Preprints
https://doi.org/10.5194/gmd-2021-225
https://doi.org/10.5194/gmd-2021-225

Submitted as: model evaluation paper 29 Jul 2021

Submitted as: model evaluation paper | 29 Jul 2021

Review status: this preprint is currently under review for the journal GMD.

Variability and extremes: Statistical validation of the AWI-ESM

Justus Contzen1,2, Thorsten Dickhaus3, and Gerrit Lohmann1,2 Justus Contzen et al.
  • 1Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany
  • 2Department of Environmental Physics, University of Bremen, Bremen, Germany
  • 3Institute for Statistics, University of Bremen, Bremen, Germany

Abstract. Coupled general circulation models are of paramount importance to assess quantitatively the magnitude of future climate change. Usual methods for validating climate models include the evaluation of mean values and covariances, but less attention is directed to the evaluation of extremal behaviour. This is a problem because many severe consequences of climate changes are due to climate extremes. We present a method for model validation in terms of extreme values based on classical extreme value theory. We further discuss a clustering algorithm to detect spacial dependencies and tendencies for concurrent extremes. To illustrate these methods, we analyse precipitation extremes of the AWI-ESM global climate model compared to the reanalysis data set CRU TS4.04. The methods presented here can also be used for the comparison of model ensembles, and there may be further applications in palaeoclimatology.

Justus Contzen et al.

Status: open (until 31 Oct 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on gmd-2021-225', Qingxiang Li, 24 Sep 2021 reply

Justus Contzen et al.

Justus Contzen et al.

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Short summary
Climate models are of paramount importance to predict future climate changes. Since many severe consequences of climate change are due to extreme events, the accurate behaviour of models in terms of extremes needs to be validated thoroughly. We present a method for model validation in terms of climate extremes, and further an algorithm to detect regions in which extremes tend to occur at the same time. These methods are applied to data from a climate model and to observational data.