Network modeling of Arctic melt ponds

The recent precipitous losses of summer Arctic sea ice have outpaced the projections of most climate models. A number of efforts to improve these models have focused in part on a more accurate accounting of sea ice albedo or reflectance. In late spring and summer, the albedo of the ice pack is determined primarily by melt ponds that form on the sea ice surface. The transition of pond configurations from isolated structures to interconnected networks is critical in allowing the lateral flow of melt water toward drainage features such as large brine channels, fractures, and seal holes, which can alter the albedo by removing the melt water. Moreover, highly connected ponds can influence the formation of fractures and leads during ice break-up. Here we develop algorithmic techniques for mapping photographic images of melt ponds onto discrete conductance networks which represent the geometry and connectedness of pond configurations. The effective conductivity of the networks is computed to approximate the ease of lateral flow. We implement an image processing algorithm with mathematical morphology operations to produce a conductance matrix representation of the melt ponds. Basic clustering and edge elimination, using undirected graphs, are then used to map the melt pond connections and reduce the conductance matrix to include only direct connections. The results for images taken during different times of the year are visually inspected and the number of mislabels is used to evaluate performance.