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Editors’ Highlights are summaries of recent papers by AGU’s journal editors. Source: AGU Advances Tracer field structure for different values of wave amplitude represented by parameter ϵ. Small scale features of the tracers are getting more structured with increasing amplitude. Credit: Sanjay and Thomas [2026], Figure 8

Ocean waves originate from a movement of energy, with limited displacement of fluid particles along the main wave direction. For this reason, they are often thought to be ineffective in stirring and mixing of oceanic tracers like heat, salt, carbon and others. However, recent observations of tracer dispersion at spatial scale lower than 10 km highlighted the urgency of gaining an improved understanding of the role of waves to determine dispersion of passive tracers.

Using numerical models and theoretical considerations, Sanjay and Thomas [2026] look at how waves affect the movement of tracers in wave-rich environments. They find that wave effects are more important for lateral stirring and mixing than was previously thought. The results suggest solutions to improve the accuracy of large-scale ocean models, therefore reducing tracer modeling biases, and ultimately improving predictions of ocean climate variability.

Citation: Sanjay, C. P., & Thomas, J. (2026). Irreversible stirring and mixing of tracers by oceanic internal gravity waves. AGU Advances, 7, e2026AV002429. https://doi.org/10.1029/2026AV002429

—Alberto Montanari, Editor-in-Chief, AGU Advances

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.

A vast sheet of volcanic rock in the Andes has preserved clues to a buried landscape that existed 22 million years ago, according to a new study led by UCL (University College London) researchers.

For the first time, important features of the Antarctic coastal zone have been mapped at high resolution around the continent by training an artificial intelligence tool to accurately recognize grounded icebergs in satellite radar images.

Northern India experiences some of the world's worst winter air pollution, with dangerous episodes often prompting emergency control efforts. In major cities like Delhi, the Air Quality Index has reached levels above 999—over 30 times the World Health Organization's safety limit.

Earth’s surface and atmosphere constantly exchange energy and gases such as water vapor and carbon dioxide through evapotranspiration (ET), photosynthesis, and respiration. Measuring these exchanges, which regulate fundamental Earth system processes affecting weather and climate and the cycling of carbon and other critical ecosystem components, has challenged scientists for centuries.

Until the 1980s, for example, technologies for measuring ET (a term sometimes used synonymously with “evaporation,” as some scientists do not distinguish between plant transpiration and other sources of evaporation) lagged behind those for other meteorological measurements. In his 1979 presidential address to the Royal Meteorological Society, John Monteith recalled how 19th century British meteorologist G. J. Symons referred to evaporation as “the most desperate branch of this desperate science” of meteorology. Monteith then explained, “One of the main reasons for despair was the lack of techniques for measuring evaporation over natural surfaces.”

Addressing urgent socioenvironmental issues, including population growth, increasing demands for food, and changing patterns of land use and water availability, demands reliable, direct measurements and robust models characterizing surface-atmosphere exchanges. Today, such measurement and modeling approaches are increasingly applied to evaluate agricultural water consumption, assess the effects of land management practices on carbon stocks, monitor and predict drought and wildfire events, and quantify the impacts of these phenomena on human and ecosystem health.

Despite theoretical and technical advances in recent decades, researchers have been plagued by the energy balance closure problem.

Since the 1980s, eddy covariance (EC) systems—comprising collocated wind and gas sensors mounted on towers—have emerged as the preferred means for measuring field-scale ET and exchanges of trace gases and heat. Yet despite theoretical and technical advances in recent decades, researchers have been plagued by the energy balance closure (EBC) problem [Hicks and Baldocchi, 2020; Mauder et al., 2020, 2024]. The crux of this problem is that the sum of surface-atmosphere fluxes measured by EC often fails to match the energy available and stored within the ecosystem, violating the fundamental energy conservation principle.

Currently, the community of EC practitioners does not coordinate research efforts and applications investigating the EBC problem. This lack of coordination has limited the advancement of techniques to address uncertainty in flux measurements, conveying doubt to nontechnical audiences and potentially influencing management and policy decisions governing agricultural and energy production, water allocations, ecosystem services, and weather adaptation strategies.

The accelerating adoption of EC approaches for scientific purposes and regulatory applications is compelling scientists to address the EBC problem collectively. As part of this effort, members of the EC community convened at an AGU Chapman Conference in 2025—outcomes of which inform this article—to discuss long-standing questions about EBC and to spur development of clear, coordinated best practices for collecting, correcting, analyzing, and applying EC measurements and datasets.

The Books Aren’t Balanced

Eddy covariance measures the sensible and latent heat fluxes in the surface energy balance (Figure 1) [e.g., Lee et al., 2005; Aubinet et al., 2012; Foken, 2017; Burba, 2022; Lalic et al., 2026]. These fluxes contribute, respectively, to changes in the temperature and the evaporation of water.

Fig. 1. The basic components of an EC system used for surface energy balance measurements are illustrated. In principle, the sum of sensible (H) and latent (LE) heat fluxes (i.e., the “turbulent flux”) should be the same as the available energy in the near-surface environment (i.e., the net radiation, Rn, minus the soil heat flux, G). Often, heat (energy) storage in the air and canopy layer below the EC system are neglected, as is advection (horizontal transport) of energy from the surrounding landscape.

