RESEARCH ARTICLE
10.1029/2022JC019216
Key Points:• A strong upwelling year and a weak
upwelling year are identified from2010 to 2018 based on model results
and observations• Coastal upwelling and Kuroshio
intrusion mainly control the nutrient
supply in nearshore and offshore
areas, respectively• Nearshore chlorophyll-a and offshore
subsurface chlorophyll maximum areaccordingly determined by Zhejiang
coastal upwelling and Kuroshio
intrusion intensities, respectively
Correspondence to:J. Shi,shijie@ouc.edu.cn
Citation:

Luo, Y., Shi, J., Guo, X., Mao, X.,
Yao, P., Zhao, B., et al. (2023). Yearlyvariations in nutrient supply in the East
China Sea due to the Zhejiang coastal
upwelling and Kuroshio intrusion.
Journal of Geophysical Research:
Oceans, 128, e2022JC019216. https://doi.
org/10.1029/2022JC019216
Received 25 AUG 2022
Accepted 17 MAR 2023

Yearly Variations in Nutrient Supply in the East China
Sea Due To the Zhejiang Coastal Upwelling and Kuroshio
Intrusion
Yifei Luo1,2 , Jie Shi1,2
and Yucheng Wang6, Xinyu Guo3

, Xinyan Mao4

, Peng Yao2,5, Bin Zhao2,5, Lin Chen2,5,

Key Laboratory of Marine Environment and Ecology, Ocean University of China, Ministry of Education, Qingdao, China,
Laboratory for Marine Ecology and Environmental Sciences, Qingdao National Laboratory for Marine Science andTechnology, Qingdao, China, 3Center for Marine Environmental Studies, Ehime University, Matsuyama, Japan, 4College of
Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao, China, 5Frontiers Science Center for Deep Ocean
Multispheres and Earth System, Key Laboratory of Marine Chemistry Theory and Technology, Ministry of Education, Ocean
University of China, Qingdao, China, 6High Performance Computing Center, Pilot National Laboratory for Marine Scienceand Technology (Qingdao), Qingdao, China
1
2

Abstract The East China Sea (ECS) is a marginal sea with high productivity, especially in summer. In
the Zhejiang coastal region of ECS, the large phytoplankton biomass in summer is attributed to the nutrients
pumped up by Zhejiang coastal upwelling (ZCU), and oceanic nutrients supplied by the Kuroshio intrusion(KI) onto continental ECS. However, the yearly variations in these two processes are not consistent, which
complicates coastal nutrient supplies. In this study, to understand the contributions of ZCU and KI to thenutrient supplies, nutrient concentrations, and phytoplankton biomass in ECS as well as their yearly variations,a physical-biochemical coupled model was applied. According to model results and observations, the years
2013 and 2018 were identified as strong and weak ZCU years during 2010–2018 period, respectively. In 2013,
nutrient and chlorophyll-a concentrations were higher than 2018 in nearshore areas, but nutrient concentrations
were lower than 2018 at the bottom of wide offshore areas. Strong wind stress in 2013 induced strong ZCU andshoreward net nutrient flux along the 60-m isobath, which was seaward in 2018. The fewer nutrient supplies
from KI in the spring of 2013 decreased the nutrient concentrations at the bottom of offshore areas, but its
influence on nearshore nutrient concentrations was not as obvious as that caused by ZCU. Phytoplankton
biomass in the nearshore and offshore areas reflected different mechanisms of nutrient supplies. In conclusion,
the intensity of ZCU, rather than KI, is the principal factor influencing the coastal primary productivity of ECS
in summer.
Plain Language Summary The Zhejiang coastal region in the East China Sea is one of the mostimportant fishing grounds in China. The highest productivity in this region appears in summer. It is reported
that the coastal productivity benefit from the nutrients transported by local upwelling in summer and Kuroshio
intrusion (KI) from the open sea in spring. To reveal the contributions of the two processes, we used a numericalmodel to investigate the nutrient supply and phytoplankton biomass influenced by the upwelling and KI. The
results showed that the more intense alongshore wind in summer induced stronger upwelling, and significantly
increased nutrient supply in nearshore areas. The KI mainly influenced the nutrient supply from the open
sea and the nutrient concentrations in offshore areas at the sea bottom. The nearshore nutrient supply andphytoplankton biomass are primarily determined by the intensity of upwelling and secondarily influenced bythat of KI. This study distinguished the effects of local and remote processes, and highlighted the influences of
their match and mismatch to the yearly variations in the nutrient supply to the nearshore area of East China Sea.
1. Introduction© 2023. American Geophysical Union.
All Rights Reserved.

LUO ET AL.

Upwelling is a worldwide phenomenon that transports deep cold water to the upper layers and decreases sea
surface temperature (SST; Jing et al., 2011; Xie & Hsieh, 1995). Deep nutrient-rich water is pumped up to theeuphotic layers, thereby promoting local primary production and the associated species interactions (Capone &Hutchins, 2013; Hu & Wang, 2016; Menge & Menge, 2013). Upwelling systems are widely distributed in open
oceans. In the Eastern Boundary Upwelling Systems, upwelling induced by alongshore winds appear over awide range of latitudes in coastal areas (Sydeman et al., 2014; B. Wang et al., 2015). Marine fish caught in these
1 of 18

Journal of Geophysical Research: Oceans

10.1029/2022JC019216

upwelling regions account for 20% of the fish caught globally (Fréon et al., 2009). In equatorial regions, upwelling
generated by trade winds can decrease iron limitation and promote primary production (Strutton et al., 2004). In
some marginal seas, upwelling induced by local seasonal winds (e.g., the Asian monsoon) also plays an important role in regulating biological productivity. In the western Arabian Sea, the area of water with high biomass
caused by the Oman Coastal Upwelling extends up to 700 km offshore (Barber et al., 2001). Correlations between
upwelling and biological production are also found in many other marginal seas, such as that south of Madagascar(Ramanantsoa et al., 2018), the eastern Great Australian Bight (van Ruth et al., 2018), the southwestern Japan Sea(Hahm et al., 2019), and the East China Sea (ECS; Hu et al., 2014).
Many quantitative indices, defined by the causes or effects of upwelling, have been used to describe the intensity of upwelling. Bakun (1973) introduced a coastal upwelling index based on wind-induced Ekman transportand evaluated the temporal and spatial variations in upwelling along the west coast of North America. Bograd
et al. (2009) further developed this index and found a delayed and weak upwelling in El Niño years in the California Current System. The Ekman pumping induced by wind stress curl is also an important cause of upwellingin some regions, such as the Peru Current System (Albert et al., 2010) and the South China Sea (B. Wanget al., 2013). The effects of upwelling, such as lower SST and higher chlorophyll-a (Chl-a) concentration, werealso used to determine the intensity of the upwelling. The weakening upwelling in the northern Arabian Sea isindicated by an increase in SST and a decrease in Chl-a concentration (Lahiri & Vissa, 2022). Using the difference in SST between the upwelling area and the surrounding area, the lifecycle of short-term upwelling processes
northeast of Taiwan was determined to be ∼15 ± 5 days (Yin & Huang, 2019). Using these indices, it was foundthat upwelling in the low-latitude regions of Eastern Boundary Upwelling Systems exists almost year-round (B.
Wang et al., 2015), but the upwelling induced by monsoons only exists in a certain season when the wind fieldfavors its formation (Ningsih et al., 2013; Villanoy et al., 2011).
The intensity of upwelling presents interannual variations that are important for biological production. Theinterannual variability of seasonal upwelling was found to be greater than that of existing year-round upwelling(Nykjær & Van Camp, 1994). Sun et al. (2016) suggested that El Niño-Southern Oscillation (ENSO) and Pacific
Decadal Oscillation (PDO) could enhance or weaken the summer monsoon in the ECS, respectively, and also
affect the intensity of upwelling. Using numerical simulations, Jacox et al. (2015) pointed out that wind andremote forcings (e.g., ENSO and PDO) drive the interannual fluctuations of the upwelled nitrate flux in the
California Current System. In the South China Sea, an abnormally strengthened coastal upwelling, indicated bysatellite-derived SST, was the reason for an unusual coastal phytoplankton bloom in the summer of 2018 (Huet al., 2021). Along the northwestern coast of the Iberian Peninsula, Picado et al. (2014) found that summer
upwelling was strong during the years with the highest Chl-a concentrations (over 7 mg/m 3).
The ECS (Figure 1) is a marginal sea in the northwestern Pacific Ocean, with high biological productivity (Shanet al., 2011; Tan & Shi, 2006). In summer, the southwest monsoon creates favorable conditions for the northeastward alongshore current and the occurrence of the Zhejiang coastal upwelling (ZCU; Gan et al., 2016; Hu &Wang, 2016; Liu, Gan, Hu, et al., 2021). Both satellite-derived data and numerical simulations indicate that theZCU occurs in nearshore areas within the 60-m isobath in summer (Lou et al., 2011; Lü et al., 2007). Although
the ZCU is close to the Changjiang River, the largest river in China, the discharged nutrient-rich freshwater
mainly flows northeastward, away from the ZCU (Liu, Gan, Wu, et al., 2021). The Kuroshio intrusion (KI) on
the continental shelf of the ECS occurs around northeast of Taiwan Island. Although the intrusion area is far
away from the coastal region, KI is the major nutrient source of the ZCU, particularly a phosphate source. The
coastal ECS is dominated by N-rich water and exhibits P-limitation (Wang, 2006; B. Wang et al., 2003). The KI
water with a low N/P ratio originates from the Kuroshio subsurface water, and forms a nearshore branch current(NKBC in Figure 1b) over the ECS continental shelf (Yue et al., 2021; Zhou et al., 2018). In climatologicalnumerical experiments, the NKBC extends shoreward and northeastward, and reaches the Zhejiang coastal (ZC)
region in summer (Xu et al., 2018; Yang et al., 2011). The ZCU then transports this water with a low N/P ratio
to the sea surface, which relieves P-limitation and promotes primary production (Xu et al., 2020). Evidence of
upwelled P-rich water was also found through in situ observations (Chen et al., 2021; B. Wang & Wang, 2007).
Therefore, the ZCU and KI, two distant but successive processes, jointly supply nutrients to the ZC region whenZCU occurs. However, the interactions between the two processes on coastal nutrient supply are not clear yet.
The intensities of the ZCU and KI are both characterized by obvious yearly variations. As indicated bysatellite-derived SST, the maximum cold-water coverage of the ZCU in a year varies from 17,000 to 25,000 km 2
LUO ET AL.

