Researchers explore the impact of sea ice change in Bering Sea

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The U.S. Coast Guard (USCG) Cutter Polar Star, pictured carving through ice in this 2017 USCG image, supported the Naval Postgraduate School research team in the field during a detailed study, sponsored by the U.S. Department of Energy and National Science Foundation, into sea ice variability in the Bering Sea and its broader impact to the Arctic region. Credit: U.S. Coast Guard photo by Chief Petty Officer David Mosley

The Bering Sea is the most productive ground fishery in the world, particularly for salmon, halibut and shellfish. About half of U.S. fish and shellfish come from that area and the fishing industry is the main driver of jobs in and around the Aleutian Islands. The freezing and melting of sea ice in the area heavily impacts the primary productivity, which is the bottom of the marine food chain.

Research Associate Professor Jaclyn Kinney, Research Professor Wieslaw Maslowski, and Research Assistant Professor Younjoo Lee—all in the Naval Postgraduate School (NPS) Department of Oceanography—looked at how sea ice variability in the Bering Sea over the last several decades might impact the cold pool and marine primary production. Their research was published in PLOS One in April 2022.

The Arctic region has become increasingly critical to U.S. security interests, and particularly to the U.S. Navy, over the past several years. Dual purpose research in predicting sea ice, and the impact of its fluctuations, is critical for navigation and understanding how systems operate.

On top of that, less sea ice also means more tourism and commercial activity in the area, potentially, which could also result in the need for more patrols, and searches and rescues. And how sea ice impacts the food supply and local jobs could dictate the economic and cultural stability of the region. All of these factors are recognized as potential challenges in the Department of the Navy’s Strategic Blueprint for the Arctic, released in 2021, underscoring the importance for oceanography team’s detailed study.

The NPS research team looked specifically at the very near the sea floor (less than 2ºC) that forms on the shelf each winter, which is called a cold pool. It’s formed by the cooling and sinking of surface water. Vertical mixing caused by salt being expelled from water as it freezes into sea ice during the autumn and winter, a process called brine rejection, increases the density of the cold pool water. This cold, salty, dense water sinks down to the bottom, forming its own marine habitat unique from other parts of the Bering Sea by the summer.

Sea ice melt is usually the first cause of water stratification during spring, but when there is not as much sea ice, stratification is mainly caused by the sun warming up surface water later in the year. Stratification is necessary for primary productivity, in the form of phytoplankton, to bloom.

“If there’s a lot of primary production in the water early, then the zooplankton are still very small and they’re not able to consume much of it,” Kinney explains. “So what happens is those phytoplankton cells will eventually settle out to the bottom, feeding the benthic community. That’s good for walruses and gray whales, which feed on the benthic community.”

But this means the pelagic community doesn’t get as much food, she says. If primary production starts later in the season, which is what happens when there’s less sea ice, it becomes a pelagic-dominated ecosystem because the zooplankton have the opportunity to get bigger. These fluctuations can heavily impact fish and shellfish populations from year to year.

Maintaining these habitat distinctions is important for maintaining the food chain for the region. Some that live in the cold pool include the snow crab and Arctic cod.

Kinney fell in love with the complexity of the Bering Sea in the early 2000s, and it was actually her first region of research.

“It’s really important for food sustainability and for people’s livelihoods,” Kinney says. “I used to study invertebrates that live in the bottom sediment, and that was what I started out doing back in the early 2000s. So I’ve just always really loved that region.”

Naturally, she keeps up with the research coming out of the region. She recently came across a paper observing the cold pool shrinking northward.

“The reduction of the cold pool means that we have a whole new potential for a brand new ecosystem moving in,” Kinney explains. “If we have much warmer water, then we’re going to get these southerly fish species moving North, and that’s going to push the Arctic species even further to the north.”

