Subseasonal forecast improvements from sea ice concentration data assimilation in the Antarctic

Abstract. This study evaluates the impact of sea ice concentration (SIC) data assimilation (DA) on subseasonal forecasts of Antarctic sea ice by comparing reforecast experiment suites initialized from two sets of initial conditions (ICs): one with SIC DA and the other without. The two ICs are evaluated against NSIDC SIC observations. Results show that the SIC DA significantly improves the climatology and interannual variability of the SIC IC. The improvement in sea ice ICs is more considerable in the Antarctic than in the Arctic. The sea ice thickness (SIT) field is mostly thinner after SIC DA except for the interior Weddell and Ross sectors. The results from reforecast experiments show that SIC DA improves the subseasonal forecast skill of Antarctic SIC in almost all initialization months except December and January, where the initial improvement is soon overtaken by the bias likely linked to the thin SIT bias. We also demonstrate that SIC DA improves the probabilistic prediction of the sea ice edge position at subseasonal time scales. The subseasonal reforecast skill of Antarctic SIC and the sea ice edge is improved the most in spring, followed by winter and summer, and has minor differences in autumn. The skill improvement associated with SIC DA is more significant in the Antarctic than the Arctic, consistent with the IC improvement. Our study demonstrates the critical role of SIC DA in the subseasonal prediction of Antarctic sea ice.