Category: English

Source: The Conversation – USA – By Claudio Villanueva, Professor of Integrative Biology and Physiology, University of California, Los Angeles

Over the past few years, a new class of medications has transformed the treatment of obesity. Drugs like Ozempic, Wegovy and Mounjaro work primarily by reducing appetite, helping people eat less and feel full sooner. Their success has demonstrated something important: Body weight is biologically regulated, and targeting the right biological pathways can lead to meaningful weight loss that can help transform lives.

But appetite is only half of the equation. Your weight reflects a balance between the calories you consume through your diet and the energy you expend through movement, exercise and maintaining basic cellular function. While recent therapies have focused on controlling energy intake, scientists are increasingly turning their attention to the other side of the ledger: the tissues that burn energy.

At the center of this conversation is an organ most people misunderstand: fat. For decades, fat – also known as adipose tissue – was thought of as passive storage: a biological pantry for excess calories. Scientists now know this view is incomplete.

Fat is not just storage

White adipose tissue, the most abundant type of fat in adults, does store energy in the form of triglycerides. But it also has several other functions.

For one, white fat is a powerful endocrine organ, releasing hormones like leptin that reduce appetite, as well as adiponectin, which regulates insulin and blood sugar levels. It also cushions organs, insulates against heat loss and acts as a metabolic buffer, safely storing excess lipids that would otherwise accumulate in the liver or muscle.

When white adipose cells expand in a healthy, flexible way, they protect the body. When they become inflamed or dysfunctional, they contribute to insulin resistance, fatty liver disease and cardiovascular risk. Obesity arises from both the expansion of white adipose cells and an increase in their number.

In other words, fat is not inherently harmful. Its health impact depends on the size of adipose cells, and when they become too large, they are unable to function optimally. Increasing the number of new fat cells can sometimes improve metabolic function.

Moreover, there are additional types of fat, and they behave in different ways.

Brown fat: The cellular furnace

Unlike white fat, brown fat is specialized to burn energy. Brown adipose cells are packed with mitochondria – the tiny power plants inside cells – and contain a protein called UCP1 that allows them to convert chemical energy directly into heat. Instead of storing calories, brown fat dissipates them.

In infants, brown fat helps maintain body temperature. For years, scientists believed it largely disappeared in adulthood. But imaging studies in the late 2000s revealed that many adults retain metabolically active brown fat, particularly in the neck and upper chest.

Exposure to cold temperatures naturally triggers the brain to stimulate brown fat cells and generate heat. As energy use rises for this process, so does calorie-burning.

If activating brown fat increases energy expenditure, could it be harnessed to treat obesity?

The challenge is that human metabolism is tightly regulated. When energy expenditure increases, the body often compensates by stimulating hunger. Studies in animals – and observations in humans – show that cold exposure not only activates brown fat but also increases appetite. The brain detects the higher energy demand and signals for greater food intake.

From an evolutionary perspective, this makes sense. For our human ancestors, cold environments meant survival required more fuel. A system that failed to replace calories burned to keep you warm would have been dangerous. This homeostatic defense of body weight is powerful. It is one reason why weight loss is difficult to sustain and why increasing energy expenditure alone may not be sufficient to lose weight.

But when coupled with GLP-1 drugs that suppress appetite, promoting energy expenditure could lead to therapies that are even more powerful at promoting weight loss.

Beige fat and metabolic plasticity

Adding further complexity to fat’s role in weight loss are beige fat cells. These cells arise within white fat depots under certain conditions – such as cold exposure or specific hormonal signals – and acquires some of the heat-producing properties of brown fat. This process, often called browning, reveals that adipose tissue is remarkably flexible.

Fat is not a static mass. It contains stem and progenitor cells capable of generating new adipocytes with distinct properties. That flexibility opens intriguing therapeutic possibilities: Instead of merely shrinking fat, could researchers reprogram it to become something else?

Researchers like me are exploring ways to safely enhance the heat-generating capacity of fat cells, potentially increasing energy expenditure without relying solely on environmental cold. Brown and beige fat are compelling targets because they are purpose-built for heat production, which is why my lab is focusing on harnessing them to treat metabolic disease.

But fat is not the only tissue in the body that consumes energy or can generate heat in the cold. Skeletal muscle accounts for a substantial portion of daily energy expenditure, particularly during activity. The liver continuously engages in metabolically expensive processes. Even subtle futile cycles – processes in which molecules are repeatedly built and broken down – consume energy and generate heat.

