The Green's Function Model Intercomparison Project (GFMIP) Protocol
Uncertainty in climate sensitivity is dominated by uncertainties in how top-of-atmosphere radiative fluxes respond to surface warming, i.e. the radiative feedbacks. One particularly recalcitrant source of uncertainty is uncertainty in the “pattern effect” – how the top-of-atmosphere radiative response depends not just on global warming, but on regional patterns of warming. Poor quantification of the pattern effect is the primary reason why the latest assessment report of the Intergovernmental Panel on Climate Change (IPCC AR6) stated that historical changes in Earth’s Energy budget cannot constrain future warming.
The Green’s Function Model Intercomparison Project (GFMIP) provides an optimal standardized protocol for quantifying how atmospheric radiation responds to regional warming patterns. The work also undertook initial experiments with seven (7) climate models. More models are either running or are committed to running GFMIP experiments. Thus, the GFMIP project provides the first quantification of the range of the pattern effect, which, in turn, will help constrain climate sensitivity and future warming.
Green's function methods are a technique for modeling the response of the atmosphere to changes in the spatial field of surface temperature. These Green’s Function are an important tool to understand changes in radiative feedbacks due to evolving patterns of warming, a phenomenon called the “pattern effect.”
In this study, researchers present a protocol for creating atmospheric Green's functions to serve as the basis for a model intercomparison project, GFMIP. The protocol has been developed using a series of sensitivity tests performed with the computationally inexpensive HadAM3 atmosphere‐only general circulation model, along with existing and new simulations from other models.
The protocol chosen on the basis of these experiments balances scientific utility with the simulation time and setup required by the Green's function approach. Running these simulations will further our understanding of many aspects of atmospheric response, from the pattern effect and radiative feedbacks to changes in circulation, cloudiness, and precipitation.