# Influence of winds, geostrophy and typhoons on the seasonal variability of the circulation in the Gulf of Tonkin: A high-resolution 3D regional modeling study

Violaine Piton, Marine Herrmann, Patrick Marsaleix, Thomas Duhaut, Trinh Bich Ngoc, Manh Cuong Tran, Kipp Shearman, Sylvain Ouillon

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## ► To cite this version:

Violaine Piton, Marine Herrmann, Patrick Marsaleix, Thomas Duhaut, Trinh Bich Ngoc, et al.. Influence of winds, geostrophy and typhoons on the seasonal variability of the circulation in the Gulf of Tonkin: A high-resolution 3D regional modeling study. Regional Studies in Marine Science, 2021, 45, 101849 [17 p.]. 10.1016/j.rsma.2021.101849. hal-04398228

HAL Id: hal-04398228

https://hal.science/hal-04398228v1

Submitted on 2 Aug 2025

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1 **Title: Influence of winds, geostrophy and typhoons on the seasonal variability of the**
2 **circulation in the Gulf of Tonkin: a high-resolution 3D regional modeling study**

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4 Authors : Violaine Piton (1,2), Marine Herrmann (1,2), Patrick Marsaleix (1), Thomas Duhaut (1),

5 Trinh Bich Ngoc (2), Manh Cuong Tran (2,3), Kipp Shearman (4), Sylvain Ouillon (1,2)

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7 (1) LEGOS, IRD, UMR556 IRD/CNES/CNRS/Université de Toulouse, 31400 Toulouse, France

8 (2) LOTUS Laboratory, University of Science and Technology of Hanoi (USTH), Vietnam

9 Academy of Science and Technology (VAST), 18 Hoang Quoc Viet, Cau Giay, Hanoi, Vietnam

10 (3) Vietnamese Center for Oceanography (CFO), Hanoi, Vietnam.

11 (4) College of Earth, Ocean and Atmospheric Sciences (CEOAS), Oregon State University,
12 Corvallis, USA.

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14 Corresponding author:

15 [violainepiton@gmail.com](mailto:violainepiton@gmail.com)

16 LEGOS, CNRS

17 14, avenue Edouard Belin

18 31400, Toulouse

19 France
20 Abstract:
21 The present study explores the seasonal variability of the circulation in the Gulf of Tonkin (GoT)
22 and associated water fluxes, and identifies the driving physical processes of this circulation. We
23 use a 9 year (2009-2017) high-resolution numerical simulation. It is evaluated with historical
24 hydrographic data, sea level data, high-frequency radar data and satellite products. Our results
25 reveal a summer anticyclonic surface circulation and a winter cyclonic surface circulation, with a
26 seasonal reversal of the circulation in the Hainan Strait (eastward in summer and westward in
27 winter). In winter, a strong (~0.4 m s⁻¹) downcoast current developing in the western GoT is
28 responsible for most of the water export out of the GoT. EOF analyses reveal that ageostrophic
29 currents (wind-driven currents) mainly drive the seasonal variability of the surface circulation in
30 the GoT. The downcoast current is however both wind and density (geostrophy) driven. Our
31 results further show the strong influence of typhoons. They can significantly modulate the
32 seasonal circulation in the GoT by reversing the summer circulation and intensifying the winter
33 circulation. Finally, it is shown that during few days of their passage, typhoon can generate a
34 total water flux up to 4 times higher than the monthly climatological flux.

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36 Keywords: seasonal circulation, water flux, typhoon, coastal current, ocean regional circulation
37 model, Gulf of Tonkin
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# 1. Introduction

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The Gulf of Tonkin (GoT) is located in the South China Sea (SCS). It receives considerable freshwater and matter (sediments, nutrients, contaminants...) inputs from the Red River watershed and rivers, and from smaller rivers along the coast of China (Fig. 1). The Red River delta - GoT system constitutes an estuarine, coastal and marine environment largely affected by natural and anthropogenic activities. The low-lying Red River delta is a densely populated area (>1000 inhab km⁻²), gathering rural areas and big urban centers. This region hosts intense agriculture and aquaculture activities, as well as large industries (General Statistics Office, 2017). It is also submitted to groundwater and surface water pumping, dam impoundments and sand dredging (Le et al., 2007). These factors generate pollution that is transferred to the Red River delta – GoT system via continental freshwaters. They also induce changes in water and matter fluxes. The Red River delta - GoT system is further submitted to atmospheric and oceanic variability at a large range of scales (typhoons, drought and flooding, seasonal monsoon, climate change, storm surges, wave and tidal forcing, large scale circulation, Wyrtki, 1961). In this context, understanding the water circulation and fluxes from the Red River to the GoT is fundamental to better understand the regional water and associated matter cycles, and to monitor and forecast the effects of anthropogenic activities on these cycles and on the Red River – GoT environment.
<figure><img src="image_3.png" /><figcaption>Figure 1: Topography of the South China Sea (a), of the Gulf of Tonkin (b) and of the Red River Delta (c), and interesting features (rivers, hydrological and HF radar stations).</figcaption></figure>

The oceanic circulation in the SCS and its seasonal variability have been the subject of numerous observation-based and numerical modeling studies (see Hu et al. (2000) and Zhu et al. (2019) for a review). The seasonally reversing Southeast Asian monsoon was identified as the primary driver of the SCS surface circulation (Wyrtki 1961; Xu et al., 1982; Chu and Li, 2000; Hu et al., 2000; Fang et al., 2002). At the daily scale, typhoons can induce strong intraseasonal variations in the wind-driven circulation and in the thermohaline structure of the SCS upper layer (Chu and Li, 2000; Lin et al., 2003; Tseng et al., 2010; Chu et al., 2000; Liu et al., 2011; Wang et al. 2014). In a review of work to date, Gao et al. (2017) pointed out the significant lack of knowledge of the circulation in the GoT compared to the SCS, resulting from the lack of recent observations and limited hydrodynamic studies and from often controversial findings.

All studies agree on a gulf-scale cyclonic circulation in winter in the GoT. Different detailed patterns of the winter circulation have however been proposed. Based on one year of observation, Tan (1987) suggested that the winter circulation in the GoT was composed of two
74 cyclonic gyres: one in the southern GoT and one in the middle of the northern GoT (see Gao et al. 2017 for a schematic view). Yu and Liu (1993), based on historical in situ observations, 75 described a gulf-scale cyclonic circulation with one small cyclonic gyre enclosed in the northern 76 GoT. Two studies described a circulation composed of a northern enclosed cyclonic gyre and a 77 southern anticyclonic gyre (Liu and Yu, 1980, based on 2D model; Yuan and Deng, 1999; based 78 on 3D model). More recent 3D modeling studies suggested that the gulf-scale cyclonic 79 circulation was composed of two cyclonic gyres: one in the southern GoT and one in the 80 northern GoT, with a southward current following the Vietnamese coast and a southwestward 81 current following the western coast of Hainan island (Manh and Yanagi, 2000; Xia et al., 2001; 82 Sun et al., 2001; Rogowski et al., 2019). 83

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85 Due to the combined effects of wind, heat flux and larger river discharge, the summer circulation
86 is complex. Previous studies suggested three different patterns (Gao et al., 2017): basin-scale
87 anticyclonic (Sun et al., 2001, based on 3D modeling), cyclonic (Xia et al., 2001, using
88 numerical modeling) or complex (Yang et al., 2003, based on field observations; Gao et al.,
89 2013, based on 3D modeling). The latter is described as a two-gyres structure with a cyclonic
90 gyre in the northern GoT and an anticyclonic gyre at the southern GoT boundary. Most recent
91 observation and model results suggested that the summer circulation is rather anticyclonic in the
92 northern GoT and eastwards in the southern GoT, with a branch exiting the GoT south of Hainan
93 island (Rogowski et al., 2019).

