Tuning without over-tuning: parametric uncertainty quantification for the NEMO ocean model
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 procedur...
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Online Access: | https://doi.org/10.5194/gmd-10-1789-2017 https://doaj.org/article/763e3aac5fcc41dd9bc78ca49b1c340f |
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ftdoajarticles:oai:doaj.org/article:763e3aac5fcc41dd9bc78ca49b1c340f 2023-05-15T17:53:53+02:00 Tuning without over-tuning: parametric uncertainty quantification for the NEMO ocean model D. B. Williamson A. T. Blaker B. Sinha 2017-04-01T00:00:00Z https://doi.org/10.5194/gmd-10-1789-2017 https://doaj.org/article/763e3aac5fcc41dd9bc78ca49b1c340f EN eng Copernicus Publications http://www.geosci-model-dev.net/10/1789/2017/gmd-10-1789-2017.pdf https://doaj.org/toc/1991-959X https://doaj.org/toc/1991-9603 1991-959X 1991-9603 doi:10.5194/gmd-10-1789-2017 https://doaj.org/article/763e3aac5fcc41dd9bc78ca49b1c340f Geoscientific Model Development, Vol 10, Iss 4, Pp 1789-1816 (2017) Geology QE1-996.5 article 2017 ftdoajarticles https://doi.org/10.5194/gmd-10-1789-2017 2022-12-31T14:55:01Z 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 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. Article in Journal/Newspaper Orca Directory of Open Access Journals: DOAJ Articles Geoscientific Model Development 10 4 1789 1816 |
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Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Geology QE1-996.5 |
spellingShingle |
Geology QE1-996.5 D. B. Williamson A. T. Blaker B. Sinha Tuning without over-tuning: parametric uncertainty quantification for the NEMO ocean model |
topic_facet |
Geology QE1-996.5 |
description |
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 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. |
format |
Article in Journal/Newspaper |
author |
D. B. Williamson A. T. Blaker B. Sinha |
author_facet |
D. B. Williamson A. T. Blaker B. Sinha |
author_sort |
D. B. Williamson |
title |
Tuning without over-tuning: parametric uncertainty quantification for the NEMO ocean model |
title_short |
Tuning without over-tuning: parametric uncertainty quantification for the NEMO ocean model |
title_full |
Tuning without over-tuning: parametric uncertainty quantification for the NEMO ocean model |
title_fullStr |
Tuning without over-tuning: parametric uncertainty quantification for the NEMO ocean model |
title_full_unstemmed |
Tuning without over-tuning: parametric uncertainty quantification for the NEMO ocean model |
title_sort |
tuning without over-tuning: parametric uncertainty quantification for the nemo ocean model |
publisher |
Copernicus Publications |
publishDate |
2017 |
url |
https://doi.org/10.5194/gmd-10-1789-2017 https://doaj.org/article/763e3aac5fcc41dd9bc78ca49b1c340f |
genre |
Orca |
genre_facet |
Orca |
op_source |
Geoscientific Model Development, Vol 10, Iss 4, Pp 1789-1816 (2017) |
op_relation |
http://www.geosci-model-dev.net/10/1789/2017/gmd-10-1789-2017.pdf https://doaj.org/toc/1991-959X https://doaj.org/toc/1991-9603 1991-959X 1991-9603 doi:10.5194/gmd-10-1789-2017 https://doaj.org/article/763e3aac5fcc41dd9bc78ca49b1c340f |
op_doi |
https://doi.org/10.5194/gmd-10-1789-2017 |
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Geoscientific Model Development |
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10 |
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4 |
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1789 |
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1816 |
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