The dominant spatial patterns and temporal variations of colored dissolved and detrital materials in the South China Sea were presented by Sea-viewing Wide Field-of-view Sensor derived data from September 1997 to December 2007. Seasonal and interannual variations of colored materials in the South China Sea were discussed for the first time. Results showed that high values of these materials were mainly within the regions with large mixing and large-scale upwelling induced by a seasonally reversing monsoon (e.g. Gulf of Tonkin and northwest of Luzon Island in winter, southeast of Vietnam in summer, etc.) and in coastal regions, particularly in the estuaries of the rivers in summer (e.g. Pearl River and Mekong River). By contrast, low values were observed in the regions of downwelling (northwest of Luzon Island in summer) and deep basin regions in the South China Sea all throughout the year. This variability is highly correlated with that of chlorophyll a, except for at some costal regions, e.g. the Pearl River estuary, where the colored materials seldom co-vary with chlorophyll-a over an established threshold. High concentration of colored material will reduce photo-biological processes at these regions in some months. The main South China Sea is still below the critical value, when comparing with the thresholds of the seasonal colored material concentrations in the Sea. Such conclusions may be useful for the management of the South China Sea environments.
Colored dissolved organic matter, which is also referred to as yellow matter (gilvin and gelbstoff) plays an important role in the light-induced biogeochemical cycling of many compounds (Siegel et al., 2002; Coble, 2007) that determines the amount and spectral quality of light available for marine photo processes. Thus, such substances badly affect the growth of phytoplankton and other aquatic organisms in the marine system (Jerlov, 1968; Bricaud et al., 1981; Siegel et al., 2002; Du et al., 2010) and therefore, colored dissolved organic matter has long been considered a dynamic quantity with important roles in ocean photochemical and photobiological processes, particularly in coastal and estuarine environments (Siegel et al., 2002; Du et al., 2010). Satellite imagery from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) can be used to characterize the global distribution of light absorption due to colored detrital and dissolved materials (CDM), which represent the quantity of colored dissolved organic matter on a routine basis (Siegel et al., 2002). Therefore, the satellite-derived global-scale or local-scale CDM distributions have been widely studied during different terms (Siegel et al., 2002; Du et al., 2010; Ortega-Retuerta et al., 2010). In this work, we focused on the South China Sea (SCS) as our region of study.
The SCS, extending from the equator to 23°N and from 99°E to 121°E, is known to be one of the largest marginal seas in the world. General surface circulation is cyclonic in winter and anticyclonic in summer because of the annual change of monsoon wind direction (Hu et al., 2000). Monsoon-driven upwelling occurs to the northwest of Luzon Island during winter, and off the east coast of Vietnam during summer (Hu et al., 2000; Chen et al., 2006; Tang et al., 2006; Tan and Shi, 2009; Qiu et al., 2012; Xian et al., 2012; Yang et al., 2012; Zhao et al., 2012). The surface currents and upwelling may also be affected by the passage of typhoons, as well as the El Niño-South Oscillation (ENSO) variation. These processes have important effects on biogeochemical cycles in the upper water column (Chen et al., 2006; Zhao and Tang, 2007; Sun et al., 2010). In addition, the SCS receives significant inputs of nutrient-rich water from the Pearl and Mekong Rivers (Ning et al., 2004). Thus, a number of factors, acting on different timescales, can affect the upwelling in, and the nutrient balance of, the SCS.
Several studies on the spatial and temporal variation of phytoplankton have been conducted in the SCS (Tang, et al., 2006; Gao and Wang, 2008; Tan and Shi, 2009). The spatiotemporal characteristics of phytoplankton are relatively clear in some typical regions of the SCS (Tang et al., 1999; Ning et al., 2004). By contrast, the long-term distributions of CDM in the SCS have seldom been studied, except for some field surveys (Hong et al., 2005; Guo et al., 2007); the spatiotemporal variations are still unexplored and unclear all over the SCS.
