Analysis of monthly variability of thermocline in the South China Sea
Tóm tắt
This study analyzes monthly variability of thermocline and its mechanism in the South China Sea (SCS). The study is based on 51-year (1960–2010) monthly seawater temperature and surface wind stress data from Simple Ocean Data Assimilation (SODA), together with heat flux, precipitation and evaporation data from the National Centers for Environmental Prediction (NCEP), the National Oceanic and Atmospheric Administration (NOAA) and the Woods Hole Oceanographic Institution, respectively. The results reveal that the upper boundary depth (Zup), lower boundary depth (Zlow), thickness (ΔZ) and intensity (T
z
) of thermocline in the SCS show remarkable monthly variability. Being averaged for the deep basin of SCS, Zup deepens gradually from May to the following January and then shoals from February to May, while Zlow varies little throughout the whole year. Further diagnostics indicates that the monthly variability of Zup is mainly caused by the buoyancy flux and wind stress curl. Using a linear method, the impacts of the buoyancy flux and wind stress curl on Zup can be quantitatively distinguished. The results suggest that Zup tends to deepen about 4.6 m when the buoyancy flux increases by 1×10 -5 kg/(m∙s 3), while it shoals about 2.5 m when the wind stress curl strengthens by 1×10 -7 N/m³.
Tài liệu tham khảo
Carton J A, Chepurin G, Cao X H, Giese B. 2000a. A simple ocean data assimilation analysis of the global upper ocean 1950-95. Part I: methodology. J. Phys. Oceanogr., 30 (2): 294–309.
Carton J A, Chepurin G, Cao X H. 2000b. A simple ocean data assimilation analysis of the global upper ocean 1950-95. Part II: results. J. Phys. Oceanogr., 30 (2): 311–326.
Carton J A, Giese B S, Grodsky S A. 2005. Sea level rise and the warming of the oceans in the Simple Ocean Data Assimilation (SODA) ocean reanalysis. J. Geophys. Res., 110 (C9): C09006, https://doi.org/10.1029/2004JC002817.
Carton J A, Giese B S. 2008. A reanalysis of ocean climate using Simple Ocean Data Assimilation (SODA). Mon. Wea. Rev., 136 (8): 2999–3017.
Chen M Y, Xie P P, Janowiak J E, Arkin P A. 2002. Global land precipitation: a 50-yr monthly analysis based on gauge observations. J. Hydrometeorol., 3 (3): 249–266.
China State Bureau of Technical Supervision (CSBTS). 2008. GB/T 12763.7-2007 The specifications for oceanographic survey—Part 7: exchange of oceanographic survey data. China Standards Press, Beijing. (in Chinese)
Fang X J, Wang C X, Xu J J. 2013. Seasenal and interannual variations of the thermocline depth in the South China Sea. Trans. Oceanol. Limnol., (3): 45–55. (in Chinese with English abstract)
Fofonoff P, Millard Jr R C. 1983. Algorithms for computation of fundamental properties of seawater. UNESCO Tech. Papers in Marine Science 44, UNESCO. 53p.
Ge R F, Qiao F L, Yu F, Jiang Z X, Guo J S. 2003. A method for calculating thermocline characteristic elements in shelf sea area—Quasi-step function approximation method. Adv. Mar. Sci., 21 (4): 393–400. (in Chinese with English abstract)
Giese B S, Ray S. 2011. El Niño variability in simple ocean data assimilation (SODA), 1871-2008. J. Geophys. Res., 116 (C2): C02024, https://doi.org/10.1029/2010JC006695.
Gill A E. 1982. Atmosphere-Ocean Dynamics. Academic Press, San Diego, USA.
Hao J J, Chen Y L, Wang F, Lin P F. 2012. Seasonal thermocline in the China Seas and northwestern Pacific Ocean. J. Geophys. Res, 117 (C2): C02022, https://doi.org/10.1029/2011JC007246.
Hao J J, Chen Y L, Wang F. 2008. A study of thermocline calculations in the China Sea. Mar. Sci., 32 (12): 17–24. (in Chinese with English abstract)
Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J, Zhu Y, Leetmaa A, Reynolds B, Chelliah M, Ebisuzaki W, Higgins W, Janowiak J, Mo K C, Ropelewski C, Wang J, Jenne R, Joseph D. 1996. The NCEP/NCAR 40-year reanalysis project. Bull. Amer. Meteor. Soc., 77 (3): 437–472.
Lan J, Bao Y, Yu F, Sun S W. 2006. Seasonal variabilities of the circulation and thermocline depth in the South China Sea deep water basin. Adv. Mar. Sci., 24 (4): 436–445. (in Chinese with English abstract)
Liu Q Y, Jia Y L, Liu P H, Wang Q, Chu P C. 2001. Seasonal and intraseasonal thermocline variability in the central South China Sea. Geophys. Res. Lett., 28 (23): 4467–4470.
Liu Q Y, Yang H J, Wang Q. 2000. Dynamic characteristics of seasonal thermocline in the deep sea region of the South China Sea. Chin. J. Oceanol. Limnol., 18 (2): 104–109.
Lozovatsky I, Figueroa M, Roget E, Fernando H J S, Shapovalov S. 2005. Observations and scaling of the upper mixed layer in the North Atlantic. J. Geophys. Res., 110 (C5): C05013, https://doi.org/10.1029/2004JC002708.
McDougall T J. 1987. Neutral surfaces. J. Phys. Oceanogr., 17 (11): 1950–1964.
Schmitt R W, Bogden P S, Dorman C E. 1989. Evaporation minus precipitation and density fluxes for the North Atlantic. J. Phys. Oceanogr., 19 (9): 1208–1221.
Yu L S, Jin X Z, Weller R A. 2008. Multidecade global flux datasets from the objectively analyzed air-sea fluxes (OAFlux) project: latent and sensible heat fluxes, ocean evaporation, and related surface meteorological variables. Woods Hole Oceanographic Institution, OAFlux Project Technical Report. OA-2008-01, Woods Hole, Massachusetts, USA. 64p.
Zhou F X, Gao R Z. 2001. Intraseasonal variability of the subsurface temperature observed in the South China Sea (SCS). Chin. Sci. Bull., 47 (4): 337–342.