Residence Time of Lagrangian Profiling Floats: Observational Assessment and Numerical Simulation of Station Keeping Ability
Year: 2026
Type: article
Abstract: Autonomous profiling floats have revolutionized global ocean observation, yet their passive drift limits their ability to remain within desired regions. Using observations and simulations with numerical ocean models, we quantify observed and potential residence time (RT) of profiling floats and explore station‐keeping strategies. Analysis of Argo float trajectories, performing fixed Argo‐style sampling, reveals strong spatial variability in RT, from <20 days in western boundary currents to >100 days in subtropical gyre interiors and marginal seas. To quantify RT for alternate sampling behaviors, trajectories are calculated using velocity fields from an assimilative ocean reanalysis model in the dynamically complex western Pacific. A simple selection criterion of choosing to park at the depth of minimum horizontal velocity increases the mean RT by tens to several hundreds of percent, compared with fixed parking at 1,000 m. More sophisticated selection strategies lengthen RT further—such as accounting for flow direction or changing the park depth multiple times during one dive. These control methods provide upper limits on the RT by assuming perfect knowledge of the ocean's velocity profile. These results demonstrate the potential of adaptive park‐depth selection for mission‐specific profiling floats and regional observing applications where enhanced RT is a primary objective. Such strategies are not intended for the core Argo array, for which consistent parking‐depth sampling is important for maintaining the long‐term middepth velocity record.
Source: Earth and Space Science
DOI: 10.1029/2026ea005018
Authors: Barry B. Ma, Zoltan B. Szuts, Thomas B. Curtin
Language: English
Subfield: Ocean Engineering
Field: Engineering
Domain: Physical Sciences
SDG: Life below water
Open Access status: gold
Estimated APC paid: $2,650
Funder: Office of Naval Research
Award: G8895947158
OpenAlex ID: W7221464800
