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Li, Wei; Ciais, Philippe; Wang, Yilong; Peng, Shushi; Broquet, Gregoire; Ballantyne, Ashley P.; Canadell, Josep G.; Cooper, Leila; Friedlingstein, Pierre; Le Quéré, Corinne; Myneni, Ranga B.; Peters, Glen P.; Piao, Shilong and Pongratz, Julia ORCID logoORCID: https://orcid.org/0000-0003-0372-3960 (2016): Reducing uncertainties in decadal variability of the global carbon budget with multiple datasets. In: Proceedings of the National Academy of Sciences of the United States of America : PNAS, Vol. 113, No. 46: pp. 13104-13108

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Conventional calculations of the global carbon budget infer the land sink as a residual between emissions, atmospheric accumulation, and the ocean sink. Thus, the land sink accumulates the errors from the other flux terms and bears the largest uncertainty. Here, we present a Bayesian fusion approach that combines multiple observations in different carbon reservoirs to optimize the land (B) and ocean (O) carbon sinks, land use change emissions (L), and indirectly fossil fuel emissions (F) from 1980 to 2014. Compared with the conventional approach, Bayesian optimization decreases the uncertainties in B by 41% and in O by 46%. The L uncertainty decreases by 47%, whereas F uncertainty is marginally improved through the knowledge of natural fluxes. Both ocean and net land uptake (B + L) rates have positive trends of 29 +/- 8 and 37 +/- 17 Tg C.y(-2) since 1980, respectively. Our Bayesian fusion of multiple observations reduces uncertainties, thereby allowing us to isolate important variability in global carbon cycle processes.

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