Increased variability of eastern Pacific El Niño under greenhouse warming

Abstract

The El Niño–Southern Oscillation (ENSO) is the dominant and most consequential climate variation on Earth, and is characterized by warming of equatorial Pacific sea surface temperatures (SSTs) during the El Niño phase and cooling during the La Niña phase. ENSO events tend to have a centre—corresponding to the location of the maximum SST anomaly—in either the central equatorial Pacific (5° S–5° N, 160° E–150° W) or the eastern equatorial Pacific (5° S–5° N, 150°–90° W); these two distinct types of ENSO event are referred to as the CP-ENSO and EP-ENSO regimes, respectively. How the ENSO may change under future greenhouse warming is unknown, owing to a lack of inter-model agreement over the response of SSTs in the eastern equatorial Pacific to such warming. Here we find a robust increase in future EP-ENSO SST variability among CMIP5 climate models that simulate the two distinct ENSO regimes. We show that the EP-ENSO SST anomaly pattern and its centre differ greatly from one model to another, and therefore cannot be well represented by a single SST ‘index’ at the observed centre. However, although the locations of the anomaly centres differ in each model, we find a robust increase in SST variability at each anomaly centre across the majority of models considered. This increase in variability is largely due to greenhouse-warming-induced intensification of upper-ocean stratification in the equatorial Pacific, which enhances ocean–atmosphere coupling. An increase in SST variance implies an increase in the number of ‘strong’ EP-El Niño events (corresponding to large SST anomalies) and associated extreme weather events.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Identifying the EP-ENSO anomaly centre in observations and models.
Fig. 2: Projected increase in EP-ENSO variance.
Fig. 3: Mechanism for the projected increase in EP-ENSO variance.

Data availability

Data related to this paper can be downloaded from the following: HadISST v1.1, https://www.esrl.noaa.gov/psd/data/gridded/data.hadsst.html; ERSST v5, https://www.ncdc.noaa.gov/data-access/marineocean-data/extended-reconstructed-sea-surface-temperature-ersst-v5; OISST v2, https://www.esrl.noaa.gov/psd/data/gridded/data.noaa.oisst.v2.html; ORA-s3, http://apdrc.soest.hawaii.edu/datadoc/ecmwf_oras3.php; ORA-s4, https://climatedataguide.ucar.edu/climate-data/oras4-ecmwf-ocean-reanalysis-and-derived-ocean-heat-content; and CMIP5 database, http://www.ipcc-data.org/sim/gcm_monthly/AR5/.

References

  1. 1.

    Ropelewski, C. F. & Halpert, M. S. Global and regional scale precipitation patterns associated with the El Niño/Southern Oscillation. Mon. Weath. Rev. 115, 1606–1626 (1987).

    ADS  Article  Google Scholar 

  2. 2.

    Glynn, P. W. & DE Weerdt, W. H. Elimination of two reef-building hydrocorals following the 1982-83 El Niño warming event. Science 253, 69–71 (1991).

    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Bove, M. C., O’Brien, J. J., Eisner, J. B., Landsea, C. W. & Niu, X. Effect of El Niño on US landfalling hurricanes, revisited. Bull. Am. Meteorol. Soc. 79, 2477–2482 (1998).

    ADS  Article  Google Scholar 

  4. 4.

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

    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Vincent, E. M. et al. Interannual variability of the South Pacific Convergence Zone and implications for tropical cyclone genesis. Clim. Dyn. 36, 1881–1896 (2011).

    Article  Google Scholar 

  6. 6.

    Valle, C. A. et al. The impact of the 1982–1983 El Niño-Southern Oscillation on seabirds in the Galapagos Islands, Ecuador. J. Geophys. Res. 92, 14437–14444 (1987).

    ADS  Article  Google Scholar 

  7. 7.

    Cai, W. et al. Increasing frequency of extreme El Niño events due to greenhouse warming. Nat. Clim. Chang. 4, 111–116 (2014).

    ADS  CAS  Article  Google Scholar 

  8. 8.

    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).

    ADS  Article  Google Scholar 

  9. 9.

