I will discuss two recent publications that are relevant for automated learning of climate and weather phenomena. The first of these is concerned with finding large-scale approximate cycles in climate data. These ideas are illustrated by finding the dominant cyclic behaviour in the Lorenz flow, and by producing an improved characterisation of the El-Nino Southern Oscillation from spatiotemporal data. The second part of the discussion addresses the data-driven identification of coherent regions in the phase space of a dynamical system. In particular, I will discuss the difficult, but common, situation where there are several regime changes in the dynamics that cause the emergence or destruction of coherence. Each of these aspects, cyclicity and coherence, contributes to learning a richer time-varying view of weather and climate dynamics.