final_program_abstracts[1]

11 IMSC Session Program
Probabilistic regional climate change projections using
Bayesian model averaging over East Asia
Thursday - Poster Session 1
Hee-Jeong Baek, Won-Tae Kwon and E-Hyung Park
National Institute of Meteorological Research, KMA, Korea
Recently Bayesian approaches have been applied to model evaluation and multimodel
ensemble averaging for weather and climate predictions. We employed similar
method used by Min et al. (2007) for regional-scale climate change projections using
IPCC AR4 multi-model data set and regional climate model data. The objective of
this study is to calculate a probabilistic climate change projection over East Asia
using Bayesian Averaging Model (BAM) and to estimate uncertainties of regional
climate projections. This information on the uncertainty is useful for decisionmakers.The
BMA technique is applied to the twenty-first century climate variables
simulated by the models of IPCC AR4 and regional climate models to produce
probabilistic predictions of regional climate over East Asia. In case of temperature
change, Bayesian training was applied to temperature data over land derived from the
Climate Research Unit and model data for the period of 1950-1999. The results from
weighted PDFs were compared with unweighted PDFs for future climate change
projection over East Asia. Comprehensive measure of model skills based on multiple
climate variables might be useful to produce more robust weighting factors and hence
more reliable probabilistic predictions of regional climate changes.
Abstracts 267