Transistors, the building blocks of modern electronics, are typically made of silicon. Because it’s a semiconductor, this material can control the flow of electricity in a circuit. But silicon has fundamental physical limits that restrict how compact and energy-efficient a transistor can be.

MIT researchers have now replaced silicon with a magnetic semiconductor, creating a magnetic transistor that could enable smaller, faster, and more energy-efficient circuits. The material’s magnetism strongly influences its electronic behavior, leading to more efficient control of the flow of electricity. 

The team used a novel magnetic material and an optimization process that reduces the material’s defects, which boosts the transistor’s performance.

The material’s unique magnetic properties also allow for transistors with built-in memory, which would simplify circuit design and unlock new applications for high-performance electronics.

“People have known about magnets for thousands of years, but there are very limited ways to incorporate magnetism into electronics. We have shown a new way to efficiently utilize magnetism that opens up a lot of possibilities for future applications and research,” says Chung-Tao Chou, an MIT graduate student in the departments of Electrical Engineering and Computer Science (EECS) and Physics, and co-lead author of a paper on this advance.

Chou is joined on the paper by co-lead author Eugene Park, a graduate student in the Department of Materials Science and Engineering (DMSE); Julian Klein, a DMSE research scientist; Josep Ingla-Aynes, a postdoc in the MIT Plasma Science and Fusion Center; Jagadeesh S. Moodera, a senior research scientist in the Department of Physics; and senior authors Frances Ross, TDK Professor in DMSE; and Luqiao Liu, an associate professor in EECS, and a member of the Research Laboratory of Electronics; as well as others at the University of Chemistry and Technology in Prague. The paper appears today in Physical Review Letters.

Overcoming the limits

In an electronic device, silicon semiconductor transistors act like tiny light switches that turn a circuit on and off, or amplify weak signals in a communication system. They do this using a small input voltage.

But a fundamental physical limit of silicon semiconductors prevents a transistor from operating below a certain voltage, which hinders its energy efficiency.

To make more efficient electronics, researchers have spent decades working toward magnetic transistors that utilize electron spin to control the flow of electricity. Electron spin is a fundamental property that enables electrons to behave like tiny magnets.

So far, scientists have mostly been limited to using certain magnetic materials. These lack the favorable electronic properties of semiconductors, constraining device performance.

“In this work, we combine magnetism and semiconductor physics to realize useful spintronic devices,” Liu says.

The researchers replace the silicon in the surface layer of a transistor with chromium sulfur bromide, a two-dimensional material that acts as a magnetic semiconductor.

Due to the material’s structure, researchers can switch between two magnetic states very cleanly. This makes it ideal for use in a transistor that smoothly switches between “on” and “off.”

“One of the biggest challenges we faced was finding the right material. We tried many other materials that didn’t work,” Chou says.

They discovered that changing these magnetic states modifies the material’s electronic properties, enabling low-energy operation. And unlike many other 2D materials, chromium sulfur bromide remains stable in air.

To make a transistor, the researchers pattern electrodes onto a silicon substrate, then carefully align and transfer the 2D material on top. They use tape to pick up a tiny piece of material, only a few tens of nanometers thick, and place it onto the substrate.

“A lot of researchers will use solvents or glue to do the transfer, but transistors require a very clean surface. We eliminate all those risks by simplifying this step,” Chou says.

Leveraging magnetism

This lack of contamination enables their device to outperform existing magnetic transistors. Most others can only create a weak magnetic effect, changing the flow of current by a few percent or less. Their new transistor can switch or amplify the electric current by a factor of 10.

They use an external magnetic field to change the magnetic state of the material, switching the transistor using significantly less energy than would usually be required.

The material also allows them to control the magnetic states with electric current. This is important because engineers cannot apply magnetic fields to individual transistors in an electronic device. They need to control each one electrically.

The material’s magnetic properties could also enable transistors with built-in memory, simplifying the design of logic or memory circuits.

A typical memory device has a magnetic cell to store information and a transistor to read it out. Their method can combine both into one magnetic transistor.

