This study uses the monthly LF dataset by for the Southern Ocean covering the months from April to September, 2003–2023. The dataset serves as the target variable in the RF regression framework. Monthly LF data are based on thermal infrared satellite imagery, which was used to detect leads as surface temperature anomalies in cold winter sea ice (). Monthly LF values were computed from daily lead observations as the fraction of clear-sky days during which a grid cell was identified as a lead (). The LF represents a temporally integrated quantity indicating the number of days a pixel is covered by a lead during a specified period relative to the number of available clear-sky observations.

To provide a more complete representation of wintertime open-water activity along the Antarctic coastal margins, the coastal polynya dataset of , was incorporated into the LF target variable. This dataset provides daily records of coastal polynya areas around Antarctica derived from passive microwave satellite observations, and is used here to supplement the MODIS-derived LF, which does not capture larger open water or thin ice areas (polynya-scale) explicitly. A monthly polynya frequency is therefore calculated based on the daily binary data. The year 2012 is excluded from the analysis because the polynya dataset is not available for that year. The combined dataset includes both pack-ice leads and coastal polynyas (LF), enabling a more physically complete analysis of wintertime lead variability across the Southern Ocean. We provide results, however, also for the leads-only data set (LF without polynyas, LFWP) to highlight the specific benefits that the polynya dataset can contribute.

For use in the RF framework, the 1 km² monthly LF data were spatially aggregated to a 2° latitude × 5° longitude grid (for basin-scale analysis) and 10×10 km² (for coastal analysis), respectively. This aggregation is meant to balance spatial resolution with computational efficiency while aligning with the resolution of predictor variables.

To investigate the drivers of lead and coastal polynya formation, this study compiled nine input predictor variables for the same period as the target variable, grouped into four categories: atmospheric forcing, sea-ice kinematics, oceanic currents, and temporal descriptor.

Atmospheric predictors include the zonal u wind and meridional v wind components of the 10 m wind, 10 m wind speed, horizontal wind divergence, and sea-level pressure from the ERA5 reanalysis () produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) and obtained from the Climate Data Store (CDS) of Copernicus Climate Change Service (C3S). ERA5 provides hourly atmospheric fields at approximately 31 km resolution, which were temporally averaged to monthly means and bilinearly interpolated to the analysis grid. The zonal u wind and meridional v wind components explicitly resolve the directional structure of near-surface wind forcing, capturing both the large-scale atmospheric circulation patterns and the local wind-driven divergence that mechanically fractures the ice cover and initiates lead opening (). Wind speed and divergence represent the principal mechanical forcing for ice motion and lead opening, capturing both direct effects through wind shear and indirect influences via pressure gradients. Wind divergence is directly used from the ERA5 reanalysis for the 10 m wind field. Sea-level pressure accounts for large-scale synoptic variability that modulates wind stress and convergence–divergence patterns over the ice pack (; ).

Sea-ice kinematic predictors in this study consist of ice velocity, and ice divergence. Ice velocity and divergence are derived from the Polar Pathfinder Daily Sea Ice Motion product () provided by the National Snow and Ice Data Center (NSIDC). This product integrates motion estimates from passive microwave sensors, visible and infrared satellite imagery, together with atmospheric reanalysis winds to generate daily ice drift vectors at 25 km spatial resolution. Ice divergence was calculated from the ice velocity components using centered finite differences. Grid cells containing land or coastline were masked using the land-sea mask of the analysis grid prior to the divergence calculation, ensuring that land contamination did not artificially inflate divergence values near the coast. For wind divergence, the ERA5 10 m wind divergence field was used directly from the reanalysis product. These variables represent the large-scale dynamic state of the ice cover, capturing mechanical deformation and motion that drive lead formation through divergent ().

Ocean surface current speed data were obtained from the ECMWF Ocean Reanalysis System 5 (ORAS5) product (). ORAS5 provides ocean surface current fields at approximately 0.25° spatial resolution, which were also monthly averaged and interpolated to the analysis grid. Ocean currents play a key mechanistic role in lead formation, as mobile sea-ice is continuously subjected to current-induced tractions that generate internal stress, weaken the ice mechanically, and promote divergent ice motion ().

Finally, the temporal descriptor, which represents the month, is included to represent the seasonal evolution of ice cover and ice thickness throughout the winter period. This descriptor captures the systematic progression of thermodynamic and ice-dynamic conditions from early winter (April) through late winter (September), allowing the model to account for seasonal shifts in the relative importance of different atmospheric, ice-dynamic and oceanic predictors. However, the month descriptor is excluded as a predictor in the coastal model performance analysis (Sect. 3.5–3.6).

