Article Type:
Research Article

Scaling Analysis of the Sea Surface Temperature Anomaly in the South China Sea

Zijun Gan LED, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, China

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Youfang Yan LED, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, and Graduate School of the Chinese Academy of Sciences, Beijing, China

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Yiquan Qi LED, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, China

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Print Publication:
01 Apr 2007
DOI:
https://doi.org/10.1175/JTECH1981.1
Page(s):
681–687
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Abstract

Based on the data of optimum interpolation sea surface temperature (OISST), the temporal correlations of the sea surface temperature anomaly (SSTA) in the South China Sea (SCS) are studied by using the rescaled range analysis (R/S) and detrended fluctuation analysis (DFA). The results show that the scaling exponents of SSTAs are larger than 0.8. This finding indicates that the SSTAs in the SCS exhibit persistent long-range time correlation of the fluctuations and the interval spreads over a wide period, from about 1 month to 4.5 yr (4∼235 weeks). In addition, the “degree” of the correlations depends very much on the geographic locations: near to the coastal regions, the value is small, while far from the coastline, the value is relatively larger. This means that SSTAs in the central SCS are smoother than those of the coastal regions. The persistence of SST in the SCS may be used as a “minimum skill” to assess the ocean models and to evaluate their performance.

Corresponding author address: Dr. Youfang Yan, South China Sea Institute of Oceanology, Chinese Academy of Sciences, 164 W. Road, Xingang, 510301 Guangzhou, China. Email: youfangyan@scsio.ac.cn

Abstract

Based on the data of optimum interpolation sea surface temperature (OISST), the temporal correlations of the sea surface temperature anomaly (SSTA) in the South China Sea (SCS) are studied by using the rescaled range analysis (R/S) and detrended fluctuation analysis (DFA). The results show that the scaling exponents of SSTAs are larger than 0.8. This finding indicates that the SSTAs in the SCS exhibit persistent long-range time correlation of the fluctuations and the interval spreads over a wide period, from about 1 month to 4.5 yr (4∼235 weeks). In addition, the “degree” of the correlations depends very much on the geographic locations: near to the coastal regions, the value is small, while far from the coastline, the value is relatively larger. This means that SSTAs in the central SCS are smoother than those of the coastal regions. The persistence of SST in the SCS may be used as a “minimum skill” to assess the ocean models and to evaluate their performance.

Corresponding author address: Dr. Youfang Yan, South China Sea Institute of Oceanology, Chinese Academy of Sciences, 164 W. Road, Xingang, 510301 Guangzhou, China. Email: youfangyan@scsio.ac.cn

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Journal of Atmospheric and Oceanic Technology

  • Bak, P., 1996: How Nature Works: The Science of Self-Organized Criticality. Springer-Verlag, 212 pp.

  • Bunde, A., Havlin S. , Koscielny B. E. , and Schellnhuber H. J. , 2001: Long term persistence in the atmosphere: Global laws and tests of climate models. Physica A, 302 , 255–267.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Chu, P. C., Chang C. P. , and Chen Y. , 1997: Temporal and spatial variabilities of the South China Sea surface temperature anomaly. J. Geophys. Res., 102 , 20937–20955.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Feder, J., 1988: Fractals. Plenum Press, 310 pp.

  • Fitch, W. M., 1983: Random sequences. J. Mol. Biol., 163 , 171–176.

  • Havlin, S., Buldyrev S. V. , Bunde A. , Goldberger A. L. , Ivanov P. C. , Peng C. K. , and Stanley H. E. , 1999: Scaling in nature: From DNA through heartbeats to weather. Physica A, 273 , 46–69.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Hurst, H. E., 1951: Long term storage of reservoirs: An experimental study. Trans. Amer. Soc. Civ. Eng., 116 , 770–799.

  • Kiraly, A., and Janosi I. , 2002: Stochastic modeling of daily temperature fluctuations. Phys. Rev. E, 65 .doi:10.1103/PhysRevE.65.051102.

    • Search Google Scholar
    • Export Citation

  • Liu, Y. H., Cizeau P. , Meyer M. , Peng C. K. , and Stanley H. E. , 1997: Correlations in economic time series. Physica A, 245 , 437–440.

  • Matsoukas, C., Islam S. , and Rodriguez-Iturbe I. , 2000: Detrended fluctuation analysis of rainfall and streamflow time series. J. Geophys. Res., 105 , 29165–29172.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • North, C. P., and Halliwell D. I. , 1994: Bias in estimating fractal dimension with the rescaled-range (R/S) technique. Math. Geol., 26 , 531–555.

  • Ose, T., Song Y. , and Kitoh A. , 1997: Sea surface temperature in the South China Sea: An index for the Asian monsoon and ENSO system. J. Meteor. Soc. Japan, 75 , 1091–1107.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Peng, C. K., Buldyrev S. V. , Havlin S. , Simons M. , Stanley H. E. , and Goldberger A. L. , 1994: Mosaic organization of DNA nucleotides. Phys. Rev. E, 49 , 1685–1689.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Peng, C. K., Havlin S. , Hausdorff J. M. , Mietus J. E. , Stanley H. E. , and Goldberger A. L. , 1995: Fractal mechanisms and heart rate dynamics—Long-range correlations and their breakdown with disease. J. Electrocardiol., 28 , 59–65.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Peters, O., Hertlein C. , and Christensen K. , 2002: A complexity view of rainfall. Phys. Rev. Lett., 88 .doi:10.1103/PhysRevLett.88.018701.

    • Search Google Scholar
    • Export Citation

  • Qu, T., 2001: The role of ocean dynamics in determining the mean seasonal cycle of the South China Sea surface temperature. J. Geophys. Res., 106 , 6943–6955.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Reynolds, R. W., and Smith T. M. , 1994: Improved global sea surface temperature analyses using optimum interpolation. J. Climate, 7 , 929–948.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Sprott, J. C., 2003: Chaos and Time-Series Analysis. Oxford University Press, 528 pp.

  • Tian, J. W., Xu J. S. , and Wei E. B. , 2000: The wavelet analysis of satellite sea surface temperature in the South China Sea and the Pacific Ocean. Chin. Sci. Bull., 45 , 2187–2192.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wang, D. X., Zhou F. X. , Fu G. , and Qin Z. H. , 1996: Numerical study on the interannual osillation of sea surface temperature in the South China Sea. Chin. J. Oceanol. Limnol., 14 , 61–67.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Welch, P. D., 1967: The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms. IEEE Trans. Audio Electroacoust., AU-15 , 70–73.

    • Search Google Scholar
    • Export Citation
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