Science
That Arctic blast can feel brutally cold, but how much colder than ‘normal’ is it really?
Richard B. (Ricky) Rood, University of Michigan
An Arctic blast hitting the central and eastern U.S. in early January 2025 has been creating fiercely cold conditions in many places. Parts of North Dakota dipped to more than 20 degrees below zero, and people as far south as Texas woke up to temperatures in the teens. A snow and ice storm across the middle of the country added to the winter chill.
Forecasters warned that temperatures could be “10 to more than 30 degrees below normal” across much of the eastern two-thirds of the country during the first full week of the year.
But what does “normal” actually mean?
While temperature forecasts are important to help people stay safe, the comparison to “normal” can be quite misleading. That’s because what qualifies as normal in forecasts has been changing rapidly over the years as the planet warms.
Defining normal
One of the most used standards for defining a science-based “normal” is a 30-year average of temperature and precipitation. Every 10 years, the National Center for Environmental Information updates these “normals,” most recently in 2021. The current span considered “normal” is 1991-2020. Five years ago, it was 1981-2010.
But temperatures have been rising over the past century, and the trend has accelerated since about 1980. This warming is fueled by the mining and burning of fossil fuels that increase carbon dioxide and methane in the atmosphere. These greenhouse gases trap heat close to the planet’s surface, leading to increasing temperature.
Because global temperatures are warming, what’s considered normal is warming, too.
So, when a 2025 cold snap is reported as the difference between the actual temperature and “normal,” it will appear to be colder and more extreme than if it were compared to an earlier 30-year average.
Thirty years is a significant portion of a human life. For people under age 40 or so, the use of the most recent averaging span might fit with what they have experienced.
But it doesn’t speak to how much the Earth has warmed.
How cold snaps today compare to the past
To see how today’s cold snaps – or today’s warming – compare to a time before global warming began to accelerate, NASA scientists use 1951-1980 as a baseline.
The reason becomes evident when you compare maps.
For example, January 1994 was brutally cold east of the Rocky Mountains. If we compare those 1994 temperatures to today’s “normal” – the 1991-2020 period – the U.S. looks a lot like maps of early January 2025’s temperatures: Large parts of the Midwest and eastern U.S. were more than 7 degrees Fahrenheit (4 degrees Celsius) below “normal,” and some areas were much colder.
But if we compare January 1994 to the 1951-1980 baseline instead, that cold spot in the eastern U.S. isn’t quite as large or extreme.
Where the temperatures in some parts of the country in January 1994 approached 14.2 F (7.9 C) colder than normal when compared to the 1991-2020 average, they only approached 12.4 F (6.9 C) colder than the 1951-1980 average.
As a measure of a changing climate, updating the average 30-year baseline every decade makes warming appear smaller than it is, and it makes cold snaps seem more extreme.
Conditions for heavy lake-effect snow
The U.S. will continue to see cold air outbreaks in winter, but as the Arctic and the rest of the planet warm, the most frigid temperatures of the past will become less common.
That warming trend helps set up a remarkable situation in the Great Lakes that we’re seeing in January 2025: heavy lake-effect snow across a large area.
As cold Arctic air encroached from the north in January, it encountered a Great Lakes basin where the water temperature was still above 40 F (4.4 C) in many places. Ice covered less than 2% of the lakes’ surface on Jan. 4.
That cold dry air over warmer open water causes evaporation, providing moisture for lake-effect snow. Parts of New York and Ohio along the lakes saw well over a foot of snow in the span of a few days.
The accumulation of heat in the Great Lakes, observed year after year, is leading to fundamental changes in winter weather and the winter economy in the states bordering the lakes.
It’s also a reminder of the persistent and growing presence of global warming, even in the midst of a cold air outbreak.
Richard B. (Ricky) Rood, Professor Emeritus of Climate and Space Sciences and Engineering, University of Michigan
This article is republished from The Conversation under a Creative Commons license. Read the original article.
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Last Updated on February 10, 2026 by Daily News Staff
Valerie Thomas is a true pioneer in the world of science and technology. A NASA engineer and physicist, she is best known for inventing the illusion transmitter, a groundbreaking device that creates 3D images using concave mirrors. This invention laid the foundation for modern 3D imaging and virtual reality technologies.
Beyond her inventions, Thomas broke barriers as an African American woman in STEM, mentoring countless young scientists and advocating for diversity in science and engineering. Her work at NASA’s Goddard Space Flight Center helped advance satellite technology and data visualization, making her contributions both innovative and enduring.
