极地研究

极地研究 ›› 2023, Vol. 35 ›› Issue (1): 34-45.DOI: 10.13679/j.jdyj.20210080

• 研究论文 • 上一篇    下一篇

基于统计与动力相结合的南极夏季海冰跨季节预测

王惠1  李双林2,1  刘娜2   

  1. 1 中国地质大学环境学院大气科学系, 湖北 武汉 430074; 
    2 中国科学院大气物理研究所, 中国科学院气候变化研究中心, 北京 l00029)
  • 出版日期:2023-03-31 发布日期:2023-04-07
  • 通讯作者: 李双林
  • 作者简介:王惠, 女, 1998年生。博士研究生, 主要从事南极海冰预测研究。E-mail: hui.wang@cug.edu.cn
  • 基金资助:
    中国科学院战略性先导专项子任务(XDA19070402)资助

A statistical-dynamical joint model for seasonal prediction of Antarctic summer sea ice

Wang Hui1, Li Shuanglin2,1, Liu Na2   

  1. 1Department of Atmospheric Sciences, China University of Geosciences, Wuhan 430074, China;
    2Climate Change Research Center, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
  • Online:2023-03-31 Published:2023-04-07

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摘要： 选取美国国家环境预测中心气候预测系统(CFS)第二版预测的南大洋(40oS—80oS)夏季海温、海洋性大陆地区(100oE—130oE, 10oN—15oS)春季海温以及南半球热带外(20oS—90oS)春季海平面气压3个预测因子, 利用奇异值分析的方法提取相关信息, 采用回归模型对南极夏季海冰场进行预测。从该模式对1983—2018年南极夏季海冰密集度异常值的回报效果看, 模型的预测技巧较CFS原始预测有显著的提高。对单格点上预测与实测的时间相关系数提高到0.76; 从交叉检验的结果来看, 预测技巧较CFS同样有显著提高; 从对南极海冰范围的预测来看, 该模型的预测效果最好, 明显优于CFS原始预测和持续性预测。这些结果表明, 该模型具有较高的预测性能。

关键词: 南极夏季海冰, 降尺度预测模型, 气候预测系统, 持续性预测

Abstract: To model summer sea ice in Antarctica, three predictors are selected: (1) sea surface temperature prediction in the Southern Ocean (40°S–80°S) from the Climate Forecast System version 2 (CFSv2), (2) observed sea surface temperature around the Maritime Continent (100°E–130°E, 10°N–15°S) in early austral spring; (3) observed sea level pressure in the southern extratropics (20°S–90°S) in early austral spring. In this study, the singular value decomposition method is used to extract relevant information, then a regression model is applied to predict the sea ice field. When applied retrospectively to simulations for 1983–2018, this model yields significantly improved results compared with raw CFSv2 predictions: temporal correlation between predicted sea ice concentrations at single grid points and observations is significant, with an average value of 0.76 over the Southern Ocean. From cross-validation results, prediction results are significantly better than those of CFSv2, with predicted sea ice extent significantly improved relatively to CFSv2 and to persistence prediction. These results indicate that the prediction performance of the joint statistical-dynamical model described in this study is high. Therefore, this model is important to predict Antarctic sea ice, for example when planning scientific survey activities in Antarctica.

Key words:  Antarctic summer sea ice, dynamic-statistical downscaling model, Climate Forecast System, persistence prediction