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

    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Kug, J.-S., Jin, F.-F. & An, S.-I. Two types of El Niño events: cold tongue El Niño and warm pool El Niño. J. Clim. 22, 1499–1515 (2009).

    ADS  Article  Google Scholar 

  11. 11.

    Kao, H. Y. & Yu, J.-Y. Contrasting eastern-Pacific and central-Pacific types of ENSO. J. Clim. 22, 615–632 (2009).

    ADS  Article  Google Scholar 

  12. 12.

    Takahashi, K., Montecinos, A., Goubanova, K. & Dewitte, B. ENSO regimes: reinterpreting the canonical and Modoki El Niño. Geophys. Res. Lett. 38, L10704 (2011).

    ADS  Article  Google Scholar 

  13. 13.

    Dommenget, D., Bayr, T. & Frauen, C. Analysis of the non-linearity in the pattern and time evolution of El Niño Southern Oscillation. Clim. Dyn. 40, 2825–2847 (2013).

    Article  Google Scholar 

  14. 14.

    Takahashi, K. & Dewitte, B. Strong and moderate nonlinear El Niño regimes. Clim. Dyn. 46, 1627–1645 (2016).

    Article  Google Scholar 

  15. 15.

    Capotondi, A. et al. Understanding ENSO diversity. Bull. Am. Meteorol. Soc. 96, 921–938 (2015).

    ADS  Article  Google Scholar 

  16. 16.

    Cai, W. et al. More extreme swings of the South Pacific convergence zone due to greenhouse warming. Nature 488, 365–369 (2012).

    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Cai, W. et al. ENSO and Greenhouse warming. Nat. Clim. Chang. 5, 849–859 (2015).

    ADS  Article  Google Scholar 

  18. 18.

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

    ADS  CAS  Article  Google Scholar 

  19. 19.

    Watanabe, M. et al. Uncertainty in the ENSO amplitude change from the past to the future. Geophys. Res. Lett. 39, L20703 (2012).

    ADS  Article  Google Scholar 

  20. 20.

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

    ADS  Article  Google Scholar 

  21. 21.

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

    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Santoso, A. et al. Late-twentieth-century emergence of the El Niño propagation asymmetry and future projections. Nature 504, 126–130 (2013).

    ADS  CAS  Article  Google Scholar 

  23. 23.

    McPhaden, M. J., Lee, T. & McClurg, D. El Niño and its relationship to changing background conditions in the tropical Pacific. Geophys. Res. Lett. 38, L15709 (2011).

    ADS  Article  Google Scholar 

  24. 24.

    Newman, M., Shin, S.-I. & Alexander, M. A. Natural variation in ENSO flavors. Geophys. Res. Lett. 38, L14705 (2011).

    ADS  Article  Google Scholar 

  25. 25.

    Yeh, S.-W., Kirtman, B. P., Kug, J.-S., Park, W. & Latif, M. Natural variability of the central Pacific El Niño event on multi-centennial timescales. Geophys. Res. Lett. 38, L02704 (2011).

    ADS  Article  Google Scholar 

  26. 26.

    Cai, W. et al. More frequent extreme La Niña events under greenhouse warming. Nat. Clim. Chang. 5, 132–137 (2015).

    ADS  Article  Google Scholar 

  27. 27.

    Choi, K.-Y., Vecchi, G. A. & Wittenberg, A. T. ENSO transition, duration, and amplitude asymmetries: role of the nonlinear wind stress coupling in a conceptual model. J. Clim. 26, 9462–9476 (2013).

    ADS  Article  Google Scholar 

  28. 28.

    Frauen, C. & Dommenget, D. El Niño and La Niña amplitude asymmetry caused by atmospheric feedbacks. Geophys. Res. Lett. 37, L18801 (2010).

    ADS  Article  Google Scholar 

  29. 29.

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

    ADS  Article  Google Scholar 

  30. 30.

    Kim, S. T. & Yu, J.-Y. The two types of ENSO in CMIP5 models. Geophys. Res. Lett. 39, L11704 (2012).

    ADS  Google Scholar 

  31. 31.

