EOS

Post based on material kindly provided by Professor Douglas H. Clark of the Geology Department at Western Washington University. Many thanks to Doug for providing this information.

On 19 March 2026, a c.2000 cubic metre rockslide blocked the northern bound lanes of Interstate 5 near to Bellingham, WA. The road will not fully reopen until later this week.

On 19 March 2026, a substantial rockslide occurred that blocked Interstate 5 just south of Bellingham in Washington State. The landslide blocked the north-bound half of the freeway, which is still closed; the Washington DOT has a decent description of it. Fortunately no one was killed by the failure. The road is not expected to full reopen until 16 April 2026. The landslide is at about [48.69293, -122.44423].

There is an excellent gallery of images of the rockslide on the Cascadia daily site. This image, from the WSDOT blog, shows the aftermath of the landslide:-

The aftermath of the 19 March 2026 landslide onto Interstate 5 near to Bellingham, WA. Image from WSDOT.

KOMO news has a good drone video of the clean up operation:-

The geologic context for the rockfall is that this section of I-5 was cut into the south side of a steep ridge of Miocene Chuckanut Formation, a thick deposit of freshwater sandstones interbedded with thinner shales and coal beds.  As a local Geotech geologist, Dan McShane notes in his blog site, the sandstone in this area is steeply dipping away from the freeway, but prominent joint sets in the sandstone beds (presumably related to their tortuous folding) cause the roadcuts along this section to be particularly susceptible to rockfall failures.  Smaller rockfalls along this stretch caused the DOT to cut the slope back from the freeway to create a rockfall collection zone.  Lidar from the Washington DNR lidar portal shows the near-vertical, north-dipping bedding in the bedrock well (red arrow shows approximate location of the slide):-

LIDAR data from Washington DNR showing the site of the 19 March 2026 landslide onto Interstate 5 near to Bellingham, WA.

A local meteorology prof at University of Washington noted that this March was the wettest March on record (since before the freeway was built) in Bellingham, which almost certainly contributed to the failure:

Total precipitation from 1 to 29 March 2026 near to Bellingham WA.

Although this particular slide-prone area was largely created by the freeway construction, the Chuckanut Formation has been the source of thousands of historic and prehistoric landslides in the area, including some truly massive valley-blocking landslides further to the east in the Cascade foothills (e.g. https://www.flickr.com/photos/wastatednr/51148697281/).   The shear number of landslides in the county is truly impressive (many involving the Chuckanut Formation): 

Mapped landslides to the east of Bellingham, WA. Data from the Washington Geologic Information Portal. Return to The Landslide Blog homepage Text © 2026. The authors. CC BY-NC-ND 3.0
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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.

After a week-and-a-half journey to and around the Moon, the Artemis II crew splashed back to Earth off the coast of San Diego at 5:07 p.m. local time (8:07 p.m. ET) on 10 April.

“From the pages of Jules Vernes to a modern day mission to the Moon, a new chapter in the exploration of our celestial neighbor is complete,” said a NASA announcer as the astronauts splashed down. “Integrity’s astronauts, back on Earth.”

In a news conference on 9 April, the day before splashdown, NASA associate administrator Amit Kshatriya described what NASA astronauts Reid Wiseman, Victor Glover, and Christina Koch, and Canadian Space Agency (CSA) astronaut Jeremy Hansen will have accomplished upon arriving home.

“They will have traveled 400,000 miles. They will have seen what no living person has seen. They will have tested every system on the spacecraft in the environment it was built for. And they will have given us 10 days of data that will shape every mission that comes after,” he said.

 
Related

• Humanity Returns to the Moon with Artemis II
•  NASA: Artemis II Mission
•  Spaceship-to-Spaceship Call
 

In the same news conference, lead flight director Jeff Radigan described the approximately “13 minutes of things that have to go right” prior to splashdown: At 4:53 p.m. local time, the spacecraft entered a 6-minute communications blackout as plasmas formed around the spacecraft in the face of heat reaching 2,200 to 2,760 °C (4,000 to 5,000 °F) and a G level of 3.9. Then, Orion jettisoned its forward bay cover, deployed drogue parachutes at 22,000 feet above Earth, and deployed three more parachutes at 6,000 feet to slow the spacecraft before splashdown.

In their journey to go farther from Earth than humans have ever traveled, the astronauts tested the Orion spacecraft’s life-support, propulsion, and navigation systems; captured images of Earth and the Moon; and conducted several trajectory correction burns.

The world watched as the astronauts on the Orion spacecraft and the International Space Station held a spaceship-to-spaceship call, and as the crew called mission control to request that a lunar crater be named “Carroll” after Commander Reid Wiseman’s late wife, Carroll Wiseman.

When the crew passed behind the Moon on 6 April, they entered a 40-minute planned communication blackout as the lunar surface blocked radio communication with Earth.

“You heard the word[s] ‘together,’ ‘togetherness’ a lot from our crew,” said NASA astronaut Victor Glover, from space, describing the blackout. “I really was hoping that, while we were waiting to get back into contact, that people could just feel that sense of togetherness, that we were all a crew on spaceship Earth.”

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

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 © 2026. AGU. CC BY-NC-ND 3.0
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Editors’ Vox is a blog from AGU’s Publications Department.

Flood inundation models are tools that predict where water flows, how deep it gets, how fast it moves and how long it remains during a flood event. But despite recent advances in flood inundation models, some flood modeling paradigms are being used beyond their range of applicability rather than leveraging the strengths of different methods.

A new article in Review of Geophysics explores the strengths and limitations of different flood modeling methods and calls for an integrated approach to flood modeling. Here, we asked the authors to give an overview of flood inundation models, the challenges of “siloing,” and future directions for research. 

In simple terms, how do flood inundation models work and why are they important?

Flood inundation models take inputs, such as rainfall, ground elevation, river flow, and infrastructure data, and simulate how flooding develops across a given area. In some ways, they function as a replica of the physical world, allowing modelers to approximate how a flood scenario may evolve.

The models matter because they support decisions across a wide range of sectors. Emergency managers use them to plan evacuations and allocate resources. Engineers rely on them to design flood control infrastructure such as levees, bridges, and drainage systems. Regulatory agencies, like FEMA in the United States, use the models to delineate flood zones, which determine where properties are subject to flood risk. Flood inundation models also inform decisions related to public health, agriculture, insurance markets, transportation infrastructure, and environmental management, among many others.

