Beneath every step we take, soils hold one of the planet’s largest reservoirs of carbon, a vast, slow-moving bank of organic matter that quietly regulates how much of the world’s carbon stays locked away and how much escapes into the atmosphere as greenhouse gas. Now a sweeping new review warns that climate-driven extremes, droughts, floods, and wildfires, are rewriting the rules of that subterranean economy in ways that conventional models and land-management practices have not yet caught up with. The analysis, led by environmental scientist Nanthi Bolan of the University of Western Australia and colleagues, including a corresponding authorship by Bolan himself, synthesizes a substantial body of global evidence on how extreme weather reshapes carbon inputs to soil, microbial processing, erosion, and the sequestration of carbon that underpins soil health, water retention, nutrient cycling, and agricultural productivity.

The work, published in the journal Carbon Research, is notable for the scale of its underlying evidence base. On 14 January 2026, the authors searched Google Scholar, PubMed, and Web of Science using terms spanning climate change, extreme weather, soil carbon, microbial activity, decomposition, erosion, dissolved organic matter, and leaching. The initial sweep returned 14,218 records; after removing duplicates and screening for eligibility, 3,754 articles remained for bibliometric and qualitative analysis. From those, 187 publications were selected for detailed synthesis based on relevance, methodological rigor, and the substantive quality of their findings. Keyword co-occurrence patterns were mapped with VOSviewer, a bibliometric tool that reveals the intellectual architecture of a research field. The result is a critical review grounded in structured literature screening rather than new experimental data, but its synthesis offers one of the most comprehensive maps yet of how extremes rearrange the soil carbon cycle.

The review’s central finding is that no two extremes act alike. Drought, the most widespread disturbance, operates largely through the plant side of the equation. When rainfall fails, photosynthesis slows, plant biomass production falls, and the total flux of carbon entering soils through roots, residues, and litter declines. There is a nuance here: under moderate water stress, plants may temporarily shift a greater proportion of their fixed carbon belowground, investing in roots to chase moisture. But prolonged drought overwhelms that compensatory response. Microbial biomass shrinks, litter decomposition stalls, and the living engine of the soil carbon cycle idles. Then comes the deceptive twist: when rain finally returns, rewetting triggers a rapid pulse of carbon dioxide as starved soil microbes seize upon the accumulated substrates left behind by months of suspended activity. That post-drought CO₂ flush, the review emphasizes, can erase much of the apparent “savings” that accumulated during the dry period. Across the assembled evidence, repeated and multi-year drought was consistently associated with declining soil organic carbon, with grasslands and arable croplands showing particularly pronounced losses, ecosystems that happen to anchor much of the world’s food production.

Flooding tells the opposite story, but it ends in many of the same places. Water saturation chokes off oxygen diffusion through soil pores, forcing microbial metabolism into anaerobic pathways. That shift changes everything downstream. Prolonged waterlogging can drive the chemical reduction of iron oxyhydroxides, minerals that in oxygenated conditions act as microscopic glues binding organic carbon to soil particles. When those minerals dissolve, the carbon they held is released as dissolved organic carbon, which can then be stripped away by erosion and leaching and exported to rivers, lakes, and ultimately the ocean. The review stresses, however, that flooding is not unambiguously a carbon loss story. Wetlands and other periodically inundated ecosystems can accumulate and retain enormous carbon stocks precisely because water constrains decomposition. Whether a flood event releases carbon or banks it depends on the interplay between plant inputs, oxygen availability, and hydrological transport, a delicate balance that shifting rainfall regimes are actively destabilizing.

Wildfire is the most dramatic and immediately visible of the three forces, and its effects cascade on multiple timescales. Combustion directly strips vegetation, litter, and soil organic matter, transferring carbon to the atmosphere in hours. But fire also rearranges the physical and biological landscape of the soil in subtler ways: it can increase erosion by removing protective ground cover, alter soil wettability in ways that change hydrological behavior, restructure microbial communities, and expose previously protected carbon, once shielded inside aggregates or mineral associations, to fresh decomposition. There is a paradoxical bright spot. Pyrogenic carbon, the charcoal-like residue of incomplete combustion, is chemically recalcitrant and can persist in soils for centuries or millennia, functioning as a long-term carbon sink. Whether a fire leaves a landscape as a net carbon source or stabilizes some of its carbon in pyrogenic form depends on fire severity, soil moisture, fuel load, ecosystem type, and the trajectory of post-fire recovery. The review is clear that this balance cannot be predicted from fire alone; it is contingent, site by site, on conditions that vary widely across the globe.

