Working Group I: The Scientific Basis

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7.5.2 Sea Ice

Figure 7.6: Idealised feedback loops involving sea ice. The signs attached to the arrows indicate the correlation between changes in the quantity of the outgoing box with that of the ingoing box, e.g., more sea ice leads to larger albedo. Resulting correlations of a loop are circled and they indicate whether a process is self-reinforced (positive sign) or damped (negative sign). Generally, resulting negative correlations can lead to oscillations, whereas resulting positive correlations may lead to instabilities. The classical ice-albedo effect is shown in the left loop, a feedback involving the overlying atmosphere is on the right.

Sea ice plays an important role in moderating heat exchange between the ocean and atmosphere at high latitudes, especially by controlling the heat flux through openings in the ice. Sea ice also interacts with the broader climate system via the ice albedo feedback, which amplifies projected climate warming at high latitudes, and by oceanic feedbacks involving ice growth and melt and the fresh water balance at the ocean surface (Curry and Webster, 1999; Lewis, 2000) Two feedbacks associated with sea ice are illustrated in Figure 7.6.

The Arctic Ocean sea-ice cover evolves from a highly reflective snow covered surface with few openings in May to a decaying sea-ice cover, mottled with melt ponds and interrupted by frequent openings in July. The seasonal changes in mean albedo in the central Arctic, from roughly 0.8 in May to 0.5 in mid-August, are known to within about ± 0.06 to 0.08. The mean spatial pattern in each summer month features lower albedos in the central Arctic and values 0.1 to 0.2 higher along the ice margins, but the spatial evolution of the pattern in any given year is variable. Averages observed at Soviet North Pole drifting stations, from 1950 to 1991, tend to be higher than satellite-based estimates (Marshunova and Mishin, 1994). Albedo depends on wavelength of radiation and on type and thickness of ice; however, ice type and thickness become unimportant if the snow cover exceeds 3 cm water equivalent. The representation of sea-ice albedo in AGCMs may take account of fractional snow cover, specified or predicted sea-ice thickness, ice surface temperature and the fraction of openings and puddles (Barry, 1996). Most models treat visible and near-infrared spectral ranges but there is still a wide variety of snow and ice albedo parametrizations among atmospheric and ocean GCMs. Important new data sets on puddle albedos (Morassutti and LeDrew, 1996) and the temporal evolution of melt pond coverage (Fetterer and Untersteiner, 1998) will enable more realistic albedo formulations to be developed.

Since the SAR, several coupled climate models have incorporated an explicit treatment of openings in sea ice, often in conjunction with ice dynamics. This is typically effected by partitioning a model grid cell into ice-free and ice-covered fractions. However, sub-grid scale variability in ice thickness, not represented in these schemes, can have a potentially important influence on sea-ice mass balance (Schramm et al., 1997), ice/ocean fluxes of heat and fresh water (Holland et al., 1997b) and the sensitivity of sea ice to thermodynamic perturbations (Holland and Curry, 1999). Recent advances in modelling the thickness distribution function make representing sub-grid scale variability, and the accompanying effects, feasible in global climate simulations (Bitz et al., 2001). Other developments since the SAR include updated parametrizations of snow ageing and associated albedo changes and the implementation in some models of a multi-layer formulation of heat conduction through the ice. Snow plays a particularly important role in sea-ice thermodynamics by modifying the surface albedo, reducing thermal conductivity (and hence ice growth rates), and in some locations causing submergence and surface flooding of ice. Considerable effort continues to be devoted to the development and testing of improved physically based parametrizations suitable for use in such models. Recent field experiments, most notably SHEBA (Randall et al., 1998; Perovich et al., 1999), have provided observational data particularly suited to evaluating climate model parametrizations of sea-ice thermodynamic processes and initial attempts at this are underway. Although sea-ice thermodynamic processes are crudely approximated in many coupled climate models (see Chapter 8, Section 8.5.3), it is unclear how these approximations contribute to errors in climate model simulations.

Ice motion is driven by wind and ocean currents and resisted by ice-ocean drag and internal ice stresses. The representation of internal stresses is the primary distinguishing feature among ice dynamics models. Sensitivity experiments with stand alone sea-ice models (Hibler, 1984; Pollard and Thompson, 1994; Arbetter et al., 1997) indicate that inclusion of ice dynamics can alter the modelled sensitivity of the ice cover to climatic perturbations. An assessment of sea-ice dynamic schemes suited to use in global climate models has been undertaken by the ACSYS Sea-Ice Model Intercomparison Project (SIMIP) (Lemke et al., 1997), which has initially focused on the evaluation of sea-ice rheologies (the relationship between internal stresses and deformation). Results are summarised in Kreyscher et al. (1997) and indicate that the elliptical yield curve, viscous-plastic scheme of Hibler (1979) generally outperforms the other schemes evaluated in terms of comparisons to observed ice drift statistics, ice thickness and Fram Strait outflow. Clearly, the effects of ice-related fresh water transports and of other potentially important processes influenced by ice dynamics are not included in climate models which ignore ice motion (see Chapter 8, Section 8.5.3). The effect on climate sensitivity remains to be assessed. Since the SAR, progress has been made at improving the efficiency of numerical sea-ice dynamic models, making them more attractive for use in coupled climate models (Hunke and Dukowicz, 1997; Zhang and Hibler, 1997).

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