Enhanced warming constrained by past trends in equatorial Pacific sea surface temperature gradient


The equatorial Pacific zonal sea surface temperature (SST) gradient, known to be a pacemaker of global warming, has strengthened since the mid-twentieth century. However, the cause is controversial because a majority of Coupled Model Intercomparison Project Phase 5 (CMIP5) models suggest weakening of the zonal SST gradient from the past to the future. Reconciling this discrepancy is important for the climate change attribution and climate sensitivity assessment. Here we use the CMIP5 ensemble and large ensemble simulations by four climate models to show that the intensifying SST gradient observed during 1951–2010 could arise from internal climate variability. Models and members that simulate historical strengthening of the SST gradient commonly exhibit reversed future trends. Using these models as a constraint, the rate of global-mean temperature rise is amplified by 9–30%, with higher values occurring in low-emission scenarios, because internal variability has a greater impact when the externally forced response is smaller.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Linear trends in the equatorial Pacific zonal SST gradient (ΔSSTeq) during 1951–2010.
Fig. 2: Linear trends in the equatorial ocean potential temperature during 1951–2010.
Fig. 3: Linear trends in ΔSSTeq for 50 years with a sliding window between 1951 and 2100.
Fig. 4: Linear trends in GSAT for 50 years with a sliding window between 1951 and 2100.

Data availability

COBE-SST2, ERSSTv5, ICOADS and Kaplan SST data sets are all available from the NOAA/OAR/ESRL PSD website (https://www.esrl.noaa.gov/psd/data/gridded/). Two other SST data sets compiled at the Met Office Hadley Centre are available from https://www.metoffice.gov.uk/hadobs/. The CMIP5 model output analysed in this study is available from the Earth System Grid Federation (ESGF) server (https://esgf-node.llnl.gov/search/cmip5/). The CESM Large Ensemble project simulation output can be obtained from http://www.cesm.ucar.edu/projects/community-projects/LENS/data-sets.html. The MPI-ESM1.1 large ensemble data are available at the MPI Grand Ensemble project website (https://www.mpimet.mpg.de/en/grand-ensemble/). A part of the IPSL-CM6-LR and MIROC6 large ensemble simulation data can be downloaded from ESGF47,48, but the full data sets are available upon request.

Code availability

The Fortran codes used for creating main plots in this study are available at https://ccsr.aori.u-tokyo.ac.jp/~hiro/sst_trends/.


  1. 1.

    Bjerknes, J. Atmospheric teleconnections from the equatorial Pacific. Mon. Weather Rev. 97, 162–172 (1969).

    Article  Google Scholar 

  2. 2.

    Jin, F.-F. Tropical ocean–atmosphere interaction, the Pacific cold tongue, and the El Niño Southern Oscillation. Science 274, 76–78 (1996).

    CAS  Article  Google Scholar 

  3. 3.

    Timmermann, A. et al. El Niño–Southern Oscillation complexity. Nature 559, 535–545 (2018).

    CAS  Article  Google Scholar 

  4. 4.

    Solomon, A. & Newman, M. Reconciling disparate twentieth-century Indo-Pacific Ocean temperature trends in the instrumental record. Nat. Clim. Change 2, 691–699 (2012).

    Article  Google Scholar 

  5. 5.

    IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).

  6. 6.

    Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteor. Soc. 93, 485–498 (2012).

    Article  Google Scholar 

  7. 7.

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

    CAS  Article  Google Scholar 

  8. 8.

    Kim, S. T. et al. Response of El Niño sea surface temperature variability to greenhouse warming. Nat. Clim. Change 4, 786–790 (2014).

    Article  Google Scholar 

  9. 9.

    Clement, A., Seager, R., Cane, M. A. & Zebiak, S. E. An ocean dynamical thermostat. J. Clim. 9, 2190–2196 (1996).

    Article  Google Scholar 

  10. 10.

    Xie, S.-P. et al. Global warming pattern formation: sea surface temperature and rainfall. J. Clim. 23, 966–986 (2010).

    Article  Google Scholar 

  11. 11.

    Held, I. M. & Soden, B. J. Robust responses of the hydrological cycle to global warming. J. Clim. 19, 5686–5699 (2006).

    Article  Google Scholar 

  12. 12.

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

    CAS  Article  Google Scholar 

  13. 13.

    Chadwick, R., Boutle, I. & Martin, G. Spatial patterns of precipitation change in CMIP5: why the rich do not get richer in the tropics. J. Clim. 26, 3803–3822 (2013).

    Article  Google Scholar 

  14. 14.

