Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 10, issue 4
Articles | Volume 10, issue 4
https://doi.org/10.5194/gmd-10-1789-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-10-1789-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Articles | Volume 10, issue 4
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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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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