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Scaling carbon fluxes from eddy covariance sites to globe: synthesis and evaluation of the FLUXCOM approach
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Abstract
FLUXNET comprises globally distributed eddy-covariance-based estimates of carbon fluxes between the biosphere and the atmosphere. Since eddy covariance flux towers have a relatively small footprint and are distributed unevenly across the world, upscaling the observations is necessary to obtain global-scale estimates of biosphere–atmosphere exchange. Based on cross-consistency checks with atmospheric inversions, sun-induced fluorescence (SIF) and dynamic global vegetation models (DGVMs), here we provide a systematic assessment of the latest upscaling efforts for gross primary production (GPP) and net ecosystem exchange (NEE) of the FLUXCOM initiative, where different machine learning methods, forcing data sets and sets of predictor variables were employed. Spatial patterns of mean GPP are consistent across FLUXCOM and DGVM ensembles ( R 2 >0.94 at 1 ∘ spatial resolution) while the majority of DGVMs show, for 70 % of the land surface, values outside the FLUXCOM range. Global mean GPP magnitudes for 2008–2010 from FLUXCOM members vary within 106 and 130 PgC yr −1 with the largest uncertainty in the tropics. Seasonal variations in independent SIF estimates agree better with FLUXCOM GPP (mean global pixel-wise R 2 ∼0.75) than with GPP from DGVMs (mean global pixel-wise R 2 ∼0.6). Seasonal variations in FLUXCOM NEE show good consistency with atmospheric inversion-based net land carbon fluxes, particularly for temperate and boreal regions ( R 2 >0.92). Interannual variability of global NEE in FLUXCOM is underestimated compared to inversions and DGVMs. The FLUXCOM version which also uses meteorological inputs shows a strong co-variation in interannual patterns with inversions ( R 2 =0.87 for 2001–2010). Mean regional NEE from FLUXCOM shows larger uptake than inversion and DGVM-based estimates, particularly in the tropics with discrepancies of up to several hundred grammes of carbon per square metre per year. These discrepancies can only partly be reconciled by carbon loss pathways that are implicit in inversions but not captured by the flux tower measurements such as carbon emissions from fires and water bodies. We hypothesize that a combination of systematic biases in the underlying eddy covariance data, in particular in tall tropical forests, and a lack of site history effects on NEE in FLUXCOM are likely responsible for the too strong tropical carbon sink estimated by FLUXCOM. Furthermore, as FLUXCOM does not account for CO 2 fertilization effects, carbon flux trends are not realistic. Overall, current FLUXCOM estimates of mean annual and seasonal cycles of GPP as well as seasonal NEE variations provide useful constraints of global carbon cycling, while interannual variability patterns from FLUXCOM are valuable but require cautious interpretation. Exploring the diversity of Earth observation data and of machine learning concepts along with improved quality and quantity of flux tower measurements will facilitate further improvements of the FLUXCOM approach overall.
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“Datadriven approaches using machine learning to integrate satellite data with site-level information (e.g., from eddy covariance measurement networks [42]) provide diagnostic constraints on global GPP, complementing process-based models [43].”
From Prithvi-EO-2.0: A Versatile Multitemporal Foundation Model for Earth Observation Applications (2026) · cites this paper as [42]
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Metadata
- Authors
- Jung, M., Schwalm, C., Migliavacca, M., Walther, S., Camps-Valls, G., Koirala, S., Anthoni, P., Besnard, S., Bodesheim, P., Carvalhais, N., Chevallier, F., Gans, F., Goll, D. S., Haverd, V., Köhler, P., Ichii, K., Jain, A. K., Liu, J., Lombardozzi, D., Nabel, J. E. M. S., Nelson, J. A., O'Sullivan, M., Pallandt, M., Papale, D., Peters, W., Pongratz, J., Rödenbeck, C., Sitch, S., Tramontana, G., Walker, A., Weber, U., & Reichstein, M.
- Affiliations
- Max Planck Institute for Biogeochemistry (DE)
- Woodwell Climate Research Center (US)
- Karlsruhe Institute of Technology (DE)
- Université Paris-Saclay (FR)
- University of Augsburg (DE)
- CSIRO Oceans and Atmosphere (AU)
- California Institute of Technology (US)
- Chiba University (JP)
- University of Illinois Urbana-Champaign (US)
- Nanjing Normal University (CN)
- NSF National Center for Atmospheric Research (US)
- Max Planck Institute for Meteorology (DE)
- Università degli Studi della Tuscia (IT)
- Wageningen University & Research (NL)
- University of Exeter (GB)
- Oak Ridge National Laboratory (US)
- Corresponding author
- Jung, Martin
- ORCID
- Jung, Martin
- Schwalm, Christopher
- Migliavacca, Mirco
- Walther, Sophia
- Camps-Valls, Gustau
- Koirala, Sujan
- Anthoni, Peter
- Besnard, Simon
- Bodesheim, Paul
- Carvalhais, Nuno
- Chevallier, Frédéric
- Gans, Fabian
- Goll, Daniel S.
- Haverd, Vanessa
- Köhler, Philipp
- Ichii, Kazuhito
- Jain, Atul K.
- Liu, Junzhi
- Lombardozzi, Danica
- Nabel, Julia E. M. S.
- Nelson, Jacob A.
- O'Sullivan, Michael
- Pallandt, Martijn
- Papale, Dario
- Peters, Wouter
- Pongratz, Julia
- Rödenbeck, Christian
- Sitch, Stephen
- Tramontana, Gianluca
- Walker, Anthony
- Weber, Ulrich
- Reichstein, Markus
- Venue
- Biogeosciences
- Publisher
- Copernicus GmbH
- Published
- 2020-03-16
- Volume
- 17
- Issue
- 5
- Pages
- 1343-1365
- ISSN
- 1726-4170 (ISSN-L)
- 1726-4189
- OpenAlex
- W2978485176
- Cited by
- 866
- References
- 91
- Citations by year
- 2026: 116 · 2025: 137 · 2024: 162 · 2023: 127 · 2022: 139 · 2021: 144 · 2020: 40
- FWCI
- 41.89
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Biogeosciences (publishedVersion, cc-by)
- CaltechAUTHORS (California Institute of Technology) (acceptedVersion)
- Socio-Environmental Systems Modeling (acceptedVersion, cc-by)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion, other-oa)
- Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, other-oa)
- OPUS (Augsburg University) (submittedVersion, cc-by)
- HAL (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- UnitusOpen (Tuscia University) (submittedVersion)
- Open access LMU (Ludwid Maxmilian's Universitat Munchen) (submittedVersion, cc-by)
- MPG.PuRe (Max Planck Society) (submittedVersion, cc-by)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, cc-by)
- Funders
- European Space Agency (ESRIN/4000123002/18/I-NB)
- H2020 European Institute of Innovation and Technology
- Topics
- Physical Sciences › Environmental Science › Global and Planetary Change › Plant Water Relations and Carbon Dynamics
- Physical Sciences › Environmental Science › Global and Planetary Change › Atmospheric and Environmental Gas Dynamics
- Physical Sciences › Environmental Science › Ecology › Remote Sensing in Agriculture
- Keywords
- FLUXCOM, gross primary productivity, net ecosystem exchange, eddy covariance flux towers, FLUXNET, solar-induced chlorophyll fluorescence, dynamic global vegetation models, temperature inversion, interannual variability, tropical forests, biosphere-atmosphere exchange, global carbon budget
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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