Computational modeling of Earth system processes often requires simplifying assumptions of the real system. These necessary assumptions result in the definition of internal model parameters that can take a number of different values but must be explicitly defined for any one model simulation. The main issue with such an uncertain multidimensional input space is that many simulations are needed to adequately explore it. This study presents a generalized parameter screening experiment for use in future earth system modeling. This approach identifies model parameters that dominate uncertainty, therefore reducing to a manageable number the simulations required to explore the input space. The approach we adopt is relatively inexpensive to implement and can be applied at both the aggregate and disaggregate (e. g., regional) level. To demonstrate the potential of such a method, it is applied to a surface mass balance model of intermediate complexity over the Greenland ice sheet. All identified parameters were related to the surface melt parameterization, with albedo parameters being identified as the most important. Spatial distributions of the parameter sensitivities show that, in recent years, most parameter sensitivities are concentrated around the southwest and northern ice sheet margins. Simulations for the 21st century indicate an increase in sensitivity in these high melt areas especially in the northeast. Melt contributions from temperature and radiative effects are shown to be important on the order of parameters identified, and as a consequence, sensitivities are dependent on the present climate used for modeling surface mass balance.