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

  • Matthieu Chevallier
  • Gregory C. Smith
  • Frédéric Dupont
  • Jean-François Lemieux
  • Gael Forget
  • Yosuke Fujii
  • Fabrice Hernandez
  • Rym Msadek
  • K. Andrew Peterson
  • Andrea Storto
  • Takahiro Toyoda
  • Maria Valdivieso
  • Guillaume Vernieres
  • Hao Zuo
  • Magdalena Balmaseda
  • You-Soon Chang
  • Nicolas Ferry
  • Gilles Garric
  • Keith Haines
  • Sarah Keeley
  • Robin M. Kovach
  • Tsurane Kuragano
  • Simona Masina
  • Yongming Tang
  • Hiroyuki Tsujino
  • Xiaochun Wang
Article

DOI: 10.1007/s00382-016-2985-y

Cite this article as:
Chevallier, M., Smith, G.C., Dupont, F. et al. Clim Dyn (2016). doi:10.1007/s00382-016-2985-y

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.

Keywords

Ice–ocean reanalysis Model intercomparison Arctic Sea ice Data assimilation Ice thickness 

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Matthieu Chevallier
    • 1
  • Gregory C. Smith
    • 2
  • Frédéric Dupont
    • 3
  • Jean-François Lemieux
    • 2
  • Gael Forget
    • 4
  • Yosuke Fujii
    • 5
  • Fabrice Hernandez
    • 6
    • 7
  • Rym Msadek
    • 8
    • 9
  • K. Andrew Peterson
    • 10
  • Andrea Storto
    • 11
  • Takahiro Toyoda
    • 5
  • Maria Valdivieso
    • 12
  • Guillaume Vernieres
    • 13
    • 14
  • Hao Zuo
    • 15
  • Magdalena Balmaseda
    • 15
  • You-Soon Chang
    • 16
  • Nicolas Ferry
    • 6
  • Gilles Garric
    • 6
  • Keith Haines
    • 12
  • Sarah Keeley
    • 15
  • Robin M. Kovach
    • 14
  • Tsurane Kuragano
    • 5
  • Simona Masina
    • 11
    • 17
  • Yongming Tang
    • 10
    • 15
  • Hiroyuki Tsujino
    • 5
  • Xiaochun Wang
    • 18
  1. 1.Centre National de Recherches Météorologiques (CNRM), Météo France/CNRS UMR3589ToulouseFrance
  2. 2.Recherche en Prévision Numérique EnvironnementaleEnvironnement et Changement Climatique CanadaDorvalCanada
  3. 3.Service Météorologique du CanadaEnvironnement et Changement Climatique CanadaDorvalCanada
  4. 4.Massachusetts Institute of TechnologyCambridgeUSA
  5. 5.Meteorological Research Institute (MRI)Japan Meteorological AgencyTsukubaJapan
  6. 6.Mercator OcéanRamonville-Saint-AgneFrance
  7. 7.Institut de Recherche pour le Développement (IRD)ToulouseFrance
  8. 8.NOAA Geophysical Fluid Dynamics Laboratory (GFDL)PrincetonUSA
  9. 9.Centre Européen de Recherche et de Formation Avancée au Calcul Scientifique (CERFACS)ToulouseFrance
  10. 10.Met Office Hadley CentreExeterUK
  11. 11.Euro-Mediterranean Centre for Climate ChangeBolognaItaly
  12. 12.National Centre for Earth Observation (NCEO)University of ReadingReadingUK
  13. 13.Science Systems and Applications, Inc.LanhamUSA
  14. 14.Global Modelling and Assimilation OfficeNASA Goddard Space Flight Center (GSFC)GreenbeltUSA
  15. 15.European Centre for Medium-Range Weather Forecasts (ECMWF)ReadingUK
  16. 16.Department of Earth Science EducationKongju National UniversityKongjuSouth Korea
  17. 17.National Institute for Geophysics and VolcanologyBolognaItaly
  18. 18.Joint Institute for Regional Earth System Science and EngineeringUCLALos AngelesUSA