1 to below 5×10-6 m s-1. However, the

302

ageostrophic BBBB# is negative, against with @ . At 25 – 50 m, @

303

smaller than ±5×10-5 m s-1, as that of BBBB" . BBBB# and BBBB$ are smaller than 1×10-5 m

304

s-1, and contribute only slightly to w @ G . Below 50 m, @ and three components are

305

less than 1×10-6 m s-1.

is generally

306

Furthermore, the observation period is divided into three stages: pre-storm (t1,

307

July 28), storm period (t2, July 29 – 31) and post-storm (t3, August 1 – 2), then the

308

vertical distributions of time-averaged vertical velocities are shown in Figs. 4b – d.

309

One can see that at the pre-storm stage (Fig. 4b), the averaged total vertical velocity

310

@

311

in the surface layer. Above 30 m, @

A

is mainly downwelling, and the maximum velocity of −1.2 ×10-4 m s-1 appears

18

A

mainly depends on the geostrophic

A

312

component BBBB"

and the unsteady component BBBB$

313

variable trend. BBBB$

314

ageostrophic component BBBB#

A

A

as they have similar vertical

becomes a dominant component below 40 m, and the A

is positive to against @

A

at the whole depth.

At the stage of storm (Fig. 4c), the total vertical velocity @

315

converts to

316

upwelling at the whole depth, with magnitudes larger than that before the storm. The

317

maximum @

318

dominated by BBBB#

319

25 m. Then it follows the geostrophic component BBBB"

320

mainly on the unsteady component BBBB$

321

component BBBB#

322

m s-1, and becomes downwelling below 15 m with the maximum value of –5 ×10-5 m

323

s-1 at 20 m.

is 4×10-4 m s-1, and decreases to 5×10-5 m s-1 below 30 m. @ in the upper 15 m, and by BBBB"

and BBBB$

in the layer of 15 –

in 25 – 40 m, and depends

in the deep layer. The ageostrophic

is positive in the surface layer with the maximum value of 2×10-4

At the post-storm stage (Fig. 4d), the total vertical velocity @

324

is

C

is negative at

325

upper 25 m with the maximum value of −1.5×10-4 m s-1, and mainly controlled by

326

the negative components BBBB#

327

@

328

s-1 at 40 m. The unsteady component BBBB$

329

positive value of about 1×10-5 m s-1.

330

C

is consistent with BBBB"

C

C

and BBBB$ C . Meanwhile BBBB"

C

is positive. At 30 – 50 m,

and has the maximum negative value up to −5×10-5 m C

plays a major role below 50 m with the

Comparing the vertical velocities at three stages of pre-storm, storm period and

331

post-storm, one can see that the total vertical velocity

332

maximum value of 4×10-4 m s-1 during the storm period, while downwelling at the 19

is upwelling with the

333

pre-storm and post-storm stages with the magnitude of about −1×10-4 m s-1.

334

Considering the time series of wind fields (not shown), downwelling might be

335

induced by onshore transports associated with the southwestward and northwestward

336

winds before and after the storm, respectively.

337 338

Fig. 4. Vertical distributions of time-averaged vertical velocities. a) Entire observation

339

period, b) pre-storm, c) storm period and d) post-storm. Red, black, blue and green

340

lines represent total vertical velocity, geostrophic, ageostrophic and unsteady

341

component, respectively.

20

342

4. Vertical mass transport

343

The vertical advection induced by the vertical velocity and the vertical turbulent

344

diffusion term induced by diapycnal mixing are the two main control mechanisms for

345

vertical mass transport (Munk, 1966). Both the vertical velocity and the turbulent

346

mixing are enhanced during the typhoon passage (Price et al., 1994). Their effects on

347

vertical mass transport are worth investigating.

348

4.1. Vertical advection transport

349 350

Based on the vertical velocity

from section 3 and the density gradient −

the vertical advection mass transport rate can be calculated as H#IJ = −

,

.

351

2D (z – t) distribution of H#IJ is shown in Fig. 5a. One can see that H#IJ

352

appears as periodic oscillation signature. Before the tropical storm arrived, there were

353

mainly downward transport with amplitudes of − 4×10-5 kg s-1 m-³. During the storm

354

passage from July 29 to 31, the transports were mainly upward. At upper 45 m, the

355

peak value reached 4×10-5 kg s-1 m-³, and 1×10-5 kg s-1 m-³ in the deeper layer. After

356

the storm on August 1, H#IJ was affected by the unsteady vertical velocity (Fig. 3),

357

and the magnitude reached to ±3×10-5 kg s-1 m-³ in the upper layer.

358

4.2. Transport by turbulent mixing

359

The vertical diffusive transport rate HKLM induced by turbulent mixing is (9

), where 9 = 0.2

N

is the turbulent eddy diffusivity, and ε

360

calculated as

361

the turbulent kinetic energy dissipation rate (Osborn, 1980). We use the 21

362

parameterization scheme proposed by Mackinnon and Gregg (2003) to derive ε from

363

the buoyancy frequency N and the sub-inertial horizontal velocity shear. Zhang et al.

364

(2014) investigated the enhanced mixing by Washi (2005) in the study area. As shown

365

in Fig. 10d in Zhang et al. (2014), 9 dramatically increased from 3×10-5 m-2 s-1 on

366

July 28 before the storm to about 2 ×10-4 m-2 s-1 on July 31 at depths above 50 m.

367

2D (z – t) distribution of diffusion transport rate HKLM is shown as in Fig. 5b.

368

One can see that in the mixed layer and the thermocline above 45 m, the mixing

369

transport is mainly upward, and the value of HKLM is larger than 1×10-6 kg s-1 m-³.

370

HKLM increased significantly when the tropical storm passed on July 29. The

371

maximum value reached 4×10-6 kg s-1 m-³. Below 50 m, HKLM is negative and

372

smaller than −5 ×10-7 kg s-1 m-³.

