Category: Science

In a new study, researchers were able to predict a person’s response to emotionally charged scenes using brain imaging and computer modeling alone.

The researchers could gauge not only whether the person’s reaction was positive, negative, or neutral, but also how strong the reaction was.

The study helps neuroscientists understand how the brain represents complex emotional natural stimuli, according to senior author Sonia Bishop, adjunct associate professor of neuroscience at the University of California, Berkeley, and the newly appointed chair of psychology at Trinity College Dublin.

The simple tasks used in the research will also make it easier to study autism spectrum disorder, where researchers seek to understand how individuals differ in processing everyday emotional stimuli.

The study appears in the journal Nature Communications.

“It is hugely important for all species to be able to recognize and respond appropriately to emotionally salient stimuli, whether that means not eating rotten food, running from a bear, approaching an attractive person in a bar, or comforting a tearful child,” says Bishop, who is also a member of UC Berkeley’s Helen Wills Neuroscience Institute.

“How the brain enables us to respond in a nuanced way to emotionally charged situations and stimuli has long been of interest, but little is known about how the brain stores schemas or neural representations to support the nuanced behavioral choices we make in response to emotional natural stimuli.”

In addition, few studies have looked beyond a simple binary reaction—approach or avoid, fight or flight—when humans clearly have a more nuanced response.

“Neuroscience studies of motivated behavior often focus on simple approach or avoidance behaviors, such as lever-pressing for food or changing locations to avoid a shock,” she says.

“However, when faced with natural emotional stimuli, humans don’t simply choose between ‘approach’ or ‘avoid.’ Rather, they select from a complex range of suitable responses. So, for example, our avoid response to a large bear—leave the area ASAP—is different to our avoid response to a weak, diseased animal—don’t get too close. Similarly, our approach response to the positive stimuli of a potential mate differs from our approach reaction to a cute baby.”

In the new study, led by former UC Berkeley doctoral student Samy Abdel-Ghaffar, who is now at Google, human volunteers were shown a variety of natural images—a baby’s face, a snarling dog, a person vomiting—chosen to evoke an emotional response. The researchers measured the participants’ 3D brain activity with a functional magnetic resonance imager (fMRI). They also asked the participants to rate the images as positive, negative, or neutral and reported the degree of emotional arousal to each.

Analysis of brain-wide activity showed that regions of the occipital temporal cortex, located in the back of the brain, are tuned to represent both the type of stimulus—single human, couple, crowd, reptile, mammal, food, object, building, landscape—and the emotional characteristics of the stimulus. For example, positive high-arousal faces were represented in slightly different regions than negative high-arousal faces or neutral low-arousal faces.

“Our research reveals that the occipital temporal cortex is tuned not only to different categories of stimuli; it also breaks down these categories based on their emotional characteristics in a way that is well suited to guide selection between alternate behaviors,” Bishop says.

Abdel-Ghaffar then used machine learning, a type of artificial intelligence, to predict the response of a second group of volunteers to the same images based solely on the stable tuning patterns in the occipital temporal cortex. He found that he could. In fact, analyzing brain activity was a better predictor of participants’ reactions than a machine learning assessment of the emotional aspects of the actual images.

“This suggests that the brain chooses which information is important or not important to represent and holds stable representations of sub-categories of animate and inanimate stimuli that integrate affective information and are optimally organized to support the selection of behaviors to different types of emotional natural stimuli,” Bishop says.

She notes also that “the paradigm used does not involve a complex task, making this approach suitable in the future, for example, to further understanding of how individuals with a range of neurological and psychiatric conditions differ in processing emotional natural stimuli.”

Additional coauthors are from UC Berkeley, the University of Texas at Austin, and the University of Nevada Reno.

Funding for the research came from the National Institutes of Health.

Source: UC Berkeley

A new gene-editing approach could offer new hope to people with sickle cell disease.

Current gene therapies to treat sickle cell disease are complex, time-consuming, and are sometimes linked to serious side effects like infertility or blood cancer.

To address these challenges, researchers have developed special nanoparticles that can send gene treatment directly to various types of cells in bone marrow to correct the disease-causing mutations.

“This gene editing approach would allow patients to receive the medicine through a transfusion,” says study lead author Xizhen Lian, an assistant research scientist affiliated with the Johns Hopkins Whiting School of Engineering’s Institute for NanoBioTechnology and the Johns Hopkins School of Medicine.

“This avoids the lengthy, difficult process of many current gene therapies, decreasing the burden on patients and the health care system while minimizing treatment side effects.”

