Climate Forcings and Climate Sensitivities Diagnosed from Coupled Climate Model Integrations

A simple technique is proposed for calculating global mean climate forcing from transient integrations of coupled Atmosphere Ocean General Circulation Models (AOGCMs). This 'climate forcing' differs from the conventionally defined radiative forcing as it includes semi-direct effects that account for certain short timescale responses in the troposphere. Firstly, we calculate a climate feedback term from reported values of 2 x CO{sub 2} radiative forcing and surface temperature time series from 70-year simulations by twenty AOGCMs. In these simulations carbon dioxide is increased by 1%/year. The derived climate feedback agrees well with values that we diagnose from equilibrium climate change experiments of slab-ocean versions of the same models. These climate feedback terms are associated with the fast, quasi-linear response of lapse rate, clouds, water vapor and albedo to global surface temperature changes. The importance of the feedbacks is gauged by their impact on the radiative fluxes at the top of the atmosphere. We find partial compensation between longwave and shortwave feedback terms that lessens the inter-model differences in the equilibrium climate sensitivity. There is also some indication that the AOGCMs overestimate the strength of the positive longwave feedback. These feedback terms are then used to infer the shortwave and longwave time series of climate forcing in 20th and 21st Century simulations in the AOGCMs. We validate the technique using conventionally calculated forcing time series from four AOGCMs. In these AOGCMs the shortwave and longwave climate forcings we diagnose agree with the conventional forcing time series within {approx}10%. The shortwave forcing time series exhibit order of magnitude variations between the AOGCMs, differences likely related to how both natural forcings and/or anthropogenic aerosol effects are included. There are also factor of two differences in the longwave climate forcing time series, which may indicate problems with the modeling of well-mixed-greenhouse-gas changes. The simple diagnoses we present provide an important and useful first step for understanding differences in AOGCM integrations, indicating that some of the differences in model projections can be attributed to different prescribed climate forcing, even for so-called standard climate change scenarios