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body {background-color: #D2D1D5;} Research & Developments is a blog for brief updates that provide context for the flurry of news that impacts science and scientists today.

A yearly analysis of climate change’s progress and effects shows a “planet on the brink” of ecological breakdown and widespread crisis and suggests that only rapid climate mitigation can avoid the worst consequences.

“We felt an ethical responsibility to document this turning point clearly.”

“We felt an ethical responsibility to document this turning point clearly and to speak directly to humanity about where we stand,” wrote William Ripple, an ecologist at Oregon State University and coauthor of the new report, in an email. “What we’re seeing now are signs of systemic distress.”

The sixth annual report, published in BioScience, analyzes global data on Earth’s atmosphere, oceans, energy, ecosystems, food systems, and more. Researchers identified our planet’s so-called vital signs, including ocean temperature, surface temperature, sea ice extent, and carbon pollution. Of the 34 vital signs, 22 were at record levels, indicating a highly stressed Earth system. 

For example, 2024 surpassed 2023 as the hottest year on record. Ocean heat and wildfire-related tree cover loss are both at all-time highs. Deadly weather disasters surged in 2024 and 2025, with floods, wildfires, and typhoons killing hundreds in the U.S. alone. Atmospheric warming is showing signs of accelerating. Ice at the poles continues to melt, contributing to sea level rise. And the Atlantic Meridional Overturning Circulation, a global network of currents critical for circulating heat on Earth, is showing signs of weakening, which could trigger further climate disruptions.

Sixteen of the 34 vital signs that researchers tracked as part of their 2025 State of the Climate report. Credit: Ripple et al. 2025, doi.org/10.1093/biosci/biaf149

“Without effective strategies, we will rapidly encounter escalating risks that threaten to overwhelm systems of peace, governance, and public and ecosystem health,” said Ripple in a press release. “In short, we’ll be on the fast track to climate-driven chaos, a dangerous trajectory for humanity.”

Another “State of the Climate” report was published in August. The Bulletin of the American Meteorological Society report, authored by more than 500 scientists, presented similar indicators of a stressed Earth system, including record-setting ice loss at the poles, unusually high global temperatures, and an increase in the frequency and severity of weather disasters and wildfires. 

Notable climate anomalies and events in 2024, based on information from NOAA’s State of the Climate reports. Credit: Blunden et al. 2025, doi.org/10.1175/2025BAMSStateoftheClimate.1 Climate & Consumption

The new report identifies resource consumption, especially meat and energy consumption, as a major driver of climate change and ecological harm. 

“We can’t fix the climate by technology alone; we have to rethink the way we live.”

Global gross domestic product, or GDP, a measure of the goods and services produced in a given year, reached an all-time high in 2025 according to preliminary data. While such economic growth is often celebrated, it is also typically coupled with ecological degradation and increased emissions, the authors point out. Reducing consumption, especially among the world’s wealthiest individuals (who tend to consume more resources), is essential to managing the effects of climate change, they write. 

“We need systemic change: rethinking what we value, shifting toward circular economies, and promoting well-being over endless growth,” Ripple wrote in an email. “We can’t fix the climate by technology alone; we have to rethink the way we live.”

 
Related

•  Read the BioScience report: “The 2025 State of the Climate Report: A Planet on the Brink”
•  Read the Bulletin of the American Meteorological Society report: “State of the Climate in 2024”
•  New “State of the Climate Report” Delivers Sobering and Stunning Data
 

Though the use of solar- and wind-powered electricity is soaring, so is fossil fuel consumption. The State of the Climate authors emphasize that a rapid reduction of fossil fuel use is both necessary and possible, and that renewable energy sources have the potential to supply most global energy needs by 2050.

“We are in a planetary emergency, but we are not powerless,” Ripple wrote. “The science is clear, the mitigation strategies are known, and every fraction of a degree matters.”

—Grace van Deelen (@gvd.bsky.social), Staff Writer

These updates are made possible through information from the scientific community. Do you have a story about science or scientists? Send us a tip at eos@agu.org. Text © 2025. AGU. CC BY-NC-ND 3.0
Except where otherwise noted, images are subject to copyright. Any reuse without express permission from the copyright owner is prohibited.

As global plastic production has ballooned, small fragments of plastic have infiltrated rivers, sea ice, and even our brains. When the minuscule fibers and shards seep into soils, they change how the soil interacts with water, according to a new study.

The study, published in Vadose Zone Journal, measured water retention and conductivity in soils from three regions of Germany with and without four different microplastics. The researchers found that a plastic concentration of just 0.4% by mass can change how quickly water flows through soil, depending on both the type of plastic and the type of soil. The altered hydraulic properties likely result from the hydrophobic nature of plastic and the microplastic particles changing the arrangement of individual soil granules, the authors said.

Tiny soil particles stick together to form clumps. The spaces between these clumps form conduits for water, nutrients, and plant roots to move through. The size and distribution of these spaces affect soil drainage and water-holding capacity, which have implications for plant growth.

