Late-twentieth-century emergence of the El Niño propagation asymmetry and future projections


The El Niño/Southern Oscillation (ENSO) is the Earth’s most prominent source of interannual climate variability, exerting profound worldwide effects1,2,3,4,5,6,7. Despite decades of research, its behaviour continues to challenge scientists. In the eastern equatorial Pacific Ocean, the anomalously cool sea surface temperatures (SSTs) found during La Niña events and the warm waters of modest El Niño events both propagate westwards, as in the seasonal cycle7. In contrast, SST anomalies propagate eastwards during extreme El Niño events, prominently in the post-1976 period7,8,9,10, spurring unusual weather events worldwide with costly consequences3,4,5,6,11. The cause of this propagation asymmetry is currently unknown10. Here we trace the cause of the asymmetry to the variations in upper ocean currents in the equatorial Pacific, whereby the westward-flowing currents are enhanced during La Niña events but reversed during extreme El Niño events. Our results highlight that propagation asymmetry is favoured when the westward mean equatorial currents weaken, as is projected to be the case under global warming12,13,14. By analysing past and future climate simulations of an ensemble of models with more realistic propagation, we find a doubling in the occurrences of El Niño events that feature prominent eastward propagation characteristics in a warmer world. Our analysis thus suggests that more frequent emergence of propagation asymmetry will be an indication of the Earth’s warming climate.

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Figure 1: Equatorial Pacific current and zonal propagation of ENSO SST anomalies.
Figure 2: Effect of total and anomalous currents on equatorial Pacific mixed layer heat balance during extreme El Niño events and all La Niña events.
Figure 3: Schematic of competing effects on zonal propagation direction during ENSO events.
Figure 4: Statistics of El Niño occurrence characterized by prominent eastward propagation in CMIP3 and CMIP5 models.


  1. 1

    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  Google Scholar 

  2. 2

    Lehodey, P., Bertignac, M., Hampton, J., Lewis, A. & Picaut, J. El Niño/Southern Oscillation and tuna in the western Pacific. Nature 389, 715–718 (1997)

    ADS  CAS  Article  Google Scholar 

  3. 3

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

    ADS  Article  Google Scholar 

  4. 4

    Wilhite, D. A., Wood, D. A. & Meyer, S. J. in Climate Crisis (eds Glantz, M., Katz, R. & Krenz, M.) 75–78 (UNEP, 1987)

    Google Scholar 

  5. 5

    Changnon, S. A. Impacts of 1997—98 El Niño generated weather in the United States. Bull. Am. Meteorol. Soc. 80, 1819–1827 (1999)

    ADS  Article  Google Scholar 

  6. 6

    Liu, Z. & Alexander, M. Atmospheric bridge, oceanic tunnel, and global climatic teleconnections. Rev. Geophys. 45, RG2005, (2007)

    ADS  Article  Google Scholar 

  7. 7

    Wallace, J. M. et al. On the structure and evolution of ENSO-related climate variability in the tropical Pacific: lessons from TOGA. J. Geophys. Res. 103, 14241–14259 (1998)

    ADS  Article  Google Scholar 

  8. 8

    Wang, B. & An, S.-I. A mechanism for decadal changes of ENSO behaviour: roles of background wind changes. Clim. Dyn. 18, 475–486 (2002)

    Article  Google Scholar 

  9. 9

    An, S.-I. & Jin, F.-F. Nonlinearity and asymmetry of ENSO. J. Clim. 17, 2399–2412 (2004)

    ADS  Article  Google Scholar 

  10. 10

    McPhaden, M. J. & Zhang, X. Asymmetry in zonal phase propagation of ENSO sea surface temperature anomalies. Geophys. Res. Lett. 36 L13703 (2009)

    ADS  Article  Google Scholar 

  11. 11

    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  Google Scholar 

  12. 12

    Vecchi, G. A. et al. Weakening of tropical Pacific atmospheric circulation due to anthropogenic forcing. Nature 441, 73–76 (2006)

