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Inaugural Article

Identifying human influences on atmospheric temperature

Benjamin D. Santer, Jeffrey F. Painter, Carl A. Mears, Charles Doutriaux, Peter Caldwell, Julie M. Arblaster, Philip J. Cameron-Smith, Nathan P. Gillett, Peter J. Gleckler, John Lanzante, Judith Perlwitz, Susan Solomon, Peter A. Stott, Karl E. Taylor, Laurent Terray, Peter W. Thorne, Michael F. Wehner, Frank J. Wentz, Tom M. L. Wigley, Laura J. Wilcox, and Cheng-Zhi Zou
  1. aProgram for Climate Model Diagnosis and Intercomparison (PCMDI), Lawrence Livermore National Laboratory, Livermore, CA 94550;
  2. bRemote Sensing Systems, Santa Rosa, CA 95401;
  3. cCentre for Australian Weather and Climate Research, Bureau of Meteorology, Melbourne, VIC 3001, Australia;
  4. dNational Center for Atmospheric Research, Boulder, CO 80307;
  5. eCanadian Centre for Climate Modelling and Analysis, Environment Canada, Victoria, BC, Canada V8W 3V6;
  6. fNational Oceanic and Atmospheric Administration (NOAA) Geophysical Fluid Dynamics Laboratory, Princeton, NJ 08542;
  7. gCooperative Institute for Research in Environmental Sciences, University of Colorado and NOAA Earth System Research Laboratory, Physical Sciences Division, Boulder, CO 80305;
  8. hEarth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139;
  9. iUnited Kingdom Meteorology Office, Hadley Centre, Exeter EX1 3PB, United Kingdom;
  10. jSciences de l’Univers au Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique (CERFACS), CERFACS/Centre National de la Recherche Scientifique, URA1875 Toulouse, France;
  11. kCooperative Institute for Climate and Satellites, North Carolina State University, and National Climatic Data Center, Asheville, NC 28801;
  12. lComputational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720;
  13. mSchool of Earth and Environmental Sciences, University of Adelaide, Adelaide, SA 5005, Australia;
  14. nNational Centre for Atmospheric Science, Department of Meteorology, University of Reading, Reading RG6 6BB, United Kingdom; and
  15. oCenter for Satellite Applications and Research, National Environmental Satellite, Data, and Information Service, College Park, MD 20740

See allHide authors and affiliations

PNAS January 2, 2013 110 (1) 26-33; first published November 29, 2012; https://doi.org/10.1073/pnas.1210514109
Benjamin D. Santer
aProgram for Climate Model Diagnosis and Intercomparison (PCMDI), Lawrence Livermore National Laboratory, Livermore, CA 94550;
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  • For correspondence: santer1@llnl.gov
Jeffrey F. Painter
aProgram for Climate Model Diagnosis and Intercomparison (PCMDI), Lawrence Livermore National Laboratory, Livermore, CA 94550;
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Carl A. Mears
bRemote Sensing Systems, Santa Rosa, CA 95401;
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Charles Doutriaux
aProgram for Climate Model Diagnosis and Intercomparison (PCMDI), Lawrence Livermore National Laboratory, Livermore, CA 94550;
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Peter Caldwell
aProgram for Climate Model Diagnosis and Intercomparison (PCMDI), Lawrence Livermore National Laboratory, Livermore, CA 94550;
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Julie M. Arblaster
cCentre for Australian Weather and Climate Research, Bureau of Meteorology, Melbourne, VIC 3001, Australia;
dNational Center for Atmospheric Research, Boulder, CO 80307;
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Philip J. Cameron-Smith
aProgram for Climate Model Diagnosis and Intercomparison (PCMDI), Lawrence Livermore National Laboratory, Livermore, CA 94550;
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Nathan P. Gillett
eCanadian Centre for Climate Modelling and Analysis, Environment Canada, Victoria, BC, Canada V8W 3V6;
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Peter J. Gleckler
aProgram for Climate Model Diagnosis and Intercomparison (PCMDI), Lawrence Livermore National Laboratory, Livermore, CA 94550;
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John Lanzante
fNational Oceanic and Atmospheric Administration (NOAA) Geophysical Fluid Dynamics Laboratory, Princeton, NJ 08542;
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Judith Perlwitz
gCooperative Institute for Research in Environmental Sciences, University of Colorado and NOAA Earth System Research Laboratory, Physical Sciences Division, Boulder, CO 80305;
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Susan Solomon
hEarth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139;
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Peter A. Stott
iUnited Kingdom Meteorology Office, Hadley Centre, Exeter EX1 3PB, United Kingdom;
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Karl E. Taylor
aProgram for Climate Model Diagnosis and Intercomparison (PCMDI), Lawrence Livermore National Laboratory, Livermore, CA 94550;
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Laurent Terray
jSciences de l’Univers au Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique (CERFACS), CERFACS/Centre National de la Recherche Scientifique, URA1875 Toulouse, France;
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Peter W. Thorne
kCooperative Institute for Climate and Satellites, North Carolina State University, and National Climatic Data Center, Asheville, NC 28801;
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Michael F. Wehner
lComputational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720;
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Frank J. Wentz
bRemote Sensing Systems, Santa Rosa, CA 95401;
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Tom M. L. Wigley
dNational Center for Atmospheric Research, Boulder, CO 80307;
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Laura J. Wilcox
nNational Centre for Atmospheric Science, Department of Meteorology, University of Reading, Reading RG6 6BB, United Kingdom; and
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Cheng-Zhi Zou
oCenter for Satellite Applications and Research, National Environmental Satellite, Data, and Information Service, College Park, MD 20740
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  1. Contributed by Benjamin D. Santer, June 22, 2012

