Articles | Volume 24, issue 4
https://doi.org/10.5194/acp-24-2555-2024
© Author(s) 2024. 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-24-2555-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
28 Feb 2024
Research article |
| 28 Feb 2024
| 28 Feb 2024
Constraining biospheric carbon dioxide fluxes by combined top-down and bottom-up approaches
Department of Biogeochemical Integration, Max Planck Institute of Biogeochemistry, Jena, Germany
Environmental Sciences Group, Wageningen University, Wageningen, the Netherlands
Markus Reichstein
Department of Biogeochemical Integration, Max Planck Institute of Biogeochemistry, Jena, Germany
Fabian Gans
Department of Biogeochemical Integration, Max Planck Institute of Biogeochemistry, Jena, Germany
Wouter Peters
Environmental Sciences Group, Wageningen University, Wageningen, the Netherlands
Centre for Isotope Research, University of Groningen, Groningen, the Netherlands
Basil Kraft
Department of Biogeochemical Integration, Max Planck Institute of Biogeochemistry, Jena, Germany
Ana Bastos
Department of Biogeochemical Integration, Max Planck Institute of Biogeochemistry, Jena, Germany
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- Influence of simulated vs. satellite-based burned areas on modelled terrestrial carbon fluxes T. Ermitão et al. https://doi.org/10.1186/s13021-025-00366-5
- WetCH4: a machine-learning-based upscaling of methane fluxes of northern wetlands during 2016–2022 Q. Ying et al. https://doi.org/10.5194/essd-17-2507-2025
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- RETRACTED: Knowledge-guided machine learning captures key mechanistic pathways for better predicting spatio-temporal patterns of growing season N2O emissions in the U.S. Midwest L. Ye et al. https://doi.org/10.1016/j.agrformet.2025.110750
- A coupled LSTM–XGBoost framework for daily estimation of Net ecosystem productivity using remote-sensing and environmental data Y. Liu et al. https://doi.org/10.1080/01431161.2026.2633777
15 citations as recorded by crossref.
- Using satellite-based Sun-induced chlorophyll fluorescence and spectral reflectance for improving terrestrial CO2 flux estimates of India A. Ravi et al. https://doi.org/10.1088/2752-664X/adabed
- Insights into terrestrial carbon and water cycling from the global eddy covariance network J. Xiao et al. https://doi.org/10.1038/s43017-025-00743-1
- Influence of simulated vs. satellite-based burned areas on modelled terrestrial carbon fluxes T. Ermitão et al. https://doi.org/10.1186/s13021-025-00366-5
- WetCH4: a machine-learning-based upscaling of methane fluxes of northern wetlands during 2016–2022 Q. Ying et al. https://doi.org/10.5194/essd-17-2507-2025
- From site to region: Performance evaluation of remote sensing-derived GPP products across China Y. Cao et al. https://doi.org/10.1016/j.asr.2026.01.038
- Knowledge‐Guided Machine Learning for Global Change Ecology Research Z. Jin et al. https://doi.org/10.1111/gcb.70742
- Carbon sink conservation: Cost-effective spatial priorities and feasible management policies for China J. Liu et al. https://doi.org/10.1016/j.landusepol.2026.107974
- Spatial indices and indicators to facilitate catchment planning with multiple objectives A. Räsänen et al. https://doi.org/10.1016/j.envdev.2025.101379
- Regionalization of Forest Landscapes in Russia to Optimize Regional Modeling of Greenhouse Gas Fluxes T. Kharitonova et al. https://doi.org/10.1134/S1028334X24604346
- Constraining a data-driven CO2 flux model by ecosystem and atmospheric observations using atmospheric transport S. Upton et al. https://doi.org/10.5194/acp-26-2561-2026
- On the added value of sequential deep learning for the upscaling of evapotranspiration B. Kraft et al. https://doi.org/10.5194/bg-22-3965-2025
- The importance of natural land carbon sinks in modelling future emissions pathways and assessing individual country progress towards net-zero emissions targets R. van der Ploeg & M. Haigh https://doi.org/10.3389/fenvs.2024.1379046
- Atmospheric CO2 observations reveal seasonality bias in Arctic–boreal carbon flux estimates J. Wen et al. https://doi.org/10.1088/1748-9326/ae6128
- RETRACTED: Knowledge-guided machine learning captures key mechanistic pathways for better predicting spatio-temporal patterns of growing season N2O emissions in the U.S. Midwest L. Ye et al. https://doi.org/10.1016/j.agrformet.2025.110750
- A coupled LSTM–XGBoost framework for daily estimation of Net ecosystem productivity using remote-sensing and environmental data Y. Liu et al. https://doi.org/10.1080/01431161.2026.2633777
Saved (final revised paper)
Latest update: 25 Jun 2026
Short summary
Data-driven eddy-covariance upscaled estimates of the global land–atmosphere net CO2 exchange (NEE) show important mismatches with regional and global estimates based on atmospheric information. To address this, we create a model with a dual constraint based on bottom-up eddy-covariance data and top-down atmospheric inversion data. Our model overcomes shortcomings of each approach, producing improved NEE estimates from local to global scale, helping to reduce uncertainty in the carbon budget.
Data-driven eddy-covariance upscaled estimates of the global land–atmosphere net CO2 exchange...
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