Solar energetic particles (SEPs) are high-energy particles consisting of protons, electrons and heavy ions emitted by the Sun during solar flares and coronal mass ejections. Energetic particles emitted from the Sun range in energy from suprathermal (several keV) up to relativistic (several GeV), and can pose significant radiation risks to spacecraft, satellites, and human astronauts. SEP events occur relatively infrequently, and their time series profiles can often exhibit complex variability. Most SEP catalogs are still curated manually by experts, but these are frequently affected by inconsistencies and varying data quality or classification standards. As a result, SEP detection and classification remain time-consuming and prone to inconsistency. While the recent rise of machine learning in the space weather field has been primarily focused on SEP forecasting, machine learning also offers a promising tool for automating and standardizing SEP detection and classification. In this study, we present a first step toward such a system by developing a binary classification convolutional neural network (CNN) trained on almost 20 years of proton time series data from seven energy channels (13.6–33.4 MeV) recorded by the High Energy Telescope (HET) onboard STEREO-A between December 2006 and May 2025. Using five-fold cross-validation, the model correctly classifies about 99% of non-SEP samples and 94% of SEP samples. Our results provide a foundation for further application of machine learning in SEP detection and classification, contribute to a more quantifiable and consistent definition of SEP events, and, once extended to additional instruments, could help pave the way toward a fully automated and standardized SEP event catalog.

The application of scientific visualization techniques focused on identifying locations where backscatter exceeds a threshold revealed unexpected locations of potential or actual venting (as well as the anticipated plumes). The plume dataset using the Cabled Observatory Vent Imaging System (COVIS) attached to the Ocean Observatories Initiative’s Regional Cabled Array in the ASHES vent field in the caldera of Axial Seamount on the Juan de Fuca Ridge. The dataset contains backscatter intensity measurements from July 2020 to September 2023. For each month, plume-imaging data — which record plume shape — were acquired on five different days (weekly). On each of those days, twelve measurements were taken at two-hour intervals, generating twelve grid files. The dataset was processed using MATLAB. First, morphological operations were used to extract the six largest clusters, and the center of mass of each was calculated from the mean of their x, y, and z coordinates. A nearest-neighbor tracking algorithm then matched each cluster with its counterpart in previous time steps. Results showed persistent cluster formation at certain locations. To validate this finding, the matched clusters over time were plotted on the bathymetry map where the original data were collected. In addition to constant cluster formation at the Inferno and Mushroom vents, significant clusters appeared at locations that are not expected to host large plumes. Unexpectedly, we identify recurring clusters 16 meters north and 18 meters west of known Inferno vent orifices (see yellow and green clusters in figure). Future work is required to investigate possible causes of the clusters at these unexpected sites. Nevertheless, this study provides valuable information for the analysis of acoustic data intended to capture the incidence of hydrothermal plumes at the ASHES vent field.

Understanding the Sun’s long-term radiative variability is essential for assessing its impact on the Earth’s upper atmosphere and long-term climate. In particular, solar extreme ultraviolet (EUV) irradiance plays a central role in heating the ionosphere, thermosphere, and mesosphere. However, continuous EUV flux measurements have only been available since 1995, leaving substantial historical gaps that constrain investigations of solar influences across multiple solar cycles. We present a Bayesian deep learning framework, SEMNet, developed to reconstruct historical solar EUV irradiance from CaII K images. Trained on SOHO/SEM EUV fluxes from 1998 to 2014 and paired with CaII K observations from the Precision Solar Photometric Telescope, SEMNet produces accurate reconstructions with associated uncertainty estimates. The model is further extended via transfer learning to recover EUV fluxes back to 1907 using historical CaII K data from the Kodaikanal Solar Observatory. Results show that SEMNet generates consistent and physically meaningful irradiance estimates, demonstrating that CaII K imaging can serve as a reliable long-term proxy. This approach offers a data-driven solution for filling gaps in solar irradiance records and enhances our ability to study solar influences on the Earth’s atmosphere and climate over more than a century.