“The water characteristics of a soil tell you how quickly water drains through the soil, which impacts crops and aquifers.”

“The water characteristics of a soil tell you how quickly water drains through the soil, which impacts crops and aquifers,” said the study’s first author, Katharina Neubert, a soil scientist at Forschungszentrum Jülich in Germany.

Earlier research has shown that microplastics can alter soil structure and its hydraulic properties, but those studies each examined only one soil type or one plastic type. The new study is the first to evaluate how multiple types of microplastics affect multiple soil types.

The researchers collected soil from three distinct agricultural regions in Germany, which had varying textures, carbon levels, and pH levels. They then obtained four widely used microplastics ranging in size from 300 micrometers to 5 millimeters—polyethylene, polypropylene, polystyrene, and polyester. They broke down the larger particles in a blender, then mixed each plastic with each soil type at a concentration of 0.4% by weight. Combined with a plastic-free control for each soil type, this yielded 15 unique soil‑microplastic combinations.

The authors poured each mixture into a metal cylinder connected to a suction device to see how quickly the suction would pull water through the soil. They performed the test on wet soil and dry soil, as moisture level also influences how quickly water drains through soil.

Unearthing a Nuanced Relationship

All four microplastics altered water flow rates in at least one of the soils, but the magnitude and direction of the effect varied widely. For example, polyester fibers, commonly shed from some types of clothing, boosted the speed at which water flowed through one soil by more than 50% when the soil was wet, yet reduced the flow rate by more than 50% under dry conditions.

“It’s very difficult to make a general statement about how soil changes with microplastics.”

“All of the results are context dependent,” said Rosolino Ingraffia, a soil scientist at Università degli Studi di Palermo in Italy, who was not involved in the research. “It’s very difficult to make a general statement about how soil changes with microplastics.”

Another recent study coauthored by Neubert showed how differences in flow rates could translate to agriculture. She grew wheat plants in the same three soil types with and without two microplastics: polyethylene and polyester. The results were similarly complicated, with the added plastic increasing, decreasing, or not affecting root growth, depending on the combination.

The plastic concentration of 0.4% used in both studies is much higher than that which most agricultural fields harbor today, said both Neubert and Ingraffia. For example, arable lands that have been treated with biosolids for a decade see concentrations closer to about 0.002%. But calculations based on the current rate of microplastic accumulation suggest that many areas could reach this 0.4% concentration in 50 or 60 years, Ingraffia added.

Neubert hopes her research leads to regulations that keep microplastics from ever reaching those levels. Germany plans to phase out the use of nutrient-rich sewage sludge as fertilizer in most agricultural fields, in part because of concerns about plastic pollution, she said. One study identified the practice as a major source of soil microplastics in Germany.

Keeping plastic out of the ground is important because “we don’t yet know what consequences it has for our soils,” Neubert said.

—Mark DeGraff (@markr4nger.bsky.social), Science Writer

Citation: DeGraff, M. (2025), Microplastics have widely varying effects on soil, Eos, 106, https://doi.org/10.1029/2025EO250396. Published on 29 October 2025. Text © 2025. The authors. CC BY-NC-ND 3.0
Except where otherwise noted, images are subject to copyright. Any reuse without express permission from the copyright owner is prohibited.

The term “ecosystem engineering” has been used for decades as a way to describe organisms that drastically alter their environment, changing the abiotic factors in their habitat as well as the lives of other organisms in their orbit. Beavers, bison, and possibly dinosaurs fall into this category.  

The idea has, over decades, helped ecologists move past the idea that animals are simply passive players in the environment toward an understanding that life itself affects its surroundings.

“Are we just a new version in a long line of the evolution of new processes that have fundamentally altered the planet? Or are we truly something new?”

A more expansive view of ecosystem engineering—one that identifies effects over geologic timescales—could be useful for Earth scientists as well as ecologists, according to a new framework recently published in Trends in Ecology and Evolution. The authors argue the framework could be useful for answering fundamental questions about humans’ role in Earth’s history.

“Are we just a new version in a long line of the evolution of new processes that have fundamentally altered the planet? Or are we truly something new?” said Kate Lyons, a paleoecologist at the University of Nebraska–Lincoln and coauthor of the new framework.

Engineering Earth

Lyons and the research team were inspired to create the new framework when they realized that many large-scale planetary changes caused by living beings didn’t fall strictly into the original definition of ecosystem engineering. “We felt we really needed a different term,” she said.

The framework distinguishes Earth system engineers from ecosystem engineers by the scale of their effects. Earth system engineers may be processes as well as organisms, and they affect their surroundings over longer periods of time and across larger, sometimes global, spatial scales. In many cases, Earth system engineers affect species they “don’t even coexist with,” Lyons said.

In fact, the fossil record shows that many biological and ecological processes both had a global impact and introduced changes in Earth systems that have lasted for millions and even billions of years. For example, the Great Oxidation Event, a period roughly 2.4 billion years ago during which single-celled organisms began to metabolize carbon dioxide and water, drastically increased the concentration of oxygen in Earth’s atmosphere. We’re still experiencing the aftermath of the Great Oxidation Event—it’s why all animals on Earth today are alive. 

