In addition to the oceanographic data available for the South China Sea (SCS) from the World Ocean Database (WOD) and Array for Real-time Geostrophic Oceanography (Argo) floats, a suite of observations has been made by the South China Sea Institute of Oceanology (SCSIO) starting from the 1970s. Here, we assemble a SCS Physical Oceanographic Dataset (SCSPOD14) based on 51,392 validated temperature and salinity profiles collected from these three datasets for the period 1919–2014. A gridded dataset of climatological monthly mean temperature, salinity, and mixed and isothermal layer depth derived from an objective analysis of profiles is also presented. Comparisons with the World Ocean Atlas (WOA) and IFREMER/LOS Mixed Layer Depth Climatology confirm the reliability of the new dataset. This unique dataset offers an invaluable baseline perspective on the thermodynamic processes, spatial and temporal variability of water masses, and basin-scale and mesoscale oceanic structures in the SCS. We anticipate improvements and regular updates to this product as more observations become available from existing and future in situ networks.
Machine-accessible metadata file describing the reported data (ISA-tab format)
Background & Summary
Much of the major progress in marine science has been closely related to the development of in situ observations. A lack of observational data can thus seriously hamper the advancement of oceanography1. The World Ocean Database (WOD) Project, established by the Intergovernmental Oceanographic Commission (IOC), represents the world’s largest collection of freely available international ocean profile data2. The WOD was first released in 1994, and is updated approximately every four years with the most recent WOD13 released in 2013 (refs 3,4). The World Ocean Atlas (WOA) series is a set of gridded, climatological, objectively analyzed fields at standard depth levels produced from the WOD5,6. As part of the successful implementation of the international Array for Real-time Geostrophic Oceanography (Argo) project, a multitude of profiling floats have been deployed since 2000 to build a real-time monitoring system for the upper and middle layers of the world’s oceans7. The Argo floats provide a valuable monitoring tool to enhance our understanding of the mixed layer and vertical temperature and salinity structures8. They allow us, for the first time, to continuously monitor ocean temperature, salinity, and velocity with all real-time data relayed and made publicly available within hours after collection9.
As the largest marginal sea in the tropics, the South China Sea (SCS) exerts a significant influence on climate change, and plays a vital role in water mass and energy exchange between the Pacific and Indian Oceans10. As part of the Indo-Pacific warm pool, the SCS is also an important component of the Asian monsoon system. It is subject to strong forcing by alternating northeast and southwest monsoon winds, frequent typhoons, seasonal intrusions of the Kuroshio Current, and fresh water inflow from rivers11,12. However, our present knowledge of the evolution of the SCS has been limited by sparse and intermittent observational data. As the only research institute focusing on the ocean science of the SCS in China, the South China Sea Institute of Oceanology (SCSIO), Chinese Academy of Sciences (CAS), has made ongoing contributions to oceanographic surveys. Between 1971 and 2014, the SCSIO performed 203 research cruises, involving multiple generations of hydrographers.
The motivation of this study is to collect all available in situ observations up to 2014 and assemble the most comprehensive physical oceanographic database for the SCS to date. SCSIO measurements between 1971 and 2014 are incorporated into the dataset, alongside WOD09 and Argo float profiles. After rigorous quality control, temperature and salinity (referred to as T/S hereafter) profiles were used to generate a gridded dataset with a horizontal resolution of 0.25° at 57 standard vertical levels (SCSPOD14 hereafter).
As detailed knowledge of the stratification in the upper waters of the SCS is very sensitive to the number of observations, the mixed layer depth (MLD), isothermal layer depth (ILD), and barrier layer thickness (BLT) were also calculated and included in this SCSPOD14 dataset. As shown in previous studies, the surface layer is especially important for marine science. It records past winter mixing events and the subsequent onset of spring re-stratification, as well as traces of all physical processes occurring in the upper ocean13. Surface properties also determine the heat content and mechanical inertia of the layer that directly interacts with the atmosphere14. It is therefore hoped that the SCSPOD14 dataset will prove useful in further studies of these scientific issues.
