The value of sustained ocean observations for sea-ice predictions in the Barents Sea

Dynamical prediction systems have shown potential to meet the emerging need for seasonal forecasts of regional Arctic sea ice. Observationally constrained initial conditions are a key source of skill for these predictions, but the direct influence of different observation types on prediction skill has not yet been systematically investigated. In this work, we perform a hierarchy of Observing System Experiments with a coupled global data assimilation and prediction system to assess the value of different classes of oceanic and atmospheric observations for seasonal sea-ice predictions in the Barents Sea. We find striking prediction skill improvements due to the inclusion of both sea-surface temperature (SST) satellite observations and subsurface temperature and salinity data from conductivity-temperature-depth (CTD) measurements. The SST data is found to provide the crucial source of interannual variability, whereas the CTD data primarily provide climatological and trend improvements. Analysis of the Barents Sea ocean heat budget suggests that ocean heat content anomalies in this region are driven by surface heat fluxes on seasonal timescales rather than variations in oceanic volume transport through the Barents Sea opening. These results highlight the imperative need for sustained and expanded ocean observational networks in the polar regions.