The moon Io, the most volcanically active body in the Solar System, functions as the “energy converter” of the Jovian environment: tidal heating by Jupiter and its Galilean satellites powers intense outgassing that sustains both the neutral and plasma tori and feeds the Jovian magnetodisk via centrifugal processes. Spacecraft traversals and remote observations, from ground‐based telescopes to JAXA’s Hisaki, have revealed the Io Plasma Torus’s (IPT) intricate spatial architecture and variability across timescales from hours to decades. Yet, fully characterizing the drivers of IPT structure and magnetospheric dynamics remains an open challenge. In this respect, NASA’s Juno mission, in its highly inclined, low‑perijove trajectory, provides in situ measurements of magnetic perturbations, plasma composition and abundance, through both plasma wave and radio occultation experiments, while refining tidal‐dissipation models through gravity science. Complementarily, a future network of longitudinally distributed Earth‑based observatories will deliver continuous, global monitoring of auroral emissions and torus brightness, capturing rapid fluctuations that single‐station views cannot. The synergy of Juno’s detailed local sampling and round‑the‑clock remote surveillance promises a holistic view of source–sink interactions driving Jupiter’s magnetosphere and the Io torus.

Land subsidence in the greater Houston region was first identified at Goose Creek Oil Field in Baytown, Texas, with three feet of measured subsidence over eight years beginning in 1918. With increased industrialization and population growth, the region relied on groundwater to support and sustain development. This excessive groundwater use caused aquifer compaction which led to land subsidence measured at over nine feet in some areas. Some subsidence impacts were increased flooding and infrastructure damage that ultimately led to the creation of the Harris-Galveston Subsidence District (HGSD) by the Texas Legislature in 1975. HGSD’s mission is to regulate groundwater withdrawal to prevent further subsidence.

In southeast Texas, abundant groundwater withdrawal has caused permanent aquifer compaction, resulting in land subsidence. With the region’s projected population growth and increased water demand, evaluating future subsidence impacts is crucial for effective water resource planning. This study presents a robust, scenario-based framework for evaluating future flood hazards and quantifying economic losses associated with inland subsidence in the Spring Creek Watershed, which spans across four counties in southeast Texas.

Serpentinization occurs due the hydration of ultramafic rocks in various tectonic settings. This hydration has been proposed as a source of geological hydrogen that could be engineered as a carbon-free energy carrier. However, the details of the hydrogen generation are complex and remain poorly understood. Water availability often limits reaction progress as the fluid-rock interactions modify permeability and generate grain-scale stresses due to the volumetric increase associated with the reaction. Most laboratory studies to date rely on powdered samples instead of solid rock cores and hence do not capture the full dynamics of fluid-rock interactions in natural settings. This study investigates how water availability and temperature influence serpentinization in intact dunite cores. Stress relaxation experiments were conducted in a Griggs-type solid medium deformation apparatus at 1.3 GPa confining pressure and 300–400 °C temperature for durations of 0 to 11 days with water contents ranging from 2 to 4 wt% relative to rock mass. The rocks were first loaded to ~200 MPa differential stress and left to creep. Microstructural analysis of post-experiment thin sections confirmed significantly increased serpentine content compared to the starting material and intricate fracture networks likely caused by both the volumetric increase during serpentinization as well as creep deformation. Continuous ultrasonic monitoring tracked in-situ P-wave velocity evolution (Vp). These results connect observed seismic velocity changes to fluid–rock reactions in the deep lithosphere and provide constraints on serpentinization kinetics in intact ultramafic rocks.

