Chinese Science Bulletin

, Volume 57, Issue 26, pp 3509–3517 | Cite as

Preliminary results of a new global ocean reanalysis

  • DongXiao Wang
  • YingHao QinEmail author
  • XianJun Xiao
  • ZuQiang Zhang
  • FengMin Wu
Open Access
Article Oceanology


Using a new global ocean reanalysis of the second generation Global Ocean Data Assimilation System of the Beijing Climate Center (BCC_GODAS2.0) spanning the period 1990–2009, we firstly quantify the accuracy of BCC_GODAS2.0 in representing the temperature and salinity by comparing with OISST and SODA data. The results show that the assimilation system may effectively improve the estimations of temperature and salinity by assimilating all kinds of observations, especially in the equatorial eastern Pacific. Moreover, the root mean square errors of monthly temperature and salinity are respectively reduced by 0.53°C and 0.28 psu, compared with the model control simulation results. Then, the applicability of this ocean reanalysis for sea surface temperature (SST) anomaly variability in the tropical Pacific is evaluated with the observational HadISST data. The NINO3 index of the new reanalysis shows a good agreement with that of HadISST, with a correlation of 93.6%. Variations in SST from BCC_GODAS2.0 are similar to those obtained from HadISST data along the equator, showing the major large zonal-scale features such as the strong magnitude of seasonal cycle. The amplitude of SST anomaly standard deviation in the equatorial eastern Pacific is also closer to observations (HadISST) than NCEP GODAS does. Besides, the first two leading empirical orthogonal function (EOF) modes of the monthly SST anomalies over the tropical Pacific region are explored. The EOF1 pattern of BCC_GODAS2.0 captures a traditional El Niño pattern, which improves magnitudes of the positive SST anomaly in the cold tongue of the eastern Pacific. The EOF2 pattern exhibits a El Niño Modoki pattern. Comparatively, the EOF2 pattern of BCC_GODAS2.0 extends more strongly toward the subtropics. It also overcomes the problem that negative loadings are confined in the narrow equatorial eastern Pacific. Consequently, the magnitude and spatial distribution of the leading EOF patterns of BCC_GODAS2.0 are well consistent with those of HadISST.


