Deep Lagrangian connectivity in the global ocean inferred from Argo floats

TL;DR

This paper introduces a novel nonlinear dynamical systems technique to infer the deep ocean's Lagrangian connectivity from Argo float data, revealing coherent regions at 1500m depth over six years.

Contribution

It applies a dynamic Laplacian method to sparse Argo data, identifying dominant coherent ocean regions and demonstrating its scalability for various scales.

Findings

Identified eight dominant coherent regions at 1500m depth.

Over 90% of floats did not record full six-year trajectories.

Method overcomes data sparsity and float entry/exit issues.

Abstract

We describe the application of a new technique from nonlinear dynamical systems to infer the Lagrangian connectivity of the deep global ocean. We approximate the dynamic Laplacian using Argo trajectories from January 2011 to January 2017 and extract the eight dominant coherent (or dynamically self-connected) regions at 1500m depth. Our approach overcomes issues such as sparsity of observed data, and floats continually leaving and entering the dataset; only 10\% of floats record for the full six years. The identified coherent regions maximally trap water within them over the six-year time frame, providing a distinct analysis of the deep global ocean, and relevant information for planning future float deployment. While our study is concerned with ocean circulation at a multi-year, global scale, the dynamic Laplacian approach may be applied at any temporal or spatial scale to identify coherent structures in ocean flow from positional time series information arising from observations or models.