The objective of this article is to explore the general CDM distributions as the first step of future studies on the CDM variations, mechanisms and impacts in the SCS. The dominant spatial patterns and temporal variations of CDM in the SCS were examined in this work using recent 10-year (September 1997–December 2007) SeaWiFS-derived data. Spatiotemporal variations of CDM were then illuminated, and the possible driving factors of such variability were also analyzed. The results will be useful for the managements of SCS environments.
Data, methods, and study area
Satellite CDM and chlorophyll a (Chl-a) observations were obtained from SeaWiFS data beginning September 1997 to December 2007, which were generated using the Garver-Siegel-Maritorena (GSM) model on 9 × 9 km grids in monthly averages (http://wiki.icess.ucsb.edu/measures/index.php/GSM; Maritorena et al., 2002). The magnitude of CDM was presented by the CDM absorption coefficient at 443 nm [acdm(443)]. To evaluate the interannual variability in CDM dynamics, we calculated monthly CDM anomalies following the method of Ortega-Retuerta et al. (2010). Positive CDM anomaly values were found to represent higher CDM concentration than usual, and vice versa.
Based on the anomalies of CDM and Chl-a, we found that in some cases that CDM and Chl-a seldom co-vary. There is a critical value, above which the positive CDM anomaly is associated with a negative Chl-a anomaly. The threshold of CDM on photo-biological process was estimated according to these cases.
Sea surface wind vector (SSW) data with a spatial resolution of 25 × 25 km from July 1999 to December 2007, were obtained from the monthly QuikSCAT provided by the Remote Sensing Systems (http://www.remss.com/). The sea surface wind stress (TAO) was calculated with the bulk formula (Garrett, 1977) using QuikSCAT-derived SSW at a height of 10 m. And the monthly sea surface wind data on a 1° × 1° grid between January 1997 and December 2007 provided by Woods Hole Oceanographic Institution (ftp://ftp.whoi.edu/pub/science/oaflux/data_v3) were utilized to assess ENSO impact on the anomaly of CDM. Sea surface temperature (SST) data with a spatial resolution of 9 × 9 km from September 1997 to December 2007 was then obtained from the monthly AVHRR Pathfinder provided by the Physical Oceanography Distributed Active Archive Center (http://podaac.jpl.nasa.gov/DATA_CATALOG/sst.html) of NASA. The Multivariate ENSO Index (MEI) data from the Physical Sciences Division are available at http://www.esrl.noaa.gov/psd/data/climateindices/list/.
The area of study was mainly the region at 6–23°N, 102–120°E selected for the detailed analysis of CDM variations in the SCS (Figure 1a). Seven small sampling boxes representing several typical regions according to previous studies were also selected (Figure 1a, Table 1). S1 was located in a much deeper basin (Tan and Shi, 2009), whereas S2 was located northwest of the Luzon Island, where there is large-scale vertical mixing and upwelling in winter (Tang et al., 1999; Tan and Shi, 2009; Qiu et al., 2012; Xian et al., 2012; Zhao et al., 2012). S3 was located in the Pearl River estuary in the south of China where coastal current, upwelling, and river discharge exist (Tan and Shi, 2009). S4 was located in the Gulf of Tonkin, a semi-closed gulf northwest of the SCS experiencing reversal seasonal monsoon (Tang et al., 2003). Finally, S5, S6, and S7 were located southeast of the Indochina Peninsula which is also prominently affected by seasonally reversing monsoon and existing coastal current and upwelling (Tang et al., 2006; Yang et al., 2012).
Spatial distribution and climatology of colored dissolved and detrital materials in South China Sea
Variation coefficient (i.e. standard deviation divided by the average) distributions of CDM showed significant variability in the estuarine regions (e.g. Pearl and Mekong River estuaries), Gulf of Tonkin, and several offshore regions (e.g. northwest of Luzon Island and both sides of the Indochina Peninsula tip) as shown in Figure 1b. This indicated that these particular regions were sensitive to variations in CDM. Therefore, we selected seven regions (see Figure 1a for their locations), with six of them having high variation coefficients (Figure 1b).
Figures 1c and d showed the 10-year seasonal mean of CDM distributions in the SCS. In general, CDM values in the coastal regions were higher than those in the offshore regions and in the open sea throughout the year. However, patches of higher CDM [acdm(443) > 0.02 m−1] were observed northeast of the Indochina Peninsula tip in summer and northwest of the Luzon Island and southwest of the Indochina Peninsula tip in winter.
