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China's Terrestrial Carbon Sink Over 2010–2015 Constrained by Satellite Observations of Atmospheric CO2 and Land Surface Variables

He, Wei ; Jiang, Fei ; Wu, Mousong LU ; Ju, Weimin ; Scholze, Marko LU ; Chen, Jing M. ; Byrne, Brendan ; Liu, Junjie ; Wang, Hengmao and Wang, Jun , et al. (2022) In Journal of Geophysical Research: Biogeosciences 127(2).
Abstract

The magnitude and distribution of China's terrestrial carbon sink remain uncertain due to insufficient constraints at large scales, whereby satellite data offer great potential for reducing the uncertainty. Here, we present two carbon sink estimates for China constrained either by satellite CO2 column concentrations (XCO2) within the Global Carbon Assimilation System or by remotely sensed soil moisture and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) in addition to in situ CO2 observations within the Carbon Cycle Data Assimilation System. They point to a moderate size of carbon sinks of 0.34 ± 0.14 (mean ± unc.) and 0.43 ± 0.09 PgC/yr during 2010–2015, which are supported by an... (More)

The magnitude and distribution of China's terrestrial carbon sink remain uncertain due to insufficient constraints at large scales, whereby satellite data offer great potential for reducing the uncertainty. Here, we present two carbon sink estimates for China constrained either by satellite CO2 column concentrations (XCO2) within the Global Carbon Assimilation System or by remotely sensed soil moisture and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) in addition to in situ CO2 observations within the Carbon Cycle Data Assimilation System. They point to a moderate size of carbon sinks of 0.34 ± 0.14 (mean ± unc.) and 0.43 ± 0.09 PgC/yr during 2010–2015, which are supported by an inventory-based estimate for forest and soil carbon sink (0.26 PgC/yr) and fall in the range of contemporary ensemble atmospheric inversions (0.25–0.48 PgC/yr). They also agree reasonably well on interannual variations, which reflect the carbon sink anomalies induced by regional droughts in southwest China. Furthermore, their spatial distributions are broadly consistent that of the forest inventory-based estimate, indicating that the largest carbon sinks locate in central and eastern China. Their estimates for forest carbon sink coincide fairly well with the inventory-based estimate across different regions, especially when aggregated to the north and south of China. Although enhanced recently by afforestation, China's carbon sink was also significantly weakened by regional droughts, which were often not fully represented in previous in situ CO2-based inversions due to insufficient observations. Our results suggest that satellite-based atmospheric CO2 and land surface observations are vital in characterizing terrestrial net carbon fluxes at regional scales.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
atmospheric CO concentration, atmospheric inversion, China's carbon sink, data assimilation, multisource satellite observations
in
Journal of Geophysical Research: Biogeosciences
volume
127
issue
2
article number
e2021JG006644
publisher
Wiley
external identifiers
  • scopus:85125142942
ISSN
2169-8953
DOI
10.1029/2021JG006644
language
English
LU publication?
yes
id
656fed0b-ed42-4146-b20d-d9d1b9a5c28c
date added to LUP
2022-06-17 11:48:04
date last changed
2022-06-17 11:48:04
@article{656fed0b-ed42-4146-b20d-d9d1b9a5c28c,
  abstract     = {{<p>The magnitude and distribution of China's terrestrial carbon sink remain uncertain due to insufficient constraints at large scales, whereby satellite data offer great potential for reducing the uncertainty. Here, we present two carbon sink estimates for China constrained either by satellite CO<sub>2</sub> column concentrations (XCO<sub>2</sub>) within the Global Carbon Assimilation System or by remotely sensed soil moisture and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) in addition to in situ CO<sub>2</sub> observations within the Carbon Cycle Data Assimilation System. They point to a moderate size of carbon sinks of 0.34 ± 0.14 (mean ± unc.) and 0.43 ± 0.09 PgC/yr during 2010–2015, which are supported by an inventory-based estimate for forest and soil carbon sink (0.26 PgC/yr) and fall in the range of contemporary ensemble atmospheric inversions (0.25–0.48 PgC/yr). They also agree reasonably well on interannual variations, which reflect the carbon sink anomalies induced by regional droughts in southwest China. Furthermore, their spatial distributions are broadly consistent that of the forest inventory-based estimate, indicating that the largest carbon sinks locate in central and eastern China. Their estimates for forest carbon sink coincide fairly well with the inventory-based estimate across different regions, especially when aggregated to the north and south of China. Although enhanced recently by afforestation, China's carbon sink was also significantly weakened by regional droughts, which were often not fully represented in previous in situ CO<sub>2</sub>-based inversions due to insufficient observations. Our results suggest that satellite-based atmospheric CO<sub>2</sub> and land surface observations are vital in characterizing terrestrial net carbon fluxes at regional scales.</p>}},
  author       = {{He, Wei and Jiang, Fei and Wu, Mousong and Ju, Weimin and Scholze, Marko and Chen, Jing M. and Byrne, Brendan and Liu, Junjie and Wang, Hengmao and Wang, Jun and Wang, Songhan and Zhou, Yanlian and Zhang, Chunhua and Nguyen, Ngoc Tu and Shen, Yang and Chen, Zhi}},
  issn         = {{2169-8953}},
  keywords     = {{atmospheric CO concentration; atmospheric inversion; China's carbon sink; data assimilation; multisource satellite observations}},
  language     = {{eng}},
  month        = {{02}},
  number       = {{2}},
  publisher    = {{Wiley}},
  series       = {{Journal of Geophysical Research: Biogeosciences}},
  title        = {{China's Terrestrial Carbon Sink Over 2010–2015 Constrained by Satellite Observations of Atmospheric CO<sub>2</sub> and Land Surface Variables}},
  url          = {{http://dx.doi.org/10.1029/2021JG006644}},
  doi          = {{10.1029/2021JG006644}},
  volume       = {{127}},
  year         = {{2022}},
}