Hierarchical evaluation of IPCC AR4 coupled climate models with systematic consideration of model uncertainties
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The capability of reproducing observed surface air temperature (SAT) changes for the twentieth century is assessed using 22 multi-models which contribute to the Intergovernmental Panel on Climate Change Fourth Assessment Report. A Bayesian method is utilized for model evaluation by which model uncertainties are considered systematically. We provide a hierarchical analysis for global to sub-continental regions with two settings. First, regions of different size are evaluated separately at global, hemispheric, continental, and sub-continental scales. Second, the global SAT trend patterns are evaluated with gradual refinement of horizontal scales (higher dimensional analysis). Results show that models with natural plus anthropogenic forcing (MME_ALL) generally exhibit better skill than models with anthropogenic only forcing (MME_ANTH) at all spatial scales for different trend periods (entire twentieth century and its first and second halves). This confirms previous studies that suggest the important role of natural forcing. For the second half of the century, we found that MME_ANTH performs well compared to MME_ALL except for a few models with overestimated warming. This indicates not only major contributions of anthropogenic forcing over that period but also the applicability of both MMEs to observationally-constrained future predictions of climate changes. In addition, the skill-weighted averages with the Bayes factors [Bayesian model averaging (BMA)] show a general superiority over other error-based weighted averaging methods, suggesting a potential advantage of BMA for climate change predictions.
KeywordsSkill Score Bayesian Model Average Couple Climate Model Eastern Hemisphere Weighted Average Method
This study was supported by the German Research Foundation (DFG) with grant He1916/8. We thank two anonymous reviewers for their constructive comments and Walter Skinner for his help on English editing. We also acknowledge the international modeling groups for providing their data for analysis, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) for collecting and archiving the model data, the JSC/CLIVAR Working Group on Coupled Modelling (WGCM) and their Coupled Model Intercomparison Project (CMIP) and Climate Simulation Panel for organizing the model data analysis activity, and the IPCC WG1 TSU for technical support. The IPCC Data Archive at Lawrence Livermore National Laboratory is supported by the Office of Science, US Department of Energy.
- Cubasch U, Meehl GA, Boer GJ, Stouffer RJ, Dix M, Noda A, Senior CA, Raper S, Yap KS (2001) Projections of future climate change. In: Houghton JT et al (eds) Climate change 2001: the scientific basis. Cambridge University Press, UK, pp 525–582Google Scholar
- International Ad Hoc Detection and Attribution Group (IDAG) (2005) Detecting and attributing external influences on the climate system: a review of recent advances. J Clim 18:1291–1314Google Scholar
- Jeffreys H (1961) Theory of probability, 3rd edn. Oxford University Press, New York, p 470Google Scholar
- McAvaney BJ, Covey C, Joussaume S, Kattsov V, Kitoh A, Ogana W, Pitman AJ, Weaver AJ, Wood RA, Zhao ZC (2001) Model evaluation, In: Houghton JT et al (eds) Climate change 2001: the scientific basis. Cambridge University Press, UK, pp 471–524Google Scholar
- Min S-K, Hense A (2007) A Bayesian assessment of climate change using multimodel ensembles. Part II: Regional and seasonal mean surface temperatures. J Clim (in press)Google Scholar
- Mitchell JFB, Karoly DJ, Hegerl GC, Zwiers FW, Allen MR, Marengo J (2001) Detection of climate change and attribution of causes. In: Houghton JT et al (eds) Climate change 2001: the scientific basis. Cambridge University Press, UK, pp 695–738Google Scholar