Investigating the drivers of wintertime Southern Ocean sea-ice leads using random forest algorithms

Abstract. Sea-ice leads and coastal polynyas play a crucial role in regulating ocean-atmosphere energy exchange, yet the physical drivers controlling their variability across the Southern Ocean remain unquantified. This study uses a machine learning approach based on random forest regression with permutation importance analysis to identify the main drivers of Southern Ocean leads and coastal polynyas during winters (April–September, 2003–2023). The model integrates nine predictors representing atmospheric (wind speed, zonal u and meridional v wind components, wind divergence, sea-level pressure), oceanic (surface current speed), and sea-ice kinematic variables (ice velocity, ice divergence), together with a seasonal descriptor (month). Evaluated on independent test data, the model achieves a correlation of r= 0.70 at the pan-Antarctic scale and r= 0.63–0.82 across regional sectors. Relative importance analysis indicates that the zonal u wind (17.7 %), current speed (13.9 %), wind speed (12.9 %), meridional v wind (12.2 %), and ice divergence (11.2 %) together account for ∼ 68 % of the model’s total permutation importance. Regional analysis reveals sector-specific drivers: the Weddell Sea is primarily controlled by zonal u wind and ocean currents; the Indian and Pacific Ocean sectors by directional wind forcing; and the Bellingshausen–Amundsen Seas are influenced by ocean currents and meridional v wind. An analysis focused on coastal areas shows that current speed dominates in the nearest coastal zones (0–50 km) at the pan-Antarctic scale, and including a polynya dataset improves r values from 0.73 to 0.89 in these zones. However, the model does not fully resolve fine-scale structures evident in observations, hence a notable portion of the lead frequency variance still remains unexplained, which points out that the individual contribution of drivers for lead formation largely depends on local conditions and coastal geometries.