EC sensors measure vertical wind velocities (updrafts and downdrafts), air temperatures, and water vapor concentrations at sampling frequencies of 10–20 hertz (10–20 observations per second). Sensible and latent heat fluxes are calculated on the basis of covariances of vertical wind speed and either temperature or water vapor concentration, respectively. In other words, the method quantifies the extent to which fluctuations in temperature and water vapor contribute to the vertical transport of sensible and latent heat. These covariances are typically evaluated over 30-minute or 1-hour averaging periods.

In principle, the sum of sensible and latent heat fluxes should equal the available energy in the near-surface environment.

In principle, the sum of sensible and latent heat fluxes should equal the available energy in the near-surface environment. The available energy is normally simplified as the net radiation—itself calculated as the difference between incoming and outgoing (reflected and emitted) shortwave (i.e., solar) and longwave radiation—minus the flux of heat conducted through soil, while other storage terms are often neglected (Figure 1).

In practice, however, the measured combined flux rarely balances the available energy, calling into question the accuracy and validity of flux measurements. Mismatches between these measurements and model calculations are more noteworthy when applied across large spatial scales, heterogeneous landscapes, and short durations.

Perceived inaccuracies influence how a wide range of data users, such as water resource managers, farmers, ranchers, and modelers, choose to interpret EC measurements and raise unanswered questions about the application of these data: Is our theoretical understanding of EC accurate? Should measured sensible and latent heat fluxes be adjusted to force EBC to be consistent with model assumptions, and if so, are adjustments based on theory possible and clearly justified? Or rather, are measured EC fluxes accurate while our knowledge of the full micrometeorological system affecting the surface energy budget is lacking?

The Colorado River Conundrum

Uncertainties in our understanding of EBC and the accuracy of EC measurements bear on many applications, including, most prominently, water use accounting in agriculture. Globally, irrigated farming accounts for nearly 70% of freshwater withdrawals and produces about 40% of the world’s food. In the United States, irrigation consumes approximately 45% of freshwater withdrawals, and the farming it supports contributes more than 50% of the country’s crop value. With water supplies limited, effective water management is critical to food security and economic stability.

EC instrumentation extends from a tower above a semiarid grassland landscape near the Santa Rita Mountains in southern Arizona. (Instrumentation appearing in the photo is shown for illustrative purposes only and does not constitute official endorsement or approval by the U.S. Department of Agriculture or the Agricultural Research Service of the manufacturer’s products or services to the exclusion of others that may be suitable.) Credit: Russ Scott/U.S. Department of Agriculture, Agricultural Research Service, Public Domain

EC measurements narrow an information gap critical in agricultural water management by helping to evaluate models of ET. Uncertainty in ET models and measurements undermines confidence in the monitoring of consumptive water use and exacerbates global concerns about water availability.

The Colorado River system, with its well-documented water shortages, offers an existential use case in which high-quality, validated evapotranspiration models are needed.

The Colorado River system, with its well-documented water shortages, offers an existential use case in which high-quality, validated ET models are needed. A century ago, the 1922 Colorado River Compact established guidelines for managing and allocating the river’s water among Arizona, California, Colorado, Nevada, New Mexico, Utah, and Wyoming. Today, the seven Colorado basin states are renegotiating these allocations while attempting to account for prolonged drought, increased evaporation, higher temperatures, and decreasing snowpacks.

This effort requires a reliable method, validated by ground truth observations, for quantifying the use of the river’s water and fairly apportioning the available supply. EC observations provide this validation of remotely sensed ET models, which in turn provide an equitable and uniform means of monitoring ET and water use across large geographic regions.

One such effort using this approach is OpenET, which produces daily ET data products across the continental United States. OpenET evaluates model outputs against EC measurements that are typically closed and has found that ensemble methods combining multiple model outputs produce the most reliable agreement with EC.

Benchmark evaluations for croplands indicate a monthly mean absolute error of roughly 15% between OpenET’s model-derived estimates of ET and estimates from EC data [Volk et al., 2024]. Evaluations comparing daily model outputs and EC data are closer to 25%, however, and evaluations for other land cover types tend to exhibit even greater errors. Dilemmas in these benchmarking validations include how to evaluate EBC and account for closure in reporting uncertainty, as well as whether to adjust measured ET values to close the theoretical energy balance.

EBC also has substantial practical implications for assessing water availability and water budgets. The FLUXNET 2015 effort offered the first globally standardized, quality-controlled, and uncertainty-quantified dataset of ecosystem exchanges of carbon dioxide and latent and sensible heat fluxes [Pastorello et al., 2020]. Analyses of FLUXNET 2015 data indicate that measured sensible and latent heat fluxes over natural ecosystems account for an average of about 80% of the available energy [Mauder et al., 2024]. With this imbalance scaled to major biomes, the missing residual energy contributes to large differences in estimated water use at basin, regional, and continental scales. At such scales, the relative uncertainty associated with incomplete EBC ranges from approximately 10% to 25%, with even greater uncertainties reported in European datasets (Figure 2).

Fig. 2. Forcing EBC has a large influence on annual global mean estimates of terrestrial latent (LE) and sensible (H) heat fluxes (top), stated as megajoules per square meter per year. The means of forcing closure represent a large source of uncertainty that is not uniform across continents (bottom). Closure was either not forced (None), applied preserving the Bowen ratio (BR = H/LE), or assuming all the missing or residual energy should be added to either LE or H (RES). For details on how these values were derived, see Jung et al. [2019]. Values in the bottom panel were calculated from the LE (blue) and H (red) estimates from different closure scenarios as a ratio of (RES − None)/None. Credit: Mauder et al. [2024], CC BY 4.0 Standardizing Methods for the Preferred Flux Measurement Technique

Despite the challenges of EBC, many scientists regard EC as the preferred approach for measuring land surface heat and gas fluxes. It is the most direct measurement of the turbulent fluxes, the theory supporting EC flux estimates is robust, and the required instrumentation has improved significantly over the past half century.

