Alternative ways of using field-based estimates to calibrate ecosystem models and their implications for carbon cycle studies

摘要: [1] Model-data fusion is a process in which field observations are used to constrain model parameters. How parameters has direct impact on the carbon cycle dynamics simulated by ecosystem models. In this study, we present an evaluation of several options for use modeling regional and explore implications those options. We calibrated Terrestrial Ecosystem Model hierarchy three vegetation classification levels Alaskan boreal forest: species level, plant-functional-type level (PFT level), biome examined differences dynamics. Species-specific field-based estimates were directly parameterize species-level simulations, while weighted averages based percent cover generate PFT- biome-level parameterization. found that key differed substantially among overlapped categorized into different PFTs. Our analysis parameter sets suggests PFT-level parameterizations primarily reflected dominant functional information some lost from parameterizations. The parameterization was representative needleleaf PFT broadleaf or function. results indicate simulations may be potentially performance result biased estimates. Improved theoretical empirical justifications grouping PFTs biomes needed adequately represent functioning structure.