Sea-level rise represents a looming hazard to coastal communities which remains difficult to quantify. Ensemble climate change predictions incorporate epistemic uncertainty in the climate modeling process and climate forcing scenarios help portray a range of radiative forcing changes. This study proposes a method for incorporating both model and scenario uncertainty in ensemble projections of thermosteric sea-level rise. A Markov Chain Monte Carlo algorithm is utilized to weigh the contributions of eight process-based climate models as well as the four Representative Concentration Pathways based on convergence criteria and observational data. Hazard analysis and deaggregation combine these contributions over a range of sea-level rise thresholds and quantify the relative contributions of each pathway and prediction model. The hazard maps generated suggest improved accuracy in modeling regional trends over typical ensembles. Deaggregations effectively represent model and scenario differences and the impacts of the methods used.