Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Robust twenty-first-century projections of El Niño and related precipitation variability


The El Niño–Southern Oscillation (ENSO) drives substantial variability in rainfall1,2,3, severe weather4,5, agricultural production3,6, ecosystems7 and disease8 in many parts of the world. Given that further human-forced changes in the Earth’s climate system seem inevitable9,10, the possibility exists that the character of ENSO and its impacts might change over the coming century. Although this issue has been investigated many times during the past 20 years, there is very little consensus on future changes in ENSO, apart from an expectation that ENSO will continue to be a dominant source of year-to-year variability9,11,12. Here we show that there are in fact robust projected changes in the spatial patterns of year-to-year ENSO-driven variability in both surface temperature and precipitation. These changes are evident in the two most recent generations of climate models13,14, using four different scenarios for CO2 and other radiatively active gases14,15,16,17. By the mid- to late twenty-first century, the projections include an intensification of both El-Niño-driven drying in the western Pacific Ocean and rainfall increases in the central and eastern equatorial Pacific. Experiments with an Atmospheric General Circulation Model reveal that robust projected changes in precipitation anomalies during El Niño years are primarily determined by a nonlinear response to surface global warming. Uncertain projected changes in the amplitude of ENSO-driven surface temperature variability have only a secondary role. Projected changes in key characteristics of ENSO are consequently much clearer than previously realized.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type



Prices may be subject to local taxes which are calculated during checkout

Figure 1: Multi-model average (MMA) of the projected change in the structure of the standardized first EOF of interannual (high-pass-filtered, ‘year-to-year’) variability for the four twenty-first-century scenarios.
Figure 2: MMA of the difference between twentieth-century and twenty-first-century filtered ST and precipitation anomalies in El Niño years.
Figure 3: Precipitation along the Equator over the Pacific in the AGCM.
Figure 4: Diagram illustrating main findings.


  1. Ropelewski, C. F. & Halpert, M. S. Precipitation patterns associated with the high index phase of the Southern Oscillation. J. Clim. 2, 268–284 (2007)

    Article  ADS  Google Scholar 

  2. Australian Bureau of Meteorology and CSIRO. Climate Change in the Pacific: Scientific Assessment and New Research (eds Hennessy, K. Power, S. & Cambers, G. ). (2011)

  3. Power, S., Casey, T., Folland, C., Colman, A. & Mehta, V. Interdecadal modulation of the impact of ENSO on Australia. Clim. Dyn. 15, 234–319 (1999)

    Article  Google Scholar 

  4. Donelly, J. P. & Woodruff, J. D. Intense hurricane activity over the past 5,000 years controlled by El Niño and the west African monsoon. Nature 447, 465–468 (2007)

    Article  ADS  Google Scholar 

  5. Callaghan, J. & Power, S. B. Variability and decline in the number of severe tropical cyclones making land-fall over eastern Australia since the late nineteenth century. Clim. Dyn. 37, 647–662 (2011)

    Article  Google Scholar 

  6. Hammer, G. L. et al. Advances in the application of climate prediction in agriculture. Agric. Syst. 70, 515–553 (2001)

    Article  Google Scholar 

  7. McPhaden, M. J., Zebiak, S. E. & Glantz, M. H. ENSO as an integrating concept in earth science. Science 314, 1740–1745 (2006)

    Article  ADS  CAS  Google Scholar 

  8. Sari Kovats, R. S. et al. El Niño and health. Nature 361, 1481–1489 (2003)

    Google Scholar 

  9. Meehl, G. A. et al. in Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the 4th Assessment Report of the Intergovernmental Panel on Climate Change (eds Solomon, S. et al.) 747– 845 (Cambridge Univ. Press, 2007)

  10. Peters, G. P. et al. The challenge to keep global warming below 2°C. Nature Clim. Change 3, 4–6 (2013)

    Article  ADS  Google Scholar 

  11. Collins, M. et al. The impact of global warming on the tropical Pacific Ocean and El Niño. Nature Geosci. 3, 391–397 (2010)

    Article  ADS  CAS  Google Scholar 

  12. Vecchi, G. A. & Wittenberg, A. T. El Niño and our future climate: where do we stand? Wiley Interdisc. Rev. Clim. Change 1, 260–270 (2010)

    Article  Google Scholar 

  13. Meehl, G. et al. The WCRP CMIP3 multimodel dataset: a new era in climate change research. Bull. Am. Meteorol. Soc. 88, 1383–1394 (2007)