Calcium carbonate biomineralization—the process by which marine life takes up calcium and carbon from seawater and produces mineral skeletons or other hard structures—is another Earth system engineering process, the authors write. This process has been a major contributor to the sequestration of carbon in the ocean, which helps to control the global climate.

Planetary History, Here and Beyond

The new paper’s authors suggest their framework could be useful for answering questions about whether the scale of humanity’s changes to Earth systems is unique in Earth’s history.

Similarly, the authors expect the framework to help in investigations of how often such planetary-scale changes happen and how they usually play out: “[Are they] typically followed by mass extinctions? [Do they] typically spur speciation?” Lyons said. 

These questions also have applications for astrobiology because knowledge of how organisms affect their environments on planetary scales—creating oxygen via photosynthesis, for example—could give insight into how and why some planets become habitable, said Peter Wagner, a paleobiologist at the University of Nebraska–Lincoln and coauthor on the new study. 

There is “growing interest” in the question “When does life leave a mark on the planet?” said Clive Jones, one of the scientists who originally developed the concept of ecosystem engineering in 1994. “It’s very worthwhile thinking about,” he said.  

What’s in a Name?

Scientists have been grappling with these questions for years, however, and the idea that ecosystem engineering could be a planetary process has been implicit in previous published work, said Erle Ellis, an ecologist at the University of Maryland, Baltimore County, who was not involved in the new paper. In fact, in 2012, Jones published a paper presenting a theory of when and why certain species create geomorphological signatures in the landscape, responding to a 2006 paper that raised the question of when life leaves a mark.

“There are some really cool interfaces between…ecosystem science and Earth system science.”

Ellis also cautioned that the line between “ecosystem engineer” and “Earth system engineer” may be arbitrary. Though it is helpful to recognize that ecosystem engineering can scale up, “there’s not really a clean break” between which processes are planetary and which aren’t, he said.

Creating a binary like the one presented in the paper “tends to divide up the community of scientists…when they’re actually working on the same thing,” Ellis said. “That’s not necessarily an advance in the sciences.”

Jones still thinks the new Earth system engineering framework could be useful. Just as the idea of ecosystem engineering helped ecologists conceptualize organisms as active agents in the abiotic environment, the idea of Earth system engineering could help scientists better understand the spatial and temporal scales of interactions between animals and Earth systems, he said.

Jones also hopes the new framework spurs additional collaboration. “There are some really cool interfaces between…ecosystem science and Earth system science,” he said. “That’s where the productive territory will be.”

—Grace van Deelen (@gvd.bsky.social), Staff Writer

This news article is included in our ENGAGE resource for educators seeking science news for their classroom lessons. Browse all ENGAGE articles, and share with your fellow educators how you integrated the article into an activity in the comments section below.

Citation: van Deelen, G. (2025), Earth system engineers take planetary alterations to extreme scales, Eos, 106, https://doi.org/10.1029/2025EO250402. Published on 29 October 2025. Text © 2025. AGU. CC BY-NC-ND 3.0
Except where otherwise noted, images are subject to copyright. Any reuse without express permission from the copyright owner is prohibited.

body {background-color: #D2D1D5;} Research & Developments is a blog for brief updates that provide context for the flurry of news that impacts science and scientists today.

An annual United Nations report, published 29 October, reveals a “yawning gap” between existing and necessary climate adaptation finance that is “putting lives, livelihoods, and entire economies at risk.”

Adaptation needs in developing countries—estimated to be at least $310 billion per year by 2035—are 12 times higher than current public international finance flows, which currently sit at about $26 billion per year. 

This so-called adaptation gap limits poor countries’ ability to withstand the changing climate, the report states.

Poorer countries are often the hardest hit by the effects of climate change, despite emitting just a fraction of the world’s greenhouse gases. These countries rely on funding from other countries, from both public and private sources, to finance climate adaptation efforts.

A comparison of adaptation financing needs from nationally determined contributions (NDCs) and national adaptation plans (NAPs) and international public adaptation finance flows in developing countries. Achieving the Glasgow Pact and delivering the multilateral development banks’ (MDB) target would help narrow the gap slightly, but would not be enough to close the gap. Credit: UNEP

“We need a global push to increase adaptation finance—from both public and private sources—without adding to the debt burdens of vulnerable nations,” said Inger Andersen, the executive director of the UN Environment Programme, in a press release.  “Even amid tight budgets and competing priorities, the reality is simple: If we do not invest in adaptation now, we will face escalating costs every year.”

“New finance providers and instruments must come on board.”

The report credits the difficulty of mobilizing necessary financial resources to “current geopolitical tensions and cuts to overseas development assistance, among other factors.” 

The Glasgow Climate Pact, an international agreement adopted in 2021, set a goal to double international public adaptation finance by 2025. The goal will not be met under current trajectories, according to the report. The most recent climate finance target of $300 billion per year by 2035, agreed upon at the UN climate change conference last year, COP 29, is also insufficient to meet the adaptation needs of developing countries.

