Investigating the Relationship between Sea Surface Chlorophyll and Major Features of the South China Sea with Satellite Information

The spatial and temporal patterns of sea surface CHL in the SCS were described using satellite observations. Satellite information for CHL (Figure 1A) and SST (Figure 1B) can be contaminated by cloud coverage, resulting in a large portion of data not being usable. The reanalyzed wind (Figure 1C) and SLA (Figure 1D) data were not impacted by daily clouds. The topography (Figure 1E) had a prominent impact on the spatial distribution of CHL. High CHL was mainly distributed along the coast, where the topography is shallow. Wind was also influenced by orography, and the lee side of mountains was characterized by weak wind; thus, a prominent WSC was identified southwest of the SCS. In contrast, the SLAs did not depend much on topography, and a region of unusually high SLAs was found in the basin of the SCS.

Figure 1: Original observations for major parameters on April 15, 2015.
(A) Sea surface chlorophyll (CHL), (B) sea surface temperature (SST), (C) wind stress curl (WSC, shading) with wind stress (WS, vector), (D) sea surface anomaly, and (E) topography for the ocean basin. Please click here to view a larger version of this figure.

Because of the severe cloud impact on satellite observations, a lot of data were either not available or spatially inconsistent. An effective and efficient method was applied to fill some data gaps and smooth the field. The data were first replaced with a 3-day average at each pixel, which can effectively fill some gaps because clouds vary daily (Figure 2B). A spatial average was further applied at each pixel such that the data were replaced by the mean of surrounding values (3 x 3 pixels). Thus, the spatial inconsistency was greatly reduced (Figure 2C).

Figure 2: SST for a single day on April 15, 2015.
(A) Original SST from MODIS, (B) three-day averaged SST, and (C) SST after spatial smoothing. Please click here to view a larger version of this figure.

The daily distribution of SST fronts was derived from the SST gradients (Figure 3A). The thresholds applied here effectively captured the location of the front (Figure 3B) and ensured the depiction of the boundaries of entire water masses (Figure 3C). The gradients and fronts were nearly identical because the front were mainly obtained from the gradient.

Figure 3: Procedure for front detection derived from SST.
(A) Magnitude of the SST gradient, (B) the distribution of SST fronts in thin black lines, and (C) front distribution based on the corresponding SST gradients. Please click here to view a larger version of this figure.

Due to cloud coverage in the CHL, SST, and front data, the monthly average time series were calculated and applied in this study. A random example is shown in Figure 4 for the month of April 2015. There was no existing gap for any of the parameters. The general patterns for different parameters were highly consistent regarding their spatial variance. For example, CHL was high near the coast and low in the central basin, while the SST was low near the coast and high in the central basin. The monthly average offered comprehensive information to depict regional features. Fronts were mainly distributed along the coast, where the dynamics are complex. A large portion of the basin was free of fronts; thus, the center of the SCS was characterized by a value close to zero (Figure 4E).

Figure 4: Monthly average for major parameters in April 2015.
(A) CHL (in logarithm scale), (B) SST, (C) WSC (shading) with WS (vector), (D) sea surface anomaly, and (E) frontal probability (FP). Please click here to view a larger version of this figure.

Most surface features were characterized by prominent seasonal variability, which was clearly observed using EOFs. The EOF is a useful mathematic method that is widely used in atmospheric and marine sciences. The method can delineate spatial patterns and temporal signals from time series over spatial domains²⁸. After spatiotemporal decomposition for sea surface features in the SCS, the first two modes are generally needed for describing the spatial and temporal variabilities. The first two EOFs for CHL described 44% and 12% of the total variance, respectively. EOF1 captured a large variance in the northern section of the SCS (Figure 5A). The corresponding monthly average of the time series (Figure 5C) showed that CHL was elevated during the winter and depressed during the summer. The region next to the southwest coast was characterized by weak magnitude, and the corresponding variability was mainly captured by EOF2 (Figure 5B). CHL values were high in the summer and low in the winter. This was mainly out of phase compared with the northern section. The monthly time series for EOFs showed clear seasonal variability, and EOF2 led EOF1 by approximately 4 months (Figure 5E).

