Abstract
ABSTRACT Land surface models (LSMs) have become indispensable for understanding the role of the terrestrial biosphere in the global climate system. However, the ability of LSMs to reproduce observed carbon, water, and energy fluxes varies considerably among models. Some of these deficiencies can be attributed to parameter uncertainties. Global sensitivity analysis (GSA) quantifies model output uncertainties caused by the uncertainty in model inputs. Our study conducts, for the very first time, a GSA for the Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC) model. Focusing on a site in the humid tropics, we evaluate the model's sensitivity for a wide range of ecosystem variables (17 in total). Considering a total of 90 parameters, we identify the top five most influential parameters using the qualitative Morris method per output variable. These influential parameters are then analysed using the quantitative Sobol' method. The analysis shows that the maximum carboxylation rate parameter has the greatest influence on almost all output variables considered. The impact of the maximum carboxylation rate is partially regulated by the canopy extinction coefficient's uncertainty. The results of this research will guide future efforts to optimize the model's performance more efficiently, focussing on a small subset of the 90 parameters.
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