Articles | Volume 9, issue 7
Geosci. Model Dev., 9, 2391–2406, 2016
https://doi.org/10.5194/gmd-9-2391-2016
Geosci. Model Dev., 9, 2391–2406, 2016
https://doi.org/10.5194/gmd-9-2391-2016

Development and technical paper 12 Jul 2016

Development and technical paper | 12 Jul 2016

Evaluating statistical consistency in the ocean model component of the Community Earth System Model (pyCECT v2.0)

Allison H. Baker et al.

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Cited articles

Baker, A. H., Xu, H., Dennis, J. M., Levy, M. N., Nychka, D., Mickelson, S. A., Edwards, J., Vertenstein, M., and Wegener, A.: A Methodology for Evaluating the Impact of Data Compression on Climate Simulation Data, in: Proceedings of the 23rd International Symposium on High-performance Parallel and Distributed Computing, HPDC '14, 203–214, 2014.
Baker, A. H., Hammerling, D. M., Levy, M. N., Xu, H., Dennis, J. M., Eaton, B. E., Edwards, J., Hannay, C., Mickelson, S. A., Neale, R. B., Nychka, D., Shollenberger, J., Tribbia, J., Vertenstein, M., and Williamson, D.: A new ensemble-based consistency test for the Community Earth System Model (pyCECT v1.0), Geosci. Model Dev., 8, 2829–2840, https://doi.org/10.5194/gmd-8-2829-2015, 2015.
Box, G. E. P. and Draper, N. R.: Response Surfaces, Mixtures, and Ridge Analyses, Second Edition, John Wiley and Sons, 2007.
Carson, II, J. S.: Model Verification and Validation, in: Proceedings of the 2002 Winter Simulation Conference, 52–58, 2002.
Clune, T. and Rood, R.: Software Testing and Verification in Climate Model Development, IEEE Software, 28, 49–55, https://doi.org/10.1109/MS.2011.117, 2011.
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Short summary
Software quality assurance is critical to detecting errors in large, complex climate simulation codes. We focus on ocean model simulation data in the context of an ensemble-based statistical consistency testing approach developed for atmospheric data. Because ocean and atmosphere models have differing characteristics, we develop a new statistical tool to evaluate ocean model simulation data that provide a simple, subjective, and systematic way to detect errors and instil model confidence.