CODC-S: A quality-controlled global ocean salinity profiles dataset

Details

• Journal Title:
  Scientific Data
• Personal Author:
  Tan, Zhetao ; Zhu, Yujing ; Cheng, Lijing ; Gouretski, Viktor ; Pan, Yuying ; Yuan, Huifeng ; Wang, Zhankun ; Li, Guancheng ; Song, Xinyi ; Zhang, Bin ; Bao, Senliang ; Li, Yuanlong ; Zhu, Jiang
• NOAA Program & Office:
  NESDIS (National Environmental Satellite, Data, and Information Service) ; NCEI (National Centers for Environmental Information)
• Description:
  Changes in the global ocean salinity reflect the evolution of the global hydrological cycle. These secular changes are assessed using seawater salinity profiles obtained during the past ~80 years. Here, we introduce a new global ocean salinity profiles database named CODC-S (the Chinese Academy of Science (CAS) Oceanography Data Center – Salinity component), which encompasses over 11 million in-situ salinity profiles from 1940 to 2023 obtained by means of several instrument types. These salinity profiles are quality-controlled (QC-ed) using a new automated salinity quality control system named CODC-QC-S (the CODC Quality Control system – Salinity component), consisting of 11 distinct quality checks. By applying time-varying, flow-dependent, and topographical-dependent 0.5% and 99.5% quantile thresholds, the CODC-QC-S defines local climatology salinity ranges without the assumption of Gaussian distribution. The CODC-S database, together with the newly proposed QC algorithm, has undergone extensive evaluations, including comparisons with the benchmark data and climatology, as well as analyses of global and basin-scale long-term salinity changes before and after QC. These validations demonstrate that the quality of salinity data in the CODC-S is time-, depth-, region- and instrument type-dependent. The eight-decade quality-homogeneous salinity profiles from the CODC-S database can support diverse oceanographic and climatic research such as monitoring water cycle changes and freshwater/overturning transports.
• Source:
  Scientific Data, 12(1)
• DOI:
  https://doi.org/10.1038/s41597-025-05172-9
• ISSN:
  2052-4463
• Format:
  PDF
• Publisher:
  Springer Science and Business Media LLC
• Document Type:
  Journal Article
• License:
  https://creativecommons.org/licenses/by-nc-nd/4.0
• Rights Information:
  CC BY-NC-ND
• Compliance:
  Submitted
• Main Document Checksum:
  urn:sha-512:3d39e72ca93f2937baa30436ad10f61e3fff8c71cbc3b92f19128522a6c1426f72ff793b9a4d92a6f9a4887f4346e597c5d4b98170b9d0d289b83663c5a48e16
• Download URL:
  https://repository.library.noaa.gov/view/noaa/70816/noaa_70816_DS1.pdf
• File Type:
  [PDF - 11.11 MB ]