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

• Published in: Journal of glaciology 2019-10, Vol.65 (253), p.813-821
• Main Authors: Mu, Longjiang, Liang, Xi, Yang, Qinghua, Liu, Jiping, Zheng, Fei
• Format: Article
• Language: English
• Subjects: Arctic ice; Arctic research; Arctic sea ice; Assimilation; Data assimilation; Datasets; Decomposition; Ensemble forecasting; Expeditions; General circulation models; Ice; Ice cover; Ice thickness; Kalman filters; Mathematical models; Microwave radiometers; Ocean models; Oceans; Polar and subpolar oceans; Radiometers; Research expeditions; Satellites; Sea ice; Sea ice concentrations; Sea ice forecasting; Sea ice thickness; sea-ice modeling; Soil; Soil moisture; Special Sensor Microwave Imager; Summer; Thickness; Weather forecasting
• ISSN: 0022-1430; 1727-5652
• DOI: 10.1017/jog.2019.55

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.