A failure to meet international finance goals means “many more people will suffer needlessly,” Andersen wrote in the report. “New finance providers and instruments must come on board.”

“The smart choice is to invest in adaptation now,” she wrote.

The report did include some silver linings: The adaptation finance gap is slightly smaller for Least Developed Countries, the UN’s classification for low-income countries facing severe obstacles to sustainable development, and Small Island Developing States. Additionally, in-country progress to plan for climate change is improving: 172 countries have at least one national adaptation strategy in place, while 21 others have started developing one.

However, the world is failing to reach other climate goals: Another UN report, published 22 October, found that oil and gas companies are still vastly underreporting their methane emissions. And ahead of COP30, scheduled to be held next month in Belém, Brazil, only 64 of the 195 nations party to the Paris Agreement have submitted their required updates to their emissions plans.

 
Related

• Adaptation Gap Report 2025
•  Carbon Brief: Five charts which explain the ‘gap’ in finance for climate adaptation 

The new report is expected to inform discussions at COP30. The Brazilian presidency of COP30 has called for the conference to be a “mutirão global,” a global collective effort, to achieve ambitious climate action. In the report, authors advise nations attending the conference in Belém to transition away from fossil fuels, engage additional financial system stakeholders, and avoid expensive but mostly ineffective maladaptations such as seawalls or wildfire suppression. 

In a recent interview with The Guardian about COP30 priorities, Secretary-General of the UN António Guterres said the world has “failed to avoid an overshooting above 1.5°C [2.7°F] in the next few years” and urged swift action.

“It is absolutely indispensable to change course,” he said.

—Grace van Deelen (@gvd.bsky.social), Staff Writer

These updates are made possible through information from the scientific community. Do you have a story about science or scientists? Send us a tip at eos@agu.org.

Text © 2025. AGU. CC BY-NC-ND 3.0
Except where otherwise noted, images are subject to copyright. Any reuse without express permission from the copyright owner is prohibited.

Author(s): Charles Heaton, Hao Yin, Dimitri Khaghani, Hae Ja Lee, Hannah Poole, Eric Blackman, Nina Boiadjieva, Xiaoqian M. Chen, Celine Crépisson, Gilbert W. Collins, Adrien Descamps, Arianna E. Gleason, Christian Gutt, Alexander N. Petsch, Lisa Randolph, Silke Nelson, Peregrine McGehee, Rajan Plumley, Christopher Spindloe, Thomas Stevens, Charlotte Stuart, Joshua J. Turner, Hussein Aluie, Jessica K. Shang, and Gianluca Gregori

Experimental benchmarking of transport coefficients under extreme conditions is required for validation of differing theoretical models. To date, measurement of transport properties of dynamically compressed samples remains a challenge with only a limited number of studies able to quantify transport…

[Phys. Rev. E 112, 045218] Published Wed Oct 29, 2025

SummaryThis study presents an enhanced method for integrating Global Navigation Satellite System (GNSS) and Interferometric Synthetic Aperture Radar (InSAR), referred to as Iterative Decomposition-based GNSS-enhanced InSAR (ID-GInSAR), to address both spatially correlated components (SCCs) and topographically correlated components (TCCs) in interferogram errors. While traditional GInSAR (GNSS-enhanced InSAR) is effective in mitigating long-wavelength SCCs, it often overlooks TCCs, which are particularly significant in regions with steep topographic gradients. The proposed ID-GInSAR approach employs an iterative decomposition process to decouple and independently model SCCs and TCCs, utilizing a combination of exponential and statistical models. The method is validated using Sentinel-1 SAR and GNSS data from California’s southern Central Valley. Results demonstrate that ID-GInSAR significantly lowers noise in interferograms, enhances the robustness of displacement time series, and improves the accuracy of co-seismic deformation and velocity field estimates. Specifically, ID-GInSAR reduces the root mean square (RMS) between GNSS and InSAR by up to 55% in individual interferograms and by an average of 30.4% in displacement time series compared to traditional GInSAR methods. Furthermore, ID-GInSAR effectively highlights subtle transient deformation, such as coseismic offsets, and provides more robust velocity fields over shorter time spans (less than three years). Finally, we compare our method with other approaches, including Remove/Filter/Restore (RFR) and GACOS, and discuss their applicability scenarios. Collectively, ID-GInSAR provides an alternative integration method for regions with complex topography where ground-based GNSS observations are available.

