Multi-Sphere Covariance Analysis for Coupled Assimilation

Coupled data assimilation (CDA) is a core method for achieving seamless forecasting with Earth system models (ESMs). It provides high-quality initial conditions for coupled models, minimizes inter-component imbalances to the greatest extent, and better utilizes observational networks. Based on whether cross-component information transfer is achieved, CDA can be classified into weakly coupled data assimilation (WCDA) and strongly coupled data assimilation (SCDA). SCDA, which incorporates cross-component information transfer, can produce more balanced and consistent analysis fields, thereby improving forecast skill. According to ECMWF (refer to the literature Coupled data assimilation at ECMWF: current status, challenges and future developments), strongly coupled data assimilation enables the transfer of observational information across different Earth system components during the assimilation phase.

This study employs the high-resolution version of the global sea-land-air-ice system model FGOALS-g3, developed by the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG) at the Institute of Atmospheric Physics, Chinese Academy of Sciences, along with the Dimension-Reduced Projection Four-Dimensional Variational (DRP-4DVar) system, to conduct single-point coupled assimilation experiments. The aim is to investigate how cross-component covariance specifically regulates the transfer of observational information between Earth system components. The experimental results show that within the coupled framework, assimilating only a single-point surface pressure observation in the atmosphere not only generates analysis increments for atmospheric wind and temperature but also influences the state of the ocean surface layer. Similarly, assimilating only a single-point sea surface temperature observation not only produces analysis increments for sea surface height but also induces responses in the lower atmospheric state.