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Improving Arctic Sea-Ice Thickness Estimates with the Assimilation of CryoSat-2 Summer Observations

Ocean-Land-Atmosphere Research 2023年第1期2卷 298-308页

作者： Chao Min Qinghua Yang Hao Luo Dake Chen Thomas Krumpen Nabir Mamnun Xiaoyu Liu lars nerger School of Atmospheric Sciences Sun Yat-sen Universityand Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)ZhuhaiChina Alfred Wegener Institute Helmholtz Centre for Polar and Marine ResearchBremerhavenGermany

Rapidly shrinking Arctic sea ice has had substantial impacts on the Earth ***,reliably estimating the Arctic sea-ice thickness(SIT)using a combination of available observations and numerical modeling is urgently ***,for the first time,we assimilate the latest CryoSat-2 summer SIT data into a coupled ice-ocean *** particular,an incremental analysis update scheme is implemented to overcome the discontinuity resulting from the combined assimilation of biweekly SIT and daily sea-ice concentration(SIC)*** with improved estimates of sea-ice volume,our SIT estimates corrected the overestimation of SIT produced by the reanalysis that assimilates only SIC in summer in areas where the sea ice is roughest and experiences strong deformation,e.g.,around the Fram Strait and *** study suggests that the newly developed CryoSat-2 SIT product,when assimilated properly using our approach,has great potential for Arctic sea-ice simulation and prediction.

关键词： prediction Arctic CryoSat

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Towards reliable Arctic sea ice prediction using multivariate data assimilation

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Science Bulletin 2019年第1期64卷 63-72页

作者： Jiping Liu Zhiqiang Chen Yongyun Hu Yuanyuan Zhang Yifan Ding Xiao Cheng Qinghua Yang lars nerger Gunnar Spreen Radley Horton Jun Inoue Chaoyuan Yang Ming Li Mirong Song Department of Atmospheric and Environmental Sciences University at Albany State University of New York College of Ocean and Meteorology Guangdong Ocean University Department of Atmospheric and Oceanic Sciences School of Physics Peking University College of Global Change and Earth System Science Beijing Normal University Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies and School of Atmospheric Sciences Sun Yat-sen University Alfred-Wegener-Institut Helmholtz Zentrum für Polar-und Meeresforschung University of Bremen Institute of Environmental Physics Lamont-Doherty Earth Observatory Columbia University Earth Institute National Institute of Polar Research Polar Research and Forecasting Division National Marine Environmental Forecasting Center State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric Physics Chinese Academy of Sciences

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.

关键词： Arctic sea ice prediction Remote sensing Data assimilation

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