Predictability of extreme events in a nonlinear stochastic-dynamical model.
Predictability of extreme events in a nonlinear stochastic-dynamical model.
复制标题
非线性随机动力学模型中极端事件的可预测性。
DOI：
--
复制
发表时间：
2012
期刊：
影响因子：
--
通讯作者：
C. Franzke
中科院分区：
文献类型：
--
作者：
C. Franzke
这项工作的目的是评估降阶模型重现高维动力系统的极端事件和可预测性特征的潜力。使用非线性玩具模型，其中包含综合气候模型的关键特征。首先，我们证明系统随机模态简化策略可以产生一个降阶模型，该模型具有与大范围时间尺度分离的全动态模型相同的极值特征。其次，我们发现该模型中的极端事件遵循具有负形状参数的广义帕累托分布；因此，极端事件在此模型中受到限制。第三，我们表明前兆方法对极端事件具有良好的预测能力。然后我们发现简化的随机模型很好地捕捉了完整动态模型的极端事件的预测能力。与之前的研究一致，我们还发现极端事件越大，它们的可预测性就越好。我们的结果表明，系统导出的降阶模型有潜力用于与天气和气候相关的极端事件的建模和统计预测，也可能用于其他科学和工程领域。
The objective of this work is to evaluate the potential of reduced order models to reproduce the extreme event and predictability characteristics of higher dimensional dynamical systems. A nonlinear toy model is used which contains key features of comprehensive climate models. First, we demonstrate that the systematic stochastic mode reduction strategy leads to a reduced order model with the same extreme value characteristics as the full dynamical models for a wide range of time-scale separations. Second, we find that extreme events in this model follow a generalized Pareto distribution with a negative shape parameter; thus extreme events are bounded in this model. Third, we show that a precursor approach has good forecast skill for extreme events. We then find that the reduced stochastic models capture the predictive skill of extreme events of the full dynamical models well. Consistent with previous studies we also find that the larger the extreme events, the better predictable they are. Our results suggest that systematically derived reduced order models have the potential to be used for the modeling and statistical prediction of weather- and climate-related extreme events and, possibly, in other areas of science and engineering too.