Mid-Term Sea Ice Prediction System using Statistical Method in Arctic Sea Route

Abstract

Melting sea ice in the Arctic Ocean has increased the usage of the Northern Sea Route (NSR) dramatically in recent years. The necessity of real-time sea ice monitoring and prediction system has also been increased for the NSR navigation safety. Korea Institute of Ocean Science and Technology (KIOST) has developed the Arctic Voyage Environmental Information System (VEIS) for the sea ice information service with a 2.5 km grid spacing in the NSR. The VEIS system includes a mid-term (one month) prediction system providing sea ice concentration (SIC) and sea ice thickness (SIT) as well as a short-term (a week) prediction system (SIC and SIT) and also includes a satellite monitoring system producing daily AMSR2 SIC composite image. The mid-term prediction system for SIC and SIT has been developed using a statistical method mainly generated with one-week predicted SIC and SIT and previous year AMSR2 SIC (L2). The statistical method has also been based on the best correlation of the month for sea ice volume (SIV), SIC and SIT among 26 years (1991-2017) data derived from a TOPAZ re-analysis data comparing to the recent 6 months data derived from a short-term prediction data (TPZ). The mid-term prediction has been validated with field measurements observed in the NSR in summer of 2016 and 2017. It will be improved adopting a revised satellite composite data (A25) and a revised short-term predicted data (TP3) using a bias corre