How large and wide the cold pool is varies drastically from year to year, and the researchers wanted to figure out how this variability relates to sea ice variability. They used the Regional Arctic System Model (RASM), developed at NPS, to examine the variability of the cold pool’s extent and distribution to see how its size and shape is impacted by the sea ice cover. The researchers developed statistical calculations of past sea ice cover conditions in the Bering Sea from 1980 to 2018. RASM can simulate critical physical processes, feedbacks, and their impact on the Arctic climate system using several coupled models and components, including the atmosphere, ocean, sea ice, biogeochemistry, and land hydrology.

The RASM confirmed a direct correlation between the extent of sea ice and the cold pool, showing a smaller cold pool during times with less sea ice cover. In general, the researchers found that in July 2018, the cold pool was only 31 percent of what its mean was from 1980 to 2018. The researchers point to a lack of sea ice, caused by strong winds out of the south, restricting the typical southward expansion of sea ice towards the shelf break.

And as for how this impacts the food chain, the researchers found that years with low amounts of sea ice were followed by a later diatom bloom, and vice versa. These results follow the Oscillating Control Hypothesis, originally developed in 2002, which states that early ice retreat will lead to a late bloom, while late ice retreat leads to an early bloom.

Diatoms are a common type of phytoplankton that forms the base of the food chain. Diatom levels can be measured by looking at how much chlorophyll-a is found in an area, which can be done via satellite, as well as in models. A comparison of the chlorophyll-a trends in the northern Bering Sea between and RASM showed similar results, which forms the basis of another study Kinney coauthored, published in the journal Oceanography in May 2022. RASM results also provided insight into the mechanism responsible for the changes by showing the variability in nitrate concentration (a variable not measured by satellite).

The researchers were excited to see RASM’s results mirror real-life observations. But the cold pool retreat they observed in 2018 continued to be a problem in 2019 and 2020, which also saw unusually high temperatures, resulting in less sea ice. Then 2021 saw a major snow crab population collapse, likely due to a reduction of their preferred cold pool habitat. Without the cold pool, the snow crab’s predators are able to eat juvenile crabs more easily. This population collapse bankrupted communities that rely on snow crabs to make a living. The Central Bering Sea Fisherman’s Association expects to see about a 65% drop in revenue due to necessary crab quota cuts.

“We want to know, as scientists, is this reduction of the cold pool the new normal? Are we going to see sea ice come back? And then how will the population reestablish to the south if we do see the sea ice come back to normal?” Kinney said.

She points out that this isn’t the first time the area has seen a diminished cold pool, the last one being in 2001. It did recover, with sea ice prevalence peaking in 2012. But sea ice extent has declined since then. Bering Sea sea ice is hard to predict because it starts from scratch each year, resigned to the whims of seasonal and interannual variability in addition to the larger climatic trends.

“There’s no straightforward linear relationship for ice retreating,” Maslowski explains. But the team is encouraged by how well RASM was able to predict the sea ice trends so far, and see it as a powerful tool to help the Navy glance into the future of the Bering Sea.



More information:
Jaclyn Clement Kinney et al, On the variability of the Bering Sea Cold Pool and implications for the biophysical environment, PLOS ONE (2022). DOI: 10.1371/journal.pone.0266180

Jaclyn Clement Kinney et al, Observations of Declining Primary Productivity in the Western Bering Strait, Oceanography (2022). DOI: 10.5670/oceanog.2022.123

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Hexbyte Glen Cove You can help train NASA’s rovers to better explore Mars

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With AI4Mars, users outline rock and landscape features in images from NASA’s Perseverance Mars rover. The project helps train an artificial intelligence algorithm for improved rover capabilities on Mars. Credit: NASA/JPL-Caltech

Members of the public can now help teach an artificial intelligence algorithm to recognize scientific features in images taken by NASA’s Perseverance rover.

Artificial intelligence, or AI, has enormous potential to change the way NASA’s spacecraft study the universe. But because all machine learning algorithms require training from humans, a recent project asks members of the public to label features of scientific interest in imagery taken by NASA’s Perseverance Mars rover.

