13.4 Characterizing Arctic Sea Ice Leads from Space

Thursday, 18 August 2016: 5:15 PM
Madison Ballroom CD (Monona Terrace Community and Convention Center)
Jay P. Hoffman, CIMSS/Univ. of Wisconsin, Madison, WI; and S. Ackerman, Y. Liu, and J. Key

Sea ice leads (fractures) play a critical role in the exchange of mass and energy between the ocean and atmosphere in the polar regions, particularly in the Arctic. Leads absorb more solar energy than the surrounding ice, warming the water and accelerating melt. In the winter, spring, and autumn, leads impact the local atmospheric structure and cloud properties because of the large flux of heat and moisture into the atmosphere. Given the rapid thinning and loss of Arctic sea ice over the last few decades, changes in the distribution of leads can be expected in response. Leads are largely wind driven, so their distributions will also be affected by the changes in atmospheric circulation that have occurred. From a climate perspective, identifying trends in lead characteristics (width, orientation, and spatial distribution) will advance our understanding of both thermodynamic and mechanical processes. From an operational perspective, knowledge of lead characteristics can aid in navigation, with direct benefits to security, subsistence hunting, and recreation. This study presents a new methodology to detect and characterize sea ice leads with optical (visible, infrared) satellite data. Data from the Moderate Resolution Imaging Spectroradiometer (MODIS) are used to assess the spatial and temporal distributions of Arctic sea ice leads and their changes since 2000. Using reflective and emissive channels, ice concentration is derived in cloud-free regions and used to create a mask of potential lead pixels. The algorithm then identifies and characterizes leads with a combination of image processing techniques that examine shape characteristics. Lead orientation and width statistics are then derived. Results indicate that leads regularly occur in some regions, but with significant interannual variability.
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