In recent years, there has been increasing awareness of the risks of collapse or tipping points in a wide variety of complex systems, ranging from human medical conditions and pandemics to ecosystems, climate, finance, and society. Even in systems where governing equations are known, such as the atmospheric flow, predictability is limited by the chaotic nature of the system and by the limited resolution in observations and computer simulations. These phenomena are naturally modelled by strongly nonlinear stochastic processes, which permit a statistical description. In this talk, I will present methods for analyzing data from such complex systems, with an application to an important tipping element in the climate: the Atlantic Meridional Overturning Circulation.