The data collected within the SCS (100–125°E, 0–25°N) were taken from three datasets: WOD09, Argo and SCSIO. In the SCS, WOD09 has 39,673 T/S profiles from February 1907 to January 2009, including 21,125 Ocean Station Data (OSD) profiles, 15,857 conductivity–temperature–depth (CTD) profiles, and 2,961 Profiling Float (PFL) profiles. Note that there were only 7 records in 1907, followed by a gap, and measurements did not start again until 1919. In this study, these few records in 1907 are not considered. Therefore, the start time of our dataset is 1919. As for Argo data, 10,140 profiles obtained after January 17 2009 were considered for inclusion in the dataset. Note that Argo float data already present in WOD09 were not considered again.
In addition to the measurements from WOD09 and Argo, the SCSIO provided 11,774 profiles. The SCSIO performed 203 research cruises between 1971 and 2014. Table 1 (available online only) lists all SCSIO cruises and gives the number of selected T/S profiles from each. For example, the SCSIO has organized and carried out 17 Nansha comprehensive investigation cruises since the 1980s. To understand the physical, chemical, and biological aspects of the northern SCS, an annual open cruise has been conducted during late summer by the SCSIO since 2004 (refs 15,16). The original SCSIO shipboard hydrographic data consisted mainly of OSD profiles before 1983, and were obtained with a Neil Brown Mark 3 CTD between 1983 and 2002, and by Sea-Bird Electronics CTD (SBE CTD; e.g., SBE 25, SBE 911, SBE 917, and SBE-911/917 plus) instruments after 2002. The CTD was tank-calibrated for temperature and pressure before and after each cruise. Modern CTD profiles are considered accurate to approximately 0.002 °C in temperature, 0.005 psu in salinity and <0.5% of full-scale pressure in depth17. Besides the above major instruments, some complementary measurements were taken by expendable CTD profiler (XCTD) after 2013 under poor sea conditions. For each scientific voyage, all measurements were qualitatively checked, including a duplicate check for similar position and time, stability check for vertical records, and deviation check which is benchmarked against climatic range10,18,
Quality control of the observations
At the data collection stage, there are 61,580 records in total. Diverse data sources can lead to variations in the quality and sources of error. For this reason, it was necessary to rigorously control data quality. The main technical processes involved in generating the SCSPOD14 dataset are shown in the flow chart in Fig. 1.
Firstly, valid depth, temperature, and salinity measurements must be included in each observation together with an observation date21 (at least year and month). About 1.5% of the measurements that failed this requirement were discarded. Secondly, a careful position check eliminated about 4.4% duplicate stations, those with incorrect location information, stations on land, and those located in shallow water (<15 m). Thirdly, erroneous records (e.g., temperature higher than 8 °C below 1,000 m or salinity lower than 30 psu below 100 m, accounting for about 1.4% of the data) and those with exceptionally sparse vertical resolution (accounting for about 1.1% of the data) were also removed. Further quality control methods, such as deviation test, removing records that obviously deviated (by >3 standard deviation) from climatological majority, were developed and applied, resulting in about 8.1% of the stations being excluded from entering the database22,23. This quality-control process reduced the dataset to a total of 51,392 SCS profiles.
Selected temperature and salinity profiles
After the data quality control procedure, 51,392 instantaneous T/S profiles with observations at various levels were selected (Fig. 2a), of which 33,203 were profiles archived in the WOD09 from February 1907 to January 2009; 10,193 were measurements taken by the SCSIO from September 1971 to September 2014; and 8,001 were from Argo profiling floats from January 2009 to December 2013 that are not included in WOD09.
The spatial sampling of WOD09 is best in the northeastern and western SCS basin, while the data coverage in the southeastern and northwestern parts of the basin is relatively poor (Fig. 2b). The locations of the hydrographic data acquired by the SCSIO with good quality flags are shown in Fig. 2c. The SCSIO dataset compensates for the lack of measurements in the southern basin to a great extent and further enriches the observations in the northern part. In 2006, Argo floats began to enter the SCS, although these records were very limited until 2008. Figure 2d depicts the spatial coverage of recent profiles from Argo floats that are not included in the WOD09. Argo floats are mainly located in the northern basin, and are associated with surface current patterns (Fig. 2d). The distribution of station numbers in each 0.5° bin further shows the density of samples (Fig. 3). In the northern regions, the combined contributions of the WOD09, SCSIO, and Argo data accounts for the highest measurement density. Although the SCSIO tried to compensate for the sparse observations in the southern basin, the sample density is still relatively low in the southeastern basin.
