A numerical framework to understand transitions in high-dimensional stochastic dynamical systems
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Publication date
2016
Authors
Dijkstra, H. A.
Tantet, Alexis
Viebahn, J. P.
Mulder, Thomas E.
Hebbink, M.
Castellana, Daniele
van den Pol, Henri
Frank, Jason
Baars, S.
Wubs, F.W.
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Document Type
Article
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cc_by_nc
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 analyse 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.
Keywords
SDG 13 - Climate Action
Citation
Dijkstra, H A, Tantet, A J J, Viebahn, J P, Mulder, T E, Hebbink, M, Castellana, D, van den Pol, H, Frank, J E, Baars, S, Wubs, F W, Chekroun, M & Kuehn, C 2016, 'A numerical framework to understand transitions in high-dimensional stochastic dynamical systems', Dynamics and Statistics of the Climate System, vol. 1, no. 1, dzw003. https://doi.org/10.1093/climsys/dzw003