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Discussion papers | Copyright
https://doi.org/10.5194/tcd-9-5521-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 16 Oct 2015

Research article | 16 Oct 2015

Review status
This discussion paper is a preprint. It has been under review for the journal The Cryosphere (TC). The revised manuscript was not accepted.

Seasonal sea ice predictions for the Arctic based on assimilation of remotely sensed observations

F. Kauker3,1, T. Kaminski2,a, R. Ricker3, L. Toudal-Pedersen4, G. Dybkjaer4, C. Melsheimer5, S. Eastwood6, H. Sumata3, M. Karcher3,1, and R. Gerdes7,3 F. Kauker et al.
  • 1OASys, Tewessteg 4a, 20249 Hamburg, Germany
  • 2The Inversion Lab, Tewessteg 4a, 20249 Hamburg, Germany
  • 3Alfred Wegener Institute, Bussestr. 24, 27570 Bremerhaven, Germany
  • 4Danish Meteorological Institute, Lyngbyvej 100, 2100 Copenhagen, Denmark
  • 5University of Bremen, Institute for Environmental Physics, Otto-Hahn-Allee 1, 28359 Bremen, Germany
  • 6The Norwegian Meteorological Institute, Blindern 43, 0313 Oslo, Norway
  • 7Jacobs University, Campus Ring 1, 28759 Bremen, Germany
  • apreviously at: FastOpt, Hamburg, Germany

Abstract. The recent thinning and shrinking of the Arctic sea ice cover has increased the interest in seasonal sea ice forecasts. Typical tools for such forecasts are numerical models of the coupled ocean sea ice system such as the North Atlantic/Arctic Ocean Sea Ice Model (NAOSIM). The model uses as input the initial state of the system and the atmospheric boundary condition over the forecasting period. This study investigates the potential of remotely sensed ice thickness observations in constraining the initial model state. For this purpose it employs a variational assimilation system around NAOSIM and the Alfred Wegener Institute's CryoSat-2 ice thickness product in conjunction with the University of Bremen's snow depth product and the OSI SAF ice concentration and sea surface temperature products. We investigate the skill of predictions of the summer ice conditions starting in March for three different years. Straightforward assimilation of the above combination of data streams results in slight improvements over some regions (especially in the Beaufort Sea) but degrades the over-all fit to independent observations. A considerable enhancement of forecast skill is demonstrated for a bias correction scheme for the CryoSat-2 ice thickness product that uses a spatially varying scaling factor.

F. Kauker et al.
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Interactive discussion
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
F. Kauker et al.
F. Kauker et al.
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Short summary
The manuscript describes the use of remotely sensed sea ice observations for the initialization of seasonal sea ice predictions. Among other observations, CryoSat-2 ice thickness is, to our knowledge for the first time, utilized. While a direct assimilation with CryoSat ice thickness could improve the predictions only locally, the use an advanced data assimilation system (4dVar) allows to establish a bias correction scheme, which is shown to improve the seasonal predictions Arctic wide.
The manuscript describes the use of remotely sensed sea ice observations for the initialization...
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