A viral video shows sprinklers soaking a green lawn outside a denver data center during severe water restrictions.Eat more fatty fish.
That dietary advice has changed the way the world consumes salmon, pushing consumer demand ever upward and production to higher and higher levels. Most of that demand is being met with farmed salmon: Today, nearly 70 percent of the salmon consumed on the planet are raised in pens and cages.
The US Department of Agriculture’s Dietary Guidelines for Americans recommend eating at least 8 ounces of fish a week and urges people to choose salmon or other species that are similarly high in “heart-healthy” Omega-3 fatty acids. But new research led by USDA scientists finds that levels of these fatty acids in farmed salmon have dropped considerably, potentially making the agency’s own advice, and similar recommendations, including from the American Heart Association, outdated and insufficient.
© Manuel Romano/NurPhoto
For several decades, paleontologists have described Miracinonyx trumani -- an extinct species of cheetah-like felid that roamed the North American prairies and steppe terrains more than 13,000 years ago -- as North America’s answer to the African cheetah (Acinonyx jubatus).
The post Ancient DNA Rewrites Story of Enigmatic ‘American Cheetah’ appeared first on Sci.News: Breaking Science News.
Researchers tracking grunts, whimpers and laughter across two geographically separated chimpanzee populations found that vocal differences sharpen with age.
The post Young Chimps Develop Their Own Accents, New Study Finds appeared first on Sci.News: Breaking Science News.
In Boé National Park, researchers have repeatedly encountered a small number of trees marked by gouged bark and piles of stones at their bases. These signs suggest that wild chimpanzees have returned to the same trees for years to throw rocks. According to a recent field account by University of Victoria researchers Robyn Nakano and Ammie Kalan, some of these sites have remained in use for over a decade.
This behavior, called accumulative stone throwing, has been observed in only four chimpanzee populations in West Africa. While rocks and trees are widespread in chimpanzee habitats, piling stones at specific trees is rare. Nakano, Kalan, and other researchers suggest this may be a cultural practice within certain groups rather than arising from environmental factors alone.
Video recordings collected through the Pan African Programme and analyzed in a 2016 Scientific Reports study led by Hjalmar Kühl show that mostly adult male chimpanzees throw rocks at particular trees and repeatedly return to the same trunks. During these events, the chimpanzees pant-hoot, a loud call used for long-distance communication, and sometimes perform buttress drumming by repeatedly striking the tree’s buttressed base with their hands and feet.
Pant-hooting and buttress drumming are typical features of displays put on by male chimpanzees. The fact that these behaviors occur alongside stone throwing suggests that accumulative stone throwing may have developed as a variation of these existing social displays.
Researchers suspect this behavior may be cultural rather than simply a response to environmental conditions because it occurs only in certain areas, even though appropriate rocks and trees are common throughout chimpanzees’ habitats. Within one population, only particular trees are used as throwing sites. Earlier studies have suggested that the behavior might function as a form of communication or could indicate places of symbolic significance within a community’s territory.
The behavior could also offer archaeologists a rare example of how repeated stone use and accumulation can create material traces that persist after the animals that made them have moved on.
Some primates use stones as tools, including populations that use rocks to open nuts for food. Accumulative stone throwing differs because the behavior occurs in a social context rather than during extractive foraging. The purpose of this behavior for the chimpanzees remains unclear.
Nakano and Kalan conducted their fieldwork from Béli, a village where the Dutch nonprofit Chimbo operates a solar-powered research compound with local partners. The team traveled 22 kilometers by bicycle and on foot into the savanna-woodland to establish a camp, working with field assistants Djei Baldé, Balu Séra, and master’s student Taylor Tippett from the Great Ape Behaviour Lab.
The Boé chimpanzees are not used to humans and tend to avoid them, making direct observation impossible. The team instead set up two video cameras at each known stone-throwing site and placed audio recorders nearby to monitor the area.
Each day began at 6:30 a.m. with visits to two to five sites to replace SD cards and batteries, check equipment, measure trees, and create 3D scans of rocks thrown at the trees for later analysis. The team also recorded chimpanzee nests, feeding traces, and vocalizations along the trails. Their camp was located beside the Fefine River, which continues flowing through the dry season and provides an important water source for wildlife.
