Volume, heat and salt transport in the North-Eastern Bering Sea during 2007-2010 derived through the 4dvar data assimilation of in-situ and satellite observations

The rich collection of BEST-BSIERP observations and other sources of data provide an excellent opportunity for synthesis through modeling and data assimilation to improve our understanding of changes in physical forcings of the Bering ecosystem in response to climate change. Assimilating data of different origins, which may be sparse in space and time, is difficult using simple algorithms (traditional optimal interpolation, correlation analysis etc.).
The 4Dvar approach is effective for performing spatiotemporal interpolation of sparse data via interpolation (covariance) functions with scales based on ocean dynamics (Bennett, 2002).

BEST-BSIERP观测资料与其他多源数据构成的丰富数据集，为通过建模与数据同化开展综合研究提供了绝佳契机，有助于深化我们对气候变化影响下白令海生态系统物理强迫（physical forcings）因子变化的认知。针对时空分布可能较为稀疏的多源数据，采用简单算法（如传统最优插值、相关分析等）开展数据同化工作存在较大难度。
四维变分（4Dvar）方法可借助基于海洋动力学确定尺度的插值（协方差）函数，有效实现稀疏数据的时空插值（Bennett, 2002）。