Aggregated regional estimates of net atmosphere-land CO2 fluxes from the five atmospheric inversions and 16 Dynamic Global Vegetation Models, supplemental data to Bastos et al, 2019 (https://doi.org/10.1029/2019GB006393)

The data provided here are aggregated regional estimates of net atmosphere-land CO2 fluxes from the five atmospheric inversions (Chevallier et al., 2005; Rödenbeck et al., 2003; van der Laan-Luijkx et al., 2017; Saeki and Patra, 2017) and 16 Dynamic Global Vegetation Models (TRENDY-v7) that contributed to the latest Global Carbon Budget (Le Quéré et al., 2018b).
The data provided here are aggregated regional estimates of net atmosphere-land CO2 fluxes from the five atmospheric inversions (Chevallier et al., 2005; Rödenbeck et al., 2003; van der Laan-Luijkx et al., 2017; Saeki and Patra, 2017) and 16 Dynamic Global Vegetation Models (TRENDY-v7) that contributed to the latest Global Carbon Budget (Le Quéré et al., 2018b). Inversions: The five inversions provided here are all based on in-situ CO2 observation measurements from 1) the Cooperative Global Atmospheric Data Integration Project; (2017): Multi-laboratory compilation of atmospheric carbon dioxide data obspack_co2_1_GLOBALVIEWplus_v3.2_2017_11_02 for the period 1957-2016; NOAA Earth System Research Laboratory, Global Monitoring Division. http://dx.doi.org/10.15138/G3704H; (2) Carbontracker Team; (2018) : Compilation of near real time atmospheric, carbon dioxide data; obspack_co2_1_NRT_v4.2_2018-04-06; NOAA Earth System Research Laboratory, Global Monitoring Division. http://doi.org/10.15138/G3RP8K. A complete description of the different atmospheric inversions can be found in Table A3 in Le Quéré et al. (2018b). Here we add two versions of CarboScope (Rödenbeck et al., 2003), both covering a period longer than 30 years: s76 (1976-2017, 8 stations) and s85 (1985-2017, 21 stations). As in Le Quéré et al. (2018b), we adjusted the ocean and land fluxes for differences in fossil fuel emission (EFF ) priors using as reference EFF used by CAMS (EDGAR (Olivier et al., 2017) scaled to the CDIAC (Marland and Andres, 2008)) over large latitudinal bands. However, this is only a first-order correction as the biases in EFF not only affect the flux estimation of the region in question but also the neighbouring regions (Saeki and Patra, 2017). The inversion surface fluxes were remapped to a regular 1x1 degree lat/lon grid and then aggregated to the 18 land regions in Tian et al. (2018). Based on the data-driven estimates of fluvial exports of organic and inorganic C to the coast, Zsche