In this study, we use an RF regression model () to model LF in each grid cell as a function of nine input predictor variables described in Sect. 2.2. RF is an ensemble learning method based on two key principles of randomness: bootstrap aggregating (bagging) and feature selection at each split. This combination allows the RF model to capture complex non-linear relationships and interactions among predictors without requiring predefined assumptions, making it well-suited for representing the non-linear processes driving sea-ice lead and coastal polynya formation.

The RF algorithm builds multiple independent decision trees using bootstrap samples of the training data. Each tree is trained on a randomly selected subset of observations drawn with replacement from the original training data. This bootstrap sampling results in about one-third of the training samples being used in each tree, with the remaining two-thirds (out-of-bag observations) available for internal validation. The aggregation of many diverse trees through averaging significantly reduces variance and overfitting risk while maintaining predictive accuracy.

At each node split during tree construction, the RF algorithm randomly selects a subset of predictor variables rather than evaluating all available features. In this study, we followed standard recommendations for regression problems and set the number of candidate predictors at each split to the square root of the total number of predictors (). This feature of randomness further decorrelates the decision trees by encouraging each tree to explore different combinations of predictors, preventing single dominant predictors from dominating all trees. Node splits were optimized using mean squared error as the dissimilarity metric.

The model was implemented in Python 3.10 using the scikit-learn library (; version 1.3). The RF configuration employed 100 decision trees, ensuring sufficient ensemble diversity while maintaining computational efficiency as increasing the number of trees beyond this number provided no measurable improvement in performance. Each tree was allowed a maximum depth, with most leaves containing only a single observation from the training data, and the minimum number of samples required to split an internal node was set to two.

The dataset was randomly split into training and testing subsets in an 80:20 ratio, ensuring balanced representation across months and regional sectors. The RF model was trained on 80 % training subset and subsequently evaluated on the independent 20 % testing subset. Model performance was evaluated using standard regression metrics such as bias (i.e. mean error), mean absolute error (MAE), root mean square error (RMSE), and the Pearson correlation coefficient (r).

To identify the most influential predictors for sea-ice lead and coastal polynya formation, we assessed predictor importance in the RF model using permutation importance (). This method quantifies the increase in prediction error when the values of a single predictor are randomly shuffled, effectively breaking its association with the target variable while preserving relationships among all remaining predictors. Relative importance is calculated on held-out test data, avoiding overfitting bias, and accounts for both direct and interaction effects among predictors. It also allows a fair comparison between continuous and categorical (e.g., month) predictors (see Sect. 2.2).

To compare the influence of different predictors, we calculated the relative importance by normalizing the individual importance scores (measured as the increase in mean square error) so that they sum to 100 %. This analysis enabled the identification of the dominant atmospheric, oceanic, and ice-dynamic predictors of lead variability across the Southern Ocean. We note that the RF permutation importance values used in this study quantify only the magnitude of each predictor’s contribution to model skill, not the sign of its relationship with LF. The resulting relative importance distributions and their regional patterns are presented in Sect. 3.3.

To further evaluate the incremental contribution of predictor categories, we conducted a stepwise predictor addition experiment. The analysis began with a baseline RF model including only atmospheric variables (10 m wind speed, wind divergence, zonal u wind, meridional v wind, and sea-level pressure) and progressively added sea-ice kinematic variables (ice velocity and divergence), ocean current speed, and a temporal descriptor (month). Each nested model was trained on 80 % of the data and evaluated on the independent 20 % testing subset. At each stage, model skill was assessed using MAE, RMSE, and r. Differences in these metrics between consecutive models quantified the marginal gain attributable to the newly added variables. This incremental framework isolates the added value of each predictor group, revealing how these variables collectively improve LF reconstruction. All data preprocessing, model training, and evaluation were performed in Python 3.10, and the performance metrics for all nested models are summarized in Fig. 2. All results presented in Sect. 3 are derived from model predictions and evaluated using an independent test subset (20 % of the dataset) that was withheld during model training.

We here used the RF in different configurations: First, to evaluate lead drivers on a large scale, we average target and predictor data into 2° latitude × 5° longitude bins and then run the RF model for data from the entire Southern Ocean and five sub-sections (Sect. 3.1–3.4). In this configuration, a seasonal descriptor (month) is included. Second, we exemplarily identify smaller areas, mostly in coastal regions, to address changes in main lead drivers depending on the region and ice regime (Sect. 3.5). Third, we derive target and predictor variables as averages for four distance categories from the coast (0–50, 50–150, 150–300, > 300 km) to address coastal process explicitly and to evaluate how the RF performance and the main drivers change with increasing distance from the coast (Sect. 3.6). In the last two configurations (Sect. 3.5–3.6), target and predictors are used on a 10×10 km² grid.

