2009 - 2020年GOSAT和OCO-2卫星观测的全球陆地1°XCO2制图数据集

IF 4.2 3区 地球科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Mengya Sheng, L. Lei, Z. Zeng, Weiqiang Rao, Hao Song, Changjiang Wu
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引用次数: 11

摘要

全球大气二氧化碳(CO2)浓度制图数据可以帮助我们更好地了解CO2的时空变化及其驱动因素,从而为减排和控制行动提供支持。测量大气二氧化碳的温室气体卫星,如温室气体观测卫星(GOSAT)和轨道碳观测站(OCO-2),自2009年以来一直提供全球柱平均干空气中二氧化碳摩尔分数(XCO2)的观测数据。然而,这些XCO2的提取在空间和时间上是不规则的,并且存在许多间隙。本文利用2009年4月至2020年12月期间GOSAT和OCO-2的XCO2检索数据,基于地理统计学方法,对全球时空连续XCO2数据集(Mapping-XCO2)进行了映射,填补了这些数据空白。数据集覆盖56°S至65°N、169°W至180°E的地理范围,空间网格间隔为1°,时间间隔为3天。绘制的XCO2值的不确定度一般小于百万分之1.5。Mapping-XCO2揭示的全球XCO2的时空特征与CarbonTracker获得的模式数据相似。与地面观测相比,总体标准偏差为1.13 ppm。结果表明,该长期数据集可用于研究全球大气XCO2的时空变化,并可为碳循环和人为CO2排放相关研究提供支持。该数据集可在http://www.doi.org/10.7910/DVN/4WDTD8和https://www.scidb.cn/en/detail?dataSetId=c2c3111b421043fc8d9b163c39e6f56e上获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Global land 1° mapping dataset of XCO2 from satellite observations of GOSAT and OCO-2 from 2009 to 2020
ABSTRACT A global mapping data of atmospheric carbon dioxide (CO2) concentrations can help us to better understand the spatiotemporal variations of CO2 and the driving factors of the variations to support the actions for emissions reduction and control. Greenhouse gases satellites that measure atmospheric CO2, such as the Greenhouse Gases Observing Satellite (GOSAT) and Orbiting Carbon Observatory (OCO-2), have been providing global observations of the column averaged dry-air mole fractions of CO2 (XCO2) since 2009. However, these XCO2 retrievals are irregular in space and time with many gaps. In this paper, we mapped a global spatiotemporally continuous XCO2 dataset (Mapping-XCO2) using the XCO2 retrievals from GOSAT and OCO-2 during the period from April 2009 to December 2020 based on a geostatistical approach that fills those data gaps. The dataset covers a geographic range from 56° S to 65° N and 169° W to 180° E for a 1° grid interval in space and 3-day time interval. The uncertainties of the mapped XCO2 values are generally less than 1.5 parts per million (ppm). The spatiotemporal characteristics of global XCO2 that are revealed by the Mapping-XCO2 are similar to the model data obtained from CarbonTracker. Compared to the ground observations, the overall standard bias is 1.13 ppm. The results indicate that this long-term Mapping-XCO2 dataset can be used to investigate the spatiotemporal variations of global atmospheric XCO2 and can support studies related to the carbon cycle and anthropogenic CO2 emissions. The dataset is available at http://www.doi.org/10.7910/DVN/4WDTD8 and https://www.scidb.cn/en/detail?dataSetId=c2c3111b421043fc8d9b163c39e6f56e.
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来源期刊
Big Earth Data
Big Earth Data Earth and Planetary Sciences-Computers in Earth Sciences
CiteScore
7.40
自引率
10.00%
发文量
60
审稿时长
10 weeks
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