Research in climate modeling and complex systems wins Nobel Prize in Physics

Pioneering modeling work on the causes and signs of human-induced climate change and on disordered physical systems—squeezed together under the umbrella of complex systems—are honored with this year’s Nobel Prize in Physics. Half of the prize goes to Syukuro Manabe of Princeton University and Klaus Hasselmann of the Max Planck Institute for Meteorology (MPI-M) for showing how, despite the variability of the weather, computer models can make concrete predictions about the warming effect of rising carbon dioxide (CO2) in Earth’s atmosphere—and finger human activity as the cause.

The other half of the award goes to Giorgio Parisi of the Sapienza University of Rome for developing a way to understand chaotic atomic behavior in certain magnetic alloys. Despite vast variability in the behavior of the materials, Parisi showed it was possible to identify underlying patterns. His work has impacted math, biology, neuroscience, machine learning—and even helps explain how a murmuration of thousands of starlings seems to move in concert.

The focus on climate science was intended as a message for “world leaders that haven’t gotten the message yet,” Thors Hans Hansson, a physicist at Stockholm University and chair of the Nobel Committee for Physics, said at this morning’s announcement. “What we are saying is that the modeling of climate is solidly based in physical theory, and well-known physics.”

The award for Manabe and Hasselmann delighted climate scientists. “It is a fantastic recognition that climate is part of the physics discipline, which should have been done long ago,” says Sandrine Bony, a climate scientist with France’s CNRS at Sorbonne University. Bjorn Stevens, a climate scientist at MPI-M, says: “First, it’s fantastic news. Second, they chose exactly the perfect candidates.”

Scientists had long understood that Earth is warmed by the Sun’s rays striking the surface and cooled by the atmosphere radiating infrared light into space, with the temperature set by the balance of the two effects. Atmospheric CO2 absorbs some of that infrared light, making the cooling less efficient and skewing the balance to raise global temperatures. As early as the 1890s, Swedish physicist Svante Arrhenius had tried to predict the warming effect of rising CO2. However, early attempts were too simple because they assumed energy transfer from one layer of the atmosphere to the next takes place purely through radiation.

Manabe, who had left Japan after World War II to work at Princeton’s Geophysical Fluid Dynamics Laboratory, injected a key piece of physics into the problem: convection. As any schoolchild knows, in the atmosphere, hot air rises and cold air falls, and that flow also transports energy. Manabe knew the atmosphere could not be understood without accounting for the phenomenon, Stevens says. “That was a genius stroke.”

In a monumental 1967 study, Manabe reduced the complexity of the atmosphere to a simple 1D model of a column of air 40 kilometers high, which still required hundreds of hours of run time on the rudimentary computers of the day. The model showed that CO2, at levels of just hundreds of parts per million (ppm), had a profound impact on the climate. If CO2 levels doubled from their current levels—then about 300 ppm—to 600 ppm, global temperatures would rise by 2.3℃.

In 1975, Manabe published the first 3D climate model that linked the atmosphere and oceans. The oceans absorb heat and CO2 from the atmosphere and can store them for centuries, so the interchange is critical for any long-term modeling of the climate. When Manabe used the model to run the same CO2 doubling experiment, he found a temperature rise of 2.93℃—a finding that is remarkably similar to the answers given by high-powered computer models today. “That’s really amazing,” Bony says. “It really shows the power of the physical understanding of how the climate works.” 

Hasselmann laid the foundation for proving from observations that climate change is real and that human activity is driving it. That’s no easy task as it requires separating the stately trends of climate change from the faster, dramatic changes of weather and seasons. Borrowing from textbook statistical physics, Hasselmann developed a climate model that had chance built into it and treated the rapidly changing weather patterns as noise. In 1979, using that model, Hasselmann was among the first to identify human fingerprints of global warming, separating natural climatic drivers such as volcanic emissions from the rapidly growing human one: greenhouse gases from the burning of fossil fuels.

That fingerprint is complex and multifaceted, but it has some stark and telling features, Stevens says. For example, warming caused by the buildup of CO2 should cause the stratosphere—the layer of atmosphere from about 10 kilometers to 50 kilometers in altitude—to cool while the lower troposphere warms. “From [heating] from the Sun, you don’t expect that,” Stevens says.

Parisi’s work sought the hidden rules that govern the properties of disordered solid materials like glass. He focused originally on exotic magnetic materials known as “spin glasses,” in which iron atoms are randomly sprinkled into a grid of copper atoms. The spins of the iron atoms act like tiny magnets, and in ordinary iron all the atoms point in the same direction, yielding a single, easily identified lowest energy “ground” state that governs the material’s behavior. In spin glass, however, neighboring spins want to point in opposite directions, but not all of them can because of the arrangement of the surrounding copper atoms. Such “frustration” means the material has a vast array of different but effectively equivalent low energy states, which makes predicting its behavior difficult.

Parisi developed a new theoretical paradigm to deal with such systems and their countless low–energy-state landscapes, oddly, by thinking of them as occupying an abstract space with a vast number dimension, one for each spin, says Lenka Zdeborová, a statistical physicist at the Swiss Federal Institute of Technology, Lausanne. In that space, she says, the various low-energy states form a treelike structure, and Parisi showed a statistical analysis of the branches could predict the spin glass behavior. That may sound like an exercise in useless abstraction, but those mathematical tools have proved indispensable in fields ranging from the flocking of birds to computer science, says Zdeborová, who is using them to try to explain how artificial neural networks learn. “He really is one of these scientists who has a whole community following up and unleashing the power of theories he came up with,” she says. “He deserves [the prize] so much.”

The two seemingly disparate worlds of Manabe’s climate modeling and Parisi’s work into atomic behavior of solids share a connection through Hasselmann’s work, says John Wettlaufer, a member of the Nobel Committee for Physics. “We do not understand predictability unless we understand variability,” he said at the press conference announcing the prize.

And the expert on spin glasses appears to have no problem being lumped in with the climate scientists. Reached by phone by the prize committee, Parisi urged nations to take action on global warming at the upcoming November climate summit in Glasgow, U.K. “It’s clear that for the future generation, we have to act now, in a very fast way.”

Update, 5 October, 1:30 p.m.: This story has been updated.

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