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Mid-Pliocene El Niño/Southern Oscillation suppressed by Pacific intertropical convergence zone shift

Nature Geoscience volume 15, pages 726–734 (2022)Cite this article

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Abstract

The El Niño/Southern Oscillation (ENSO), the dominant driver of year-to-year climate variability in the equatorial Pacific Ocean, impacts climate pattern across the globe. However, the response of the ENSO system to past and potential future temperature increases is not fully understood. Here we investigate ENSO variability in the warmer climate of the mid-Pliocene (~3.0–3.3 Ma), when surface temperatures were ~2–3 °C above modern values, in a large ensemble of climate models—the Pliocene Model Intercomparison Project. We show that the ensemble consistently suggests a weakening of ENSO variability, with a mean reduction of 25% (±16%). We further show that shifts in the equatorial Pacific mean state cannot fully explain these changes. Instead, ENSO was suppressed by a series of off-equatorial processes triggered by a northward displacement of the Pacific intertropical convergence zone: weakened convective feedback and intensified Southern Hemisphere circulation, which inhibit various processes that initiate ENSO. The connection between the climatological intertropical convergence zone position and ENSO we find in the past is expected to operate in our warming world with important ramifications for ENSO variability.

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Fig. 1: Simulated mid-Pliocene tropical variability changes.
Fig. 2: Equatorial Pacific Ocean changes.
Fig. 3: ITCZ–ENSO relationship.
Fig. 4: Changes to potential ENSO triggers.
Fig. 5: Energetic constraints for the ITCZ position.
Fig. 6: Schematic of the drivers of suppressed ENSO activity in the mPWP.

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Data availability

PlioMIP2 data (with exception of IPSL-CM6A and GISS2.1 G) are available upon request to Alan M. Haywood (a.m.haywood@leeds.ac.uk). PlioMIP2 data from CESM2, EC-Earth3.3, NorESM1-F, IPSL-CM6A and GISS2.1 G can be obtained directly through the Earth System Grid Federation repository (ESGF; https://esgf-node.llnl.gov/search/cmip6/). Observational SST and precipitation data can be found in the NOAA-USA (NOAA Extended Reconstructed SST version 5) and NCAR-USA (Global precipitation climatology project) online repositories, respectively.

Code availability

Computer codes are available at https://github.com/gmpontes/Nature_Geoscience_ENSO_ITCZ_PlioMIP.git or upon request to the corresponding author.

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Acknowledgements

This work was supported by the São Paulo Research Foundation (FAPESP-Brazil grants no. 2016/23670-0, no. 2019/0882-1 and no. 2021/11035-6), the Australian Research Council (ARC FT160100495) including the ARC Centre of Excellence for Climate Extremes (CE110001028) and the NCI National Facility, Canberra. A.S. is supported by CSHOR, a joint research centre between QNLM and CSIRO, and the Australian Government’s National Environmental Science Program. PlioMIP2 experiments were supported by FP7 Ideas Programme: European Research Council, Past Earth Network, CEMAC—University of Leeds, JSPS, Earth Simulator at JAMSTEC, Helmholtz Climate Initiative REKLIM, Alfred Wegener Institute’s research programme Marine, Coastal and Polar Systems, Swedish Research Council, Swedish National Infrastructure for Computing, Canadian Innovation Foundation, UNINETT Sigma2—the National Infrastructure for High Performance Computing and Data Storage in Norway, Très Grand Centre de calcul du CEA—GENCI, National Science Foundation (NSF—USA), SURFsara Dutch National Computing and Netherlands Organisation for Scientific Research, Exact Sciences. This research is sponsored by National Science Foundation Grants 2103055 to R.F. and 1418411 to B.O.-B. The CESM project is supported primarily by the National Science Foundation. This material is based on work supported by NCAR, which is a major facility sponsored by the National Science Foundation under Cooperative Agreement 1852977. Computing and data storage resources, including the Cheyenne supercomputer (https://doi.org/10.5065/D6RX99HX), were provided by the Computational and Information Systems Laboratory (CISL) at NCAR.

Author information

Authors and Affiliations

  1. Oceanographic Institute, University of São Paulo, São Paulo, Brazil

    Gabriel M. Pontes & Ilana Wainer

  2. Climate Change Research Centre, The University of New South Wales, Sydney, New South Wales, Australia

    Andréa S. Taschetto, Alex Sen Gupta & Agus Santoso

  3. ARC Centre of Excellence for Climate Extremes, The University of New South Wales, Sydney, New South Wales, Australia

    Andréa S. Taschetto, Alex Sen Gupta & Agus Santoso

  4. Centre for Southern Hemisphere Oceans Research CSHOR, CSIRO Oceans and Atmosphere, Hobart, Tasmania, Australia

    Agus Santoso

  5. School of Earth and Environment, University of Leeds, Leeds, UK

    Alan M. Haywood, Stephen J. Hunter & Julia C. Tindall

  6. Centre for Earth System Dynamics, Atmosphere and Ocean Research Institute, University of Tokyo, Tokyo, Japan

    Wing-Le Chan & Ayako Abe-Ouchi

  7. Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany

    Christian Stepanek & Gerrit Lohmann

  8. NASA/GISS, Columbia CCSR, Columbia University, New York, NY, USA

    Mark A. Chandler & Linda E. Sohl

  9. Department of Physics, University of Toronto, Toronto, Ontario, Canada

    W. Richard Peltier & Deepak Chandan

  10. Faculty of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Japan

    Youichi Kamae

  11. NORCE Norwegian Research Centre, Bjerknes Centre for Climate Research, Bergen, Norway

    Kerim H. Nisancioglu & Zhongshi Zhang

  12. Laboratoire des Sciences du Climat et de l’Environnement, LSCE/IPSL, CEA-CNRS-UVSQ Université Paris-Saclay, Gif-sur-Yvette, France

