Climate Dynamics

, Volume 37, Issue 11–12, pp 2511–2539 | Cite as

An assessment of oceanic variability in the NCEP climate forecast system reanalysis

  • Yan XueEmail author
  • Boyin Huang
  • Zeng-Zhen Hu
  • Arun Kumar
  • Caihong Wen
  • David Behringer
  • Sudhir Nadiga


At the National Centers for Environmental Prediction (NCEP), a reanalysis of the atmosphere, ocean, sea ice and land over the period 1979–2009, referred to as the climate forecast system reanalysis (CFSR), was recently completed. The oceanic component of CFSR includes many advances: (a) the MOM4 ocean model with an interactive sea-ice, (b) the 6 h coupled model forecast as the first guess, (c) inclusion of the mean climatological river runoff, and (d) high spatial (0.5° × 0.5°) and temporal (hourly) model outputs. Since the CFSR will be used by many in initializing/validating ocean models and climate research, the primary motivation of the paper is to inform the user community about the saline features in the CFSR ocean component, and how the ocean reanalysis compares with in situ observations and previous reanalysis. The net ocean surface heat flux of the CFSR has smaller biases compared to the sum of the latent and sensible heat fluxes from the objectively analyzed air-sea fluxes (OAFlux) and the shortwave and longwave radiation fluxes from the International Satellite Cloud Climatology Project (ISCCP-FD) than the NCEP/NCAR reanalysis (R1) and NCEP/DOE reanalysis (R2) in both the tropics and extratropics. The ocean surface wind stress of the CFSR has smaller biases and higher correlation with the ERA40 produced by the European Centre for Medium-Range Weather Forecasts than the R1 and R2, particularly in the tropical Indian and Pacific Ocean. The CFSR also has smaller errors compared to the QuickSCAT climatology for September 1999 to October 2009 than the R1 and R2. However, the trade winds of the CFSR in the central equatorial Pacific are too strong prior to 1999, and become close to observations once the ATOVS radiance data are assimilated in late 1998. A sudden reduction of easterly wind bias is related to the sudden onset of a warm bias in the eastern equatorial Pacific temperature around 1998/1999. The sea surface height and top 300 m heat content (HC300) of the CFSR compare with observations better than the GODAS in the tropical Indian Ocean and extratropics, but much worse in the tropical Atlantic, probably due to discontinuity in the deep ocean temperature and salinity caused by the six data streams of the CFSR. In terms of climate variability, the CFSR provides a good simulation of tropical instability waves and oceanic Kelvin waves in the tropical Pacific, and the dominant modes of HC300 that are associated with El Nino and Southern Oscillation, Indian Ocean Dipole, Pacific Decadal Oscillation and Atlantic Meridional Overturning Circulation.


Wind Stress Pacific Decadal Oscillation Atlantic Meridional Overturning Circulation Indian Ocean Dipole Mixed Layer Depth 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors thank Mike Halpert and Wanqiu Wang, and the two anonymous reviewers for their thorough reviews of the manuscript. We are also thankful for (1) the altimeter products produced by Ssalto/Duacs and distributed by Aviso with support from CNES, (2) the seasonal mean temperature and 5-year mean salinity analysis and the World Ocean Atlas by National Oceanographic Data Center, (3) the TAO mooring data by NOAA, (4) the Objectively Analyzed air-sea Fluxes (OAFlux) by Woods Hole Oceanographic Institution, (5) the ISCCP global radiative flux by NASA Goddard Institute for Space Studies, (6) the Ocean Surface Current Analysis-Real Time (OSCAR) by Earth and Space Research, (7) the TRMM Microwave Imager (TMI) SST by Remote Sensing Systems.


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Copyright information

© Springer-Verlag (outside the USA) 2010

Authors and Affiliations

  • Yan Xue
    • 1
    Email author
  • Boyin Huang
    • 1
    • 2
  • Zeng-Zhen Hu
    • 1
  • Arun Kumar
    • 1
  • Caihong Wen
    • 1
    • 2
  • David Behringer
    • 3
  • Sudhir Nadiga
    • 3
  1. 1.Climate Prediction Center, NCEP/NOAACamp SpringsUSA
  2. 2.Wyle Information SystemCamp SpringsUSA
  3. 3.Environmental Modeling Center, NCEP/NOAACamp SpringsUSA

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