Figure 5: The EOF for CHL.
(A) Magnitude of EOF1, (B) magnitude of EOF2, (C) monthly averaged time series for EOF1, (D) monthly average time series for EOF2, and (E) monthly time series of EOF1 (black) and EOF2 (blue). Please click here to view a larger version of this figure.

The explained variance in the first two EOFs for SST was prominently high, equaling 91% and 5% for EOF1 and EOF2, respectively. It is important to emphasize that the overall average must be removed before conducting EOF; thus, the mean field was excluded. EOF1 dominated the total variance, and its magnitude was largest in the northern SCS and decreased southward (Figure 6A). The corresponding monthly average of the time series (Figure 6C) showed that the SST was elevated during summer and depressed during winter. The southern SCS was characterized by a weak magnitude, attributed to persistent high temperatures at low latitudes. The variability in the southern section was mainly captured by EOF2 (Figure 6B). The corresponding SST was enhanced between March and June, while low values persisted in the remaining months. Prominent warming occurred in 2010 and 2016, where the SST off the coast southwest of the SCS was much higher than that in the other years (Figure 6E). This interannual variability is mainly attributed to El Niño events that reduce the southwest summer monsoon and result in weak upwelling¹². Because seasonal variability is the major focus of the current study, this feature is not discussed further.

Figure 6: The EOF for SST.
(A) Magnitude of EOF1, (B) magnitude of EOF2, (C) monthly averaged time series for EOF1, (D) monthly averaged time series for EOF2, and (E) monthly time series of EOF1 (black) and EOF2 (blue). Please click here to view a larger version of this figure.

Because of the noisy nature of the gradient, the derived front explained much less of the variance. Indeed, EOF1 and EOF2 of FP only explained 19% and 9% of the total variance, respectively. EOF1 captured the variances in the north and northeast SCS (Figure 7A). The corresponding monthly average of the time series (Figure 7C) showed that in those regions, more FP occurred during winter and less during summer. The phase off the coast southwest of the SCS was the opposite, although the corresponding variability was much less prominent. EOF2 captured the spring enhancement of FP (Figure 7D) in the western SCS (Figure 7B). The monthly time series of EOF1 and EOF2 were characterized by weak interannual variability.

Figure 7: The EOF for FP.
(A) Magnitude of EOF1, (B) magnitude of EOF2, (C) monthly averaged time series for EOF1, (D) monthly averaged time series for EOF2, and (E) monthly time series of EOF1 (black) and EOF2 (blue). Please click here to view a larger version of this figure.

Different factors were investigated for their relationships with CHL (Figure 8). For example, SST can be used to understand the fundamental features of the ocean that can influence the growth rate of phytoplankton and subsequently impact CHL. For the majority of the SCS, there were high correlations between SST and CHL (Figure 8A), and most of the correlations reached more than -0.8. It is important to point out that high correlation does not indicate causation between these two factors. As SST reached its annual maximum in summer, the MLD became shallowest²¹. Nutrients supplied to the euphotic layer were low because vertical mixing was blocked by intensive stratification¹³. As a result, low nutrients limited the growth rate of phytoplankton and resulted in low CHL. In contrast, high CHL occurred in winter when the MLD was deeper, and low SST induced weak stratification³⁵.

Figure 8: Correlations between CHL and other factors at the seasonal scale.
(A) SST, (B) WS, (C) WSC, (D) FP, and (E) SLA. The gray color indicates that the correlation is nonsignificant. Spatially averaged variables are calculated based on the green box in panel A. Their time series are used to obtain the correlation coefficients in Table 1. This figure has been modified from Yu et al.¹⁷. Please click here to view a larger version of this figure.