SummaryRupture directivity significantly increases horizontal peak ground acceleration, elongates aftershock clouds, and enlarges meizoseismal areas beyond the fault end in front of the direction of rupture propagation. In this study, we examine the directivity of 25 moderate to large earthquakes (Mw ≥ 6) from 1968 to 2017 in the Iranian plateau by employing relocated earthquake clusters, mapped surface ruptures, focal mechanisms of earthquakes, slip distribution models, spatial distribution of Peak Ground Acceleration (PGA) amplitudes and macroseismic effects. The methodology overcomes the lack of dense seismic networks required to study directivity using methods based on the azimuthal variation of the spectrum of seismic waves. We show that 16 out of the 25 (i.e., 64%) of the earthquakes investigated have mostly unidirectional rupture. This implies that unidirectional ruptures in a slow deforming continental collision zone such as the Iranian Plateau is only slightly less common than those observed globally. With the understanding that unidirectional rupture increases the probability of ground shaking off the termination of the causative faults, our findings highlight the importance of considering the directivity effect in earthquake hazard assessment in Iran and also in other slow deforming continental regions.

SummaryEvidence from seismic studies, mineral physics, thermal evolution models and geomagnetic observations is inconclusive about the presence of a stably stratified layer at the top of the Earth’s fluid outer core. Such a convectively stable layer could have a strong influence on the internal fluid waves propagating underneath the core-mantle boundary (CMB) that are used to probe the outermost region of the core through the wave interaction with the geomagnetic field and the rotation of the mantle. Here, we numerically investigate the effect of a top stable layer on the outer core fluid waves by calculating the eigenmodes in a neutrally stratified sphere permeated by a magnetic field with and without a top stable layer. We use a numerical model, assuming a flow with an m-fold azimuthal symmetry, that allows for radial motions across the lower boundary of the stable layer and angular momentum exchanges across the CMB through viscous and electromagnetic coupling. On interannual time-scales, we find torsional Alfvén waves that are only marginally affected by weak to moderate stratification strength in the outer layer. At decadal time-scales similarly weak stable layers promote the appearance of waves that propagate primarily within the stable layer itself and resemble Magneto-Archimedes-Coriolis (MAC) waves, even though they interact with the adiabatic fluid core below. These waves can exert viscous and electromagnetic torques on the mantle that are several orders of magnitude larger than those in the neutrally stratified case.

SummaryA novel time-lapse modelling scheme for Airborne Electromagnetics (AEM) monitoring datasets is presented, using data from multiple surveys applied to study the hydro-related evolution of the Bookpurnong floodplain in South Australia. Additionally, it introduces a new wide-ranging approach for this type of study, incorporating new processing, validation, and interpretation tools.Time-Lapse studies are widespread in the literature but are not commonly applied to model EM data, particularly AEM data. This is linked to the challenges of performing overlapping data acquisition with inductive systems. The key features of the new time-lapse scheme, which address these issues, include the definition of independent forward and model meshes, essential for considering discrepancies in the location of soundings which arise in multitemporal AEM data acquisition, and the incorporation of system flight height in the inversion. This proved crucial for achieving satisfactory data fitting and limiting artifact propagation in the time-lapse models.Additionally, a novel processing workflow for AEM multitemporal datasets is presented. This has proven important for effectively processing the multitemporal datasets, which presents new challenges in identifying noise coupling arising from the use of different systems across vintages of data, possible variations in acquisition settings operated by different field crews, and changes in subsurface resistivity in the survey area. Results generated from the time-lapse modelling are evaluated with an Independent Hydrogeological Validation (IHV), designed to support the geophysical models validation and interpretation by providing a first-step hydrogeological evaluation.At Bookpurnong, along a sector of the Murray River floodplain, multitemporal AEM surveys were collected in 2015, 2022 and 2024, to study natural and engineered changes in the groundwater system over time. The time-lapse models show significantly smaller variations compared to those determined with individually modelled survey data sets, while delineating sharply bounded changes in resistivity across the floodplain. This demonstrates the effectiveness of the new time-lapse scheme in minimizing inversion variations typically encountered with independently modelled results affected by larger equivalence issues.Here, AEM models are first compared with resistivity borehole measurements, revealing a strong match between the two methodologies and spatial variations in resistivity consistent with a meandering river across the floodplain. These variations are further validated and interpreted using the IHV approach, which revealed a direct correlation between the hydrological stress of the Murray River and the response of shallow aquifers. Additionally, time-lapse geophysical models, combined with a hydrostratigraphic analysis, allow for a direct correlation between shallow and deep hydrogeological responses.We believe that the time-lapse methodology described here can be widely applied to multitemporal studies using AEM datasets, enabling the study of a broad range of natural processes with great accuracy and at the basin scale.

body {background-color: #D2D1D5;} Research & Developments is a blog for brief updates that provide context for the flurry of news regarding law and policy changes that impact science and scientists today.

A judge has announced that the government cannot issue further reduction-in-force (RIF) notices to federal employees because of the government shutdown, nor implement RIFs that had already been issued during the shutdown.

The ruling by U.S. District Judge Susan Illston will mark the latest in a months-long court battle over RIFs at federal agencies.

“I think it’s important that we remember that, although we are here talking about statutes and administrative procedure and the like, we are also talking about human lives, and these human lives are being dramatically affected by the activities that we’re discussing this morning,” Judge Illston said at the top of the hearing, which was held at the headquarters of the Northern District of California in San Francisco.

