Extended Abstract (768K)5.17
Bayesian climate change assessment using multi-AOGCM ensembles: global and regional surface temperatures
Seung-Ki Min, Univ. of Bonn, Bonn, Germany; and A. Hense
A Bayesian approach is applied to the observed surface air temperature (SAT) changes using multi-AOGCM ensembles (MMEs) of the IPCC AR4 simulations and single-AOGCM ensembles (SMEs) with the ECHO-G model. A Bayesian decision method is used as a tool for classifying observations into scenarios (or hypotheses) concerned. The Bayes factor (or likelihood ratio) provides an observational evidence for the each scenario against a predefined reference scenario. Four scenarios are used to explain observed SAT changes as 'CTL' (control or no change), 'Nat' (natural forcing induced change), 'GHG' (greenhouse-gas induced change), and 'All' (natural plus anthropogenic forcing induced change). Parameters needed to define the four scenarios are estimated from SMEs or MMEs. Taking the 'CTL' scenario as a reference one, application results for global mean SAT changes for the whole 20th century (1900-1999) show 'decisive' evidences (logarithm of Bayes factor > 5) for the 'All' scenario only. While 'strong' evidences (log of Bayes factor > 2.5) for both 'All' and 'Nat' scenarios are found in SAT changes for the first half (1900-1949), there are 'decisive' evidences for the 'All' scenario for SAT changes in the second half (1950-1999), supporting previous results. It is demonstrated that our Bayesian decision results for global mean SATs are largely insensitive to both inter-model uncertainties and prior probabilities. Extension to regional and seasonal SATs will be also presented considering more scenarios such as 'S' (sulfate aerosol forcing only) and 'ANTHRO' ('GHG' plus 'S').
Session 5, Climate Modeling: Studies of climate change
Wednesday, 1 February 2006, 8:30 AM-5:00 PM, A313
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