This short editorial comment discusses challenges and methods to quantifying climate change uncertainty. The author acknowledges that decision makers need descriptions of uncertainty that address future costs, benefits, and impacts of potential choices. These descriptions must not only incorporate the uncertainty of today but also how uncertainty is expected to change over time. In concept, the following uncertainty analysis procedure could be followed: 1) perform sensitivity analysis of all model assumptions; 2) assign descriptions of the full range of possible alternatives for each assumption and the relative likelihood of each assumption; and 3) conduct Monte Carlo simulations. This procedure is challenging for climate change applications because past data can not be used to construct the distributions since one source of uncertainty is future economic growth and technological change. Data is also sparse to completely constrain probability distributions. Expert judgments will ultimately be required but this will also have inherent bias and uncertainty. This article is useful for describing one method of uncertainty analysis to be used in descriptions of climate change uncertainty.