The RF regression model demonstrates skill in reproducing observed LF using nine predictor variables based on an independent test data sample across the Southern Ocean during winter months (April–September) for the period 2003–2023.

The model captures the large-scale pan-Antarctic pattern of LF and is broadly consistent with the 20-year observational climatology presented in . Reconstructed spatial maxima occur in the same general regions as the main lead-prone zones along the coastal margins, particularly in the Weddell Sea, Prydz Bay, Pacific sector and Ross Sea coastal zones, where LF values exceed 0.18, indicating that leads were present on more than about 18 % of winter days during the study period shown in Fig. 1. The spatial pattern of model bias is shown in Fig. A2 (Appendix), which shows that the largest under-predictions are concentrated along the Antarctic coastal margins, particularly in the Weddell Sea, Prydz Bay, and Ross Sea coastal zones, while the offshore pack ice shows modest over-prediction.

The scatter plot of observed versus predicted LF from the test subset reveals tight clustering around the 1:1 line, particularly for mid-range values between 0.05 and 0.30, suggesting skillful reconstructions for lead frequencies encountered across the Southern Ocean pack ice (Fig. 2a). Density contours in the scatter plots further show that models are more reliable for moderate LF values (0.08–0.20), with a larger spread at both the lowest and highest ends of the distribution. A slight underprediction of strong lead events is evident (LF > 0.20). When evaluated on the test dataset, the model achieved values of r= 0.70, MAE of 0.025, and RMSE of 0.034 for the Southern Ocean in Fig. 2a. For comparison, the model without the inclusion of the polynya dataset (LFWP) achieves r= 0.66 (shown in red) at the pan-Antarctic scale, demonstrating that the representation of coastal polynya activity provides a slight improvement in overall model performance.

Regional validation reveals considerable spatial variability in model performance across the five Southern Ocean sectors (Fig. 2b–f). The Indian Ocean sector shows the highest performance (r= 0.82), followed by the Pacific Ocean (r= 0.79) and the Weddell Sea and Ross Sea (both r= 0.73). The Bellingshausen–Amundsen Seas show the comparatively lowest correlation (r= 0.63). The improvement from including the polynya dataset is most pronounced in the Ross Sea, where r increases from 0.64 to 0.73, and in the Weddell Sea, where r increases from 0.71 to 0.73. In the Indian and Pacific Ocean sectors, the improvement is more modest (r increasing from 0.81 to 0.82 and from 0.77 to 0.79, respectively).

Figure 3 shows that the RF model captures the seasonal evolution of monthly mean LF throughout the winter season, 2003–2023. At the pan-Antarctic scale, predicted monthly LF closely follows observations from April to September, capturing both the timing and magnitude of the rise toward mid-winter and the subsequent late-season decline. Predicted LF increases from April to a June peak near 0.14, before decreasing slightly toward September at lowest LF value (Fig. 3a). Regional temporal evolution shows close agreement across sectors (Fig. 3b–f). The Weddell Sea displays a clear mid-winter maximum, while the Indian Ocean sector shows a gradual decline from April to September, with both observed and predicted values remaining in close agreement throughout winter (Fig. 3c). Seasonal mean LF value gaps are higher between observed and predicted LF in the Pacific sector and in May in the Ross Sea (Fig. 3d–e). The Bellingshausen–Amundsen Seas show the lowest seasonal values in September, and maintain moderate LF values throughout winter (Fig. 3f). Across all sectors, the generally close match between predicted and observed monthly means indicates that the model captures much of the large-scale temporal variability of LF, although noticeable discrepancies remain in some regions and months.

Figure 4 presents how different predictor variables contribute to overall model performance based on the test subset. For the Southern Ocean, wind speed alone provides limited skill (r∼0.10), and performance improves steadily as predictors are progressively introduced. The sequential addition of zonal u wind and meridional v wind yields the largest step-gains, raising r to ∼0.45 and ∼0.55, respectively, highlighting the important role of directional wind (zonal u and meridional v winds) forcing in driving lead formation across the Southern Ocean. Ice velocity and ice divergence progressively improve correlation to r∼0.65, ocean current speed to r∼0.68, and the final inclusion of the temporal descriptor (month) brings the correlation to its full value (r= 0.70) at the Southern Ocean scale (Fig. 4a).

