Changing the Climate Sensitivity of an Atmospheric General Circulation Model through Cloud Radiative Adjustment
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Sokolov-2012-Changing the Climate Sensitivity of an Atmospheric General.pdf
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Author(s)
Sokolov, Andrei P.
Monier, Erwan
Date Issued
October 2012
Journal
Journal of Climate
Publisher
American Meteorological Society
Citation
Sokolov, Andrei P., and Erwan Monier. “Changing the Climate Sensitivity of an Atmospheric General Circulation Model through Cloud Radiative Adjustment.” Journal of Climate 25.19 (2012): 6567–6584. ©2013 American Meteorological Society
Version
Final published version
Abstract
Conducting probabilistic climate projections with a particular climate model requires the ability to vary the model’s characteristics, such as its climate sensitivity. In this study, the authors implement and validate a method to change the climate sensitivity of the National Center for Atmospheric Research (NCAR) Community Atmosphere Model, version 3 (CAM3), through cloud radiative adjustment. Results show that the cloud radiative adjustment method does not lead to physically unrealistic changes in the model’s response to an external forcing, such as doubling CO2 concentrations or increasing sulfate aerosol concentrations. Furthermore, this method has some advantages compared to the traditional perturbed physics approach. In particular, the cloud radiative adjustment method can produce any value of climate sensitivity within the wide range of uncertainty based on the observed twentieth century climate change. As a consequence, this method allows Monte Carlo–type probabilistic climate forecasts to be conducted where values of uncertain parameters not only cover the whole uncertainty range, but cover it homogeneously. Unlike the perturbed physics approach that can produce several versions of a model with the same climate sensitivity but with very different regional patterns of change, the cloud radiative adjustment method can only produce one version of the model with a specific climate sensitivity. As such, a limitation of this method is that it cannot cover the full uncertainty in regional patterns of climate change.
MIT Department
Massachusetts Institute of Technology. Center for Global Change Science
Massachusetts Institute of Technology. Joint Program on the Science & Policy of Global Change
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DOI of Published Version
https://doi.org/10.1175/jcli-d-11-00590.1