373

4.3. Comparison of advection and mixing transports

374

The variations of vertical convection transport rate H#IJ and turbulent mixing

375

transport rate HKLM , at three stages, i.e., pre-storm, storm passage and post-storm, are

376

shown in Figs. 5c and d. One can see that H#IJ is generally one order larger than

377

HKLM . In particular, at the pre-storm stage, the time-averaged advection mass transport

378

BBBBBB H#IJ

379

A BBBBBBB HKPM . The maximum of BBBBBB H#IJ

380

the maximum of BBBBBBB HKPM

381

decreases rapidly to −4 ×10-6 kg s-1 m-³, and BBBBBBB HKPM

382

BBBBBB During storm passage, H #IJ

A

is downward (negative), in contrary to upward of the mixing transport

A

A

reaches −6×10-6 kg s-1 m-³ at the upper 25 m, and

is 3 ×10-7 kg s-1 m-³ at about 20 m. Below 25 m, BBBBBB H#IJ A

A

turns to −4 ×10-7 kg s-1 m-³.

turns to upward with the maximum of 4 ×10-6 kg s-1 22

383

m-³, and BBBBBBB HKPM

384

of post-storm, BBBBBB H#IJ

385

BBBBBB KPM

386

thermocline rises up with respect to that during the storm passage. The larger values

387

in the mixed layer after the storm further indicates the delayed strong mixing after

388

storm forcing (Zhang et al., 2014; Zhang and Tian, 2014).

C

is upward with the maximum of 4 ×10-7 kg s-1 m-³ at 25 m. At stage C

tends to recover the case of pre-storm. On the other hand,

is enhanced to the maximum of 6.2 ×10-7 kg s-1 m-³ at 20 m, implying that the

389 390

Fig. 5. a-b) 2D (z – t) distribution of vertical convection mass transport rate H#IJ and

391

the mixing transport rate HKLM . c-d) Vertical distributions of time-averaged BBBBBB H#IJ

392

and BBBBBBB HKPM at three stages: pre-storm (black lines), storm passage (red lines) and

393

post-storm (blue lines). Black dashed lines represent the thermocline. 23

394

5. Discussion

395

5.1. Ekman pumping velocity (EPV)

396

As mentioned in the Introduction, previous investigators mostly calculated the

397

EPV as the main vertical motion induced by tropical cyclones (Chang et al., 2008;

398

Sun et al., 2010; Chen et al., 2012). In this paper, we derived the total vertical velocity

399

profiles during the tropic storm passage using the diagnostic equation developed by

400

Sèvellec et al. (2015). Here, the diagnostic total vertical velocity is compared with the

401

wind stress curl determined EPV.

402

The EPV induced by the wind stress curl is given by -

T>X

403

UV

Y U

d& =

Z

=

U

U[

\]

' *−

U

U^

\

' _*

(2)

404

where

405

Coriolis parameter, +(= 1.025 × 10C `a ∙ c>C ) is the mean density in the Ekman

406

layer, d[ and d^ are zonal and meridional components of the wind stress, eZ =

407

fgh /

408

averaged 9 in the upper layer is about 5 × 10>i m s >A (Zhang et al., 2014), so

409

that the Ekman layer thickness is about 30 m, close to the thickness of the mixed layer

410

as shown in Fig. 1. The sea surface wind stresses d[ and d^ are calculated by the

411

bulk formulas:

Z

is the Ekman pumping vertical velocity at the Ekman layer depth,

is the

is Ekman layer thickness. Assuming that the viscosity h = 9 , and the

412

d[ = +# kI

A- A-

(3a)

413

d^ = +# kI

A- A-

(3b)

24

414

where +# = 1.293 kg ∙ m>C is the air density,

415

and

416

calculated by (Large and Pond, 1981)

417

is the wind speed at 10 m,

are zonal and meridional wind components, and kI is the drag coefficient

kI × 10C =1.2

< 11 m s >A

418

=0.49+0.065

419

=1.364+0.0234

420

A-

11 <

AA-

−0.00023158

A- 19

<

< 19 m s >A

AA-

< 100 m s >A

Using the wind stress curl during the storm passages,

Z

at the Ekman depth is

421

calculated assuming the vertical velocity as 0 m s-1 on the surface. In comparison, we

422

calculate the averaged EPV in the upper layer as BBBBZ =

423

are shown in Fig. 3a. One can see that BBBBZ was mainly positive with values within a

424

range from −1×10-5 to 1×10-4 m s-1 during the observation period. Before the storm

425

passage on July 29, the maximum BBBBZ reached 1×10-4 m s-1, and gradually reduced to

426

−1×10-5 m s-1 after July 30. In comparison, the averaged diagnostic vertical velocity

427

in the upper layer @

428

peak value of @

429

the change in the wind speed as shown in Fig. 1c.

430

A

A

-pVY XY

. The time series of BBBBZ

reached a higher peak value of 1×10-3 m s-1. Furthermore, the

occurred one day later than that of the averaged EPV BBBB, Z but to

According to geostrophic adjustment theory, an upwelling process requires a = 1/

431

geostrophic adjustment time of at least

432

storm Washi (2005) had a longer forcing time (>60 h) than the required time (~35 h),

433

thus, the EPV can be achieved for the upwelling velocity. On the other hand, if the

434

forcing time lasts long enough, the upwelling velocity should be greater than EPV

#

25

to be well established. The tropical

435

(Sun et al., 2010). In our case, as mentioned above, the diagnostic total upwelling

436

velocity is one order larger than the EPV of the Ekman layer, indicating that

437

geostrophic movements have been developed. This is true that

438

the total vertical velocity

"

is important for

as mentioned in section 3.

439

Due to the limitations of the mooring observations, the vertical velocities are

440

missing in the surface layer above 14 m. According to the Ekman pumping theory, the

441

EPV is set to be zero at the sea surface and reaches a maximum at the bottom of the

442

Ekman layer. Assuming that the vertical velocity increases linearly with the depth, the

443

depth-mean vertical velocity at depth 0 – 14 m should be less than that in the lower

444

layer. As shown by previous numerical model studies, the total vertical velocities

445

were stronger near the thermocline and decreased upward to the surface (Zhang et al.,

446

2016; Prakash and Pant, 2017). Thus, we propose that the depth-mean vertical

447

velocity in the surface layer (0-14 m) might be smaller than the average value of

448

O(1×10-3) m s-1 in the mixed layer.

