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Standard finite volume or finite difference methods may produce unphysical negative solutions of phase space density when applied to radiation belt diffusion equation with cross diffusion terms. In this work, we apply a recently proposed positivity-preserving finite volume (PPFV) method to a 2D diffusion problem of radiation belt electrons with both structured and unstructured meshes. Our test using a model problem shows that the new method does not produce unphysical negative solutions with both types of meshes even with strong cross-diffusion terms. By applying the method to the 2D pitch angle and energy diffusion problem, we demonstrate that the method achieves positivity of solutions without requiring excessive number of grid points and shows good agreement with previous results obtained using a layer method. The ability of preserving positivity of the solution with unstructured meshes allows the method to handle complex boundary configurations. Our results suggest that the new PPFV method could be useful in modeling radiation belt diffusion processes or in building a physics-based forecast model.

Distributed acoustic sensing (DAS) has emerged as a promising seismic technology for monitoring microearthquakes (MEQs) with high spatial resolution. Efficient algorithms are needed for processing large DAS data volumes. This study introduces a deep learning (DL) model based on a Residual Convolutional Neural Network (ResNet) for detecting MEQs using DAS data, named as DASEventNet. The test data were collected from the Utah FORGE 16A (78)-32 hydraulic stimulation experiments conducted in April 2022. The DASEventNet model achieves a remarkable accuracy of 100% when discriminating MEQs from noise in the raw test set of 260 examples. Surprisingly, the model identified weak MEQ signatures that have been manually categorized as noise. The decision-making process with the model is decoded by the classic activation map, which illuminates learning features of the DASEventNet model. These features provide clear illustrations of weak MEQs and varied noise types. Finally, we apply the trained model to the entire period (∼7 days) of continuous DAS recordings and find that it discovers >5,700 new MEQs, previously unregistered in the public Silixa DAS catalog. The DASEventNet model significantly outperforms the traditional seismic method Short-Term Average/Long-Term Average (STA/LTA), which detected only 1,307 MEQs. The DASEventNet detection threshold is M w −1.80 compared to the minimum magnitude of M w −1.14 detected by STA/LTA. The spatiotemporal distribution of the newly identified MEQs defines an extensive stimulation zone and more accurately characterizes fracture geometry. Our results highlight the potential of DL for long-term, real-time microseismic monitoring that can improve enhanced geothermal systems and other activities that include subsurface hydraulic fracturing.

Recently, more advanced synchronous global-scale satellite observations, the Soil Moisture Active Passive enhanced Level 3 (SMAP L3) soil moisture product and the Orbiting Carbon Observatory 2 (OCO-2) solar-induced chlorophyll fluorescence (SIF) product, provide an opportunity to improve the predictive understanding of both water and carbon cycles in land surface modeling. The Simplified Simple Biosphere Model version 4 (SSiB4) was coupled with the Top-down Representation of Interactive Foliage and Flora Including Dynamics Model (TRIFFID) and a mechanistic representation of SIF. Incorporating dynamic vegetation processes reduced global SIF root-mean-squared error (RMSE) by 12%. Offline experiments were conducted to understand the water and carbon cycles and their interactions using satellite data as constraints. Results indicate that soil hydraulic properties, the soil hydraulic conductivity at saturation (Ks) and the water retention curve, significantly impact soil moisture and SIF simulation, especially in the semi-arid regions. The wilting point and maximum Rubisco carboxylation rate (Vmax) affect photosynthesis and transpiration, then soil moisture. However, without atmospheric feedback processes, their effects on soil moisture are undermined due to the compensation between soil evaporation and transpiration. With optimized parameters based on SMAP L3 and OCO-2 data, the global RMSE of soil moisture and SIF simulations decreased by 15% and 12%, respectively. These findings highlight the importance of integrating advanced satellite data and dynamic vegetation processes to improve land surface models, enhancing understanding of terrestrial water and carbon cycles.

