Arctic ozone soars in 2024 due to series of extraordinary atmospheric events

Ocean-Land-Atmosphere Research (OLAR)

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The graph depicts the total column ozone (TCO), or the total amount of ozone in a vertical column extending from the Earth's surface to space, over time. The annual (blue) and March month (red) TCO measurements are illustrated in the graph by satellite (top panel), reanalysis (second from the top), and ground-based (Lerwick, Oslo, Sodankylä, and Scoresbysund) observations in the Arctic between 1978–2024. The pink lines in the background show the observed high TCO years. 

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Credit: Authors and OLAR

Scientists discover the atmospheric processes behind the highest Arctic stratospheric ozone levels observed in more than 45 years, providing new insight into how large-scale climate variability shapes the ozone layer.

The Arctic ozone layer is a protective layer of gas that sits in the stratosphere above the North Pole, filtering ultraviolet radiation from the sun. Recently, scientists discovered that the Arctic ozone layer recorded its highest ozone levels in March 2024 since satellite observations began in 1979. What puzzled researchers was why.

 

Ozone is a molecule of three highly reactive oxygen atoms (O3). In the atmosphere, it serves a protective function, but on the ground, ozone is a harmful gas that forms when pollutants from tailpipes, power plants and factories react with sunlight. Inhaling ozone can cause severe respiratory issues.

 

The researchers found record-high total column ozone of 477 Dobson Units (DU), a standard measure of the total amount of ozone, from merged satellite observations, supported by exceptionally high measurements from monitoring stations across the Arctic, including Lerwick, Scotland; Oslo, Norway; Sodankylä, Finland; and Scoresbysund, a deep inlet in the Greenland Sea. Balloon observations and satellite measurements also confirmed unusually high ozone concentrations throughout the lower and middle stratosphere.

 

Rather than simply reporting these observations, a team of scientists from CORAL at the Indian Institute of Technology Kharagpur in Kharagpur, India, investigated what caused the record-setting levels of ozone above the Arctic.

 

The team published their paper, entitled “Intense Wave Activity and Climate Oscillations Drive Record-High Arctic Ozone in March 2024,”on July 21st  in Ocean-Land-Atmosphere Research.

 

“Although the record ozone values were evident in satellite and ground-based observations, the atmospheric processes responsible for this extraordinary event were not well understood. Our goal was to identify the mechanisms that led to this unprecedented increase and determine how large-scale climate variability influenced the Arctic ozone layer,” said Jayanarayanan Kuttippurath, associate professor at CORAL, IIT Khargapur and lead scientist of the research study.

 

The team performed a comprehensive dynamical analysis, which uses mathematical, analytical, and numerical methods to model how a system evolves over time, that showed exceptionally strong planetary waves, or large, meandering waves that form naturally in rotating fluids like the Earth's atmosphere and oceans, propagating from the lower atmosphere into the stratosphere, triggering three warming events during the winter of 2023–2024. These warming events weakened and disturbed the Arctic polar vortex, allowing ozone-rich air to accumulate over the polar region.

 

The study further demonstrated that this sequence of events was amplified by the combined influence of a strong El Niño, or warmer than average water across the equatorial Pacific; the Madden–Julian Oscillation, a massive, eastward-moving atmospheric disturbance that travels along the equator, circling the entire globe every 30 to 60 days; and the Quasi-Biennial Oscillation, a regular variation of stratospheric winds over the equator that alternate between easterlies and westerlies.

 

The team’s integration of advanced wave diagnostics with observations from multiple independent datasets has provided the most robust explanation of the atmospheric mechanisms responsible for the record-breaking Arctic ozone levels to date.

 

"This was not simply an unusual ozone year," said Kuttippurath. "It was the result of an exceptionally rare alignment of atmospheric processes operating from the Earth's surface to the upper atmosphere."

 

The findings highlight the growing importance of atmospheric dynamics in controlling Arctic ozone variability. “As the recovery of the ozone layer continues under the Montreal Protocol [a 1987 global treaty designed to protect Earth's ozone layer], year-to-year ozone levels will increasingly depend on natural climate variability and large-scale atmospheric circulation rather than changes in ozone-depleting substances alone,” said Anjali Sathyanath, research scholar at CORAL, IIT Kharagpur and lead author of the study.