449

5.2. Accuracy assessment of diagnostic vertical velocity

450 451

As suggested by Sèvellec et al. (2015), convergence of the vertical velocities can be used to assess the accuracy of the method used in this study, i.e., A

q = \T

452

r p\ r

@ d%

(4)

453

where @

is the depth-averaged vertical velocity and d is the time interval for

454

integration. From Fig. 6, one can see that the variability of the estimated vertical

455

velocity depends greatly on the number of samples. If the number of samples is less 26

456

than 144, the maximum variability of vertical velocity may reach −1.8 ×10-4 m s-1.

457

When the number of samples are more than 300, the total vertical velocity

458

components tend to be stable.

and its

459

For the impact of measurement uncertainty, we propagate the uncertainty of

460

temperature and horizontal velocity in Eq. (1). The overall errors are about 5.6×10-6

461

m s-1 for the total vertical velocity w and 7.3×10-7 m s-1, 6.7×10-5 m s-1, and 7.4×10-5

462

m s-1 for three components

463

values, the errors are scaled by sample number as

464

more than 100, the errors of the vertical velocity are about 1×10-8 - 1×10-6m s-1,

465

which are one to two orders of magnitude lower than the calculated vertical velocities.

466

On the other hand, the calculated values of

467

the magnitude from 1×10-5 to 1×10-4 m s-1 before the storm and reached the

468

maximum upwelling of O(1×10-3) m s-1 as Washi (2005) passed by. The magnitude

469

and direction of vertical velocity are consistent with previous studies (Pallàs-Sanz and

470

Viúdez, 2005; Liang et al., 2017; Xie et al., 2017b), implying the reliability of the

471

diagnostic method. Thus, this study provides a new test for the method of vertical

472

velocity estimation based on single mooring observation of horizontal velocity and

473

density.

",

#

and

27

$,

respectively. For the time-averaged A

√u

. When sample numbers are

varied within a range of one order of

474 475

Fig. 6. Convergence of the depth-averaged vertical velocity vs. the number of

476

samples. Red, black, blue and green lines represent qV , qVv , qVw , and qVx ,

477

respectively.

478

A climatologically mean profile is used to calculate the density and the buoyancy

479

frequency profiles due to the missing salinity observations in section 2. The possible

480

uncertainties induced by the salinity data are discussed here. It is true that the salinity

481

may differ from the climatological values during the storm passage. However, such

482

differences may have little effect on the calculated vertical velocities. As shown in Eq.

483

(1), the vertical velocity depends mainly on the density variations of

484

rather than on the density itself. Previous observations of time series of salinity and

485

temperature have shown that the density variation mainly followed that of

486

temperature during the typhoon passage (Pan et al., 2012; Zhang and Tian, 2014; Wu

487

et al., 2016; Song and Tang, 2017). Furthermore, the previous salinity observations

488

near the mooring station have showed that the salinity variation was less than 0.3 28

U

U

and

U

U

,

489

during storm passage (Zhang and Tian, 2014). Assuming a lager salinity variation of

490

0.1 – 1 from climatology values, the overall errors propagating into the diagnosed

491

vertical velocity are within a range of 7.2×10-8 – 7.2×10-7 m s-1, which are more than

492

two orders of magnitude lower than the diagnostic vertical velocities. The

493

salinity-induced density errors have tiny increment with the depth (<0.0005 kg m-3

494

within 100 m for the salinity error of 1) and such variation can be ignored. On the

495

other hand, the salinity variation from the climatological values with the depths is

496

smaller and the overall errors propagating into the vertical velocity is also smaller

497

compared to the variation with time series during storm passage. Thus, the

498

uncertainties due to the use of climatological salinity profile are negligible in this

499

study.

500

5.3. Comparison to similar studies

501

In the study area east of Hainan Island, previous investigators have estimated the

502

vertical velocity under general wind conditions based on conservation of the salinity

503

flux, generalized omega equation or numerical models (Deng et al., 1995; Su et al.,

504

2009; Xie et al., 2017b). As shown in Table 1, the results vary within a range of one

505

order of magnitude from 3×10-5 to 3×10-4 m s-1, generally consistent with the

506

calculated values of

before passage of Washi (2005) in this study.

507

For typhoon processes, the vertical velocity in the upper layer is usually

508

estimated based on the Ekman pumping theory. The EPVs are generally of the order

509

of O(1×10-3) m s-1 during typhoon passage (Table 1), similar to the calculated vertical 29

510

velocity in the upper layer in this study. However, the EPV can only give the values at

511

the bottom of the Ekman layer, the vertical structure and temporal variation of the

512

vertical velocity profile were lack of investigation. Despite the use of numerical

513

models of ocean response to tropical cyclones, discussion of the vertical velocity is

514

scarce. Using a 3D Price-Weller-Pinkel model (3DPWP), Zhang et al. (2016) derived

515

the 3D circulation in response to severe tropical storm Kalmaegi (2014) in the

516

northern SCS. They found alternately appearing upwelling and downwelling

517

velocities with amplitudes of 1×10-3 m s-1 along the typhoon track, which are close to

518

the results obtained in this study. Prakash and Pant (2017) investigated the upper

519

ocean response to severe tropical storm Phailin (2013) in the southeastern Bay of

520

Bengal with the Coupled Atmosphere-Ocean-Wave-Sediment Transport (COAWST)

521

modelling system. They obtained a maximum upwelling velocity of 2.5×10-3 m s-1

522

when the storm passed by. Their results also show negative vertical velocities at a

523

short period before the arrival of tropical cyclone, probably due to shoreward water

524

transport.

