<b>Jacobson</b>, A. R., J. B. <b>Miller</b>, A. Ballantyne, S. Basu, L. Bruhwiler, A. Chatterjee, S. Denning, and L. Ott, 2018: Chapter 8: Observations of atmospheric carbon dioxide and methane. In Second State of the Carbon Cycle Report (SOCCR2): A Sustained Assessment Report [Cavallaro, N., G. Shrestha, R. Birdsey, M. A. Mayes, R. G. Najjar, S. C. Reed, P. Romero-Lankao, and Z. Zhu (eds.)]. U.S. Global Change Research Program, Washington, DC, USA, pp. 337-364, https://doi.org/10.7930/SOCCR2.2018.Ch8.
Observations of Atmospheric Carbon Dioxide and Methane
In a potential future when carbon emissions have a significant economic cost and international agreements to control emissions are in place, verifying claims of emissions mitigation and assessing the efficacy of mitigation strategies will be necessary. In addition to international agreements, 18 states have plans in place to reduce GHG emissions. Bottom-up methods based on economic, agricultural, and forest inventories provide much of the basis for these calculations. These methods are susceptible to systematic errors, including incomplete sectoral coverage, misreporting, and the use of uncertain emissions factors. Top-down methods derive emissions budgets consistent with atmospheric concentrations of GHGs, but they also contain systematic errors resulting from imperfect knowledge of atmospheric transport and lack of observations. Although these uncertainties place limits on the accuracy of top-down emissions estimates, atmospheric data still provide strong constraints on GHG emissions from local to global scales (e.g., Levin et al., 2010). As shown by the example of Brandt et al. (2014), natural gas super emitters can be localized from in situ observations even when they have not previously been identified by inventories. As described in this chapter, both existing and new technologies can provide independent and complementary information and help reconcile emissions estimates from the bottom-up and top-down approaches. From a carbon management and decision perspective, collecting and utilizing information from atmospheric data could provide additional information in regions and sectors where uncertainties in bottom-up inventories are large. Top-down emissions estimates can be produced with low latency and with robust uncertainty quantification. Together, these two methods can provide robust observational constraints on emissions at a variety of scales.
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