Meteorological observations of surface air temperature have provided fundamental data for climate change detection and attribution. However, the weather stations are unevenly distributed, and are still very sparse in remote regions. The possible sampling error is well known, but not well quantified because we are lack of the adequate and regularly distributed measurements. The high resolution of satellite land surface temperature retrieval during night time provide a nice proxy for near surface temperature as both temperatures controlled by surface longwave radiative cooling and the nocturnal temperature inversion depress land-atmosphere turbulent exchange. The sampling error of mean value and trend were assessed by comparing station point measurements (pixel of ∼0.01°) with grid (1°) mean and national mean from 2001 to 2021. This method permits us to make the first assessment of under-sampling error and spatial representative error on both national mean and trend of air temperature during nighttime collected at ∼2,400 weather stations over China. The sampling error in national mean temperature is more than 3°C. The under-sampling error due to lack of observation explains two thirds and the spatial representative error due to the difference between station and grid/regional mean elevation contribute the other one third. The sampling error in trend account for one third of the national mean trend. The urban heat island effect associated with urbanization around the weather stations (spatial representative error) can explain four fifths of the sampling error in trend, which is consistent with existing studies based on air temperature collected at paired weather station.

The magnetotail plasma sheet boundary layer (PSBL) is a dynamic boundary layer between the hot-denser plasma sheet and the cold-tenuous tail lobes. It plays an important role in exchanging mass and energy in the magnetotail. In previous studies, the local current carried by the electron beams has been well understood. The strong energy conversion (E ⋅ J, E is electric field and J is current density), however, is barely reported at the PSBL. Here, using magnetospheric multiscale mission, we find a strong dawn-dusk current with a magnitude of 80 nA/m2 at the magnetotail PSBL. The strong current appears during crossing the PSBL and is primarily contributed by the perpendicular electron currents. Cooperating with an intense fluctuating electric field (reaching ∼40 mV/m) carried by the lower-hybrid drift waves, this dawn-dusk current leads to a strong energy conversion with a magnitude larger than 3 nW/m3. This study enhances the understanding of local energy-conversion processes at the PSBL.

Ambient noise interferometry has become a common technique for monitoring slight changes in seismic velocity in a variety of contexts. However, the physical origin of the resolved small velocity fluctuations is not well established for long-term seasonal effects. Here we propose a physical forward model of scattered waves in a deformable medium that includes acousto-elastic effect, which refers to non-linear elasticity with third-order elastic constants. The model shows that small pressure perturbations of a few kPa due to seasonal variations in the water table can induce seismic velocity changes compatible with those measured at the surface by ambient noise interferometry. The results are consistent with field observations near the deep geothermal site of Rittershoffen (France). They illustrate the capability in modeling the diffuse wavefield from scattering synthetic waves to reproduce ambient noise signals for monitoring environmental and/or deep reservoir signals.

Detection of earthquake precursors has long been a controversial issue with regard to its possibility and realizability. Here we present the detection of electromagnetic anomalous signals before large earthquakes using an observation network of very high frequency radio wave receivers close to major tectonic lines in Japan. The receivers are equipped with specifically designed narrowband filters to suppress noises and to detect extremely weak signals. We detected different types of electromagnetic anomalies before earthquakes around mountainous and coastal regions, where presence of electric charges is anticipated on the surface located in the middle of the radio wave paths near major tectonic lines in Japan. We use numerical electromagnetic wave analysis to show that when electric charges are present on a ground surface as a consequence of tectonic activity, the surface charges interact strongly with radio waves and eventually cause strong diffraction of the radio waves. The analysis was performed using the three-dimensional finite-difference time-domain method with digital elevation models of the actual geographical landforms on a massively parallel supercomputer. The results confirm the consistent mechanisms of the electromagnetic precursors, which explains the anomalous electromagnetic signals observed by the authors before large earthquakes.

To investigate the combined impacts of anthropogenic and biogenic emissions on the formation of secondary organic aerosols (SOA), SOA molecular tracers, their corresponding volatile organic compound precursors, and other air pollutants were measured online during the winter and summer seasons of 2022 in an industrial city, Zibo, China. The results indicate that the average concentrations of SOA tracers were 16.1 ± 9.8 ng m−3 in winter and 99.4 ± 57.2 ng m−3 in summer. During winter, anthropogenic SOA (ASOA, the sum of SOA derived from naphthalene and mono-aromatic volatile organic compounds) dominated, whereas isoprene SOA (SOAI) prevailed in summer. Correlation analysis between SO4 2− and both SOAI and high-order monoterpene SOA tracers (SOAM-H) (R = 0.46–0.72, p < 0.001) revealed that higher aerosol acidity facilitated the formation of SOAI and SOAM-H, with SO2 emissions playing a significant role in leading to higher acidity. Most biogenic SOA (BSOA) tracers exhibited a significant positive correlation with NO3 −, particularly in winter, implying the remarkable influence of NO x emissions on BSOA formation. The levels of BSOA tracers increased with NH3, indicating that NH3 can enhance the formation of BSOA. In summer, SOA formation correlated with O x (O x  = O3 + NO2), indicating the substantial impact of atmospheric oxidizing capacity on SOA formation. During winter, aerosol liquid water content (ALWC) correlated well with SOAI tracers (i.e., 3-hydroxyglutaric acid (3-HGA) and 3-hydroxy-4,4-dimethylglutaric acid (3-HDMGA)), and 2,3-dihydroxy-4-oxopentanoic acid (DHOPA) (R > 0.5, p < 0.001), indicating the important contribution of aqueous-phase formation of SOA. These findings underscore the significant role of anthropogenic pollutant emissions in the formation of ASOA and BSOA in urban environments.