525

Previous estimations of the vertical velocity in the coastal ocean or during

526

passage of tropical cyclones are summarized in Table 1. One can see that the

527

magnitude and direction of the vertical velocity are generally consistent with our

528

calculations, of which the values are generally less than 1×10-4 m s-1 under the

529

ordinary weather conditions and increase to larger than 1×10-3 m s-1 when moderate

530

tropical cyclone passes by. 30

531

Table 1 Estimation of vertical velocities in coastal ocean or under tropical cyclone in

532

previous studies

Ocean area

Method

Weather condition

Maximum vertical velocity (m s-1)

East of Hainan Island

Conservation of salinity flux

Summer monsoon

4.51 ×10-5

Deng et al., 1995

East of Hainan Island

Generalized omega equation

Summer monsoon

–5 ~ 7×10-5

Xie et al. 2017b

East of Hainan Island

2D nonlinear model

Summer monsoon

0 ~3.2 ×10-5

Guo et al., 1998

East of Hainan Island

HAMburg Shelf Ocean Model (HAMSOM)

Summer monsoon

-1 ~ 3 ×10

Su and Pohlmann, 2009

Southern East China Sea

EPV

Central South China Sea

EPV

West of Luzon Strait

EPV

Northern South China Sea

3D Price-Weller-Pinkel model (3DPWP)

Southeastern Bay of Bengal

Coupled atmosphere-ocean model

Super typhoon Hai-Tang (2005) Typhoon ‘Hagibis’ (2007) Typhoon Kai-Tak (2000)

-4

Authors, year

-1 ~ 6.8 ×10-5

Chang et al., 2008

3 ~ 6×10-3

Sun et al., 2010

-2 ~ 5×10-3

Lin et al., 2003

Severe tropical storm Kalmaegi (2014)

-1 ~ 1×10-3

Zhang et al., 2016

Severe tropical storm Phailin (2013)

2.5 ×10

Prakash and Pant, 2017

-3

533 534

5.4. Dynamic processes associated with storm

535

As mentioned above, the diagnosed vertical velocity from mooring observations

536

shows significant change as the storm passes by. The reason why the storm would 31

537

change the vertical velocity is implicitly explained by the storm-induced variations of

538

horizontal velocity and stratification. As shown in Fig. 1a, the mooring station is on

539

the right side of the storm track. According to previous observational and numerical

540

studies, there would be strong two-layer near-inertial motions on the right side of the

541

storm track (Price, 1983; Zhang et al., 2016). In our case, as shown in Figs. 1d and 1e,

542

the magnitudes of zonal and meridional velocities 0 increased after the storm passed

543

by on July 29, 2005 and the velocity directions were opposite in the upper mixed layer

544

and the deep layer. This implies that the horizontal advection and the rotation of the

545

horizontal velocities with the depth

546

stir the ocean, enhance the mixing and decreased the stratification as shown in Figs. 1f

547

and 1i. Thus, the squared Brunt-Väisälä frequency

548

passes by. As indicated by Eq. (1), the increase of 0 and

549

result in the increase of the geostrophic vertical velocity

550

vertical velocity

551

component

552

horizontal velocities indicate that

553

the storm passage because of smaller

554

isopycnal undulation associated with the internal waves has larger values in the

555

deeper layer.

556 557

#,

∅

are increased. Furthermore, the storm winds

decreases after the storm ∅

and the decrease of "

and thus the total

in the mixed layer. For the ageostrophic vertical velocity

the calculation of

and the vertical and temporal gradients of the #

is mainly controlled by

. It increases during

due to homogeneous mixing. For

$,

the

Zhang et al. (2016) investigated the dynamic responses to storm Kalmaegi (2014) in the northern SCS using the 3DPWP model. They found that the mixed layer 32

558

currents are directly wind driven in the beginning, while the opposite currents below

559

are driven by the pressure gradient generated by inertial pumping. Thus, strong

560

upwelling and downwelling occur in accordance with the horizontal flow convergence

561

and divergence.

562

As discussed above, the interaction of storm-induced strong near-inertial motion,

563

the derived geostrophic motion from pressure gradient, and the ageostrophic motions

564

driven by winds together change the vertical velocity, and thus the vertical transport.

565

5.5 Effects of turbulent friction

566

As indicated by the scale analysis in section 2.2, the turbulent friction term is

567

ignored in the dynamic equation diagnosed vertical velocity. It is reasonable to

568

consider the friction is relatively weaker than the other dynamic terms discussed

569

above. Near the upper boundary, the EPV associated with the surface wind friction

570

and the turbulent viscosity in the upper ocean is calculated in section 5.1. The

571

comparison shows that the averaged EPV in the upper layer is one order smaller than

572

the diagnosed vertical velocity without the turbulent friction. In the deep layer, the

573

studied water column is 42 m far from the bottom, and the bottom friction may have

574

little effect. Furthermore, the vertical velocity derived from the advection-turbulent

575

diffusion equation (not shown) is also one order smaller than the diagnostic vertical

576

velocity without the friction. This implies that the turbulent forcing has weak direct

577

influence on

578

turbulent friction and mixing may have been implicitly included in the diagnosis since

in the study case. On the other hand, the indirect effects of the

33

579

the observed horizontal velocity and density had been changed by the turbulent

580

friction and mixing.

581

5.6 Broad impacts

582

As mentioned in the Introduction, tropical cyclone-induced vertical velocity and

583

mass transport are of great importance for ocean biogeochemical processes and

584

primary productivity. Thus, tropical cyclones are not only hazardous for the coastal

585

ocean but favor the raising of the concentration of phytoplankton and the fishery

586

production (Hong et al., 2011; Lachkar and Gruber, 2013; Di Lorenzo, 2015; Nowald

587

et al., 2015; Zhang et al., 2018). In this study, we provide quantitative estimates of the

588

vertical velocity and mass transport induced by a typical tropical storm Washi (2005)

589

in the northwestern SCS. The upward transport was enhanced at least one order of

590

magnitude during storm passage, implying possible phytoplankton bloom after storm

591

passage. In fact, the concentration of Chl-a around the mooring station indeed

592

increased 23% after Washi (2005) passed by (Zhang et al., 2014). Furthermore, we

593

find that the vertical advection is the dominant factor for the cyclone-induced vertical

594

mass transport, instead of the vertical mixing. Thus, diagnosing the vertical velocities

595

is crucial for studies of biogeochemical response to and variation of coastal ecosystem

596

due to tropical cyclone passage.

