In the Gulf of Mexico, the Loop Current (LC) is a salient oceanographic feature, being a warm-water current originating from the Caribbean Sea, traveling northward, protruding into the Gulf, and eventually departing through the Florida Strait. This dynamic stream influences the intensity of hurricanes, the vitality of coastal and estuarine ecosystems, the efficiency of petroleum exploration and extraction, and the prosperity of the fishing sector. These intricacies, in turn, profoundly affect the region’s overall economic framework. Given its overarching impact, the compelling question arises: How can we accurately forecast the likelihood of future Loop Current phenomena and anticipate the evolving oceanic conditions within the Gulf? We develop and utilize differential Dynamical-Orthogonal primitive-equations (DO-PEs) for efficient and high-resolution stochastic ocean forecasting in regions with complex ocean dynamics. We can then perform the equivalent of massive ensemble simulations of 10⁶ members in a stochastic subspace while ensuring that the initial statistics respect the physical processes, modeled complex dynamics, and uncertainty in initial conditions of the Gulf of Mexico. We demonstrate the convergence of DO probabilistic forecasts as the number of modes increases, effectively replicating full-order Monte Carlo ensemble simulations. Our analysis quantifies predictability boundaries, predictive capacities, and variability from initial and boundary conditions. Additionally, we formulate ensemble hindcasts for varying periods and LC states. We complete some targeted multi-resolution modeling experiments. We also compute Mutual Information (MI) and correlation fields to determine the most informative observations and their impacts on ocean predictions. Ultimately, our work can be used to provide rich probabilistic forecasts and inform the design of shorter- and longer-term observing campaigns.