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Saturday, August 15, 2009

B. D. Santer et al., PNAS (2009), Incorporating model quality information in climate change detection and attribution studies

Proceedings of the National Academy of Sciences, published online before print August 14, 2009; doi: 10.1073/pnas.0901736106

Incorporating model quality information in climate change detection and attribution studies

  1. B. D. Santera,1,
  2. K. E. Taylora,
  3. P. J. Glecklera,
  4. C. Bonfilsa,
  5. T. P. Barnettb,
  6. D. W. Pierceb,
  7. T. M. L. Wigleyc,
  8. C. Mearsd,
  9. F. J. Wentzd,
  10. W. Brüggemanne,
  11. N. P. Gillettf,
  12. S. A. Kleina,
  13. S. Solomong,
  14. P. A. Stotth and
  15. M. F. Wehneri
  1. aProgram for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, Livermore, CA 94550;
  2. bScripps Institution of Oceanography, La Jolla, CA 92037;
  3. cNational Center for Atmospheric Research, Boulder, CO 80307;
  4. dRemote Sensing Systems, Santa Rosa, CA 95401;
  5. eInstitut für Unternehmensforschung, Universität Hamburg, 20146 Hamburg, Germany;
  6. fCanadian Centre for Climate Modelling and Analysis, University of Victoria, Victoria, BC, Canada V8W 3V6;
  7. gChemical Sciences Division, National Oceanic and Atmospheric Administration Earth System Research Laboratory, Boulder, CO 80305;
  8. hHadley Centre, U.K. Meteorological Office, Exeter EX1 3PB, United Kingdom; and
  9. iLawrence Berkeley National Laboratory, Berkeley, CA 94720


In a recent multimodel detection and attribution (D&A) study using the pooled results from 22 different climate models, the simulated “fingerprint” pattern of anthropogenically caused changes in water vapor was identifiable with high statistical confidence in satellite data. Each model received equal weight in the D&A analysis, despite large differences in the skill with which they simulate key aspects of observed climate. Here, we examine whether water vapor D&A results are sensitive to model quality. The “top 10” and “bottom 10” models are selected with three different sets of skill measures and two different ranking approaches. The entire D&A analysis is then repeated with each of these different sets of more or less skillful models. Our performance metrics include the ability to simulate the mean state, the annual cycle, and the variability associated with El Niño. We find that estimates of an anthropogenic water vapor fingerprint are insensitive to current model uncertainties, and are governed by basic physical processes that are well-represented in climate models. Because the fingerprint is both robust to current model uncertainties and dissimilar to the dominant noise patterns, our ability to identify an anthropogenic influence on observed multidecadal changes in water vapor is not affected by “screening” based on model quality.

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