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The satellite derived environmental factors and their relationships with dimethylsulfide in the East Marginal Seas of China
Journal of Marine Systems 204 (2020) 103305
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The satellite derived environmental factors and their relationships with dimethylsulfide in the East Marginal Seas of China
T
Bo Qua,⁎,1, Gui-Peng Yangb, Li-Yan Guoc, Li Zhaod a
School of Science, Nantong University, Nantong, China Key Laboratory of Marine Chemistry Theory and Technology, Ministry of Education/Institute for Advanced Ocean Study, Ocean University of China, Qingdao, China c Nantong Fudadianjing Focus Educational Institution, Nantong, China d Jiangsu Vocational College of Business, Nantong, China b
ARTICLE INFO
ABSTRACT
Keywords: Chlorophyll-a Aerosol optical depth Sea surface temperature Wind speed Mixed layer depth Dimethylsulfide sea-to-air flux
Biogenic dimethylsulfide (DMS) sea-to-air flux and its closely linked environment factors are studied in the East Marginal Seas of China (EMSC) (25°N-40°N, 120°E-130°E) from 2011 to 2015. Higher chlorophyll-a (CHL) up north during blooming season is due to the southeasterly wind brought more nutrients from the Changjiang River waters. The elevated CHL during autumn and winter periods down south is more related with the higher sea surface temperature (SST). The lower CHL in the middle region in the later half year was mainly influenced by terrestrial runoff and the diluted Changjiang River waters. The enriched Changjiang River discharges brought more nutrients to the Zhejiang coastal waters and enriched the local phytoplankton, where aerosol optical depth (AOD) had the highest correlation with CHL (0.96). Peak SST was in August and had a negative correlation with wind speed (WIND). With increased WIND up north, AOD reduced accordingly. Lower CHL in 2015 was mainly related to the southeastern wind brought less nutrient water from Open Sea during blooming season. DMS sea-to-air fluxes are calculated and compared with OUC (Ocean University of China) field data. The reasonable agreements are made. DMS concentrations ranged from 0.32 to 5.64 nM with a mean of 2.19 nM, it had high positive correlations with CHL in the summer and autumn periods. DMS sea-to-air fluxes ranged within 2.14–59.06 μmolm−2d−1 with a mean of 18.96 μmolm−2d−1. The elevated DMS sea-to-air fluxes in ECMS indicate the contributions of DMS emission in the study region cannot be ignored on the global climate evaluation.
1. Introduction
1.1. Current
The East Marginal Seas of China (EMSC) is situated on the margin of the Northwest Pacific Ocean, is one of the best river dominated ocean margin. In the northern region is where Bohai Sea located with Yellow River discharge into the region from west coast; middle region is where Yellow Sea outlet mouse located; and the southern region is where the most East China Sea located. It is an extremely dynamic oceanic region which is influenced by the South China Sea waters passing through the Taiwan Strait, the Yellow Sea waters by the China Coast Current, the Changjiang River, one of the biggest rivers by discharge volume in the world, and Kuroshio where its brunches interact strongly with coastal currents. The EMSC circulation pattern is largely controlled by the Kuroshio, which influences the water and heat exchanges of the ECS (East China Sea) with the open sea (Jing et al., 2007; Yang et al., 2011a; Jian et al., 2018).
The Kuroshio mainstream intrudes into the EMSC from the east of Taiwan Island and flows in northeastward direction over outer continental shelf through the Luzon Strait then turns northeastward (Fig. 1). The Kuroshio is strongly influenced by the bottom topography. Kuroshio surface water is characterized by oligotrophic nutrients, relatively high temperature, and high salinity (Jing et al., 2007; Jian et al., 2018). In the West boundary of EMSC, waters exhibit high nutrients low temperature, low salinity characteristics. The EMSC coastal current is a wind-driven current. The coastal waters have southerly wind driven current and flow northwardly along coast. However, in October and November, due to reduced Changjiang River discharge and prevailing northeast monsoon, surface flow changed to southward along the shelf (Jing et al., 2007). Therefore, the mixing of the shelf and oceanic waters is an important factor controlling primary productivity.
Corresponding author. E-mail address: [email protected] (B. Qu). 1 This work is funded by National Nature Science Funding No. 41276097. ⁎
https://doi.org/10.1016/j.jmarsys.2020.103305 Received 18 September 2019; Received in revised form 15 December 2019; Accepted 31 December 2019 Available online 10 January 2020 0924-7963/ © 2020 Elsevier B.V. All rights reserved.
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Fig. 1. The study region in the EMSC. Kurosiho Current is indicted in red. Changjiang River and Yellow River is also indicated in red. The bathymetry is indicated by color in the figure. The figure is based on the figure produced by Science China Press (https://phys.org/news/2017-09-unravelling-mechanisms-sst-yellow-sea.html). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
1.2. Dimethylsulfide
consumption to DMS removal (85%) was higher than those of photolysis (10%) and sea-to-air exchanges (4%) (Jian et al., 2018). It was reported that there is a significant relationship between DMS and CHL (Yang et al., 2011b; Zhang et al., 2014; Zhang et al., 2017; Jian et al., 2018) in the surface layer of EMSC. Phytoplankton is the main source of DMS. Therefore, the species and biomass of phytoplankton may be the primary factors controlling DMS concentrations in surface water. Diatoms occupied for 97% of the total biomass in EMSC (Zhang et al., 2014). It is well known that marine phytoplankton is significant source of marine sulphate aerosol.
Dimethylsulfide (DMS) produced by marine phytoplankton, as a volatile sulfur, plays an important role in the global biogeochemical cycles. DMS can balance the global sulfur budget and impact the global climate due to its oxidation products (Lovelock et al., 1972). Sulfate produced by this process can increase the natural acidity of atmospheric deposition. It was estimated that 28.1Tg of DMS sulfur is transferred annually from ocean to atmosphere (Lana et al., 2011). When DMS emitted into the atmosphere, it can be oxidized rapidly by OH (daytime) and NO3 (nighttime) and form various sulfur-containing products, such as sulfur dioxide, methanesulfonic acid, and non-sea-salt sulfate, hence these sulfate is also the important source of cloud condensation nuclei (CCN) in remote marine regions (Quinn and Bates, 2011). CCN can reduce the amount of solar radiation reaching the Earth's surface by altering cloud droplet concentration and size, hence cooling the Earth's surface (Charlson et al., 1987). Recent measurements show that the higher concentrations of DMS appeared in spring in ECMS (Zhang et al., 2017; Jian et al., 2018). In spring, bacterial abundance influenced DMS distribution significantly. Enhanced UV radiation could increase DMS photodegradation rate, and low seawater pH could facilitate DMS degradation rate under UVB radiation. Compared with total DMS emission of the global ocean to atmosphere, the contribution of the EMSC to global DMS emissions cannot be neglected (Yang et al., 2011b; Zhang et al., 2014; Zhang et al., 2017). Near the coast regions, the contribution of biological
1.3. Phytoplankton It was found that the peaks of CHL appeared in the 0-10 m of surface water for spring, summer and autumn seasons in EMSC (Zhang et al., 2016). Low temperature in spring and autumn and low salinity in spring and summer are suitable for phytoplankton growth; Active phosphate may be an important factor limiting spring and autumn CHL. Researchers found that CHL in EMSC is higher inshore and lower offshore (Jing et al., 2007; Yang et al., 2011b; Zheng et al., 2012; Zhang et al., 2014; Zhang et al., 2016; Zhang et al., 2017). High CHL was in the Changjiang River estuary and the periphery of the Hangzhou Bay, whereas low CHL was in the South Yellow Sea Cold Water Mass (Zhang et al., 2014). The peak CHL located near the mainland which indicates the influence of anthropogenic activities on the coastal environment where nutrients and phytoplankton biomass are both higher (Yang 2
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et al., 2011b; Zhang et al., 2014). The oxidation of DMS into atmosphere (DMS air-to-sea flux) has significant impact on climate changes by altering cloud properties through CCN. Previous studies in ECMS were mainly based on the field data analysis and lacked of consistent historical data comparisons (Yang et al., 2011b; Zhang et al., 2014; Zhang et al., 2017; Jian et al., 2018). Satellite derived data can overcome this shortage and give a much better environmental pictures. The purpose of studying the relationship among several important environmental factors (including CHL, AOD, SST, WIND, Cloud Cover and MLD) for a recent year consistent time period is to better understand the EMSC ecosystem and the link between ocean and atmosphere. The regional time series of DMS concentrations and sea-to-air DMS fluxes will be elucidated for predicting the contemporary regional climate changes.
(Fig. 3), higher in the east and lower in the west (not show in the figure). The highest WIND appeared in (127°E, 33–37°N) in the south of Korea where several currents meet from south, west and northwest directions. WIND reduced towards the open ocean down south. Monthly mean WIND profiles are compared between the 3 sub-regions (Fig. 3). Northern region (35°N-40°N, 120°E-130°E) had higher WIND comparing to southern region (25°N-30°N, 120°E-130°E). The highest WIND appeared in December and the lowest WIND appeared in June. The range of mean WIND in the study region is within 3–12 m/s. In general, mean wind direction is between southeast and southwest directions (Fig. 4(a)). Year 2015 had more southeast direction wind during phytoplankton blooming season (April–May), which means year 2015 had more wind influenced from Pacific Ocean coming from southeast direction. Mean wind directions in sub-regions are compared (Fig. 4(b)). Wind directions were mainly southwestwards up north and southeastwards down south. The southwestern wind up north brought warmer nutrients water from the Changjiang River. Winds in southern region were mainly in southeast directions, the currents are more coming from Pacific Ocean. Turbulence generated in the ocean by wind, convective cooling, breaking waves, current shear, and other physical processes creates an almost uniform density surface layer that had active vertical mixing and high dissipation (Wijesekera and Gregg, 1996). The depth of the mixing layer is determined by a balance between the destabilizing effects of mechanical mixing and the stabilizing effects of surface buoyancy flux (Thomson and Fine, 2003). In our MLD calculation, the correspondent depth is defined as the MLD grid value in the following way using R software. For one month, about 100 observed data are displayed in the study region. For a particular grid point, 5 nearest points are automatically selected by R software. The grid value of MLD is defined by averaging the 3 medium values selected from the 5 nearest points (the largest and smallest values are not used for the mean value calculation). The monthly mean MLD together with SST and WIND for year 2011–2015 in the study region are calculated (Fig. 5). MLD had peak in March. The MLD in March 2013 reached >200 m. After June, MLD had similar patterns for the 5 years (not show in the figure). The lowest MLD was in June or July. The MLD was higher up north and lower down south. Generally SST and WIND had negative correlations with average correlation coefficient of −0.512 (p < 0.001) for the 5 years. The mean SST could reach to the highest (28 °C) in August in 2014 and down to 11.3 °C in February in 2011. The lowest average WIND occurred in May and highest WIND occurred in January with range from 1.97 to 12.8 m/s for the 5 years. MLD had strong negative correlations to SST with correlation coefficient − 0.94 and p < 0.0001.
