A Robust Estimate of Continental-Scale Terrestrial Carbon Sinks Using GOSAT XCO2 Retrievals

Lingyu Zhang; Fei Jiang; Wei He; Mousong Wu; Jun Wang; Weimin Ju; Hengmao Wang; Yongguang Zhang; Stephen Sitch; Anthony P. Walker; Xu Yue; Shuzhuang Feng; Mengwei Jia; Jing M. Chen

doi:10.1029/2023GL102815

A Robust Estimate of Continental-Scale Terrestrial Carbon Sinks Using GOSAT XCO₂ Retrievals

Lingyu Zhang
, Fei Jiang
, Wei He
, Mousong Wu
, Jun Wang
, Weimin Ju
, Hengmao Wang
, Yongguang Zhang
, Stephen Sitch
, Anthony P. Walker
, Xu Yue
, Shuzhuang Feng
, Mengwei Jia
, Jing M. Chen

Ecosystem Processes

Research output: Contribution to journal › Article › peer-review

13 Scopus citations

Abstract

Satellite XCO₂ retrievals could improve the estimates of surface carbon fluxes, but it remains unknown on what scales these estimates are robust. Here, we use the time-dependent Bayesian synthesis top-down method and prior net ecosystem exchanges (NEEs) from 12 terrestrial biosphere models (TBMs) to infer the monthly carbon fluxes of 51 land regions with constraints by GOSAT XCO₂ retrievals. We find that the uncertainty (standard deviation of 12 TBMs) reduction rates (uncertainty reduction rate (URR)) decrease significantly at decreasing spatial scales. On the continental-scale, the mean URR is about 57%, and the annual and seasonal cycle estimates of NEE are rather robust. The evaluation shows that the posterior CO₂ concentrations are significantly improved at the continental scale. Our study suggests that the GOSAT XCO₂ can only promise a robust continental-scale NEE estimate, and improving the XCO₂ accuracy is an effective way to achieve robust estimates on smaller scales under current spatial coverage.

Original language	English
Article number	e2023GL102815
Journal	Geophysical Research Letters
Volume	50
Issue number	6
DOIs	https://doi.org/10.1029/2023GL102815
State	Published - Mar 28 2023

Funding

This work is supported by the National Key R&D Program of China (Grant 2021YFB3901001), Fengyun Application Pioneering Project (Grant FY-APP-2022.0505), the Research Funds for the Frontiers Science Center for Critical Earth Material Cycling, Nanjing University (Grant 090414380031), and the National Natural Science Foundation of China (Grant 41907378). We acknowledge all atmospheric data providers to obspack_CO2_1_GLOBALVIEWplus_ v7.0_2021-08-18. We acknowledge the CONTRAIL project team members for the public availability of their data as part of the ObsPack data package. The GOSAT data are produced by the ACOS/OCO-2 project at the Jet Propulsion Laboratory, California Institute of Technology, and obtained from the JPL website, CO2.jpl.nasa.gov. We acknowledge the members of the GOSAT Project for their contribution to this work. We are also grateful to the High-Performance Computing Center (HPCC) of Nanjing University for doing the numerical calculations in this paper on its blade cluster system. This work is supported by the National Key R&D Program of China (Grant 2021YFB3901001), Fengyun Application Pioneering Project (Grant FY‐APP‐2022.0505), the Research Funds for the Frontiers Science Center for Critical Earth Material Cycling, Nanjing University (Grant 090414380031), and the National Natural Science Foundation of China (Grant 41907378). We acknowledge all atmospheric data providers to obspack_CO_1_GLOBALVIEWplus_ v7.0_2021‐08‐18. We acknowledge the CONTRAIL project team members for the public availability of their data as part of the ObsPack data package. The GOSAT data are produced by the ACOS/OCO‐2 project at the Jet Propulsion Laboratory, California Institute of Technology, and obtained from the JPL website, CO.jpl.nasa.gov. We acknowledge the members of the GOSAT Project for their contribution to this work. We are also grateful to the High‐Performance Computing Center (HPCC) of Nanjing University for doing the numerical calculations in this paper on its blade cluster system. 2 2