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95 Physical processes governing this seasonal circulation also remain a topic of debate. Several
96 studies identified density-gradient as the governing mechanisms (Xia et al., 2001; Yang et al.,
97 2003, based on 3D modeling; Gao et al. 2014b, based on 3D modeling) while others identified
98 the monsoon winds (Yuan and Deng, 1999; Manh and Yanahi, 2000; Xia et al., 2001; Sun et al.,
99 2001; Rogowski et al., 2019).

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101 The circulation in the strait of Hainan also remains widely discussed and is classified into two
102 patterns (Gao et al., 2017). (i) The first pattern consists in seasonal current reversal (eastward in
103 summer, westward in winter, Liu et al. 2001; Gao et al., 2013; Rogowski et al., 2019). Based on
104 field SLA (Sea Level Anomaly) observations, Liu et al. (2001) associated the circulation reversal
to the seasonal monsoon. Ke (1983), using 2D modeling, reported that the eastward current occurred under southwesterly wind conditions. Using a 3-D numerical model, Rogowski et al. (2019) suggested that the westward buoyant flow observed in fall-winter comes from the adjacent Pearl River discharge, which enters the GoT through wind-induced circulation. However, Gao et al. (2013), with a 3D modeling study, reported that the sea level difference between the two entrances of the strait had a stronger effect on current direction than local winds. (ii) The second pattern describes a year-lasting westward circulation. The driving mechanisms behind this current are discussed. Some studies identified the tidal rectification over variable bottom topography (Shi et al., 2001, based on current observations; Chen et al. 2007, 2009, both based on 3D modeling). Others identified the coastal circulation off the western coasts of China (Bao et al., 2005; Rogowski et al., 2019, both 3D model studies).

Finally, Manh and Yanagi (2000) were the first to depict a downcoast current off the Vietnamese coast, flowing southward along the shorelines during winter. Ding et al. (2013) later suggested that this current was generated by the wind-driven circulation. This downcoast current was however found to last through the year by Rogowski et al. (2019). The authors suggested that it is forced by buoyant patterns, generated by intense Red River freshwater summer inputs.

The general objective of this study is to improve the knowledge of the Red River delta and GoT system in terms of circulation and water fluxes using a high-resolution model configuration. For that, our first goal is to characterize the patterns of the seasonal surface circulation in the GoT and to identify the associated governing physical processes. Our second goal is to assess the influence of this seasonal circulation on the water fluxes at the boundaries of the domain, including oceanic boundaries (fluxes between the GoT and the SCS) and continental boundaries (fluxes from the rivers). Our third goal is to examine the influence of typhoons on seasonal circulation and water fluxes. Typhoons are indeed associated with significant momentum, heat and water fluxes and influence the SCS circulation, but their effect on the GoT ocean dynamics has barely been studied until now. After this introduction, we present the hydrodynamical model and the data used to evaluate our simulation in section 2. In section 3, we present the evaluation of the model, examine the patterns of the seasonal circulation in the GoT, their associated water
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fluxes and governing mechanisms, and assess the influence of typhoons **on the circulation**. In section 4, we discuss our results. Final conclusions are given in section 5.

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## 2. Methods

### 2.1 Study site

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The GoT is a shallow basin (< 90 m) opened to the SCS through the south of the gulf and to the east through the narrow Hainan Strait (Fig. 1). The Red River is the principal source of freshwater and sediments to the GoT. The Red River delta system is composed of nine distributaries: the Day, the Ninh Co, the Ba Lat, the Tra Ly, the Thai Binh, the Van Uc, the Lach Tray, the Cam and the Bach Dang. This system brings on average 3500 m³ s⁻¹ of water along 150 km of coastlines (Dang et al., 2010), with on average 85 10⁹ m³ of water issued during the wet season (June-October) against 21 10⁹ m³ during the dry season (December-April) (Vinh et al., 2014). The Red River discharge rate is approximately 10 times larger than the sum of discharge from all remaining rivers along the Guangxi coast (Ding et al., 2013).

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The climate in the GoT is mainly governed by the South-East Asian sub-tropical monsoon climate system, characterized by an alternation of summer and winter monsoons (Wirtky 1961; Wang and LinHo, 2002) (Fig. 2). The wet summer season (June to September) accounts for 85-95% of the total annual rainfall in the region and is dominated by southwest (SW) winds. During the dry winter season, northeast (NE) winds bring cold and dry conditions over the GoT (Vinh et al., 2018). At shorter time-scales, the GoT is under the influence of typhoons. They hit northern Vietnam about 4-5 times every year and predominantly from June to November, with a frequency that has increased during the second half of the 20th century (Thanh et al., 1997). 2013 was recorded as the most active typhoon season over the Pacific since 2004 (Mori et al., 2014). It included the super typhoon Yolanda, one of the most powerful cyclones ever recorded (Schiermeier 2013; Philippine National Disaster Risk Reduction and Management Council, 2015).
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The circulation in the GoT is also under the influence of tides. The SCS and the GoT are one of the few regions in the world where diurnal tides dominate the semidiurnal tides (Fang, 1986).

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The main tidal constituents in the SCS are O1, K1, M2, S2 (classified by their importance in amplitudes) (Wyrtki, 1961).

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<caption>Figure 2: Winter (DJF) and summer (JJA) wind distribution (in m s⁻¹) over the GoT over the period 2005-2017 from CF-1.0 analysis of the European Centre for Medium-Range Weather Forecasts.</caption>

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## 2.2 Model and forcing

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We use the three-dimensional SYMPHONIE ocean circulation model (Marsaleix et al. 2008, 2009, 2012, 2019), which is widely used to simulate the hydrodynamic conditions in the Mediterranean Sea and specific processes as the Rhone river plume dynamics (Estournel et al., 1997; Reffray et al., 2004), coastal dense water formation (Ulses et al., 2008a; Herrmann et al.

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2008), wind-induced circulation over the Gulf of Lions shelf (Estournel et al., 2003; Petrenko et al., 2008; Ulses et al., 2008b), shelf-slope exchanges and along slope circulation (Bouffard et al., 2008).

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The numerical grid used in this study is the same as the one reported in detail in Piton et al. (2020), with 20 vertical irregular levels based on the sigma coordinate system. The horizontal grid corresponds to an orthogonal bipolar curvilinear system, with variable resolution increasing

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with the distance from the Red River delta (0.15 x 0.15 km near the delta and 5 x 5 km at the
open lateral boundaries of the numerical domain). The bathymetry data consist of a merged
product of GEBCO 2014 dataset gridded bathymetry and digitalized nautical charts built by
Piton et al. (2020).

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The surface atmospheric conditions (i.e. wind intensity, potential temperature, atmospheric pressure, relative humidity, precipitation rate, short and long waves radiative flux) are prescribed every 3 hours from forecast CF-1.0 analysis of the European Centre for Medium-Range Weather Forecasts (ECMWF). The turbulent fluxes at the ocean-atmosphere interface are computed following formulations of Large and Yeager (2004).

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Initial and lateral ocean boundary conditions are provided by the daily mean fields from the
Global Ocean 1/12° Physics Analysis and Forecast simulation produced by Mercator Ocean,
available at http://marine.copernicus.eu/services-portfolio/access-to-products/). This product is
referred to hereafter as GOM (for Global Ocean Model).

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Open boundary tidal conditions are prescribed by the FES2014b global tidal model resolved on
unstructured meshes but distributed on a resolution coherent 1/16° x 1/16° grid (Carrère et al.,
2016). Our configuration considers nine barotropic tidal components as tidal forcing: M2, S2,
N2, K2 (semi-diurnal tides), K1, P1, O1, Q1 (diurnal tides) and M4 (non-linear tide)
implemented as in Pairaud et al, 2008.

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Our domain includes 16 rivers with 9 of them belonging to the Red River delta (Fig. 1). The 7
remaining rivers are the Lam, the Lac Giang, the Ma, the Beilun, the Qinjiang, the Nanliu and
the Jiuzhou. The construction of the Red River waterways is described in Piton et al. (2020).
Daily mean water discharges are collected by the National Hydro-Meteorological Service
(NHMS) of Vietnam at the hydrographic station of Son Tay (Fig. 1). Following the work of Vinh
et al. (2014), a percentage of the total Son Tay's daily discharges is attributed accordingly to
each distributary: 7% of the total discharge is attributed to the Bach Dang channel, 13.2% to the
Cam channel, 14.5% to the Van Uc channel, 6.4% to the Thai Binh channel, 30.3% to the Ba Lat
channel, 5.6% to the Ninh Co channel and 23% to the Day channel. For the rivers located outside
the Red River delta, we impose climatological monthly discharges provided by the NHMS.