Eddy covariance measurements have been performed over thousands of sites worldwide, covering diverse landscapes and climates.

As a result, EC measurements have been performed over thousands of sites worldwide, covering diverse landscapes and climates. Many studies have compared EC to other measurement techniques and found that it is the most reliable method for measuring surface fluxes at the hectare scale and answering critical questions about water and carbon fluxes [e.g., Pastorello et al., 2020].

Paradoxically, the scientific community has not yet developed and agreed on standards for applying the EC method. Several observation networks involving national and international collaborations, such as AmeriFlux, the Integrated Carbon Observation System, the National Ecological Observatory Network, and others, have contributed to emerging standards by providing instrument deployment guidelines, data processing protocols, and standardized datasets.

Yet most scientists experienced in micrometeorology struggle to commit to specific standards when deploying EC instrumentation. At the same time, growing demand for near-real-time data is pushing scientists to deploy EC systems rapidly and with diminished emphasis on methodological nuances and sensor uncertainties. While the scientific community acknowledges the need for better surface flux measurements for addressing societal challenges, methodological developments are not yet able to fully meet those needs.

This dilemma has motivated a new community effort to establish accepted EC standards and progress toward solving the EBC problem. Aims for this effort include improving understanding of errors in surface energy balance terms, reducing systematic EC biases contributing to EBC gaps, cataloging the influence of ecosystem type and land cover on closure and measurement uncertainties, and describing corrections from theory that can compensate for measurement biases.

Those aims serve as objectives for a forthcoming special collection of studies emerging from last year’s Chapman conference. The collection will explore the design of EC observational networks and the effects of land surface heterogeneity and data processing on flux estimates and EBC (Figure 3). The collection will also consider how to define EC data quality related to the use of best measurement practices and EBC metrics for practical management applications and socioeconomic decisionmaking, as well as the implications of EBC for model validation.

Fig. 3. This schematic offers a qualitative interpretation of the magnitude of EBC as a function of measurement timescale and landscape homogeneity based on a plethora of EC observations and discussions at the 2025 AGU Chapman Conference. Terms in the definition of EBC beside the color scale bar are defined in Figure 1. Higher closure values, which indicate better agreement between total heat fluxes measured by EC and the available energy, typically occur when longer timescales are considered and when landscape cover is more homogeneous.            

Ultimately, this community effort will identify critical research priorities and fill knowledge gaps after nearly half a century of EC research. It also looks to further develop the technique for real-world needs, from quantifying water consumption and resource availability to illuminating how changing landscapes are affecting weather and human and ecosystem health.

Acknowledgments

More information about the special collection, which will be published across several AGU journals, including Geophysical Research Letters, Journal of Advances in Modeling Earth Systems, Journal of Geophysical Research: Atmospheres, Journal of Geophysical Research: Biogeosciences, and Water Resources Research, is available in the “Call for Papers.” Additional contributions to the collection are encouraged.

References

Aubinet, M., T. Vesala, and D. Papale (Eds.) (2012), Eddy Covariance: A Practical Guide to Measurement and Data Analysis, Springer, Dordrecht, Netherlands, https://doi.org/10.1007/978-94-007-2351-1.

Burba, G. (2022), Eddy Covariance Method for Scientific, Regulatory, and Commercial Applications, LI-COR, Lincoln, Neb., www.licor.com/resources/books/ec-book.

Foken, T. (2017), Micrometeorology, 2nd ed., Springer, Berlin, https://doi.org/10.1007/978-3-642-25440-6.

Hicks, B. B., and D. D. Baldocchi (2020), Measurement of fluxes over land: Capabilities, origins, and remaining challenges, Boundary Layer Meteorol., 177, 365–394, https://doi.org/10.1007/s10546-020-00531-y.

Jung, M., et al. (2019), The FLUXCOM ensemble of global land-atmosphere energy fluxes, Sci. Data, 6, 1–14, https://doi.org/10.1038/s41597-019-0076-8.

Lalic, B., et al. (Eds.) (2026), Micrometeorological Measurements: An Introduction for Beginners, Springer, Cham, Switzerland, https://doi.org/10.1007/978-3-032-03884-5.

Lee, X., W. Massman, and B. Law (Eds.) (2005), Handbook of Micrometeorology: A Guide for Surface Flux Measurement and Analysis, Atmos. Oceanogr. Sci. Library, vol. 29, Springer, Dordrecht, Netherlands, https://doi.org/10.1007/1-4020-2265-4.

Mauder, M., T. Foken, and J. Cuxart (2020), Surface energy balance closure over land: A review, Boundary Layer Meteorol., 177, 395–426, https://doi.org/10.1007/s10546-020-00529-6.

Mauder, M., et al. (2024), Energy balance closure at FLUXNET sites revisited, Agric. For. Meteorol., 358, 110235, https://doi.org/10.1016/j.agrformet.2024.110235.

Pastorello, G., et al. (2020), The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data, Sci. Data, 7, 225, https://doi.org/10.1038/s41597-020-0534-3.

