A Framework to Calibrate Ecosystem Demography Models Within Earth System Models Using Parallel Surrogate Global Optimization

The climatic feedbacks from vegetation, particularly from tropical forests, can alter climate through land-atmospheric interactions. Expected shifts in species composition can alter these interactions with profound effects on climate and terrestrial ecosystem dynamics. Ecosystem demographic (ED) models can explicitly represent vegetation dynamics and are a key component of next-generation Earth System Models (ESMs). Although ED models exhibit greater fidelity and allow more direct comparisons with observations, their interacting parameters can be more difficult to calibrate due to the complex interactions among vegetation groups and physical processes. In addition, while representation of forest successional coexistence in ESMs is necessary to accurately capture forest-climate interactions, few models can simulate forest coexistence and few studies have calibrated coexisted forest species. Furthermore, although both vegetation characteristics and soil properties affect vegetation dynamics, few studies have paid attention to jointly calibrating parameters related to these two processes. In this study, we develop a computationally-efficient and physical model structure-based framework that uses a parallel surrogate global optimization algorithm to calibrate ED models. We calibrate two typically coexisted tropical tree species, early and late successional plants, in a state-of-the-art ED model that is capable of simulating successional diversity in forests. We concurrently calibrate vegetation and soil parameters and validate results against carbon, energy, and water cycle measurements collected in Barro Colorado Island, Panama. The framework can find optimal solutions within 4–12 iterations for 19-dimensional problems. The calibration for tropical forests has important implications for predicting land-atmospheric interactions and responses of tropical forests to environmental changes.