Turn off MathJax
Article Contents
Qi Shu, Fangli Qiao, Jiping Liu, Zhenya Song, Zhiqiang Chen, Jiechen Zhao, Xunqiang Yin, Yajuan Song. Arctic sea ice concentration and thickness data assimilation in the FIO-ESM climate forecast system[J]. Acta Oceanologica Sinica.
Citation: Qi Shu, Fangli Qiao, Jiping Liu, Zhenya Song, Zhiqiang Chen, Jiechen Zhao, Xunqiang Yin, Yajuan Song. Arctic sea ice concentration and thickness data assimilation in the FIO-ESM climate forecast system[J]. Acta Oceanologica Sinica.

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

Funds:  The National Key Research and Development Program of China under contract Nos 2018YFC1407205 and 2018YFA0605901; 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.
More Information
  • 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 PIOMAS (the Pan-Arctic Ice-Ocean Modeling and Assimilation System) 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 assimilation. Assimilation experiment results show that ice concentration assimilation can help to get better modeled ice concentration and ice extent. All the biases of ice concentration, ice cover, ice volume, and ice thickness can be reduced dramatically through ice concentration and thickness assimilation. The near-real-time forecast results indicate that ice data assimilation can improve the forecast skill significantly in the FIO-ESM climate forecast system. The forecasted Arctic integrated ice edge error is reduced by around 1/3 by sea ice data assimilation. Compared with the six near-real-time Arctic sea ice forecast results from the subseasonal-to-seasonal (S2S) 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 forecast. Since 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-real-time Arctic sea ice seasonal prediction.
  • loading
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (15) PDF downloads(1) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return