A New Graphical Method to Diagnose the Impacts of Model Changes on Climate Sensitivity

摘要

Equilibrium climate sensitivity (ECS) is defined as the change in the global mean surface air temperature due to the doubling or quadrupling of CO 2 in a climate model simulation. This metric is used to determine the uncertainty in future climate projections, and therefore, the impact of model changes on ECS is of large interest to the climate modeling community. In this paper, we propose a new graphical method, which is an extension of Gregory's linear regression method, to represent the impact of model changes on ECS, climate forcing, and climate feedbacks in a single diagram. Using this visualization method, one can (a) quantify whether the model or process change amplifies, reduces, or has no impact on global warming, (b) evaluate the percentage changes in ECS, climate forcing, and climate feedbacks, and (c) quantify the ranges of the uncertainties in the estimated changes. We demonstrate this method using an example of climate sensitivity simulations with and without interactive chemistry. This method can be useful for multimodel assessments where the response of multiple models for the same model experiment (e.g., usage of interactive chemistry compared with the prescribed chemistry as shown here) can be assessed simultaneously, which is otherwise difficult to compare and comprehend. We also demonstrate how this method can be used to examine the spread in ECS, climate forcing, and climate feedbacks with respect to the multimodel mean (or one benchmark model) for multimodel frameworks such as Coupled Model Intercomparison Project Phase 5 or for different ensemble members in a large ensemble of simulations conducted using a single model.