The variability of chlorophyll-a and its relationship with dynamic factors in the basin of the South China Sea

Journal of Marine Systems 200 (2019) 103230

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The variability of chlorophyll-a and its relationship with dynamic factors in the basin of the South China Sea

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Yi Yua, Xiaogang Xinga, Hailong Liub, Yeping Yuana,c, Yuntao Wanga,b, , Fei Chaia,d a

State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, China State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Beijing, China c Ocean College, Zhejiang University, Zhoushan, China d School of Marine Sciences, University of Maine, Orono, ME, United States of America b

A R T I C LE I N FO

A B S T R A C T

Keywords: Chlorophyll-a Coastal upwelling Seasonal cycle Anomalous field Monsoon South China Sea

Satellite observations from 2002 to 2017 were used to investigate the spatial distribution and temporal variability of chlorophyll-a (Chl-a) in the South China Sea (SCS). High levels of Chl-a were mostly found near the coasts of China and Vietnam, with a significantly long-term increasing trend. Large seasonal variability was found in the northern SCS and southeast of Vietnam. The Chl-a levels reached the maximum in winter and minimum in summer for majority of the SCS, while the seasonal variability to the southeast of Vietnam was out of phase. The monsoon winds and sea surface temperatures were the most important determinants impacting the distribution and variability of Chl-a along with other associated and influential environmental drivers, e.g., wind stress curl, frontal activity, and sea level anomalies. High correlation coefficients for the seasonal variability between Chl-a and other factors were found in a majority of the SCS, especially in the northern and central parts. The coefficients to the southeast of Vietnam were not valid at seasonal scales, but Chl-a and other factors were significantly correlated at the monthly anomalous fields. This observation occurred because the nutrient supply was mainly determined by local dynamics, e.g., wind-induced coastal upwelling and offshore transport. The interannual variability indicated low levels of Chl-a southeast of Vietnam during El Niño years because of the weakened southwest monsoon. The study offered the first comprehensive description of Chl-a in the SCS at seasonal, anomalous, and interannual variability scales and an analysis of the potential contributing dynamical processes.

1. Introduction Chlorophyll-a (Chl-a) has been widely used as the most important descriptor of phytoplankton biomass because it is strongly coupled with phytoplankton carbon (Ryther and Yentsch, 1957). Both in situ measurements (Boss and Behrenfeld, 2010) and satellite observations (Behrenfeld and Falkowski, 1997; Boyce et al., 2010) have revealed that Chl-a is a reliable proxy for assessing changes in phytoplankton biomass. The development of satellite remote sensing technologies and the evolution of the Chl-a retrieval algorithm (Maritorena et al., 2002; Hu et al., 2012) helped in understanding the features of Chl-a. Substantial volumes of research literature have described the spatial distributions of Chl-a in different regions, e.g., over the globe (Gregg et al., 2005; Siegel et al., 2013), in boundary currents (Legaard and Thomas, 2006), in coastal zones (Gohin et al., 2008; Philippart et al., 2010), in estuaries (Liu et al., 2010) and in upwelling regions (Liu et al., 2012); the

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corresponding temporal variability from the diurnal (e.g., LeBouteillier and Herbland, 1982; Mercado et al., 2006), seasonal (e.g., HolmHansen et al., 2004) to interannual (e.g., Signorini et al., 2015) scales have also been well documented. At the global scale, Chl-a distribution is largely controlled by largescale ocean circulations (Signorini et al., 2015). Diverse physical drivers dominate or modulate the Chl-a variability, which is particularly true for mesoscale dynamics. For instance, increasing variation in Chl-a levels was associated with the increasing variability of the energy of mesoscale eddies in coastal oceans (Piontkovski et al., 2012). Kahru et al. (2007) found that eddy pumping played an important role in determining the Chl-a variability in the northeastern tropical Pacific. Fronts, defined as the boundary between different water masses, are mesoscale processes that are mostly characterized by convergence (Greer et al., 2015); frontal zones are usually associated with high Chl-a and biomass levels (Signorini and McClain, 2007; Wang et al., 2015a).

State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, China E-mail address: [email protected] (Y. Wang).

https://doi.org/10.1016/j.jmarsys.2019.103230 Received 14 June 2019; Received in revised form 14 August 2019; Accepted 25 August 2019 Available online 29 August 2019 0924-7963/ © 2019 Published by Elsevier B.V.

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of Vietnam is responsible for Chl-a enhancement (Liu et al., 2002). The high-nutrient water coming from river discharges (south of Vietnam) and upwellings supports the growth of phytoplankton (Gao and Wang, 2008). The offshore jet and anticyclonic circulation transport coastal water with high nutrient levels eastward into the SCS basin (Li et al., 2017). As the high-nutrient water moves offshore, the grazing pressure is reduced, and the Chl-a levels become high (Chen et al., 2014). Furthermore, interannual climate oscillations can also influence the Chl-a level variability in the SCS. For example, low Chl-a levels were observed in the SCS after Eastern Pacific El Niño events (Racault et al., 2017), which was particularly true for the region southeast of Vietnam (Gao et al., 2013). The summer monsoon and coastal upwelling decreased following recent El Niño events, and phytoplankton was generally low (Liu et al., 2012). A significant negative correlation was identified between Chl-a levels and the multivariate ENSO index (MEI), with Chl-a lagging by 9 months (Tang et al., 2011). Boyce et al. (2014) estimated that the global Chl-a levels have declined by > 60% over the past century and that the trend would continue into the future. However, they also discovered large differences in the global oceans, e.g., the Chl-a level in the SCS was predicted to increase. To the best of our knowledge, there has been limited documentation describing the trends of the Chl-a levels in the SCS, and there is great uncertainty among different studies (Loisel et al., 2017). Additionally, there are large spatial variations within the SCS, which have not been thoroughly studied (Palacz et al., 2011); the previous studies on the variability in the SCS were limited to research on specific dynamic processes, e.g., mesoscale eddies, typhoons, upwelling, or mixing (e.g., Tang et al., 2014). However, an overview of the Chl-a levels through long-term observations at high resolution throughout the SCS basin is not yet available. In this study, we investigate the Chl-a distribution trends over the past one and a half decades and describe the seasonal and anomalous variability and associated underlying physical mechanisms. Section 2 describes the dataset and methods used in this study, followed by results in Section 3. The major findings are discussed in Section 4.

