Articles | Volume 8, issue 3
https://doi.org/10.5194/gmd-8-805-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-8-805-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Model description paper
| 
25 Mar 2015
Model description paper |
| 25 Mar 2015
A global carbon assimilation system using a modified ensemble Kalman filter
S. Zhang
College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
Joint Center for Global Change Studies, Beijing, China
X. Zheng
CORRESPONDING AUTHOR
College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
Joint Center for Global Change Studies, Beijing, China
J. M. Chen
Joint Center for Global Change Studies, Beijing, China
Department of Geography, University of Toronto, Toronto, Canada
International Institute for Earth System Science, Nanjing University, Nanjing, China
Z. Chen
College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
Joint Center for Global Change Studies, Beijing, China
B. Dan
College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
Joint Center for Global Change Studies, Beijing, China
X. Yi
College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
Joint Center for Global Change Studies, Beijing, China
L. Wang
Department of Statistics, University of Manitoba, Winnipeg, Canada
College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
Joint Center for Global Change Studies, Beijing, China
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Cited
16 citations as recorded by crossref.
- The potential of CO2 satellite monitoring for climate governance: A review G. Pan et al. 10.1016/j.jenvman.2020.111423
- China's Terrestrial Carbon Sink Over 2010–2015 Constrained by Satellite Observations of Atmospheric CO2 and Land Surface Variables W. He et al. 10.1029/2021JG006644
- Soil Moisture Assimilation Using a Modified Ensemble Transform Kalman Filter Based on Station Observations in the Hai River Basin G. Wu et al. 10.1155/2016/4569218
- Global and regional carbon budget for 2015–2020 inferred from OCO-2 based on an ensemble Kalman filter coupled with GEOS-Chem Y. Kong et al. 10.5194/acp-22-10769-2022
- A Regional multi-Air Pollutant Assimilation System (RAPAS v1.0) for emission estimates: system development and application S. Feng et al. 10.5194/gmd-16-5949-2023
- High‐dimensional ensemble Kalman filter with localization, inflation, and iterative updates H. Sun et al. 10.1002/qj.4846
- Regional CO<sub>2</sub> fluxes from 2010 to 2015 inferred from GOSAT XCO<sub>2</sub> retrievals using a new version of the Global Carbon Assimilation System F. Jiang et al. 10.5194/acp-21-1963-2021
- Constraining global terrestrial gross primary productivity in a global carbon assimilation system with OCO-2 chlorophyll fluorescence data J. Wang et al. 10.1016/j.agrformet.2021.108424
- Terrestrial carbon cycle model-data fusion: Progress and challenges X. Li et al. 10.1007/s11430-020-9800-3
- A meteorologically adjusted ensemble Kalman filter approach for inversing daily emissions: A case study in the Pearl River Delta, China G. Jia et al. 10.1016/j.jes.2021.08.048
- 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
- Assimilating shallow soil moisture observations into land models with a water budget constraint B. Dan et al. 10.5194/hess-24-5187-2020
- Atmospheric CO<sub>2</sub> inversions on the mesoscale using data-driven prior uncertainties: quantification of the European terrestrial CO<sub>2</sub> fluxes P. Kountouris et al. 10.5194/acp-18-3047-2018
- CO2 Flux Inversion With a Regional Joint Data Assimilation System Based on CMAQ, EnKS, and Surface Observations Z. Peng et al. 10.1029/2022JD037154
- Improved Constraints on the Recent Terrestrial Carbon Sink Over China by Assimilating OCO‐2 XCO2 Retrievals W. He et al. 10.1029/2022JD037773
16 citations as recorded by crossref.
- The potential of CO2 satellite monitoring for climate governance: A review G. Pan et al. 10.1016/j.jenvman.2020.111423
- China's Terrestrial Carbon Sink Over 2010–2015 Constrained by Satellite Observations of Atmospheric CO2 and Land Surface Variables W. He et al. 10.1029/2021JG006644
- Soil Moisture Assimilation Using a Modified Ensemble Transform Kalman Filter Based on Station Observations in the Hai River Basin G. Wu et al. 10.1155/2016/4569218
- Global and regional carbon budget for 2015–2020 inferred from OCO-2 based on an ensemble Kalman filter coupled with GEOS-Chem Y. Kong et al. 10.5194/acp-22-10769-2022
- A Regional multi-Air Pollutant Assimilation System (RAPAS v1.0) for emission estimates: system development and application S. Feng et al. 10.5194/gmd-16-5949-2023
- High‐dimensional ensemble Kalman filter with localization, inflation, and iterative updates H. Sun et al. 10.1002/qj.4846
- Regional CO<sub>2</sub> fluxes from 2010 to 2015 inferred from GOSAT XCO<sub>2</sub> retrievals using a new version of the Global Carbon Assimilation System F. Jiang et al. 10.5194/acp-21-1963-2021
- Constraining global terrestrial gross primary productivity in a global carbon assimilation system with OCO-2 chlorophyll fluorescence data J. Wang et al. 10.1016/j.agrformet.2021.108424
- Terrestrial carbon cycle model-data fusion: Progress and challenges X. Li et al. 10.1007/s11430-020-9800-3
- A meteorologically adjusted ensemble Kalman filter approach for inversing daily emissions: A case study in the Pearl River Delta, China G. Jia et al. 10.1016/j.jes.2021.08.048
- 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
- Assimilating shallow soil moisture observations into land models with a water budget constraint B. Dan et al. 10.5194/hess-24-5187-2020
- Atmospheric CO<sub>2</sub> inversions on the mesoscale using data-driven prior uncertainties: quantification of the European terrestrial CO<sub>2</sub> fluxes P. Kountouris et al. 10.5194/acp-18-3047-2018
- CO2 Flux Inversion With a Regional Joint Data Assimilation System Based on CMAQ, EnKS, and Surface Observations Z. Peng et al. 10.1029/2022JD037154
- Improved Constraints on the Recent Terrestrial Carbon Sink Over China by Assimilating OCO‐2 XCO2 Retrievals W. He et al. 10.1029/2022JD037773
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Latest update: 26 Dec 2024
Short summary
A Global Carbon Assimilation System based on the Ensemble Kalman filter (GCAS-EK) is developed for assimilating atmospheric CO2 data into an ecosystem model to simultaneously estimate the surface carbon fluxes and atmospheric CO2 distribution. This assimilation approach is similar to CarbonTracker, but with several new developments. The results showed that this assimilation approach can effectively reduce the biases and uncertainties of the carbon fluxes simulated by the ecosystem model.
A Global Carbon Assimilation System based on the Ensemble Kalman filter (GCAS-EK) is developed...
