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Technical Note: A new coupled system for global-to-regional downscaling of CO2 concentration estimation

MPG-Autoren
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Trusilova,  K.
Department Biogeochemical Systems, Prof. M. Heimann, Max Planck Institute for Biogeochemistry, Max Planck Society;

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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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Gerbig,  C.
Airborne Trace Gas Measurements and Mesoscale Modelling, Dr. habil. C. Gerbig, Department Biogeochemical Systems, Prof. M. Heimann, Max Planck Institute for Biogeochemistry, Max Planck Society;

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

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Zitation

Trusilova, K., Rödenbeck, C., Gerbig, C., & Heimann, M. (2010). Technical Note: A new coupled system for global-to-regional downscaling of CO2 concentration estimation. Atmospheric Chemistry and Physics, 10(7), 3205-3213. doi:10.5194/acp-10-3205-2010.


Zitierlink: https://hdl.handle.net/11858/00-001M-0000-000E-DB11-F
Zusammenfassung
We introduce a global-to-regional nesting scheme for atmospheric transport models used in simulating concentrations of green house gases from globally distributed surface fluxes. The coupled system of the regional Stochastic Time-Inverted Lagrangian Transport (STILT) model and the global atmospheric transport model (TM3) is designed to resolve atmospheric trace gas concentrations at high temporal and spatial resolutions in a specified domain e. g. for regional inverse applications. The nesting technique used for the coupling is based on a decomposition of the atmospheric concentration signal into a far-field and a near-field contribution enabling the usage of different model types for global (Eulerian) and regional (Lagrangian) scales. For illustrating the performance of the coupled TM3-STILT system we compare simulated mixing ratios of carbon dioxide with available observations at 10 sites in Europe. For all chosen sites the TM3-STILT provided higher correlations between the modelled and the measured time series than the TM3 global model. Autocorrelation analysis demonstrated that the TM3-STILT model captured temporal variability of measured tracer concentrations better than TM3 at most sites.