A global four-dimensional gridded dataset of ocean dissolved oxygen concentration retrieval from Argo profiles

IF 3.3 3区 地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY
Cunjin Xue, Zhenguo Wang, Linfeng Yue, Chaoran Niu
{"title":"A global four-dimensional gridded dataset of ocean dissolved oxygen concentration retrieval from Argo profiles","authors":"Cunjin Xue,&nbsp;Zhenguo Wang,&nbsp;Linfeng Yue,&nbsp;Chaoran Niu","doi":"10.1002/gdj3.251","DOIUrl":null,"url":null,"abstract":"<p>Lack of a long-term time series of dataset with a high spatiotemporal resolution at a global scale poses a great challenge to clarify the characteristics of DOC in space and depth, and its trend in time. Thus, there is an urgent need for the development of a global DOC gridded dataset in space, time and depth. The Biogeochemical Argo (BGC-Argo) provides an important data source for obtaining global DOC, but is limited by irregular spatial sampling locations. Besides, BGC-Argo has shorter time series coverage and fewer profiles compared to Core-Argo. Thus, this manuscript aims at reconstructing the DOC profiles based on the Core-Argo and BGC-Argo profiles and then developing a spatial, temporal and depth-specific gridded DOC dataset, named G4D-DOC. Validation results demonstrate that G4D-DOC has a good overall consistency with WOA18 and GLODAPv2 datasets, particularly at depths of 10 dbar and 1000 dbar, where it surpasses consistency at other standard depths. In addition, compared to WOA18, G4D-DOC has achieved a breakthrough in a temporal resolution from a climatological monthly to monthly, and compared to GLODAPv2, G4D-DOC has achieved a breakthrough in space from irregular discrete locations to regular grids. Further, G4D-DOC can be widely used to conduct the characteristics of DOC in space and depth and its trends at global and regional scales. The metadata of G4D-DOC is as follows: four dimensions mean two dimensions in space (longitude and latitude), one in time and one in depth; data format is standard Hierarchical Data Format Version 4 (HDF4) with a spatial resolution of 1 degree and temporal resolutions of annual, seasonal and monthly intervals at 26 standard layers above 2000 dbar in depth; the spatial coverage is global and the time period is from 2005 to 2022.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":null,"pages":null},"PeriodicalIF":3.3000,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.251","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Geoscience Data Journal","FirstCategoryId":"89","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/gdj3.251","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

Abstract

Lack of a long-term time series of dataset with a high spatiotemporal resolution at a global scale poses a great challenge to clarify the characteristics of DOC in space and depth, and its trend in time. Thus, there is an urgent need for the development of a global DOC gridded dataset in space, time and depth. The Biogeochemical Argo (BGC-Argo) provides an important data source for obtaining global DOC, but is limited by irregular spatial sampling locations. Besides, BGC-Argo has shorter time series coverage and fewer profiles compared to Core-Argo. Thus, this manuscript aims at reconstructing the DOC profiles based on the Core-Argo and BGC-Argo profiles and then developing a spatial, temporal and depth-specific gridded DOC dataset, named G4D-DOC. Validation results demonstrate that G4D-DOC has a good overall consistency with WOA18 and GLODAPv2 datasets, particularly at depths of 10 dbar and 1000 dbar, where it surpasses consistency at other standard depths. In addition, compared to WOA18, G4D-DOC has achieved a breakthrough in a temporal resolution from a climatological monthly to monthly, and compared to GLODAPv2, G4D-DOC has achieved a breakthrough in space from irregular discrete locations to regular grids. Further, G4D-DOC can be widely used to conduct the characteristics of DOC in space and depth and its trends at global and regional scales. The metadata of G4D-DOC is as follows: four dimensions mean two dimensions in space (longitude and latitude), one in time and one in depth; data format is standard Hierarchical Data Format Version 4 (HDF4) with a spatial resolution of 1 degree and temporal resolutions of annual, seasonal and monthly intervals at 26 standard layers above 2000 dbar in depth; the spatial coverage is global and the time period is from 2005 to 2022.

Abstract Image

从 Argo 剖面提取海洋溶解氧浓度的全球四维网格数据集
全球范围内缺乏高时空分辨率的长期时间序列数据集,这对阐明 DOC 在空间和深度上的特征及其在时间上的变化趋势构成了巨大挑战。因此,迫切需要开发一个全球 DOC 空间、时间和深度网格数据集。生物地球化学 Argo(BGC-Argo)为获取全球 DOC 提供了一个重要的数据源,但受限于不规则的空间取样位置。此外,与 Core-Argo 相比,BGC-Argo 的时间序列覆盖范围更短,剖面更少。因此,本稿件旨在基于 Core-Argo 和 BGC-Argo 剖面重建 DOC 剖面,然后建立一个空间、时间和深度特定的网格化 DOC 数据集,命名为 G4D-DOC。验证结果表明,G4D-DOC 与 WOA18 和 GLODAPv2 数据集具有良好的整体一致性,尤其是在 10 dbar 和 1000 dbar 深度,其一致性超过了其他标准深度。此外,与 WOA18 相比,G4D-DOC 在时间分辨率上实现了从气候月度到月度的突破;与 GLODAPv2 相比,G4D-DOC 在空间分辨率上实现了从不规则离散位置到规则网格的突破。此外,G4D-DOC 还可广泛应用于全球和区域尺度的 DOC 空间和深度特征及其变化趋势的研究。G4D-DOC 的元数据如下:四维指空间两维(经度和纬度)、时间一维和深度一维;数据格式为标准分层数据格式第 4 版(HDF4),空间分辨率为 1 度,时间分辨率为年、季和月,深度为 2000 dbar 以上的 26 个标准层;空间覆盖范围为全球,时间段为 2005 年至 2022 年。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Geoscience Data Journal
Geoscience Data Journal GEOSCIENCES, MULTIDISCIPLINARYMETEOROLOGY-METEOROLOGY & ATMOSPHERIC SCIENCES
CiteScore
5.90
自引率
9.40%
发文量
35
审稿时长
4 weeks
期刊介绍: Geoscience Data Journal provides an Open Access platform where scientific data can be formally published, in a way that includes scientific peer-review. Thus the dataset creator attains full credit for their efforts, while also improving the scientific record, providing version control for the community and allowing major datasets to be fully described, cited and discovered. An online-only journal, GDJ publishes short data papers cross-linked to – and citing – datasets that have been deposited in approved data centres and awarded DOIs. The journal will also accept articles on data services, and articles which support and inform data publishing best practices. Data is at the heart of science and scientific endeavour. The curation of data and the science associated with it is as important as ever in our understanding of the changing earth system and thereby enabling us to make future predictions. Geoscience Data Journal is working with recognised Data Centres across the globe to develop the future strategy for data publication, the recognition of the value of data and the communication and exploitation of data to the wider science and stakeholder communities.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信