Climate reddening increases the chance of critical transitions.
In electrical engineering - and many other disciplines - "white noise" is referred to as an effect tied to random fluctuations that give signals as output that can obscure or bury "real" signals. The most familiar form of white noise is static, and the elevation of signal over static is a very important feature to - for one example - audiophiles who might pay many thousands of dollars to hear a minor scratch of a bow against a string in a recording of Benjamin Britten's "War Requiem".
In physics one of the most famous examples of "white noise" is Brownian motion, which is famously the effect, explained by Albert Einstein, thus proving the reality of atomic theory (which in 1905 still had prominent doubters), by which small particles observed under a microscope in solution seem to vibrate and move in a way that cannot be predicted.
The "signal to noise ratio" is an important issue in many branches of science, and is, in fact, an important feature of many important international regulations wherever scientific expertise applies, for example, in drug development, aviation, and many areas of engineering.
It is common to think of "white noise" as having no meaning other than providing difficulties for instrument makers, but this is not exactly true; random fluctuations can result in the generation of very real and important effects on a macroscopic scale. This is known as the "Butterfly Effect," an important feature of chaos theory.
A famous electrical engineering paper proposed an example and some mathematics of how white noise can generate real signals:
A statistical model of flicker noise (Barnes Allan Proceedings of the IEEE Vol: 54, Issue: 2, Feb. 1966 pp 176-178)
The effect they described has become known as "red noise," in which seemingly random effects result in changes to the overall state of a system by generating low frequency ("red" as opposed to "white" ) signals that persist for a long time, and in fact can result in permanent changes to the state of a system.
A paper from which the title of this post is taken has just been published in the journal Nature Climate Change to show how the climate can be permanently placed rapidly into an alternate and different state owing to the seemingly random fluctuations in the weather (as distinct from climate, climate being the integration of individual weather events.)
The paper is here: Nature Climate Change Volume 8, pages 478–484 (2018).
Some excerpts from the paper:
...To explore how changes in climate variability may affect systems with tipping points, we first ask how the size and duration of single environmental perturbations affect these systems. Next, we examine systematically how the autocorrelation and variance of climate variability separately affect the likelihood of a critical transition and how the autocorrelation of climate variability affects the duration of extreme events using an established ecological model as an example. Subsequently, we discuss evidence from five systems in which the duration of anomalously warm or dry events has been shown to elevate the chance of critical transitions: forests, coral reefs, the poverty trap, human conflict and the West Antarctic Ice Sheet (WAIS).
Note that not all of these five transitions are physical in nature, specifically two are social effects.
The authors continue:
As a first step to see how changes in the dynamic regime of climatic drivers may affect the likelihood of critical transitions, consider the effect of an idealized single perturbation such as a temporary change in environmental conditions (Fig. 1, red arrow). Because it takes time for the system to respond to a change in conditions (Fig. 1, black arrows), the moment at which the conditions are reversed to the original (Fig. 1, green arrows) determines the fate of the system. If the conditions recover quickly, the system reverts to the original state (Fig. 1, trajectory 1→ 2→ 3→ 4→ 1). However, recovery of the conditions at a later moment can cause the system to settle in the alternative equilibrium (Fig. 1, trajectory 1→ 2→ 3’→ 4’→ 5).
One example, one close to my heart since my liberalism does not involve worship of Elon Musk's stupid car for billionaires and millionaires, but concern for those who lack basic resources:
People whose livelihoods depend directly on natural resources and ecosystem services are particularly vulnerable to climate change and changes in weather variables61. For example, the herds of pastoralists in East Africa graze extensively and the growth of the forage mainly depends on rainfall. Consequently, drought can lead to substantial herd losses. The effect of drought on the livelihood of people depends mostly on its duration — in 1981 the seasonal rainfall totals in Brazil were slightly above average, but longer periods of drought resulted in yield losses that year62. Interhousehold differences in the capacity to deal with these losses can lead to variability in income and wealth63. The poverty trap is a critical minimal asset threshold, below which families are unable to build up stocks of assets over time64. When households are close to such a situation, losses as a result of weather variability can have permanent adverse consequences as they invoke a transition into a poverty trap. An example of the differential effects of a prolonged weather event on poverty is the three-year drought in Ethiopia in the late 1990s; wealthy households were able to rebuild their assets, while the adverse effects for the low-income households lasted longer65...
An closing remarks from the paper:
The transitions we have reviewed illustrate the potentially wideranging implications of our theoretical prediction that a reddening of climate fluctuations may promote the likelihood of inducing self-sustained shifts into alternative stable states of climate-sensitive systems. Clearly, there is a huge gap between our limited understanding of such real-world cases and the simple models we have analysed. Our initial theoretical analysis captures the essence of how the duration of an event may affect the likelihood of invoking a shift to an alternative attractor. Subsequently, our red-noise simulations illustrate that this basic conclusion may indeed apply more broadly to include the effect of temporal correlation in regimes of permanent fluctuations. Clearly, we merely scratched the surface of the question of how the timescale and magnitude of fluctuations may affect the scenarios we outlined. In any of the discussed systems, reality is much more complex than the schematic representation in the deterministic and stochastic parts of our models...
An interesting paper, well worth a read.
We're playing with fire, and in saying this, I'm not merely referring to the rapidly increasing reliance on oxidative combustion to power the world, no matter what you may have read on those self declared "green" websites where they hype the failure of so called "renewable energy" as a grand success.
I trust you will have a pleasant Sunday.
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