基于TanSat XCO2反演的全球陆地生态系统碳通量

遥感学报 Pub Date : 2022-01-12 DOI:10.34133/2022/9816536
Hengmao Wang, Fei Jiang, Yi Liu, Dongxu Yang, Mousong Wu, W. He, Jun Wang, Jing Wang, W. Ju, Jing M. Chen
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引用次数: 8

摘要

卫星是中国第一颗温室气体观测卫星。近年来,在提取塔平均CO2干空气摩尔分数(XCO2)方面取得了实质性进展。然而,利用TanSat XCO2反演估算陆地生态系统净交换(NEE)的尝试相对较少。本研究基于GEOS-Chem 4D-Var数据同化系统,利用TanSat XCO2推断2017年4月至2018年3月全球NEE。反演估计全球NEE为- 3.46 PgC年-1,明显高于先前的估计,并提高了对全球大气CO2增长率的估计。从区域上看,我们的逆温显著增加了北部中高纬度地区的碳吸收量,显著增强了热带和南部土地的碳释放,尤其是非洲和印度半岛。北方陆地后汇的增加主要是由于非生长期碳释放减少,热带和南方陆地碳吸收的减少基本发生在全年。对独立CO2观测资料的评价和与以往估算值的比较表明,虽然北部中纬度地区和南温带地区的陆地汇有一定程度的改善,但北部高纬度地区的陆地汇明显高估,热带地区(主要是北非)的陆地汇明显低估。这些结果表明,TanSat XCO2反演在北部高纬度地区可能存在系统的负偏差,而在北非地区可能存在较大的正偏差,需要进一步努力消除这些地区的偏差,以便更好地估计全球和区域新东东。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Global Terrestrial Ecosystem Carbon Flux Inferred from TanSat XCO2 Retrievals
TanSat is China’s first greenhouse gases observing satellite. In recent years, substantial progresses have been achieved on retrieving column-averaged CO2 dry air mole fraction (XCO2). However, relatively few attempts have been made to estimate terrestrial net ecosystem exchange (NEE) using TanSat XCO2 retrievals. In this study, based on the GEOS-Chem 4D-Var data assimilation system, we infer the global NEE from April 2017 to March 2018 using TanSat XCO2. The inversion estimates global NEE at −3.46 PgC yr-1, evidently higher than prior estimate and giving rise to an improved estimate of global atmospheric CO2 growth rate. Regionally, our inversion greatly increases the carbon uptakes in northern mid-to-high latitudes and significantly enhances the carbon releases in tropical and southern lands, especially in Africa and India peninsula. The increase of posterior sinks in northern lands is mainly attributed to the decreased carbon release during the nongrowing season, and the decrease of carbon uptakes in tropical and southern lands basically occurs throughout the year. Evaluations against independent CO2 observations and comparison with previous estimates indicate that although the land sinks in the northern middle latitudes and southern temperate regions are improved to a certain extent, they are obviously overestimated in northern high latitudes and underestimated in tropical lands (mainly northern Africa), respectively. These results suggest that TanSat XCO2 retrievals may have systematic negative biases in northern high latitudes and large positive biases over northern Africa, and further efforts are required to remove bias in these regions for better estimates of global and regional NEE.
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来源期刊
遥感学报
遥感学报 Social Sciences-Geography, Planning and Development
CiteScore
3.60
自引率
0.00%
发文量
3200
期刊介绍: The predecessor of Journal of Remote Sensing is Remote Sensing of Environment, which was founded in 1986. It was born in the beginning of China's remote sensing career and is the first remote sensing journal that has grown up with the development of China's remote sensing career. Since its inception, the Journal of Remote Sensing has published a large number of the latest scientific research results in China and the results of nationally-supported research projects in the light of the priorities and needs of China's remote sensing endeavours at different times, playing a great role in the development of remote sensing science and technology and the cultivation of talents in China, and becoming the most influential academic journal in the field of remote sensing and geographic information science in China. As the only national comprehensive academic journal in the field of remote sensing in China, Journal of Remote Sensing is dedicated to reporting the research reports, stage-by-stage research briefs and high-level reviews in the field of remote sensing and its related disciplines with international and domestic advanced level. It focuses on new concepts, results and progress in this field. It covers the basic theories of remote sensing, the development of remote sensing technology and the application of remote sensing in the fields of agriculture, forestry, hydrology, geology, mining, oceanography, mapping and other resource and environmental fields as well as in disaster monitoring, research on geographic information systems (GIS), and the integration of remote sensing with GIS and the Global Navigation Satellite System (GNSS) and its applications.
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