    Ham, Y.-G. & Kug, J.-S. How well do current climate models simulate two types of El Nino? Clim. Dyn. 39, 383–398 (2012).

    Article  Google Scholar 

  32. 32.

    Karamperidou, C., Jin, F.-F. & Conroy, J. L. The importance of ENSO nonlinearities in tropical Pacific response to external forcing. Clim. Dyn. 49, 2695–2704 (2017).

    Article  Google Scholar 

  33. 33.

    Stevenson, S. et al. Will there be a significant change to El Niño in the twenty-first century? J. Clim. 25, 2129–2145 (2012).

    ADS  Article  Google Scholar 

  34. 34.

    Cane, M. A. & Sarachik, E. S. The response of a linear baroclinic equatorial ocean to periodic forcing. J. Mar. Res. 39, 651–693 (1981).

    Google Scholar 

  35. 35.

    Zebiak, S. E. & Cane, M. A. A model El Niño–Southern Oscillation. Mon. Weath. Rev. 115, 2262–2278 (1987).

    ADS  Article  Google Scholar 

  36. 36.

    Dewitte, B., Reverdin, G. & Maes, C. Vertical structure of an OGCM simulation of the equatorial Pacific Ocean in 1985–1994. J. Phys. Oceanogr. 29, 1542–1570 (1999).

    ADS  Article  Google Scholar 

  37. 37.

    Dewitte, B. et al. Low frequency variability of temperature in the vicinity of the equatorial thermocline in SODA: role of equatorial wave dynamics and ENSO asymmetry. J. Clim. 22, 5783–5795 (2009).

    ADS  Article  Google Scholar 

  38. 38.

    An, S.-I. & Jin, F.-F. Collective role of thermocline and zonal advective feedbacks in the ENSO mode. J. Clim. 14, 3421–3432 (2001).

    ADS  Article  Google Scholar 

  39. 39.

    Thual, S., Dewitte, B., An, S.-I. & Ayoub, N. Sensitivity of ENSO to stratification in a recharge-discharge conceptual model. J. Clim. 4, 4331–4348 (2011).

    Google Scholar 

  40. 40.

    Thual, S., Dewitte, B., An, S.-I., Illig, S. & Ayoub, N. Influence of recent stratification changes on ENSO stability in a conceptual model of the equatorial Pacific. J. Clim. 26, 4790–4802 (2013).

    ADS  Article  Google Scholar 

  41. 41.

    Gent, P. R. & Luyten, J. R. How much energy propagates vertically in the equatorial oceans? J. Phys. Oceanogr. 15, 997–1007 (1985).

    ADS  Article  Google Scholar 

  42. 42.

    Kim, W. & Cai, W. The importance of the eastward zonal current for generating extreme El Niño. Clim. Dyn. 42, 3005–3014 (2014).

    Article  Google Scholar 

  43. 43.

    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 

  44. 44.

    Huang, B. et al. Extended reconstructed sea surface temperature version 5 (ERSSTv5), upgrades, validations, and intercomparisons. J. Clim. 30, 8179–8205 (2017).

    ADS  Article  Google Scholar 

  45. 45.

    Reynolds, R. W. et al. An improved in situ and satellite SST analysis for climate. J. Clim. 15, 1609–1625 (2002).

    ADS  Article  Google Scholar 

  46. 46.

    Balmaseda, M. A., Vidard, A. & Anderson, D. L. T. The ECMWF ocean analysis system: ORA-S3. Mon. Weath. Rev. 136, 3018–3034 (2008).

    ADS  Article  Google Scholar 

  47. 47.

    Balmaseda, M. A., Mogensen, K. & Weaver, A. T. Evaluation of the ECMWF ocean reanalysis system ORAS4. Q. J. R. Meteorol. Soc. 139, 1132–1161 (2013).

    ADS  Article  Google Scholar 

  48. 48.

    Kalnay, E. et al. The NCEP/NCAR 40-year reanalysis project. Bull. Am. Meteorol. Soc. 77, 437–472 (1996).

    ADS  Article  Google Scholar 

  49. 49.