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The simulation performed with the SYMPHONIE model runs from 1 January 2009 to 31
December 2017. The model run is referred to hereafter as SIMU.

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215 2.3 In situ observations and satellite data sets

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217 The performance of SIMU are evaluated against several datasets.

218 We use SST and SSS (sea surface salinity) time series provided hourly at two hydrographic
219 stations (Hon Dau and Bach Long Vi, see Fig. 1) by the NHMS over the period 2009-2017.

220 We also use CTD profiles collected along a 25 km long cross-shelf survey line (horizontal
221 resolution: 2.5 km; vertical resolution: 0.5 m), in December 2015 and in July 2016, provided by
222 the Vietnamese Administration of Seas and Islands (Center for Oceanography).

223 Data from three CODAR stations located on the Vietnamese coast at Hon Dau, Nghi Xuan and
224 Dong Hoi (see Fig. 1 for location) and part of the Global High Frequency (HF) Radar Network
225 (Roarty et al., 2019) are used to assess the variability of the modeled surface coastal current. In
226 particular, we use three years of those monthly averaged HF radar observations, from January
227 2015 to December 2017. Details on the computation of the monthly averaged surface currents
228 fields are available in Tran et al. (2021).

229 For the SST evaluation over the GoT, we use the 1/18° daily Operational Sea Surface
230 Temperature and Sea Ice Analysis (OSTIA) distributed by the UK Met Office (Stark et al.,
231 2007), available since 2006 at https://podaac.jpl.nasa.gov/dataset/UKMO-L4HRfnd-GLOB-
232 OSTIA.

233 The 1/4° L3_DEBIAS_LOCEAN_v3 product from the Soil Moisture and Ocean Salinity
234 (SMOS) satellite mission of the European Space Agency (ESA) launched in 2009 is used for
235 validation of the model's SSS (Boutin et al., 2018). These data have been produced by
236 LOCEAN/IPSL (UMR CNRS/UPMC/IRD/MNHN) laboratory and ACRI-ST company and are
237 distributed by the Ocean Salinity Expertise Center (CECOS) of the CNES-IFREMER Centre
238 Aval de Traitement des Données SMOS (CATDS) at https://www.catds.fr/Products/Available-
239 products-from-CEC-OS/CEC-Locean-L3-Debiased-v3.

240 The modeled sea level anomaly (SLA) is evaluated with the delayed-time Global Ocean Gridded
241 SSALTO/DUACS Sea Surface Height L4 product (called ALTI hereafter) available from:
242 http://marine.copernicus.eu/services-portfolio/access-to-
243 products/?option=com_csw&view=details&product_id=SEALEVEL_GLO PHY L4 REP OB
244 SERVATIONS 008 047. This is a global product with 1/4° resolution at daily timescale, from
245 1993 to present in which the SLA is computed with respect to a twenty-year mean. For
comparison with the model outputs, the mean SLA over the model period (2009-2017) is subtracted from the SLA at each grid point, both for the model and the data.

SIMU performances to reproduce observations are evaluated with the Nash-Sutcliffe efficiency coefficient (NSE, Nash and Sutcliffe, 1970), as:

$$NSE = 1 - \frac{\sum(x_t - y_t)^2}{\sum(x_t - \bar{x})} \quad (\text{Eq. 1})$$

with y the simulated values and x the observed values. NSE varies from 1.0 (perfect fit) to -∞; negative values suggesting a poor efficiency to reproduce the observations (Krause et al., 2005).

## 3. Results

### 3.1 Model evaluation

We evaluate the realism of our reference simulation SIMU in reproducing the GoT oceanic circulation and water masses, and their seasonal variability. For that we compare SIMU outputs with the available data presented in section 2.3, focusing on the summer (June-August, JJA) and winter (December-February, DJF) seasons. When modeled and observational data have different spatial/temporal resolutions, the finer one is resampled at the same resolution as the coarser one before making comparisons.

The spatial distribution of OSTIA SST in DJF is well reproduced by SIMU (Fig. 3 a, b), with a highly significant spatial correlation of R=0.94 (p-value p<0.01) and a cold 0.5°C bias. The observed winter SST is also well reproduced by GOM with a highly significant correlation of R=0.95 (p<0.01) and a cold 0.1°C bias (Fig. 3 a, c). The northeast winter monsoon brings a cold tongue of surface water into the GoT from the SCS, causing a strong north-south SST gradient similar in observations and models, with temperatures varying from ~17 to 25 °C. The observed summer JJA SST is quite homogeneous over the GoT, with temperatures varying from ~28.5 to 30 °C (Fig. 3 e). Two cold water areas are observed near the southernmost Vietnamese coast and along the south-east coast of Hainan island in OSTIA, well reproduced in SIMU (Fig. 3 d, e). SIMU however overestimates SST along the Chinese coast by ~1 °C, (possibly due to the ~1.5°C overestimation in GOM), and underestimates it by ~1°C along the southern Hainan coast. The spatial correlation between OSTIA and SIMU SST in summer is therefore overall weak (R=0.39,
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p<0.05), also due to the smaller spatial range of SST, with an overall warm (0.2°C) bias (Fig. 3 d). The spatial correlation between GOM and OSTIA SST is even smaller (R=0.25, p<0.05) with an overall warm 0.4°C bias (Fig. 3 f).

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The comparison of modeled and observed monthly climatological SST anomaly averaged over the GoT (Fig. 4 a) shows that the annual SST cycle is well reproduced by SIMU and GOM (R=0.99, p<0.01; NSE≥0.98). The average bias is smaller in SIMU (0.24 °C) than in GOM (0.43°C). SST annual cycle observed in in-situ data at Bach Long Vy (Fig. 4 d) and Hon Dau (Fig. 4 g) is also well reproduced by both SIMU and GOM, with R=0.99 (p<0.01) and NSE≥0.99 for both models. SIMU, OSTIA and GOM all slightly underestimate the winter negative and summer positive SST anomalies at Bach Long Vi by ~0.4 °C, with a slightly weaker bias in SIMU compared to GOM.

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<figure><img src="image_22.png" /><figcaption>Figure 3: Spatial distributions in winter (DJF) and summer (JJA) of SST (°C, a, b, c, d, e, f), SSS (g, h, i, j, k, l), SLA (m) and geostrophic currents velocity (m s⁻¹, m, n, o, p, q, r) from SIMU and</figcaption></figure>
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GOM outputs and for observations (OSTIA, SMOS, ALTI). Spatial means and spatial
correlations between simulated and observed fields are computed over the SIMU domain.

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The realism of simulated seasonal SSS fields is evaluated by comparison with SMOS satellite
data. As the coverage of SMOS is coarse (1/4°) and sparse in coastal areas, this comparison must
be considered with caution. Fig. 3 g, h, i shows SIMU, SMOS and GOM DJF SSS fields.
Observed winter SSS are homogeneous over the GoT (~33) as rainfalls and associated river
discharge are low (Fig. 3 h). A fresher water patch (~32) is observed near Hainan Strait, possibly
pushed from the strait along the northern coast by the northeast monsoonal winds. These spatial
patterns are well reproduced in SIMU with a highly significant spatial correlation (R=0.81,
p<0.01), and in GOM with a slightly weaker correlation (R=0.67, p<0.01). In JJA, the summer
southwest monsoon, associated with heavy rainfalls and strong river discharge, triggers a NW-
SE SSS gradient in SMOS (Fig. 3 k). SIMU exhibits a similar SSS gradient, with weaker values
along the coast (Fig. 3 j), corresponding to the Red River plume. Due to its low resolution,
SMOS misses most of this plume. The correlation between SIMU and SMOS JJA SSS is
consequently rather weak (R=0.21, p=0.12). GOM and SMOS JJA SSS are uncorrelated
(R=0.04, p=0.2).