Regions show pronounced differences in how predictor additions affect skill across regions. The Indian Ocean sector (Fig. 4c) demonstrates particularly sharp performance gains from the addition of zonal u and meridional v wind components, with correlation increasing steeply from r∼0.25 after adding wind divergence to r∼0.65 after meridional v wind is included, potentially reflecting the strong directional wind control on lead formation in this sector. The Pacific Ocean (Fig. 4d) shows a similar pattern, ultimately reaching r= 0.79 with the complete predictor set. The Ross Sea (Fig. 4e) shows more gradual improvement throughout the predictor sequence, reaching a final value of r= 0.73. The Bellingshausen–Amundsen Seas (Fig. 4f) show consistent incremental gains across all predictor groups, ultimately reaching r= 0.63 with the full predictor set.

The largest improvements in both correlation and error metrics typically occur when both directional wind components and ocean current speed are introduced, indicating the coupled nature of the atmosphere–ice–ocean system in driving lead variability. While relative importance (Sect. 3.3) quantifies the independent contribution of each predictor in the full model, the incremental analysis reveals the marginal gain of each predictor group and identifies the minimum predictor set required for skillful LF reconstruction.

Figure 5 summarizes the permutation importance analysis, which reveals the dominant predictors of LF in the RF framework across the Southern Ocean and its regions based on the test sample. At the pan-Antarctic scale, the zonal u wind emerges as the most influential predictor (17.7 %), followed by ocean current speed (13.9 %), wind speed (12.9 %), meridional v wind (12.2 %), and ice divergence (11.2 %). Wind divergence, ice velocity, sea-level pressure, and month provide additional contributions (Fig. 5a).

Regional importance patterns reveal sector-to-sector differences shown in Fig. 5b–f. The Weddell Sea is dominated by zonal u wind (19.9 %) and ocean current speed (16.4 %), with ice divergence (11.9 %), and wind speed (10.8 %) contributing substantially. The Indian Ocean sector displays the strongest zonal wind control, with zonal u wind accounting for 26.8 % of importance, complemented by meridional v wind (15.9 %), current speed (11.6 %), wind divergence (10.5 %), and ice divergence (9 %). The Pacific Ocean shows meridional v wind influence with 29.6 %, followed by zonal u wind (14 %), ice divergence (11.6 %), and current speed (11.1 %). The Ross Sea exhibits more balanced contributions with current speed (17.3 %), zonal u wind (15.6 %), meridional v wind (13.9 %), wind divergence (12.3 %), and ice divergence (10 %), reflecting its dynamical environment. The Bellingshausen–Amundsen Seas are dominated by ocean currents (14.5 %), with substantial contributions from meridional v wind (14.4 %), wind speed (11.8 %), month (11.8 %), and wind divergence (11.5 %).

Among the predictors at the pan-Antarctic scale, the directional wind components, ocean current speed, and ice divergence show the strongest influences, which point towards the essential roles of atmospheric wind forcing, oceanic traction, and sea-ice deformation in shaping lead and coastal polynya variability (see Sect. 4). Overall, the permutation importance patterns indicate that lead formation arises from coupled non-linear interactions among multiple physical processes rather than from any single dominant predictor, and that the relative importance of these processes varies considerably across the five regional sectors of the Southern Ocean.

Figure 6 presents the test subset monthly model performance metrics and top-5 predictors for each winter month (April–September, 2003–2023) across the Southern Ocean and regional sectors. The month is excluded as a predictor in this single-month analysis. This demonstrates the monthly and regional evolution of model skill and progressive shifts in predictor dominance throughout the winter.

At the pan-Antarctic scale (Fig. 6a), the model performance varies across months, with r ranging between approximately 0.60 and 0.70, with April showing the highest correlation, while August shows comparatively lower skill. The MAE remains relatively stable across months at approximately 0.020–0.030. The dominant predictors show a relatively balanced distribution throughout winter, such as zonal u wind (∼ 18 %–21 %), current speed (∼ 15 %–19 %), and meridional v wind (∼ 13 %–16 %) contributing consistently across all months. Overall, wind field and current speed are higher in importance, indicating a major role of wind-ocean forcing on lead and coastal polynya formation across all winters in the Southern Ocean.

