Articles | Volume 9, issue 1
Research article
21 Feb 2018
Research article |  | 21 Feb 2018

Selecting a climate model subset to optimise key ensemble properties

Nadja Herger, Gab Abramowitz, Reto Knutti, Oliver Angélil, Karsten Lehmann, and Benjamin M. Sanderson

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Cited articles

Abramowitz, G.: Model independence in multi-model ensemble prediction, Aust. Meteorol. Oceanogr. J., 59, 3–6, 2010. a
Abramowitz, G. and Bishop, C. H.: Climate model dependence and the ensemble dependence transformation of CMIP projections, J. Climate, 28, 2332–2348,, 2015. a, b, c
Abramowitz, G. and Gupta, H.: Toward a model space and model independence metric, Geophys. Res. Lett., 35, L05705,, 2008. a
Annan, J. D. and Hargreaves, J. C.: Understanding the CMIP3 multimodel ensemble, J. Climate, 24, 4529–4538,, 2011. a, b
Annan, J. D. and Hargreaves, J. C.: On the meaning of independence in climate science, Earth Syst. Dynam., 8, 211–224,, 2017. a
Short summary
Users presented with large multi-model ensembles commonly use the equally weighted model mean as a best estimate, ignoring the issue of near replication of some climate models. We present an efficient and flexible tool that finds a subset of models with improved mean performance compared to the multi-model mean while at the same time maintaining the spread and addressing the problem of model interdependence. Out-of-sample skill and reliability are demonstrated using model-as-truth experiments.
Final-revised paper