2021 Nobel Prize in Physics

This year’s Nobel Prize in Physics was shared in half. One half was awarded to Syukuro Manabe and Klaus Hasselmann for the physical modelling of Earth’s climate, quantifying variability and reliably predicting global warming; and the other half to Giorgio Parisi for the discovery of the interplay of disorder and fluctuations in physical systems from atomic to planetary scales.

The three Laureates share this year’s Nobel Prize in Physics for their work on chaotic and seemingly random phenomena. Syukuro Manabe and Klaus Hasselmann laid the foundation for our knowledge about the Earth’s climate and how humanity’s effect on it. Giorgio Parisi was awarded for his revolutionary contributions to the theory of disordered materials and random processes.

Complex systems are characterized by randomness and disorder, and they are difficult to understand. Earth’s climate is one of these complex systems, which is vital for humanity. Syukuro Manabe showed how rising carbon dioxide levels in the Earth’s atmosphere lead to increased temperatures at the surface of our planet. In the 1960s, he pioneered the development of physical models regarding the Earth’s climate and was the first to investigate the interaction between radiation balance and vertical transport of air masses. Current climate models were all developed based on his work.

About a decade later, Klaus Hasselmann created a model linking weather and climate, thus answering the question of why climate models can still be reliable although the weather can change and become chaotic. He also developed methods to identify specific signals that both natural phenomena and human activities imprint in the climate. His methods were used to prove that the increase in atmospheric temperatures was caused by anthropogenic carbon dioxide emissions.

In the 1980s, Giorgio Parisi discovered hidden patterns in disordered complex materials. His discoveries made it possible to understand and describe various random materials and phenomena in a number of fields including physics, biology, neuroscience, and machine learning.

Pioneering model for the effect of carbon dioxide

In the 1950s, Japanese atmospheric physicist Syukuro Manabe was one of the young and talented researchers who left war-ravaged Japan to pursue a career in the United States. The goal of Manabe’s research, like the goal of Arrhenius about seven decades before that time, was to understand how increasing carbon dioxide levels could cause a rise in temperatures. However, while Arrhenius focused on radiation balance, Manabe pioneered the development of physical models to include the vertical transport of air masses due to convection besides the latent heat of water vapour. He found that while oxygen and nitrogen had negligible effects on surface temperature, carbon dioxide had a clear effect: When its levels doubled, global temperature increased by over 2°C. His model confirmed the warming was indeed due to the increase in carbon dioxide, as it predicted a rise in temperatures closer to the ground while the upper atmosphere got colder. If the rise in temperature was caused by changes in solar radiation instead, the entire atmosphere should have been warming up at the same time.

This was a one-dimensional model, to keep things relatively simple as the computers were much slower sixty years ago than their current counterparts. However, Manabe himself succeeded in creating a three-dimensional climate model in 1975, another milestone in understanding the secrets of climate.

Chaotic Weather

About 10 years after Manabe, Klaus Hasselmann succeeded in linking weather and climate by finding a way to beat the rapid and chaotic changes in weather that make calculations way too painful. Solar radiation is unevenly distributed in the Earth’s atmosphere, both geographically and over time, resulting in great variation in weather around our planet.

It is not exactly possible to determine the air temperature, pressure, humidity or wind conditions for every point in the atmosphere. This fact itself prevents making precise accurate calculations. Questioning if a butterfly flapping its wings in Brazil could cause a hurricane in Texas, this phenomenon was called “the butterfly effect”, meaning that it is impossible create long-term weather forecasts. This discovery (chaotic weather) was made in the 1960s by the American meteorologist Edward Lorenz, who laid the foundations for today’s chaos theory.

So, how can we produce reliable climate models for the next few decades or hundreds of years when weather is a classic example of a chaotic system? In the 1980s, Klaus Hasselmann showed how chaotically changing weather events could be described as rapidly changing noise, thus placing long-term climate forecasts on a strong scientific basis. He also developed methods to determine the human impact on observed global temperature.

Hasselmann created a stochastic climate model, meaning that he also included the element of chance in the model. The inspiration came from Albert Einstein’s theory of Brownian Motion, also called a random walk. Using this theory, Hasselmann showed that the rapidly changing atmosphere can actually cause slow changes in the ocean.

Climate models now clearly show an accelerating greenhouse effect; atmospheric carbon dioxide levels have increased by 40 percent since the mid-19th century. The Earth’s atmosphere never contained this much carbon dioxide for hundreds of thousands of years. Accordingly, temperature measurements show that the Earth has warmed by 1°C in the past 150 years.

Syukuro Manabe and Klaus Hasselmann contributed to the greatest good for humanity by providing a firm physical foundation for our knowledge on the Earth’s climate. Since their work, we know that climate models are clear. The world is warming. It is caused by the increase of greenhouse gases in the atmosphere? Can this be explained only by natural factors? No. Are the temperatures rising caused by the emissions of humans? Yes!

 Methods for disordered systems

Around 1980, Giorgio Parisi presented his discoveries of how seemingly random phenomena were governed by hidden rules. His work is now considered among the most important contributions to the theory of complex systems.

Particles in a gas can be considered as tiny balls moving at increasing speeds as the temperature rises. When the temperature drops, or the pressure increases, these balls condense into a liquid and then into a solid – the latter usually being a crystal in which the balls are arranged in a regular pattern. However, if this change happens quickly, the balls can form an irregular pattern that does not change even as the liquid is further cooled or compressed. If you repeat the experiment, the balls form a new pattern, although the change is exactly the same. Why the different results?

These compacted balls constitute a simple model for ordinary glass or granular materials such as sand and gravel. However, the subject of Parisi’s original work was a different type of system -spin glass, a special type of metal alloy where the atoms of a metal are randomly mixed between the atoms of another metal. Say, even if a few iron atoms are mixed into copper atoms, they still change the material’s magnetic properties in a surprising way.

Spin glasses and their exotic properties provide a model for complex systems. However, their mysterious and annoying nature has long eluded the researchers. A mathematical method called the replica trick, which processes many copies of the system in a simultaneous fashion, was among techniques used to define spin glasses, although it also provided relatively unfeasible calculations results. However, Parisi made a breakthrough when he showed in 1979 how the replica trick could be used to solve a spin glass problem. His solution took years to be mathematically verified, but since then, has been used for many disordered systems and become a cornerstone of the theory of complex