To improve Antarctic sea-ice simulations and estimations, an ensemble-based Data Assimilation System for the Southern Ocean (DASSO) was developed based on a regional sea ice–ocean coupled implementation of MITgcm and the parallel data assimilation framework (PDAF), which assimilates sea-ice thickness (SIT) together with sea-ice concentration (SIC) derived from satellites. The result of experiments conducted from 15 April to 14 October 2016 shows that assimilating SIC and SIT can suppress the overestimation of sea ice in the model-free run. However, a covariance inflation procedure is required in data assimilation to improve the simulation of Antarctic sea ice, partially due to the underestimation of atmospheric uncertainties.
Thus, a multivariate balanced atmospheric ensemble forcing is further developed for DASSO based on the high-resolution ERA5 reanalysis, which considers the relationship between different variables and adjacent times. The model-free run of 2016 shows that this newly generated atmospheric ensemble forcing can suppress model errors of SIC and produce better estimates of simulation uncertainties. Further analysis reveals the improvement stems from a better representation of atmosphere-ocean and sea ice-ocean thermodynamic processes in the model. This makes it possible to improve the background error estimate of DASSO.
Based on this improvement, the observation error estimate and the localization scheme are further optimized for DASSO. The preliminary result of the long-term data assimilation experiments shows that compared with our initial configuration, optimized DASSO can better reproduce the condition of Antarctic sea ice and decrease reliance on the covariance inflation procedure significantly. Along with more Antarctic sea ice observations due to be released soon, the prospects look bright for reconstructing long-term Antarctic sea ice conditions, especially SIT and volume, through sea-ice data assimilation.

南大洋,南极海冰,资料同化