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

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

doi:https://doi.org/10.5194/gmd-10-1789-2017

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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Final revised paper (published on 27 Apr 2017)
Supplement to the final revised paper
Preprint (discussion started on 30 Aug 2016)
Supplement to the preprint

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

- Printer-friendly version

- Supplement

RC1: 'Referee comment on gmd-2016-185', Anonymous Referee #1, 03 Oct 2016
- AC1: 'Author's response to reviewer 1', Danny Williamson, 24 Nov 2016
RC2: 'Referee comment', Richard Wilkinson, 06 Oct 2016
- AC2: 'Author's response to Dr Wilkinson', Danny Williamson, 24 Nov 2016
RC3: 'Referee Comment', Anonymous Referee #3, 11 Oct 2016
- AC3: 'Author's response to reviewer 3', Danny Williamson, 24 Nov 2016

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

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

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.