The team found that most sites first identified by the Pan African Programme and revisited in 2017 remained active when Nakano and Kalan’s team returned in 2026. Chimpanzees have returned to these same trees to throw rocks for more than ten years.
The video and audio collected during this trip will allow the team to identify the age and sex of individual stone-throwers and observe how nearby chimpanzees respond to these events. This data could help the researchers determine the purpose of this behavior.
Chimpanzees’ habitat in Guinea-Bissau is becoming increasingly threatened by industrial mining. During fieldwork, the researchers discovered boreholes dug to explore bauxite, the ore used to produce aluminum. Although bauxite mining might help promote economic development, comparable operations in neighboring Guinea have caused habitat loss and pollution, with negative effects on both wildlife and local communities. The country’s political instability also presents challenges for environmental oversight.
Nakano and Kalan emphasize that losing these stone-throwing sites would be both a cultural and ecological loss, erasing a documented example of primate material culture.
Nakano and Kalan are still analyzing the footage from this field season, but the main question of why chimpanzees throw stones at these specific trees remains unanswered. Still, the evidence collected by the cameras and recorders could help determine whether the behavior functions as territorial signaling, long-distance communication, or something else entirely.
Austin Burgess is a writer and researcher with a background in sales, marketing, and data analytics. He holds an MBA, a Bachelor of Science in Business Administration, and a data analytics certification. His work focuses on breaking scientific developments, with an emphasis on emerging biology, cognitive neuroscience, and archaeological discoveries.
At Earth’s poles, much of the heat leaving the planet is invisible to human eyes. NASA’s two PREFIRE CubeSats are measuring that far-infrared energy directly, and a new animation built from two complete seasonal cycles shows just how differently the Arctic and Antarctic warm and cool. The Polar Radiant Energy in the Far-InfraRed Experiment began […]
The post NASA’s PREFIRE Satellites Show How the Poles Trade Heat With Space appeared first on Curiosmos.
The economist John Maynard Keynes once remarked, “In the long run we are all dead.” From an astronomical standpoint, this is true for the Earth itself. In roughly 5 billion years, the Sun will exhaust the hydrogen at its core and enter the next phase of its life as a Red Giant, where it will swell up large enough to engulf and destroy the Earth.
Currently, the Sun is still in its main-sequence phase, where it fuses hydrogen into helium in its core. Although it remains relatively stable during this phase, it will gradually grow brighter with time. As the Sun shines brighter, the increased heat and radiation will raise Earth’s surface temperature, potentially pushing it past the threshold for life to survive.
One of Earth’s geological processes that helps regulate its surface temperature is the carbon cycle. Over millions of years, carbon moves from the Earth’s interior into the atmosphere as carbon dioxide or CO2, where it functions as a greenhouse gas that traps heat. Plants take in this CO2 through photosynthesis and move carbon through the biosphere.
Meanwhile, rain and rock weathering draw carbon into the oceans and rocks. By pulling CO2 out of the atmosphere, the carbon cycle could mitigate Earth’s warming as the Sun brightens over time. Today, human activities are increasing atmospheric CO2 and warming the planet. However, over billions of years, the carbon cycle could gradually remove enough CO2 from the atmosphere that eventually too little would remain to support photosynthesis.
With these challenges to life on Earth in mind, the researchers Jacob Haqq‐Misra and Eric Wolf investigated how long Earth’s biosphere could last in the distant future. The team used computer models to calculate the balance of energy that Earth would receive and emit over time, accounting for the effects of increasing sunlight and changing atmospheric CO2 concentrations. They followed this up with calculations using the ExoCAM model to determine the temperature and atmospheric conditions of future Earth across different latitudes. They related these conditions to a habitability metric to determine their impact on the biosphere.
To estimate the biosphere’s maximum possible lifespan, the team conducted 29 simulations representing the planet’s conditions over the next 2 billion years. In one set of simulations, the Earth’s carbon cycle contributed little to regulating surface temperature, with the atmosphere’s CO2 concentration fixed at approximately modern levels of 400 parts per million (ppm) and surface temperatures continuing to increase. In another, the carbon cycle worked to keep Earth’s average surface temperature fixed at the modern average of approximately 59℉ (15℃), no matter how much it had to reduce CO2 in the atmosphere.
