Ray Arnob, Rakshit Sarbendu, Basak Gopal K, Dana Syamal K, Ghosh Dibakar
Physics and Applied Mathematics Unit, Indian Statistical Institute, Kolkata 700108, India.
Stat-Math Unit, Indian Statistical Institute, Kolkata 700108, India.
Phys Rev E. 2020 Jun;101(6-1):062210. doi: 10.1103/PhysRevE.101.062210.
We investigate a low-dimensional slow-fast model to understand the dynamical origin of El Niño southern oscillation. A close inspection of the system dynamics using several bifurcation plots reveals that a sudden large expansion of the attractor occurs at a critical system parameter via a type of interior crisis. This interior crisis evolves through merging of a cascade of period-doubling and period-adding bifurcations that leads to the origin of occasional amplitude-modulated extremely large events. More categorically, a situation similar to homoclinic chaos arises near the critical point; however, atypical global instability evolves as a channellike structure in phase space of the system that modulates variability of amplitude and return time of the occasional large events and makes a difference from the homoclinic chaos. The slow-fast timescale of the low-dimensional model plays an important role on the onset of occasional extremely large events. Such extreme events are characterized by their heights when they exceed a threshold level measured by a mean-excess function. The probability density of events' height displays multimodal distribution with an upper-bounded tail. We identify the dependence structure of interevent intervals to understand the predictability of return time of such extreme events using autoregressive integrated moving average model and box-plot analysis.
我们研究了一个低维快慢模型,以理解厄尔尼诺 - 南方涛动的动力学起源。通过几个分岔图对系统动力学进行仔细检查后发现,在一个临界系统参数处,吸引子会通过一种内部危机突然大幅扩张。这种内部危机是通过一系列倍周期分岔和加周期分岔的合并而演化的,这导致了偶尔出现的振幅调制的极大事件的产生。更确切地说,在临界点附近会出现类似于同宿混沌的情况;然而,非典型的全局不稳定性在系统相空间中以一种通道状结构演化,这种结构调节了偶尔出现的大事件的振幅变化和返回时间,与同宿混沌有所不同。低维模型的快慢时间尺度在偶尔出现的极大事件的发生中起着重要作用。此类极端事件的特征在于其超过由平均超额函数测量的阈值水平时的高度。事件高度的概率密度显示出具有上界尾部的多峰分布。我们使用自回归积分移动平均模型和箱线图分析来识别事件间隔的依赖结构,以理解此类极端事件返回时间的可预测性。