2 of 18

Journal of Geophysical Research: Oceans

10.1029/2022JC019216

Figure 1. (a) Map of the model domain and (b) enlarged coastal East China Sea (ECS). Scatters in (a) denote coastal nutrient
sampling sites in the summer of 2013 and 2018, among which red circles are the sites of this study, blue squares are the sites
from Yu (2014), black triangles are the sites from Lyu et al. (2020). Blue-white dots in (a, b) are the positions of inflow fromrivers. ZC region: Zhejiang coastal region, EZC region: East Zhejiang coastal region, NKBC: nearshore Kuroshio Branch
Current. Sections N27, B60, and N30 are the open boundaries of the ZC region. A, B, C, and D represent the endpoints of thesections N27, B60, and N30. Section M is a cross-shelf section. Section B200 is the location where NKBC intrudes into the
ECS. The gray lines represent the isobaths.

(Lou et al., 2011). Through numerical simulation, it was found that the transport of phosphate from KI to thewhole continental ECS varies from −0.25 to 0.30 kmol/s, which is attributed to the shift of the Kuroshio axis
(Lin et al., 2019). However, due to their different mechanisms, the yearly variations in the intensities of these twoprocesses seem inconsistent. The ZCU is mainly induced by the local winds, and influenced by tide, topography,
and so on (Hu & Wang, 2016), while KI is controlled by density field, large scale remote winds, and topography(Guo et al., 2006; Yang et al., 2018). The intensity of the ZCU, indicated by SST derived indices, demonstrated
a decreasing trend in the period 1993–2007 (Yang et al., 2020). The intensity of KI, as indicated by the onshore
flux, showed an increasing trend during the same period (Li et al., 2013). In addition, the most intense years forthe two processes were different. Since each of ZCU and KI can significantly influence coastal nutrient supply,
such inconsistency probably affects and complicates the yearly nutrient supply to the ECS shelf as well as coastal
primary production, which cannot be determined by climatological research. However, this inconsistency has not
drawn sufficient attention, and the contributions of these two processes have not yet been investigated.
In this study, a three-dimensional physical-biochemical coupled model was used to distinguish the yearly effects
of ZCU and KI on nutrient supply and phytoplankton biomass in the ECS. Section 2 describes the model andobservation data. Section 3 presents the distributions of the water temperature, vertical velocity, nutrients, andChl-a concentrations. Section 4 discusses the contributions of the ZCU and KI to nutrient fluxes and phytoplankton biomass, and the factors controlling the two processes. Finally, a summary of the study is presented in
Section 5.

2. Methods
2.1. Model Description
The three-dimensional physical-biochemical coupled model used in this study consists of two modules. The
physical module is based on the Princeton Ocean Model (Blumberg & Mellor, 1987; Mellor, 2003), and thebiochemical module is based on the biological part of the NORWECOM (Skogen & Søiland, 1998). The physicalmodule provides the water temperatures, current velocities, and turbulent viscosity coefficient to the biochemical
module. There are seven state variables in the biochemical module: dissolved inorganic nitrogen (DIN), dissolved
inorganic phosphorus (DIP), silicate, diatoms, flagellates, detritus, and biogenic silica. The biomass of both
LUO ET AL.

3 of 18

Journal of Geophysical Research: Oceans
Table 1
Forcing Conditions for the Numerical Experiments

ExperimentWind
stressOther atmosphericforcings except forwind stress

Southern open
boundary
conditions

ChangjiangRiverrunoff

WIND

2013

2018

2018

2018

OTHERS

2018

2013

KURO

2018

2018

RIVER

2018

2018

10.1029/2022JC019216

diatoms and flagellates was quantified using the concentrations of Chl-a.
Detailed information on the physical module and coupled model has alreadybeen given in the previous studies (Guo et al., 2006; Q. Wang et al., 2008;
Y. Wang et al., 2019; Zhao & Guo, 2011). The horizontal resolution of themodel was 1/18° (∼6 km), and there were 21 terrain-following sigma layers
in the vertical direction.

The model domain is 117.5°–131.5°E, 24.0°–41.0°N, which covered theBohai Sea, Yellow Sea, and ECS (Figure 1a). The ZC region is defined as
2013
2018
the enclosed area surrounded by three open boundaries and the coastline of
2018
2013
Zhejiang Province (Figure 1b). Sections N27 and N30 are the southern andnorthern open boundaries, which are located along the latitudes of 27°N
and 30°N, respectively. Section B60 is set as the 60-m isobath, separating
the ZC region from the outer sea. The endpoints of the three sections are denoted as A, B, C, and D. The
cross-shelf section M extends from (121.5°E, 28.5°N) to (122.5°E, 27.5°N). According to previous studies onwater exchange, the Kuroshio intrudes into the continental ECS through the 200-m isobath from the area northeast of Taiwan Island (Li et al., 2013; Liu et al., 2014; Zhang et al., 2019; Zhou et al., 2015). Therefore, in thisstudy, the southwestern part of the 200-m isobath is defined as section B200 (Figure 1b). The flux of nutrients
through B200 was calculated to evaluate the effects of KI.
2018

2018

2.2. Model Configurations
The model results of the climatologically seasonal variations in the physical and biochemical variables of the
ECS have been validated and analyzed by Y. Wang et al. (2019). In this study, the climatological results on 1
January from Y. Wang et al. (2019) were used as the initial conditions. The model was integrated for 3 years,
driven by the forcings of 2010, and then integrated continuously for 9 years, from 1 January 2010 to 31 December
2018. The model results from 2010 to 2018 were analyzed.
Atmospheric forcing data from 2010 to 2018, including wind stress, sea surface pressure, surface net heat fluxes,
evaporation, and precipitation, were derived from the ERA-interim reanalysis data set of the European Center
for Medium-Range Weather Forecasts (ECMWF; Dee et al., 2011). The data with a spatial resolution of 1/8°
were interpolated to model grids. The model was driven by the 6-hourly wind stress data and the daily means ofother atmospheric fluxes. The monthly runoff of the Changjiang River from 2010 to 2018 was obtained from the
China River and Sediment Bulletin. The riverine DIN and DIP concentrations of the Changjiang River were set
as constants (118.4 mmol/m 3 for DIN, 1.3 mmol/m 3 for DIP) according to observations in 2014 by Liang and
Xian (2018). The physical open boundary conditions from 2010 to 2018, including the monthly means of the sea
level, water temperature, salinity, and velocity, were obtained from the results of an assimilative model of theJapan Coastal Ocean Predictability Experiment (JCOPE2; Miyazawa et al., 2009). The other forcing data were set
to be the same as those of Y. Wang et al. (2019), therefore, they did not have yearly variations. These forcing data
included (a) the nutrients from atmospheric deposition; (b) the freshwater runoff and riverine nutrient concentrations from rivers except for the Changjiang River; (c) the monthly concentrations of suspended particulate matterin the water; and (d) the nutrient concentrations and tidal constituents along the open boundary.
To quantitatively investigate the effects of the atmospheric forcing, river runoff from the Changjiang River, andKI on the nutrient supply and Chl-a concentration in the ZC region, four numerical experiments were carried out
for the reference years 2013 and 2018. The details of each numerical experiment are listed in Table 1. The 2018
simulation was set as the CONTROL run. The wind stress and other atmospheric conditions, including evaporation, precipitation, sea level pressure, and air-sea heat flux, were changed to those of 2013 in the experiments
labeled as WIND and OTHERS, respectively. In the KURO experiment, the southern open boundary conditions
were changed to those of 2013 to represent the change of KI northeast of Taiwan. In the RIVER experiment, theChangjiang River runoff was changed to that of 2013.
2.3. Observations
The daily SST data from 2010 to 2018 are acquired from the Operational Sea Surface Temperature and Sea Ice
Analysis (OSTIA) system (Donlon et al., 2012). The spatial resolution of this SST data is 1/20°. In addition, the
LUO ET AL.

