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Intercomparison of the Arctic sea ice cover in global ocean–sea ice reanalyses from the ORA-IP project

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An Erratum to this article was published on 13 May 2016

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Abstract

Ocean–sea ice reanalyses are crucial for assessing the variability and recent trends in the Arctic sea ice cover. This is especially true for sea ice volume, as long-term and large scale sea ice thickness observations are inexistent. Results from the Ocean ReAnalyses Intercomparison Project (ORA-IP) are presented, with a focus on Arctic sea ice fields reconstructed by state-of-the-art global ocean reanalyses. Differences between the various reanalyses are explored in terms of the effects of data assimilation, model physics and atmospheric forcing on properties of the sea ice cover, including concentration, thickness, velocity and snow. Amongst the 14 reanalyses studied here, 9 assimilate sea ice concentration, and none assimilate sea ice thickness data. The comparison reveals an overall agreement in the reconstructed concentration fields, mainly because of the constraints in surface temperature imposed by direct assimilation of ocean observations, prescribed or assimilated atmospheric forcing and assimilation of sea ice concentration. However, some spread still exists amongst the reanalyses, due to a variety of factors. In particular, a large spread in sea ice thickness is found within the ensemble of reanalyses, partially caused by the biases inherited from their sea ice model components. Biases are also affected by the assimilation of sea ice concentration and the treatment of sea ice thickness in the data assimilation process. An important outcome of this study is that the spatial distribution of ice volume varies widely between products, with no reanalysis standing out as clearly superior as compared to altimetry estimates. The ice thickness from systems without assimilation of sea ice concentration is not worse than that from systems constrained with sea ice observations. An evaluation of the sea ice velocity fields reveals that ice drifts too fast in most systems. As an ensemble, the ORA-IP reanalyses capture trends in Arctic sea ice area and extent relatively well. However, the ensemble can not be used to get a robust estimate of recent trends in the Arctic sea ice volume. Biases in the reanalyses certainly impact the simulated air–sea fluxes in the polar regions, and questions the suitability of current sea ice reanalyses to initialize seasonal forecasts.

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  • 13 May 2016

    An erratum to this article has been published.

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Acknowledgments

The authors wish to thank François Massonnet and one anonymous reviewer for their helpful comments on a first version of the manuscript. The authors wish to thank Fanny Girard-Ardhuin, David Salas-Mélia, Charles-Emmanuel Testut and Ed Blanchard-Wrigglesworth for valuable discussions. During the initial preparation of this intercomparison, our co-author Nicolas Ferry passed away. He was an active member of the GODAE OceanView and CLIVAR GSOP community, and was leading the ocean reanalysis scientific activity and the GLORYS project at Mercator Océan. We will greatly miss the scientific expert, the gentle colleague and the good friend. The GLORYS reanalysis project has been partially funded by the European Commission funded projects FP7 MyOcean and MyOcean2 projects. The development and production of C-GLORS05V3 has received funding from the Italian Ministry of Education, University and Research and the Italian Ministry of Environment, Land and Sea under the GEMINA project and from the European Commission Copernicus program, previously known as GMES program, under the MyOcean and MyOcean2 projects. ECMWF also acknowledges the MyOcean2 project for the ORAP5 reanalysis.

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Authors and Affiliations

  1. Centre National de Recherches Météorologiques (CNRM), Météo France/CNRS UMR3589, Toulouse, France

    Matthieu Chevallier

  2. Recherche en Prévision Numérique Environnementale, Environnement et Changement Climatique Canada, Dorval, Quebec, Canada

    Gregory C. Smith & Jean-François Lemieux

  3. Service Météorologique du Canada, Environnement et Changement Climatique Canada, Dorval, Quebec, Canada

    Frédéric Dupont

  4. Massachusetts Institute of Technology, Cambridge, MA, USA

    Gael Forget

  5. Meteorological Research Institute (MRI), Japan Meteorological Agency, Tsukuba, Japan

    Yosuke Fujii, Takahiro Toyoda, Tsurane Kuragano & Hiroyuki Tsujino

  6. Mercator Océan, Ramonville-Saint-Agne, France

    Fabrice Hernandez, Nicolas Ferry & Gilles Garric

  7. Institut de Recherche pour le Développement (IRD), Toulouse, France

    Fabrice Hernandez

  8. NOAA Geophysical Fluid Dynamics Laboratory (GFDL), Princeton, NJ, USA

    Rym Msadek

  9. Centre Européen de Recherche et de Formation Avancée au Calcul Scientifique (CERFACS), Toulouse, France

    Rym Msadek

  10. Met Office Hadley Centre, Exeter, UK

    K. Andrew Peterson & Yongming Tang

  11. Euro-Mediterranean Centre for Climate Change, Bologna, Italy

    Andrea Storto & Simona Masina

  12. National Centre for Earth Observation (NCEO), University of Reading, Reading, UK

    Maria Valdivieso & Keith Haines

  13. Science Systems and Applications, Inc., Lanham, MD, USA

    Guillaume Vernieres

  14. Global Modelling and Assimilation Office, NASA Goddard Space Flight Center (GSFC), Greenbelt, MD, USA

    Guillaume Vernieres & Robin M. Kovach

  15. European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, UK

    Hao Zuo, Magdalena Balmaseda, Sarah Keeley & Yongming Tang

  16. Department of Earth Science Education, Kongju National University, Kongju, South Korea

    You-Soon Chang

  17. National Institute for Geophysics and Volcanology, Bologna, Italy

    Simona Masina

  18. Joint Institute for Regional Earth System Science and Engineering, UCLA, Los Angeles, CA, USA

    Xiaochun Wang

Authors
  1. Matthieu Chevallier
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  2. Gregory C. Smith
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  3. Frédéric Dupont
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  4. Jean-François Lemieux
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  5. Gael Forget
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  6. Yosuke Fujii
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  7. Fabrice Hernandez
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  8. Rym Msadek
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  9. K. Andrew Peterson
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  10. Andrea Storto
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  11. Takahiro Toyoda
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  12. Maria Valdivieso
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  13. Guillaume Vernieres
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  14. Hao Zuo
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  15. Magdalena Balmaseda
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  16. You-Soon Chang
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  17. Nicolas Ferry
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  18. Gilles Garric
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  19. Keith Haines
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  20. Sarah Keeley
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  21. Robin M. Kovach
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  22. Tsurane Kuragano
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  23. Simona Masina
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  24. Yongming Tang
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  25. Hiroyuki Tsujino
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  26. Xiaochun Wang
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Corresponding author

Correspondence to Matthieu Chevallier.

Additional information

This paper is a contribution to the special issue on Ocean estimation from an ensemble of global ocean reanalyses, consisting of papers from the Ocean ReAnalyses Intercomparsion Project (ORA-IP), coordinated by CLIVAR-GSOP and GODAE OceanView. The special issue also contains specific studies using single reanalysis systems.

An erratum to this article is available at https://doi.org/10.1007/s00382-016-3155-y.

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Chevallier, M., Smith, G.C., Dupont, F. et al. Intercomparison of the Arctic sea ice cover in global ocean–sea ice reanalyses from the ORA-IP project. Clim Dyn 49, 1107–1136 (2017). https://doi.org/10.1007/s00382-016-2985-y

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  • DOI: https://doi.org/10.1007/s00382-016-2985-y

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