 

The research also has broader implications for seasonal climate prediction. Improving our understanding of how planetary waves, tropical climate modes that determine the recurring shifts in temperature, atmospheric pressure, and rainfall patterns between the Tropic of Cancer and the Tropic of Capricorn, and the polar vortex interact could improve forecasts of Arctic stratospheric conditions and their influence on global weather patterns.

 

“In the longer term, we hope this research will contribute to a new generation of Earth System Models capable of providing more reliable predictions of atmospheric extremes in a warming climate,” said Kuttippurath.

 

This study did not receive any specific funding.

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Journal

Ocean-Land-Atmosphere Research

DOI

10.34133/olar.0164 

Method of Research

Observational study

Article Title

Intense Wave Activity and Climate Oscillations Drive Record-High Arctic Ozone in March 2024

Article Publication Date

21-Jul-2026

 

Orcas filmed ramming one of the world’s heaviest fish so hard it explodes, possibly for fun

Study documenting previously undescribed orca behavior in the Gulf of California finds hold-and-ram strategy could be parental investment – or entertainment

Frontiers

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A group of orcas in the Gulf of California has been observed applying a hold-to-ram strategy to process a sharp-tail sunfish. This is a previously undescribed behavior which could be a form of parental investment – or entertainment.

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Credit: Kathryn Ayres

Orcas are known to be smart and ruthless hunters. They’ve been observed hunting a variety of prey, with some species, such as whales, larger than them. They use a wide range of strategies to hunt and kill prey, but now, in the Gulf of California, researchers have observed one that hasn’t been described before.

“We document how one orca holds the sunfish and lets go just before another orca hits it at high speed, causing the tissue to break apart into thousands of pieces,” said first author of the Frontiers in Ethology article, Dr Kathryn Ayres, a scientist at Beneath The Waves, a non-profit organization promoting ocean health. “We think this may help younger orcas feed more easily or it could also just be for fun. Orcas are known for playing with their food.”

Sunfish blown to bits

Sunfish can grow over three meters long and weigh up to 2,000 kg. They move through various depth layers but come to the surface to rest, thermoregulate, or seek parasite removal. In surface waters, they are vulnerable to predation. In the Gulf of California, they are one of the species most commonly hunted and eaten by orcas. The current observations are based on two independent hunting events that occurred a little more than one year apart.

In July 2024, Ayres observed how a group of orcas, including a juvenile, interacted with a dead sharp-tail sunfish, which they had killed previously. A female orca held the sunfish by its large tail fin – an effective point of grip – while a male orca accelerated and swam toward both. Shortly before impact, the female released the sunfish. The ramming caused the sunfish’s tissue to fragment and float in the water. The juvenile of the group then began consuming smaller bits of sunfish. The adults fed on what remained of the body but didn’t consume the smaller fragments.

In September 2025, a similar event, which was filmed by Héctor Franz, followed the same hold-to-ram and feeding behavior.

Small bites

The high-impact ramming behavior caused substantial disintegration of prey tissue. Ramming has not been previously described as a predation strategy by orcas. Both events suggest that the orcas followed a coordinated and cooperative strategy, with one orca stabilizing the carcass so the other could hit it more precisely. The sunfish were already deceased in both events, so the ramming is thought to be a processing technique rather than a means to deliver a killing blow.

The gelatinous layer that covers sunfish, known as capsule, and their skin host a distinct microbiome. If this tissue is shattered, the microbial taxa inhabiting it are dispersed into the water where they alter the nutrient composition. Tissue fragmentation could therefore facilitate contact between sunfish and orca microbiomes, which could provide nutritional or other benefits, such as immune regulation.

The observed behavior could be interpreted as a form of parental investment, the researchers pointed out. Dividing the carcass into more manageable pieces for the calf aligns with previously observed behaviors.

“Orcas often tear apart prey and share it with other members of the group, including calves and juveniles,” said Ayres.

‘They continue to surprise us’

Yet, this doesn’t mean that this interaction could not also have served other social purposes: it could simply have been a game for the killer whales.

As far as the sunfish are concerned, the interaction could provide new insights into the species’ characteristics. So far, fragmentation of tissue has only been observed in sharp-tail sunfish. This suggests that disintegration could be a species-specific structural response of sharp-tail sunfish tissue to high-energy impacts that may not apply to other, larger sunfish species.

It’s unclear if the same orcas were involved in both events. This highlights the need for more high-quality footage – especially of orca’s dorsal fins and eye patches – to be able to identify individuals.