Applications of flood inundation models in different sectors. Credit: Nazari et al. [2026], Figure 1

How have flood inundation models evolved since they first started being developed?

Flood inundation models have evolved significantly over the past century, driven primarily by advances in mathematics, computational power, and data availability. Early models were relatively simple and could only track water moving in one direction along a channel. As the field advanced, models expanded to simulate how floodwater propagates across the broader landscape, including areas far beyond waterbodies.

The availability of high-resolution terrain data, remote sensing, and satellite imagery further transformed the field. Modelers could work with detailed representations of the landscape at regional, continental and even global scales, a scale that was computationally out of reach just decades earlier. High-performance computing made it possible to run complex simulations faster and over much larger areas.

Rather than these different approaches growing together and complementing each other, they increasingly develop in isolation.

More recently, the rise of data-driven approaches, artificial intelligence and machine learning, introduced an additional modeling paradigm, one that learns patterns from observed data rather than solving physical equations. These methods often offer computational efficiency in data-rich environments. However, this rapid diversification has also introduced a challenge. Rather than these different approaches growing together and complementing each other, they increasingly develop in isolation, each evolving within its own methodological boundaries. This divergence and what it means for the future of the field is a defining concern in flood modeling today.

What are the flood inundation modeling methods described in your review article?

Our review groups flood inundation modeling into four broad methods. First are computational models, which are physics-based models that numerically solve equations representing conservation of mass and momentum and are often very robust for representing flood dynamics. Second, with the rise of big data, artificial intelligence and machine learning algorithms proliferated. These methods can be fast and efficient, but they often rely heavily on data, lack physical constraints and offer limited generalizability beyond their training conditions, which is particularly concerning since those “unseen” conditions could be the very extreme events that matter far more than data-rich frequent and milder scenarios. Third are observational and experimental methods, which use field measurements, satellite data, and laboratory studies to describe or analyze flooding; these can help with calibration and validation but usually have limited predictive skill on their own. Fourth are conceptual models, which simplify flood behavior into transparent and efficient rules. These can be useful for planning and broad analyses, but they overlook important hydraulic details.

What is “siloing” in flood inundation modeling and why does it occur?

In our review, “siloing” refers to the tendency of different modeling approaches evolving independently within their own methodological boundaries, with limited exchange or integration across paradigms. A concern is substantial investment on methods with a limited scope, assuming that methods can ultimately overcome their own simplifications and replace other methods. This has particularly been observed in the push to use data-driven and remote sensing paradigms to replace physics-based models, rather than integrating their strengths. This can be due to several reasons. Different applications demand different levels of accuracy, efficiency, predictive skill, and computing power. Some methods are easier to use or better matched with available data. In other cases, modelers may be more familiar with one method than with alternatives, so they continue refining that method even when another approach could solve part of the problem better. Siloing also grows when simplified methods are adopted for convenience or justified by data limitations and computing power constraints, gradually being treated as full replacements for more physically grounded models.

What are some of the challenges that siloing presents?

Siloing slows progress by underusing the strengths of complementary methods.

Siloing creates both scientific and practical problems. One major challenge is that models may be pushed beyond the scope they were designed for. For example, some simplified or data-driven methods can miss key flood dynamics, such as backwater effects, transient flow behavior, meaning how floods change rapidly over time, or infrastructure controls, yet still be used in consequential decisions. Another problem is that siloing slows progress by underusing the strengths of complementary methods. Siloing also makes it difficult to objectively evaluate model assumptions because each modeling community tends to focus on improving its own methods rather than testing where those methods perform best and where they fall short.

What are the pathways for future research in flood inundation modeling?

The main pathway we propose is synergistic integration of various modeling; moving away from developing modeling methods in isolation and toward integrating them so that each method contributes what it does best. This means, for example, using simple or data-driven models to identify where detailed hydrodynamic modeling is most needed, leveraging satellite and field observations to improve other models’ inputs and calibration, and incorporating machine learning in ways that are guided by physical constraints rather than data alone. It also means investing more in physics-based models, experiments and data collection, such as detailed surveys of ground elevation and physical infrastructure, rather than defaulting to simplification as a substitute for that investment. Advances in high-performance computing make this level of integration increasingly feasible.

The goal is not to sacrifice physics just to arrive at faster or more convenient approaches.

The goal is not to sacrifice physics just to arrive at faster or more convenient approaches, but to develop actionable models that are physically grounded, reliable across a range of conditions, and informative for the decisions that depend on them. Advances across all these fronts can help close the gap between physical realism and computational efficiency, making integrated modeling not just an aspiration but an achievable practice.

—Behzad Nazari (behzadnazari@gmail.com, 0009-0000-5568-4735), The University of Texas at Arlington, United States; Ebrahim Ahmadisharaf (eahmadisharaf@eng.famu.fsu.edu, 0000-0002-9452-7975), Florida State University: Tallahassee, United States

Editor’s Note: It is the policy of AGU Publications to invite the authors of articles published in Reviews of Geophysics to write a summary for Eos Editors’ Vox.

Citation: Nazari, B., and E. Ahmadisharaf (2026), Synergistic integration of flood inundation modeling methods, Eos, 107, https://doi.org/10.1029/2026EO265015. Published on 10 April 2026. This article does not represent the opinion of AGU, Eos, or any of its affiliates. It is solely the opinion of the author(s). Text © 2026. 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 Great Salt Lake is disappearing. Driven by decades of water diversions for agriculture, development, and mining, as well as by the warming climate, Utah’s famed lake has lost roughly 73% of its volume since 1850, exposing more than 54% of the lake bed.

The ecological and economic consequences of this decline are well documented, with the latter estimated at more than $2 billion in annual losses.

But a more insidious crisis is also rising as the lake vanishes: Dust from the exposed lake bed, picked up and blown by the wind, appears to be having a measurable mental health impact on the state’s residents.

Our recent research established a desiccated lake–to–mental health pathway, linking declining Great Salt Lake water levels to increased concentrations of hazardous, fine-grained particulate matter (PM2.5) in the air and, ultimately, to a higher prevalence of major depressive episodes (MDEs). In this context, lake desiccation acts as a potent threat multiplier. It does not merely create a new environmental hazard; it compounds existing social vulnerabilities, transforming a hydrological crisis into a chronic public health burden.