Wind-induced mixing can enhance Chl-a levels by entraining additional nutrients into the upper ocean in most tropical and subtropical areas (Kahru et al., 2010). Nababan et al. (2011) found higher Chl-a levels near the coasts induced by river discharges and wind-driven upwelling, and there were anomalously high Chl-a levels from 1997 to 1998 in the northeastern Gulf of Mexico that coincided with a strong El Niño event and an unusually strong upwelling. Moreover, a substantial amount of literature describes the response of regional Chl-a levels to basin-scale climate indices, e.g., the Pacific decadal oscillation (PDO) can impact the distribution of Chl-a in the basin of the Pacific Ocean (Martinez et al., 2009). Waliser et al. (2005) quantified the influence of the Madden-Julian oscillation (MJO) on Chla variability in the tropical Indian and Pacific Oceans. They found that large-scale disturbances could alter the wind patterns and consequently offer an important indicator for predictions within the fishing industry. The El Niño Southern Oscillation (ENSO) impacts the global growth of phytoplankton via changes in temperature, wind and sea surface heights (Wilson and Adamec, 2001; Racault et al., 2017). Satellite remote sensing provides an important approach not only to estimate the global primary productivity but also to promote more studies of physical-biological coupling processes, marine ecosystems, and carbon cycles at both global and regional levels. The South China Sea (SCS) is a semi-closed basin that is connected to the Pacific Ocean through the Luzon Channel and the Indian Ocean via the Strait of Malacca. The SCS is identified as a typical oligotrophic region where primary productivity is limited by nutrient availability (Tang et al., 1999). Over the past two decades, numerous studies have been conducted in the SCS to advance the understanding of regional biological processes and their related physical drivers. Many factors, as described above, were found to influence the variability of Chl-a levels; however, their individual importance and interrelationships in the basin of the SCS are not well known (Xian et al., 2012), and the variation at different temporal scales has not yet been investigated. The production and Chl-a levels of the SCS are mainly limited by the availability of nutrients (Pauly and Christensen, 1993; Gao et al., 2013). With abundant solar radiation in the SCS, Chl-a levels increase as the nutrients are transported into the euphotic layer (Chen, 2005). The SCS is unique because of its wind system and the associated interaction with the topography, which largely determines the regional dynamics. Monsoon winds are the most dominant factor that impacts the regional circulation and dynamics and subsequently determines the seasonal cycles of Chl-a levels (Palacz et al., 2011; Tang et al., 2014). In winter, the northeast monsoon is stronger than that in other seasons, and the basin is characterized by a cyclonic gyre (Wu et al., 1998). The resultant upwelling and strong mixing deepen the mixed layer depth (MLD) (Qu et al., 2007) and induce more nutrients into the euphotic layer (Chen et al., 2006); thus, the corresponding Chl-a levels reach the annual maximum (Zhang et al., 2016). During summer, the monsoon changes to a southwesterly monsoon, and the intensity is comparably weak (Liu et al., 2013b). The shoaling of the MLD and weak mixing result in low Chl-a levels throughout the basin (Zeng et al., 2016). The coeffect of wind and topography on Chl-a levels is prominent in certain regions. For example, to the northwest of Luzon Island, the winter monsoon can induce upwelling and carry river discharges offshore (Gao and Wang, 2008). At the same time, the intrusion of the Kuroshio Current induces cyclonic eddies and increases upwelling (Wang et al., 2012), which brings more nutrients to the surface (Chen et al., 2006). In the northern section of the Luzon Chanel, anticyclonic eddies trap low-nutrient water from the Kuroshio Current (Zhang et al., 2017), which is usually less favorable for supporting the growth of phytoplankton (Xiu et al., 2016). However, anticyclones can increase mixing as they start to decay, and biological productivity subsequently increases (Chen et al., 2015). Guo et al. (2017) found that the front of the Kuroshio intrusion induces upwelling, which causes the Luzon bloom. The upwelling induced by summer monsoons off the southeast coast

2. Data and methods Daily Chl-a observations were obtained as L3 data from the Moderate Resolution Imaging Spectroradiometer onboard NASA's satellite EOS-Aqua (MODIS). The temporal coverage from October 2002 to September 2017, ranging over 15 years, was used in this study. The spatial resolution was 4.5 km, and observations < 5 km from the coast were eliminated to avoid the influence from land. Observations obscured by clouds were removed from the dataset. The Chl-a data were logarithmically transformed because the data had a log-normal distribution (Campbell, 1995; Siegel et al., 2013). The monthly Chl-a time series was calculated using a 60-day running mean filter, which included 30 days before and after the 15th of each month, to reduce the impact of cloud coverage. The overall mean Chl-a level at each pixel was subtracted before applying an empirical orthogonal function (EOF). The locations where missing observations exceeded 20% were removed in the EOF following the example of Wang et al. (2015a). The first four modes of the EOFs explained > 60% of the total variance. To investigate Chl-a trends, the original daily Chl-a data were smoothed with a 365-day running mean filter at each grid point. The filter effectively eliminated the seasonal variability and high-frequency signals, following the example of Thomson and Emery (2014). Long-term trends were subsequently calculated using linear regression to obtain a smoothed time series for the Chl-a data (Signorini et al., 2015). Wind speed was obtained from the ERA-Interim reanalysis product, which is the latest global atmospheric reanalysis dataset developed by the European Center for Medium-Range Weather Forecasts (ECMWF, Dee et al., 2011). The spatial resolution of the product is ¼°, and data from October 2002 to September 2017 were used in this study, 2

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levels were characterized by different area extents. For example, high Chl-a levels in the Beibu Gulf extended > 150 km from the coast, while high values to the northeast of Vietnam were limited to areas < 50 km offshore. Chl-a levels were also high in the Taiwan Channel, although the intensity was weaker than that in the coastal zones. Chl-a levels were low around Luzon Island and in the Luzon Channel, even though these areas are close to the island coasts. Notably, low Chl-a levels were mainly found in the basin center of the SCS and east of the Luzon Channel. Significant linear trends over the past 15 years were identified in some coastal regions. Large positive trends of Chl-a levels were found in the Beibu Gulf, east of Vietnam and Hainan Island, in a narrow band off Guangdong Province and to the southwest of the Taiwan Channel (Fig. 1). This condition was particularly true in the southwest of the Taiwan Channel, where the trend was larger than 0.04 mg/m3 per year. A continuous region near the entire coast of Vietnam showed weak increases in Chl-a levels up to 200 km offshore. Palacz et al. (2011) found that the increase in the Chl-a level was due to the intensification of the local wind speed in the region. The trends in the regions directly near the coast were not significant. The variability of the Chl-a level in the Beibu Gulf was determined by tidal mixing along with other dynamical factors (Hu et al., 2003). The long-term intensification of wind in the region led to a trend with increasing Chl-a levels (Yu et al., 2019). Negative trends were found in the eastern basin of the SCS, especially to the west and southwest of Luzon Island, although the corresponding rate was comparatively small. The largest decreasing trend was found in a small frontal zone near west Hainan Island, where tidal mixing and wind induced upwelling had been identified (Hu et al., 2003).

consistent with the data on Chl-a levels. A more detailed description of the ERA-Interim reanalysis products can be found in Dee et al. (2011). Wind stress (WS) and wind stress curl (WSC) were calculated as follows:

→ τ = ρC→ u ∙ |→ u| τ = ∇×→

∂τy ∂x

−

(1)

∂τx ∂y

(2) → → where u is the wind speed vector, τ is the WS and its direction is the same as the direction of the wind speed vector, τx and τy are the eastward and northward components of WS, respectively, ρ is the air density above the sea surface, and C is the drag coefficient for the neutral stability condition (Hellerman, 1965). Because no gaps exist in the wind field, monthly WS was defined as a 30-day average for each month. The alongshore WS was calculated as a vector component of WS in the direction of the nearest coastline. The direction was obtained for each location of the coastline by fitting a straight line to the coastline with a distance of < 100 km. Wind data < 200 km offshore were then averaged to represent the local alongshore wind (Wang et al., 2015a). For each pixel of Chl-a data, the corresponding alongshore WS was based on wind observations at the nearest coastline. The corresponding WSC was calculated as the average of the wind observations that were within 100 km. The sea surface temperature (SST) data were also obtained from MODIS for the same period. The spatial resolution was 4.5 km, and the daily SST data were used. Because of limited cloud impacts compared with Chl-a observations, monthly SST series were calculated as 30-day averages for each month. SST fronts were derived using the modified gradient method at daily intervals (Wang et al., 2015a). The monthly frontal probability (FP) was defined at each pixel as the ratio between the number of times the pixel was identified as a front and the number of times the pixel was cloud-free in the month. The daily sea level anomaly (SLA) data used in this study were obtained from the Copernicus Marine and Environment Monitoring Service (CMEMS). The delayed time all-sat (DT all-sat) product was used for improved precision. The spatial resolution was 25 km, and the temporal range was at daily intervals from 2002 to 2017 (Ducet et al., 2000). The SLA was computed as the difference between the instantaneous sea surface height and a reference, which was the average over 20 years (1993–2012). A detailed description of the data processing can be found in Schaeffer et al. (2012). The SLA data were spatially high-pass filtered with half-power filter cutoffs of 20° longitude by 10° latitude over the global ocean. This process removed the SLA variability associated with large-scale Rossby waves and removed steric heating and cooling effects (Liu et al., 2013a). Positive (negative) SLAs represented the sea surface height that was higher (lower) than the average. The spatial grid for the SLA was interpolated to the same grid as the wind data. The possible impacts of basin-scale climate indices on interannual variability of Chl-a were investigated. The MEI was downloaded (https://www.esrl.noaa.gov/) and used in this study. The index was calculated based on six main observed variables, including sea level pressure, zonal and meridional wind, SST, surface air temperature, and total cloudiness fraction of the sky over the tropical Pacific Ocean. The MEI was available at monthly intervals, and the data from 2002 to 2017 were used; a detailed description can be found in Wolter and Timlin (2011).

