Articles | Volume 15, issue 14
https://doi.org/10.5194/gmd-15-5511-2022
© 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/gmd-15-5511-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
19 Jul 2022
Development and technical paper |
| 19 Jul 2022
| 19 Jul 2022
Improving the joint estimation of CO2 and surface carbon fluxes using a constrained ensemble Kalman filter in COLA (v1.0)
State Key Laboratory of Numerical Modeling for Atmospheric Sciences
and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, China
Department of Atmospheric and Oceanic Science, University of Maryland, College Park, Maryland, USA
Earth System Science Interdisciplinary Center, College Park, Maryland, USA
State Key Laboratory of Numerical Modeling for Atmospheric Sciences
and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
Yun Liu
International Laboratory for High-Resolution Earth System Model and
Prediction (iHESP), Texas A&M University, College Station, Texas, USA
Department of Oceanography, Texas A&M University, College Station, TX, USA
Eugenia Kalnay
Department of Atmospheric and Oceanic Science, University of Maryland, College Park, Maryland, USA
Ghassem Asrar
Universities Space Research Association, Columbia, Maryland, USA
Bo Wu
State Key Laboratory of Numerical Modeling for Atmospheric Sciences
and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
Qixiang Cai
State Key Laboratory of Numerical Modeling for Atmospheric Sciences
and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
Di Liu
Research Center for Eco-Environmental Sciences, Chinese Academy of
Sciences, Beijing, China
Pengfei Han
Carbon Neutrality Research Center, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
State Key Laboratory of Numerical Modeling for Atmospheric Sciences
and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
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Cited
12 citations as recorded by crossref.
- Impact of the horizontal resolution of GEOS-Chem on land‒ocean and tropic‒extratropic partitioning and seasonal cycle in CO2 inversion Z. Liu et al. 10.1088/1748-9326/ad7870
- Improved Constraints on the Recent Terrestrial Carbon Sink Over China by Assimilating OCO‐2 XCO2 Retrievals W. He et al. 10.1029/2022JD037773
- Recovery of ecosystem productivity in China due to the Clean Air Action plan H. Zhou et al. 10.1038/s41561-024-01586-z
- Detection of Chinese Spring Festival in Beijing using in-situ CO2 observations and atmospheric inversion Z. Liu et al. 10.1016/j.atmosenv.2024.120446
- Invert global and China's terrestrial carbon fluxes over 2019–2021 based on assimilating richer atmospheric CO2 observations J. Li et al. 10.1016/j.scitotenv.2024.172320
- Satellite-detected large CO2 release in southwestern North America during the 2020–2021 drought and associated wildfires H. Chen et al. 10.1088/1748-9326/ad3cf7
- National CO2budgets (2015–2020) inferred from atmospheric CO2observations in support of the global stocktake B. Byrne et al. 10.5194/essd-15-963-2023
- Global Carbon Budget 2023 P. Friedlingstein et al. 10.5194/essd-15-5301-2023
- Regional CO2 Inversion Through Ensemble‐Based Simultaneous State and Parameter Estimation: TRACE Framework and Controlled Experiments H. Chen et al. 10.1029/2022MS003208
- Weaker regional carbon uptake albeit with stronger seasonal amplitude in northern mid-latitudes estimated by higher resolution GEOS-Chem model Z. Liu et al. 10.1016/j.scitotenv.2023.169477
- Anomalous Net Biome Exchange Over Amazonian Rainforests Induced by the 2015/16 El Niño: Soil Dryness‐Shaped Spatial Pattern but Temperature‐dominated Total Flux J. Wang et al. 10.1029/2023GL103379
- Contaminant source characterization in a coastal aquifer influenced by tidal forces and density-driven flow A. Panjehfouladgaran & M. Rajabi 10.1016/j.jhydrol.2022.127807
11 citations as recorded by crossref.
- Impact of the horizontal resolution of GEOS-Chem on land‒ocean and tropic‒extratropic partitioning and seasonal cycle in CO2 inversion Z. Liu et al. 10.1088/1748-9326/ad7870
- Improved Constraints on the Recent Terrestrial Carbon Sink Over China by Assimilating OCO‐2 XCO2 Retrievals W. He et al. 10.1029/2022JD037773
- Recovery of ecosystem productivity in China due to the Clean Air Action plan H. Zhou et al. 10.1038/s41561-024-01586-z
- Detection of Chinese Spring Festival in Beijing using in-situ CO2 observations and atmospheric inversion Z. Liu et al. 10.1016/j.atmosenv.2024.120446
- Invert global and China's terrestrial carbon fluxes over 2019–2021 based on assimilating richer atmospheric CO2 observations J. Li et al. 10.1016/j.scitotenv.2024.172320
- Satellite-detected large CO2 release in southwestern North America during the 2020–2021 drought and associated wildfires H. Chen et al. 10.1088/1748-9326/ad3cf7
- National CO2budgets (2015–2020) inferred from atmospheric CO2observations in support of the global stocktake B. Byrne et al. 10.5194/essd-15-963-2023
- Global Carbon Budget 2023 P. Friedlingstein et al. 10.5194/essd-15-5301-2023
- Regional CO2 Inversion Through Ensemble‐Based Simultaneous State and Parameter Estimation: TRACE Framework and Controlled Experiments H. Chen et al. 10.1029/2022MS003208
- Weaker regional carbon uptake albeit with stronger seasonal amplitude in northern mid-latitudes estimated by higher resolution GEOS-Chem model Z. Liu et al. 10.1016/j.scitotenv.2023.169477
- Anomalous Net Biome Exchange Over Amazonian Rainforests Induced by the 2015/16 El Niño: Soil Dryness‐Shaped Spatial Pattern but Temperature‐dominated Total Flux J. Wang et al. 10.1029/2023GL103379
Latest update: 22 Nov 2024
Short summary
We described the application of a constrained ensemble Kalman filter (CEnKF) in a joint CO2 and surface carbon fluxes estimation study. By assimilating the pseudo-surface and OCO-2 observations, the annual global flux estimation is significantly biased without mass conservation. With the additional CEnKF process, the CO2 mass is strictly constrained, and the estimation of annual fluxes is significantly improved.
We described the application of a constrained ensemble Kalman filter (CEnKF) in a joint CO2 and...
