This research implemented a Bayesian statistical method to calibrate a widely used process-based simulator BIOME-BGC against estimates of gross primary production (GPP) data. Six parameters of BIOME-BGC were calibrated, which were also allowed to vary month-by-month to investigate the hypothesis that the phenology exhibited a seasonal cycle that was not accurately reproduced by the simulator. Time varying parameters substantially improved the simulated GPP as compared to GPP obtained with constant parameters.
A file list and description of data (i.e., metadata) in each file are provided in the uploaded pdf file "BayesianIntegration_BIOME-BGC_codebook_Version1.pdf". This pdf file also provides a brief description of the methods adopted in this research.