3.2. Seasonal distribution and variability of Chl-a The seasonal distribution of Chl-a (Fig. 2) aligned with the general overall pattern of its averages, although clear seasonal variability could be observed. During winter, the region was characterized by the largest seasonal average, especially for the northern SCS, e.g., the coast of China and northwest of Luzon Island. The regions with high Chl-a levels had similar spatial patterns compared to the overall averages, but the intensity was stronger than the average. The lowest levels of Chl-a were identified during spring, and the region with high Chl-a levels was limited to a very narrow band near the coast in the northern SCS. The mean Chl-a levels for the entire region slightly increased in summer, although the average Chl-a level for the basin was still low. High Chl-a levels were found to the south of Vietnam, covering a large region offshore. A weak but visible jet of high Chl-a levels was found to originate from the southeast of Vietnam extending eastward to the ocean basin. The Chl-a levels increased in autumn, especially for the region near the coast of China and to the northwest of Luzon Island. The variability of Chl-a was further investigated using the EOF method (Figs. 3 and 4). The first mode of the EOF, accounting for 42% of the total variance, described large portion of the Chl-a variability. The magnitude of the variability was generally the same throughout the area, with large values in the northern SCS, the Beibu Gulf and to the east of Vietnam (Fig. 3a). Corresponding temporal evolution was characterized by strong seasonal cycles with peaks in January and troughs in June (Fig. 4). The positive phase with a large amplitude lasted for only five months, while the negative phase occurred over seven months was relatively weak. Thus, the northern SCS was generally characterized by persistently low Chl-a levels, except with a strong enhancement between November and March. The regions with small values indicated a different temporal pattern of Chl-a variability, which will be captured in other EOF modes. The second mode of the EOF (12%) was mostly described by the variability of Chl-a levels in the region south and southeast of Vietnam (Fig. 3b), which expanded northeast to the basin of the SCS. The largest value was identified at the lee side south of Vietnam. A weak but prominent signal was also observed at the mouth of the Pearl River extending east. The

3. Results 3.1. Average field and the overall trend of Chl-a distribution The overall mean distribution of Chl-a (Fig. 1) showed that high Chl-a concentrations (> 2 ≈ 100.3mg/m3) were generally found near the coast, especially off the coast of China, Northern and Southern Vietnam, and around Borneo Island. These regions with high Chl-a 3

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Fig. 1. Total average Chl-a levels (left) and the linear trend (right) over 15 years. The Chl-a levels are on a logarithmic scale, but the trend is shown in regular scale. The major locations are labeled in the left figure (PR: Pearl River). The 200-m isobath is plotted as a bold black contour. Regions where the linear trend was not significant at the 95% confidence level were masked in the right figure.

4. Dynamical factors related to the variability of Chl-a

corresponding time series revealed predominant seasonal variability in Chl-a with maximum (minimum) values in August (April) in the regions with positive magnitudes. There was clear difference, e.g., both for the spatial and temporal pattern, between the first two modes of EOFs. Thus, the method successfully distinguished the variability at different regions, and the corresponding dynamics will be discussed in next section. The third mode of the EOF described 3.3% of the total variance in the coastal regions of Guangdong, Hainan Island and east Vietnam and the offshore region southeast of Vietnam (Fig. 3c). The magnitude near the coast occupied large regions extending 200 km off Guangdong and 100 km off Vietnam. The variability in a large area southeast of Vietnam was captured by EOF3, exhibiting an opposite phase compared with the other regions. The time series of the third mode mainly captured interannual signals associated with weak semiannual variability. The time series was positive for only four months, with two recognizable peaks in June and November, while negative phase lasted for two periods, from January to April and from June to August, but with weak amplitude. The fourth mode of the EOF was also included in this study and described the Chl-a level variability near Guangdong and Vietnam (Fig. 3d). Although the mode contributed only 3% of the total variance, the local variance explained by EOF4 was > 40% if only the Pearl River area was considered (not shown). The monthly mean of the time series revealed semiannual variability with peaks in January and July (April and October) for regions with positive (negative) magnitudes. The variability in the Chl-a levels near Guangdong Province was impacted by the Pearl River discharge; the highest Chl-a levels have been found to be associated with large river discharges (Shen et al., 2008).

Previous studies have described multiple dynamic factors that influence the variability of Chl-a levels over the globe. In this study, we focused on investigating how different factors influenced the Chl-a levels in the basin of the SCS. The parameters investigated in this study include SST, WS magnitude, WSC, frontal activities, and SLA. SST is an important physical characteristic of the ocean, relating to stratification, average light exposure, and circulation, along with other processes. These processes were expected to impact the growth of phytoplankton and Chl-a concentrations. Strong correlations between SST and Chl-a were found for a majority of the SCS basin (Fig. 5a), especially for the region west of Luzon Channel, where the correlation reached more than −0.8. Both Chl-a and SST were dominated by seasonal cycles with opposite phases (Zeng et al., 2016), and the highest correlation was reached without any lags. This result was consistent with a substantial number of former observations (e.g., Zhang et al., 2016). During summer, the SST reached the annual maximum, and the corresponding MLD was the shallowest (Zeng et al., 2016). The strong stratification blocked the mixing between surface and subsurface waters, leading to low nutrient amounts supplied into the euphotic layer (Chen, 2005; Xian et al., 2012). Thus, the growth of phytoplankton was highly limited, and low Chl-a levels were observed. In contrast, the MLD was deeper during winter when low SST induced weak stratification, and the Chl-a level was high in the basin. Wind-driven mixing, which is approximately gauged by WS (Qu et al., 2007), plays an important role in driving vertical mixing. Because the wind pattern generally develops ahead of Chl-a levels, the time 4

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Fig. 2. Seasonal distribution of Chl-a levels in winter (January to March), spring (April to June), summer (July to September) and winter (October to December).

correlation was weak in the southern SCS, although it was still significant at the 95% confidence level. The Chl-a levels to the southeast of Vietnam and east of the Pearl River estuary were not significantly correlated with WS, indicating that other processes impacted the distribution of Chl-a. The influence of WSC on Chl-a was also investigated with wind measurements leading by one month. Significant correlations were found in most parts of the SCS basin (Fig. 5c), although they showed opposite signs. A large correlation coefficient (larger than 0.7) could be identified from the tip of northwest Luzon Island, extending southwest

series for wind was advanced one month before the Chl-a measurement (Fig. 5). The result was mostly identical when a zero lag was applied; thus, it is not shown here. The WS and Chl-a levels were strongly related, and the correlation coefficients were as large as 0.8 (Fig. 5b). A strong correlation was found in the northern SCS, especially west of the Luzon Channel, where the winter WS was strongest in the entire basin. Shen et al. (2008) found that stronger winds induce more mixing and deepen the MLD; thus, there were more nutrients to enhance Chl-a. The increase (decrease) trend in Chl-a levels was expected to be related to the strengthened (weakened) upwelling (Chen et al., 2006). The 5

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Fig. 3. The amplitudes of the first four EOFs for Chl-a levels.

