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| Titel |
Structure of the transport uncertainty in mesoscale inversions of CO2 sources and sinks using ensemble model simulations |
| VerfasserIn |
T. Lauvaux, O. Pannekoucke, C. Sarrat, F. Chevallier, P. Ciais, J. Noilhan, P. J. Rayner |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1726-4170
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| Digitales Dokument |
URL |
| Erschienen |
In: Biogeosciences ; 6, no. 6 ; Nr. 6, no. 6 (2009-06-19), S.1089-1102 |
| Datensatznummer |
250003840
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| Publikation (Nr.) |
 copernicus.org/bg-6-1089-2009.pdf |
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| Zusammenfassung |
We study the characteristics of a statistical ensemble of mesoscale
simulations in order to estimate the model error in the simulation of
CO2 concentrations. The ensemble consists of ten members and
the reference simulation using the operationnal short range forecast
PEARP, perturbed using the Singular Vector technique. We then used this
ensemble of simulations as the initial and boundary conditions for the
meso scale model (Méso-NH) simulations, which uses CO2
fluxes from the ISBA-A-gs land surface model. The final ensemble
represents the model dependence to the boundary conditions, conserving
the physical properties of the dynamical schemes, but excluding the
intrinsic error of the model.
First, the variance of our ensemble is estimated over the domain, with
associated spatial and temporal correlations. Second, we extract the
signal from noisy horizontal correlations, due to the limited size
ensemble, using diffusion equation modelling. The computational cost
of such ensemble limits the number of members (simulations) especially
when running online the carbon flux and the atmospheric models. In the
theory, 50 to 100 members would be required to explore the overall
sensitivity of the ensemble. The present diffusion model allows us to
extract a significant part of the noisy error, and makes this study
feasable with a limited number of simulations. Finally, we compute the
diagonal and non-diagonal terms of the observation error covariance
matrix and introduced it into our CO2 flux matrix inversion
for 18 days of the 2005 intensive campaign CERES over the South West
of France. Variances are based on model-data mismatch to ensure we
treat model bias as well as ensemble dispersion, whereas spatial and
temporal covariances are estimated with our method.
The horizontal structure of the ensemble variance manifests the
discontinuities of the mesoscale structures during the day, but
remains locally driven during the night. On the vertical, surface
layer variance shows large correlations with the upper levels in the
boundary layer (> 0.6), dropping to 0.4 with the lower levels of
the free troposphere. Large temporal correlations were found during
the afternoon (> 0.5 for several hours), reduced during the
night. The diffusion equation model extracted relevant error
covariance signals horizontally, with reduced correlations over
mountain areas and during the night over the continent. The posterior
error reduction on the inverted CO2 fluxes accounting for the
model error correlations illustrates the predominance of the temporal
over the spatial correlations when using tower-based CO2 concentration observations. |
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