Called AI4Mars, the project is the continuation of one launched last year that relied on imagery from NASA’s Curiosity rover. Participants in the earlier stage of that project labeled nearly half a million images, using a tool to outline features like sand and rock that rover drivers at NASA’s Jet Propulsion Laboratory typically watch out for when planning routes on the Red Planet. The end result was an algorithm, called SPOC (Soil Property and Object Classification), that could identify these features correctly nearly 98% of the time.

SPOC is still in development, and researchers hope it can someday be sent to Mars aboard a future spacecraft that could perform even more autonomous driving than Perseverance’s AutoNav technology allows.

Images from Perseverance will further improve SPOC by expanding the kinds of identifying labels that can be applied to features on the Martian surface. AI4Mars now provides labels to identify more refined details, allowing people to choose options like float rocks (“islands” of rocks) or nodules (BB-size balls, often formed by water, of minerals that have been cemented together).

The goal is to hone an algorithm that could help a future rover pick out needles from the haystack of data sent from Mars. Equipped with 19 cameras, Perseverance sends anywhere from dozens to hundreds of images to Earth each day for scientists and engineers to comb through for specific geological features. But time is tight: After those images travel millions of miles from Mars to Earth, the team members have a matter of hours to develop the next set of instructions, based on what they see in those images, to send to Perseverance.

“It’s not possible for any one scientist to look at all the downlinked images with scrutiny in such a short amount of time, every single day,” said Vivian Sun, a JPL scientist who helps coordinate Perseverance’s daily operations and consulted on the AI4Mars project. “It would save us time if there was an algorithm that could say, ‘I think I saw rock veins or nodules over here,’ and then the science team can look at those areas with more detail.”

Especially during this developmental stage, SPOC requires lots of validation from scientists to ensure it’s labeling accurately. But even when it improves, the algorithm is not intended to replace more complex analyses by human scientists.

It’s all about the data

Key to any successful algorithm is a good dataset, said Hiro Ono, the JPL AI researcher who led the development of AI4Mars. The more individual pieces of data available, the more an algorithm learns.

The robotic arm of NASA’s Perseverance rover is visible in this image used by the AI4Mars project. Users outline and identify different rock and landscape features to help train an artificial intelligence algorithm that will help improve the capabilities of Mars rovers. Credit: NASA/JPL-Caltech

“Machine learning is very different from normal software,” Ono said. “This isn’t like making something from scratch. Think of it as starting with a new brain. More of the effort here is getting a good dataset to teach that brain and massaging the data so it will be better learned.”

AI researchers can train their Earth-bound algorithms on tens of thousands of images of, say, houses, flowers, or kittens. But no such data archive existed for the Martian surface before the AI4Mars project. The team would be content with 20,000 or so images in their repository, each with a variety of features labeled.

The Mars-data repository could serve several purposes, noted JPL’s Annie Didier, who worked on the Perseverance version of AI4Mars. “With this algorithm, the rover could automatically select science targets to drive to,” she said. It could also store a variety of images onboard the rover, then send back just images of specific features that scientists are interested in, she said.

That’s on the horizon; scientists may not have to wait that long for the algorithm to benefit them, however. Before the algorithm ever makes it to space, it could be used to scan NASA’s vast public archive of Mars data, allowing researchers to find surface features in those images more easily.

Ono noted it’s important to the AI4Mars team to make their own dataset publicly available so that the entire data science community can benefit.

“If someone outside JPL creates an algorithm that works better than ours using our dataset, that’s great, too,” he said. “It just makes it easier to make more discoveries.”

More about the mission

A key objective for Perseverance’s mission on Mars is astrobiology, including the search for signs of ancient microbial life. The rover will characterize the planet’s geology and past climate, pave the way for human exploration of the Red Planet, and be the first mission to collect and cache Martian rock and regolith (broken rock and dust).

Subsequent NASA missions, in cooperation with ESA (European Space Agency), would send spacecraft to Mars to collect these sealed samples from the surface and return them to Earth for in-depth analysis.

The Mars 2020 Perseverance mission is part of NASA’s Moon to Mars exploration approach, which includes Artemis missions to the Moon that will help prepare for human exploration of the Red Planet.



