Marine Pollution Bulletin 169 (2021) 112517

Contents lists available at ScienceDirect

Marine Pollution Bulletinjournal homepage: www.elsevier.com/locate/marpolbul

Controlling factors of annual cycle of dimethylsulfide in the Yellow andEast China seas
Jia-Wei Shen a, Liang Zhao b, *, Hong-Hai Zhang c, d, Hao Wei a, Xinyu Guo eaSchool of Marine Science and Technology, Tianjin University, Tianjin 300072, PR China
College of Marine and Environmental Sciences, Tianjin University of Science and Technology, TEDA, Tianjin 300457, PR Chinac
Frontiers 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 266100, PR ChinadLaboratory of Marine Ecology and Environmental Science, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, PR ChinaeCenter for Marine Environmental Studies, Ehime University, Matsuyama 790-8577, Japanb

A R T I C L E I N F O

A B S T R A C T

Keywords:
Dimethylsulfide
Annual cycle
Ecological model
Marginal sea

We developed a dimethylsulfide (DMS) module coupled to an ecological dynamics model studying the annual
DMS cycle of the Yellow and East China seas (YECS). The model results showed that surface DMS concentrations([DMS]) peaked in August along the coast, and there exhibited several DMS peaks offshore annually. In addition,
surface [DMS] were higher in the Yellow Sea than that in the East China Sea. The annual mean surface [DMS] of
the YECS reached to 4.55 nmol/L, and oceanic DMS emissions from this sea area was 6.78 μmol/(m2 day).
Several sensitivity experiments demonstrated that phytoplankton community and sea water temperature exerted
crucial effects on seasonal variations of surface [DMS]; and phytoplankton community or temperature changed
the timing of surface DMS peak while photolysis affected the magnitude of [DMS]. Moreover, the effect size ofphytoplankton community or water temperature varied spatially.

1. Introduction
Quantitative assessments of climate response to global change
induced by human activities, including eutrophication, acidification andglobal warming, contain many uncertainties, which can be reduced by
an improved understanding of biogeochemical feedbacks (Kloster et al.,2006). Marine dimethylsulfide (DMS) is an abundant biogenic volatilesulfur compound mainly originated from dimethylsulfoniopropionate(DMSP). And the transfer of oceanic DMS to the atmosphere is ofimportance for influencing aerosol formation and cloud albedo over theocean (Charlson et al., 1987; Fiddes et al., 2018); and future increases ofthis flux could alleviate warming (Bopp et al., 2004; Gabric et al., 2004;
Grandey and Wang, 2015).
Surface DMS concentration and DMS emissions are higher in mar­
ginal seas (Kettle et al., 1999; Gypens and Borges, 2014), and changes insurface DMS and its flux under a climate change scenarios vary spatially(Bopp et al., 2004; Kloster et al., 2007). Thus, additional uncertainty
may be introduced in assessments of DMS effects on climate if the spatiotemporal variations of DMS level especially in marginal seas, which are

involved in complex environmental changes, is not well-researched.
The area encompassing the Yellow and East China seas (YECS) is oneof the most extensive marginal sea basins in the western Pacific Ocean.
The YECS, which accounts for a small part of the global ocean, con­tributes greatly to the release of DMS (Yang et al., 2014; Jian et al.,2018; Xu et al., 2019). And a significant relationship between chloro­
phyll a (Chla) and DMS concentrations has been observed, indicatingthat phytoplankton biomass might play a crucial role in controllingvariations in DMS (Yang et al., 2011; Gao et al., 2017; Jian et al., 2018).
Some controlled experiments have quantified contributions of different
processes to the DMS stock (Jian et al., 2018; Xu et al., 2019; Zhai et al.,2019), and Xu et al. (2019) suggested that both microbial consumption
and photooxidation determine the removal of surface DMS.
Observational data contain large spatio-temporal discontinuities(Yang et al., 2011). The relationship between DMS and other factors(Gao et al., 2017; Jian et al., 2018; Shen et al., 2019), and the contri­
bution of each DMS sink (Xu et al., 2019), varies spatially and tempo­
rally. Therefore, we still need to deepen our understanding on seasonaland horizontal variations of DMS concentrations and their controlling* Corresponding author at: College of Marine and Environmental Sciences, Tianjin University of Science and Technology, 29 Thirteenth Avenue, TEDA, Tianjin
300457, PR China.
E-mail address: zhaoliang@tust.edu.cn (L. Zhao).
https://doi.org/10.1016/j.marpolbul.2021.112517
Received 6 February 2021; Received in revised form 13 May 2021; Accepted 15 May 2021
0025-326X/© 2021 Elsevier Ltd. All rights reserved.

J.-W. Shen et al.

Marine Pollution Bulletin 169 (2021) 112517

factors. In addition to in-situ observations and controlled experiments,
numerical modeling is also a tool to understand annual cycle of DMS.
And model studies have focused on the marine DMS cycle of some othermarginal seas (Gabric et al., 1999; Archer et al., 2002; Gypens et al.,2014). In this study, we developed a DMS module and coupled it to athree-dimensional ecological dynamics model to studying the annual
cycle of the YECS climatologic DMS and discussing its controlling
factors.

covers the YECS with an open boundary along the southern and easternboundaries (Fig. 1). The time step was 6 s for the external mode and 360
s for the internal mode. Tidal forcing (M2, S2, K1 and O1 tides) wereadded along the lateral boundary (Wang et al., 2008).
The biological module developed for the YECS (Zhao and Guo, 2011)is derived from the biological part of the Norwegian Ecological Modelsystem (Skogen and Søiland, 1998). The details of this module areavailable in Zhao and Guo (2011) and Wang et al. (2019). Someboundary conditions for the biogeochemical variables were adjusted,
including nutrient concentrations at the western part of the open
southern boundary and atmospheric nitrogen deposition (Wang et al.,2019). To decouple the nutrient cycles of nitrate and phosphate, wecategorized the detritus into particulate organic nitrogen and particulateorganic phosphorus.

