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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Volume 10, issue 2
Geosci. Model Dev., 10, 945–958, 2017
https://doi.org/10.5194/gmd-10-945-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Geosci. Model Dev., 10, 945–958, 2017
https://doi.org/10.5194/gmd-10-945-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Development and technical paper 23 Feb 2017

Development and technical paper | 23 Feb 2017

A cloud feedback emulator (CFE, version 1.0) for an intermediate complexity model

David J. Ullman1,a and Andreas Schmittner1 David J. Ullman and Andreas Schmittner
  • 1College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, OR 97331 USA
  • anow at: Northland College, Ashland, WI, USA

Abstract. The dominant source of inter-model differences in comprehensive global climate models (GCMs) are cloud radiative effects on Earth's energy budget. Intermediate complexity models, while able to run more efficiently, often lack cloud feedbacks. Here, we describe and evaluate a method for applying GCM-derived shortwave and longwave cloud feedbacks from 4 × CO2 and Last Glacial Maximum experiments to the University of Victoria Earth System Climate Model. The method generally captures the spread in top-of-the-atmosphere radiative feedbacks between the original GCMs, which impacts the magnitude and spatial distribution of surface temperature changes and climate sensitivity. These results suggest that the method is suitable to incorporate multi-model cloud feedback uncertainties in ensemble simulations with a single intermediate complexity model.

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Short summary
One major source of uncertainty in the prediction of climate relates to how models simulate clouds and their impact on surface temperatures. We have developed a new method for incorporating the cloud results as derived from complex climate models and applying these results to a more simplified model. The benefit with this approach is that a more simplified model is able to be run more efficiently, while still maintaining complicated cloud effects and their effect on surface temperatures.
One major source of uncertainty in the prediction of climate relates to how models simulate...
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