    Coats, S. & Karnauskas, K. B. Are simulated and observed twentieth century tropical Pacific sea surface temperature trends significant relative to internal variability? Geophys. Res. Lett. 44, 9928–9937 (2017).

    Article  Google Scholar 

  15. 15.

    England, M. H. et al. Recent intensification of wind-driven circulation in the Pacific and the ongoing warming hiatus. Nat. Clim. Change 4, 222–227 (2014).

    Article  Google Scholar 

  16. 16.

    Deser, C., Phillips, A. S. & Alexander, M. A. Twentieth century tropical sea surface temperature trends revisited. Geophys. Res. Lett. 37, L10701 (2010).

    Article  Google Scholar 

  17. 17.

    Bordbar, M. H., Martin, T., Latif, M. & Park, W. Role of internal variability in recent decadal to multidecadal tropical Pacific climate changes. Geophys. Res. Lett. 44, 4246–4255 (2017).

    Article  Google Scholar 

  18. 18.

    Andrews, T. et al. Accounting for changing temperature patterns increases historical estimates of climate sensitivity. Geophys. Res. Lett. 45, 8490–8499 (2018).

    Article  Google Scholar 

  19. 19.

    Ceppi, P. & Gregory, J. M. Relationship of tropospheric stability to climate sensitivity and Earth’s observed radiation budget. Proc. Natl Acad. Sci. USA 114, 13126–13131 (2017).

    CAS  Article  Google Scholar 

  20. 20.

    Zhou, C., Zelinka, M. D. & Klein, S. A. Impact of decadal cloud variations on the Earth’s energy budget. Nat. Geosci. 9, 871–874 (2016).

    CAS  Article  Google Scholar 

  21. 21.

    Dong, Y., Proistosescu, C., Armour, K. C. & Battisti, D. S. Attributing historical and future evolution of radiative feedbacks to regional warming patterns using a Green’s function approach: the preeminence of the western Pacific. J. Clim. 32, 5471–5491 (2019).

    Article  Google Scholar 

  22. 22.

    Fyfe, J. C. et al. Making sense of the early-2000s warming slowdown. Nat. Clim. Change 6, 224–228 (2016).

    Article  Google Scholar 

  23. 23.

    Luo, J.-J., Wang, G. & Dommenget, D. May common model biases reduce CMIP5’s ability to simulate the recent Pacific La Niña-like cooling? Clim. Dynam. 50, 1335–1351 (2018).

    Article  Google Scholar 

  24. 24.

    Seager, R. et al. Strengthening tropical Pacific zonal sea surface temperature gradient consistent with rising greenhouse gases. Nat. Clim. Change 9, 517–522 (2019).

    Article  Google Scholar 

  25. 25.

    Chung, E.-S. et al. Reconciling opposing Walker circulation trends in observations and model projections. Nat. Clim. Change 9, 405–412 (2019).

    Article  Google Scholar 

  26. 26.

    Power, S. et al. Inter-decadal modulation of the impact of ENSO on Australia. Clim. Dyn. 15, 319–324 (1999).

    Article  Google Scholar 

  27. 27.

    Henley, B. J. et al. Spatial and temporal agreement in climate model simulations of the Interdecadal Pacific Oscillation. Environ. Res. Lett. 12, 044011 (2017).

    Article  Google Scholar 

  28. 28.

    Meehl, G., Hu, A. & Teng, H. Initialized decadal prediction for transition to positive phase of the Interdecadal Pacific Oscillation. Nat. Commun. 7, 11718 (2016).

    CAS  Article  Google Scholar 

  29. 29.

    Kosaka, Y. & Xie, S.-P. The tropical Pacific as a key pacemaker of the variable rates of global warming. Nat. Geosci. 9, 669–673 (2016).

    CAS  Article  Google Scholar 

  30. 30.

    Bordbar, M. H. et al. Uncertainty in near-term global surface warming linked to tropical Pacific climate variability. Nat. Commun. 10, 1990 (2019).

    Article  CAS  Google Scholar 

  31. 31.

    Kociuba, G. & Power, S. B. Inability of CMIP5 models to simulate recent strengthening of the Walker circulation: implications for projections. J. Clim. 28, 20–35 (2015).

    Article  Google Scholar 

  32. 32.

    Takahashi, C. & Watanabe, M. Pacific trade winds accelerated by aerosol forcing over the past two decades. Nat. Clim. Change 6, 768–772 (2016).

    Article  Google Scholar 

  33. 33.

    McGregor, S. et al. Recent Walker circulation strengthening and Pacific cooling amplified by Atlantic warming. Nat. Clim. Change 4, 888–892 (2014).

