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An assessment of scale-dependent variability and bias in global prediction models

  • Nedjeljka ŽagarEmail author
  • Katarina Kosovelj
  • Elisa Manzini
  • Martin Horvat
  • José Castanheira
Article

Abstract

The paper presents a method for the scale-dependent validation of the spatio-temporal variability in global weather or climate models and for their bias quantification in relation to dynamics. The method provides a relationship between the bias and simulated spatial and temporal variance by a model in comparison with verifying reanalysis data. For the low resolution (T30L8) subset of ERA-20C data, it was found that 80–90% (depending on season) of the global interannual variance is at planetary scales (zonal wavenumbers k = 0−3), and only about 1% of the variance is at scales with \(k>7\). The reanalysis is used to validate a T30L8 GCM in two configurations, one with the prescribed sea-surface temperature (SST) and another using a slab ocean model. Although the model with the prescribed SST represents the average properties of surface fields well, the interannual variability is underestimated at all scales. Similar to variability, model bias is strongly scale dependent. Biases found in the experiment with the prescribed SST are largely increased in the experiment using a slab ocean, especially in \(k=0\), in scales with missing variability and in seasons with poorly simulated energy distribution. The perfect model scenario (a comparison between the GCM coupled to a slab ocean vs. the same model with prescribed SSTs) shows that the representation of the ocean is not critical for synoptic to subsynoptic variability, but essential for capturing the planetary scales.

Keywords

Spatio-temporal variability Bias spectra Variability quantification Climate models Model validation 

Notes

Acknowledgements

The authors are grateful to the comments of two anonymous reviewers that led to the paper improvements. Discussions with Grant Branstator, Akira Kasahara, Fred Kucharski, Franco Molteni and Joe Tribbia are gratefully acknowledged. Nedjeljka Žagar and Martin Horvat were partly supported by the Slovenian Research Agency, program P1-0188 and project J1-9431. Funded by the European Research Council, Grant Agreement no. 280153.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Meteorologisches Institut, CENUniversität HamburgHamburgGermany
  2. 2.Faculty of Mathematics and PhysicsUniversity of LjubljanaLjubljanaSlovenia
  3. 3.University of LjubljanaLjubljanaSlovenia
  4. 4.Max-Planck-Institut fur MeteorologieHamburgGermany
  5. 5.CESAM and Department of PhysicsUniversity of AveiroAveiroPortugal

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