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Biogeosciences An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/bg-2019-368
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-2019-368
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: reviews and syntheses 02 Oct 2019

Submitted as: reviews and syntheses | 02 Oct 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Biogeosciences (BG).

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

Martin Jung1, Christopher Schwalm2, Mirco Migliavacca1, Sophia Walther1, Gustau Camps-Valls3, Sujan Koirala1, Peter Anthoni4, Simon Besnard1,5, Paul Bodesheim1,6, Nuno Carvalhais1,7, Frederic Chevallier8, Fabian Gans1, Daniel S. Groll9, Vanessa Haverd10, Kazuhito Ichii12,13, Atul K. Jain14, Junzhi Liu1,15, Danica Lombardozzi16, Julia E. M. S. Nabel17, Jacob A. Nelson1, Martijn Pallandt19, Dario Papale20,21, Wouter Peters22, Julia Pongratz23,17, Christian Rödenbeck19, Stephen Sitch18, Gianluca Tramontana20,3, Ulrich Weber1, Markus Reichstein1, Philipp Koehler11, Michael O'Sullivan18, and Anthony Walker24 Martin Jung et al.
  • 1Department of Biogeochmical Integration, Max Planck Institute for Biogeochemistry, Jena, 07745, Germany
  • 2Woods Hole Research Center, Falmouth, MA, 02540-1644, USA
  • 3Image Processing Laboratory (IPL), Universitat de València, Paterna, 46980, Spain
  • 4Institute of Meteorology and Climate Research – Atmospheric Environmental Research (IMK-IFU), Karlsruhe Institute of Technology, Garmisch-Partenkirchen, 82467, Germany
  • 5Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, Wageningen, 6708 PB, the Netherlands
  • 6Department of Mathematics and Computer Science, Friedrich-Schiller Universität Jena, Jena, 07743, Germany
  • 7Departamento de Ciências e Engenharia do Ambiente (DCEA), Faculdade de Ciências e Tecnologia, FCT, Universidade Nova de Lisboa, Caparica, 2829-516, Portugal
  • 8Laboratoire des Sciences du Climat et de l’Environnement (LSCE/IPSL), Université Paris-Saclay, Gif-sur- Yvette, 91198, France
  • 9Department of Geography, University of Augsburg, Augsburg, 86159, Germany
  • 10Department Continental Biogeochemical Cycles, CSIRO Oceans and Atmosphere, Canberra, 2601, Australia
  • 11Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, USA
  • 12Center for Environmental Remote Sensing (CEReS), Chiba University, Chiba, 263-8522, Japan
  • 13Center for Global Environmental Research, National Institute for Environmental Studies, Tsukuba, 305-8506, Japan
  • 14Department of Atmospheric Science, University of Illinois, Urbana, IL 61801, USA
  • 15School of Geography, Nanjing Normal University, Nanjing, 210023, China
  • 16Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, CO 80307, USA
  • 17Department Land in the Earth System (LES), Max Planck Institute for Meteorology, Hamburg, 20146, Germany
  • 18College of Life and Environmental Sciences, University of Exeter, Exeter, EX4 4QE, UK
  • 19Department of Biogeochmical Systems, Max Planck Institute for Biogeochemistry, Jena, 07745, Germany
  • 20Department of Innovation in Biology, Agri-food and Forest systems (DIBAF), University of Tuscia, Viterbo, 01100, Italy
  • 21Impacts on Agriculture, Forests and Ecosystem Services (IAFES), EuroMediterranean Center on Climate Change (CMCC), Lecce, 01100, Italy
  • 22Department of Meteorology and Air Quality, Wageningen University and Research, Wageningen, 6700 AA, the Netherlands
  • 23Department of Geography, Ludwig-Maximilans-Universität München, München, 80333, Germany
  • 24Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, USA

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 biosphere-atmosphere exchange from the flux tower network. Based on cross-consistency checks with atmospheric inversions, sun-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 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 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 DGVM-based 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 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. 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.

Martin Jung et al.
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Status: open (until 13 Nov 2019)
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Short summary
We test the approach of producing global gridded carbon fluxes based on combining machine learning with local measurements, remote sensing and climate data. We show that we can reproduce seasonal variations of carbon assimilated by plants via photosynthesis and of ecosystems net carbon balance. The ecosystem’s mean carbon balance and carbon flux trends require cautious interpretation. The analysis paves the way for future improvements of the data-driven assessment of carbon fluxes.
We test the approach of producing global gridded carbon fluxes based on combining machine...
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