Anaerobic digestion of organic waste coupled with the reduction of sulfate in industrial waste gypsum has been proposed as a strategy for mitigating methane emissions from conventional digestion of organic waste. To improve controls on the structure and function of microbial communities used in this process, we investigate factors that determine the partitioning of electron donors in sulfate- and organic-rich anaerobic systems by tracking the metabolic fluxes, microbial community composition, and organic matter profiles in continuous flow-through reactors and batch cultures grown on sulfate-rich sewage sludge. Methane suppression in batch cultures depended on the presence of sulfate-reducing bacteria capable of oxidizing acetate to CO2 (complete oxidizers). These microbes outcompeted methanogens for acetate, while cultures that contained more sulfate reducers incapable of oxidizing acetate (incomplete oxidizers) produced methane. The stoichiometry of the associated metabolic reactions revealed that only complete oxidation was possible under a chemical oxygen demand (COD) to sulfate ratio of less than around 1. Above a ratio of around 2.1, both metabolisms were possible, and the faster growing incomplete oxidizers are relatively more abundant than complete oxidizers in batch cultures based on 16S rRNA gene sequencing. In high-throughput bioreactors that operated over 90 days, incomplete oxidizers comprised a higher proportion of the sulfate-reducing community early into operation, suggesting faster growth rates of these organisms. Complete oxidizers became more abundant later. The compositions of microbial communities in bioreactors inoculated by the communities known to contain complete oxidizers and those that received only microbes from sewage sludge were similar after 79 days, but complete oxidizers became more abundant than the incomplete oxidizers more than two weeks earlier in the former reactors. The microbial diversity in the established reactors depended primarily on the organic loads rather than the addition of inocula. These results enable targeted control of microbial community function to inform the design of anaerobic digesters to reduce greenhouse gas emissions and remove organic and industrial waste.

Starting in November 2023, the Houthi militia occupying northern Yemen has attacked ships passing through the Bab al-Mandab Strait, a chokepoint on the route between Europe and Asia via the Suez Canal. As a result, cargo ship traffic through the Red Sea has plummeted, with ships instead taking the longer route around the Cape of Good Hope. The increase in traffic in the southeast Atlantic Ocean and decrease in the Red Sea is readily apparent in satellite retrievals of column nitrogen dioxide. Within the stratocumulus deck covering much of the southeast Atlantic, a previously detectible cloud microphysical perturbation due to ship pollution had largely disappeared following the International Maritime Organization’s sulfur-limiting regulations in 2020 but returns during 2024 due to the increase in ship traffic despite the lower cloud brightening efficacy per ship. Because nitrogen dioxide pollution per unit of fuel oil burned is not affected by switching to low-sulfur fuel or installing sulfur scrubbers, quantifying the ratio of shipping-enhanced cloud droplet number and nitrogen dioxide concentrations before and after the fuel sulfur limits went into effect provides a strong constraint on the cloud changes from the regulations. We find that the ~80% reduction in sulfur emissions leads to a 66% (95% confidence: 25-88%) reduction in the increase in cloud droplet number concentration per unit marine fuel oil burned.

Aerosol-cloud interactions exert a strong but highly uncertain negative radiative forcing, masking current greenhouse gas-driven warming and obscuring our ability to estimate future warming as well. ”Natural experiments” like ship tracks have been identified as a promising means of constraining aerosol effects on clouds; however, questions remain about the extent to which cloud adjustments observed in ship tracks generalize to other pollution effects. At the same time, inadvertent aerosol perturbations like ship tracks have inspired proposals to offset global warming impacts with deliberate aerosol cooling [e.g., marine cloud brightening (MCB)]. Ship track aerosol are a mixture of carbonaceous material and sulfate, but changes in fuel composition due to international regulations have shifted aerosol composition from larger sulfate-coated to smaller soot-dominated particles. There is currently a debate in the literature about whether reduced cloudiness observed in some ship tracks is due to cloud adjustments to aerosol-driven microphysical perturbations or to aerosol absorption, which is negligible for sea salt (as would be used for MCB). Through satellite observations both before and after these regulations went into effect and large eddy simulation modeling of cloud adjustments under sulfur-dominated, soot-dominated, and salt ”tracks”, we test the hypothesis that ship tracks observed in the historical record are good analogues for what salt tracks would behave like under MCB. If there are large differences between sulfur and salt tracks in particular, then current knowledge based on satellite imagery and field measurements of ship tracks would need to be interpreted cautiously for the case of MCB and may indeed underestimate efficacy, with implications for the merit of small-scale outdoor perturbative studies focusing on salt tracks in particular.

