DOI: 10.18160/1svh-3dnb
title: 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)

description: 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, Zscheischler et al. (2017) produced a spatially-explicit data set of climatological land-ocean C transfers at 1x1 degree lat/lon resolution and includes the fluxes from dissolved inorganic carbon from atmospheric origin and from weathering and Dissolved and Particulate Organic Carbon (DOC and POC). In this study, the DOC and POC exports of this dataset were rescaled per basin to match the estimates of Resplandy et al. (2018). After aggregating these rescaled estimates to the 18 land regions, we subtracted the fluvial C exports from the inversion net surface CO2 flux over land, to calculate regional net biospheric production (NBP) that can be compared with the DGVM estimates. The references to the original datasets, user requirements, and links to their repositories are provided in the spreadsheet. Dynamic Global Vegetation Models (DGVMs): Here we use the simulations of for GCB2018 (simulation S3) all 16 models are forced with: (i) observed climate from CRU (Harris et al., 2014) and JRA-55 (Kobayashi et al., 2015) datasets following the methodology in Viovy (2016) (CRUJRA); global CO2 concentration from NOAA/ESRL (Dlugokencky and Tans, 2018); (iii) land-use change transitions the and land-management fields from the harmonised land-use change data (LUH2v2.1h) dataset (Hurtt et al., 2011, 2017) based on HYDE 3.1 5 (Klein Goldewijk et al., 2011); and (iv) gridded data of nitrogen deposition when nitrogen-cycling is simulated by models. Some models include natural disturbance (mainly fires) and nutrient cycling (nitrogen in ten models, and nitrogen and phosphorus in one model). The processes simulated by each model can be found in Table A1 of Le Quéré et al. (2018). We further provide results for S2 simulation, which is forced with CO2 and climate changes only, and keeping a fixed land-cover map (Le Quéré et al., 2018b). Outputs of monthly net biospheric production (NBP) from simulations S2 and S3 were first resampled to a common 1x1 degree lat/lon grid, and then aggregated for each region. The references of the model versions and user requirements are provided in the spreadsheet. Chevallier, F., Fisher, M., Peylin, P., Serrar, S., Bousquet, P., Bréon, F.-M., Chédin, A., and Ciais, P.: Inferring CO2sources and sinks from satellite observations: Method and application to TOVS data, J. Geophys. Res., 110, https://doi.org/10.1029/2005jd006390, 2005. Rödenbeck, C., Houweling, S., Gloor, M., and Heimann, M.: CO 2 flux history 1982–2001 inferred from atmospheric data using a global inversion of atmospheric transport, Atmospheric Chemistry and Physics, 3, 1919–1964, 2003. van der Laan-Luijkx, I. T., van der Velde, I. R., van der Veen, E., Tsuruta, A., Stanislawska, K., Babenhauserheide, A., Zhang, H. F., Liu, Y., He,W., Chen, H., Masarie, K. A., Krol, M. C., and Peters,W.: The CarbonTracker Data Assimilation Shell (CTDAS) v1.0: implementation and global carbon balance 2001–2015, Geosci. Model Dev., 10, 2785–2800, https://doi.org/10.5194/gmd-10-2785-2017, 2017. Patra, P. K., Takigawa, M., Watanabe, S., Chandra, N., Ishijima, K., and Yamashita, Y.: Improved Chemical Tracer Simulation by MIROC4.0-based Atmospheric Chemistry-Transport Model (MIROC4-ACTM), Sola, 14, 91–96, 2018. Le Quéré, C., et al. Global Carbon Budget 2018, Earth System Science Data, 10, 2141–2194, https://doi.org/10.5194/essd-10-2141-2018, https://www.earth-syst-sci-data.net/10/2141/2018/, 2018b. Olivier, J. G. J., Schure, K. M., and Peters, J. A. H. W.: Trends in global CO2 and total greenhouse gas emissions, PBL Netherlands Environmental Assessment Agency, p. 5, 2017. Marland, G., T. B. and Andres, R.: Global, Regional, and National Fossil Fuel CO2 Emissions. In Trends: A Compendium of Data on Global Change., Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, Oak Ridge, Tenn., U.S.A., 2008. Saeki, T. and Patra, P. K.: Implications of overestimated anthropogenic CO 2 emissions on East Asian and global land CO 2 flux inversion, Geoscience Letters, 4, 9, 2017. Tian, H., Yang, J., Lu, C., Xu, R., Canadell, J., Jackson, R., Arneth, A., Chang, J., Chen, G., Ciais, P., et al.: The global N2O Model Intercomparison Project (NMIP), B. Am. Meteorol. Soc., 99, 1231–1251, 2018 Zscheischler, J., Mahecha, M. D., Avitabile, V., Calle, L., Carvalhais, N., Ciais, P., Gans, F., Gruber, N., Hartmann, J., Herold, M., et al.: Reviews and syntheses: An empirical spatiotemporal description of the global surface-atmosphere carbon fluxes: opportunities and data limitations, Biogeosciences, 14, 3685–3703, 2017. Resplandy, L., Keeling, R., Rödenbeck, C., Stephens, B., Khatiwala, S., Rodgers, K., Long, M., Bopp, L., and Tans, P.: Revision of global carbon fluxes based on a reassessment of oceanic and riverine carbon transport, Nature Geoscience, 11, 504, 2018.