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Future high-resolution El Niño/Southern Oscillation dynamics


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 ( and upon request ( The HadISST64 can be obtained from UK Meteorological Office, Hadley Centre ( NOAA High Resolution SST data53 were provided by the NOAA/OAR/ESRL PSL from their website The European Centre for Medium-range Weather Forecast (ECMWF) (2011): The ERA-Interim reanalysis dataset65, Copernicus Climate Change Service (C3S) is available from The Ocean ReAnalysis System 5 (ORAS5)66 can be obtained from the ECMWF at

Code availability

The CESM source code and the standard setup files for the ultra-high-resolution model simulation can be obtained from More details on the simulations and the PE layout for a Cray XC50 computer can be found at The data analysis was conducted using the software CDO (, Ferret ( and MATLAB ( The code that was used for data processing, model analysis and figure production is freely available at


  1. Karamperidou, C. et al. in El Niño Southern Oscillation in a Changing Climate (eds McPhaden, M. J. et al.) 471–484 (Wiley, American Geophysical Union, 2020).

  2. Jin, F. F., Neelin, J. D. & Ghil, M. El Nino Southern Oscillation and the annual cycle: subharmonic frequency-locking and aperiodicity. Physica D 98, 442–465 (1996).

    Article  Google Scholar 

  3. Timmermann, A., Latif, M., Grötzner, A. & Voss, R. Modes of climate variability as simulated by a coupled general circulation model. Part I: ENSO-like climate variability and its low-frequency modulation. Clim. Dynam. 15, 605–618 (1999).

    Article  Google Scholar 

  4. Timmermann, A. et al. The influence of a weakening of the Atlantic meridional overturning circulation on ENSO. J. Clim. 20, 4899–4919 (2007).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  6. Timmermann, A. et al. Increased El Nino frequency in a climate model forced by future greenhouse warming. Nature 398, 694–697 (1999).

    Article  CAS  Google Scholar 

  7. Cai, W. J. et al. Increased variability of eastern Pacific El Nino under greenhouse warming. Nature 564, 201–206 (2018).

    Article  CAS  Google Scholar 

  8. Kohyama, T. & Hartmann, D. L. Nonlinear ENSO Warming Suppression (NEWS). J. Clim. 30, 4227–4251 (2017).

    Article  Google Scholar 

  9. Ashfaq, M., Skinner, C. B. & Diffenbaugh, N. S. Influence of SST biases on future climate change projections. Clim. Dynam. 36, 1303–1319 (2011).

    Article  Google Scholar 

  10. Bellenger, H., Guilyardi, E., Leloup, J., Lengaigne, M. & Vialard, J. ENSO representation in climate models: from CMIP3 to CMIP5. Clim. Dynam. 42, 1999–2018 (2014).

    Article  Google Scholar 

  11. Li, G. & Xie, S. P. Tropical biases in CMIP5 multimodel ensemble: the excessive equatorial Pacific cold tongue and double ITCZ problems. J. Clim. 27, 1765–1780 (2014).

    Article  Google Scholar 

  12. Misra, V., Marx, L., Brunke, M. & Zeng, X. The equatorial Pacific cold tongue bias in a coupled climate model. J. Clim. 21, 5852–5869 (2008).

    Article  Google Scholar 

  13. Bayr, T. et al. Mean-state dependence of ENSO atmospheric feedbacks in climate models. Clim. Dynam. 50, 3171–3194 (2018).

    Article  Google Scholar 

  14. Wengel, C., Latif, M., Park, W., Harlass, J. & Bayr, T. Seasonal ENSO phase locking in the Kiel Climate Model: the importance of the equatorial cold sea surface temperature bias. Clim. Dynam. 50, 901–919 (2018).

    Article  Google Scholar 

  15. Wittenberg, A. T. Are historical records sufficient to constrain ENSO simulations? Geophys. Res. Lett. 36, L12702 (2009).

    Article  Google Scholar 

  16. 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. Dynam. 36, 1609–1627 (2011).

    Article  Google Scholar 

  17. Kim, S. T., Cai, W., Jin, F.-F. & Yu, J.-Y. ENSO stability in coupled climate models and its association with mean state. Clim. Dynam. 42, 3313–3321 (2014).

    Article  Google Scholar 

  18. Bayr, T., Domeisen, D. I. V. & Wengel, C. The effect of the equatorial Pacific cold SST bias on simulated ENSO teleconnections to the North Pacific and California. Clim. Dynam. 53, 3771–3789 (2019).

    Article  Google Scholar 

  19. Bayr, T. et al. Error compensation of ENSO atmospheric feedbacks in climate models and its influence on simulated ENSO dynamics. Clim. Dynam. 53, 155–172 (2019).