From the temporal distribution of the records it is clear that measurements in the SCS are relatively scarce in WOD09 prior to 1960 (Fig. 4a and b). The number of data records steadily increases after 1965, reaching its maximum in 1998. After 1970, the number of SCSIO observations was greatly expanded, and complement the WOD09 (Fig. 4b). The distribution of T/S profiles at different depths is shown in Fig. 4c. Most data were collected within the upper 1,000 m of the water column, and there are very limited records below 1,500 m.
Definitions of upper layer stratification
The analysis of individual profiles allows a more detailed and realistic investigation of physical characteristics, and thus gives more confidence in the description of processes involved24. The most widely favored and simplest scheme for finding the ILD is the threshold method13. Various temperature thresholds, such as 0.1 °C (ref. 25), 0.2 °C (refs 14,26,27), 0.5 °C (refs 28,
Following previous studies, the MLD is defined where the potential density has increased from its value at the reference level by a given threshold24,25,27,30,
Generation of the gridded data
The gridded field was produced in three steps. In the first step, each profile was vertically interpolated to the 57 standard depths used in WOA13 (0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450, 475, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1,000, 1,050, 1,100, 1,150, 1,200, 1,250, 1,300, 1,350, 1,400, 1,450, and 1,500 m). Cubic spline interpolation was used to obtain the values at these new standard layer depths for Argo floats and profiles available at previous standard depths. Linear interpolation was used for SCSIO measurements with higher vertical resolution of 1 m or less. In the second step, we binned the data by month, and then produced a 0.25° field by gridding all temperature and salinity data using an inverse distance weighting method at each vertical standard layer. In this step, to further filter out high frequency variations, such as internal solitary waves in the SCS34, the daily mean procedure is executed at each grid before obtaining the gridded value. Given that there are limited observations around some grid points or some standard layers, a variable search radius was adopted; at each grid point, the search radius was gradually increased until it contained at least five historical observations. A standard deviation test was performed during the gridding process, so that the selected profiles were discarded if they deviated from the bin-averaged values by more than three times the standard deviation. In the third step, to partially reduce noise in the observations, three passes of a nine-point smoothing filter were performed after the gridding process at standard levels for monthly compositing periods for the SCS. The two-dimensional nine-point smoothing uses 50% self-weight and 50% adjacent weight from the eight neighboring observation values14. Such a weighting scheme enhances the quality and continuity of the climatology compared with a simple linear average approach35.
The MLD, ILD and BLT fields estimated from individual profiles were then averaged in 0.5°×0.5° boxes for each month regardless of the year of observation. To eliminate any bias in sampling density, a variable scan radius was chosen so that each average is based on at least 10 samples. If an estimate from an individual profile deviated from the grid mean by more than three times the standard deviation, it was excluded. The monthly gridded MLD, ILD, and BLT fields were then generated using a two-dimensional Kriging method and smoothed by two passes of the nine-point filter. Kriging is an optimal interpolation that generates the best linear unbiased estimate at each location. It is based on regression against observed values of surrounding samples and weighted according to spatial covariance values36. The methodology is based on the assumption that irregularly placed MLD/ILD/BLT values are treated regionally. The regionalized MLD/ILD/BLT can then be considered as intermediate between a truly random variable and a completely deterministic variable37.
The gridded SCSPOD14 contains two zipped folders from the figshare records (Data Citation 1: Figshare http://dx.doi.org/10.6084/m9.figshare.c.1513842). The temperature and salinity dataset is available as a zipped folder called Gridded_Temp&Salt_SCSPOD14.zip. The gridded mixed and isothermal layer depth dataset is available in another zipped folder, Gridded_Stratifications_SCSPOD14.zip. Both folders contain asci data files, binary data files (combined with.ctl and.gs files from which GRADS software can read data and output figures) and Matlab data files (.mat) that can be read directly by Matlab software. Additional material such as figures and a README with a detailed description of the content are also available in Data Citation 1: Figshare http://dx.doi.org/10.6084/m9.figshare.c.1513842.