4 of 18

Journal of Geophysical Research: Oceans

10.1029/2022JC019216

Figure 2. The modeled climatological (2010–2018) temperature. (a–d) Sea surface temperature and (e–h) the vertical structures of temperature along section M from
June to September. Section M is noted by the thick red line in (a–d). The Zhejiang coastal region and East Zhejiang coastal region are noted by the thick black line in (b).

hourly satellite surface Chl-a concentrations were obtained from the Geostationary Ocean Color Imager (GOCI)
provided by Korea Ocean Satellite Center (Ryu et al., 2012). The spatial resolution of this Chl-a data is 500 m.
The satellite-derived SST and surface Chl-a concentrations are used to validate the modeled results.
In this study, samples for nutrient analysis were collected by R/V haili at 57 stations in the coastal areas of the
ECS in the summer (5–19 August) of 2018 (Figure 1a). The concentrations of DIN and DIP (unpublished data)
were measured using a continuous-flow analyzer (AutoAnalyzer 3, Seal Analytical Ltd., UK; SEAL AnalyticalLtd., UK; Zhao et al., 2018). Besides, the DIN and DIP concentrations in August of 2013 derived from Yu (2014),
and the DIN concentrations in July of 2018 derived from Lyu et al. (2020) are also collected (Figure 1a) to validate the model results in summer when the ZCU prevails.

3. Results
3.1. Developing Processes of the ZCUThe ZCU occurs in summer when the southwest summer monsoon prevails along the coastline of the ECS.
Wind-induced Ekman transport creates surface offshore transport, and results in coastal upwelling. Along withthis phenomenon, coastal water temperature shows significant variation. Thus, the distributions of the 9-year
average SST from late spring to early autumn indicate the developing processes of the ZCU (Figure 2). In June
(Figure 2a), the SST decreased from south to north owing to the meridional variations in solar radiation. Coldwater with a temperature lower than 26°C extended southward along the coastline of Zhejiang Province. The SST
decreased from the open sea to the coast. In July (Figure 2b), SST became warmer than that in June. The warm
water extended northwestward from the open sea to the coastal areas. The 28°C isotherm reached north to thelatitude of 30°N. Near the coast of Zhejiang Province from the latitude of 28°N to 31°N, there was an isolated
cold-water band enclosed by the 26°C isotherm, which is thought to be the evidence of the formation of the ZCU.
In August (Figure 2c), the SST was high and became comparably uniform throughout the ECS owing to thestrong solar radiation. The cold-water band enclosed by the 28°C isotherm could still be noticed near the coastof Zhejiang Province, but was not as obvious as that in July. Until early autumn (Figure 2d), the SST increased
southward and seaward. The isolated cold-water band caused by the ZCU tended to disappear.
LUO ET AL.

5 of 18

Journal of Geophysical Research: Oceans

Figure 3. The model (black) and OSTIA (red) regionally averaged sea surface
temperature for July from 2010 to 2018. The averaged area is the Zhejiang
coastal region denoted in Figure 1b.

10.1029/2022JC019216

Section M lies across the cold-water band near the coast (Figures 2b and 2c).
The vertical structures of the water temperature along section M from latespring to early autumn also indicate the developing processes of the ZCU.
The isotherms tilted upward near the coastal area, indicating the occurrenceof upwelling. In June (Figure 2e), the 22°C isotherm reached a depth of
20 m in the nearshore area but remained much deeper (about 50 m) in theoffshore area. In July (Figure 2f), the water was well-mixed in the nearshore
area shallower than 15 m. Next to the well-mixed area was the cold-waterband. The 22°C isotherm reached up to a depth of 15 m, which was shallower
than that in June, indicating more intense upwelling. In the offshore area,
the water column was well-stratified. Until August (Figure 2g), although thecold-water band at the sea surface was not as obvious as that in July due to thestrengthened solar radiation (Figure 2c), the upwelling in the nearshore area
was still robust, as indicated by the upward-tilted isotherms. In September(Figure 2h), upwelling disappeared, as indicated by the horizontally extended
isotherms.

The modeled SST and vertical structures of temperature suggest that the ZCU starts to appear in June, strengthensin July and August, and then attenuates in September. The cold-water band at the sea surface is most obvious inJuly, therefore the SST distributions in July are used to analyze the yearly variations in the ZCU intensity from2010 to 2018.
3.2. Yearly Variations in ZCU Intensity During 2010–2018
The modeled SST averaged in the ZC region was compared with the satellite-derived SST from OSTIA for Julyfrom 2010 to 2018 (Figure 3). The yearly variations of two time series in July were consistent with a correlation
coefficient of 0.87, which suggests that the model was able to reproduce the yearly variations in summer SST in
the ZC region. The modeled variations of SST in the ZC region were obvious, with a maximum value of 28.4°C
in 2018 and a minimum value of 25.8°C in 2013. The SST in the ZC region also presented an obvious increasing
trend from 2015 to 2018 and fluctuated from 2010 to 2014.
Although upwelling pumps deep cold water up to the sea surface, decreases local SST and forms a cold-waterband, the SST is also influenced by other factors like sea surface heat flux. Therefore, the zonal difference in
SST between the upwelling region and the adjacent region, rather than SST itself, is typically used to quantify
the intensity of an upwelling (Jayaram et al., 2010; Lahiri & Vissa, 2022; Xu et al., 2017). In this study, the zonal
SST difference (UISST) is defined asUISST = SSTEZC − SSTZC

where SSTZC is the mean SST of the ZC region, and SSTEZC is the mean SSTof the East ZC (EZC) region, which is defined by shifting the ZC region
eastward by 2° (see Figure 1b). A larger UISST indicates a stronger ZCU. The
mean vertical velocity (w) in the entire ZC region is regarded as an explicit
index of the ZCU intensity. In this study, both the UISST and w were used todefine the strong and weak years of the ZCU.

Figure 4. Time series of UISST (black) and w (red) for July from 2010 to2018. UISST is the zonal sea surface temperature difference between the East
Zhejiang coastal and Zhejiang coastal (ZC) regions. w is the vertical velocity
averaged in the entire ZC region.

LUO ET AL.

The UISST is shown in Figure 4. The average SST in the EZC region was muchhigher than that in the ZC region (see Figure 2b), thus the UISST was positivein July for all the simulated years. The average value of UISST was 2.0°C from2010 to 2018, with a maximum value of 2.8°C in 2011, and a minimum valueof ∼1.2°C in 2014 and 2018. The larger the UISST, the stronger the upwelling.
The UISST in 2011, 2013, and 2017 was much higher, exceeding one standard
deviation during the 9 years. The ZCU was weak in 2014 and 2018, as indicated by the lower UISST.
The yearly variations in w, which was averaged vertical velocity in the ZC
region in July, are shown in Figure 4 for a comparison with those of the UISST.
6 of 18

Journal of Geophysical Research: Oceans

10.1029/2022JC019216

Figure 5. Comparisons of (a, b) sea surface temperature, vertical distribution of (c, d) water temperature and (e, f) vertical velocity along section M in July of thestrong (2013) and weak (2018) Zhejiang coastal upwelling years. Section M is noted by thick red line in (a, b). Positive values of the vertical velocity in (e, f) represent
upward one.

The two variables exhibited similar yearly variations with a correlation coefficient of 0.80 (Figure 4). Therefore,both the UISST and w gave the same result when they were used to define the strong and weak years of the ZCU.
In the following analyses, 2013 was chosen to represent the strong ZCU year, when UISST was high and w was thestrongest among the 9 years. Between 2014 and 2018, we chose 2018 to represent the weak ZCU year. The reason
for this choice is that there were intensive cruise observations in the coastal areas of the ECS in the summer of
2018, and the observed DIN and DIP concentrations can be used to validate the model results.
3.3. Comparisons of Temperatures and Vertical Velocities in the Strong and Weak ZCU Years
The modeled SST in 2013 and 2018 were both characterized by low SST along the coast of Zhejiang Province inthe summer (Figures 5a and 5b), which indicated the occurrence of ZCU. Comparing the SST in these two years,
it was obvious that the cold-water band was much more intense in 2013 than in 2018. The average SST in the ZC
region was 25.8°C in 2013 and 28.4°C in 2018. Meanwhile, the UISST was 2.7°C and 1.2°C in 2013 and 2018,
respectively. The larger UISST indicates a stronger ZCU in 2013 than in 2018.
The vertical structures of water temperature along section M also showed obvious differences caused by thedifferent intensities of ZCU (Figures 5c and 5d). The isotherms were located at a shallower place in 2013 than in
2018. Taking the 26°C isotherm as an example, it reached the sea surface of the coastal area in 2013 but remained
at a depth of ∼10 m in 2018.
The average vertical velocity along section M was 1.11 m/day in 2013, which was ∼5.2 times the value in 2018
(Figures 5e and 5f). The upward vertical velocities appeared from near the coast to a longitude of 122.2°E alongsection M. The upward velocities occupied a larger area along section M in 2013 than in 2018. The maximum
upward vertical velocity was 9.1 m/day occurring at the bottom of the longitude of 122.0°E in 2013, but it was
only 5.1 m/day in 2018.
3.4. Comparisons of Nutrient Concentrations in the Strong and Weak ZCU Years
The climatologically seasonal variations in nutrient concentrations in the ECS have already been validated inprevious studies (Y. Wang et al., 2019; Zhang et al., 2019). In this study, intensive observations of nutrient
concentrations in the coastal areas of the ECS in the summer of 2013 and 2018 (see Section 2.3 for detailed
information) overlapped with the modeled distributions (Figures 6 and 7). Most of modeled data shows linear
correlation with observed data (Figures 6d, 6h and 7d, 7h). The agreement suggest that the model successfully
reproduced the observational features of nutrient concentrations in summer when the ZCU occurred.
The concentrations of DIN and DIP at the sea surface and bottom in 2013 and 2018 were compared to quantify
the influence of the ZCU on nutrient supply. At the surface, the distributions of DIN concentrations in both 2013
and 2018 were similar (Figures 6a and 6b). The area with a concentration greater than 50 mmol/m 3 appeared near
LUO ET AL.

