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  Constraining modelled global vegetation dynamics and carbon turnover using multiple satellite observations

Forkel, M., Drüke, M., Thurner, M., Dorigo, W., Schaphoff, S., Thonicke, K., von Bloh, W., Carvalhais, N. (2019): Constraining modelled global vegetation dynamics and carbon turnover using multiple satellite observations. - Scientific Reports, 9, 18757.
https://doi.org/10.1038/s41598-019-55187-7

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Forkel, M.1, Autor
Drüke, Markus2, Autor              
Thurner, M.1, Autor
Dorigo, W.1, Autor
Schaphoff, Sibyll2, Autor              
Thonicke, Kirsten2, Autor              
von Bloh, Werner2, Autor              
Carvalhais, N.1, Autor
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Zusammenfassung: The response of land ecosystems to future climate change is among the largest unknowns in the global climate-carbon cycle feedback. This uncertainty originates from how dynamic global vegetation models (DGVMs) simulate climate impacts on changes in vegetation distribution, productivity, biomass allocation, and carbon turnover. The present-day availability of a multitude of satellite observations can potentially help to constrain DGVM simulations within model-data integration frameworks. Here, we use satellite-derived datasets of the fraction of absorbed photosynthetic active radiation (FAPAR), sun-induced fluorescence (SIF), above-ground biomass of trees (AGB), land cover, and burned area to constrain parameters for phenology, productivity, and vegetation dynamics in the LPJmL4 DGVM. Both the prior and the optimized model accurately reproduce present-day estimates of the land carbon cycle and of temporal dynamics in FAPAR, SIF and gross primary production. However, the optimized model reproduces better the observed spatial patterns of biomass, tree cover, and regional forest carbon turnover. Using a machine learning approach, we found that remaining errors in simulated forest carbon turnover can be explained with bioclimatic variables. This demonstrates the need to improve model formulations for climate effects on vegetation turnover and mortality despite the apparent successful constraint of simulated vegetation dynamics with multiple satellite observations.

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 Datum: 2019
 Publikationsstatus: Final veröffentlicht
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 Identifikatoren: DOI: 10.1038/s41598-019-55187-7
PIKDOMAIN: RD1 - Earth System Analysis
eDoc: 8673
Research topic keyword: Biodiversity
Research topic keyword: Ecosystems
Model / method: LPJmL
Model / method: Open Source Software
Regional keyword: Global
Organisational keyword: RD1 - Earth System Analysis
Working Group: Earth System Model Development
Working Group: Ecosystems in Transition
Working Group: Terrestrial Safe Operating Space
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Titel: Scientific Reports
Genre der Quelle: Zeitschrift, SCI, Scopus, p3, OA
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Seiten: - Band / Heft: 9 Artikelnummer: 18757 Start- / Endseite: - Identifikator: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals2_395