final_program_abstracts[1]

11 IMSC Session Program
Bayesian estimation of local signal and noise in CMIP3
simulations of climate change
Wednesday - Parallel Session 2
Qinqyun Duan 1 and Thomas J. Phillips 2
1
Beijing Normal University, Beijing, China
2
Lawrence Livermore National Laboratory, Livermore, USA
The Bayesian Model Averaging (BMA) algorithm of Raftery et al. is employed to
estimate local probability distributions of projected changes in continental surface
temperature T and precipitation P, as simulated by some 20 CMIP3 coupled climate
models for two 21st century greenhouse emissions scenarios of different severity.
Bayesian-weighted multi-model consensus estimates of the local climate-change
signal and noise are determined from the statistical agreement of each model’s
historical climate simulation with observational estimates of T and P, and of its 21st
century climate projection with suitably chosen T or P target data. The multi-model
consensus estimate of the local climate-change signal and noise proves to be
surprisingly insensitive to different methods for choosing these future-climate target
data.
It is found that the Bayesian-estimated local climatic changes in continental T are
universally positive and statistically significant under either future emissions scenario.
In contrast, changes in continental P vary locally in sign and are statistically
significant only in limited regions under the more severe scenario. Bayesian
estimation of 21st century climate change that jointly considers more than one climate
variable or statistical parameter is also explored in the BMA framework. A bi-variate
approach allows estimation of the probability distribution of the joint projected
climate change in T and P which differs qualitatively by region, while inclusion of
both first- and second-moment statistics of either T or P results in a greater
differentiation of the Bayesian weightings of the individual simulations and a general
enhancement of the estimated signal-noise ratio of the projected climate change.
We gratefully acknowledge the support provided by Beijing Normal University. A
portion of the work also was performed under the auspices of the U.S. Department of
Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-
07NA27344.
Abstracts 185