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Hexbyte Glen Cove Two months at sea to explore the Southern Ocean's contribution to climate regulation thumbnail

Hexbyte Glen Cove Two months at sea to explore the Southern Ocean’s contribution to climate regulation

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A team coordinated by two CNRS researchers and involving colleagues from Sorbonne University, Toulouse III–Paul Sabatier University, the University of Western Brittany and Aix-Marseille University, will traverse the Southern Ocean from January 11 to March 8, 2021, aboard the Marion Dufresne II research vessel chartered by the French Oceanographic Fleet. Their goal is to better understand the sequestration of atmospheric CO2 in the ocean, and especially how the chemical elements essential to this storage are supplied, transported and transformed by the ocean

The Southern Ocean, which surrounds the Antarctic continent, south of the Atlantic, Pacific and Indian Oceans, is a wild region that is difficult to explore. It plays a key, yet complex, role in the capture and storage of atmospheric CO2. A wide range of factors need to be taken into account, including biological activity (surface photosynthesis, carbon export to the deep ocean and its sequestration in sediments) and ocean circulation.

To understand these processes it is necessary to quantify them, which can be done by measuring what are known as geochemical elements (silica, nitrate, iron, zinc, as well as elements such as thorium, radium and rare earths). The vast majority of these tracers are present in minute concentrations in seawater.

The SWINGS1 oceanographic cruise, starting on January 11 and involving 48 scientists, is part of the international GEOTRACES program, which since 2010 has been constructing a chemical atlas of the oceans, compiling data describing the biogeochemical cycles of these trace elements and their isotopes in the world’s oceans. The data is acquired using very strict protocols, compared and validated among the different countries, and made available in an open database. This is the first time that such a comprehensive marine survey has been carried out in the Southern Ocean. Its goal is to determine the sources (atmospheric, sedimentary, hydrothermal, etc) of these elements, some of which (iron and zinc for example) play a crucial role in the photosynthetic activity of phytoplankton. The scientists will be studying their physical, chemical and biological transformations at all depths of the Southern Ocean, as well as their ultimate fate, when they sink into the deep ocean and are stored in sediments.

In addition to the SWINGS scientists, a team from OISO (Indian Ocean Observation Service), which is assessing the proportion of CO2 from anthropogenic emissions and the resulting ocean acidification, will embark on the Marion Dufresne II during the cruise. Another temporal data monitoring program, THEMISTO, will be studying open- ecosystems. Finally, a third project (MAP-IO) will use the Marion Dufresne II to carry out, among other things, physical measurements of the distribution of aerosols and trace gases. With these three projects complementing the SWINGS goals, scientific cooperation lies at the heart of the new cruise.

The laboratories involved in the SWINGS program are:

  • Laboratoire des Sciences de l’Environnement Marin (CNRS/IFREMER/IRD/Université de Bretagne occidentale)
  • Laboratoire d’Etudes en Géophysique et Océanographie Spatiales (CNRS/CNES/IRD/Université Toulouse III—Paul Sabatier)
  • Laboratoire de Météorologie Dynamique (CNRS/ENS- PSL/ École polytechnique-Institut Polytechnique de Paris/Sorbonne Université)
  • Laboratoire d’Océanographie et du Climat : Expérimentations et Approches Numériques (CNRS/IRD/MNHN/Sorbonne Université)
  • Centre Européen de Recherche et d’Enseignement de Géosciences de l’Environnement (CNRS/INRAE/IRD/Aix-Marseille Université)
  • Laboratoire d’Océanographie Microbienne (CNRS/Sorbonne Université)
  • Institut Méditerranéen d’Océanologie (CNRS/IRD/Université de Toulon/Aix-Marseille Université)
  • Laboratoire Climat, Environnement, Couplages et Incertitudes (CNRS/CERFACS)
  • Technical Division of CNRS-INSU

The expedition was funded by France’s National Research Agency ANR, the French Oceanographic Fleet operated by the National Institute for Ocean Science IFREMER, the CNRS National Institute for Earth Sciences and Astronomy INSU, and the ISBlue University Research School.