597

The diagnostic method used in this study is derived from the general

598

thermodynamic and momentum equations with assumptions of negligible viscosity,

599

advection and diffusion terms. Thus, it can be applied to cases with low viscosity, 34

600

small inertial term and low diffusion. As estimated in section 2.3, the dynamic

601

parameters during tropical cyclone processes generally satisfy the assumption,

602

implying the applicability of the method for other typhoon processes. For the coastal

603

ocean where Ro <<1 and Pe >> 1, the method can also be used. This study provides a

604

test for obtaining vertical velocity from mooring observations in the coastal ocean and

605

tropical cyclone processes. Furthermore, considering difficulties of in-situ

606

measurements under harsh weather conditions, the vertical velocity derived from

607

mooring observations of horizontal velocity and stratification is valuable for better

608

understanding of 3D dynamic and biogeochemical responses of the ocean to tropical

609

cyclones.

610

6. Conclusions

611

Based on mooring observations of the horizontal velocities and the temperature

612

on the northwestern continental shelf of the SCS during the period from July 28 to

613

August 2, 2005, variations of the vertical velocity and the mass transport are analyzed

614

using the dynamic diagnostic method. The major findings are summarized as follows:

615

1) During the observation period, 80% of the vertical velocity was of the order of

616

O(1×10-4) m s-1 in the upper mixed layer above 25 m, with the maximum

617

upwelling of O(1×10-3) m s-1 as tropical storm Wash (2005) passed by on July 30 –

618

31, 2005. Below 25 m, the vertical velocity decreased one order of magnitude to

619

O(1×10-5) m s-1.

35

620

2) The governing mechanisms of the vertical velocity are distinct in different layers.

621

Near the surface, the ageostrophic component

622

layer, the geostrophic component

623

velocity with the maximum of 4.2×10-4 m s-1. Below the thermocline, the vertical

624

velocity component is mainly induced by the unsteady behavior of the density

625

associated with the near-inertial internal waves.

"

#

is significant. In the deep mixed

is the dominant component of the vertical

626

3) The vertical advection and the mixing transport are calculated from the diagnostic

627

vertical velocity and the parameterized mixing rate. The results are of the order of

628

O(1×10-5) kg s-1 m-3 and O(1×10-6) kg s-1 m-3, respectively. Both transports are

629

upward during the storm passage.

630

4) The Ekman pumping rate caused by wind forcing is calculated. The maximum

631

depth-averaged EPV was 1.1×10-4 m s-1 at the upper 30 m, which occurred on July

632

29 before the maximum wind speed arrived and one order smaller than the total

633

diagnosed vertical velocity of 1×10-3 m s-1 during the storm Washi (2005) passage

634

on July 30 – 31, 2005.

635

Acknowledgments

636

This work is supported by the National Nature Science Foundation of China

637

[Grants 41776034, and 41476009], the National Program on Global Change and

638

Air-Sea Interaction [Grants GASI-02-SCS-YGST2-02, and GASI-IPOVAI-01-02]

639

and the Innovation program of Guangdong Ocean University [Grant CYL231419012]. 36

640

References

641

Allen, J.T., Brown, L., Sanders, R., Moore, C.M., Mustard, A., Fielding, S., Lucas, M., Rixen,

642

M., Savidqe, G., Henson, S., Mayor, D., 2005. Diatom carbon export enhanced by silicate

643

upwelling in the northeast Atlantic. Nature 437, 728-732. 10.1038/nature03948.

644

Aoki, K., Kuroda, H., Setou, T., Okazaki, M., Yamatogi, T., Hirae, S., Ishida, N., Yoshida, K.,

645

Mitoya, Y., 2019. Exceptional red-tide of fish-killing dinoflagellate Karenia mikimotoi

646

promoted by typhoon-induced upwelling. Estuar. Coast. Shelf Sci. 219, 14-23.

647

https://doi.org/10.1016/j.ecss.2019.01.014.

648

Barceló-Llull, B., Mason, E., Capet, A., Pascual, A., 2016. Impact of vertical and horizontal

649

advection on nutrient distribution in the southeast Pacific. Ocean Sci. 12, 1003-1011.

650

https://doi.org/10.5194/os-12-1003-2016.

651 652

Bryden, H.L., 1980. Geostrophic vorticity balance in midocean. J. Geophys. Res. 85, 2825-2828. https://doi.org/10.1029/JC085iC05p02825.

653

Chang, Y., Liao, H.T., Lee, M.A., Chan, J.W., Shieh, W.J., Lee, K.T., Wang, G.H., Lan, Y.C.,

654

2008. Multisatellite observation on upwelling after the passage of typhoon Hai-Tang in

655

the southern East China Sea. Geophys. Res. Lett. 35, L03612.

656

https://doi.org/10.1029/2007GL032858.

657

Chen, X.Y., Pan, D.L., He, X.Q., Bai, Y., Wang, D.F., 2012. Upper ocean responses to

658

category 5 typhoon Megi in the western north Pacific. Acta Oceanol. Sin. 31, 51-58.

659

Deng, S., Zhong, H.L., Wang, W.M., Yun, F.J., 1995. On relation between upwelling off 37

660

Qionghai and fishery. J. Oceanogr. in Taiwan Strait. 1, 51-56. (In Chinese with English

661

abstract)

662 663 664

Di Lorenzo, E., 2015. Climate science: The future of coastal ocean upwelling. Nature, 518, 310-311. 10.1038/518310a.

Gierach, M.M., Subrahmanyam, B., 2008. Biophysical responses of the upper ocean to major

665

Gulf

666

https://doi.org/10.1029/2007JC004419.

of

Mexico

hurricanes

in

2005.

J.

Geophys.

Res.

113,

C04029.