2. Model description
We developed a DMS module that included two prognostic variables,
DMS and dissolved dimethylsulfoniopropionate (DMSPd, a precursor of
DMS), and coupled it to the East China Sea Ecological Model (ECSE­
COM). The ECSECOM has a hydrodynamic and a biological module andwas used to study seasonal variations in nutrients and phytoplankton(Zhao and Guo, 2011), nutrient transport and dynamic (Wang et al.,2019) and role of oceanic and terrestrial nutrients in primary production(Zhang et al., 2019). Water temperature, currents and diffusivity co­efficients from the hydrodynamic module, and irradiance and phyto­plankton biomass from the biological module were used as inputs to the
DMS module.

2.2. Dimethylsulfide module
We added DMSPd and DMS into the DMS module. The governing
equations of DMSPd and DMS in sea water (concentrations in nmol/L)
are given in Eqs. (1) and (2), and the parameters (C1 to C9) used in thestandard run are provided in Table 1:∂(DMSPd)+ adv(DMSPd) = diff (DMSPd) + C1 *(C3 *f(T)*PDIA + DDIA )⏟̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅⏞⏞̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅⏟⏟̅̅̅̅̅̅̅̅̅⏞⏞̅̅̅̅̅̅̅̅̅⏟ ⏟̅̅̅̅̅̅̅̅̅⏞⏞̅̅̅̅̅̅̅̅̅⏟∂(t)

2.1. Hydrodynamic and biological modules

advection term

diffusion term

phytoplankton active exudation and autolysis+ C2 *(C4 *f(T)*PFLA + DFLA ) − C5 *f(T)*DMSPd −⏟̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅⏞⏞̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅⏟⏟̅̅̅̅̅̅̅̅̅̅̅̅̅⏞⏞̅̅̅̅̅̅̅̅̅̅̅̅̅⏟The hydrodynamic module is a nested model of the YECS with a highhorizontal resolution (1/18◦ ) derived from the Princeton Ocean Model(Guo et al., 2003). The module has 21 sigma (σ) levels, and its domain

phytoplankton active exudation and autolysis

free DMSP− lyase cleavage

C6 *DMSPd⏟̅̅̅̅̅̅̅̅⏞⏞̅̅̅̅̅̅̅̅⏟microbial DMSPd degradation(1)∂(DMS)+ adv(DMS) = diff (DMS) + C5 *f(T)*DMSPd + C7 *C6 *DMSPd⏟̅̅̅̅̅̅̅̅̅̅̅⏞⏞̅̅̅̅̅̅̅̅̅̅̅⏟⏟̅̅̅̅̅̅⏞⏞̅̅̅̅̅̅⏟ ⏟̅̅̅̅̅̅̅⏞⏞̅̅̅̅̅̅̅⏟ ⏟̅̅̅̅̅̅̅̅̅̅̅̅̅⏞⏞̅̅̅̅̅̅̅̅̅̅̅̅̅⏟∂(t)advection term−

C8 *DMS⏟̅̅̅̅̅⏞⏞̅̅̅̅̅⏟microbial DMS degradation

diffusion term

free DMSP− lyase cleavage

microbial DMSP cleavage− C9 *Iz*DMS⏟̅̅̅̅̅̅̅̅⏞⏞̅̅̅̅̅̅̅̅⏟photolysis(2)
where adv and diff are advection and diffusion terms of the two prog­
nostic variables, P (mgN/m3) and D (mgN/m3) are phytoplankton
biomass stock and stock of dead phytoplankton, respectively, and thesubscripts DIA and FLA represent diatoms and flagellates.
We did not consider the DMS flux at the bottom boundary because of
the lack of in-situ data. And the sea-to-air DMS flux was specified as thesurface boundary condition (Eq. (3)):)(
KH ∂(DMS)= − kflux *(DMSsea − DMSair )(3)H∂σwhere KH (m2/s) is the vertical diffusion coefficient, H (m) is the water
Table 1
Parameters used in the dimethylsulfide (DMS) module for the standard run.

Fig. 1. Model domain and bathymetry. Red dots denote rivers along thecoastline. Black contours with number denote 50, 100 and 200 m isobaths. (Forinterpretation of the references to colour in this figure legend, the reader isreferred to the web version of this article.)

Parameter description

Symbol

Value

Unit

Intercellular S(DMSPp): N in a cell of DIA
Intercellular S(DMSPp): N in a cell of FLA
Active exudation rate constant at 0 degree
Celsius for DIA
Active exudation rate constant at 0 degree
Celsius for FLA
Free DMSP-lyase cleavage rate constant at
0 degree Celsius
Microbial DMSPd degradation rate constant
Proportion of consumed DMSPd cleaved to
DMS by microbe
Microbial DMS degradation rate constant
DMS photochemical oxidation rate per unit ofradiation

C1
C2

0.30
0.80

mg S/mg N
mg S/mg N

C3

0.034

1/day

C4

0.046

1/day

C5

0.18

1/day

C6

4.00

1/day

C7

0.45

/

C8

3.00

C9

0.0033

1/day
1/(day⋅W/
m2)

DMSPp = particulate dimethylsulfoniopropionate; DIA = diatoms; FLA = fla­gellates; DMSPd = dissolved dimethylsulfoniopropionate.
2

J.-W. Shen et al.

Marine Pollution Bulletin 169 (2021) 112517

depth plus sea surface elevation known as total water depth, and kflux(m/s) represents sea-to-air DMS exchange rate. DMS in the air (DMSair)
was assumed to be 0 nmol/L throughout this study given the results from
Zhu et al. (2019).