2. Data and methods Our study region is in the East Marginal Seas of China (EMSC) (25°N-40°N, 120°E-130°E) (Fig. 1) for the period of year 2011 to 2015. Global CHL and AOD were archived from MODIS (Aqua), 8-day, 4-km, level 3, mapped global database (http://oceandata.sci.gsfc.nasa.gov/ MODISA/Mapped/8Day). SeaDAS 6.4 (The image analysis package data analysis system) (seadas.gsfc.nasa.gov/) is used to subside the regional data. CHL spatial images are obtained from Giovanni site (https://giovanni.gsfc.nasa.gov/giovanni/) NASA GESDISC Channel. SST, WIND and wind direction (WIRD), were obtained from ftp://ftp. remss.com/windsat/bmaps_v07.0.1/weeks/. Cloud Cover (CLD) is downloaded from https://acdisc.gesdisc.eosdis.nasa.gov/data/Aqua_ AIRS_Level3/AIRX3STM.006/). Mixed layer depth (MLD) is obtained.by calculating the temperature difference criterion (0.5 °C lower than surface temperature), where the depth varying temperature is obtained from ftp://101.71.255.12/pub/ARGO/BOA_Argo. R statistical software is used to calculate the grid data from scattered data points. 3. Results 3.1. Cloud cover (CLD), sea surface temperature (SST), wind speed (WIND) and mixed layer depth(MLD) Cloud cover is the most important condition affecting the amount of radiation reaching to the earth's surface. It has direct relationship with surface temperature, hence to climate. The presence of clouds known as the solar albedo effect, reduces the solar flux available to the earth and the atmosphere for absorption, but also it enhances the trapping of the outgoing of the greenhouse effect. EMSC had less cloud with mean cloud cover (CLD) within 40%–60% in the study region. In general, CLD was higher in winter and spring and lower in summer and autumn. October had less cloud than the rest of months. Year 2013 summer had less cloud than other years. SST and WIND are prime ocean surface parameters for calculating DMS sea-to-air flux and other exchanges with in situ observations. It is critical to calculate the SST and WIND accurately, in order to understand the air-sea interactions. In our study region, mean SST spatial distribution for year 2011–2015 was higher in southeast and lower up north. SST weekly mean time series and correspondent standard deviation for year 2013 is shown in Fig. 2(a). SST had similar patterns for the 5 years with the peaks in the middle or later August and valleys in February. In the southern region, SST is more likely influenced by South China Sea warm currents from southwest and Pacific Ocean currents from southeast. We focused on the mean SST for southern region (25–29°N) and from++ 124–130°E where had less missing values from satellite data (Fig. 2(b)). Within this southern region, year 2015 had the lowest SST and year 2013 had the highest SST. SST is getting higher when longitude increases. In general, WIND was higher up north and lower down south
3.2. CHL distributions CHL is used to indicate the phytoplankton biomass in the ocean. Satellite ocean data has a long time history of global CHL data. The mean CHL time series with 8-day time interval is calculated in the study region as well as the means for the three sub-regions divided by the 5degree latitude difference (Fig. 6). Overall, CHL had their peaks in April. Compare to the northern (35–40°N), middle (30–35°N) and southern regions (25–30°N), CHL had a decreased trend from north to south. Elevated CHL up north may be due to the prevailing southwest wind (Fig. 5(b)). The Changjiang River water merging to the ocean has a tendency to turn northeast direction. The water carries substantial amounts of particulates, nutrients, and organic compounds (Zhang, 1996) to the northern region and had significant impact on the phytoplankton biomass up north. Moreover, cooler SST up north is favorable for phytoplankton growth. The lower CHL down south is probably due to the badly P-limited waters caused poor nutrients in the southern region (Zhang et al., 2007; Yang et al., 2011a; Zhang et al., 2014; Zhang et al., 2017). 3
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Fig. 2. (a) SST mean time series for year 2013 (the error bars are the standard deviations). (b) Mean SST in southern region (25–30°N) along longitude 124–130°E).
Fig. 3. Monthly mean WIND comparison among three sub-regions: Southern, middle and northern regions. 4
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Fig. 4. Monthly mean wind directions in the study region (a) for year 2011–2015; (b) for the three sub-regions (south, middle and north).
The peaks of CHL appeared from early April to early May. During blooming season, the peaks of CHL would shift ahead when locations moved towards south (Fig. 6). From late June to late July, southern part of CHL would increase and jump to the top. CHL would come back to the higher values again in the northern region from August to the end of
September. During this time period, middle part of CHL dropped to the bottom and stayed to the bottom until the end of December. The reasons of lower CHL in middle region in later half year are likely due to the influenced terrestrial runoff and the diluted Changjiang River waters, with high turbidity and low transparency in the region, limit the
Fig. 5. Monthly mean WIND, SST and MLD in the study region for year 2011–2015. 5
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Fig. 6. Comparison mean CHL time series among the three different sub-regions throughout a year.
mainly in southeast direction rather from southwest direction during blooming season (Fig. 4(a)), less enriched open sea water would flow into the middle regions during 2015, and enriched Changjiang River water would have less chance to flow into the regions (Fig. 7(b)). CHL in southern region had significant changes (Fig. 8(c)). Year 2011 had about one and half months peak period from early April to late May. During August and September, year 2011 had two diurnal peaks. The diurnal peaks would most likely relate to the summer monsoon (Mackey et al., 2017). Year 2012 had much earlier peaks right started from beginning of February. The second larger peak was in early March. However, year 2012 had even higher winter peaks in November. Different from northern and middle regions, year 2015 had highest autumn peak in middle of October lasted for one week. Year 2013 and 2014 had generally lower profiles throughout the year. The reasons could be due to the badly P-limited coastal waters in the region as well as the wind directions changes (refer to Fig. 4(a) for year 2014 winter mainly southeast direction and Fig. 7(d) showing the lower spatial distributions in year 2014 winter season). Although CHL in southern region was generally low comparing to northern region during the first half year (Fig. 6), the bloom periods were longer for year 2011 and earlier for year 2012 (Fig. 8(c)). The longer and earlier bloom seasons are probably due to the warmer SST during spring season while SST and CHL were positive correlated during spring (Qu et al., 2006; Qu et al., 2014; Qu et al., 2018). During autumn and winter, CHL were much higher down south. The warmer currents down south bring out rich nutrients from bottom layers of water and coastal waters, the different currents confluence together enriched local phytoplankton biomass.
phytoplankton growth in the upper layer (Zhou et al., 2004). From October to end of December, southern region CHL came back to the top due to relevant warmer SST during winter season where rising temperature is the main drive for the phytoplankton development especially in coastal regions (Trombetta et al., 2019). Spatial mean CHL images in phytoplankton blooming season (April–May) and winter season (October–December) are compared for year 2011 (Fig. 7(a), (c)), 2015 (Fig. 7(b)) and 2014 (Fig. 7(d)). The same satellite data resource is used (MODIS Aqua 4 km). During phytoplankton blooming season, the obviously higher CHL appeared in northern region in Bohai Sea. Year 2011 was higher in northern and middle regions. The influence of the Changjiang River discharge shows in the north of 34°N for year 2011 and less influence for year 2015. General high CHL appeared in the southeast coast (30–33°N, 122–126°E) in Zhejiang coastal region especially in year 2015. The two of the richest fishing grounds in the world are located in this southern coastal region (Huang et al., 2019). It was reported that carbon and nutrient supplies are rich in the southern region especially near the east coast of Zhejiang province. However the southern region (ECS) is severely phosphate P-limited (Huang et al., 2019). The increase of badly needed P from the Taiwan Strait is more than that from the Changjiang River (Huang et al., 2019). The coastal acidification was induced by remineralization of biogenic particles. These biogenic particles were supplied either by coastal red tides or by near-shore marine aquaculture (Yang et al., 2011a; Liu et al., 2014). Winter CHL was generally lower due to lower SST and limited light, especially in the middle region where Changjiang River located (Fig. 7(c), (d)). However, there is relevant high CHL along the coast, especially for year 2011. CHL exhibited totally different profiles in the three sub-regions (Fig. 8). In the northern region (Fig. 8(a)), 2015 mean CHL had its highest peak in late April. Year 2012 had double peaks during blooming season. Year 2014 had its late higher double peaks from early May to late May. Year 2013 had several peaks from early April to late May. Year 2015 still had lowest profile in late half year. Middle region shows different profiles (Fig. 8(b)) Year 2012 had the highest CHL peak in late March and second peak around 21st April. Year 2011 also had higher CHL peak in early April. Year 2013 had longer peak during 3th -15th May. However, year 2015 was lower in general. Year 2011 had even higher winter CHL than the northern region. The elevated CHL in middle region in 2011 and 2012 related to the Changjiang River discharge and increased coastal water nutrients. One reason of lower profiles in the middle regions in year 2015 is due to the wind direction's changes. In 2015, the mean wind direction was
3.3. AOD distributions AOD is a function of sea salt, mineral dust, organic compounds, as well as non-sea-salt sulphate and MSA, which is derived from oxidation of atmospheric DMS (Gabric et al., 2005). Gabric et al. (2005) studied correlations between CHL and AOD in Southern Ocean, and found that there was a strong correlation between CHL and AOD in the band of 50°S to 60°S, where sea-ice melting and associated release of biogenic sulfur species contributed to the aerosol concentration. In our study region, AOD had average higher concentration in year 2012 in the study region (not show in the figure). The mean AOD in the 5 years in study region is general higher in spring and lower from summer towards winter. AOD in the three sub-regions are shown in Fig. 9. In general, southern region had higher AOD especially during winter time. Northern region had the lowest AOD. However, from early 6
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Fig. 7. Spatial mean CHL for April–May period comparison between year 2011 (a) and 2015 (b) in the study region (from MODIS-Aqua MODISA–L3m-CHL v2018 CHL monthly 4 km in https://giovanni.gsfc.nasa.gov/giovanni/, unit: mg/m3, Equidistant cylindrical projection with linear scale and smooth option are used for the plotting).