7 of 18

Journal of Geophysical Research: Oceans

10.1029/2022JC019216

Figure 6. Comparisons of (a, b) dissolved inorganic nitrogen (DIN) and (e, f) dissolved inorganic phosphorus (DIP) concentrations at the sea surface in July of thestrong (2013) and weak (2018) Zhejiang coastal upwelling years. The contour plot represents model results while the colored marks represent in situ observations in thesummer of 2013 and 2018. The circles, squares, and triangles represent the observation sites in this study, Yu (2014), and Lyu et al. (2020), respectively. Differences of(c) DIN and (g) DIP concentrations between 2013 and 2018. Comparisons of modeled results and observations (d, h).

the Changjiang Estuary (see Figure 1b) and extended northeastward along with the extension of the Changjiang
diluted water, which was also validated by observations (Figures 6a, 6b, and 6d). This indicated that the Changjiang River is an important source of DIN for the northern ECS. DIN concentrations were high along the coastlineof Zhejiang Province within the 60-m isobath, and decreased sharply seaward. The concentrations remained low(<1 mmol/m 3) in areas with depths greater than 60 m. To compare the DIN concentrations between 2013 and2018, the differences in DIN concentrations between the 2 years (the values in 2013 minus the values in 2018)
were calculated (Figure 6c). In the ZC region, DIN concentrations were much higher in the strong upwellingyear (2013), as indicated by the positive differences. Positive DIN differences were also found in the Changjiang
Estuary and its northeastward area. The Changjiang runoff was 2.7 × 10 11 m 3 from April to June 2013, which
was larger than that in 2018 (2.2 × 10 11 m 3). Since the nutrient concentrations of the Changjiang River water
were set as constants in the calculations (see Section 2.2), it is the greater amount of river discharge in 2013 thatinduced higher DIN concentrations near the Changjiang Estuary. Moreover, the stronger southwest monsoon in2013 contributed to the northeastward extension of N-rich river water.
The modeled concentrations of DIP were low throughout the entire ECS at the surface (Figures 6e and 6f), during
the summers of both 2013 and 2018. This is because DIP acts as the limiting nutrient in the ECS, and was nearly used
up by phytoplankton growth (Yu, 2014). The DIP concentrations had comparably high values (over 0.2 mmol/m 3)
in coastal regions, and then decreased seaward, as indicated by both model results and observations (Figures 6e,
6f, and 6h). In 2018, scattered high DIP concentrations (over 0.6 mmol/m 3) appeared near the Changjiang Estuaryand Hangzhou Bay (see Figure 1b), which were caused by the episodic supply contributed by the ChangjiangRiver, but they were not captured by the model. In the ZC region, there was an area with weak positive differencesin DIP concentrations (Figure 6g), whose position was consistent with that of the cold-water band induced by the
ZCU (Figure 5a). However, the difference in the DIP concentrations between these 2 years were not as obvious as
those of the DIN (Figure 6c). Because DIP was the limiting nutrient, the standing stocks did not reflect the amountLUO ET AL.

8 of 18

Journal of Geophysical Research: Oceans

10.1029/2022JC019216

Figure 7. Same as Figure 6, but for the nutrients at the sea bottom. The sea bottom is defined as the deepest sigma layer in the model that is 97.5% of the total water
depth. For the grid points whose water column depth is deeper than 220 m, the results are replaced by the mean nutrient concentrations between 220 and 300 m.

supplied by the ZCU. In contrast to the DIN, the differences in DIP concentrations were negative near the Changjiang Estuary, indicating that the DIP concentration in the ECS was not determined sorely by Changjiang Riverrunoff. Biological processes played a more important role in the distribution of DIP concentrations.
At the bottom, the highest DIN concentrations also appeared near the Changjiang Estuary in both 2013 and 2018
(Figures 7a and 7b). The DIN concentrations were much higher at the bottom than at the surface on the continental shelf of the ECS. A higher DIN concentration band (over 5 mmol/m 3), originating from the shelf break northeast of Taiwan Island, passed through the 200-m isobath (section B200, see Figure 1b), extended northwestward
to the coast, and then turned northeastward along the coastline to the Changjiang Estuary. The location of theband was consistent with the intrusion path of NKBC in the ECS, which is known to deliver the nutrients from
KI (Yang et al., 2011, 2013). The differences in DIN concentrations were positive within the nearshore area, but
negative in the wide deeper areas of the continental shelf (Figure 7c).
The DIP concentrations at the bottom (Figures 7e and 7f) were significantly higher than those at the surface(Figures 6e and 6f), which agreed well with the observations. The concentrations in the coastal areas were over
0.6 mmol/m 3 and remained at high values of 0.4 mmol/m 3 out of the 60-m isobath. The path of NKBC was also
indicated by the extension of the band with higher DIP concentrations (over 0.6 mmol/m 3). Therefore, KI canbe regarded as a critical source of nutrients to the bottom of the ZC region in summer, especially for DIP (Xuet al., 2020; Yang et al., 2013). In nearshore areas, there were positive DIP differences, indicating higher DIP
concentrations in 2013 with a stronger ZCU (Figure 7g). The much wider areas of the ECS were dominated bynegative differences at the bottom. The differences in DIN and DIP concentrations between 2013 and 2018 exhibited similar distributions at the bottom (Figures 7c and 7g).
3.5. Comparisons of the Phytoplankton Biomass in the Strong and Weak ZCU YearsIn summer, the surface phytoplankton biomass was much higher in the Changjiang Estuary and the adjacent
ZC region, with Chl-a concentrations higher than 2 mg/m 3, as indicated by both the model results and satellite
LUO ET AL.

9 of 18

Journal of Geophysical Research: Oceans

10.1029/2022JC019216

Figure 8. Comparisons of (a, b) modeled and (d, e) Geostationary Ocean Color Imager sea surface Chl-a concentrations in July of the strong (2013) and weak (2018)
Zhejiang coastal upwelling years, and (c, f) differences between these 2 years.

observations from GOCI for these 2 years (Figures 8a, 8b, 8d, and 8e). The high surface Chl-a concentrationsin the Changjiang Estuary were supported by the large input of nutrients from the Changjiang River, whereas
the high values along the coast of Zhejiang Province were attributed to the supply of nutrients due to the ZCU.
Comparing the situations in 2013 and 2018, the modeled differences in Chl-a concentrations were positive in the
ZC region (Figure 8c). The distributions of satellite-derived differences were patchier and not as uniform as themodel results (Figure 8f). However, they both suggested that Chl-a concentrations were higher in the ZC regionin 2013. In the deeper continental shelf region, there were no obvious differences in the Chl-a concentrations
between these 2 years.

4. Discussion
4.1. The Nutrient Transports of the ZCU and KI
The ZC region is surrounded by three open boundaries (N27, B60, and N30) and the coastline (see Figure 1b). The
fluxes of DIN and DIP through these three open boundaries in July 2013 and 2018 were calculated to interpret the
LUO ET AL.

10 of 18

Journal of Geophysical Research: Oceans

10.1029/2022JC019216

Figure 9. Spatial distributions of (a, b) dissolved inorganic nitrogen and (c, d) dissolved inorganic phosphorus fluxes along the three open boundaries of the Zhejiang
coastal (ZC) region (N27, B60 and N30, see Figure 1b) in July of the strong (2013) and weak (2018) Zhejiang coastal upwelling years. Positive values represent the flux
into the ZC region. The flux at B60 was horizontally five-points running mean. The A, B, C, and D represent the endpoints of the sections.

nutrient budget of the ZC region (Figure 9). In July 2013 and 2018, nutrients were transported into the ZC region
through the southern boundary (N27) for both DIN and DIP, and were transported out of the region through thenorthern boundary (N30). The northeastward currents along the coastline, induced by the summer monsoon(Gan et al., 2016), were responsible for the transport of nutrients across N27 and N30. However, the distributionof nutrient fluxes at B60 was much more complicated. Shoreward transport dominated the lower water layers(deeper than 20 m), which was induced by the ZCU going up along the sea bottom. Meanwhile, seaward transport
occupied the upper layers, which was the effect of offshore Ekman transport induced by alongshore wind stress.
The nutrient fluxes at these three open boundaries exhibited clear differences between 2013 and 2018 (Figure 9).
Taking DIP as an example (Figures 9c and 9d; Table 2), the amounts of DIP transported into the ZC region across
N27 were 4.54 × 10 8 and 3.24 × 10 8 mol in July 2013 and 2018, respectively. During the same period, the DIP
fluxes across N30 were −2.88 × 10 8 mol in 2013 and −1.95 × 10 8 mol in 2018. Negative values indicate theexport of DIP at N30. The transport amounts were much larger in 2013 than those in 2018 for both imports at N27
and exports at N30, which was attributed to the stronger alongshore currents in 2013. The distributions of DIP
fluxes at B60 were similar in 2013 and 2018, with seaward transport in the upper 20 m, and shoreward transport
in the deeper water layers; nevertheless, the fluxes in 2013 were much more intense. At deeper layers of B60(20–60 m), 4.23 × 10 8 mol of DIP was transported into the ZC region by the ZCU in July 2013, which was about
10 times the value in 2018 (0.40 × 10 8 mol). This indicated greater transport by the stronger ZCU in 2013. The net
flux over the entire B60 was 1.40 × 10 8 mol in July 2013. However, the value was −0.83 × 10 8 mol in 2018. At
B60, the net inflow in 2013 and net outflow in 2018 resulted in positive differences in DIP concentrations in thenearshore areas (Figures 6g and 7g). The net DIP flux through these three open boundaries was 3.06 × 10 8 mol in
July 2013, which was approximately six times that of 2018 (0.46 × 10 8 mol). DIP acted as a limiting nutrient in
the ZC region. More DIP supplies in 2013 promoted biogeochemical cycles of N and P in the seawater. Taking thevariations in DIP as an example, the net contributions of biological processes, including photosynthesis, respiration, mineralization, and benthic release was −2.45 × 10 8 mol in July 2013, but only −0.38 × 10 8 mol in July
2018. The greater absorption of DIP resulted in higher Chl-a concentrations in the ZC region in 2013 (Figure 8c).
KI is regarded as an important source of nutrients in the ECS, especially for DIP (Lin et al., 2019; Yanget al., 2013). The amounts of nutrients transported across B200 between 2013 and 2018 were compared to evaluate the effects of KI. Given that there is a time lag between the occurrence of the KI at B200 and its effect in the
coastal areas, Xu et al. (2020) used a group of numerical experiments to point out that the nutrients from the KI
integrated from March to June influenced the growth of phytoplankton in July in the coastal areas of the ECS.
Based on the results of our model, 1.21 × 10 11 mol DIN and 1.63 × 10 10 mol DIP were transported to the continental ECS across B200 from March to June in 2013, whereas in 2018 the values were 1.64 × 10 11 mol DIN andLUO ET AL.

11 of 18

Journal of Geophysical Research: Oceans

10.1029/2022JC019216

Table 2Comparisons Among the Cases 2013, WIND, KURO, and 2018 (CONTROL)
2013

WIND

KURO

2018 (CONTROL)ZCU intensity (m/day)

1.51

1.46

0.41

0.42

Alongshore wind stress (dyn/cm 2)

0.70

0.70

0.10

0.10

4.54

4.93

2.98

3.24

DIP flux (10 mol)
8

N27
B60

1.40

1.55

−0.62

−0.83

N30

−2.88

−3.02

−1.86

−1.95

Sum
DIP variation due to biological processes (10 8 mol)

3.06

3.46

0.50

0.46

−2.45

−2.83

−0.36

−0.38

Chl-a concentration (mg/m 3)

1.85

2.06

1.31

1.34

KI intensity (10 10 mol DIP)

1.63

2.04

1.58

2.10

Note. The ZCU intensity is quantified as the average vertical velocity in July in the ZC region. Alongshore wind stress, DIP
flux, DIP variation due to biological processes, and Chl-a concentration are quantified in July in the ZC region. KI intensity
is quantified as the DIP flux through B200 from March to June.

2.10 × 10 10 mol DIP. It was apparent that the supplies from KI in 2013 were less than those in 2018, which was
the reason for the negative differences in DIN and DIP concentrations at the bottom of the wide continental ECS
between 2013 and 2018 (Figures 7c and 7g).
4.2. The Key Factors Determining Nutrient Supply in the ZC Region
There were obvious differences in the nutrient supply, nutrient standing stocks, and Chl-a concentrations between
2013 and 2018. To probe the key factors determining these differences, a group of numerical experiments werecarried out as described in Table 1.
In WIND, the wind stress was changed to that of 2013, and the other conditions were the same as those in 2018
(CONTROL), which means the alongshore wind stress in July significantly increased from 0.10 to 0.70 dyn/cm 2(Table 2). The ZCU intensity in WIND varied significantly compared with that in 2018. The simulated average w
in the ZC region was 1.46 m/day, which was approximately three times the value in 2018 (0.42 m/day). Comparing the results of WIND and 2013, the same stronger wind stress induced the stronger upwelling, as indicated by
the similar w. Therefore, wind stress along with wind-induced Ekman transport were important factors in deciding the ZCU intensity. The amount of DIP supplied by KI from March to June was 2.04 × 10 10 mol in WIND,
which was similar to the value of 2.10 × 10 10 mol in 2018, but larger than the value of 1.63 × 10 10 mol in 2013.
Therefore, wind stress did not have a significant influence on the nutrient supply by KI. In the ZC region, the DIP
fluxes through N27 and N30 were larger in WIND than in 2018, and a stronger ZCU also transported more DIP
across B60 into the ZC region. Therefore, the local alongshore wind in the summer determined the ZCU intensityand played an important role in establishing the nutrient supply of coastal regions. As a result, a greater nutrient
supply in the ZC region supported the growth of phytoplankton, which was reflected in the higher Chl-a concentrations of 2.06 mg/m 3 in WIND and 1.85 mg/m 3 in 2013 than the value of 1.34 mg/m 3 in 2018.
In KURO, the southern open boundary conditions of the model were changed to those of 2013. The amount of DIP
supplied by KI from March to June in KURO (1.58 × 10 10 mol) was smaller than that in 2018 (2.10 × 10 10 mol)but was similar to that in 2013 (1.64 × 10 10 mol). Therefore, the southern open boundary conditions determined
the nutrients supplied by KI to the continental ECS. However, with the same weaker alongshore wind stress in
KURO and in 2018, the ZCU intensity and DIP fluxes at the three open boundaries of the ZC region were similar(Table 2). This suggests that the different amounts of nutrients supplied by KI between 2013 and 2018 did not
significantly influence the nutrient budget of the ZC region. As a result, the Chl-a concentration in the ZC region
in KURO was as low as that in 2018.
The results of the experiments OTHERS and RIVER were similar to those of 2018 (not shown in Table 2).
This suggests that the other atmospheric forcings except wind stress did not influence the nutrient supply or
phytoplankton biomass in the ZC region. The Changjiang diluted water extended northeastward, driven by the
LUO ET AL.

12 of 18

Journal of Geophysical Research: Oceans

10.1029/2022JC019216

Figure 10. The differences in dissolved inorganic phosphorus concentrations between numerical experiments andCONTROL (2018). The differences between WIND and CONTROL at (a) surface and (b) bottom. The differences between
KURO and CONTROL at (c) surface and (d) bottom. For the grid points whose water column depth is deeper than 220 m, theresults are replaced by the mean nutrient concentrations between 220 and 300 m.

southwest summer monsoon. Therefore, nutrients from the Changjiang River tend to influence the areas north of
the ZC region. Previous studies have also noted that the input from the Changjiang River does not influence thephytoplankton biomass in the coastal areas of Zhejiang Province (Xu et al., 2020).
To identify the main areas where the ZCU and KI distribute DIP, the results of WIND and KURO were comparedwith those of 2018. Figure 10 shows the differences in DIP concentrations in WIND and KURO minus those in
CONTROL (2018) at the surface and bottom. In WIND (Figures 10a and 10b), the positive differences dominatedthe coastal areas of Zhejiang Province at both the surface and bottom, which is consistent with the shoreward
nutrient supply induced by the stronger ZCU in WIND (Table 2). The differences in the DIP concentrations
between 2013 and 2018 in the nearshore areas (Figures 6g and 7g) can also be attributed to the effects of the
ZCU. However, the negative differences in DIP concentration between 2013 and 2018 dominated the bottomof the wide continental areas (Figure 7g), which cannot be explained by the results of WIND (Figure 10b). The
weaker KI was the main influencing factor, as indicated by the similar distribution of negative DIP differences atthe bottom in 2013 (Figure 7g) and in KURO (Figure 10d). However, the KI did not have an obvious influenceon the DIP concentrations at the surface (Figure 10c).
LUO ET AL.

13 of 18

Journal of Geophysical Research: Oceans

10.1029/2022JC019216

Figure 11. Vertical structures of Chl-a concentrations horizontally averaged in the (a) Zhejiang coastal and (b) East Zhejiang
coastal regions in July of the cases 2013, WIND, KURO, and CONTROL (2018). Scatters represent observed nearshore andoffshore Chl-a concentration profiles in summer.

