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. FLUXNET comprises globally distributed eddy-covariance-based estimates of carbon fluxes between the biosphere and theatmosphere. Since eddy covariance flux towers have a relatively smallfootprint and are distributed unevenly across the world, upscaling theobservations is necessary to obtain global-scale estimates ofbiosphere–atmosphere exchange. Based on cross-consistency checks withatmospheric inversions, sun-induced fluorescence (SIF) and dynamic globalvegetation models (DGVMs), here we provide a systematic assessment of thelatest upscaling efforts for gross primary production (GPP) and netecosystem exchange (NEE) of the FLUXCOM initiative, where different machinelearning methods, forcing data sets and sets of predictor variables wereemployed. Spatial patterns of mean GPP are consistent across FLUXCOM and DGVMensembles (R2&gt;0.94 at 1∘ spatial resolution)while the majority of DGVMs show, for 70 % of the land surface, valuesoutside the FLUXCOM range. Global mean GPP magnitudes for 2008–2010 fromFLUXCOM members vary within 106 and 130 PgC yr−1 with the largestuncertainty in the tropics. Seasonal variations in independent SIF estimatesagree better with FLUXCOM GPP (mean global pixel-wise R2∼0.75) than with GPP from DGVMs (mean global pixel-wiseR2∼0.6). Seasonal variations in FLUXCOM NEE show goodconsistency with atmospheric inversion-based net land carbon fluxes,particularly for temperate and boreal regions (R2&gt;0.92).Interannual variability of global NEE in FLUXCOM is underestimated comparedto inversions and DGVMs. The FLUXCOM version which also uses meteorologicalinputs shows a strong co-variation in interannual patterns with inversions(R2=0.87 for 2001–2010). Mean regional NEE from FLUXCOM shows largeruptake than inversion and DGVM-based estimates, particularly in the tropicswith discrepancies of up to several hundred grammes of carbon per square metre per year. Thesediscrepancies can only partly be reconciled by carbon loss pathways that areimplicit in inversions but not captured by the flux tower measurements suchas carbon emissions from fires and water bodies. We hypothesize that acombination of systematic biases in the underlying eddy covariance data, inparticular in tall tropical forests, and a lack of site history effects onNEE in FLUXCOM are likely responsible for the too strong tropical carbonsink estimated by FLUXCOM. Furthermore, as FLUXCOM does not account forCO2 fertilization effects, carbon flux trends are not realistic.Overall, current FLUXCOM estimates of mean annual and seasonal cycles of GPPas well as seasonal NEE variations provide useful constraints of globalcarbon cycling, while interannual variability patterns from FLUXCOM arevaluable but require cautious interpretation. Exploring the diversity ofEarth observation data and of machine learning concepts along with improvedquality and quantity of flux tower measurements will facilitate furtherimprovements of the FLUXCOM approach overall.

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