Using OCO-2 Observations to Constrain Regional CO<sub>2</sub> Fluxes Estimated with the Vegetation, Photosynthesis and Respiration Model
A good quantitative knowledge of regional sources and sinks of atmospheric carbon dioxide (CO<sub>2</sub>) is essential for understanding the global carbon cycle. It is also a key prerequisite for elaborating cost-effective national strategies to achieve the goals of the Paris Agreement....
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2025-01-01
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| author | Igor B. Konovalov Nikolai A. Golovushkin Evgeny A. Mareev |
| author_facet | Igor B. Konovalov Nikolai A. Golovushkin Evgeny A. Mareev |
| author_sort | Igor B. Konovalov |
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| description | A good quantitative knowledge of regional sources and sinks of atmospheric carbon dioxide (CO<sub>2</sub>) is essential for understanding the global carbon cycle. It is also a key prerequisite for elaborating cost-effective national strategies to achieve the goals of the Paris Agreement. However, available estimates of CO<sub>2</sub> fluxes for many regions of the world remain uncertain, despite significant recent progress in the remote sensing of terrestrial vegetation and atmospheric CO<sub>2</sub>. In this study, we investigate the feasibility of inferring reliable regional estimates of the net ecosystem exchange (NEE) using column-averaged dry-air mole fractions of CO<sub>2</sub> (XCO<sub>2</sub>) retrieved from Orbiting Carbon Observatory-2 (OCO-2) observations as constraints on parameters of the widely used Vegetation Photosynthesis and Respiration model (VPRM), which predicts ecosystem fluxes based on vegetation indices derived from multispectral satellite imagery. We developed a regional-scale inverse modeling system that applies a Bayesian variational optimization algorithm to optimize parameters of VPRM coupled to the CHIMERE chemistry transport model and which involves a preliminary transformation of the input XCO<sub>2</sub> data that reduces the impact of the CHIMERE boundary conditions on inversion results. We investigated the potential of our inversion system by applying it to a European region (that includes, in particular, the EU countries and the UK) for the warm season (May–September) of 2021. The inversion of the OCO-2 observations resulted in a major (more than threefold) reduction of the prior uncertainty in the regional NEE estimate. The posterior NEE estimate agrees with independent estimates provided by the CarbonTracker Europe High-Resolution (CTE-HR) system and the ensemble of the v10 OCO-2 model intercomparison (MIP) global inversions. We also found that the inversion improves the agreement of our simulations of XCO<sub>2</sub> with retrievals from the Total Carbon Column Observing Network (TCCON). Our sensitivity test experiments using synthetic XCO<sub>2</sub> data indicate that the posterior NEE estimate would remain reliable even if the actual regional CO<sub>2</sub> fluxes drastically differed from their prior values. Furthermore, the posterior NEE estimate is found to be robust to strong biases and random uncertainties in the CHIMERE boundary conditions. Overall, this study suggests that our approach offers a reliable and relatively simple way to derive robust estimates of CO<sub>2</sub> ecosystem fluxes from satellite XCO<sub>2</sub> observations while enhancing the applicability of VPRM in regions where eddy covariance measurements of CO<sub>2</sub> fluxes are scarce. |
| format | Article |
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| institution | Kabale University |
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| language | English |
| publishDate | 2025-01-01 |
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| spelling | doaj-art-5ebaa405b0ae4db9a797dccb1307d8222025-01-24T13:47:38ZengMDPI AGRemote Sensing2072-42922025-01-0117217710.3390/rs17020177Using OCO-2 Observations to Constrain Regional CO<sub>2</sub> Fluxes Estimated with the Vegetation, Photosynthesis and Respiration ModelIgor B. Konovalov0Nikolai A. Golovushkin1Evgeny A. Mareev2A.V. Gaponov-Grekhov Institute of Applied Physics, Russian Academy of Sciences, 603950 Nizhny Novgorod, RussiaA.V. Gaponov-Grekhov Institute of Applied Physics, Russian Academy of Sciences, 603950 Nizhny Novgorod, RussiaA.V. Gaponov-Grekhov Institute of Applied Physics, Russian Academy of Sciences, 603950 Nizhny Novgorod, RussiaA good quantitative knowledge of regional sources and sinks of atmospheric carbon dioxide (CO<sub>2</sub>) is essential for understanding the global carbon cycle. It is also a key