In the field of stochastic dynamics of marine structures, environmental conditions play a vital role. Considering wind and waves as random processes, determining the environmental parameters which correspond to an annual exceedance probability for a certain structural concept is of vital importance for the respective assessment of the loads and their effects. The accuracy in predicting the conditions, especially those corresponding to the sea, is of a great relevance when a probabilistic design is performed in order to ensure the structural integrity of an offshore wind turbine. In particular, models are not always completely perfect and accurate data is not always available. The Environmental Contour Method (ECM), which is based on the IFORM methodology, is one of the most popular methods in the offshore industry when determining the environmental conditions, for a given annual exceedance probability, is required. The ECM allows analysing proper sea states for operational and extreme conditions with lower computational efforts than the most accurate method (Full Long-Term Analysis). In the present study, effects of progressive variations (uncertainties) of the sea states parameters (i.e. significant wave height, spectral peak period) on the dynamic response of a Monopile Wind Turbine (NREL 5MW) are analysed. Two operative conditions are considered: rated wind and cut-out wind speed. In each case, the 50-year environmental contour (EC) is plotted for a site located in the North Sea. Some sea states are selected from the EC (base cases) and then derived cases with percentage variations are generated. All the cases are simulated in FAST (NREL) and the standard deviations of the time series are compared with its respective values of base cases. The results for the dynamic responses at mudline (e.g. overturning moments and shear forces) are presented as the most important parameters governing the design of the monopile. In this analysis, the wave height shows more influence on the response variation percentage than the peak period. This work shows the importance of accurately setting up the input parameters and their impact on the calculation of the dynamic responses.