Nobel Prize in Physics addresses complexity, both general and climatic

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Enlarge / The output of a modern climate model allows us to detect trends despite the chaotic nature of the underlying system.

Complex behavior is all around us. Think of something like the economy. It has many components, each with its own set of rules and all of them interacting in complicated ways. Trying to keep track of what’s going on from scratch is almost impossible. Yet some reasonably consistent behaviors emerge from this complexity, allowing us to understand some general rules for it.

This mixture of complexity and emerging behavior appears in many other systems involving global human behavior, as well as in the fields of physics, chemistry and biology. This year’s Nobel Prize in Physics is split equally between two aspects of the study of these systems. Half the price goes to Giorgio Parisi, who helped find methods for understanding complex systems that can be applied more generally. And the other half is shared between two climate modelers, Syukuro Manabe and Klaus Hasselmann, who helped develop systems that we now use to understand how climate behavior emerges from the complex interplay of its components and influences, including the growing influence of greenhouse gases.

Complex systems and emerging behaviors

Giorgio Parisi’s work has its roots in the early days of statistical mechanics, notably the work of James Clerk Maxwell (of fame for Maxwell’s Demon) and Ludwig Boltzmann, who applied a statistical approach to the second law of thermodynamics. (entropy). Finally, physicists had a mathematical tool capable of describing how properties at the macroscopic scale, such as the temperature and pressure of a gas, emerge from the random and disordered motions of particles at the microscopic scale. Parisi’s work uncovered the hidden rules that govern these types of complex disordered systems and their emerging properties.

What does it mean for a property to be emergent? Think of a piece of gold. It has properties like hardness or color, but these properties are not found in the individual atoms that make up the lump. On the contrary, they emerge from the collective interactions between the atoms that make up gold.

This is a fairly simple and straightforward example. It is often more difficult to predict the behavior of a very complex system like weather or a granular material like sand or gravel. This is because of the large number of individual components, the randomness of their interactions, and the many variables that can impact these interactions.

For example, sand can behave as both a liquid and a solid. Dry sand easily pours like fluid out of a bucket, but if you place a rock on the same sand, the collective grains are strong enough to support it, even though, technically, the rock is denser than sand.

The usual ordered equations that govern the phase transition from a liquid to a solid simply do not apply. The grains appear to act as individual particles as they flow from the bucket, but can quickly cluster together when solidarity is needed. The large number of individual grains makes it difficult to predict the behavior of the system from time to time, such as determining when an avalanche is likely to occur. Each grain interacts with several immediate neighboring grains simultaneously, and the behavior of neighboring grains is constantly changing from moment to moment.

A different turn

Parisi’s Nobel Prize-winning discoveries came from his work with spin glasses, a metal alloy in which iron atoms randomly mix in a grid of copper atoms. The spins of the atoms in a regular magnet all point in the same direction. This is not the case in spin glass, in which each iron atom is influenced by other nearby iron atoms. So you get an atomic-scale showdown: some pairs of nearby spins naturally want to point in the same direction, but others want to point in the opposite direction. They are caught in a “frustrated” state.

Parisi himself drew an analogy with the characters in a Shakespeare play, where one character wishes to have peace with two others, but those other two are sworn enemies. Likewise, in a spin glass, if two spins want to point in opposite directions, a third spin cannot point in both directions at the same time. In a way, the spin glass finds an optimal orientation which constitutes a compromise between the two opposite spins.

In the 1970s, physicists attempted to describe these frustrated complex systems by trying to process many copies of the system (replicas) simultaneously. It was a clever mathematical trick, but it did not produce the expected results. Parisi found the hidden messy structure hiding underneath, causing the case to crack. Parisi has shown that even if you are considering many exact replicas of the system, each replica can end up in a different state because there are so many possible states and it is difficult to transition between them. The analysis therefore reproduces the symmetry breaking, a characteristic common to many physical systems.

Its breakthrough therefore applies to much more than spin glasses. In the decades that followed, scientists used his knowledge to describe complex disordered systems in a wide range of fields: mathematics, biology, neuroscience, laser science, materials science, and machine learning, to name but a few. some. All of these systems look very different on the surface, but they share a common underlying mathematical framework.

For example, biological swarms (such as midges) and flight behavior among starlings and jackdaws are both examples of emerging collective behavior; the patterns that form arise from underlying rules of interaction, which can change in response to different environmental signals. Parisi’s work has been influential in solving the traveling salesman puzzle (a classic optimization problem) and in the study of neural networks. It may also be relevant for the study of social networks, such as how political polarization or social perception bias can be treated as emergent properties resulting from the complex interactions of millions of people.

The emergence of climate models

Through this year’s prize, the Nobel committee argues that Parisi’s breakthrough has parallels with how the incredibly complex behaviors that produce climate can still be understood by following the underlying physics. In other words, if you model things like the mixing of gases and their interactions with radiation, clear behaviors can emerge from these processes, although there is a lot of variation superimposed on that behavior. This is exactly what we ended up doing with the climate models.

The prize for climate modeling recognizes two very distinct aspects of its development. While climate models have only gained public attention in recent decades, attempts to model the influence of the composition of the atmosphere on its temperature date back to the work of Svante Arrhenius in 1896. Early work, however, viewed the system as static and made no distinction between land and ocean surfaces under the atmosphere. While these efforts have grown more sophisticated over the decades, they have primarily involved incorporating some of Earth’s complexities while finding the point at which the incoming and outgoing energy balances out.

The work of Syukuro Manabe, honored today, was essential in initiating the transition to the modern modeling approach. Manabe began working at the Princeton Geophysical Fluid Dynamics Laboratory in 1959; a decade later, he had developed a computer model that simulated a one-dimensional column of the atmosphere. This allowed the model to include more realistic conditions, such as uneven distribution of gases at different levels of the atmosphere and redistribution of heat by convection.

By 1975, he and his colleagues had achieved an astonishing feat: to create an entirely global model that tracked heat, radiation and the movement of atmospheric gases, all in a computer with half a megabyte of RAM. Surprisingly, this study produced a climate sensitivity to greenhouse gases that is within the range of uncertainties produced by current models.

Klaus Hasselmann is recognized for his key contributions to comparing climate model results with real-world data, allowing us to identify fingerprints of increasing greenhouse warming. Hasselmann entered this field by focusing on the natural variability of the climate system. Determining the limits of these natural variations leads directly to the ability to identify when the system has exceeded these limits and therefore must undergo additional influences.

Between 1979 and 1997, Hasselmann was one of the authors of three papers essential to establishing a framework for the comparison of models with real world data. These included influential ideas on how best to identify greenhouse warming signals, recognizing that it is sometimes better to measure those parts of the climate where the noise of natural variability is low rather than there. where the greenhouse warming signal is strongest. Other scientists have called his work “the first serious effort to provide a solid statistical framework for identifying a human-caused warming signal.”

There is still some unease among the research communities about the specific individuals who win the Nobel Prize, and this is likely to be exacerbated here. Climate modeling is a multidisciplinary activity carried out by many large teams around the world and relies heavily on the work of previous modelers, so choosing a limited number of people to honor was always going to be problematic. While the Nobel Committee made a reasonable attempt to honor milestones in the evolution of climate models in the systems we use today, it is not surprising that some climatologists are express a little unease on the price.



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