Optimal detection and attribution of climate change: sensitivity of results to climate model differences

Fingerprint techniques for the detection of anthropogenic climate change ai m to distinguish the climate response to anthropogenic forcing from respons es to other external influences and from internal climate variability. All these responses and the characteristics of internal variability are typical ly estimated from climate model data. We evaluate tho sensitivity of detect ion and attribution results to the use of response and variability estimate s from two different coupled ocean atmosphere general circulation models (H adCM2, developed at the Hadley Centre, and ECHAM3/LSG from the MPI fur Mete orologie and Deutsches Klimarechenzentrum). The models differ in their resp onse to greenhouse gas and direct sulfate aerosol forcing and also in the s tructure of their internal variability. This leads to differences in the es timated amplitude and the significance level of anthropogenic signals in ob served 50-year summer (June, July, August) surface temperature trends. Whil e the detection of anthropogenic influence on climate is robust to intermod el differences, our ability to discriminate between the greenhouse gas and the sulfate;aerosol signals is not. An analysis of the recent warming, and the warming that occurred in the first half of the twentieth century, sugge sts that simulations forced with combined changes in natural (solar and vol canic) and anthropogenic (greenhouse gas and sulfate aerosol) forcings agre e best with the observations.