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| Titel |
An objective prior error quantification for regional atmospheric inverse applications |
| VerfasserIn |
P. Kountouris, C. Gerbig, K.-U. Totsche, A. J. Dolman, A. G. C. A. Meesters, G. Broquet, F. Maignan, B. Gioli, L. Montagnani, C. Helfter |
| 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 ; 12, no. 24 ; Nr. 12, no. 24 (2015-12-16), S.7403-7421 |
| Datensatznummer |
250118217
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| Publikation (Nr.) |
 copernicus.org/bg-12-7403-2015.pdf |
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| Zusammenfassung |
| Assigning proper prior uncertainties for inverse modelling of CO2 is of
high importance, both to regularise the otherwise ill-constrained inverse
problem and to quantitatively characterise the magnitude and structure of
the error between prior and "true" flux. We use surface fluxes derived from
three biosphere models – VPRM, ORCHIDEE, and 5PM – and compare them against
daily averaged fluxes from 53 eddy covariance sites across Europe for the
year 2007 and against repeated aircraft flux measurements encompassing
spatial transects. In addition we create synthetic observations using modelled
fluxes instead of the observed ones to explore the potential to infer prior
uncertainties from model–model residuals. To ensure the realism of the
synthetic data analysis, a random measurement noise was added to the modelled
tower fluxes which were used as reference. The temporal autocorrelation time
for tower model–data residuals was found to be around 30 days for both VPRM
and ORCHIDEE but significantly different for the 5PM model with 70 days.
This difference is caused by a few sites with large biases between the data
and the 5PM model. The spatial correlation of the model–data residuals for
all models was found to be very short, up to few tens of kilometres but with
uncertainties up to 100 % of this estimation. Propagating this error
structure to annual continental scale yields an uncertainty of 0.06 Gt C
and strongly underestimates uncertainties typically used from atmospheric
inversion systems, revealing another potential source of errors. Long spatial
e-folding correlation lengths up to several hundreds of kilometres were determined
when synthetic data were used. Results from repeated aircraft transects in
south-western France are consistent with those obtained from the tower sites
in terms of spatial autocorrelation (35 km on average) while temporal
autocorrelation is markedly lower (13 days). Our findings suggest that the
different prior models have a common temporal error structure. Separating the
analysis of the statistics for the model data residuals by seasons did not
result in any significant differences of the spatial e-folding correlation
lengths. |
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