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

Abstract

Abstract. 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 seasonal-to-decadal dynamics of vegetation greenness.

A data-driven approach to identify controls on global fire activity from satellite and climate observations (SOFIA V1)

“…1). Similar model structures like SOFIA where a response variable is controlled by a product of several functions have been previously applied in environmental modelling, for example, in light-use efficiency models to simulate NPP (Cai et al, 2014;Nemani et al, 2003) or in phenology models to simulate leaf development (Forkel et al, 2014;Jolly et al, 2005;Stöckli et al, 2011). The response value of the functional relationship can also be used to map sensitivities of burned area to environmental or socioeconomic variables.…”

“…The response value of the functional relationship can also be used to map sensitivities of burned area to environmental or socioeconomic variables. Such a mapping of controls was previously done for plant productivity (Nemani et al, 2003) and phenology (Forkel et al, 2014;Jolly et al, 2005) based on redgreen-blue (RGB) composite maps. Here we will demonstrate how this approach can be used to investigate spatial patterns of sensitivities between burned area and climatic, environmental, and socioeconomic controls on fire activity.…”

“…BFGS) (Mebane and Sekhon, 2011). GENOUD was already previously used to estimate parameters in a dynamic global vegetation model (Forkel et al, 2014). Here we applied GENOUD by using 500 individuals (i.e.…”

“…However, in order to apply modeldata integration for global process-oriented vegetation-fire models, multiple datasets on vegetation, hydrological, and fire-related variables should be used to realistically constrain vegetation-fire interactions. Hence there is a need to develop appropriate observation operators and to extend currently existing model-data integration frameworks of global vegetation models (Forkel et al, 2014;Kaminski et al, 2013;MacBean et al, 2016;Schürmann et al, 2016) to the corresponding fire modules in order to formally assess model structures and to constrain model parameters. In summary, model-data integration frameworks need to be developed that make use of multiple satellite datasets on vegetation and moisture proxies in order to improve the representation of fire in global vegetation models and thus to better understand interactions of fire with ecosystems and the atmosphere within the Earth system.…”

Implementing the Nitrogen cycle into the dynamic global vegetation, hydrology and crop growth model LPJmL (version 5)

“…Note that Schaphoff et al (2017b, a) provide the most comprehensive model description available, which includes a few model features that have been added to the model after the development of the N modules had begun and which are thus not part of the LPJmL 5 version described here. These include several minor amendments of the code as well as the updated grass allocation scheme ) and the updated phenology scheme for the natural vegetation (Forkel et al, 2014).…”