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  • Soden, BJ, IM Held, R Colman, K Shell, J Kiehl, C Shields,, 2007: Quantifying Climate Feedbacks using Radiative Kernels. J. Climate. Submitted.

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. Differences
in the representation of these feedbacks are directly responsible for the uncertainty in current
model estimates of climate sensitivity. There are a variety of issues that complicate the analysis
of radiative feedbacks in global climate models (GCMs), resulting in some confusion regarding
their strength and distribution. In this paper, we present a method for quantifying climate
feedbacks based on “radiative kernels” which describe the differential response of the top-ofatmosphere
radiative fluxes to incremental changes in the feedback variables. The use of
radiative kernels enables one to decompose the feedback into one part that depends on the
radiative transfer algorithm and the unperturbed climate state, and a second part 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 has the advantage of requiring fewer computations and being easier
to implement than “partial radiative perturbation” methods. More importantly, it 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. Our results using this method 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.

Last Updated: 2007-07-27

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