An improved coupled data assimilation system with a CGCM using multi-time-scale high-efficiency EnOI-like filtering

Traditional ensemble Kalman filter (EnKF) CDA algorithms, while convenient to implement in multiple DA components in a coupled system, are, however, expensive and lack sufficient representativeness for low-frequency background flows.

Here, a multi-time-scale high-efficiency approximate filter with ensemble optimal interpolation (MSHea-EnOI) scheme has been implemented with a global fully coupled model. It consists of stationary, low-frequency, and high-frequency filters constructed from the time series of a single-model solution with improved representativeness for low-frequency background error statistics and enhanced computational efficiency. The MSHea-EnOI is evaluated in a biased twin experiment framework with synthetic '' observations '' produced by another coupled model, and a three-decade coupled reanalysis experiment with real observations.