Letter

Decadal modulation of global surface temperature by internal climate variability

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Published online:

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.

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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

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

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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.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Aiguo Dai.

Supplementary information