In our latest short video, we highlight Valerie Thomas’ remarkable journey—from her early passion for science to her groundbreaking work at NASA. Watch and be inspired by a true STEM pioneer whose legacy continues to shape the future of space and technology.
🎥 Watch the video here: https://youtu.be/P5XTgpcAoHw
Dive into “The Knowledge,” where curiosity meets clarity. This playlist, in collaboration with STMDailyNews.com, is designed for viewers who value historical accuracy and insightful learning. Our short videos, ranging from 30 seconds to a minute and a half, make complex subjects easy to grasp in no time. Covering everything from historical events to contemporary processes and entertainment, “The Knowledge” bridges the past with the present. In a world where information is abundant yet often misused, our series aims to guide you through the noise, preserving vital knowledge and truths that shape our lives today. Perfect for curious minds eager to discover the ‘why’ and ‘how’ of everything around us. Subscribe and join in as we explore the facts that matter. https://stmdailynews.com/the-knowledge/
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Beneath the Waves: The Global Push to Build Undersea Railways
Undersea railways are transforming transportation, turning oceans from barriers into gateways. Proven by tunnels like the Channel and Seikan, these innovations offer cleaner, reliable connections for passengers and freight. Ongoing projects in China and Europe, alongside future proposals, signal a new era of global mobility beneath the waves.
For most of modern history, oceans have acted as natural barriers—dividing nations, slowing trade, and shaping how cities grow. But beneath the waves, a quiet transportation revolution is underway. Infrastructure once limited by geography is now being reimagined through undersea railways.
Undersea rail tunnels—like the Channel Tunnel and Japan’s Seikan Tunnel—proved decades ago that trains could reliably travel beneath the ocean floor. Today, new projects are expanding that vision even further.
Around the world, engineers and governments are investing in undersea railways—tunnels that allow high-speed trains to travel beneath oceans and seas. Once considered science fiction, these projects are now operational, under construction, or actively being planned.
Undersea Rail Is Already a Reality
Japan’s Seikan Tunnel and the Channel Tunnel between the United Kingdom and France proved decades ago that undersea railways are not only possible, but reliable. These tunnels carry passengers and freight beneath the sea every day, reshaping regional connectivity.
Undersea railways are cleaner than short-haul flights, more resilient than bridges, and capable of lasting more than a century. As climate pressures and congestion increase, rail beneath the sea is emerging as a practical solution for future mobility.
What’s Being Built Right Now
China is currently constructing the Jintang Undersea Railway Tunnel as part of the Ningbo–Zhoushan high-speed rail line, while Europe’s Fehmarnbelt Fixed Link will soon connect Denmark and Germany beneath the Baltic Sea. These projects highlight how transportation and technology are converging to solve modern mobility challenges.
The Mega-Projects Still on the Drawing Board
Looking ahead, proposals such as the Helsinki–Tallinn Tunnel and the long-studied Strait of Gibraltar rail tunnel could reshape global affairs by linking regions—and even continents—once separated by water.
Why Undersea Rail Matters
The future of transportation may not rise above the ocean—but run quietly beneath it.
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Special Education Is Turning to AI to Fill Staffing Gaps—But Privacy and Bias Risks Remain
With special education staffing shortages worsening, schools are using AI to draft IEPs, support training, and assist assessments. Experts warn the benefits come with major risks—privacy, bias, and trust.
Seth King, University of Iowa
In special education in the U.S., funding is scarce and personnel shortages are pervasive, leaving many school districts struggling to hire qualified and willing practitioners.
Amid these long-standing challenges, there is rising interest in using artificial intelligence tools to help close some of the gaps that districts currently face and lower labor costs.
Over 7 million children receive federally funded entitlements under the Individuals with Disabilities Education Act, which guarantees students access to instruction tailored to their unique physical and psychological needs, as well as legal processes that allow families to negotiate support. Special education involves a range of professionals, including rehabilitation specialists, speech-language pathologists and classroom teaching assistants. But these specialists are in short supply, despite the proven need for their services.
As an associate professor in special education who works with AI, I see its potential and its pitfalls. While AI systems may be able to reduce administrative burdens, deliver expert guidance and help overwhelmed professionals manage their caseloads, they can also present ethical challenges – ranging from machine bias to broader issues of trust in automated systems. They also risk amplifying existing problems with how special ed services are delivered.
Yet some in the field are opting to test out AI tools, rather than waiting for a perfect solution.
A faster IEP, but how individualized?
AI is already shaping special education planning, personnel preparation and assessment.
One example is the individualized education program, or IEP, the primary instrument for guiding which services a child receives. An IEP draws on a range of assessments and other data to describe a child’s strengths, determine their needs and set measurable goals. Every part of this process depends on trained professionals.