 
Related

• Judge pauses shutdown layoffs at more than 30 federal agencies
•White House threatens layoffs — not furloughs — if the government shuts down
• Mass Firing of Probationary Federal Employees Was Illegal, Judge Rules
• Judge rules that probationary federal workers were fired illegally — AGU’s plaintiff coalition prevails
 

The case, American Federation of Government Employees, AFL-CIO v. United States Office of Personnel Management (OPM) (3:25-cv-01780), was first filed in February. AGU joined as a plaintiff in the case in March. Other plaintiffs include Climate Resilient Communities, the Coalition to Protect America’s National Parks, and the American Public Health Association.

Judge Illston granted a temporary restraining order earlier this month, which prevented the government from executing RIFs during the shutdown until further notice.

However, the Trump administration only paused some RIFs, arguing that most of the thousands of layoffs announced since the shutdown are not covered by the court order.

As part of the temporary restraining order, the court ordered the government to provide an accounting of “all RIFs, actual or imminent,” that it planned to execute during the shutdown. The list included 143 Fish and Wildlife Service employees, 355 USGS employees, 272 National Park Service employees, and 474 Bureau of Land Management employees.

On 22 October, Judge Illston broadened the reach of who was protected by the temporary restraining order by adding several unions representing federal employees as plaintiffs.

In today’s hearing, the plaintiffs argued for a preliminary injunction, a move that essentially preserves the status quo before the final judgement of a trial. Danielle Leonard, an attorney representing the plaintiffs, argued that, in this case, the state of affairs prior to the government shutdown should be considered the “status quo.” In essence, this meant seeking for a halt to RIFs that have occurred since the shutdown, not just future RIFs.

The plaintiffs sought prove that the RIFs were “arbitrary or capricious,” a legal standard that is part of the Administrative Procedure Act, which governs how federal agencies operate.

Michael Velchick, an attorney representing the U.S. government, argued that the government’s actions were not only not arbitrary or capricious, but good policy, and “the right thing to do.”

“Morally it’s the right thing to do, and it’s the democratic thing to do,” he said. “The American people selected someone known above all else for his eloquence in communicating to employees that, ‘You’re fired.’”

This was seemingly a reference to the president’s former reality TV show, The Apprentice.

Leonard argued that Velchick’s statement was offensive to the 1.5 million federal employees represented by her clients. She summed up the defendant’s argument like this:

“There is some general authority, and therefore that blesses the specific actions that are happening here for the reasons that the government has given, regardless of how poor those reasons are. And that’s just not the way the law works.”

Judge Illston seemed to agree, stating that the Office of Personnel Management and Office of Management and Budget were prohibited from issuing more RIF notices or implementing those already issued.

The judge noted that she will likely hold an evidentiary hearing to settle a potential dispute over whether specific RIF notices were issued because of the shutdown, or were “already in the works” and unrelated to the shutdown.

—Emily Gardner (@emfurd.bsky.social), Associate Editor

These updates are made possible through information from the scientific community. Do you have a story about how changes in law or policy are affecting scientists or research? Send us a tip at eos@agu.org. Text © 2025. AGU. CC BY-NC-ND 3.0
Except where otherwise noted, images are subject to copyright. Any reuse without express permission from the copyright owner is prohibited.

A team of U.S. scientists has discovered the oldest directly dated ice and air on the planet in the Allan Hills region of East Antarctica.

Publication date: Available online 24 October 2025

Source: Advances in Space Research

Author(s): P.S. Marrocchesi

Publication date: Available online 24 October 2025

Source: Advances in Space Research

Author(s): Husan Eshkuvatov, Shuanggen Jin, Bobomurat Ahmedov, Shukhrat Mardonov, Shahzod Numonjonov

Publication date: Available online 23 October 2025

Source: Advances in Space Research

Author(s): Shengyun Zhao, Linyan Cui, Rui Zhong

Publication date: Available online 23 October 2025

Source: Advances in Space Research

Author(s): M.E. Kalyashova, A.M. Bykov, D.V. Badmaev

Using a mix of airborne and satellite images as well as data from ground sensors, a UCLA-led research team has reconstructed how the shape and reach of the methane plumes from the 2015–16 Aliso Canyon gas blowout evolved during the 112-day disaster.

This image captured by the Copernicus Sentinel-3 mission on 26 October 2025 shows the "brightness temperature" at the top of Hurricane Melissa as it barreled through the Caribbean Sea toward Jamaica, where it is expected to make landfall.

Irrigation canal maintenance in western Nebraska is taking a giant step forward thanks to an innovative, non-invasive method by Husker geoscientist Mohamed Khalil to check canal integrity. His sophisticated time-lapse analysis pinpoints canal seepage and structural settlement far more accurately and efficiently than traditional approaches—using a technology that can have wide-ranging uses statewide for agriculture, industry and natural resources management.

This story was originally published by Knowable Magazine.

When the biggest earthquake in more than a decade rattled Russia’s remote Kamchatka Peninsula in July, seismologists around the world knew within moments. For earthquakes big or small, sensors around the globe detect the tremors and relay that information to researchers, who quickly analyze the observations and issue alerts.

