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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))
Article

Abstract

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.

Keywords

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.

Notes

Acknowledgements

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.

References

  1. Achutarao K, Sperber KR in collaboration with the CMIP modeling groups (2002) Simulation of the El Niño southern oscillation: results from the coupled model intercomparison project. Clim Dyn 19:191–209CrossRefGoogle Scholar
  2. Allen MR, Stainforth DA (2002) Towards objective probabilistic climate forecasting. Nature 419:228CrossRefPubMedGoogle Scholar
  3. Basnett TA, Parker DE (1997) Development of the global mean sea level pressure data set GMSLP2. Hadley Centre Climate Research Technical Note CRTN 79Google Scholar
  4. Buizza R, Miller M, Palmer TN (1999) Stochastic representation of model uncertainties in the ECMWF EPS. Quart J R Met Soc 125:2887–2908CrossRefGoogle Scholar
  5. Cane MA, Clement AC, Kaplan A, Kushnir Y, Pozdnyakov D, Seager SE, Zebiak SE, Murtugudde R (1997) Twentieth century sea surface temperature trends. Science 275:957–960CrossRefPubMedGoogle Scholar
  6. Clement A, Seager R (1999) Climate and tropical oceans. J Clim 12:3383–3401CrossRefGoogle Scholar
  7. Collins M (2000) Understanding uncertainties in the response of ENSO to greenhouse warming. Geophys Res Lett 27(21):3509–3513CrossRefGoogle Scholar
  8. Corti S, Molteni F, Palmer TN (1999) Signature of recent climate change in frequencies of natural atmospheric circulation regimes. Nature 398:799–802CrossRefGoogle Scholar
  9. Covey C, AchutaRao KM, Cubasch U, Jones P, Lambert SJ, Mann ME, Phillips TJ, Taylor KE (2003) An overview of the results of the coupled model intercomparison project. Global Planetary Change 37:103–133CrossRefGoogle Scholar
  10. Cox PM, Betts RA, Jones CD, Spall SA, Totterdell I (2000) Acceleration of global warming due to carbon cycle feedbacks in a coupled climate model. Nature 408:184–187CrossRefPubMedGoogle Scholar
  11. Cox PM, Betts RA, Collins M, Harris P, Huntingford C, Jones CD (2004) Amazon dieback under climate-carbon cycle projections for the 21st century. Theor Appl Climatol 78:137–156. DOI: 10.1007/s00704-004-0049-4 CrossRefGoogle Scholar
  12. Cubasch U, Meehl GA et al (2001) The scientific basis. Contribution of working group I to the third assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press, CambridgeGoogle Scholar
  13. Ebbesmeyer CC, Cayan DR, McLain DR, Nichols FH, Peterson DH, Redmond KT (1991). 1976 step in the Pacific climate: forty environmental changes between 1968–1975 and 1977–1984. In: Betancourt JL, Sharp VL (eds) Proceedings of the 7th annual Pacific climate (PACLIM) workshop, April 1990. California Department of Water Resources. Interagency Ecological Studies Program Technical Report 26Google Scholar
  14. Graham NE (1994) Decadal-scale climate variability in the tropical and North Pacific during the 1970s and 1980s: observations and model results. Clim Dyn 10:135–162CrossRefGoogle Scholar
  15. Guilderson TM, Schrag D (1998) Abrupt shift in subsurface temperature in the tropical Pacific associated with changes in El Niño. Science 281:240–243CrossRefPubMedGoogle Scholar
  16. Jin F-F, Hu Z-Z, Latif M, Bengtsson L, Roeckner E (2001) Dynamical and cloud–radiation feedbacks in El Niño and greenhouse warming. Geophys Res Lett 28:1539–1542CrossRefGoogle Scholar
  17. Knutson TR, Manabe S (1995) Time-mean response over the tropical Pacific to increased CO2 in a coupled ocean–atmosphere model. J Climate 8:2181–2199CrossRefGoogle Scholar
  18. Kuntson TR, Manabe S (1998) Model assessment of decadal variability and trends in the tropical Pacific ocean. J Climate 11:2273–2296CrossRefGoogle Scholar
  19. Latif M, Roeckner E, Mikolajewicz U, Voss R (2000) Tropical stabilization of the thermohaline circulation in a greenhouse warming simulation. J Climate 13:1809–1813CrossRefGoogle Scholar
  20. Li T, Hogan TF, Chang C-P (2000) Dynamic and thermodynamic regulation of ocean warming. J Atmos Sci 57:3353–3365CrossRefGoogle Scholar
  21. McDonald RE, Bleaken DG, Cresswell DR, Pope VD, Senior CA (2004) Tropical storms: representation and diagnosis in climate models and the impacts of climate change. Clim Dyn (in press). DOI:10.1007/s00382-004-0491-0 Google Scholar
  22. Meehl GA, Washington WM (1999) El Niño-like climate change in model with increased atmospheric CO2 concentrations. Nature 382:56–60CrossRefGoogle Scholar
  23. Meehl GA, Collins W, Boville B, Kiehl JT, Wigley TML, Arblaster JM (2000) Response of the NCAR climate system model to increased CO2 and the role of physical processes. J Climate 13:1879–1898CrossRefGoogle Scholar
  24. Noda A, Yoshimatsu K, Yukimoto S, Yamahuchi K, Yamaki S (1999) Relationship between natural variability and CO2-induced warming pattern: MRI AOGCM experiment. In: 10th symposium on global change studies, American Meteorological Society PublicationGoogle Scholar
  25. Pierrehumbert RT (1995) Thermostats, radiator fins, and the local runaway greenhouse. J Atmos Sci 52:1784–1806CrossRefGoogle Scholar
  26. Rayner NA, Parker DE, Horton EB, Folland CK, Alexander LV, Rowell DP, Kent EC, Kaplan, A (2004) Globally complete analyses of SST, sea-ice and night marine air temperature, 1871–2000. J Geophys Res (in press)Google Scholar
  27. Senior CA (1999) Comparison of mechanisms of cloud–climate feedbacks in a GCM. J Climate 12:1480–1489CrossRefGoogle Scholar
  28. Sun, DZ, Liu, Z (1996) Dynamic ocean–atmosphere coupling: a thermostat for the tropics. Science 272:1148–1150PubMedGoogle Scholar
  29. Timmermann A, Oberhuber J, Bacher A, Esch M, Latif M, Roeckner E (1999) Increased El Niño frequency in a climate model forced by future greenhouse warming. Nature 398:694–696CrossRefGoogle Scholar
  30. Trenberth KE, Hurrell JW (1994) Decadal atmosphere–ocean variations in the Pacific. Clim Dyn 9:303–319CrossRefGoogle Scholar
  31. Xie P, Arkin PA (1997) Global precipitation: a 17-year monthly analysis based on gauge observations, satellite estimates and numerical model outputs. Bull Am Met Soc 78:2539–2558CrossRefGoogle Scholar

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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