The water level of the Great Salt Lake dropped substantially over the past several decades, as shown by these composite images taken by Landsat satellites in June 1985 and July 2022. Credit: NASA Earth Observatory, Public Domain

Previous studies documented important parts of this pathway separately, including links between drying lakes and dust or degraded air quality, and broader associations between PM2.5 exposure and mental health outcomes. Our study brought those links together by analyzing and combining information from various open-access, long-standing datasets collected by different agencies to study changing mental health conditions in Utah between 2006 and 2018.

This integration required more than data assembly. It also required a fundamental shift in how scientists from different fields framed the problem and spoke to one another.

The Friction of Interdisciplinary Collaboration

We had to assemble a research team representing a variety of specializations. Once the team formed, we faced immediate barriers regarding language and standards of evidence.

Our study began with a bold hypothesis: Air pollution from the Great Salt Lake might be affecting both physical and mental health. To investigate this idea, we had to assemble a research team representing a variety of specializations across hydrology, atmospheric science, and mental health—a challenging task considering some potential collaborators indicated they thought the research was too speculative or too far outside conventional disciplinary boundaries to pursue.

Once the team formed, we faced immediate barriers regarding language and standards of evidence. An early challenge involved weighing how different disciplines frame the concept of “ground truth.” In the geosciences, ground truth often refers to calibrated physical measurements from, say, a lake gauge, a monitoring station, or a satellite-validated observation. In mental health research, the evidence base often relies on self-reported symptoms, survey-derived prevalence estimates, and clinical interpretations. Bridging those traditions required trust and a shared understanding that no single dataset could capture the full picture.

We also had to reconcile the ways different disciplines consider a phenomenon’s time frame and impact. Physical scientists are trained to notice anomalies, such as sharp spikes in PM2.5 levels and abrupt departures from recognized patterns in climatology. But depression and other mental health disorders are rarely explained by a single environmental event. More often, depression emerges in the context of multiple events and experiences in someone’s life, as well as of genetic vulnerabilities and epigenetic influences. That understanding led us away from focusing only on short-lived pollution extremes and toward metrics that better captured sustained exposures from multiple environmental factors.

A third challenge involved scale. We had to harmonize high-resolution environmental observations with mental health estimates available only at broad geographic and temporal scales (because public health data are necessarily aggregated and deidentified to protect privacy). This integration forced us to consider what kinds of comparisons we could make responsibly and what kinds of claims the data could genuinely support.

Overcoming these research challenges shaped our study in fundamental ways. Geoscientists are accustomed to looking at environmental variables as direct drivers of change, hence the framing of our initial hypothesis. In public health, however, causality is notoriously difficult to prove when multiple confounding variables from socioeconomic status to personal medical history are at play.

We thus reframed our entire approach to address the question of whether an ecological relationship plausibly exists between pollution and depression based on ecosocial models and data on mental illnesses.

This reframing wasn’t just semantic; it changed our analytical methodology. For example, instead of using simple tests of direct cause-and-effect relationships, we needed statistical approaches that could evaluate grouped differences, main effects, and interaction effects across multiple datasets. For this, we used analysis of variance models to test whether social vulnerability modified the relationship between PM2.5 exposure and major depressive episodes—in other words, whether the same pollution burden translated into different mental health outcomes in counties with different levels of vulnerability.

Reconciling Incompatible Data

The technical backbone of our study involved merging massive public datasets representing several fields of study:

• Hydrology: daily lake level and volume measurements at Great Salt Lake collected by the U.S. Geological Survey (USGS)
• Atmospheric science: daily EPA measurements of PM2.5 concentrations collected by ground stations across each county in Utah, as well as monthly PM2.5 data from NASA’s MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, version 2) reanalysis product to isolate the contribution to overall PM2.5 levels of Great Salt Lake–derived dust
• Sociology: the Centers for Disease Control and Prevention (CDC) Social Vulnerability Index (SVI), a county-level measure released biennially that summarizes community vulnerability to external stressors on the basis of factors such as poverty, disability, minority status, housing, and transportation access
• Mental health: annual, deidentified records of MDE prevalence from the Substance Abuse and Mental Health Services Administration (SAMHSA) harmonized in our analysis to the county level

Figuring out how to use these datasets together presented a significant hurdle because they were never designed to be interoperable.

Figuring out how to use these datasets together presented a significant hurdle because they were never designed to be interoperable and because of temporal and spatial measurement gaps in the datasets. Raw, daily data on fluctuating PM2.5 levels do not easily map onto representations of mental health trends in annual surveys, especially the slow-burning, cumulative experiences of depressive episodes.

We used multiple approaches to solve this incompatibility problem.

We screened the EPA station records of PM2.5 and the MERRA-2 time series for statistical outliers using Z scores. This screening filters out extreme contributions to PM2.5 pollution, such as wildfire-driven spikes, to ensure that any correlations between pollution and MDEs reflected chronic exposure to lake desiccation–derived dust rather than to temporary anomalies.

We also moved beyond raw particulate concentration data and identified a pollution metric that reflects harm to humans. We looked to two key regulatory benchmark thresholds that are based on extensive scientific evidence linking PM2.5 exposure to serious respiratory and cardiovascular health risks: the EPA’s National Ambient Air Quality Standards 24-hour PM2.5 standard of 35 micrograms per cubic meter and the World Health Organization’s more stringent 24-hour guideline of 15 micrograms per cubic meter. (These thresholds are not specific to mental health outcomes, a gap that points to the need for future work evaluating mental health–relevant PM2.5 thresholds more directly.)

By applying these thresholds to the daily PM2.5 data, we determined the number of exceedance days—days during which the 24-hour average exceeded these safety limits—on a county-by-county basis. This metric allowed us to quantify annual county-level doses of exceedance days. It also created a common denominator with the health surveys, making it possible to statistically compare the occurrence of high dust levels resulting from environmental degradation of the Great Salt Lake to population-level mental health outcomes.

Detailing a Dose-Response Relationship

The results of our study revealed a concerning dose-response relationship. Mental health outcomes in our analysis came from grouped county-level SAMHSA estimates of MDE prevalence, which we analyzed and classified into five categories of severity ranging from “very low” to “very high.” We found that higher MDE categories were associated with exposure to more PM2.5 exceedance days. Annual average exceedance days rose from about 9.7 days for the very low MDE group to about 21.7 days for the very high group. Seasonal effects were also apparent, with average exceedance days for those in the high MDE group in winter exceeding 35 days.