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Fig. 3b shows the observed and modeled time series of climatological monthly SSS anomaly
averaged over the GoT. SIMU SSS annual cycle closely follows SMOS (R=0.96, p<0.01;
NSE=0.92). SIMU SSS annual cycle also exhibits highly significant correlation with in situ data
at Bach LongVy (R=0.76, p<0.01) and Hon Dau (R=0.99, p<0.01, NSE=0.88), a station located
at the coast, where tidal and river influences are strong with strong negative anomalies in
summer (up to -5, Fig. 4 h). GOM fails to represent the seasonal SSS cycle over the GoT and at
both stations, showing non-significant correlation with in situ observations (R<0.4, p>0.2, Fig. 4
b, e, h), and much higher biases compared to SIMU (0.63 vs. 0.43 over the GoT, 0.11 vs. 0.01 at
Bach LongVy and 4.32 vs. 1.81 at Hon Dau). GOM's weaker performance in reproducing the
annual cycle and seasonal spatial patterns of SSS in the GoT is beyond the scope of this study
but may be attributed to inaccurate representation of the Red River discharge in GOM model
(personal comments of Romain Bourdallé-Badie, Mercator).

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<figure><img src="image_23.png" /><figcaption>b) Annual SSS cycle in the GoT</figcaption></figure>

<figure><img src="image_24.png" /><figcaption>c) Annual SLA cycle in the GoT</figcaption></figure>

<figure><img src="image_25.png" /><figcaption>d) Annual SST cycle at Bach LongVy</figcaption></figure>

<figure><img src="image_26.png" /><figcaption>e) Annual SSS cycle at Bach LongVy</figcaption></figure>

<figure><img src="image_27.png" /><figcaption>f) Annual SLA cycle at Bach LongVy</figcaption></figure>

<figure><img src="image_28.png" /><figcaption>g) Annual SST cycle at Hon Dau</figcaption></figure>

<figure><img src="image_29.png" /><figcaption>h) Annual SSS cycle at Hon Dau</figcaption></figure>

<figure><img src="image_30.png" /><figcaption>i) Annual SLA cycle at Hon Dau</figcaption></figure>

<figure><img src="image_31.png" /></figure>
Figure 4: Time series of climatological monthly SST anomalies (a, d, g), SSS anomalies (b, e, h), SLA (c, f, i) in the GoT (a, b, c), at Bach Long Vi (d, e, f) and at Hon Dau (g, h, i) in SIMU, OSTIA, SMOS, ALTI and from the hydrographic stations.

We then examine SIMU capability to represent the ocean surface circulation by comparing SIMU outputs with altimetry data (ALTI) and HF radar measurements (HF) over their common period (2015-2017 for HF and 2009-2017 for ALTI). The SLA is calculated from both the model and ALTI from the sea surface height (SSH) fields by removing at each grid point the mean sea surface over the common period:

$$SLA(i,j,t) = SSH(i,j,t) - \overline{SSH}(i,j,2009-2017) \quad (\text{Eq. 2})$$

Fig. 3 m-r shows the climatological DJF and JJA mean SLA and surface geostrophic currents in SIMU, ALTI and GOM. Winter SLA patterns are similar in SIMU and ALTI with highly significant spatial correlations (R=0.71, p<0.01) and similar mean values (0.062 m for SIMU and 0.061 m for ALTI, Fig. 3 m, n). GOM SLA winter fields show a slightly weaker correlation (R=0.68, p<0.01) and an overall positive 0.03 m bias (Fig. 3 o). HF Radar data show a strong southward coastal current along the Vietnamese coast (~0.4 m s⁻¹) in DJF (Fig. 5 a), which is well reproduced by SIMU in terms of direction and intensity (~0.2-0.3 m s⁻¹, Fig. 5 c). This current is not observed in ALTI, which can be explained by altimetry limitations in correctly capturing SLA in coastal areas (Carrère et al., 2003; Bouffard et al., 2008). The meridional SLA gradient observed in ALTI and SIMU is possibly enhanced by the northeast winter monsoon winds which push the water masses towards the south. The observed geostrophic circulation is composed of a double gyre (of ~0.1-0.2 m s⁻¹), well reproduced by SIMU but with weaker intensity (<0.1 m s⁻¹): a cyclonic gyre in the northern GoT, and a anticyclonic gyre in the southern GoT (Fig. 3 n).

The meridional SLA gradient is reversed in summer (Fig. 3 p, q), possibly due to the southwest summer monsoon winds, which push the water towards the northern GoT. The mean observed JJA SLA (-0.108 m) is well reproduced in SIMU (-0.109 m) even though SIMU and ALTI spatial correlation is low (R=0.31, p<0.1). GOM underestimates the mean SLA (-0.153 m) and shows a weaker correlation with ALTI (R=0.21, p=0.13) (Fig. 3 r). The JJA geostrophic
circulation is complex with a cyclonic eddy along the southernmost coast of Vietnam, a strong (~0.15 m s⁻¹) southeastern current exiting the GoT south of Hainan island and a general westward circulation in the northern GoT (Fig. 3 q). SIMU reproduces well those patterns, though with a 10 to 30 % underestimation (Fig. 3 p). GOM fails to reproduce the observed summer observed geostrophic circulation (Fig. 3 r). HF show a weak downcoast current (~0.2 m s⁻¹) and northeastward weak currents in the northern part of the radar domain in JJA, well reproduced by SIMU (Fig. 5 b-d).

<figure><img src="image_32.png" /><figcaption>Figure 5: Seasonally averaged surface current intensities (colors, in m s⁻¹) and corresponding current directions (arrows) for DJF (a,c) and JJA (b,c) over the period 2015-2017 from HF radar (a,b) and from SIMU (c,d).</figcaption></figure>

We finally evaluate the capability of SIMU to reproduce the vertical structure of coastal water masses by comparing SIMU outputs to 25 km-long CTD cross-sections (see Fig. 1) collected on
23 December 2015 (Fig. 6 a, b, d, e) and on 25 July 2016 (Fig. 6 g, h, j, k). SIMU correctly reproduces the observed temperature and salinity structures, whether conditions are homogeneous along the water column in December (Fig. 6 a, b, d, e) or stratified in July (Fig. 6 g, h, j, k). It however underestimates the lowest December temperatures and salinities by up to respectively 1 °C and 2 (Fig. 6 c, f), and overestimates the higher July temperatures and salinities by up to 2 °C and 1 (Fig. 6 i, l). Despite the complexity of the **hydrodynamics** of the very-near coastal GoT, SIMU therefore realistically reproduces the near-shore spatial and temporal patterns of temperature and salinity.

<figure><img src="image_33.png" /><figcaption>Figure 6: Temperature (in °C, a, b, g, h) and salinity (d, e, j, k) transects along a 25 km across-shore line from SIMU (a, d, g, j) and from CTD observations (b, e, h, k), and biases between SIMU and observations (c, f, i, l).</figcaption></figure>

## 3.2 Seasonal circulation in the GoT and governing processes

Based on SIMU results, this section provides insights on surface circulation and associated water fluxes and on the driving mechanisms of the seasonal variability of the currents.

Surface geostrophic currents ($U_{geos}$) are computed from SIMU SSH fields, and the ageostrophic currents ($U_{ageos}$) are deduced from: $U_{total} = U_{geos} + U_{ageos}$ where $U_{total}$ is the total current.

Here, we consider the ageostrophic circulation as a proxy of the Ekman transport.
Mean seasonal fields and drivers of the circulation

Winter (DJF) and summer (JJA) maps of mean surface currents $U_{\text{total}}$, $U_{\text{ageos}}$ and $U_{\text{geos}}$ are presented in Fig. 7 (a, b, c, d, e, f).

