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：2020
卷号：117
期号：24
页码：13300-13307
DOI：10.1073/pnas.1919032117
出版社：The National Academy of Sciences of the United States of America
摘要：We report national scale estimates of CO 2 emissions from fossil-fuel combustion and cement production in the United States based directly on atmospheric observations, using a dual-tracer inverse modeling framework and CO 2 and Δ 14 CO 2 measurements obtained primarily from the North American portion of the National Oceanic and Atmospheric Administration’s Global Greenhouse Gas Reference Network. The derived US national total for 2010 is 1,653 ± 30 TgC yr −1 with an uncertainty ( 1 σ ) that takes into account random errors associated with atmospheric transport, atmospheric measurements, and specified prior CO 2 and 14 C fluxes. The atmosphere-derived estimate is significantly larger ( > 3 σ ) than US national emissions for 2010 from three global inventories widely used for CO 2 accounting, even after adjustments for emissions that might be sensed by the atmospheric network, but which are not included in inventory totals. It is also larger ( > 2 σ ) than a similarly adjusted total from the US Environmental Protection Agency (EPA), but overlaps EPA’s reported upper 95% confidence limit. In contrast, the atmosphere-derived estimate is within 1 σ of the adjusted 2010 annual total and nine of 12 adjusted monthly totals aggregated from the latest version of the high-resolution, US-specific “Vulcan” emission data product. Derived emissions appear to be robust to a range of assumed prior emissions and other parameters of the inversion framework. While we cannot rule out a possible bias from assumed prior Net Ecosystem Exchange over North America, we show that this can be overcome with additional Δ 14 CO 2 measurements. These results indicate the strong potential for quantification of US emissions and their multiyear trends from atmospheric observations.
关键词：fossil fuel CO 2 ; radiocarbon ; atmospheric inverse modeling