667

Guo, F., Shi, M.C., Xia, Z.W., 1998. Two-dimension diagnose model to calculate upwelling

668

on offshore of the east coast of Hainan Island. Acta Oceanol. Sin. 20, 109-116. (In

669

Chinese with English abstract)

670

Halpern, D., Knox, R. A., Luther, D. S., George, S., Philander, H., 1989. Estimates of

671

equatorial upwelling between 140° and 110°W during 1984. J. Geophys. Res. 94,

672

8018-8020. https://doi.org/10.1029/JC094iC06p08018.

673

Hong, H., Chai, F., Zhang, C., Huang, B., Jiang, Y., Hu, J., 2011. An overview of physical

674

and biogeochemical processes and ecosystem dynamics in the Taiwan Strait. Cont. Shelf

675

Res. 31, S3-S12. https://doi.org/10.1016/j.csr.2011.02.002.

676 677

Hoskins, B.J., Draghici, I., Davies, H.C., 1978. A new look at the ω-equation. Quart. J. R. Met. Soc. 104, 31-38. https://doi.org/10.1002/qj.49710443903.

38

678 679

Hu, J.Y., Wang, X.H., 2016. Progress on upwelling studies in the China seas. Rev. Geophys. 54, 653-673. https://doi.org/10.1002/2015RG000505.

680

Jing, Z.Y., Qi, Y.Q., Hua, Z.L., Zhang, H., 2009. Numerical study on the summer upwelling

681

system in the northern continental shelf of the South China Sea. Cont. Shelf Res. 29,

682

467-478. https://doi.org/10.1016/j.csr.2008.11.008.

683

Jing, Z.Y., Qi, Y.Q., Du, Y., Zhang, S.W., Xie, L.L., 2015. Summer upwelling and thermal

684

fronts in the northwestern South China Sea: observational analysis of two mesoscale

685

mapping

686

https://doi.org/10.1002/2014JC010601.

surveys. J.

Geophys.

Res. 120,

1993-2006.

687

Kashem, M., Ahmed, K., Qiao, F.L., Akhter, M.A.E., Chowdhury, K.A., 2019. The response

688

of the upper ocean to tropical cyclone Viyaru over the Bay of Bengal. Acta Oceanol. Sin.

689

38, 61-70.

690

Lachkar, Z., Gruber, N., 2013. Response of biological production and air–sea CO2 fluxes to

691

upwelling intensification in the California and Canary Current Systems. J. Mar.

692

Syst. 109, 149-160. https://doi.org/10.1016/j.jmarsys.2012.04.003.

693

Large, W.G., Pond, P., 1981. Open ocean momentum flux measurements in moderate to

694

strong

695

https://doi.org/10.1175/1520-0485(1981)011<0324:OOMFMI>2.0.CO;2.

winds.

J.

Phys.

Oceanogr.

11,

324-336.

696

Lévy, M., Gavart, M., Mmmery, L., Caniaux, G., Paci, A., 2005. A four℃dimensional

697

mesoscale map of the spring bloom in the northeast Atlantic (POMME experiment): 39

698

results of a prognostic model. J. Geophys. Res. 110, C07S21.

699

https://doi.org/10.1029/2004JC002588.

700

Li, C.Y., Weeks, E., Brian, W. B., 2010. Storm surge induced flux through multiple tidal

701

passes of Lake Pontchartrain estuary during Hurricanes Gustav and Ike. Estuar. Coast.

702

Shelf Sci., 87, 517-525. https://doi.org/10.1016/j.ecss.2010.02.003.

703

Liang, X.S., Spall, M., Wunsch, C., 2017. Global ocean vertical velocity from a dynamically

704

consistent ocean state estimate. J. Geophys. Res. 122, 8208-8224.

705

https://doi.org/10.1002/2017JC012985.

706

Lin, I.I., 2012. Typhoon-induced phytoplankton blooms and primary productivity increase in

707

the western North Pacific subtropical ocean. J. Geophys. Res. 117, 1-15.

708

https://doi.org/10.1029/2011JC007626.

709

Lin, I., Liu, W. T., Wu, C. C., Wong, G. T., Hu, C.M., Chen, Z.Q., Liang, W.D., Yang, Y.,

710

Liu, K. K., 2003. New evidence for enhanced ocean primary production triggered by

711

tropical cyclone. Geophys. Res. Lett. 30, 1718.

712

https://doi.org/10.1029/2003GL017141.

713

Lindstrom, S.S., Randolph Watts, D., 1994. Vertical motion in the Gulf Stream near 68°W. J.

714

Phys. Oceanogr. 24, 2321-2333.

715

https://doi.org/10.1175/1520-0485(1994)024<2321:VMITGS>2.0.CO;2.

716

Liu, J.L., Cai, S.Q., Wang, S.G., 2011. Observations of strong near-bottom current after the

717

passage of typhoon Pabuk in the South China Sea. J. Mar. Syst. 87, 102-108. 40

718

https://doi.org/10.1016/j.jmarsys.2011.02.023.

719

Mackinnon, J.A., Gregg, M.C., 2003. Mixing on the late-summer New England

720

shelf—Solibores, shear, and stratification. J. Phys. Oceanogr. 33, 1476-1492.

721

https://doi.org/10.1175/1520-0485(2003)033<1476:MOTLNE>2.0.CO;2.

722

McDougall, T.J., Barker, P.M., 2011. Getting started with TEOS-10 and the Gibbs Seawater

723

(GSW) oceanographic toolbox. SCOR/IAPSO WG127, 28 pp. [Available online at

724

www.teos-10.org/pubs/Getting_Started.pdf.]

725 726

Munk,

W.H.,

1966.

Abyssal

recipes.

Deep

Sea

Res.

13,

707-730.

https://doi.org/10.1016/0011-7471(66)90602-4.

727

Nowald, N., Iversen, M.H., Fischer, G., Ratmeyer, V., Wefer, G., 2015. Time series of in-situ

728

particle properties and sediment trap fluxes in the coastal upwelling filament off Cape

729

Blanc, Mauritania. Prog. Oceanogr. 137, 1-11.

730

https://doi.org/10.1016/j.pocean.2014.12.015.

