Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
doi:10.5194/gmd-2016-185
© Author(s) 2016. This work is distributed
under the Creative Commons Attribution 3.0 License.
Methods for assessment of models
30 Aug 2016
Review status
A revision of this discussion paper was accepted for the journal Geoscientific Model Development (GMD) and is expected to appear here in due course.
Tuning without over-tuning: parametric uncertainty quantification for the NEMO ocean model
Daniel Williamson1, Adam T. Blaker2, and Bablu Sinha2 1College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK
2National Oceanography Centre, Southampton, UK, SO14 3ZH
Abstract. In this paper we discuss climate model tuning and present an iterative automatic tuning method from the statistical science literature. The method, which we refer to here as iterative refocussing (though also known as history matching), avoids many of the common pitfalls of automatic tuning procedures that are based on optimisation of a cost function; principally the over-tuning of a climate model due to using only partial observations. This avoidance comes by seeking to rule out parameter choices that we are confident could not reproduce the observations, rather than seeking the model that is closest to them (a procedure that risks over-tuning). We comment on the state of climate model tuning and illustrate our approach through 3 waves of iterative refocussing of the NEMO ORCA2 global ocean model run at 2° resolution. We show how at certain depths the anomalies of global mean temperature and salinity in a standard configuration of the model exceeds 10 standard deviations away from observations and show the extent to which this can be alleviated by iterative refocussing without compromising model performance spatially. We show how model improvements can be achieved by simultaneously perturbing multiple parameters, and illustrate the potential of using low resolution ensembles to tune NEMO ORCA configurations at higher resolutions.

Citation: Williamson, D., Blaker, A. T., and Sinha, B.: Tuning without over-tuning: parametric uncertainty quantification for the NEMO ocean model, Geosci. Model Dev. Discuss., doi:10.5194/gmd-2016-185, in review, 2016.
Daniel Williamson et al.
Daniel Williamson et al.

Viewed

Total article views: 230 (including HTML, PDF, and XML)

HTML PDF XML Total Supplement BibTeX EndNote
155 68 7 230 13 4 9

Views and downloads (calculated since 30 Aug 2016)

Cumulative views and downloads (calculated since 30 Aug 2016)

Viewed (geographical distribution)

Total article views: 230 (including HTML, PDF, and XML)

Thereof 229 with geography defined and 1 with unknown origin.

Country # Views %
  • 1

Saved

Discussed

Latest update: 29 Mar 2017
Publications Copernicus
Download
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.
We present a method from the statistical science literature to assist in the tuning of global...
Share