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Journal Article

TransCom 3 CO2 inversion intercomparison: 2. Sensitivity of annual mean results to data choices

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Gloor,  M.
Tall Tower Atmospheric Gas Measurements, Dr. J. Lavrič, 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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Citation

Law, R. M., Chen, Y.-H., Gurney, K. R., Baker, D., Bousquet, P., Bruhwiler, L., et al. (2003). TransCom 3 CO2 inversion intercomparison: 2. Sensitivity of annual mean results to data choices. Tellus, Series B - Chemical and Physical Meteorology, 55(2), 580-595.


Cite as: https://hdl.handle.net/11858/00-001M-0000-000E-D09F-9
Abstract
TransCom 3 is an intercomparison project for CO2 source inversions. Annual mean CO2 concentration data are used to estimate CO2 sources using 16 different atmospheric transport models. Here we test the sensitivity of the inversion to the concentration data. We examine data network choice, time period of data, baseline data selection and the choice of data uncertainty used. We find that in most cases regional source estimates lie within the source uncertainty range of the control inversion. This indicates that the estimated sources are relatively insensitive to the changes in data that were tested. In the data network tests, only the Australian region source estimates varied over a much larger range than that given by the control case uncertainty estimate. For the other regions, the sensitivity to data network was within or close to the uncertainty range. Most of the sensitivity was found to be associated with a small number of sites (e.g. Darwin, Easter Island). These sites are often identified by the inability of the inversion to fit the data at these locations. The model-mean inversion values are mostly insensitive to the time period of data used, with the exception of temperate North America and the tropical Indian ocean. Data selection has a small impact on source estimates for the mean across models, but individual model sensitivity can be large. The magnitude of data uncertainties controls the relative magnitude of the estimated source uncertainty and the spread in model source estimates. Smaller data uncertainties lead to larger differences in source estimates between models. Overall, the data sensitivity tests performed here support the robustness of the control inversion source estimates presented in Gurney et al. (2003. Tellus 55B, this issue). The test results also provide guidance in setting up and interpreting other inversions.