During winter, the surface simulated circulation ($U_{\text{total}}$) shows a gulf-scale cyclonic circulation. This circulation is composed of a strong downcoast current off Vietnam (of ~0.4 m s⁻¹) (zone B on Fig. 7 a), a westward flow through the Hainan strait (of ~0.2 m s⁻¹) (zone A), which joins the coastal current, and a northeastward branch (~0.2 m s⁻¹) of an unclosed gyre in the south of the GoT that also joins the coastal current (zone C) (Fig. 7 a). $U_{\text{ageos}}$ circulation patterns are similar to $U_{\text{total}}$ patterns over most of the GoT, with only slightly reduced velocities (by ~20%, Fig. 7 c).

This suggests that $U_{\text{ageos}}$ dominates most of the GoT winter circulation, especially the Hainan strait westward current (zone A) and the branch that enters the basin south of Hainan island (zone C) (Fig. 7 g). $U_{\text{geos}}$ circulation patterns are similar to $U_{\text{total}}$ patterns in the western coastal part of the basin (zone B) with reduced velocities (by ~80%). However, $U_{\text{geos}}$ shows large differences to $U_{\text{total}}$ in the eastern part (zones A and C), with velocities much smaller than $U_{\text{total}}$ (Fig. 7 e). $U_{\text{geos}}$ only dominates the strong boundary coastal current (zone B) (at ~60%, Fig. 7 i).

During summer, $U_{\text{total}}$ shows a gulf scale anticyclonic circulation composed of an eastward circulation in the Hainan strait (<0.1 m s⁻¹) (zone A), a strong eastward current (~0.4 m s⁻¹) flowing outside the GoT south of Hainan island (zone C), which is itself fed by an eastward current coming from the Vietnamese coast (Fig. 7 b). $U_{\text{ageos}}$ circulation patterns are very similar to $U_{\text{total}}$ but ~20% weaker almost all over the GoT (Fig. 7 d). Besides dominating the circulation at ~80%, $U_{\text{ageos}}$ is responsible for the strong eastward current flowing south of Hainan island (zone C, Fig. 7 h). On the contrary, $U_{\text{geos}}$ shows very small current velocities (<0.1 m s⁻¹) with summer circulation patterns largely different from $U_{\text{total}}$ summer patterns (Fig. 7 f), except in Hainan strait (zone A) and along the zonal band 18.5-20° N where circulation appears to be roughly equally controlled by $U_{\text{ageos}}$ and $U_{\text{geos}}$ (Fig. 7 h, j). The SLA JJA fields (Fig. 3 p, q) reveal an anticyclonic and a cyclonic gyre in the northern and southern GoT respectively, which trigger a westward geostrophic current capable of compensating the ageostrophic circulation along this 18.5-20° N band. Overall, the results suggest that the gulf-scale summer circulation is mostly wind-driven.
<figure><img src="image_34.png" /></figure>
Figure 7: Mean DJF (a, c, e) and JJA (b, d, f) surface fields of $U_{\text{total}}$ (a, b), $U_{\text{ageos}}$ (c, d), $U_{\text{geos}}$ (e, f) (in m s⁻¹) with arrows denoting current direction, and mean DJF (g, i) and JJA (h, j) fields of relative proportions of $U_{\text{ageos}}$ (g, h) and $U_{\text{geos}}$ (i, j) with arrows corresponding to $U_{\text{total}}$ surface currents fields (in m s⁻¹).

These results suggest that the basin-scale seasonal surface current variability is dominated by the ageostrophic circulation rather than by the geostrophic circulation, during both DJF and JJA. However, $U_{\text{geos}}$ dominates the western boundary circulation and the circulation in the Hainan strait.

To complement this analysis, Empirical Orthogonal Functions (EOFs) decomposition was performed on $U_{\text{total}}$, $U_{\text{ageos}}$, $U_{\text{geos}}$ monthly mean fields from 2009 to 2017, for both zonal (u) and meridional (v) components, to extract the dominant pattern of variability. EOFs was performed on detrended datasets. Note that, trends spatially vary throughout the GoT, from -0.9 10⁻⁴ to 2.6 10⁻⁴ m s⁻¹ y⁻¹ for u and from -1.6 10⁻⁴ to 0.7 10⁻⁴ m s⁻¹ y⁻¹ for v (not shown), and thus are 4 to 5 times smaller than $U_{\text{total}}$ surface currents. EOF decomposition is also performed on detrended zonal and meridional wind fields (ECMWF, see section 2.1) over the same period. Only statistically consistent EOFs, based on North et al. (1982) are presented here.

The first EOF mode ($EOF_1$) on the zonal and meridional component of $U_{\text{total}}$ accounts for 90% and 85% of the total variance, respectively (Fig. 8 a, f). The corresponding spatial patterns show negative values all over the GoT, except along the southern coast of Vietnam for the zonal component (Fig. 8 a) and except around the Hainan island for the meridional component (Fig. 8 f). This indicates that the circulation in the GoT is mostly influenced by this first mode of variability, with eastern and western opposite patterns of variability. The associated temporal functions (or principal components, PC) denotes a clear seasonal variability (Fig. 8 e, j). During summer, the product of the spatial and temporal functions shows that this variability is responsible for zonal currents to flow eastwards almost all over the GoT at velocities varying from ~0.15 m s⁻¹ in the central part of the basin to 0.4 m s⁻¹ in the Hainan Strait and south of Hainan island. The seasonal variability is responsible for meridional currents to flow northward in the western part of the GoT and southward in the eastern part, with velocities as high as 0.3 m s⁻¹ along the coast of Vietnam. These patterns of circulation correspond to the basin-scale
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summer anticyclonic circulation described above. During winter, zonal and meridional
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circulation reverses with velocities of approximately the same order of magnitude, corresponding
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The EOF₁ on the zonal and meridional components of U<sub>ageos</sub> presents patterns of spatial variability similar to those of U<sub>total</sub> (Fig. 8 b, g), with highly significant spatial correlations of 0.97 (p<0.01) and 0.90 (p<0.01) for u and v, respectively (Fig. 8 a, b, f, g). The associated temporal functions also show a clear seasonal signal as a first mode of variability (at 68% for u and at 62% for v, Fig. 8 b, g) and are each highly significantly correlated with the temporal functions of U<sub>total</sub> EOF₁ for u (R=0.86, p<0.01) and for v (R=0.68, p<0.01) (Fig. 8 e, j). The product of the spatial and temporal functions shows that this mode of variability is responsible for zonal ageostrophic currents to flow eastward all over the basin at up to ~0.5 m s<sup>-1</sup>, and meridional ageostrophic currents to flow northward in the western part of the GoT and southward in the eastern part during summer (anticyclonic circulation). Again, the circulation reverses during winter (cyclonic circulation).

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The EOF₁ on the zonal and meridional components of U<sub>geos</sub> account both for less than 60 % of the total variance and also reveal a strong seasonal variability. They are well correlated with the EOF₁ on U<sub>total</sub>, both in space (spatial correlation with U<sub>total</sub> of 0.92, p<0.01 for u; of 0.90, p<0.01 for v) and time (R=0.82, p<0.01 for u, R=0.75, p<0.01 for v). The influence on the GoT of the EOF₁ on U<sub>geos</sub> is however much weaker than the EOF₁ on U<sub>total</sub> and U<sub>ageos</sub> (Fig. 8 a, b, c, f, g, h): the product of the temporal and spatial functions gives small current velocities. A maximum in zonal geostrophic currents of -7.5x10<sup>-2</sup> m s<sup>-1</sup> is observed in spring 2010 (Fig. 8 c, e), while U<sub>total</sub> and U<sub>ageos</sub> are respectively responsible for currents up to 0.3 and 0.6 m s<sup>-1</sup> at the same period (Fig. 8 a, b, e). Again, a maximum of meridional geostrophic currents of 0.1 m s<sup>-1</sup> is reached in winter 2015 (Fig. 8 h, j) while at the same period, U<sub>total</sub> and U<sub>ageos</sub> are respectively responsible for meridional currents up 0.2 and 0.6 m s<sup>-1</sup> (Fig. 8 f, g, j).

