 Different types of ensemble study explore the consequences of different sources of uncertainty in modeling. A multi-model ensemble (MME) study probes structural uncertainty, which is uncertainty about the form that modeling equations should take as well as how those equations should be solved. An example of an MME study is the Coupled Model Intercomparison Project Version 5 (CMIP5), which produced projections using a few dozen climate models developed at different modeling centers around the world (see Flato et al. 2013: Table 9.1). A perturbed-physics ensemble (PPE) consists of multiple versions of the same climate model, where the versions differ in the numerical values assigned to one or more parameters within the model. Examples of studies employing PPEs include the climateprediction.net project (Stainforth et al. 2005) and the UK Climate Projections (UKCP09) project (Murphy et al. 2009). PPEs probe parameter uncertainty, i.e., uncertainty about the numerical values that should be assigned to parameters within a given model structure. In an initial condition ensemble (ICE), the same model or model version is assigned different initial conditions. ICEs can probe initial condition uncertainty, which is uncertainty about the initial model state from which simulations should be launched, and they are often used in combination with one of the other two types of ensemble (see Werndl forthcoming for further discussion).