Offshore Freshwater Pathways in the Northern Gulf of Mexico: Impacts of Modeling Choices

Abstract

The Gulf of Mexico is a very productive and economically important system where riverine runoff acts as a linkage between the continental shelf and the open ocean, providing nutrients in addition to freshwater. This work investigates the three-dimensional transport and pathway structure of this river runoff offshore the continental shelf using ensembles of numerical simulations with different configurations regarding grid resolution (mesoscale resolving and submesoscale permitting) and river setup using suites of 5-months long integrations covering nearly 3 years. The riverine forcing is applied only at the surface over an area around the river mouth, a strategy often adopted in numerical studies, or as a meridional flux with a vertical extension. The simulated flow captures the southward offshore transport of river runoff driven by its interaction with the largest mesoscale circulations in the basin, the Loop Current and Loop Current eddies. This pathway is strong and well-document during summer but also active and relevant in winter, despite a less obvious surface signature. The most intense transport occurs primarily at the peripheries of the Loop Current and the detached eddies, and the freshwater is subducted as deep as 600 m around the mesoscale anticyclonic eddies. Submesoscale motions strengthen slightly the spread of freshwater plumes in summer but their contribution is negligible, if not negative, in winter. Differences in the freshwater distribution and transport volume among runs are small and generally less than 10% among ensembles, with overall slightly higher volume of freshwater transported off-shore and at depth in submesoscale permitting runs that include a velocity flux in their riverine input representation.

The Role of Freshwater Forcing on Surface Predictability in the Gulf of Mexico

“…This is more evident for temperature than for salinity. We further investigate how the configuration changes the propagation of freshwater anomalies throughout the water column in a separate study (Liu et al., 2022).…”

Systematic Bias Correction in Ocean Mesoscale Forecasting Using Machine Learning

“…The domain, current system, and an example of LCE formation are presented in Figure 1. Forecasting the LC and LCEs evolution is fundamental to predicting tropical storms and hurricane intensity given that the LC system carries large heat anomalies (e.g., Jaimes et al, 2016), and impacts the transport of nutrients, freshwater and pollutants in the basin (e.g., Liu et al, 2018Liu et al, , 2022 as well, with relevance to ecosystem services, given the relatively low nutrient content of the water carried by the LC system.…”