The CORA 5.2 dataset for global in situ temperature and salinity measurements: data description and validation

Type Article
Date 2019-12
Language English
Author(s) Szekely Tanguy1, Gourrion Jerome1, Pouliquen SylvieORCID2, Reverdin Gilles3
Affiliation(s) 1 : Societe Coopérative OceanScope, 115 rue Claude Chape, 29290, Plouzané, Brest, France
2 : IFREMER, BP 70, Plouzané, 29280, France
3 : Sorbonne-Université, CNRS/IRD/MNHN (LOCEAN), Paris, France
Source Ocean Science (1812-0784) (Copernicus GmbH), 2019-12 , Vol. 15 , N. 6 , P. 1601-1614
DOI 10.5194/os-15-1601-2019
WOS© Times Cited 1
Note Special issue The Copernicus Marine Environment Monitoring Service (CMEMS): scientific advances Editor(s): J. M. Huthnance, P.-Y. Le Traon, A. Melet, M. Tonani, E. Stanev, M. Grégoire, and A. Pascual
Abstract

We present the Copernicus in situ ocean dataset of temperature and salinity (version 5.2). Ocean subsurface sampling varied widely from 1950 to 2017 as a result of changes in instrument technology and the development of in situ observational networks (in particular, tropical moorings for the Argo program). Thus, global ocean temperature data coverage on an annual basis grew from 10 % in 1950 (30 % for the North Atlantic basin) to 25 % in 2000 (60 % for the North Atlantic basin) and reached a plateau exceeding 80 % (95 % for the North Atlantic Ocean) after the deployment of the Argo program. The average depth reached by the profiles also increased from 1950 to 2017. The validation framework is presented, and an objective analysis-based method is developed to assess the quality of the dataset validation process. Objective analyses (OAs) of the ocean variability are calculated without taking into account the data quality flags (raw dataset OA), with the near-real-time quality flags (NRT dataset OA), and with the delayed-time-mode quality flags (CORA dataset OA). The comparison of the objective analysis variability shows that the near-real-time dataset managed to detect and to flag most of the large measurement errors, reducing the analysis error bar compared to the raw dataset error bar. It also shows that the ocean variability of the delayed-time-mode validated dataset is almost exempt from random-error-induced variability.

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