The future of metabolic therapeutics for weight loss may involve carefully increasing energy flux across multiple tissues. The challenge is doing so without triggering compensatory hunger or unintended side effects. Any intervention that dramatically raises metabolic demand risks being interpreted by the brain as a threat to survival.

A two-sided strategy to maximize weight loss

The success of GLP-1–based medications has demonstrated that targeting appetite pathways can overcome some of the body’s resistance to weight loss. The next generation of therapies may build on that foundation.

One possibility is combining medications that modulate appetite with interventions that enhance energy expenditure. By influencing both sides of the energy balance equation – intake and output – it might be possible to achieve more durable metabolic improvements.

Equally important is shifting the public narrative. Fat is not merely an enemy to eliminate. It is a dynamic, multifunctional organ that protects, communicates, adapts and, under the right conditions, burns energy.

Understanding that complexity moves society beyond simplistic views of weight regulation. It also points toward a future in which therapies are not just about eating less, but about strategically harnessing the body’s own metabolic machinery.

The era of appetite control has begun. I believe the era of precision energy expenditure will be next.

Claudio Villanueva receives funding from National Institutes of Health.

– ref. Fat cells burn energy to make heat – making them the next frontier of weight loss therapies – https://theconversation.com/fat-cells-burn-energy-to-make-heat-making-them-the-next-frontier-of-weight-loss-therapies-277596

Source: The Conversation – USA – By Jon R. Lindsay, Associate Professor of Cybersecurity and Privacy and of International Affairs, Georgia Institute of Technology

The U.S. military was able “to strike a blistering 1,000 targets in the first 24 hours of its attack on Iran” thanks in part to its use of artificial intelligence, according to The Washington Post. The military has used Claude, the AI tool from Anthropic, combined with Palantir’s Maven system, for real-time targeting and target prioritization in support of combat operations in Iran and Venezuela.

While Claude is only a few years old, the U.S. military’s ability to use it, or any other AI, did not emerge overnight. The effective use of automated systems depends on extensive infrastructure and skilled personnel. It is only thanks to many decades of investment and experience that the U.S. can use AI in war today.

In my experience as an international relations scholar studying strategic technology at Georgia Tech, and previously as an intelligence officer in the U.S. Navy, I find that digital systems are only as good as the organizations that use them. Some organizations squander the potential of advanced technologies, while others can compensate for technological weaknesses.

Myth and reality in military AI

Science fiction tales of military AI are often misleading. Popular ideas of killer robots and drone swarms tend to overstate the autonomy of AI systems and understate the role of human beings. Success, or failure, in war usually depends not on machines but the people who use them.

In the real world, military AI refers to a huge collection of different systems and tasks. The two main categories are automated weapons and decision support systems. Automated weapon systems have some ability to select or engage targets by themselves. These weapons are more often the subject of science fiction and the focus of considerable debate.

Decision support systems, in contrast, are now at the heart of most modern militaries. These are software applications that provide intelligence and planning information to human personnel. Many military applications of AI, including in current and recent wars in the Middle East, are for decision support systems rather than weapons. Modern combat organizations rely on countless digital applications for intelligence analysis, campaign planning, battle management, communications, logistics, administration and cybersecurity.

Claude is an example of a decision support system, not a weapon. Claude is embedded in the Maven Smart System, used widely by military, intelligence and law enforcement organizations. Maven uses AI algorithms to identify potential targets from satellite and other intelligence data, and Claude helps military planners sort the information and decide on targets and priorities.

The Israeli Lavender and Gospel systems used in the Gaza war and elsewhere are also decision support systems. These AI applications provide analytical and planning support, but human beings ultimately make the decisions.

The long history of military AI

Weapons with some degree of autonomy have been used in war for well over a century. Nineteenth-century naval mines exploded on contact. German buzz bombs in World War II were gyroscopically guided. Homing torpedoes and heat-seeking missiles alter their trajectory to intercept maneuvering targets. Many air defense systems, such as Israel’s Iron Dome and the U.S. Patriot system, have long offered fully automatic modes.

Robotic drones became prevalent in the wars of the 21st century. Uncrewed systems now perform a variety of “dull, dirty and dangerous” tasks on land, at sea, in the air and in orbit. Remotely piloted vehicles like the U.S. MQ-9 Reaper or Israeli Hermes 900, which can loiter autonomously for many hours, provide a platform for reconnaissance and strikes. Combatants in the Russia-Ukraine war have pioneered the use of first-person view drones as kamikaze munitions. Some drones rely on AI to acquire targets because electronic jamming precludes remote control by human operators.

