Kenneth Golden, a mathematician at the University of Utah, was perusing images of Arctic sea ice when he noticed a pattern that seemed familiar. When seen from above, the melting sea ice looked like a field of white mottled with dark splotches where the ice had turned to liquid. To Golden it seemed awfully similar to the arrangement of atoms in a magnetic material. There’s no obvious reason for magnets to have a relationship with aerial photos of ice, but the thought stuck with him. More than a decade later, this intuition has finally solidified into a model that could be used to better predict the effects of climate change on sea ice.

Melt ponds are exactly what they sound like: pools of water that form on top of sea ice when the ice's top layer melts in the spring and summer. The ponds are important because they change the reflectivity of ice. Ice has a high albedo, meaning it reflects most of the sunlight that hits it. Water, however, has a low albedo and absorbs a large portion of sunlight as heat. This produces a feedback loop: As ice melts to form melt ponds, a higher percentage of the ice's surface absorbs sunlight as heat, which melts even more ice, producing more, larger melt ponds.

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Knowing what percentage of the ice's surface is made of melt ponds is therefore critical to knowing the rate at which Arctic ice is melting, which contributes to the global climate. But because the Arctic is so big, and satellite imaging has limited resolution, measuring the overall area of melt ponds is a hard problem. This is where Golden comes in.

Golden started studying sea ice as a math major at Dartmouth College, even traveling to Antarctica his senior year. He focused his career on more theoretical math, but ten years after his first Antarctic expedition, he got a call from his undergraduate research advisor inviting him to join a large polar research project with the US Navy.

The project was to characterize sea ice from satellite data, and the team needed someone like Golden to create an algorithm that made sense of its optical properties. Over the next few years, Golden went on multiple expeditions to Antarctica and the Arctic with "real sea ice people," as he put it, researchers who waded ankle- or knee-deep in frigid puddles. He also analyzed images of these melt ponds taken from helicopters, and realized that he recognized in their patterns a ferromagnet model from his physics classes: the Ising model.

Named after Ernst Ising, the model started out as a problem given to Ising by his thesis adviser in the 1920s; now it's commonly taught in statistical mechanics textbooks and classes.

Magnets work because individual atoms can be thought of as mini-magnets, with north and south poles. The direction of their north pole is called their magnetic moment, and because atoms are quantum in nature, they only have two choices of direction: spin up or spin down. When all the atoms in a piece of material align their magnetic moments, the entire material becomes a magnet; this is the lowest-energy configuration the atoms can take. "Somehow by hanging out with those people in melt ponds, seeing all these images, it struck me that they look like pictures I'd seen of the Ising model," says Golden.

The Ising model, which explains magnetism, helped simulate Arctic melt ponds. Kenneth Golden

In that model, magnetic moments are arranged in a grid, where each atom's moment can only interact with—and potentially change—the moment of a next-door neighbor. This makes patches of same-spin atoms form in the material. As Golden flipped through photos of the melt ponds, he noticed that they interacted with the surrounding ice in much the same way.