Seasonal sea ice predictions for the Arctic based on assimilation of remotely sensed observations
F. Kauker1,3, T. Kaminski2,a, R. Ricker3, L. Toudal-Pedersen4, G. Dybkjaer4, C. Melsheimer5, S. Eastwood6, H. Sumata3, M. Karcher1,3, and R. Gerdes3,71OASys, 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
Received: 08 Sep 2015 – Accepted for review: 22 Sep 2015 – Discussion started: 16 Oct 2015
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.
Kauker, F., Kaminski, T., Ricker, R., Toudal-Pedersen, L., Dybkjaer, G., Melsheimer, C., Eastwood, S., Sumata, H., Karcher, M., and Gerdes, R.: Seasonal sea ice predictions for the Arctic based on assimilation of remotely sensed observations, The Cryosphere Discuss., 9, 5521-5554, doi:10.5194/tcd-9-5521-2015, 2015.