Salt Lake City sits just southeast of Great Salt Lake. Credit: Ken Lund/Flickr, CC BY-SA 2.0

The frequency of high-pollution exceedance days was highest in Salt Lake County, which is home to Salt Lake City and more than 1.2 million people and lies directly downwind of Great Salt Lake. Duchesne County, farther east but also notably downwind, also had a high frequency of exceedance days.

In many cities, socioeconomic vulnerability is a strong predictor of an area’s pollution exposure. In Utah, looking at a natural rather than human-made source of pollution, we found the opposite.

Another important finding challenged a traditional environmental justice assumption. In many cities, socioeconomic vulnerability—as gauged by the SVI, for example—is a strong predictor of an area’s pollution exposure because lower-income neighborhoods are often located near industrial centers, transportation corridors, and other emissions sources. In Utah, looking at a natural rather than human-made source of pollution, we found the opposite: The most socially vulnerable counties, such as rural San Juan County in the state’s southeast, saw the lowest PM2.5 exposures because they are far from the lake bed.

Yet social vulnerability still mattered. Our interaction model revealed that social vulnerability significantly modified how exposure to PM2.5 lake dust related to mental health outcomes. In plain terms, the model tested whether the relationship between PM2.5 exceedance days and county-level prevalence of MDEs was the same across counties with different levels of social vulnerability.

Although social vulnerability by itself did not directly affect MDE prevalence to a significant extent, it significantly modified the PM2.5-MDE relationship, indicating that for a given level of pollution exposure, more socially vulnerable counties experienced a disproportionately higher prevalence of MDEs. This trend may arise because these populations have less access to protective buffers that shield against dust exposure and its effects, such as high-efficiency air filtration, stable housing, health care, and coping resources to limit outdoor exposure during peak pollution events, than affluent populations do.

Protecting Public Health

Our findings revealed that the desiccation of the Great Salt Lake is not merely an ecological crisis. It is also a compounding public health challenge that demands responses across sectors and scales. Depression is expected to become the world’s largest disease burden by 2030. And it is already more common among the most vulnerable in society, the very populations that will have the hardest time finding protections against climate change.

A few visitors stand along the shoreline of the Great Salt Lake in 2021. Credit: Farragutful/Wikimedia Commons, CC BY-SA 4.0

At the community level, one approach to the challenge is to deploy interventions to shield vulnerable communities. Current air quality alerts are framed mainly around respiratory and cardiovascular health risks. Expanding these systems to include mental health considerations would better reflect the full range of potential harms associated with repeated dust exposure. Beyond alerts, local governments and health departments can also consider targeted interventions to help those least able to avoid exposure. These interventions could include opening indoor clean-air shelters during severe pollution events—much like cooling centers used during heat waves—and subsidizing air filtration systems and home weatherization.

Regionally, public health cannot be separated from hydrological stability. Shielding people from, and treating the symptoms of, dust exposure without addressing the shrinking lake bed of the Great Salt Lake (or other changes in blue spaces) is an incomplete strategy. Reversing the lake’s decline will require difficult conversations among stakeholders about watershed management, including the possibility of reducing consumptive water use and rethinking the balance between immediate gains from continued diversions and longer-term benefits of ecological preservation. Accounting for the compounding costs of public health crises, infrastructure degradation, and lost ecological services suggests that preserving the Great Salt Lake is not simply an environmental priority but also a long-term investment in regional resilience.

This research demonstrates the critical value of long-term, open-access public data infrastructure while also highlighting a major practical barrier: Environmental and health datasets remain difficult to integrate.

On a broader scale, physical scientists, public health researchers, clinicians, policymakers, and others—who each still largely work in silos—must work across disciplines if we are to anticipate, measure, and reduce the cascading risks posed by climate-driven environmental change.

Our capabilities for tracking environmental cascades—from drought to lake bed desiccation or from wildfire to smoke exposure, for example—have grown increasingly precise. What remains far less developed is our ability to translate physical signals into a fuller understanding of the public health burden presented by these cascades. That disconnect limits both understanding and response and points to the need for integrative approaches that treat environmental change and health as connected parts of a system of exposure, vulnerability, and human consequences.

Further, this research demonstrates the critical value of long-term, open-access public data infrastructure while also highlighting a major practical barrier: Environmental and health datasets remain difficult to integrate across temporal and spatial scales. The challenge we faced in aligning daily atmospheric data with annual health surveys underscores the need to improve interoperability across data systems maintained by agencies such as NASA, NOAA, USGS, EPA, CDC, SAMHSA, and others.

Greater alignment across these datasets—for example, through satellite imaging of blue spaces and air quality alongside exposure sampling in regions of concern—would make it easier to connect environmental change with health outcomes. It would also help to translate knowledge of emerging risks into actionable public health strategies to protect the mental and physical health of the residents of Utah and beyond.

Author Information

Maheshwari Neelam (maheshwari.neelam@nasa.gov), Universities Space Research Association and NASA Marshall Space Flight Center, Huntsville, Ala.; and Kamaldeep Bhui, Department of Psychiatry and Wadham College, University of Oxford, Oxford, U.K.

Citation: Neelam, M., and K. Bhui (2026), Lessons from linking Great Salt Lake desiccation and depression, Eos, 107, https://doi.org/10.1029/2026EO260113. Published on 10 April 2026. Text © 2026. 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.

Editors’ Highlights are summaries of recent papers by AGU’s journal editors. Source: AGU Advances

Machine learning (ML)-based models hold great potential to enhance and perhaps transform simulations of the Earth’s weather and climate across the range from synoptic to seasonal to annual to multi-decadal time scales. However, ML-based models should also produce results consistent with the physical laws of the Earth system. While ML-based models have been tested for weather forecasting, it remains uncertain whether they can produce reasonable responses in long-term simulations under forcings relevant across weather to climate time scales. Therefore, it is essential to perform a broad evaluation across different timescales. In addition, it is important to understand how well the emergent ML techniques can complement conventional physics-based models.

Chen et al. [2026] perform a series of tests that cover systems at the synoptic scale, interannual scale, and under long-term out-of-distribution forcings. This study uses a hybrid model called NeuralGCM, which combines traditional Earth system modeling with ML approaches. For a set of idealized experiments, NeuralGCM produces performs similarly to conventional physics-based Earth system models. However, some limitations were found in simulating extratropical cyclone strength, atmospheric wave responses, and stratospheric warming and circulation responses. In general, the combination of ML with established physics-based modeling represents a promising path forward in achieving weather and climate analyses that require less computing time.