“Now, not only are transistors turning on and off, they are also remembering information. And because we can switch the transistor with greater magnitude, the signal is much stronger so we can read out the information faster, and in a much more reliable way,” Liu says.

Building on this demonstration, the researchers plan to further study the use of electrical current to control the device. They are also working to make their method scalable so they can fabricate arrays of transistors.

This research was supported, in part, by the Semiconductor Research Corporation, the U.S. Defense Advanced Research Projects Agency (DARPA), the U.S. National Science Foundation (NSF), the U.S. Department of Energy, the U.S. Army Research Office, and the Czech Ministry of Education, Youth, and Sports. The work was partially carried out at the MIT.nano facilities.

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MIT is often recognized as one of the leading institutions of higher learning not only in the United States, but in the world, by several publications, including U.S. News & World Report, QS World University Rankings, Times Higher Education, and Forbes.

Now, MIT also has the distinction of being one of just 30 colleges and universities out of hundreds recognized by Princeton Review’s 2026 Mental Health Services Honor Roll for providing exemplary mental health and well-being services to its students. This is the second year in a row that MIT has received this honor.

The honor roll was created to be a resource for enrolled students and prospective students who may seek such services when applying to colleges. The survey asked more than a dozen questions about training for students, faculty, and staff; provisions for making new policies and procedures; peer-to-peer offerings; screenings and referral services available to all students; residence hall mental health resources; and other criteria, such as current online information that is updated and accessible.

Overall, the 2025 survey findings for all participating institutions are noteworthy, with Princeton Review reporting double-digit increases in campus counseling, wellness, and student support programs compared with its 2024 survey results. Earning a place on the honor roll underscores MIT’s commitment to providing exceptional services for graduate and undergraduate students alike.

Karen Singleton, deputy chief health officer and chief of mental health and counseling services at MIT Health, says, “This honor highlights the hard work and collaboration that we do here at MIT to support students in their well-being journey. This is a recognition of how we are doing those things effectively, and a recognition of MIT’s investment in these support services.”

MIT Health hosts 36 clinicians to meet the needs of the community, and it recently added an easy online scheduling system at the request of students.

Many mental health and well-being services are offered through several departments housed in the Division of Student Life (DSL). They often collaborate with MIT Health and partners across the Institute, including in the Division of Graduate and Undergraduate Education, to provide the best services for the best outcomes for MIT students. 

Support resources in DSL are highly utilized and valued by students. For instance, 82 percent of the Class of 2025 had visited Student Support Services (S3) at least once before graduating, and on a regular satisfaction survey, 91 percent of students who visited S3 said they would return if needed.

“Student Support Services supports over 80 percent of all undergraduates by the time they graduate, and over 60 percent each year. Our offices, including ORSEL, GradSupport, S3, SMHC, the CARE Team, and Residential and Community Life work incredibly well together to support our students,” says Kate McCarthy, senior associate dean of support, wellbeing, and belonging.

“The magic in our support system is the deeply collaborative nature of it. There are many different places students can enter the support network, and each of these teams works closely together to ensure students get connected to the help they need. We always say that students shouldn’t think too much about where they turn … if they get to one of us, they get to all of us,” says David Randall, dean of student life.

Division of Student Life Vice Chancellor Suzy Nelson adds, “It is an honor to see MIT included among colleges and universities recognized for excellent mental health services. Promoting student well-being is central to our mission and guides so much of what we do. This recognition reflects the work of many in our community who are dedicated to creating a campus environment where students can thrive academically and personally.”

Palo Alto’s crosswalk signals were hacked last year. Turns out the city never changed the default passwords.

Withdrawing from the world’s premier climate science organization supports the president’s views about global warming.

The prospect of ramping up the country’s heavy crude production goes against global efforts to phase down fossil fuels.

“We might as well focus on what we can control,” said Environment and Public Works Chair Shelley Moore Capito (R-W.Va.).

The justice holds stock in an oil company that withdrew from the lawsuit right before the high court took up the case.

Beset by crises in Ukraine and Greenland, leaders let the U.S. withdrawal from the world’s most important climate body pass with barely a mention.