Their results appear in Nature Nanotechnology.

The research team, which included scientists at the University of Texas Southwestern Medical Center, St. Jude Children’s Research Hospital, Harvard University, and Johns Hopkins School of Medicine, used CRISPR/Cas and base gene-editing techniques in a mouse model of sickle cell disease to activate a form of hemoglobin and correct the sickle cell mutation. The team also found the approach effective in targeting leukemia cells.

“One challenge we encountered is that the stem cell population is very small; only 0.1% of cells in bone marrow are stem cells. They are also protected in a micro-environment that can prevent the delivery of drugs from circulation,” Lian says.

The team solved this problem by adding a special fat molecule into their tiny delivery particles. This new molecule helped the delivery particles find and strongly attach to the stem cells, delivering important gene therapy.

The team’s next step is to optimize this technology on a humanized animal model that can better mimic clinical scenarios, as they are currently working solely with rodent blood cells and components. Humanized animal models have been genetically modified to express human genes, cells, and proteins, allowing researchers to study human diseases in a living system that closely resembles that of humans.

“Our approach promises to help patients avoid invasive treatment procedures, which will significantly reduce the side effects of blood cancer because there is no random gene insertion into the patient’s genes. We are targeting a specific gene that causes the disease and that’s it,” Lian says.

“The only way to cure such genetic diseases is to correct the genetic mutation in the stem cell populations.”

Source: Johnny Moseman for Johns Hopkins University

New research finds that while some teens experience negative feelings when using Instagram, the dominant feeling they have around the platform is boredom.

Concern that social media is driving the teen mental health crisis has risen to such a pitch that the majority of states in the country have filed lawsuits against Meta (which owns Instagram and Facebook) and the US surgeon general called last month for warning labels on platforms, similar to those on tobacco.

The new research finds, however, that teens open the Instagram app because they’re bored. Then they sift through largely irrelevant content, mostly feeling bored, while seeking interesting bits to share with their friends in direct messages—the most constant source of connection they found on the platform. Then, eventually bored with what researchers call a “content soup,” they log off.

The study tracked the experiences of 25 US teens moment by moment as they used the app. Teens leaned on a few techniques to stabilize their experiences—such as using likes, follows, and unfollows to curate their feeds, and racing past aggravating content.

The researchers used these results to make a few design recommendations, including prompts to cue reflection while using the app or features that clarify and simplify how users can curate their feeds.

The team presented its research on June 18 at the ACM Interaction Design and Children Conference in Delft, Netherlands.

“A lot of the talk about social media is at the extremes,” says lead author Rotem Landesman, a University of Washington doctoral student in the Information School. “You either hear about harassment or bullying—which are real phenomena—or this kind of techno-utopian view of things, where companies like Meta, among others, seem to say they are thinking about wellbeing constantly but we’ve yet to see concrete results of that. So we really wanted to study the mundane, daily experience of teens using Instagram.”

To capture this in-the-moment experience, the team first trained the participants in mindfulness techniques and had them download an app called AppMinder. The simple interface, which the researchers developed, would pop up five minutes after the teens started using Instagram and have them fill out a quick survey about how they were feeling emotionally and why. The pop-ups came once every three hours. Teens were supposed to use Instagram and fill out at least one response a day for seven days, though many submitted multiple responses each day.

Finally, researchers interviewed teens about their responses and had them open Instagram again and narrate how they were feeling in real time and explain how they were experiencing certain features.

“We saw teens turning to Instagram in moments of boredom, looking for some kind of stimulation,” says co-senior author Alexis Hiniker, an associate professor in the iSchool. “They were finding enough moments of closeness and connection with their friends on the app to keep them coming back. That value is definitely there, but it’s really buried in gimmicks, attention-grabbing features, content that’s sometimes upsetting or frustrating, and a ton of junk.”

Much of what Instagram’s algorithm served up was not what the teens were looking for. Yet they’d keep wading through hundreds of posts to find a single meme or piece of fashion inspiration to share with their friends. Overall, they found the most value in the app’s direct message function, not in this scrolling.

Because they found value in specific experiences, teens employed several mitigation strategies to focus their time on the app:

• Trying to curate their feeds to emphasize posts that made them feel good rather than bad or bored, by following, unfollowing, hiding, and liking
• Scrolling quickly, skipping, or logging off when content made them feel bad
• Toggling Instagram features—hiding like-counts, turning off certain notifications—to reduce negative emotions

“Instagram’s push notifications and algorithmically curated feeds forever hold out the promise of teens experiencing a meaningful interaction, while delivering on this promise only intermittently,” says co-senior author Katie Davis, an associate professor in the iSchool.

