Characterization of water masses in the Sulu Sea using temperature–salinity (T-S) diagrams, dissolved oxygen, and chlorophyll-a distribution
DOI:
https://doi.org/10.29080/mrcm.v7i2.2606Keywords:
chlorophyll-a, dissolved oxygen, T–S diagram, vertical profile, water massAbstract
The Sulu Sea is a semi-enclosed tropical marginal basin influenced by complex exchanges between the South China Sea and the western North Pacific, resulting in distinctive hydrographic and biogeochemical characteristics. This study aims to characterize the water masses of the Sulu Sea using temperature–salinity (T–S) analysis and to examine the vertical distributions of dissolved oxygen (DO) and chlorophyll-a as indicators of biophysical processes. A key novelty of this study is the integration of T–S analysis with DO and chlorophyll-a observations to provide a comprehensive assessment of the relationships among water-mass structure, ventilation processes, and biological productivity in the Sulu Sea. The analysis was based on Conductivity–Temperature–Depth (CTD) observations collected at 22 stations during the SEAFDEC cruise in March–April 2015, with profiling depths reaching approximately 750 m. The T–S analysis identified two dominant upper-ocean water masses: Subtropical Lower Water (SLW), characterized by a salinity maximum of 34.5–34.9 psu within the thermocline layer (150–250 m), and North Pacific Upper Thermocline Water (NPUTW), which was primarily observed in the eastern stations and indicates the influence of Pacific inflow through the Bohol–Dipolog pathway. Dissolved oxygen exhibited strong vertical stratification, with high concentrations (>5 mg L⁻¹) in the surface layer and reduced values (~2 mg L⁻¹) below the thermocline due to limited ventilation and organic matter remineralization. Chlorophyll-a was concentrated within the upper euphotic zone and near the thermocline, reflecting the combined influence of stratification and nutrient availability. The integrated analysis demonstrates that water-mass distribution, oxygen dynamics, and phytoplankton variability are closely linked to circulation pathways and vertical stratification. These findings provide new insight into the coupling between physical and biogeochemical processes in the Sulu Sea and contribute to improving the understanding of tropical marginal sea ecosystems.
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Q. Yang, W. Zhao, M. Li, and J. Tian. 2014. Spatial Structure of Turbulent Mixing in the Northwestern Pacific Ocean. J. Phys. Oceanogr. 44(8): 2235–2247. https://doi.org/10.1175/JPO-D-13-0148.1
G. Fang, Y. Wang, Z. Wei, Y. Fang, F. Qiao, and X. Hu. 2009. Interocean circulation and heat and freshwater budgets of the South China Sea based on a numerical model. Dynamics of Atmospheres and Oceans. 47(1–3): 55–72. https://doi.org/10.1016/j.dynatmoce.2008.09.003
Z. Liu and J. Gan. 2017. Three-dimensional pathways of water masses in the South China Sea: A modeling study. J. Geophys. Res. Oceans. 122(7): 6039–6054. https://doi.org/10.1002/2016JC012511
T. Qu, Y. T. Song, and T. Yamagata 2009. An introduction to the South China Sea throughflow: Its dynamics, variability, and application for climate. https://doi.org/10.1016/j.dynatmoce.2008.05.001
Y. Du and T. Qu. 2010. Three inflow pathways of the Indonesian throughflow as seen from the simple ocean data assimilation. Dynamics of Atmospheres and Oceans. 50(2): 233–256. https://doi.org/10.1016/j.dynatmoce.2010.04.001
D. Xu and P. Malanotte-Rizzoli. 2013. The seasonal variation of the upper layers of the South China Sea (SCS) circulation and the Indonesian through flow (ITF): An ocean model study. Dynamics of Atmospheres and Oceans. 63: 103–130. https://doi.org/10.1016/j.dynatmoce.2013.05.002
