Uncertainty quantification in climate models is challenged by the sparsity and bifurcative character of the available climate data. To circumvent these challenges we propose a methodology that employs Bayesian inference to locate discontinuities in the model output, followed by an efficient propagation of uncertain quantities using spectral expansions of random parameters/fields. Stochastic emulators are used to assess the performance of the proposed approach.