Volk, J. M., et al. (2024), Assessing the accuracy of OpenET satellite-based data to support water resource and land management applications, Nat. Water, 2, 193–205, https://doi.org/10.1038/s44221-023-00181-7.

Author Information

William P. Kustas (bill.kustas@usda.gov), Agricultural Research Service, U.S. Department of Agriculture, Beltsville, Md.; Jeffrey Wood, University of Missouri, Columbia; Jason Kelley, University of Idaho, Moscow; Nicolas Bambach, University of California, Davis; and Jose D. Fuentes, Pennsylvania State University, University Park

Citation: Kustas, W. P., J. Wood, J. Kelley, N. Bambach, and J. D. Fuentes (2026), Seeking closure to better understand Earth’s energy balance and water resources, Eos, 107, https://doi.org/10.1029/2026EO260281. Published on 11 September 2026. Text not subject to copyright.
Except where otherwise noted, images are subject to copyright. Any reuse without express permission from the copyright owner is prohibited.

It takes Pluto 248 years to complete one journey around the Sun. Humanity has known of its existence for less than half of that time, and we are still discovering the myriad ways that Pluto is unlike other celestial objects. Among other oddities: Pluto is the only object beyond Neptune’s orbit to have an atmosphere.

But that might not always be the case.

Because Pluto experiences such a drastic change in the amount of sunlight it receives throughout its 2-century year, planetary scientists have theorized that Pluto’s nitrogen-dominated atmosphere might come and go with its seasons. Now, new research suggests that Pluto’s atmospheric pressure is beginning to drop, potentially heralding atmospheric collapse ahead of Plutonian winter.

Power of the Occult

Pluto was discovered in 1930, but astronomers didn’t detect its atmosphere until 1988. They carefully observed as Pluto briefly blocked the light from a distant star and noticed that the starlight traveled through a thin gaseous layer of mostly nitrogen, with a little bit of methane, carbon monoxide, and hydrocarbons. This method of detection, called stellar occultation, allows scientists to monitor the bulk properties of Pluto’s atmosphere, like the height and pressure, as well as some finer details like atmospheric waves and the presence of haze.

Then in 2015, NASA’s New Horizons spacecraft flew through the Pluto system. It provided the clearest pictures to date of Pluto’s atmosphere and revealed surprising complexity. Since then, planetary scientists have continued to monitor the dwarf planet’s atmosphere via occultations.

Pluto’s atmosphere and surface ice, like in the smooth western half of the heart-shaped Tombaugh Regio, regularly exchange nitrogen particles. The details of these surface-air exchanges partially govern where and how quickly the atmosphere will freeze out during Plutonian winter. Credits: NASA/JHUAPL/SwRI, Public Domain

“Stellar occultations provide a snapshot of the physical properties of Pluto’s atmosphere [at] a given time. The path that the starlight takes through a thin atmosphere is a function of its composition as well as the temperature and density profiles,” Amanda Sickafoose, a planetary scientist at the Planetary Science Institute in Tucson, Ariz., told Eos via email. Sickafoose is the lead author on the new discovery.

But occultations happen only when Pluto and a background star line up just right, which is maybe once or twice a year. What’s more, given how subtle the signal from Pluto’s atmosphere is, the answers inferred from one telescope and analysis method might differ from those of another method. These challenges make it difficult for astronomers to put together a uniform and cohesive dataset that shows the subtle changes in Pluto’s atmosphere over time.

An Almost Airless Winter

Sickafoose and her colleagues sought to overcome these challenges by compiling Pluto occultation data spanning 1988–2023, including 10 new occultations from 2017 to 2023, and analyzing them in a consistent way. The observations were made at more than 15 different telescope sites around the world. But by reanalyzing the data in a uniform way, the team minimized telescope-specific differences and was able to tease out details of how Pluto’s atmosphere has evolved.

The team’s analysis showed that Pluto’s upper and lower atmospheres have responded differently to weakening sunlight.

“In terms of size and pressure, we find that Pluto’s atmosphere remained roughly stable from the New Horizons flyby in 2015 through 2021,” Sickafoose said. “Our most recent datasets in 2022 and 2023 suggest that the pressure has dropped, at a level of 16%, in the lower atmosphere.”

As Pluto’s atmosphere cools down, the haze particles in its atmosphere will fall down as nitrogen snow. Credit: NASA/JHUAPL/SwRI, Public Domain

The data also indicate that while Pluto’s upper atmosphere remains haze free, its lower atmosphere haze has started to clear up. Astronomers have noted the change in haze before and suspect that haze particles are snowing down as Pluto’s atmosphere cools. The team published these results in The Planetary Science Journal in July.

“This is great work!” said Perianne Johnson, a Pluto climate scientist at Purdue University in West Lafayette, Ind., who was not involved with this research. “We are hoping to identify slight changes in Pluto’s atmosphere, and it can be difficult to ascertain if reported differences are truly changes to the atmosphere or just differences between the way two scientists processed the data and different assumptions they made about unknown atmospheric properties. In this work, we can trust that the reported changes to the atmospheric pressure are real.”

Nitrogen Snow

Like many trans-Neptunian objects (TNOs), Pluto’s orbit is very noncircular and angled steeply from the ecliptic plane. Its temperature changes drastically throughout its year, depending on how far it is from the Sun. Since the discovery of Pluto’s atmosphere, astronomers have theorized that the atmosphere may freeze out, or collapse, as Pluto’s temperature drops.

