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The Cryosphere An interactive open-access journal of the European Geosciences Union
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Volume 8, issue 1
The Cryosphere, 8, 229–243, 2014
https://doi.org/10.5194/tc-8-229-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
The Cryosphere, 8, 229–243, 2014
https://doi.org/10.5194/tc-8-229-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 14 Feb 2014

Research article | 14 Feb 2014

Sea-ice extent and its trend provide limited metrics of model performance

D. Notz D. Notz
  • Max Planck Institute for Meteorology, Hamburg, Germany

Abstract. We examine how the evaluation of modelled sea-ice coverage against reality is affected by uncertainties in the retrieval of sea-ice coverage from satellite, by the usage of sea-ice extent to overcome these uncertainties, and by internal variability. We find that for Arctic summer sea ice, model biases in sea-ice extent can be qualitatively different from biases in sea-ice area. This is because about half of the CMIP5 models and satellite retrievals based on the Bootstrap and the ASI algorithm show a compact ice cover in summer with large areas of high-concentration sea ice, while the other half of the CMIP5 models and satellite retrievals based on the NASA Team algorithm show a loose ice cover. For the Arctic winter sea-ice cover, differences in grid geometry can cause synthetic biases in sea-ice extent that are larger than the observational uncertainty. Comparing the uncertainty arising directly from the satellite retrievals with those that arise from internal variability, we find that the latter by far dominates the uncertainty estimate for trends in sea-ice extent and area: most of the differences between modelled and observed trends can simply be explained by internal variability. For absolute sea-ice area and sea-ice extent, however, internal variability cannot explain the difference between model and observations for about half the CMIP5 models that we analyse here. All models that we examined have regional biases, as expressed by the root-mean-square error in concentration, that are larger than the differences between individual satellite algorithms.

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