Schematic diagram summarizing the NeuralGCM and Earth System Models. The panels illustrate the core structure of the NeuralGCM model and a simplistic representation of processes included in an ensemble of analyses using an Earth System Model. Credit: Chen et al. [2026], Figure 1 (top panels)

Citation: Chen, Z., Leung, L. R., Zhou, W., Lu, J., Lubis, S. W., Liu, Y., et al. (2026). Hierarchical testing of a hybrid machine learning-physics global atmosphere model. AGU Advances, 7, e2025AV002075. https://doi.org/10.1029/2025AV002075

—Don Wuebbles, Editor, AGU Advances

Text © 2026. The authors. CC BY-NC-ND 3.0
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In March 2026 I recorded 61 fatal landslides causing 520 fatalities, the highest March total on record.

This is my regular update for the number of fatal global landslides, focusing on March 2026. AAs usual, this data has been collected in line with the methodology described in Froude and Petley (2018) and in Petley (2012). References are listed below – please cite these articles if you use this analysis. Data presented in these updates should be treated as being provisional at this stage.

The headline figures are as follows:

March 2026: 61 fatal landslides causing 520 fatalities;

This is very surprising total once again – 61 fatal landslides is the highest March total in my long term dataset – the previous record was 49 events in 2024. The baseline mean (2004-2016) is c.23 fatal landslides.

Loyal readers will know that my preferred way to present the annual data is using the cumulative total number of fatal landslides calculated in pentads (five day blocks). To make this easier to interpret, I have converted the pentads into day numbers through the year (so 1 January is day number 1, 31 December is day number 365).

This is the data for 2026 to the end of March:-

The cumulative total number of fatal landslides through to March 2026, plotted with the long term mean number and the exceptional year of 2024 for comparison.

The factors that are driving this very high level of recorded fatal landslides are not clear to me at this point. Perhaps it is a change in the quality of information I’m collating, although this seems unlikely to be the sole cause. Perhaps it is associated with the rapid degradation that is occurring in mountain areas (more on this to come). Perhaps it is the result of climate change. Interestingly, March 2026 was exceptionally warm compared to the long term record, globally, but it was “only” the fourth warmest March on record. March 2024 was the warmest on record.

This all requires more detailed analysis, which I have yet to do. But, at the moment, 2026 is proving to be a bad year for fatal landslides. A major caveat though is that the early months of the year are not a good predictor of what might happen through the Northern Hemisphere summer months, driven mainly by the SW monsoon in South Asia, the summer monsoon in East Asia and patterns of tropical cyclones.

References

Froude, M. and Petley, D.N. 2018.  Global fatal landslide occurrence from 2004 to 2016.  Natural Hazards and Earth System Sciences 18, 2161-2181.

Petley, D.N. 2012. Global patterns of loss of life from landslides. Geology 40 (10), 927-930.

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When it comes to wildfires, the story may seem straightforward: As forests burn, they release greenhouse gases like carbon dioxide, carbon monoxide, and methane that warm the planet. But in the far northern parts of North America, wildfires don’t always follow the same script.

In a new study published in Nature Geoscience, researchers found that forest fires in Alaska tend to have a warming effect on Earth’s atmosphere but those in western Canada can contribute to net cooling.

“The most surprising aspect is that if you take away this permafrost component, fires in general in Alaska would switch” from a net warming to cooling effect.

Geography and permafrost help explain the discrepancy. When forest fires burn in Alaska, they not only burn the forest but also thaw permafrost. Both of these phenomena release carbon into the atmosphere. Northern Canada also has permafrost, and blazes there also burn trees and the soil layer that anchors them. However, as reported in an influential 2006 study, these fires are more likely to leave behind open spaces that can be blanketed by bright snow in winter. This brighter surface reflects more sunlight, triggering a net cooling effect.

“The most surprising aspect is that if you take away this permafrost component, fires in general in Alaska would switch” from a net warming to cooling effect, said Max J. van Gerrevink, a climate scientist at Vrije Universiteit Amsterdam in the Netherlands and lead author of the study.

Missing Permafrost

Van Gerrevink’s research builds on the landmark 2006 study, which provided an innovative approach to assessing the climate-warming potential of boreal wildfires but didn’t address a key contributing factor: carbon emissions from permafrost. This exclusion meant that while the 2006 finding held true for some boreal regions, it couldn’t be generalized across the board.

“We know that there’s more carbon released than was actually implemented in that study,” van Gerrevink said.

Van Gerrevink and his team tracked the satellite data of all wildfires in Alaska and western Canada from 2001 to 2019. They accounted for possible warming processes such as greenhouse gases released during a fire and permafrost thawing after a fire. They also considered possible cooling processes, including snow-covered landscapes or atmospheric aerosols reflecting sunlight and forest regrowth absorbing carbon dioxide.

“We also trained models, first on historical climate data making the models quite robust and then substituting climate data with future projections,” van Gerrevink added.

They found that even a small number of fires that burn intensely and thaw the carbon-rich permafrost can have a large warming effect. Importantly, as climate warms and snow cover declines, even fires that have a cooling effect may increasingly shift toward a warming in the future.

A 360° View of Wildfires

“Every fire is really ecosystem dependent. When a fire burns, it’s going to burn differently depending on what the surrounding ecosystem structure is,” said Kimberley Miner, an Earth scientist at the NASA Jet Propulsion Laboratory who was not involved in the study. “What this study is pointing out is that’s true in the Arctic too.”

In the new paper, van Gerrevink and his coauthors found that “climate-warming fires occur preferentially in dry, high-elevation, steep permafrost landscapes,” while “climate-cooling fires are driven by longer spring snow exposure and occur more frequently in continental regions near the tree line.”

“I think the study motivates us to think of fires as being more complex than [just] good or bad.”

Dense permafrost layers in some areas of the Northern Hemisphere, Miner explained, mean “we have to think about fires in a really different way, in a much more complete, almost 360° way—not just what’s happening aboveground,” but below the surface too.

Christopher Williams, an Earth system scientist at Clark University in Worcester, Mass., who also was not involved with the study, said its consideration of the relationship between permafrost and wildfire-related emissions could reshape the way scientists think about the ecological effects of fires.

