Quantifying climate feedbacks using radiative kernels

  • Soden B
  • Held I
  • Colman R
 et al. 
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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.

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