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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
PNAS published ahead of print 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;mSchool of Earth and Environmental Sciences, University of Adelaide, Adelaide, SA 5005, Australia;
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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.

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

  • *Other CHEM models (such as GISS-E2-R [p2] and GFDL-CM3) substantially overestimate observed ozone loss in certain regions and at certain times of year. The fact that some CHEM models underpredict observed ozone loss and others overestimate observed ozone trends helps to explain why we do not find even larger TLS trend differences between the O3+V case (which excludes CHEM models) and the BASE case (which includes CHEM results).

  • †Previous multimodel studies have found either small (45) or large (47) impacts of stratospheric ozone changes on tropospheric temperature. The ozone-induced tropospheric temperature signals inferred from such multimodel analyses can be obscured by intermodel differences in other applied external forcings and model differences in climate sensitivity (48).

  • ‡Model temperature fields are spatially complete and sampled at uniform time intervals, whereas MSU-based temperature measurements are not spatially complete and not sampled at uniform time intervals. These sampling differences tend to inflate the high-frequency variance of the observations. The RSS percentile realizations attempt to account for this variance inflation (26).

  • §For each of the four atmospheric layers except TLT, the O3+V fingerprint is searched for in 14 individual observational datasets (RSS v3.3, UAH v5.4, STAR v2.0, and 11 RSS percentile realizations). For TLT, there are only 13 model vs. observed comparisons, because STAR does not provide TLT information. There are, therefore, a total of (3 × 14) + 13 comparisons.

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 Nov 2012, 201210514; 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 Nov 2012, 201210514; DOI: 10.1073/pnas.1210514109
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