“I think the study motivates us to think of fires as being more complex than [just] good or bad,” he said.

—Saugat Bolakhe (@scigat.bsky.social), Science Writer

Citation: Bolakhe, S. (2026), Alaska’s wildfires heat the planet, but Canada’s cool it, Eos, 107, https://doi.org/10.1029/2026EO260112. Published on 9 April 2026. Text © 2026. 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.

Editors’ Highlights are summaries of recent papers by AGU’s journal editors. Source: Journal of Advances in Modeling Earth Systems

Thunderstorms play a central role in tropical weather as they do not only produce local, extreme rainfall, but also interact with their environment. These interactions, from local to large-scale, can strongly influence both the mean climate and its variability. A new generation of kilometer-scale Global Storm-Resolving Models (GSRMs) is expected to represent these multi-scale processes more realistically by explicitly resolving deep convection. Understanding how storms interact with environmental moisture and temperature, and how these interactions shape the climate system’s internal variability, remains a central challenge for GSRMs.

In a new study, Takasuka et al. [2026] analyze multi-year simulations from three GSRMs (ICON, IFS, and NICAM) to examine how these next-generation models represent convective storms and how these representations relate to their different approaches to modeling convection. Although the models capture the timing of peak rainfall over ocean well, they tend to simulate storms that are too numerous and too small. Moreover, the models differ in the lifecycle of convection, particularly in the transition from shallow to deep convection and in the storage of atmospheric moisture, resulting in different large-scale mean state (e.g. precipitation) and variability (e.g. the Madden-Julian oscillation).

The study highlights how mesoscale coupling between convection and the thermodynamic environment shapes larger-scale tropical weather and climate characteristics, while revealing persisting challenges in representing complex storm processes in GSRMs and identifying key areas where a more realistic representation of convective–environment interactions could lead to more reliable simulations.

Time-height evolution of moisture (color shading) and temperature (blue contours) from 48 hours before to 48 hours after the peak of deep convective storm events over the tropical ocean, shown for reanalysis data (a; observational reference) and three kilometer-scale global storm-resolving models: (b) ICON, (c) IFS, and (d) NICAM. Both moisture and temperature are expressed as deviations from the ±48-hour mean. Credit: Takasuka et al. [2026], Figure 6 (a-d)

Citation: Takasuka, D., Becker, T., & Bao, J. (2026). Precipitation characteristics and thermodynamic-convection coupling in global kilometer-scale simulations. Journal of Advances in Modeling Earth Systems, 18, e2025MS005343. https://doi.org/10.1029/2025MS005343

—Jiwen Fan, Editor, JAMES

Text © 2026. 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.

While Saturn has 274 confirmed moons in its orbit, its largest moon, Titan, is of particular interest to researchers due to its similarities to Earth. A new article in Reviews of Geophysics explores the geophysical parallels between Earth and Titan, and how scientists could use field work on Earth to learn more about both worlds. Here, we asked the lead author to give an overview of Titan…

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Researchers have found evidence of a sandstorm on Mars that occurred about 3.6 billion years ago, marking the first time a sandstorm has been recognized in the Martian stratigraphic record. They published their findings in Geology. It’s not that scientists didn’t know that wind once blew on Mars. It does so now, and features on the planet’s surface, like dry riverbeds…

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The basic ingredients for life as we know it are common in the cosmos. Scientists are still learning which of those ingredients were present on primordial Earth, and how they combined to make life remains an unsolved mystery. However, many researchers now think many of the molecules necessary for life were already present in the nebula that grew into our solar system, which would mean the…

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Earth’s ice sheets are changing rapidly in response to anthropogenic climate change, and these changes are modulated by their basal topography. Visualizing the landscape that lies beneath the East Antarctic Ice Sheet not only allows glaciologists to improve model projections of future ice sheet change, but also provides a glimpse of a landscape hidden beneath ice. Paxman et al. [2026] used…

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An Iron Age burial mound in Norway has been reinterpreted as being a memorial for a catastrophic landslide during a period of climatic instability. There is a very interesting article (Gustavsen 2026) in the European Journal of Archaeology that re-examines an Iron Age mound known as Raknehaugen (Rakni’s Mound) in Norway. This mound has, until now, been interpreted as being the burial…

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Because they are used in everything from cosmetics to dental floss to nonstick pans, per- and polyfluoroalkyl substances (PFAS) are frustratingly abundant in our environment, including in our food, rain, and drinking water. They’re persistent, too, earning their nickname “forever chemicals,” and have been linked to health effects ranging from cancers to liver toxicity to reduced fertility.

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Machine learning allows us to detect millions of tiny earthquakes, but our current tools struggle to process this “data tsunami” with high precision. While a popularized mathematical approach called “Bayesian inference” can tell us exactly how reliable an earthquake’s location is, it is usually too slow to handle such massive amounts of information. This is especially true for “double-difference”…

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Every year, the World Meteorological Organization, or WMO, tracks a set of key climate indicators—including the amount of carbon dioxide in the atmosphere and the Earth’s temperature—to assess how global warming is progressing. In their latest report, released last Sunday, the authors decided to include a new measure: the Earth’s energy imbalance. “Climate change is often discussed in…

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Distributed Acoustic Sensing (DAS) is a relatively new technique that uses optic cable to measure ground motion along its length, effectively turning the cable into thousands of vibration sensors and which measures dynamic strain over long distances. DAS is now widely used as an alternative to conventional geophones and seismometers. In practice, DAS cables are typically buried to ensure good…

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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.

In a short markup meeting this morning, a Senate committee passed a 17-bill package aimed at strengthening NOAA’s weather research programs and forecasting capabilities.

After years of development, the Weather Research and Forecasting Innovation Reauthorization Act of 2026 was officially introduced to the Senate last week by a bipartisan group of Senators from Alaska, Delaware, Hawaii, Kansas, Montana, Nevada, Texas, and Washington.

The bill was passed without markup and will now go to the Senate floor for a full chamber vote.

 
Related

•  The Weather Act
• Cantwell, Cruz, Colleagues Introduce Weather Act Reauthorization to Modernize Weather Forecasting and Research
•  Markup of NASA Reauthorization, NOAA Weather Bill
 

The Weather Act “is aimed at improving the accuracy and actionability of forecasts and weather warnings, as well as modernizing weather systems,” said Sen. Ted Cruz (R-Texas), Chairman of the Senate Committee on Commerce, Science, and Transportation, in his opening statement. “It addresses hurricanes, tornadoes, wildfires, landslides, droughts, and atmospheric rivers.”