In the scenario with a fixed CO2 concentration, the team found that in 1.7 billion years, Earth’s surface reached an average of 122°F (50°C), which is too hot for most land plants. After 1.9 billion years, it reached 149°F (65°), becoming too hot for all land plants to survive as temperatures continued to rise.
They also found that in the fixed average surface temperature scenario, atmospheric CO2 dropped below the 150 ppm level that most plants require for photosynthesis within 500 million years. After 1.4 billion years, CO2 levels fell below 10 ppm, leaving only a few land photosynthesizers and aquatic plants alive. After about 1.8 billion years, atmospheric CO2 dropped to 1 ppm, leaving only microbes capable of photosynthesis.
The researchers’ findings generally extend the estimated lifetime of Earth’s future biosphere compared with earlier, simpler models that suggested maximum lifespans of 100 million to 1.5 billion years. However, they clarified that other scientists should verify their results by running tests with other models, especially under extreme conditions of high incoming solar radiation and low atmospheric CO2.
Lastly, Haqq‐Misra and Wolf noted that their model did not account for major evolutionary changes that could help life adapt to future conditions. Over time, plants could evolve to tolerate higher temperatures or lower atmospheric CO2 levels. Future humans or another intelligent species might also engage in large-scale geoengineering projects to prevent the end of life on Earth. If life on Earth proves adaptable enough, the biosphere may endure until a Red Giant fills the daytime sky.
The post The long-term fate of Earth’s biosphere appeared first on Sciworthy.
Apple has increased its estimated trade-in values for most current iPhone, iPad, Mac, and Apple Watch models in the United States. The adjustments give customers slightly more credit toward a new device, although some older Apple hardware and several Android phones have moved in the opposite direction.
The updated values are maximum estimates rather than guaranteed payments. Apple calculates the final credit after confirming the model and inspecting its condition. A damaged display, enclosure problems, missing functionality, or an inaccurate description can reduce the amount offered.
Most of the increases are modest, but they arrive across several of Apple’s largest product categories at the same time. Recent iPhones receive between $10 and $25 more, every iPad category listed in Apple’s summary gains value, and several popular Macs receive larger increases.
The revisions also reinforce the role of trade-ins within Apple’s retail operation. Credit can reduce the cost of a regular purchase, be delivered through an Apple Gift Card, or lower payments at the beginning of an eligible Apple Upgrade lease.
Every iPhone 16 model included in the update receives a higher maximum estimate. The iPhone 16 Pro Max moves from $685 to $695, retaining the highest listed value among the affected iPhones.
The iPhone 16 Pro increases from $550 to $560, while the iPhone 16 Plus moves from $455 to $465. The standard iPhone 16 receives the largest increase within the family, rising by $25 from $435 to $460.
These changes narrow the difference between the standard iPhone 16 and the larger Plus model. The Pro models retain substantially higher estimates, reflecting their higher original prices and stronger position in the used-device market.
An additional $10 or $25 will not transform the cost of an upgrade, but it can offset part of a case, charger, AppleCare plan, or storage upgrade. The difference also becomes more relevant for customers comparing Apple’s direct program with a third-party reseller offering a similar amount.
Apple’s iPad adjustments are more consistent. The maximum estimate for an iPad Pro rises from $670 to $690, an increase of $20. The iPad Air moves from $445 to $460, while the standard iPad rises from $220 to $235.
The iPad mini also gains $15, moving from a maximum of $250 to $265. That means every current iPad category displayed in Apple’s trade-in summary receives a higher estimate.
Apple’s direct trade-in values are generally lower than the prices an owner might obtain through a private sale. The program instead emphasizes convenience: the device can be returned at an Apple Store or sent using Apple’s online process, with the credit connected directly to a new purchase.
A private sale can produce a better return, especially for a recent device in excellent condition. It also requires the owner to set a price, find a buyer, manage payment, and handle the transfer independently. Apple’s program trades some resale value for a simpler exchange.
Mac trade-in values receive some of the largest increases, but the category also includes notable reductions.
The MacBook Air rises from a maximum of $485 to $520, adding $35. The Mac mini receives the same increase, moving from $340 to $375. The MacBook Pro changes only slightly, increasing from $685 to $690.
Apple leaves the current iMac estimate unchanged at $355. The Mac Studio receives a larger increase from $975 to $1,045, reflecting the higher value retained by recent desktop configurations.