T/S scatter diagrams
The water properties and exchanges between the SCS and the West Pacific (WP) through the Luzon Strait have been widely documented38,
Temperature and salinity: comparison with AMSR-E SST and WOA13 data
To evaluate the SCSPOD14, data inter-comparisons are conducted with the monthly climatological SST measured by microwave sensors from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E41), and monthly climatological temperature and salinity data from the WOA13. The AMSR-E SST is used in the comparison because of the high accuracy of satellite observations.
Bimonthly distributions of AMSR-E SST, WOA13 SST, and SCSPOD14 SST are shown in Fig. 7a–c. Both WOA13 SST and SCSPOD14 SST depict the seasonal variability of SST in the SCS. Compared with the satellite AMSR-E SST, WOA13 and SCSPOD14 have better spatial coverage in shallow waters. Both WOA13 and SCSPOD14 SST are lower than the satellite AMSR-E SST in continental slope areas. In general, SCSPOD14 SST is higher than WOA13 SST in the northern region, but lower in the southern region. The most significant quantitative differences between these two datasets occur in the northeastern corner in March, where the difference is larger than 0.5 °C (Fig. 7d). The distinct spatial differences between these two datasets occur in the summer monsoon season (e.g., May and July) as shown in Fig. 7b and c. In May, the SCSPOD14 SST field is much closer to that of AMSR-E SST, while the WOA13 SST is much cooler, especially in the central and eastern basin. The difference between SCSPOD14 and WOA13 SST is positive over almost the entire basin. In July, WOA13 SST was still lower than AMSR-E and SCSPOD14 in the northern basin. Unlike their differences in the northern SCS, WOA13 and SCSPOD14 are relatively consistent in the central and southern basin. Both WOA13 and SCSPOD14 show a low temperature core caused by upwelling around 12°N, slightly south of the position of this feature in AMSR-E. In the southern basin, the values in WOA13 and SCSPOD14 are both lower than in AMSR-E. The difference between SCSPOD14 and WOA13 SST is relatively small in July compared with other months. By September, the characteristics of WOA13 and SCSPOD14 SST both differ to some extent from the AMSR-E measurements. In contrast to earlier months, from September the SCSPOD14 SST is cooler than WOA13 SST in continental slope areas, particularly around Taiwan where their difference is about –0.5 °C
Due to the limitations of satellite salinity data and their poor accuracy in the SCS, this study only shows the comparison between WOA13 and SCSPOD14 SSS. The results indicate that SSS from the WOA13 and SCSPOD14 have similar spatial patterns, with a high-salinity band that spreads from southwest of Taiwan to east of Hainan Island, and forms a high-salinity core in the central northern SCS in winter. In summer and autumn, SSS is lower in the basin with obvious low-salinity zones located near the Mekong River Estuary where intense runoff produces freshwater lenses, and along the east boundary of the SCS where there is abundant precipitation. As shown in Fig. 8a and b, differences in SSS between the datasets are smaller than for SST. The major differences between SCSPOD14 and WOA13 SSS occur in the southeastern basin (Fig. 8c). As described in the data collection section, the difference in observation numbers of the two datasets is also the greatest in this region. The additional measurements in SCSPOD14 might lead to the difference in SSS. Another discrepancy occurs around 18°N where SCSPOD14 also contains many measurements. We believe that more observations will change the salinity dataset. Vertical distributions of temperature and salinity are given in Fig. 9a–d, in addition to SST and SSS. The transect along 18°N in July is selected due to the large differences that occur in that region (Figs 7d and 8c). Located between the tropics and subtropics, this transect is important for studying meridional ocean transport in the SCS. The SCSIO open cruises in the northern SCS have surveyed the 18°N transect on a regular basis since 2004 and obtained multi-year measurements, greatly enriching the observations of this region10. Figure 8 shows that the main differences are in the upper ocean, rather than in the subsurface and intermediate waters (not shown). There is a warmer (around 0.5 °C) and fresher (around –0.05 psu) mixed layer in the SCSPOD14 along 18°N compared with WOA13 observations (Fig. 9e and f). The contrast is more obvious in the western basin than in the eastern basin. In contrast to the WOA13, a secondary high salinity core around 150 m appears in the SCSOD14.
