Evaluation of land surface subprocesses and their impacts on model performance with global flux data

Meanwhile, uncertainty in model structure derived from the many possible parameterization schemes for the same physical subprocess is a primary source of land model uncertainties. To attribute structural errors and model parameterization scheme uncertainties, it is critical to identify the key subprocesses involved and investigate the interactions of these subprocesses on LSM behavior, which will ultimately help us identify the "optimal" parameterization schemes for various plant functional types, soil types, and different locations.

Here, we conduct physical ensemble simulations for multiple sites from FLUXNET and then apply a variance-based sensitivity analysis method to quantitatively assess the impacts of uncertainties in the parameterization schemes of subprocesses in the Noah with multiparameterization (Noah-MP) LSM on model performance. The results show that three subprocesses—surface exchange coefficient, runoff and groundwater, and surface resistance to evaporation—have the most significant impacts on the performance of the simulated sensible heat flux, latent heat flux, and net absorbed radiation in the Noah-MP LSM.