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Finally, the EOF₁ on the zonal and meridional wind also shows a clear seasonal variability over the GoT, corresponding to the monsoon wind reversal (Fig. 8 d, e, i, j). The zonal winds blow eastward during summer (up to 0.5 m s⁻¹) and westward during winter (up to 0.3 m s⁻¹), while the meridional winds blow northward during summer (up to 0.2 m s⁻¹) and southward during winter (up to 0.2 m s⁻¹), corresponding to the alternation of NE winter monsoon and SW summer monsoon. The temporal functions of the EOF₁ on the zonal and meridional wind are highly

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<figure><img src="image_35.png" /><figcaption>Figure 8: Spatial functions (a, b, c, d, f, g, h, i) and associated principal components (noted PC) of the temporal variability (e, j) of the first EOF mode of the zonal and meridional components of $U_{\text{total}}$ (a, f), of $U_{\text{ageos}}$ (b, g), of $U_{\text{geos}}$ (c, h) and wind (d, i). The product between the spatial and</figcaption></figure>
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temporal functions denote the intensity of the considered current. On panels b, c, d, g, h, i, R corresponds to the correlations between EOF₁ Uₜotal and EOF₁ Uₐgeos, EOF₁ Uₗeos and EOF₁ on the wind, respectively. On panels e and j, R in cyan corresponds to the correlation between EOF₁ Uₜotal and EOF₁ Uₐgeos, R in grey corresponds to the correlation between EOF₁ Uₜotal and EOF₁ Uₗeos, and R in blue to the correlation between EOF₁ Uₜotal and EOF₁ on the wind.

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### Associated water fluxes

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In this section, we use SIMU results to evaluate the influence of the seasonal circulation variability in the GoT on the water fluxes over the domain, at the GoT boundaries and along the Red River Delta.

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Fig. 9 a presents the modeled winter and summer water fluxes across five sections, located across the trajectories of the Hainan strait current (section 1), of the current south of Hainan island (section 2), and of the coastal boundary current (sections 3, 4, 5), computed over 2009-2017. Atmospheric water fluxes are computed over the GoT area encompassed in between sections 1, 2 and 3.

<figure><img src="image_36.png" /></figure>

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Figure 9: (a) Mean summer (JJA, red) and winter (DJF, blue) fluxes (in Sv) through sections 1 to 5 computed from SIMU over the period 2009-2017, (b) Climatological cross-section, rivers and surface monthly fluxes (in Sv) over the period 2009-2017. Negative values indicate flux flowing outside the GoT.

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First, fluxes direction are consistent with the surface circulation seasonal variability described above, with a winter cyclonic circulation induced by northeast monsoon winds and a summer anticyclonic circulation induced by southwest monsoon winds. Second, winter fluxes all over the GoT are more intense than summer fluxes: from ~1.5 times larger during winter than during summer across section 1, to 5 times larger across section 3 and 38 times larger across section 2 (Fig. 9 a). Furthermore, winter flux along the south of the delta (0.140 Sv, section 4) is 6 times larger than along the northern delta (0.023 Sv, section 5), resulting from a 0.114 Sv gain of water from offshore in between these sections (river inputs is only 0.003 Sv). This is consistent with the winter circulation described above (Fig. 7 a), which shows strong W-SW currents flowing towards the delta. On the contrary, in summer, combined river and section 4 fluxes correspond to 0.014 Sv, whereas flux through section 5 is 0.008 Sv, showing a 0.006 Sv loss of water in between section 4 and 5 associated with a non-negligible eastward offshore flux. Summer surface currents (Fig. 7 d) indeed indicate a N-NE circulation along the delta, responsible for those eastward offshore fluxes.

The monthly fluxes responsible for input/export of water inside/outside the GoT, i.e. for controlling the water renewal in the GoT, are detailed on Fig. 9 b.

During the autumn-winter dry season, from September to March, the water input to the GoT mostly occurs through section 2 south of Hainan island (from 50 to 90%, corresponding to ~1.1 Sv over the period). The rest of the inputs (from 10 to 50%, corresponding to ~0.5 Sv over the period) occurs through the Hainan Strait (section 1) (Fig. 9 b). This is consistent with the results from Shi et al. (2001) who estimated that the fluxes via the Hainan Strait (section 1) renew less than 44% of the GoT water in winter. Contribution of rivers to the total water input remains very limited (<1%) during this period. Export of water outside the GoT almost only occurs through section 3, i.e., through the coastal boundary current (~1.7 Sv over the period). Less than 0.5% (<0.009 Sv over the period) is exported via the atmospheric flux (i.e. evaporation). Cumulated fluxes from September to January (5 months) represent ~2/3 of the yearly fluxes.

During summer (June to August), the GoT water input enters mostly from the SW GoT through section 3, i.e. via the coastal flow (corresponding to ~0.3 Sv over the period), and to much less extent (~5%, corresponding to 0.02 Sv over the period) from rivers and precipitation inputs. Water is mainly exported outside the GoT through the Hainan Strait (section 1, between 60 and 95% corresponding to ~0.2 Sv), while the rest (10 to 50%, corresponding to < 0.08 Sv over the
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period) is exported through the section south of Hainan island (section 2, Fig. 9 b). Cumulated
fluxes during this period represent less than 15% of the total yearly flux.

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These results show that the GoT water renewal mostly occurs during winter, with water entering
the GoT mostly through the southeastern boundary of the domain, south of Hainan island. These
winter waters are exported out of the GoT via the winter coastal current along the Vietnam coast.

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Hainan Strait fluxes are important for winter water inputs, and crucial in summer for water
export outside the GoT. The general circulation drives the flux patterns, leading to offshore flux
(eastward) near the delta area in summer, whereas **the flow in the GoT is westward in winter.**

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## 3.3 Typhoon-induced variability of currents

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As previously mentioned in section 2.1, 2013 was recorded as one of the most active typhoon
seasons over the Pacific since 2004 and the deadliest since 1975 (Mori et al., 2014, Lea and
Saunders, 2015, 2016 and 2017). Among the 18 typhoons passing over the SCS in 2013, four hit
the GoT (Joint Typhoon Warning Center): Typhoon Fabian (June 22nd), Typhoon Jolina
(August 2nd), Typhoon Paolo (September 30th) and Typhoon Yolanda (November 10th).
Characteristics of these typhoons and their wind intensities are given in Piton (2019).

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In this section, daily water budgets are computed for each of these four typhoons on the day it
passed over the GoT, and are compared to the total monthly (climatology) water budget of the
corresponding month (Fig. 10).

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First, each of the four typhoons generated a cyclonic circulation over the GoT (Fig. 10 a, c, e, g)
and a westward current through Hainan Strait (Fig. 10 a, c, e, g). Typhoon Fabian and Jolina
generated strong southeastward current south of Hainan island (up to 1 m s⁻¹) (Fig. 10 a,c). On
the contrary, Typhoon Paolo and Yolanda generated strong northwestward currents south of the
island (Fig. 10 e,g). Along the delta (section 4 and 5), all typhoons generated a southward
circulation, which can be a determining factor in terms of sediment transport.

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Typhoons thus generate a cyclonic circulation in the GoT that enhance the cyclonic circulation in
winter but reverse the anticyclonic summer surface circulation. This is further discussed below.

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<figure><img src="image_37.png" /><figcaption>Figure 10: Daily fields of surface $U_{\text{total}}$ on (a) June 22nd (c) August 2nd (e) September 30th and (g) November 10th of 2013, corresponding to the passages of typhoons Fabian, Jolina, Paolo and</figcaption></figure>
Yolanda respectively. Orange arrows correspond to the approximate trajectory of each typhoon; and daily fluxes (in $10^3$) generated by typhoons (red arrows) and climatological (for the period 2009-2017) accumulated monthly fluxes (grey arrows) for (b) typhoon Fabian, (d) typhoon Jolina, (f) typhoon Paolo and (g) typhoon Yolanda. Percent values correspond to the ratio between typhoon daily fluxes and monthly climatological fluxes.

