El Niño and La Niña, collectively referred to as the El Niño-Southern Oscillation (ENSO), are not only highly consequential1-6 but also strongly nonlinear7-14. For example, the maximum warm anomalies of El Niño, which occur in the equatorial eastern Pacific Ocean, are larger than the maximum cold anomalies of La Niña, which are centred in the equatorial central Pacific Ocean7-9. The associated atmospheric nonlinear thermal damping cools the equatorial Pacific during El Niño but warms it during La Niña15,16. Under greenhouse warming, climate models project an increase in the frequency of strong El Niño and La Niña events, but the change differs vastly across models17, which is partially attributed to internal variability18-23. Here we show that like a butterfly effect24, 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 perturbation24,25; it also operates in a large ensemble generated with another model commencing from different initial conditions25,26 and across climate models participating in the Coupled Model Intercomparison Project27,28. Thus, if the greenhouse-warming-induced increase in ENSO variability29 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.

中文翻译：

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蝴蝶效应和对全球变暖的自我调节厄尔尼诺反应

厄尔尼诺和拉尼娜统称为厄尔尼诺-南方涛动 (ENSO)，不仅后果严重1-6，而且非线性7-14。例如，发生在赤道东太平洋的厄尔尼诺最大暖距平大于集中在赤道中太平洋的拉尼娜最大冷距平7-9。相关的大气非线性热阻尼在厄尔尼诺现象期间使赤道太平洋冷却，但在拉尼娜现象期间使赤道太平洋变暖15,16。在温室变暖的情况下，气候模型预测强厄尔尼诺和拉尼娜事件的频率会增加，但模型之间的变化差异很大 17，这部分归因于内部可变性 18-23。在这里，我们表明，就像蝴蝶效应24，对相同初始条件的无限小随机扰动会导致截然不同的初始 ENSO 变异性，这会系统地影响其对一个世纪后温室变暖的反应。在初始变率较高的实验中，ENSO 热阻尼引起的更大累积海洋热损失减少了赤道上太平洋的分层，导致在随后的温室变暖下 ENSO 变率的增加较小。这种自调制机制在使用两个不同模型生成的两个大型集合中运行，每个模型都从相同的初始条件开始，但具有蝴蝶扰动 24,25；它还在一个大型集合中运行，该集合由另一个模型生成，该模型从不同的初始条件开始 25,26 以及参与耦合模型比对项目 27,28 的跨气候模型。因此，如果温室变暖引起的 ENSO 变异性增加 29 最初被内部变异性抑制，那么未来的 ENSO 变异性可能会增强，反之亦然。这种将 ENSO 随时间变化的自调制联系起来，为理解气候变化中多个时间尺度上 ENSO 变化的动态提供了不同的视角。