The Royal Swedish Academy of Sciences has decided to award the 2021 Nobel Prize in Physics
"For his revolutionary contributions to our understanding of complex physical systems"
with one half jointly with:
Syukuro Manabe - Princeton University, USA
Klaus Hasselmann - Max Planck Institute for Meteorology, Hamburg, Germany
"For physical modeling of the Earth's climate, to quantify variability and reliably predict global warming "
and the other half to:
Giorgio Parisi - Sapienza University of Rome, Italy
"For the discovery of the interaction of disorder and fluctuations of physical systems from atomic scale to planetary scale"
Physics of climate and other complex phenomena
Three laureates share this year's Nobel Prize in Physics for their studies of chaotic and seemingly random phenomena. Syukuro Manabe and Klaus Hasselmann laid the foundation for our knowledge of Earth's climate and how humanity influences it. Giorgio Parisi is recognized for his revolutionary contributions to the theory of disordered materials and random processes.
Complex systems are characterized by chance and disorder and are difficult to understand. This year's Prize recognizes new methods of describing them and predicting their long-term behavior.
An example of a complex system of vital importance to humanity is that of the Earth's climate. Syukuro Manabe demonstrated how increasing levels of carbon dioxide in the atmosphere lead to increased temperatures on the Earth's surface. In the 1960s, he led the development of physical models of Earth's climate and was the first person to explore the interaction between radiation balance and the vertical transport of air masses. His work laid the foundations for the development of current climate models.
A decade later, Klaus Hasselmann created a model that links time and climate, thus answering the question of why climate models can be reliable despite variable and chaotic weather. He also developed methods to identify specific signals, fingerprints, that natural phenomena and human activities imprint in the climate. His methods have been used to prove that the increase in temperature in the atmosphere is due to human emissions of carbon dioxide.
Around 1980, Giorgio Parisi discovered patterns hidden in complex messy materials. His discoveries are among the most important contributions to the theory of complex systems. They make it possible to understand and describe many different and seemingly entirely random materials and phenomena, not only in physics , but also in other very different fields, such as mathematics, biology, neuroscience and machine learning.
“The findings recognized this year demonstrate that our knowledge of the climate rests on a solid scientific basis, based on a rigorous analysis of observations. This year's laureates have all contributed to our understanding of the properties and evolution of complex physical systems, ”said Thors Hans Hansson, Chairman of the Nobel Committee for Physics.
They found patterns hidden in the climate and other complex phenomena
All complex systems are made up of many different interacting parts. They have been studied by physicists for a few centuries and can be difficult to describe mathematically - they can have huge numbers of components or be ruled by chance. They could also be chaotic, like the weather, where small deviations from the initial values lead to huge differences at a later stage. This year's winners have all helped us gain a better understanding of these systems and their long-term development.
The greenhouse effect is vital for life
Two hundred years ago, the French physicist Joseph Fourier studied the energy balance between solar radiation towards the ground and radiation from the ground. He understood the role of the atmosphere in this balance; At the Earth's surface, incoming solar radiation is transformed into outgoing radiation - “dark heat” - which is absorbed by the atmosphere, warming it. The protective role of the atmosphere is now called the greenhouse effect. This name comes from its similarity to the glass panes of a greenhouse, which allow through the heating rays of the sun, but trap the heat inside. However, the radiative processes in the atmosphere are much more complicated.
The task remains the same as that undertaken by Fourier - to study the balance between the shortwave solar radiation coming towards our planet and the infrared radiation leaving the long waves of the Earth. Details were added by many climatologists over the next two centuries. Contemporary climate models are incredibly powerful tools, not only for understanding the climate, but also for understanding the global warming for which humans are responsible.
These models are based on the laws of physics and were developed from models that have been used to predict the weather. Weather is described by meteorological quantities such as temperature, precipitation, wind, or clouds, and is affected by what happens in the oceans and on land. Climate models are based on the calculated statistical properties of time, such as mean values, standard deviations, highest and lowest measured values, etc. They can't tell us what the weather will be like in Stockholm on December 10 next year, but we can get an idea of the temperature or how much precipitation we can expect on average in Stockholm in December.