Remote sensing data have identified numerous chloride salt-bearing deposits across the Martian southern highlands. Due to the high solubility of chloride salts, these deposits may indicate the last stable liquid water on the Martian surface. Analyses of the deposits’ morphologies, ages, and surrounding topographies may provide crucial clues for understanding the hydrologic past of Mars. Many hypotheses regarding the formation of these deposits have been proposed (for example, ponding and evaporation of upwelled groundwater, surface runoff, or formation in shallow lacustrine environments), but no consensus has yet been reached, and it is possible that multiple formation pathways resulted in chloride salt deposition. In this work, we use salt map/digital terrain model (DTM) overlays to perform geomorphological analyses of chloride depositional environments. A Hapke-based abundance estimation technique was used to create salt abundance maps from Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) imagery that overlies chloride-bearing deposits. We used Context Camera (CTX), High Resolution Imaging Science Experiment (HiRISE), and High Resolution Stereo Camera (HRSC) datasets to create DTMs. We have identified 4 target regions (Terra Sirenum, N-NE Noachis Terra, W of Hesperia Planum, and E of Hesperia Planum to Terra Cimmeria) in the southern highlands and focused our analyses on ~4-5 deposit clusters within each target region, forming a representative sample of deposit variability. We have selected clusters based on diversities in how chloride deposits map with elevation, underlying deposit age (ranging from late Noachian to early/mid Amazonian), valley network density, and surrounding geologic features such as channels or flat expanses. The combination of elevation data from DTMs, high resolution imagery for surrounding context, and CRISM salt abundance maps enables us to place constraints on deposit thickness and extent, chloride salt volume, and brine-forming pond/lake volumes if applicable. Studying variability among deposits will assist in determining if chlorides formed from a universal process or multiple, localized processes. The chloride salt map/DTM overlays created in this work will enhance understanding of these deposits and assist in the determination of their origin.

Data and scientific methods are becoming more complex in today’s landscape of affordable sensors, open data, and machine learning. In addition, collaboration and team science are growing to meet the increasing demand for interdisciplinary expertise. With larger teams, more data, and higher complexity analyses, making science reproducible is difficult. Skills in data and code management are often those most needed to meet these growing challenges, but scientists often lack formal training in these areas. In this presentation, we will introduce concepts and techniques to iteratively improve the reproducibility of scientific analyses using a published water data workflow shared publicly on GitHub. The reproducibility improvements will be presented in a stepwise fashion to introduce a set of recommendations for different skillsets: 1) beginner, focused on improved documentation and organization; 2) intermediate, focused on code modularity; and 3) advanced, focused on automation. Furthermore, the recommended improvements for each skill level will be tracked in GitHub version control, enabling transparent review of changes via pull requests and commits. Injecting good data and code practices to support reproducibility throughout a project can enhance a team’s ability to pivot and test new hypotheses, have greater confidence in results, and speed up the publication process. Demystifying how to improve scientific reproducibility is key to having these skills and approaches valued, practiced, and taught.

Recent modeling and observational studies of the terrestrial hydrogen exosphere report that during a geomagnetic storm, exospheric density can increase by up to 60% in the 2 – 10RE radial distance range, driven by dynamic upper atmospheric heating. This density enhancement suggests an important role of the exosphere in inner magnetospheric dynamics, as neutral – ion charge exchange is a key energy loss mechanism for ring current ions. To investigate the interaction between the exosphere and the ring current, we conduct a geomagnetic storm simulation using the Model for Analyzing Terrestrial Exosphere (MATE), coupled with the Thermosphere – Ionosphere – Mesosphere – Electrodynamics General Circulation Model (TIME-GCM) and the Comprehensive Inner-Magnetosphere Ionosphere (CIMI) model. Strong high-latitude forcing during the storm heats the global upper atmosphere, increasing the number of ballistic and escaping hydrogen atoms entering the exosphere, thereby enhancing exospheric density in the inner magnetosphere. Subsequently, charge exchange between neutral hydrogens and ring current ions intensifies, accelerating energy decay in the ring current and reducing ion precipitation from the ring current to the upper atmosphere. These simulation results suggest a non-negligible role of the exosphere in magnetosphere – ionosphere – thermosphere coupling through neutral ion charge exchange. Upcoming NASA missions such as the Carruthers Geocoronal Observatory and the Storm Time O+ Ring Current Imaging Evolution (STORIE) will help illuminate these important physical processes.

