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Future high-resolution El Niño/Southern Oscillation dynamics

Nature Climate Change volume 11, pages 758–765 (2021)Cite this article

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Abstract

The current generation of climate models does not properly resolve oceanic mesoscale processes in tropical oceans, such as tropical instability waves. The associated deficit in explicit vertical and lateral heat exchange can further contribute to large-scale equatorial temperature biases, which in turn impact the representation of the El Niño/Southern Oscillation (ENSO) and its sensitivity to greenhouse warming. Here, using a mesoscale-resolving global climate model with an improved representation of tropical climate, we show that a quadrupling of atmospheric CO2 causes a robust weakening of future simulated ENSO sea surface temperature variability. This sensitivity is caused mainly by stronger latent heat flux damping and weaker advective feedbacks. Stratification-induced weakening of tropical instability wave activity and the corresponding growth of ENSO instability partly offset the effect of other negative dynamical feedbacks. Our results demonstrate that previous lower-resolution greenhouse warming projections did not adequately simulate important ENSO-relevant ocean mesoscale processes.

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Fig. 1: Simulated tropical Pacific mean-state SST biases in UHR-CESM and CMIP6.
Fig. 2: Simulated mean state, variability and changes in tropical SST on three timescales in UHR-CESM.
Fig. 3: Snapshots of TIW activity in terms of SST and EKE during a major El Niño and La Niña event.
Fig. 4: ENSO BJ stability index analysis within the mixed layer in UHR-CESM.
Fig. 5: Schematic diagram describing greenhouse warming leading to future ENSO suppression.

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Data availability

The data from the UHR-CESM simulations are available on the IBS Center for Climate Physics climate data server (https://climatedata.ibs.re.kr/) and upon request (https://ibsclimate.org/research/ultra-high-resolution-climate-simulation-project/). The HadISST64 can be obtained from UK Meteorological Office, Hadley Centre (https://www.metoffice.gov.uk/hadobs/hadisst/). NOAA High Resolution SST data53 were provided by the NOAA/OAR/ESRL PSL from their website https://psl.noaa.gov. The European Centre for Medium-range Weather Forecast (ECMWF) (2011): The ERA-Interim reanalysis dataset65, Copernicus Climate Change Service (C3S) is available from https://www.ecmwf.int/en/forecasts/datasets/archive-datasets/reanalysis-datasets/era-interim. The Ocean ReAnalysis System 5 (ORAS5)66 can be obtained from the ECMWF at https://www.ecmwf.int/en/research/climate-reanalysis/ocean-reanalysis.

Code availability

The CESM source code and the standard setup files for the ultra-high-resolution model simulation can be obtained from http://www.cesm.ucar.edu/models/cesm1.2/. More details on the simulations and the PE layout for a Cray XC50 computer can be found at https://ibsclimate.org/research/ultra-high-resolution-climate-simulation-project/. The data analysis was conducted using the software CDO (https://code.mpimet.mpg.de/projects/cdo/embedded/cdo.pdf), Ferret (https://ferret.pmel.noaa.gov) and MATLAB (https://de.mathworks.com/products/matlab.html). The code that was used for data processing, model analysis and figure production is freely available at https://doi.org/10.5281/zenodo.5006068.

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Acknowledgements

C.W., S.-S.L., M.F.S, A.T. and J.-E.C. were supported by the Institute for Basic Science (IBS-R028-D1). C.W. was also supported by the H2020 European Research Council (CONSTRAIN (grant no. 820829)). F.S. was supported by NASA grant no. 80NSSC17K0564. C.W. acknowledges valuable discussions with W. Cai and K. J. Stein. S.-S.L. and A. T. are grateful to J. Small for providing advice on the setup of the CESM1.2.2 model. This is IPRC publication no. 1527 and SOEST contribution no. 11365. The simulations were conducted on the IBS/ICCP supercomputer “Aleph”, 1.43-petaflop high-performance Cray XC50-LC Skylake computing system with 18,720 processor cores, 9.59-petabyte storage and 43-petabyte tape archive space.

