Abstract:
With the recent changes in the Arctic climate, transportation in the Arctic Ocean has increased. It
is important to accurately predict the ice conditions to navigate safely in the Arctic Ocean. In this
study we have improved the ice-ocean coupled Ice-POM model by implementing a data
assimilation system. This system incorporates sea ice observation data such as sea ice
concentration, sea ice thickness and sea ice velocity to improve the numerical predictions. Ocean
part of the model is based on the Princeton Ocean Model while the Ice model considers the
discrete characteristics of the ice along the ice edge. The model domain consists of Arctic Ocean,
Greenland-Iceland-Norwegian (GIN) seas and the Northern Atlantic Ocean. The model grid is
with 25kms resolutions. Several data assimilation techniques have been tested in the study. We
have used a nudging method that incorporates background and observation errors and an
ensemble assimilation approach that uses different atmospheric forcing conditions. The
observation errors and background errors were taken in to account. Observation errors were
varied according to the location and the season. Assimilation has improved the ocean conditions
significantly. This is evident from the changes in the ocean salinity compared to the model
predictions. We have also compared the results from different assimilation techniques. Ensemble
Kalman filter method outperforms the nudging method however it comes with a greater
computational cost. The assimilation time interval was also varied. While daily assimilation
window produced the most accurate results, the weekly and monthly assimilation window also
produce results with reasonable accuracy about 5 months after assimilation is begun.