    Article  Google Scholar 

  20. Li, G., Xie, S.-P., Du, Y. & Luo, Y. Effects of excessive equatorial cold tongue bias on the projections of tropical Pacific climate change. Part I: the warming pattern in CMIP5 multi-model ensemble. Clim. Dynam. 47, 3817–3831 (2016).

    Article  Google Scholar 

  21. Brown, J. N. et al. Implications of CMIP3 model biases and uncertainties for climate projections in the western tropical Pacific. Clim. Change 119, 147–161 (2013).

    Article  Google Scholar 

  22. Lian, T. et al. Effects of tropical cyclones on ENSO. J. Clim. 32, 6423–6443 (2019).

    Article  Google Scholar 

  23. Wang, Q. Y. et al. Tropical cyclones act to intensify El Nino. Nat. Commun. 10, 3793 (2019).

    Article  CAS  Google Scholar 

  24. An, S. I. Interannual variations of the tropical ocean instability wave and ENSO. J. Clim. 21, 3680–3686 (2008).

    Article  Google Scholar 

  25. Holmes, R. M., McGregor, S., Santoso, A. & England, M. H. Contribution of tropical instability waves to ENSO irregularity. Clim. Dynam. 52, 1837–1855 (2019).

    Article  Google Scholar 

  26. Small, R. J. et al. A new synoptic scale resolving global climate simulation using the Community Earth System Model. J. Adv. Modeling Earth Syst. 6, 1065–1094 (2014).

    Article  Google Scholar 

  27. Chang, P. et al. An unprecedented set of high-resolution Earth system simulations for understanding multiscale interactions in climate variability and change. J. Adv. Model. Earth Syst. 12, e2020MS002298 (2020).

    Article  Google Scholar 

  28. Chu, J.-E. et al. Reduced tropical cyclone densities and ocean effects due to anthropogenic greenhouse warming. Sci. Adv. 6, eabd5109 (2020).

    Article  CAS  Google Scholar 

  29. Gu, D. & Philander, S. G. H. Interdecadal climate fluctuations that depend on exchanges between the tropics and extratropics. Science 275, 805–807 (1997).

    Article  CAS  Google Scholar 

  30. Vimont, D. J., Battisti, D. S. & Hirst, A. C. Footprinting: a seasonal connection between the tropics and mid-latitudes. Geophys. Res. Lett. 28, 3923–3926 (2001).

    Article  Google Scholar 

  31. Stuecker, M. F. et al. Strong remote control of future equatorial warming by off-equatorial forcing. Nat. Clim. Change 10, 124–129 (2020).

    Article  Google Scholar 

  32. Kim, S. T. & Jin, F. F. An ENSO stability analysis. Part I: Results from a hybrid coupled model. Clim. Dynam. 36, 1593–1607 (2011).

    Article  Google Scholar 

  33. Timmermann, A., Jin, F. F. & Collins, M. Intensification of the annual cycle in the tropical Pacific due to greenhouse warming. Geophys. Res. Lett. (2004).

  34. Menkes, C. E. R., Vialard, J. G., Kennan, S. C., Boulanger, J. P. & Madec, G. V. A modeling study of the impact of tropical instability waves on the heat budget of the eastern equatorial Pacific. J. Phys. Oceanogr. 36, 847–865 (2006).

    Article  Google Scholar 

  35. Jochum, M. & Murtugudde, R. Temperature advection by tropical instability waves. J. Phys. Oceanogr. 36, 592–605 (2006).

    Article  Google Scholar 

  36. Graham, T. The importance of eddy permitting model resolution for simulation of the heat budget of tropical instability waves. Ocean Model. 79, 21–32 (2014).

    Article  Google Scholar 

  37. Moum, J. N. et al. Sea surface cooling at the Equator by subsurface mixing in tropical instability waves. Nat. Geosci. 2, 761–765 (2009).

    Article  CAS  Google Scholar 

  38. Holmes, R. & Thomas, L. The modulation of equatorial turbulence by tropical instability waves in a regional ocean model. J. Phys. Oceanogr. 45, 1155–1173 (2015).

    Article  Google Scholar 

  39. Liu, C. et al. The subsurface mode tropical instability waves in the equatorial Pacific ocean and their impacts on shear and mixing. Geophys. Res. Lett. 46, 12270–12278 (2019).

    Article  Google Scholar 

  40. Pezzi, L. P., Vialard, J., Richards, K. J., Menkes, C. & Anderson, D. Influence of ocean–atmosphere coupling on the properties of tropical instability waves. Geophys. Res. Lett. (2004).

  41. Seo, H., Jochum, M., Murtugudde, R., Miller, A. J. & Roads, J. O. Feedback of tropical instability-wave-induced atmospheric variability onto the ocean. J. Clim. 20, 5842–5855 (2007).

    Article  Google Scholar 

  42. Stein, K., Schneider, N., Timmermann, A. & Jin, F.-F. Seasonal synchronization of ENSO events in a linear stochastic model. J. Clim. 23, 5629–5643 (2010).