“Collecting good identification photographs whenever possible is key: we are still seeing new techniques of how orcas hunt and process their prey,” concluded Ayres. “They're one of the most iconic marine predators in the world, yet they continue to surprise us.”

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Journal

Frontiers in Ethology

DOI

10.3389/fetho.2026.1835536 

Method of Research

Observational study

Subject of Research

Animals

Article Title

Fragmentation of Sharp-Tail Sunfish (Masturus lanceolatus) Caused by High-Impact Ramming Behaviour in Orcas (Orcinus orca)

Article Publication Date

23-Jul-2026

Multidisciplinary framework bridges earth science and environmental engineering to combat waterborne pathogens

KeAi Communications Co., Ltd.

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Graphic Abstract

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Credit: Zheng-Yang Huo, et al.

Waterborne pathogens remain a leading cause of infectious disease globally, responsible for 24% of all deaths and over 10 million fatalities each year. Despite decades of progress in water treatment, conventional approaches have struggled to keep pace with the evolving threat—pathogens are becoming more resistant, more mobile, and harder to predict in the face of climate change, urbanization, and chemical pollution. In a new perspective published in Water & Ecology, a China-UK research team led by Zheng-Yang Huo from Renmin University of China proposes a multidisciplinary research framework that integrates earth science and environmental engineering to mitigate the escalating risks of waterborne pathogens.

“The main issue is that we have been fighting this battle from two separate fronts,” says Huo. “Earth scientists understand how pathogens evolve and spread in natural environments, while engineers develop technologies to kill them in treatment plants. But neither perspective alone can solve the full problem.”

The researchers identified critical gaps in current research. “Earth science provides powerful tools to characterize geochemical processes and pathogen transmission networks across watersheds, yet lacks effective microbial control technologies,” explains Huo. “Environmental engineering delivers efficient disinfection methods for municipal facilities, but fails to account for coordinated microbial responses to environmental perturbations, leaving emerging risks unpredicted.”

To bridge this divide, the authors proposed a unified research framework spanning the entire pathogen life cycle. From the earth science perspective, the framework emphasizes real-time detection, numerical simulation, and multi-omics approaches, including metagenome-assembled genomes and environmental DNA metabarcoding, to track pathogens, harmful genes, and virulence factors across air, water, and solid media. From the engineering perspective, it prioritizes cost-effective, reliable, and safe disinfection technologies tailored to diverse scenarios, from decentralized rural systems to centralized urban treatment plants, while strictly controlling secondary risks such as disinfection by-products and viable-but-non-culturable states.

“The key is vertical integration,” says Huo. “Geochemical insights inform where and how pathogens evolve, while engineering solutions determine how to interrupt that evolution before it reaches human populations.”

The authors highlighted how integrating epidemic information, environmental parameters, and local health data can enable accurate, location-specific risk assessment models. They stated that in treatment facilities, understanding ecological processes that drive pathogenicity changes can improve disinfection design, while in natural environments, identifying pathogen transmission hotspots allows targeted deployment of in situ control methods.

“These cases show that cross-disciplinary intelligence can reveal hidden mechanisms and provide actionable strategies,” says Huo. “It redefines pathogen control from reactive treatment to predictive, full-chain governance.”

Nonetheless, the authors acknowledged implementation challenges, including data integration complexity, high technical costs, and the need for standardized monitoring networks. “Phased implementation starting with priority watersheds and high-risk facilities, coupled with expanded interdisciplinary collaboration, offers the most realistic pathway forward,” says Huo.

###

Contact the author:

Zheng-Yang Huo

School of Chemistry and Life Resources, Renmin University of China, Beijing 100872, China

zhengyanghuo.edu@ruc.edu.cn

The publisher KeAi was established by Elsevier and China Science Publishing & Media Ltd to unfold quality research globally. In 2013, our focus shifted to open access publishing. We now proudly publish more than 200 world-class, open access, English language journals, spanning all scientific disciplines. Many of these are titles we publish in partnership with prestigious societies and academic institutions, such as the National Natural Science Foundation of China (NSFC).

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Journal

Water & Ecology

DOI

10.1016/j.wateco.2026.100048 

Method of Research

Systematic review

Subject of Research

Not applicable

Article Title

Beyond the Boundary: Bridging Earth Science and Environmental Engineering for Waterborne Pathogen Mitigation

COI Statement

Zheng-Yang Huo is a guest editor for Water & Ecology but was not involved in the editorial review or the decision to publish this article. All authors declare that they have no competing interests.

