Arctic Ice Ocean Prediction System: evaluating sea-ice forecasts during Xuelong's first trans-Arctic Passage in summer 2017

• Published in: Journal of Glaciology
• Main Authors: Longjiang Mu, Xi Liang, Qinghua Yang, Jiping Liu, Fei Zheng
• Format: Article in Journal/Newspaper
• Language: English
• Published: Cambridge University Press 2019
• Subjects: Polar and subpolar oceans; sea ice; sea-ice modeling; Environmental sciences; GE1-350; Meteorology. Climatology; QC851-999; Arctic; Beaufort Sea; Journal of Glaciology; Sea ice
• Online Access: https://doi.org/10.1017/jog.2019.55; https://doaj.org/article/72848c5610fb40f5b2d05421a3078455

In an effort to improve the reliability of Arctic sea-ice predictions, an ensemble-based Arctic Ice Ocean Prediction System (ArcIOPS) has been developed to meet operational demands. The system is based on a regional Arctic configuration of the Massachusetts Institute of Technology general circulation model. A localized error subspace transform ensemble Kalman filter is used to assimilate the weekly merged CryoSat-2 and Soil Moisture and Ocean Salinity sea-ice thickness data together with the daily Advanced Microwave Scanning Radiometer 2 (AMSR2) sea-ice concentration data. The weather forecasts from the Global Forecast System of the National Centers for Environmental Prediction drive the sea ice–ocean coupled model. The ensemble mean sea-ice forecasts were used to facilitate the Chinese National Arctic Research Expedition in summer 2017. The forecasted sea-ice concentration is evaluated against AMSR2 and Special Sensor Microwave Imager/Sounder sea-ice concentration data. The forecasted sea-ice thickness is compared to the in-situ observations and the Pan-Arctic Ice-Ocean Modeling and Assimilation System. These comparisons show the promising potential of ArcIOPS for operational Arctic sea-ice forecasts. Nevertheless, the forecast bias in the Beaufort Sea calls for a delicate parameter calibration and a better design of the assimilation system.