The optimally tuned system is then said to be in stochastic resonance. Nicolis
[83] and Benzi et al. [5], by tuning the noise parameter ε to appropriate values, were
able to give qualitative explanations for glaciation cycles based on this phenomenon.
Stochastic resonance proved to be relevant in other elementary climate models
than the energy balance models considered so far. In Penland et al. [87], Wang
et al. [107, 106], a two-dimensional stochastic model for a qualitative explanation
of the ENSO (El Ni˜ no Southern Oscillation) phenomenon also leads to stochastic
resonance effects: for certain parameter ranges the model exhibits random tuned
transitions between two stable sea surface temperatures. New evidence for the pres-
ence of stochastic resonance phenomena in paleo-climatic time series was added by
Ganopolski and Rahmstorf [45]. Their paper interprets the GRIP ice core record
representing temperature proxies from the Greenland glacier that extend over a
period of roughly 90 000 years, and showing the fine structure of the temperature
record of the last glacial period. The time series shows about 20 intermediate warm-
ings during the last glacial period commonly known under the name of Dansgaard–
Oeschger events. These events are clearly marked by rapid spontaneous increases
of temperature by about 6K followed by slower coolings to return to the initial
basic cold age temperature. It was noted in [45] that a histogram of the number of
Dansgaard–Oeschger events with a duration of k · 1480 years, with k = 1, 2, 3,...
exhibits the typical shape of a stochastic resonance spike train consistent for in-
stance with the results of Herrmann and Imkeller [49] for Markov chains describing
the effective diffusion dynamics, or Berglund and Gentz [13] for diffusion processes
with periodic forcing.
1.2. Heuristics of our mathematical approach
The rigorous mathematical elaboration of the concept of stochastic resonance
is the main objective of this book. We start its mathematically sound treatment
by giving a heuristical outline of the main stream of ideas and arguments based on
the methods of large deviations for random dynamical systems in the framework of
the Freidlin–Wentzell theory. Freidlin [39] is able to formulate Kramers’ [65] very
old seminal approach mathematically rigorously in a very general setting, and this
way provides a lower estimate for the good tuning (see also the numerical results
by Milstein and Tretyakov [77]). To obtain an upper estimate, we finally argue by
embedding time discrete Markov chains into the diffusion processes that describe
the effective dynamics of noise induced transitions. Optimal tuning results obtained
for the Markov chains will then be transferred to the original diffusion processes.
To describe the idea of our approach, let us briefly return to our favorite ex-
ample explained in the preceding section. Recall that the function
f(t, T ) = Q(t) 1 a(T ) γ T
T, t 0,
describes a multiple of Rin Rout, and its very slow periodicity in t is initiated
by the assumption on the solar constant Q(t) = Q0 + b sin(ωt). Let us compare
this quantity, sketched in Figure 1.9 schematically for two times, say t1, t2 such
that Q takes its minimum at t1 and its maximum at t2. The graph of f moves
periodically between the two extreme positions. Note that in the one-dimensional
situation considered, f(t, ·) can be seen as the negative gradient of a potential
function U(t, ·) which depends periodically on time t.
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