Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Letter
  • Published:

Decadal modulation of global surface temperature by internal climate variability

Nature Climate Change volume 5, pages 555–559 (2015)Cite this article

Subjects

Abstract

Despite a steady increase in atmospheric greenhouse gases (GHGs), global-mean surface temperature (T) has shown no discernible warming since about 2000, in sharp contrast to model simulations, which on average project strong warming1,2,3. The recent slowdown in observed surface warming has been attributed to decadal cooling in the tropical Pacific1,4,5, intensifying trade winds5, changes in El Niño activity6,7, increasing volcanic activity8,9,10 and decreasing solar irradiance7. Earlier periods of arrested warming have been observed but received much less attention than the recent period, and their causes are poorly understood. Here we analyse observed and model-simulated global T fields to quantify the contributions of internal climate variability (ICV) to decadal changes in global-mean T since 1920. We show that the Interdecadal Pacific Oscillation (IPO) has been associated with large T anomalies over both ocean and land. Combined with another leading mode of ICV, the IPO explains most of the difference between observed and model-simulated rates of decadal change in global-mean T since 1920, and particularly over the so-called ‘hiatus’ period since about 2000. We conclude that ICV, mainly through the IPO, was largely responsible for the recent slowdown, as well as for earlier slowdowns and accelerations in global-mean T since 1920, with preferred spatial patterns different from those associated with GHG-induced warming or aerosol-induced cooling. Recent history suggests that the IPO could reverse course and lead to accelerated global warming in the coming decades.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Time series of the near-global (60° S–75° N) mean surface temperature anomalies (T′, all relative to the 1961–1990 mean) from 1920 to 2013.
Figure 2: Time series of the principal components and spatial patterns of the first and fourth leading EOFs in the near-global surface temperature fields during 1920–2013.
Figure 3: Evolution of decadal trends in global-mean surface temperatures.
Figure 4: Simulated time series of the near-global mean surface temperature anomalies (relative to the 1961–1990 mean) from 1920 to 2013
Figure 5: Time series of the ECEP surface temperature anomalies (T′, all relative to the 1961–1990 mean) from 1920–2013.

Similar content being viewed by others

References

  1. Kosaka, Y. & Xie, S-P. Recent global-warming hiatus tied to equatorial Pacific surface cooling. Nature 501, 403–407 (2013).

    Article  CAS  Google Scholar 

  2. Fyfe, J. C., Gillett, N. P. & Zwiers, F. W. Overestimated global warming over the past 20 years. Nature Clim. Change 3, 767–769 (2013).

    Article  Google Scholar 

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

    Google Scholar 

  4. Trenberth, K. E. & Fasullo, J. An apparent hiatus in global warming? Earth’s Future 1, 19–32 (2013).

    Article  Google Scholar 

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

    Article  Google Scholar 

  6. Kaufmann, R. K., Kauppi, H., Mann, M. L. & Stock, J. H. Reconciling anthropogenic climate change with observed temperature 1998–2008. Proc. Natl Acad. Sci. USA 108, 11790–11793 (2011).

    Article  CAS  Google Scholar 

  7. Schmidt, G. A., Shindell, D. T. & Tsigaridis, K. Reconciling warming trends. Nature Geosci. 7, 158–160 (2014).

    Article  CAS  Google Scholar 

  8. Solomon, S. et al. The persistently variable ”background” stratospheric aerosol layer and global climate change. Science 333, 866–870 (2011).

    Article  CAS  Google Scholar 

  9. Fyfe, J. C., von Salzen, K., Cole, J. N. S., Gillett, N. P. & Vernier, J-P. Surface response to stratospheric aerosol changes in a coupled atmosphere–ocean model. Geophys. Res. Lett. 40, 584–588 (2013).

    Article  Google Scholar 

  10. Santer, B. et al. Volcanic contribution to decadal changes in tropospheric temperature. Nature Geosci. 7, 185–189 (2014).

    Article  CAS  Google Scholar 

  11. Zhang, Y., Wallace, J. M. & Battisti, D. S. ENSO-like interdecadal variability: 1900–93. J. Clim. 10, 1004–1020 (1997).

    Article  Google Scholar 

  12. Mantua, N. J., Hare, S. R., Zhang, Y., Wallace, J. M. & Francis, R. C. A Pacific interdecadal climate oscillation with impacts on salmon production. Bull. Am. Meteorol. Soc. 78, 1069–1079 (1997).

    Article  Google Scholar 

  13. Power, S, T., Casey, C., Folland, A., Colman, A & Mehta, V. Interdecadal modulation of the impact of ENSO on Australia. Clim. Dynam. 15, 319–324 (1999).

    Article  Google Scholar 

  14. Deser, C, Phillips, A. S. & Hurrell, J. W. Pacific interdecadal climate variability: Linkages between the tropics and the North Pacific during boreal winter since 1900. J. Clim. 17, 3109–3124 (2004).

    Article  Google Scholar 

  15. Dai, A. The influence of the Inter-decadal Pacific Oscillation on US precipitation during 1923–2010. Clim. Dynam. 41, 633–646 (2013).

    Article  Google Scholar 

  16. Meehl, G. A., Arblaster, J. M., Fasullo, J. T., Hu, A. & Trenberth, K. E. Model-based evidence of deep-ocean heat uptake during surface-temperature hiatus periods. Nature Clim. Change 1, 360–364 (2011).

