Factors influencing the climatological mixed layer depth in the South China Sea: numerical simulations

Conghui Fan; Juanjuan Wang; Jinbao Song

doi:10.1007/s00343-010-0002-6

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Factors influencing the climatological mixed layer depth in the South China Sea: numerical simulations

Updated：2023-05-16

- Factors influencing the climatological mixed layer depth in the South China Sea: numerical simulations
- Journal of Oceanology and Limnology Vol. 28, Issue 5, Pages: 1112-1118(2010)
- Affiliations：
  
  1. Institute of Oceanology Chinese Academy of Sciences
  2. Key Laboratory of Ocean Circulation and Waves (KLOCAW) Chinese Academy of Sciences
  3. Graduate University of Chinese Academy of Sciences
- Author bio：
- Funds：
- DOI：10.1007/s00343-010-0002-6
  CLC：
- Online First：11 September 2010，
  
  Published：2010
- Accepted：
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Fan, C., Wang, J. & Song, J. Factors influencing the climatological mixed layer depth in the South China Sea: numerical simulations., Chin. J. Ocean. Limnol. 28, 1112–1118 (2010). https://doi.org/10.1007/s00343-010-0002-6
DOI：

Fan, C., Wang, J. & Song, J. Factors influencing the climatological mixed layer depth in the South China Sea: numerical simulations., Chin. J. Ocean. Limnol. 28, 1112–1118 (2010). https://doi.org/10.1007/s00343-010-0002-6 DOI：

摘要

Abstract

The mixed layer depth (MLD) in the upper ocean is an important physical parameter for describing the upper ocean mixed layer. We analyzed several major factors influencing the climatological mixed layer depth (CMLD)

and established a numerical simulation in the South China Sea (SCS) using the Regional Ocean Model System (ROMS) with a high-resolution (1/12°×1/12°) grid nesting method and 50 vertical layers. Several ideal numerical experiments were tested by modifying the existing sea surface boundary conditions. Especially

we analyzed the sensitivity of the results simulated for the CMLD with factors of sea surface wind stress (SSWS)

sea surface net heat flux (SSNHF)

and the difference between evaporation and precipitation (DEP). The result shows that of the three factors that change the depth of the CMLD

SSWS is in the first place

when ignoring the impact of SSWS

CMLD will change by 26% on average

and its effect is always to deepen the CMLD; the next comes SSNHF (13%) for deepening the CMLD in October to January and shallowing the CMLD in February to September; and the DEP comes in the third (only 2%). Moreover

we analyzed the temporal and spatial characteristics of CMLD and compared the simulation result with the ARGO observational data. The results indicate that ROMS is applicable for studying CMLD in the SCS area.

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Keywords

references

Alexander M A, Penland C. 1996. Variability in a mixed layer model of the upper ocean driven b stochastic atmospheric surface fluxes. Journal of Climate, 9(10): 2 424–2 442.

Amante C, Eakins B W. 2009. ETOPO1 1 Arc-Minute Global Relief Model: Procedures, Data Sources and Analysis. NOAA Technical Memorandum NESDIS NGDC-24, 19 pp

Chen D, Busalacchi A J, Rothstein L M. 1994. The roles of vertical mixing, solar radiation, and wind stress in a model simulation of the sea surface temperature seasonal cycle in the tropical Pacific Ocean. J. Geophys. Res., 99(C10): 20 345–20 359.

da Silva A C, Young, Levitus S. 1994. Atlas of Surface Marine Data 1994, Volume 1: Algorithms and Procedures. Technical Report 6, U.S. Department of Commerce, NOAA, NESDIS.

de Boyer Montégut C, Madec G, Fischer A S, Lazar A, Iudicone D. 2004. Mixed layer depth over the global ocean: an examination of profile data and a profile-based climatology. J. Geophys Res., 109(C12): C12003.1–C12003.20

di Lorenzo E. 2003. Seasonal dynamics of the surface circulation in the southern California Current System. Deep-Sea Res. Part II, 50(14–16): 2 371–2 388.

Dinniman M S, Klinck J M, Smith Jr W O. 2003. Cross shelf exchange in a model of the Ross Sea circulation and biogeochemistry. Deep-Sea Res. Part II, 50(22–26): 3 103–3 120.

Gill A E, Niller P P. 1973. The theory of the seasonal variability in the ocean. Deep-Sea Res., 20(2): 141–177.

Haidvogel D B, Arango H G, Hedstrom K, Beckmann A, Malanotte-Rizzoli P, Shchepetkin A F. 2000. Model evaluation experiments in the North Atlantic Basin: Simulations in nonlinear terrain-following coordinates. Dyn. Atmos. Oceans, 32(3–4): 239–281.

Kara A B, Rochford P A, Hurlburt Harley E. 2000. An optimal definition for ocean mixed layer depth. J. Geophys. Res., 105(C7): 16 803–16 822.

Kelly K A, Qiu B. 1995. Heat flux estimates for the western North Atlantic, Part I, Assimilation of satellite data into a mixed layer model. J. Phys. Oceanogr., 25(10): 2 344–2 360.

Large W G, McWilliams J C, Doney S C. 1994. Oceanic Vertical Mixing: A Review and a Model with a Nonlocal Boundary Layer Parameterization. Rev. Geophys., 32(4): 363–403.

Large W G, Yeager S G. 2004. Diurnal to Decadal Global Forcing for Ocean and Sea-Ice Models: The data Sets and Flux Climatologies, Technical Report TN-460+STR, NCAR. 105p.

Liu Q Y, Sun J L, Jia X J. 2002. Numerical simulation and experiments of the upper mixed layer in the northern south china sea in spring. Oceanologia et Limnologia Sinica, 33(5): 526–525. (in Chinese with English abstract)

Marchesiello P, McWilliams J C, Shchepetkin A. 2003. Equilibrium structure and dynamics of the California Current System. J. Phys. Oceanogr., 33(4): 753–783.

Miller J R. 1976. The salinity effect in a mixed layer ocean model. J. Phys. Oceanogr., 6(1): 29–35.

Monterey G, Levitus S. 1997. Seasonal Variability of Mixed Layer Depth for the World Ocean. U.S. Gov. Printing Office, Wash. DC. 96p.

Ohlmann J C, Siegel D A, Gautier C. 1996. Ocean mixed layer radiant heating and solar penetration: a global analysis. J. Climate, 9(10): 2 265–2 280.

Price J F, Weller R A, Pinkel R. 1986. Diurnal cycling: observations and models of the upper ocean response to diurnal heating, Cooling and wind mixing. J. Geophys. Res., 91(C7): 8 411–8 427.

Sprintall J, Tomczak M. 1992. Evidence of the barrier layer in the surface layer of the tropics. J. Geophys. Res., 97(C6): 7 305–7 316.

Suga T, Motoki K, Aoki Y, Macdonald A M. 2004. The North Pacific climatology of winter mixed layer and mode waters. J. Phys. Oceanogr., 34(1): 3–22.

Thomson R E, Fine I V. 2003. Estimating mixed layer depth for oceanic profile data. J. Atmos. Oceanic. Technol., 20(2): 319–329.

Udaya Bhaskar T V S, Swain D, Ravichandran M. 2006. Inferring mixed-layer depth variability from Argo observations in the western Indian Ocean. Journal of Marine Research, 64(3): 393–406.

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