Tuning without over-tuning: parametric uncertainty quantification for the NEMO ocean model.
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Williamson, Daniel B.
Blaker, Adam T.
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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 .....
JournalGeoscientific Model Development
Sustainable Development Goals (SDG)14.A
Best Practice TypeManual (incl. handbook, guide, cookbook etc)
CitationWilliamson, 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-10-1789-2017
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