low. Therefore, our DMS module did not include riverine inputs of
DMSPd or DMS. Lana et al. (2011) provided a global distribution of
surface DMS level, which showed that the concentration is 1–2 nmol/L
off south of Taiwan and to the east of the Ryukyu Islands. In this study,
the DMS and the DMSPd levels along the southern and southeastern
open boundaries were set to 1 nmol/L (above 200 m) and 0 nmol/L(under 200 m). YECS DMS concentrations reached stable seasonal values
after a spin-up of 3 year, and the model outputs from the fourth year
were used for examining the annual cycle of climatologic marine DMS.
Phytoplankton biomass, community and temperature all have an
important impact on the marine DMS cycle (Stefels et al., 2007; Yanget al., 2014; Jian et al., 2018; Speeckaert et al., 2018). Hence, several
sensitivity experiments were designed to investigate the direct influenceof these biogeochemical variables on the seasonality of surface DMS
concentrations (Table 2). Run 1 is the standard run including all direct
effects. Run 2 does not include the direct effect of phytoplankton com­munity (i.e., two phytoplankton function types (DIA and FLA) have thesame intercellular S:N and active exudation rate). To obtain realistic
DMS concentrations, we set C1 and C2 (C3 and C4) to 0.50 mg S/mg N(0.034/day) for the Run 2. In Run 3, we eliminated the impact of watertemperature on phytoplankton active exudation and free DMSP-lyase
cleavage after setting f(T) to 1, moreover, we trebled the C3, C4 andC5 for obtaining a realistic result. Run 4 closed the photolysis term (seenin Eq. (2)), focusing on the direct effect of radiation on the seasonal DMS
concentration variations.

2.2.1. Sources and sinks of dissolved dimethylsulfoniopropionate
In this module, biological sources of DMSPd included phytoplankton
active exudation and autolysis while sinks included free DMSP–lyase
cleavage and microbial degradation (Eq. (1)). Phytoplankton active
exudation is a pathway where particulate dimethylsulfoniopropionate(DMSPp) in cells can be released into the DMSPd phase. The ratio ofintercellular DMSP sulfur to nitrogen (S:N) in a DIA or FLA cell werereferred to as C1 and C2; C3 (or C4) is the exudation rate constant of DIA(or FLA) at 0 degree Celsius, and f(T) is a temperature dependent coef­
ficient suggested by Geider et al. (1998), details of this coefficient wasprovided in Eq. (4). The intercellular S:N of a DIA (C1) is 0.06 ± 0.17 mg
S/mg N (Keller et al., 1989), and the intercellular S:N of a FLA (C2) is0.33 ± 0.48 mg S/mg N (Stefels et al., 2007). For the standard run, C1
and C2 were set to 0.30 and 0.80 mg S/mg N after adjustments which ismainly based on the performance of surface DMS concentrations(Table 1), and following Archer et al. (2002), we set C3 and C4 to 0.034
and 0.046/day (Table 1). Autolysis represents DMSPp released byphytoplankton at death. As for free DMSP-lyase cleavage, Archer et al.
(2002) showed that setting for free DMSP–lyase cleavage rate constant
was 0.125/day. However, in this manuscript, free DMSP–lyase cleavage
shown in Eq. (1) included the cleavage of DMSPd not only by free en­zymes or free-living bacteria, but also by phytoplankton intercellular
DMSP-lyase, and this cleavage rate constant at 0 degree Celsius (C5) was
set to 0.18/day (Table 1). DMSPd can be consumed by microbial com­munities. Previous studies showed that microbial DMSPd degradation
rate constant (C6) of the YECS is 0.91 to 14.10/day (Jian et al., 2018; Xuet al., 2019; Zhai et al., 2019), and it was set to 4.00/day (Table 1).
f(T) = e0.063*T , T ≤ 30◦ C
f(T) = e0.063*30 , T > 30◦ C

2.4. Description of in-situ data
We compiled an observational dataset containing in-situ data ac­quired during 48 survey flights over the YECS between 2005 and 2017from literatures. In total, surface data were collected from 1885 stations.
Large spatio-temporal discontinuities are present in this dataset; forexample, more data were collected in July and November (Fig. S1a). To
better optimize the model performance, a gridded climatology concen­
tration field of monthly DMS, DMSPd and Chla data (hereafter referred
to as YECS_DMS, YECS_DMSPd and YECS_CHL) were constructed basedon in-situ dataset (the details of in-situ dataset and gridded climatology
are available in the Supplementary Material). Fig. S1b shows monthly
climatology grids; lower grid numbers were noted in August and
September, which was taken into account when we verified the seasonal
cycle.

(4)

2.2.2. Sources and sinks of dimethylsulfide
Sources of marine DMS include free DMSP-lyase cleavage and mi­
crobial DMSP–lyase cleavage, and sinks for DMS include microbial DMS
degradation, photolysis and air–sea exchange. Microbial DMSP–lyase
cleavage is a pathway where DMSPd is consumed by microbes (Stefels
et al., 2007). The proportion of consumed DMSPd cleaved to DMS (C7)
was assumed to 0.45 (Table 1) based on previous studies that suggested
C7 may range from 0.09 to 0.71 with an average of 0.40 of the YECS(Jian et al., 2018; Xu et al., 2019; Zhai et al., 2019). Microbial degra­
dation is usually the main sink of marine DMS (Stefels et al., 2007). And
its degradation rate constant (C8) is in the range of 0.14 to 7.53/day in
the YECS (Jian et al., 2018; Xu et al., 2019; Zhai et al., 2019), and was set
to 3.00/day (Table 1). In this module, the photolysis rate was assumed to
be a linear function of incident solar radiation (IZ, W/m2). Some studies
showed that it ranges from 0.51 to 3.85/day in the YECS (Jian et al.,2018; Xu et al., 2019), we trebled the photolysis rate per unit of irra­
diance (C9) gained from Kloster et al. (2006). And kflux (cm/h) was
calculated according to Eq. (5) (Nightingale et al., 2000):()) Sc(T) − 0.5(
kflux = 0.222*U10 2 + 0.333*U10 *(5)600

3. Results
3.1. Model evaluation
3.1.1. Validation of surface dimethylsulfide concentrations
The horizontal distribution of annual mean surface (at a depth of 2m) DMS concentrations ([DMS]) from the standard run is shown in
Fig. 2a with observational data from the YECS_DMS dataset plotted as
small dots arrayed on top of the model result. The small dots indicated
Table 2
Settings for the sensitivity experiments used to study the direct impact ofphytoplankton community, temperature and radiation on dimethylsulfide cycle.

where U10 (m/s) is wind speed at 10 m above sea surface derived from
ERA-Interim database (https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim). Sc, the Schmidt number of DMS, isa temperature dependent variable (Saltzman et al., 1993).
2.3. Model setup
The concentrations of DMSPd and DMS in river water are relatively

Run

C1–C4 affected by
PFTStress active exudation and free DMSP
cleavage

Photolysis

Run
1
Run
2
Run
3
Run
4

Yes

Yes

Yes

No

Yes

Yes

Yes

No

Yes

Yes

Yes

No

PFT = phytoplankton function type; DMSP = dimethylsulfoniopropionate.
3

J.-W. Shen et al.