Establish the role of carbon dioxide
The greenhouse effect is essential for life on Earth. It regulates temperature because greenhouse gases in the atmosphere - carbon dioxide, methane, water vapor, and other gases - first absorb infrared radiation from the Earth, then release this absorbed energy, warming the earth. surrounding air and the ground below.
Greenhouse gases actually make up only a very small proportion of the Earth's dry atmosphere, which is largely made up of nitrogen and oxygen - these make up 99% by volume. Carbon dioxide is only 0.04% by volume. The most powerful greenhouse gas is water vapor, but we cannot control the concentration of water vapor in the atmosphere, while we can control the concentration of carbon dioxide.
The amount of water vapor in the atmosphere is highly dependent on temperature, which leads to a water return mechanism. More carbon dioxide in the atmosphere makes it warmer, which helps hold more water vapor in the air, which increases the greenhouse effect and raises temperatures even higher. If the level of carbon dioxide drops, some of the water vapor will condense and the temperature will drop.
An important first piece of the puzzle on the impact of carbon dioxide came from Swedish researcher and Nobel laureate Svante Arrhenius. Incidentally, it was his colleague, meteorologist Nils Ekholm who, in 1901, was the first to use the word greenhouse to describe the storage and re-radiation of heat in the atmosphere.
Arrhenius understood the physics responsible for the greenhouse effect at the end of the 19th century - that the outgoing radiation is proportional to the absolute temperature (T) of the radiant body to the power of four (T⁴). The hotter the source of the radiation, the shorter the wavelength of the rays. The Sun has a surface temperature of 6000 ° C and mainly emits rays in the visible spectrum. The Earth, with a surface temperature of only 15 ° C, re-irradiates infrared radiation invisible to us. If the atmosphere did not absorb this radiation, the surface temperature would barely exceed -18 ° C.
Arrhenius was actually trying to figure out what was causing the recently discovered phenomenon of Ice Ages. He came to the conclusion that if the level of carbon dioxide in the atmosphere were halved, it would be enough for the Earth to enter a new ice age. And vice versa - doubling the amount of carbon dioxide would raise the temperature by 5-6 ° C, a result which, somewhat fortuitously, is surprisingly close to current estimates.
Pioneering model for the effect of carbon dioxide
In the 1950s, Japanese atmospheric physicist Syukuro Manabe was one of the young and talented Tokyo researchers who left war-devastated Japan and continued their career in the United States. The goal of Manabes' research, like that of Arrhenius some seventy years earlier, was to understand how rising carbon dioxide levels can cause temperatures to rise. However, while Arrhenius had focused on radiation balance, in the 1960s, Manabe conducted work on the development of physical models to incorporate the vertical transport of air masses due to convection, as well as the latent heat of water vapor.
To make these calculations manageable, he chose to reduce the model to one dimension - a vertical column, 40 kilometers into the atmosphere. Despite this, it took hundreds of hours of precious computation to test the model by varying the gas levels in the atmosphere. Oxygen and nitrogen had negligible effects on surface temperature, while carbon dioxide had a clear impact: when the level of carbon dioxide doubled, the global temperature rose by more than 2 ° vs.
The model confirmed that this warming was really due to the increase in carbon dioxide, as it predicted a rise in temperatures closer to the ground as the upper atmosphere became colder. If changes in solar radiation were responsible for the temperature rise, the whole atmosphere should have heated up at the same time.
Sixty years ago, computers were hundreds of thousands of times slower than they are now, so this model was relatively straightforward, but Manabe got the key features right. You always have to simplify, he says. You can't compete with the complexity of nature - there is so much physics involved in every drop of rain that it would never be possible to calculate absolutely everything. The teachings of the one-dimensional model led to a three-dimensional climate model, which Manabe published in 1975; it was yet another important step on the road to understanding the secrets of climate.
The weather is chaotic
A decade after Manabe, Klaus Hasselmann succeeded in connecting weather and climate by finding a way to outsmart the rapid and chaotic weather changes that were so troublesome for the calculations. Our planet has vast changes in its weather because solar radiation is so unevenly distributed, both geographically and over time. The Earth is round, so less of the sun's rays reach the upper latitudes than the lower latitudes around the equator. Not only that, but the Earth's axis is tilted, producing seasonal differences in the incoming radiation. The differences in density between warmer and colder air cause colossal heat transports between different latitudes, between ocean and land, between higher and lower air masses, which cause the time on our planet.
