Abstract
An extension of a regression-based methodology for constraining climate forecasts using a multi-thousand member ensemble of perturbed climate models is presented, using the multi-model CMIP-3 ensemble to estimate the systematic model uncertainty in the prediction, with the caveat that systematic biases common to all models are not accounted for. It is shown that previous methodologies for estimating the systematic uncertainty in predictions of climate sensitivity are dependent on arbitrary choices relating to ensemble sampling strategy. Using a constrained regression approach, a multivariate predictor may be derived based upon the mean climatic state of each ensemble member, but components of this predictor are excluded if they cannot be validated within the CMIP-3 ensemble. It is found that the application of the CMIP-3 constraint serves to decrease the upper bound of likelihood for climate sensitivity when compared with previous studies, with 10th and 90th percentiles of probability at 1.5 K and 4.3 K respectively.
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Acknowledgments
Thanks to Claudio Piani, David Sexton and Charles Jackson for their extensive comments. This work was only made possible with the efforts of the team who developed and continue to support the climateprediction.net project, as well as the members of the public who donated their idle computing time to the project. We acknowledge the modeling groups, the Program for Climate Model Diagnosis and Inter-comparison (PCMDI) and the WCRP’s Working Group on Coupled Modeling (WGCM) for their roles in making available the WCRP CMIP3 multi-model dataset. Support of this dataset is provided by the Office of Science, U.S. Department of Energy.
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Sanderson, B.M. On the estimation of systematic error in regression-based predictions of climate sensitivity. Climatic Change 118, 757–770 (2013). https://doi.org/10.1007/s10584-012-0671-6
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DOI: https://doi.org/10.1007/s10584-012-0671-6