A. L. Gordon, J. Sprintall, and A. Field. 2011. Regional oceanography of the Philippine archipelago. Oceanography. 24(1): 15–27. https://doi.org/10.5670/oceanog.2011.01
Z. D. Tessler, A. L. Gordon, L. J. Pratt, and J. Sprintall 2010. Transport and Dynamics of the Panay Sill Overflow in the Philippine Seas. J. Phys. Oceanogr. 40(12): 2679–2695. https://doi.org/10.1175/2010JPO4395.1
J. Wei, M. T. Li, P. Malanotte-Rizzoli, A. L. Gordon, and D. X. Wang. 2016. Opposite variability of indonesian throughflow and south china sea throughflow in the Sulawesi Sea. J. Phys. Oceanogr. 46(1): 3165–3180. https://doi.org/10.1175/JPO-D-16-0132.1
W. Han et al. 2009. Seasonal surface ocean circulation and dynamics in the Philippine Archipelago region during 2004-2008. Dynamics of Atmospheres and Oceans. 47(1–3): 114–137. https://doi.org/10.1016/j.dynatmoce.2008.10.007
S. Cai and Y. He. 2010. Association of the Sulu Sea surface circulation with the South China Sea. Journal of Marine Systems. 81(4): 335–340. https://doi.org/10.1016/j.jmarsys.2010.02.010
T. Qu and Y. T. Song. 2009. Mindoro Strait and Sibutu Passage transports estimated from satellite data. Geophys. Res. Lett. 36(9). https://doi.org/10.1029/2009GL037314
H. Matsuura, S. Nishida, and J. Nishikawa. 2010 Species diversity and vertical distribution of the deep-sea copepods of the genus Euaugaptilus in the Sulu and Celebes Seas. Deep. Sea. Res. 2. Top. Stud. Oceanogr. 57(24–26): 2098–2109. https://doi.org/10.1016/j.dsr2.2010.09.013
J. Nishikawa, H. Matsuura, L. V. Castillo, W. L. Campos, and S. Nishida. 2007. Biomass, vertical distribution and community structure of mesozooplankton in the Sulu Sea and its adjacent waters. Deep. Sea. Res. 2. Top. Stud. Oceanogr. 54(1–2): 114–130. https://doi.org/10.1016/j.dsr2.2006.09.005
L. Li and Z. Gan. 2014. Ventilation of the Sulu Sea retrieved from historical data. Acta Oceanologica Sinica. 33(9): 1–11. https://doi.org/10.1007/s13131-014-0517-3
Q. Yang, W. Zhao, M. Li, and J. Tian. 2014. Spatial structure of turbulent mixing in the Northwestern Pacific Ocean. J. Phys. Oceanogr. 44(8): 2235–2247. https://doi.org/10.1175/JPO-D-13-0148.1
C. T. A. Chen, W. P. Hou, T. Gamo, and S. L. Wang. 2006. Carbonate-related parameters of subsurface waters in the West Philippine, South China and Sulu Seas. Marine Chemistry. 151–161. https://doi.org/10.1016/j.marchem.2005.05.008
T. Gamo et al. 2007. Geochemical implications for the mechanism of deep convection in a semi-closed tropical marginal basin: Sulu Sea. Deep. Sea. Res. 2. Top. Stud. Oceanogr. 54(1–2): 4–13. https://doi.org/10.1016/j.dsr2.2006.06.004
X. Xing, G. Qiu, E. Boss, and H. Wang. 2019. Temporal and Vertical Variations of Particulate and Dissolved Optical Properties in the South China Sea. J. Geophys. Res. Oceans. 124(6): 3779–3795. https://doi.org/10.1029/2018JC014880
J. Wang, Y. Qi, and I. S. F. Jones. 2006. An analysis of the characteristics of chlorophyll-a in the Sulu Sea. Journal of Marine Systems. 59(1–2): 111–119. https://doi.org/10.1016/j.jmarsys.2005.09.004
Y. Chen, Q. P. Li, and J. Yu. 2022. Submesoscale variability of subsurface chlorophyll-a across eddy-driven fronts by glider observations, Prog. Oceanogr. 209. https://doi.org/10.1016/j.pocean.2022.102905
Y. Chen and H. Zhao. 2021. Spatial distribution of the summer subsurface chlorophyll-a maximum in the North South China Sea. PLoS One. 16(4). https://doi.org/10.1371/journal.pone.0248715
M. L. Wu. 2022. Vertical patterns of chlorophyll-a a in the euphotic layer are related to mesoscale eddies in the South China Sea. Front. Mar. Sci. 9. https://doi.org/10.3389/fmars.2022.948665
Z. Tao, S. Wang, X. Chi, F. Zhang, and S. Sun. 2025. Zooplankton community structure and vertical distribution in the South China Sea during the summer monsoon. Mar. Pollut. Bull. 25. https://doi.org/10.1016/j.marpolbul.2025.117857
