Glider observations of thermohaline staircases in the tropical North Atlantic using an automated classifier

Rollo, Callum ORCID: https://orcid.org/0000-0002-5134-7886, Heywood, Karen J. ORCID: https://orcid.org/0000-0001-9859-0026 and Hall, Rob A. ORCID: https://orcid.org/0000-0002-3665-6322 (2022) Glider observations of thermohaline staircases in the tropical North Atlantic using an automated classifier. Geoscientific Instrumentation, Methods and Data Systems, 11 (2). 359–373. ISSN 2193-0856

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Abstract

Thermohaline staircases are stepped structures of alternating thick mixed layers and thin high-gradient interfaces. These structures can be up to several tens of metres thick and are associated with double-diffusive mixing. Thermohaline staircases occur across broad swathes of the Arctic and tropical and subtropical oceans and can increase rates of diapycnal mixing by up to 5 times the background rate, driving substantial nutrient fluxes to the upper ocean. In this study, we present an improved classification algorithm to detect thermohaline staircases in ocean glider profiles. We use a dataset of 1162 glider profiles from the tropical North Atlantic collected in early 2020 at the edge of a known thermohaline staircase region. The algorithm identifies thermohaline staircases in 97.7 % of profiles that extend deeper than 300 m. We validate our algorithm against previous results obtained from algorithmic classification of Argo float profiles. Using fine-resolution temperature data from a fast-response thermistor on one of the gliders, we explore the effect of varying vertical bin sizes on detected thermohaline staircases. Our algorithm builds on previous work by adding improved flexibility and the ability to classify staircases from profiles with noisy salinity data. Using our results, we propose that the incidence of thermohaline staircases is limited by strong background vertical gradients in conservative temperature and absolute salinity.

Item Type: Article
Additional Information: Financial support: This research has been supported by the Natural Environment Research Council (grant no. NE/N012070/1) and the European Research Council, H2020 European Research Council (COMPASS (grant no. 741120)).
Faculty \ School: Faculty of Science > School of Environmental Sciences
Depositing User: LivePure Connector
Date Deposited: 25 Oct 2022 09:32
Last Modified: 25 Oct 2022 10:31
URI: https://ueaeprints.uea.ac.uk/id/eprint/89329
DOI: 10.5194/gi-11-359-2022

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