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title = "A Robust Estimate of Continental-Scale Terrestrial Carbon Sinks Using GOSAT XCO2 Retrievals",

abstract = "Satellite XCO2 retrievals could improve the estimates of surface carbon fluxes, but it remains unknown on what scales these estimates are robust. Here, we use the time-dependent Bayesian synthesis top-down method and prior net ecosystem exchanges (NEEs) from 12 terrestrial biosphere models (TBMs) to infer the monthly carbon fluxes of 51 land regions with constraints by GOSAT XCO2 retrievals. We find that the uncertainty (standard deviation of 12 TBMs) reduction rates (uncertainty reduction rate (URR)) decrease significantly at decreasing spatial scales. On the continental-scale, the mean URR is about 57\%, and the annual and seasonal cycle estimates of NEE are rather robust. The evaluation shows that the posterior CO2 concentrations are significantly improved at the continental scale. Our study suggests that the GOSAT XCO2 can only promise a robust continental-scale NEE estimate, and improving the XCO2 accuracy is an effective way to achieve robust estimates on smaller scales under current spatial coverage.",

author = "Lingyu Zhang and Fei Jiang and Wei He and Mousong Wu and Jun Wang and Weimin Ju and Hengmao Wang and Yongguang Zhang and Stephen Sitch and Walker, \{Anthony P.\} and Xu Yue and Shuzhuang Feng and Mengwei Jia and Chen, \{Jing M.\}",

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T1 - A Robust Estimate of Continental-Scale Terrestrial Carbon Sinks Using GOSAT XCO2 Retrievals

AU - Zhang, Lingyu

AU - Jiang, Fei

AU - He, Wei

AU - Wu, Mousong

AU - Wang, Jun

AU - Ju, Weimin

AU - Wang, Hengmao

AU - Zhang, Yongguang

AU - Sitch, Stephen

AU - Walker, Anthony P.

AU - Yue, Xu

AU - Feng, Shuzhuang

AU - Jia, Mengwei

AU - Chen, Jing M.

PY - 2023/3/28

Y1 - 2023/3/28

N2 - Satellite XCO2 retrievals could improve the estimates of surface carbon fluxes, but it remains unknown on what scales these estimates are robust. Here, we use the time-dependent Bayesian synthesis top-down method and prior net ecosystem exchanges (NEEs) from 12 terrestrial biosphere models (TBMs) to infer the monthly carbon fluxes of 51 land regions with constraints by GOSAT XCO2 retrievals. We find that the uncertainty (standard deviation of 12 TBMs) reduction rates (uncertainty reduction rate (URR)) decrease significantly at decreasing spatial scales. On the continental-scale, the mean URR is about 57%, and the annual and seasonal cycle estimates of NEE are rather robust. The evaluation shows that the posterior CO2 concentrations are significantly improved at the continental scale. Our study suggests that the GOSAT XCO2 can only promise a robust continental-scale NEE estimate, and improving the XCO2 accuracy is an effective way to achieve robust estimates on smaller scales under current spatial coverage.

AB - Satellite XCO2 retrievals could improve the estimates of surface carbon fluxes, but it remains unknown on what scales these estimates are robust. Here, we use the time-dependent Bayesian synthesis top-down method and prior net ecosystem exchanges (NEEs) from 12 terrestrial biosphere models (TBMs) to infer the monthly carbon fluxes of 51 land regions with constraints by GOSAT XCO2 retrievals. We find that the uncertainty (standard deviation of 12 TBMs) reduction rates (uncertainty reduction rate (URR)) decrease significantly at decreasing spatial scales. On the continental-scale, the mean URR is about 57%, and the annual and seasonal cycle estimates of NEE are rather robust. The evaluation shows that the posterior CO2 concentrations are significantly improved at the continental scale. Our study suggests that the GOSAT XCO2 can only promise a robust continental-scale NEE estimate, and improving the XCO2 accuracy is an effective way to achieve robust estimates on smaller scales under current spatial coverage.

UR - https://www.scopus.com/pages/publications/85152530005

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DO - 10.1029/2023GL102815

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JF - Geophysical Research Letters

IS - 6

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ER -

A Robust Estimate of Continental-Scale Terrestrial Carbon Sinks Using GOSAT XCO₂ Retrievals

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