Forces that govern the motion of planetary flows originate from interactions occurring at scales that are orders of magnitude smaller than the flow itself. These mesoscale and sub–mesoscale eddies contribute to large-scale flow mixing, particle entrainment, and tracer transport. Therefore, oceanic simulations must accurately characterize their contributions to capture the flow dynamics. In this work, a stochastic representation of the primitive equations, derived within the framework of modeling under location uncertainty (LU), is proposed. This framework decomposes the velocity into a smooth–in–time large–scale component and a random field, accounting for unresolved mesoscale and sub–mesoscale motions. The LU framework is unique in its derivation, as it is obtained from physical conservation principles through a stochastic version of the Reynolds transport theorem. Two data-driven approaches are developed to model the random noise using proper orthogonal decomposition and dynamic mode decomposition methods. For higher resolution, model based noise as well as mixture of model based and data driven noise with observational constraints are proposed and compared. Numerical simulations demonstrate that the LU framework improves flow prediction for gyre circulation compared to its deterministic counterpart. Enhanced flow mixing, jet characteristics, and tracer transport are observed. Additionally, terms in the LU framework are analyzed for their contribution to structuring the flow.