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Revealing the Southeast Greenland physical environment to enhance biological knowledge
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  • Twila Moon,
  • Benjamin Cohen,
  • Kristin Laidre,
  • Harry Stern,
  • Taryn Black
Twila Moon
University of Colorado Boulder

Corresponding Author:[email protected]

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Benjamin Cohen
University of Washington
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Kristin Laidre
Polar Science Center, Applied Physics Lab, University of Washington, Seattle
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Harry Stern
Applied Physics Laboratory University of Washington
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Taryn Black
Applied Physics Laboratory, University of Washington
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Southeastern Greenland (SEG) provides a complex habitat area consisting of dozens of deep fjords that connect land-based ice with the open ocean. Within these fjords, glacial ice mixes with sea ice and intricate topography can create niche local conditions. This area is important for a number of species, including polar bears, and better understanding of both land ice and floating glacial ice as biological habitat motivates the need to study the physical environment itself. Studying SEG, however, posed a variety of challenges, including difficult access for in-situ work or instrument deployment, cloudy conditions that can obscure optical satellite instruments, and steep, complex terrain that can complicate identification of surface conditions. Here, we discuss our work to leverage several remote sensing products to determine the geospatial patterns of landfast sea ice and solid glacial ice during 2015-2019 in five SEG fjords with high polar bear use. We further connect these data with measurements of solid glacial ice discharge and glacial and terrestrial freshwater flux across SEG. The landfast sea ice season in our focus fjords is quite short, extending on average only ~2-4 months, and including substantial variability. Because of the fjord connections to marine-terminating glaciers, however, solid glacial ice creates a potential alternative ice platform on the fjord surfaces that can complement the short sea ice season. Challenges remain in automating this type of surface classification, and we discuss how our manual digitization work can be leveraged to support other ongoing efforts to create machine learning surface identification algorithms.