School of Mathematics and Statistics, University of Sydney, NSW 2006, Australia.
Chaos. 2012 Jun;22(2):023115. doi: 10.1063/1.4704805.
We study the effect of regime switches on finite size Lyapunov exponents (FSLEs) in determining the error growth rates and predictability of multiscale systems. We consider a dynamical system involving slow and fast regimes and switches between them. The surprising result is that due to the presence of regimes, the error growth rate can be a non-monotonic function of initial error amplitude. In particular, troughs in the large scales of FSLE spectra are shown to be a signature of slow regimes, whereas fast regimes are shown to cause large peaks in the spectra where error growth rates far exceed those estimated from the maximal Lyapunov exponent. We present analytical results explaining these signatures and corroborate them with numerical simulations. We show further that these peaks disappear in stochastic parametrizations of the fast chaotic processes, and the associated FSLE spectra reveal that large scale predictability properties of the full deterministic model are well approximated, whereas small scale features are not properly resolved.
我们研究了在确定多尺度系统的误差增长率和可预测性时,状态转换对有限大小李雅普诺夫指数(FSLE)的影响。我们考虑了一个涉及慢态和快态以及它们之间转换的动力系统。令人惊讶的结果是,由于状态的存在,误差增长率可能是初始误差幅度的非单调函数。特别是,FSLE 谱大尺度中的低谷被证明是慢态的特征,而快态则导致谱中出现大峰值,误差增长率远远超过从最大李雅普诺夫指数估计的值。我们提出了解释这些特征的分析结果,并通过数值模拟加以证实。我们进一步表明,这些峰值在快混沌过程的随机参数化中消失,并且相关的 FSLE 谱表明,完整确定性模型的大尺度可预测性特性得到了很好的近似,而小尺度特征则没有得到适当的解决。