Coupled human-natural system models are often used to explore potential future outcomes for emissions, economics, energy, land, water, climate, etc. There are many uncertain assumptions in the underlying models that drive those futures, and in previous work we have use a traditional Monte Carlo approach to explore socio-economic and climate uncertainties in the MIT Integrated Global Systems Modeling (IGSM) and generate probabilistic ensembles. These ensembles produce a large amount of data, and it can be difficult to sort through and extract relevant insights. In this work, we apply a variety of scenario discovery techniques to the probabilistic ensembles in order to find insights about potential future outcomes. In doing so, we develop a new scenario discovery visualization tool, which we make publicly available as an open-science web platform. This tool can be used to explore the distributions of model outputs, the input distributions driving the ensembles, relationships between inputs and outputs and how they change over time and across regions and scenarios, relationships between different outputs and potential tradeoffs, output patterns over time and their drivers, and geographic maps of outputs. As such, this tool enables the ability to quickly conduct analyses of interest. We demonstrate its capabilities by extracting insights related to energy futures, with a particular focus on the penetration of renewable energy. We find that the key drivers of renewables can vary significantly based on the policy scenario, region and time period. In particular, time series clustering reveals interesting dynamics that are missed by looking at individual years. Through this work we demonstrate the value of scenario discovery techniques in drawing insights from large ensembles of coupled human-natural system models by facilitating the identification of drivers, relationships among variables, areas of the uncertainty space that are particularly interesting or relevant, and the identification of individual scenarios of interest.