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Time-dependent atmospheric CO2 inversions based on interannually varying tracer transport

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/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;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons62417

Houweling,  S.
Department Biogeochemical Systems, Prof. M. Heimann, Max Planck Institute for Biogeochemistry, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons62385

Gloor,  M.
Tall Tower Atmospheric Gas Measurements, Dr. J. Lavrič, Department Biogeochemical Systems, Prof. M. Heimann, Max Planck Institute for Biogeochemistry, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons62402

Heimann,  M.
Department Biogeochemical Systems, Prof. M. Heimann, Max Planck Institute for Biogeochemistry, Max Planck Society;

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Zitation

Rödenbeck, C., Houweling, S., Gloor, M., & Heimann, M. (2003). Time-dependent atmospheric CO2 inversions based on interannually varying tracer transport. Tellus, Series B - Chemical and Physical Meteorology, 55(2), 488-497. doi:10.1034/j.1600-0889.2003.00033.x.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000E-D0DC-0
Zusammenfassung
The use of inverse calculations to estimate surface CO2 fluxes from atmospheric concentration measurements has gained large attention in recent years. The success of an inversion will, among other factors, depend strongly on how realistically atmospheric tracer transport is represented by the employed transport model, as it links surface CO2 fluxes to modelled concentrations at the location of measurement stations. We present sensitivity studies demonstrating that transport modelling should be based on interannually varying meteorology, as compared to the traditional use of repeating a single year's winds only. Moreover, we propose an improved procedure of representing the concentration sampling in the model, which allows consistency with the measurements and uses their information content more efficiently. In further sensitivity tests, we estimate the effect of different spatial transport model resolutions and different meteorological driver data sets. Finally, we assess the quality of the inversion results with the help of independent measurements and flux estimates, and preliminarily discuss some of the resulting features.