东北亚海域温度和盐度气候图谱。

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2025-02-04 DOI:10.1038/s41597-025-04543-6
Yong Sun Kim, Soo-Hyun Seok, Jae-Ho Lee, Sung-Dae Kim
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引用次数: 0

摘要

本研究描述了东北亚海域2023 (ANAS23)的月度地图集,水平分辨率为1/10°,垂直高度为73。对于ANAS23,使用简单的克里格插值技术分析了160多万水文剖面,该技术考虑了数据密度及其在每个网格点的协方差,并采用了剖面稳定方法来最大限度地减少对水团结构的破坏。将ANAS23与先前发表的地图集、重复剖面观测和卫星地转流场进行比较,发现ANAS23提供了水团、流、热盐锋和中尺度漩涡的空间分布的可靠描述,同时避免了尖形噪声、垂直不稳定性和人工水域,特别是在大地形特征上。ANAS23可作为评估气候平均场动态及其随气候变化的基线。地图集之间的不确定性仍然很明显,特别是在观测资料稀少的地区,这一事实要求国际合作努力收集合格的水文观测资料,以编制性能更好的区域地图集,从而提高对未来气候的预测技能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Climatological Atlas of Temperature and Salinity for the Northeast Asian Seas.

This study describes a monthly Atlas for the Northeast Asian Seas 2023 (ANAS23) with a 1/10° horizontal resolution and 73 vertical levels. For ANAS23, over 1.6 million hydrographic profiles were analyzed, utilizing a simple kriging interpolation technique, which considers data density and their covariance at each grid point, along with a profile stabilizing method to minimize damage to water-mass structures. Comparison of ANAS23 with previously published atlases, repeated sectional observations, and satellite-based geostrophic current fields reveals that the ANAS23 provides reliable descriptions of the spatial distribution of water masses, currents, thermohaline fronts, and mesoscale eddies while avoiding spike-shape noises, vertical instabilities, and artificial waters, particularly over large-topographic features. The ANAS23 could be utilized as a baseline to assess the dynamic state of climatological mean fields and their changes under evolving climates. The fact that uncertainty among atlases is still apparent, particularly in a region of scarce observations, calls for a collaborative international effort to collect qualified hydrographic observations for a better-performing regional atlas, thus improving predictive skills for future climate.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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