 

AI study reveals the biodiversity cost of green energy minerals

A new approach using remote sensing and machine learning reveals mining for minerals like lithium has unexpected consequences on biodiversity.

Tohoku University

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Figure 1 Spatial Variation in Deforestation-to-Mining Area Ratio for Iron, Gold, Aluminium, and Lithium. Panels show (a) Iron, (b) Gold, (c) Aluminium, and (d) Lithium. Each grid cell represents approximately 100,000 km² (displayed in the Global Mercator projection) and is coloured by the deforestation-to-mining area ratio (deforestation area/mining area, %): 0, 10-20, 20-30, 30-40, 40-50, 50-60, 60-70, 70-80, 80-90, and 90-100, with darker colours indicating higher ratios. Grey areas indicate locations without mapped mining cells for the respective commodity.
 

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Credit: Cheng et al.

A new study led by Japanese researchers from Tohoku University and National Institute for Environmental Studies (NIES) reveals that some of the minerals most essential for green technologies may have a significantly negative impact on biodiversity. Using remote sensing and machine learning, the scientists produced an unprecedented global map of mined commodities. Combining this map with public datasets on forest loss and species extinction risk, they then quantified how the extraction of these commodities is reshaping forests and biodiversity worldwide.

Analyzing approximately 70,000 mining sites across 20 commodities, the study found that mining activities caused the loss of 16,268 km² of forest between 2001 and 2022, an area roughly comparable to the size of Beijing, with the majority of that loss occurring in rainforests in the Amazon, Southeast Asia, and the Congo Basin.

"Advances in remote sensing and machine learning offer new opportunities for automated analysis of mining at a global scale. The transition to green technologies is driving demand for a different set of minerals, but there is insufficient data to fully understand the environmental implications," explained Keiichiro Kanemoto, an associate professor at Tohoku University and author of the study.

While the study shows that commodities such as gold and coal remain the largest drivers of deforestation, it reveals a different pattern when biodiversity is considered. Green energy minerals such as lithium show some of the highest biodiversity risks despite being associated with lower levels of forest loss. This contrast highlights a critical shift: while traditional mining has been defined by large-scale deforestation, the extraction of minerals central to the energy transition may exert subtle - but equally serious - pressures on ecologically sensitive habitats. For example, lithium extraction in salt-flat and wetland environments can disrupt fragile ecosystems and endanger species despite minimal visible deforestation.

Minerals such as lithium, cobalt, and nickel are indispensable for technologies like electric vehicle batteries and renewable energy storage, yet their extraction can impose substantial environmental costs. The findings highlight the need to balance decarbonization goals with biodiversity protection, particularly as demand for these materials is expected to rise in the coming decades.

The resulting dataset offers valuable insights for policymakers and industry leaders. Governments can use the data to design targeted, commodity-specific environmental regulations, while companies can better anticipate policy shifts and assess risks within their supply chains. By identifying which commodities and regions carry the greatest environmental costs, the research supports more informed decision-making toward sustainable resource use.
Kanemoto said: "This study represents a major step forward in understanding the true global footprint of mining and demonstrates that the path to a greener future must also account for the hidden environmental costs of the materials that make it possible."

Details of the results were published in the journal Nature Communications on May 28, 2026.

 

Spatial Variation in ERI for Iron, Copper, Nickel and Cobalt, and Lithium. Panels show (a) Iron, (b) Copper, (c) Nickel and cobalt, and (d) Lithium. ERI = Extinction Risk Index. Each grid cell represents approximately 100,000 km² (displayed in the Global Mercator projection) and is coloured using a bivariate legend that combines mean ERI (y-axis; intervals of 0.15) and mining area (x-axis; km², log10 scale). Blue tones indicate lower ERI, yellow-to-dark tones indicate higher ERI, and darker shades toward the right indicate larger mining area classes.

Credit

Cheng et al.

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Journal

Nature Communications

DOI

10.1038/s41467-026-73792-9 

Article Title

Mapping Global Resource Driven Nature Loss in the Mining Sector from 2001 to 2022

AI-powered closed-loop system could accelerate the discovery of energy materials

By combining AI models, automated experiments and continuous feedback into a single research platform, researchers have reduced the time and cost of developing next-generation materials for batteries, hydrogen storage and other clean energy technologies.

