Abstract

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.

Highlights

  • Estimating the temperature and salinity ocean state is critical for documenting the evolution of the ocean and its role in the present climate

  • We present CORA (Coriolis Ocean dataset for ReAnalysis), a dataset distributed by the Copernicus Marine Environment Monitoring Service and produced by Coriolis

  • The CORA dataset is an extensive dataset of temperature and salinity measurements

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Summary

Introduction

Estimating the temperature and salinity ocean state is critical for documenting the evolution of the ocean and its role in the present climate. Et al.: The CORA 5.2 dataset requires the production of two datasets: a near-real-time validated dataset distributing the profiles within days after collection and a delayed-time validated dataset covering in year n the historical period up to year n − 1 This choice, made in the early versions of CORA, has been retained in the latest one that we describe here. In addition to the influence of the mapping method and the baseline climatology (Abraham et al, 2013; Cheng and Zhu, 2015; Boyer et al, 2016; Gouretski, 2018), the data validation performed on in situ measurements has a direct influence on the estimation of global ocean indicators such as GOHC, global freshwater content, and sea level height (Abraham et al, 2013; Gouretski, 2018).

Data providers
Dataset description
Data quality control
Near-real-time validation
Delayed-time-mode validation tests
Data file consistency test
Level disorder and duplicated levels
Global range
Findings
Conclusions
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