The seasonal variations in CDM concentrations in the selected regions (Figure 2) were obtained by integrating the monthly averaged CDM concentrations from September 1997 to December 2007. And the values of SST and TAO (Figure 2) of the corresponding regions were averaged from September 1997 to December 2007 and from July 1999 to December 2007, respectively. CDM concentrations in S1, S2, and S5 were comparatively lower than those in S3, S4, S6, and S7 (Figure 2). CDM values oscillated seasonally within a range of 0.005–0.015 m−1 between the maxima and minima at regions 1, 2, and 5 (Figure 2). By contrast, the observed seasonal amplitudes at regions 3, 4, 6, and 7 were considerably higher (0.04–0.073 m−1, Figure 2).
Figure 2 clearly demonstrates that the CDM concentrations visibly increased when TAO increased and SST decreased, except for S3 and S5. In S3, there was one dominant December–January peak with a possible secondary June–August peak of CDM values, a December–January peak of TAO, and a June–August peak of SST. In S5, there was a July–August peak of CDM and a December peak of TAO. Furthermore, the monthly CDM was significantly correlated with TAO for all seven regions (Table 1). The monthly CDM was negatively correlated with SST, whereas no significant correlations were obtained in S5 and S6 (Table 1).
Spatial and temporal drivers of colored dissolved and detrital materials in South China Sea
At a monthly temporal resolution, changes in CDM and Chl-a were synchronous at the seven regions with no lagged response (Table 1), which would indicate that their variations were controlled by the same factors. According to previous studies (Tang et al., 1999, 2003; Ning et al., 2004; Gao and Wang, 2008; Tang et al., 2006; Tan and Shi, 2009; Qiu et al., 2012; Xian et al., 2012; Yang et al., 2012; Zhao et al., 2012), seasonally reversed monsoon, episodic upwellings and river discharges influence phytoplankton biomass and productivity in the SCS by changing the transport and distribution of nutrient-rich water.
Furthermore, the elevated CDM values were observed when terrestrial discharge or deep mixing and upwelling could bring elevated subsurface CDM concentrations to the surface layer (Siegel et al., 2002). Due to locating in the open sea with a large depth (depth >4000 m), CDM in S1 had few source injections (e.g. river discharges) to elevate. In addition, the lowest CDM values were observed in downwelling regions (e.g. S1 and S2 in summer). Single peaks of CDM (Figure 2) were mainly found within the large-scale vertical mixing and upwelling regions induced by seasonally reversing monsoon (e.g. S2, S4, S5 and S7). The two peak facts in S3 were likely relative to South China coastal upwelling and current induced by northeast monsoon in winter, and also the Pearl River discharge increase and continental runoff into the SCS enhanced by the summer that rainy season brings. Coastal upwelling forced by southwest/northeast monsoon in summer/winter occurs in S6. In addition, the Mekong River discharge increases and continental runoff enhances in summer rainy season. These factors lead to very high CDM concentration in S6 from July to December. In comparison with single-peak CDM regions, it was obvious that river discharge and continental runoff had an important contribution to double-peak CDM regions which were located in estuarine regions.
In summary then, CDM seasonal distribution in the SCS was regulated by the interaction among mixing, upwelling, coastal current and river discharge.
According to previous studies, typhoons can also influence the seasonal variation of hydrological conditions in the upper water of the SCS (Lin et al., 2003; Sun et al., 2010). Hence, CDM variations can also be influenced by typhoons. Shang et al. (2008) pointed out that CDM contributed 64% to the increase in total absorption immediately after typhoon Lingling, which passed over S1 in November 2001. An extremely high value was found in S1 during November 2001 (Figure 3). An exceptionally high value that also appeared in November and December 2007 in S5 (Figure 3) was considered to be caused by typhoon Hagibis in November 2007 (Sun et al., 2010). The aforementioned analysis suggested that typhoons have an irregular and non-negligible role in the distribution and dynamics of local CDM in the SCS.
