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Scaling carbon fluxes from eddy covariance sites to globe: synthesis and evaluation of the FLUXCOM approach

MPS-Authors
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Jung,  Martin
Global Diagnostic Modelling, Dr. Martin Jung, Department Biogeochemical Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;

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Migliavacca,  Mirco
Biosphere-Atmosphere Interactions and Experimentation, Dr. M. Migliavacca, Department Biogeochemical Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;

/persons/resource/persons230565

Walther,  Sophia
Global Diagnostic Modelling, Dr. Martin Jung, Department Biogeochemical Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;

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Koirala,  Sujan
Model-Data Integration, Dr. Nuno Carvalhais, Department Biogeochemical Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;

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Besnard,  Simon
Model-Data Integration, Dr. Nuno Carvalhais, Department Biogeochemical Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;

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Bodesheim,  Paul
Empirical Inference of the Earth System, Dr. Miguel D. Mahecha, Department Biogeochemical Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;

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Carvalhais,  Nuno
Model-Data Integration, Dr. Nuno Carvalhais, Department Biogeochemical Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;

/persons/resource/persons62378

Gans,  Fabian
Empirical Inference of the Earth System, Dr. Miguel D. Mahecha, Department Biogeochemical Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;

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Nelson,  Jacob A.
Global Diagnostic Modelling, Dr. Martin Jung, Department Biogeochemical Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;
IMPRS International Max Planck Research School for Global Biogeochemical Cycles, Max Planck Institute for Biogeochemistry, Max Planck Society;
Model-Data Integration, Dr. Nuno Carvalhais, Department Biogeochemical Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;

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Pallandt,  Martijn
Department Biogeochemical Systems, Prof. M. Heimann, Max Planck Institute for Biogeochemistry, Max Planck Society;
IMPRS International Max Planck Research School for Global Biogeochemical Cycles, Max Planck Institute for Biogeochemistry, Max Planck Society;

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Rödenbeck,  Christian
Inverse Data-driven Estimation, Dr. C. Rödenbeck, Department Biogeochemical Systems, Prof. M. Heimann, Max Planck Institute for Biogeochemistry, Max Planck Society;

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Weber,  Ulrich
Department Biogeochemical Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;

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Reichstein,  Markus
Department Biogeochemical Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;

External Resource

http://dx.doi.org/10.5194/bg-17-1343-2020
(Publisher version)

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BGC3137.pdf
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Supplementary Material (public)

BGC3137s1.pdf
(Supplementary material), 6MB

Citation

Jung, M., Schwalm, C., Migliavacca, M., Walther, S., Camps-Valls, G., Koirala, S., et al. (2020). Scaling carbon fluxes from eddy covariance sites to globe: synthesis and evaluation of the FLUXCOM approach. Biogeosciences, 17(5), 1343-1365. doi:10.5194/bg-17-1343-2020.


Cite as: https://hdl.handle.net/21.11116/0000-0004-CDE4-4
Abstract
FLUXNET assembles 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 in order to obtain global-scale estimates of biosphereatmosphere


exchange from the flux tower network. Based on cross-consistency checks with atmospheric inversions, sun50


induced fluorescence (SIF) and dynamic global vegetation models (DGVM), we provide here 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 datasets, and sets of predictor variables were employed.


Spatial patterns of mean GPP are consistent among FLUXCOM and DGVM ensembles (R2>0.94 at 1° spatial resolution)


while the majority of DGVMs are outside the FLUXCOM range for 70% of the land surface. Global mean GPP magnitudes


55 for 2008-2010 from FLUXCOM members vary within 106 and 130 PgC yr-1 with the largest uncertainty in the tropics.


Seasonal variations of independent SIF estimates agree better with FLUXCOM GPP (mean global pixel-wise R2 ~ 0.75) than


with GPP from DGVMs (mean global pixel wise R2 ~ 0.6). Seasonal variations of FLUXCOM NEE show good consistency


with atmospheric inversion-based net land carbon fluxes, particularly for temperate and boreal regions (R2>0.92).


Interannual variability of global NEE in FLUXCOM is underestimated compared to inversions and DGVMs. The


60 FLUXCOM version which uses also meteorological inputs shows a strong co-variation of interannual patterns with


inversions (R2=0.88 for 2001-2010). Mean regional NEE from FLUXCOM shows larger uptake than inversion and DGVMbased


estimates, particularly in the tropics with discrepancies of up to several hundred gC m2 yr-1. 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


65 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 CO2 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.


70 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