The nutrient supply revealed by the numerical experiments indicated that KI intensity determined the nutrient
concentrations at the bottom of the ECS. The amount of these nutrients transported to the surface in the ZC region
depended on the intensity of the wind-induced ZCU.
4.3. The Effects of the ZCU and KI on Phytoplankton Biomass
To investigate the response of phytoplankton biomass to nutrient supply, the vertical structures of the Chl-a
concentrations, averaged in the ZC and EZC regions (see Figure 1b) in July, were analyzed for cases 2013,
WIND, KURO, and 2018 (Figure 11).
The vertical structures of the Chl-a concentrations in the ZC and EZC regions were significantly different insummer. In the ZC region, the Chl-a concentrations remained higher (over 2.0 mg/m 3) in the upper water layersthat were shallower than 10 m, and then decreased with increasing water depth (Figure 11a). At a depth of 30 m,
the Chl-a concentration decreased to approximately zero. These modeled vertical characteristics were consistent
with the nearshore in situ observations in Figure 11a (Chen et al., 2021; Wei et al., 2021). In the offshore EZC
region, the water was well-stratified in summer, and nutrients were depleted by phytoplankton at the surface.
Under the influence of both nutrients and light, the subsurface chlorophyll maximum formed. This phenomenon
has been confirmed in many historical observations of the offshore areas of the ECS (Furuya et al., 2003; Leeet al., 2017; Xu et al., 2022). The modeled depth of subsurface chlorophyll maximum was ∼50 m, which was in
the range of observed depths (20–50 m). The modeled maximum value (2.0–3.0 mg/m 3) was also in the range of
observations (1.0–3.0 mg/m 3).
In WIND and 2013, more nutrients were pumped up by the stronger ZCU, which supported greater Chl-a concentrations (∼4.0 mg/m 3) at the sea surface in the ZC region (red and orange lines in Figure 11a). The surface Chl-a
concentrations in KURO and in 2018 (blue and black lines in Figure 11a) were only half of those in WIND andin 2013, which were induced by a weaker ZCU. At the depth of 50 m in the EZC region, the maximum Chl-a
LUO ET AL.

14 of 18

Journal of Geophysical Research: Oceans

10.1029/2022JC019216

concentrations in WIND and in 2018 reached about 3.0 mg/m 3 under the influence of stronger KI in these twocases (orange and black lines in Figure 11b), whereas the concentrations were only ∼2.0 mg/m 3 in KURO andin 2013, with weaker KI (red and blue lines in Figure 11b). The comparison of these four cases indicated that
the ZCU mainly determined the phytoplankton biomass in nearshore areas, whereas the KI mainly regulated theintensity of the subsurface chlorophyll maximum in the offshore areas. The intensity of the ZCU was similar in
WIND and 2013. However, more nutrients supplied by the KI in WIND also resulted in higher Chl-a concentrations at the surface in the ZC region in WIND than those in 2013 (red and orange lines in Figure 11a; Table 2).

5. Conclusions
The ZCU prevails near the coastal area of the ECS in summer, and KI is regarded as an important oceanic sourceof DIP. The primary production is reported to be high in summer when the ZCU pumps up DIP oriented from theKuroshio to the euphotic layers. However, the yearly variations in both ZCU and KI intensities are not consistent.
Therefore, their interaction on coastal nutrient supply may vary a lot.
In this study, a three-dimensional physical-biochemical model was applied to evaluate the influence of the
ZCU and KI on the yearly variations of nutrient concentration and phytoplankton biomass. After validating themodeled SST with the satellite-derived data in the ZC region in 2010–2018 period, the years 2013 and 2018 were
determined as the strong and weak year of the ZCU, as indicated by both UISST and w. The surface DIP concentrations were lower than 0.30 mmol/m 3 in the 2 years. Comparing the results at the sea bottom in 2013 and 2018,
the bottom DIP concentrations in 2013 were ∼0.20 mmol/m 3 higher than in 2018 in nearshore areas, whereas
were ∼0.08 mmol/m 3 lower than in 2018 in offshore areas. The stronger ZCU in July 2013 induced net shoreward
rather than seaward DIP transport at the 60-m isobath (1.40 × 10 8 mol in 2013, −0.83 × 10 8 mol in 2018), which
resulted in higher nearshore DIP concentrations. The weaker KI in the spring (March to June) of 2013 decreased
the amount of DIP supply from the open ocean (1.63 × 10 10 mol in 2013, 2.10 × 10 10 mol in 2018), and resulted inlower offshore DIP concentrations. The greater nutrient supply in nearshore areas in 2013 significantly promoted
primary production and increased phytoplankton biomass (1.85 mg/m 3 in 2013, 1.34 mg/m 3 in 2018).
Through a group of numerical experiments, the wind stress and the southern open boundary conditions wereregarded as the key factors influencing the yearly variations of nutrient and Chl-a concentrations in the ECS. Thisis because wind stress decided the ZCU intensity, whereas the southern open boundary conditions represented
the KI intensity. With similar KI intensities, the ZCU intensities between 2013 and 2018 determined the nutrient
supply and concentrations of the ZC region within the 60-m isobath. The KI intensities influenced the nutrients
supplied from B200 and the nutrient concentrations at the bottom of the wide ECS continental shelf. The subsurface chlorophyll maximum in the offshore ECS in summer was controlled by the KI intensity, while the nearshore
phytoplankton biomass was mainly determined by the ZCU intensity.
The intensity of KI was weak in 2013 with a strong ZCU, while KI was strong in 2018 with a weak ZCU. The
intensities of KI and ZCU happened to be inconsistent in these 2 years. In this study, the ZCU-induced influence
on coastal nutrient supply and phytoplankton biomass was highlighted. But the KI-induced influence can also be
noticed from the information in Table 2. For example, in cases 2013 and WIND, which have a similar intensity of
ZCU, the DIP flux through N27 was higher in WIND (4.93 × 10 8 mol) than in 2013 (4.54 × 10 8 mol), owing to
the stronger KI in WIND. Moreover, the effect of KI can be impeded by weaker ZCU. Because when the intensity
of ZCU was weak in KURO and 2018, the DIP fluxes through N27 were 2.98 × 10 8 and 3.24 × 10 8 mol, respectively, which represented a smaller difference. This result suggests that there exist nonlinear interactions between
the ZCU and KI to regulate coastal nutrient supply in the ECS.

Data Availability Statement
The OSTIA SST data used in this study are the Reprocessed foundation SST analyses data available at https://
ghrsst-pp.metoffice.gov.uk/ostia-website/index.html. The GOCI chlorophyll data are obtained from http://kosc.kiost.ac.kr/gociSearch/list.nm?menuCd=50&lang=en&url=gociSearch. The ERA-interim data set isavailable at https://apps.ecmwf.int/datasets/data/interim-full-daily/levtype=sfc. The China River and Sediment Bulletin are available at http://www.mwr.gov.cn/sj. The JCOPE2 data are provided by Japan Agency for
Marine-Earth Science and Technology (JAMSTEC, http://www.jamstec.go.jp/jcope). The model results that
LUO ET AL.

15 of 18

Journal of Geophysical Research: Oceans

10.1029/2022JC019216

support the findings of this study are available online at https://data.mendeley.com/datasets/jm3h5mgn3n/draft?a=1aacb6b2-ba0d-4aac-aaec-0048a16488d9.
Acknowledgments
This study was supported by the National
Nature Science Foundation of China(NSFC; 42176022, 41630966, and42076034). X. Guo was supported by
a Grant-in-Aid for Scientific Research(MEXT KAKENHI, Grant 20H04319,
22H05206) and the Environment
Research and Technology Development
Fund (JPMEERF20212001) of the Environmental Restoration and Conservation
Agency of Japan. Y. Luo was supportedby the Ministry of Education, Culture,
Sports, Science and Technology, Japan(MEXT) to a project on Joint Usage/
Research Center-Leading Academia
in Marine and Environment Pollution
Research (LaMer; 22-57).

LUO ET AL.

