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\begin{document}

\title{Revealing the Southeast Greenland physical environment to enhance
biological knowledge}



\author[1]{Twila Moon}%
\author[2]{Benjamin Cohen}%
\author[3]{Kristin Laidre}%
\author[4]{Harry Stern}%
\author[5]{Taryn Black}%
\affil[1]{University of Colorado Boulder}%
\affil[2]{University of Washington}%
\affil[3]{Polar Science Center, Applied Physics Lab, University of Washington, Seattle}%
\affil[4]{Applied Physics Laboratory University of Washington}%
\affil[5]{Applied Physics Laboratory, University of Washington}%


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  \date{June 11, 2024}


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\begin{abstract}
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
\textasciitilde{}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.%
\end{abstract}%



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