The Integration of Ecological processes into a Multi-layer Higher order closure Land Surface Model

The ecological impacts on biogeophysical and biogeochemical processes were investigated by a series of simulations conducted by a multi-layer higher order closure land surface model (UCD-ACASA) driven by a variety of meteorological and ecological conditions. The results show that the implementation of a more realistic ecological dataset, once carefully quality controlled, can significantly improve the biogeophysical and biogeochemical simulations, which suggests that the ecological impacts on surface layer simulations are as important as the reliability of the selected land surface model. Therefore, the ability to simulate realistic ecological conditions is imperative and beneficial to improve weather and climate simulations. We coupled the ecological processes in UCD-ACASA by adapting the fully prognostic plant carbon and nitrogen dynamics from the version 4.5 of the Community Land Model (CLM4.5). The simulated ecological conditions are sensitive to both radiative transfer processes and leaf distribution inside the canopy, and the multi-layer feature built in UCD-ACASA enables it to describe these properties more realistically as compared to the other big-leaf models. We conducted another set of simulations to examine the reliability of the simulated biogeophysical, biogeochemical and ecological results. The simulated Leaf Area Index (LAI) was compared with a high resolution remotely sensed LAI dataset, and the results show that the simulated LAI tends to overestimate mean LAI and underestimate annual LAI variation at the selected sites. However, the simulated LAI is reasonable enough to produce comparable simulation results against the simulations driven directly by remotely sensed LAI for the tested biogeophysical and biogeochemical fluxes. The results show that ecological impacts on biogeophysical and biogeochemical simulations are significant, and the implementation of biogeochemical processes into a land surface model has the potential to improve weather and climate simulations by avoiding the inaccurate surface feedback mechanisms from static vegetation distribution.