Estimation of the uncertainty in model predictions is an important topic given a wide range of attention in the current research within the hydrologic community. Using a variety of single- and multiple criterion methods for sensitivity analysis and inverse modeling we analyze and compare the behavior of a state of the art land surface model, the Simple Biosphere Model 3 (SiB3) and estimate the uncertainty in model predictions associated with parameter uncertainty. In particular, we focus on the behavior of model predictions for turbulent heat and carbon fluxes, soil moisture, and soil temperature. This is done using data from hydrometeorological towers collected at several locations within the LBA domain (Amazon tropical forest) and at locations in Arizona (semi-arid grass and shrubland). Estimations of the overall uncertainty of the model associated with both parameter and data uncertainties are presented. The methods used are based on Markov Chain Monte Carlo simulations with several Metropolis type algorithms for the uncertainty estimation while generalized sensitivity and variance methods are used for the sensitivity analysis. The influence that the specific location exerts upon the model simulation and the associated uncertainty is also analyzed.