BCC_GODAS2.0 temperature salinity EOF El Niño El Niño Modoki 


  1. 1.
    Talagrand O. Assimilation of observations: An introduction. J Meteor Soc Jpn, 1997, 75: 191–209Google Scholar
  2. 2.
    You X B, Zhou G Q, Zhu J, et al. Sea temperature data assimilation system for the China Sea and adjacent areas. Chin Sci Bull, 2003, 48: 70–76CrossRefGoogle Scholar
  3. 3.
    Shu Y Q, Wang D X, Zhu J, et al. The 4-D structure of upwelling and Pearl River plume in the northem South China Sea during summer 2008 revealed by a data assimilation model. Ocean Model, 2011, 36: 228–241CrossRefGoogle Scholar
  4. 4.
    Bell M J, Forbes R M, Hines A. Assessment of the FOAM global data assimilation system for real-time operational ocean forecasting. J Mar Syst, 2000, 25: 1–22CrossRefGoogle Scholar
  5. 5.
    Behringer D W, Ji M, Leetmaa A. An improved coupled model for ENSO prediction and implications for ocean initialization. Part I: The ocean data assimilation system. Mon Weather Rev, 1998, 126: 1013–1021CrossRefGoogle Scholar
  6. 6.
    De Mey P, Benkiran M. A multivariate reduced-order optimal interpolation method and its application to the Mediterranean basin-scale circulation, In: Pinardi N, Woods J, eds. Ocean Forecasting, Conceptual basis and Applications. Berlin: Springer-Verlag, 2002Google Scholar
  7. 7.
    Zhu J, Zhou G Q, Yan C, et al. A three-dimensional variational ocean data assimilation system: Scheme and preliminary results. Sci China Ser D-Earth Sci, 2006, 49: 1212–1222CrossRefGoogle Scholar
  8. 8.
    Yan C X, Zhu J, Zhou G Q. Impacts of XBT, TAO, altimetry, and ARGO observations on the tropic Pacific Ocean data assimilation. Adv Atmos Sci, 2007, 24: 383–398CrossRefGoogle Scholar
  9. 9.
    Xiao X J, Wang D X, Yan C X, et al. Evaluation of a 3dVAR system for the South China Sea. Prog Nat Sci, 2008, 18: 547–554CrossRefGoogle Scholar
  10. 10.
    Liu Y M, Zhang R H, Yin Y H, et al. The application of ARGO data to the global ocean data assimilation operational system of NCC. Acta Meteor Sin, 2005, 29: 355–365Google Scholar
  11. 11.
    Liu Y M, Li W J, Zhang P Q. A global 4-dimensional ocean data assimilation system and the studies on its results in the tropic Pacific (in Chinese). Acta Oceanol Sin, 2005, 27: 27–35Google Scholar
  12. 12.
    Li X Y, Qin D H, Xiao C D, et al. Progress regarding climate change during recent years (in Chinese). Chin Sci Bull (Chin Ver), 2011, 56: 3029–3040CrossRefGoogle Scholar
  13. 13.
    Xiao X J, He N, Zhang Z Q, et al. Variation assimilation using satellite data of sea surface temperature and altimeter (in Chinese). J Trop Oceanogr, 2011, 30: 1–8Google Scholar
  14. 14.
    Haines K. A direct method for assimilating sea surface height data into ocean models with adjustments to the deep circulation. J Phys Oceanogr, 1991, 21: 843–868CrossRefGoogle Scholar
  15. 15.
    Hayden C M, Purser R J. Recursive filter objective analysis of meteorological fields: Applications to NESDIS operational processing. J Appl Meteor, 1995, 34: 3–15CrossRefGoogle Scholar
  16. 16.
    Troccoli A, Haines K. Use of the temperature-salinity relation in a data assimilation context. J Atmos Oceanic Technol, 1999, 16: 2011–2025CrossRefGoogle Scholar
  17. 17.
    Yan C X, Zhu J, Li R, et al. Roles of vertical correlation of the background error and T-S relation in estimation temperature and salinity profiles from sea surface dynamic height. J Geophys Res, 2004, 109: C08010CrossRefGoogle Scholar
  18. 18.
    Zhu J, Yan C X. Nonlinear balance constraints in 3DVAR data assimilation. Sci China Ser D-Earth Sci, 2006, 49: 331–336CrossRefGoogle Scholar
  19. 19.
    Griffies S M, Harrison M J, Pacanowski R C, et al. A Technical Guide to MOM4, 2003. 295Google Scholar
  20. 20.
    Smith W H F, Sandwell D T. Global seafloor topography from satellite altimetry and ship depth soundings. Science, 1997, 277: 1957–1962Google Scholar
  21. 21.
    Edwards M O. Global gridded elevation and bathymetry (ETOPO5), digital raster data on a 5-minute geographic (lat/lon) 2160*4320 (centroid-registeredg) grid. Boulder, NOAA Natl Geophys Data Cent, 1989Google Scholar
  22. 22.
    Jakobsson M, Macnab R, Mayer L, et al. An improved bathymetric portrayal of the Arctic Ocean: Implications for ocean modeling and geological, geophysical and oceanographic analyses. Geophys Res Lett, 2008, 35: L07602CrossRefGoogle Scholar
  23. 23.
    Kanamitsu M, Ebisuzaki W, Woollen J, et al. NCEP-DOE AMIP-II Reanalysis (R-2). Bull Amer Meteor Soc, 2002, 83: 1631–1643CrossRefGoogle Scholar
  24. 24.
    Gill A E, Niiler P P. The theory of the seasonal variability in the ocean. Deep-Sea Res, 1973, 20: 141–177Google Scholar
  25. 25.
    Reynolds R W, Rayner N A, Smith T M, et al. An improved in situ and satellite SST analysis for climate. J Clim, 2002, 15: 1609–1625CrossRefGoogle Scholar
  26. 26.
    Carton J A, Chepurin G, Cao X, et al. A simple ocean data assimilation analysis of the global upper ocean 1950–95. Part I: Methodology. J Phys Oceanogr, 2000, 30: 294–309CrossRefGoogle Scholar
  27. 27.
    Carton J A, Chepurin G, Cao X, et al. A simple ocean data assimilation analysis of the global upper ocean 1950–95. Part II: Results. J Phys Oceanogr, 2000, 30: 311–326CrossRefGoogle Scholar
  28. 28.
    Rayner N A, Parker D E, Horton E B, et al. Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J Geophys Res, 2003, 108: 4407CrossRefGoogle Scholar
  29. 29.
    Xie S P. Interaction between the annual and interannual variations in the equatorial Pacific. J Phys Oceanogr, 1995, 25: 1930–1941CrossRefGoogle Scholar
  30. 30.
    Webster P J, Yang S. Monsoon and ENSO: Selectively interactive systems. Quart J Roy Meteor Soc, 1992, 118: 877–926CrossRefGoogle Scholar
  31. 31.
    Wyrtki K. Water displacements in the Pacific and the genesis of El Niño cycles. J Geophys Res, 1985, 90: 7129–7132CrossRefGoogle Scholar
  32. 32.
    Rasmusson E M, Carpenter T H. Variations in tropical sea surface temperature and surface wind fields associated with the Southern Oscillation/El Niño. Mon Weather Rev, 1982, 110: 354–384CrossRefGoogle Scholar
  33. 33.
    Ashok K, Behera S K, Rao S A, et al. El Niño Modoki and its possible teleconnection. J Geophys Res, 2007, 112: C11007CrossRefGoogle Scholar

Copyright information

© The Author(s) 2012

Authors and Affiliations

  • DongXiao Wang
    • 1
  • YingHao Qin
    • 1
    • 2
    Email author
  • XianJun Xiao
    • 3
  • ZuQiang Zhang
    • 3
  • FengMin Wu
    • 4
  1. 1.State Key Laboratory of Tropical Oceanography, South China Sea Institute of OceanologyChinese Academy of SciencesGuangzhouChina
  2. 2.Graduate University of Chinese Academy of SciencesBeijingChina
  3. 3.National Climate CenterBeijingChina
  4. 4.Nanjing University of Information Science and TechnologyNanjingChina

Personalised recommendations