Now artificial intelligence is poised to make almost everything about earthquake research much faster—and to rewrite researchers’ very understanding of how earthquakes happen.

“Machine learning opened a whole new window.”

By using a subfield of AI called machine learning, some scientists are identifying up to millions of tiny, previously unseen earthquakes in data gathered from seismically active places. These new and improved databases are helping researchers to better understand the geological faults along which quakes happen, and can help to illuminate the risks of future quakes. Some scientists are even using machine learning to improve their forecasts of how many aftershocks may rattle a location that has just experienced a large and damaging earthquake.

More broadly, researchers hope that machine learning, with its ability to crunch through huge amounts of information and learn from the patterns within, will reveal fresh insights into some of the biggest mysteries about earthquakes, including how a quake unfolds in its first devastating seconds.

“Machine learning opened a whole new window,” says Mostafa Mousavi, a seismologist at Harvard University.

Shaking Earth, Exploding Data

Earthquakes happen when geological stress builds up in the ground, such as when two plates of Earth’s crust grind alongside one another, as they do at California’s San Andreas Fault. At some point, the stress reaches a critical threshold and the fault ruptures, breaking the rock and causing seismic energy to ripple outward and shake the ground.

The San Andreas fault, seen here as a dramatic slash across the Carrizo Plain in Southern California, is an example of a geologically active area where seismologists are using AI to better understand earthquake patterns. Credit: John Wiley, Wikimedia Commons, CC BY 3.0

That energy is recorded by seismometers and other instruments around the world, which are positioned in great numbers in geologically active areas like California and Japan. The data feed into national and international systems for tracking earthquakes and alerting the world. The amount of data has exploded in recent years as seismologists find new ways to gather information on ground movements—like detecting seismic signals over fiber optic networks, or using the accelerometers built into smartphones to create a phone-based earthquake warning network.

Just a decade or two ago, much of the analysis of seismic signals was done by hand, with scientists working as quickly as possible to assess recordings coming in from their observing networks. But today, there are just too many data points. “Now the only—almost—way that you can deal with the seismic data is to go to automatic processing,” says Mousavi, who coauthored a 2023 article in the Annual Review of Earth and Planetary Sciences on machine learning in earthquake seismology.

One of the most common uses of machine learning in seismology is measuring the arrival time of seismic waves at a particular location, a process known as phase picking. Earthquakes generate two kinds of seismic waves, known as P and S waves, that affect the ground in different ways and show up as different types of squiggles on a seismogram. In the past, a seismologist would analyze data arriving from seismic sensors and hand-select what they gauged to be the start of P waves or S waves on those seismogram plots. Picking the starts of those waves accurately and precisely is important for understanding factors such as where exactly the earthquake hit. But phase picking is very time consuming.

An earthquake’s energy appears as a squiggly waveform on measurements made by seismometers. The first type of signal to arrive is a ground movement known as a P wave, followed by a type known as an S wave. Picking where the waves first arrive on a seismometer reading is an important part of understanding an earthquake’s impact—this has typically been done by human seismologists but in recent years the process has been made much quicker by incorporating machine-learning algorithms. Credit: Knowable Magazine, adapted from USGS.gov

In the past few years, seismologists have been using machine learning algorithms to pick seismic phases much faster than a human can. There are a number of automated methods that can do phase picking, but machine learning algorithms, which have been trained on huge volumes of data on past quakes, can identify a wide variety of signals from different types of tremors in a way that was not possible before. The practice is now so standard that the term “machine learning” is no longer stated in the titles of research papers, says Mousavi. “By default, everybody knows.”

AI-based phase picking is faster than phase picking by humans and at least as accurate, Mousavi says. Seismologists are now working to expand these tools to other types of seismic analysis.

Expanding Quake Catalogs

One area that has already seen big discoveries is the use of machine learning to expand earthquake catalogs—basically, lists of what earthquakes happened where in a particular region. Earthquake catalogs include all the quakes that seismologists can identify from recorded signals—but AI can find exponentially more tremors than human scientists can.

Essentially, machine learning can trawl through the data to identify small earthquakes that people don’t have the ability or time to flag. “Either you don’t see them by eye, or there’s no time to go and look at all those tiny events,” says Leila Mizrahi, a seismologist with the Swiss Seismological Service at ETH Zürich. Often, these tremors are obscured by background noise in the data.

Tiny earthquakes are important as a window into how larger earthquakes begin.

In a pioneering 2019 study in Science, researchers used an AI algorithm that matched patterns of seismic waves to identify more than 1.5 million tiny earthquakes that happened in Southern California between 2008 and 2017 but had not been spotted before. These are itty-bitty quakes that most people wouldn’t feel even if they were standing on top of them. But knowing they exist is important in helping seismologists understand patterns of behavior along a geological fault.