The plasmasphere accounts for the majority of the mass of Earth's magnetosphere and contains most of the cold ion (1 eV) population. The plasmasphere is extremely dynamic, undergoing a constant cycle of erosion and refilling. In this paper we perform a statistical study of erosion and refilling rates using 6 years of data from the Van Allen Probes from the beginning of 2013 through the end of 2018. Using in-situ density measurements derived from the upper hybrid resonance line, we create global maps of the erosion and refilling rates over a wide range of L shells and local times. Sorting the data by L shell, magnetic local time, and distance to the plasmapause, we characterize the absolute and relative rates of erosion and refilling during a variety of geomagnetic conditions. We also examine three case studies of geomagnetic storms and compare their density evolutions during the recovery period. Our results are in agreement with refilling rates found by previous statistical studies using different methods, but somewhat lower than many of the case studies reported. We find median erosion rates of 164, 83, and 43 cm−3/day and refilling rates of 87, 42, and 27 cm−3/day at L = 3, 4 and 5, respectively when Kp ≤ ${\le} $ 3. We also find little local time dependence for both erosion and refilling rates.

We study how the normal stiffness and the permeability of a realistic rough fracture at the field scale are linked and evolve during its closure up to the percolation threshold. We base our approach on a well-established self-affine geometric model for fracture roughness, which has proven to be a relevant proxy from laboratory to multi-kilometer scales. We explore its implications for fracture apertures in reservoir-scale open channels. We build our approach on a finite element model using the MOOSE/GOLEM framework and conduct numerical flow-through experiments in a 256×256× $256\times 256\times $ 256 m3 ${\mathrm{m}}^{3}$ granite reservoir hosting a single, partially sealed fracture under variable normal loading conditions and undrained conditions. Navier-Stokes flow is solved in the embedded 3-dimensional rough fracture, and Darcy flow is solved in the surrounding poroelastic matrix. We study the evolution of the mechanical stiffness and fluid permeability of the fracture-rock system during fracture closure including mechanisms that impact the contact surface geometry like asperity yield and deposit of fracture-filling material in the open space of the rough fracture. The largely observed stiffness characteristic is shown to be related to the self-affine property of the fracture surface. A strong anisotropy of the fracture permeability is evidenced when the fluid percolation thresholds are exceeded in two orthogonal directions of the imposed pressure gradient. We propose a unifying physically based law for the evolution of stiffness and permeability in the form of an exponential increase in stiffness as permeability decreases.

Volcanic eruptions and wildfires can impact stratospheric chemistry. We apply tracer-tracer correlations to satellite data from Atmospheric Chemistry Experiment—Fourier Transform Spectrometer and the Halogen Occultation Experiment at 68 hPa to consistently compare the chemical impact on HCl after multiple wildfires and volcanic eruptions of different magnitudes. The 2020 Australian New Year (ANY) fire displayed an order of magnitude less stratospheric aerosol extinction than the 1991 Pinatubo eruption, but showed similar large changes in mid-latitude lower stratosphere HCl. While the mid-latitude aerosol loadings from the 2015 Calbuco and 2022 Hunga volcanic eruptions were similar to the ANY fire, little impact on HCl occurred. The 2009 Australian Black Saturday fire and 2021 smoke remaining from 2020 yield small HCl changes, at the edge of the detection method. These observed contrasts across events highlight greater reactivity for smoke versus volcanic aerosols at warm temperatures.

