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

Longjiang Mu; Xi Liang; Qinghua Yang; Jiping Liu; Fei Zheng

doi:10.1017/jog.2019.55

Journal of Glaciology (Oct 2019)

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

Longjiang Mu,
Xi Liang,
Qinghua Yang,
Jiping Liu,
Fei Zheng

Affiliations

Longjiang Mu: Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, and School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, China State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany Polar Research and Forecasting Division, National Marine Environmental Forecasting Center, Beijing, China
Xi Liang: Polar Research and Forecasting Division, National Marine Environmental Forecasting Center, Beijing, China
Qinghua Yang: ORCiD; Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, and School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, China State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China Polar Research and Forecasting Division, National Marine Environmental Forecasting Center, Beijing, China Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, China
Jiping Liu: Department of Atmospheric and Environmental Sciences, University at Albany, State University of New York, New York, USA
Fei Zheng: International Center for Climate and Environment Science, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China

DOI: https://doi.org/10.1017/jog.2019.55
Journal volume & issue: Vol. 65
pp. 813 – 821

Abstract

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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.

Published in Journal of Glaciology

ISSN: 0022-1430 (Print); 1727-5652 (Online)
Publisher: Cambridge University Press
Country of publisher: United Kingdom
LCC subjects: Geography. Anthropology. Recreation: Environmental sciences; Science: Physics: Meteorology. Climatology
Website: https://www.cambridge.org/core/journals/journal-of-glaciology

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