Arizona Rep. Eli Crane has called efforts to limit planet-warming emissions "climate conspiracy nonsense."

“If someone did care about environment or climate change, one would have a hard time signing onto these new dietary guidelines,” a Harvard epidemiologist said.

“God only knows what the Trump administration will do” if the EU expands its aviation emissions scheme, an EU official says.

Temperatures rose above 113 degrees Fahrenheit in some places as the heat baked Adelaide and South Australia, before being driven east.

There were no immediate reports of major injuries or deaths.

Artificial intelligence has captured headlines recently for its rapidly growing energy demands, and particularly the surging electricity usage of data centers that enable the training and deployment of the latest generative AI models. But it’s not all bad news — some AI tools have the potential to reduce some forms of energy consumption and enable cleaner grids.

One of the most promising applications is using AI to optimize the power grid, which would improve efficiency, increase resilience to extreme weather, and enable the integration of more renewable energy. To learn more, MIT News spoke with Priya Donti, the Silverman Family Career Development Professor in the MIT Department of Electrical Engineering and Computer Science (EECS) and a principal investigator at the Laboratory for Information and Decision Systems (LIDS), whose work focuses on applying machine learning to optimize the power grid.

Q: Why does the power grid need to be optimized in the first place?

A: We need to maintain an exact balance between the amount of power that is put into the grid and the amount that comes out at every moment in time. But on the demand side, we have some uncertainty. Power companies don’t ask customers to pre-register the amount of energy they are going to use ahead of time, so some estimation and prediction must be done.

Then, on the supply side, there is typically some variation in costs and fuel availability that grid managers need to be responsive to. That has become an even bigger issue because of the integration of energy from time-varying renewable sources, like solar and wind, where uncertainty in the weather can have a major impact on how much power is available. Then, at the same time, depending on how power is flowing in the grid, there is some power lost through resistive heat on the power lines. So, as a grid operator, how do you make sure all that is working all the time? That is where optimization comes in.

Q: How can AI be most useful in power grid optimization?

A: One way AI can be helpful is to use a combination of historical and real-time data to make more precise predictions about how much renewable energy will be available at a certain time. This could lead to a cleaner power grid by allowing us to handle and better utilize these resources.

AI could also help tackle the complex optimization problems that power grid operators must solve to balance supply and demand in a way that also reduces costs. These optimization problems are used to determine which power generators should produce power, how much they should produce, and when they should produce it, as well as when batteries should be charged and discharged, and whether we can leverage flexibility in power loads. These optimization problems are so computationally expensive that operators use approximations so they can solve them in a feasible amount of time. But these approximations are often wrong, and when we integrate more renewable energy into the grid, they are thrown off even farther. AI can help by providing more accurate approximations in a faster manner, which can be deployed in real-time to help grid operators responsively and proactively manage the grid.

AI could also be useful in the planning of next-generation power grids. Planning for power grids requires one to use huge simulation models, so AI can play a big role in running those models more efficiently. The technology can also help with predictive maintenance by detecting where anomalous behavior on the grid is likely to happen, reducing inefficiencies that come from outages. More broadly, AI could also be applied to accelerate experimentation aimed at creating better batteries, which would allow the integration of more energy from renewable sources into the grid.

Q: How should we think about the pros and cons of AI, from an energy sector perspective?

A: One important thing to remember is that AI refers to a heterogeneous set of technologies. There are different types and sizes of models that are used, and different ways that models are used. If you are using a model that is trained on a smaller amount of data with a smaller number of parameters, that is going to consume much less energy than a large, general-purpose model.

In the context of the energy sector, there are a lot of places where, if you use these application-specific AI models for the applications they are intended for, the cost-benefit tradeoff works out in your favor. In these cases, the applications are enabling benefits from a sustainability perspective — like incorporating more renewables into the grid and supporting decarbonization strategies.

Overall, it’s important to think about whether the types of investments we are making into AI are actually matched with the benefits we want from AI. On a societal level, I think the answer to that question right now is “no.” There is a lot of development and expansion of a particular subset of AI technologies, and these are not the technologies that will have the biggest benefits across energy and climate applications. I’m not saying these technologies are useless, but they are incredibly resource-intensive, while also not being responsible for the lion’s share of the benefits that could be felt in the energy sector.

