Improving numerical sea ice predictions in the Arctic Ocean by data assimilation using satellite observations

Abstract

This study focuses on improving sea ice predictions in the Arctic Ocean by introducing data assimilation into an ice-ocean coupled Ice-POM model that is used to predict sea ice conditions in the Arctic sea routes. Ocean part of the model used in this study is based on the Princeton Ocean Model (POM). The ice model considers discrete characteristics of ice along the ice edge. The model domain consists of the Arctic Ocean, Greenland-Iceland-Norwegian (GIN) seas and the Northern Atlantic Ocean. The model grid is with 25km horizontal resolution. An improved nudging method that takes the observation errors into account is used in this study. Observation errors are varied in accordance with the season and the location. Sea ice concentration, sea ice thickness and sea ice velocity are assimilated simultaneously. Assimilation improved ocean and ice conditions significantly. This is evident from the changes in sea ice extent, sea ice thickness and ocean salinity.