Articles | Volume 10, issue 6
https://doi.org/10.5194/gmd-10-2321-2017
https://doi.org/10.5194/gmd-10-2321-2017
Methods for assessment of models
 | 
23 Jun 2017
Methods for assessment of models |  | 23 Jun 2017

A Bayesian posterior predictive framework for weighting ensemble regional climate models

Yanan Fan, Roman Olson, and Jason P. Evans

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Cited articles

Bhat, K. S., Haran, M., Terando, A., and Keller, K.: Climate Projections Using Bayesian Model Averaging and Space-Time Dependence, J. Agric. Biol. Envir. S., 16, 606?628, https://doi.org/10.1007/s13253-011-0069-3, 2011.
Buser, C. M., Künsch, H. R., Lüthi, D., Wild, M., and Schär, M. C.: Bayesian multi-model projections of climate: bias assumptions and interannual variability, Clim. Dynam., 33, 849–868, 2010.
Buser, C. M., Künsch, H. R., and Schär, C.: Bayesian multi-model projections of climate: generalization and application to ENSEMBLES results, Climate Res., 44, 227–241, 2010.
Christensen, J. H., Carter, T. R., Rummukainen, M., and Amanatidis, G.: Evaluating the performance and utility of regional climate models: the PRUDENCE project, Climatic Change, 81, 1–6, https://doi.org/10.1007/s10584-006-9211-6, 2007.
Cortés-Hernández, V. E., Zheng, F., Evans, J. P., Lambert, M., Sharma, A., and Westra, S.: Evaluating regional climate models for simulating sub-daily rainfall extremes, Clim. Dynam., 47, 1613–1628, https://doi.org/10.1007/s00382-015-2923-4, 2015.
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Short summary
We develop a novel and principled Bayesian statistical approach to computing model weights in climate change projection ensembles of regional climate models. The approach accounts for uncertainty in model bias, trend and internal variability. The weights are easily interpretable and the ensemble weighted models are shown to provide the correct coverage and improve upon existing methods in terms of providing narrower confidence intervals for climate change projections.