Uncertainties arising from future decisions driving the makeup of the energy system globally affect multiple sectors in the human-Earth system on diverse spatiotemporal scales. The complex interplay between sectors requires a thorough examination of these uncertainties, usually conducted through large scenario ensembles encompassing a wide range of potential futures. However, previous efforts have overlooked the methodological choice of aggregation measures across the ensemble, despite potential consequences. In this study, we leverage a large ensemble dataset that captures the uncertainties associated with the energy system generated using the Global Change Analysis Model. Using the ensemble, we first explore how energy-related uncertainties are propagated to both the global and regional water-energy-food sectors. We then conduct a rank correlation analysis across diverse cross-ensemble aggregation measures that are used to aggregate ensemble members for further analysis and highlight the potential downsides arising from relying on a single measure. Our results highlight that the influences that arise from low-carbon transitions can increase the uncertainties of all sectors at the end of the century, each with its unique dynamic. Moreover, the most severe outcomes in the majority of regions take place under scenarios with extreme socioeconomic assumptions in combination with low-carbon transitions. Our findings emphasize that threshold-based classification measures that have been frequently adopted to identify critical outcomes in multisectoral systems may overlook the dynamics embedded in the scenario ensemble. As an alternative, using appropriate cross-ensemble aggregation measures in order to derive robust insights from the outcomes holds promise.