But systems that automate reconnaissance and strikes are merely the most visible parts of the automation revolution. The ability to see farther and hit faster dramatically increases the information processing burden on military organizations. This is where decision support systems come in. If automated weapons improve the eyes and arms of a military, decision support systems augment the brain.

Cold War era command and control systems anticipated modern decision support systems such as Israel’s AI-enabled Tzayad for battle management. Automation research projects like the United States’ Semi-Automatic Ground Environment, or SAGE, in the 1950s produced important innovations in computer memory and interfaces. In the U.S. war in Vietnam, Igloo White gathered intelligence data into a centralized computer for coordinating U.S. airstrikes on North Vietnamese supply lines. The U.S. Defense Advanced Research Projects Agency’s strategic computing program in the 1980s spurred advances in semiconductors and expert systems. Indeed, defense funding originally enabled the rise of AI.

Organizations enable automated warfare

Automated weapons and decision support systems rely on complementary organizational innovation. From the Electronic Battlefield of Vietnam to the AirLand Battle doctrine of the late Cold War and later concepts of network-centric warfare, the U.S. military has developed new ideas and organizational concepts.

Particularly noteworthy is the emergence of a new style of special operations during the U.S. global war on terrorism. AI-enabled decision support systems became invaluable for finding terrorist operatives, planning raids to kill or capture them, and analyzing intelligence collected in the process. Systems like Maven became essential for this style of counterterrorism.

The impressive American way of war on display in Venezuela and Iran is the fruition of decades of trial and error. The U.S. military has honed complex processes for gathering intelligence from many sources, analyzing target systems, evaluating options for attacking them, coordinating joint operations and assessing bomb damage. The only reason AI can be used throughout the targeting cycle is that countless human personnel everywhere work to keep it running.

AI gives rise to important concerns about automation bias, or the tendency for people to give excessive weight to automated decisions, in military targeting. But these are not new concerns. Igloo White was often misled by Vietnamese decoys. A state-of-the-art U.S. Aegis cruiser accidentally shot down an Iranian airliner in 1988. Intelligence mistakes led U.S. stealth bombers to accidentally strike the Chinese embassy in Belgrade, Serbia, in 1999.

Many Iraqi and Afghan civilians died due to analytical mistakes and cultural biases within the U.S. military. Most recently, evidence suggests that a Tomahawk cruise missile struck a girls school adjacent to an Iranian naval base, killing about 175 people, mostly students. This targeting could have resulted from a U.S. intelligence failure.

Automated prediction needs human judgment

The successes and failures of decision support systems in war are due more to organizational factors than technology. AI can help organizations improve their efficiency, but AI can also amplify organizational biases. While it may be tempting to blame Lavender for excessive civilian deaths in the Gaza Strip, lax Israeli rules of engagement likely matter more than automation bias.

As the name implies, decision support systems support human decision-making; AI does not replace people. Human personnel still play important roles in designing, managing, interpreting, validating, evaluating, repairing and protecting their systems and data flows. Commanders still command.

In economic terms, AI improves prediction, which means generating new data based on existing data. But prediction is only one part of decision-making. People ultimately make the judgments that matter about what to predict and how to use predictions. People have preferences, values and commitments regarding real-world outcomes, but AI systems intrinsically do not.

In my view, this means that increasing military use of AI is actually making humans more important in war, not less.

Jon R. Lindsay does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

– ref. US military leans into AI for attack on Iran, but the tech doesn’t lessen the need for human judgment in war – https://theconversation.com/us-military-leans-into-ai-for-attack-on-iran-but-the-tech-doesnt-lessen-the-need-for-human-judgment-in-war-277831

Source: The Conversation – USA – By André O. Hudson, Dean of the College of Science, Professor of Biochemistry, Rochester Institute of Technology

Tensions in the Middle East often trigger concerns about rising gasoline prices. But disruptions to oil supplies could affect much more than the cost of filling up a car. That’s because crude oil is not only burned as fuel. It is also the raw material for thousands of products that modern societies depend on, including plastics, fertilizers, clothing fibers, medicines and electronics.

As a biochemist, I’m interested in how certain chemicals can shape society, and oil is a prime example.

The stakes become clearer when looking at the Strait of Hormuz, a narrow waterway between Iran and Oman. About one-fifth of the world’s petroleum liquids consumption passes through the strait each day, making it one of the most important oil shipping routes on Earth. If conflict significantly disrupts traffic there, the effects could ripple far beyond energy markets.