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Fig. 4. The time series (left) and monthly average (right) of the first four EOFs for Chl-a levels. The explained variance was labeled for each mode.

of Luzon Island. Our results revealed a strong correlation between the WSC and Chl-a levels, but the relationship varied widely for different locations. Nutrient levels are often increased and associated with elevated Chla levels in a frontal zone (Greer et al., 2015). This study was the first to seek the relationship between frontal activity and Chl-a level variability in the basin of the SCS. The correlation between a monthly time series of FP and Chl-a levels (Fig. 5d) showed that they were highly correlated throughout the eastern SCS. This result was particularly true for the region west of Luzon Island, southeast of Vietnam, and in the northern Beibu Gulf. The corresponding correlations were > 0.5, indicating that increased Chl-a levels were associated with more fronts (Hu et al., 2003). No significant or weak correlation was found in other regions;

up to a few hundred kilometers. In the center of the SCS and to the southeast of Vietnam, the Chl-a levels were significantly correlated with WSC with coefficients > 0.4. In the northern SCS, the Chl-a level was negatively correlated with WSC, except to the west of Taiwan and the Hainan Islands. There was a band with nonsignificant correlations that extended southwestward from the Luzon Channel to the east of Vietnam. This band was identified as the region where winter WS was largest and WS strongly correlated with Chl-a levels (Fig. 5b); thus, the WSC was close to zero. A former study used a dataset from two Bio-Argo floats and found that the Chl-a level was not related to the WSC (Zhang et al., 2016). However, the trajectories of the two floats were exactly within the band where the correlation between the Chl-a level and the WSC were not significant, i.e., west of the Luzon Chanel and southwest

Fig. 5. The correlation coefficient between Chl-a levels and SST, wind stress (WS), wind stress curl (WSC), frontal probability (FP), and sea level anomaly (SLA). Locations with nonsignificant correlations are plotted in gray. The green box in the left panel indicates the region used to calculate the correlation coefficients between different variables for Table 1. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) 7

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thus, the local frontal activity was not a major force driving the seasonal variability of Chl-a levels. The SLA is related to local dynamics, such as vertical transport and eddy activities, which can influence the distribution of Chl-a (Xian et al., 2012; Chen et al., 2014). The winter monsoon induced positive WSC, which drove basin-scale upwelling (Qu et al., 2007). The dense upwelled water can depress sea levels and enhance the Chl-a levels, and negative correlations are expected. At the mesoscale, cyclonic (anticyclonic) eddies can drive upwelling (downwelling) and are subsequently associated with depressed (elevated) sea surfaces (Chelton et al., 2011). During winter, the northeast monsoon was favorable for driving the cyclonic eddy near Luzon Island, and the low SLA was associated with high Chl-a levels (Wang et al., 2012). Indeed, a negative correlation between SLA and Chl-a was identified in an area extending southwestward from the Luzon Chanel to the basin of the SCS (Fig. 5e). In the Beibu Gulf, the winds drove an Ekman transport onshore (Jing et al., 2016), which piled up near the coast and increased the sea levels. Though the onshore transport was less favorable for transporting nutrients, the high SLA was accompanied by high Chl-a levels because of seasonal variability, and a positive correlation developed. The Chl-a level in the region southeast of Vietnam was not significantly correlated with SST, WS, or SLA at the seasonal scale, and its correlations with WSC and frontal activities were weak (Fig. 5; Tang et al., 2014). This region is known for its coastal upwelling (Liu et al., 2002), which largely determines its dynamics. Thus, the time series of alongshore WS and Chl-a levels was assessed to identify their relationship. The Chl-a level was found to be significantly correlated with the alongshore WS in the upwelling zone to the southeast of Vietnam (Fig. 6a). The correlation coefficient gradually decreased offshore, which may be because the Chl-a level induced by upwelling was initially limited to the coast and extended further distances through multiple processes, e.g., current and eddy processes (Chen et al., 2014). In regard to the temporal period required for Chl-a to extend offshore, different time lags were tested between alongshore WS and Chl-a levels. The map of the lags where the correlation coefficients were the highest is plotted in Fig. 6b. The WS led the Chl-a by one month in the upwelling region, and the lag increased gradually for the surrounding regions. The speed of offshore extension, V, was calculated using the following equation:

