[ad_1]
Kenneth Golden, a A mathematician from the University of Utah went through images of the Arctic sea ice when he noticed a pattern that sounded familiar. From above, the melting sea-ice resembled a field of white speckled with dark spots where the ice had become liquid. For Golden, it looked terribly like the arrangement of atoms in a magnetic material. There is no obvious reason for magnets to have a relationship with aerial shots of ice, but this thought has remained true to him. More than a decade later, this intuition finally materialized into a model that could be used to better predict the effects of climate change on sea ice.
Cast iron ponds look exactly like what they look like: pools of water that form over sea ice when the top layer of ice melts in the spring and summer. Ponds are important because they alter the reflectivity of the ice. Ice has a high albedo, which means that it reflects most of the sunlight. The water, however, has a weak albedo and absorbs much of the sunlight in the form of heat. This produces a feedback loop: when ice melts to form cast-iron ponds, a higher percentage of the ice surface absorbs sunlight as heat, melting more ice, producing more larger pools of fusion.
Knowing what percentage of the ice surface is made up of melt ponds is therefore essential to know the melting rate of Arctic ice, which contributes to the global climate. But since the Arctic is very large and the resolution of satellite images is limited, it is difficult to measure the total area of the melt ponds. That's where Golden comes in.
Golden started studying ice floes as a mathematician at Dartmouth College, even going to Antarctica in his final year. He focused his career on more theoretical mathematics, but ten years after his first expedition to Antarctica, his undergraduate research advisor called him to join a major polar research project with the US Navy. .
The project consisted of characterizing sea ice from satellite data and the team needed someone like Golden to create an algorithm giving meaning to its optical properties. Over the following years, Golden undertook multiple expeditions to Antarctica and the Arctic with "real packers", researchers wading around their ankles or knees in puddles of icy water. He also analyzed images of these cast iron basins taken from helicopters and realized that he recognized in their models a ferromagnetic model derived from his physics lessons: the Ising model.
Named after Ernst Ising, the model began as a problem given to Ising by his supervisor in the 1920s; now it is commonly taught in textbooks and courses in statistical mechanics.
Magnets work because individual atoms can be considered as mini-magnets, with north and south poles. The direction of their North Pole is called their magnetic moment and, as the atoms are of quantum nature, they have only two choices of direction: upward or downward rotation. When all the atoms of a piece of material align their magnetic moments, all the material becomes a magnet; it is the lowest energy configuration that atoms can take. "In one way or another, while hanging out with these people in cast ponds, seeing all these images, it seemed to me that they looked like pictures that I Had views of the Ising model, "Golden said.
The Ising model, which explains magnetism, has been used to simulate Arctic melt ponds.
Kenneth Golden
In this model, the magnetic moments are organized in a grid, where the moment of each atom can only interact with, and potentially change, the moment of a neighbor next door. This creates plates of atoms of the same spin in the material. As Golden flipped through photos of the cast-iron ponds, he noticed that they were interacting with the surrounding ice in the same way.
"Then I had the idea: instead of going around in circles, what about water and ice?" Golden said.
Golden began playing with Ising's model simulations out of curiosity, trying to see how he could relate these seemingly disparate ideas. It would start with a random topography of the ice, an uneven surface with depressions and hills, and let the ice begin to melt – in other words, the magnetic spins would start to tilt. The resulting images of the simulations show islands of light or dark light for atoms with upward or downward rotation, water or ice, the edges of their irregular shapes and fractal nature. He showed the result of such a simulation to a colleague who was analyzing images of cast ponds. The latter had initially thought that Golden was showing him one of his own pictures.
"It does not just create ponds with the correct geometry, but they really look like ponds," Golden said. To verify his results, Golden compared the distribution of areas and pond perimeters predicted by his model to those observed in the wild. They closely matched the distribution of natural cast pools, and the model was published in New Journal of Physics.
Arctic melt pools, often captured in aerial photos, are crucial elements of global climate models.
Donald Perovich
The realism of such a simple model, often called "toy model" by scientists, has its limitations. So, Golden plans to add the effects of the Arctic winds, which could reshape the edges of the ponds. It can not account for all facets of the real world, but the length scales of the Golden model, about a meter, are already much smaller than those used in typical climate models.
"These are big global models," says Elizabeth Hunke, lead designer of Los Alamos sea ice model. "We use grids of more than one kilometer apart.As melting basins are much smaller than grid cells, we need a way to describe the fraction of the cell of grid that is covered by melting basins. " Golden model, she says: "provides a statistical way to do it that represents the essential dynamics."
Donald Perovich, a geophysicist at Dartmouth College, who is familiar with Golden's research, has found an immediate way to connect the model to his own work on the Arctic. "This model helps us to specify the type of observations we are going to make, and these observations can then be used to evaluate this model."
In addition to its applicability, Perovich also finds a deeper value in the model. "I find it amazing how mathematics helps us understand the world around us," he says.
For Golden, who has spent his career at the interface of theory and reality, the idea is natural. "Mathematics is the operating system of science," he says.
More great cable stories
[ad_2]
Source link