Articles | Volume 22, issue 2
https://doi.org/10.5194/os-22-1377-2026
https://doi.org/10.5194/os-22-1377-2026
Technical note
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28 Apr 2026
Technical note | Highlight paper |  | 28 Apr 2026

A method for quantifying correlation in the shape of oceanographic profile data

Mark Taylor and Stephanie Henson

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Biogeosciences Discuss., https://doi.org/10.5194/bg-2023-182,https://doi.org/10.5194/bg-2023-182, 2023
Revised manuscript not accepted
Short summary

Cited articles

Abbott, M. R. and Letelier, R. M.: Decorrelation scales of chlorophyll as observed from bio-optical drifters in the California Current, Deep-Sea Res. Pt. II, 45, 1639–1667, https://doi.org/10.1016/S0967-0645(98)80011-8, 1998. a
Alhassan, Y., Siekmann, I., and Petrovskii, S.: Mathematical model of oxygen minimum zones in the vertical distribution of oxygen in the ocean, Sci. Rep., 14, 22248, https://doi.org/10.1038/s41598-024-72207-3, 2024. a
Barbieux, M., Uitz, J., Gentili, B., Pasqueron de Fommervault, O., Mignot, A., Poteau, A., Schmechtig, C., Taillandier, V., Leymarie, E., Penkerc'h, C., D'Ortenzio, F., Claustre, H., and Bricaud, A.: Bio-optical characterization of subsurface chlorophyll maxima in the Mediterranean Sea from a Biogeochemical-Argo float database, Biogeosciences, 16, 1321–1342, https://doi.org/10.5194/bg-16-1321-2019, 2019. a
Bock, N., Cornec, M., Claustre, H., and Duhamel, S.: Biogeographical Classification of the Global Ocean From BGC-Argo Floats, Global Biogeochem. Cy., 36, e2021GB007233, https://doi.org/10.1029/2021GB007233, 2022. a, b
Brewin, R. J., Dall'Olmo, G., Gittings, J., Sun, X., Lange, P. K., Raitsos, D. E., Bouman, H. A., Hoteit, I., Aiken, J., and Sathyendranath, S.: A conceptual approach to partitioning a vertical profile of phytoplankton biomass into contributions from two communities, J. Geophys. Res.-Oceans, 127, e2021JC018195, https://doi.org/10.1029/2021JC018195, 2022. a
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Editorial statement
This is the first application of a particular mathematical framework to oceanographic observations, which could potentially be used widely for many applications, for example, to data from moorings, autonomous platforms and ocean models, with possible use in observing system optimisation, data assimilation and the analysis of vertically structured ocean processes.
Short summary
Oceanographic profiles comprise measurements of variables across depths. Here, a method is presented to calculate the correlation between profiling datasets by quantifying profile shape variability. This enables dependencies between multiple variables, and spatial or temporal changes in a single variable, to be described. Two case studies demonstrate the method using profiling data from a stationary mooring and drifting floats.
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