    ADS  CAS  Article  PubMed  Google Scholar 

  13. 13

    DiNezio, P. et al. Climate response of the equatorial Pacific to global warming. J. Clim. 22, 4873–4892 (2009)

    ADS  Article  Google Scholar 

  14. 14

    Sen Gupta, A., Ganachaud, A., McGregor, S., Brown, J. N. & Muir, L. Drivers of the projected changes to the Pacific Ocean equatorial circulation. Geophys. Res. Lett. 39 L09605 (2012)

    ADS  Article  Google Scholar 

  15. 15

    Graham, N. E. & Barnett, T. P. Sea surface temperature, surface wind divergence, and convection over tropical oceans. Science 238, 657–659 (1987)

    ADS  CAS  Article  PubMed  Google Scholar 

  16. 16

    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 

  17. 17

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

    ADS  Article  Google Scholar 

  18. 18

    Jin, F.-F. & Neelin, J. D. Modes of interannual tropical ocean-atmosphere interaction—a unified view. Part I: Numerical results. J. Atmos. Sci. 50, 3477–3503 (1993)

    ADS  Article  Google Scholar 

  19. 19

    Fedorov, A. & Philander, S. G. H. A stability analysis of tropical ocean-atmosphere interactions: bridging measurements and theory for El Niño. J. Clim. 14, 3086–3101 (2001)

    ADS  Article  Google Scholar 

  20. 20

    An, S.-I., Jin, F.-F. & Kang, I.-S. The role of zonal advection feedback in phase transition and growth of ENSO in the Cane-Zebiak model. J. Met. Soc. Jpn 77, 1151–1160 (1999)

    Article  Google Scholar 

  21. 21

    Kang, I.-S., An, S.-I. & Jin, F.-F. A systematic approximation of the SST anomaly equation for ENSO. J. Met. Soc. Jpn 79, 1–10 (2001)

    Article  Google Scholar 

  22. 22

    An, S.-I. & Jin, F.-F. An eigenanalysis of the interdecadal changes in the structure and frequency of ENSO mode. Geophys. Res. Lett. 27, 2573–2576 (2000)

    ADS  Article  Google Scholar 

  23. 23

    Fedorov, A. & Philander, S. G. H. Is El Niño changing? Science 288, 1997–2002 (2000)

    ADS  CAS  Article  PubMed  Google Scholar 

  24. 24

    Siedel, H. & Giese, B. S. Equatorial currents in the Pacific Ocean 1992–1997. J. Geophys. Res. 104, 7849–7863 (1999)

    ADS  Article  Google Scholar 

  25. 25

    Jin, F.-F., An, S.-I., Timmermann, A. & Zhao, J. Strong El Niño events and nonlinear dynamical heating. Geophys. Res. Lett. 30, 1120 (2003)

    ADS  Article  Google Scholar 

  26. 26

    Choi, J., An, S.-I., Dewitte, B. & Hsieh, W. W. Interactive feedback between the tropical Pacific decadal oscillation and ENSO in a coupled general circulation model. J. Clim. 22, 6597–6611 (2009)

    ADS  Article  Google Scholar 

  27. 27

    Liang, J., Yang, X.-Q. & Sun, D.-Z. The effect of ENSO events on the tropical Pacific mean climate: insights from an analytical model. J. Clim. 25, 7590–7606 (2012)

    ADS  Article  Google Scholar 

  28. 28

    Guilyardi, E. El Niño-mean state-seasonal cycle interactions in a multi-model ensemble. Clim. Dyn. 26, 329–348 (2006)

    Article  Google Scholar 

  29. 29

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

    ADS  CAS  Article  Google Scholar 

  30. 30

    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 

  31. 31

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

    ADS  Article  Google Scholar 

  32. 32

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

    ADS  Article  Google Scholar 

  33. 33

    Carton, J. A. & Giese, B. S. A reanalysis of ocean climate using simple ocean data assimilation (SODA). Mon. Weath. Rev. 136, 2999–3017 (2008)

