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| Titel |
Regional-scale geostatistical inverse modeling of North American CO2 fluxes: a synthetic data study |
| VerfasserIn |
S. M. Gourdji, A. I. Hirsch, K. L. Mueller, V. Yadav, A. E. Andrews, A. M. Michalak |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1680-7316
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Chemistry and Physics ; 10, no. 13 ; Nr. 10, no. 13 (2010-07-08), S.6151-6167 |
| Datensatznummer |
250008608
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| Publikation (Nr.) |
 copernicus.org/acp-10-6151-2010.pdf |
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| Zusammenfassung |
| A series of synthetic data experiments is performed to investigate the
ability of a regional atmospheric inversion to estimate grid-scale CO2 fluxes
during the growing season over North America. The inversions are performed
within a geostatistical framework without the use of any prior flux estimates
or auxiliary variables, in order to focus on the atmospheric constraint
provided by the nine towers collecting continuous, calibrated CO2
measurements in 2004. Using synthetic measurements and their associated
concentration footprints, flux and model-data mismatch covariance parameters
are first optimized, and then fluxes and their uncertainties are estimated at
three different temporal resolutions. These temporal resolutions, which
include a four-day average, a four-day-average diurnal cycle with 3-hourly
increments, and 3-hourly fluxes, are chosen to help assess the impact of
temporal aggregation errors on the estimated fluxes and covariance
parameters. Estimating fluxes at a temporal resolution that can adjust the
diurnal variability is found to be critical both for recovering covariance
parameters directly from the atmospheric data, and for inferring accurate
ecoregion-scale fluxes. Accounting for both spatial and temporal a
priori covariance in the flux distribution is also found to be necessary for
recovering accurate a posteriori uncertainty bounds on the estimated
fluxes. Overall, the results suggest that even a fairly sparse
network of 9 towers collecting continuous CO2 measurements across the
continent, used with no auxiliary information or prior estimates of the flux
distribution in time or space, can be used to infer relatively accurate
monthly ecoregion scale CO2 surface fluxes over North America within
estimated uncertainty bounds. Simulated random transport error is shown to
decrease the quality of flux estimates in under-constrained areas at the
ecoregion scale, although the uncertainty bounds remain realistic. While
these synthetic data inversions do not consider all potential issues
associated with using actual measurement data, e.g. systematic transport
errors or problems with the boundary conditions, they help to highlight the
impact of inversion setup choices, and help to provide a baseline set of CO2
fluxes for comparison with estimates from future real-data
inversions. |
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