A numerical framework to understand transitions in high-dimensional stochastic dynamical systems
| Autor: | Dijkstra, H.A., Tantet, A.J.J., Viebahn, J.P., Mulder, T.E., Hebbink, M., Castellana, D., van den Pol, Henri, Frank, J.E., Baars, Sven, Wubs, F.W., Chekroun, Mickael, Kuehn, C., Mathematical Modeling, Sub Physical Oceanography, Sub Dynamics Meteorology, Dep Wiskunde, Sub Mathematical Modeling |
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| Přispěvatelé: | Computational and Numerical Mathematics |
| Rok vydání: | 2016 |
| Předmět: |
Mathematical optimization
010504 meteorology & atmospheric sciences Dynamical systems theory Climate dynamics High dimensional 01 natural sciences Sketch 010305 fluids & plasmas Stochastic partial differential equation 0103 physical sciences Applied mathematics Climate model 0105 earth and related environmental sciences Mathematics |
| Zdroj: | Dynamics and Statistics of the Climate System: An Interdisciplinary Journal, 1(1) Dynamics and Statistics of the Climate System: An Interdisciplinary Journal, 1(1). Oxford University Press |
| ISSN: | 2059-6987 |
| DOI: | 10.1093/climsys/dzw003 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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