Magnet and Neuron Model Also Predicts Arctic Sea Ice Melt

Credit: NASA Goddard This May the Arctic sea ice cover was 8.5 percent below the 1981–2010 average— a drop of 436,000 square miles —meaning the ocean is absorbing a lot of sunlight that would normally be reflected. This added heat affects marine ecosystems and may contribute to warming the Northern Hemisphere, so predicting such changes is a key part of modeling what the earth’s climate is likely to do. Now a new study offers a model that predicts the formation of telltale "melt ponds" on Arctic sea ice remarkably well—despite being developed in the 1920s to show how objects become magnetic. The study , published this summer in the New Journal of Physics , vividly illustrates the surprising connections that can exist among seemingly unrelated systems in nature. In 1920 physicist Wilhelm Lenz proposed what is now called the Ising model to study ferromagnets: iron and other materials with inherent magnetic properties. And this is not the first time this model has found an unexpected application; neuroscientists have also used it since the 1980s to study neuron firing. The common ground among ferromagnets, neuron patterns and melt ponds lies in the mechanism behind the Ising model. Lenz proposed that ferromagnets could be thought of as evenly spaced atoms arranged in a grid, or lattice. His model randomly assigned an upward or downward "spin" (a characteristic that affects magnetization) to each atom in the hypothetical grid; the arrangement of spins determines the state of the system, including the strength of its magnetization and how much energy it has. The more neighboring atoms develop the same spin, the lower the system’s overall energy state becomes. And since natural systems tend toward lower energy states, it turns out that the principle behind this model is remarkably versatile. Advertisement University of Utah mathematician […]

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