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Fig. 8. Mean CHL time series in (a) the northern study region; (b) the middle study region; (c) the southern study region.
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Fig. 9. Mean AOD in the three sub-regions.
June to early July, AOD had higher profile in middle region where usually summer enrichment and stratification occurred near the Changjiang River mouth. In general, AOD was higher down south and lower up north. Year 2015 had lower AOD due to less influenced from Changjiang River (not in the figure).
with reasonable deep of MLD plus nutrients from coastal water and the Changjiang River (can be indicated by higher AOD) can make a significant difference for the phytoplankton biomass in the region. 4. DMS concentrations and sea-to-air fluxes in EMSC 4.1. DMS concentrations
3.4. Relationships between CHL, AOD and WIND
Simó and Dachs (2002) formula is used to calculate the regional DMS (unit is nanomoles (nM)) for different years). In the formula, DMS is derived according to two different scenarios:
The seasonal correlations between CHL and AOD are listed in Table 1. From July to September, the correlations are all negative. The relationships are always positive for the following periods: Jan-Mar, Apr-Jun and Oct-Dec. During CHL blooming season, CHL and AOD had almost positive correlations, although there is a lag between them. Summer AOD had negative correlations with CHL (Jul-Sep). In the middle region where the sea waters are more influenced by the Changjiang River waters, CHL and AOD had quite strong positive correlation in the first half year especially during CHL blooming season. Winter AOD had reasonable good positive correlations with CHL, especially in southern and northern sub-regions (correlation coefficients were >0.7). The correlations are all significant (with p < 0.01). From previous results, CHL is higher up north and lower down south (Fig. 6) while mean AOD was lower up north and higher down south (Fig. 9). The peaks of CHL were always a few weeks after peaks of AOD. That means AOD influenced CHL with a time lag of half month to more than one month (not show in the figure). WIND was stronger in the northern region (Fig. 3). There was obviously negative correlation between AOD and WIND. The correlation coefficient is −0.274 (with p < 0.01). Therefore, with increase of WIND, AOD would reduce accordingly. Our results show WIND is an important factor on controlling phytoplankton biomass in our study region. With relative low WIND along west coast where shallow water locates, it can increase the stability of water column and favorable for the growth of phytoplankton (Rey et al., 1987). However, it was reported that higher WIND in deep water could bring nutrients to the surface and also helped the growth of phytoplankton (Zheng et al., 2012). The combination of WIND and SST
DMS =
DMS = 55.8(CHL/MLD) + 0.6 if CHL/MLD
North Middel South
Jan–Mar
Apr–Jun
Jul–Sep
Oct–Dec
0.53 0.42 −0.14
0.76 0.84 0.75
0.06 0.96 0.77
−0.33 −0.67 −0.2
0.71 0.44 0.77
0.02
(2)
CHL and MLD time series (with 8-day time interval) are calculated from Satellite data. DMS is calculated according to the formula (1) and (2) for the 5 different years. DMS, CHL and MLD profiles in year 2011 is shown in Fig. 10. MLD was higher in spring (>140 m) and decreased after 20th March and reached to minimum in June and July (<20 m). CHL increased from late March and reached to its peak in 13th April. The increase of CHL accompanied with decrease of MLD. DMS had its first peak in 13th April (the same as CHL) and then increased to its second peak in 25th May. The slight increment of CHL in 28th July led the significant increase of DMS in 11th August. The peak of DMS in 2011 was in middle August. It is coincident with the field measurement from Ocean University of China (OUC, the cross sign in the figure) (Zhang et al., 2014). In July 2011, DMS average value is 5.415 nM from our calculation, which is close to the real mean data from OUC (5.3 nM) (Zhang et al., 2014). DMS mean time series (with 8-days time interval) is calculated. Generally, the peak months are in July and August. Due to lower mean CHL in year 2015, lower DMS profile almost throughout the year comparing to the 5 years mean values (not show in the figure). Same as CHL profile, DMS in year 2011 had higher winter values than other years. DMS ranged from 0.32 to 5.64 nM with a mean of 2.19 nM for the 5 years. The correlations between CHL and DMS for the 4 different time periods are listed in Table 2, where p values are calculated and most correlations are significant, apart from Jan-Mar period for year 2012, 2014 and 2015 (p > 0.05). There are no obvious correlations for the whole year. However, there are more significant correlations for the shorter periods. The most correlated period is during June and August. In general, DMS and CHL had more correlations during summer and autumn periods.
Table 1 The correlation coefficient between CHL and AOD for different time periods. Whole year
(1)
ln(MLD) + 5.7 if CHL/MLD < 0.02
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Fig. 10. Mean DMS, CHL and MLD profiles in year 2011 in the study region.
are within 33.9–59 μmol m−2d−1. The range of DMS sea-to-air flux is from 2.15 to 59 μmol m−2d−1.
4.2. DMS sea-to-air fluxes in EMSC The sea-to-air fluxes of DMS calculation is essential to understand the regional cycle of biogenic sulfur and its effect on the Earth's radiation. The most commonly used DMS sea-to-air flux calculations are from Liss and Merlivat (1986), Wanninkhof (1992) or Nightingale et al. (2000) (Liss and Merlivat, 1986; Wanninkhof, 1992; Nightingale et al., 2000). Liss and Merlivat's (1986) method for the calculation of DMS sea-to-air flux is used here:
4.3. DMS concentration and sea-to-air flux validations from the field data During July 2011, seawater samples were collected on board in EMSC about 60 stations by research group in OUC (Zhang et al., 2014). They found that diatoms occupied the dominant positions in EMSC. There are positive correlations between DMS and CHL. During this period, field data shows that DMS ranges from 0.63 nM to 41.19 nM with average value of 5.3 nM in the whole study region. The highest CHL concentrations are found in Changjiang River estuary and the periphery of the Hangzhou Bay (Zhang et al., 2014) which is close to our spatial mean satellite image (Fig. 8). They found that the main reason of elevated CHL is due to the upwelling in this region. The upwelling phenomenon replenished nutrients to support the growth of phytoplankton in the surface waters. The lowest DMS is found further east of Changjiang River Estuary (around A (124°E, 31°N)). The high nutrient showed in southeast of Hangzhou Bay (around B (122°E, 29°N)) in Zhejiang coastal waters. The reasons of lower CHL concentrations around A location are mainly due to the terrestrial runoff, high turbidity and low transparency in the estuary (Zhou et al., 2004). As a result, the concentrations DMS were also reduced. DMS concentrations are compared from our calculated data with the field data from OUC (Table 3) (Zhang et al., 2014; Zhang et al., 2017). Our calculated DMS in July 2011 (5.415 nM) is very close to the field DMS mean (5.3 nM) (Zhang et al., 2014). Our result is only increased 2.17%. However, in October 2015, Our calculated mean DMS in October 2015 (2.77 nM) is quite low comparing to the same time period mean field data (3.63 nM) (Zhang et al., 2017). Our result is 23.7% lower than the field data. Due to the different data calculation methods, range data is not comparable. OUC field data is from individual measurements in some special locations. Our range data is from spatial
(3)
FluxDMS = k w DMS
where kw is the DMS sea-to-air transfer velocity. It is determined by WIND (w) (at 10 m above sea surface) and SST.
k w = 0.17 w for w k w = (2.85w kw = (5.9w
(4a)
3.6
10.6) + 0.612 for 3.6
w
49.91) + 0.612 for w > 13 where
=
(4b)
13 2 (600/Sc ) 3 ,
=
2 (600/Sc ) 1 ,
(4c)
and
Sc = 3628.5
243.58(SST ) + 7.801(SST )2
0.1148(SST )3
(5)
Here Sc is the DMS Schmidt number. Monthly profiles of DMS, transfer velocity kw and DMS sea-to-air flux in year 2011 are shown in Fig. 11(a). DMS sea-to-air flux had similar increase trend to DMS concentration and also reached to peak in August. There is a second flux peak in November when transfer velocity increased. Monthly mean DMS sea-to-air flux is calculated for 2011–2015 in the study region (Fig. 11(b)). DMS flux had peaks in August and the highest peak was in year 2012. Year 2015 had least DMS sea-to-air flux due to its lower DMS concentrations throughout the year. DMS sea-to-air flux are within range of 14–22 μmolm−2d−1. Minimum values are within 2.15–5.2 μmol m−2d−1, maximum values Table 2 Correlations between CHL and DMS in the study region. 2011 Whole Year Jan-May Jun-Aug Sep-Dec
−0.178 (p 0.302(p < 0.940(p < 0.532(p <
< 0.01) 0.01) 0.01) 0.01)
2012
2013
0.061(p < 0.01) 0.475*(p > 0.05) 0.980(p < 0.01) 0.920(p < 0.05)
0.155(p 0.595(p 0.730(p 0.923(p
10
< < < <
0.01) 0.01) 0.01) 0.01)
2014
2015
0.098(p < 0.01) 0.669*(p > 0.05) 0.858(p < 0.01) 0.908(p < 0.01)
−0.013(p > 0.01) 0.539*(p > 0.05) 0.957(p < 0.01) 0.693(p > 0.01)
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Fig. 11. (a) Mean DMS concentration, DMS flux and transfer velocity in the study region for year 2011. (b) Monthly mean DMS sea-to-air flux in the study region for year 2011–2015.
mean ranges. The only meaningful data for comparison is mean data, although the mean data is also from different angles. OUC mean data is averaged from daily mean of individual measurements of some special locations, our mean data is monthly spatial mean for all the grid in the region. OUC data stations are not so evenly spread out like our grid data. To our surprise, year 2011 had rather closed mean DMS for both field DMS and calculated DMS. Hence, Simó's formula (Simó and Dachs, 2002) can be used in our study region to get reasonable results and not too far away from the reality. The differences of DMS sea-to-air flux between our calculation using
Table 3 DMS concentrations comparison with the field data (unit: nM). DMS in July 2011
Range Monthly peak Mean Rate changed
DMS in October 2015
OUC field data
Our data
OUC field data
Our data
0.63–41.19
– 5.415 5.415
0.96–9.64
– 3.96 2.77
5.3 +2.17%
3.63 −23.7%
11
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and direction of monsoon winds can affect the trajectory of aerosols from the continent to the marine environment, hence influence the phytoplankton biomass.
Table 4 DMS air-sea flux comparisons with the field calculated data (unit: umolm−2d−1).
