文献类型：期刊文献

英文题名：Towards reliable Arctic sea ice prediction using multivariate data assimilation

作者：Liu, Jiping[1];Chen, Zhiqiang[2];Hu, Yongyun[3];Zhang, Yuanyuan[4];Ding, Yifan[4];Cheng, Xiao[4];Yang, Qinghua[5,6];Nerger, Lars[7];Spreen, Gunnar[8];Horton, Radley[9];Inoue, Jun[10];Yang, Chaoyuan[1];Li, Ming[11];Song, Mirong[12]

机构：[1]SUNY Albany, Dept Atmospher & Environm Sci, Albany, NY 12222 USA;[2]Guangdong Ocean Univ, Coll Ocean & Meteorol, Zhanjiang 524088, Peoples R China;[3]Peking Univ, Sch Phys, Dept Atmospher & Ocean Sci, Beijing 100871, Peoples R China;[4]Beijing Normal Univ, Coll Global Change & Earth Syst Sci, Beijing 100875, Peoples R China;[5]Sun Yat Sen Univ, Guangdong Prov Key Lab Climate Change & Nat Disas, Zhuhai 519082, Peoples R China;[6]Sun Yat Sen Univ, Sch Atmospher Sci, Zhuhai 519082, Peoples R China;[7]Alfred Wegener Inst, Helmholtz Zentrum Polar & Meeresforsch, D-27570 Bremerhaven, Germany;[8]Univ Bremen, Inst Environm Phys, D-28359 Bremen, Germany;[9]Columbia Univ, Lamont Doherty Earth Observ, Earth Inst, Palisades, NY 10964 USA;[10]Natl Inst Polar Res, Tachikawa, Tokyo 1908518, Japan;[11]Natl Marine Environm Forecasting Ctr, Polar Res & Forecasting Div, Beijing 100081, Peoples R China;[12]Chinese Acad Sci, Inst Atmospher Phys, State Key Lab Numer Modeling Atmospher Sci & Geop, Beijing 100029, Peoples R China

年份：2019

卷号：64

期号：1

起止页码：63

外文期刊名：SCIENCE BULLETIN

收录：SCI-EXPANDED(收录号：WOS:000460871100010)、、EI(收录号：20185006231063)、Scopus(收录号：2-s2.0-85058053933)、WOS

基金：This work was supported by the National Key R&D Program of China (2018YFA0605901), the NOAA Climate Program Office (NA15OAR4310163), the National Natural Science Foundation of China (41676185), and the Key Research Program of Frontier Sciences of Chinese Academy of Sciences (QYZDY-SSW-DQC021).

语种：英文

外文关键词：Arctic sea ice prediction; Remote sensing; Data assimilation

外文摘要：Rapid declines in Arctic sea ice have captured attention and pose significant challenges to a variety of stakeholders. There is a rising demand for Arctic sea ice prediction at daily to seasonal time scales, which is partly a sea ice initial condition problem. Thus, a multivariate data assimilation that integrates sea ice observations to generate realistic and skillful model initialization is needed to improve predictive skill of Arctic sea ice. Sea ice data assimilation is a relatively new research area. In this review paper, we focus on two challenges for implementing multivariate data assimilation systems for sea ice forecast. First, to address the challenge of limited spatiotemporal coverage and large uncertainties of observations, we discuss sea ice parameters derived from satellite remote sensing that (1) have been utilized for improved model initialization, including concentration, thickness and drift, and (2) are currently under development with the potential for enhancing the predictability of Arctic sea ice, including melt ponds and sea ice leads. Second, to strive to generate the "best" estimate of sea ice initial conditions by combining model simulations/forecasts and observations, we review capabilities and limitations of different data assimilation techniques that have been developed and used to assimilate observed sea ice parameters in dynamical models. (C) 2018 Science China Press. Published by Elsevier B.V. and Science China Press. All rights reserved.