Articles | Volume 26, issue 12
https://doi.org/10.5194/acp-26-9257-2026
https://doi.org/10.5194/acp-26-9257-2026
Research article
 | 
01 Jul 2026
Research article |  | 01 Jul 2026

Investigating information transfer in CO2 flux inversions: an analysis of ensemble Kalman filter based on Monte Carlo simulations

Shidong Fan and Ying Li

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Cited articles

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Basu, S., Miller, J. B., and Lehman, S.: Separation of biospheric and fossil fuel fluxes of CO2 by atmospheric inversion of CO2 and 14CO2 measurements: Observation System Simulations, Atmos. Chem. Phys., 16, 5665–5683, https://doi.org/10.5194/acp-16-5665-2016, 2016. 
Basu, S., Lehman, S. J., Miller, J. B., Andrews, A. E., Sweeney, C., Gurney, K. R., Xu, X., Southon, J., and Tans, P. P.: Estimating US fossil fuel CO2 emissions from measurements of C-14 in atmospheric CO2, P. Natl. Acad. Sci. USA, 117, 13300–13307, https://doi.org/10.1073/pnas.1919032117, 2020. 
Beck, V., Koch, T., Kretschmer, R., Marshall, J., Ahmadov, R., Gerbig, C., Pillai, D., and Heimann, M.: The WRF Greenhouse Gas Model (WRF-GHG), Max-Planck-Institut für Biogeochemie, https://www.bgc-jena.mpg.de/5363366/tech_report25.pdf (last access: 23 June 2026), 2011. 
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Short summary
Atmospheric CO2 inversions infer surface fluxes from concentration measurements, yet results vary widely across systems. Using ensemble simulations as well as variational theory, this study shows that the assumed spatial and temporal correlations of surface fluxes largely determine how observational information propagates. Transport shapes patterns, but prior correlations control scale and strength, explaining signal amplification, dilution, and flux misattribution.
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