Butterfly effect and a self-modulating El Nino response to global warming

Modelling experiments show that the El Nino response to global warming is self-modulating and depends on its historical variability; if current variability is high, future variability will be low. El Nino and La Nina, collectively referred to as the El Nino-Southern Oscillation (ENSO), are not only highly consequential(1-6)but also strongly nonlinear(7-14). For example, the maximum warm anomalies of El Nino, which occur in the equatorial eastern Pacific Ocean, are larger than the maximum cold anomalies of La Nina, which are centred in the equatorial central Pacific Ocean(7-9). The associated atmospheric nonlinear thermal damping cools the equatorial Pacific during El Nino but warms it during La Nina(15,16). Under greenhouse warming, climate models project an increase in the frequency of strong El Nino and La Nina events, but the change differs vastly across models(17), which is partially attributed to internal variability(18-23). Here we show that like a butterfly effect(24), an infinitesimal random perturbation to identical initial conditions induces vastly different initial ENSO variability, which systematically affects its response to greenhouse warming a century later. In experiments with higher initial variability, a greater cumulative oceanic heat loss from ENSO thermal damping reduces stratification of the upper equatorial Pacific Ocean, leading to a smaller increase in ENSO variability under subsquent greenhouse warming. This self-modulating mechanism operates in two large ensembles generated using two different models, each commencing from identical initial conditions but with a butterfly perturbation(24,25); it also operates in a large ensemble generated with another model commencing from different initial conditions(25,26)and across climate models participating in the Coupled Model Intercomparison Project(27,28). Thus, if the greenhouse-warming-induced increase in ENSO variability(29)is initially suppressed by internal variability, future ENSO variability is likely to be enhanced, and vice versa. This self-modulation linking ENSO variability across time presents a different perspective for understanding the dynamics of ENSO variability on multiple timescales in a changing climate.