In her opening statement, Sen. Maria Cantwell (D-Wash.) cited recent examples of costly natural disasters in the United States, including atmospheric rivers in western Washington in December 2025, September 2024 floods in North Carolina in the wake of Hurricane Helene, and the January 2025 Los Angeles fires. Other examples include the devastating July 2025 floods in Texas, and the hundreds of tornadoes across the country last year. In 2025 alone, Cantwell noted, weather disasters cost the United States $115 billion.

AGU’s executive director Janice Lachance voiced AGU’s support for the bill in a press release from the Senate Committee on Commerce, Science, and Transportation.

“The Weather Act Reauthorization Act strengthens the nation’s weather enterprise so scientific advances move more quickly from the lab to forecasts, helping emergency managers, farmers, and families make informed decisions when it matters most. AGU strongly supports this bipartisan effort to ensure science continues to protect public safety, support economic stability, and build national resilience,” she said.

If passed, the Weather Act would, among other changes:

• Update or expand the Tsunami Forecasting and Warning Program, the Hurricane Forecast Improvement Project, and the Tornado Warning Improvement and Extension Program
• Establish an atmospheric river forecast improvement program and require the U.S. Geological Survey and NOAA to consider the risks of atmospheric rivers in programs to prepare for and respond to landslides
• Create a project to improve marine fog forecasts
• Establish an official Fire Weather Services Program within NOAA
• Improve drought monitoring capability
• Advance the accuracy of space weather forecasting

At the markup meeting, the committee also approved the NASA Transition Authorization Act of 2025, which includes dozens of priorities, such as directing NASA to develop a permanent Moon base, extending the ISS through 2032, and requiring that two commercial space stations be launched before the ISS is retired.

“Both of these pieces of legislation represent, I believe, critical green lights that use science to basically move the United States forward on technology and innovation so the United States can lead in both space and weather,” said Sen. Maria Cantwell (D-Wash.)

—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 © 2026. AGU. CC BY-NC-ND 3.0
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Editors’ Vox is a blog from AGU’s Publications Department.

After a 6-year term as Editor-in-Chief of Tectonics, Taylor Schildgen has handed over the reins to Giulio Viola. Here, Dr. Schildgen reflects on her tenure as Dr. Viola discusses his priorities for the journal moving forward.

Wrapping up – Reflections from Outgoing Editor-in-Chief Taylor Schildgen

Taylor Schildgen, the outgoing Editor-in-Chief of Tectonics.

It has been a great privilege, and sometimes a challenge, to serve as Editor-in-Chief of Tectonics for the past six years. Only a few months into my term, which started in January 2020, many of us long accustomed to field-based work and teaching scrambled to adjust to travel restrictions, loss of access to classrooms, offices, and laboratories, and all of the personal challenges associated with the isolation and illness of the pandemic. Writing manuscripts quickly proved to be one relatively clear path of productivity for those of us with at least a little data on hand.

The associated onslaught of manuscripts (approximately 30% increase in submissions above pre-pandemic levels) quickly highlighted our need for greater geographic diversity on our editorial board, and an overall greater number of Associate Editors to help shepherd manuscripts through the review process. I’m delighted that in that process, we also managed to bring a higher percentage of women onto the editoral board, as well as people whose specialities helped to increase the range of the board’s expertise. The editorial board’s need to evolve and remain representative of the authorship community, with regards to geographic, methodological, and process expertise, will remain a challenge as the field itself continues to evolve, incorporating new technologies and new priorities.

Parallel to these efforts, and together with the other AGU journals, we have aimed to better clarify the Aims and Scope of each journal, to ensure the best chances that manuscripts submitted will be handled by Editors, Associate Editors, and reviewers that are most capable of providing constructive reviews. Since these adjustments, for manuscripts that go out to review, the median time to first decision has been about 2.5 months in the last three years, and the median time to final decision has ranged from 5 to 6 months.

Since 2022, our need to make our science more open and accessible led to the implementation of FAIR (findable, accessible, interoperable, readable) data guidelines at all AGU journals, together with Plain Language Summaries in most journals. I was pleased by the speed and relative ease with which these guidelines were taken up, and that the members of our community and editorial board were among the first to suggest modifications of the guidelines and helped to craft guidelines for authors.

Political threats to science have always existed, and on some level have always directly impacted our ability to conduct research. But for the AGU journals, never has the political threat to science and scientific publication been more acute than with the inauguration of Donald Trump as United States president in January 2025. Withholding of grant money awarded by the U.S. National Science Foundation, widespread layoffs at nearly all governmental research agencies, and uncertainty in the funding future have hampered both ongoing projects and the future careers of many of our deeply valued, often early-career colleagues. The Editors of Tectonics, led by Djordje Grujic, published an editorial in April 2025, “Tectonics in Turbulence: Defending Science in Unstable Times,” to highlight the impacts of these threats and provide links to effective counter actions.

Moreover, the hypocritically named “Restoring Gold Standard Science” executive order of 25 May 2025 purports a need for political nominees to assess which science can and cannot be published. AGU journal Editors-in-Chief, led by Michael Wysession, published a response in August 2025 titled “The Executive Order ‘Restoring Gold Standard Science’ is Dangerous for America” in AGU Advances to this blatant move toward censorship and attempt to sow distrust in science. Most recently, threats of detention by Immigration and Customs Enforcement, even for those with work permits, have chilled what was once a vibrant and open research environment. These changes not only affect U.S. based researchers, but also international colleagues who had plans for or who still hope to conduct research stays in the U.S. Regardless of the outcome of the next mid-term and subsequent presidential election, vulnerabilities to a global powerhouse in scientific research have been vividly exposed.

Finding ways to articulate the value of our research, both on practical, applied levels and for the pure satisfaction of human curiosity, remains crucial.

How do we move forward? Finding ways to articulate the value of our research, both on practical, applied levels and for the pure satisfaction of human curiosity, remains crucial. Sharing our research widely, with appropriate context so that results are reproducible and can be built upon, is a necessity. Holding ourselves and our colleagues to the highest level of scientific rigor and ethical behavior is a basic tenet of our work. And reaching out for new perspectives from throughout the diverse membership of our community is likely the best route to solving our hardest problems. Publications at Tectonics play but a small role in these aims, but facilitate the kinds of broad international collaborations and networks that accomplish far more lofty goals, and can provide a buffer against the negative impacts of any given political administration.

