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Sensitivity of optimal extension of CO2 observation networks to model transport

MPG-Autoren
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

Patra, P. K., Maksyutov, S., Baker, D., Bousquet, P., Bruhwiler, L., Chen, Y.-H., et al. (2003). Sensitivity of optimal extension of CO2 observation networks to model transport. Tellus, Series B - Chemical and Physical Meteorology, 55(2), 498-511. doi:10.1034/j.1600-0889.2003.00056.x.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000E-D0C8-B
Zusammenfassung
Optimal extensions of the surface CO2 observation network have been determined using 15 global transport models and a time-independent inverse model. The regional average CO2 flux estimate uncertainty is minimized based on the TransCom-3 (level 1) framework. An ensemble model calculation shows that the regional average CO2 flux uncertainties could be reduced to about 0.36, 0.32, 0.28 or 0.26 Gt C yr(-1) per region, from about 0.53 Gt C yr(-1) per region corresponding to the basic network, after adding 5, 10, 15 or 20 optimally located stations, respectively. The additional station locations are mostly found in continental South America and Africa. The distribution of the efficiency in estimation of flux uncertainty reduction per station tends to become more uniform with the extension of the network. We show that the multimodel approach to network design converges if a large enough extension is considered; about 20 stations in this inverse model framework. The reduction in the flux uncertainty for the first few stations depends on the model of atmospheric transport, and is nearly proportional to the simulated signal from local emissions in the surface layer. In addition, it is seen that the simulated spatial and temporal variability of CO2 concentration has significant influence on the distribution of the additional stations as well as determining the regional flux estimate uncertainty.