Increasing levels of harmful air pollutants, particularly particulate matter smaller than 2.5 µm (PM2.5), pose a significant global public health risk. While accurate monitoring is essential, a critical challenge persists: high-cost, government-grade sensors are sparsely deployed, while more accessible low-cost sensors lack accuracy. Deep learning models, such as Long Short-Term Memory (LSTM) networks, offer a powerful solution for calibrating these sensors, yet they often lack reliability as they do not report the confidence of their predictions. This absence of Uncertainty Quantification (UQ) undermines trust and can lead to flawed decision-making in environmental management. This study aims to address this gap by separating and quantifying two forms of uncertainty: aleatoric (inherent data noise) and epistemic (the model’s own confidence). To achieve this, an LSTM model was developed to predict both a mean value and its corresponding variance by training with a Negative Log-Likelihood (NLL) loss function, allowing it to learn aleatoric uncertainty directly. To estimate the model’s epistemic uncertainty, Monte Carlo Dropout (MCD) was applied during inference. By performing multiple forward passes with Dropout active, a prediction distribution was generated whose variance serves as a strong measure of model confidence. An experiment was conducted to find the optimal dropout probability by training and evaluating models with rates from 0.05 to 0.5. While the model with a 0.05 dropout rate achieved the lowest Test RMSE (0.9174) and the 0.35 rate yielded the lowest ECE (0.0228), the results revealed that the model with a dropout rate of 0.10 demonstrated the best overall performance. It achieved the lowest Test NLL of 0.2825, a Test RMSE of 0.9200 (R² of 0.9846), and well-calibrated uncertainty with 97.63% coverage and an ECE of 0.0338. This work presents a validated framework for enhancing the trustworthiness of deep learning models in air quality applications, ensuring that predictions are not only accurate but also reliable.

In recent years, air quality has decreased substantially because of increasing levels of harmful pollutants from various industrial practices, vehicle emissions, and wildfires. Particulate matter with diameters less than 2.5 µm or PM2.5 is considered a leading air pollutant, causing over 8 million deaths annually worldwide. Hence, accurate monitoring and prediction of air pollution, specifically PM2.5, is essential for public health protection; however, current air quality monitoring methods face limitations. Currently, air quality data is collected from ground-level monitoring stations, which utilize Environmental Protection Agency (EPA) sensors. While these sensors provide accurate data, their high cost prevents them from widespread use. On the other hand, low-cost air quality sensors can be distributed across different areas and fill geographic gaps in sensor coverage, but they are not very accurate. This trade-off between cost and accuracy presents a challenge in effectively monitoring widespread air quality. To address this challenge, this study presents a new approach to improving the accuracy of air quality measurements of low-cost sensors by calibrating them using various machine learning (ML) and deep learning (DL) techniques, allowing them to attain the high accuracy of EPA sensors while still remaining inexpensive. ML/DL models were trained on spatiotemporal data, including PM2.5 levels, temperature, and humidity from the low-cost sensors and PM2.5 levels from the EPA sensors. Then, using these models, the temperature, humidity, and PM2.5 levels from the low-cost sensors were used to predict their corresponding highly accurate PM2.5 measurements from EPA sensors with RMSEs as low as 5.53. Better accuracy in low-cost sensors can lead to more extensive and reliable air quality monitoring networks. This approach can be applied globally, enabling better response strategies to air pollution and its associated health risks.

The study of the effect of statistically non-stationary turbulence in the Earth’s outer core on the effective turbulent electromotive force generated by the convectively driven flow of liquid iron reveals very interesting features of the geodynamo process, as non-equilibrium processes in MHD turbulence can lead to Earth-like evolution characteristics of the dynamo generated magnetic field. The non-stationarity means that interactions of distinct waves are crucial and the effect of beat between close-frequency modes induces slow time variation of the large-scale electromotive force. This provides an attractive and fairly simple physical mechanism for random appearances of short-lived geomagnetic excursions and reversals separating long periods of relatively stable field, through non-synchronized evolution of the amplifying α-effect and turbulent diffusion. Rare and random appearances of simultaneous suppression of the α-effect and enhancement of diffusion lead to sudden magnetic energy drops, i.e. an excursions or reversals. The turbulent field of what is termed MAR waves (Magnetic-Archemedean-Rossby) is analyzed. The dispersion relation and structure of such waves involving the joint effect of the Lorentz, buoyancy and Coriolis forces together with curvature of the core-mantle boundary are obtained and utilized for estimation of the non-stationary electromotive force in the core. The solutions for the large-scale dipole possess an Earth-like behavior, magnitude and time scales, and the physical mechanism of the process, including identification of two dynamically important parameters is discussed.