Marine Pollution Bulletin 169 (2021) 112517

Fig. 2. Validation of surface dimethylsulfide (DMS).
(a) Comparison of annual mean DMS concentrations(nmol/L) for the standard run (contour plot) to theresults from the YECS_DMS (colored dots). Black
contours denote 50 and 200 m isobaths. Boxplot of
DMS concentrations (nmol/L) for the (b) gridded
model output and (c) YECS_DMS. The red lines withineach box are the median, the boxes define the hinge(25%–75% quartile), the horizontal lines outsideeach box are 1.5 times the hinge, and black solid lines
provide seasonal variations in the arithmetical mean.
The Cost Function (CF) is reported in the literature(Allen et al., 2007; Dabrowski et al., 2014) and its
expression is given in Eq. (6). (For interpretation of
the references to colour in this figure legend, thereader is referred to the web version of this article.)higher annual mean surface [DMS] (>5 nmol/L) in the Yalu Estuary, off
the coast of northern Shandong Peninsula, Haizhou Bay, east of the
Changjiang Estuary, off the coast of Zhejiang and Fujian provinces;
surface [DMS] was higher in the coast, and [DMS] was greater in the
Yellow Sea (YS) than that in the East China Sea (ECS; Fig. 2a). The
distribution of surface DMS level from the model output was consistent
with that of the observational data (Fig. 2a). The model output also
captured the characteristics of seasonal variation of surface DMS level.
Both higher simulated or observed [DMS] occurred between April andJuly; and [DMS] was higher in autumn as compared to that in winter(Fig. 2b and c). The discrepancy was that we overestimated surface[DMS] of the YS, underestimated [DMS] of the ECS (Fig. 2a); and themodel also underestimated it in winter (Fig. 2b and c).
The Cost Function (CF; Eq. (6)) is an evaluating indicator of thegoodness of fit, defined as the ratio of mean absolute error (MAE) to thestandard deviation of the observational data (Allen et al., 2007; Dab­
rowski et al., 2014):∑|CS − CM |
CF =
(6)
n*σ M

simulating surface DIA Chla concentrations ([Chla]) plus FLA [Chla]
known as total [Chla] is shown in Fig. 3. The data from the YECS_CHL
dataset (small dots) indicated higher annual mean surface [Chla] (>1μg/L) in the Yalu Estuary and adjacent areas, off the coast of northern
Shandong Peninsula, Haizhou Bay, Subei Shoal and the ECS inner shelf;
the model output was able to capture these leading features (Fig. 3). And
the annual mean surface [Chla] calculated from the YECS_CHL was 0.83
μg/L, and the model output (1.17 μg/L) was relatively higher for thesame area; moreover, and it was 1.19 μg/L in the YECS (<200 m water
depth), which was close to the locally modified satellite Chla concen­
trations (1.12 μg/L) provided by Hao et al. (2019). The discrepancy wasthat the ECSECOM output overestimated annual mean total [Chla] in the
YS especially the western 34.5◦ N transect, resulting in thewhere CS is model result, CM is observational data, n is data number andσM is the standard deviation of all observational data. A score of CF < 1indicates excellent/very good performance; a score of CF = 1–2 in­dicates good performance; a score of CF = 2–3 indicates reasonable
performance; a score of CF > 3 indicates poor/bad performance (Dab­rowski et al., 2014).
As far as the performance assessment in modeling DMS based on the
CF value is concerned, the MAE was within one σM (CF = 0.98) meaningthat our model output performance was excellent/very good in the
YECS. The annual mean surface [DMS] calculated from the YECS_DMS
was 3.74 nmol/L, and the model output of the same sea areas reached to
4.18 nmol/L, overestimated by 12% or so. And the annual mean simu­
lated surface DMSPd concentration ([DMSPd]) was also higher than thevalue derived from the YECS_DMSPd. In general, our model reproduced
the spatial and seasonal variations in surface [DMS] of the YECS.
3.1.2. Validation of surface total chlorophyll a concentrations andphytoplankton community structure
Phytoplankton biomass is an core indicator of marine DMS in the
YECS (Yang et al., 2014; Jian et al., 2018). The model performance in

Fig. 3. Comparison of annual mean surface concentrations of total chlorophylla (Chla; μg/L) for the standard run (contour plot) to the results from the
YECS_CHL (scatter diagram). Black contours denote 50 and 200 m isobaths.
4

J.-W. Shen et al.

Marine Pollution Bulletin 169 (2021) 112517

overestimation of [DMSPd], [DMS]; and model output underestimatedtotal [Chla] in the ECS as a whole.
The structure of the phytoplankton community can affect annual
patterns of marine [DMS] (Vogt et al., 2010; Speeckaert et al., 2018). So,
we also evaluated phytoplankton community here. In nearshore YECS
regions (<50 m water depth), annual mean proportion of the DIA [Chla]of which the nearest corresponding satellite-derived data was [Chla] of
microphytoplankton, contributed the most to the annual average of total[Chla] (Fig. 4a), whereas FLA [Chla] of which the nearest corresponding
satellite-derived data was the summation of nanophytoplankton [Chla]and picophytoplankton [Chla], explained the highest proportion in
offshore regions (>50 m water depth; Fig. 4b). This was in better
agreement with the results of satellite-derived dataset gained from lit­
eratures (Sun et al., 2019a; Sun et al., 2019b).

Table 3
Surface dimethylsulfide concentrations (nmol/L) in selected areas.
Regions

Annualmean

Spring(Mar. toMay)

Summer(Jun. toAug.)

Autumn(Sep. to
Nov.)

Winter
(Dec. toFeb.)

