Articles | Volume 26, issue 4
https://doi.org/10.5194/npg-26-359-2019
https://doi.org/10.5194/npg-26-359-2019
Review article
 | 
08 Oct 2019
Review article |  | 08 Oct 2019

Numerical bifurcation methods applied to climate models: analysis beyond simulation

Henk A. Dijkstra

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Cited articles

Abraham, R. and Shaw, C.: Dynamics: The Geometry of Behavior, Addison-Wesley, Redwood City, California, USA, 1992. a
Ashwin, P., Wieczorek, S., Vitolo, R., and Cox, P.: Tipping points in open systems: bifurcation, noise-induced and rate-dependent examples in the climate system, Philos. T. Roy. Soc. A, 370, 1166–1184, 2012. a
Baars, S., Viebahn, J. P., Mulder, T. E., Kuehn, C., Wubs, F. W., and Dijkstra, H. A.: Continuation of probability density functions using a generalized Lyapunov approach, J. Comput. Phys., 336, 627–643, 2017. a, b, c
Berloff, P. S., Hogg, A. M., and Dewar, W. K.: The turbulent oscillator: a mechanism of low-frequency variability of the wind-driven ocean gyres, J. Phys. Oceanogr., 37, 2362–2386, 2007. a
Castellana, D., Wubs, F., and Dijkstra, H.: A statistical significance test for sea-level variability, Dynamics and Statistics of the Climate System, 3, dzy008, https://doi.org/10.1093/climsys/dzy008, 2018. a
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I provide a personal view on the role of bifurcation analysis of climate models in the development of a theory of variability in the climate system. By outlining the state of the art of the methodology and by discussing what has been done and what has been learned from a hierarchy of models, I will argue that there are low-order phenomena of climate variability, such as El Niño and the Atlantic Multidecadal Oscillation.