Range Mean Rate changed
DMS flux in July 2011
DMS flux in October 2015
OUC field data
Our data
OUC field data
Our data
0.03–102.4 16.73 +23%
5.2–48.61 20.57
0.31–33.7 12.97 +8%
3.72–33.93 14
5. Conclusions The EMSC environmental forcings are studied and the relationships among them are analyzed. Our study region was divided into three subregions (northern, middle and southern) for more detailed study. In general, cloud cover was between 40%–60% with more cloudy in winter and spring and less cloudy in summer and autumn in the study region. WIND was higher up north and lower down south. Wind directions were mainly between southeast direction down south and southwest direction up north. Peak SST occurred in August. Year 2015 had lower SST in southern region. SST and WIND had negative correlations. CHL had peak period in April. CHL was higher near shore and lower off shore. During first half year, CHL were higher up north and lower down south. The reasons are due to relevant cooler temperature and southeasterly wind up north, brought enriched Changjiang River waters northwards, and favored phytoplankton growth. However, during autumn and winter seasons, CHL was higher in southern region, lower in middle region, year 2011 and 2012 had higher CHL in spring in the middle region, due to the more enriched Changjiang river discharge and increased coastal water nutrients. Year 2015 had the lowest CHL profile and year 2013, 2014 had lowest winter profiles. The major reason could be due to the southeastern wind from Open Sea, brought sea water with less nutrients to the region. Badly P-limited southern coastal water is another reason. Year 2011 had much higher CHL winter profile in middle region and much higher and longer spring CHL down south. The highest CHL concentrations are mainly located along west coast of the ECMS near Zhejiang coastal waters, southeast of Hangzhou Bay, where most fish farmers located (Yang et al., 2011a). CHL and AOD had quite strong positive correlation in the first half year especially during CHL blooming season. Winter AOD also had reasonable positive correlations with CHL, especially in southern and northern sub-regions. The highest positive correlations between CHL and AOD are located in the middle region where Changjiang River mouth is. AOD influenced CHL. The peaks of AOD were ahead of CHL peaks about half or one month more. With the WIND increased, AOD reduced accordingly. This again proved the less AOD up north due to the higher WIND in the region. Year 2015 had lower CHL as well as lower AOD in general, mainly due to the southeast direction wind. MLD was higher up north and lower down south and higher in winter and lower in summer. DMS concentrations and DMS sea-to-air flux are calculated for the study region using Simó's (Simó and Dachs, 2002) formula and DMS flux calculation scheme is from Liss and Merlivat (1986). The comparisons are made for July 2011 and Oct. 2015 with OUC field data. The reasonable agreements are made. Our mean DMS concentration in July 2011 and DMS sea-to-air flux in October 2015 are quite close to OUC field data. DMS had high positive correlations with CHL in the summer and autumn periods. Our results show that DMS ranged from 0.32–5.64 nM with a mean of 2.19 nM for the 5 years. DMS sea-to-air flux ranged within 2.14–59.06 μmolm−2d−1 with mean of 18.96 μmolm−2d−1. Elevated CHL, DMS and DMS sea-to-air flux in east coast of EMSC during year 2011 to 2015 indicate that the EMSC region cannot be ignored for the global DMS flux emission evaluation. Hence, our study region is a relatively important region that will have impact on the local climate and even global climate change. More study is urged for detailed DMS model simulation and DMS sea-to-air flux predictions based on the OUC field data.
Liss and Merlivat's scheme and the calculation from OUC based on their field data are compared (Table 4). The minimum DMS sea-to-air fluxes calculated by OUC are very small comparing to our calculated minimum values. However, year 2015 maximum DMS fluxes are close between our calculation (33.93 μmolm−2d−1) and OUC field data (33.7 μmolm−2d−1). It is an excellent matching for the peak values in October 2015. The difference is only 0.7%. The mean values are also not far away (8% difference). However, the maximum DMS flux in July 2011 had quite big difference. The mean values had 23% difference. The different DMS flux calculation methods also caused different results. OUC DMS flux used parameterization of Nightingale et al. (2000). If they also use the same method from Liss and Merlivat (1986), the flux of DMS would range from 0.01 μmol m2/day to 74.7 μmol m2/day (Zhang et al., 2014). Hence, the maximum value difference would be reduced. In general, our calculated DMS and DMS flux are not too far away from the field data. Some values had very good matching. 4.4. Discussions CHL was lower down south and higher up north in our study region. Apart from the factor of SST, other CHL inflencing factors such as WIND, MLD and AOD are discussed. The severely P-limited ECS region (southern region) and declined dissolved oxygen and PH value in the northern region (Bohai Sea) coastal area were the other important factors influences phytoplankton biomass in ECMS (Liu et al., 2014; Huang et al., 2019). It was also found that summer Kuroshio subsurface water is rich in phosphate which can transport the phosphate continuously from the east of Taiwan to the area off the mouth of the Changjiang River where harmful algal bloom occurs frequently in summer (Zhang et al., 2007; Yang et al., 2011a). It was reported that phytoplankton blooms are triggered by upwelling especially in open or deep-coastal zones which provides nutrients from deep nutrient-rich water (Zhang et al., 2014; Zhang et al., 2017; Trombetta et al., 2019). The upwelling phenomenon potentially increases nutrient inputs and thus bloom event. Higher tidal amplitude can influence CHL timing and magnitude (Trombetta et al., 2019). Eutrophication of Chinese coastal waters is manifested by increasingly frequent dinoflagellate blooms (Lu et al., 2005), and intensification of spring diatom blooms in ECS. Increasing ratio of nitrogen to phosphorus (N:P) blooms including severe P limitation and trace metal micronutrient inventories observed in ECS coastal (Mackey et al., 2017). Spring blooms were mainly associated with diatom blooms, where diatom growth was correlated with elevated trace metal and nutrient content from aerosols (Mackey et al., 2017). Aerosol had no much effect on phytoplankton growth in the Changjiang River estuary area where CHL was the lowest (Zhang et al., 2014; Zhang et al., 2017). The diatom-dominated spring blooms have a tendency of transition to more frenquent spring and summer dinoflagellate blooms in ECS (Mackey et al., 2017). Numerous researchers have focused on eutrophication caused by Changjiang River discharge, which brings substantial anthropogenic nutrient input into the East China Sea. Strong seasonal signals of monsoon effect are observed in ECS (Wang et al., 2001; Mackey et al., 2017), with winds coming from the southeast in the summer monsoon phase and from the northwest during the winter phase. The strength 12
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Acknowledgements
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Special thanks to several students: Rui Wang, Yang Yang, Zheng Liu, Shan Yan and Zhiwei Xu for calculating Cloud Cover, AOD, CHL, SST, MLD data and DMS flux in the study region. Sincerely thanks should go to NASA Ocean Biology Processing Group and Goddard Space Flight Centre of SeaWiFS Project group for providing MODIS CHL, AOD satellite data. Thanks should also go to NOAA NCEP EMC CMB GLOBAL ReynSmithOIv2 for providing weekly sea-ice concentration data. Special thanks go to the Naval Research Laboratory Remote Sensing Division, the Naval Center for Space Technology, and the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Integrated Program Office (IPO) for providing satellite-based wind speed, sea surface temperature data. Thanks to NASA Goddard Earth Science (GES) and Information Services Center (DISC) team for providing the Cloud Cover data. Special thanks to Giovanni EARTHDATA site (https:// giovanni.gsfc.nasa.gov/giovanni/) from NASA GESDISC Channel for providing CHL spatial visualization pages. Again, thanks to Dr. David L. Carroll from CU Aerospace, for the genetic algorithm Fortran code. Thanks also should go to Gleb Panteleev and Jacob Stroh from International Arctic Research Center, UAF (University of Alaska) for providing the Arctic CTD Database (for MLD calculations). We gratefully acknowledge the National Natural Science Foundation of China (Funding No. 41276097) for providing research funding for this project. References Charlson, R.J., Lovelock, J.E., Andreae, M.O., Warren, S.G., 1987. Oceanic phytoplankton, atmospheric sulphur, cloud albedo and climate. Nature 326, 655–661. Gabric, A.J., Shephard, J.M., Knight, J.M., Jones, G., Trevena, A.J., 2005. Correlations between the satellite-derived seasonal cycles of phytoplankton biomass and aerosol optical depth in the Southern Ocean: evidence for the influence of sea ice. Glob. Biogeochem. Cycles 19, GB4018. https://doi.org/10.1029/2005GB002546. Huang, T.-H., Chen, C.-T.A., Lee, J., Wu, C.-R., Wang, Y.-L., Bai, Y., Yang, Y.J., 2019. East China Sea increasingly gains limiting nutrient P from South China Sea. Sci. Rep. 9 (1), 5648. https://doi.org/10.1038/s41598-019-42020-4. 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. https://doi.org/10.1002/lno.10737. Jing, Z.Y., Hua, Z.L., Qi, Y.Q., Cheng, X.H., 2007. Numerical study on the coastal upwelling and its seasonal variation in the East China Sea. J. Coast. Res. 50, 555–563. Lana, A., Bell, T.G., Simo, R., Vallina, S.M., Ballabrera-Poy, J., Kettle, A.J., Dachs, J., Bopp, L., Saltzman, E.S., Stefels, J., Johnson, J.E., Liss, P.S., 2011. An updated climatology of surface dimethlysulfide concentrations and emission fluxes in the global ocean. Glob. Biogeochem. Cycles 25, GB1004. https://doi.org/10.1029/ 2010GB003850. Liss, P.S., Merlivat, L., 1986. Air-Sea Gas Exchange Rates: Introduction and Synthesis. P. Buat-Menard. Reidel, Hingham, MA. Liu, Y., Peng, Z., Zhou, R., Song, S., Liu, W., You, C.-F., Shen, C.-C., 2014. Acceleration of modern acidification in the South China Sea driven by anthropogenic CO2. Sci. Rep. 4, 5148. https://doi.org/10.1038/srep05148. Lovelock, J.E., Maggs, R.J., Rasmussen, R.A., 1972. Atmospheric dimethylsulphide and the natural sulphur cycle. Nature 237, 452–453. Lu, D., Göbel, J., Qi, Y., Zou, J., Han, X., Gao, Y., Li, Y., 2005. Morphological and genetic study of Lu from the East China Sea, and comparison with some related species. Harmful Algae 4, 493–505. https://doi.org/10.1016/j.hal.2004.08.015. Mackey, K.R.M., Kavanaugh, M.T., Wang, F., Chen, Y., Liu, F., Glover, D.M., Paytan, A., 2017. Atmospheric and fluvial nutrients fuel algal blooms in the East China Sea. Front. Mar. Sci. 4 (2). https://doi.org/10.3389/fmars.2017.00002.