The way typhoons influence flux intensities indeed varies from a typhoon to another, a season to another and a section to another (Fig. 10 b, d, f, h).

First of all, all typhoons generated strong southward fluxes along the delta (sections 4 and 5), which represented ~25 to ~50 % of the climatological monthly water fluxes at section 4, and 7% to 63% at section 5 (Fig. 10 a, d). Through the Hainan Strait, the westward daily flux generated by the typhoons represented 2% to 12% of the climatological monthly flux through section 1. Typhoon Paolo for example generated a daily water input to the GoT through the Hainan Strait of 14 $10^3$ m$^3$, i.e. 12% of the total monthly input of 117 $10^3$ m$^3$ in November (Fig. 10 f). At the southern GoT boundaries, all typhoons generated daily exports of water through section 3 ranging from 9% to 16 % of the climatological monthly flux. All typhoons further generated daily inputs of water through section 2 of 8 and 20% of the climatological monthly flux (for Paolo and Yolanda respectively, Fig. 10 f, h) and exports ranging of 38 and 100% (for Jolina and Fabian respectively, Fig. 10 b, d). Typhoon Fabian for example generated in one day a southward water export of 26 $10^3$ m$^3$ through section 2, equal to the total southward climatological export of water through this section in June (Fig. 10 b).

These results show that typhoons significantly modify the seasonal circulation and associated water fluxes in the GoT. They generate a strong enhancement of winter cyclonic circulation vs. a strong reversal of summer anticyclonic circulation, and daily fluxes anomalies of the same order as the fluxes accumulated in one month. Moreover, it has been shown that the influence of a typhoon on the circulation can last up to 3-4 days after its passage over the SCS (Ke et al., 1987). In the present study, we only considered the daily budget generated by a typhoon on the day of its passage over the GoT. This aspect is further discussed in section 4.3.
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4 Discussion

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4.1 Seasonal patterns of the circulation in the GoT

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597 As explained in the Introduction, the current knowledge about the circulation in the GoT remains
598 uncertain. In particular, while most studies agree on a predominance of gulf-scale cyclonic flows
599 during winter, the circulation structure during summer is a topic of debate (Gao et al., 2017). The
600 circulation through the Hainan Strait (Fig. 1) is also controversial: either westwards all year long
601 or alternating with monsoon winds reversal. Moreover mechanisms driving the seasonal
602 variability of the surface circulation are still under discussion: some studies point out the role of
603 the monsoon wind-induced currents (ageostrophic circulation) while other highlight the density-
604 gradient currents (geostrophic circulation). Our results from on a carefully validated high
605 resolution numerical study provide new insights on the surface circulation patterns and on the
606 driving mechanisms of the seasonal variability of the currents.

607 First, our results describe the winter surface circulation patterns as a gulf-scale cyclonic
608 circulation composed of a strong downcoast current off Vietnam, a westward flow through the
609 Hainan strait, which joins the coastal current, and a northeastward branch of an unclosed gyre in
610 the south of the GoT that also joins the coastal current. These circulation patterns agree with the
611 winter circulation described by Manh and Yanagi (2000), Xia et al. (2001) and Sun et al. (2001).
612 Our analysis moreover suggests that the ageostrophic currents drive most of the GoT winter
613 circulation, especially the Hainan strait westward current and the branch that enters the basin
614 south of Hainan island (Fig. 7 g). We further show that the geostrophic circulation dominates the
615 strong boundary coastal current, consistently with the fact that river plumes generate intense
616 density-gradients by inputs of fresher waters which in return, induce currents to follow these
617 slopes. These results confirm results of previous studies suggesting that northeast winter
618 monsoon winds are the primary driving factor of winter surface circulation over the density-
619 gradient term in the GoT (Gao et al. 2014b; Manh and Yanahi, 2000; Sun et al. 2001; Xia et al.,
620 2001). Presumably due to the coarser resolution of the models and to the scarce in situ data, none
621 of these studies reported the dominant contribution of the geostrophic currents to the coastal
622 boundary current.

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Second, our results describe a gulf-scale anticyclonic summer circulation composed of an eastward circulation in the Hainan strait and a strong eastward current flowing outside the GoT south of Hainan island, itself fed by an eastward current coming from the Vietnamese coast (Fig. 7 b). This circulation patterns agree with those described by Wang (1998) and Sun et al. (2001). These authors however did not describe an eastward flow in the Hainan strait, but rather a westward flow. This difference is once again presumably due to the coarser resolution of the 3D models associated with scarce in situ data. We showed that the gulf-scale summer circulation is also mostly wind-driven, confirming conclusions from the few available previous studies (Manh and Yanahi, 2000; Sun et al. 2001).

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Third, our EOF analysis confirms that the temporal variability of the total current ($U_{total}$) is dominated by the seasonal variability, which explains almost 90% of the signal. Seasonal variability also explains most of the variability of the ageostrophic signal ($U_{ageos}$), while it explains only ~ ½ of the geostrophic signal ($U_{geos}$) variability. We show that $U_{ageos}$ seasonal cycle generates basin-scale currents intensities one order of magnitude stronger than the ones generated by $U_{geos}$ seasonal cycle. Both ageostrophic and geostrophic seasonal circulations affect the Hainan strait circulation and the southern boundary flow. However in both cases, $U_{ageos}$ triggers stronger current intensities. The seasonal variability of the zonal geostrophic current thus plays a second order role, except for the coastal boundary current. This confirms that the ageostrophic transport drives the seasonal variability of the total circulation. This result is consistent with previous studies suggesting that the summer circulation is driven by the ageostrophic circulation, in this case by the Ekman transport triggered by monsoon winds (Manh and Yanahi, 2000; Sun et al., 2001).

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4.2 Hainan strait flux comparison with existing values

Our estimate of the summer (JJA) water flux through Hainan strait (section 1) of -0.067 Sv (Fig. 9 a and Table 1) falls within the range of the fluxes observed by Shi et al. (2001) (-0.2 to 0.1 Sv, Table 1) but is slightly lower than the ones computed by Gao et al. (2013) from their model (-0.28 to -0.18 Sv). The winter (DJF) modeled flux of 0.098 Sv in the Hainan strait is also slightly weaker than the ones from Shi et al. (2001, 0.2 to 0.4 Sv) and Gao et al. (2013, 0.3 to 0.4 Sv),
but are close to the results computed by Chen et al. (2009b, 0.116 Sv). Discrepancies among those studies may be caused by the different methods (the use of in situ data or numerical modelling) and time-periods (from 1 year simulation to a 37 year-long in situ dataset) considered by each team (see Table 1). Another reason could be that the flux computations depend on the size of the section and thus on the bathymetry, which is particularly difficult to represent in straits, due to data availability and grid construction methods. In SIMU configuration, the bathymetry smoothing scheme induces for example an underestimation of the depths of ~15% (~10 m) in the Hainan strait bathymetry, compared to the raw bathymetry. In order to evaluate the sensitivity of the water fluxes to changes in bathymetry, a sensitivity simulation with a bathymetry deepened by ~10 m in the Hainan strait was performed (noted SIMU_DEEP). Results from this simulation show that a deeper strait moderately increases the summer water flux by 7% and the winter water flux by 4% compared to SIMU (Table 1). These results confirm the role of bathymetry representation in the flux computation through the Hainan Strait, but they mitigate its importance.

