Climate Dynamics

, Volume 42, Issue 7–8, pp 1999–2018 | Cite as

ENSO representation in climate models: from CMIP3 to CMIP5

  • H. BellengerEmail author
  • E. Guilyardi
  • J. Leloup
  • M. Lengaigne
  • J. Vialard


We analyse the ability of CMIP3 and CMIP5 coupled ocean–atmosphere general circulation models (CGCMs) to simulate the tropical Pacific mean state and El Niño-Southern Oscillation (ENSO). The CMIP5 multi-model ensemble displays an encouraging 30 % reduction of the pervasive cold bias in the western Pacific, but no quantum leap in ENSO performance compared to CMIP3. CMIP3 and CMIP5 can thus be considered as one large ensemble (CMIP3 + CMIP5) for multi-model ENSO analysis. The too large diversity in CMIP3 ENSO amplitude is however reduced by a factor of two in CMIP5 and the ENSO life cycle (location of surface temperature anomalies, seasonal phase locking) is modestly improved. Other fundamental ENSO characteristics such as central Pacific precipitation anomalies however remain poorly represented. The sea surface temperature (SST)-latent heat flux feedback is slightly improved in the CMIP5 ensemble but the wind-SST feedback is still underestimated by 20–50 % and the shortwave-SST feedbacks remain underestimated by a factor of two. The improvement in ENSO amplitudes might therefore result from error compensations. The ability of CMIP models to simulate the SST-shortwave feedback, a major source of erroneous ENSO in CGCMs, is further detailed. In observations, this feedback is strongly nonlinear because the real atmosphere switches from subsident (positive feedback) to convective (negative feedback) regimes under the effect of seasonal and interannual variations. Only one-third of CMIP3 + CMIP5 models reproduce this regime shift, with the other models remaining locked in one of the two regimes. The modelled shortwave feedback nonlinearity increases with ENSO amplitude and the amplitude of this feedback in the spring strongly relates with the models ability to simulate ENSO phase locking. In a final stage, a subset of metrics is proposed in order to synthesize the ability of each CMIP3 and CMIP5 models to simulate ENSO main characteristics and key atmospheric feedbacks.


CMIP5 Model Zonal Wind Stress CMIP5 Ensemble ENSO Amplitude Atmospheric 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.



We acknowledge the support from the European Union EUCLIPSE project (ENV/244067, FP7), the Agence Nationale pour la Recherche projects ANR-10-Blanc-616 METRO. We also acknowledge the CMIP3 and CMIP5 modelling groups, the ESG and PRODIGUER data distribution systems. We thank the two anonymous reviewers who helped to improve this manuscript.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • H. Bellenger
    • 1
    Email author
  • E. Guilyardi
    • 1
    • 2
  • J. Leloup
    • 1
  • M. Lengaigne
    • 1
  • J. Vialard
    • 1
  2. 2.NCAS-ClimateUniversity of ReadingReadingUK

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