@article{562c4b487ed5405f81e3c0002050fcae,
title = "Estimating US fossil fuel CO2 emissions from measurements of 14C in atmospheric CO2",
abstract = "We report national scale estimates of CO2 emissions from fossilfuel combustion and cement production in the United States based directly on atmospheric observations, using a dual-tracer inverse modeling framework and CO2 and Δ14CO2 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 (1σ) that takes into account random errors associated with atmospheric transport, atmospheric measurements, and specified prior CO2 and 14C fluxes. The atmosphere-derived estimate is significantly larger (>3σ) than US national emissions for 2010 from three global inventories widely used for CO2 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 (>2σ) 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 1σ 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 Δ14CO2 measurements. These results indicate the strong potential for quantification of US emissions and their multiyear trends from atmospheric observations.",
keywords = "Atmospheric inverse modeling, Fossil fuel CO, Radiocarbon",
author = "Sourish Basu and Lehman, {Scott J.} and Miller, {John B.} and Andrews, {Arlyn E.} and Colm Sweeney and Gurney, {Kevin R.} and Xiaomei Xu and John Southon and Tans, {Pieter P.}",
note = "Funding Information: ACKNOWLEDGMENTS. Most inversions in Table 2 were run on NOAA{\textquoteright}s Theia high-performance computing system. Inversion ensembles for error estimates were run on the NASA High End Computing system Pleiades under Grant HEC-SMD-18-1805. This work was supported by NOAA grants (S.J.L. and J.B.M.) and NASA+NOAA grants (K.R.G., J.B.M., and S.J.L.). Chad Wolak, Stephen Morgan, and Patrick Cappa prepared samples from the GGGRN for ∆14CO2 measurement. Tomohiro Oda provided estimates of emissions from aviation. Ingeborg Levin provided measurements of ∆14CO2 at Cape Grimm (CGO) in Australia. ∆14CO2 samples from Argyle, Maine, were analyzed under the auspices of the US Department of Energy by the Center for Accelerator Mass Spectrometry at Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. Atmospheric CO2 measurements were courtesy of the data providers to ObsPack GV+ 3.2. Funding Information: Most inversions in Table 2 were run on NOAA's Theia high-performance computing system. Inversion ensembles for error estimates were run on the NASA High End Computing system Pleiades under Grant HEC-SMD-18-1805. This work was supported by NOAA grants (S.J.L. and J.B.M.) and NASA+NOAA grants (K.R.G., J.B.M., and S.J.L.). Chad Wolak, Stephen Morgan, and Patrick Cappa prepared samples from the GGGRN for ?14CO2 measurement. Tomohiro Oda provided estimates of emissions from aviation. Ingeborg Levin provided measurements of ?14CO2 at Cape Grimm (CGO) in Australia. ?14CO2 samples from Argyle, Maine, were analyzed under the auspices of the US Department of Energy by the Center for Accelerator Mass Spectrometry at Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. Atmospheric CO2 measurements were courtesy of the data providers to ObsPack GV+ 3.2. Publisher Copyright: {\textcopyright} 2020 National Academy of Sciences. All rights reserved.",
year = "2020",
month = jun,
day = "16",
doi = "10.1073/pnas.1919032117",
language = "English (US)",
volume = "117",
pages = "13300--13307",
journal = "Proceedings of the National Academy of Sciences of the United States of America",
issn = "0027-8424",
publisher = "National Academy of Sciences",
number = "24",
}