Li Wei, Ciais Philippe, Wang Yilong, Peng Shushi, Broquet Grégoire, Ballantyne Ashley P, Canadell Josep G, Cooper Leila, Friedlingstein Pierre, Le Quéré Corinne, Myneni Ranga B, Peters Glen P, Piao Shilong, Pongratz Julia
Laboratoire des Sciences du Climat et de l'Environnement/Institut Pierre Simon Laplace, Commissariat à l'Énergie Atomique et aux Énergies Alternatives-CNRS-Université de Versailles Saint-Quentin, Université Paris-Saclay, F-91191 Gif-sur-Yvette, France;
Laboratoire des Sciences du Climat et de l'Environnement/Institut Pierre Simon Laplace, Commissariat à l'Énergie Atomique et aux Énergies Alternatives-CNRS-Université de Versailles Saint-Quentin, Université Paris-Saclay, F-91191 Gif-sur-Yvette, France.
Proc Natl Acad Sci U S A. 2016 Nov 15;113(46):13104-13108. doi: 10.1073/pnas.1603956113. Epub 2016 Oct 31.
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 since 1980, respectively. Our Bayesian fusion of multiple observations reduces uncertainties, thereby allowing us to isolate important variability in global carbon cycle processes.
全球碳预算的传统计算方法将陆地碳汇推断为排放、大气碳积累和海洋碳汇之间的残差。因此，陆地碳汇累积了其他通量项的误差，并且具有最大的不确定性。在此，我们提出一种贝叶斯融合方法，该方法结合了不同碳库中的多种观测数据，以优化1980年至2014年期间的陆地（B）和海洋（O）碳汇、土地利用变化排放（L）以及间接的化石燃料排放（F）。与传统方法相比，贝叶斯优化使B的不确定性降低了41%，O的不确定性降低了46%。L的不确定性降低了47%，而F的不确定性通过自然通量知识略有改善。自1980年以来，海洋和陆地净吸收（B + L）速率分别具有29±8和37±17 Tg C⋅y的正趋势。我们对多种观测数据的贝叶斯融合降低了不确定性，从而使我们能够分离出全球碳循环过程中的重要变异性。
