Data collation for climate-cooling gas dimethylsulfide in Antarctic snow, sea ice and underlying seawater.

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2024-10-31 DOI:10.1038/s41597-024-04038-w

Gabrielle Burke, Pat Wongpan, Delphine Lannuzel, Hakase Hayashida

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Abstract

Dimethylsulfide (DMS) is a climatically active volatile sulfur compound found in Earth's oceans and atmosphere that plays an important role in cloud formation. DMS originates from its precursor dimethylsulfoniopropionate (DMSP), which is produced by several classes of phytoplankton. Concentrations of DMS and DMSP in Antarctic sea ice, snow and underlying seawater are not well documented and there is currently no dataset available to find the existing data. The purpose of this project was to compile historical measurements into a publicly available dataset. A total of 220 samples collected since 1992 were compiled using the Antarctic Sea ice Processes and Climate program template, in accordance with the existing datasets for chlorophyll-a, macronutrients, and dissolved iron. Analyses performed on the completed DMS dataset showed that the spatial and temporal coverages are limited; there are barely any measurements in autumn and winter, nor in the Amundsen or Ross seas. These findings provide a basis for future sampling efforts in the Antarctic region.

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南极雪、海冰和底层海水中气候致冷气体二甲基硫化物的数据整理。

二甲基硫醚（DMS）是一种在地球海洋和大气中发现的具有气候活性的挥发性硫化合物，在云的形成过程中发挥着重要作用。DMS 源于其前体二甲基硫代丙酸酯（DMSP），由几类浮游植物产生。南极海冰、雪和底层海水中 DMS 和 DMSP 的浓度没有得到很好的记录，目前也没有数据集可以查找现有数据。该项目的目的是将历史测量数据汇编成一个可公开获取的数据集。根据现有的叶绿素-a、宏量营养素和溶解铁数据集，利用南极海冰过程与气候计划模板，汇编了自 1992 年以来收集的 220 个样本。对已完成的 DMS 数据集进行的分析表明，其空间和时间覆盖范围有限；几乎没有秋季和冬季的测量数据，也没有阿蒙森海或罗斯海的测量数据。这些发现为今后在南极地区开展采样工作提供了依据。

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来源期刊

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.