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Authors and Affiliations

  1. Center for Climate Physics, Institute for Basic Science, Busan, Republic of Korea

    Christian Wengel, Sun-Seon Lee, Axel Timmermann & Jung-Eun Chu

  2. Pusan National University, Busan, Republic of Korea

    Christian Wengel, Sun-Seon Lee, Axel Timmermann & Jung-Eun Chu

  3. Max Planck Institute for Meteorology, Hamburg, Germany

    Christian Wengel

  4. Department of Oceanography and International Pacific Research Center, School of Ocean and Earth Science and Technology, University of Hawaiʻi at Mānoa, Honolulu, HI, USA

    Malte F. Stuecker & Fabian Schloesser

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  1. Christian Wengel
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Contributions

C.W., M.F.S. and A.T. designed the study. C.W. conducted the analysis, wrote the initial manuscript draft and produced all figures. S.-S.L. conducted the model experiments. All authors contributed to the interpretation of the results and to the improvement of the manuscript.

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Correspondence to Christian Wengel or Axel Timmermann.

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Peer review information Nature Climate Change thanks Ryan Holmes, Tsubasa Kohyama and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Observed and simulated PD tropical SST mean state and variability on three timescales.

As in Fig. 2 but for observations and PD. Tropical SST (a–c) mean state, (d–f) ENSO variability, (g–i) TIW variability and (j–l) annual cycle in (first column) observations, (second column) PD and (third column) PD-Obs. difference. Variability is estimated by the standard deviation. HadISSTv1.164 is used for SST mean state, ENSO variability, and the annual cycle and NOAA OI SST V253 is used for TIWs. Unit is °C for all figure panels.

Extended Data Fig. 2 SSTA Niño3 timeseries and spectrum.

(a, c, e) SSTA Niño3 timeseries (°C) in PD, 2 × CO2, and 4 × CO2, respectively, and (b, d, f) Welch power spectral density of normalized SSTA Niño3 timeseries in PD, 2 × CO2, and 4 × CO2, respectively. The dashed lines in (b, d, f) denote the significance with respect to 95% confidence bounds. The grey vertical line denotes the frequency of the spectral maximum in PD.

Extended Data Fig. 3 Mean-state quantities in observations and UHR-CESM.

Climatological mean of (a, b) equatorial Pacific Ocean temperature, (c, d) tropical Pacific zonal wind stress and (e, f) zonal ocean currents at 110°W in (first column) observations and (second column) the UHR-CESM PD simulation. For observations, ERA-Interim65 and ORAS566 are used.

Extended Data Fig. 4 Future change in mean zonal ocean current in UHR-CESM.

Meridional-vertical section of zonal ocean current along 110°W in UHR-CESM (a) PD, (b) 4 × CO2 and (c) the change in 4 × CO2 relative to PD. Unit is cm s−1.

Extended Data Fig. 5 Empirical Orthogonal Functions (EOFs) of SSTA.

(a, e, i) EOF1, (b, f, j) EOF2, (c, g, k) EOF1-EOF2, (d, h, l) EOF1+EOF2 in PD, 2 × CO2, and 4 × CO2, respectively. Unit is °C. Explained variances of EOF1 and EOF2 are given in the title of each panel.

Extended Data Fig. 6 Change in ENSO state-dependent noise.

(a) 2 × CO2, (b) 4 × CO2. The state-dependent noise was calculated by linearly regressing the monthly-mean running variance (152 days) of band pass-filtered (2–180 days) daily-mean zonal wind stress anomalies onto central-eastern equatorial Pacific (180°W–80°W; 5°S–5°N) SSTA. Anomalies were obtained by subtracting the long term-mean seasonal cycle after removing the quadratic trend. Unit is 10-2 Pa °C−1.

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Wengel, C., Lee, SS., Stuecker, M.F. et al. Future high-resolution El Niño/Southern Oscillation dynamics. Nat. Clim. Chang. 11, 758–765 (2021). https://doi.org/10.1038/s41558-021-01132-4

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