A sea-ice sensitivity study with a global ocean-ice model

The sensitivity of the global sea-ice distribution in the Australian Climate Ocean Model (AusCOM) to a range of parameter values related to sea-ice physics was explored. The sea-ice component of AusCOM is the U.S. Los Alamos National Laboratory Sea Ice Model (CICE4.1) and the ocean component is the Modular Ocean Model developed at NOAA’s Geophysical Fluid Dynamics Laboratory (GFDL MOM4p1). We aimed to determine optimal sets of parameter values to produce as realistic a global sea-ice distribution as possible. A small number of sets of optimal parameter values was selected based on the closest match between the model and observationally constrained model climatologies. New detailed information is provided on the sensitivity of the global sea-ice distribution to the parameters not studied this extensively before. The sea-ice distribution shows a similar degree of sensitivity to parameters determining ice-ocean stress, mechanical redistribution, oceanic heat and shortwave radiation. Accordingly, AusCOM can be effectively tuned to produce realistic sea ice by parameters internal to the sea-ice model. The sensitivity of ice volume is stronger than that of ice area indicating that the internal ice model parameters mostly influence the ice thickness. The sea-ice area has significantly weaker sensitivity to the sea-ice model parameters considered, particularly in winter. Then, the evolution of sea ice is dominated by external factors, such as location of land, and atmospheric and oceanic forcing. The performance of the ocean model is crucial in producing a realistic sea-ice cover, in both the Arctic and the Antarctic.

► Provides limits to where a sea-ice model can be tuned in a coupled environment.
► We employ a global ocean-ice model having a realistic ocean-ice interaction.
► Coupled model can produce realistic sea ice by tuning internal sea-ice parameters.
► The sensitivity of ice volume is stronger than that of ice area.
► The sea ice area has a weaker sensitivity to the sea-ice model parameters in winter.