Oil is a chemical starting point

Crude oil is a complex mixture of hydrocarbons – molecules made mainly of carbon and hydrogen. Refineries and chemical plants separate and transform these molecules into smaller chemical building blocks known as petrochemicals.

Some of the most important petrochemical building blocks include chemicals such as ethylene, propylene and benzene. Manufacturers can then convert these building blocks into more complex forms, which make up plastics, solvents, synthetic rubber and other industrial materials.

While fuel is a well-known product, fuels actually represents only a portion of what is produced from crude oil. The refining process generates a wide range of petroleum-based materials used to manufacture everyday items, such as plastics, medicines, electronics, cosmetics, clothing fibers and household goods.

Plastics that shape modern life

One of the most visible uses of oil is the production of plastics. Scientists can link individual petrochemical molecules to form polymers, which are long chains of repeating units that create materials such as polyethylene, polypropylene and polystyrene.

Because plastics are lightweight, durable and relatively inexpensive, they have become essential to global manufacturing.

These plastics appear in countless products, including food packaging and water bottles; medical equipment, such as syringes and IV bags; electronics casings and appliances; automotive parts; and construction materials, such as pipes and insulation.

Even technologies designed to reduce carbon emissions depend on them. Wind turbines, solar panels and electric vehicles all contain plastic components derived from petrochemicals.

Fertilizer that feeds billions

Oil and natural gas also play a critical role in agriculture. Modern fertilizers rely on nitrogen compounds, such as ammonia. Ammonia is produced through the Haber-Bosch process, which uses hydrogen typically derived from natural gas or other fossil fuels.

These fertilizers replenish nutrients in soil and dramatically increase crop yields. Without them, global food production would be far lower. Petrochemicals are also used to produce pesticides, herbicides and plastics used in irrigation systems and agricultural equipment.

Clothing, cosmetics and medicines

Petrochemicals also appear in many everyday consumer goods. Synthetic fabrics, such as polyester, nylon and acrylic, are made from petrochemical feedstocks. These feedstocks are the basic chemicals, made from crude oil or natural gas, that serve as the starting ingredients for products widely used in clothing, carpets and furniture.

Petroleum-derived ingredients are also common in cosmetics and personal care products. Certain lotions, shampoos and lipsticks rely on these compounds because they help stabilize formulas and extend shelf life.

Petrochemicals are also important in medicine. Petroleum-derived chemical intermediates − compounds made during the process of turning raw materials into a final product − are used to manufacture pharmaceuticals, medical tubing, sterile packaging and disposable gloves.

These materials help hospitals maintain sterility and safety in health care environments.

Why the Strait of Hormuz matters

Because oil and petrochemical feedstocks move through global shipping routes, disruptions in one region will affect supply chains worldwide. The Strait of Hormuz is particularly important. If conflict or political tensions continue to interrupt shipping through the Strait, oil prices will rise quickly. Energy analysts have long warned that disruptions to the strait could send shock waves through global markets. The impact would not be limited to transportation fuels.

Petrochemical industries depend on steady supplies of crude oil and natural gas liquids as raw materials. If those supplies become more expensive or harder to obtain, manufacturers could face higher production costs.

The proportion of crude oil used for petrochemical feedstocks to create plastics, fertilizers and other materials represents around 10% to 20% of oil consumption. Most crude oil is refined for fuel production, including gasoline, diesel and jet fuel, so these fuel supply chains would likely be the first to take a hit. But over time, disruptions could affect the availability and price of products ranging from plastics and packaging to fertilizers, synthetic clothing fibers and even food.

A hidden foundation of modern economies

Because petrochemicals are often used behind the scenes as ingredients rather than finished products, the connection many agricultural, medical and consumer goods have to oil is easy to overlook. Yet, petrochemicals form a hidden foundation for modern economies. They enable large-scale agriculture, advanced health care systems and global manufacturing supply chains.

At the same time, concerns about climate change and plastic pollution are driving research into alternatives. Scientists are developing bio-based plastics made from plant materials, improving recycling technologies and exploring new ways to produce fertilizers with lower carbon emissions.

For now, the modern world remains deeply dependent on oil, not only for energy but also for the materials that shape everyday life. When news headlines focus on disruptions to oil supply, the consequences may extend far beyond the gas pump, affecting the products that underpin modern society.