Older professional desktops move in the other direction. The iMac Pro falls from $325 to $315, while the top Mac Pro estimate declines from $2,090 to $2,045.
That $45 reduction at the top of the Mac range means Apple’s maximum Mac trade-in offer decreases even though several mainstream models receive more credit. The changes appear more closely connected to the resale position of individual product families than to a uniform increase across every computer.
Mac estimates can vary considerably because one model name can cover different years, processors, memory configurations, storage capacities, and physical designs. Apple asks for a serial number before producing a more specific estimate for a Mac.
The Apple Watch adjustments are similarly mixed. Apple Watch Ultra 2 rises from $295 to $305, while Apple Watch Series 9 increases from $120 to $130.
Apple Watch Series 10 remains unchanged at a maximum of $150. The original Apple Watch Ultra falls from $215 to $205, losing $10.
Wearable trade-in values can decline quickly because watches receive frequent updates and are exposed to greater physical wear than many computers or tablets. Battery condition, display damage, case wear, and working buttons can influence whether the device receives the estimated credit.
Apple also accepts devices that no longer qualify for monetary value. Those products can be returned for recycling, including older hardware and accessories that would otherwise remain unused.
Apple accepts selected smartphones from Samsung, Google, and other manufacturers when customers move to an iPhone. The latest revisions are less favorable to several Android devices.
The maximum Android estimate across Apple’s displayed range decreases from $370 to $360. Individual changes vary by model.
The Samsung Galaxy S23 Ultra drops from $230 to $200, a $30 reduction. Google’s Pixel 8 Pro declines from $170 to $165.
Other devices remain unchanged. The standard Galaxy S23 continues to carry an estimated value of up to $125, while the OnePlus 12 remains at up to $200.
Apple’s Android offers are primarily designed to remove friction for people moving to the iPhone. They do not necessarily match the amount available from the manufacturer, a carrier promotion, or a private buyer.
Carrier promotions can advertise considerably higher trade-in credits than Apple, particularly around a new iPhone launch. Those offers commonly require an eligible unlimited plan, a new line, monthly bill credits, or a commitment lasting two or three years.
Apple’s direct credit is easier to understand because it is tied to the assessed value of the device rather than a long carrier agreement. The best choice depends on whether the customer values flexibility, immediate credit, or the largest possible promotional discount.
Apple’s published figures represent the maximum available for qualifying devices in suitable condition. The online estimate is based on the answers supplied before the product is inspected.
After receiving the device, Apple or its trade-in partner verifies its identity and checks whether it powers on, functions properly, and matches the reported condition. A difference can produce a revised offer.
Customers can accept or reject a lower revised value. Rejecting it cancels the trade-in and returns the device, although the payment arrangement for the accompanying purchase may also be adjusted. If the inspected value is higher than the original estimate, Apple says it automatically increases the credit.
The way the credit is delivered depends on the purchase. When a customer pays in full for a new iPhone, iPad, Mac, or Apple Watch, the confirmed value can be returned to the original payment method up to the price of the new product. Any remaining amount can be issued through an Apple Gift Card.
With an eligible monthly payment arrangement, the estimated credit can be applied directly to the new device’s purchase price. Under Apple Upgrade, a qualifying trade-in owned by the customer can reduce payments across the initial lease term. Another device cannot be traded in each time the customer exercises the lease’s upgrade option.
Values also differ between countries and can change without a product announcement. The revised dollar amounts apply to Apple’s United States program, while local market prices, available models, currencies, and trade-in partners determine estimates elsewhere.
Anyone considering an exchange should obtain a fresh estimate shortly before purchasing. Apple requires the trade-in device to arrive within the specified return period and says an online estimate is generally protected for 14 days after the customer receives the new product.
The post Apple Raises Trade-In Values for Most iPhone, iPad, Mac, & Watch Models appeared first on AppleMagazine.
Paleontologists working in southern Wyoming have identified a previously unknown species of omomyid primate, adding a new chapter to the story of one of the best-studied fossil lineages in North America.
The post Tiny New Primate Species Emerges from 50-Million-Year-Old Wyoming Sandstone appeared first on Sci.News: Breaking Science News.