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The satellite derived environmental factors and their relationships with dimethylsulfide in the East Marginal Seas of China
T
Bo Qua,⁎,1, Gui-Peng Yangb, Li-Yan Guoc, Li Zhaod a
School of Science, Nantong University, Nantong, China Key Laboratory of Marine Chemistry Theory and Technology, Ministry of Education/Institute for Advanced Ocean Study, Ocean University of China, Qingdao, China c Nantong Fudadianjing Focus Educational Institution, Nantong, China d Jiangsu Vocational College of Business, Nantong, China b
ARTICLE INFO
ABSTRACT
Keywords: Chlorophyll-a Aerosol optical depth Sea surface temperature Wind speed Mixed layer depth Dimethylsulfide sea-to-air flux
Biogenic dimethylsulfide (DMS) sea-to-air flux and its closely linked environment factors are studied in the East Marginal Seas of China (EMSC) (25°N-40°N, 120°E-130°E) from 2011 to 2015. Higher chlorophyll-a (CHL) up north during blooming season is due to the southeasterly wind brought more nutrients from the Changjiang River waters. The elevated CHL during autumn and winter periods down south is more related with the higher sea surface temperature (SST). The lower CHL in the middle region in the later half year was mainly influenced by terrestrial runoff and the diluted Changjiang River waters. The enriched Changjiang River discharges brought more nutrients to the Zhejiang coastal waters and enriched the local phytoplankton, where aerosol optical depth (AOD) had the highest correlation with CHL (0.96). Peak SST was in August and had a negative correlation with wind speed (WIND). With increased WIND up north, AOD reduced accordingly. Lower CHL in 2015 was mainly related to the southeastern wind brought less nutrient water from Open Sea during blooming season. DMS sea-to-air fluxes are calculated and compared with OUC (Ocean University of China) field data. The reasonable agreements are made. DMS concentrations ranged from 0.32 to 5.64 nM with a mean of 2.19 nM, it had high positive correlations with CHL in the summer and autumn periods. DMS sea-to-air fluxes ranged within 2.14–59.06 μmolm−2d−1 with a mean of 18.96 μmolm−2d−1. The elevated DMS sea-to-air fluxes in ECMS indicate the contributions of DMS emission in the study region cannot be ignored on the global climate evaluation.
1. Introduction
1.1. Current
The East Marginal Seas of China (EMSC) is situated on the margin of the Northwest Pacific Ocean, is one of the best river dominated ocean margin. In the northern region is where Bohai Sea located with Yellow River discharge into the region from west coast; middle region is where Yellow Sea outlet mouse located; and the southern region is where the most East China Sea located. It is an extremely dynamic oceanic region which is influenced by the South China Sea waters passing through the Taiwan Strait, the Yellow Sea waters by the China Coast Current, the Changjiang River, one of the biggest rivers by discharge volume in the world, and Kuroshio where its brunches interact strongly with coastal currents. The EMSC circulation pattern is largely controlled by the Kuroshio, which influences the water and heat exchanges of the ECS (East China Sea) with the open sea (Jing et al., 2007; Yang et al., 2011a; Jian et al., 2018).
The Kuroshio mainstream intrudes into the EMSC from the east of Taiwan Island and flows in northeastward direction over outer continental shelf through the Luzon Strait then turns northeastward (Fig. 1). The Kuroshio is strongly influenced by the bottom topography. Kuroshio surface water is characterized by oligotrophic nutrients, relatively high temperature, and high salinity (Jing et al., 2007; Jian et al., 2018). In the West boundary of EMSC, waters exhibit high nutrients low temperature, low salinity characteristics. The EMSC coastal current is a wind-driven current. The coastal waters have southerly wind driven current and flow northwardly along coast. However, in October and November, due to reduced Changjiang River discharge and prevailing northeast monsoon, surface flow changed to southward along the shelf (Jing et al., 2007). Therefore, the mixing of the shelf and oceanic waters is an important factor controlling primary productivity.
Corresponding author. E-mail address: [email protected] (B. Qu). 1 This work is funded by National Nature Science Funding No. 41276097. ⁎
https://doi.org/10.1016/j.jmarsys.2020.103305 Received 18 September 2019; Received in revised form 15 December 2019; Accepted 31 December 2019 Available online 10 January 2020 0924-7963/ © 2020 Elsevier B.V. All rights reserved.
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Fig. 1. The study region in the EMSC. Kurosiho Current is indicted in red. Changjiang River and Yellow River is also indicated in red. The bathymetry is indicated by color in the figure. The figure is based on the figure produced by Science China Press (https://phys.org/news/2017-09-unravelling-mechanisms-sst-yellow-sea.html). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
1.2. Dimethylsulfide
consumption to DMS removal (85%) was higher than those of photolysis (10%) and sea-to-air exchanges (4%) (Jian et al., 2018). It was reported that there is a significant relationship between DMS and CHL (Yang et al., 2011b; Zhang et al., 2014; Zhang et al., 2017; Jian et al., 2018) in the surface layer of EMSC. Phytoplankton is the main source of DMS. Therefore, the species and biomass of phytoplankton may be the primary factors controlling DMS concentrations in surface water. Diatoms occupied for 97% of the total biomass in EMSC (Zhang et al., 2014). It is well known that marine phytoplankton is significant source of marine sulphate aerosol.
Dimethylsulfide (DMS) produced by marine phytoplankton, as a volatile sulfur, plays an important role in the global biogeochemical cycles. DMS can balance the global sulfur budget and impact the global climate due to its oxidation products (Lovelock et al., 1972). Sulfate produced by this process can increase the natural acidity of atmospheric deposition. It was estimated that 28.1Tg of DMS sulfur is transferred annually from ocean to atmosphere (Lana et al., 2011). When DMS emitted into the atmosphere, it can be oxidized rapidly by OH (daytime) and NO3 (nighttime) and form various sulfur-containing products, such as sulfur dioxide, methanesulfonic acid, and non-sea-salt sulfate, hence these sulfate is also the important source of cloud condensation nuclei (CCN) in remote marine regions (Quinn and Bates, 2011). CCN can reduce the amount of solar radiation reaching the Earth's surface by altering cloud droplet concentration and size, hence cooling the Earth's surface (Charlson et al., 1987). Recent measurements show that the higher concentrations of DMS appeared in spring in ECMS (Zhang et al., 2017; Jian et al., 2018). In spring, bacterial abundance influenced DMS distribution significantly. Enhanced UV radiation could increase DMS photodegradation rate, and low seawater pH could facilitate DMS degradation rate under UVB radiation. Compared with total DMS emission of the global ocean to atmosphere, the contribution of the EMSC to global DMS emissions cannot be neglected (Yang et al., 2011b; Zhang et al., 2014; Zhang et al., 2017). Near the coast regions, the contribution of biological
1.3. Phytoplankton It was found that the peaks of CHL appeared in the 0-10 m of surface water for spring, summer and autumn seasons in EMSC (Zhang et al., 2016). Low temperature in spring and autumn and low salinity in spring and summer are suitable for phytoplankton growth; Active phosphate may be an important factor limiting spring and autumn CHL. Researchers found that CHL in EMSC is higher inshore and lower offshore (Jing et al., 2007; Yang et al., 2011b; Zheng et al., 2012; Zhang et al., 2014; Zhang et al., 2016; Zhang et al., 2017). High CHL was in the Changjiang River estuary and the periphery of the Hangzhou Bay, whereas low CHL was in the South Yellow Sea Cold Water Mass (Zhang et al., 2014). The peak CHL located near the mainland which indicates the influence of anthropogenic activities on the coastal environment where nutrients and phytoplankton biomass are both higher (Yang 2
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et al., 2011b; Zhang et al., 2014). The oxidation of DMS into atmosphere (DMS air-to-sea flux) has significant impact on climate changes by altering cloud properties through CCN. Previous studies in ECMS were mainly based on the field data analysis and lacked of consistent historical data comparisons (Yang et al., 2011b; Zhang et al., 2014; Zhang et al., 2017; Jian et al., 2018). Satellite derived data can overcome this shortage and give a much better environmental pictures. The purpose of studying the relationship among several important environmental factors (including CHL, AOD, SST, WIND, Cloud Cover and MLD) for a recent year consistent time period is to better understand the EMSC ecosystem and the link between ocean and atmosphere. The regional time series of DMS concentrations and sea-to-air DMS fluxes will be elucidated for predicting the contemporary regional climate changes.
(Fig. 3), higher in the east and lower in the west (not show in the figure). The highest WIND appeared in (127°E, 33–37°N) in the south of Korea where several currents meet from south, west and northwest directions. WIND reduced towards the open ocean down south. Monthly mean WIND profiles are compared between the 3 sub-regions (Fig. 3). Northern region (35°N-40°N, 120°E-130°E) had higher WIND comparing to southern region (25°N-30°N, 120°E-130°E). The highest WIND appeared in December and the lowest WIND appeared in June. The range of mean WIND in the study region is within 3–12 m/s. In general, mean wind direction is between southeast and southwest directions (Fig. 4(a)). Year 2015 had more southeast direction wind during phytoplankton blooming season (April–May), which means year 2015 had more wind influenced from Pacific Ocean coming from southeast direction. Mean wind directions in sub-regions are compared (Fig. 4(b)). Wind directions were mainly southwestwards up north and southeastwards down south. The southwestern wind up north brought warmer nutrients water from the Changjiang River. Winds in southern region were mainly in southeast directions, the currents are more coming from Pacific Ocean. Turbulence generated in the ocean by wind, convective cooling, breaking waves, current shear, and other physical processes creates an almost uniform density surface layer that had active vertical mixing and high dissipation (Wijesekera and Gregg, 1996). The depth of the mixing layer is determined by a balance between the destabilizing effects of mechanical mixing and the stabilizing effects of surface buoyancy flux (Thomson and Fine, 2003). In our MLD calculation, the correspondent depth is defined as the MLD grid value in the following way using R software. For one month, about 100 observed data are displayed in the study region. For a particular grid point, 5 nearest points are automatically selected by R software. The grid value of MLD is defined by averaging the 3 medium values selected from the 5 nearest points (the largest and smallest values are not used for the mean value calculation). The monthly mean MLD together with SST and WIND for year 2011–2015 in the study region are calculated (Fig. 5). MLD had peak in March. The MLD in March 2013 reached >200 m. After June, MLD had similar patterns for the 5 years (not show in the figure). The lowest MLD was in June or July. The MLD was higher up north and lower down south. Generally SST and WIND had negative correlations with average correlation coefficient of −0.512 (p < 0.001) for the 5 years. The mean SST could reach to the highest (28 °C) in August in 2014 and down to 11.3 °C in February in 2011. The lowest average WIND occurred in May and highest WIND occurred in January with range from 1.97 to 12.8 m/s for the 5 years. MLD had strong negative correlations to SST with correlation coefficient − 0.94 and p < 0.0001.
