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We first applied the WIBCS algorithm for the purpose of a GSA to determine the sensitivity the Accelerated Climate Model for Energy (ACME) Land Model of ALM to 65 parameters. Several model output variables are analyzed including gross primary productivity (GPP), leaf area index (LAI), vegetation carbon and soil organic matter carbon at nearly 100 FLUXNET sites covering a broad range of multiple plant functional types (PFTs) and climates. We find for all PFTs, generally 15 or fewer parameters drive most of the variance in the outputs. Within a PFT for a given output, generally the same parameters appear as sensitive at each site while differences in parameters are evident among PFTs and different outputs. This sensitivity analysis then serves as the basis for more focused, lower-dimensional surrogate construction that will help for parameter calibration and improved land-surface model predictions.
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