I’m excited to develop AI algorithms that respect the physical constraints of the power grid so that we can credibly deploy them. This is a hard problem to solve. If an LLM says something that is slightly incorrect, as humans, we can usually correct for that in our heads. But if you make the same magnitude of a mistake when you are optimizing a power grid, that can cause a large-scale blackout. We need to build models differently, but this also provides an opportunity to benefit from our knowledge of how the physics of the power grid works.

And more broadly, I think it’s critical that those of us in the technical community put our efforts toward fostering a more democratized system of AI development and deployment, and that it’s done in a way that is aligned with the needs of on-the-ground applications.

Nature Climate Change, Published online: 09 January 2026; doi:10.1038/s41558-025-02534-4

The El Niño–Southern Oscillation threatens human health, and its impacts are likely to intensify under climate change. This research examines how historical El Niño–Southern Oscillation events have caused life expectancy and economic losses across the Pacific Rim and projects future impacts and vulnerable groups.

Nature Climate Change, Published online: 09 January 2026; doi:10.1038/s41558-025-02537-1

Channelized subsurface melting is an important process in the dynamics of ice shelves. Here the authors present observational data from Antarctic ice shelves and show that their basal melt is up to 50% higher than previously assumed.

ICE has been invading U.S. cities, targeting, surveilling, harassing, assaulting, detaining, and torturing people who are undocumented immigrants. They also have targeted people with work permits, asylum seekers, permanent residents (people holding “green cards”), naturalized citizens, and even citizens by birth. ICE has spent hundreds of millions of dollars on surveillance technology to spy on anyone—and potentially everyone—in the United States. It can be hard to imagine how to defend oneself against such an overwhelming force. But a few enterprising hackers have started projects to do counter surveillance against ICE, and hopefully protect their communities through clever use of technology. 

Let’s start with Flock, the company behind a number of automated license plate reader (ALPR) and other camera technologies. You might be surprised at how many Flock cameras there are in your community. Many large and small municipalities around the country have signed deals with Flock for license plate readers to track the movement of all cars in their city. Even though these deals are signed by local police departments, oftentimes ICE also gains access. 

Because of their ubiquity, people are interested in finding out where and how many Flock cameras are in their community. One project that can help with this is the OUI-SPY, a small piece of open source hardware. The OUI-SPY runs on a cheap Arduino compatible chip called an ESP-32. There are multiple programs available for loading on the chip, such as “Flock You,” which allows people to detect Flock cameras and “Sky-Spy” to detect overhead drones. There’s also “BLE Detect,” which detects various Bluetooth signals including ones from Axon, Meta’s Ray-Bans that secretly record you, and more. It also has a mode commonly known as “fox hunting” to track down a specific device. Activists and researchers can use this tool to map out different technologies and quantify the spread of surveillance. 

There’s also the open source Wigle app which is primarily designed for mapping out Wi-Fi, but also has the ability to make an audio alert when a specific Wi-Fi or Bluetooth identifier is detected. This means you can set it up to get a notification when it detects products from Flock, Axon, or other nasties in their vicinity. 

One enterprising YouTuber, Benn Jordan, figured out a way to fool Flock cameras into not recording his license plate simply by painting some minor visual noise on his license plate. This is innocuous enough that any human will still be able to read his license plate, but it completely prevented Flock devices from recognizing his license plate as a license plate at the time. Some states have outlawed drivers obscuring their license plates, so taking such action is not recommended. 

Jordan later went on to discover hundreds of misconfigured Flock cameras that were exposing their administrator interface without a password on the public internet. This would allow anyone with an internet connection to view a live surveillance feed, download 30 days of video, view logs, and more. The cameras pointed at parks, public trails, busy intersections, and even a playground. This was a massive breach of public trust and a huge mistake for a company that claims to be working for public safety.