2. Data and methods Our study region is in the East Marginal Seas of China (EMSC) (25°N-40°N, 120°E-130°E) (Fig. 1) for the period of year 2011 to 2015. Global CHL and AOD were archived from MODIS (Aqua), 8-day, 4-km, level 3, mapped global database (http://oceandata.sci.gsfc.nasa.gov/ MODISA/Mapped/8Day). SeaDAS 6.4 (The image analysis package data analysis system) (seadas.gsfc.nasa.gov/) is used to subside the regional data. CHL spatial images are obtained from Giovanni site (https://giovanni.gsfc.nasa.gov/giovanni/) NASA GESDISC Channel. SST, WIND and wind direction (WIRD), were obtained from ftp://ftp. remss.com/windsat/bmaps_v07.0.1/weeks/. Cloud Cover (CLD) is downloaded from https://acdisc.gesdisc.eosdis.nasa.gov/data/Aqua_ AIRS_Level3/AIRX3STM.006/). Mixed layer depth (MLD) is obtained.by calculating the temperature difference criterion (0.5 °C lower than surface temperature), where the depth varying temperature is obtained from ftp://101.71.255.12/pub/ARGO/BOA_Argo. R statistical software is used to calculate the grid data from scattered data points. 3. Results 3.1. Cloud cover (CLD), sea surface temperature (SST), wind speed (WIND) and mixed layer depth(MLD) Cloud cover is the most important condition affecting the amount of radiation reaching to the earth's surface. It has direct relationship with surface temperature, hence to climate. The presence of clouds known as the solar albedo effect, reduces the solar flux available to the earth and the atmosphere for absorption, but also it enhances the trapping of the outgoing of the greenhouse effect. EMSC had less cloud with mean cloud cover (CLD) within 40%–60% in the study region. In general, CLD was higher in winter and spring and lower in summer and autumn. October had less cloud than the rest of months. Year 2013 summer had less cloud than other years. SST and WIND are prime ocean surface parameters for calculating DMS sea-to-air flux and other exchanges with in situ observations. It is critical to calculate the SST and WIND accurately, in order to understand the air-sea interactions. In our study region, mean SST spatial distribution for year 2011–2015 was higher in southeast and lower up north. SST weekly mean time series and correspondent standard deviation for year 2013 is shown in Fig. 2(a). SST had similar patterns for the 5 years with the peaks in the middle or later August and valleys in February. In the southern region, SST is more likely influenced by South China Sea warm currents from southwest and Pacific Ocean currents from southeast. We focused on the mean SST for southern region (25–29°N) and from++ 124–130°E where had less missing values from satellite data (Fig. 2(b)). Within this southern region, year 2015 had the lowest SST and year 2013 had the highest SST. SST is getting higher when longitude increases. In general, WIND was higher up north and lower down south
3.2. CHL distributions CHL is used to indicate the phytoplankton biomass in the ocean. Satellite ocean data has a long time history of global CHL data. The mean CHL time series with 8-day time interval is calculated in the study region as well as the means for the three sub-regions divided by the 5degree latitude difference (Fig. 6). Overall, CHL had their peaks in April. Compare to the northern (35–40°N), middle (30–35°N) and southern regions (25–30°N), CHL had a decreased trend from north to south. Elevated CHL up north may be due to the prevailing southwest wind (Fig. 5(b)). The Changjiang River water merging to the ocean has a tendency to turn northeast direction. The water carries substantial amounts of particulates, nutrients, and organic compounds (Zhang, 1996) to the northern region and had significant impact on the phytoplankton biomass up north. Moreover, cooler SST up north is favorable for phytoplankton growth. The lower CHL down south is probably due to the badly P-limited waters caused poor nutrients in the southern region (Zhang et al., 2007; Yang et al., 2011a; Zhang et al., 2014; Zhang et al., 2017). 3
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Fig. 2. (a) SST mean time series for year 2013 (the error bars are the standard deviations). (b) Mean SST in southern region (25–30°N) along longitude 124–130°E).
Fig. 3. Monthly mean WIND comparison among three sub-regions: Southern, middle and northern regions. 4
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Fig. 4. Monthly mean wind directions in the study region (a) for year 2011–2015; (b) for the three sub-regions (south, middle and north).
The peaks of CHL appeared from early April to early May. During blooming season, the peaks of CHL would shift ahead when locations moved towards south (Fig. 6). From late June to late July, southern part of CHL would increase and jump to the top. CHL would come back to the higher values again in the northern region from August to the end of
September. During this time period, middle part of CHL dropped to the bottom and stayed to the bottom until the end of December. The reasons of lower CHL in middle region in later half year are likely due to the influenced terrestrial runoff and the diluted Changjiang River waters, with high turbidity and low transparency in the region, limit the
Fig. 5. Monthly mean WIND, SST and MLD in the study region for year 2011–2015. 5
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Fig. 6. Comparison mean CHL time series among the three different sub-regions throughout a year.
mainly in southeast direction rather from southwest direction during blooming season (Fig. 4(a)), less enriched open sea water would flow into the middle regions during 2015, and enriched Changjiang River water would have less chance to flow into the regions (Fig. 7(b)). CHL in southern region had significant changes (Fig. 8(c)). Year 2011 had about one and half months peak period from early April to late May. During August and September, year 2011 had two diurnal peaks. The diurnal peaks would most likely relate to the summer monsoon (Mackey et al., 2017). Year 2012 had much earlier peaks right started from beginning of February. The second larger peak was in early March. However, year 2012 had even higher winter peaks in November. Different from northern and middle regions, year 2015 had highest autumn peak in middle of October lasted for one week. Year 2013 and 2014 had generally lower profiles throughout the year. The reasons could be due to the badly P-limited coastal waters in the region as well as the wind directions changes (refer to Fig. 4(a) for year 2014 winter mainly southeast direction and Fig. 7(d) showing the lower spatial distributions in year 2014 winter season). Although CHL in southern region was generally low comparing to northern region during the first half year (Fig. 6), the bloom periods were longer for year 2011 and earlier for year 2012 (Fig. 8(c)). The longer and earlier bloom seasons are probably due to the warmer SST during spring season while SST and CHL were positive correlated during spring (Qu et al., 2006; Qu et al., 2014; Qu et al., 2018). During autumn and winter, CHL were much higher down south. The warmer currents down south bring out rich nutrients from bottom layers of water and coastal waters, the different currents confluence together enriched local phytoplankton biomass.
phytoplankton growth in the upper layer (Zhou et al., 2004). From October to end of December, southern region CHL came back to the top due to relevant warmer SST during winter season where rising temperature is the main drive for the phytoplankton development especially in coastal regions (Trombetta et al., 2019). Spatial mean CHL images in phytoplankton blooming season (April–May) and winter season (October–December) are compared for year 2011 (Fig. 7(a), (c)), 2015 (Fig. 7(b)) and 2014 (Fig. 7(d)). The same satellite data resource is used (MODIS Aqua 4 km). During phytoplankton blooming season, the obviously higher CHL appeared in northern region in Bohai Sea. Year 2011 was higher in northern and middle regions. The influence of the Changjiang River discharge shows in the north of 34°N for year 2011 and less influence for year 2015. General high CHL appeared in the southeast coast (30–33°N, 122–126°E) in Zhejiang coastal region especially in year 2015. The two of the richest fishing grounds in the world are located in this southern coastal region (Huang et al., 2019). It was reported that carbon and nutrient supplies are rich in the southern region especially near the east coast of Zhejiang province. However the southern region (ECS) is severely phosphate P-limited (Huang et al., 2019). The increase of badly needed P from the Taiwan Strait is more than that from the Changjiang River (Huang et al., 2019). The coastal acidification was induced by remineralization of biogenic particles. These biogenic particles were supplied either by coastal red tides or by near-shore marine aquaculture (Yang et al., 2011a; Liu et al., 2014). Winter CHL was generally lower due to lower SST and limited light, especially in the middle region where Changjiang River located (Fig. 7(c), (d)). However, there is relevant high CHL along the coast, especially for year 2011. CHL exhibited totally different profiles in the three sub-regions (Fig. 8). In the northern region (Fig. 8(a)), 2015 mean CHL had its highest peak in late April. Year 2012 had double peaks during blooming season. Year 2014 had its late higher double peaks from early May to late May. Year 2013 had several peaks from early April to late May. Year 2015 still had lowest profile in late half year. Middle region shows different profiles (Fig. 8(b)) Year 2012 had the highest CHL peak in late March and second peak around 21st April. Year 2011 also had higher CHL peak in early April. Year 2013 had longer peak during 3th -15th May. However, year 2015 was lower in general. Year 2011 had even higher winter CHL than the northern region. The elevated CHL in middle region in 2011 and 2012 related to the Changjiang River discharge and increased coastal water nutrients. One reason of lower profiles in the middle regions in year 2015 is due to the wind direction's changes. In 2015, the mean wind direction was
3.3. AOD distributions AOD is a function of sea salt, mineral dust, organic compounds, as well as non-sea-salt sulphate and MSA, which is derived from oxidation of atmospheric DMS (Gabric et al., 2005). Gabric et al. (2005) studied correlations between CHL and AOD in Southern Ocean, and found that there was a strong correlation between CHL and AOD in the band of 50°S to 60°S, where sea-ice melting and associated release of biogenic sulfur species contributed to the aerosol concentration. In our study region, AOD had average higher concentration in year 2012 in the study region (not show in the figure). The mean AOD in the 5 years in study region is general higher in spring and lower from summer towards winter. AOD in the three sub-regions are shown in Fig. 9. In general, southern region had higher AOD especially during winter time. Northern region had the lowest AOD. However, from early 6
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Fig. 7. Spatial mean CHL for April–May period comparison between year 2011 (a) and 2015 (b) in the study region (from MODIS-Aqua MODISA–L3m-CHL v2018 CHL monthly 4 km in https://giovanni.gsfc.nasa.gov/giovanni/, unit: mg/m3, Equidistant cylindrical projection with linear scale and smooth option are used for the plotting).
