Articles | Volume 10, issue 1
https://doi.org/10.5194/esd-10-91-2019
https://doi.org/10.5194/esd-10-91-2019
Review
 | 
13 Feb 2019
Review |  | 13 Feb 2019

ESD Reviews: Model dependence in multi-model climate ensembles: weighting, sub-selection and out-of-sample testing

Gab Abramowitz, Nadja Herger, Ethan Gutmann, Dorit Hammerling, Reto Knutti, Martin Leduc, Ruth Lorenz, Robert Pincus, and Gavin A. Schmidt

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A daily 25 km short-latency rainfall product for data-scarce regions based on the integration of the Global Precipitation Measurement mission rainfall and multiple-satellite soil moisture products
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Cited articles

Abramowitz, G.: Model independence in multi-model ensemble prediction, Aust. Meteorol. Ocean., 59, 3–6, 2010. 
Abramowitz, G. and Gupta, H.: Toward a model space and model independence metric, Geophys. Res. Lett., 35, L05705, https://doi.org/10.1029/2007GL032834, 2008. 
Abramowitz, G. and Bishop, C. H.: Climate Model Dependence and the Ensemble Dependence Transformation of CMIP Projections, J. Climate, 28, 2332–2348, 2015. 
Annan, J. D. and Hargreaves, J. C.: Reliability of the CMIP3 ensemble, Geophys. Res. Lett., 37, L02703, https://doi.org/10.1029/2009GL041994, 2010. 
Annan, J. D. and Hargreaves, J. C.: Understanding the CMIP3 ensemble, J. Climate, 24, 4529–4538, 2011. 
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Best estimates of future climate projections typically rely on a range of climate models from different international research institutions. However, it is unclear how independent these different estimates are, and, for example, the degree to which their agreement implies robustness. This work presents a review of the varied and disparate attempts to quantify and address model dependence within multi-model climate projection ensembles.
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