• 海洋气象学 •

### 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

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)

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.

• P732.4