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Fig. 8. Mean CHL time series in (a) the northern study region; (b) the middle study region; (c) the southern study region.
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Fig. 9. Mean AOD in the three sub-regions.
June to early July, AOD had higher profile in middle region where usually summer enrichment and stratification occurred near the Changjiang River mouth. In general, AOD was higher down south and lower up north. Year 2015 had lower AOD due to less influenced from Changjiang River (not in the figure).
with reasonable deep of MLD plus nutrients from coastal water and the Changjiang River (can be indicated by higher AOD) can make a significant difference for the phytoplankton biomass in the region. 4. DMS concentrations and sea-to-air fluxes in EMSC 4.1. DMS concentrations
3.4. Relationships between CHL, AOD and WIND
Simó and Dachs (2002) formula is used to calculate the regional DMS (unit is nanomoles (nM)) for different years). In the formula, DMS is derived according to two different scenarios:
The seasonal correlations between CHL and AOD are listed in Table 1. From July to September, the correlations are all negative. The relationships are always positive for the following periods: Jan-Mar, Apr-Jun and Oct-Dec. During CHL blooming season, CHL and AOD had almost positive correlations, although there is a lag between them. Summer AOD had negative correlations with CHL (Jul-Sep). In the middle region where the sea waters are more influenced by the Changjiang River waters, CHL and AOD had quite strong positive correlation in the first half year especially during CHL blooming season. Winter AOD had reasonable good positive correlations with CHL, especially in southern and northern sub-regions (correlation coefficients were >0.7). The correlations are all significant (with p < 0.01). From previous results, CHL is higher up north and lower down south (Fig. 6) while mean AOD was lower up north and higher down south (Fig. 9). The peaks of CHL were always a few weeks after peaks of AOD. That means AOD influenced CHL with a time lag of half month to more than one month (not show in the figure). WIND was stronger in the northern region (Fig. 3). There was obviously negative correlation between AOD and WIND. The correlation coefficient is −0.274 (with p < 0.01). Therefore, with increase of WIND, AOD would reduce accordingly. Our results show WIND is an important factor on controlling phytoplankton biomass in our study region. With relative low WIND along west coast where shallow water locates, it can increase the stability of water column and favorable for the growth of phytoplankton (Rey et al., 1987). However, it was reported that higher WIND in deep water could bring nutrients to the surface and also helped the growth of phytoplankton (Zheng et al., 2012). The combination of WIND and SST
DMS =
DMS = 55.8(CHL/MLD) + 0.6 if CHL/MLD
North Middel South
Jan–Mar
Apr–Jun
Jul–Sep
Oct–Dec
0.53 0.42 −0.14
0.76 0.84 0.75
0.06 0.96 0.77
−0.33 −0.67 −0.2
0.71 0.44 0.77
0.02
(2)
CHL and MLD time series (with 8-day time interval) are calculated from Satellite data. DMS is calculated according to the formula (1) and (2) for the 5 different years. DMS, CHL and MLD profiles in year 2011 is shown in Fig. 10. MLD was higher in spring (>140 m) and decreased after 20th March and reached to minimum in June and July (<20 m). CHL increased from late March and reached to its peak in 13th April. The increase of CHL accompanied with decrease of MLD. DMS had its first peak in 13th April (the same as CHL) and then increased to its second peak in 25th May. The slight increment of CHL in 28th July led the significant increase of DMS in 11th August. The peak of DMS in 2011 was in middle August. It is coincident with the field measurement from Ocean University of China (OUC, the cross sign in the figure) (Zhang et al., 2014). In July 2011, DMS average value is 5.415 nM from our calculation, which is close to the real mean data from OUC (5.3 nM) (Zhang et al., 2014). DMS mean time series (with 8-days time interval) is calculated. Generally, the peak months are in July and August. Due to lower mean CHL in year 2015, lower DMS profile almost throughout the year comparing to the 5 years mean values (not show in the figure). Same as CHL profile, DMS in year 2011 had higher winter values than other years. DMS ranged from 0.32 to 5.64 nM with a mean of 2.19 nM for the 5 years. The correlations between CHL and DMS for the 4 different time periods are listed in Table 2, where p values are calculated and most correlations are significant, apart from Jan-Mar period for year 2012, 2014 and 2015 (p > 0.05). There are no obvious correlations for the whole year. However, there are more significant correlations for the shorter periods. The most correlated period is during June and August. In general, DMS and CHL had more correlations during summer and autumn periods.
Table 1 The correlation coefficient between CHL and AOD for different time periods. Whole year
(1)
ln(MLD) + 5.7 if CHL/MLD < 0.02
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Fig. 10. Mean DMS, CHL and MLD profiles in year 2011 in the study region.
are within 33.9–59 μmol m−2d−1. The range of DMS sea-to-air flux is from 2.15 to 59 μmol m−2d−1.
4.2. DMS sea-to-air fluxes in EMSC The sea-to-air fluxes of DMS calculation is essential to understand the regional cycle of biogenic sulfur and its effect on the Earth's radiation. The most commonly used DMS sea-to-air flux calculations are from Liss and Merlivat (1986), Wanninkhof (1992) or Nightingale et al. (2000) (Liss and Merlivat, 1986; Wanninkhof, 1992; Nightingale et al., 2000). Liss and Merlivat's (1986) method for the calculation of DMS sea-to-air flux is used here:
4.3. DMS concentration and sea-to-air flux validations from the field data During July 2011, seawater samples were collected on board in EMSC about 60 stations by research group in OUC (Zhang et al., 2014). They found that diatoms occupied the dominant positions in EMSC. There are positive correlations between DMS and CHL. During this period, field data shows that DMS ranges from 0.63 nM to 41.19 nM with average value of 5.3 nM in the whole study region. The highest CHL concentrations are found in Changjiang River estuary and the periphery of the Hangzhou Bay (Zhang et al., 2014) which is close to our spatial mean satellite image (Fig. 8). They found that the main reason of elevated CHL is due to the upwelling in this region. The upwelling phenomenon replenished nutrients to support the growth of phytoplankton in the surface waters. The lowest DMS is found further east of Changjiang River Estuary (around A (124°E, 31°N)). The high nutrient showed in southeast of Hangzhou Bay (around B (122°E, 29°N)) in Zhejiang coastal waters. The reasons of lower CHL concentrations around A location are mainly due to the terrestrial runoff, high turbidity and low transparency in the estuary (Zhou et al., 2004). As a result, the concentrations DMS were also reduced. DMS concentrations are compared from our calculated data with the field data from OUC (Table 3) (Zhang et al., 2014; Zhang et al., 2017). Our calculated DMS in July 2011 (5.415 nM) is very close to the field DMS mean (5.3 nM) (Zhang et al., 2014). Our result is only increased 2.17%. However, in October 2015, Our calculated mean DMS in October 2015 (2.77 nM) is quite low comparing to the same time period mean field data (3.63 nM) (Zhang et al., 2017). Our result is 23.7% lower than the field data. Due to the different data calculation methods, range data is not comparable. OUC field data is from individual measurements in some special locations. Our range data is from spatial
(3)
FluxDMS = k w DMS
where kw is the DMS sea-to-air transfer velocity. It is determined by WIND (w) (at 10 m above sea surface) and SST.
k w = 0.17 w for w k w = (2.85w kw = (5.9w
(4a)
3.6
10.6) + 0.612 for 3.6
w
49.91) + 0.612 for w > 13 where
=
(4b)
13 2 (600/Sc ) 3 ,
=
2 (600/Sc ) 1 ,
(4c)
and
Sc = 3628.5
243.58(SST ) + 7.801(SST )2
0.1148(SST )3
(5)
Here Sc is the DMS Schmidt number. Monthly profiles of DMS, transfer velocity kw and DMS sea-to-air flux in year 2011 are shown in Fig. 11(a). DMS sea-to-air flux had similar increase trend to DMS concentration and also reached to peak in August. There is a second flux peak in November when transfer velocity increased. Monthly mean DMS sea-to-air flux is calculated for 2011–2015 in the study region (Fig. 11(b)). DMS flux had peaks in August and the highest peak was in year 2012. Year 2015 had least DMS sea-to-air flux due to its lower DMS concentrations throughout the year. DMS sea-to-air flux are within range of 14–22 μmolm−2d−1. Minimum values are within 2.15–5.2 μmol m−2d−1, maximum values Table 2 Correlations between CHL and DMS in the study region. 2011 Whole Year Jan-May Jun-Aug Sep-Dec
−0.178 (p 0.302(p < 0.940(p < 0.532(p <
< 0.01) 0.01) 0.01) 0.01)
2012
2013
0.061(p < 0.01) 0.475*(p > 0.05) 0.980(p < 0.01) 0.920(p < 0.05)
0.155(p 0.595(p 0.730(p 0.923(p
10
< < < <
0.01) 0.01) 0.01) 0.01)
2014
2015
0.098(p < 0.01) 0.669*(p > 0.05) 0.858(p < 0.01) 0.908(p < 0.01)
−0.013(p > 0.01) 0.539*(p > 0.05) 0.957(p < 0.01) 0.693(p > 0.01)
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Fig. 11. (a) Mean DMS concentration, DMS flux and transfer velocity in the study region for year 2011. (b) Monthly mean DMS sea-to-air flux in the study region for year 2011–2015.
mean ranges. The only meaningful data for comparison is mean data, although the mean data is also from different angles. OUC mean data is averaged from daily mean of individual measurements of some special locations, our mean data is monthly spatial mean for all the grid in the region. OUC data stations are not so evenly spread out like our grid data. To our surprise, year 2011 had rather closed mean DMS for both field DMS and calculated DMS. Hence, Simó's formula (Simó and Dachs, 2002) can be used in our study region to get reasonable results and not too far away from the reality. The differences of DMS sea-to-air flux between our calculation using
Table 3 DMS concentrations comparison with the field data (unit: nM). DMS in July 2011
Range Monthly peak Mean Rate changed
DMS in October 2015
OUC field data
Our data
OUC field data
Our data
0.63–41.19
– 5.415 5.415
0.96–9.64
– 3.96 2.77
5.3 +2.17%
3.63 −23.7%
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and direction of monsoon winds can affect the trajectory of aerosols from the continent to the marine environment, hence influence the phytoplankton biomass.
Table 4 DMS air-sea flux comparisons with the field calculated data (unit: umolm−2d−1).
