Articles | Volume 15, issue 8
Methods for assessment of models
19 Apr 2022
Methods for assessment of models |  | 19 Apr 2022

An ensemble-based statistical methodology to detect differences in weather and climate model executables

Christian Zeman and Christoph Schär

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Cited articles

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,, 2015. a, b, c, d, e, f, g, h, i, j
Baker, A. H., Hu, Y., Hammerling, D. M., Tseng, Y.-H., Xu, H., Huang, X., Bryan, F. O., and Yang, G.: Evaluating statistical consistency in the ocean model component of the Community Earth System Model (pyCECT v2.0), Geosci. Model Dev., 9, 2391–2406,, 2016. a, b, c, d
Baldauf, M., Seifert, A., Förstner, J., Majewski, D., Raschendorfer, M., and Reinhardt, T.: Operational Convective-Scale Numerical Weather Prediction with the COSMO Model: Description and Sensitivities, Mon. Weather Rev., 139, 3887–3905,, 2011. a
Bartlett, M. S.: The Effect of Non-Normality on the t Distribution, Math. Proc. Cambridge, 31, 223–231,, 1935. a
Bauer, P., Thorpe, A., and Brunet, G.: The quiet revolution of numerical weather prediction, Nature, 525, 47–55,, 2015. a
Short summary
Our atmosphere is a chaotic system, where even a tiny change can have a big impact. This makes it difficult to assess if small changes, such as the move to a new hardware architecture, will significantly affect a weather and climate model. We present a methodology that allows to objectively verify this. The methodology is applied to several test cases, showing a high sensitivity. Results also show that a major system update of the underlying supercomputer did not significantly affect our model.