731

Osborn, T.R., 1980. Estimate of the local rate of vertical diffusion from dissipation

732

measurements. J. Phys. Oceanogr. 10, 83-89.

733

https://doi.org/10.1175/1520-0485(1980)010<0083:EOTLRO>2.0.CO;2.

734

Pallàs-Sanz, E., Viúdez, A., 2005. Diagnosing mesoscale vertical motion from horizontal

735

velocity and density data. J. Phys. Oceanogr. 35, 1744-1762.

736

https://doi.org/10.1175/JPO2784.1.

41

737

Pan, A., Guo, X., Xu, J., Huang, J., Wan, X., 2012. Responses of Guangdong coastal

738

upwelling to the summertime typhoons of 2006. Sci. China Ear. Sci. 55, 495-506.

739

10.1007/s11430-011-4321-z.

740

Poulain, P.M., 1993. Estimates of horizontal divergence and vertical velocity in the equatorial

741

Pacific. J. Phys. Oceanogr. 23, 601-607.

742

https://doi.org/10.1175/1520-0485(1993)023<0601:EOHDAV>2.0.CO;2.

743

Prakash, K. R., Pant, V., 2017. Upper oceanic response to tropical cyclone Phailin in the Bay

744

of Bengal using a coupled atmosphere-ocean model. Ocean Dyn. 67, 51-64.

745

10.1007/s10236-016-1020-5.

746

Price, J. F., 1983. Internal wave wake of a moving storm. Part I. Scales, energy budget and

747

observations. J.

748

https://doi.org/10.1175/1520-0485(1983)013<0949:IWWOAM>2.0.CO;2.

749 750

Phys.

Oceanogr. 13,

949-965.

Price, J.F., Sanford, T.B., Forristall, G.Z., 1994. Forced stage response to a moving hurricane. J. Phys. Oceanogr. 4, 233-260

751

Qu, T.D., 2000. Upper-layer circulation in the South China Sea. J. Phys. Oceanogr. 30,

752

1450-1460. https://doi.org/10.1175/1520-0485(2000)030<1450:ULCITS>2.0.CO;2.

753

Sévellec, F., Naveira, Garabato, A.C., Brearley, J.A., Sheen, K.L., 2015. Vertical flow in the

754

Southern Ocean estimated from individual moorings. J. Phys. Oceanogr. 45, 2209-2220.

755

https://doi.org/10.1175/JPO-D-14-0065.1.

42

756

Shi, Y.X., Xie, L.L., Zheng, Q.A., Zhang, S.W., Li, M.M., Li, J.Y., 2019. Unusual coastal

757

ocean cooling in the northern South China Sea by a katabatic cold jet associated with

758

Typhoon

759

https://doi.org/10.1007/s13131-019-1440-4.

Mujigea

(2015). Acta.

Oceanol.

Sin.

38,

62-75.

760

Song, Y. J., Tang, D. L. 2017. Mixed layer depth responses to tropical cyclones Kalmaegi and

761

Fung-Wong in the northeastern South China Sea. J. Trop. Oceanogr., 36, 15-24. (In

762

Chinese with English abstract)

763

State Bureau of Technical Supervision, 1992. Marine survey specification - Marine survey

764

data processing (National standards of People's Republic of China GB12763). Beijing:

765

China Standard Press, 67-70.

766

Su, J., Pohlmann, T., 2009. Wind and topography influence on an upwelling system at the

767

eastern

768

https://doi.org/10.1029/2008JC005018.

Hainan

coast.

J.

Geophys.

Res.

114,

C06017.

769

Su, J.L., 2004. Overview of the South China Sea circulation and its influence on the coastal

770

physical oceanography outside the Pearl River Estuary. Cont. Shelf Res. 24, 1745-1760.

771

https://doi.org/10.1016/j.csr.2004.06.005.

772

Sun, L., Yang, Y.J., Xian, T., Lu, Z.M., Fu, Y.F., 2010. Strong enhancement of chlorophyll a

773

concentration by a weak typhoon. Mar. Ecol. Prog. Ser. 404, 39-50.

774

https://doi.org/10.3354/meps08477.

43

775

Tseng, Y.H., Jan, S., Dietrich, D.E., Lin, I.I., Chang, Y.T., 2010. Modeled oceanic response

776

and sea surface cooling to typhoon Kai-Tak. Terr. Atmos. Ocean. Sci. 21, 85-98.

777

10.3319/TAO.2009.06.08.02(IWNOP).

778

van Aken, H. M., van Haren, H., Maas, L. R., 2007. The high-resolution vertical structure of

779

internal tides and near-inertial waves measured with an ADCP over the continental slope

780

in

781

https://doi.org/10.1016/j.dsr.2007.01.003.

the

Bay

of

Biscay.

Deep

Sea

Res.

54,

533-556.

782

Wang, G.H., Su, J.L., Ding, Y.H., Chen, D.K., 2007. Tropical cyclone genesis over the South

783

China Sea. J. Mar. Syst. 68, 318-326. https://doi.org/10.1016/j.jmarsys.2006.12.002.

784

Williams, M. J., Coles, R., Primavera, J. H., 2007. A lesson from cyclone Larry: An untold

785

story of the success of good coastal planning. Estuar. Coast. Shelf Sci. 71, 364-367.

786

https://doi.org/10.1016/j.ecss.2006.09.010.

787

Wu, X., Wang, H., Bi, N., Song, Z., Zang, Z., Kineke, G. C., 2016. Bio-physical changes in

788

the coastal ocean triggered by typhoon: A case of Typhoon Meari in summer

789

2011. Estuar. Coast. Shelf Sci. 183,413-421. https://doi.org/10.1016/j.ecss.2016.04.014.

790

Xie, L.L., Cao, R.X., Sahng, Q.T., 2012a. Progress of study on coastal circulation near the

791

shore of western Guangdong. J. Guangdong Ocean Univ. 32, 94-98. (In Chinese with

792

English abstract)

793

Xie, L.L., He, C.F., Li, M.M., Tian, J.J., Jing, Z.Y., 2017a. Response of sea surface

794

temperature to typhoon passages over the upwelling zone east of Hainan Island. Adv. 44

795

Mar. Sci. 35, 8-19. (In Chinese with English abstract)

796

Xie, L.L., Pallàs-Sanz, E., Zheng, Q.A., Zhang S.W., Zong, X.L., Yi, X.F., Li, M.M., 2017b.

