Abstract Subseasonal prediction systems based on coupled atmosphere‐ice‐ocean models are vital tools for polar environmental forecasting. However, their predictive skill for Antarctic sea ice thickness (SIT), which is a critical variable governing atmosphere‐ocean energy and mass exchange, remains inadequately assessed. In this study, we evaluated Antarctic SIT prediction skill in four dynamical systems (China Meteorological Administration, Environment and Climate Change Canada, European Centre for Medium‐Range Weather Forecasts, and Météo‐France) participating in the Subseasonal to Seasonal Prediction Project using the Soil Moisture and Ocean Salinity satellite‐derived thin SIT retrievals. Results indicate that dynamical systems show limited overall skill against observation‐based benchmarks, primarily due to excessive initial errors. Removing climatological biases reduces errors, yielding skill in the Ross Sea comparable to the damped anomaly persistence prediction benchmark. Furthermore, the dynamical system surpassed the benchmark significantly from March to April, with three systems skillfully predicting region‐wide SIT anomaly evolution. Predictive skill for thin ice in the Weddell Sea and the coastal regions around Antarctica is a key driver of inter‐model skill differences. Overall, these results suggest that dynamical systems have the potential to surpass benchmark skill via improved SIT initialization, reduced climatological bias, and improved process representation of newly formed sea ice that enhances coastal thin‐ice simulation. Daily pan‐Antarctic SIT observations are urgently needed for future comprehensive evaluation.
Wang et al. (Tue,) studied this question.