P. C. Chu, Q. Liu, Y. Jia, and C. Fan. 2002. Evidence of a Barrier Layer in the Sulu and Celebes Seas.
B. Liu and E. J. D’Sa. 2019. Oceanic Internal Waves in the Sulu-Celebes Sea under Sunglint and Moonglint,” IEEE Transactions on Geoscience and Remote Sensing. 57(6119–6129). https://doi.org/10.1109/TGRS.2019.2904402
X. Zhang and X. Li. 2021. Combination of Satellite Observations and Machine Learning Method for Internal Wave Forecast in the Sulu and Celebes Seas,” IEEE Transactions on Geoscience and Remote Sensing. 59(2822–2832). https://doi.org/10.1109/TGRS.2020.3008067
X. Zhao. 2021. Satellite investigation of semidiurnal internal tides in the Sulu-Sulawesi Seas. Remote Sens. (Basel). 13(13). https://doi.org/10.3390/rs13132530
M. Firdaus. 2021. Indirect estimation of turbulent mixing in western route of Indonesian throughflow. In IOP Conference Series: Earth and Environmental Science, IOP Publishing Ltd. https://doi.org/10.1088/1755-1315/944/1/012059
Z. D. Tessler, A. L. Gordon, and C. R. Jackson. 2012. Early stages soliton observations in Sulu seas. J. Phys. Oceanogr. 42(1327–1336). https://doi.org/10.1175/JPO-D-11-0165.1
C. M. Ferrera. 2017. Carbonate parameters in high and low productivity areas of the Sulu Sea, Philippines,” Mar. Chem. 195(2–14). https://doi.org/10.1016/j.marchem.2017.08.005
S. Takeda. 2007. Biological and chemical characteristics of high-chlorophyll-a, low-temperature water observed near the Sulu Archipelago. Deep. Sea. Res. 2. Top. Stud. Oceanogr. 54(81–102). https://doi.org/10.1016/j.dsr2.2006.08.020
C. M. Ferrera, G. S. Jacinto, C. T. A. Chen, and H. K. Lui. 2018. Organic carbon concentrations in high- and low-productivity areas of the Sulu Sea. Sustainability (Switzerland). 10(6). https://doi.org/10.3390/su10061867
M. Valera, R. K. Walter, B. A. Bailey, and J. E. Castillo. 2020. Machine learning based predictions of dissolved oxygen in a small coastal embayment. J. Mar. Sci. Eng. 8(1–16). https://doi.org/10.3390/jmse8121007
A. S. Parab, M. Ghose, and C. S. Manohar. 2026. Seasonal variability in primary productivity and associated shifts in bacterial community structure at chlorophyll-a maximum depths in the eastern Arabian Sea. Journal of Marine Systems. 255. https://doi.org/10.1016/j.jmarsys.2026.104234
Y. Yu, X. Xing, H. Liu, Y. Yuan, Y. Wang, and F. Chai. 2019. The variability of chlorophyll-a-a and its relationship with dynamic factors in the basin of the South China Sea. Journal of Marine Systems. 200. https://doi.org/10.1016/j.jmarsys.2019.103230
H. Lv, G. Wang, W. Xu, Y. Zhang, and L. Jiang. 2023. Seasonal variability of satellite-derived primary production in the South China Sea from an absorption-based model. Front. Mar. Sci. 10. https://doi.org/10.3389/fmars.2023.1087604
Y. Zheng. 2024. A novel method to estimate the 3D chlorophyll-a a distribution in the South China Sea surface waters using hydrometeorological parameters. Sci. Rep. 14(1). https://doi.org/10.1038/s41598-024-76748-5
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