    ADS  Article  Google Scholar 

  34. 34

    Giese, B. S. & Ray, S. El Niño variability in simple ocean data assimilation (SODA), 1871-2008. J. Geophys. Res. 116, C02024 (2011)

    ADS  Article  Google Scholar 

  35. 35

    Behringer, D. W. The Global Ocean Data Assimilation System at NCEP. In 11th Symp. on ‘Integrated Observing and Assimilation Systems for Atmosphere, Oceans, and Land Surface’ 1–12 (AMS 87th Annual Meeting, Henry B. Gonzales Convention Center, 2007)

    Google Scholar 

  36. 36

    Roberts, J. & Roberts, T. D. Use of the Butterworth low-pass filter for oceanographic data. J. Geophys. Res. 83 (C11). 5510–5514 (1978)

    ADS  Article  Google Scholar 

  37. 37

    Smith, T. M., Reynolds, R. W., Peterson, T. C. & Lawrimore, J. Improvements to NOAA's historical merged land–ocean surface temperature analysis (1880–2006). J. Clim. 21, 2283–2296 (2008)

    ADS  Article  Google Scholar 

  38. 38

    Efron, B. & Tibshirani, R. J. An Introduction to the Bootstrap 168–232, Chs 13–16 (Chapman & Hall, 1993)

    Google Scholar 

  39. 39

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

    ADS  Article  Google Scholar 

  40. 40

    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 

  41. 41

    Guilyardi, E. El Niño-mean state-seasonal cycle interactions in a multi-model ensemble. Clim. Dyn. 26, 329–348 (2006)

    Article  Google Scholar 

  42. 42

    Santoso, A., England, M. & Cai, W. Impact of Indo-Pacific feedback interactions on ENSO dynamics diagnosed using ensemble climate simulations. J. Clim. 25, 7743–7763 (2012)

    ADS  Article  Google Scholar 

  43. 43

    Aiken, C. M., Santoso, A., McGregor, S. & England, M. H. The 1970's shift in ENSO dynamics: a linear inverse model perspective. Geophys. Res. Lett. 40, 1612–1617 (2013)

    ADS  Article  Google Scholar 

  44. 44

    Trenberth, K. E. & Stepaniak, D. P. Indices of El Niño evolution. J. Clim. 14, 1697–1701 (2001)

    ADS  Article  Google Scholar 

  45. 45

    Kim, S.-T. & Jin, F.-F. An ENSO stability analysis. Part II: results from the twentieth and twenty-first century simulations of the CMIP3 models. Clim. Dyn. 36, 1609–1627 (2011)

    Article  Google Scholar 

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We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling and all modelling groups for making the CMIP data available. We thank F. Avila and J. Kajtar for downloading and processing the climate models data sets. A.S., S.M. and M.H.E. are supported by the Australian Research Council. W.C. is supported by the Australian Climate Change Science Programme. S.-I.A. was supported by the National Research Foundation of Korea funded by the Korean government (MEST) (grant number NRF-2009-C1AAA001-2009-0093042). M.J.M. is supported by NOAA. This is PMEL contribution number 3977.

Author information




A.S. and S.M. conceived the study in discussion with F.-F.J. A.S. designed and conducted the analysis. W.C. and A.S. wrote the initial draft of the paper. All authors contributed to interpreting results, presentation, and improvement to the paper.

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Correspondence to Agus Santoso.

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The authors declare no competing financial interests.

