IPCC Fourth Assessment Report: Climate Change 2007
Climate Change 2007: Working Group I: The Physical Science Basis Sea Ice

Sea ice components of current AOGCMs usually predict ice thickness (or volume), fractional cover, snow depth, surface and internal temperatures (or energy) and horizontal velocity. Some models now include prognostic sea ice salinity (Schmidt et al., 2004). Sea ice albedo is typically prescribed, with only crude dependence on ice thickness, snow cover and puddling effects.

Since the TAR, most AOGCMs have started to employ complex sea ice dynamic components. The complexity of sea ice dynamics in current AOGCMs varies from the relatively simple ‘cavitating fluid’ model (Flato and Hibler, 1992) to the viscous-plastic model (Hibler, 1979), which is computationally expensive, particularly for global climate simulations. The elastic-viscous-plastic model (Hunke and Dukowicz, 1997) is being increasingly employed, particularly due to its efficiency for parallel computers. New numerical approaches for solving the ice dynamics equations include more accurate representations on curvilinear model grids (Hunke and Dukowicz, 2002; Marsland et al., 2003; Zhang and Rothrock, 2003) and Lagrangian methods for solving the viscous-plastic equations (Lindsay and Stern, 2004; Wang and Ikeda, 2004).

Treatment of sea ice thermodynamics in AOGCMs has progressed more slowly: it typically includes constant conductivity and heat capacities for ice and snow (if represented), a heat reservoir simulating the effect of brine pockets in the ice, and several layers, the upper one representing snow. More sophisticated thermodynamic schemes are being developed, such as the model of Bitz and Lipscomb (1999), which introduces salinity-dependent conductivity and heat capacities, modelling brine pockets in an energy-conserving way as part of a variable-layer thermodynamic model (e.g., Saenko et al., 2002). Some AOGCMs include snow ice formation, which occurs when an ice floe is submerged by the weight of the overlying snow cover and the flooded snow layer refreezes. The latter process is particularly important in the antarctic sea ice system.

Even with fine grid scales, many sea ice models incorporate sub-grid scale ice thickness distributions (Thorndike et al., 1975) with several thickness ‘categories’, rather than considering the ice as a uniform slab with inclusions of open water. An ice thickness distribution enables more accurate simulation of thermodynamic variations in growth and melt rates within a single grid cell, which can have significant consequences for ice-ocean albedo feedback processes (e.g., Bitz et al., 2001; Zhang and Rothrock, 2001). A well-resolved ice thickness distribution enables a more physical formulation for ice ridging and rafting events, based on energetic principles. Although parametrizations of ridging mechanics and their relationship with the ice thickness distribution have improved (Babko et al., 2002; Amundrud et al., 2004; Toyota et al., 2004), inclusion of advanced ridging parametrizations has lagged other aspects of sea ice dynamics (rheology, in particular) in AOGCMs. Better numerical algorithms used for the ice thickness distribution (Lipscomb, 2001) and ice strength (Hutchings et al., 2004) have also been developed for AOGCMs.