YECS (<200 m
water depth)
YS
YS coast
central YS
ECS
inner ECS shelf
middle andouter ECS
shelf

4.55

4.91

7.19

5.48

0.63

6.45
8.11
4.30
2.98
3.98
2.57

6.85
7.45
6.08
3.30
3.87
3.07

9.60
14.26
3.59
5.19
7.35
4.30

8.37
9.75
6.60
3.07
3.89
2.74

0.96
1.00
0.91
0.36
0.82
0.17

YECS = the Yellow and East China seas; YS = Yellow Sea; ECS = East China Sea.

3.2. Annual cycle of surface dimethylsulfide concentrations

structure (>10 nmol/L) developed; and the average [DMS] of thesouthern core was greater. And surface [DMS] decreased in May overall,however, [DMS] in the southeastern of central SYS increased. In June,dual-core structure disappeared entirely. Following the autumn bloom,
surface [DMS] rose considerably in the eastern and southwestern park of
the central SYS, and a different dual-core structure (>10 nmol/L)
developed after September where average [DMS] in the east was greater
as compared with that in the west. And these spatial and temporalvariations were basically consistent with surface total [Chla] (Figs. 5 and
7).
Along the inner ECS shelf, both the surface [DMS] and [DMSPd] rosefrom early spring and reached a maximum during August, while theprominent peak of total surface [Chla] occurred in late spring (Fig. 6c).
And the seasonal mismatch between surface [DMS] and total [Chla] wasmainly found off the coast of Zhejiang Province during summer (Figs. 5
and 7). In the middle and outer ECS shelf, higher surface [DMS] and[DMSPd] occurred in April or summer with [DMS] that exceeded 10
nmol/L mainly occurring in the region east of the Zhoushan Islands(Table 3; Fig. 5 and 6d); however, there was totally no peak of total[Chla] in summer (Fig. 6d). During September and October, surface[DMS] maintained at a higher level (Fig. 6d), and [DMS] that went
beyond 10 nmol/L were mainly found in east and southeast of theZhoushan Islands during September, and southwest of Jeju Island and

The annual mean surface [DMS] arrived at 4.55 nmol/L in the YECS(<200 m water depth; Table 3). And monthly surface [DMS] varied
considerably spatially; it decreased from inshore to offshore over themost of the year (Fig. 5). So, a quantitative analysis of the annual cycle ofsurface [DMS] was conducted for four regions. The YS was divided into
two regions, coast zone (<50 m water depth) and central zone (≥50 m),
the ECS was also divided into two areas: the inner shelf (<50 m) and the
middle and outer shelf (50–200 m).
Along the YS coast, surface [DMS] as well as surface [DMSPd], weremaintained at a peak level between May and September, while total
surface [Chla] reached a maximum in May and obviously decreased
during May and September (Fig. 6a). This seasonal mismatch between[DMS] and total [Chla] were found e.g., off the coast of southern
Shandong Peninsula and the west coast of Korea during later spring andsummer (Figs. 5 and 7). As for the central YS, the seasonal variations ofsurface [DMS] was totally in agreement with that of [DMSPd] or total[Chla]; [DMS], [DMSPd] and total [Chla] all peaked in spring andautumn, and the value of the spring peak was greater than the autumn
DMS peak (Fig. 6b). Moreover, [DMS] varied considerably in the YS
spatially especially the central South Yellow Sea (SYS; Fig. 5). Surface[DMS] rose rapidly from March with higher [DMS] (>8 nmol/L)
occurring in the western part of the central SYS. Then, a dual-core

Fig. 4. The distribution of (a) the ratio of diatoms (DIA)-derived chlorophyll a (Chla) to total Chla and (b) the ratio of flagellates (FLA)-derived Chla to total Chla.
Black contours denote 50 and 200 m isobaths.
5

J.-W. Shen et al.

Marine Pollution Bulletin 169 (2021) 112517

Fig. 5. The distribution of surface dimethylsulfide (DMS) concentrations (nmol/L). Black contours denote 50 and 200 m isobaths.
Fig. 6. Surface total chlorophyll a (Chla) concentra­
tions (μg/L; green solid line), Chla concentrations offlagellates (FLA, μg/L; green dotted line), Chla con­centrations of diatoms (DIA, μg/L; green dash dotline), dimethylsulfide (DMS) concentrations (nmol/L;
blue solid line) and dissolved dimethylsulfoniopro­
pionate (DMSPd) concentrations (nmol/L; blue
dotted line) for the (a) Yellow Sea (YS) coast (<50 m
water depth), (b) central YS (≥50 m), (c) East China
Sea (ECS) inner shelf (<50 m), and (d) middle andouter shelf of the ECS (50–200 m). (For interpretationof the references to colour in this figure legend, thereader is referred to the web version of this article.)east of the Zhoushan Islands during October (Fig. 5). Surface [DMS] waslower in the middle and outer ECS shelf than that along the inner shelf,
and the ECS [DMS] were lower compared to those in the YS (Table 3). In
total, the seasonal variations of surface [DMS] were generally consistent
with that of surface total [Chla], but there were mismatches between
them.

community affected surface [DMS]. Obviously, it can change the timingof a prominent peak; the output from Run 2 showed that the maximum
monthly surface [DMS] occurred in May other than in August along the
YS coast, and along the ECS coast, it peaked in July other than in August(Fig. 8a and c). However, the change of peak occurrence was not foundin the central YS (Fig. 8b) suggesting that the effect size of phyto­plankton community varied spatially. Fig. 9a showed the horizontal
distribution of correlation coefficient (r) between monthly surface[DMS] from Runs 1 and 2. Lower r values (<0.90) were mainly found in
the eastern North YS (NYS), the northern SYS, off the coast of southern
Shandong Peninsula, northeast of the Zhoushan Islands, and along theZhejiang coast, suggesting that the phytoplankton community played acrucial role on surface DMS seasonality in these sea areas.
Eliminating the influence of temperature on phytoplankton active
exudation and free DMSP-lyase cleavage also changed the timing ofprominent peak along the coast, furthermore, in the middle and outer
shelf of ECS, surface [DMS] peaked in April and decreased during latespring and summer in Run 3 (Fig. 8). And Fig. 9b shows that lower r(<0.90) between monthly surface [DMS] from Runs 1 and 3 were mainly
found in the northern NYS and the middle and outer ECS shelf. Hence,

4. Discussions
4.1. Controlling factors of the annual dimethylsulfide cycle
Surface [Chla] of FLA may be an important variable to explain themismatch between concentrations of total Chla and DMS (Fig. 6). For
example, surface [DMS] as well as FLA [Chla], were maintained at apeak value along the YS coast during late summer and early autumn,
while total [Chla] reduced between May and September (Fig. 6a). These
results indicated that both total [Chla] and phytoplankton community
structure may determine the seasonal cycle of surface [DMS] in the
YECS.
Runs 1 and 2 were designed to determine how phytoplankton
6

J.-W. Shen et al.

