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Atmospheric CO2 inversion validation using vertical profile measurements: Analysis of four independent inversion models

MPS-Authors
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Marshall,  J.
Satellite-based Remote Sensing of Greenhouse Gases, Dr. J. Marshall, Department Biogeochemical Systems, Prof. M. Heimann, Max Planck Institute for Biogeochemistry, Max Planck Society;

/persons/resource/persons62529

Rödenbeck,  C.
Inverse Data-driven Estimation, Dr. C. Rödenbeck, Department Biogeochemical Systems, Prof. M. Heimann, Max Planck Institute for Biogeochemistry, Max Planck Society;

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http://dx.doi.org/10.1029/2010jd014887
(Publisher version)

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Citation

Pickett-Heaps, C. A., Rayner, P. J., Law, R. M., Ciais, P., Patra, P. K., Bousquet, P., et al. (2011). Atmospheric CO2 inversion validation using vertical profile measurements: Analysis of four independent inversion models. Journal of Geophysical Research: Atmospheres, 116, D12305. doi:10.1029/2010jd014887.


Cite as: https://hdl.handle.net/11858/00-001M-0000-000E-DC5A-7
Abstract
We present the results of a validation of atmospheric inversions of CO(2) fluxes using four transport models. Each inversion uses data primarily from surface stations, combined with an atmospheric transport model, to estimate surface fluxes. The validation (or model evaluation) consists of running these optimized fluxes through the forward model and comparing the simulated concentrations with airborne concentration measurements. We focus on profiles from Cape Grim, Tasmania, and Carr, Colorado, while using other profile sites to test the generality of the comparison. Fits to the profiles are generally worse than to the surface data from the inversions and worse than the expected model-data mismatch. Thus inversion estimates are generally not consistent with the profile measurements. The TM3 model does better by some measures than the other three models. Models perform better over Tasmania than Colorado, and other profile sites bear out a general improvement from north to south and from continental to marine locations. There are also errors in the interannual variability of the fit, consistent in time and common across models. This suggests real variations in sources visible to the profile but not the surface measurements.