08 Feb 2023
 | 08 Feb 2023
Status: this preprint is open for discussion.

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

Abstract. We present a framework for evaluating multi-model ensembles based on common empirical orthogonal functions ('common EOFs') that emphasise salient features connected to spatio-temporal covariance structures embedded in large climate data volumes. In other words, this framework enables the extraction of the most pronounced spatial patterns of coherent variability within the joint data set and provides a set of weights for each model in terms of principal components which refer to exactly the same set of spatial patterns of covariance. In other words, common EOFs provide a means for extracting information from large volumes of data. Moreover, they can provide an objective basis for evaluation that can be used to accentuate ensembles more than traditional methods for evaluation, which tend to focus on individual models. Our demonstration of the capability of common EOFs reveals a statistically significant improvement of the sixth generation of the World Climate Research Programme (WCRP) Climate Model Intercomparison Project (CMIP6) simulations over the previous generation (CMIP5) in terms of their ability to reproduce the mean seasonal cycle in air surface temperature, precipitation, and mean sea-level pressure over the Nordic countries. The leading common EOF principal component for annually/seasonally aggregated temperature, precipitation and pressure statistics suggest that their simulated interannual variability is generally consistent with that seen in the ERA5 reanalysis. We also demonstrate how common EOFs can be used to analyse whether CMIP ensembles reproduce the observed historical trends over the historical period 1959–2021, and the results suggest that the trend statistics provided by both CMIP5 RCP4.5 and CMIP6 SSP245 are consistent with observed trends. An interesting finding is also that the leading common EOF principal component for annually/seasonally aggregated statistics seems to be approximately normally distributed, which is useful information about the multi-model ensemble data.

Rasmus E. Benestad et al.

Status: open (until 13 Apr 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-1385', Abdel Hannachi, 05 Mar 2023 reply
    • AC1: 'Reply on RC1', Rasmus Benestad, 22 Mar 2023 reply
  • RC2: 'Comment on egusphere-2022-1385', Anonymous Referee #2, 23 Mar 2023 reply
    • CC1: 'Reply on RC2', Rasmus Benestad, 30 Mar 2023 reply
    • CC2: 'Reply on RC2', Rasmus Benestad, 30 Mar 2023 reply

Rasmus E. Benestad et al.

Video supplement

common EOFs for evaluation of geophysical data and global climate models. Rasmus Benestad

A brief presentation of common EOFs in R-studio Rasmus Benestad

Rasmus E. Benestad et al.


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Short summary
A mathematical method known as 'common EOFs' is not widely used within the climate research community, but they offer innovative ways of evaluating climate models. We show how they 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 they represent a kind of machine learning (ML) for dealing with "Big data".