Upper ocean stratification: comparison with IFREMER/LOS MLD climatology
The differences seen in the T/S profiles in the upper ocean can affect the calculation of stratification in the SCS. Therefore, new upper layer stratification data is also provided in the SCSPOD14 dataset. Because of the various definitions and calculations of upper layer stratification, there are no direct reference data for comparison. Here, we only evaluate the SCSPOD14 stratifications by comparison with global climatological data, the IFREMER/LOS Mixed Layer Depth Climatology14 produced by de Boyer Montégut et al. Their two kinds of MLD calculation are used: MLD-T02 uses the temperature threshold of 0.2 °C, while MLD-R003 uses a density criterion of 0.03 kg m−3 difference from the reference depths as 10 m.
From Fig. 10a,b, both MLD-R003 and MLD-T02 in the IFREMER/LOS climatology cannot provide high-resolution MLD data in the SCS. It is also clear that the coverage of MLD-R003, which depends on the density and thus requires both the temperature and salinity profiles, is more insufficient compared to MLD-T02, which depends only on temperature profiles. The difference between the two MLD calculation methods is significant over the SCS, which further provides evidence for the existence of the barrier layer in the SCS. This emphasizes the utility of our new MLD, ILD, and BLT calculations in the SCSPOD14 dataset.
The monthly MLD distribution is shown in Fig. 10c. As a result of numerous in situ measurements, the SCSPOD14 MLD depicts the seasonal change in detail. During winter, the deep tongue extending southwestward from the Luzon Strait along the continental slope in the northern basin exceeds 50 m to the west of 119°E. Zones of deep ILD and MLD form in this region, associated with predominantly northeasterly winds, surface cooling, and convergence. Our maps of the climatology also clearly show a shallow center around the West Luzon Eddy northwest of the Philippines. During summer, a deep core occurs in the southeastern basin and spreads southwestward, reaching a maximum depth of 45 m. Our climatology also clearly shows a band of shallow MLD, connecting two shallow centers, one around the West Luzon Eddy northwest of the Philippines during winter, and another located in the West Vietnam Cyclonic Eddy east of Vietnam during summer. Note that only the figures and MLD validation are provided here; figures and related documents for the ILD and BLT are provided in Data Citation 1: Figshare http://dx.doi.org/10.6084/m9.figshare.c.1513842.
These comparisons of the gridded SCSPOD14 data with WOA13 and IFREMER/LOS MLD climatology clearly show the value of SCSPOD14 for the study of oceanic structure, water masses, and the assessment of the accuracy of numerical simulations. We are confident that this dataset will be a useful complement to other gridded products. This work also highlights the need for more observations in the southeastern basin to fill spatial gaps in the dataset.
How to cite this article: Zeng, L. et al. SCSPOD14, a South China Sea physical oceanographic dataset derived from in situ measurements during 1919–2014. Sci. Data 3:160029 doi: 10.1038/sdata.2016.29 (2016).
Zeng, L., Wang, D., Chen, J., Wang , W., & Chen , R. Figshare http://dx.doi.org/10.6084/m9.figshare.c.1513842 (2016)
Great thanks are given to the scientists and crews of cruises organized by the SCSIO for providing the valuable in situ observations. The construction of the reanalysis was jointly supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA11010203), the National Natural Science Foundation of China (41206011, 41476014, 41376024, 41306012, 41476011, 41476012, 41406038), the 100 Talent Program of Chinese Academy of Sciences, the project of Guangdong Provincial Department of Science and Technology (2012A032100004), the development and application of SCADCP in the observation of west boundary current in the northern South China Sea, and Xisha Deep Sea Marine Environment Observation and Research Station, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Sansha 573199, China. WOD09 data and WOA data are available online (http://www.nodc.noaa.gov/OC5/WOD09/data09geo.html and http://www.nodc.noaa.gov/cgi-bin/OC5/woa13/woa13.pl?parameter=t). Argo data was collected and made freely available by the International Argo Program and the national programs that contribute to it. (http://www.argo.ucsd.edu, http://argo.jcommops.org). The global mixed layer depth climatology data by de Boyer Montégutis available on IFREMER/LOS Mixed Layer Depth Climatology website (www.ifremer.fr/cerweb/deboyer/mld). We thank Drs Yunkai He, Jian Li, Qiang Wang, and Qiang Xie for their help with data collection and useful discussions.