Bayesian Climate Change Assessment

Abstract

A Bayesian fingerprinting methodology for assessing anthropogenic impacts on climate was developed. This analysis considers the effect of increased CO2 on near-surface temperatures. A spatial CO2 fingerprint based on control and forced model output from the National Center for Atmospheric Research Climate System Model was developed. The Bayesian approach is distinguished by several new facets. First, the prior model for the amplitude of the fingerprint is a mixture of two distributions: one reflects prior uncertainty in the anticipated value of the amplitude under the hypothesis of ?no climate change.? The second reflects behavior assuming?climate change forced by CO2.? Second, within the Bayesian framework, a new formulation of detection and attribution analyses based on practical significance of impacts rather than traditional statistical significance was presented. Third, since Bayesian analyses can be very sensitive to prior inputs, a robust Bayesian approach, which investigates the ranges of posterior inferences as prior inputs are varied, was used. Following presentation of numerical results that enforce the claim of changes in temperature patterns due to anthropogenic CO2 forcing, the article concludes with a comparative analysis for another CO2 fingerprint and selected discussion.