Navigation for autonomous underwater vehicles (AUVs) in mid-depth water column is notoriously challenging due to the lack of features for reference. In this paper, we propose a flow-aided cooperative navigation (FACON) strategy to improve the navigation performance of a team of AUVs when neither frequent surfacing nor persistent bottom-locking is available. Preloaded ocean current forecasts are referenced by each individual vehicle as it performs dead-reckoning with an inertial navigation system and measures relative current velocities during navigation. A marginalized particle filter is applied by each AUV to track its location, velocity, sensor biases, and local flow perturbation unresolved by the ocean forecast. Meanwhile, AUVs perform distributed cooperative localization based on relative pose measurements and asynchronized local communication when they enter the communication range of one another. Opportunistic information fusion among AUVs is realized through covariance intersection. The performance of the flow-aided navigation scheme of a single AUV is analyzed within a simulated experiment based on field test data. The feasibility of FACON is discussed through simulation in a turbulent, multi-gyre flow field.