Other hackers have taken on the task of open-source intelligence and community reporting. One interesting example is deflock.me and alpr.watch, which are crowdsourced maps of ALPR cameras. Much like the OUI-SPY project, this allows activists to map out and expose Flock surveillance cameras in their community. 

There have also been several ICE reporting apps released, including apps to report ICE sightings in your area such Stop ICE Alerts, ICEOUT.org, and ICE Block. ICEBlock was delisted by Apple at the request of Attorney General Pam Bondi, a fact we are suing over. There is also Eyes Up, an app to securely record and archive ICE raids, which was taken down by Apple earlier this year. 

Another interesting project documenting ICE and creating a trove of open-source intelligence is ICE List Wiki which contains info on companies that have contracts with ICE, incidents and encounters with ICE, and vehicles ICE uses. 

People without programming knowledge can also get involved. In Chicago, people used whistles to warn their neighbors that ICE was present or in the area. Many people 3D-printed whistles along with instructional booklets to hand out to their communities, allowing a wider distribution of whistles and consequently earlier warnings for their neighbors. 

Many hackers have started hosting digital security trainings for their communities or building web sites with security advice, including how to remove your data from the watchful eyes of the surveillance industry. To reach a broader community, trainers have even started hosting trainings on how to defend their communities and what to do in an ICE raid in video games, such as Fortnight. 

There is also EFF’s own Rayhunter project for detecting cell-site simulators, about which we have written extensively. Rayhunter runs on a cheap mobile hotspot and doesn’t require deep technical knowledge to use.

It’s important to remember that we are not powerless. Even in the face of a domestic law enforcement presence with massive surveillance capabilities and military-esque technologies, there are still ways to engage in surveillance self-defense. We cannot give into nihilism and fear. We must continue to find small ways to protect ourselves and our communities, and when we can, fight back. 

EFF is not affiliated with any of these projects (other than Rayhunter) and does not endorse them. We don’t make any statements about the legality of using any of these projects. Please consult with an attorney to determine what risks there may be. 

Related Cases: EFF v. DOJ, DHS (ICE tracking apps)

MIT’s senior capstone course 2.009 (Product Engineering Processes), an iconic class known colloquially on campus as “two double-oh nine,” emulates what engineers experience while working as part of a design team at a product development firm. The annual prototype launch is a colorful and exciting culmination of a semester’s worth of work.

“This fall, 97 students split into six teams entered the rapid-fire cycle of product engineering, looping between ideas, prototypes, failures, fixes, and breakthroughs,” said Josh Wiesman, 2.009 lecturer, in the program’s opening remarks. “They pushed themselves out of their comfort zone and learned to oscillate between creativity and technical rigor. Thermal, fluids, mechanics, materials, instrumentation — everything you can imagine came back around in new and unexpected ways.”

Wiesman’s remarks hinted at this year’s theme, which co-instructor Peko Hosoi, the Pappalardo Professor of Mechanical Engineering, reminded spectators was announced this year as “Cycles!”

“Engineering doesn’t move in a straight line,” Hosoi elaborated. “It loops, it resets, accelerates, and builds momentum, just like our students.” She continued, “Tonight, we’re celebrating the energy, grit, and creativity that comes from embracing those cycles.”

Starting with ideation, the teams ventured out to talk to people from a variety of walks of life and uncover what Hosoi referred to as “exciting problems worth solving.” From there — with mentors, access to makerspaces, and a budget to turn their ideas into working products — the teams, each represented by a color, spent 13 weeks designing, building, and drafting a business plan for their product.

Spectators packed Kresge Auditorium on Dec. 8, waiving colorful pompoms and cheering on the teams, with thousands more watching online. The six teams demonstrated their prototypes and shared business plans, with breaks between presentations featuring dance and musical performances by MIT Ridonkulous, MIT Ohms, and MIT Live, and short animated films created by the 2.009 team which, this year, incorporated popular movie references.

A recording of the event livestream is available on the 2.009 website, which includes full demonstrations of the product prototypes discussed below, along with audience questions.

Green Team

In the United States, some 350,000 people suffer cardiac arrest each year. Immediate intervention by bystanders can be the difference between life and death. The Green Team presented HeartBridge, an automated CPR device.