    Camille Contoux & Ning Tan

  13. Key Laboratory of Cenozoic Geology and Environment, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, China

    Ning Tan

  14. Department of Physical Geography and Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden

    Qiong Zhang

  15. National Center for Atmospheric Research, Boulder, CO, USA

    Bette L. Otto-Bliesner & Esther C. Brady

  16. Department of Geosciences, College of Liberal Arts and Sciences, University of Connecticut, Storrs, CT, USA

    Ran Feng

  17. Institute for Marine and Atmospheric research Utrecht (IMAU), Department of Physics, Utrecht University, Utrecht, the Netherlands

    Anna S. von der Heydt, Michiel L. J. Baatsen & Arthur M. Oldeman

Authors
  1. Gabriel M. Pontes
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  2. Andréa S. Taschetto
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  21. Ning Tan
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Contributions

G.M.P., A.S.T., A.S.G. and A.S. designed the study. G.M.P., A.S.T., A.S.G., A.S. and I.W. contributed to the interpretation of the data and discussions. G.M.P. conducted the analysis, prepared the figures and wrote the original manuscript. A.S.G. produced the schematic in Fig. 6. A.M.H., W.-L.C., A.A.-O., C.S., G.L., S.J.H., J.C.T., M.A.C., L.E.S., W.R.P., D.C., Y.K., K.H.N., Z.Z., C.C., N.T., Q.Z., B.L.O.-B., E.C.B., R.F., A.S.v.d.H. and M.L.J.B. performed the PlioMIP2 simulations. G.M.P. and A.S.T. performed the CAM4 experiments. All authors commented and reviewed the manuscript.

Corresponding author

Correspondence to Gabriel M. Pontes.

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The authors declare no competing interests.

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Nature Geoscience thanks Matthew Collins, Soon-Il An and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: James Super, in collaboration with the Nature Geoscience team.

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Extended data

Extended Data Fig. 1 Change in amplitude of Sea Surface Temperature anomalies in the PlioMIP1 models.

SST anomalies were computed by removing the mean annual cycle. Amplitude is defined as the standard deviation of the SST anomaly timeseries. Stippling indicates where the change is significant at the 95% level. Map created using the Basemap library for Python.

Extended Data Fig. 2 Mid-Pliocene changes in eastern Pacific SST variability by frequency bands.

Percentage change in Niño3 (5°N-5°S; 150°-90°W) standard deviation separated by interannual (dark blue) and low-frequency (>10 yr; light blue) variability. The amplitude of low-frequency oscillations is evaluated through the variance of the 11-year running mean Niño3 time series in each model. The amplitude of the interannual period is estimated as the variance of the residual time series, that is, original Niño3 timeseries subtracted from the Niño3 decadal timeseries.

Extended Data Fig. 3 Relationship between ENSO amplitude change and zonal SST difference.

Zonal SST change evaluated as the difference between the cold tongue (5°S-5°N; 120°W-100°W) and warm pool regions (5°S-5°N; 150°E-170°E) in the equatorial Pacific. PlioMIP1 models are represented by magenta squares while PlioMIP2 models are represented by red circles.

Extended Data Fig. 4 Changes in the low-level wind (850hPa) divergence.

a PlioMIP1 and b PlioMIP2 from preindustrial. Stippling indicates where changes are significant at the 95% level. A consistent increased convergence in the tropical North Pacific indicates a northward ITCZ shift across the Pacific Ocean. Maps created using the Basemap library for Python.

Extended Data Fig. 5 ENSO non-linear characteristics for each PlioMIP2 model.

Relationship between Niño3 SST anomalies (x-axis, °C) and Niño3 rainfall (y-axis, mm/day) for the last 100 years of the pre-industrial control simulation of each model. Observed relationship was computed from GPCP and ERSSTv5 datasets from 1979 to 2020. Models that simulate rainfall skew greater than 1 and rainfall anomalies greater than 5 mm/day are marked with a ‘red star’.

Extended Data Fig. 6 ITCZ-ENSO relationship for selected models.

Relationship between the change in the Niño3 amplitude and the mean October-to-February ITCZ shift for the models that correctly captured ENSO non-linear characteristics.

Extended Data Fig. 7 North Pacific Meridional Mode amplitude change.

Simulated change in the amplitude of the North Pacific Meridional Mode.

Extended Data Fig. 8 Atmospheric circulation changes for PlioMIP1 models.

a multi-model mean DJF precipitation change (mPWP minus pre-industrial). Stippling indicates where changes are significant at the 95% level. b Changes in the meridional streamfunction in the AGCM experiment forced with climatological PlioMIP1 SST and sea-ice (see Methods). c multi-model mean change in low-level (850 hPa) winds and streamfunction. Wind changes are only plotted where there is a significant change at the 95% level. Map created using the Basemap library for Python.

Extended Data Table 1 Models used in the present study
Full size table
Extended Data Table 2 PlioMIP phases 1 and 2 boundary conditions
Full size table

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Pontes, G.M., Taschetto, A.S., Sen Gupta, A. et al. Mid-Pliocene El Niño/Southern Oscillation suppressed by Pacific intertropical convergence zone shift. Nat. Geosci. 15, 726–734 (2022). https://doi.org/10.1038/s41561-022-00999-y

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