Astronomers “anticipated a drop in pressure sometime in the upcoming decades, based on atmospheric models that consider things like Pluto’s orbit, the amount of Sun received at different locations on its surface, and the surface ice properties,” Sickafoose said.

“Pluto is the best studied TNO, and what we learn about Pluto helps put other TNOs in context.”

These data suggest that that collapse might be starting. And while a 16% drop in atmospheric pressure may sound small, Johnson said, “it is important to note that that change occurred over a period of just a few years, which is only a few percent of Pluto’s orbital timescale. So this is rapid, exciting change and is indicative of ice condensation occurring somewhere on Pluto.”

Most theories predict that once the collapse starts, Pluto’s atmospheric pressure will continue to drop for more than 150 years before eventually bouncing back in the 23rd century. Pinning down when and how fast the collapse happens holds clues for where atmospheric nitrogen is condensing and snowing down onto Pluto’s southern hemisphere ice deposits.

The next opportunity for high-quality occultation observations of Pluto is in 2027 and could verify whether atmospheric collapse is imminent.

“Occultations are an incredibly powerful tool for observing the distant solar system from Earth,” Johnson said. “Pluto is the best studied TNO, and what we learn about Pluto helps put other TNOs in context.”

—Kimberly M. S. Cartier (@astrokimcartier.bsky.social), Staff Writer

Citation: Cartier, K. M. S. (2026), Pluto’s atmosphere may be collapsing, Eos, 107, https://doi.org/10.1029/2026EO260288. Published on 11 September 2026. Text © 2026. 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.

The southern region of the Democratic Republic of Congo (DRC) is known as the Copperbelt because of its rich deposits of copper and other minerals.

SummaryUnderstanding induced seismicity is a critical challenge for seismic risk mitigation, particularly in Europe where geo-resource exploitation increasingly occurs in densely populated areas. The Lacq site in southwestern France represents one of the most significant and long-lasting cases of induced seismicity in Western Europe. Despite decades of observations, the characterization of Lacq seismicity remains limited by insufficient instrumentation and monitoring, leading to poorly constrained earthquake locations and uncertainties regarding whether seismic events occur within or below the reservoir. In this study, we present a high-resolution seismicity catalog covering a five-year period (2021–2026), representing the most comprehensive dataset available for the Lacq area. To achieve this, a temporary seismic network was deployed through a collaboration between GFZ (Potsdam), OMP (Toulouse), and UPPA (Pau). Event detection and location were performed using two independent approaches: a detailed manual picking and relocation workflow, and a fully automated deep-learning-based method. Combined with the use of a pre-existing local 3D velocity model, this dual approach significantly reduces location uncertainties and improves the robustness and completeness of the catalog. The resulting dataset constitutes a valuable resource for future studies.

Dozens of glacial lakes have formed across northern India in the last three decades, while existing lakes have expanded rapidly, highlighting the need to monitor a landscape reshaped by climate change.

The historic droughts of 2023 and 2024 in the Amazon reduced river flows and altered the region's rainfall composition. Daily monitoring in Manaus, the capital of the Brazilian state of Amazonas, revealed these changes through the use of isotopes, which act as a kind of water "fingerprint." This method demonstrated how El Niño and warming in the North Atlantic affected the origin of moisture and precipitation dynamics.

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

Leaf area index, which measures the ratio of leaf area to ground area, is commonly used by researchers to track foliage coverage and ecosystem health. One consequence of global warming, observed by satellite remote sensing, has been a long-term increase in leaf area index. This Earth “greening” is predicated on increased terrestrial carbon uptake under increasing CO2.

Chang et al. [2026] examine water controls on vegetation greening by isolating structural (e.g. leaf area index, LAI) and physiological (e.g., water use efficiency, WUE) impacts under increasing CO2. Their analysis shows worldwide patterns of increased plant water stress (PWS) and ecological drought, attributed to soil moisture, atmospheric aridity, and regional climate. This study demonstrates water limits on Earth greening emerging from local interdependencies between the carbon and water cycles. 

Attribution of variables, including meteorological factors (air temperature, Ta; and net radiation, Rn), soil and atmospheric aridity (VPD; surface soil moisture, SMsurf; and root-zone soil moisture, SMroot), and atmospheric CO2 to the changes of PWS indicated by ESILAI over the past four decades with a Random Forest model. ESILAI is the evaporative stress index that accounts for dynamical changes in Leaf Arae Index (LAI). a. Spatial distribution of dominant factor; b. Area ratio for the distribution of each dominant factor in a. Credit: Chang et al. [2026], Figure 7

Citation: Chang, Q., Wang, L., Barnes, M. L., Ficklin, D. L., Benson, M. C., & Novick, K. A. (2026). Widespread increase in global plant water stress obscured by greening. AGU Advances, 7, e2025AV002243. https://doi.org/10.1029/2025AV002243

—Ana P. Barros, Editor, AGU Advances

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.

Some 550 million years ago, Earth looked very different than it does today. The Southern Hemisphere was dominated by an enormous single continent known as Gondwana.

A University of the Basque Country-EHU research group has determined the causes of a 255 m (837 feet) crustal uplift of the Atlantic slope and a 167 m (548 feet) uplift of the Mediterranean slope, as well as the development of rivers in these areas.

Peatlands store large amounts of carbon. However, when they are drained, they can become substantial sources of the greenhouse gas carbon dioxide (CO₂).

Editors’ Vox is a blog from AGU’s Publications Department.

