A Semiprognostic Phenology Model for Simulating Multidecadal Dynamics of Global Vegetation Leaf Area Index.

Vegetation leaf phenology, often reflected by the dynamics in leaf area index (LAI), influences a variety of land surface processes. Robust models of vegetation phenology are pivot components in both land surface models and dynamic global vegetation models but remain challenging in terms of the model accuracy. This study develops a semiprognostic phenology model that is suitable for simulating time series of vegetation LAI. This method establishes a linear relationship between the steady‐state LAI (i.e., the LAI when the environment conditions remain unchanging) and gross primary productivity, meaning that the LAI an unchanging environment can carry is proportional to the photosynthetic products produced by plant leaves and implements with a simple light use efficiency algorithm of MOD17 to form a closed set of equations. We derive an analytical solution based on the Lambert W function to the closed equations and then apply a simple restricted growth process model to simulate the time series of actual LAI. The results modeled using global climate data demonstrate that the model is able to capture both the spatial pattern and intra‐annual and interannual variation of LAI derived from the satellite‐based product on a global scale. The results modeled using the flux tower data suggest that the developed model is able to explain over 70% variation in daily LAI for each plant functional type except evergreen broadleaf forest. The developed semiprognostic approach provides a simple solution to modeling the spatiotemporal variation in vegetation LAI across plant functional types on the global scale. Plain Language Summary: Modeling vegetation leaf phenology remains challenging in terms of the model accuracy. We develop a semiprognostic vegetation phenology method that is suitable for simulating the variation in the time series of leaf area index. This approach establishes a linear relationship between LAI and vegetation productivity and implements with a simple light use efficiency algorithm of MOD17. The results modeled using global climate data demonstrate that the model is able to capture both the spatial pattern and intra‐annual and interannual variation of LAI derived from the remote sensing data on the global scale. Key Points: We developed a semiprognostic phenology model for simulating the dynamics of vegetation leaf area index on the global scaleThe novel model is able to capture the spatiotemporal variation of global vegetation leaf area index as compared with observationsThe developed approach provides a simple solution to modeling multidecadal variation of global vegetation leaf area index [ABSTRACT FROM AUTHOR]