Quantitative assessment of the parameterization sensitivity of the Noah-MP land surface model with dynamic vegetation using ChinaFLUX data

Abstract The Noah land surface model with multiparameterization options (Noah-MP) depicts the water, heat, and carbon exchange processes occurring between the land and atmosphere. Including dynamic vegetation in the model gives tens of thousands of possible model parameterization combinations, making it difficult to select the optimal scheme for practical applications. Quantitative assessment of parameterization sensitivity can mitigate this difficulty. In this study, we combined summertime observation data from eight sites in the ChinaFLUX network with the multiple parameterization schemes of four physical subprocesses to design 72 simulation tests for each site to assess the model. Subsequently, Sobol sensitivity analysis was performed to quantitatively evaluate the sensitivity of the four physical subprocesses in simulating sensible heat flux (SHF), latent heat flux (LHF), and net ecosystem exchange (NEE). These subprocesses include the soil moisture factor which controls stomatal resistance (BTR), surface turbulence exchange coefficient (SFC), runoff and groundwater (RUN), and radiation transfer (RAD). Finally, the role of the sensitive subprocess parameterization schemes on the simulation results was explored. The results showed that a combination of parameterization schemes reasonably simulated diurnal variations in SHF and LHF, although a significant difference was shown in simulated NEE diurnal variation. The SFC and RAD subprocess parameterizations were sensitive to simulated SHF at most sites, as were the BTR and SFC subprocesses to NEE. With respect to LHF, the sensitive processes were not consistent at each site. Comparison of the parameterization schemes of the sensitive processes showed that RAD and SFC process parameterization affected heat, water, and carbon flux mainly via the canopy and ground temperature, BTR by transpiration and carbon assimilation, and RUN by soil moisture.