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Increased variability of eastern Pacific El Niño under greenhouse warming

Nature volume 564, pages 201–206 (2018)Cite this article

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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.

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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/.

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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

Authors and Affiliations

  1. Key Laboratory of Physical Oceanography, Institute for Advanced Ocean Studies, Ocean University of China and Qingdao National Laboratory for Marine Science and Technology, Qingdao, China

    Wenju Cai, Guojian Wang & Lixin Wu

  2. Centre for Southern Hemisphere Oceans Research (CSHOR), CSIRO Oceans and Atmosphere, Hobart, Tasmania, Australia

    Wenju Cai, Guojian Wang & Agus Santoso

  3. Centro de Estudios Avanzados en Zonas Áridas, Coquimbo, Chile

    Boris Dewitte

  4. Departamento de Biología, Facultad de Ciencias del Mar, Universidad Católica del Norte, Coquimbo, Chile

    Boris Dewitte

  5. Millennium Nucleus for Ecology and Sustainable Management of Oceanic Islands, Coquimbo, Chile

    Boris Dewitte

  6. Laboratoire d’Etudes en Géophysique et Océanographie Spatiales, Toulouse, France

    Boris Dewitte & Aude Carréric

  7. Australian Research Council Centre of Excellence for Climate Extremes, The University of New South Wales, Sydney, New South Wales, Australia

    Agus Santoso

  8. Servicio Nacional de Meteorología e Hidrología del Perú—SENAMHI, Lima, Peru

    Ken Takahashi

  9. College of Global Change and Earth System Science, Beijing Normal University and University Corporation for Polar Research, Beijing, China

    Yun Yang

  10. NOAA/Pacific Marine Environmental Laboratory, Seattle, WA, USA

    Michael J. McPhaden

Authors
  1. Wenju Cai
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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.

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Correspondence to Wenju Cai or Lixin Wu.

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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.

a–d, 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.

a–d, 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
Full size table

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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

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Keywords

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

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