André O. Hudson receives funding from the National Science Foundation and the National Institutes of Health

– ref. Oil isn’t just fuel: Iran conflict could disrupt markets for everything from plastics to fertilizers – https://theconversation.com/oil-isnt-just-fuel-iran-conflict-could-disrupt-markets-for-everything-from-plastics-to-fertilizers-277946

Source: The Conversation – USA – By Arryn Robbins, Assistant Professor of Psychology, University of Richmond

Even with no fur in frame, you can easily see that a photo of a hairless Sphynx cat depicts a cat. You wouldn’t mistake it for an elephant.

But many artificial intelligence vision systems would. Why? Because when AI systems learn to categorize objects, they often rely on visual cues – like surface texture or simple patterns in pixels. This tendency makes them vulnerable to getting confused by small changes that have little effect on human perception.

A vision system aligned more closely with human perception – one that perhaps emphasizes shape, for instance – might still confuse the cat for another similarly shaped mammal, like a tiger; but it is unlikely to indicate an elephant.

The kinds of mistakes an AI makes reveal how it organizes visual information, with potential limitations that become concerning in higher-stakes settings.

Imagine an autonomous vehicle approaching a vandalized stop sign. While a human driver recognizes the sign from its shape and context, an AI that relies on pixel patterns may misclassify it, pushing the altered sign out of the category “sign” altogether and into a different group of images that it identifies as similar, such as a billboard, advertisement or other roadside object.

Together, these problems point to a misalignment between how humans perceive the visual world and how AI represents it.

We are experts in visual perception, and we work at the intersection of human and machine perception. People organize visual input into objects, meaning and relationships shaped by experience and context. AI models don’t organize visual information the same way. This key difference explains why AI sometimes fails in surprising ways.

Seeing objects, not features

Imagine that in front of you is a small, opaque object with both straight and curved edges. But you don’t see those features; you just see your coffee mug.

Vision isn’t a camera, passively recording the world. Instead, your brain rapidly turns the light your eyes absorb into objects you recognize and understand, organizing experience into structured mental representations.

Researchers can understand how these representations are structured by examining how people judge similarity. Your coffee mug is not like your computer, but it’s similar to a glass of water despite differences in appearance. That judgment reflects how the mug is mentally represented: not just in terms of appearance, but also what the mug is used for and how it fits into everyday activities.

Importantly, the mental organization of representations is flexible. Which aspects of an object stand out change with context and goals. If packing a moving box, shape and size matter most, so your mug might be placed anywhere it fits. But when putting it away in a cupboard, it goes next to other drinkware. The mug hasn’t changed, only the way it is organized in your mind.

Human visual perception is adaptive, driven by meaning and tied to how we interact with the world.

Aligning AI with humans

AI systems, however, organize visual input in fundamentally different ways than people – not because they are machines, but because of how narrowly they are trained. When an AI is trained to categorize a cat or an elephant, it only needs to learn which visual patterns lead to the correct label, not how those animals relate to each other or fit into the broader world.

In contrast, humans learn within a broader context. When we learn what an elephant is, we weave that representation into the tapestry of everything else we have learned: animals, size, habitats and more. Because AI is graded only on label accuracy, it can rely on shortcuts that work in training but sometimes fail in the real world.

The issue of representational alignment refers to whether AI organizes information in ways that resemble how people do. It’s not to be confused with value alignment, which refers to the challenge of making sure AI systems pursue outcomes and goals that humans intend.

Because human learning embeds new information into a web of prior knowledge, the relationships between new and existing concepts can be studied and measured. This means that representational alignment may be a solvable problem and a step toward addressing broader alignment challenges.

One approach to representational alignment focuses on building AI systems that behave like humans on psychological tasks, allowing researchers to compare representations directly. For example, if people judge a cat as more similar to a dog than to an elephant, the goal is to build AI models that arrive at those same judgments.

One promising technique involves training AI on human similarity judgments collected in the lab. In these studies, human participants might be shown three images and asked which two objects are more similar; for example, whether a mug is more like a glass or a bowl. Including this data during training encourages AI systems to learn how objects relate to one another, producing representations that better reflect how people understand the world.

Alignment beyond vision

Representational alignment matters beyond vision systems, and AI researchers are taking notice. As AI increasingly supports high-stakes decisions, differences between how machines and humans represent the world will have real consequences, even when an AI system appears highly accurate. For example, if an AI analyzing medical images learns to associate the source of an image or repeated image artifacts with disease rather than the real visual signs of the disease itself, that is obviously problematic.

AI doesn’t necessarily need to process information exactly the way people think, but training AI using principles drawn from human perception and cognition – such as similarity, context and relational structure – can lead to safer, more accurate and more ethical systems.

