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

Urheber*innen

Forkel,  M.
External Organizations;

/persons/resource/markus.drueke

Drüke,  Markus
Potsdam Institute for Climate Impact Research;

Thurner,  M.
External Organizations;

Dorigo,  W.
External Organizations;

/persons/resource/Sibyll.Schaphoff

Schaphoff,  Sibyll
Potsdam Institute for Climate Impact Research;

/persons/resource/Kirsten.Thonicke

Thonicke,  Kirsten
Potsdam Institute for Climate Impact Research;

/persons/resource/Werner.von.Bloh

von Bloh,  Werner
Potsdam Institute for Climate Impact Research;

Carvalhais,  N.
External Organizations;

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(Verlagsversion), 5MB

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Zitation

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


Zitierlink: https://publications.pik-potsdam.de/pubman/item/item_23449
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.