The Weddell Sea (Fig. 6b) exhibits notable seasonal variation in both model skill and predictor importance. In April and May, the model achieves its correlation (r∼0.70 and 0.60), and for both months ice divergence (∼ 20 %–25 %) is the leading contributor. From June to August, current speed increase in prominence as leading drivers, reflecting stronger oceanic forcing during mid-winter. By September, zonal u wind importance rises markedly to approximately 28 %, while the model correlation also recovers to approximately 0.70, indicating stronger zonal u wind control on lead formation in late winter under a thickening ice pack.

The Indian Ocean sector (Fig. 6c) is majorly dominated by v and u winds throughout winter (∼ 15 % in April, rising again to ∼ 42 % in September), while ice divergence in April and wind divergence in July provide primary contributions. Model performance is highest in April and September (r∼0.80), with a slight reduction during mid-winter months. The Pacific Ocean sector (Fig. 6d) shows consistent dominance of meridional v wind accounting for approximately 22 %–31 % of total predictor importance across all months. Model skill remains relatively stable across months (r∼ 0.60–0.75), with the highest correlation in April and September.

The Ross Sea (Fig. 6e) displays more variable predictor importance throughout winter. In April, zonal u wind (∼ 25 %) and current speed (∼ 15 %) are the leading predictors (r∼ 0.75). During May, current speed increases in importance (∼ 25 %), reflecting stronger mechanical deformation during early winter ice consolidation. From June to August, wind divergence and ice divergence together contribute approximately 35 %–40 %, with wind divergence peaking in August (∼ 23 %). By September, ice divergence and zonal u wind regain prominence, suggesting a return to wind-driven control in late winter.

The Bellingshausen–Amundsen Seas (Fig. 6f) show the variable model performance, with r ranging from approximately 0.65 in April to approximately 0.40 in July before recovering slightly toward September. In April, current speed (∼ 19 %), zonal u wind (∼ 18 %), and meridional v wind (∼ 17 %) are the leading predictors. Through May and June, meridional v wind and current speed increase in importance, while from August onward, the predictor importance becomes more evenly distributed among current speed and ice divergence.

Overall, these patterns suggest that lead and coastal polynya formation result from distinct combinations of atmospheric, oceanic, and ice-dynamic physical processes that vary across sectors and evolve throughout the winter season.

To investigate lead and coastal polynya variability and the sensitivity of dominant physical drivers based to regional conditions, we exemplarily identified four coastal regions, shown in Fig. 7, for which the regionally averaged LF was reconstructed from the same predictors as above. These boxes were selected to identify key coastal regions where the model demonstrates statistically significant predictive skill (p<0.05 with r>0.60), and where leads and coastal polynya formation are governed by distinctly different main physical drivers (see Fig. 9). These regions span both the East Antarctic sector (boxes 1–2) and the West Antarctic sector (boxes 3–4), covering the East Weddell coastal zone (box 1, Lazarev Sea), the East Antarctic coastal margin (box 2, D'Urville Sea), the Bellingshausen–Amundsen Seas coast (box 3, Amundsen Sea), and the western Weddell coastal zone (box 4, West Weddell Sea). The observed mean LF for the wintertime period 2003–2023 in these coastal regions is generally high (>0.15), including coastal polynya activity along the Antarctic continental margins (; ).

Model performance within the four coastal regions is evaluated in Fig. 8, which shows scatter plots of observed versus predicted monthly mean LF for the period April–September, 2003–2023. Figure 8 shows the model performance when the polynya dataset of is included (LF, blue dots) and when it is excluded (LFWP, orange dots), allowing a direct assessment of the added value of polynya information in each coastal region. The highest correlation is found in box 3 in the Amundsen Sea (r= 0.83), followed by box 4 in West Weddell Sea (r= 0.71), box 1 along the Lazarev Sea (r= 0.65), and box 2 (r= 0.64) in the D'Urville Sea. Boxes of similar size in the Ross Sea did not provide significant LF reconstruction and are therefore not shown here. However, it must be mentioned that this strongly depends on the location, boundaries, and size of the box.

The comparison between LF and LFWP results reveals regional differences in the added value of the polynya dataset. An improvement is found in box 3, where including the polynya dataset raises r from 0.73 (LFWP) to 0.83 (LF), suggesting that the investigated drivers are well suited to explain polynya activity in this region. In contrast, box 2 in the D'Urville Sea shows a notable decrease from LFWP (r= 0.84) to LF (r= 0.64), indicating that the polynya dataset here rather blurs the connection between predictors and the target variable, and that the pack-ice lead signal already captures the dominant variability in this region. Boxes 1 and 4 show broadly comparable performance with and without the polynya dataset, suggesting that both pack-ice leads and coastal polynyas contribute comparably to the observed LF variability in these regions.