Eben W. Daggett receives funding from the NMSU Institute for Applied Practice in AI and Machine Learning (IAAM). He is currently employed by Medtronic PLC.

Michael Hout has received funding from the New Mexico State University Institute for Applied Practice in Artificial Intelligence and Machine Learning.

Arryn Robbins does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

– ref. AI doesn’t ‘see’ the way that you do, and that could be a problem when it categorizes objects and scenes – https://theconversation.com/ai-doesnt-see-the-way-that-you-do-and-that-could-be-a-problem-when-it-categorizes-objects-and-scenes-271481

Source: The Conversation – USA (2) – By Allie Mazurek, Engagement Climatologist and Researcher, Colorado Climate Center, Colorado State University

Curious Kids is a series for children of all ages. If you have a question you’d like an expert to answer, send it to CuriousKidsUS@theconversation.com.

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Why do we see snow on mountaintops that are closer to the Sun but not near the ground? – Ms. Drews’ third grade class, Beechview Elementary School, Farmington Hills, Michigan

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There’s not much better than a bluebird day in the mountains – a crisp, sunny day accompanied by a fresh blanket of snow. But why doesn’t the Sun quickly melt all that high altitude snow away?

It all boils down to our atmosphere, which is what I research as a scientist in Colorado. Let’s dive in!

Our atmosphere: Earth’s armor

Earth’s atmosphere begins right at its surface and extends to outer space, and it is filled with a mixture of many different gases. Gases in the atmosphere include the oxygen we breathe and the water vapor that makes it rain and snow. They are essential to supporting life on Earth in several ways.

One of the most important jobs those gases have is to protect us from harmful things in space, including our closest star: the Sun.

The Sun’s radiation provides heat to our planet, but too much of it can be a problem. If you’ve ever gotten a sunburn, then you’re already familiar with this idea.

Some of our atmospheric gases limit the amount of radiation from the Sun that can reach the Earth’s surface by absorbing some of it, which prevents temperatures from being way too warm in the daytime. At night, certain atmospheric gases also trap some of the heat that the Earth’s surface releases as it cools down, protecting us from unsurvivable cold.

The way the atmosphere regulates Earth’s temperatures is known as the greenhouse effect. You’ll often hear this term used alongside climate change or global warming. That is because global warming is caused by enhancing the greenhouse effect: As people burn fossil fuels in cars and factories, the amount of greenhouse gases in the atmosphere increases. These extra gases allow the Earth’s atmosphere to trap more heat, causing an increase in temperatures.

The atmosphere likes to stay grounded

If you were to compare the Earth’s atmosphere along a Caribbean beach to that surrounding the top of Mount Everest, it would look quite different.

That is because as you go higher up in the atmosphere, it gets “thinner,” meaning that there are less gases present at higher elevations and altitudes.

Why? Blame it on gravity.

In the same way that gravity keeps people and objects from flying away to outer space, Earth’s gravitational force pulls on the gases in our atmosphere, trying to keep them as close to Earth as possible.

As a result, there are fewer gas molecules in the atmosphere as you go higher up in altitude, making the air thinner, or less dense. Humans can sometimes experience altitude sickness at high elevations because there is less oxygen present in the air as a result of this phenomenon.

Closer to the Sun, but still cold and snowy?

Our high-elevation mountains protrude into higher altitudes of the atmosphere, where the air has fewer gas molecules. While this thinner air allows more of the Sun’s radiation to pass through compared with the atmosphere at sea level, thinner air tends to be colder for two reasons:

First, collisions between gas molecules generate heat, and if you have fewer molecules available to run into each other, that heat generation is lower.

Second, a thinner atmosphere is less effective at maintaining heat because there are fewer molecules available to trap and hold on to heat.

Colder temperatures can create more opportunities for precipitation to fall in the form of snow rather than rain, which is why some mountains can be so snowy.

And if the ground is habitually covered in snow, as is the case in many mountain ranges, it can be even easier to maintain cooler temperatures. That’s because snow-covered surfaces are very reflective, making them highly effective at causing the Sun’s incoming rays to bounce back toward space instead of getting absorbed by the ground.

So if you visit the mountains to have fun in the snow, be sure to pack your jacket, but don’t forget that sunscreen too.

Allie Mazurek does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

– ref. Why do mountaintops stay snowy even when it’s dry at the base? – https://theconversation.com/why-do-mountaintops-stay-snowy-even-when-its-dry-at-the-base-277560