Marine Pollution Bulletin 169 (2021) 112517

Fig. 7. The distribution of surface total chlorophyll a (Chla) concentrations (μg/L). Black contours denote 50 and 200 m isobaths.

Fig. 8. Monthly surface dimethylsulfide (DMS) concentrations from Runs 1 (black line), 2 (green line), 3 (blue line) and 4 (red line) for the (a) Yellow Sea (YS) coast(<50 m water depth), (b) central YS (≥50 m), (c) East China Sea (ECS) inner shelf (<50 m), (d) middle and outer ECS shelf (50–200 m). (For interpretation of thereferences to colour in this figure legend, the reader is referred to the web version of this article.)temperature played an important role on surface DMS seasonality in the
middle and outer ECS shelf.
If we removed the photolysis term, approximately 11% of the surface[DMS] was added in the YECS, and the seasonal variations in surface[DMS] from Run 4 were in agreement with Run 1 in every selected re­gion (Fig. 8). In addition, lower r values (<0.90) between [DMS] from
Runs 1 and 4 were only found in waters east of Taiwan (not shown).
However, the [DMS] elevated with a wider margin in the offshore re­gions compared with that in the nearshore regions. Because more light isabsorbed by particles in nearshore waters, and Iz is relatively lower.
These results revealed that both the phytoplankton community andsea water temperature exerted crucial effects on seasonal variations ofsurface [DMS] in the YECS, but that these impacts notably varied overspace.

some other productive marginal seas which are widely studied (e.g., NW
and NE Atlantic Shelves, Eastern Canary Coastal Current, NW Arabian
Sea upwelling zone, North Pacific epicontinental seas), similar to theannual mean of the Humboldt Coastal Current and lower than someother water masses (e.g., Alaska Coastal Current, West India Coastal
Currents; Fig. 2 exhibited by Lana et al. (2011)). In addition, the annual
mean surface [DMS] or the oceanic DMS source to the atmosphere (6.78μmol/(m2 day)) of the YECS from our model output was about 1.94 or
1.42 times, respectively, to the global annual mean concentrations
provided by Lana et al. (2011) or the annual mean sea-to-air DMS flux
given by a recent research (Wang et al., 2020), indicating that the YECSDMS emissions was not negligible.

4.2. Annual mean surface dimethylsulfide concentrations

We developed a three-dimensional hydrodynamic module coupledwith DMS biophysical and geochemical processes for studying annual
DMS cycle of the YECS and discussing its controlling factors. This model

5. Conclusions

The annual mean surface [DMS] in the YECS was higher than those in7

J.-W. Shen et al.

Marine Pollution Bulletin 169 (2021) 112517

Fig. 9. The distribution of correlation coefficient (r) between monthly surface dimethylsulfide concentrations from (a) Runs 1 and 2, (b) Runs 1 and 3. Black contours
denote isolines where the p value is equal to 0.0001.

reproduced spatial and seasonal variations in surface concentrations ofclimatologic DMS. Our main results from standard run and sensitivity
experiments were as follows:

the work reported in this paper.

1. Surface [DMS] varied temporally and spatially in the YECS. The[DMS] peaked in summer along the coast, while there were several
DMS peaks in the central YS and the middle and outer ECS shelf. In
each season, surface [DMS] was higher in the nearshore regions thanthat in the offshore regions, and it was higher in the YS compared tothat in the ECS.
2. The annual mean surface [DMS] reached to 4.55 nmol/L in the YECS,
and the annual emissions of marine DMS from this area to the at­mosphere arrived at 0.0705 Tg(S)/yr, accounting for 0.35% of the
global oceanic DMS emissions despite this area accounting for only
0.25% of the global ocean area.
3. Seasonality of surface [DMS] was driven by phytoplankton biomass,
phytoplankton community and sea water temperature; the peakmonth of [DMS] was affected by phytoplankton community or
temperature, while photolysis mainly changed the magnitude ofsurface [DMS]. Moreover, effect size of phytoplankton community or
temperature varied spatially.

This work was supported by the National Key Research and Devel­
opment Program of China [No. 2016YFA0601301]; National Natural
Science Foundation of China [No. 41876018]; the Tianjin Natural Sci­
ence Foundation [No. 19JCZDJC40600]; grant from the Ministry of
Education, Culture, Sports, Science and Technology, Japan (MEXT) to
the project on Joint Usage/Research Center - Leading Academia in
Marine and Environmental Research (LaMer). The initial wind speed at
10 m above sea surface was obtained from ERA-Interim (https://www.
ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim).

