In this study, a Bayesian maximum-likelihood method is used to estimate local probability distributions of projected climate changes in continental temperature T and precipitation P under greenhouse emission scenarios of two different severity levels. These estimates are derived from multimodel climate simulatinos of the 20th and 21st centuries. Bayesian-weighted multimodel consensus estimates of the local climate-change signal and noise are determined from the statistical agreement of each model's simulation of historical climate with observations and of its 21st-century climate projection with suitably chosen target data. The consensus estimates of climatic changes in T are found to be universally positive and statistically significant under either future emissions scenario. In contrast, changes in P vary locally in sign, and they are statistically significant only in limited regions under the more severe scenario. The impacts of jointly considering more than one variable or statistical parameter on the Bayesian estimation of 21st-century climate change also are explored. The multivariate approach allows estimation of a probability distribution of the joint projected climate change in T and P, while inclusion of both first- and second-moment statistics of either variable results in a greater differentiation of the Bayesian weights and a general enhancement of the local signal-noise ratio.

Full Article: http://www.agu.org/journals/jd/jd1018/2009JD013654/2009JD013654.pdf

Last Updated: 2012-01-18