As we all know, making reliable weather forecasts for more than the next ten days is a challenge. Two hundred years ago, the famous French scientist, Pierre-Simon de Laplace, claimed that if we simply knew the position and speed of all the particles in the universe, it should be possible to calculate both what s has happened and what will happen in our world. In principle, this should be true; Newton's three-century-old laws of motion, which also describe airlift in the atmosphere, are entirely deterministic - they are not ruled by chance.
However, nothing could be more wrong when it comes to the weather. This is partly because, in practice, it is impossible to be precise enough - to indicate the air temperature, pressure, humidity or wind conditions for each point in the atmosphere. In addition, the equations are nonlinear; small deviations from the initial values can cause a weather system to evolve in entirely different ways. Based on the question of whether a butterfly flapping its wings in Brazil could cause a tornado in Texas, the phenomenon has been named the butterfly effect. In practice, this means that it is impossible to produce long-term weather forecasts - the weather is chaotic; this discovery was made in the 1960s by the American meteorologist Edward Lorenz, who laid the foundations for today's chaos theory.
Making sense of noisy data
How can we produce reliable climate models for decades or hundreds of years into the future, although weather is a classic example of a chaotic system? Around 1980, Klaus Hasselmann demonstrated how chaotic weather phenomena can be described as rapidly changing noise, thus placing long-term climate predictions on a solid scientific basis. In addition, he developed methods to identify the human impact on the observed global temperature.
As a young physics doctoral student in Hamburg, Germany, in the 1950s, Hasselmann worked on fluid dynamics and then began to develop observations and theoretical models for ocean waves and currents. He settled in California and continued his oceanography, meeting colleagues such as Charles David Keeling, with whom the Hasselmanns founded a choir of madrigals. Keeling is legendary for having started, in 1958, what is now the longest series of atmospheric carbon dioxide measurements at the Mauna Loa Observatory in Hawaii. Hasselmann was unaware that in his later work he would regularly use the Keeling curve, which shows changes in carbon dioxide levels.
Obtaining a climate model from noisy weather data can be illustrated by walking a dog: the dog runs off the leash, back and forth, side to side, and around your legs. How can you use dog tracks to see if you are walking or are standing still? Or if you walk quickly or slowly? The dog's tracks are the weather changes, and your walk is the calculated climate. Is it even possible to draw conclusions about long-term climate trends using chaotic and noisy weather data?
An additional difficulty is that the fluctuations that influence the climate are extremely variable over time - they can be fast, like the force of the wind or the temperature of the air, or very slow, like the melting of the ice caps and the warming of the sea. oceans. For example, uniform heating of just one degree can take a thousand years for the ocean, but only a few weeks for the atmosphere. The decisive trick was to incorporate the rapid changes in the weather into the calculations as noise, and to show how this noise affects the climate.
Hasselmann created a stochastic climate model, which means that chance is built into the model. Its inspiration came from Albert Einstein's theory of Brownian motion, also called a random walk. Using this theory, Hasselmann demonstrated that the rapidly changing atmosphere can actually cause slow variations in the ocean.
Discerning traces of human impact
Once the climate variation model was completed, Hasselmann developed methods to identify the human impact on the climate system. He found that the models, as well as observations and theoretical considerations, contain adequate information about the properties of noise and signals. For example, changes in solar radiation, volcanic particles, or greenhouse gas levels leave unique signals, fingerprints, that can be separated. This method of fingerprint identification can also be applied to the effect humans have on the climate system. Hasselman thus paved the way for further studies on climate change, which have shown evidence of human impact on the climate using a large number of independent observations.
Climate models have become increasingly sophisticated as the processes involved in complex climate interactions are mapped in greater depth, including through satellite measurements and meteorological observations. The models clearly show an acceleration of the greenhouse effect; since the mid-19th century, levels of carbon dioxide in the atmosphere have increased by 40%. Earth's atmosphere has not contained as much carbon dioxide for hundreds of thousands of years. As a result, temperature measurements show that the world has warmed by 1 ° C over the past 150 years.