Temporal pattern of monthly CDM anomalies and MEI in the three selected regions (S1, S2 and S5) showed opposite trends between the CDM anomaly and MEI in S2 and S5 (Figure 3). Furthermore, CDM anomalies were correlated with Chl-a anomalies, SST anomalies and MEI for all seven regions (Table 1), but not significantly correlated with MEI only in S1. However, CDM anomalies were also correlated with TAO anomalies in S1, S2, S3, and S7, but not significantly in S4, S5 and S6.
Studies on CDM anomalies in single locations revealed that CDM dynamics for some regions were regulated not only by seasonal drivers (i.e. dynamics of monsoon), but also by interannual climatic forcings, such as the variation in the ENSO (Zhao and Tang, 2007; Tan and Shi, 2009). For example, 1997/1998 was the most intense El Niño year in the 20th century. The seasonal evolution of ocean surface conditions in the SCS was strongly modulated by the 1997/1998 El Niño (Figures 4a and b). Under the influence of El Niño, the weakened northeast (winter) and southwest (summer) monsoons (Figures 4a and b) caused the decrease of coastal upwelling along shore and attenuation of mixing/Ekman upwelling offshore, resulting in lower CDM concentrations (Figures 4c and d). Thus, the factor of ENSO must exert significant control on CDM annual changes in the SCS as indicated by the above.
The variation of Chl-a is the result of the competition between the dynamics process and the photo-inhibited process. CDM may play important roles in ocean photo-biological processes, particularly in coastal and estuarine environments. It is found that the positive CDM anomaly is associated with the negative Chl-a anomaly, given CDM reaching to a threshold. Take S3 as an example, the threshold of CDM concentration respectively was 0.037 m−1, 0.039 m−1, 0.05 m−1 and 0.063 m−1 in spring, summer, autumn and winter, which would start reducing photo-biological processes. For the whole SCS, such values are 0.024 m−1, 0.026 m−1, 0.03 m−1 and 0.046 m−1, respectively. Compared with the monthly CDM values and the thresholds, only coastal and estuarine CDM might reach critical values in some months. It may be concluded that the coastal and estuarine environments (e.g. the Pearl River estuary) should be under caution, but that the CDM in the SCS are still safely below the critical value.
The present analyses showed the climatological CDM distribution in the SCS. The CDM concentrations were higher in the coastal regions (e.g. Gulf of Tonkin and southeast of Vietnam) than in the open sea basin. Similar to that of Chl-a, the seasonal variations in the CDM distribution were mainly imposed by the vertical mixing and/or large-scale upwelling of the seasonally reversing monsoon, as well as the runoff of the rivers (especially in the Pearl River estuary and Mekong River estuary in summer). Moreover, the irregularly passing typhoons and ENSO may have effects on the CDM concentration. These forcing mechanisms in the CDM are similar to those in Chl-a, such that the CDM variations are significantly correlated with Chl-a, except for at some costal regions (e.g. the Pearl River estuary), where the CDM seldom co-vary with Chl-a over a certain threshold of CDM. It may be concluded that the coastal and estuarine environments (e.g. the Pearl River estuary) should be under caution, but that the CDM in the main SCS are still safely below the critical value. Such conclusions may be useful for the management of the SCS environments.
We thank GSM ocean color work Group for CDM products from SeaWiFS (http://wiki.icess.ucsb.edu/measures/index.php/GSM), the Remote Sensing Systems for the QuikSCAT wind-vector data, NASA’S Physical Oceanography Distributed Active Archive Center for AVHRR SST data, WHOI for providing OAflux data, and Physical Sciences Division (PSD) for the Multivariate ENSO Index (MEI) data. This work is supported by the Knowledge Innovation Program of the Chinese Academy of Sciences (Nos. KZCX2-YW-QN514 and KZCX2-YW-Q11–04), the National Basic Research Program of China (Nos. 2007CB816004 and 2012CB417402), The Open Fund of State Key Laboratory of Satellite Ocean Environment Dynamics (No. SOED1209), and the National Foundation of Natural Science (No. 41075041). We also thank the anonymous reviewers for their constructive suggestions.