Application of regularised optimal fingerprinting to attribution. Part II: application to global near-surface temperature

Attribution of global near-surface temperature changes is revisited using simulations from the coupled model intercomparison project 5 and methodological improvements from the regularised optimal fingerprinting approach. The analysis of global mean temperature shows that changes can be robustly detected and attributed to anthropogenic influence. However, the differences between results from individual models and observations are found to be larger than the simulated internal variability in several cases. Discrimination between greenhouse gases and other anthropogenic forcings, based on the global mean only, is more difficult due to collinearity of temporal response patterns. Using spatio-temporal data provides less robust conclusions with respect to detection and attribution, as the results tend to deteriorate as the spatial resolution increases. More importantly, some inconsistencies between individual models and observations are found in this case. Such behaviour is not observed in a perfect model framework, where pseudo-observations and the expected response patterns are provided by the same model. However, using response patterns from a model other than the one used for pseudo-observations may lead to the same behaviour as real observations. Our results suggest that additional sources of uncertainty, such as modeling uncertainty or observational uncertainty, should not be neglected in detection and attribution.