“Unfortunately, it’s much easier to identify the problem than to fix it. The current business model of most social media platforms depends on keeping users scrolling as often and for as long as possible. Legislation is needed to compel platforms to change the status quo.”

Based on their findings, the researchers offered three design changes to improve teens’ experiences:

• Notifications, like those from AppMinder, that prompt teens to consider what they’re on Instagram to do and to reflect in the moment
• Features that make curating feeds easier, such as a “This is good for me” button that clearly highlights positive content
• The use of data to track signs of well-being and its opposite— or example, tracking when users skip past content or log off and pairing this with other data

This summer, the team will take the data from the study and examine it with a separate group of teens, aiming for further insights and recommendations.

“It is not and should not be the sole responsibility of teens to make their experiences better, to navigate these algorithms without knowing how they work, exactly,” Landesman says. “The responsibility also lies with companies running social media platforms.”

This research was partially funded by the Oread Fund and the CERES network.

Source: University of Washington

Researchers have developed a new method for predicting what data wireless computing users will need before they need it, making wireless networks faster and more reliable.

The new method makes use of a technique called a “digital twin,” which effectively clones the network it is supporting.

At issue is something called edge caching. Caching refers to storing data on a server that a system or network thinks users will be using (or reusing) in the near future. This allows the system to meet user demands more quickly than if the system had to retrieve the data from the original source.

Edge caching is when a system is caching data in the server that is closest to the end user, such as computers that are incorporated into network routers or colocated with those routers.

“Two big challenges here are determining which data need to be cached and how much data the edge server should store at any given point in time,” says Yuchen Liu, corresponding author of a paper on the work and an assistant professor of computer science at North Carolina State University.

“Systems can’t put everything in edge caches, and storing too much redundant data on an edge server can slow down the server if the data are using too many computational resources. As a result, systems are constantly making decisions about which data packages to store and which data packages can be evicted.

“The more accurate a system is at predicting which data users will actually want, and how much data the edge servers should be storing, the better the system’s performance,” Liu says. “Our work here focused on improving those predictions.”

The new edge caching optimization method, called D-REC, makes use of a computational modeling technique called a digital twin. A digital twin is a virtual model of a real object. In the case of D-REC, the digital twin is a virtual model of a defined wireless network—whether that’s a cellular network or a Wi-Fi network.

“The method can be applied to any wireless network, depending on the system administrator or network operator’s needs,” says Liu. “D-REC can be adjusted depending on the needs of the user.”

In D-REC, the digital twin takes real-time data from the wireless network and uses it to conduct simulations to predict which data are most likely to be requested by users. These predictions are then sent back to the network to inform the network’s edge caching decisions. Because the simulations are performed by a computer that is outside of the network, this does not slow down network performance.

The researchers used open-source datasets to determine whether a wireless network operated more efficiently with D-REC. The researchers ran extensive experiments designed to account for many variables, such as the scale of the network, the number of users on a network, and so on.

“D-REC outperformed conventional approaches,” says Liu. “Our technique improved the network’s ability to accurately predict which data should be edge cached. D-REC also helped systems do a better job of balancing data storage across their networks.”

In addition, because D-REC’s digital twin focuses on predicting network behavior, it can identify potential problems in advance.

“For example, if the digital twin thinks there is a high likelihood that a specific base station, or server, will be overloaded, the network can be notified—allowing it to redistribute data across the network in order to preserve network performance and reliability,” says Liu.

“At this point, we’re open to working with network operators to explore how D-REC can improve network performance and reliability in real-world situations.”

The paper appears in the IEEE Journal on Selected Areas in Communications. Additional coauthors are from NC State, the University of Miami, and the Chinese University of Hong Kong.

Support for this work came from the National Science Foundation.

Source: North Carolina State University

According to a new study, the quick, athletic learners among us really are built differently—inside their brains.

You join a swing dance class, and at first you’re all left feet. But—slowly, eyes glued to the teacher—you pick up a step or two and start to feel the rhythm of the big band beat. A good start.

Then you look over and realize the couple next to you has picked up twice the steps in half the time. Why? The new research may have answers.

Daniel Ferris, a University of Florida professor of biomedical engineering, and his former doctoral student, Noelle Jacobsen, hooked up dozens of healthy people to brain-monitoring electrodes and had them walk on a treadmill with two belts moving at different speeds. The treadmill forced people to rapidly learn a new way to walk.