A new study is challenging one of the most comforting assumptions in urban climate design: that adding vegetation to dense cities always makes hot weather safer. Research published in npj Urban Sustainability reports that compact, high-rise urban forms can amplify humid heat risks associated with vegetation transpiration—a process usually considered an essential cooling service. The finding does not suggest that trees and plants should be removed from cities. Instead, it reveals that the success of green infrastructure depends heavily on building geometry, airflow, humidity, and the way heat and moisture move through the urban atmosphere.
Vegetation transpiration occurs when plants release water vapor through microscopic openings in their leaves. This process consumes energy and can lower leaf and surrounding air temperatures, much like evaporation from human skin. In a well-ventilated environment, the added moisture may be dispersed efficiently, allowing evaporative cooling to provide a net thermal benefit. But in tightly packed high-rise districts, the same moisture can become trapped between buildings. The result is a more humid microclimate in which the body’s ability to cool itself through sweating is weakened.
That distinction matters because humid heat is more dangerous than air temperature alone suggests. The human body relies on evaporation to transfer heat away from the skin. When atmospheric moisture is already high, sweat evaporates more slowly, forcing the body to work harder to maintain a stable internal temperature. Heat-stress indicators such as wet-bulb temperature and the Wet-Bulb Globe Temperature account for this interaction between heat and humidity. A neighborhood with slightly lower temperatures but substantially higher humidity can therefore feel more oppressive—and pose greater health risks—than a drier location with hotter air.
The study focuses on the urban “canyon” effect created by tall buildings arranged close together. These structures can obstruct wind, reduce the exchange of air with the atmosphere above, and alter the amount of sunlight reaching streets and façades. When vegetation in such spaces transpires, the released water vapor may accumulate rather than disperse. The compact geometry can also create complex circulation patterns, with warm and moist air repeatedly moving through pedestrian-level spaces. In this setting, greenery may cool surfaces while simultaneously increasing the moisture burden experienced by people outdoors.
This is a crucial refinement of the popular idea that urban greening is universally beneficial. Plants perform several valuable functions: they provide shade, intercept solar radiation, reduce surface temperatures, store carbon, and improve the visual and psychological quality of public space. Yet their cooling performance is not fixed. It changes with species, soil moisture, canopy density, irrigation, wind conditions, building height, street width, and background humidity. The new research emphasizes that these factors must be evaluated together rather than treating vegetation as an isolated solution to urban heat.
The implications are particularly significant for rapidly growing cities where vertical development is being used to accommodate rising populations. High-rise construction can reduce land consumption, but dense towers may produce localized atmospheric conditions that differ dramatically from those in surrounding neighborhoods. A cooling strategy designed for an open suburban street may behave differently in a narrow urban canyon surrounded by tall façades. Planners could therefore face a difficult balancing act: maximizing shade and ecological benefits without creating stagnant pockets where moisture and heat combine into dangerous conditions.
The findings also raise questions about how cities measure the success of climate adaptation projects. A street may record a lower surface temperature after trees are planted, yet still expose pedestrians to greater physiological stress if humidity increases and air movement declines. This means future assessments should include multiple variables, including air temperature, relative humidity, wind speed, radiant heat, and human thermal response. Satellite observations and conventional weather stations may not fully capture conditions at street level, where building walls, tree canopies, pavement, and human activity interact within a few meters.
The research is likely to intensify debate over “smart” urban greening rather than green infrastructure in general. Possible responses include selecting vegetation according to local humidity and wind conditions, designing wider or better-connected ventilation corridors, combining trees with reflective or permeable surfaces, and ensuring that shaded areas do not become poorly ventilated enclosures. Building orientation and spacing may be as important as the number of trees planted. Irrigation practices could also matter, since additional water supplied to vegetation may increase transpiration during precisely the periods when atmospheric moisture is already high.
For residents, the message is both cautionary and practical. A leafy street can still be healthier than an exposed one, particularly when shade reduces direct solar radiation. But during extreme heat, people should not assume that every green space offers the same level of protection. Dense, humid, windless areas may require additional measures such as cooling centers, drinking-water access, timed outdoor activities, and heat alerts that account for humidity. The study’s central contribution is to show that urban climate risk is not determined by temperature alone—or by the presence of greenery alone—but by the interaction between city form, plant physiology, and human vulnerability.