Wind-driven mixing can be approximately gauged by WS and was used to describe vertical mixing¹⁸. Large correlation coefficients, with values of approximately 0.8, were identified between the WS and CHL levels north of the SCS (Figure 8B), particularly in the regions with the strongest winter wind located on the northern shelf of the SCS. Weak but significant correlations were found to the south. Correlations between WSC and CHL were significant in the majority of the SCS (Figure 8C), although they showed opposing trends in the north and south. A positive correlation coefficient between CHL and WSC was identified to the south, with negative values were in the north. The correlation in the region between them was not significant. The WS and CHL were found to be strongly correlated in the corresponding region where the winter WS was largest.

Fronts can also induce CHL variability. A large correlation was found in the northeast and southwest of the SCS (Figure 8D). CHL increased as frontal activities became more active³⁶. The SLA showed a significant negative correlation with CHL from the northeast SCS towards the southwest and a positive correlation along the west coast of the SCS (Figure 8E). It is interesting to note that the positive correlations were limited to the region with shallow topography.

To the northeast of the SCS, all correlations were large (Figure 8). Thus, the correlations of monthly time series between CHL and other parameters were calculated using the spatial average in a designated box (Figure 8A), and most of the factors were intercorrelated with significant correlations (top right section of Table 1). Because the seasonal cycle dominated the time series, the correlation was no longer valid after removing the monthly average (bottom left section of Table 1).

Table 1: Correlation coefficients of the time series among factors, located northeast of the SCS, e.g., SST (sea surface temperature), FP (frontal probability), WSC (wind stress curl) and WS (wind stress), using the box shown in Figure 8A. The monthly averages and anomalies are shown in the top right section and left bottom section, respectively. Numbers in bold and italics indicate that the correlation does not meet the 95% confidence level. The table has been modified from Yu et al.¹⁷.

The correlations in the seasonal cycle were not significant for some regions, such as the southwest of the SCS (Figure 8). The region is dominated by dynamic processes (e.g., upwelling and wind-induced offshore transport) that determine the variability in CHL¹⁷. A significant correlation between CHL and other factors (e.g., SST, WS, fronts, and WSC) was identified in anomalous fields (Figure 9). The anomalies were calculated for the monthly time series by removing the corresponding monthly average. The effective number of degrees of freedom could be increased, but prior studies have shown that it does not impact the underlying relationships among their time series^28,37.

Figure 9: Correlation between CHL and other factors in the anomalous fields.
(A) SST, (B) WS, (C) WSC, (D) FP, and (E) SLA. The gray color indicates that the correlation is nonsignificant. Spatially averaged variables are calculated based on the green box in panel A. The time series are used to obtain the correlation coefficients shown in Table 2. This figure has been modified from Yu et al.¹⁷. Please click here to view a larger version of this figure.

In the anomalous fields, CHL and SST were significantly correlated in the majority of the SCS (Figure 9A). When SSTs were unusually high, CHL became unusually low, and vice versa. Similarly, an unusually high WSC and fronts to the southwest of the SCS induced high levels of CHL, and vice versa (Figure 9C, 9D). In addition, a negative correlation was found between the SLAs and CHL levels (Figure 9E). Different lags were tested, and the correlation only became significant if no lag was employed. Thus, CHL was simultaneously impacted by anomalies in SST, WSC, and fronts, as well as SLA. Their relationship was further investigated using the spatially averaged monthly time series southwest of the SCS, designated as a green box in Figure 9A. The results show that most of the factors were intercorrelated with significant correlations in the anomalous field (bottom left section of Table 2).

Table 2: Correlation coefficients of the time series among factors, located southwest of the SCS, e.g., SST (sea surface temperature), FP (frontal probability), WSC (wind stress curl) and WS (wind stress), using the box shown in Figure 9A. The monthly average and anomalies are shown in the top right section and left bottom section, respectively. Numbers in bold and italics indicate that the correlation does not meet the 95% confidence level. The table has been modified from Yu et al.¹⁷.

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