“Noelle was able to analyze brain activity of the best learners versus the slow learners and, lo and behold, some of the areas that were important were very clear in their brains,” Ferris says.

“The biggest surprise to us was that the visual cortex was very involved in the differences between the slow and fast learners. That suggests there’s something about visual information that is key to how you’re learning to move your body.”

This isn’t the first evidence for the role of visual information in acquiring new skills. Ferris’ lab has also shown that briefly interrupting vision can speed up learning how to walk on a balance beam.

In addition to hinting at how some of us pick up dance moves more quickly, the importance of visual processing could add to understanding the well-known link between vision problems and fall risks among older adults.

In addition to making it harder to spot trip hazards, “if you’re having trouble with vision, you may have problems learning new motor skills,” Ferris says.

Quick learners took about a minute to adjust and develop a comfortable walking cadence on the treadmill; the slower group took four times as long on average.

In addition to using the visual processing areas of their brains, fast learners also showed high activity in the regions involved in processing and planning muscle movements, as the scientists predicted. An error-correction region of their brains, known as the anterior cingulate cortex, was also activated to respond to the unusual gait.

The findings appear in the journal eNeuro.

Source: University of Florida

A new study that examined parenthood and “care work” found that mothers get paid less than either men or women without children, even in fields that are stereotypically thought of as being “women’s work.”

Men, on the other hand, generally received higher pay after becoming fathers—though white fathers benefited from this more than Black, Asian, or Hispanic fathers.

“We know that there is a parenthood wage gap in professional work—in which women make less money after having children, while men do not,” says Anna Manzoni, coauthor of the study and professor of sociology at North Carolina State University.

“We were interested in exploring whether a parenthood wage gap occurs among care workers.”

The researchers defined care work as occupations that involve providing for someone’s needs and well-being who typically cannot provide for their own. This includes occupations such as nursing, health care aides, K-12 teachers, child-care workers, religious clergy, and social workers.

“We were interested in care workers because we thought employers may use stereotypes linked to parental identity to determine rewards, and care workers may leverage their parental identity to signal appropriateness for work, possibly leading to higher, rather than lower, earnings,” Manzoni says.

“We were also interested in the role of race and gender, which may signal different levels of appropriateness to employers according to how they fulfill employer-held gender and racial stereotypes.”

To that end, the researchers drew on data from the US Census Bureau’s American Community Survey, which collects socio-demographic information, as well as data on work, pay, family status and related subjects—a nationally representative sample of more than 3 million people. The researchers used data from the years 1980, 1990, and then yearly from 2000 through 2018. Specifically, the researchers analyzed data from 805,786 care workers between the ages of 18 and 37.

“The findings were very straightforward,” Manzoni says. “Wages for women without children were more than 12% higher than wages for mothers, once all factors were accounted for. This penalty was more pronounced for white women than for Asian, Hispanic or Black women—though all of them saw their wages decline after parenthood. We think the difference between white women and women of color stems from the racialized belief that women of color are appropriate for caring labor.”

Meanwhile, the researchers also found that wages for fathers tended to be higher than wages for men without children. However, race comes into play here as well. Once other variables came into play, Black fathers actually saw a slight decline in wages after becoming parents.

“Put simply, our findings suggest that being seen as appropriate often does not result in wage advantages in care work,” Manzoni says. “Organizational practices and culture continue to disadvantage mothers and people of color, reinforcing inequality.

“In short, our results highlight the permanency of the belief that mothers are not compatible with paid work.”

The paper appears in The Sociological Quarterly.

Source: North Carolina State University

A new camera could prevent companies from collecting embarrassing and identifiable photos and videos from devices like smart home cameras and robotic vacuums.

The camera, called PrivacyLens, uses both a standard video camera and a heat-sensing camera to spot people in images from their body temperature. The person’s likeness is then completely replaced by a generic stick figure, whose movements mirror those of the person it stands in for.

The accurately animated stick figure allows a device relying on the camera to continue to function without revealing the identity of the person in view of the camera.

That extra anonymity could prevent private moments from leaking onto the internet, which is increasingly common in today’s world laden with camera-equipped devices that collect and upload information.

In 2020, a photo of a person on the toilet appeared on an online forum. The person didn’t realize their iRobot Roomba had wandered into the bathroom, and that all its photos were sent to a start-up company’s cloud server. From there, the photos were accessed and shared on social media groups, according to an investigation by MIT Technology Review.