As climate change increases the frequency and intensity of heat extremes, these interactions could become more consequential. The research presents cities with a deceptively simple lesson: cooling the urban environment is not just a matter of adding more plants. It requires understanding where the water released by vegetation goes, how buildings control its movement, and how the resulting air feels to a human body. In the race to make cities greener, the next generation of design may need to be equally focused on keeping them breathable.
Subject of Research: The relationship between compact high-rise urban form, vegetation transpiration, humidity, and humid heat risk.
Article Title: Urban high-rise compact form amplifies humid heat risk from vegetation transpiration.
Article References: Zhang, Y., Yang, K., Zhu, Y. et al. “Urban high-rise compact form amplifies humid heat risk from vegetation transpiration.” npj Urban Sustainability (2026). https://doi.org/10.1038/s42949-026-00459-1
Image Credits: AI Generated
DOI: 10.1038/s42949-026-00459-1
Environmental Research: Climate was launched in 2022, with the aim of uniting multidisciplinary communities. With a scope that is intentionally broad, the mission of the journal is to publish innovative research examining the causes, consequences, and solutions of climate variability and change. Since our launch, we have published more than 270 articles covering physical and biogeochemical processes that shape Earth’s climate across spatial and temporal scales; the human and ecological dimensions that shape climate-related risks and impacts; and the design, analysis and evaluation of climate mitigation, adaptation, and risk-management strategies and solutions. This webinar will explore the current state of climate research, including changes in the field over the last five years since the launch of the journal, and direct us toward the future.
Moderator: Noah Diffenbaugh, Stanford University
Noah Diffenbaugh is the William Wrigley Professor and Kimmelman Family Senior Fellow in Stanford’s Doerr School of Sustainability, and the Olivier Nomellini Family University Fellow in Undergraduate Education. His research is focused on understanding what aspects of the climate system most directly and acutely impact people and ecosystems. Noah has served as a Lead Author for the Intergovernmental Panel on Climate Change (IPCC), and as editor-in-chief of the peer-review journals Geophysical Research Letters and Environmental Research: Climate. He has provided testimony and scientific expertise to federal, state and local officials. Noah is an elected Fellow of the American Geophysical Union (AGU), a recipient of the James R. Holton Award and William Kaula Award from the AGU, and has been recognized as a Kavli Fellow by the U.S. National Academy of Sciences.
Katharine Mach, University of Miami
Katharine Mach is professor and chair of the Department of Environmental Science and Policy at the University of Miami Rosenstiel School of Marine, Atmospheric, & Earth Science. During spring 2025, she was the Coleman P. Burke Distinguished Visiting Professor at the Yale School of Environment. Her research assesses climate-related risks and response options to address increased flooding, extreme heat, wildfire and other hazards. Through innovative approaches to integrating evidence, she informs effective adaptations to the risks.
Katharine was the 2020 recipient of the Piers Sellers Prize for world-leading contribution to solution-focused climate research. She has served as a chapter author or lead in the Fourth through Sixth US National Climate Assessments and the IPCC Sixth Assessment Report and as a member of the National Academies Climate Security Roundtable. She serves as co-editor-in-chief for Climate Risk Management, an editorial board member for Oxford Open Climate Change and Environmental Research: Climate, and an advisory committee member for the Aspen Global Change Institute. Across all of her research projects, she engages in relevant policy processes and explores and discusses climate risk and adaptation with the media, the private sector, nongovernmental organizations and communities.
Andrew Pershing, Climate Central
As chief programme officer, Andrew Pershing, PhD, leads Climate Central’s climate science activity. His role includes bridging primary research and media analysis to amplify critical work on climate change and climate impacts and make it accessible to audiences around the world. As the director of attribution science and climate fingerprinting, Andrew is developing tools like the Climate Shift Index to quantify how climate change has increased the likelihood or severity of conditions in the atmosphere and ocean. He works closely with communications experts at Climate Central and our partners to help the media and others to incorporate this information into their content.
Andrew has led interdisciplinary research teams to study the impact of global warming on marine ecosystems in the northwest Atlantic. More recently, his work has focused on how climate trends interact with decisions that people make and on how marine ecosystems store and process carbon. As a communicator, Andrew has deep experience working with journalists to present climate science on air and in print.