“For every minute someone who needs it goes without effective CPR, their chance of survival decreases by roughly 10 percent,” Green Team presenters told the audience. But, they added, CPR is exhausting at the recommended speed and compression depth, with research showing decreases in effectiveness of manual compressions after just three minutes.

HeartBridge is a durable mechanical device that administers steady compressions to a patient and provides textual, visual, and auditory cues to users.

Purple Team

The Purple Team painted the picture of a quintessential fall activity in New England, inviting the audience to imagine “it’s a beautiful Saturday in October, and you decide to go apple picking.” At family-run orchards, thousands of apples fall to the ground each season, creating more than just a mess. Rotting apples invite pests or can spread fungus, decreasing crop yield.

AgriSweep, the Purple Team’s prototype, is a hydraulic powered tractor attachment that collects fallen apples into a produce bin, saving time and labor costs, decreasing the need for sprays, and potentially generating revenue for farmers who sell the windfalls for hard cider, livestock feed, or compost.

Nodding to the video references punctuating the show, the team closed their presentation with a reference to an iconic film with an MIT connection: “How do you like them apples?”

Red Team

Hand embroidery is a popular pastime, but drawing or transferring patterns can be time-consuming or messy. The Red Team aims to solve this problem with their product, Scribbly, a “user-friendly and software-free printer” designed to let hobbyists to create their own designs and make transfers easier.

The machine, which can accommodate a variety of fabrics and embroidery hoop sizes up to 10 inches in diameter, reads design files from a USB, then transfers the image via a pen that can be “erased” with heat if the user wants to change the design.

To demonstrate their product, the team created a transfer pattern of the MIT Department of Mechanical Engineering logo.

Blue Team

Boating safety was top-of-mind for the Blue Team. Propeller-related injuries are a big concern for recreational boaters. Fixed propeller guards, or prop guards, are the most common solution but have drawbacks, including reducing fuel efficiency and decreasing maneuverability. DORI, the Blue Team prototype, is a deployable prop guard that is stowed above the waterline and can be lowered into place when needed.

Yellow Team

The Yellow Team tackled a problem faced by “pond skating enthusiasts and people who maintain their own backyard rinks,” namely, rough patches, bumps, and uneven ice. Their product, Polar, is a compact device that smooths out backyard surfaces to improve skate-ability.

The system includes a chassis on a welded steel frame with a motorized drivetrain, a cutter to shave the ice surface, and an onboard water distribution system with heating mechanism and drip bar for resurfacing.

Pink Team

The final team of the night, the Pink Team, conquered a challenge rooted in one of the most demanding and real-world contexts: rescue diving. In a drowning emergency, rescue divers have just minutes to save a life. Using a retractable strap, carabiner, and locking mechanism, the Pink Team’s product, HydroHold, attaches directly to a diver’s buoyancy control device and offers a hands-free way to secure a drowning victim during a rescue mission.

The product was developed following consultations with divers from local fire departments, the state police, and Woods Hole Oceanographic Institute. “When we took these prototypes to rescue divers, we heard them ask for two things over and over,” the presenters said. “Something simple, and something safe.”

Rather than choosing complexity, Hosoi told the audience, the Pink Team pursued refinement. “They kept testing with users, shaping the interface, and polishing the details until everything felt natural.”

Wiesman added that the product is a reminder that “powerful engineering isn’t about flashy things … sometimes it’s about reducing friction, elevating usability, and building something that just works when it matters.”

Thank you and goodnight

The night ended with a final “thank you” song celebrating the products, the teams, and all the contributors who make the class possible because, as Hosoi said, “It really does take a team to make this class ‘cycle’ forward.” 

The clever AI-generated tribute, which weaves in the names of class participants and instructors, while rhyming “pizza with pepperoni” and “pond-sized Zamboni,” can also be watched in its entirety at the end of the livestream recording, following the product demonstrations. 

