  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.