    Article  Google Scholar 

  34. 34.

    Hirahara, S., Ishii, M. & Fukuda, Y. Centennial-scale sea surface temperature analysis and its uncertainty. J. Clim. 27, 57–75 (2014).

    Article  Google Scholar 

  35. 35.

    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. 108, 4407 (2003).

    Article  Google Scholar 

  36. 36.

    Kennedy, J. J., Rayner, N. A., Smith, R. O., Saunby, M. & Parker, D. E. Reassessing biases and other uncertainties in sea-surface temperature observations since 1850. Part 1: measurement and sampling errors. J. Geophys. Res. 116, D14103 (2011).

    Article  Google Scholar 

  37. 37.

    Huang, B. et al. Extended reconstructed sea surface temperature, version 5 (ERSSTv5): upgrades, validations, and intercomparisons. J. Clim. 30, 8179–8205 (2017).

    Article  Google Scholar 

  38. 38.

    Woodruff, S. D. et al. ICOADS release 2.5: extensions and enhancements to the surface marine meteorological archive. Int. J. Climatol. 31, 951–967 (2011).

    Article  Google Scholar 

  39. 39.

    Kaplan, A. et al. Analyses of global sea surface temperature 1856–1991. J. Geophys. Res. 103, 18567–18589 (1998).

    Article  Google Scholar 

  40. 40.

    Ishii, M. & Kimoto, M. Reevaluation of historical ocean heat content variations with time-varying XBT and MBT depth bias corrections. J. Oceanogr. 65, 287–299 (2009).

    Article  Google Scholar 

  41. 41.

    Kay, J. E. et al. The Community Earth System Model (CESM) large ensemble project: a community resource for studying climate change in the presence of internal climate variability. Bull. Am. Meteor. Soc. 96, 1333–1349 (2015).

    Article  Google Scholar 

  42. 42.

    Maher, N. et al. The Max Planck Institute grand ensemble: enabling the exploration of climate system variability. J. Adv. Model. Earth Sys. 11, 1–21 (2019).

    Article  Google Scholar 

  43. 43.

    Tatebe, H. et al. Description and basic evaluation of simulated mean state, internal variability, and climate sensitivity in MIROC6. Geo. Model Dev. 12, 2727–2765 (2019).

    CAS  Article  Google Scholar 

  44. 44.

    Boucher, O. et al. Presentation and evaluation of the IPSL-CM6A-LR climate model. J. Adv. Model. Earth Sys. 12, e2019MS002010 (2020).

    Google Scholar 

  45. 45.

    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 

  46. 46.

    Kimoto, M. & Ghil, M. Multiple flow regimes in the Northern Hemisphere winter. Part I: methodology and hemispheric regimes. J. Atmos. Sci. 50, 2625–2643 (1993).

    Article  Google Scholar 

  47. 47.

    Boucher, O. et al. IPSL-CM6A-LR model output prepared for Coupled Model Intercomparison Project Phase 6 (CMIP6) historical run. Earth System Grid Federation https://doi.org/10.22033/ESGF/CMIP6.5195 (2018).

  48. 48.

    Tatebe, H. et al. MIROC6 model output prepared for Coupled Model Intercomparison Project Phase 6 (CMIP6) historical run. Earth System Grid Federation https://doi.org/10.22033/ESGF/CMIP6.5603 (2018).

Download references


We acknowledge the modelling groups, the PCMDI and the WCRP’s WGCM for their efforts in making the CMIP5 multi-model data set available. We also thank T. Suzuki for processing CMIP5 ocean temperature data. M.W., Y.K. and H.T. were supported by Grant-in-Aid 26247079 and the Integrated Research Program for Advancing Climate Models (JPMXD0717935457) from the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan. J.-L.D. was supported by the European Union Horizon 2020 project #820829. T.M. acknowledges funding from European Research Council grant #770765 and European Union Horizon 2020 project #820829.

Author information




M.W. designed the research and wrote the paper. H.T. conducted the MIROC large ensemble experiments. J.-L.D., Y.K. and T.M. helped analyse the large ensemble simulations. All authors discussed the results and commented on the manuscript.

Corresponding author

Correspondence to Masahiro Watanabe.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Climate Change thanks Dong Eun Lee and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1–15 and Table 1.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Watanabe, M., Dufresne, JL., Kosaka, Y. et al. Enhanced warming constrained by past trends in equatorial Pacific sea surface temperature gradient. Nat. Clim. Chang. (2020). https://doi.org/10.1038/s41558-020-00933-3

Download citation


Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing