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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Cited articles

Beck, J. and Guillas, S.: Sequential design with Mutual Information for Computer Experiments (MICE): Emulation of a Tsunami model, arXiv, 2015.
Brynjarsdottir, J. and O'Hagan, A.: Learning about physical parameters: The importance of model discrepancy, Inverse Prob., 30, 114007 24 pp., 2014.
Conti, S., Gosling, J. P., Oakley, J. E., and O'Hagan, A.: Gaussian process emulation of dynamic computer codes, Biometrika, 96, 663–676, 2009.
Craig, P. S., Goldstein, M., Seheult, A. H., and Smith, J. A.: Bayes Linear Strategies for Matching Hydrocarbon Reservoir History, in: Bayesian Statistics 5, edited by: Bernado, J. M., Berger, J. O., Dawid, A. P., and Smith, A. F. M., Oxford University Press, 69–95, 1996.
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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.
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