Articles | Volume 16, issue 10
https://doi.org/10.5194/gmd-16-2899-2023
https://doi.org/10.5194/gmd-16-2899-2023
Methods for assessment of models
 | 
26 May 2023
Methods for assessment of models |  | 26 May 2023

Various ways of using empirical orthogonal functions for climate model evaluation

Rasmus E. Benestad, Abdelkader Mezghani, Julia Lutz, Andreas Dobler, Kajsa M. Parding, and Oskar A. Landgren

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Cited articles

Ambaum, M. H. P., Hoskins, B. J., and Stephenson, D. B.: Arctic Oscillation or North Atlantic Oscillation?, J. Climate, 14, 3495–3507, https://doi.org/10.1175/1520-0442(2001)014<3495:AOONAO>2.0.CO;2, 2001. a
Barnett, T. P.: Comparison of Near-Surface Air Temperature Variability in 11 Coupled Global Climate Models, J. Climate, 12, 511–518, 1999. a, b, c
Becker, R. A., Chambers, J. M., and Wilks, A. R.: The new S language: a programming environment for data analysis and graphics, Wadsworth & Brooks/Cole computer science series, Wadsworth & Brooks/Cole Advanced Books & Software, Pacific Grove, Calif., ISBN 9780534091927, 9780534091934, 053409192X, 0534091938; OCLC Number (WorldCat Unique Identifier): 17677647, 1988. a
Benestad, R.: Common EOFs for model evaluation, Figshare [data set], https://doi.org/10.6084/M9.FIGSHARE.21641756.V3, 2022. a, b, c, d
Benestad, R.: Common EOFs for evaluation of geophysical data and global climate models, Youtube [video], https://youtu.be/32mtHHAoq6k, last access: 25 May 2023a. a
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Short summary
A mathematical method known as common EOFs is not widely used within the climate research community, but it offers innovative ways of evaluating climate models. We show how common EOFs can be used to evaluate large ensembles of global climate model simulations and distill information about their ability to reproduce salient features of the regional climate. We can say that they represent a kind of machine learning (ML) for dealing with big data.
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