期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2016
卷号：113
期号：46
页码：13104-13108
DOI：10.1073/pnas.1603956113
语种：English
出版社：The National Academy of Sciences of the United States of America
摘要：SignificanceThe conventional approach of calculating the global carbon budget makes the land sink the most uncertain of all budget terms. This is because, rather than being constrained by observations, it is inferred as a residual in the budget equation. Here, we overcome this limitation by performing a Bayesian fusion of different available observation-based estimates of decadal carbon fluxes. This approach reduces the uncertainty in the land sink by 41% and in the ocean sink by 46%. These results are significant because they give unprecedented confidence in the role of the increasing land sink in regulating atmospheric CO2, and shed light on the past decadal trend. 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.