Additional information

The NOAA SST and GODAS reanalysis data are provided by the NOAA/OAR/ESRL PSD via All other reanalysis data were downloaded from the Asia-Pacific Data-Research Center of the IPRC at

Extended data figures and tables

Extended Data Figure 1 Zonal propagation of SST anomalies and effect of current on mixed layer heat balance during ENSO events.

a, SST37 anomalies along the equatorial Pacific (averaged between 5° S–5° N) over January 1959 to December 2011, with seasonal cycle and linear trend (referenced to the entire 1959–2011) removed. The arrows, whose slopes are calculated from the multi-reanalysis ensemble average, indicate zonal propagation directions. b, Composite evolution of interannual-scale heating rate (colour scale; in units of °C per month) of the equatorial Pacific mixed layer during post-1976 moderate El Niño events. The phase transition (dashed black line) tracks the evolving peak of temperature anomaly (red contours are positive and blue contours are negative) with a statistically significant linear fit slope (green line; β = −0.97, P < 0.01). c, As in b but with advection due to the total current (arrow) removed, resulting in a β* = −0.29 (red dashed line) value that is statistically significant (P < 0.05). Only statistically significant values above the 95% confidence level are shaded in colour, contoured, or marked by black arrows (grey arrows otherwise). d, As in c but for extreme El Niño events (1982, 1997) with the effect of mean current (arrows) removed. e, As in d but for post-1976 La Niña events. The β* values are 1.44 in d and 0.61 in e and are statistically significant (P < 0.01). Source data

Extended Data Figure 2 Time evolution of equatorial Pacific zonal current and wind stress.

a, The same as Fig. 1c for zonal current velocity averaged across the reanalysis products. The dashed curve highlights interdecadal variation using a 13-year running mean. Grey shading denotes two standard deviation about each mean value, representing monthly spread and variations across reanalyses. b, As in a but for surface zonal wind stress. Source data

Extended Data Figure 3 Time evolution of the equatorial Pacific zonal current across reanalysis products.

Raw time series of zonal current velocity averaged over 5° S–5° N, 160° E–90° W, capturing the Niño4 to Niño3 regions, and over the ENSO development phase (August to December). The ensemble average (1980–2006) is marked by the thick horizontal dashed line. Source data

Extended Data Figure 4 Observed characteristics of equatorial Pacific current associated with ENSO.

a, Total current evolution composited over developing phase of ENSO: extreme El Niño (dark red shading/black line), strong El Niño (red shading/dark red line), weak El Niño (pink shading/red line), and La Niña (blue shading/dark blue line). Thick lines indicate the mean composites, and the coloured shades are for one standard deviation unit above and below the means representing the spread across the different reanalyses and each classified events. b, Lead-lag monthly correlation between the reanalysis ensemble average current and Niño3 with eastward current anomalies leading warm Niño3 anomalies at three months. c, Total current velocity (vel.) averaged over September–December versus Niño3 (December–February) associated with extreme (dark red circles), strong (red circles), and weak (green circles) El Niño events in each pre-1976 and post-1976 period, with a correlation coefficient of 0.82, significant at the 99% level. Open circles indicate non-statistically significant β. The correlation (r = 0.84) remains significant at the 99% level even when these points are excluded. d, As in c but for total current versus β during all ENSO events (blue circles for La Niña). The correlation coefficient (coef.) between current and statistically significant β for El Niño is r = 0.75, which is significant at the 99% level. The dashed horizontal line in d marks half a standard deviation unit of all the El Niño slopes. Source data

Extended Data Figure 5 ENSO propagation structure in CMIP models.

a, Propagation structure in each CMIP model (grey circles for 1907–1999; black triangles for 2006–2098 period) and observations (1959–2011; the large open circle labelled ‘Obs.’). The propagation structure is defined by summing up the proportion of westward events (negative proportion) and eastward events (positive proportion) identified as El Niño (red bar for 1907–1999; red line for 2006–2098) and La Niña (blue bar for 1907–1999; blue line for 2006–2098) with statistically significant β. The colour intensities for the bars and lines indicate the four types of propagating events. The proportions of propagating events and non-propagating events add up to 1, and so the net propagation structure (grey circle or black triangle) can range from a scale of −1 if all events propagate westwards to +1 if all propagate eastwards. Eastward (westward) propagation structure is an indication for a more dominant thermocline (zonal advective) feedback mechanism. b, Propagation structure versus long-term annually averaged zonal current velocity across all CMIP models (coloured markers) in the past simulation, revealing a positive correlation (r = 0.40) significant at the 95% level (r = 0.44 for future). Open circle marks the observed counterpart using data from 1959 to 2011 for a larger event sample. c, As in b but for mean zonal wind stress, exhibiting no significant correlation (r = 0.01; r = 0.14 for the future). Models marked by dotted horizontal lines in a and squares in b and c indicate those selected for future projections (Fig. 4). Models marked with diamonds in b and c simulate realistic flow features but are saturated with eastward-propagating events that they have already produced in the past simulation (see Extended Data Fig. 10a). Source data