<table><caption>Table 1: Seasonal water transport (in Sv) through the Hainan strait from previous studies and from our simulations (negative values indicate an eastward flux).</caption><thead><tr><th></th><th>Summer</th><th>Winter</th><th>Method</th><th>Period</th></tr></thead><tbody><tr><td>Shi et al., 2001</td><td>-0.2 to 0.1</td><td>0.2 to 0.4</td><td>in situ SLA observations</td><td>1963-1999</td></tr><tr><td>Chen et al., 2009</td><td>0.026</td><td>0.116</td><td>Estuarine and Coastal Ocean Model<br>1.8 km resolution<br>11 σ layers</td><td>1-year<br>simulation</td></tr><tr><td>Gao et al., 2013</td><td>-0.28 to -0.18</td><td>0.3 to 0.4</td><td>Princeton Ocean Model<br>0.9 to 2 km resolution<br>21 σ layers</td><td>2006-2007</td></tr><tr><td>SIMU</td><td>-0.067</td><td>0.098</td><td>SYMPHONIE model<br>0.15 to 5 km resolution<br>20 σ layers</td><td>2009-2017</td></tr><tr><td>SIMU_DEEP</td><td>-0.072</td><td>0.102</td><td>SYMPHONIE model<br>0.15 to 5 km resolution<br>20 σ layers</td><td>2009-2017</td></tr></tbody></table>
671 4.3 Influence of typhoon duration on flux estimates

672 In the SCS, typhoons can impact circulation days after their passages (Ke et al., 1987). The  
673 current response to a typhoon passage decays through dispersion and propagation of near-inertial  
674 wave (Gill 1984; Park et al. 2009) with an e-folding time from days to week. Yang and Hou  
675 (2014) observed near-inertial currents associated with the passage of Typhoon Nesat (2011) in  
676 the northern SCS that lasted up to 10 days after the typhoon's passage. Chen et al. (2013)  
677 suggested that near-intertial kinetic energy associated with the passage of large typhoons in the  
678 SCS last more than 7 days. However, the decay of current is not monotonous if near-inertial  
679 baroclinic wave resonate, re-intensifying the mixed layer current (Gill 1984).

680 Using SIMU, we estimate the total duration of the typhoon's influence on circulation by 681 examining the time series of daily fluxes through sections 2 and 3 (for Fabian, Paolo and 682 Yolanda) and sections 1 and 5 (for Jolina) (Fig. 11). In summer, typhoon Fabian triggered flux 683 decreases and reversals through sections 2 and 3, and Typhoon Jolina through section 1 684 approximately 4 days before the typhoon's passages. Fluxes reversed back to their initial 685 direction 2 to 3 days later. In winter, Typhoon Paolo and Yolanda generated flux increases 686 through section 2 and 3, approximately 3 days before their passages and their impacts started 687 vanishing 2 to 3 days later (Fig. 11). This confirms that the influence of typhoons on the 688 circulation can last several days (from 6 to 8 days) before and after the event in the GoT. For an 689 estimation of the total water fluxes generated by a typhoon, taking into consideration water 690 fluxes 3 days before and 3 days after a typhoon's passage should be considered. For example, we 691 showed that Typhoon Fabian generated in one day a southward water input of 26 10³ m³ through 692 section 2, which is equal to the total southward export of water through this section in 693 climatological June (Fig. 10 a). The influence of Typhoon Fabian on the fluxes through section 2 694 lasted 6 days in total (Fig. 11) and have generated an integrated export of water outside the GoT 695 over this period of ~144 10³ m³ through section 2, i.e. 410% of the climatological monthly 696 output. The influence of Typhoon Paolo on the water fluxes through section 3 corresponded to 697 67 10³ m³ on the day of its passage (10% of the climatological monthly accumulated outflow), 698 and to 327 10³ m³ for the total duration (5.5 days) of the typhoon's influence on the GoT: over 699 this period, the typhoon induced an export of water equal to half of the 674 10³ m³ usually 700 exported monthly. The influence of Typhoon Jolina on the fluxes through section 1 lasted for 701 ~5.5 days. In this case, the typhoon induced an input of water of about 10% (~13 10³ m³) of the
climatological monthly output through Hainan Strait. Finally, Typhoon Yolanda generated a water input of 547 10³ m³ through section 2 over a period of 6 days, which corresponds to 106% of the climatological monthly accumulated inputs. These results suggest that the duration of a typhoon influence on the water fluxes in the GoT is shorter than on the near-inertial waves. To our knowledge, no previous studies reported the influence of a typhoon on the water fluxes prior to its passage.

<figure><img src="image_38.png" /><figcaption>Figure 11: Daily temporal series of fluxes (m³) through sections 2 and 3 for Typhoon Fabian, Paolo and Yolanda and through section 1 and 5 for Typhoon Jolina, computed between 10 days before and after the passage (T) of the typhoons over the GoT. The convention positive (respectively negative) corresponds to input to (respectively output from) the GoT.</figcaption></figure>

## 5 Conclusion

This study aimed to bridge the scientific gaps in previous regional studies regarding the circulation in the GoT. For that, we developed a high-resolution closed-budget model configuration, specifically dedicated to the study of the circulation in the GoT. It integrates an improved a bathymetric and coastline dataset and is forced by state of the art products. Results of
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our 9-year simulation was compared with in situ and remote sensing data. Our model reproduces realistically the GoT annual cycles of surface temperature, salinity, sea level anomaly and circulation, with better performances than the lateral boundary forcing model.

We used this simulation, to describe the circulation and water fluxes patterns of the contrasted winter and summer seasons, and to identify the mechanisms responsible for the seasonal variability of the circulation. The winter circulation is basin-scale cyclonic, with a strong downcoast current flowing off Vietnam. Winter waters enter the GoT through the Hainan Strait and south off Hainan island, and exit the basin through the south-western boundary via the downcoast current. The summer circulation is basin-scale anticyclonic, with a strong eastward current flowing outside the GoT from the Vietnamese coast and south of Hainan island. Summer waters enter the GoT mostly through its south-western boundary and exit through the Hainan Strait and south of Hainan island. GoT water renewal occurs mostly during winter. EOF decompositions revealed that the circulation in the GoT is governed first by this seasonal variability. It is triggered by the wind-driven ageostrophic currents (i.e. Ekman transport), hence by the monsoon-induced winds. The downcoast coastal current flowing off Vietnam in winter is however density-driven, generated by the Red River plume freshwater inputs.

We showed that typhoons generate a gulf-scale cyclonic circulation capable of reversing the anticyclonic summer circulation and intensifying the winter cyclonic circulation. Typhoons-induced currents can generate daily water fluxes of the same order as monthly climatological fluxes. Typhoons influence the circulation up to 3 days prior and after their passages over the GoT. They can consequently generate fluxes equivalent to 10-410% of the climatological monthly water fluxes during the total duration of their influence. For future studies, we therefore recommend considering a total of six to seven days to better account for the effect of typhoons in the GoT.

Further work is still necessary to better model and understand the circulation in the GoT and the mechanisms involved in its functioning. First, SYMPHONIE evaluation should be completed using longer periods of HF radar surface currents data (Rogowski et al., 2019). We further encourage the development of continuous current measurements at key locations (Hainan Strait,
750 Red River Delta coast, downcoast current) for HF radar data and models evaluation. Second, 751 using a coupling between SYMPHONIE and a wave generation and propagation model such as 752 Wave Watch III (Tolman, 2009), as done over the Gulf of Lions (Mikolajczak, 2019), should 753 allow a better representation of near-shore currents, in particular during storms (Michaud et al., 754 2012 & 2013). 755

756 Lastly, at interannual time-scales, many studies examined the influence of El Niño Southern 757 Oscillation (ENSO) on the SCS region : regarding the summer monsoon (Liu et al. 2012; 758 Dippner et al., 2013), sea surface temperature (Liu et al., 2014), sea level (Peng et al., 2013; 759 Rong et al., 2007), surface winds (Huynh et al., 2016), precipitation (Räsänen et al., 2016), 760 cyclone frequency occurrences (Camargo and Sobel, 2005; Wu et al., 2005), river runoff 761 (Räsänen and Kummu, 2013; Ha et al., 2018) and upwelling (Da et al., 2019). However, no 762 studies (in english) have focused on ENSO effects over the GoT. The interannual variability of 763 the GoT surface circulation and the influence of ENSO will be the scope of a forthcoming paper. 764
764 Acknowledgements

765 This work is a part of LOTUS international joint laboratory (lotus.usth.edu.vn). Numerical

766 simulations were performed using the cluster OCCIGEN from the CINES group (project DARI

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768 Research Physical Oceanography Program, grant number N00014-17-1-2839.

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