Improvement of Arctic Sea Ice Multi-Parameter Assimilation on Sea Ice Concentration Simulation

Based on the CICE sea ice model and the PDAF parallel data assimilation framework, this paper uses the local error subspace transform Kalman filter method (LESTKF) to assimilate the sea ice concentration, sea ice thickness and sea ice freeboard data into the model, and designs experiments to study the improvement of multi-parameter assimilation on the simulation of Arctic sea ice concentration and range. The results show that data assimilation has a good improvement effect on the simulation of Arctic sea ice concentration and range. The average deviation, root mean square error and mean absolute error of the assimilation experiment are significantly reduced compared with the control experiment. The assimilation experiment improves the simulation of sea ice concentration and range most obviously in summer. Multi-parameter assimilation can improve the prediction accuracy and reliability of Arctic sea ice change.