In particular, Mousavi says, tiny earthquakes are important as a window into how larger earthquakes begin. Large earthquakes may happen along a particular fault once every century or more—far too long a time period for scientists to observe in order to understand the rupture process. Tiny quakes behave much the same as big ones, but they happen much more frequently. So studying the pattern of tiny quakes in the newly expanded earthquake catalogs could help scientists better understand what gets everything going. In this way, the richer catalogs “have potential to help us to understand and to model better the seismic hazard,” Mousavi says.

Expanded earthquake catalogs can also illuminate the structure of geological faults below a region much better than before. It’s like going from a simplistic sketch of how the faults are arranged to a painting with more photorealistic details. In 2022, a team led by seismologist Yongsoo Park, then at Stanford University, used machine learning to build an expanded catalog of quakes in Oklahoma and Kansas between 2010 and 2019, many of them induced by oil and gas companies injecting wastewater into the ground. The work illuminated fault structures that weren’t visible before, allowing the scientists to map the faults more precisely and to better understand seismic risk.

This dramatic example shows the power of machine learning to enhance scientists’ knowledge of earthquakes. Top is a depiction of an earthquake swarm that occurred near Pawnee, Oklahoma, in September 2016. Each dot represents an earthquake measured by seismometers (with yellow representing quakes that occurred early in the swarm, and red representing quakes that occurred later). Bottom is the same earthquake swarm, but in this case when scientists used machine learning to pinpoint additional, smaller quakes in the observations. The enhanced earthquake catalog shows far more detail of where the quakes occurred, including illuminating the underlying geological faults. Credit: Courtesy of Yongsoo Park

Park and his colleagues showed that 80 percent of the larger earthquakes that happened could have been anticipated based on the smaller earthquakes that occurred before the big ones. “There is always a possibility that the next major earthquake can occur on a fault that is still not mapped,” says Park, who is now at Los Alamos National Laboratory in New Mexico. “Routinely capturing smaller earthquakes might be able to reveal such hidden faults before a major earthquake happens.”

Scientists are applying this approach around the globe. Researchers in Taiwan, for instance, recently used machine learning to produce a more detailed catalog of a magnitude 7.3 tremor in April 2024 that killed at least 18 people on the island and damaged hundreds of buildings. The study, reported at a seismology meeting in April 2025, found the AI-based catalog to be about five times more complete than the one produced by human analysts, and it was made within a day rather than taking months. It revealed new details on the location and orientation of geological faults—information that can help officials better prepare for how the ground might move in future quakes. Such catalogs “will become the standard in every earthquake-prone region in the future,” says team leader and seismologist Hsin-Hua Huang of Academia Sinica in Taiwan.

Forecasting is Still a Problem

So far, AI hasn’t been as successful in tackling another of seismology’s biggest challenges—forecasting the probability of future quakes.

The field of earthquake forecasting deals with general probabilities—such as the chances of a quake of magnitude X happening in region Y over time period Z. Currently, seismologists create quake forecasts using mathematical analyses of past earthquakes, such as a statistical method that relies on observations of how past earthquakes triggered subsequent quakes. This approach works well enough for specific tasks, like understanding how many aftershocks may rattle a region after a Big One. That sort of information can help people in a disaster zone know whether it’s safe to return to their houses or whether more aftershocks might be on the way, threatening to collapse more buildings.

But this kind of analysis can’t always accurately capture the real seismic risk, especially along faults that only rarely yield big quakes and thus aren’t well represented in the seismic record. Seismologists are testing AI-based algorithms for earthquake forecasting to see if they might do better, but so far, the news is tepid. In their best performances, the machine learning analyses are about as good as the standard methods of quake forecasting. “They are not outperforming the traditional ones yet,” says Mousavi, who summarized the state of the field in an August 2025 article in Physics Today.

Overall, though, seismologists see a bright future in using AI to understand earthquakes better.

In one of the more promising experiments, Mizrahi has been trying to use AI to speed up producing aftershock forecasts in the crucial minutes and hours after a large earthquake hits. She and a colleague trained a machine-learning algorithm on the older statistical method of quake forecasting, then unleashed it on its own to see how the AI would do. It did perform much faster than the older, non-AI approach, but there’s still more work to do. “We’re in the process of evaluating how happy we are with it,” says Mizrahi, who published the findings last year in Seismological Research Letters.

In the future, researchers hope to speed up these types of forecasting analyses. Other areas of seismology could eventually benefit, too. Some early research hints that machine learning could be used in earthquake early warning, for instance estimating exactly how much the ground will move in the seconds after an earthquake has started nearby. But the usefulness of this is limited to the few parts of the world that have early warning systems in place, like California and Japan.

Park also cautions about relying too much on machine learning tools. Scientists need to be careful about maintaining quality control so they can be sure they are interpreting the results of any AI analysis correctly, he says.

Overall, though, seismologists see a bright future in using AI to understand earthquakes better. “We’re on the way,” Mizrahi says.

—Alexandra Witze, Knowable Magazine

This article originally appeared in Knowable Magazine, a nonprofit publication dedicated to making scientific knowledge accessible to all. Sign up for Knowable Magazine’s newsletter. Read the original article here.

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