This paper, exploiting the properties of mixed causal and noncausal models, proposes strategies to detect time reversibility in stochastic processes. These novel strategies can be implemented to verify the time reversibility of stationary stochastic processes. We show that they can also be used for nonstationary processes when the trend component is computed using the Hodrick-Prescott filter characterized by a time-reversible closed-form solution. We then establish a linkage between the concept of environmental tipping point and the statistical property of time irreversibility and investigate whether time reversibility is a feature of climate change using nine climate indicators. While we detect time irreversibility in GHG emissions, global temperatures and fundamental natural oscillations do not show this feature. Under a constructive view, our findings then give hope that correction policies might still help avoid the worst consequences of climate change