Perhaps the most consequential theme running through the synthesis is that carbon responses cannot be inferred from the type of extreme event alone. Land use, soil mineralogy, hydrology, salinity, vegetation composition, microbial community makeup, and the timing of events within a season all determine whether carbon ends up stored, mobilized, or released. Iron and aluminum oxyhydroxides and clay minerals, for instance, provide mineral protection that can shield organic matter even under stress, while sandy or low-clay soils offer little such refuge. Managed croplands respond differently from native grasslands; saline coastal soils behave differently from acidic forest soils. And compound events, drought followed by wildfire, or alternating drought and flooding in rapid succession, may produce effects that are qualitatively different from those of isolated disturbances, as when a dried landscape burns more intensely, or when post-fire soils, stripped of vegetation, wash away in the first heavy rain. The review argues that the climate science and soil science communities have largely studied these disturbances one at a time, while the real world increasingly delivers them in combination.

The implications for climate mitigation and adaptation are substantial. Soil organic carbon is frequently invoked in national climate pledges and voluntary carbon markets as a bankable sink, yet the review suggests that the security of that bank depends heavily on how often and how severely extremes strike. If multi-year droughts steadily drain carbon from grasslands and croplands, or if fires repeatedly reset accumulation cycles, the effective permanence of soil carbon storage becomes far more fragile than many accounting frameworks assume. Conversely, protecting and rebuilding soil carbon delivers benefits well beyond climate: it improves water retention in drought-prone regions, supports nutrient cycling, stabilizes soil structure against erosion, and sustains the microbial diversity on which fertility depends. Carbon stewardship, in other words, is not separable from food security or ecosystem resilience; it is the same problem viewed from different angles.

The authors are candid about the limits of the current evidence base. The article does not report newly generated datasets and does not present a dedicated limitations section, and its conclusions rest on a qualitative synthesis of selected publications. Consequently, they call for improved and more consistent evidence across soil types, land uses, and both terrestrial and aquatic environments, and across multiple temporal scales, from the instantaneous CO₂ pulse after rewetting to the multi-decadal trajectory of carbon storage. Standardizing measurements of carbon pools, microbial functions, and event intensity, they argue, would make results comparable across ecosystems and enable more confident generalization, something the field currently struggles to achieve given heterogeneous methods and site-specific reporting.

Looking forward, the review sketches a research agenda that is as technologically ambitious as it is mechanistically focused. The authors recommend integrating high-resolution remote sensing with ground observations, advanced spectroscopy, molecular analyses, and integrated modeling to track carbon as it moves through soils and into waterways. They single out several priorities: characterizing microbial genomic and functional diversity as it responds to stress, quantifying carbon stoichiometry, probing the interactions between organic matter and iron and aluminum oxyhydroxides, understanding clay-mineral protection mechanisms, tracking long-term storage outcomes, and systematically studying the sequencing of compound extreme events. Each of these threads addresses a gap in the mechanistic chain that links a weather event in the atmosphere to a molecule of carbon, stabilized or released, in the soil.

What emerges from the review is a picture of the soil carbon cycle not as a stable background process but as a dynamic, contested system whose behavior under climate extremes is far less predictable than once assumed. The same storm that floods a rice paddy and exports carbon to a river delta may, a few hundred kilometers away, end a drought and briefly green a grassland, with opposite consequences for the atmosphere. As extremes grow more frequent and more compound, the review makes the case that understanding, and safeguarding, the carbon held beneath our feet will require science that is as interconnected and as restless as the weather itself.

Keywords

soil organic carbon, extreme weather, drought, flooding, wildfire, carbon sequestration, microbial activity, dissolved organic carbon, erosion, pyrogenic carbon, climate change, soil health

Proxima Fusion said Wednesday it plans to build a €140 million ($162.6 million) factory to produce fusion-grade high-temperature superconducting (HTS) tape, which will provide the startup with key component to its reactor design.

The four-year agreement will cover rice farms across three Indian states, reaching about 100,000 hectares at peak delivery.