Range Mean Rate changed
DMS flux in July 2011
DMS flux in October 2015
OUC field data
Our data
OUC field data
Our data
0.03–102.4 16.73 +23%
5.2–48.61 20.57
0.31–33.7 12.97 +8%
3.72–33.93 14
5. Conclusions The EMSC environmental forcings are studied and the relationships among them are analyzed. Our study region was divided into three subregions (northern, middle and southern) for more detailed study. In general, cloud cover was between 40%–60% with more cloudy in winter and spring and less cloudy in summer and autumn in the study region. WIND was higher up north and lower down south. Wind directions were mainly between southeast direction down south and southwest direction up north. Peak SST occurred in August. Year 2015 had lower SST in southern region. SST and WIND had negative correlations. CHL had peak period in April. CHL was higher near shore and lower off shore. During first half year, CHL were higher up north and lower down south. The reasons are due to relevant cooler temperature and southeasterly wind up north, brought enriched Changjiang River waters northwards, and favored phytoplankton growth. However, during autumn and winter seasons, CHL was higher in southern region, lower in middle region, year 2011 and 2012 had higher CHL in spring in the middle region, due to the more enriched Changjiang river discharge and increased coastal water nutrients. Year 2015 had the lowest CHL profile and year 2013, 2014 had lowest winter profiles. The major reason could be due to the southeastern wind from Open Sea, brought sea water with less nutrients to the region. Badly P-limited southern coastal water is another reason. Year 2011 had much higher CHL winter profile in middle region and much higher and longer spring CHL down south. The highest CHL concentrations are mainly located along west coast of the ECMS near Zhejiang coastal waters, southeast of Hangzhou Bay, where most fish farmers located (Yang et al., 2011a). CHL and AOD had quite strong positive correlation in the first half year especially during CHL blooming season. Winter AOD also had reasonable positive correlations with CHL, especially in southern and northern sub-regions. The highest positive correlations between CHL and AOD are located in the middle region where Changjiang River mouth is. AOD influenced CHL. The peaks of AOD were ahead of CHL peaks about half or one month more. With the WIND increased, AOD reduced accordingly. This again proved the less AOD up north due to the higher WIND in the region. Year 2015 had lower CHL as well as lower AOD in general, mainly due to the southeast direction wind. MLD was higher up north and lower down south and higher in winter and lower in summer. DMS concentrations and DMS sea-to-air flux are calculated for the study region using Simó's (Simó and Dachs, 2002) formula and DMS flux calculation scheme is from Liss and Merlivat (1986). The comparisons are made for July 2011 and Oct. 2015 with OUC field data. The reasonable agreements are made. Our mean DMS concentration in July 2011 and DMS sea-to-air flux in October 2015 are quite close to OUC field data. DMS had high positive correlations with CHL in the summer and autumn periods. Our results show that DMS ranged from 0.32–5.64 nM with a mean of 2.19 nM for the 5 years. DMS sea-to-air flux ranged within 2.14–59.06 μmolm−2d−1 with mean of 18.96 μmolm−2d−1. Elevated CHL, DMS and DMS sea-to-air flux in east coast of EMSC during year 2011 to 2015 indicate that the EMSC region cannot be ignored for the global DMS flux emission evaluation. Hence, our study region is a relatively important region that will have impact on the local climate and even global climate change. More study is urged for detailed DMS model simulation and DMS sea-to-air flux predictions based on the OUC field data.
Liss and Merlivat's scheme and the calculation from OUC based on their field data are compared (Table 4). The minimum DMS sea-to-air fluxes calculated by OUC are very small comparing to our calculated minimum values. However, year 2015 maximum DMS fluxes are close between our calculation (33.93 μmolm−2d−1) and OUC field data (33.7 μmolm−2d−1). It is an excellent matching for the peak values in October 2015. The difference is only 0.7%. The mean values are also not far away (8% difference). However, the maximum DMS flux in July 2011 had quite big difference. The mean values had 23% difference. The different DMS flux calculation methods also caused different results. OUC DMS flux used parameterization of Nightingale et al. (2000). If they also use the same method from Liss and Merlivat (1986), the flux of DMS would range from 0.01 μmol m2/day to 74.7 μmol m2/day (Zhang et al., 2014). Hence, the maximum value difference would be reduced. In general, our calculated DMS and DMS flux are not too far away from the field data. Some values had very good matching. 4.4. Discussions CHL was lower down south and higher up north in our study region. Apart from the factor of SST, other CHL inflencing factors such as WIND, MLD and AOD are discussed. The severely P-limited ECS region (southern region) and declined dissolved oxygen and PH value in the northern region (Bohai Sea) coastal area were the other important factors influences phytoplankton biomass in ECMS (Liu et al., 2014; Huang et al., 2019). It was also found that summer Kuroshio subsurface water is rich in phosphate which can transport the phosphate continuously from the east of Taiwan to the area off the mouth of the Changjiang River where harmful algal bloom occurs frequently in summer (Zhang et al., 2007; Yang et al., 2011a). It was reported that phytoplankton blooms are triggered by upwelling especially in open or deep-coastal zones which provides nutrients from deep nutrient-rich water (Zhang et al., 2014; Zhang et al., 2017; Trombetta et al., 2019). The upwelling phenomenon potentially increases nutrient inputs and thus bloom event. Higher tidal amplitude can influence CHL timing and magnitude (Trombetta et al., 2019). Eutrophication of Chinese coastal waters is manifested by increasingly frequent dinoflagellate blooms (Lu et al., 2005), and intensification of spring diatom blooms in ECS. Increasing ratio of nitrogen to phosphorus (N:P) blooms including severe P limitation and trace metal micronutrient inventories observed in ECS coastal (Mackey et al., 2017). Spring blooms were mainly associated with diatom blooms, where diatom growth was correlated with elevated trace metal and nutrient content from aerosols (Mackey et al., 2017). Aerosol had no much effect on phytoplankton growth in the Changjiang River estuary area where CHL was the lowest (Zhang et al., 2014; Zhang et al., 2017). The diatom-dominated spring blooms have a tendency of transition to more frenquent spring and summer dinoflagellate blooms in ECS (Mackey et al., 2017). Numerous researchers have focused on eutrophication caused by Changjiang River discharge, which brings substantial anthropogenic nutrient input into the East China Sea. Strong seasonal signals of monsoon effect are observed in ECS (Wang et al., 2001; Mackey et al., 2017), with winds coming from the southeast in the summer monsoon phase and from the northwest during the winter phase. The strength 12
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Acknowledgements
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Special thanks to several students: Rui Wang, Yang Yang, Zheng Liu, Shan Yan and Zhiwei Xu for calculating Cloud Cover, AOD, CHL, SST, MLD data and DMS flux in the study region. Sincerely thanks should go to NASA Ocean Biology Processing Group and Goddard Space Flight Centre of SeaWiFS Project group for providing MODIS CHL, AOD satellite data. Thanks should also go to NOAA NCEP EMC CMB GLOBAL ReynSmithOIv2 for providing weekly sea-ice concentration data. Special thanks go to the Naval Research Laboratory Remote Sensing Division, the Naval Center for Space Technology, and the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Integrated Program Office (IPO) for providing satellite-based wind speed, sea surface temperature data. Thanks to NASA Goddard Earth Science (GES) and Information Services Center (DISC) team for providing the Cloud Cover data. Special thanks to Giovanni EARTHDATA site (https:// giovanni.gsfc.nasa.gov/giovanni/) from NASA GESDISC Channel for providing CHL spatial visualization pages. Again, thanks to Dr. David L. Carroll from CU Aerospace, for the genetic algorithm Fortran code. Thanks also should go to Gleb Panteleev and Jacob Stroh from International Arctic Research Center, UAF (University of Alaska) for providing the Arctic CTD Database (for MLD calculations). We gratefully acknowledge the National Natural Science Foundation of China (Funding No. 41276097) for providing research funding for this project. References Charlson, R.J., Lovelock, J.E., Andreae, M.O., Warren, S.G., 1987. Oceanic phytoplankton, atmospheric sulphur, cloud albedo and climate. Nature 326, 655–661. Gabric, A.J., Shephard, J.M., Knight, J.M., Jones, G., Trevena, A.J., 2005. Correlations between the satellite-derived seasonal cycles of phytoplankton biomass and aerosol optical depth in the Southern Ocean: evidence for the influence of sea ice. Glob. Biogeochem. Cycles 19, GB4018. https://doi.org/10.1029/2005GB002546. Huang, T.-H., Chen, C.-T.A., Lee, J., Wu, C.-R., Wang, Y.-L., Bai, Y., Yang, Y.J., 2019. East China Sea increasingly gains limiting nutrient P from South China Sea. Sci. Rep. 9 (1), 5648. https://doi.org/10.1038/s41598-019-42020-4. 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. https://doi.org/10.1002/lno.10737. Jing, Z.Y., Hua, Z.L., Qi, Y.Q., Cheng, X.H., 2007. Numerical study on the coastal upwelling and its seasonal variation in the East China Sea. J. Coast. Res. 50, 555–563. Lana, A., Bell, T.G., Simo, R., Vallina, S.M., Ballabrera-Poy, J., Kettle, A.J., Dachs, J., Bopp, L., Saltzman, E.S., Stefels, J., Johnson, J.E., Liss, P.S., 2011. An updated climatology of surface dimethlysulfide concentrations and emission fluxes in the global ocean. Glob. Biogeochem. Cycles 25, GB1004. https://doi.org/10.1029/ 2010GB003850. Liss, P.S., Merlivat, L., 1986. Air-Sea Gas Exchange Rates: Introduction and Synthesis. P. Buat-Menard. Reidel, Hingham, MA. Liu, Y., Peng, Z., Zhou, R., Song, S., Liu, W., You, C.-F., Shen, C.-C., 2014. Acceleration of modern acidification in the South China Sea driven by anthropogenic CO2. Sci. Rep. 4, 5148. https://doi.org/10.1038/srep05148. Lovelock, J.E., Maggs, R.J., Rasmussen, R.A., 1972. Atmospheric dimethylsulphide and the natural sulphur cycle. Nature 237, 452–453. Lu, D., Göbel, J., Qi, Y., Zou, J., Han, X., Gao, Y., Li, Y., 2005. Morphological and genetic study of Lu from the East China Sea, and comparison with some related species. Harmful Algae 4, 493–505. https://doi.org/10.1016/j.hal.2004.08.015. Mackey, K.R.M., Kavanaugh, M.T., Wang, F., Chen, Y., Liu, F., Glover, D.M., Paytan, A., 2017. Atmospheric and fluvial nutrients fuel algal blooms in the East China Sea. Front. Mar. Sci. 4 (2). https://doi.org/10.3389/fmars.2017.00002.
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