prerequisite for elaborating cost-effective national strategies to achieve the goals of the Paris Agreement. However, available estimates of CO<sub>2</sub> fluxes for many regions of the world remain uncertain, despite significant recent progress in the remote sensing of terrestrial vegetation and atmospheric CO<sub>2</sub>. In this study, we investigate the feasibility of inferring reliable regional estimates of the net ecosystem exchange (NEE) using column-averaged dry-air mole fractions of CO<sub>2</sub> (XCO<sub>2</sub>) retrieved from Orbiting Carbon Observatory-2 (OCO-2) observations as constraints on parameters of the widely used Vegetation Photosynthesis and Respiration model (VPRM), which predicts ecosystem fluxes based on vegetation indices derived from multispectral satellite imagery. We developed a regional-scale inverse modeling system that applies a Bayesian variational optimization algorithm to optimize parameters of VPRM coupled to the CHIMERE chemistry transport model and which involves a preliminary transformation of the input XCO<sub>2</sub> data that reduces the impact of the CHIMERE boundary conditions on inversion results. We investigated the potential of our inversion system by applying it to a European region (that includes, in particular, the EU countries and the UK) for the warm season (May–September) of 2021. The inversion of the OCO-2 observations resulted in a major (more than threefold) reduction of the prior uncertainty in the regional NEE estimate. The posterior NEE estimate agrees with independent estimates provided by the CarbonTracker Europe High-Resolution (CTE-HR) system and the ensemble of the v10 OCO-2 model intercomparison (MIP) global inversions. We also found that the inversion improves the agreement of our simulations of XCO<sub>2</sub> with retrievals from the Total Carbon Column Observing Network (TCCON). Our sensitivity test experiments using synthetic XCO<sub>2</sub> data indicate that the posterior NEE estimate would remain reliable even if the actual regional CO<sub>2</sub> fluxes drastically differed from their prior values. Furthermore, the posterior NEE estimate is found to be robust to strong biases and random uncertainties in the CHIMERE boundary conditions. Overall, this study suggests that our approach offers a reliable and relatively simple way to derive robust estimates of CO<sub>2</sub> ecosystem fluxes from satellite XCO<sub>2</sub> observations while enhancing the applicability of VPRM in regions where eddy covariance measurements of CO<sub>2</sub> fluxes are scarce.https://www.mdpi.com/2072-4292/17/2/177net ecosystem exchange (NEE)CO<sub>2</sub> fluxessatellite CO<sub>2</sub> observationsOCO-2regional-scale inversionBayesian estimation |
| spellingShingle | Igor B. Konovalov Nikolai A. Golovushkin Evgeny A. Mareev Using OCO-2 Observations to Constrain Regional CO<sub>2</sub> Fluxes Estimated with the Vegetation, Photosynthesis and Respiration Model Remote Sensing net ecosystem exchange (NEE) CO<sub>2</sub> fluxes satellite CO<sub>2</sub> observations OCO-2 regional-scale inversion Bayesian estimation |
| title | Using OCO-2 Observations to Constrain Regional CO<sub>2</sub> Fluxes Estimated with the Vegetation, Photosynthesis and Respiration Model |
| title_full | Using OCO-2 Observations to Constrain Regional CO<sub>2</sub> Fluxes Estimated with the Vegetation, Photosynthesis and Respiration Model |
| title_fullStr | Using OCO-2 Observations to Constrain Regional CO<sub>2</sub> Fluxes Estimated with the Vegetation, Photosynthesis and Respiration Model |
| title_full_unstemmed | Using OCO-2 Observations to Constrain Regional CO<sub>2</sub> Fluxes Estimated with the Vegetation, Photosynthesis and Respiration Model |
| title_short | Using OCO-2 Observations to Constrain Regional CO<sub>2</sub> Fluxes Estimated with the Vegetation, Photosynthesis and Respiration Model |
| title_sort | using oco 2 observations to constrain regional co sub 2 sub fluxes estimated with the vegetation photosynthesis and respiration model |
| topic | net ecosystem exchange (NEE) CO<sub>2</sub> fluxes satellite CO<sub>2</sub> observations OCO-2 regional-scale inversion Bayesian estimation |
| url | https://www.mdpi.com/2072-4292/17/2/177 |
| work_keys_str_mv | AT igorbkonovalov usingoco2observationstoconstrainregionalcosub2subfluxesestimatedwiththevegetationphotosynthesisandrespirationmodel AT nikolaiagolovushkin usingoco2observationstoconstrainregionalcosub2subfluxesestimatedwiththevegetationphotosynthesisandrespirationmodel AT evgenyamareev usingoco2observationstoconstrainregionalcosub2subfluxesestimatedwiththevegetationphotosynthesisandrespirationmodel |