    Lorenz, E. N. Empirical Orthogonal Functions and Statistical Weather Prediction. Statistical Forecast Project Report 1 (MIT Department of Meteorology, 1956).

  50. 50.

    Fjelstad, J. E. Interne Wellen (Cammermeyer in Komm., 1933).

  51. 51.

    Dewitte, B., Yeh, S.-W., Moon, B.-K., Cibot, C. & Terray, L. Rectification of the ENSO variability by interdecadal changes in the equatorial background mean state in a CGCM simulation. J. Clim. 20, 2002–2021 (2007).

    ADS  Article  Google Scholar 

  52. 52.

    Yeh, S.-W., Dewitte, B., Yim, B. Y. & Noh, Y. Role of the upper ocean structure in the response of ENSO-like SST variability to global warming. Clim. Dyn. 35, 355–369 (2010).

    Article  Google Scholar 

  53. 53.

    Blumenthal, M. B. & Cane, M. A. Accounting for parameter uncertainties in model verification: an illustration with tropical sea surface temperature. J. Phys. Oceanogr. 19, 815–830 (1989).

    ADS  Article  Google Scholar 

Download references

Acknowledgements

This work is supported by the Centre for Southern Hemisphere Oceans Research, a joint research centre between QNLM and CSIRO. W.C., G.W. and A.S. are also supported by the Earth Systems and Climate Change Hub of the Australian Government’s National Environmental Science Program, and a CSIRO Office of Chief Executive Science Leader award. B.D. was supported by Fondecyt (grant number 1171861) and LEFE-GMMC. PMEL contribution number: 4817.

Reviewer information

Nature thanks Y.-G. Ham and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information

Affiliations

Authors

Contributions

W.C. conceived the study and wrote the initial manuscript in collaboration with L.W. and B.D. G.W. performed the model analysis and generated the final figures. B.D. analyses the dynamical coupling between the atmosphere and the ocean. A.S., B.D., K.T., A.C., Y.Y. and M.J.M. contributed to interpretation of the results, discussion of the associated dynamics and improvement of the paper.

Corresponding authors

Correspondence to Wenju Cai or Lixin Wu.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Properties of the observed ENSO diversity, the associated CP and EP regimes, and the nonlinear Bjerknes feedback.

a, b, The diversity means that the pattern of any ENSO event may be reconstructed by a combination of the first (a) and second (b) principal pattern from an EOF analysis on monthly SST anomalies (colour scale) and the associated wind-stress vectors (scale shown top right). The associated monthly PC time series are used to describe their evolution, and the CP- and EP-ENSO regimes by the C-index (\(({\rm{PC}}1+{\rm{PC}}2)/\sqrt{2}\)) and E-index (\(({\rm{PC}}1-{\rm{PC}}2)/\sqrt{2}\)), respectively. c, d, The anomaly pattern associated with the EP-ENSO (c) and CP-ENSO (d) for December–February (DJF), the season in which ENSO events typically mature. e, f, Response to the E-index (e) or C-index (f) of monthly zonal wind-stress (Tauu) anomalies (in units of N m−2) at the anomaly centre (see Methods) associated with the E- or C-index, respectively. The monthly wind-stress anomalies were binned in 0.25-s.d. E- or C-index intervals, and the median wind-stress anomaly and index are identified for each bin (circles). A separate linear regression was carried out for positive (red) and negative (blue) median index values. The ratio of the slope for the positive indices (S2) over that for the negative indices (S1) is taken as an indication of the nonlinear Bjerknes feedback, which operates in the EP-ENSO.

Extended Data Fig. 2 Inter-model relationship between α and the zonal wind response to SST.

a, Relationship between α and the response of monthly zonal wind anomalies to positive E-index values. Zonal wind anomalies are taken at the anomaly centre associated with the E-index. b, Relationship between α and the response of zonal wind anomalies to negative C-index values. Zonal wind anomalies are taken at the anomaly centre associated with the C-index.

Extended Data Fig. 3 Properties of the selected models in terms of ENSO diversity, the associated CP and EP regimes, and the nonlinear Bjerknes feedback.