    Article  ADS  Google Scholar 

  14. Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of the CMIP5 and the experimental design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012)

    Article  ADS  Google Scholar 

  15. Moss, R. H. et al. The next generation of scenarios for climate change research and assessment. Nature 463, 747–756 (2010)

    Article  ADS  CAS  Google Scholar 

  16. van Vuuren et al. The representative concentration pathways: an overview. Clim. Change 109, 5–31 (2011)

  17. Nakicenovic, N. et al. Special Report on Emissions Scenarios: A Special Report of Working Group III of the Intergovernmental Panel on Climate Change (Cambridge Univ. Press, 2000)

  18. Neelin, J. D. et al. ENSO theory. J. Geophys. Res. 103, 14261–14290 (1998)

    Article  ADS  Google Scholar 

  19. Coelho, C. A. S. & Goddard, L. El Niño-induced tropical droughts in climate change projections. J. Clim. 22, 6456–6476 (2009)

    Article  ADS  Google Scholar 

  20. Kug, J. S., An, S. I., Ham, Y. G. & Kang, I. S. Changes in El Niño and La Niña teleconnections over North Pacific–America in the global warming simulations. Theor. Appl. Climatol. 100, 275–282 (2010)

    Article  ADS  Google Scholar 

  21. Meehl, G. A. & Teng, H. Multi-model changes in El Niño teleconnections over North America in a future warmer climate. Clim. Dyn. 29, 779–790 (2007)

    Article  Google Scholar 

  22. Lorenz, E. N. Empirical Orthogonal Functions and Statistical Weather Prediction (Massachusetts Inst. Technology Sci. Rep. no. 1, 1956)

    Google Scholar 

  23. Xie, S.-P. et al. Global warming pattern formation: SST and rainfall. J. Clim. 23, 966–986 (2010)

    Article  ADS  Google Scholar 

  24. Yeh, S.-W. et al. El Niño in a changing climate. Nature 461, 511–514 (2009)

    Article  ADS  CAS  Google Scholar 

  25. Guilyardi, E. et al. Understanding El Niño in ocean–atmosphere general circulation models: progress and challenges. Bull. Am. Meteorol. Soc. 90, 325–340 (2009)

    Article  ADS  Google Scholar 

  26. Power, S. B., Delage, F., Colman, R. & Moise, A. Consensus on twenty-first-century rainfall projections in climate models more widespread than previously thought. J. Clim. 25, 3792–3809 (2012)

    Article  ADS  Google Scholar 

  27. Chung, C. T. Y. et al. Nonlinear precipitation response to El Niño and global warming in the Indo-Pacific. Clim. Dyn.. (2013)

  28. Ashok, K., Behera, S. K., Rao, S. A., Weng, H. & Yamagata, T. El Niño Modoki and its possible teleconnection. J. Geophys. Res. 112, C11007 (2007)

    Article  ADS  Google Scholar 

  29. Xie, P. & Arkin, P. A. Global precipitation: a 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull. Am. Meteorol. Soc. 78, 2539–2558 (1997)

    Article  ADS  Google Scholar 

  30. Rayner, N. A. et al. Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res. 108, 4407 (2003)

    Article  Google Scholar 

Download references


We thank L. Hanson, H. Ye and A. Moise for making the CMIP data available to us, and R. Colman, A. Handasyde and H. G. Power for comments on an earlier draft. This work is supported by the Australian Climate Change Science Program (ACCSP) and the Pacific–Australia Climate Change Science and Adaptation planning Program (PACCSAP). PACCSAP is supported by AusAID, in collaboration with the Department of Industry, Innovation, Climate Change, Science, Research and Tertiary Education. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups for producing and making available their model output. For CMIP the US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led the development of software infrastructure in partnership with the Global Organization for Earth System Science Portals.

Author information

Authors and Affiliations



S.P. drafted the paper, devised the hypotheses and was primarily responsible for designing the methods used. F.D., C.C., G.K. and K.K. contributed to method design, implemented the very complex code required, performed extensive data analyses, contributed to the writing of this paper, produced the plots and assisted in their interpretation. C.C. also conducted the AGCM experiments.

Corresponding author

Correspondence to Scott Power.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Leading patterns (standardized, first EOFs) of interannual variability in surface temperature (ST) and precipitation.

