热带海洋学报 ›› 2022, Vol. 41 ›› Issue (3): 75-90.doi: 10.11978/2021112

• 海洋气象学 • 上一篇    下一篇

海洋资料同化对气候季节-年际预测技巧及初始场的影响试验

孙惠杭1(), 王意国2, 罗京佳1()   

  1. 1.南京信息工程大学气候与应用前沿研究院, 江苏 南京 210044
    2.挪威南森环境与遥感中心和皮耶克尼斯气候研究中心, 挪威 卑尔根
  • 收稿日期:2021-08-30 修回日期:2021-12-24 发布日期:2021-12-31
  • 通讯作者: 罗京佳
  • 作者简介:孙惠杭(1996—), 男, 江苏省泰州市人, 硕士研究生, 从事海洋资料同化和气候预测研究。email: 978982001@qq.com
  • 基金资助:
    国家自然科学基金(42030605);国家重点研发计划(2020YFA0608004)

Impact of ocean data assimilation on initial conditions and skills of seasonal-to- interannual climate prediction

SUN Huihang1(), WANG Yiguo2, LUO Jingjia1()   

  1. 1. Institute for Climate and Application Research (ICAR), Nanjing University of Information Science and Technology, Nanjing 210044, China
    2. Nansen Environmental and Remote Sensing Center, Bjerknes Centre for Climate Research, Bergen, Norway
  • Received:2021-08-30 Revised:2021-12-24 Published:2021-12-31
  • Contact: LUO Jingjia
  • Supported by:
    National Natural Science Foundation of China(42030605);National Key R&D Program of China(2020YFA0608004)

摘要:

集合卡尔曼滤波(Ensemble Kalman filter, EnKF)是一种国内外广泛使用的海洋资料同化方案, 用集合成员的状态集合表征模式的背景误差协方差, 结合观测误差协方差, 计算卡尔曼增益矩阵, 有效地将观测信息添加到模式初始场中。由于季节、年际预测很大程度上受到初始场的影响, 因此资料同化可以提高模式的预测性能。本文在NUIST-CFS1.0预测系统逐日SST nudging的初始化方案上, 利用EnKF在每个月末将全场(full field)海表温度(sea surface temperature, SST)、温盐廓线(in-situ temperature and salinity profiles, T-S profiles)以及卫星观测海平面高度异常(sea level anomalies, SLA)观测资料同化到模式初始场中, 对比分析了无海洋资料同化以及加入同化后初始场的区别、加入海洋资料同化后模式提前1~24个月预测性能的差异以及对于厄尔尼诺-南方涛动(El Niño-southern oscillation, ENSO)预测技巧的影响。结果表明, 加入海洋资料同化能有效地改进初始场, 并且呈现随深度增加初始场改进越显著的特征。加入同化后, 对全球SST、次表层海水温度的平均预测技巧均有一定的提高, 也表现出随深度增加预测技巧改进越明显的特征。但加入海洋资料同化后, 模式对ENSO的预测技巧有所下降, 可能是由于模式误差的存在, 使得同化后的预测初始场从接近观测的状态又逐渐恢复到与模式动力相匹配的状态, 加剧了赤道太平洋冷舌偏西、中东部偏暖的气候平均态漂移。

关键词: 海洋资料同化, 集合卡尔曼滤波, 气候预测

Abstract:

Ensemble Kalman filter (EnKF) is a widely used ocean data assimilation scheme. It uses the members' state to represent background error covariance of the model and uses the observation error covariance to calculate the Kalman gain matrix; and then it adds the information of observation to the model's initial conditions. Data assimilation can effectively improve climate prediction skill since seasonal-to-interannual climate prediction is largely influenced by initial conditions. Based on the NUIST-CFS1.0, previously SINTEX-F, that used coupled sea-surface temperature (SST)-nudging initialization method, we employ EnKF to assimilate SST, gridded altimeter satellite sea-level anomalies, and in-situ temperature and salinity profiles at the end of each month. We assess the differences in the initial fields and climate (including the ENSO) prediction skills at lead times of up to 24 months with and without the ocean data assimilation. The results show that the initial conditions are improved largely with the ocean data assimilation, and the improvement of the initial fields is getting better with the increase of depth. The global mean prediction skills of SST and subsurface temperature with EnKF are improved at 1-24 months lead, and the improved prediction also becomes more obvious in deep layers. However, the prediction skill of the ENSO is reduced after the EnKF assimilation. This might be due to the existence of model errors, the predicted initial field with EnKF is gradually restored from a state close to the observed state to match the model dynamics again, which displays a larger cold SST bias in the west and warm bias in the central-eastern equatorial Pacific, compared to the original NUIST-CFS1.0.

Key words: ocean data assimilation, ensemble Kalmen filter, climate prediction

中图分类号: 

  • P732.4