Meteorology and Atmospheric Physics

, Volume 61, Issue 3–4, pp 153–186 | Cite as

ENSO cycle in a coupled ocean-atmosphere model and its negative feedback mechanism

  • X. -Q. Yang
  • Q. Xie
  • Y. -Q. Ni
  • S. -S. Huang


A coupled ocean-atmosphere anomaly model has been developed for simulating ENSO cycle and its mechanism-study in this paper. After a long model run, the coupled model is successful in demonstrating ENSO-like irregular interannual variability and corresponding horizontal spatial structures. Based on the simulated results, the dynamics and the thermodynamics of the model ENSO cycle have been investigated, and in particular the negative feedback mechanisms that act to oppose instability of air-sea interaction, inducing termination of warm and cold events, have been examined. A detailed analysis of the oceanic wave dynamical properties and heat budget of the SST changes in a representative cycle suggest that the negative feedback mechanism to check the unstable growth of a warm event obviously differs from that of a cold event. The mechanism that induces decay and termination of a cold event is closely related to the negative, delayed feedback effect produced by the oceanic dynamical wave reflection at the western boundary. However, independent of the wave reflection effect, the negative feedback mechanism by which the coupled system returns from a warm event is associated with a slowly eastward-propagating coupling mode. Accompanied with the strong unstable development of the equatorial positive SST anomaly, the anomalous upwelling of cold water generated off the equator and the nonlinear anomalous meridional advection generated in the equator west of instability area jointly restrain the instability and finally plunge the system from a mature warm phase into a weak cold phase. A comparison between the results from the present model and the previous works is also discussed in this paper.


Wave Reflection Cold Event Negative Feedback Mechanism Warm Event ENSO Cycle 
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 1996

Authors and Affiliations

  • X. -Q. Yang
    • 1
  • Q. Xie
    • 2
  • Y. -Q. Ni
    • 1
  • S. -S. Huang
    • 1
  1. 1.Department of Atmospheric SciencesNanjing UniversityNanjingP. R. China
  2. 2.Department of MeteorologyAir Force Institute of MeteorologyNanjingP. R. China

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