| 误差订正对2018年夏季次季节尺度海冰预测的作用 |
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| 引用本文： | 赵杰臣,舒启,李春花,吴兴仁,宋振亚,乔方利.误差订正对2018年夏季次季节尺度海冰预测的作用[J].海洋学报(英文版),2020,39(9):50-59. |
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| 作者姓名： | 赵杰臣 舒启 李春花 吴兴仁 宋振亚 乔方利 |
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| 作者单位： | 国家海洋环境预报中心, 国家海洋局海洋灾害预报技术研究重点实验室, 北京 100081;青岛海洋科学与技术试点国家试验室, 区域海洋动力学与数值模拟功能实验室, 青岛266237;自然资源部第一海洋研究所, 青岛 266061;中国海洋大学, 海洋与大气学院, 青岛 266100;青岛海洋科学与技术试点国家试验室, 区域海洋动力学与数值模拟功能实验室, 青岛266237;自然资源部第一海洋研究所, 青岛 266061;海洋环境科学与数值模拟自然资源部重点试验室, 青岛 266061;美国海洋与大气局, 国家环境预报中心环境模拟中心, 大学公园, 马里兰 20740, 美国 |
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| 基金项目： | The National Key Research and Development Program of China under contract No. 2018YFC1407206; the National Natural Science Foundation of China under contract Nos 41821004 and U1606405; the Basic Scientific Fund for National Public Research Institute of China (Shu Xingbei Young Talent Program) under contract No. 2019S06. |
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| 摘 要： | 北极海冰次季节尺度预测在针对破冰船和商船的实际服务中十分重要，但常常受制于气候模拟的模拟能力。本研究提出了一种误差订正方法并分别应用到两个气候模式：海洋一所地球系统模式（FIOESM）和美国国家环境预报中心（NCEP）的气候预报系统（CFS），来改善北极海冰60天尺度的预测。本研究的预测工作是中国第9次北极科学考察和2018年夏季中远集团北极商业航行的业务化海冰服务保障的重要部分。模式起报时间分别是2018年7月1日、8月1日和9月1日，预报时效均是60天。结果显示，FIOESM整体上低估了海冰密集度的数值，平均偏差可达30%。误差订正对海冰密集度（SIC）的均方根偏差（RMSE）的改进比例可达27%，对海冰外缘线（SIE）的整体偏差（IIEE）的改进比例为10%。而对于CFS，SIE在边缘区域的过高估计是其主要特点。误差订正导致了SIC的RMSE改进了7%，而对SIE的IIEE改进了17%。在海冰范围预测方面，FIOESM预测的最小范围数值和时间点都和观测接近，而CFS的预测结果偏差较大。另外和其他S2S模式的结果比较发现，本研究提出的误差订正方法对存在较大偏差的预测结果改进更为有效。
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| 关 键 词： | 误差订正 北极海冰 次季节尺度预测 业务化服务 |
| 收稿时间： | 2019/12/3 0:00:00 |
The role of bias correction on subseasonal prediction of Arctic sea ice during summer 2018 |
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| Zhao Jiechen,Shu Qi,Li Chunhu,Wu Xingren,Song Zheny,Qiao Fangli.The role of bias correction on subseasonal prediction of Arctic sea ice during summer 2018[J].Acta Oceanologica Sinica,2020,39(9):50-59. |
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| Authors: | Zhao Jiechen Shu Qi Li Chunhu Wu Xingren Song Zheny Qiao Fangli |
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| Institution: | Key Laboratory of Marine Hazards Forecasting, National Marine Environmental Forecasting Center, Ministry of Natural Resources, Beijing 100081, China;Laboratory for Regional Oceanography and Numerical Modeling, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao 266237, China;First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China;College of;Laboratory for Regional Oceanography and Numerical Modeling, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao 266237, China;First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China;Key Laboratory for Marine Science and Numerical Modeling, Ministry of Natural Resources, Qingdao 266061, China;Key Laboratory of Marine Hazards Forecasting, National Marine Environmental Forecasting Center, Ministry of Natural Resources, Beijing 100081, China;IMSG at Environmental Modeling Center, National Centers for Environmental Prediction, National Oceanic and Atmospheric Administration, College Park, MD 20740, USA |
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| 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. |
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| Keywords: | bias correction Arctic sea ice subseasonal prediction operational service |
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