The predictor relative importance analysis for each coastal region (Fig. 9) reveals pronounced spatial heterogeneity in the dominant drivers of coastal lead variability, with each box characterized by a distinctly different main driver (see also Fig. A3). Ocean current speed is the main predictor in box 1 (30.4 %), highlighting a critical role of coastal ocean current dynamics in driving lead formation in this region. In box 2, meridional v wind emerges as the leading driver (29.4 %), indicating that the meridional wind field plays a particularly important role along this section of the Pacific coastal margin, where offshore katabatic winds are likely to drive divergent ice motion and coastal lead formation. Wind divergence is found to be the dominant predictor in box 3 (36.7 %) along the Amundsen Sea coast. Zonal u wind is the dominant predictor in box 4 (34.1 %), reflecting the importance of zonal wind forcing perpendicular to the Antarctic Peninsula in controlling lead opening along the western Weddell Sea coast.

The presented results so far indicate that the individual contribution of drivers for lead and coastal polynya formation shows a pronounced spatial variability with a strong dependence on local coastal geometries, as well as distance to coast, distance to marginal ice zone and probably also local bathymetry. To better focus on the effect of coastal distance, Fig. 10 further summarizes the overall RF model skill and dominant predictor importance as a function of distance from the Antarctic coast, across four distance categories (0–50, 50–150, 150–300, and > 300 km). The model correlation in LF is highest in the nearest coastal zone (0–50 km, r= 0.89), where ocean current speed and ice velocity are the leading drivers, and decreases progressively with distance from the coast. Notably, the main and secondary drivers remain consistent across all distance categories for both LF and LPWP except the 50–150 km zone: in the 50–150 km zone, ice divergence becomes the secondary predictor while wind speed emerges as the main predictor for LF, while meridional v wind appears as a co-dominant main predictor for LFWP. In the 150–300 km zone, meridional v wind dominates alongside wind speed as the secondary driver. Beyond 300 km from the coast, ice divergence and ice velocity remain the primary controls, though the overall model correlation drops to approximately r∼ 0.46 for LF. Importantly, almost the same dominant predictors are found for both LF and LFWP dataset, except in the 50–150 km zone, but model skill increases substantially in the nearest coastal zone (Fig. 10), rising from r= 0.73 (LFWP) to r= 0.89 (LF) in the 0–50 km band, while it decreases at larger coastal distances (from r∼ 0.80 (LFWP) to r ∼ 0.62 (LF)) in the 50–150 km band, demonstrating that the representation of coastal polynya activity significantly improves model skill in the regions closest to the Antarctic coastline where polynya and lead dynamics are most tightly coupled whereas model performance decreases as the distance increases from the coast, potentially indicating a lower accuracy of the polynya area data in the pack ice.

Figure 11 further extends this analysis by presenting the dominant driver and model performance for each region/distance category combination, for both LF (Fig. 11a) and LFWP (Fig. 11b), with grey cells indicating no statistically significant relationship at the 0.1 level. Regional patterns reveal sector-to-sector variability in both the main drivers and the statistical significance of results across distance categories. In the nearest coastal zone, current speed is the main driver in the Weddell Sea (42 %, r= 0.52), Indian Ocean (30 %, r= 0.79), and Pacific Ocean (29 %, r= 0.86). In the Weddell Sea, no significant relationships are found farther from the coast. In the Indian Ocean, the dominant control shifts from current speed near the coast to ice divergence (21 %–46 %) at larger distances. In the Pacific Ocean, meridional v wind (17 %–27 %) becomes more important at intermediate distances, likely reflecting the influence of offshore winds. In the Ross Sea, significant results appear only in the nearest and outermost distance bands, where current speed (25 %, r= 0.59) near the coast and ice divergence (55 %, r= 0.79) dominate beyond 300 km. The Bellingshausen–Amundsen Seas are the only region where meridional v wind is the main driver close to the coast (29 %), suggesting an important role of offshore wind forcing in this sector.

The dominance of drivers for LF and LFWP is generally similar across most regions and distance bands. The largest difference is found in the nearest coastal zone, where including polynyas improves the model skill at the Southern Ocean scale in the 0–50 km band. In the Bellingshausen–Amundsen Seas, the dominant driver also changes from meridional v wind in LF to current speed in LFWP. This suggests that the pack-ice lead activity in this region is more strongly linked to ocean currents, whereas offshore winds play a larger role in controlling lead and coastal polynya variability.