Quantifying Climate Feedbacks Using Radiative Kernels

TLDR

Radiative feedbacks largely determine climate change magnitude, yet their analysis in global models is hampered by methodological uncertainties that obscure their strengths and spatial distributions. The authors propose a method that quantifies climate feedbacks by applying radiative kernels, which describe how top‑of‑atmosphere radiative fluxes respond to incremental changes in feedback variables. This approach decomposes feedbacks into a radiative‑transfer component and a climate‑response component, estimates cloud feedbacks from differences between full‑sky and clear‑sky kernels, and generates regional maps to compare model results. The method shows that models typically yield globally averaged cloud feedbacks that are largely positive or neutral, contrasts with the mixed sign of cloud forcing, and offers a simple, accurate way to compare feedbacks across models while clarifying spatial patterns and intermodel differences.

Abstract

Abstract 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.