The world's southernmost ocean, the Southern Ocean that surrounds Antarctica, plays an important role in the global climate because its waters contain large amounts of carbon dioxide. A new international study, in which researchers from the University of Gothenburg participated, has examined the complex processes driving air-sea fluxes of gasses, such as carbon dioxide. The research group is now delivering new findings that shed light on the area's important role in climate change. "We show how the intense storms that often occur in the region increase ocean mixing and bring carbon dioxide-rich waters from the deep to the surface. There has been a lack of knowledge about these complex processes, so the study is an important key to understanding the Southern Ocean's significance for the climate and the global carbon budget," says Sebastiaan Swart, professor of oceanography at the University of Gothenburg and co-author of the study.

This content can also be viewed on the site it originates from. Last summer, the science-fiction writer Kim Stanley Robinson went on a backpacking trip with some friends. They headed into the High Sierra, hiking toward Deadman Canyon--a fifty-mile walk through challenging terrain. Now sixty-nine, Robinson has been hiking and camping in the Sierras for half a century. At home, in Davis, California, he tracks his explorations on a wall-mounted map, its topography thick with ink.

This story was originally published by Wired and is reproduced here as part of the Climate Desk collaboration. Two Decembers ago, Erin Pettit layered up, slapped on goggles, cued up an audio book, and went on a hike--across Thwaites Glacier in Antarctica. Behind her, she dragged a sled loaded with a ground-penetrating radar, which fired pulses through a thousand feet of ice and analyzed the radio waves that bounced off the seawater below, thus building a detailed image of the glacier beneath her feet. Pettit--a glaciologist and climate scientist at Oregon State University--hiked alone through the snow, sometimes eschewing headphones for the absolute auditory stillness of the most remote landscape on Earth. "It was actually kind of an amazing, meditative field season," she says, "I just bundled up, I went out there and pulled my sled, and just walked for miles and miles."

NASA's Perseverance Mars rover has run into another snag on its mission to find evidence of ancient microbial life. 

Ada Palmer is a professor of European history at the University of Chicago. Her four-volume science fiction series, Terra Ignota, was inspired by 18th-century philosophers such as Voltaire and Diderot. "I wanted to write a story that Voltaire might have written if Voltaire had been able to read the last 70 years' worth of science fiction and have all of those tools at his disposal," Palmer says in Episode 495 of the Geek's Guide to the Galaxy podcast. Palmer says that Voltaire could actually be considered the first science fiction writer, thanks to a piece he wrote in 1752. "Voltaire has a short story called'Micromégas,' in which an alien from Saturn and an alien from a star near Sirius come to Earth, and they are enormous, and they explore the Earth and have trouble finding life-forms because to them a whale is the size of a flea," she says.

Two Decembers ago, Erin Pettit layered up, slapped on goggles, cued up an audio book, and went on a hike--across Thwaites Glacier in Antarctica. Behind her, she dragged a sled loaded with a ground-penetrating radar, which fired pulses through a thousand feet of ice and analyzed the radio waves that bounced off the seawater below, thus building a detailed image of the glacier beneath her feet. Pettit--a glaciologist and climate scientist at Oregon State University--hiked alone through the snow, sometimes eschewing headphones for the absolute auditory stillness of the most remote landscape on Earth. "It was actually kind of an amazing, meditative field season," she says, "I just bundled up, I went out there and pulled my sled, and just walked for miles and miles." In case you were worried, her colleagues always knew where Pettit was; every so often someone would roll out on a snow machine to bring her supplies or to swap out the radar's battery.

The expedition to find the wreck of Sir Ernest Shackleton's Endurance is set to sail next month, it was announced today on the centenary of the polar explorer's death. Endurance was one of two ships used by the Imperial Trans-Antarctic expedition of 1914–1917, which hoped to make the first land crossing of the Antarctic. Carrying an expedition crew of 28 men, the 144-foot-long Endurance was a three-masted schooner barque sturdily built for operations in polar waters. Aiming to land at Vahsel Bay, the vessel became stuck in pack ice on the Weddell Sea on January 18, 1915 -- where she and her crew would remain for many months. In late October, however, a drop in temperature from 42 F to -14 F saw the ice pack begin to steadily crush the Endurance, which finally sank on November 21, 1915.

Inference of causal relations from data now has become an important field in artificial intelligence. During the past 16 years, causality analysis (in a quantitative sense) has been developed independently in physics from first principles. This short note is a brief summary of this line of work, including part of the theory and several representative applications.

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Optical devices lie at the heart of most of the technology we see around us. When one actually wants to make such an optical device, one can predict its optical behavior using computational simulations of Maxwell's equations. If one then asks what the optimal design would be in order to obtain a certain optical behavior, the only way to go further would be to try out all of the possible designs and compute the electromagnetic spectrum they produce. When there are many design parameters, this brute force approach quickly becomes too computationally expensive. We therefore need other methods to create optimal optical devices. An alternative to the brute force approach is inverse design. In this paradigm, one starts from the desired optical response of a material and then determines the design parameters that are needed to obtain this optical response. There are many algorithms known in the literature that implement this inverse design. Some of the best performing, recent approaches are based on Deep Learning. The central idea is to train a neural network to predict the optical response for given design parameters. Since neural networks are completely differentiable, we can compute gradients of the response with respect to the design parameters. We can use these gradients to update the design parameters and get an optical response closer to the one we want. This allows us to obtain an optimal design much faster compared to the brute force approach. In my thesis, I use Deep Learning for the inverse design of the Fabry-P\'erot resonator. This system can be described fully analytically and is therefore ideal to study.