“Most consumers do not think about what happens to the data collected by their favorite smart home devices. In most cases, raw audio, images, and videos are being streamed off these devices to the manufacturers’ cloud-based servers, regardless of whether or not the data is actually needed for the end application,” says Alanson Sample, associate professor of computer science and engineering at the University of Michigan and corresponding author of the study.

“A smart device that removes personally identifiable information before sensitive data is sent to private servers will be a far safer product than what we currently have.”

Raw photos are never stored anywhere on the device or in the cloud, completely eliminating access to unprocessed images. With this level of privacy protection, the engineering team hopes to make patients more comfortable with using cameras to monitor chronic health conditions and fitness at home.

“Cameras provide rich information to monitor health. It could help track exercise habits and other activities of daily living, or call for help when an elderly person falls,” says Yasha Iravantchi, a doctoral student in computer science and engineering who will present PrivacyLens at the Privacy Enhancing Technologies Symposium in Bristol, UK.

“But this presents an ethical dilemma for people who would benefit from this technology. Without privacy mitigations, we present a situation where they must weigh giving up their privacy in exchange for good chronic care. This device could allow us to get valuable medical data while preserving patient privacy.”

Replacing patients with stick figures helps make them more comfortable having a camera in even the most private parts of the home, according to an initial survey of 15 participants. The team has incorporated a sliding privacy scale into the device that allows users to control how much of their faces and bodies are censored.

“Our survey suggested that people might feel comfortable only blurring their face when in the kitchen, but in other parts of the home they may want their whole body removed from the image,” Sample says. “We want to give people control over their private information and who has access to it.”

The device could not only make patients more comfortable with chronic health monitoring, but it could also help protect privacy in public spaces. Vehicle manufacturers could potentially use PrivacyLens to prevent their autonomous vehicles from being used as surveillance drones, and companies that use cameras to collect data outdoors might find the device useful for complying with privacy laws.

“There’s a wide range of tasks where we want to know when people are present and what they are doing, but capturing their identity isn’t helpful in performing the task. So why risk it?” Iravantchi says.

The Rackham Graduate School and a Meta faculty research gift funded the work.

Sample has filed a provisional patent for the device, with the help of University of Michigan Innovation Partnerships, and hopes to eventually bring it to market.

Source: Derek Smith for University of Michigan

A medication used to treat diabetic neuropathy may make chemotherapy treatments more effective for patients with lung cancer, according to a new study.

Despite surgical and chemotherapy treatment, more than 50% of non-metastatic, non-small lung cancer patients see recurrences, in large part because of drug-resistant cancer cells.

Researchers have identified a way to make these cells more susceptible to chemotherapy, says study author Jussuf Kaifi, a thoracic surgeon at the University of Missouri Health Care and an assistant professor of surgery at the university’s School of Medicine.

“Traditional treatments for lung cancer, including chemotherapy, often have little to no effect on the cancer because of drug resistance,” Kaifi says.

“It is a major cause of mortality in patients, so finding ways to circumvent drug and chemotherapy resistance is vital to improving patient outcomes.”

The study examined 10 non-small cell lung cancer tumors, half of which were identified as drug resistant. The drug-resistant tumors showed overexpression of a certain enzyme, AKR1B10. When treated with the diabetic neuropathy medication, epalrestat, the tumors became less drug resistant, causing their sensitivity to chemotherapy to significantly increase.

Epalrestat is available in several countries and well-tolerated by patients, but it is not yet approved for use by the Food and Drug Administration in the United States. The medication is currently in high-level clinical trials as part of the FDA’s approval process. If given FDA approval, epalrestat could be fast-tracked as an anti-cancer drug for lung cancer patients.

“In general, developing new drugs for cancer treatment is an extremely lengthy, expensive and inefficient process,” Kaifi says.

“In contrast, ‘repurposing’ these drugs to other diseases is much faster and cheaper. In view of overcoming drug resistance, epalrestat can rapidly be advanced to the clinic to improve cure rates in lung cancer patients.”

The research appears in Clinical Cancer Research.

Source: University of Missouri

New research suggests that consuming foods rich in beta-glucan, a type of fiber found in oats and barley, can reduce body weight and obesity.

Ozempic—known generically as semaglutide—has taken the weight-loss market by storm, promising to help people shed pounds quickly. Though many use the drug to reduce body weight, it is mostly prescribed for treating type 2 diabetes in adults and carries a high price without insurance coverage.

But what if you could achieve weight loss and improved glucose control without medication?

The new study in The Journal of Nutrition analyzed the impact of different fibers on gut microbiota—the community of tiny microbes living in the digestive system that are responsible for breaking down the food we eat.