References
Albert, A., Echevin, V., Lévy, M., & Aumont, O. (2010). Impact of nearshore wind stress curl on coastal circulation and primary productivity in
the Peru upwelling system. Journal of Geophysical Research, 115, C12033. https://doi.org/10.1029/2010JC006569
Bakun, A. (1973). Coastal upwelling indices, west coast of North America, 1946–71. NOAA Technical Report NMFS SSRF-671. U.S. Department of Commerce.
Barber, R. T., Marra, J., Bidigare, R. C., Codispoti, L. A., Halpern, D., Johnson, Z., et al. (2001). Primary productivity and its regulation in
the Arabian Sea during 1995. Deep Sea Research Part II: Topical Studies in Oceanography, 48(6–7), 1127–1172. https://doi.org/10.1016/
S0967-0645(00)00134-X
Blumberg, A. F., & Mellor, G. L. (1987). A description of a three-dimensional coastal ocean circulation model. In N. S. Heaps (Ed.),
Three-dimensional coastal ocean models (Vol. 4, pp. 1–16). American Geophysical Union. https://doi.org/10.1029/co004p0001
Bograd, S. J., Schroeder, I., Sarkar, N., Qiu, X., Sydeman, W. J., & Schwing, F. B. (2009). Phenology of coastal upwelling in the California
Current. Geophysical Research Letters, 36, L01602. https://doi.org/10.1029/2008GL035933
Capone, D., & Hutchins, D. (2013). Microbial biogeochemistry of coastal upwelling regimes in a changing ocean. Nature Geoscience, 6(9),711–717. https://doi.org/10.1038/ngeo1916
Chen, C.-C., Shiah, F.-K., Gong, G.-C., & Chen, T.-Y. (2021). Impact of upwelling on phytoplankton blooms and hypoxia along the Chinese coastin the East China Sea. Marine Pollution Bulletin, 167, 112288. https://doi.org/10.1016/j.marpolbul.2021.112288
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., et al. (2011). The ERA-Interim reanalysis: Configurationand performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society, 137(656), 553–597. https://doi.
org/10.1002/qj.828
Donlon, C. J., Martin, M., Stark, J., Roberts-Jones, J., Fiedler, E., & Wimmer, W. (2012). The operational sea surface temperature and sea ice
analysis (OSTIA) system. Remote Sensing of Environment, 116, 140–158. https://doi.org/10.1016/j.rse.2010.10.017
Fréon, P., Barange, M., & Arístegui, J. (2009). Eastern boundary upwelling ecosystems: Integrative and comparative approaches. Progress in
Oceanography, 83(1–4), 1–14. https://doi.org/10.1016/j.pocean.2009.08.001
Furuya, K., Hayashi, M., Yabushita, Y., & Ishikawa, A. (2003). Phytoplankton dynamics in the East China Sea in spring and summer as revealedby HPLC-derived pigment signatures. Deep Sea Research Part II: Topical Studies in Oceanography, 50(2), 367–387. https://doi.org/10.1016/
s0967-0645(02)00460-5
Gan, J., Liu, Z., & Liang, L. (2016). Numerical modeling of intrinsically and extrinsically forced seasonal circulation in the China Seas: A kinematic study. Journal of Geophysical Research: Oceans, 121, 4697–4715. https://doi.org/10.1002/2016JC011800
Guo, X., Miyazawa, Y., & Yamagata, T. (2006). The Kuroshio onshore intrusion along the shelf break of the East China Sea: The origin of the
Tsushima warm current. Journal of Physical Oceanography, 36(12), 2205–2231. https://doi.org/10.1175/jpo2976.1
Hahm, D., Rhee, T. S., Kim, H.-C., Jang, C. J., Kim, Y. S., & Park, J.-H. (2019). An observation of primary production enhanced by coastal
upwelling in the Southwest East/Japan sea. Journal of Marine Systems, 195, 30–37. https://doi.org/10.1016/j.jmarsys.2019.03.005
Hu, B., Li, J., Zhao, J., Wei, H., Yin, X., Li, G., et al. (2014). Late Holocene elemental and isotopic carbon and nitrogen records from the East China
Sea inner shelf: Implications for monsoon and upwelling. Marine Chemistry, 162, 60–70. https://doi.org/10.1016/j.marchem.2014.03.008
Hu, J., & Wang, X. H. (2016). Progress on upwelling studies in the China seas. Reviews of Geophysics, 54, 653–673. https://doi.
org/10.1002/2015RG000505
Hu, Q., Chen, X., Huang, W., & Zhou, F. (2021). Phytoplankton bloom triggered by eddy-wind interaction in the upwelling region east of Hainan
Island. Journal of Marine Systems, 214, 103470. https://doi.org/10.1016/j.jmarsys.2020.103470
Jacox, M. G., Bograd, S. J., Hazen, E. L., & Fiechter, J. (2015). Sensitivity of the California current nutrient supply to wind, heat, and remoteocean forcing. Geophysical Research Letters, 42, 5950–5957. https://doi.org/10.1002/2015GL065147
Jayaram, C., Chacko, N., Joseph, K. A., & Balchand, A. N. (2010). Interannual variability of upwelling indices in the southeastern Arabian Sea:
A satellite based study. Ocean Science Journal, 45(1), 27–40. https://doi.org/10.1007/s12601-010-0003-6
Jing, Z., Qi, Y., & Du, Y. (2011). Upwelling in the continental shelf of northern South China Sea associated with 1997–1998 El Niño. Journal ofGeophysical Research, 116, C02033. https://doi.org/10.1029/2010JC006598
Lahiri, S. P., & Vissa, N. K. (2022). Assessment of Indian Ocean upwelling changes and its relationship with the Indian Monsoon. Global andPlanetary Change, 208, 103729. https://doi.org/10.1016/j.gloplacha.2021.103729
Lee, K., Matsuno, T., Endoh, T., Ishizaka, J., Zhu, Y., Takeda, S., & Sukigara, C. (2017). A role of vertical mixing on nutrient supply into
the subsurface chlorophyll maximum in the shelf region of the East China Sea. Continental Shelf Research, 143, 139–150. https://doi.
org/10.1016/j.csr.2016.11.001
Li, J., Wei, H., Zhang, Z., & Lu, Y. (2013). A modelling study of inter-annual variation of Kuroshio intrusion on the shelf of East China Sea.
Journal of Ocean University of China, 12(4), 537–548. https://doi.org/10.1007/s11802-013-2203-z
Liang, C., & Xian, W. (2018). Changjiang nutrient distribution and transportation and their impacts on the estuary. Continental Shelf Research,
165, 137–145. https://doi.org/10.1016/j.csr.2018.05.001
Lin, Z., Wang, X., Xiu, P., Chai, F., & Wu, Q. (2019). Boundary phosphate transport of the East China Sea and its influence on biological process.
Journal of Geoscience and Environment Protection, 7(9), 79–104. https://doi.org/10.4236/gep.2019.79007
Liu, C., Wang, F., Chen, X., & von Storch, J.-S. (2014). Interannual variability of the Kuroshio onshore intrusion along the East China Sea shelf break:Effect of the Kuroshio volume transport. Journal of Geophysical Research: Oceans, 119, 6190–6209. https://doi.org/10.1002/2013JC009653
Liu, Z., Gan, J., Hu, J., Wu, H., Cai, Z., & Deng, Y. (2021). Progress on circulation dynamics in the East China Sea and southern Yellow Sea: Origination, pathways, and destinations of Shelf Currents. Progress in Oceanography, 193, 102553. https://doi.org/10.1016/j.pocean.2021.102553
Liu, Z., Gan, J., Wu, H., Hu, J., Cai, Z., & Deng, Y. (2021). Advances on coastal and estuarine circulations around the Changjiang estuary in the
recent decades (2000–2020). Frontiers in Marine Science, 8, 615929. https://doi.org/10.3389/fmars.2021.615929
Lou, X., Shi, A., Xiao, Q., & Zhang, H. (2011). Satellite observation of the Zhejiang coastal upwelling in the East China Sea during 2007–2009.
Proceedings of SPIE, 8175, 283–304. https://doi.org/10.1117/12.898140
Lü, X. G., Qiao, F. L., Xia, C. S., & Yuan, Y. L. (2007). Tidally induced upwelling off Yangtze River estuary and in Zhejiang coastal waters insummer. Science in China-Series D: Earth Sciences, 50(3), 462–473. https://doi.org/10.1007/s11430-007-2050-0
Lyu, W., Sun, Q., Wang, S., & Li, B. (2020). Summer vertical turbulent nitrate flux in the Yellow Sea and the East China Sea. Oceanologia et
Limnologia Sinica, 51(6), 1379–1390.

