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| Titel |
Failure analysis of parameter-induced simulation crashes in climate models |
| VerfasserIn |
D. D. Lucas, R. Klein, J. Tannahill, D. Ivanova, S. Brandon, D. Domyancic, Y. Zhang |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1991-959X
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| Digitales Dokument |
URL |
| Erschienen |
In: Geoscientific Model Development ; 6, no. 4 ; Nr. 6, no. 4 (2013-08-07), S.1157-1171 |
| Datensatznummer |
250084970
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| Publikation (Nr.) |
 copernicus.org/gmd-6-1157-2013.pdf |
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| Zusammenfassung |
| Simulations using IPCC (Intergovernmental Panel on Climate Change)-class climate models are subject to fail or crash for a variety of
reasons. Quantitative analysis of the failures can yield useful insights to better understand and
improve the models. During the course of uncertainty quantification (UQ) ensemble simulations to
assess the effects of ocean model parameter uncertainties on climate simulations, we experienced
a series of simulation crashes within the Parallel Ocean Program (POP2) component of the Community
Climate System Model (CCSM4). About 8.5% of our CCSM4 simulations failed for numerical
reasons at combinations of POP2 parameter values. We applied support vector machine (SVM)
classification from machine learning to quantify and predict the probability of failure as
a function of the values of 18 POP2 parameters. A committee of SVM classifiers readily predicted
model failures in an independent validation ensemble, as assessed by the area under the receiver
operating characteristic (ROC) curve metric (AUC > 0.96). The causes of the simulation failures
were determined through a global sensitivity analysis. Combinations of 8 parameters related to
ocean mixing and viscosity from three different POP2 parameterizations were the major sources of
the failures. This information can be used to improve POP2 and CCSM4 by incorporating correlations
across the relevant parameters. Our method can also be used to quantify, predict, and understand
simulation crashes in other complex geoscientific models. |
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