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| Titel |
Improving the LPJ-GUESS modelled carbon balance with a particle filter data assimilation technique |
| VerfasserIn |
Andrew McRobert, Marko Scholze, Sarah Kemp, Ben Smith |
| Konferenz |
EGU General Assembly 2015
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 17 (2015) |
| Datensatznummer |
250111061
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| Publikation (Nr.) |
 EGU/EGU2015-11125.pdf |
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| Zusammenfassung |
| \begin{document}
The recent increases in anthropogenic carbon dioxide ($CO_2$)
emissions have disrupted the equilibrium in the global carbon cycle
pools with the ocean and terrestrial pools increasing their respective
storages to accommodate roughly half of the anthropogenic
increase. Dynamic global vegetation models (DGVM) have been developed
to quantify the modern carbon cycle changes. In this study, a particle
filter data assimilation technique has been used to calibrate the
process parameters in the DGVM LPJ-GUESS (Lund-Potsdam-Jena
General Ecosystem Simulator). LPJ-GUESS simulates individual
plant function types (pft) as a competitive balance within high
resolution forest patches. Thirty process parameters have been
optimized twice, using both a sequential and iterative method of
particle filter. The iterative method runs the model for the full time
period of thirteen years and then evaluates the cost function from the
mismatch of observations and model results before adjusting the
parameters and repeating the full time period. The sequential method
runs the model and particle filter for each year of the time series in
order, adjusting the parameters between each year, then loops back to
beginning of the series to repeat. For each particle, the model output
of NEP (Net Ecosystem Productivity) is compared to eddy flux
measurements from ICOS flux towers to minimize the cost function. A
high-resolution regional carbon balance has been simulated for central
Sweden using a network of several ICOS flux towers.
\end{document} |
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