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| Titel |
A comparison of different inverse carbon flux estimation approaches for application on a regional domain |
| VerfasserIn |
L. F. Tolk, A. J. Dolman, A. G. C. A. Meesters, W. Peters |
| 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 ; 11, no. 20 ; Nr. 11, no. 20 (2011-10-18), S.10349-10365 |
| Datensatznummer |
250010131
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| Publikation (Nr.) |
 copernicus.org/acp-11-10349-2011.pdf |
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| Zusammenfassung |
We have implemented six different inverse carbon flux estimation methods in
a regional carbon dioxide (CO2) flux modeling system for the
Netherlands. The system consists of the Regional Atmospheric Mesoscale
Modeling System (RAMS) coupled to a simple carbon flux scheme which is run
in a coupled fashion on relatively high resolution (10 km). Using an Ensemble
Kalman filter approach we try to estimate spatiotemporal carbon exchange
patterns from atmospheric CO2 mole fractions over the Netherlands for a
two week period in spring 2008. The focus of this work is the different
strategies that can be employed to turn first-guess fluxes into optimal
ones, which is known as a fundamental design choice that can affect the
outcome of an inversion significantly.
Different state-of-the-art approaches with respect to the estimation of net
ecosystem exchange (NEE) are compared quantitatively: (1) where NEE is
scaled by one linear multiplication factor per land-use type, (2) where the
same is done for photosynthesis (GPP) and respiration (R) separately with
varying assumptions for the correlation structure, (3) where we solve for
those same multiplication factors but now for each grid box, and (4) where
we optimize physical parameters of the underlying biosphere model for each
land-use type. The pattern to be retrieved in this pseudo-data experiment is
different in nearly all aspects from the first-guess fluxes, including the
structure of the underlying flux model, reflecting the difference between
the modeled fluxes and the fluxes in the real world. This makes our study a
stringent test of the performance of these methods, which are currently
widely used in carbon cycle inverse studies.
Our results show that all methods struggle to retrieve the spatiotemporal
NEE distribution, and none of them succeeds in finding accurate domain
averaged NEE with correct spatial and temporal behavior. The main cause is
the difference between the structures of the first-guess and true CO2
flux models used. Most methods display overconfidence in their estimate as a
result. A commonly used daytime-only sampling scheme in the transport model
leads to compensating biases in separate GPP and R scaling factors that are
readily visible in the nighttime mixing ratio predictions of these systems.
Overall, we recommend that the estimate of NEE scaling factors should not be
used in this regional setup, while estimating bias factors for GPP and R for
every grid box works relatively well. The biosphere parameter inversion
performs good compared to the other inversions at simultaneously producing
space and time patterns of fluxes and CO2 mixing ratios, but
non-linearity may significantly reduce the information content in the
inversion if true parameter values are far from the prior estimate. Our
results suggest that a carefully designed biosphere model parameter
inversion or a pixel inversion of the respiration and GPP multiplication
factors are from the tested inversions the most promising tools to optimize
spatiotemporal patterns of NEE. |
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