Abstract:
With the recent changes in the Arctic climate,
increased transportation can be observed in the Arctic Ocean.
For safe navigation along the Arctic Sea routes, it is important
to accurately predict the ice conditions. In this study the iceocean
coupled Ice-POM model is improved by a Kalman filter
based 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 ice
along the ice edge. In an ice-ocean coupled model, atmospheric
forcing directly affects the accuracy of predictions. However,
different atmospheric data sets produced by different weather
agencies show large differences in the Arctic region. Model
errors largely depend upon the inaccuracies in forcing data.
This study uses an ensemble of multiple atmospheric data sets
collected from different weather agencies and the spread of the
ensemble is taken as an indicator of the model error covariance.
The Observation errors were varied according to the location
and the season. Assimilation has improved the predictions of
sea ice variables. It has also indirectly improved the ocean
conditions. This Atmospheric forcing based Kalman filter (AFKF)
method outperforms other assimilation methods such as direct
assimilation and nudging methods.
