Articles | Volume 27, issue 4
Nonlin. Processes Geophys., 27, 501–518, 2020
https://doi.org/10.5194/npg-27-501-2020
Nonlin. Processes Geophys., 27, 501–518, 2020
https://doi.org/10.5194/npg-27-501-2020

Research article 14 Nov 2020

Research article | 14 Nov 2020

Detecting flow features in scarce trajectory data using networks derived from symbolic itineraries: an application to surface drifters in the North Atlantic

David Wichmann et al.

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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by David Wichmann on behalf of the Authors (20 Aug 2020)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (27 Aug 2020) by Ana M. Mancho
RR by Anonymous Referee #2 (08 Sep 2020)
ED: Publish subject to minor revisions (review by editor) (08 Sep 2020) by Ana M. Mancho
AR by David Wichmann on behalf of the Authors (11 Sep 2020)  Author's response    Manuscript
ED: Publish as is (23 Sep 2020) by Ana M. Mancho
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Short summary
The surface transport of heat, nutrients and plastic in the North Atlantic Ocean is organized into large-scale flow structures. We propose a new and simple method to detect such features in ocean drifter data sets by identifying groups of trajectories with similar dynamical behaviour using network theory. We successfully detect well-known regions such as the Subpolar and Subtropical gyres, the Western Boundary Current region and the Caribbean Sea.