FYFD

Category: Research

  • Retreating Glaciers Risk Tsunamis

    Retreating Glaciers Risk Tsunamis

    On 10 August 2025, the slopes of Alaska’s Tracy Arm Fjord gave way, sliding into the water. The resulting tsunami was the second-largest ever recorded, with a 481-meter runup after a 100-meter initial wave that moved at more than 70 meters per second. The fjord was fortunately empty at the time, though it is regularly visited by cruise ships. After the landslide, a seiche ricocheted through the fjord for 36 hours.

    With no earthquake to trigger the tsunami, researchers had to piece together the accident through forensics. Their study concluded that the glacier’s retreat had left unstable slopes exposed, likening it to a child’s closet overstuffed with hastily gathered toys. The moment the door is no longer held closed, everything comes crashing out.

    Ultimately, the landslide-induced tsunami is, therefore, a result of climate change. That result is disconcerting, given the increasing frequency of cruise ships visiting glacial fjords. Unlike earthquake-induced tsunamis, landslide-induced ones like the Tracy Arm event don’t come with a seismic warning. With rapid climate change and frequent tourism, risk management is critical. (Image credit: C. Read/USGS; research credit: D. Shugar et al.; via Eos)

    An image showing the aftermath of the 10 August, 2025 landslide in Alaska's Tracy Arm Fjord, which caused the second largest tsunami recorded. The light rock slope shows where material fell from. On the lower right, the foot of the South Sawyer Glacier is just visible.
    An image showing the aftermath of the 10 August, 2025 landslide in Alaska’s Tracy Arm Fjord, which caused the second largest tsunami recorded. The light rock slope shows where material fell from. On the lower right, the foot of the South Sawyer Glacier is just visible.
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  • Testing Coffee With Current

    Testing Coffee With Current

    Coffee is a key ingredient in the scientific process for many researchers, so it’s no wonder that researchers often develop an interest in the drink’s physics and chemistry. In a new study, a research team devised an objective method to test both a coffee’s strength and its roast color.

    The researchers used a potentiostat to test how an electric current interacted with the brewed coffee and showed how the measurements related to the coffee’s flavor. The method was even robust enough that they could identify which coffee sample came from a batch of beans that had failed a roaster’s quality controls.

    While you’re unlikely to use such a method at home, it could be helpful in coffee shops, where baristas try to pin down the variables to produce the same flavor in every cup. (Image credit: M. Kenneally; research credit: R. Bumbaugh et al.; via Ars Technica)

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  • Cloudy Mornings and Clear Evenings

    Cloudy Mornings and Clear Evenings

    In the past few decades, our knowledge of exoplanets has exploded, but we’re still relatively limited in what we can learn about these worlds. That’s due, in large part, to the indirect way we observe them. Most exoplanets are found when we see them transit, passing between Earth and their star. During a transit, the planet blocks a portion of the light we would otherwise detect from the star, letting us know that something’s there. We’re often able to measure the spectra of light passing through the exoplanet’s atmosphere, giving us a glimpse of chemical signatures.

    Today’s study looks at exoplanet WASP-94A b, a gas giant tidally-locked so that only one side ever faces its star. In its transit, researchers could clearly measure different spectra from the morning and evening sides of the planet. The asymmetry seems to indicate that the exoplanet develops thick clouds on the nightside, which then dissipate during the daytime. (Image credit: H. Robbins/JHU; research credit: S. Mukherjee et al.; via Nature)

    Artist's conception of an exoplanet with clouds forming on the nightside and dissipating on the dayside.
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  • Buckling in Rings

    Buckling in Rings

    From oil drums to–yes–soda cans, liquid-filled cylindrical shells are everywhere. And, it turns out, these structures fail differently than empty shells or ones filled with a solid. Liquid-filled cylinders buckle in sequential rings, as seen in the video below. Researchers found that the buckling resulted from the shell softening and re-stiffening under the compressive load–repeating that process over and over for each ring. Their findings could help us detect containers that are in danger of failing. (Video, image, and research credit: S. Jain et al.; via Ars Technica)

    Animation of a liquid-filled cylindrical shell buckling sequentially under compression.
    Animation of a liquid-filled cylindrical shell buckling sequentially under compression.
  • Regelation Lets Glaciers Flow

    Regelation Lets Glaciers Flow

    Under the cold temperatures and immense pressures of a glacier, ice does not always behave in ways we’d expect. For example, cutting through ice using the pressure of a weighted wire does not break an ice block in two; as the wire passes through the ice, the melted water refreezes in its wake, leaving an intact block. Known as regelation, this process is one way that glaciers flow past obstacles in their path.

    Although many experiments demonstrate regelation for ice with temperatures near freezing, the process occurs in colder ice, too. A new study combines data across a wide range of temperatures with a new physical model of regelation to show how the process changes with temperature. It seems that relatively small temperature changes drastically affect how much meltwater forms around the wire and how slowly the ice refreezes. (Image credit: S. Ferrara; video credit: SciTube; research credit: C. Meyer et al.)

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  • On Dolphin Turbulence

    On Dolphin Turbulence

    Dolphins are such fast and agile swimmers that, naturally, scientists have long wanted to understand how they swim so well. A recent study draws on numerical simulation to analyze the flow a dolphin creates when flapping its tail.

