A numerical framework to understand transitions in high-dimensional stochastic dynamical systems

Citation:

Dijkstra, Henk A, Alexis Tantet, Jan Viebahn, Erik Mulder, Mariët Hebbink, Daniele Castellana, Henri van den Pol, et al. 2016. “A numerical framework to understand transitions in high-dimensional stochastic dynamical systems.” Dynamics and Statistics of the Climate System 1 (1): 1-27.

Abstract:

Dynamical systems methodology is a mature complementary approach to forward simulation which can be used to investigate many aspects of climate dynamics. With this paper, a review is given on the methods to analyze deterministic and stochastic climate models and show that these are not restricted to low-dimensional toy models, but that they can be applied to models formulated by stochastic partial differential equations. We sketch the numerical implementation of these methods and illustrate these by showing results for two canonical problems in climate dynamics.

Publisher's Version

Last updated on 04/19/2020