Articles | Volume 22, issue 16
https://doi.org/10.5194/acp-22-10769-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-22-10769-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Global and regional carbon budget for 2015–2020 inferred from OCO-2 based on an ensemble Kalman filter coupled with GEOS-Chem
Yawen Kong
Ministry of Education Key Laboratory for Earth System Modeling,
Department of Earth System Science, Tsinghua University, Beijing 100084,
China
Institute of Environment and Ecology, Tsinghua Shenzhen International
Graduate School, Tsinghua University, Shenzhen 518055, China
State Environmental Protection Key Laboratory of Sources and Control
of Air Pollution Complex, Beijing 100084, China
Qiang Zhang
Ministry of Education Key Laboratory for Earth System Modeling,
Department of Earth System Science, Tsinghua University, Beijing 100084,
China
Kebin He
State Environmental Protection Key Laboratory of Sources and Control
of Air Pollution Complex, Beijing 100084, China
State Key Joint Laboratory of Environment Simulation and Pollution
Control, School of Environment, Tsinghua University, Beijing 100084, China
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Cited
13 citations as recorded by crossref.
- Improved estimates of net ecosystem exchanges in mega-countries using GOSAT and OCO-2 observations L. Zhang et al. 10.1038/s43247-024-01910-w
- Significant co-benefits of air pollutant and CO2 emission reduction from biomass energy utilization in power plants in China Q. Cai et al. 10.1016/j.scitotenv.2023.164116
- Evaluating the consistency between OCO-2 and OCO-3 XCO2 estimates derived from the NASA ACOS version 10 retrieval algorithm T. Taylor et al. 10.5194/amt-16-3173-2023
- Impacts of different biomass burning emission inventories: Simulations of atmospheric CO2 concentrations based on GEOS-Chem M. Su et al. 10.1016/j.scitotenv.2023.162825
- CHEEREIO 1.0: a versatile and user-friendly ensemble-based chemical data assimilation and emissions inversion platform for the GEOS-Chem chemical transport model D. Pendergrass et al. 10.5194/gmd-16-4793-2023
- Global Evaluation and Intercomparison of XCO2 Retrievals from GOSAT, OCO-2, and TANSAT with TCCON J. Fang et al. 10.3390/rs15205073
- To what extent does the CO2 diurnal cycle impact flux estimates derived from global and regional inversions? S. Munassar et al. 10.5194/acp-25-639-2025
- A New Method for Top-Down Inversion Estimation of Carbon Dioxide Flux Based on Deep Learning H. Wang et al. 10.3390/rs16193694
- A new global carbon flux estimation methodology by assimilation of both in situ and satellite CO2 observations W. Su et al. 10.1038/s41612-024-00824-w
- Global Carbon Budget 2023 P. Friedlingstein et al. 10.5194/essd-15-5301-2023
- Global Carbon Budget 2022 P. Friedlingstein et al. 10.5194/essd-14-4811-2022
- An inversion model based on GEOS-Chem for estimating global and China's terrestrial carbon fluxes in 2019 C. Wu et al. 10.1016/j.accre.2023.01.001
- The Orbiting Carbon Observatory-2 (OCO-2) and in situ CO2 data suggest a larger seasonal amplitude of the terrestrial carbon cycle compared to many dynamic global vegetation models R. Lei et al. 10.1016/j.rse.2024.114326
13 citations as recorded by crossref.
- Improved estimates of net ecosystem exchanges in mega-countries using GOSAT and OCO-2 observations L. Zhang et al. 10.1038/s43247-024-01910-w
- Significant co-benefits of air pollutant and CO2 emission reduction from biomass energy utilization in power plants in China Q. Cai et al. 10.1016/j.scitotenv.2023.164116
- Evaluating the consistency between OCO-2 and OCO-3 XCO2 estimates derived from the NASA ACOS version 10 retrieval algorithm T. Taylor et al. 10.5194/amt-16-3173-2023
- Impacts of different biomass burning emission inventories: Simulations of atmospheric CO2 concentrations based on GEOS-Chem M. Su et al. 10.1016/j.scitotenv.2023.162825
- CHEEREIO 1.0: a versatile and user-friendly ensemble-based chemical data assimilation and emissions inversion platform for the GEOS-Chem chemical transport model D. Pendergrass et al. 10.5194/gmd-16-4793-2023
- Global Evaluation and Intercomparison of XCO2 Retrievals from GOSAT, OCO-2, and TANSAT with TCCON J. Fang et al. 10.3390/rs15205073
- To what extent does the CO2 diurnal cycle impact flux estimates derived from global and regional inversions? S. Munassar et al. 10.5194/acp-25-639-2025
- A New Method for Top-Down Inversion Estimation of Carbon Dioxide Flux Based on Deep Learning H. Wang et al. 10.3390/rs16193694
- A new global carbon flux estimation methodology by assimilation of both in situ and satellite CO2 observations W. Su et al. 10.1038/s41612-024-00824-w
- Global Carbon Budget 2023 P. Friedlingstein et al. 10.5194/essd-15-5301-2023
- Global Carbon Budget 2022 P. Friedlingstein et al. 10.5194/essd-14-4811-2022
- An inversion model based on GEOS-Chem for estimating global and China's terrestrial carbon fluxes in 2019 C. Wu et al. 10.1016/j.accre.2023.01.001
- The Orbiting Carbon Observatory-2 (OCO-2) and in situ CO2 data suggest a larger seasonal amplitude of the terrestrial carbon cycle compared to many dynamic global vegetation models R. Lei et al. 10.1016/j.rse.2024.114326
Latest update: 30 Jan 2025
Short summary
We developed a Bayesian atmospheric inversion system based on the 4D local ensemble transform Kalman filter (4D-LETKF) algorithm coupled with GEOS-Chem from the latest Orbiting Carbon Observatory-2 (OCO-2) V10r XCO2 retrievals. This is the first adaptation of 4D-LETKF to an OCO-2-based global carbon inversion system. We inferred global gridded carbon fluxes and investigated their magnitudes, variations, and partitioning schemes to understand the global and regional carbon budgets for 2015–2020.
We developed a Bayesian atmospheric inversion system based on the 4D local ensemble transform...
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