Prize for understanding complex systems, including weather.
There is a complex behavior around us. Think of something like the economy. It is made up of many components, each with its own set of rules, all of which interact in complex ways. It is almost impossible to try to follow what is happening from the beginning. However, some logical and logical behaviors come out of this complexity and allow us to understand some general rules for it.
This combination of complexity and emerging behaviors is reflected in many other systems, including general human behavior as well as in the fields of physics, chemistry and biology. This year's Nobel Prize in Physics is divided equally between the two sides of the study of these systems. Half of the award goes to Giorgio Baresi, who helped find ways to understand complex systems that can be used in general. The other half is split between two climate modelers, Siukuru Manabe and Klaus Haselmann, who have helped develop systems that allow us to understand how the climate behaves from the interactions of complex components and their effects - including the improvement of greenhouse gas emissions - we use.
Complex Systems and Emerging Behaviors
The work of Giorgio Baresi has its roots in the early days of statistical mechanics, most notably the work of James Clerk Maxwell (better known as Dave Maxwell) and Ludwig Boltzmann, who is best known for his use of a statistical method in law The second is thermodynamics (entropy). Finally, the physicists had a mathematical tool that could show how large-scale properties - such as temperature and gas pressure - arose from the random and irregular motions of small-sized particles. Paris' work revealed the hidden laws that govern this kind of turbulent complex systems and their emerging properties.
What does the emergence of ownership mean? Think of a piece of gold. It has properties such as hardness or color, but these properties are not found in the single atoms that make up the mass. Instead, they emerge from the collective interactions between the atoms of the gold component.
This is a fairly simple and straightforward example. It is often difficult to predict the behavior of a very complex system, such as weather or granular materials such as sand or gravel. This is due to the large number of individual components, the randomness of their interactions, and the many variables that can influence these interactions.
For example, sand can act both as a liquid and as a solid: dry sand drips like liquid that comes out of a bucket easily, but if a rock is placed on the same sand, the aggregate is strong enough to hold it—even if The rocks were technically denser than sand. The usual sequential equations that govern the phase transition from the liquid state to the solid state simply do not apply. The granules appear to act as individual particles when they exit the bucket, but can be quickly combined if binding is required. The large number of individual grains makes it difficult to predict how the system will operate from moment to moment - such as when an avalanche will occur. Each grain interacts with several adjacent grains simultaneously, and the behavior of neighboring grains changes from moment to moment. Working with rotating glass, a metal alloy in which iron atoms are randomly mixed into a lattice of copper atoms. The rotation of the atoms in a regular magnet is in one direction. This is not the case with rotating glass, where each iron atom is affected by other neighboring iron atoms. So you have a kind of tension on the atomic scale: some pairs of adjacent cycles naturally want to point in one direction, but others want to point in the opposite direction. They are in a state of "desperation".
Paris himself introduced the character analogy in Shakespeare's play, in which one character desires peace with two others, while the other two are ferocious enemies. Similarly, in rotating glass, if two rotations want to be in opposite directions, the third rotation cannot point in both directions at the same time. Somehow, the rotating glass finds a suitable orientation, which is the opposite of leveling between two turns.
In the 1970s, physicists attempted to describe these highly complex systems by manipulating large copies of the system (mockups). Synchronized. It was a clever math trick but it didn't produce the desired results. Paris found the dysfunctional, hidden hull lurking beneath her and tore off the frame. Paris showed that even if you consider several replicas of a system, each replica can reach a different state, since there are many possible modes and it is difficult to move between them. Thus, the analysis repeats frequently, a common feature of many physical systems.
Therefore, its advancement applies to most rotating glasses. In the decades since, scientists have used his insights to describe turbulent complex systems in a wide variety of fields: mathematics, biology, neuroscience, laser science, materials science, and machine learning. All of these systems look very different in appearance, but have a common sporting framework.
For example, biological engorgement (eg muscles), interstellar group behavior and swallowing are examples of emerging group behaviors. ; The patterns formed arise from the basic rules of interaction, which can change in response to different environmental cues. Paris's work has been instrumental in tackling the riddle of the seller and passengers (the classic optimization problem) and in the study of neural networks. It may also be related to the study of social networks, such as how political polarization or biases in social cognition can be seen as emerging features of the complex interactions of millions.
The Origin of the Climate Model
With this year's prize, the Nobel Committee sees progress made in Paris bears similarities to the highly complex behaviors of climate-creation to trace fundamental physics. In other words, if you use things like mixing gases and interacting with radiation, then clear behaviors of these processes will appear, even if there are many changes in the different layers of this behavior. That's exactly what we did with climate models.Advertising
The Climate Modeling Award honors two very distinguished aspects of their development. Although it has only been in the public eye for the past few decades, the attempt to model how the composition of the atmosphere affects its temperature dates back to the work of Svante Arrhenius in 1896. There is no difference between the Earth's surface and the oceans beneath the atmosphere. As these efforts have become more complex over the decades, they have largely involved combining some of the complexities of the Earth with finding a point to balance the input and output energies. Begin the transition to a modern modeling approach. Manabe began working at the Princeton Laboratory of Geophysical Fluid Dynamics in 1959. A decade later, he created a computer model that simulated a one-dimensional plume of the atmosphere. This allows the model to include more realistic conditions, such as the uneven distribution of gases at different levels of the atmosphere and the redistribution of heat through convection. A fully global model that tracks heat, radiation, and movement of atmospheric gases on a computer with half a megabyte of RAM. Surprisingly, this made the climate more sensitive to greenhouse gases, which are within the range of uncertainty generated by today's models. World data allow us to identify the hallmarks of increasing greenhouse heating. Hesselmann entered the field by focusing on the natural diversity of the climate system. Detecting the limitations of these natural changes leads directly to the ability to detect when a system exceeds this limit and, therefore, should be more affected. Between 1979 and 1997, Hesselmann was one of the authors of three papers that were important in creating a framework for comparing models with real-world data. This includes powerful ideas on how to better detect greenhouse warming signals, because you know that sometimes it's better to focus on parts of the climate where natural variability noise is low rather than where the greenhouse heating signal is strongest. , do the measurement. Other scientists have described his work as "the first serious attempt to provide an adequate statistical framework for determining the human-caused warming signal". Climate modeling is a multidisciplinary activity pursued by many great teams around the world and is one of the activities of past modelers, so the selection of a limited number of proud people has always been done. While the Nobel Committee has made a reasonable effort to respect milestones during the evolution of climate models in the systems we use today, it is not surprising that some climate scientists have done little about this award, they are upset.
The Nobel Prize in Physics deals with complexity, general and climate
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