The Bayesian method for the calibration of complex models, applying Markov Chain Monte Carlo (MCMC) algorithm, was used to quantify the uncertainty associated with the parameterization of the Pasture Simulation Model (PaSim) for the evaluation of climate change impacts. A case study of applying the method to calibrate 2008 and 2009 model parameters in a mountain grassland in the Central Eastern Alps (Stubai, Austria, 47° 05′ North, 11° 11′ East) is presented to assess the effects of changes in climate conditions (with or without temperature extremes). In the case study, the prior distributions of the 17 most influential parameters used to estimate vegetation and soil variables were based on both literature and previous studies, and subsequently updated using the likelihood distributions resulting from site-specific measurements. The posterior uncertainty distribution of the model results was generated using Monte Carlo simulations with posterior parameter probability distributions. It was shown that using site-specific information to update the prior uncertainty distribution, the resulting uncertainty associated with the model results could be reduced. As an example, for the parameter „maximum specific leaf area‟, the coefficient of variation was reduced of about 99% compared to the prior probability. Posterior estimations of output variables were closer to observations, e.g. the RMSE of the leaf area index was reduced to about the half of that of prior distribution. Similar results were also obtained with other parameters and model outputs. These results indicate that, thanks to the incorporation of quantitative uncertainty analysis into grassland simulation, a better informed decision can be made with respect to climate change where specific impacts and issues of adaptation arise.