Various ways of using empirical orthogonal functions for climate model evaluation

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

doi:https://doi.org/10.5194/gmd-16-2899-2023

Articles | Volume 16, issue 10

https://doi.org/10.5194/gmd-16-2899-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-16-2899-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 16, issue 10

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

Supplement

https://doi.org/10.5194/gmd-16-2899-2023-supplement

Data sets

Common EOFs for model evaluation. figshare. Rasmus E. Benestad https://doi.org/10.6084/m9.figshare.21641756.v3

Video supplement

Common EOFs for evaluation of geophysical data and global climate models Rasmus Benestad https://youtu.be/32mtHHAoq6k

A brief presentation of common EOFs in R-studio Rasmus Benestad https://youtu.be/E01hthVL9pY

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.