The role of bias correction on subseasonal prediction of Arctic sea ice during summer 2018
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The role of bias correction on subseasonal prediction of Arctic sea ice during summer 2018 Jiechen Zhao1, 2, 3, 4, Qi Shu2, 3, 5, Chunhua Li1, Xingren Wu6, Zhenya Song2, 3, 5, Fangli Qiao2, 3, 5* 1 Key Laboratory of Marine Hazards Forecasting, National Marine Environmental Forecasting Center, Ministry of
Natural Resources, Beijing 100081, China 2 Laboratory for Regional Oceanography and Numerical Modeling, Pilot National Laboratory for Marine Science and
Technology (Qingdao), Qingdao 266237, China 3 First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China 4 College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao 266100, China 5 Key Laboratory for Marine Science and Numerical Modeling, Ministry of Natural Resources, Qingdao 266061, China 6 IMSG at Environmental Modeling Center, National Centers for Environmental Prediction, National Oceanic and
Atmospheric Administration, College Park, MD 20740, USA Received 3 December 2019; accepted 25 December 2019 © Chinese Society for Oceanography and Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract
Subseasonal Arctic sea ice prediction is highly needed for practical services including icebreakers and commercial ships, while limited by the capability of climate models. A bias correction methodology in this study was proposed and performed on raw products from two climate models, the First Institute Oceanography Earth System Model (FIOESM) and the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS), to improve 60 days predictions for Arctic sea ice. Both models were initialized on July 1, August 1, and September 1 in 2018. A 60-day forecast was conducted as a part of the official sea ice service, especially for the ninth Chinese National Arctic Research Expedition (CHINARE) and the China Ocean Shipping (Group) Company (COSCO) Northeast Passage voyages during the summer of 2018. The results indicated that raw products from FIOESM underestimated sea ice concentration (SIC) overall, with a mean bias of SIC up to 30%. Bias correction resulted in a 27% improvement in the Root Mean Square Error (RMSE) of SIC and a 10% improvement in the Integrated Ice Edge Error (IIEE) of sea ice edge (SIE). For the CFS, the SIE overestimation in the marginal ice zone was the dominant features of raw products. Bias correction provided a 7% reduction in the RMSE of SIC and a 17% reduction in the IIEE of SIE. In terms of sea ice extent, FIOESM projected a reasonable minimum time and amount in mid-September; however, CFS failed to project both. Additional comparison with subseasonal to seasonal (S2S) models suggested that the bias correction methodology used in this study was more effective when predictions had larger biases. Key words: bias correction, Arctic sea ice, subseasonal prediction, operational service Citation: Zhao Jiechen, Shu Qi, Li Chunhua, Wu Xingren, Song Zhenya, Qiao Fangli. 2020. The role of bias correction on subseasonal prediction of Arctic sea ice during summer 2018. Acta Oceanol
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