This work is devoted to the analysis of models having functional inputs and is motivated by the intensive use of climatic variables in crop models. The main output of these models is the crop yield, which is estimated, among others, from daily-sampled climatic variables (Temperature, Rain, Radiation, Evapotranspiration). We want to test to what extent this fine temporal resolution is mandatory to generate accurate predictions and quantify how much a priori simplifications, such as lowering the temporal resolution, would affect the model results. This may lead to a better understanding of model behavior as well as to a simplification of the model and/or of the acquisition of its input variables. To this aim, we introduce the use of filtering operators into Global Sensitivity Analysis using switching factors [1]. Low pass filters are used to reduce the temporal resolution of climatic variables. GSA is required because we want to explore the impact of this input simplification in a global exploration of model inputs. Switching factors have been proposed [1], [2] in the context of spatially distributed inputs and further analyzed in [3]. They were initially introduced to assess the sensitivity to the presence of stochastic errors in spatial functional inputs. We use them here to test the sensitivity of a model to simplifications of the temporal structure of its climatic inputs.