Joint ECMWF/OceanPredict workshop on Advances in Ocean Data Assimilation

A multi-timescale high-efficiency approximate EnKF (MSHea-EnKF), which consists of stationary, slow-varying, and fast-varying filter using the time series of a single-model solution, has been implemented in the Geophysical Fluid Dynamics Laboratory’s global fully coupled climate model (CM2.1) to increase the representation of low-frequency background error statistics and enhance the computational efficiency. Here, the MSHea-EnKF is evaluated in a biased twin experiment framework and a 27-year real-obs coupled data assimilation (CDA) experiment. Results show that while the computing only costs duodecimal of traditional ensemble coupled data assimilation (ECDA), the ocean state estimation quality improves 30.3% and 10.7% for upper 500m salinity and temperature, respectively, and the atmosphere state estimation has almost the same quality as traditional ECDA. It’s mainly because MSHea-EnKF improves representation primarily on slow-varying background flows. The MSHea-EnKF also gets a more reasonable standard deviation distribution for Atlantic meridional overturning circulation (AMOC) and stronger meridional transport at 26.5°N below 2000m, which is closer to Rapid estimates.