Inês Azevedo, Stanford University
Inês M Lima Azevedo is a professor in the Energy Science & Engineering department at Stanford University. She is a senior fellow at both Stanford Woods Institute for the Environment and for Precourt Institute for Energy. She has a BSc in environmental engineering (2004) and a MSc in engineering policy and management of technology from IST-Portugal, and a PhD in engineering and public policy from Carnegie Mellon University (2009). Inês is passionate about solving problems that include environmental, technical, economic and policy issues, where traditional engineering approaches play an important role but cannot provide a complete answer. In particular, she is interested in assessing how energy systems are likely to evolve, which requires comprehensive knowledge of the technologies that can address future energy needs and the decision-making process followed by various agents in the economy.
About this journal
Environmental Research: Climate explores the causes, consequences, and solutions of climate variability and change, by uniting research communities across the entire spectrum of the climate system.
Editor-in-chief: Noah Diffenbaugh Stanford University, USA
The post Environmental Research: Climate, fireside chat appeared first on Physics World.
The frequency of extreme weather events can be predicted more accurately than presently possible by combining artificial intelligence (AI) with physical climate modelling, using a protocol called rare-event sampling. That’s the conclusion of a study from researchers in the US and France. The researchers used the approach to model extreme heat events such as the one currently roasting Europe, but they believe it could also be applicable to many other extreme events in climate science.
As the global climate warms, extreme weather is becomingly increasingly deadly and difficult to predict, and quantifying the risks of such events is important for climate change adaptation and mitigation. “Very often the rarest events carry the largest impacts,” says climate physicist Amaury Lancelin of France’s Laboratory of Dynamic Meteorology.
Full global climate model simulations will need to run for an infeasibly long time to produce sufficient data for an estimate of the precise frequencies of rare events. Alexander Wikner of the University of Chicago in Illinois gives the example of an event that, on average, occurs once every century: “Each year you’re going to flip a coin that has a one-in-a-hundred chance of coming up heads,” he says. “On average, once every hundred years, you’ll get one head, but you could very easily get no heads or two or three heads.” A meaningful estimation of the frequency therefore would traditionally require around a thousand years’ worth of simulation – which is not computationally feasible in a physical model.
Deep-learning algorithms, which ignore the underlying climate physics and simply train themselves using pattern recognition, require up to 10,000 times less computing power. However, the reliability of these in modelling extremely rare events is questionable. For example, there is evidence that an AI model will not predict more extreme cyclones than it has seen in its training data, “which is a quite unnerving idea if we imagine that tropical cyclones will be getting more extreme in the future and we want to use an AI model to forecast them,” Wikner explains. They also produce no insight into the physics of the events.
Rare-event sampling (RES) refers to techniques that preferentially allocate computing resources to specific rare events of interest, thereby avoiding modelling long periods of time in which none of those events occur. The problem, of course, is that one has to have some idea what kinds of conditions increase the probability of a rare event occurring in order to know which periods to model in more detail.
In the new research, scheduled for publication in Physical Review Letters, Lancelin, Wikner and colleagues developed the AI+RES framework. An AI algorithm runs climatic simulations repeatedly and selects those that it predicts are most likely to lead to the rare event. A full climate model then simulates only these. The researchers used this protocol to compare the frequency of heatwaves at mid-latitudes, using the predictions of a relatively coarse-grained direct numerical simulation called PlaSim as the ground truth.
They found that their technique produced similar results to PlaSim with up to 1000 times lower computational resources. The researchers now hope to apply the technique to predict other types of extreme events and apply it to more sophisticated models.
“The reason we used PlaSim is that it is computationally quite cheap compared to state-of-the-art climate models so that we can actually verify that our methodology works,” says Lancelin. “Otherwise there is no way to know whether we have made a garbage prediction or not without the ground truth.” Knowing that the AI+RES model reproduces the PlaSim results correctly, however, the researchers now hope to apply computationally expensive models in situations where it was previously impossible, with confidence that the results are likely correct.
Climate scientist Robin Noyelle of ETH Zurich believes the work marks a significant contribution to ideas that were already “floating out there”. He says that, although RES has been in use for nearly ten years, selecting the best candidates for full modelling has always proved challenging: “People were using things that we thought were reasonable – I did that during my PhD – so if I want to look at hot summers, then I select on temperature.”
