Dr Ivan Sudakow on A Statistical Mechanics Model for Melt Ponds on Arctic Sea Ice
When snow on the surface of Arctic sea ice begins to melt in late spring, small pools of water form. As the melt season progresses, these simply shaped pools grow and coalesce into beautiful aqua blue regions with complex, self-similar boundaries of fractal dimension close to two. While the white snowy sea ice surface reflects most of the incident sunlight, the darker melt ponds allow significant light to penetrate into the upper ocean. Melt ponds are thus one of the key controls on how much solar energy is absorbed by the ice pack and upper ocean, and strongly influence ice melting rates and polar marine ecology. In order to help develop a predictive theoretical approach to studying melting sea ice, we introduce a two-dimensional random field Ising model which accounts for only the most basic physics in the system. The ponds are identified as metastable states in the model, where the binary spin variable corresponds to the presence of melt water or ice on the sea ice surface. In this talk, I will give an overview of how we are building this model to capture the essential mechanism of pattern formation of Arctic melt ponds. I will also discuss how we are using statistical physics to improve the modeling of the Arctic climate system.