Lagrangian dynamical geography of the Gulf of Mexico

TL;DR

This paper models the surface-ocean Lagrangian dynamics in the Gulf of Mexico using satellite drifter data, identifying regions with weak interactions to inform environmental and resource management.

Contribution

It introduces a Markov-chain framework to delineate the Gulf's dynamical provinces based on satellite data, enhancing understanding of ocean connectivity.

Findings

Identification of almost-invariant attracting sets

Decomposition of the Gulf into weakly interacting provinces

Implications for oil spill and fish larval connectivity

Abstract

We construct a Markov-chain representation of the surface-ocean Lagrangian dynamics in a region occupied by the Gulf of Mexico (GoM) and adjacent portions of the Caribbean Sea and North Atlantic using satellite-tracked drifter trajectory data, the largest collection so far considered. From the analysis of the eigenvectors of the transition matrix associated with the chain, we identify almost-invariant attracting sets and their basins of attraction. With this information we decompose the GoM's geography into weakly dynamically interacting provinces, which constrain the connectivity between distant locations within the GoM. Offshore oil exploration, oil spill contingency planning, and fish larval connectivity assessment are among the many activities that can benefit from the dynamical information carried in the geography constructed here.