Predictions of critical transitions with non-stationary reduced order models

摘要: Abstract Here we demonstrate the ability of stochastic reduced order models to predict statistics non-stationary systems undergoing critical transitions. First, show that are able accurately autocorrelation function and probability density functions (PDF) higher dimensional with time-dependent slow forcing either resolved or unresolved modes. Second, whether system tips early repeatedly jumps between two equilibrium points (flickering) depends on strength coupling modes time scale separation. Both kinds behaviour have been found precede transitions in earlier studies. Furthermore, also timing The skill various proposed tipping indicators discussed.