V=

∑ Di ∑ Li

(3)

where i denotes each pixel along the 11°N position from the coast to 800 km offshore, and Di and Li denote the offshore distance and time lag, respectively. The calculated speed was 0.04 m/s, which was consistent with the coastal upwelling intensity during local summer (with an Ekman depth equal to 100 m, Castelao and Wang, 2014). Therefore, the wind-induced upwelling and the Ekman transport were the major drivers of Chl-a level variability and its offshore movement off the southeast of Vietnam. The variability of Chl-a levels in regions with lags longer than 6 months, e.g., northeast Vietnam and near Borneo Island, was out of phase with the alongshore WS. In addition, the corresponding Chl-a variability should be determined by other processes, as mentioned earlier. The seasonal cycle of Chl-a was highly correlated with SST, wind, and SLA for the majority of the SCS, and the variability of Chl-a levels to the southeast of Vietnam was shown to be dominated by the upwelling and offshore transport induced by alongshore WS. The correlation between Chl-a and other factors, e.g., SST, fronts, WS and WSC, was calculated in the fields of monthly anomalies to investigate their dynamic relationship. The fields of monthly anomalies of each factor were obtained by subtracting the monthly average from the corresponding time series. This procedure increased the effective number of degrees of freedom without removing any true relationships between the time series (Wang et al., 2015a) because the seasonal variation in the signal was never purely harmonic (Chelton, 1982). A significant correlation was identified between the anomalous Chla levels and the anomalous SST in a majority of the SCS (Fig. 7a). This correlation revealed that anomalously high (low) SSTs were associated with anomalously low (high) Chl-a levels, which is consistent with the findings from Tang et al. (2011). Similarly, abnormally high (lower) WSC (Fig. 7c) and frontal activities (Fig. 7d) can induce increased (decreased) Chl-a levels southeast of Vietnam. In addition, the anomalous SLA and Chl-a levels were negatively correlated (Fig. 7e) to the east of Vietnam. The significant correlation can only be observed without any lags, indicating that the SST anomalies, WSC, fronts, and SLA simultaneously impacted the Chl-a levels. Previous studies have found that anomalous Chl-a was significantly correlated with anomalous WS and SST (Liu et al., 2013b). However, these studies considered

Fig. 6. (Left) The correlation coefficient between Chl-a levels and alongshore wind stress with wind leading by one month. Locations with correlations less than −0.3 are plotted in gray. The correlations < 0.21 were not significant. (Right) The lags (wind leading) between Chl-a levels and wind stress when their correlation reached a maximum and significant. Regions where no significant correlations ever existed are plotted in gray. 8

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Fig. 7. Same as Fig. 5 but for fields of monthly anomalies. The green box in panel (a) indicates the region used to calculate correlation coefficients between different variables for. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) Fig. 8. The time series (left) of the EOF3 anomalies for Chl-a levels (black) and the MEI (blue). The MEI lagged by six months. Correlation coefficients (right) between the time series of EOF3 anomalies and the MEI with different lags (MEI leading). The solid dots represent correlations that were significant at the 95% confidence level. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

the Chl-a levels (Zhang et al., 2016). On the other hand, the upwelling brought cold and deep water to the surface layer, which decreased the surface temperature, and convective mixing deepened the MLD and cooled the ocean surface (Zeng et al., 2016). Thus, in addition to the impact on Chl-a, strong wind also induced cold SSTs via multiple dynamics. At the same time, upwelled or mixed cold water was characterized by high densities, which depressed the SLA (Chelton et al., 2011). As a result, the high Chl-a level was associated with low SST and SLA, strong WS (WSC), and high frontal activity (Fig. 5). Because the prominent monsoon pattern was associated with the seasonal cycle of solar radiation, the strongest (weakest) wind in winter (summer) was accompanied by the lowest (highest) SST (Liu et al., 2013b). Thus, the seasonal variability of the system was strongly coupled. The strong intercorrelation made it difficult to distinguish the individual impact of each process, and each factor could be used to explain the variability of Chl-a to some extent. The largest correlations for Chl-a and other factors were consistently found in the areas west of Luzon Chanel, consistent with Xian et al. (2012). The intercorrelation coefficients among monthly time series and the corresponding anomalies were calculated in Table 1 where bold numbers indicated they were significant at 95% confidence level (p < 0.05). The correlation coefficient between Chl-a and other factors was usually higher than the intercorrelation among those factors. For example, the correlation between SST and WS was −0.46, which was much less than the correlations between Chl-a and SST (−0.80) and between Chl-a and WS (0.79). This finding revealed that the coupling between Chl-a and other factors was strong and that each factor can independently impact Chl-a to some extent. The majority of the SCS basin showed strong coupling among Chl-a and other factors discussed in the paper, although a substantial spatial difference could be identified (Fig. 5). For instance, Chl-a was correlated with SST, WS, and FP with the same sign throughout the basin,

the SCS as a single average, and their approaches could not resolve the relationships in different regions. Our results indicated that the correlation between the anomaly of WS and the anomaly of Chl-a levels was weak, and it was significant for only limited areas, e.g., northwest of Luzon Island and in the central basin (Fig. 7b). The impact of large-scale ocean signals was analyzed by comparing Chl-a levels with the MEI. The amplitudes of the EOFs anomalies were correlated with the MEI using different lags, but most of the results were not statistically meaningful. Only EOF3 was found to be significantly correlated with the ENSO signal at a 95% confidence level. The largest correlation coefficient was 0.44 when the MEI led EOF3 of Chl-a by 6 months (Fig. 8). The time series showed abnormally low (high) Chl-a levels southeast of Vietnam (along the coast of Guangdong) in 2010 and between 2016 and 2017 when El Niño events started a half year earlier. After the El Niño events, the summer monsoon and coastal upwelling southeast of Vietnam decreased (Gao et al., 2013); thus, the corresponding Chl-a level was low. However, the favorable wind corresponding to the upwelling event along the coast of Guangdong was dramatically enhanced (Jing et al., 2011; Xiu et al., 2018), which elevated the Chl-a levels. An abnormally high Chl-a level southeast of Vietnam was observed in late 2007, which was found to be related to the impact of a positive Indian Ocean Dipole and La Nina (Liu et al., 2012).

