Scaling carbon fluxes from eddy covariance sites to globe: synthesis and evaluation of the FLUXCOM approach
Authors: 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 
Published: March 2020

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 (R2>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 R2∼0.75) than with GPP from DGVMs (mean global pixel-wise
R2∼0.6). Seasonal variations in 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 also uses meteorological
inputs shows a strong co-variation in interannual patterns with inversions
(R2=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
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.