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Biogeosciences An interactive open-access journal of the European Geosciences Union
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Volume 13, issue 14
Biogeosciences, 13, 4237–4251, 2016
https://doi.org/10.5194/bg-13-4237-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Biogeosciences, 13, 4237–4251, 2016
https://doi.org/10.5194/bg-13-4237-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 26 Jul 2016

Research article | 26 Jul 2016

Evaluation of 4 years of continuous δ13C(CO2) data using a moving Keeling plot method

Sanam Noreen Vardag et al.
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Alden, C. B., Miller, B., J., and White, J. W.: Can bottom-up ocean CO2 fluxes be reconciled with atmospheric 13C observations?, Tellus B, 62, 369–388, https://doi.org/10.1111/j.1600-0889.2010.00481.x, 2010.
Andres, R. J., Marland, G., Boden, T., and Bischof, S.: Carbon Dioxide Emissions from Fossil Fuel Consumption and Cement Manufacture, 1751–1991; and an Estimate of Their Isotopic Composition and Latitudinal Distribution, Environmental Sciences, http://www.osti.gov/scitech/biblio/10185357 (last access: 22 July 2016), 1994.
Ballantyne, A. P., Miller, J. B., and Tans, P. P.: Apparent seasonal cycle in isotopic discrimination of carbon in the atmosphere and biosphere due to vapor pressure deficit, Global Biogeochem. Cy., 24, gB3018, https://doi.org/10.1029/2009GB003623, 2010.
Ballantyne, A. P., Miller, J. B., Baker, I. T., Tans, P. P., and White, J. W. C.: Novel applications of carbon isotopes in atmospheric CO2: what can atmospheric measurements teach us about processes in the biosphere?, Biogeosciences, 8, 3093–3106, https://doi.org/10.5194/bg-8-3093-2011, 2011.
Bastos, A., Janssens, I. A., Gouveia, C. M., Trigo, R. M., Ciais, P., Chevallier, F., Peñuelas, J., Rödenbeck, C., Piao, S., Friedlingstein, P., and Running, S. W.: European land CO2 sink influenced by NAO and East-Atlantic Pattern coupling, Nature Communications, 7, 10315, https://doi.org/10.1038/ncomms10315, 2016.
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Using a synthetic dataset, we show how to best determine the mean source signature, δS, at high temporal resolution using continuous CO2 and δ13C(CO2) data. We apply this method to measured data from Heidelberg and find a distinct seasonal cycle of δS. Disentangling this record into its source components requires the isotopic end members of CO2 from the biosphere and those from the fuel mix. They can be estimated from the δS record, but only when their relative share is close to 100 %.
Using a synthetic dataset, we show how to best determine the mean source signature, δS, at high...
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