Articles | Volume 19, issue 3
https://doi.org/10.5194/os-19-887-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-19-887-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Improving the thermocline calculation over the global ocean
Emmanuel Romero
Departamento de Oceanología, Instituto Politécnico Nacional–Centro Interdisciplinario de Ciencias Marinas (IPN–CICIMAR), Av. IPN s/n, La Paz, B.C.S., 23096, Mexico
Leonardo Tenorio-Fernandez
CORRESPONDING AUTHOR
Departamento de Oceanología, Instituto Politécnico Nacional–Centro Interdisciplinario de Ciencias Marinas (IPN–CICIMAR), Av. IPN s/n, La Paz, B.C.S., 23096, Mexico
CONAHCyT, Consejo Nacional de Humanidades Ciencia y Tecnología, Av. Insurgentes Sur 1582, Col. Crédito Constructor, Alcaldía Benito Juárez, C.P. 03940, Mexico City, Mexico
Esther Portela
School of Environmental Sciences, University of East Anglia, Norwich, UK
Univ. Brest, Laboratoire d'Océanographie Physique et Spatiale, CNRS, IRD, Ifremer, Plouzané, France
Institute for Marine and Antarctic Studies, University of Tasmania, Hobart 7001, Australia
Jorge Montes-Aréchiga
Departamento de Física, Universidad de Guadalajara, Gral. Marcelino García Barragán 1421, Olímpica, 44430 Guadalajara, Jal, Mexico
Laura Sánchez-Velasco
Departamento de Oceanología, Instituto Politécnico Nacional–Centro Interdisciplinario de Ciencias Marinas (IPN–CICIMAR), Av. IPN s/n, La Paz, B.C.S., 23096, Mexico
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Due to the need to obtain a greater amount of data in less time in areas with little in situ hydrographic data, an algorithm based on cluster analysis is proposed. This algorithm allows real-time quality control of Argo data which has patterns similar to data in delayed mode. To test this, a study area of high scientific interest but with little concentration of in situ data was chosen. In this area 80 % of the data normally discarded because of salinity drifts was recovered.
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Oceanic dissolved oxygen (DO) is fundamental for ocean biogeochemical cycles and marine life. To ease the computation of the ocean oxygen budget from in situ DO data, mapping of data on a regular 3D grid is useful. Here, we present a new DO gridded product from the Argo database. We compare it with existing DO mapping from a historical dataset. We suggest that the ocean has generally been losing oxygen since the 1980s, but large interannual and regional variabilities should be considered.
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Due to the need to obtain a greater amount of data in less time in areas with little in situ hydrographic data, an algorithm based on cluster analysis is proposed. This algorithm allows real-time quality control of Argo data which has patterns similar to data in delayed mode. To test this, a study area of high scientific interest but with little concentration of in situ data was chosen. In this area 80 % of the data normally discarded because of salinity drifts was recovered.
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Short summary
In this study, we present a methodology to locate the minimum and maximum depths of the strongest thermocline, its thickness, and its strength by adjusting the sigmoid function to the temperature profiles in the global ocean. The results of the methodology are compared with the results of other methods found in the literature, and an assessment of the ocean regions where the adjustment is valid is provided. The method proposed here has shown to be robust and easy to apply.
In this study, we present a methodology to locate the minimum and maximum depths of the...