Articles | Volume 16, issue 18
Atmos. Chem. Phys., 16, 12005–12038, 2016
https://doi.org/10.5194/acp-16-12005-2016
Atmos. Chem. Phys., 16, 12005–12038, 2016
https://doi.org/10.5194/acp-16-12005-2016
Research article
26 Sep 2016
Research article | 26 Sep 2016

Greenhouse gas simulations with a coupled meteorological and transport model: the predictability of CO2

Saroja M. Polavarapu et al.

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Cited articles

Agusti-Panareda, A., Diamantakis, M., Bayona, V., Klappenbach, F., and Butz, A.: Improving the inter-hemispheric gradient of total column atmospheric CO2 and CH4 in simulations with the ECMWF semi-Lagrangian atmospheric global model, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2016-143, in review, 2016.
Aliabadi, A. A, Staebler, R. M., de Grandpré, J., Zadra, A., and Vaillancourt, P. A.: Comparison of Estimated Atmospheric Boundary Layer Mixing Height in the Arctic and Southern Great Plains under Statically Stable Conditions: Experimental and Numerical Aspects, Atmos.-Ocean, 54, 60–74, 2016.
Andrews, D. G., Holton, J. R., and Leovy, C. B.: Middle Atmosphere Dynamics, Academic Press, San Diego, California, 1987.
Baker, D. F., Law, R. M., Gurney, K. R., Rayner, P., Peylin, P., Denning, A. S., Bousquet, P., Bruhwiler, L., Chen, Y.-H., Ciais, P., Fung, I. Y., Heimann, M., John, J., Maki, T., Maksyutov, S., Masarie, K., Prather, M., Pak, B., Taguchi, S., and Zhu, Z.: TransCom 3 inversion intercomparison: Impact of transport model errors on the interannual variability of regional CO2 fluxes, 1988–2003, Global Biogeochem. Cy., 20, GB1002, https://doi.org/10.1029/2004GB002439, 2006a.
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Short summary
CO2 predictions are used to compute model–data mismatches when estimating surfaces fluxes using atmospheric observations together with an atmospheric transport model. By isolating the component of transport error which is due to uncertain meteorological analyses, it is demonstrated that CO2 can only be defined on large spatial scales. Thus, there is a spatial scale below which we cannot infer fluxes simply due to the fact that meteorological analyes are imperfect.
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