“We know that fiber is important and beneficial; the problem is that there are so many different types of fiber,” says Frank Duca, associate professor in the University of Arizona animal and comparative biomedical sciences department in the College of Agriculture, Life, and Environmental Sciences.

“We wanted to know what kind of fiber would be most beneficial for weight loss and improvements in glucose homeostasis so that we can inform the community, the consumer, and then also inform the agricultural industry.”

The researchers looked at the effect of five different plant-based fibers in rodent diets: pectin, beta-glucan, wheat dextrin, starch, and cellulose. Only beta-glucan resulted in reduction of body weight and fat, as well as improvements in glucose homeostasis.

Beta-glucan is a unique fiber that is found in many foods, including oats, barley, mushrooms, and yeasts, and future studies will examine how different sources of beta-glucan could differ in their effectiveness.

Changes in metabolites—the molecules produced when gut bacteria interact with fiber—seemed to be responsible for the weight-loss effects, particularly a specific metabolite called butyrate. Butyrate is a key fuel source for colon cells, promoting a healthy gut barrier to reduce systemic inflammation. Butyrate also induces the release of gut peptides, or messengers that regulate the functions of the gut, such as the glucagon-like peptide-1, or GLP-1.

Drugs like semaglutide are synthetic versions of GLP-1, which stimulate insulin and can also help people feel full. One key difference of naturally occurring GLP-1 is its rapid degradation near the intestine, whereas semaglutide is made to last longer and target the brain.

“Part of the benefits of consuming dietary fiber is through the release of GLP-1 and other gut peptides that regulate appetite and body weight,” Duca says.

“However, we don’t think that’s all of the effect. We think that there are other beneficial things that butyrate could be doing that are not gut peptide related, such as improving gut barrier health and targeting peripheral organs like the liver.”

Duca is researching other types of fiber that can be beneficial for weight reduction. In a previous study, the Duca Lab discovered that barley flour was the most effective in promoting weight loss compared to several other commercially available flours. Other studies involving oligofructose have also demonstrated beneficial effects.

In the future, Duca hopes to collaborate with other researchers to develop enhanced fibers that can optimize the release of butyrate.

Source: Elena Lopez for University of Arizona

New research reveals how a climate model commonly used by geoscientists currently overestimates a key physical property of Earth’s climate system called albedo.

Albedo is the degree to which ice reflects planet-warming sunlight into space.

As the planet continues to warm due to human-driven climate change, accurate computer climate models will be key in helping illuminate exactly how the climate will continue to be altered in the years ahead.

“We found that with old model versions, the ice is too reflective by about 5%,” says Chloe Clarke, a project scientist in University of California, Irvine professor Charlie Zender’s group. “Ice reflectivity was much too high.”

The amount of sunlight the planet receives and reflects is important for estimating just how much the planet will warm in the coming years. Previous versions of the model, called the Energy Exascale Earth System Model (E3SM), overestimated albedo because they did not account for what Clarke described as the microphysical properties of ice in a warming world.

Those properties include the effects things like algae and dust have on albedo. Dark-colored algae and dust can make snow and ice less reflective and less able to reflect sunlight.

To do the analysis, Clarke and her team studied satellite data to track the albedo of the Greenland Ice Sheet. They found that E3SM reflectivity overestimates the reflectivity of the ice sheet, “meaning the model estimates less melt than what would be expected from the ice microphysical properties,” says Clarke.

But with the new ice reflectivity incorporated into the model, the Greenland Ice Sheet is melting at a rate of about six gigatons more than in older model versions. This is based on albedo measurements that are more consistent with satellite observations.

Clarke hopes her team’s study stresses the importance of the seemingly minuscule properties that can have far-reaching consequences for the overall climate.

“I think our work is going to help models do a much better job of helping us capture snow and ice-related climate feedbacks,” she says.

Next, Clarke wants to study different icy parts of the planet to gauge how widespread the albedo discrepancy is in E3SM.

“Our next steps are to get it so it is functional globally and not just valid over Greenland,” says Clarke, who also intends to compare the new Greenland Ice Sheet melt rates to observations to measure how much more accurate the new ice albedo is.

“It would be useful to apply it to glaciers in places like the Andes and Alaska.”

The study appears in the Journal of Geophysical Research: Atmospheres.

Additional authors are from Lamont-Doherty Earth Observatory, University of Michigan, National Oceanic and Atmospheric Administration, and UC Irvine.

Funding information is listed in the study.

Source: UC Irvine