What will the weather be like tomorrow, or next week or, indeed, in a month’s time? Today’s numerical weather predictions can easily answer the first two parts of this question, but they fall short beyond about 14 days. New work on predictability limits by scientists at the University of Miami and the NOAA Cooperative Institute for Marine and Atmospheric Studies (CIMAS) say that this maximum could be extended to around 129 days – but probably no further. Whether this limit can be reached in practice, however, will depend on future improvements in forecasting technologies, they say.

Numerical weather prediction was developed in the late 1940s. Indeed, it was one of the first major applications of electronic computers. As these have advanced, so have the predictions. Indeed, thanks to ever more sophisticated mathematical models capable of analysing increasing amounts of observational data, we can now predict, for example, when hurricanes will occur as far as eight days in advance – something that was deemed impossible even a few decades ago. But how far ahead can such predictions be made?

Previous attempts to determine this predictability limit have largely relied on analysing the behaviour of very small perturbations in the atmosphere. These tiny errors, however, are inaccessible either observationally or via numerical modelling, explains Zoltan Toth of the NOAA who led this new study together with his colleague Wei Zhang. “These past studies therefore had to make some assumptions about the behaviour of the small errors, and these assumptions are necessarily somewhat arbitrary.”

Analysing the energetic balance of the atmosphere

The method developed by Toth, Zhang and their colleagues makes no reference to this error behaviour and instead goes back to analysing the energetic balance of the atmosphere, which continually absorbs solar radiation on the molecular level. In their approach, the researchers began by considering how our planet’s atmosphere would behave as a closed system, the deterministic dynamics of which preserves information about its initial state, assumed to be perfectly known. In theory, such a hypothesis would allow forecasts of the atmosphere’s behaviour right out to infinity. This is not possible in reality, they explain, because the atmosphere is obviously not a closed system: it receives and emits radiation. In the real atmosphere, the phases of photons in sunlight are completely random and cannot be determined, injecting quantum-scale uncertainty into the atmosphere.

These unknown quantum characteristics act as noise and destroy any retained information, beginning first of all on the smallest scales, explain the researchers. As these scales become bigger, noise eventually affects all parts of the system. When the total energy in the atmosphere is entirely replaced, the ability to make any prediction is completely lost.

“Using the relatively well-measured quantities of total energy in the atmosphere and the incoming and outgoing solar radiation fluxes at its upper boundary, we estimate that the range of skilful forecasts could potentially be extended from 14 days as at present to 129 (±7) days at the most,” explains Toth.

Very different from the mainstream discourse

“This result is very different from the mainstream discourse about atmospheric predictability, which to this day is strongly influenced by early publications in the field,” adds Toth. “More recently even, the authors of a 2018 article in the Bulletin of the American Meteorological Society speculated on whether current forecast systems are reaching their limits for when it comes to predicting tropical cyclones. In 2020, we argued that this limit is at least decades, if not much farther away.”

The theoretical foundation for the new methodology to determine the time limit of predictability is rather simple, he says. “At its core is a conceptual realization, which came to us intuitively. This is that the energy turnover time (that is, the time it takes for all energy in the atmosphere to be replaced by incoming solar radiation) is equivalent to the upper limit of predictability.”

While there is no complex mathematics involved, Toth notes that he and his colleagues had to find a way to describe the technique in a readily understandable way – something that was not easy, he admits, given the complexity of the subject.

Looking ahead, the researchers, who detail their present work in Advances in Atmospheric Sciences, are now looking into several other independent ways to estimate the limit of predictability. “There is no doubt, this limit has a theoretical feel to it,” Toth tells Physics World. “Yet there are a number of very practical implications for both traditional equation- and AI-based modelling of the atmosphere following on from the theory of predictability that we are also exploring in a series of ongoing studies.”

The post How far ahead can we predict the weather? appeared first on Physics World.

A viral video shows sprinklers soaking a green lawn outside a denver data center during severe water restrictions.

Fusionality is developing control systems and simulation environments to help fusion power startups move faster.

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.

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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.

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.

Battery materials startup Sila will use a $1.4 billion loan from the U.S. Department of Defense to scale production at its factory in Washington state.

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.

Heat stress from marine heatwaves can create a toxic relationship between seagrasses and a hidden ecosystem of bacteria,

As part of a planned Texas data center, Amazon is investing in an on-site power plant that could reportedly become the largest source of climate pollution in the United States.

The Trump administration has now convinced developers to abandon 12 offshore wind leases. The latest will cost taxpayers $1.2 billion.

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.

Black or grey swans

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.

A large sample of record-breaking events

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.

A difficult study

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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