Common EOFs: a tool for multi-model comparison and evaluation

Abstract

With the increase in the volume of climate model simulations for past, present and future climate, from various institutions across the globe, there is a need for efficient and robust methods for model comparison and/or evaluation. This manuscript discusses common empirical orthogonal function analysis with a step-wise algorithm, which can be used for the above objective. The method looks for simultaneous diagonalisation of several covariance matrices in a step-wise fashion ensuring thus simultaneous monotonic decrease of the eigenvalues in all groups, and allowing therefore for dimension reduction. The method is applied to a number of tropospheric and stratospheric fields from the main four reanalysis products, and also to several historical climate model simulations from CMIP6, the Coupled Model Intercomparison Project (Phase 6). Monthly means as well as winter daily gridded data are considered over the Northern Hemisphere. The method shows consistency between mass fields as well as mid-tropospheric and stratospheric fields of the reanalyses, but also reveals significant differences in the 2 m surface-air temperature in terms of explained variance. CMIP6 models, on the other hand, show differences reflected in the percentage of explained variance of the leading common EOFs with inter-group variation ranging from 5–10% in the troposphere to about 25% in the stratosphere. Higher order statistics within the leading common modes of variability, in addition to further merits of the method are also discussed.