Complex sequences of sound produced by humpback whales were first scientifically described as “song” more than a half century ago and have since been recognized as shared and transmitted culture within the species. Do these whale songs contain elements of human musical composition?A standard tool for analyzing whale songs is a spectrogram, a graph representing sound intensity as a function of time (x-axis) and frequency (y-axis). Visual examination of humpback whale songs represented in spectrograms, together with listening, reveals structure similar to forms of human musical composition. For instance, the song represented below can be interpreted as having a sonata form. The defining structure of a sonata includes an exposition, a development, and a recapitulation, in which the recapitulation repeats the exposition. In this example, the core structure of a sonata is flanked by an introduction at the start and a coda at the end.The introduction begins at high frequency and gradually descends in pitch. The exposition theme is constructed by phrases of downward-upward glissando pairs followed by a short low-pitched unit. Next, the development theme consists of phrases of two short high-pitched units followed by a low-pitched droning unit. The high-pitched units gradually increase then decrease in pitch; meanwhile the low-pitched units descend to a flat pitch. The recapitulation section repeats the exposition’s theme exactly. Finally, the song ends with a coda of low-pitched units. A high-pitched introduction and low-pitched coda is a common structure in starting and closing whale songs.In the development theme, the high-pitched units mimic a soprano part, while the droning units act as the bass part. The humpback whale’s voice (spanning more than nine octaves) has a much wider range than the human voice. A single whale can encompass multiple singing parts while a human choir needs individuals of varying vocal ranges to form sopranos, altos, tenors, and basses.In conclusion, preliminary analysis of whale songs show that they can share commonalities with human musical compositions. Further, an individual whale can fill the parts of a full human choir. Rather than accidental patterns scattered throughout an arbitrary tune, whales compose distinct and beautiful themes in their songs, exhibiting the grandeur of nature.

The top meter of the Earth’s crust holds nearly equal amounts of rock-derived organic carbon (OCpetro) and soil organic carbon. Due to this large reservoir size, the oxidation of OCpetro may be an important flux of CO2 to the atmosphere over geologic time. However, determining the magnitude and drivers of OCpetro oxidation remains difficult at the watershed scale. To understand the balance of OC sources eroded from a landscape and prone to breakdown, we measured the OC concentrations, stable isotope ratios, and the radiocarbon content of suspended sediment samples from the East River: a shale-dominated alpine watershed near Crested Butte, Colorado. Suspended sediment samples were studied because they integrate multiple sources of sediment across a watershed. With these OC measurements, we employ a Monte Carlo mass balance inversion to constrain inputs of carbon from plants, soil, and bedrock. We find that along the main stem of the river, δ13C values range from -27 to -26 ‰ VPDB and fraction modern OC (FmC) ranges from 0.73 to 0.95, suggesting millennial-scale floodplain residence times. In contrast, lighter δ13C values (-29 to -27 ‰) and older FmC values are observed within tributaries. Notably, samples collected at different river stages at the downstream-most locality exhibit the widest variability in carbon isotope data. A decrease in sediment OC abundances and FmC values with increasing discharge is likely the result of an increased proportion in bedrock carbon in transport. These data illuminate hydrologic and geomorphic controls on the balance of OC export, which has consequences for watershed-scale OC fluxes. Whereas the river main stem primarily transports young, labile, plant- and soil-derived organic carbon, small steeper tributaries appear to transport older, rock-derived organic carbon, likely due to erosion of bedrock. However, the relative contributions of bedrock and soil-derived carbon in floodplains can fluctuate in response to changing hydrologic states, which are influenced by climate. Ongoing measurements of rhenium and carbon isotopes on suspended sediment, bedrock, and soil will help refine endmembers and shed light on the role of bedrock OC in riverine carbon fluxes.