Global climate goals require a transition to a deeply decarbonized energy system. Meeting the objectives of the Paris Agreement through countries’ Nationally Determined Contributions and Long-Term Strategies represents a complex problem with consequences across multiple systems shrouded by deep uncertainty. Robust, large-ensemble methods and analyses mapping a wide range of possible future states of the world are needed to help policymakers design effective strategies to meet emissions reduction goals. This study contributes a scenario discovery analysis applied to a large ensemble of 5,760 model realizations generated using the Global Change Analysis Model. Eleven energy-related uncertainties are systematically varied, representing national mitigation pledges, institutional factors, and techno-economic parameters, among others. The resulting ensemble maps how uncertainties impact common energy system metrics used to characterize national and global pathways toward deep decarbonization. Results show globally consistent but regionally variable energy transitions as measured by multiple metrics, including electricity costs and stranded assets. Larger economies and developing regions experience more severe economic outcomes across a broad sampling of uncertainty. The scale of CO2 removal globally determines how much the energy system can continue to emit, but the relative role of different CO2 removal options in meeting decarbonization goals varies across regions. Previous studies characterizing uncertainty have typically focused on a few scenarios, and other large-ensemble work has not (to our knowledge) combined this framework with national emissions pledges or institutional factors. Our results underscore the value of large-ensemble scenario discovery for decision support as countries begin to design strategies to meet their goals.

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.