Advanced Institute for Materials Research (AIMR), Tohoku University

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The evolution of scientific paradigms and application in AI-driven energy materials. This figure depicts the sequential evolution of scientific paradigms from empirical, theoretical, computational to data-driven science, culminating in the 4th+ generative paradigm empowered by AI and data science, and showcases their applications in energy materials research.

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Credit: Hao Li et al.

Developing the materials needed for cleaner energy technologies has traditionally been a slow and expensive process, often relying on years of trial and error. Now, researchers have outlined a new artificial intelligence (AI)-powered framework that could dramatically accelerate the discovery of advanced energy materials while reducing development costs.

Details were published in Digital Discovery on June 17, 2026.

Researchers have described a "closed-loop" research system that combines multiple AI technologies with automated experiments. Rather than treating each stage of materials research separately, the approach continuously feeds new experimental results back into AI models, allowing them to learn, improve predictions and guide the next round of experiments.

The framework is designed to address a longstanding challenge in materials science. Conventional methods - including laboratory experiments, theoretical calculations and computer simulations - have each contributed to major advances, but they often require researchers to balance accuracy, speed and computational cost. As a result, discovering promising new materials can take many years.

The researchers propose what they call the "4th+ paradigm," an evolution of the data-driven "fourth paradigm" of scientific research. The approach combines large materials databases, machine learning interatomic potentials (MLIPs), large language models (LLMs), intelligent AI agents and automated laboratory workflows into a single integrated system.

Together, these technologies enable researchers to predict the properties of materials with near atomic-level accuracy, rapidly analyze scientific literature and experimental data, and automatically recommend the most promising candidates for further testing. The closed-loop system then uses the experimental results to continuously refine its predictions, creating an increasingly efficient discovery process.

The team also outlines a future research framework consisting of four interconnected modules that support the entire materials discovery pipeline - from data collection and AI modeling to autonomous experimentation and industrial application. Such platforms could significantly shorten the time needed to develop high-performance materials for batteries, hydrogen storage, fuel cells and other clean energy technologies.

"Artificial intelligence is transforming materials science from a process driven largely by experience into one guided by data and autonomous decision-making," says Hao Li, Distinguished Professor at Tohoku University's Advanced Institute for Materials Research (WPI-AIMR). "By creating a closed-loop system that connects AI models with experiments, we can dramatically improve the efficiency of discovering new energy materials and accelerate their translation into practical technologies."

Faster development of energy materials could have wide-ranging benefits. Improved batteries could increase the range of electric vehicles, better hydrogen storage materials could support cleaner fuel technologies, and more efficient energy conversion materials could help reduce greenhouse gas emissions and support the global transition to low-carbon energy systems.

While significant challenges remain - including creating standardized materials databases, improving the reliability of AI models and reducing the cost of experimental validation - the researchers believe these obstacles can be overcome through continued advances in AI and laboratory automation.

Looking ahead, the team plans to develop more reliable machine learning models, establish open materials databases and build fully automated research platforms that seamlessly integrate AI with experimental laboratories. Such systems could help bring new energy materials from discovery to real-world applications far more quickly than is possible today.

 

About the World Premier International Research Center Initiative (WPI)

The WPI program was launched in 2007 by Japan's Ministry of Education, Culture, Sports, Science and Technology (MEXT) to foster globally visible research centers boasting the highest standards and outstanding research environments. Numbering more than a dozen and operating at institutions throughout the country, these centers are given a high degree of autonomy, allowing them to engage in innovative modes of management and research. The program is administered by the Japan Society for the Promotion of Science (JSPS).

See the latest research news from the centers at the WPI News Portal: https://www.eurekalert.org/newsportal/WPI
Main WPI program site:  www.jsps.go.jp/english/e-toplevel

Advanced Institute for Materials Research (AIMR)
Tohoku University
Establish a World-Leading Research Center for Materials Science
AIMR aims to contribute to society through actions as a world-leading research center for materials science and continuous challenges to research frontiers. To this end, the institute gathers excellent researchers in the fields of physics, chemistry, materials science, engineering, and mathematics and provides world-class research environment.

AIMR site: https://www.wpi-aimr.tohoku.ac.jp/en/

 

Evolution of materials databases.

 

LLMs and AI agents for materials research.

Credit

Hao Li et al.

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Journal

Digital Discovery

DOI

10.1039/D6DD00218H 

Article Title

Closed-loop discovery of energy materials empowered by artificial intelligence models