论文标题

极端事件影响的北极海冰系统中的临界时间

The tipping times in an Arctic sea ice system under influence of extreme events

论文作者

Yang, Fang, Zheng, Yayun, Duan, Jinqiao, Fu, Ling, Wiggins, Stephen

论文摘要

鉴于北极海冰最近快速撤退,极端天气事件引发了北极冰覆盖的变化,引起了人们的注意。非高斯$α$稳定的莱维过程被认为是描述这种极端事件的合适模型。基于非本地fokker-Planck方程的最大轨迹可用于在$α$稳定的lévy噪声下的非自主北极海冰系统。两种类型的临界时间,早期的小费时间和造成灾难的临界时间,用于预测最大可能从长期冰覆盖的状态到季节性无冰的状态,从季节性的无冰状态到多年生的无冰冰球的关键时间。我们发现,极端事件的强度增加会导致海冰融化的警告时间较短,并且增强的温室效应将增强这种影响,从而使警告时间的到来显着更早。同时,对于增强的温室效应,我们发现极端事件的强度和频率的增加将推动灾难性的小费时间,在该时间中,全年在北极海洋中保持无冰状态。最后,我们确定了Lévy指数$α$的值和噪声强度$ε$在$αε$ -Space中,这可以触发北极海冰状态之间的过渡。这些结果为在极端事件的影响下研究了研究北极海冰变化的有效理论框架。

In light of the rapid recent retreat of Arctic sea ice, the extreme weather events triggering the variability in Arctic ice cover has drawn increasing attention. A non-Gaussian $α$-stable Lévy process is thought to be an appropriate model to describe such extreme event. The maximal likely trajectory, based on the nonlocal Fokker-Planck equation, is applied to a nonautonomous Arctic sea ice system under $α$-stable Lévy noise. Two types of tipping times, the early-warning tipping time and the disaster-happening tipping time, are used to predict the critical time for the maximal likely transition from a perennially ice-covered state to a seasonally ice-free one, and from a seasonally ice-free state to a perennially ice-free one, respectively. We find that the increased intensity of extreme events results in shorter warning time for sea ice melting, and that an enhanced greenhouse effect will intensify this influence, making the arrival of warning time significantly earlier. Meanwhile, for the enhanced greenhouse effect, we discover that increased intensity and frequency of extreme events will advance the disaster-happening tipping time, in which an ice-free state is maintained throughout the year in the Arctic Ocean. Finally, we identify values of Lévy index $α$ and noise intensity $ε$ in $αε$-space that can trigger a transition between the Arctic sea ice state. These results provide an effective theoretical framework for studying Arctic sea ice variations under the influence of extreme events.

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