极地研究 ›› 2025, Vol. 37 ›› Issue (4): 742-756.DOI: 10.13679/j.jdyj.20240028

• 研究论文 • 上一篇    下一篇

基于FIO-CPS v1.0对北极夏季海冰预测能力的评估

谭啸1张录军1宋振亚2,3,4舒启2,3,4   

  1. 1南京大学大气科学学院, 江苏 南京 210033;
    2自然资源部第一海洋研究所, 山东 青岛 266061;
    3自然资源部海洋环境科学与数值模拟重点实验室, 山东 青岛 266061;
    4山东省海洋环境科学与数值模拟重点实验室, 山东 青岛 266061
  • 收稿日期:2024-03-14 修回日期:2024-05-21 出版日期:2025-12-30 发布日期:2026-01-12
  • 通讯作者: 张录军
  • 基金资助:
    中央级公益性科研院所基本科研业务费专项资金、国家自然科学基金和山东省自然科学基金资助

Assessment of FIO-CPS v1.0 Arctic summer sea ice prediction skill

TAN Xiao1, ZHANG Lujun1, SONG Zhenya2,3,4, SHU Qi2,3,4   

  1. 1School of Atmospheric Sciences, Nanjing University, Nanjing 210033, China;
    2First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China;
    3Key Laboratory of Marine Science and Numerical Modeling, Ministry of Natural Resources, Qingdao 266061, China;
    4Shandong Key Laboratory of Marine Science and Numerical Modeling, Qingdao 266061, China
  • Received:2024-03-14 Revised:2024-05-21 Online:2025-12-30 Published:2026-01-12

摘要: 随着北极地区气候变暖加剧, 北冰洋海冰范围快速减小和北退, 北极海冰预测对北极科考和商业适航性评估重要性日益凸显本文基于自然资源部第一海洋研究所研发的短期气候预测系统(FIO-CPS v1.0)的历史回报试验, 在对北极海冰预测结果进行误差订正后, 评估了该系统对北极夏季(69)海冰覆盖范围和海冰密集度的预测能力评估结果表明: (1)FIO-CPS v1.0对北极夏季海冰范围的预测存在系统性偏差, 经过误差订正后, 预测性能改善效果显著, 7月预测性能改进最大; (2)误差订正后FIO-CPS v1.0对北极海冰范围具有良好的预测性能, 逐月与逐日预测数据的均方根误差均小于0.5×106 km2, 异常相关系数均超过0.7; (3)FIO-CPS v1.0的预测技巧显著高于线性趋势气候态预测, 且在一定程度上优于阻尼异常持续性预测, 但其优势随预测提前量的增大而逐渐减弱; (4)在北极海冰范围极小值年份, FIO-CPS v1.0的预测性能偏弱, 但相对于线性趋势气候态预测仍具有显著优势; (5)误差订正后FIO-CPS v1.0对海冰密集度也具有较高的预测能力, 预测的海冰密集度均方根误差普遍小于30%

关键词: 短期气候预测系统, FIO-CPS v1.0,  海冰预测能力评估, 海冰密集度, 海冰覆盖范围北极

Abstract: As the climate warms in the Arctic region, the sea ice extent (SIE) in the Arctic Ocean is rapidly decreasing and retreating northward, making Arctic sea ice forecasting increasingly important for scientific expeditions and commercial navigability assessments. This study utilizes hindcast data from the First Institute of Oceanography-Climate Prediction System (FIO-CPS) v1.0. After applying bias correction to the Arctic sea ice predictions, the system’s predictive capability for summer (June–September) Arctic sea ice extent and sea ice concentration (SIC) was evaluated. The evaluation results indicate that: (1) There is a systematic bias in the FIO-CPS v1.0 predictions of summer Arctic SIE. After bias correction, the prediction performance improves significantly, with the most notable improvement observed in July; (2) Following bias correction, FIO-CPS v1.0 demonstrates good performance in predicting Arctic SIE, with root mean square errors for both monthly and daily predictions below 0.5×106 km2, and anomaly correlation coefficients exceeding 0.7; (3) The prediction skill of FIO-CPS v1.0 is significantly higher than that of linear trend climatology predictions and, to some extent, superior to damped anomaly persistence predictions. However, this advantage gradually diminishes as the lead time increases; (4) During years with extremely low Arctic SIE, the prediction performance of FIO-CPS v1.0 is relatively weaker, but it still holds a significant advantage over linear trend climatology predictions; (5) After bias correction, FIO-CPS v1.0 also exhibits high predictive capability for SIC, with the root mean square error of predicted sea ice concentration generally below 30%.

Key words: short-term climate prediction system, FIO-CPS v1.0, assessment of sea ice prediction skills, sea ice concentration, Arctic