Identifying environmental controls on vegetation greenness phenology through model-data integration

Existing dynamic global vegetation models (DGVMs) have a limited ability in reproducing phenology and decadal dynamics of vegetation greenness as observed by satellites. These limitations in reproducing observations reflect a poor understanding and description of the environmental controls on phenol...

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Main Authors: Forkel, M., Carvalhais, N., Schaphoff, S., v. Bloh, W., Migliavacca, M., Thurner, M., Thonicke, K.
Format: Article in Journal/Newspaper
Language:English
Published: München : European Geopyhsical Union 2014
Subjects:
550
Online Access:https://doi.org/10.34657/1024
https://oa.tib.eu/renate/handle/123456789/536
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spelling ftleibnizopen:oai:oai.leibnizopen.de:CpNR04kBdbrxVwz63gX_ 2023-10-01T03:49:55+02:00 Identifying environmental controls on vegetation greenness phenology through model-data integration Forkel, M. Carvalhais, N. Schaphoff, S. v. Bloh, W. Migliavacca, M. Thurner, M. Thonicke, K. 2014 application/pdf https://doi.org/10.34657/1024 https://oa.tib.eu/renate/handle/123456789/536 eng eng München : European Geopyhsical Union CC BY 3.0 Unported https://creativecommons.org/licenses/by/3.0/ Biogeosciences, Volume 11, Issue 23, Page 7025-7050 Biogeochemistry boreal forest data set decadal variation ecological modeling environmental conditions growing season net primary production phenology seasonal variation vegetation dynamics water availability 550 article Text 2014 ftleibnizopen https://doi.org/10.34657/1024 2023-09-03T23:25:19Z Existing dynamic global vegetation models (DGVMs) have a limited ability in reproducing phenology and decadal dynamics of vegetation greenness as observed by satellites. These limitations in reproducing observations reflect a poor understanding and description of the environmental controls on phenology, which strongly influence the ability to simulate longer-term vegetation dynamics, e.g. carbon allocation. Combining DGVMs with observational data sets can potentially help to revise current modelling approaches and thus enhance the understanding of processes that control seasonal to long-term vegetation greenness dynamics. Here we implemented a new phenology model within the LPJmL (Lund Potsdam Jena managed lands) DGVM and integrated several observational data sets to improve the ability of the model in reproducing satellite-derived time series of vegetation greenness. Specifically, we optimized LPJmL parameters against observational time series of the fraction of absorbed photosynthetic active radiation (FAPAR), albedo and gross primary production to identify the main environmental controls for seasonal vegetation greenness dynamics. We demonstrated that LPJmL with new phenology and optimized parameters better reproduces seasonality, inter-annual variability and trends of vegetation greenness. Our results indicate that soil water availability is an important control on vegetation phenology not only in water-limited biomes but also in boreal forests and the Arctic tundra. Whereas water availability controls phenology in water-limited ecosystems during the entire growing season, water availability co-modulates jointly with temperature the beginning of the growing season in boreal and Arctic regions. Additionally, water availability contributes to better explain decadal greening trends in the Sahel and browning trends in boreal forests. These results emphasize the importance of considering water availability in a new generation of phenology modules in DGVMs in order to correctly reproduce observed ... Article in Journal/Newspaper albedo Arctic Tundra LeibnizOpen (The Leibniz Association) Arctic Browning ENVELOPE(164.050,164.050,-74.617,-74.617)
institution Open Polar
collection LeibnizOpen (The Leibniz Association)
op_collection_id ftleibnizopen
language English
topic Biogeochemistry
boreal forest
data set
decadal variation
ecological modeling
environmental conditions
growing season
net primary production
phenology
seasonal variation
vegetation dynamics
water availability
550
spellingShingle Biogeochemistry
boreal forest
data set
decadal variation
ecological modeling
environmental conditions
growing season
net primary production
phenology
seasonal variation
vegetation dynamics
water availability
550
Forkel, M.
Carvalhais, N.
Schaphoff, S.
v. Bloh, W.
Migliavacca, M.
Thurner, M.
Thonicke, K.
Identifying environmental controls on vegetation greenness phenology through model-data integration
topic_facet Biogeochemistry
boreal forest
data set
decadal variation
ecological modeling
environmental conditions
growing season
net primary production
phenology
seasonal variation
vegetation dynamics
water availability
550
description Existing dynamic global vegetation models (DGVMs) have a limited ability in reproducing phenology and decadal dynamics of vegetation greenness as observed by satellites. These limitations in reproducing observations reflect a poor understanding and description of the environmental controls on phenology, which strongly influence the ability to simulate longer-term vegetation dynamics, e.g. carbon allocation. Combining DGVMs with observational data sets can potentially help to revise current modelling approaches and thus enhance the understanding of processes that control seasonal to long-term vegetation greenness dynamics. Here we implemented a new phenology model within the LPJmL (Lund Potsdam Jena managed lands) DGVM and integrated several observational data sets to improve the ability of the model in reproducing satellite-derived time series of vegetation greenness. Specifically, we optimized LPJmL parameters against observational time series of the fraction of absorbed photosynthetic active radiation (FAPAR), albedo and gross primary production to identify the main environmental controls for seasonal vegetation greenness dynamics. We demonstrated that LPJmL with new phenology and optimized parameters better reproduces seasonality, inter-annual variability and trends of vegetation greenness. Our results indicate that soil water availability is an important control on vegetation phenology not only in water-limited biomes but also in boreal forests and the Arctic tundra. Whereas water availability controls phenology in water-limited ecosystems during the entire growing season, water availability co-modulates jointly with temperature the beginning of the growing season in boreal and Arctic regions. Additionally, water availability contributes to better explain decadal greening trends in the Sahel and browning trends in boreal forests. These results emphasize the importance of considering water availability in a new generation of phenology modules in DGVMs in order to correctly reproduce observed ...
format Article in Journal/Newspaper
author Forkel, M.
Carvalhais, N.
Schaphoff, S.
v. Bloh, W.
Migliavacca, M.
Thurner, M.
Thonicke, K.
author_facet Forkel, M.
Carvalhais, N.
Schaphoff, S.
v. Bloh, W.
Migliavacca, M.
Thurner, M.
Thonicke, K.
author_sort Forkel, M.
title Identifying environmental controls on vegetation greenness phenology through model-data integration
title_short Identifying environmental controls on vegetation greenness phenology through model-data integration
title_full Identifying environmental controls on vegetation greenness phenology through model-data integration
title_fullStr Identifying environmental controls on vegetation greenness phenology through model-data integration
title_full_unstemmed Identifying environmental controls on vegetation greenness phenology through model-data integration
title_sort identifying environmental controls on vegetation greenness phenology through model-data integration
publisher München : European Geopyhsical Union
publishDate 2014
url https://doi.org/10.34657/1024
https://oa.tib.eu/renate/handle/123456789/536
long_lat ENVELOPE(164.050,164.050,-74.617,-74.617)
geographic Arctic
Browning
geographic_facet Arctic
Browning
genre albedo
Arctic
Tundra
genre_facet albedo
Arctic
Tundra
op_source Biogeosciences, Volume 11, Issue 23, Page 7025-7050
op_rights CC BY 3.0 Unported
https://creativecommons.org/licenses/by/3.0/
op_doi https://doi.org/10.34657/1024
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