    The resulting flow is highly turbulent–researchers were only able to simulate up to a fraction of a dolphin’s actual Reynolds number–with both large-scale vortices and a cascade of smaller ones. The largest vortices, shown here in white, form on the upper and lower surface of the dolphin’s tail, then slide off the tail in a vortex ring. It’s these vortex rings, the researchers found, that provide the bulk of a dolphin’s thrust.

    The smaller-scale vortices, in contrast, get formed by the large vortices, and they make little to no contribution to the dolphin’s propulsion. Interestingly, these results suggest that we might be able to describe the propulsion of dolphins and other highly turbulent swimmers by focusing only on the largest scales in the flow. (Video, image, and research credit: Y. Motoori et al.; via Ars Technica)

    Animation of the simulated flow from a swimming dolphin.
    Animation of the simulated flow from a swimming dolphin.
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  • AI-Based Weather Forecasting Has Blind Spots

    AI-Based Weather Forecasting Has Blind Spots

    Traditional weather forecasting models are physics-based and rely on supercomputers. Practically speaking, this means that they start from the basic governing equations (like the Navier-Stokes equations) and use approximations to model aspects of the problem in order to make the physics solvable, given constraints on time, computational power, spatial resolution, and so on.

    So-called AI models approach the problem differently, training a model on past weather conditions in order to predict future weather. In some respects, this approach is very successful; AI-based models require less computational infrastructure to run and, in recent years, have greatly improved their predictions of everyday weather.

    However, these AI models do poorly when predicting extreme weather events, because their training data contain relatively few examples of these events. They show limited ability to extrapolate their predictions to more extreme events. But these events–like the unprecedented 2021 heatwave in the Pacific Northwest or many of the Category 5 hurricanes we’ve seen in the last decade–are happening increasingly often due to climate change. Those events will keep happening, more frequently, as warming continues. Physics-based models can predict and forecast these events in ways that AI-based models fail to because they are limited by their trained experiences.

    Researchers are working to find ways to better equip AI-based models with more physical sense, but, as these models proliferate, it’s important for their users (and those of us using their forecasts) to know what their current weaknesses are. (Image credit: B. McGowan; research credit: Y. Sun et al.; see also S. Nath and T. Palmer; via Gizmodo)

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  • Predicting Volcanic Eruptions

    Predicting Volcanic Eruptions

    People have long hoped to reliably predict volcanic eruptions. An automated system at Piton de la Fournaise in France has been doing so since 2014 with an impressive 92% accuracy. The tool, called Jerk, makes its predictions based on real-time measurements of subtle ground movements associated with magma fracturing rock on its way to the surface. Its predictions have ranged from minutes to hours before the start of an eruption.

    So far, the team has only tested the system at one volcano, but they are working to install a second version at Mount Etna, where they’ll see whether other volcanoes produce a similar signal ahead of eruption. If so, Jerk could provide valuable warnings in populated areas and give geologists an automated alternative for monitoring remote volcanoes.

    To learn more, check out the team’s open access paper and this interview with the team leaders over at Gizmodo. (Image credit: F. Beauducel; research credit: F. Beauducel et al.; via Gizmodo)

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  • Understanding Pollen Dispersal

    Understanding Pollen Dispersal

    When the wind blows, trees shift and sway, reconfiguring their shape and their leaves in response. For parts of the year, that flow can also pluck pollen grains off the tree, carrying them on the winds. A new computational simulation models this pollen dispersal from a tree, with the aim of eventually integrating into a tool for urban planners.

    Trees are an important component to fighting climate change, especially in cities, because they cool their surroundings in addition to providing fresh oxygen. But urban planners recognize the downsides to trees, too–allergies, anyone?–and, with the right tools, they could maximize the trees’ advantages while minimizing pollen spread for allergy-sufferers. (Image credit: M. Köles; research credit: T. Dbouk et al.; via Physics World)

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  • Waves on Other Planets

    Waves on Other Planets

    On Earth, most waves form when wind blows across the water. The shear and added energy from the wind ripples the surface, eventually building up waves (through the Kelvin-Helmholtz instability). The same process should happen anywhere else where wind and open liquid surfaces meet–even on other planets. To explore this, researchers built a new model, PlanetWaves, that predicts the waves based on a planet’s gravity, atmospheric conditions, and the density, viscosity, and surface tension of its surface liquid.

    After validating the model with conditions on Earth, the team explored wave conditions for Titan, ancient Mars, and several exoplanets. They found that Titan’s lighter gravity and liquid ethane (which is less dense than water) combined to make waves on Titan much taller than those generated at the same wind speed on Earth (top image). You can watch them in action in the video below. Standing in a light breeze on Titan, you’d watch giant 3-meter waves rolling in.

    The team also found that waves on Mars would have gotten shorter as Mars lost its atmosphere and the air pressure dropped. Over time, the same wind speed would have elicited smaller and smaller waves. Wave action has a big effect on a landscape’s erosion, so understanding how waves look on other planets will help us parse their geography. (Video, image, and research credit: U. Schneck et al.; via MIT News; submitted by Joseph S.)

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