I wish to express my sincerest gratitude to our authors, who decide to publish their excellent work and innovative ideas in Tectonics; to our reviewers, who generously contribute their time to providing feedback and guidance to authors; to our Associate Editors, who help guide authors through this process and often provide additional constructive comments; to the AGU staff, who help manage communication and trouble-shoot the challenges we all encounter with the GEMS online submission system; to the Editors-in-Chief of the other AGU journals, who create a community of shared experiences and support that help AGU journals as a whole to adapt to changes in the publication landscape and maintain a forward-looking perspective; and to the other Editors of Tectonics (Laurent Jolivet, Margi Rusmore, Djordje Grugic, Federico Rossetti), who have kept a watchful eye over all, including the direction of the journal, the work load of the board, and were a continuous source of helpful advice to both me and the AGU staff regarding what we can do to improve the experience for everyone involved with the journal.

I have full confidence that the new Editor-in-Chief will take up leadership of the journal with the level of energy, enthusiasm, and care that this flagship of our community deserves.

Finally, I thank John Geissmann, the previous Editor-in-Chief of Tectonics who first brought me onto the editorial board in 2014, and since that time has been a true friend and mentor in various aspects of publications and life. It has been a tremendous pleasure and honor to work with you all. I have full confidence that the new Editor-in-Chief of Tectonics, Guilio Viola, will take up leadership of the journal with the level of energy, enthusiasm, and care that this flagship of our community deserves. And I look forward to reading your work in Tectonics.

Looking forward – Aspirations from Incoming Editor-in-Chief Giulio Viola

Giulio Viola, the new Editor-in-Chief of Tectonics.

I am grateful and honored to succeed Prof. Taylor Schildgen as Editor-in-Chief of Tectonics. I have long considered this journal to be at the forefront of publishing high-impact, multidisciplinary research on the evolution, structure, and deformation of the Earth’s lithosphere through time. Since publishing my first paper in Tectonics more than twenty-five years ago, I have always admired its blend of methodological rigor, vision, and editorial quality, all aspects that have grown even stronger under Taylor’s leadership.

After a career devoted to studying deformation from the grain to the plate scale, mentoring young scientists, and serving on editorial boards, I am thankful for the opportunity to help guide the journal through the scientific and publishing challenges ahead. Together with the Editors and the renewed board of Associate Editors, I hope to build further on the journal’s already strong foundation, continue to develop its strategic vision, and explore new topics and directions for our community.

My immediate priority is to improve turnaround times while maintaining the highest scientific standards and adhering to a clear and well-defined strategic plan for the journal’s scope. I want to offer authors a transparent, straightforward, and efficient editorial path, which I believe is one of the most important aspects of scientific publishing. By making our processes more transparent and efficient, we can allow authors to focus on what matters most, i.e., producing excellent science for our readers, and attract even more outstanding contributions.

Looking ahead, I see important opportunities for Tectonics. The societal relevance of our field has never been greater. Research in tectonics informs earthquake hazard assessment, critical metals exploration, waste disposal, energy storage, and the broader energy transition. Inspired by the “Challenges and Opportunities for Research in Tectonics” white paper prepared for the U.S. National Science Foundation, I hope to see even more contributions that address these pressing needs while continuing to support fundamental, curiosity-driven research.

We will continue to broaden the geographical representation of our editorial board and reviewer community, and we will explore mentorship initiatives to support early-career researchers, especially those from underrepresented regions, as well as early-stage Editors.

I am committed to fostering an open dialogue in which anyone interested in the journal and in the future of the journal can contribute.

The publication landscape is evolving rapidly. How do we keep authors, reviewers, and readers engaged? What role should new technologies, including generative AI, play in scholarly publishing? How can we reach a broader, truly global audience beyond the English-speaking community, especially given the societal impact of much of our research? Not all the answers are clear to me, but I am committed to fostering an open dialogue in which anyone interested in the journal and in the future of the journal can contribute.

No Editor-in-Chief works alone. The success of Tectonics depends on the dedication of editors, associate editors, reviewers, authors, readers, and our publisher. I hope to contribute scientific breadth, editorial experience, and a genuine commitment to an engaged community.

Tectonics has been an important part of my professional life for over two decades, and I take on this role with both enthusiasm and a strong sense of responsibility. I hope that new generations of researchers will feel the same appreciation for the journal and will continue to choose it to publish their very best work.

—Taylor Schildgen (tschild@gfz.de, 0000-0002-4236-4609), GFZ Helmholtz Centre for Geosciences, Germany; and Giulio Viola (giulio.viola3@unibo.it, 0000-0002-8383-3328), Università di Bologna, Italy

Citation: Schildgen, T., and G. Viola (2026), Editorial handover at Tectonics, Eos, 107, https://doi.org/10.1029/2026EO265005. Published on 4 March 2026. This article does not represent the opinion of AGU, Eos, or any of its affiliates. It is solely the opinion of the author(s). Text © 2026. 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.

Editors’ Highlights are summaries of recent papers by AGU’s journal editors. Source: AGU Advances

On very large scales, the precipitation response to warming is sometimes summarized as the “wet gets wetter and the dry gets drier.” This wet-gets-wetter response suggests that regions of tropical rainfall contract and intensify with warming. Ample evidence supports this response for the case of the annual-mean thermally driven Hadley circulation, in which moist air ascends near the equator and descends in the subtropics.

Sokol et al. [2026] test whether this response also applies to east-west overturning circulations, like the Pacific Walker circulation, in which air ascends in the western tropical Pacific and descends in the Eastern Pacific. In their idealized simulations of the Walker circulation, they find the opposite response: rainy regions expand as the surface warms, and the mean rainfall within them decreases, i.e., a “wet-gets-drier” response. They show that this response is driven by a rapid slowdown of the Walker circulation with warming, which is connected to changes in the vertical structure of the circulation. 

Citation: Sokol, A. B., Merlis, T. M., & Fueglistaler, S. (2026). No “wet gets wetter” in kilometer-scale mock-Walker circulations. AGU Advances, 7, e2025AV002040. https://doi.org/10.1029/2025AV002040

—Don Wuebbles, Editor, AGU Advances

Text © 2026. The authors. CC BY-NC-ND 3.0
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