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Two months at sea to explore the Southe

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Hexbyte Glen Cove Studies explore fluids in pancakes, beer, and the kitchen sink thumbnail

Hexbyte Glen Cove Studies explore fluids in pancakes, beer, and the kitchen sink

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Mechanical engineer Roberto Zenit spent the summer of 2019 trying to solve a problem that now plagues science departments around the world: How can hands-on fluid dynamics experiments, usually carried out in well-stocked lab rooms, be moved off campus? Since the pandemic hit, leading researchers like Zenit have found creative ways for students to explore flow at home.

Zenit’s answer, ultimately, came down to pancakes. He teaches a lab class at Brown University, and one experiment requires students to measure viscosity, which is often done by measuring how quickly small spheres fall through thick liquids and settle at the bottom. But Zenit realized he didn’t have to do it that way. The kitchen is rich with viscous fluids, and all he had to do was pick one.

Why not pancake batter?

This fall, students in his class, wherever they were sequestered, had to mix up pancake batter, pour it on a horizontal surface, and measure how quickly the radius expanded. “By measuring the rate at which this blob grows in time you can back-calculate the viscosity,” said Zenit.

Zenit described the experiment during a mini-symposium on kitchen flows at the 73rd Annual Meeting of the American Physical Society’s Division of Fluid Dynamics. In addition to his viscosity-through-pancakes project, the symposium included new research on how fluids mix with each other and how they incorporate solid particles (as in batter or dough). Researchers from the University of Cambridge described new findings on hydraulic jumps—those eerily smooth circles of water, surrounded by turbulence, that form directly beneath a running kitchen faucet.

Chemical engineer Endre Mossige, a postdoctoral researcher at Stanford University, organized the symposium. “Kitchen flow experiments are so easy to do,” he said. “You need so little equipment to extract such useful information about dynamics.”

The kitchen is a natural place to look for inspiration, said Jan Vermant, an engineer at ETH Zurich. “In the kitchen we do a lot with high-interface materials,” he said. “You have to mix fluids and air and make emulsions, and work with bubbles. This is a fundamental problem of food projects, and one known by chefs all over the world.”

Vermant reported on his group’s recent work, which tackled a beer problem by turning it into a fluid dynamics problem. He studies thin films, and in recent research he’s been studying the stability of foam in beers and breads. Beermakers, he said, check on the fermentation progress of new brews by looking at the stability of foam. But, he said, the process is very “hand-wavy.” When he began looking at beer brewing through the lens of fluid dynamics, he found a rich research environment.

Beer bubbles contain a rich variety of environments: capillary flows, soap films, and protein aggregation. “Basically, they have all the mechanisms one can design as an engineer,” he said. His group found, to their surprise, that even though most beers have foam, different beers have different mechanisms behind those foams. Some foams act like soap films; others develop robust protein networks at the surface.

“They each highlight different aspects of the problem nicely,” said Vermant. In subsequent work, his group took a similarly close look at interfacial phenomena in breads—and similarly found a variety of behaviors. “They have this rich diversity of mechanisms to stabilize structures,” he said.

Vermant said the work isn’t just about beer and bread; it may also serve as inspiration for new materials. “We can mimic those systems and might make foams using the same principles as foams,” he said, which could be useful for applications ranging from spray insulation to protective foams for crops.

At Brown, Zenit said not every student successfully completed the experiment. “Some of them took my advice too literally, and did it in a hot pan,” he said. Cooking the pancake changed the viscosity—freezing the batter in place—which meant the students don’t have usable data. (But they did have breakfast.)

He said turning to pancakes during the pandemic has opened his eyes to different ways to teach fundamental ideas like viscosity. “In the regular experiments, you drop this sphere in a container and measure it,” he said. The fluid, he says, is reduced to its measurement. With batter, the student experiences the concept. “With the pancakes, you get to feel the viscosity.”



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