SDUST2020MGCR: a global marine gravity change rate model determined from multi-satellite altimeter data

IF 11.2 1区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY Earth System Science Data Pub Date : 2024-05-06 DOI:10.5194/essd-16-2281-2024
Fengshun Zhu, Jinyun Guo, Huiying Zhang, Lingyong Huang, Heping Sun, Xin Liu
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Abstract

Abstract. Investigating the global time-varying gravity field mainly depends on GRACE/GRACE-FO gravity data. However, satellite gravity data exhibit low spatial resolution and signal distortion. Satellite altimetry is an important technique for observing the global ocean and provides many consecutive years of data, which enables the study of high-resolution marine gravity variations. This study aims to construct a high-resolution marine gravity change rate (MGCR) model using multi-satellite altimetry data. Initially, multi-satellite altimetry data and ocean temperature–salinity data from 1993 to 2019 are utilized to estimate the altimetry sea level change rate (SLCR) and steric SLCR, respectively. Subsequently, the mass-term SLCR is calculated. Finally, based on the mass-term SLCR, the global MGCR model on 5′ × 5′ grids (SDUST2020MGCR) is constructed by applying the spherical harmonic function method and mass load theory. Comparisons and analyses are conducted between SDUST2020MGCR and GRACE2020MGCR resolved from GRACE/GRACE-FO gravity data. The spatial distribution characteristics of SDUST2020MGCR and GRACE2020MGCR are similar in the sea areas where gravity changes significantly, such as the eastern seas of Japan, the western seas of the Nicobar Islands, and the southern seas of Greenland. The statistical mean values of SDUST2020MGCR and GRACE2020MGCR in global and local oceans are all positive, indicating that MGCR is rising. Nonetheless, differences in spatial distribution and statistical results exist between SDUST2020MGCR and GRACE2020MGCR, primarily attributable to spatial resolution disparities among altimetry data, ocean temperature–salinity data, and GRACE/GRACE-FO data. Compared with GRACE2020MGCR, SDUST2020MGCR has higher spatial resolution and excludes stripe noise and leakage errors. The high-resolution MGCR model constructed using altimetry data can reflect the long-term marine gravity change in more detail, which is helpful in studying seawater mass migration and its associated geophysical processes. The SDUST2020MGCR model data are available at https://doi.org/10.5281/zenodo.10701641 (Zhu et al., 2024).
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SDUST2020MGCR:根据多卫星高度计数据确定的全球海洋重力变化率模型
摘要。研究全球时变重力场主要依赖于 GRACE/GRACE-FO 重力数据。然而,卫星重力数据空间分辨率低、信号失真。卫星测高是观测全球海洋的重要技术,可提供连续多年的数据,从而能够研究高分辨率的海洋重力变化。本研究旨在利用多卫星测高数据构建高分辨率海洋重力变化率(MGCR)模型。首先,利用 1993 年至 2019 年的多卫星测高数据和海洋温度-盐度数据,分别估算测高海平面变化率(SLCR)和立体海平面变化率(SLCR)。随后,计算质量项海平面变化率。最后,在质量项海平面变化率的基础上,应用球谐函数方法和质量负荷理论,构建了 5′×5′ 网格的全球海平面变化率模型(SDUST2020MGCR)。对 SDUST2020MGCR 和根据 GRACE/GRACE-FO 重力数据解析的 GRACE2020MGCR 进行了比较和分析。在日本东部海域、尼科巴群岛西部海域和格陵兰岛南部海域等重力变化较大的海域,SDUST2020MGCR 和 GRACE2020MGCR 的空间分布特征相似。SDUST2020MGCR和GRACE2020MGCR在全球和局部海洋的统计平均值均为正值,表明MGCR正在上升。然而,SDUST2020MGCR 和 GRACE2020MGCR 在空间分布和统计结果上存在差异,这主要归因于测高数据、海洋温盐度数据和 GRACE/GRACE-FO 数据在空间分辨率上的差异。与 GRACE2020MGCR 相比,SDUST2020MGCR 的空间分辨率更高,并排除了条纹噪声和泄漏误差。利用测高数据构建的高分辨率 MGCR 模型能够更详细地反映长期海洋重力变化,有助于研究海水质量迁移及其相关地球物理过程。SDUST2020MGCR模型数据可在https://doi.org/10.5281/zenodo.10701641(Zhu等,2024)。
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来源期刊
Earth System Science Data
Earth System Science Data GEOSCIENCES, MULTIDISCIPLINARYMETEOROLOGY-METEOROLOGY & ATMOSPHERIC SCIENCES
CiteScore
18.00
自引率
5.30%
发文量
231
审稿时长
35 weeks
期刊介绍: Earth System Science Data (ESSD) is an international, interdisciplinary journal that publishes articles on original research data in order to promote the reuse of high-quality data in the field of Earth system sciences. The journal welcomes submissions of original data or data collections that meet the required quality standards and have the potential to contribute to the goals of the journal. It includes sections dedicated to regular-length articles, brief communications (such as updates to existing data sets), commentaries, review articles, and special issues. ESSD is abstracted and indexed in several databases, including Science Citation Index Expanded, Current Contents/PCE, Scopus, ADS, CLOCKSS, CNKI, DOAJ, EBSCO, Gale/Cengage, GoOA (CAS), and Google Scholar, among others.
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