A Bayesian Climate Change Detection and Attribution Assessment

Abstract

Abstract A Bayesian analysis of the evidence for human-induced climate change in global surface temperature observations is described. The analysis uses the standard optimal detection approach and explicitly incorporates prior knowledge about uncertainty and the influence of humans on the climate. This knowledge is expressed through prior distributions that are noncommittal on the climate change question. Evidence for detection and attribution is assessed probabilistically using clearly defined criteria. Detection requires that there is high likelihood that a given climate-model-simulated response to historical changes in greenhouse gas concentration and sulphate aerosol loading has been identified in observations. Attribution entails a more complex process that involves both the elimination of other plausible explanations of change and an assessment of the likelihood that the climate-model-simulated response to historical forcing changes is correct. The Bayesian formalism used in this study deals with this latter aspect of attribution in a more satisfactory way than the standard attribution consistency test. Very strong evidence is found to support the detection of an anthropogenic influence on the climate of the twentieth century. However, the evidence from the Bayesian attribution assessment is not as strong, possibly due to the limited length of the available observational record or sources of external forcing on the climate system that have not been accounted for in this study. It is estimated that strong evidence from a Bayesian attribution assessment using a relatively stringent attribution criterion may be available by 2020.