Abstract
Abstract. Proper specification of model parameters is critical to the performance of land surface models (LSMs). Due to high dimensionality and parameter interaction, estimating parameters of an LSM is a challenging task. Sensitivity analysis (SA) is a tool that can screen out the most influential parameters on model outputs. In this study, we conducted parameter screening for six output fluxes for the Common Land Model: sensible heat, latent heat, upward longwave radiation, net radiation, soil temperature and soil moisture. A total of 40 adjustable parameters were considered. Five qualitative SA methods, including local, sum-of-trees, multivariate adaptive regression splines, delta test and Morris methods, were compared. The proper sampling design and sufficient sample size necessary to effectively screen out the sensitive parameters were examined. We found that there are 2–8 sensitive parameters, depending on the output type, and about 400 samples are adequate to reliably identify the most sensitive parameters. We also employed a revised Sobol' sensitivity method to quantify the importance of all parameters. The total effects of the parameters were used to assess the contribution of each parameter to the total variances of the model outputs. The results confirmed that global SA methods can generally identify the most sensitive parameters effectively, while local SA methods result in type I errors (i.e., sensitive parameters labeled as insensitive) or type II errors (i.e., insensitive parameters labeled as sensitive). Finally, we evaluated and confirmed the screening results for their consistency with the physical interpretation of the model parameters.
Highlights
A land surface model (LSM) is an integral component of any numerical weather prediction (NWP) and climate models
Common Land Model (CoLM) (Dai et al, 2003) is a widely used land surface model. It combines the advantages of three existing land surface models: Land Surface Model (LSM) (Bonan, 1996), Biosphere-atmosphere transfer scheme (BATS) (Dickinson et al, 1993) and Institute of Atmospheric Physics landsurface model (IAP94) (Dai and Zeng, 1997)
The result of validation shows that the most sensitive parameters selected by delta test (DT) are nearly the same to that given by the other global methods, even though the medium sensitive parameters may differ from the ones identified by other Sensitivity analysis (SA) methods
Summary
A land surface model (LSM) is an integral component of any numerical weather prediction (NWP) and climate models. Hou et al (2012) introduced an uncertainty quantification framework to analyze the sensitivity of 10 hydrologic parameters in CLM4SP (Community Land Model Version 4 with satellite phenology) with a generalized linear model (GLM) method. They found that the simulation of sensible heat and latent heat is sensitive to subsurface runoff generation parameters. The work has two objectives: (1) to test and compare different qualitative SA methods for separating sensitive parameters from insensitive ones; and (2) to validate the screening results using a quantitative SA method.
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