Bayesian Hierarchical Model for Assessment of Climate Model Biases

  • Maeregu Woldeyes Arisido
  • Carlo Gaetan
  • Davide Zanchettin
  • Angelo Rubino
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 194)

Abstract

Studies of climate change rely on numerical outputs simulated from Global Climate Models coupling the dynamics of ocean and atmosphere (GCMs). GCMs are, however, notoriously affected by substantial systematic errors (biases), whose assessment is essential to assert the accuracy and robustness of simulated climate features. This contribution focuses on constructing a Bayesian hierarchical model for the quantification of climate model biases in a multi-model framework. The method combines information from a multi-model ensemble of GCM simulations to provide a unified assessment of the bias. It further individuates different bias components that are characterized as non-stationary spatial fields accounting for spatial dependence. The approach is illustrated based on the case of near-surface air temperature bias over the tropical Atlantic and bordering regions from a multi-model ensemble of historical simulations from the fifth phase of the Coupled Model Intercomparison Project.

Keywords

Bayesian hierarchical model Climate bias CMIP5 Posterior inference Spatial analysis 

References

  1. 1.
    Arisido, M.W.: Functional measure of ozone exposure to model short-term health effects. Environometrics 27, 306–317 (2016)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Buser, C.M., Knsch, H.R., Lthi, D., Wild, M., Schr, C.: Bayesian multi-model projection of climate: bias assumptions and interannual variability. Clim. Dyn. 33, 849–868 (2009)CrossRefGoogle Scholar
  3. 3.
    Cressie, N.: Statistics for Spatial Data. Wiley-Interscience, New York (1993)MATHGoogle Scholar
  4. 4.
    Furrer, R., Geinitz, S., Sain, S.R.: Assessing variance components of general circulation model output fields. Environmetrics 23, 440–450 (2012)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Gelfand, A.E., Diggle, P., Guttorp, P., Fuentes, M. (eds.) Handbook of Spatial Statistics. CRC Press, Boca Raton (2010)Google Scholar
  6. 6.
    Gelman, A., Carlin, J.B., Stern, H.S., Rubin, D.B.: Bayesian Data Analysis. Chapman and Hall/CRC Texts in Statistical Science, London (2003)Google Scholar
  7. 7.
    Gilks, W.R., Richardson, S., Spiegelhalter, D.J.: Markov chain Monte Carlo in practice. Chapman and Hall, London (1996)MATHGoogle Scholar
  8. 8.
    Kang, E.L., Cressie, N., Sain, S.R.: Combining outputs from the North American regional climate change assessment program by using a Bayesian hierarchical model. J. R. Stat. Soc. Ser. C (Appl. Stat.) 61, 291–313 (2012)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Stroud, J.R., Muller, P., Sansó, B.: Dynamic models for spatiotemporal data. J. R. Stat. Soc. Ser. B. Stat. Methodol. 63, 673–689 (2001)MathSciNetCrossRefMATHGoogle Scholar
  10. 10.
    Tebaldi, C., Smith, R.L., Nychka, D., Mearns, L.O.: Quantifying uncertainty in projections of regional climate change: a Bayesian approach to the analysis of multimodel ensembles. J. Clim. 18, 1524–1540 (2005)CrossRefGoogle Scholar
  11. 11.
    Wang, C., Zhang, L., Lee, S.K., Wu, L., Mechoso, C.R.: A global perspective on CMIP5 climate model biases. Nat. Clim. Change 4, 201–205 (2014)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Maeregu Woldeyes Arisido
    • 1
  • Carlo Gaetan
    • 1
  • Davide Zanchettin
    • 1
  • Angelo Rubino
    • 1
  1. 1.Department of Environmental Sciences, Informatics and StatisticsCa’ Foscari University of VeniceVeniceItaly

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