• DOI:10.1007/s00332-012-9158-x
• Corpus ID: 17643896

A Mathematical Framework for Critical Transitions: Normal Forms, Variance and Applications

@article{Kuehn2013AMF,
  title={A Mathematical Framework for Critical Transitions: Normal Forms, Variance and Applications},
  author={Christian Kuehn},
  journal={Journal of Nonlinear Science},
  year={2013},
  volume={23},
  pages={457-510}
}

• C. Kuehn
• Published 14 January 2011
• Mathematics
• Journal of Nonlinear Science

Critical transitions occur in a wide variety of applications including mathematical biology, climate change, human physiology and economics. Therefore it is highly desirable to find early-warning signs. We show that it is possible to classify critical transitions by using bifurcation theory and normal forms in the singular limit. Based on this elementary classification, we analyze stochastic fluctuations and calculate scaling laws of the variance of stochastic sample paths near critical transitions for fast-subsystem bifurcations up to codimension two. The theory is applied to several models: the Stommel–Cessi box model for the thermohaline circulation from geoscience, an epidemic-spreading model on an adaptive network, an activator–inhibitor switch from systems biology, a predator–prey system from ecology and to the Euler buckling problem from classical mechanics. For the Stommel–Cessi model we compare different detrending techniques to calculate early-warning signs. In the epidemics model we show that link densities could be better variables for prediction than population densities. The activator–inhibitor switch demonstrates effects in three time-scale systems and points out that excitable cells and molecular units have information for subthreshold prediction. In the predator–prey model explosive population growth near a codimension-two bifurcation is investigated and we show that early-warnings from normal forms can be misleading in this context. In the biomechanical model we demonstrate that early-warning signs for buckling depend crucially on the control strategy near the instability which illustrates the effect of multiplicative noise.