    Article  Google Scholar 

  43. Lloyd, J., Guilyardi, E. & Weller, H. The role of atmosphere feedbacks during ENSO in the CMIP3 Models. Part III: The shortwave flux feedback. J. Clim. 25, 4275–4293 (2012).

    Article  Google Scholar 

  44. Ellison, T. H. & Turner, J. S. Turbulent entrainment in stratified flows. J. Fluid Mech. 6, 423–448 (1959).

    Article  Google Scholar 

  45. Cai, W. J. et al. ENSO and greenhouse warming. Nat. Clim. Change 5, 849–859 (2015).

    Article  Google Scholar 

  46. Kohyama, T., Hartmann, D. L. & Battisti, D. S. Weakening of nonlinear ENSO under global warming. Geophys. Res. Lett. 45, 8557–8567 (2018).

    Article  Google Scholar 

  47. 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).

    Article  Google Scholar 

  48. Masina, S., Philander, S. G. H. & Bush, A. B. G. An analysis of tropical instability waves in a numerical model of the Pacific Ocean: 2. Generation and energetics of the waves. J. Geophys. Res. Oceans 104, 29637–29661 (1999).

    Article  Google Scholar 

  49. Grodsky, S. A. et al. Tropical instability waves at 0° N, 23° W in the Atlantic: a case study using Pilot Research Moored Array in the Tropical Atlantic (PIRATA) mooring data. J. Geophys. Res. Oceans (2005).

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

    Article  Google Scholar 

  51. An, S.-I. & Choi, J. Inverse relationship between the equatorial eastern Pacific annual-cycle and ENSO amplitudes in a coupled general circulation model. Clim. Dynam. 40, 663–675 (2013).

    Article  Google Scholar 

  52. Kug, J.-S., Jin, F.-F., Sooraj, K. P. & Kang, I.-S. State-dependent atmospheric noise associated with ENSO. Geophys. Res. Lett. (2008).

  53. Reynolds, R. W. et al. Daily high-resolution-blended analyses for sea surface temperature. J. Clim. 20, 5473–5496 (2007).

    Article  Google Scholar 

  54. Hurrell, J. W. et al. The community earth system model a framework for collaborative research. Bull. Am. Meteorol. Soc. 94, 1339–1360 (2013).

    Article  Google Scholar 

  55. Neale, R. B. et al. Description of the NCAR Community Atmosphere Model (CAM 5.0) NCAR/TN-4861 STR (NCAR, 2010).

  56. Hunke, E. C. & Lipscomb, W. H. CICE: The Los Alamos Sea Ice Model. Documentation and Software User’s Manual, Version 4.0 Technical Report LA-CC-06-012 (NCAR, 2008).

  57. Smith, R. D. & Gent, P. R. Reference Manual for the Parallel Ocean Program (POP), Ocean Component of the Community Climate System Model (CCSM2.0 and 3.0) Technical Report LA-UR-02-2484 (Los Alamos National Laboratory, 2002).

  58. Lawrence, D. M. et al. Parameterization improvements and functional and structural advances in Version 4 of the Community Land Model. J. Adv. Model. Earth Syst. (2011).

  59. Eyring, V. et al. Overview of the coupled model intercomparison project phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).

    Article  Google Scholar 

  60. Jin, F. F., Kim, S. T. & Bejarano, L. A coupled-stability index for ENSO. Geophys. Res. Lett. (2006).

  61. Luebbecke, J. F. & McPhaden, M. J. Assessing the twenty-first-century shift in ENSO variability in terms of the Bjerknes stability index. J. Clim. 27, 2577–2587 (2014).

    Article  Google Scholar 

  62. Graham, F. S. et al. Effectiveness of the Bjerknes stability index in representing ocean dynamics. Clim. Dynam. 43, 2399–2414 (2014).

    Article  Google Scholar 

  63. Wengel, C., Dommenget, D., Latif, M., Bayr, T. & Vijayeta, A. What controls ENSO-amplitude diversity in climate models? Geophys. Res. Lett. 45, 1989–1996 (2018).

    Article  Google Scholar 

  64. 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. Atmospheres (2003).

  65. Dee, D. P. et al. The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc. 137, 553–597 (2011).

    Article  Google Scholar 

  66. Zuo, H., Balmaseda, M. A., Tietsche, S., Mogensen, K. & Mayer, M. The ECMWF operational ensemble reanalysis–analysis system for ocean and sea ice: a description of the system and assessment. Ocean Sci. 15, 779–808 (2019).

    Article  Google Scholar 

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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



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.

Corresponding authors

Correspondence to Christian Wengel or Axel Timmermann.

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The authors declare no competing interests.

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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 (ac) mean state, (df) ENSO variability, (gi) TIW variability and (jl) 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).

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