The influence of the ground and atmosphere on sound generation and propagation from wind turbines creates uncertainty in sound level estimations. Realistic simulations of wind turbine noise thus require quantifying the overall uncertainty on sound pressure levels induced by environmental phenomena. This study proposes a method of uncertainty quantification using a quasi-Monte Carlo method of sampling influential input data (i.e., environmental parameters) to feed an Amiet emission model coupled with a Parabolic Equation propagation model. This method allows for calculation of the probability distribution of the output data (i.e., sound pressure levels). As this stochastic uncertainty quantification method requires a large number of simulations, a metamodel of the global (emission-propagation) wind turbine noise model was built using the kriging interpolation technique to drastically reduce calculation time. When properly employed, the metamodeling technique can quantify statistics and uncertainties in sound pressure levels at locations downwind from wind turbines. This information provides better knowledge of sound pressure variability and will help to better control the quality of wind turbine noise prediction for inhomogeneous outdoor environments.