Extended Data Figure 6 Projected changes of long-term mean zonal wind stress, zonal current velocity, and propagation structure across the CMIP3 and CMIP5 models.

a, Future and past difference (diff.) in long-term mean zonal wind stress and zonal current velocity. b, Future and past difference in long-term mean zonal wind stress and ENSO propagation structure (Extended Data Fig. 5). c, Future and past difference in long-term mean zonal current velocity and ENSO propagation structure. The correlations between each of the variables are shown in the panels and are statistically significant at the 99% level. Removing the model outlier (miroc3-2-hires) reduces correlations in a, b and c to 0.61, 0.47 and 0.59, respectively, but are still statistically significant up to the 99% level. Source data

Extended Data Figure 7 Effect of current reversals on zonal phase propagation and future projection.

The analysis incorporates the 24 models that simulate realistic flow features (see Methods). a, Correlation between total current and phase transition slope during El Niño events in the past simulation (1907–1999). The positive correlation (r = 0.46), significant above the 99% level (with 472 data points), confirms the relationship seen in the limited observational record (Extended Data Fig. 4). b, Probability density of β for westward (grey) and eastward (red) El Niño events with (darker shading) and without (lighter shading) current reversals. c, Probability density of the difference in phase transition slope before and after the effect of total current removed from the heat balance (β − β*), for all La Niña events (blue), all El Niño events (light red), and El Niño events that co-occur with current reversals (darker red). The probability density for strong El Niño events (greater than one standard deviation) is shown by the dashed curve. d, Probability density of number of current reversals associated with any events in the past (1907–1999; blue) and future (2006–2098; red) periods. Vertical lines in d indicate the respective mean values (6.4 and 9.5 for past and future periods, respectively). The statistics in b, c and d are generated using a bootstrap sampling technique with 5,000 simulations. Source data

Extended Data Figure 8 Monthly evolution of the total current during the developing year of El Niño events in the selected CMIP3 and CMIP5 models.

Red and grey curves respectively represent El Niño events in both past and future simulations that are classified as above and below 1.5 standard deviations of Niño3 (December–February average), normalized by the standard deviation of the past period. Only events with statistically significant transition slopes are considered. The corresponding dashed curves indicate the sample averages. Each panel displays the correlation coefficient between the equatorial Pacific current (September–December average) and the Niño3 anomalies, following the observed counterpart (Extended Data Fig. 4c). Source data

Extended Data Figure 9 As Extended Data Fig. 8, but for the excluded models.

Correlation coefficients displayed in red are not statistically significant. Source data

Extended Data Figure 10 Occurrences of El Niño with prominent eastward propagation and future projection as a function of mean current, ENSO amplitude, and current reversals in the CMIP3 and CMIP5 models.

a, Number of events for each of the 40 models for the past (1907–1999; blue) and future (2006–2098; red) periods (see Methods for event criteria). The number of events over the 93 model years expected from the observed occurrences is four (dotted vertical line). For future projection, we consider models that produce an occurrence of 0–8 events (that is, doubling; dashed vertical line). Dotted horizontal lines indicate the selected models. b, Future and past difference (diff.) in event occurrences against that of the long-term mean zonal current velocity (vel.). c, As in b but against the future and past difference in ENSO amplitude as defined by the standard deviation of Niño3 index. d, ENSO amplitude difference against the difference in number of eastward-propagating events with current reversals. The correlation coefficients displayed in the panels are significant at the 95% level. Source data

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

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