Climate Dynamics

, Volume 39, Issue 1–2, pp 365–381 | Cite as

Mixed layer heat budget of the El Niño in NCEP climate forecast system

  • Boyin Huang
  • Yan Xue
  • Hui Wang
  • Wanqiu Wang
  • Arun Kumar


The mechanisms controlling the El Niño have been studied by analyzing mixed layer heat budget of daily outputs from a free coupled simulation with the Climate Forecast System (CFS). The CFS is operational at National Centers for Environmental Prediction, and is used by Climate Prediction Center for seasonal-to-interannual prediction, particularly for the prediction of the El Niño and Southern Oscillation (ENSO) in the tropical Pacific. Our analysis shows that the development and decay of El Niño can be attributed to ocean advection in which all three components contribute. Temperature advection associated with anomalous zonal current and mean vertical upwelling contributes to the El Niño during its entire evolutionary cycle in accordance with many observational, theoretical, and modeling studies. The impact of anomalous vertical current is found to be comparable to that of mean upwelling. Temperature advection associated with mean (anomalous) meridional current in the CFS also contributes to the El Niño cycle due to strong meridional gradient of anomalous (mean) temperature. The surface heat flux, non-linearity of temperature advection, and eddies associated with tropical instabilities waves (TIW) have the tendency to damp the El Niño. Possible degradation in the analysis and closure of the heat budget based on the monthly mean (instead of daily) data is also quantified.


Climate Forecast System Vertical Advection Mixed Layer Temperature Zonal Advection Thermocline Feedback 
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.


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Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Boyin Huang
    • 1
  • Yan Xue
    • 1
  • Hui Wang
    • 1
  • Wanqiu Wang
    • 1
  • Arun Kumar
    • 1
  1. 1.National Climate Data Center, Climate Prediction CenterNOAAAshevilleUSA

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