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Schlagwörter:
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Zusammenfassung:
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
5 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 to enhance the understanding of processes that control seasonal
to long-term vegetation greenness dynamics. Here we implemented a new phenol10
ogy 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 en15
vironmental 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
20 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
25 a new generation of phenology modules in DGVMs in order to correctly reproduce
observed seasonal to decadal dynamics of vegetation greenness.
