Data‐Driven Emulation of Numerically Simulated Baltic Sea Surface Currents With a Deep Convolutional U‐Net: Explainability and Potential Forecast Skill

Ocean models can represent surface circulation at kilometer scales, but their computational cost limits broad experimentation. We present DeepCUN, a deep convolutional encoder–decoder (U‐Net) that emulates daily mean Baltic Sea surface current components on a 1‐nautical‐mile grid. DeepCUN is trained on 2015–2023 reanalysis currents with atmospheric reanalysis forcing. An occlusion‐sensitivity channel ablation indicates that wind provides the dominant predictive information when combined with the antecedent surface‐current state, while additional atmospheric variables contribute negligibly. Accordingly, DeepCUN is configured to use only the prior surface‐current state and the subsequent‐day wind components to predict next‐day surface current fields. On an independent 2024 test year, DeepCUN captures the dominant spatial patterns and temporal variability, with the highest errors concentrated in the southwestern boundary exchange corridor and narrow straits and lower errors across the basin interior, where correlations exceed 0.9 in most locations. For post hoc explainability of this data‐driven mapping, we apply layer‐wise relevance propagation to characterize attributed support and a diagonal Jacobian elasticity metric to quantify local responsiveness, revealing spatially varying reliance on state memory in the southwestern boundary‐influenced region and wind‐modulated adjustment across the basin interior. In addition, to assess the potential forecast skill of this approach, we perform recursive rollouts, which show domain‐mean absolute error increasing smoothly from at 1 day to at 21 days while correlation decreases from to , indicating stable multi‐week behavior under idealized forcing conditions. The framework provides an efficient and interpretable template for configuring, pruning, and stress‐testing reduced‐input emulators of regional surface circulation.