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Designing Lagrangian experiments to measure regional-scale trace gas fluxes


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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Lin, J. C., Gerbig, C., Wofsy, S. C., Chow, V. Y., Gottlieb, E., Daube, B. C., et al. (2007). Designing Lagrangian experiments to measure regional-scale trace gas fluxes. Journal of Geophysical Research-Atmospheres, 112(13), D13312. doi:10.1029/2006JD008077.

Knowledge of trace gas fluxes at the land surface is essential for understanding the impact of human activities on the composition and radiative balance of the atmosphere. An ability to derive fluxes at the regional scale (on the order of 10(2)-10(4) km 2), at the scale of ecosystems and political borders, is crucial for policy and management responses. Lagrangian ("air mass-following") aircraft experiments have potential for providing direct estimates of regional-scale fluxes by measuring concentration changes in air parcels as they travel over the landscape. Successful Lagrangian experiments depend critically on forecasts of air parcel locations, rate of dispersion of air parcels, and proper assessment of forecast errors. We describe an operational tool to forecast air parcel locations and dispersion and to guide planning of flights for air mass-following experiments using aircraft. The tool consists of a particle dispersion model driven by mesoscale model forecasts from operational centers. The particle model simulates time-reversed motions of air parcels from specified locations, predicting the source regions which influence these locations. Forecast errors are incorporated into planning of Lagrangian experiments using statistics of wind errors derived by comparison with radiosonde data, as well as the model-to-model spread in forecast results. We illustrate the tool's application in a project designed to infer regional CO2 fluxes-the CO2 Budget and Rectification Airborne study, discuss errors in the forecasts, and outline future steps for further improvement of the tool. [References: 53]