5. Discussion The variability of Chl-a levels was highly correlated with ocean dynamics, e.g., SST, WS (WSC), frontal activities, and SLA (Fig. 5). However, these factors were intercorrelated. For example, the wind played an important role in influencing the vertical transport and mixing of water columns (Qu et al., 2007), which would impact the nutrient distribution (Chen et al., 2006) and subsequently determine 9

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impacted by El Niño. An anticyclonic (cyclonic) wind anomaly was generated in the southern (northern) SCS during the El Niño years (Jing et al., 2011; Yan et al., 2015). As a result, for the region southeast of Vietnam, the summer monsoon and upwelling became weaker (Gao et al., 2013), and few nutrients were available for the growth of phytoplankton (Racault et al., 2017). Indeed, our results indicated that low Chl-a levels lagged El Niño events by 6 months. Tang et al. (2011) found that the MEI led the offshore and coastal variabilities of Chl-a by nine months and one month, respectively. In addition, the upwelling was enhanced off the coast of China (Jing et al., 2011; Xiu et al., 2018); thus, corresponding increased Chl-a levels were observed (EOF3 in Figs. 3 and 4). The variability of the Chl-a levels near the mouth of the Pearl River was highly complex and was only briefly described here because it was not the main focus of the current study. Although the suspended sediment transported by river discharge can impact the accuracy of satellite measurements (Hu et al., 2012), in situ Chl-a observations showed good agreement with satellite data (Loisel et al., 2017). The majority of river discharge occurred during the southwestern monsoon from April to September (Chen and Chen, 2006). During the summer, river discharge and an upwelling coastal jet transported nutrient-rich water along the coast to the east (Gan et al., 2010). Our results captured the eastward expansion of Chl-a during the summer, while the simultaneous Chl-a levels to the west of the mouth of the Pearl River reached its annual minimum (EOF 2&4). During the period when the summer monsoon switched to (September to November) or developed from (March to May) a winter monsoon, the local wind was weak with varying directions; thus, the overall impact was less prominent (Chen et al., 2017). The Chl-a levels west of the Pearl River were high because the river discharge advected westward under the influence of the Coriolis effect (Zu et al., 2014). Weather events, such as typhoons, have been observed to induce phytoplankton blooms in the SCS over the past few decades (Lin et al., 2003). The corresponding intensities of these blooms were strong with short durations (Shang et al., 2008), and the associated intraseasonal variability of Chl-a levels can be clearly observed in the basin (Zheng and Tang, 2007). Because the monthly averaged Chl-a levels will filter out signals with short periods (Wu et al., 2005), the intraseasonal variability of the Chl-a levels cannot be resolved in the current study. Additionally, the satellite observations were confined to the surface of the ocean. However, subsurface features can be quite different and require in situ observations for describing the three-dimensional patterns. Future studies focusing on the high-frequency and three-dimensional variability of Chl-a are important for a better understanding of regional biological processes. It is clear that Chl-a levels vary not only with phytoplankton carbon biomass but also with environmental conditions such as light, temperature, and nutrient availability (Geider et al., 1998); thus, there are some debates regarding the accuracy of Chl-a estimations. Phytoplankton cells adjust their intracellular Chl-a contents in response to changes in light conditions (Falkowski and Laroche, 1991; Behrenfeld et al., 2005). This condition is particularly true in subtropical gyres, where remarkable decoupling was found for the seasonal cycles of Chl-a levels and phytoplankton biomass (Behrenfeld et al., 2005; Barbieux et al., 2018). Wang et al. (2015b) evaluated the biological responses of phytoplankton cells in the SCS, and their results showed that Chl-a could be used as a proxy for phytoplankton biomass for the majority of the SCS. However, phytoplankton biological processes might impact the accuracy of using Chl-a to represent biomass. More in situ observations are needed to consolidate the relationship between Chl-a and biomass in the SCS.

Table 1 Correlation coefficients (r, calculated by Pearson's method) of the time series for different variables (SST: sea surface temperature, FP: frontal probability, WSC: wind stress curl, WS: wind stress magnitude) in the region northwest of Luzon Island (see Fig. 5 for the location). The top right section shows correlations for the total monthly averages; the bottom left section shows the correlations for the anomalous monthly averages. The gray font indicates that the correlation was not significant at the 95% confidence level. Chl-a Chl-a

SST

WS

WSC

FP

SLA

−0.80

0.78

0.67

0.74

−0.71

−0.47

−0.51

−0.79

0.86

0.63

0.51

−0.38

0.52

−0.37

SST

−0.41

WS

0.32

0.04

WSC

−0.00

0.08

−0.02

FP

0.21

−0.09

0.03

0.15

SLA

−0.25

0.42

0.07

0.13

−0.74 -0.08

indicating a monotonic relationship. The correlations between Chl-a and WSC and between Chl-a and SLA in the southeast and northwest were the opposite. This finding was mainly because the largest wind intensity was found in winter and was 500 km offshore and parallel with the coast of China (Chu et al., 1997). Thus, the WSC (Qu, 2000) and Ekman pumping (Wang et al., 2015a) on each side were out of phase. Although they were significantly correlated with Chl-a in the northwest, the local dynamics was not major driver for the variability of Chl-a; instead, their seasonal cycle contributed to the large correlation. Additionally, Chl-a was not correlated with SST, wind, or SLA for the region southeast of Vietnam (Table 2). Instead, the local Chl-a was strongly determined by dynamic processes, such as upwelling and advection. The summer monsoon to the north and northeast can induce offshore Ekman transport and coastal upwelling (Liu et al., 2002), which brings nutrient-rich deep water to the surface layer (Gao and Wang, 2008; Castelao and Wang, 2014). As more nutrients were brought to the euphotic zone, the Chl-a level was anticipated to increase (Chen, 2005). The dynamic impact was more obvious in the fields of monthly anomalies. High Chl-a levels were found when SST and SLA were anomalously low, and the WSC and frontal activities were anomalously high (Fig. 7). In winter, the coastal zones off Vietnam were characterized by downwelling-favorable winds, which did not have a significant effect on local production (Chen et al., 2014). This study is the first to resolve the anomalous variability of Chl-a levels in southeast of Vietnam and offers a comprehensive description of the Chl-a levels in the SCS. The interannual variability of Chl-a was mainly identified in the southeast of Vietnam and off the coast of China, which were largely

