Critical slowing down in dynamical systems driven by nonstationary correlated noise

Precursor signals for bifurcation-induced critical transitions have recently gained interest across many research fields. Common indicators, including variance and autocorrelation increases, rely on the dynamical system being driven by white noise. Here, we show that these metrics raise false alarms for systems driven by time-correlated noise, if the autocorrelation of the noise process increases with time. We introduce an indicator for systems driven by nonstationary short-term memory noise, and show that this indicator performs well in situations where the classical methods fail.