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Abstract:
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Sea ice has substantial effects on the climate of Polar regions and the Earth overall. For example, open ocean tends to absorb heat from solar radiation while ice covered surfaces tend to reflect radiation. Large, narrow cracks in the ice’s surface, called leads, affect how sea ice grows and melts. Information about when and where leads form is needed to understand sea ice behavior and feedbacks between the ocean and atmosphere. To develop this understanding, scientists need an efficient way to identify sea ice leads in observational data and climate model output. For sea ice, remote sensing data and climate model output provide gridded fields showing the proportion of area in each grid box that is ice-covered. This granular identification, however, does not directly identify leads as distinct and coherent features. We introduce a likelihood-based method to efficiently identify sea ice leads from data of this form. Our method also provides uncertainty estimates of the presence and location of leads. We apply this identification method to high-resolution model output to assess the frequency of lead formation, structure of typical leads, and environmental conditions when leads form.
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