  In this paper we predict sea surface salinity (SSS) in the Arctic Ocean based
on satellite measurements. SSS is a crucial indicator for ongoing changes in
the Arctic Ocean and can offer important insights about climate change. We
particularly focus on areas of water mistakenly flagged as ice by satellite
algorithms. To remove bias in the retrieval of salinity near sea ice, the
algorithms use conservative ice masks, which result in considerable loss of
data. We aim to produce realistic SSS values for such regions to obtain more
complete understanding about the SSS surface over the Arctic Ocean and benefit
future applications that may require SSS measurements near edges of sea ice or
coasts. We propose a class of scalable nonstationary processes that can handle
large data from satellite products and complex geometries of the Arctic Ocean.
Barrier overlap-removal acyclic directed graph GP (BORA-GP) constructs sparse
directed acyclic graphs (DAGs) with neighbors conforming to barriers and
boundaries, enabling characterization of dependence in constrained domains. The
BORA-GP models produce more sensible SSS values in regions without satellite
measurements and show improved performance in various constrained domains in
simulation studies compared to state-of-the-art alternatives. An R package is
available at https://github.com/jinbora0720/boraGP.