Acknowledgements

Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.marpolbul.2021.112517.
References
Allen, J.I., Somerfield, P.J., Gilbert, F.J., 2007. Quantifying uncertainty in highresolution coupled hydrodynamic-ecosystem models. J. Mar. Syst. 64 (1), 3–14.
Archer, S.D., Gilbert, F.J., Nightingale, P.D., Zubkov, M.V., Taylor, A.H., Smith, G.C.,
Burkill, P.H., 2002. Transformation of dimethylsulphoniopropionate to dimethyl
sulphide during summer in the North Sea with an examination of key processes via amodelling approach. Deep-Sea Res. II Top. Stud. Oceanogr. 49 (15), 3067–3101.
Bopp, L., Boucher, O., Aumont, O., Belviso, S., Dufresne, J.L., Mai, P., Monfray, P., 2004.
Will marine dimethylsulfide emissions amplify or alleviate global warm. Can. J. Fish.
Aquat. Sci. 61 (5), 826–835.
Charlson, R.J., Lovelock, J.E., Andreae, M.O., Warren, S.G., 1987. Oceanic
phytoplankton, atmospheric sulphur, cloud albedo and climate. Nature 326 (6114),
655–661.
Dabrowski, T., Lyons, K., Berry, A., Cusack, C., Nolan, G.D., 2014. An operational
biogeochemical model of the north-east Atlantic: model description and skill
assessment. J. Mar. Syst. 129, 350–367.
Fiddes, S., Woodhouse, M.T., Nicholls, Z., Lane, T.P., Schofield, R., 2018. Cloud,precipitation and radiation responses to large perturbations in global dimethyl
sulfide. Atmos. Chem. Phys. 18 (14), 10177–10198.
Gabric, A.J., Matrai, P.A., Vernet, M., 1999. Modelling the production and cycling ofdimethylsulphide during the vernal bloom in the Barents Sea. Tellus Ser. B Chem.
Phys. Meteorol. 51 (5), 919–937.
Gabric, A.J., Simó, R., Cropp, R., Hirst, A.C., Dachs, J., 2004. Modeling estimates of the
global emission of dimethylsulfide under enhanced greenhouse conditions. Glob.
Biogeochem. Cycles 18 (2), GB2014.

Unsynchronized changes in nutrient levels found in Chinese coastal
waters may lead to a series of ecological effects (Xin et al., 2019; Wanget al., 2021a; Wang et al., 2021b). In the future, we need to focus on the
response of surface [DMS] to the altered nutrient level in the YECS.
CRediT authorship contribution statement
Jia-Wei Shen: Conceptualization, Data curation, Visualization,
Writing – original draft. Liang Zhao: Conceptualization, Methodology,
Writing – review & editing, Supervision. Hong-Hai Zhang: Investiga­
tion, Data curation. Hao Wei: Writing – review & editing, Supervision.
Xinyu Guo: Writing – review & editing.
Declaration of competing interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence8

J.-W. Shen et al.

Marine Pollution Bulletin 169 (2021) 112517
Speeckaert, G., Borges, A.V., Champenois, W., Royer, C., Gypens, N., 2018. Annual cycleof dimethylsulfoniopropionate (DMSP) and dimethylsulfoxide (DMSO) related tophytoplankton succession in the Southern North Sea. Sci. Total Environ. 622–623,
362–372.
Stefels, J., Steinke, M., Turner, S., Malin, G., Belviso, S., 2007. Environmental constraints
on the production and removal of the climatically active gas dimethylsulphide(DMS) and implications for ecosystem modelling. Biogeochemistry 83 (1–3),
245–275.
Sun, D.Y., Huan, Y., Wang, S.Q., Qiu, Z.F., Ling, Z.B., Mao, Z.H., He, Y.J., 2019a. Remotesensing of spatial and temporal patterns of phytoplankton assemblages in the Bohai
Sea, Yellow Sea, and East China Sea. Water Res. 157, 119–133.
Sun, X.R., Shen, F., Brewin, R., Liu, D.Y., Tang, R.G., 2019b. Twenty-year variations insatellite-derived chlorophyll-a and phytoplankton size in the Bohai Sea and Yellow
Sea. J. Geophys. Res. Oceans 124 (12), 8887–8912.
Vogt, M., Vallina, S.M., Buitenhuis, E.T., Bopp, L., Quere, C.L., 2010. Simulating
dimethylsulphide seasonality with the dynamic Green Ocean model PlankTOM5.
J. Geophys. Res. Oceans 115, C06021.
Wang, J., Bouwman, A.F., Liu, X., Beusen, A.H.W., Van Dingenen, R., Dentener, F.,
Yao, Y., Glibert, P.M., Ran, X., Yao, Q., Xu, B., Yu, R., Middelburg, J.J., Yu, Z., 2021a.
Harmful algal blooms in Chinese coastal waters will persist due to perturbed nutrient
ratios. Environmental Science & Technology Letters 8 (3), 276–284.
Wang, Q., Guo, X., Takeoka, H., 2008. Seasonal variations of the Yellow River plume in
the Bohai Sea: a model study. J. Geophys. Res. Oceans 113, C08046.
Wang, W.-L., Song, G.-S., Primeau, F.W., Saltzman, E.S., Moore, J.K., 2020. Global ocean
dimethyl sulfide climatology estimated from observations and an artificial neuralnetwork. Biogeosciences 17, 5335–5354.
Wang, Y., Xu, H., Li, M., 2021b. Long-term changes in phytoplankton communities in
China's Yangtze Estuary driven by altered riverine fluxes and rising sea surface
temperature. Geomorphology 376, 107566.
Wang, Y.C., Guo, X., Zhao, L., Zhang, J., 2019. Seasonal variations in nutrients andbiogenic particles in the upper and lower layers of East China Sea shelf and theirexport to adjacent seas. Prog. Oceanogr. 176, 102138.
Xin, M., Wang, B., Xie, L., Sun, X., Wei, Q., Liang, S., Chen, K., 2019. Long-term changes
in nutrient regimes and their ecological effects in the Bohai Sea, China. Mar. Pollut.
Bull. 146, 562–573.
Xu, F., Jin, N., Ma, Z., Zhang, H.H., Yang, G.P., 2019. Distribution, occurrence, and fateof biogenic dimethylated sulfur compounds in the Yellow Sea and Bohai Sea during
spring. J. Geophys. Res. Oceans 124 (8), 5787–5800.
Yang, G.P., Zhang, H.H., Zhou, L.M., Yang, J., 2011. Temporal and spatial variations ofdimethylsulfide (DMS) and dimethylsulfoniopropionate (DMSP) in the East China
Sea and the Yellow Sea. Cont. Shelf Res. 31 (13), 1325–1335.
Yang, G.P., Song, Y.Z., Zhang, H.H., Li, C.X., Wu, G.W., 2014. Seasonal variation andbiogeochemical cycling of dimethylsulfide (DMS) and dimethylsulfoniopropionate(DMSP) in the Yellow Sea and Bohai Sea. J. Geophys. Res. Oceans 119 (12),8897–8915.
Zhai, X., Li, J.L., Zhang, H.H., Tan, D.D., Yang, G.P., 2019. Spatial distribution andbiogeochemical cycling of dimethylated sulfur compounds and methane in the East
China Sea during spring. J. Geophys. Res. Oceans 124 (2), 1074–1090.
Zhang, J., Guo, X., Zhao, L., 2019. Tracing external sources of nutrients in the East China
Sea and evaluating their contributions to primary production. Prog. Oceanogr. 176,102122.
Zhao, L., Guo, X., 2011. Influence of cross-shelf water transport on nutrients andphytoplankton in the East China Sea: a model study. Ocean Sci. 7 (1), 27–43.
Zhu, R., Yang, G.P., Zhang, H.H., 2019. Temporal and spatial distributions of carbonylsulfide, dimethyl sulfide, and carbon disulfide in seawater and marine atmosphere of
the Changjiang estuary and its adjacent East China Sea. Limnol. Oceanogr. 64 (2),632–649.

