Climate Dynamics

, Volume 24, Issue 1, pp 89–104 | Cite as

El Niño- or La Niña-like climate change?

  • Matthew CollinsEmail author
  • The CMIP Modelling Groups (BMRC (Australia), CCC (Canada), CCSR/NIES (Japan), CERFACS (France), CSIRO (Austraila), MPI (Germany), GFDL (USA), GISS (USA), IAP (China), INM (Russia), LMD (France), MRI (Japan), NCAR (USA), NRL (USA), Hadley Centre (UK) and YNU (South Korea))


The potential for the mean climate of the tropical Pacific to shift to more El Niño-like conditions as a result of human induced climate change is subject to a considerable degree of uncertainty. The complexity of the feedback processes, the wide range of responses of different atmosphere–ocean global circulation models (AOGCMs) and difficulties with model simulation of present day El Niño southern oscillation (ENSO), all complicate the picture. By examining the components of the climate-change response that projects onto the model pattern of ENSO variability in 20 AOGCMs submitted to the coupled model inter-comparison project (CMIP), it is shown that large-scale coupled atmosphere–ocean feedbacks associated with the present day ENSO also operate on longer climate-change time scales. By linking the realism of the simulation of present day ENSO variability in the models to their patterns of future mean El Niño-like or La Niña-like climate change, it is found that those models that have the largest ENSO-like climate change also have the poorest simulation of ENSO variability. The most likely scenario (p=0.59) in a model-skill-weighted histogram of CMIP models is for no trend towards either mean El Niño-like or La Niña-like conditions. However, there remains a small probability (p=0.16) for a change to El Niño-like conditions of the order of one standard El Niño per century in the 1% per year CO2 increase scenario.


Empirical Orthogonal Function Couple Model Intercomparison Project Trend Pattern Couple Model Intercomparison Project Model Share Component 
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.



This study could not have been performed without the contributions of all the CMIP participants and the excellent work of the PCMDI CMIP team. This work was supported by the UK Department of the Environment, Food and Rural Affairs under Contract PECD/7/12/37 and by the UK National Environment Research Council under the COAPEC programme.


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Copyright information

© Springer-Verlag 2004

Authors and Affiliations

  • Matthew Collins
    • 1
    Email author
  • The CMIP Modelling Groups (BMRC (Australia), CCC (Canada), CCSR/NIES (Japan), CERFACS (France), CSIRO (Austraila), MPI (Germany), GFDL (USA), GISS (USA), IAP (China), INM (Russia), LMD (France), MRI (Japan), NCAR (USA), NRL (USA), Hadley Centre (UK) and YNU (South Korea))
  1. 1.Hadley Centre for Climate Prediction and Research, Met OfficeExeterUK

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