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dc.contributor.authorPost, Hanna
dc.contributor.authorVrugt, Jasper A.
dc.contributor.authorFox, Andrew
dc.contributor.authorVereecken, Harry
dc.contributor.authorHendricks Franssen, Harrie-Jan
dc.date.accessioned2017-06-02T22:24:31Z
dc.date.available2017-06-02T22:24:31Z
dc.date.issued2017-03
dc.identifier.citationEstimation of Community Land Model parameters for an improved assessment of net carbon fluxes at European sites 2017, 122 (3):661 Journal of Geophysical Research: Biogeosciencesen
dc.identifier.issn21698953
dc.identifier.doi10.1002/2015JG003297
dc.identifier.urihttp://hdl.handle.net/10150/623871
dc.description.abstractThe Community Land Model (CLM) contains many parameters whose values are uncertain and thus require careful estimation for model application at individual sites. Here we used Bayesian inference with the DiffeRential Evolution Adaptive Metropolis (DREAM((zs))) algorithm to estimate eight CLM v. 4.5 ecosystem parameters using 1 year records of half- hourly net ecosystem CO2 exchange (NEE) observations of four central European sites with different plant functional types (PFTs). The posterior CLM parameter distributions of each site were estimated per individual season and on a yearly basis. These estimates were then evaluated using NEE data from an independent evaluation period and data from " nearby" FLUXNET sites at similar to 600 km distance to the original sites. Latent variables (multipliers) were used to treat explicitly uncertainty in the initial carbon- nitrogen pools. The posterior parameter estimates were superior to their default values in their ability to track and explain the measured NEE data of each site. The seasonal parameter values reduced with more than 50% (averaged over all sites) the bias in the simulated NEE values. The most consistent performance of CLM during the evaluation period was found for the posterior parameter values of the forest PFTs, and contrary to the C3-grass and C3-crop sites, the latent variables of the initial pools further enhanced the quality-of-fit. The carbon sink function of the forest PFTs significantly increased with the posterior parameter estimates. We thus conclude that land surface model predictions of carbon stocks and fluxes require careful consideration of uncertain ecological parameters and initial states.
dc.description.sponsorshipEU FP7 project ExpeER [262060]; European Commission through the Seventh Framework Programme for Research and Technical Development,Transregional Collaborative Research Centre 32 [TR32]; German Research Foundation (DFG); Terrestrial Environmental Observatories (TERENO); Helmholtz Association; Julich Supercomputing Centre (JSC)en
dc.language.isoenen
dc.publisherAMER GEOPHYSICAL UNIONen
dc.relation.urlhttp://doi.wiley.com/10.1002/2015JG003297en
dc.rights©2017. American Geophysical Union. All Rights Reserved.en
dc.titleEstimation of Community Land Model parameters for an improved assessment of net carbon fluxes at European sitesen
dc.typeArticleen
dc.contributor.departmentUniv Arizona, Sch Nat Resources & Environmen
dc.identifier.journalJournal of Geophysical Research: Biogeosciencesen
dc.description.note6 month embargo; First published: 22 March 2017en
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en
dc.eprint.versionFinal published versionen
dc.contributor.institutionDepartment of Geography; University of Cologne; Cologne Germany
dc.contributor.institutionAgrosphere (IBG-3), Forschungszentrum Jülich GmbH; Jülich Germany
dc.contributor.institutionSchool of Natural Resources and the Environment; University of Arizona; Tucson Arizona USA
dc.contributor.institutionAgrosphere (IBG-3), Forschungszentrum Jülich GmbH; Jülich Germany
dc.contributor.institutionAgrosphere (IBG-3), Forschungszentrum Jülich GmbH; Jülich Germany
refterms.dateFOA2017-09-23T00:00:00Z
html.description.abstractThe Community Land Model (CLM) contains many parameters whose values are uncertain and thus require careful estimation for model application at individual sites. Here we used Bayesian inference with the DiffeRential Evolution Adaptive Metropolis (DREAM((zs))) algorithm to estimate eight CLM v. 4.5 ecosystem parameters using 1 year records of half- hourly net ecosystem CO2 exchange (NEE) observations of four central European sites with different plant functional types (PFTs). The posterior CLM parameter distributions of each site were estimated per individual season and on a yearly basis. These estimates were then evaluated using NEE data from an independent evaluation period and data from " nearby" FLUXNET sites at similar to 600 km distance to the original sites. Latent variables (multipliers) were used to treat explicitly uncertainty in the initial carbon- nitrogen pools. The posterior parameter estimates were superior to their default values in their ability to track and explain the measured NEE data of each site. The seasonal parameter values reduced with more than 50% (averaged over all sites) the bias in the simulated NEE values. The most consistent performance of CLM during the evaluation period was found for the posterior parameter values of the forest PFTs, and contrary to the C3-grass and C3-crop sites, the latent variables of the initial pools further enhanced the quality-of-fit. The carbon sink function of the forest PFTs significantly increased with the posterior parameter estimates. We thus conclude that land surface model predictions of carbon stocks and fluxes require careful consideration of uncertain ecological parameters and initial states.


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