    Article  Google Scholar 

  17. Meehl, G. A., Hu, A., Arblaster, J., Fasullo, J. T. & Trenberth, K. E. Externally forced and internally generated decadal climate variability in the Pacific. J. Clim. 26, 7298–7310 (2013).

    Article  Google Scholar 

  18. Seager, R., Harnik, N., Kushnir, Y., Robinson, W. & Miller, J. Mechanisms of hemispherically symmetric climate variability. J. Clim. 16, 2960–2978 (2003).

    Article  Google Scholar 

  19. Trenberth, K. E., Caron, J. M., Stepaniak, D. P. & Worley, S. Evolution of El Niño–Southern Oscillation and global atmospheric surface temperatures. J. Geophys. Res. 107, 4065 (2002).

    Article  Google Scholar 

  20. Balmaseda, M. A., Trenberth, K. E. & Källén, E. Distinctive climate signals in reanalysis of global ocean heat content. Geophys. Res. Lett. 40, 1754–1759 (2013).

    Article  Google Scholar 

  21. Neelin, J. D. Climate Change and Climate Modeling (Cambridge Univ. Press, 2011).

    Google Scholar 

  22. DelSole, T., Tippett, M. K. & Shukla, J. A significant component of unforced multidecadal variability in twentieth century global warming. J. Clim. 24, 909–926 (2011).

    Article  Google Scholar 

  23. Wu, Z., Huang, N. E., Wallace, J. M., Smoliak, B. V. & Chen, X. On the time-varying trend in global-mean surface temperature. Clim. Dynam. 37, 759–773 (2011).

    Article  Google Scholar 

  24. Liu, Z. Y. Dynamics of interdecadal climate variability: A historical perspective. J. Clim. 25, 1963–1995 (2012).

    Article  Google Scholar 

  25. Chen, J., Del Genio, A. D., Carlson, B. E. & Bosilovich, M. G. The spatiotemporal structure of twentieth-century climate variations in observations and reanalyses. Part II: Pacific pan-decadal variability. J. Clim. 21, 2634–2650 (2008).

    Article  Google Scholar 

  26. Hansen, J., Ruedy, R., Sato, M. & Lo, K. Global surface temperature change. Rev. Geophys. 48, RG4004 (2010).

    Article  Google Scholar 

  27. Morice, C. P., Kennedy, J. J., Rayner, N. A. & Jones, P. D. Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 dataset. J. Geophys. Res. 117, D08101 (2012).

    Article  Google Scholar 

  28. Barnston, A. G. & Livezey, R. E. Classification, seasonality and persistence of low-frequency atmospheric circulation patterns. Mon. Wealth. Rev. 115, 1083–1126 (1987).

    Article  Google Scholar 

  29. 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. Meteorol. Soc. http://dx.doi.org/10.1175/BAMS-D-13-00255.1 (2014)

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

    Article  Google Scholar 

  31. Santer, B. et al. Identifying human influences on atmospheric temperature. Proc. Natl Acad. Sci. USA 110, 26–33 (2013).

    Article  CAS  Google Scholar 

  32. Lanzante, J. R. Resistant, robust and non-parametric techniques for the analysis of climate data: Theory and examples, including applications to historical radiosonde station data. Int. J. Climatol. 16, 1197–1226 (1996).

    Article  Google Scholar 

Download references

Acknowledgements

We thank K. Trenberth, B. Merryfield and G. Boer for constructive comments, P. Kushner for sharing the CCSM4 ensemble simulations used in Supplementary Fig. 10, and H. Wang for providing the data used in Supplementary Fig. 1b. We acknowledge the CMIP5 modeling groups and NCAR CESM large ensemble project, the Program for Climate Model Diagnosis and Intercomparison and the WCRP’s Working Group on Coupled Modelling for their roles in making available the WCRP CMIP multi-model data sets. Support for this data set is provided by the Office of Science, US Department of Energy. A.D. is supported by the National Science Foundation (AGS-1353740) and the US Department of Energy’s Office of Science (DE-SC0012602); S-P.X. is supported by the NSF (AGS- 1305719).

Author information

Authors and Affiliations

  1. Department of Atmospheric & Environmental Sciences, University at Albany, SUNY, Albany, New York 12222, USA

    Aiguo Dai

  2. National Center for Atmospheric Research, PO Box 3000 Boulder, Colorado 80307, USA,

    Aiguo Dai

  3. Canadian Centre for Climate Modeling and Analysis, Environment Canada, Victoria British Columbia V8W 2Y2, Canada,

    John C. Fyfe

  4. Scripps Institution of Oceanography, University of California at San Diego, La Jolla, California 92093, USA

    Shang-Ping Xie

  5. RCE-TEA, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China

    Xingang Dai

Authors
  1. Aiguo Dai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. John C. Fyfe
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shang-Ping Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xingang Dai
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

A.D. designed the study, performed all the calculations, made most of the figures, and wrote the draft of the paper; J.C.F. helped improve the manuscript and made Supplementary Fig. 10; S-P.X. helped improve the manuscript; X.D. helped initiate the study.

Corresponding author

Correspondence to Aiguo Dai.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dai, A., Fyfe, J., Xie, SP. et al. Decadal modulation of global surface temperature by internal climate variability. Nature Clim Change 5, 555–559 (2015). https://doi.org/10.1038/nclimate2605

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nclimate2605

This article is cited by

Search

Advanced search

Quick links

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