The extent to which the climate will change due to an external forcing depends largely on radiative feedbacks, which act to amplify or damp the surface temperature response. There are a variety of issues that complicate the analysis of radiative feedbacks in global climate models, resulting in some confusion regarding their strengths and distributions. In this paper, the authors present a method for quantifying climate feedbacks based on “radiative kernels” that describe the differential response of the top-of-atmosphere radiative fluxes to incremental changes in the feedback variables. The use of radiative kernels enables one to decompose the feedback into one factor that depends on the radiative transfer algorithm and the unperturbed climate state and a second factor that arises from the climate response of the feedback variables. Such decomposition facilitates an understanding of the spatial characteristics of the feedbacks and the causes of intermodel differences. This technique provides a simple and accurate way to compare feedbacks across different models using a consistent methodology. Cloud feedbacks cannot be evaluated directly from a cloud radiative kernel because of strong nonlinearities, but they can be estimated from the change in cloud forcing and the difference between the full-sky and clear-sky kernels. The authors construct maps to illustrate the regional structure of the feedbacks and compare results obtained using three different model kernels to demonstrate the robustness of the methodology. The results confirm that models typically generate globally averaged cloud feedbacks that are substantially positive or near neutral, unlike the change in cloud forcing itself, which is as often negative as positive.

[1] An atmospheric general circulation model, coupled to a mixed layer ocean, is subjected to a broad range of forcing away from the current climate between 1/16 to 32 times current CO 2 in halving/doubling steps. As climate warms climate sensitivity weakens (although not monotonically), albedo feedback weakens (driving much of the sensitivity weakening), water vapour feedback strengthens (at a rate slightly larger than it would if relative humidity remained unchanged), and lapse rate feedback increases (negatively); this latter change essentially offsetting the water vapour increases. Longwave cloud feedbacks are relatively stable (moderate and positive) across the full range; shortwave cloud feedback remains relatively weak, apart from under the coldest climates. Cloud optical property related components (from total water content, water/ice fraction and cloud thickness) remain remarkably stable. Cloud 'amount' feedbacks show the greatest trends: weakening as temperatures increase. Although cloud feedbacks show an overall consistency of features in different latitudes, precise patterns of changes differ substantially for different baseline climates. Citation: Colman, R., and B. McAvaney