Assessments of the global anthropogenic greenhouse and sulfate signal using different types of simplified climate models

Summary The problem of global climate change forced by anthropogenic emissions of greenhouse gases (GHG) and sulfur components (SU) has to be addressed by different methods, including the consideration of concurrent forcing mechanisms and the analysis of observations. This is due to the shortcoming and uncertainties of all methods, even in case of the most sophisticated ones. In respect to the global mean surface air temperature, we compare the results from multiple observational statistical models such as multiple regression (MRM) and neural networks (NNM) with those of energy balance (EBM) and general circulation models (GCM) where, in the latter case, we refer to the recent IPCC Report. Our statistical assessments, based on the 1866–1994 period, lead to a GHG signal of 0.8–1.3 K and a combined GHG-SU signal of 0.5–0.8 K detectable in observations. This is close to GCM simulations and clearly larger than the volcanic, solar and ENSO (El Niño/southern oscillation) signals also considered.With 2 Figures