Observations29,30 (top: a, ST; b, precipitation), CMIP5 models (c, ST; d, precipitation) and CMIP3 models (e, ST; f, precipitation). The model patterns are MMAs of the first EOF of each individual model. All data were spectrally filtered to remove variability with periods greater than 13 years before EOF analysis and are standardized as described in Methods.

Extended Data Figure 2 Scatter plot showing the amplitude of EOF1 time series in the twenty-first century (y axis) and the twentieth century (x axis) in individual models under the four scenarios.

Dots above the line (indicated by the dashed line) are projected increases; dots below the line y = x are projected decreases. The amplitude is measured here using the standard deviation. The time series for each model was re-scaled as described in the text. The vertical (dotted) line gives an observational estimate. The figures in the bottom right of the plot give the percentage of models above the diagonal; that is, with a projected increase.

Extended Data Figure 3 The SST anomalies (SSTAs) used in the AGCM experiments.

a, SST_EN; b, SSTA_GW (RCP8.5); c, SSTA_GW (A2); d, ΔSSTA_EN (RCP8.5); e, ΔSSTA_EN (A2). SSTA_EN is the observed SST anomaly averaged over all El Niño events between 1978 and 2009, SSTA_GW is the change in background SST projected for the late twenty-first century, and ΔSSTA_EN is the projected multi-model average change in the filtered El Niño SST pattern obtained from climate models.

Extended Data Figure 4 Multi-model average (MMA) of projected change in mean SST under the four scenarios.

Note the high degree of similarity in the spatial structures between the scenarios and the enhanced equatorial warming towards the east.

Extended Data Figure 5 Impact of bias correction (or ‘shifting’) on the MMA of ΔEOF1 of surface temperature and precipitation for the four different twenty-first-century scenarios.

Each model’s first EOF was shifted east to maximize the spatial correlation coefficient with the observed first EOF. On average, roughly 20° (CMIP3 models) and 14° (CMIP5 models) of shifting was required. Some models did not require adjustment; others required eastward shifts up to 22.5°. The MMA of both unadjusted EOFs (‘raw’) and bias-corrected EOFs (‘shifted’) are shown. The dateline is indicated by the red dashed vertical line in each panel for reference only. ST: ad, raw; eh, shifted. Precipitation: hl, raw; mp, shifted. Stippling is applied if more than 70% of models agree on the sign of change. See Methods for further details.

Extended Data Figure 6 Precipitation along the Equator over the Pacific in AGCM experiments 6 and 7.

See Methods and Extended Data Table 2 for more details on these experiments. Values in the top two panels represent differences between precipitation with α ≥ 1 and the corresponding experiment with α = 0. The twentieth-century figures, for example, are differences relative to precipitation in the experiment with α = 0 under twentieth-century conditions. The twenty-first-century figures are differences relative to precipitation in the experiment with α = 0 and SSTA_GW, and either twentieth-century CO2 concentrations (dotted lines) or late twenty-first-century CO2 concentrations (dashed lines) from experimental sets 6 and 7. a, RCP8.5; b, A2. The lower two panels depict the impact of warming only (21C − 20C, solid lines) and the impact of increasing CO2 concentrations only (21C SST&CO2 − 21C, dotted lines). c, RCP8.5; d, A2. The results show that the SST changes are primarily responsible for the precipitation response (a, b), and that the impact of CO2 change—over and above the impact it has as a result of the changes in SST it causes—is small compared with the nonlinear response (c, d).

Extended Data Figure 7 Contribution of PTH, PMCD, PCOV and E to the response in the AGCM.

a, RCP8.5; b, A2. α = 1 and 4 only. Symbols are defined in Methods.

Extended Data Figure 8 Ability of CMIP5 models to simulate the spatial structure of observed EOF2 of ST, and projected changes in the relative frequency of central Pacific and eastern Pacific El Niños.

a, Observed EOF2 of ST. b, Spatial correlation coefficient between observed EOF2 and both EOF2 and EOF3 in each model. c, Change in the relative frequency of central Pacific and eastern Pacific El Niños under the RCP8.5 scenario.

Extended Data Table 1 Climate models analysed
Extended Data Table 2 Description of the AGCM experiments conducted

PowerPoint slides

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Power, S., Delage, F., Chung, C. et al. Robust twenty-first-century projections of El Niño and related precipitation variability. Nature 502, 541–545 (2013).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing