| 引用本文： |
-
张思晗,赵杰臣,邹文峰,吴杰,王英政,陈子怡,赵丁珑,牟芳如.北极海冰多参数同化对海冰密集度模拟的改进[J].海洋开发与管理,2024,41(6):3-14
|
|
| 摘要: |
| 文章基于CICE海冰模式和PDAF并行数据同化框架，使用局地误差子空间变换卡尔曼滤波方法(LESTKF)，将海冰密集度、海冰厚度和海冰干舷资料同化到模式中，设计实验研究了多参数同化对北极海冰密集度模拟的改进。结果显示，数据同化对北极海冰密集度模拟具有良好的改善作用，同化实验的平均偏差、均方根误差和平均绝对误差相对于控制实验均有明显减小，同化实验在夏季对海冰密集度和范围的模拟改善最为明显，多参数同化可以提高海冰密集度和范围模拟的精度和可靠性。 |
| 关键词: 北极海冰 数据同化 CICE PDAF 海冰密集度 海冰范围 |
| DOI：10.20016/j.cnki.hykfygl.2024.06.005 |
| 投稿时间：2024-06-12修订日期：2024-06-14 |
| 基金项目:哈尔滨工程大学青年科学家培育基金项目(79000012/006)，山东省泰山学者工程(2023). |
|
| Improvement of Arctic Sea Ice Multi-Parameter Assimilation on Sea Ice Concentration Simulation |
| ZHANG Sihan,ZHAO Jiechen,ZOU Wenfeng,WU Jie,WANG Yingzheng,CHEN Ziyi,ZHAO Dinglong,MU Fangru |
| Qingdao Innovation Development Base of Harbin Engineering University; Qingdao Harbin Engineering University Innovation and Development Center;Qingdao Innovation Development Base of Harbin Engineering University; Qingdao Harbin Engineering University Innovation and Development Center; Key Laboratory of Polar Ocean Acoustics and Technology Applications, Ministry of Education (Harbin Engineering University);COSCO Shipping (Guangzhou) Co., Ltd.;COSCO Shipping Specialized Carriers Co., Ltd. |
| Abstract: |
| Based on the CICE sea ice model and the PDAF parallel data assimilation framework, this paper uses the local error subspace transform Kalman filter method (LESTKF) to assimilate the sea ice concentration, sea ice thickness and sea ice freeboard data into the model, and designs experiments to study the improvement of multi-parameter assimilation on the simulation of Arctic sea ice concentration and range. The results show that data assimilation has a good improvement effect on the simulation of Arctic sea ice concentration and range. The average deviation, root mean square error and mean absolute error of the assimilation experiment are significantly reduced compared with the control experiment. The assimilation experiment improves the simulation of sea ice concentration and range most obviously in summer. Multi-parameter assimilation can improve the prediction accuracy and reliability of Arctic sea ice change. |
| Key words: Arctic sea ice, Data assimilation, CICE, PDAF, Sea ice concentration, Sea ice extent |
