The CORA 5.2 dataset for global in situ temperature and salinity measurements: data description and validation Tanguy Szekely, Jérôme Gourrion, Sylvie Pouliquen, Gilles Reverdin To cite this version: Tanguy Szekely, Jérôme Gourrion, Sylvie Pouliquen, Gilles Reverdin. The CORA 5.2 dataset for global in situ temperature and salinity measurements: data description and validation. Ocean Science, European Geosciences Union, 2019, 15 (6), pp.1601-1614. 10.5194/os-15-1601-2019. hal-02417887 HAL Id: hal-02417887 https://hal.sorbonne-universite.fr/hal-02417887 Submitted on 18 Dec 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Ocean Sci., 15, 1601–1614, 2019 https://doi.org/10.5194/os-15-1601-2019 © Author(s) 2019. This work is distributed under the Creative Commons Attribution 4.0 License. The CORA 5.2 dataset for global in situ temperature and salinity measurements: data description and validation Tanguy Szekely1, Jérôme Gourrion1, Sylvie Pouliquen2, and Gilles Reverdin3 1Societe Coopérative OceanScope, 115 rue Claude Chape, 29290, Plouzané, Brest, France 2IFREMER, BP 70, Plouzané, 29280, France 3Sorbonne-Université, CNRS/IRD/MNHN (LOCEAN), Paris, France Correspondence: Tanguy Szekely ([email protected]) Received: 17 December 2018 – Discussion started: 21 January 2019 Revised: 9 August 2019 – Accepted: 7 September 2019 – Published: 4 December 2019 Abstract. We present the Copernicus in situ ocean dataset 1 Introduction of temperature and salinity (version 5.2). Ocean subsurface sampling varied widely from 1950 to 2017 as a result of Estimating the temperature and salinity ocean state is critical changes in instrument technology and the development of in for documenting the evolution of the ocean and its role in situ observational networks (in particular, tropical moorings the present climate. To do so, the scientific community relies for the Argo program). Thus, global ocean temperature data on in situ measurements at a global scale and from global coverage on an annual basis grew from 10 % in 1950 (30 % datasets. for the North Atlantic basin) to 25 % in 2000 (60 % for the Among the global datasets, one can cite the World Ocean North Atlantic basin) and reached a plateau exceeding 80 % Database (Boyer et al., 2013; hereafter WOD) and the EN4 (95 % for the North Atlantic Ocean) after the deployment of database (Good et al., 2013; http://www.metoffice.org/, last the Argo program. The average depth reached by the profiles access: May 2018) distributed by the UK Meteorological Of- also increased from 1950 to 2017. The validation framework fice. Here, we present CORA (Coriolis Ocean dataset for is presented, and an objective analysis-based method is de- ReAnalysis), a dataset distributed by the Copernicus Ma- veloped to assess the quality of the dataset validation pro- rine Environment Monitoring Service (hereafter CMEMS) cess. Objective analyses (OAs) of the ocean variability are and produced by Coriolis. CORA differs from these ear- calculated without taking into account the data quality flags lier datasets in terms of choices in the construction and the (raw dataset OA), with the near-real-time quality flags (NRT production of the dataset. Indeed, WOD is validated with dataset OA), and with the delayed-time-mode quality flags the highest quality control methods at 102 vertical levels, (CORA dataset OA). The comparison of the objective analy- whereas the EN4 profiles are limited to a maximum of 400 sis variability shows that the near-real-time dataset managed vertical levels and are automatically validated (Ingleby and to detect and to flag most of the large measurement errors, re- Huddleston, 2007). CORA conversely retains data at the ducing the analysis error bar compared to the raw dataset er- highest vertical resolution. The choice of reducing the num- ror bar. It also shows that the ocean variability of the delayed- ber of levels in the data validation and in the dataset con- time-mode validated dataset is almost exempt from random- struction helps to quickly cluster new measurements in the error-induced variability. dataset and provides easy-to-handle datasets. On the other hand, these methodologies result in a loss of measurements potentially available for the scientific community through the vertical sampling of the profiles or in the data validation. In the construction of CORA, all the measurements available are kept, and then an automatic validation is first performed, followed by a manual and/or individual check (Gaillard et al., 2009; Cabanes et al., 2013). This validation framework Published by Copernicus Publications on behalf of the European Geosciences Union. 