Arctic sea ice concentration and thickness data assimilation in the FIO-ESM climate forecast system

作     者：Qi Shu Fangli Qiao Jiping Liu Zhenya Song Zhiqiang Chen Jiechen Zhao Xunqiang Yin Yajuan Song Qi Shu;Fangli Qiao;Jiping Liu;Zhenya Song;Zhiqiang Chen;Jiechen Zhao;Xunqiang Yin;Yajuan Song

作者机构：First Institute of OceanographyMinistry of Natural ResourcesQingdao 266061China Laboratory for Regional Oceanography and Numerical ModelingPilot National Laboratory for Marine Science and Technology(Qingdao)Qingdao 266237China Key Laboratory of Marine Science and Numerical ModelingMinistry of Natural ResourcesQingdao 266061China Department of Atmospheric and Environmental SciencesUniversity at AlbanyState University of New YorkAlbanyNY 12222USA College of Ocean and MeteorologyGuangdong Ocean UniversityZhanjiang 524088China National Marine Environmental Forecasting CenterBeijing 100081China 

出 版 物：《Acta Oceanologica Sinica》 (海洋学报（英文版）)

年 卷 期：2021年第40卷第10期

页      面：65-75页

核心收录：

学科分类：07[理学] 0707[理学-海洋科学] 

基　　金：The National Key Research and Development Program of China under contract Nos 2018YFC1407205 and2018YFA0605901 the Basic Scientific Fund for National Public Research Institute of China(ShuXingbei Young Talent Program)under contract No.2019S06 the National Natural Science Foundation of China under contract Nos 41821004,42022042 and 41941012 the China-Korea Cooperation Project on Northwestern Pacific Climate Change and its Prediction 

主　　题：FIO-ESM sea ice data assimilation sea ice forecast 

摘      要：To improve the Arctic sea ice forecast skill of the First Institute of Oceanography-Earth System Model(FIO-ESM)climate forecast system,satellite-derived sea ice concentration and sea ice thickness from the Pan-Arctic IceOcean Modeling and Assimilation System(PIOMAS)are assimilated into this system,using the method of localized error subspace transform ensemble Kalman filter(LESTKF).Five-year(2014–2018)Arctic sea ice assimilation experiments and a 2-month near-real-time forecast in August 2018 were conducted to study the roles of ice data *** experiment results show that ice concentration assimilation can help to get better modeled ice concentration and ice *** the biases of ice concentration,ice cover,ice volume,and ice thickness can be reduced dramatically through ice concentration and thickness *** near-real-time forecast results indicate that ice data assimilation can improve the forecast skill significantly in the FIO-ESM climate forecast *** forecasted Arctic integrated ice edge error is reduced by around 1/3 by sea ice data *** with the six near-real-time Arctic sea ice forecast results from the subseasonal-toseasonal(S2 S)Prediction Project,FIO-ESM climate forecast system with LESTKF ice data assimilation has relatively high Arctic sea ice forecast skill in 2018 summer sea ice *** sea ice thickness in the PIOMAS is updated in time,it is a good choice for data assimilation to improve sea ice prediction skills in the near-realtime Arctic sea ice seasonal prediction.