Estimating shortwave radiative forcing and response in climate models

K. E. Taylor, M. Crucifix, P. Braconnot, C. D. Hewitt, C. Doutriaux, A. J. Broccoli, J. F.B. Mitchell, M. J. Webb

Research output: Contribution to journalArticlepeer-review

153 Scopus citations

Abstract

Feedback analysis in climate models commonly involves decomposing any change in the system's energy balance into radiative forcing terms due to prescribed changes, and response terms due to the radiative effects of changes in model variables such as temperature, water vapor, clouds, sea ice, and snow. The established "partial radiative perturbation" (PRP) method allows an accurate separation of these terms, but requires processing large volumes of model output with an offline version of the model's radiation code. Here, we propose an "approximate PRP (APRP) method for the shortwave that provides an accurate estimate of the radiative perturbation, but derived from a quite modest amount of monthly mean model output. The APRP method is based on a simplified sho rtwave radiative model of the atmosphere, where surface absorption and atmospheric scattering and absorption are represented by means of three parameters that are diagnosed for overcast and clear-sky portions of each model grid cell. The accuracy of the method is gauged relative to full PRP calculations in two experiments: one in which carbon dioxide concentration is doubled and another in which conditions of the Last Glacial Maximum (LGM) are simulated. The approximate PRP method yields a shortwave cloud feedback accurate in the global mean to within 7%. Forcings and feedbacks due to surface albedo and noncloud atmospheric constituents are also well approximated with errors of order 5%-10%. Comparison of two different model simulations of the LGM shows that the regional and global differences in their ice sheet albedo forcing fields are clearly captured by the APRP method. Hence this method is an efficient and satisfactory tool for studying and intercomparing shortwave forcing and feedbacks in climate models.

Original languageEnglish (US)
Pages (from-to)2530-2543
Number of pages14
JournalJournal of Climate
Volume20
Issue number11
DOIs
StatePublished - Jun 1 2007

All Science Journal Classification (ASJC) codes

  • Atmospheric Science

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