The impact of data assimilation and atmospheric forcing data on predicting short-term sea ice distribution along the Northern sea route

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With the recent rapid decrease in summer sea ice in the Arctic Ocean extending the navigation period in the Northern sea routes (NSR), the precise prediction of ice distribution is crucial for safe and efficient navigation in the Arctic Ocean. Precise ice distribution prediction in the short-term (5-days scale) is one of the key issues to realize safe and efficient navigation in the NSR. Ensemble predictions of short-term sea-ice conditions along the Northern sea route have been carried out using a high–resolution (about 2.5 km) ice–ocean coupled model that explicitly treats ice floe collisions in marginal ice zones. In this study, the ensembles are constructed by using forecasted atmospheric forcing data sets from THORPEX Interactive Grand Global Ensemble (TIGGE) project in 2015. We also discussed the influence of data assimilation on high-resolution model ice and ocean initial conditions estimated by the whole Arctic medium-resolution (about 25 km) model. The correlation score of ice–edge error and sea ice concentration distribution quantifies forecast skill. Skill scores are computed from 5-days ensemble forecasts initialized in each month between May 2015 to October 2015. Comparison of different ensemble atmospheric forecasts, using different months initial data sets, revealed that our ice-POM numerical model skillfully predicts the ice distribution during the NSR operational period. The average forecast skill of ice-POM model in the melting season is 9.28±2.68 km and in the freezing season without assimilated initial conditions is 15.43±6.29 km and with assimilation 13.85±5.77 km with the 15% thresholds of ice concentration for the ice edge. With data assimilation, there is 10% improvement of average ice edge error within 5-days simulation.

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