Sources of Uncertainty in Regional and Global Terrestrial CO₂ Exchange Estimates

Bastos, A.; O'Sullivan, M.; Ciais, P.; Makowski, D.; Sitch, S.; Friedlingstein, P.; Chevallier, F.; Rödenbeck, C.; Pongratz, J.; Luijkx, I. T.; Patra, P. K.; Peylin, P.; Canadell, J. G.; Lauerwald, R.; Li, W.; Smith, N. E.; Peters, W.; Goll, D. S.; Jain, A.K.; Kato, E.; ... (2020). Sources of Uncertainty in Regional and Global Terrestrial CO₂ Exchange Estimates. Global biogeochemical cycles, 34(2) American Geophysical Union 10.1029/2019GB006393

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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 GCB 2018: (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 GCB 2018 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.

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