But persistent workforce shortages mean districts often struggle to complete assessments, update plans and integrate input from parents. Most districts develop IEPs using software that requires practitioners to choose from a generalized set of rote responses or options, leading to a level of standardization that can fail to meet a child’s true individual needs.
Preliminary research has shown that large language models such as ChatGPT can be adept at generating key special education documents such as IEPs by drawing on multiple data sources, including information from students and families. Chatbots that can quickly craft IEPs could potentially help special education practitioners better meet the needs of individual children and their families. Some professional organizations in special education have even encouraged educators to use AI for documents such as lesson plans.
Training and diagnosing disabilities
There is also potential for AI systems to help support professional training and development. My own work on personnel development combines several AI applications with virtual reality to enable practitioners to rehearse instructional routines before working directly with children. Here, AI can function as a practical extension of existing training models, offering repeated practice and structured support in ways that are difficult to sustain with limited personnel.
Some districts have begun using AI for assessments, which can involve a range of academic, cognitive and medical evaluations. AI applications that pair automatic speech recognition and language processing are now being employed in computer-mediated oral reading assessments to score tests of student reading ability.
Practitioners often struggle to make sense of the volume of data that schools collect. AI-driven machine learning tools also can help here, by identifying patterns that may not be immediately visible to educators for evaluation or instructional decision-making. Such support may be especially useful in diagnosing disabilities such as autism or learning disabilities, where masking, variable presentation and incomplete histories can make interpretation difficult. My ongoing research shows that current AI can make predictions based on data likely to be available in some districts.
Privacy and trust concerns
There are serious ethical – and practical – questions about these AI-supported interventions, ranging from risks to students’ privacy to machine bias and deeper issues tied to family trust. Some hinge on the question of whether or not AI systems can deliver services that truly comply with existing law.
The Individuals with Disabilities Education Act requires nondiscriminatory methods of evaluating disabilities to avoid inappropriately identifying students for services or neglecting to serve those who qualify. And the Family Educational Rights and Privacy Act explicitly protects students’ data privacy and the rights of parents to access and hold their children’s data.
What happens if an AI system uses biased data or methods to generate a recommendation for a child? What if a child’s data is misused or leaked by an AI system? Using AI systems to perform some of the functions described above puts families in a position where they are expected to put their faith not only in their school district and its special education personnel, but also in commercial AI systems, the inner workings of which are largely inscrutable.
These ethical qualms are hardly unique to special ed; many have been raised in other fields and addressed by early-adopters. For example, while automatic speech recognition, or ASR, systems have struggled to accurately assess accented English, many vendors now train their systems to accommodate specific ethnic and regional accents.
But ongoing research work suggests that some ASR systems are limited in their capacity to accommodate speech differences associated with disabilities, account for classroom noise, and distinguish between different voices. While these issues may be addressed through technical improvement in the future, they are consequential at present.
Embedded bias
At first glance, machine learning models might appear to improve on traditional clinical decision-making. Yet AI models must be trained on existing data, meaning their decisions may continue to reflect long-standing biases in how disabilities have been identified.
Indeed, research has shown that AI systems are routinely hobbled by biases within both training data and system design. AI models can also introduce new biases, either by missing subtle information revealed during in-person evaluations or by overrepresenting characteristics of groups included in the training data.
Such concerns, defenders might argue, are addressed by safeguards already embedded in federal law. Families have considerable latitude in what they agree to, and can opt for alternatives, provided they are aware they can direct the IEP process.
By a similar token, using AI tools to build IEPs or lessons may seem like an obvious improvement over underdeveloped or perfunctory plans. Yet true individualization would require feeding protected data into large language models, which could violate privacy regulations. And while AI applications can readily produce better-looking IEPs and other paperwork, this does not necessarily result in improved services.
Filling the gap
Indeed, it is not yet clear whether AI provides a standard of care equivalent to the high-quality, conventional treatment to which children with disabilities are entitled under federal law.
The Supreme Court in 2017 rejected the notion that the Individuals with Disabilities Education Act merely entitles students to trivial, “de minimis” progress, which weakens one of the primary rationales for pursuing AI – that it can meet a minimum standard of care and practice. And since AI really has not been empirically evaluated at scale, it has not been proved that it adequately meets the low bar of simply improving beyond the flawed status quo.
But this does not change the reality of limited resources. For better or worse, AI is already being used to fill the gap between what the law requires and what the system actually provides.
Seth King, Associate Profess of Special Education, University of Iowa
This article is republished from The Conversation under a Creative Commons license. Read the original article.
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