We introduce a novel class of stochastic blockmodel for multilayer weighted networks that accounts for the presence of a global ambient noise governing between-block interactions. We induce a hierarchy of classifications in weighted multilayer networks by assuming that all but one cluster are governed by unique local signals, while a single block behaves identically as interactions across differing blocks . Hierarchical variational inference is employed to jointly detect and typologize blocks as signal or noise. We call this model for multilayer weighted networks the Stochastic Block with Ambient Noise Model, SBANM)and develop an associated community detection algorithm. Then we apply this method to subjects in the Philadelphia Neurodevelopmental Cohort to discover communities of subjects with similar psychopathological symptoms in relation to psychosis.