797

Diagnosis of 3D vertical circulation in the upwelling and frontal zones east of Hainan

798

Island, China. J. Phys. Oceanogr. 47, 755-774. https://doi.org/10.1175/JPO-D-16-0192.1.

799

Xie, L.L., Zhang, S.W., Zhao, H., 2012b. Overview of studies on Qiongdong upwelling. J.

800

Trop. Oceanogr. 31, 35-41. (In Chinese with English abstract)

801

Xu, Z.H., Yin, B.S., Hou, Y. J., 2011. Multimodal structure of the internal tides on the

802

continental shelf of the northwestern South China Sea. Estuar. Coast. Shelf Sci. 95,

803

178-185. https://doi.org/10.1016/j.ecss.2011.08.026.

804 805

806

Yang, Y.X., 2005. Occurrence regularity of tropical cyclone in South China Sea. J. Shanghai Mar. Univ. 26, 16-19. (In Chinese with English abstract)

Ye, H.J., Sui, Y., Tang, D.L., Afanasyev, Y.D., 2013. A subsurface chlorophyll-a bloom

807

induced by typhoon in the South China Sea. J. Mar. Syst. 128, 138-145.

808

https://doi.org/10.1016/j.jmarsys.2013.04.010.

809

Zhan, L., Chen, L., Zhang, J., Li, Y., Wu, M., Liu, J., 2018. Contribution of upwelling to

810

air-sea N2O flux at the tip of the Antarctica Peninsula. Limnol. Oceanogr. 63,

811

2737-2750. https://doi.org/10.1002/lno.11004.

812 813

Zhang, H., Chen, D.K., Zhou, L., Liu, X.H., Ding, T., Zhou, B.F., 2016. Upper ocean response to typhoon Kalmaegi (2014). J. Geophys. Res. 121, 6520-6535.

45

814

https://doi.org/10.1002/2016JC012064.

815

Zhang, J.J., Li, H.L., Xuan, J.L., Wu, Z.Z., Yang, Z., Wiesner, M.G., Chen, J.F., 2018.

816

Enhancement of mesopelagic sinking particle fluxes due to upwelling, aerosol deposition,

817

and monsoonal influences in the northwestern South China Sea. J. Geophys. Res. 124,

818

99-112. https://doi.org/10.1029/2018JC014704.

819

Zhang, S.W., Xie, L.L., Hou, Y.J., Zhao, H., Qi, Y.Q., Yi, X.F., 2014. Tropical storm-induced

820

turbulent mixing and chlorophyll-a enhancement in the continental shelf southeast of

821

Hainan Island. J. Mar. Syst. 129, 405-414. https://doi.org/10.1016/j.jmarsys.2013.09.002.

822

Zhang, Y.W., Tian, J.W., 2014. Enhanced turbulent mixing induced by strong wind on the

823

South

824

https://link.springer.com/article/10.1007/s10236-014-0710-0.

China

Sea

shelf. Ocean

Dyn. 64,

781-796.

825

Zhao, H., Pan, J.Y, Han, G.Q., Devlin, A.T., Zhang, S.W., Hou Y.J., 2017. Effect of a fast℃

826

moving tropical storm Washi on phytoplankton in the northwestern South China Sea. J.

827

Geophys. Res. 122, 3404-3416. https://doi.org/10.1002/2016JC012286.

828

Zheng, M.L., Li, M.M., Xie, L.L., Hong, Y.B., He, Y.K., Zong, X.L., 2018. Observation of

829

hydrographic characteristics of northwestern shelf of the South China Sea in winter 2012.

830

Oceanol. Limnol. Sin. 49, 734-745. (In Chinese with English abstract)

831

Zheng, Q.A., Zhu, B.L., Li, J.Y., Sun, Z.Y., Xu, Y., 2015. Growth and dissipation of typhoon

832

forced solitary continental shelf waves in the northern South China Sea. Clim. Dyn. 45,

833

853-865. 10.1007/s00382-014-2318-y. 46

Affiliations Guangdong Key Laboratory of Coastal Ocean Variation and Disaster Prediction, College of Ocean and Meteorology, Guangdong Ocean University, Zhanjiang 524088, China Liju Wang, Lingling Xie, Junyi Li, Mingming Li Marine Resources Big Data Center of South China Sea, Southern Marine Science and Engineering Guangdong Laboratory (Zhanjiang), Zhanjiang 524025, China Lingling Xie, Junyi Li, Mingming Li Department of Atmospheric and Oceanic Science, University of Maryland, College Park, Maryland 20742, USA Quanan Zheng Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China Yijun Hou

Contributions Liju Wang analyzed the data and prepared the manuscript. Lingling Xie initiated the idea, designed the study and revised the manuscript. Quanan Zheng contributed to the design of the study and revised the manuscript. Junyi Li and Mingming Li contributed to the data analysis and discussion. Yijun Hou contributed the raw data. All of the authors have read and approved the final manuscript.

Corresponding author Correspondence to Lingling Xie.

January 8, 2020 Editor Estuarine, Coastal and Shelf Science

Dear Editor: We declare that we have no conflicts of interest to this work entitled “Tropical cyclone enhanced vertical transport in the northwestern South China Sea I: Mooring observation analysis for Washi (2005)” authored by Liju Wang, myself, Quanan Zheng, Junyi Li, Mingming Li and Yijun Hou. We appreciate your help with our manuscript. If you have further questions, please contact me at Dr. Lingling Xie College of Ocean and Meteorology Guangdong Ocean University 1 Haida Road, Mazhang District Zhanjiang, Guangdong 524088 Tel.: 0759-2396037 e-mail: [email protected] Sincerely yours,

Lingling Xie, Ph.D. Professor