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Biases and Uncertainty in Climate Projections

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  • CHRISTOPH M. BUSER
  • HANS R. KÜNSCH
  • ALAIN WEBER

Abstract

. We study statistical procedures to quantify uncertainty in multivariate climate projections based on several deterministic climate models. We introduce two different assumptions – called constant bias and constant relation respectively – for extrapolating the substantial additive and multiplicative biases present during the control period to the scenario period. There are also strong indications that the biases in the scenario period are different from the extrapolations from the control period. Including such changes in the statistical models leads to an identifiability problem that we solve in a frequentist analysis using a zero sum side condition and in a Bayesian analysis using informative priors. The Bayesian analysis provides estimates of the uncertainty in the parameter estimates and takes this uncertainty into account for the predictive distributions. We illustrate the method by analysing projections of seasonal temperature and precipitation in the Alpine region from five regional climate models in the PRUDENCE project.

Suggested Citation

  • Christoph M. Buser & Hans R. Künsch & Alain Weber, 2010. "Biases and Uncertainty in Climate Projections," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(2), pages 179-199, June.
    • Handle: RePEc:bla:scjsta:v:37:y:2010:i:2:p:179-199
    DOI: 10.1111/j.1467-9469.2009.00686.x
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    References listed on IDEAS

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    1. Claudia Tebaldi & Bruno Sansó, 2009. "Joint projections of temperature and precipitation change from multiple climate models: a hierarchical Bayesian approach," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(1), pages 83-106, January.
    2. Smith, Richard L. & Tebaldi, Claudia & Nychka, Doug & Mearns, Linda O., 2009. "Bayesian Modeling of Uncertainty in Ensembles of Climate Models," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 97-116.
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