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

View/ Open
Average rating
votes
Date
2017Author
Williamson, Daniel B.
Blaker, Adam T.
Sinha, Bablu
Metadata
Show full item recordAbstract
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 three waves of iterative refocussing of the NEMO (Nucleus for European Modelling of the Ocean) 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 .....
Resource URL
https://www.geosci-model-dev.net/10/1789/2017/Journal
Geoscientific Model DevelopmentVolume
10Page Range
pp.1789–1816Document Language
enSustainable Development Goals (SDG)
14.ABest Practice Type
Manual (incl. handbook, guide, cookbook etc)DOI Original
:10.5194/gmd-10-1789-2017Citation
Williamson, D. B.; Blaker, A. T. and Sinha, B. (2017) Tuning without over-tuning: parametric uncertainty quantification for the NEMO ocean model, Geoscientfic. Model Development, 10, pp.1789–1816, DOI: https://doi.org/10.5194/gmd-10-1789-2017Collections
The following license files are associated with this item: