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ACP | Articles | Volume 20, issue 3
Atmos. Chem. Phys., 20, 1795–1816, 2020
https://doi.org/10.5194/acp-20-1795-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Atmos. Chem. Phys., 20, 1795–1816, 2020
https://doi.org/10.5194/acp-20-1795-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 14 Feb 2020

Research article | 14 Feb 2020

Uncertainty analysis of a European high-resolution emission inventory of CO2 and CO to support inverse modelling and network design

Ingrid Super et al.

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Cited articles

Amann, M., Bertok, I., Borken-Kleefeld, J., Cofala, J., Heyes, C., Höglund-Isaksson, L., Klimont, Z., Nguyen, B., Posch, M., Rafaj, P., Sandler, R., Schöpp, W., Wagner, F., and Winiwarter, W.: Cost-effective control of air quality and greenhouse gases in Europe: Modeling and policy applications, Environ. Modell. Softw., 26, 1489–1501, https://doi.org/10.1016/j.envsoft.2011.07.012, 2011. 
Andres, R. J., Boden, T. A., and Higdon, D.: A new evaluation of the uncertainty associated with CDIAC estimates of fossil fuel carbon dioxide emission, Tellus B, 66, 1–15, https://doi.org/10.3402/tellusb.v66.23616, 2014. 
Andres, R. J., Boden, T. A., and Higdon, D. M.: Gridded uncertainty in fossil fuel carbon dioxide emission maps, a CDIAC example, Atmos. Chem. Phys., 16, 14979–14995, https://doi.org/10.5194/acp-16-14979-2016, 2016. 
Berner, R. A.: The long-term carbon cycle, fossil fuels and atmospheric composition, Nature, 426, 323–326, https://doi.org/10.1038/nature02131, 2003. 
Boon, A., Broquet, G., Clifford, D. J., Chevallier, F., Butterfield, D. M., Pison, I., Ramonet, M., Paris, J. D., and Ciais, P.: Analysis of the potential of near-ground measurements of CO2 and CH4 in London, UK, for the monitoring of city-scale emissions using an atmospheric transport model, Atmos. Chem. Phys., 16, 6735–6756, https://doi.org/10.5194/acp-16-6735-2016, 2016. 
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Emission data contain uncertainties introduced by the methodology and the data used. We quantified uncertainties in gridded emissions using the uncertainty in underlying data, showing that disaggregation in space and time significantly increases the uncertainty. Understanding uncertainties helps to interpret atmospheric measurements and the gap with modelled concentrations. Moreover, our analyses help identify regions with large uncertainties, which require further scrutiny.
Emission data contain uncertainties introduced by the methodology and the data used. We...
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