Acknowledgements. We would like to thank Martin Jung, Jakob A. Nelson, Sophia Walther, and the FLUXCOM team for their structuralsupport, feedback and discussion. The Authors would like to thank the producers of the Inversion data included in this study: Ingrid Luijkxand Wouter Peters (CTE), Frederic Chevallier and the Copernicus Atmosphere Monitoring Service (CAMS), Christian Roedenbeck (JenaCarboscope sEXTocNEET), Yosuke Niwa (NISMON-CO2), and Liang Feng and Paul Palmer (UoE). This research was funded by theEuropean Research Council (ERC) Synergy Grant ’Understanding and modeling the Earth System with Machine Learning (USMILE)’under the Horizon 2020 research and innovation programme (Grant Agreement No. 855187) This work used eddy covariance data acquired by the FLUXNET community and in particular by the following networks: AmeriFlux(U.S. Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program (DE-FG02-04ER63917 and DE-FG02-04ER63911)), AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada (supported by CFCAS,NSERC, BIOCAP, Environment Canada, and NRCan), GreenGrass, KoFlux, LBA, NECC, OzFlux, TCOS-Siberia, USCCC. We acknowl-edge the financial support to the eddy covariance data harmonization provided by CarboEuropeIP, FAO-GTOS-TCO, iLEAPS, Max PlanckInstitute for Biogeochemistry, National Science Foundation, University of Tuscia, Université Laval and Environment Canada and US Depart-ment of Energy and the database development and technical support from Berkeley Water Center, Lawrence Berkeley National Laboratory,Microsoft Research eScience, Oak Ridge National Laboratory, University of California - Berkeley, University of Virginia