An ensemble optimal interpolation (EnOI) data assimilation method is applied in the BCC_CSM1.1 to investigate the impact of ocean data assimilations on seasonal forecasts in an idealized twin experiment framework. Pseudo-observations of sea surface temperature (SST), sea surface height (SSH), sea surface salinity (SSS), temperature and salinity (T/S) profiles were first generated in a free model run. Then, a series of sensitivity tests initialized with predefined bias were conducted for a one-year period; this involved a free run (CTR) and seven assimilation runs. These tests allowed us to check the analysis field accuracy against the “truth”. As expected, data assimilation improved all investigated quantities; the joint assimilation of all variables gave more improved results than assimilating them separately. One-year predictions initialized from the seven runs and CTR were then conducted and compared. The forecasts initialized from joint assimilation of surface data produced comparable SST root mean square errors to that from assimilation of T/S profiles, but the assimilation of T/S profiles is crucial to reduce subsurface deficiencies. The ocean surface currents in the tropics were better predicted when initial conditions produced by assimilating T/S profiles, while surface data assimilation became more important at higher latitudes, particularly near the western boundary currents. The predictions of ocean heat content and mixed layer depth are significantly improved initialized from the joint assimilation of all the variables. Finally, a central Pacific El Niño was well predicted from the joint assimilation of surface data, indicating the importance of joint assimilation of SST, SSH, and SSS for ENSO predictions.

在 BCC_CSM1.1 中应用了集合最优插值 (EnOI) 数据同化方法，以在理想化的双实验框架中研究海洋数据同化对季节预报的影响。海面温度（SST）、海面高度（SSH）、海面盐度（SSS）、温度和盐度（T/S）的伪观测) 配置文件首先在免费模型运行中生成。然后，在为期一年的时间内进行了一系列以预定义偏差初始化的敏感性测试；这包括一次自由运行 (CTR) 和七次同化运行。这些测试使我们能够根据“真相”检查分析字段的准确性。正如预期的那样，数据同化改善了所有调查量；所有变量的联合同化比单独同化它们提供了更多的改进结果。然后进行并比较从七次运行和点击率中初始化的一年预测。由地表资料联合同化初始化的预报产生的 SST 均方根误差与T/S剖面同化产生的 SST 均方根误差相当，但T/S同化剖面对于减少地下缺陷至关重要。当通过同化T/S剖面产生的初始条件时，可以更好地预测热带地区的海面洋流，而在高纬度地区，特别是在西部边界洋流附近，地表数据同化变得更加重要。通过对所有变量的联合同化初始化，对海洋热含量和混合层深度的预测得到了显着改善。最后，通过联合同化地表数据很好地预测了中太平洋厄尔尼诺现象，表明联合同化 SST、SSH 和 SSS 对 ENSO 预测的重要性。