Syukuro Manabe and Klaus Hasselmann have contributed to the greatest benefit to humanity, in the spirit of Alfred Nobel, by providing a solid physical basis for our knowledge of the Earth's climate. We can no longer say we didn't know - the climate models are unequivocal. Is the Earth getting warmer? Yes. Is this the cause of the increase in the amounts of greenhouse gases in the atmosphere? Yes. Can this be explained only by natural factors? No. Are mankind's emissions the reason for the temperature rise? Yes.
Methods for disordered systems
Around 1980, Giorgio Parisi presented his findings on how seemingly random phenomena are governed by hidden rules. His work is now considered one of the most important contributions to the theory of complex systems.
Modern studies of complex systems are rooted in statistical mechanics developed in the second half of the 19th century by James C. Maxwell, Ludwig Boltzmann and J. Willard Gibbs, who named this field in 1884. Statistical mechanics evolved from the idea that a new kind of method was needed to describe systems, such as gases or liquids, which consist of a large number of particles. This method had to take into account the random movements of the particles, so the basic idea was to calculate the average effect of the particles instead of studying each particle individually. For example, the temperature in a gas is a measure of the average value of the energy of the gas particles. Statistical mechanics is very successful because it provides a microscopic explanation of macroscopic properties in gases and liquids, such as temperature and pressure.
The particles in a gas can be thought of as tiny balls, flying at speeds that increase with higher temperatures. When the temperature drops or the pressure increases, the beads condense first to a liquid and then to a solid. This solid is often a crystal, where the balls are arranged in a regular pattern. However, if this change occurs quickly, the balls can form an irregular pattern that does not change even if the liquid is cooled or squeezed together. If the experiment is repeated, the balls will take on a new pattern, despite the change occurring in exactly the same way. Why are the results different?
Understanding the complexity
These compressed balls are a simple model for ordinary glass and for granular materials, such as sand or gravel. However, the subject of Parisi's original work was another type of system - spin glass. This is a special type of metal alloy in which iron atoms, for example, are randomly mixed in a grid of copper atoms. Even though there are only a few atoms of iron, they alter the magnetic properties of the material in a drastic and very confusing way. Each iron atom behaves like a small magnet, or spin (The spin is, in quantum physics, one of the internal properties of particles, just like mass or electric charge. Like other quantum observables, its measurement gives discrete values and is subject to the uncertainty principle) , which is affected by other iron atoms close to it. In an ordinary magnet all the spins point in the same direction, but in a spin glass they are frustrated; some pairs of spins want to point in the same direction and others in the opposite direction - so how do they find an optimal orientation?
In the introduction to his book on Spin Glass, Parisi writes that the study of spin glass is like watching human tragedies in Shakespeare's plays. If you want to make friends with two people at the same time, but they hate each other, it can be frustrating. This is even more the case in a classic tragedy, where very emotional friends and enemies meet on stage. How to minimize the tension in the room?
Spin glasses and their exotic properties provide a model for complex systems. In the 1970s, many physicists, including several Nobel Prize winners, searched for a way to describe the mysterious and frustrating spin glasses. One method they used was the replication trick, a mathematical technique in which many copies, replicas, of the system are processed at the same time. However, in terms of physics, the results of the initial calculations were impractical.
In 1979, Parisi made a breakthrough when he demonstrated how the replica trick could be ingeniously used to solve a spin glass problem. He discovered a structure hidden in the aftershocks and found a way to describe it mathematically. It took many years for Parisi's solution to be proven mathematically correct. Since then, his method has been used in many disordered systems and has become a cornerstone of complex systems theory.
The Fruits of Frustration Are Many and Varied. Spin glass and granular materials are examples of frustrated systems, in which various constituents must organize themselves in a way that is a compromise between the counteracting forces. The question is how they behave and what the results are. Parisi is a master at studying these questions for many different materials and phenomena. His fundamental discoveries on the structure of spin glasses were so deep that they not only influenced physics, but also mathematics, biology, neuroscience, and machine learning, as all of these areas include issues directly related to frustration.
Parisi has also studied many other phenomena in which random processes play a decisive role in how structures are created and how they develop, and has addressed questions such as: Why do we have periodic ice ages? Is there a more general mathematical description of chaos and turbulent systems? Or - how do the patterns appear in a whisper of thousands of starlings? This question may seem like a far cry from spin glass. However, Parisi said most of her research has focused on how simple behaviors result in complex collective behaviors, and this applies to both spin glasses and starlings.