Table 2 Correlation coefficients of the time series for different variables in the region southeast of Vietnam (see Fig. 7 for the location). The top right section shows the correlations for the total monthly averages; the bottom left section shows the correlations for the anomalous monthly averages. The gray font indicates that the correlation was not significant at the 95% confidence level. Chl-a Chl-a

SST

WS

WSC

FP

SLA

−0.15

0.36

0.35

0.26

−0.15

−0.48

0.61

0.07

0.17

−0.14

−0.02

0.10

0.53

−0.21

SST

−0.59

WS

0.25

−0.24

WSC

0.29

−0.10

0.41

FP

0.57

−0.42

0.24

0.29

SLA

−0.30

0.54

−0.23

−0.29

6. Summary

−0.42 −0.47

The spatial distribution and temporal variability of Chl-a levels in the SCS basin were investigated using satellite observations. High Chl-a 10

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levels were mostly found near the coast and to the northwest of Luzon Island with large seasonal variability. The Chl-a levels in the northern region peaked in the winter, while southeast of Vietnam, the Chl-a levels peaked in summer. The distribution and variability were impacted by different dynamic processes, and the impacts of corresponding factors on Chl-a levels were subsequently investigated. The seasonal variability of Chl-a was strongly correlated with SST, WS, WSC, frontal activity, and SLA for the northern and central SCS. The relationship, however, was not valid to the southeast of Vietnam because coastal upwelling and other dynamics were predominant. Indeed, significant correlations were identified between Chl-a and other factors in the field of monthly anomalies. The current study offered a comprehensive description of Chl-a levels and the corresponding dynamics throughout the SCS. The most striking finding was the quantification of the relationship between alongshore WS and offshore propagation of Chl-a southeast of Vietnam. The nutrients induced by coastal upwelling were carried by Ekman transport, advection and anticyclonic circulation eastward to the basin of the SCS. In the future, three-dimensional observations of Chl-a should allow for the analysis of how physical processes enhance and decrease the Chl-a levels.

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Acknowledgments We are very thankful to the National Aeronautics and Space Administration (NASA) for sharing the satellite dataset (https://podaactools.jpl.nasa.gov/), the European Center for Medium-Range Weather Forecasts (ECMWF) for releasing the ERA-Interim reanalysis product, the Copernicus Marine and Environment Monitoring Service (CMEMS) for producing sea level anomaly (SLA) data and the National Oceanic and Atmospheric Administration (NOAA) for producing the MEI (https://www.esrl.noaa.gov/psd/enso/mei/). The study was supported by the National Key Research and Development Program of China [no. 2016YFC1401601], the Scientific Research Fund of the Second Institute of Oceanography, SOA [no. JB1806], the National Natural Science Foundation of China [no. 41806026, 41890805, 41806041 and 41730536] and the Project of State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography [no. SOEDZZ1902, SOEDZZ1601]. References Barbieux, M., Uitz, J., Bricaud, A., Organelli, E., Poteau, A., Schmechtig, C., et al., 2018. Assessing the variability in the relationship between the particulate backscattering coefficient and the chlorophyll a concentration from a global biogeochemical-Argo database. Journal of Geophysical Research: Oceans 123, 1229–1250. https://doi.org/ 10.1002/2017JC013030. Behrenfeld, M.J., Falkowski, P.G., 1997. Photosynthetic rates derived from satellite-based chlorophyll concentration. Limnol. Oceanogr. 42 (1), 1–20. Behrenfeld, M.J., Boss, E., Siegel, D.A., Shea, D.M., 2005. Carbon-based ocean productivity and phytoplankton physiology from space. Glob. Biogeochem. Cycles 19, GB1006. https://doi.org/10.1029/2004GB002299. Boss, E., Behrenfeld, M., 2010. In situ evaluation of the initiation of the North Atlantic phytoplankton bloom. Geophysical Research Letter 37, L18603. Boyce, D.G., Lewis, M.R., Worm, B., 2010. Global phytoplankton decline over the past century. Nature 466, 591–596. Boyce, D.G., Dowd, M., Lewis, M.R., Worm, B., 2014. Estimating global chlorophyll changes over the past century. Prog. Oceanogr. 122, 163–173. Campbell, J., 1995. The lognormal distribution as a model for bio-optical variability in the sea. J. Geophys. Res. 100 (C7), 13237–13254. Castelao, R.M., Wang, Y., 2014. Wind-driven variability in sea surface temperature front distribution in the California current system. Journal of Geophysical Research: Oceans 119 (3), 1861–1875. Chelton, D.B., 1982. Large-scale response of the California current to forcing by the wind stress curl. CalCOFI Rep 23, 130–148. Chelton, D.B., Gaube, P., Schlax, M.G., Early, J.J., Samelson, R.M., 2011. The influence of nonlinear mesoscale eddies on near-surface oceanic chlorophyll. Science 334 (6054), 328–332. Chen, Y.-L., 2005. Spatial and seasonal variations of nitrate-based new production and primary production in the South China Sea. Deep-Sea Res. I 52 (2), 319–340. Chen, Y.L., Chen, H., 2006. Seasonal dynamics of primary and new production in the northern South China Sea: the significance of river discharge and nutrient advection. Deep-Sea Res. I 53 (6), 971–986. Chen, C.-C., Shiah, F.-K., Chung, S.-W., Liu, K.-K., 2006. Winter phytoplankton blooms in

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