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Abstract Climate engineering (CE) measures are increasingly discussed when dealing with the adverse impacts of climate change. While much research has focused on individual methods, few studies attempt to compare and rank the effectiveness of these measures. Furthermore, model uncertainties are seldom acknowledged and lesser still, estimated when CE scenarios are assessed. In this work, we quantify the variance in outcomes due to poorly constrained model parameters under several idealized CE scenarios. The four scenarios considered are (1) warming under the high emission scenario Representative Concentration Pathway 8.5 without CE applied and the same emission scenario with (2) afforestation,(3) solar radiation management, and (4) artificial ocean alkalinization. By considering the parametric uncertainty in model outputs, we demonstrate the problems with comparing these scenarios using a single parameter setting. Using statistical emulation, we estimate the probability distributions of several model outcomes. Based on such distributions, we suggest an approach to ranking the effectiveness of the scenarios considered according to their probability of avoiding climate thresholds.