A Bayesian approach is applied to the observed regional and seasonal surface air temperature (SAT) changes using single-model ensembles (SMEs) with the ECHO-G model and multi-model ensembles (MMEs) of the IPCC AR4 simulations. Bayesian decision classifies observations into the most probable scenario out of six available scenarios: CTL (control), N (natural forcing), ANTHRO (anthropogenic forcing), G (greenhouse-gas), S (Sulfate aerosols), and ALL (natural plus anthropogenic forcing). Space-time vectors of detection variable are constructed for six continental regions (North America, South America, Asia, Africa, Australia, and Europe) by combining temporal components of SATs (expressed as Legendre coefficients) from two or three subregions of each continental region.

Bayesian decision results show that over most of the regions observed SATs are classified into ALL or ANTHRO scenarios for the whole 20th century and its second half. N and ALL scenarios are decided during the first half of the 20th century, but only in the low latitude region (Africa and South America), which might be related to response patterns to solar forcing. Overall seasonal decisions follow annual results, but there are notable seasonal dependences which differ between regions. A comparison of SME and MME results demonstrates that the Bayesian decisions for regional-scale SATs are largely robust to inter-model uncertainties as well as prior probability and temporal scales as found in the global results.

Last Updated: 2007-07-27