When studying the subsurface of the Earth, scientists commonly use geophysical inverse modeling, which aims to infer key physical properties, like geological structures, from indirect observations. But these modeling techniques are typically high-dimensional and computationally demanding. To overcome these limitations, the scientific community is embracing Bayesian inference methods, which could offer a solution by integrating prior geological knowledge with observed data, enabling systematic uncertainty quantification.

A new article in Reviews of Geophysics explores recent advances in Bayesian methods for modeling the Earth’s subsurface from indirect geophysical data. Here, we asked the authors to give an overview of geophysical inverse modeling, how scientists use Bayesian inference, and what challenges remain.

In simple terms, what is geophysical inverse modeling?

Many decisions depend on what lies beneath our feet: where to drill, whether carbon dioxide can be stored safely, how groundwater may move, or where a hidden fault may create risk. Yet most of the subsurface cannot be observed directly. Geophysical surveys provide clues by measuring seismic waves, electromagnetic fields, gravity, and other responses of the Earth. Inverse modeling works backward from those measurements. Scientists propose an underground model, use physical laws to predict the signals it would produce, compare those predictions with the observations, and revise the model. The result is not a photograph, but a scientifically constrained description of underground structures and properties.

Conceptual overview of the multidisciplinary landscape of geophysical inverse modeling in subsurface applications. Credit: Liu et al. [2026], Figure 1

What are the benefits and limitations of using geophysical inverse modeling?

Geophysical inverse modeling can investigate large and deep regions without extensive drilling or excavation. It helps estimate rock velocity, electrical resistivity, density, porosity, fluid content, and the shape of faults, aquifers, or reservoirs. Its central limitation is that the data are noisy, incomplete, and unevenly informative. Different underground models can therefore produce very similar measurements. A single “best” model may look more certain than the evidence allows, while important alternatives remain hidden. For practical decisions, the key question is not only whether an image looks sharp. It is whether the remaining ambiguity could change a prediction or action, such as a drilling location, a monitoring plan, or a safety assessment.

What is Bayesian inference?

Bayesian inference is a way to update what we believe when new evidence arrives. A “prior” represents geological knowledge and plausible underground scenarios before the latest measurements are used. A “likelihood” describes how well each scenario explains the observed data, while accounting for measurement noise and modeling errors. Combining the two gives a “posterior,” a probability-weighted collection of scenarios that remain plausible after the evidence is considered. Rather than producing only one answer, Bayesian inversion shows which features are strongly supported, which remain uncertain, and how alternative interpretations compare. Those scenarios can then be carried forward into predictions and decisions, connecting data, models, possible outcomes, and action.

How has Bayesian inference improved subsurface modeling?

Bayesian inference has shifted the goal from finding one preferred underground model to evaluating a range of plausible models and predictions. This makes uncertainty an explicit result rather than an afterthought. Scientists can identify poorly constrained regions, reveal trade-offs between properties, combine different data types, and estimate the risk of costly misinterpretation. Bayesian analysis can also help determine which additional measurement would be most valuable. Recent gradient-informed methods use derivatives to navigate large model spaces more efficiently when the simulator is differentiable. Deep learning can represent realistic geological patterns, accelerate expensive physical simulations, and support rapid repeated inference. Together, these advances make uncertainty more useful for monitoring, planning, and decision-making.

Overview of Bayesian inference approaches for geophysical inverse problems. RW-MCMC enables general-purpose uncertainty quantification without requiring gradients. Ensemble-based methods offer efficiency in high-dimensional, weak non-linear, and non-Gaussian settings. Gradient-informed techniques leverage posterior geometry for improved sampling when gradients are available. Differentiable Bayesian frameworks combine physics-based modeling, machine learning, and automatic differentiation. Credit: Liu et al. [2026], Figure 2

What is Differentiable Bayesian Inversion, and what are its benefits?

Differentiable Bayesian Inversion, or DBI, is not a single new sampling algorithm. It is a unifying framework for linking geological knowledge, physical or learned simulators, models of measurement uncertainty, and Bayesian inference in one computational workflow. “Differentiable” means that software can track how a small change in an underground model affects the predicted data and how plausible that model is. Automatic differentiation can calculate these sensitivities through the full workflow. This common computational language allows physics-based models and machine-learning components to work together rather than remain separate boxes. Because the framework is modular, individual components can be improved or replaced. DBI could support faster, geologically realistic, uncertainty-aware inversion for monitoring and operational decisions.

What challenges remain?

The largest challenge is scale. Real three-dimensional problems may contain millions of unknowns, and every candidate model can require an expensive seismic, electromagnetic, or flow simulation. Plausible answers may also form several disconnected geological scenarios that are difficult to explore. AI models used to represent geology and fast surrogate simulators introduce further risks: they may fail when field conditions differ from their training data, and their errors can distort uncertainty estimates. Progress will require efficient differentiable solvers, more robust samplers, better uncertainty calibration, and validation on realistic field data. Gradient-free and hybrid methods will remain essential when models are not differentiable. The aim is not to eliminate uncertainty, but to identify which uncertainties matter and use them wisely.

—Mingliang Liu (mingliangliu@sdu.edu.cn; 0000-0002-5783-4490), Shandong University, China, and Stanford University, United States; Dario Grana (0000-0003-4220-053X), University of Wyoming, United States; Klaus Mosegaard (0000-0001-5292-5249), University of Copenhagen, Denmark; Mrinal K. Sen (0000-0002-5525-0467), The University of Texas at Austin, United States; Minghui Xu (0000-0001-9567-5569), Stanford University, United States; and Tapan Mukerji (0000-0003-1711-1850), Stanford University, 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: Liu, M., D. Grana, K. Mosegaard, M. K. Sen, M. Xu, and T. Mukerji (2026), How can we make better decisions about what lies underground?, Eos, 107, https://doi.org/10.1029/2026EO265036. Published on 10 September 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.