16 of 18

Journal of Geophysical Research: Oceans

10.1029/2022JC019216

Mellor, G. (2003). The three-dimensional current and surface wave equations. Journal of Physical Oceanography, 33(9), 1978–1989. https://doi.
org/10.1175/1520-0485(2003)033<1978:ttcasw>2.0.co;2
Menge, B. A., & Menge, D. N. L. (2013). Dynamics of coastal meta-ecosystems: The intermittent upwelling hypothesis and a test in rocky intertidal regions. Ecological Monographs, 83(3), 283–310. https://doi.org/10.1890/12-1706.1
Miyazawa, Y., Zhang, R., Guo, X., Tamura, H., Ambe, D., Lee, J.-S., et al. (2009). Water mass variability in the western North Pacific detected ina 15-year eddy resolving ocean reanalysis. Journal of Oceanography, 65(6), 737–756. https://doi.org/10.1007/s10872-009-0063-3
Ningsih, N. S., Rakhmaputeri, N., & Harto, A. B. (2013). Upwelling variability along the southern coast of Bali and in Nusa Tenggara waters.
Ocean Science Journal, 48(1), 49–57. https://doi.org/10.1007/s12601-013-0004-3
Nykjær, L., & Van Camp, L. (1994). Seasonal and interannual variability of coastal upwelling along Northwest Africa and Portugal from 1981 to
1991. Journal of Geophysical Research, 99(C7), 14197–14207. https://doi.org/10.1029/94JC00814
Picado, A., Alvarez, I., Vaz, N., Varela, R., Gomez-Gesteira, M., & Dias, J. M. (2014). Assessment of chlorophyll variability along the northwestern coast of Iberian Peninsula. Journal of Sea Research, 93, 2–11. https://doi.org/10.1016/j.seares.2014.01.008
Ramanantsoa, J. D., Krug, M., Penven, P., Rouault, M., & Gula, J. (2018). Coastal upwelling south of Madagascar: Temporal and spatial variability. Journal of Marine Systems, 178, 29–37. https://doi.org/10.1016/j.jmarsys.2017.10.005
Ryu, J.-H., Han, H.-J., Cho, S., Park, Y.-J., & Ahn, Y.-H. (2012). Overview of geostationary ocean color imager (GOCI) and GOCI data processing system (GDPS). Ocean Science Journal, 47(3), 223–233. https://doi.org/10.1007/s12601-012-0024-4
Shan, X., Jin, X., Zhou, Z., & Dai, F. (2011). Fish community diversity in the middle continental shelf of the East China Sea. Chinese Journal of
Oceanology and Limnology, 29(6), 1199–1208. https://doi.org/10.1007/s00343-011-0321-2
Skogen, M. D., & Søiland, H. (1998). A user’s guide to NORWECOM v2.0. The Norwegian ecological model system. Institute of Marine Research.
Strutton, P. G., Chavez, F. P., Dugdale, R. C., & Hogue, V. (2004). Primary productivity in the central equatorial Pacific (3°S 130°W) during
GasEx-2001. Journal of Geophysical Research, 109, C08S06. https://doi.org/10.1029/2003JC001790
Sun, Y., Dong, C., He, Y., Yu, K., Renault, L., & Ji, J. (2016). Seasonal and interannual variability in the wind-driven upwelling along the southern
East China Sea Coast. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(11), 5151–5158. https://doi.
org/10.1109/jstars.2016.2544438
Sydeman, W. J., García-Reyes, M., Schoeman, D. S., Rykaczewski, R. R., Thompson, S. A., Black, B. A., & Bograd, S. J. (2014). Climate change
and wind intensification in coastal upwelling ecosystems. Science, 345(6192), 77–80. https://doi.org/10.1126/science.1251635
Tan, S., & Shi, G. (2006). Satellite-derived primary productivity and its spatial and temporal variability in the China Seas. Journal of Geographical Sciences, 16(4), 447–457. https://doi.org/10.1007/s11442-006-0408-4
van Ruth, P. D., Patten, N. L., Doubell, M. J., Chapman, P., Rodriguez, A. R., & Middleton, J. F. (2018). Seasonal- and event-scale variations in
upwelling, enrichment and primary productivity in the Eastern Great Australian Bight. Deep Sea Research Part II: Topical Studies in Oceanography, 157–158, 36–45. https://doi.org/10.1016/j.dsr2.2018.09.008
Villanoy, C., Cabrera, O., Yniguez, A., Camoying, M., de Guzman, A., David, L., & Flament, P. (2011). Monsoon-driven coastal upwelling off
Zamboanga Peninsula, Philippines. Oceanography, 24(01), 156–165. https://doi.org/10.5670/oceanog.2011.12
Wang, B. (2006). Cultural eutrophication in the Changjiang (Yangtze river) plume: History and perspective. Estuarine, Coastal and Shelf Science,69(3–4), 471–477. https://doi.org/10.1016/j.ecss.2006.05.010
Wang, B., & Wang, X. (2007). Chemical hydrography of coastal upwelling in the East China Sea. Chinese Journal of Oceanology and Limnology,
25(1), 16–26. https://doi.org/10.1007/s00343-007-0016-x
Wang, B., Wang, X., & Zhang, R. (2003). Nutrient conditions in the Yellow Sea and the East China Sea. Estuarine Coastal Shelf Sciences, 58(1),127–136. https://doi.org/10.1016/S0272-7714(03)00067-2
Wang, D., Gouhier, T. C., Menge, B. A., & Ganguly, A. R. (2015). Intensification and spatial homogenization of coastal upwelling under climatechange. Nature, 518(7539), 390–394. https://doi.org/10.1038/nature14235
Wang, D., Wang, H., Li, M., Liu, G., & Wu, X. (2013). Role of Ekman transport versus Ekman pumping in driving summer upwelling in the South
China Sea. Journal of Ocean University of China, 12(3), 355–365. https://doi.org/10.1007/s11802-013-1904-7
Wang, Q., Guo, X., & Takeoka, H. (2008). Seasonal variations of the Yellow River Plume in the Bohai Sea: A model study. Journal of Geophysical Research, 113, C08046. https://doi.org/10.1029/2007JC004555
Wang, Y., Guo, X., Zhao, L., & Zhang, J. (2019). Seasonal variations in nutrients and biogenic particles in the upper and lower layers of East
China Sea Shelf and their export to adjacent seas. Progress in Oceanography, 176, 102138. https://doi.org/10.1016/j.pocean.2019.102138
Wei, Q., Yao, P., Xu, B., Zhao, B., Ran, X., Zhao, Y., et al. (2021). Coastal upwelling combined with the river plume regulates hypoxia in the
Changjiang estuary and adjacent Inner East China Sea shelf. Journal of Geophysical Research: Oceans, 126, e2021JC017740. https://doi.
org/10.1029/2021JC017740
Xie, L., & Hsieh, W. W. (1995). The global distribution of wind-induced upwelling. Fisheries Oceanography, 4(1), 52–67. https://doi.
org/10.1111/j.1365-2419.1995.tb00060.x
Xu, L., Yang, D., Benthuysen, J. A., & Yin, B. (2018). Key dynamical factors driving the Kuroshio subsurface water to reach the Zhejiang coastal
area. Journal of Geophysical Research: Oceans, 123, 9061–9081. https://doi.org/10.1029/2018JC014219
Xu, L., Yang, D., Greenwood, J., Feng, X., Gao, G., Qi, J., et al. (2020). Riverine and oceanic nutrients govern different algal bloom
domain near the Changjiang estuary in Summer. Journal of Geophysical Research: Biogeosciences, 125, e2020JG005727. https://doi.
org/10.1029/2020JG005727
Xu, Q., Wang, S., Sukigara, C., Goes, J. I., Gomes, H. D., Matsuno, T., et al. (2022). High-resolution vertical observations of phytoplankton
groups derived from an in-situ fluorometer in the East China Sea and Tsushima Strait. Frontiers in Marine Science, 8, 756180. https://doi.
org/10.3389/fmars.2021.756180
Xu, Q., Zhang, S., Cheng, Y., & Zuo, J. (2017). Interannual feature of summer upwelling around the Zhoushan Islands in the East China Sea.
Journal of Coastal Research, 331, 125–134. https://doi.org/10.2112/jcoastres-d-15-00197.1
Yang, D., Yin, B., Liu, Z., & Feng, X. (2011). Numerical study of the ocean circulation on the East China Sea shelf and a Kuroshio bottom branchnortheast of Taiwan in summer. Journal of Geophysical Research, 116, C05015. https://doi.org/10.1029/2010JC006777
Yang, D., Yin, B., Sun, J., & Zhang, Y. (2013). Numerical study on the origins and the forcing mechanism of the phosphate in upwelling areas off
the coast of Zhejiang Province, China in Summer. Journal of Marine Systems, 123–124, 1–18. https://doi.org/10.1016/j.jmarsys.2013.04.002
Yang, D.-Z., Huang, R. X., Yin, B.-S., Feng, X.-R., Chen, H.-Y., Qi, J.-F., et al. (2018). Topographic beta spiral and onshore intrusion of theKuroshio Current. Geophysical Research Letters, 45, 287–296. https://doi.org/10.1002/2017GL076614
Yang, S., Mao, X., & Jiang, W. (2020). Interannual variation of coastal upwelling in summer in Zhejiang, China (in Chinese). Periodical of Ocean
University of China, 50(2), 1–8.
Yin, W., & Huang, D. (2019). Short-term variations in the surface upwelling off northeastern Taiwan observed via satellite data. Journal ofGeophysical Research: Oceans, 124, 939–954. https://doi.org/10.1029/2018JC014537

LUO ET AL.

17 of 18

Journal of Geophysical Research: Oceans

10.1029/2022JC019216

Yu, Z. (2014). Characteristics of nutrient in the southern Yellow Sea and northern East China Sea from 2011 to 2013 (Master’s thesis in Chinese).
Ocean University of China. Retrieved from https://kns.cnki.net/KCMS/detail/detail.aspx?dbname=CMFD201501&filename=1014204213.nh
Yue, J., Noman, M. A., & Sun, J. (2021). Kuroshio intrusion drives the Trichodesmium assemblage and shapes the phytoplankton community
during spring in the East China Sea. Journal of Oceanology and Limnology, 39(2), 536–549. https://doi.org/10.1007/s00343-020-9344-x
Zhang, J., Guo, X., & Zhao, L. (2019). Tracing external sources of nutrients in the East China Sea and evaluating their contributions to primary
production. Progress in Oceanography, 176, 102122. https://doi.org/10.1016/j.pocean.2019.102122
Zhao, B., Yao, P., Bianchi, T. S., Arellano, A. R., Wang, X., Yang, J., et al. (2018). The remineralization of sedimentary organic carbon in different
sedimentary regimes of the Yellow and East China Seas. Chemical Geology, 495, 104–117. https://doi.org/10.1016/j.chemgeo.2018.08.012
Zhao, L., & Guo, X. (2011). Influence of cross-shelf water transport on nutrients and phytoplankton in the East China Sea: A model study. Ocean
Science, 7(1), 27–43. https://doi.org/10.5194/os-7-27-2011
Zhou, F., Xue, H., Huang, D., Xuan, J., Ni, X., Xiu, P., & Hao, Q. (2015). Cross-shelf exchange in the shelf of the East China Sea. Journal ofGeophysical Research: Oceans, 120, 1545–1572. https://doi.org/10.1002/2014JC010567
Zhou, P., Song, X., Yuan, Y., Wang, W., Chi, L., Cao, X., & Yu, Z. (2018). Intrusion of the Kuroshio subsurface water in the Southern East
China Sea and its variation in 2014 and 2015 traced by dissolved inorganic iodine species. Progress in Oceanography, 165, 287–298.
https://doi.org/10.1016/j.pocean.2018.06.011

LUO ET AL.

18 of 18