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Global net land carbon uptake or net biome production (NBP) has increased during recent decades1. Whether its temporal variability and autocorrelation have changed during this period, however, remains elusive, even though an increase in both could indicate an increased potential for a destabilized carbon sink2,3. Here, we investigate the trends and controls of net terrestrial carbon uptake and its temporal variability and autocorrelation from 1981 to 2018 using two atmospheric-inversion models, the amplitude of the seasonal cycle of atmospheric CO2 concentration derived from nine monitoring stations distributed across the Pacific Ocean and dynamic global vegetation models. We find that annual NBP and its interdecadal variability increased globally whereas temporal autocorrelation decreased. We observe a separation of regions characterized by increasingly variable NBP, associated with warm regions and increasingly variable temperatures, lower and weaker positive trends in NBP and regions where NBP became stronger and less variable. Plant species richness presented a concave-down parabolic spatial relationship with NBP and its variability at the global scale whereas nitrogen deposition generally increased NBP. Increasing temperature and its increasing variability appear as the most important drivers of declining and increasingly variable NBP. Our results show increasing variability of NBP regionally that can be mostly attributed to climate change and that may point to destabilization of the coupled carbon–climate system.

Global net land carbon uptake or net biome production (NBP) has increased during recent decades1. Whether its temporal variability and autocorrelation have changed during this period, however, remains elusive, even though an increase in both could indicate an increased potential for a destabilized carbon sink2,3. Here, we investigate the trends and controls of net terrestrial carbon uptake and its temporal variability and autocorrelation from 1981 to 2018 using two atmospheric-inversion models, the amplitude of the seasonal cycle of atmospheric CO2 concentration derived from nine monitoring stations distributed across the Pacific Ocean and dynamic global vegetation models. We find that annual NBP and its interdecadal variability increased globally whereas temporal autocorrelation decreased. We observe a separation of regions characterized by increasingly variable NBP, associated with warm regions and increasingly variable temperatures, lower and weaker positive trends in NBP and regions where NBP became stronger and less variable. Plant species richness presented a concave-down parabolic spatial relationship with NBP and its variability at the global scale whereas nitrogen deposition generally increased NBP. Increasing temperature and its increasing variability appear as the most important drivers of declining and increasingly variable NBP. Our results show increasing variability of NBP regionally that can be mostly attributed to climate change and that may point to destabilization of the coupled carbon–climate system.

The Global Carbon Budget 2018 (GCB2018) estimated by the atmospheric CO2 growth rate, fossil fuel emissions, and modeled (bottom-up) land and ocean fluxes cannot be fully closed, leading to a “budget imbalance,” highlighting uncertainties in GCB components. However, no systematic analysis has been performed on which regions or processes contribute to this term. To obtain deeper insight on the sources of uncertainty in global and regional carbon budgets, we analyzed differences in Net Biome Productivity (NBP) for all possible combinations of bottom-up and top-down data sets in GCB2018: (i) 16 dynamic global vegetation models (DGVMs), and (ii) 5 atmospheric inversions that match the atmospheric CO2 growth rate. We find that the global mismatch between the two ensembles matches well the GCB2018 budget imbalance, with Brazil, Southeast Asia, and Oceania as the largest contributors. Differences between DGVMs dominate global mismatches, while at regional scale differences between inversions contribute the most to uncertainty. At both global and regional scales, disagreement on NBP interannual variability between the two approaches explains a large fraction of differences. We attribute this mismatch to distinct responses to El Niño–Southern Oscillation variability between DGVMs and inversions and to uncertainties in land use change emissions, especially in South America and Southeast Asia. We identify key needs to reduce uncertainty in carbon budgets: reducing uncertainty in atmospheric inversions (e.g., through more observations in the tropics) and in land use change fluxes, including more land use processes and evaluating land use transitions (e.g., using high-resolution remote-sensing), and, finally, improving tropical hydroecological processes and fire representation within DGVMs.

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