In early 2017 and 2023, California experienced two catastrophic and record-breaking floods that caused billions of dollars in damage, triggered mudslides and forced thousands of people to evacuate. Why this happened left scientists scratching their heads. Both disasters struck during La Niña conditions, which normally bring drier-than-normal weather to California.

Extreme heat has become more common—both research and lived experience document this—but when it arrives is a different question. A new study finds that, rather than occurring only during warm seasons, extreme heat events have become more frequent in the months before and after, too.

This is the highest-resolution image of the Sun ever taken.

It was captured by the Daniel K. Inouye Solar Telescope (DKIST) in Hawaii, the largest solar telescope in the world, on 14 April 2025.

The image zooms in on a small patch of the Sun’s surface in visible light. The whole image spans 5,800 kilometers (3,604 miles) horizontally and 4,350 kilometers (2,703 miles) vertically, covering a mere 0.0004% of the Sun’s surface. Each pixel is 6 kilometers (3.7 miles) per side.

Take a close look at the grid-like pattern and see if you can spot small swirls among the feathered edges.

A high-resolution image of the Sun at 416 nanometers taken by the Inouye Solar Telescope. Credit: NSF/NSO/AURA/MPS, CC BY 4.0

Here are two spots with swirling plasma.

The inset here zooms in on a small patch of the Sun’s surface with the telltale swirl of Kelvin-Helmholtz instabilities (KHI) just tens of kilometers across. Credit: NSF/NSO/AURA/MPS, CC BY 4.0 Another region of the same image shows even more KHI fingerprints, suggesting that these instabilities might be common on the Sun’s surface. Credit: NSF/NSO/AURA/MPS, CC BY 4.0

The tiny swirls and vortices are called Kelvin-Helmholtz instabilities, and this is the first observational confirmation that they exist on the Sun. The swirls are manifestations of a physical process that could play a crucial role in how the Sun’s plasma bubbles and bursts.

“Kelvin-Helmholtz instabilities are one of the fundamental instability processes in magnetofluids, fluids, and gases,” said David Kuridze. Kuridze studies plasma in the solar atmosphere at the National Solar Observatory (NSO) in Boulder, Colo., and is colead researcher on the discovery.

“We did not set out to find Kelvin-Helmholtz instabilities,” added colead researcher Friedrich Wöger, who studies flow dynamics of the solar photosphere at NSO. “The experiment was targeted at finding the most efficient way to reach the diffraction limit of the telescope. And the nice thing is, not only did we succeed with that but we also found Kelvin-Helmholtz instabilities.”

Kelvin-Helmholtz instabilities might be everywhere on the Sun. Credit: NSF/NSO/AURA/MPS, CC BY 4.0 When Plasmas Flow By

Kelvin-Helmholtz instabilities (KHI) are caused by the edges of two streams of fluid flowing past each other at different speeds. And “fluid” is quite a broad term, geophysically speaking. Scientists have spotted these swirls and vortices in estuaries and oceans, as well as in Earth’s clouds; the skies of Mars, Jupiter, and Saturn; and interactions between the solar wind and planetary magnetospheres, Kuridze explained.

Astronomers have long suspected that KHI exist on the Sun’s surface. The solar photosphere is a fluidlike plasma, so it would make sense for it to follow the same rules as any other fluid, albeit with the added complication of the Sun’s magnetic field. What’s more, the existence of KHI could explain some of the Sun’s more mysterious phenomena, like braided magnetic field lines, eruptions, and the ultrahot corona.

“Kelvin-Helmholtz instabilities are a very effective mechanism to twist and bend magnetic structures” and generate magnetic energy, Kuridze said.

“One of the big questions is, How much energy can these vortices create and transport up into the outer atmosphere of the Sun, and is it enough to heat it up to millions of degrees kelvin?” Wöger said.

But until recently, actually spotting these instabilities on the Sun’s surface was impossible. Picking out small vortices within the solar plasma requires specialized instruments installed on solar telescopes, and previous generations of solar telescopes were simply not powerful enough to see them. When DKIST came online in 2019, its 4-meter (13-foot) mirror immediately changed the game by allowing astronomers to view the bubbling, boiling solar surface at several wavelengths and at smaller scales than ever before.

“DKIST’s resolving capacity is equivalent to finding a quarter from a distance of 50 kilometers,” said Michail Mathioudakis, a solar astrophysicist at Queen’s University Belfast in the United Kingdom.

Roil and Toil

Kuridze, Wöger, and their team pushed DKIST’s resolving power to the max to zoom in on a small part of the Sun near a sunspot for a few minutes. This slow roiling is what they saw.

As the Sun’s plasma surface roils, the different flows of plasma shear against each other and create Kelvin-Helmholtz instabilities. Credit: NSF/NSO/AURA/MPS, CC BY 4.0

Here are those same two zoomed-in spots from before.

When scientists zoomed in on the high-resolution video of the Sun, they could watch KHI actively swirling. Credit: NSF/NSO/AURA/MPS, CC BY 4.0

“When we looked at these data and the movie, we immediately recognized the signature of Kelvin-Helmholtz instability,” Wöger said.

