Abstract
Abstract A coupled atmosphere–sea-ice model is used to assess the effect of Antarctic polynyas on the heat exchange between the surface and the atmosphere, the ice production rate within the polynyas, and the possible mechanisms of formation and maintenance of the polynyas. Results are presented for sensitivity studies with the model using prescribed minimum open-water fractions (25% and 30%) at a number of locations around the coast of East Antarctica and in the Amundsen, Ross and Weddell Seas. Although to some extent unphysical for the ocean, this technique allows the heat fluxes and freezing rates to be computed for the constrained conditions compared with an unconstrained control simulation. The model simulations suggest that Antarctic coastal polynyas, including the Mertz Glacier polynya, are primarily latent heat polynyas (where the heat loss from open water is balanced by the latent heat released with ice production) rather than sensible heat ones (where the heat loss is balanced by heat from the deep ocean). When a polynya exists, the turbulent latent heat flux from the ocean surface to the atmosphere can increase several times in magnitude and the average latent heat flux (over open-water and sea-ice fractions) can be sustained at over 60 W m −2 . The sensible heat flux changes sign and increases by about 2 orders of magnitude. This loss of turbulent heat (sensible plus latent) is offset only slightly by increased absorbed short-wave radiation in winter, but this offset increases greatly in spring when more short-wave radiation is available. In the polynyas, the large heat loss to the atmosphere is mostly balanced by the heat released due to ice formation, and this is accompanied by a high rate of salt rejection. The average ice production simulated in the polynyas is more than 11 m/year, with 5 m formed during 3 months of winter. These results provide quantitative support for the sustained high ice-freezing rates in polynyas estimated in other studies, which have used satellite and in situ observational data.
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