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Abstract

We perform a multimodel detection and attribution study with climate model simulation output and satellite-based measurements of tropospheric and stratospheric temperature change. We use simulation output from 20 climate models participating in phase 5 of the Coupled Model Intercomparison Project. This multimodel archive provides estimates of the signal pattern in response to combined anthropogenic and natural external forcing (the fingerprint) and the noise of internally generated variability. Using these estimates, we calculate signal-to-noise (S/N) ratios to quantify the strength of the fingerprint in the observations relative to fingerprint strength in natural climate noise. For changes in lower stratospheric temperature between 1979 and 2011, S/N ratios vary from 26 to 36, depending on the choice of observational dataset. In the lower troposphere, the fingerprint strength in observations is smaller, but S/N ratios are still significant at the 1% level or better, and range from three to eight. We find no evidence that these ratios are spuriously inflated by model variability errors. After removing all global mean signals, model fingerprints remain identifiable in 70% of the tests involving tropospheric temperature changes. Despite such agreement in the large-scale features of model and observed geographical patterns of atmospheric temperature change, most models do not replicate the size of the observed changes. On average, the models analyzed underestimate the observed cooling of the lower stratosphere and overestimate the warming of the troposphere. Although the precise causes of such differences are unclear, model biases in lower stratospheric temperature trends are likely to be reduced by more realistic treatment of stratospheric ozone depletion and volcanic aerosol forcing.

  • climate change detection and attribution
  • climate modeling
  • multimodel analysis

Footnotes

  • ↵1To whom correspondence should be addressed. E-mail: santer1{at}llnl.gov.
  • This article is part of the special series of Inaugural Articles by members of the National Academy of Sciences elected in 2011.

  • Author contributions: B.D.S., C.A.M., S.S., K.E.T., L.T., F.J.W., and T.M.L.W. designed research; B.D.S., J.F.P., C.A.M., C.D., P.C., J.P., and L.J.W. performed research; B.D.S., J.F.P., C.A.M., C.D., P.C., P.J.C.-S., P.J.G., J.P., S.S., L.T., and L.J.W. analyzed data; C.A.M., F.J.W., and C.-Z.Z. contributed key observational datasets; and B.D.S., J.F.P., C.A.M., C.D., P.C., J.A., P.J.C.-S., N.P.G., P.J.G., J.L., J.P., S.S., P.A.S., K.E.T., L.T., P.W.T., M.F.W., F.J.W., T.M.L.W., L.J.W., and C.-Z.Z. wrote the paper.

  • The authors declare no conflict of interest.

  • This article is a PNAS Direct Submission.

  • See QnAs on page 3.

  • This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1210514109/-/DCSupplemental.

Freely available online through the PNAS open access option.

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Human influences on atmospheric temperature
Benjamin D. Santer, Jeffrey F. Painter, Carl A. Mears, Charles Doutriaux, Peter Caldwell, Julie M. Arblaster, Philip J. Cameron-Smith, Nathan P. Gillett, Peter J. Gleckler, John Lanzante, Judith Perlwitz, Susan Solomon, Peter A. Stott, Karl E. Taylor, Laurent Terray, Peter W. Thorne, Michael F. Wehner, Frank J. Wentz, Tom M. L. Wigley, Laura J. Wilcox, Cheng-Zhi Zou
Proceedings of the National Academy of Sciences Jan 2013, 110 (1) 26-33; DOI: 10.1073/pnas.1210514109

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Human influences on atmospheric temperature
Benjamin D. Santer, Jeffrey F. Painter, Carl A. Mears, Charles Doutriaux, Peter Caldwell, Julie M. Arblaster, Philip J. Cameron-Smith, Nathan P. Gillett, Peter J. Gleckler, John Lanzante, Judith Perlwitz, Susan Solomon, Peter A. Stott, Karl E. Taylor, Laurent Terray, Peter W. Thorne, Michael F. Wehner, Frank J. Wentz, Tom M. L. Wigley, Laura J. Wilcox, Cheng-Zhi Zou
Proceedings of the National Academy of Sciences Jan 2013, 110 (1) 26-33; DOI: 10.1073/pnas.1210514109
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  • Physical Sciences
  • Earth, Atmospheric, and Planetary Sciences

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    - Nov 29, 2012
Proceedings of the National Academy of Sciences: 110 (1)
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  • Article
    • Abstract
    • Observational and Model Temperature Data
    • Global Mean Temperature Changes in TLS and TLT
    • Zonal Mean Temperature Trends
    • Geographical Patterns of Temperature Trends
    • Evaluating Model Noise Estimates
    • Fingerprint Method
    • Fingerprint Results
    • Conclusions
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