 Intertwined with the issue of ensemble interpretation is the issue of weighting, i.e.,  whether to assign greater weight to some projections than others. This is part and parcel of some methods, but in MME studies it has been common to give equal weight to participating models, as the IPCC approach illustrates. This is sometimes referred to as “model democracy” or “one-model one-vote” (Knutti 2010). Part of the original motivation for model democracy was the difficulty of determining which of the state-of-the-art models included in MMEs would give the most accurate projections of future conditions (see  Section 4.4).  This difficulty notwithstanding, recently weighting has been advocated on the grounds that, for at least some projected variables, there is good reason to think that some models are more skillful than others and, moreover, the one-model one-vote approach fails to take account of dependence among models, i.e., of the fact that an MME can include several models that are variants of one another (Knutti et al. 2017). A key challenge, however, is to select and combine relevant metrics of performance and other criteria to assign appropriate weights for a given projected variable (Gleckler et al. 2008; Knutti et al. 2010b; Weigel et al. 2010). The basic issue of interpretation discussed above also remains, i.e., how to interpret the weighted ensemble results.