Climate Dynamics

, Volume 30, Issue 2–3, pp 277–291 | Cite as

Twentieth century ENSO characteristics in the IPCC database

  • Julie LeloupEmail author
  • Matthieu Lengaigne
  • Jean-Philippe Boulanger


In this paper, we assess and compare to observations the spatial characteristics of the twentieth Century ENSO SST variability simulated by 23 models of the IPCC-AR4/CMIP3 database. The analysis is confined to the SST anomalies along the equatorial Pacific and is based on the use of a non-linear neural classification algorithm, the Self-Organizing Maps. Systematic biases include a larger than observed proportion for modelled ENSO maximum variability occurring in the Western Pacific. No clear relationship is found between this bias and the characteristics of the modelled mean state bias in the equatorial Pacific. This bias is mainly related to a misrepresentation of both El Niño and La Niña termination phases for most of the models. In contrast, the onset phase is quite well simulated. Modelled El Niño and La Niña peak phases display an asymmetric bias. Whereas the main bias of the modelled El Niño peak is to exhibit a maximum in the western Pacific, the simulated La Niña bias mainly occurs in the central Pacific. In addition, some models are able to capture the observed El Niño peak characteristics while none of them realistically simulate La Niña peaks. It also arises that the models closest to the observations score unevenly in reproducing the different phases, preventing an accurate classification of the models quality to reproduce the overall ENSO-like variability.


Main Bias CMIP3 Database SSTA Maximum Referent Vector 
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 modelling groups for providing their data for analysis, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) for collecting and archiving the model output, and the JSC/CLIVAR Working Group on Coupled Modelling (WGCM) for organizing the model data analysis activity. The multi-model data archive is supported by the Office of Science, US Department of Energy. We also acknowledge the modeling group of INGV-SXG. SOM Toolbox is Copyright (C) 2000–2005 by Esa Alhoniemi, Johan Himberg, Juha Parhankangas and Juha Vesanto and freely available at The authors are grateful to the reviewers which comments helped improving the original manuscript. Authors are thankful to Éric Guilyardi, Aymeric Chazottes, Sylvie Thiria, and Julien Brajard for stimulating discussions. J. L. was founded by the european project claris (, vartrop team/locean/cnrs, and aci-fns French Program under the project mc2 and M. L. thanks the LEFE project.


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Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  • Julie Leloup
    • 1
    Email author
  • Matthieu Lengaigne
    • 1
  • Jean-Philippe Boulanger
    • 1
  1. 1.Locean-ipsl/ird/cnrs, UPMC, Boîte 100Paris Cedex 05France

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