Gao, N., Yang, G.P., Zhang, H.H., Liu, L., 2017. Temporal and spatial variations of three
dimethylated sulfur compounds in the Changjiang estuary and its adjacent area
during summer and winter. Environ. Chem. 14 (3), 160–177.
Geider, R.J., Maclntyre, H.L., Kana, T.M., 1998. A dynamic regulatory model ofphytoplanktonic acclimation to light, nutrients, and temperature. Limnol. Oceanogr.
43 (4), 679–694.
Grandey, B.S., Wang, C., 2015. Enhanced marine Sulphur emissions offset global
warming and impact rainfall. Sci. Rep. 5, 13055.
Guo, X., Hukuda, H., Miyazawa, Y., Yamagata, T., 2003. A triply Nested Ocean model for
simulating the Kuroshio—roles of horizontal resolution on JEBAR. J. Phys.
Oceanogr. 33 (1), 146–169.
Gypens, N., Borges, A.V., 2014. Increase in dimethylsulfide (DMS) emissions due toeutrophication of coastal waters offsets their reduction due to ocean acidification.
Front. Mar. Sci. 1 (4), 1–6.
Gypens, N., Borges, A.V., Speeckaert, G., Lancelot, C., 2014. The dimethylsulfide cycle in
the Eutrophied Southern North Sea: a model study integrating phytoplankton andbacterial processes. PLoS One 9 (1), e85862.
Hao, Q., Chai, F., Xiu, P., Bai, Y., Chen, J.F., Liu, C.G., Le, F.F., Zhou, F., 2019. Spatial
and temporal variation in chlorophyll a concentration in the Eastern China Seas
based on a locally modified satellite dataset. Estuar. Coast. Shelf Sci. 220, 220–231.
Jian, S., Zhang, H.H., Zhang, J., Yang, G.P., 2018. Spatiotemporal distribution
characteristics and environmental control factors of biogenic dimethylated sulfur
compounds in the East China Sea during spring and autumn. Limnol. Oceanogr. 63(S1), S280–S298.
Keller, M.D., Bellows, W.K., Guillard, R.R.L., 1989. Dimethyl sulfide production inmarine phytoplankton. In: Saltzman, E., Cooper, W. (Eds.), Biogenic Sulfur in theEnvironment. American Chemical Society, Washington, DC, pp. 167–182.
Kettle, A.J., Andreae, M.O., Amouroux, D., Andreae, T.W., Bates, T.S., Berresheim, H.,
Bingemer, H., Boniforti, R., Curran, M.A.J., Ditullio, G.R., 1999. A global database of
sea surface dimethylsulfide (DMS) measurements and a procedure to predict sea
surface DMS as a function of latitude, longitude, and month. Glob. Biogeochem.
Cycles 13 (2), 399–444.
Kloster, S., Feichter, J., Maier-Reimer, E., Six, K.D., Stier, P., Wetzel, P., 2006. DMS cyclein the marine ocean-atmosphere system-a global model study. Biogeosciences 3 (1),29–51.
Kloster, S., Six, K.D., Feichter, J., Maier-Reimer, E., Roeckner, E., Wetzel, P., Stier, P.,
Esch, M., 2007. Response of dimethylsulfide (DMS) in the ocean and atmosphere to
global warming. J. Geophys. Res. Biogeosci. 112, G03005.
Lana, A., Bell, T.G., Simó, R., Vallina, S.M., Ballabrera-Poy, J., Kettle, A.J., Dachs, J.,
Bopp, L., Saltzman, E.S., Stefels, J., 2011. An updated climatology of surface
dimethlysulfide concentrations and emission fluxes in the global ocean. Glob.
Biogeochem. Cycles 25 (1), GB1004.
Nightingale, P.D., Malin, G., Watson, A.J., Liss, P.S., Liddicoat, M.I., Boutin, J.,
Upstillgoddard, R.C., 2000. In situ evaluation of air-sea gas exchange
parameterizations using novel conservative and volatile tracers. Glob. Biogeochem.
Cycles 14 (1), 373–387.
Saltzman, E.S., King, D.B., Holmen, K., Leck, C., 1993. Experimental determination of the
diffusion coefficient of dimethylsulfide in water. J. Geophys. Res. 98 (C9),16481–16486.
Shen, J.W., Zhao, L., Wang, S.J., Li, Y.X., 2019. Multivariate analysis between
dimethylsulfide and environmental factors and modeling in the Yellow Sea and theEast China Sea. China Environ. Sci. 39 (6), 2514–2522.
Skogen, M.D., Søiland, H., 1998. A user’s guide to NORWECOM v2.0, a coupled 3
dimensional physical chemical biological ocean-model. In: the NORWegian
ecological model system. Bergen, Institute of Marine Research, Technical Report
Fisken og Havet 18/98 available online at: http://www.imr.no/morten/norwecom/userguide2_0.ps.gz.

9