This approach fails for short-term extreme events, however, because of the dynamic nature of the atmosphere. The AI model itself also failed to produce good accuracy when emulating the events, but it was “basically free” in terms of the computing power required, and provided a good starting point for the physical models. “This coupling between the two ideas is really new,” Noyelle says.
The post AI-enhanced rare-event sampling helps predict extreme weather appeared first on Physics World.
Artificial intelligence (AI)-based weather models are not as good as physics-based forecasting systems at predicating extreme weather events, say researchers at the University of Geneva, Switzerland and the Karlsruhe Institute of Technology, Germany. After comparing the outputs of different models on the same dataset of extreme events, the team found that AI models systematically erred on the side of normality, underestimating temperatures for extremely hot events while overestimating them for cold ones. This could be because the models learn from what has already happened and struggle to forecast events outside their training data.
With extreme weather events growing more common and intense due to our rapidly warming climate, being able to predict them is becoming ever more important. In recent years, meteorologists have sought to address this by developing a new generation of AI models that vie with physics-based numerical weather prediction (NWP) systems in the accuracy and extent of their forecasts – at least for everyday weather events.
In the new work, researchers led by Zhongwei Zhang and Sebastian Engelke sought to understand whether AI could also be competitive when forecasting extreme weather episodes. These episodes are usually defined by variables such as wind, atmospheric pressure or temperature falling well outside norms for location and season, and are sometimes termed “black swans” or “grey swans” depending on how extreme they are.
“In the past, only relatively moderate extremes were documented,” Zhang and Engelke observe. “But given the current rate of high rate of global warming, record-breaking events now sometimes exceed previous record levels by large margins.”
That’s potentially a problem for AI, they add, because several recent studies have shown that such models come up short when asked to extrapolate beyond their training data. For example, in 20205, researchers at the US National Oceanic and Atmospheric Administration (NOAA) and the Allen Institute for Artificial Intelligence in Washington, US, found that a seasonal AI forecasting model could not predict values for the North Atlantic Oscillation, which plays a crucial role in Europe’s weather and climate. Another study found that models tend to underpredict the intensity of the most extreme storms (as measured by mean sea-level pressure) or other high-impact events such as heat waves.
To test this hypothesis, Zhang and Engelke constructed a large dataset of record-breaking events for heat, cold, and wind extremes during 2018 and 2020. This dataset included several well-known events, such as the Siberian heatwave in early 2020 and the US heatwave of August 2020, but also tens of thousands of less-heralded ones. The 2020 dataset, for example, included 162,751 heat, 32,991 cold, and 53,345 wind records spread across different seasons and climatic zones.
The researchers assessed how well the three leading deterministic AI weather models –GraphCast, Pangu-Weather (and operational variants) and Fuxi – performed when extrapolating from this dataset of record-breaking events. They then compared these models’ performance to that of the physics-based High RESolution forecast (HRES) model developed by the European Centre for Medium-Range Weather Forecasts (ECMWF), which is widely acknowledged as today’s best physics-based NWP model.
In line with previous studies, the researchers found that both GraphCast and Fuxi were better than HRES at forecasting normal weather events. However, for record-breaking temperature and wind events in 2020, the situation was reversed, with the physics-based HRES model consistently outperforming all AI models for hot and cold temperature records as well as wind speed records.
The researchers report that the most difficult aspect of the study was the sheer length of computation time required to analyse huge AI and numerical forecast datasets. To complicate things further, Zhang notes that some of the most recent AI weather models are being developed by big tech companies and are not publicly available.
As well as showing that purely data-driven AI models struggle to forecast record-breaking extreme weather events, Engelke says the team’s work also provides a protocol for systematically evaluating forecasts of extreme events. “We hope this will motivate the research community to thoroughly evaluate the next generations of AI forecasts to advance our understanding of their advantages and their limitations compared to conventional physics-based models,” he says.
In this study, which appears in Science Advances, the Geneva/Karlsruhe researchers focused on evaluating the forecasts of deterministic AI weather models. They are now doing the same for forecasts made by recent probabilistic AI weather models, which they suspect will face similar extrapolation limitations.
“We are also working on building AI models ourselves that have improved forecast performance on extreme events,” Zhang and Engelke reveal. “This might be achieved this by creating hybrid models, which are a smart combination of physical and AI-based weather models.”
The post Physics-based models still beat AI for predicting extreme weather events appeared first on Physics World.
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