From the Weizmann Institute of Science – Eat, Prey, Rain
Photo: Tamar Deutsch
What do a herd of gazelles and a fluffy mass of clouds have in common? A mathematical formula that describes the population dynamics of such prey animals as gazelles and their predators has been used to model the relationship between cloud systems, rain and tiny floating particles called aerosols. This model may help climate scientists understand, among other things, how human-produced aerosols affect rainfall patterns. The research recently appeared in the Proceedings of the National Academy of Sciences (PNAS).
Clouds are major contributors to the climate system. In particular the shallow marine stratocumulus clouds that form huge cloud decks over the subtropical oceans cool the atmosphere by reflecting part of the incoming solar energy back to space. Drs. Ilan Koren of the Weizmann Institute’s Environmental Sciences and Energy Research Department (Faculty of Chemistry) and Graham Feingold of the NOAA Earth System Research Laboratory, Colorado, found that equations for modeling prey-predator cycles in the animal world were a handy analogy for cloud-rain cycles: Just as respective predator and prey populations expand and contract at the expense of one another, so too rain depletes clouds, which grow again once the rain runs out. And just as the availability of grass affects herd size, the relative abundance of aerosols – which “feed” the clouds as droplets condense around them – affects the shapes of those clouds. A larger supply of airborne particles gives rise to more droplets, but these droplets are smaller and thus remain high up in the cloud rather than falling as rain.
In previous research, Feingold and Koren had “zoomed in” to discover oscillations in convective cells in marine stratocumulus. Now they returned to their data, but from a “top down” angle to see if a generalized formula could reveal something about these systems. Using just three simple equations, they developed a model showing that cloud-rain dynamics mimic three known predator-prey modes. Like gazelles and lions, the two can oscillate in tandem, the “predator” rain cycle following a step behind peak cloud formation. Or the two can reach a sort of steady state in which the clouds are replenished at the same rate as they are diminished (as in a light, steady drizzle). The third option is chaos – the crash that occurs when predator populations get out of hand or a strong rain destroys the cloud system.
The model shows that as the amounts of aerosols change, the system can abruptly shift from one state to another. It also reveals a bifurcation – two scenarios at different ends of the aerosol scale that lend themselves to stable patterns. In the first, relatively low aerosol levels lead to clouds in which development depends heavily on aerosol concentrations. In the second, high levels produce saturation; these clouds depend solely on the initial environmental conditions.
Using this so-called systems approach, says Koren, “can open new windows to view and understand the emergent behavior of the complex relationships between clouds, rain and aerosols, giving us a more useful view of the big picture and helping us to understand how shifting aerosol levels can lead to different climate patterns.”
Dr. Ilan Koren’s research is supported by the Yeda-Sela Center for Basic Research. Dr. Koren is the incumbent of the Benjamin H. Swig and Jack D. Weiler Career Development Chair in Perpetuity.
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Would really like to see something on clouds and relationships with Cirrus Clouds and the Jetstream. Any pilots out there to help me?
steveta_uk says:
July 25, 2011 at 9:51 am
“high levels produce saturation; these clouds depend solely on the initial environmental conditions”
is apparently derived from “high levels of food produce saturation in gazelle; these herd sizes depend solely on initial number of lions” – which doesn’t sound plausible.
If the “food” requirement is satisfied then the “prey” population is limited solely by the “predator” population. In an actual prey-predator cycle the “saturation” steady state” would be unlikely since the predator population would expand in response to the availability of food. In clouds, “prey” and “predator” are metaphorical, as is “food,” so a potential steady state where moisture, seed, and rain balance out is conceivable.
If you add a third variable, it is time to employ chaos theory. For example, if you attempt to create a “balance of nature,” using green dots for grass plants, brown dots for rabbits, and red dots for foxes, any “balance” you manage is a precarious perfection, and can be knocked out of kilter by the accidental death of a single plant, rabbit or fox. Very swiftly populations explode and then crash, and your model displays either a grassland devoid of both rabbits and foxes, or a total wasteland devoid of all life. (I know about this because I had a friend who worked with such models.)
Nature is very hard to copy. There have been various attempts to create “capsules” cut off from all outside influences except sunshine, to replicate the conditions in a space craft flying to a nearby star, and after a month or so the people in the capsule have to open the hatch and let in some fresh air, because things in the capsule get too out of whack. Even if the plants in the capsule use up the CO2 and make O2 for the people in the capsule to breathe, strange trace gasses build up towards poisonous levels.
Considering people haven’t even learned to mimic what nature does, to claim we understand it is absurd.
Let’s analyze of the Lotka-Voltera equation: it was developed and used to examine population dynamics. It says the populayion of (n + 1) generation depends on the initial population, the birth ratre, death rate, a time factor and “K”- a numerical value describing the carrying capacity (including the interaction of a predator-prey or parasite-host system). By mainpulating the values of these factors and iterating, any number of amazing graphs can be generated including those accurately describing the population spikes of the 17-yr locust, the waxing & waning of deer-wolf populations or the periodic marches to the sea of lemmings, etc.
By cleverly realizing that the cyclic changes in cloud formations ( or even weather) might also be modeled using this equation, these authors substituted values they thought important in cloud dynamics and coincidently found an iteration that accurately reflected observations.
Now the trick is to prove if their assumptions about the important factors are in fact the operative factors or if they just fortuitiously chose numerical values that iterated out to the desired result.