1602 T. Szekely et al.: The CORA 5.2 dataset requires the production of two datasets: a near-real-time val- to the community. Coriolis also collects XBT, CTD (con- idated dataset distributing the profiles within days after col- ductivity, temperature, depth), and XCTD measurements lection and a delayed-time validated dataset covering in year from French and European research programs as well as n the historical period up to year n − 1. This choice, made in from the Global Telecommunication System (GTS), Vol- the early versions of CORA, has been retained in the latest untary Ship System (VOS), and subtropical mooring net- one that we describe here. works (TAO/TRITON/RAMA/PIRATA programs from the A global ocean heat content (GOHC) increase has been Pacific Marine Environmental Laboratory – PMEL). A ma- observed on decadal timescales, whether it is in the upper jor effort has also been made to include smaller datasets layers of the ocean (Domingues et al., 2008; Ishii and Ki- in the Coriolis dataset that are available in delayed-time moto, 2009; Levitus et al., 2009), below the thermocline (Von mode, such as ice-tethered profiler (ITP) and CTD profiles Schuckmann and Le Traon, 2011), or in the abyss (Purkey from the ICES program, sea mammal measurements from and Johnson, 2010). In addition to the influence of the map- MEOP (http://www.meop.net, last access: May 2018), and ping method and the baseline climatology (Abraham et al., validated surface drifter data. Delayed-time-mode measure- 2013; Cheng and Zhu, 2015; Boyer et al., 2016; Gouretski, ments have also been downloaded from the Word Ocean 2018), the data validation performed on in situ measurements Database (WOD13) and the French Service Hydrographique has a direct influence on the estimation of global ocean in- de la Marine (SHOM). It should be noted that in the case of a dicators such as GOHC, global freshwater content, and sea profile distributed by Coriolis in real-time mode and by one level height (Abraham et al., 2013; Gouretski, 2018). As an of these datasets in delayed-time mode, the delayed-time- example, differences in the GOHC estimation in the Johnson mode validated profile replaces the real-time-mode profile in et al. (2012) analysis compared to the Lyman et al. (2010) the CORA database. analysis have been shown to result from quality control is- Last, recent comparisons of the CORA profile positions sues. The particular case of expendable bathythermograph with the EN4 dataset (https://www.metoffice.gov.uk/, last ac- (XBT) measurement (Levitus et al., 2009; Cheng et al., 2016) cess: May 2018) have shown that some of the profiles dis- influence on the GOHC estimation is well documented. Sys- tributed in EN4 were not in the CORA previous versions. A tematic errors in other instrument types may also introduce partnership with the EN4 teams allowed us to detect and to systematic biases, leading to biases in the GOHC estimation import most of those profiles. A total of 5 069 864 profiles (Lyman et al., 2006; Willis et al., 2007). The validation of a have been imported in this way, covering the period 1950– quality control method is thus a critical task to ensure that the 2015. However, contrary to the other measurements, the pro- dataset flags are accurate enough to flag erroneous measure- files from the EN4 database are not reported with a pressure ments without biasing the dataset. The uncertainty surround- measurement, but instead with depth and with a maximum ing the quality assessment of a large oceanographic dataset number of reported levels in an individual profile set to 400. being a critical topic in ocean climate studies, we propose The issue of the inhomogeneity in the dataset with respect to here a method of global dataset quality assessment and ap- the vertical sampling will be discussed. ply it to near-real-time validated and delayed-time-mode val- idated datasets. We will first list the data sources of the CORA measure- 3 Dataset description ments in Sect. 2. A description of the CORA data space and time repartition will be reported on Sect. 3. Then, the quality CORA aims to provide a comprehensive dataset of in situ control procedure will be described in Sect. 4. Lastly, grid- temperature and salinity measurements from 1950 to 2017. ded temperature and salinity fields are calculated using an The oceanic temperature and salinity measuring instruments objective mapping that is presented in Sect. 5. The results of have, however, radically changed during the last 70 years. the dataset validation and quality assessment are finally dis- As a result, the origin and characteristics of data distributed cussed in Sect. 6. in the CORA dataset widely vary in time (Fig. 1). Most of the profiles collected prior to 1965 are mechanical bathythermo- graph (MBT) measurements or Nansen casts.