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

Acta Oceanologica Sinica 2021年第10期40卷 65-75页

作者： Qi Shu Fangli Qiao Jiping Liu Zhenya Song Zhiqiang Chen Jiechen Zhao Xunqiang Yin Yajuan Song First Institute of Oceanography Ministry of Natural ResourcesQingdao 266061China Laboratory for Regional Oceanography and Numerical Modeling Pilot National Laboratory for Marine Science and Technology(Qingdao)Qingdao 266237China Key Laboratory of Marine Science and Numerical Modeling Ministry of Natural ResourcesQingdao 266061China Department of Atmospheric and Environmental Sciences University at AlbanyState University of New YorkAlbanyNY 12222USA College of Ocean and Meteorology Guangdong Ocean UniversityZhanjiang 524088China National Marine Environmental forecasting Center Beijing 100081China

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.

关键词： FIO-ESM sea ice data assimilation sea ice forecast

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ice concentration assimilation in a regional ice-ocean coupled model and its application in sea ice forecasting

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Advances in Polar Science 2013年第4期24卷 258-264页

作者： LI Qun ZHANG Zhanhai SUN Li WU Huiding SOA Key Laboratory for Polar Science Polar Research Institute of China Shanghai Flood Control Information Center National Marine Environmental forecasting Center State Oceanic Administration

A reasonable initial state of ice concentration is essential for accurate short-term forecasts of sea ice using ice-ocean coupled models. In this study, sea ice concentration data are assimilated into an operational ice forecast system based on a com- bined optimal interpolation and nudging scheme. The scheme produces a modeled sea ice concentration at every time step, based on the difference between observational and forecast data and on the ratio of observational error to modeled error. The impact and the effectiveness of data assimilation are investigated. Significant improvements to predictions of sea ice extent were obtained through the assimilation of ice concentration, and minor improvements through the adjustment of the upper ocean properties. The assimilation of ice thickness data did not significantly improve predictions. forecast experiments show that the forecast accuracy is higher in summer, and that the errors on five-day forecasts occur mainly around the marginal ice zone.

关键词： ice concentration assimilation combined optimal interpolation and nudging sea ice forecast skills core

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