As in Extended Data Fig. 1, but for only the 17 selected models.

Extended Data Fig. 4 Examples of the nonlinear relationship between the PC1 and PC2 time series in some selected models.

ad, December–February averages, with an apparent inverted V-shaped nonlinear relationship between PC1 and PC2 for FIO-ESM (a), CCSM4 (b), CESM1-CAM5 (c) and GFDL-ESM2M (d).

Extended Data Fig. 5 Properties of the non-selected models in terms of ENSO diversity, the associated CP and EP regimes, and nonlinear Bjerknes feedback.

As in Extended Data Fig. 3, but for only the 17 non-selected models. In this case, the nonlinear Bjerknes feedback is much weaker.

Extended Data Fig. 6 Examples of the nonlinear relationship between the PC1 and PC2 time series in some non-selected models.

ad, December–February averages for ACCESS1-3 (a), inmcm4 (b), IPSL-CM5A-MR (c) and bcc-csm1-1 (d). In contrast to the selected models (Extended Data Fig. 4), these models display a weak or no nonlinear relationship between PC1 and PC2.

Extended Data Fig. 7 Histograms of 10,000 realizations of a bootstrap method for the present-day (control) and future (climate change) periods.

Each realization is averaged over 17 models, independently resampled randomly from the 17 selected models. The standard deviation of the 10,000 inter-realization is calculated for each period. a, For the E-index, the standard deviations are 0.0263 (blue) and 0.0234 (red) for the two periods. b, For occurrences with E-index > 1.5 s.d., the standard deviations are 0.87 (blue) and 1.06 (red) for the two periods. c, For the wind-projection coefficient, the standard deviations are 0.036 (blue) and 0.042 (red) for the two periods. The difference between the future and the present-day periods is greater than the sum of the two inter-realization standard deviation values (each indicated by half of the grey shaded region). The blue and red vertical lines indicate the mean values of 10,000 inter-realizations for the present-day and future periods, respectively.

Extended Data Fig. 8 Projected change in EP-ENSO variability using the E-index and the Niño3 SST index.

a, Comparison of the standard deviation of the E-index in the present-day (1900–1999) and future (2000–2099) 100-year periods for all 34 models. 24 of the 34 models show an increase in variance (the other 10 are greyed out). b, The same as a, but for the Niño3 SST index. Error bars in the multi-model mean are calculated as the standard deviation of the 10,000 inter-realizations. The multi-model-mean change in the E-index variance (a) is statistically significant at more than the 95% confidence level, but that in the Niño3 SST index is not significant (b). The vertical line separates the selected (left) from the non-selected (right) models.

Extended Data Fig. 9 Relationship between SST warming and change in E-index for selected models.

a, Multi-model-mean warming pattern (in °C per °C of global warming (GW); colour scale). First, for each model we construct a warming pattern by calculating the difference between the average SST anomalies over the future (2000–2099) and present-day (1900–1999) periods. Second, we scale this difference by the increase in global-mean SST simulated by the model over the corresponding period. Finally, we take the mean of the scaled difference over all models to construct the multi-model-mean warming pattern. b, Inter-model relationship between the intensity of the SST warming pattern (a) and change in E-index, also scaled by the corresponding increase in global-mean SST in each model. The intensity of the scaled SST warming pattern for each model is obtained by regressing the scaled SST warming pattern for each model onto the scaled multi-model-mean SST warming pattern, using the region indicated by the black box in a. The inter-model relationship is statistically significant above the 95% confidence level, with the statistical properties shown.

Extended Data Table 1 Details of the 34 models

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Cai, W., Wang, G., Dewitte, B. et al. Increased variability of eastern Pacific El Niño under greenhouse warming. Nature 564, 201–206 (2018). https://doi.org/10.1038/s41586-018-0776-9

Download citation

Keywords

  • Greenhouse Warming
  • Anomaly Center
  • El Niño Southern Oscillation (ENSO)
  • Eastern Equatorial Pacific
  • Central Pacific ENSO

Further reading

Comments

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.