After they spotted the telltale signs of KHI, the observing team asked their modeling partners to simulate the motions of the Sun’s magnetically influenced fluidlike plasma, or magnetohydrodynamics, at a similarly high resolution. Creating simulations with the level of detail needed to compare with the DKIST observations requires phenomenal computational power and likely wouldn’t have happened without these observations as motivation, Wöger said.

Those simulations, based on fundamental physics and fluid dynamics principles, confirmed that the vortices captured by the DKIST images were likely created by KHI. The DKIST observations show that KHI might be ubiquitous across the Sun’s surface. This discovery was published in Nature in August.

“Models of the solar atmosphere have shown indications of this instability, but the paper has identified this physical process observationally in some of the smallest astrophysical scales,” said Mathioudakis, who was not involved with the research. “What surprised me the most is that this discovery was made with a relatively simple imaging setup and does not involve complex instrumentation, calibration issues, or data inversions. It will therefore stand the test of time.”

Small Swirls, Big Energy

“This is a very notable observation because Kelvin-Helmholtz instability in photospheric shear flows has been predicted theoretically for decades, but the relevant spatial scales were simply too small to resolve directly,” said Claire Foullon, a solar and space physicist at the University of Exeter in the United Kingdom.

“What is more surprising is how DKIST reveals it to be so widespread,” Foullon added. “Rather than being an occasional phenomenon, the observations suggest that this may be a fundamental part of the small-scale dynamics of the magnetized photosphere.” Foullon was not involved with the new discovery.

“There’s always this nice tension between simulation and observation.”

As KHI swirl around and around in the solar photosphere, they can twist up magnetic fields and store up energy. All that energy has to go somewhere, and many solar scientists think it might contribute to heating up the Sun’s corona.

“We are effectively seeing, for the first time, dynamics on the scales at which the magnetic foot-points of the corona are being continually stirred and restructured,” Foullon said.

The degree to which KHI contribute to coronal heating is still unknown, as is whether KHI exist on even smaller physical scales on the Sun. These might remain open questions for a while, Kuridze said, as observers figure out ways to push DKIST to even smaller physical scales and as simulations stretch farther to match.

“There’s always this nice tension between simulation and observation,” Wöger said. “When we see something [in observations], then we’re pushing the numerical simulations. And sometimes in the numerical simulations you see something, and then we’re trying to see whether it actually exists on the Sun.”

—Kimberly M. S. Cartier (@astrokimcartier.bsky.social), Staff Writer

Citation: Cartier, K. M. S. (2026), Sharpest-ever image of the Sun shows small but mighty swirls, Eos, 107, https://doi.org/10.1029/2026EO260285. Published on 10 September 2026. Text © 2026. 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.

Source: AGU Advances

A process known as the biological carbon pump (BCP) moves organic carbon from near the ocean’s surface to the ocean floor. That carbon often stays there for hundreds or thousands of years, making the BCP a primary source of carbon sequestration in the oceans. Scientists have thought that relatively large phytoplankton are more efficient at exporting carbon to the deep ocean than tiny cyanobacteria, but new research from Zhang et al. now calls that belief into question.

Using sediment traps placed at a depth of 1,000 meters in the South China Sea paired with multiple years of observations of surface phytoplankton communities, the authors show that about two thirds of the carbon sequestered in the ocean comes not from eukaryotic microalgae, but from another major group: cyanobacteria. These tiny, often unicellular prokaryotic phytoplankton are so named because of the blue-green color seen when they grow in large colonies. The main reason cyanobacteria play a larger role is that much of the carbon from microalgae is lost on its journey to the deep ocean but carbon from cyanobacteria is not. These findings could change how models of ocean carbon sequestration are constructed and hold implications for our understanding of how the BCP will shift in response to climate change, the authors say.

Evidence of taxon-specific divergence in carbon sequestration comes from the amino acid carbon isotope signatures found 1,000 meters deep, which allowed the researchers to tie the carbon they collected to phytoplankton groups at the surface. The authors found that production of carbon and phytoplankton structure at the surface vary considerably by season and corresponding nutrient availability. But surprisingly, the efficiency and origin of carbon collected deeper down do not change much by season.

The linked processes of aggregation and ballasting, in which carbon from cyanobacteria is incorporated into larger clumps that also contain other minerals, help cyanobacteria carbon sink more efficiently and reduce its loss during sinking by limiting microbial degradation. These findings may explain why high-latitude systems dominated by large phytoplankton do not always exhibit higher organic carbon sequestration efficiency compared to low-latitude oligotrophic gyres rich in cyanobacteria, the authors note. (AGU Advances, https://doi.org/10.1029/2025AV002148, 2026)

—Nathaniel Scharping (@nathanielscharp), Science Writer

Citation: Scharping, N. (2026), Tiny cyanobacteria may be the secret heroes of ocean carbon sequestration, Eos, 107, https://doi.org/10.1029/2026EO260283. Published on 10 September 2026. Text © 2026. 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.

In November 2025, NASA and its European partners launched the Sentinel-6B satellite to improve hurricane forecasts, help protect infrastructure and benefit commercial industries, including shipping. The satellite is now flying 30 seconds behind its predecessor, Sentinel-6 Michael Freilich. Both satellites are providing precise sea level height measurements during what oceanographers expect to be a historic El Niño, a naturally occurring oceanic phenomenon in which warmer-than-usual Pacific waters shift global weather patterns.