Articles | Volume 10, issue 4
https://doi.org/10.5194/gmd-10-1789-2017
https://doi.org/10.5194/gmd-10-1789-2017
Methods for assessment of models
 | 
27 Apr 2017
Methods for assessment of models |  | 27 Apr 2017

Tuning without over-tuning: parametric uncertainty quantification for the NEMO ocean model

Daniel B. Williamson, Adam T. Blaker, and Bablu Sinha

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AR by Danny Williamson on behalf of the Authors (24 Nov 2016)  Author's response   Manuscript 
ED: Referee Nomination & Report Request started (05 Jan 2017) by James Annan
RR by Anonymous Referee #2 (30 Jan 2017)
ED: Publish as is (30 Jan 2017) by James Annan
AR by Danny Williamson on behalf of the Authors (13 Feb 2017)
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Short summary
We present a method from the statistical science literature to assist in the tuning of global climate models submitted to CMIP. We apply the method to the NEMO ocean model and find choices of its free parameters that lead to improved representations of depth integrated global mean temperature and salinity. We argue against automatic tuning procedures that involve optimising certain outputs of a model and explain why our method avoids common difficulties with/arguments against automatic tuning.