Every autumn, as the Northern Hemisphere moves toward winter, Judah Cohen starts to piece together a complex atmospheric puzzle. Cohen, a research scientist in MIT’s Department of Civil and Environmental Engineering (CEE), has spent decades studying how conditions in the Arctic set the course for winter weather throughout Europe, Asia, and North America. His research dates back to his postdoctoral work with Bacardi and Stockholm Water Foundations Professor Dara Entekhabi that looked at snow cover in the Siberian region and its connection with winter forecasting.

Cohen’s outlook for the 2025–26 winter highlights a season characterized by indicators emerging from the Arctic using a new generation of artificial intelligence tools that help develop the full atmospheric picture.

Looking beyond the usual climate drivers

Winter forecasts rely heavily on El Niño–Southern Oscillation (ENSO) diagnostics, which are the tropical Pacific Ocean and atmosphere conditions that influence weather around the world. However, Cohen notes that ENSO is relatively weak this year.

“When ENSO is weak, that’s when climate indicators from the Arctic becomes especially important,” Cohen says.

Cohen monitors high-latitude diagnostics in his subseasonal forecasting, such as October snow cover in Siberia, early-season temperature changes, Arctic sea-ice extent, and the stability of the polar vortex. “These indicators can tell a surprisingly detailed story about the upcoming winter,” he says. 

One of Cohen’s most consistent data predictors is October’s weather in Siberia. This year, when the Northern Hemisphere experienced an unusually warm October, Siberia was colder than normal with an early snow fall. “Cold temperatures paired with early snow cover tend to strengthen the formation of cold air masses that can later spill into Europe and North America,” says Cohen — weather patterns that are historically linked to more frequent cold spells later in winter.

Warm ocean temperatures in the Barents–Kara Sea and an “easterly” phase of the quasi-biennial oscillation also suggest a potentially weaker polar vortex in early winter. When this disturbance couples with surface conditions in December, it leads to lower-than-normal temperatures across parts of Eurasia and North America earlier in the season.

AI subseasonal forecasting

While AI weather models have made impressive strides showcasing in short-range (one-to–10-day) forecasts, these advances have not yet applied to longer periods. The subseasonal prediction covering two to six weeks remains one of the toughest challenges in the field.

That gap is why this year could be a turning point for subseasonal weather forecasting. A team of researchers working with Cohen won first place for the fall season in the 2025 AI WeatherQuest subseasonal forecasting competition, held by the European Centre for Medium-Range Weather Forecasts (ECMWF). The challenge evaluates how well AI models capture temperature patterns over multiple weeks, where forecasting has been historically limited.

The winning model combined machine-learning pattern recognition with the same Arctic diagnostics Cohen has refined over decades. The system demonstrated significant gains in multi-week forecasting, surpassing leading AI and statistical baselines.

“If this level of performance holds across multiple seasons, it could represent a real step forward for subseasonal prediction,” Cohen says

The model also detected a potential cold surge in mid-December for the U.S. East Coast much earlier than usual, weeks before such signals typically arise. The forecast was widely publicized in the media in real-time. If validated, Cohen explains, it would show how combining Arctic indicators with AI could extend the lead time for predicting impactful weather.

“Flagging a potential extreme event three to four weeks in advance would be a watershed moment,” he adds. “It would give utilities, transportation systems, and public agencies more time to prepare.”

What this winter may hold

Cohen’s model shows a greater chance of colder-than-normal conditions across parts of Eurasia and central North America later in the winter, with the strongest anomalies likely mid-season.

“We’re still early, and patterns can shift,” Cohen says. “But the ingredients for a colder winter pattern are there.”

As Arctic warming speeds up, its impact on winter behavior is becoming more evident, making it increasingly important to understand these connections for energy planning, transportation, and public safety. Cohen’s work shows that the Arctic holds untapped subseasonal forecasting power, and AI may help unlock it for time frames that have long been challenging for traditional models.

In November, Cohen even appeared as a clue in The Washington Post crossword, a small sign of how widely his research has entered public conversations about winter weather.

“For me, the Arctic has always been the place to watch,” he says. “Now AI is giving us new ways to interpret its signals.”

Cohen will continue to update his outlook throughout the season on his blog.