Atlantic Meridional Overturning Circulation (AMOC) variability originates from a large number of interacting processes with multiple time scales, with dominant processes dependent on both the latitude and timescale of interest. Here, we isolate the optimal atmospheric modes driving climate-relevant decadal AMOC variability using a novel approach combining dynamical and statistical attribution (dynamics-weighted principal component, or DPC analysis). We find that for both the subpolar (55°N) and subtropical (25°N) AMOC, the most effective independent mode of heat flux forcing closely resembles the North Atlantic Oscillation, and drives meridionally coherent AMOC anomalies through western boundary density anomalies. Conversely, established modes of wind stress variability possess limited quantitative similarity to the optimal wind stress patterns, which generate low-frequency AMOC fluctuations by rearranging the ocean buoyancy field. We demonstrate (by running a modified version of the ECCOv4r4 state estimate) that most AMOC variability on decadal time scales can be explained by the DPCs.

Lightning is the severest threaten to safe operation of wind turbines. In this letter, the authors present the first altitude-triggered lightning experiment involving an elevated 12 m-long wind turbine blade placed on the ground. A total of 50 precursors with amplitude over 62.9 A were observed through measurements of channel-base current, fast electric field, and optical data. The air gap with around 3–5 m has been bridged between the wire's lower extremity and the metal blade tip during ascending of the rocket and it is observed to be luminous by slow framing rate camera. The precursors are classified into three groups, namely bipolar pulses, unipolar pulses, and group pulses. Excluding the precursors preceding the initial stage and M-components at the late-time of the initial stage, five stages are classified. In the first stage, the current remains limited at a relatively small value, while the electric field exhibited a slow rising behavior with positive polarity. In the second stage, the current starts to increase, and the electric field rapidly intensifies due to the accumulating charge, and the wire is assumed to experience an explosion. In the third stage, the reconnection process occurs. The current is characterized by a peak value of 1.45 kA with 10%–90% risetime of 10.4 μs. The electric fields suffer from notable decrease and are characterized by a microsecond-scale V-shape pulse. The current cutoff is quite short that almost not found. In the fourth step, the current is characterized by superimposed pulses. The final stage is the channel darkening.

Subthermocline eddies (SEs) influencing ocean circulation are progressively known, yet their extensive impact on the western Pacific undercurrent system remains largely unexplored and, in some regions, often underestimated. Okubo-Weiss parameter analysis reveals a distinctive meridional pattern of cyclonic and anticyclonic SE distribution in the interior western Pacific basin that aligns with zonally elongated mean flows. These westward-propagating SEs play a pivotal role in regulating the formation of zonal undercurrents, particularly off-equatorial regions, through the convergence of eddy potential vorticity flux. Along the Pacific western boundary region, anticyclonic SEs typically enhance (reverse) the velocity of boundary currents flowing northward (southward), primarily through barotropic energy conversion, while cyclonic SEs do the opposite. To summarize, we provide a schematic map of the circulation system in the western Pacific and emphasize the interconnected framework of undercurrents, particularly in relation to SEs.

We develop a 3D full waveform inversion (FWI) method based on dynamic time warping (DTW) to address the issue of cycle-skipping, which can prohibit the convergence of conventional FWI methods. DTW globally compares data samples at different time steps in 2D matrices against the time shifts of waveforms. We introduce the concept of shape descriptors into softDTW, creating a soft-shapeDTW objective function within our waveform inversion process to improve alignment accuracy. Additionally, including constraints from Sakoe-Chiba bands in the inversion further enhances efficiency and overall performance. A synthetic test has shown that the soft-shapeDTW inversion outperforms conventional waveform inversions in overcoming the cycle-skipping challenges that arise from poor initial models. This method was applied to fit observed seismograms to reveal western Yunnan's crustal structure. Seismic waveforms were recorded by 88 broadband stations from 10 local earthquakes, which were then denoised using a continuous wavelet transform method. Generalized cut and paste waveform inversions were used to determine the source parameters of these seismic events. Our inversion well-aligned various seismic phases in the selected time windows of seismograms, and the resolved velocity models well associate with local geological structure. Results suggest that the soft-shapeDTW inversion offers a robust alternative to FWI, reducing the reliance on accurate starting models.

The 2022 eruption at Mauna Loa, Hawai'i, marked the first extrusive activity from the volcano after 38 years of quiescence. The eruption was preceded by several years of seismic unrest in the vicinity of the volcano's summit. Characterizing the structure and dynamics of seismogenic features within Mauna Loa during this pre-eruptive interval may provide insights into how pre- and co-eruptive processes manifest seismically at the volcano. In particular, the extent to which seismicity may be used to forecast the location and timing of future eruptions is unclear. To address these questions, we construct a catalog of relocated seismicity on Mauna Loa spanning 2011–2023. Our earthquake locations image complex, sub-kilometer-scale seismogenic structures in the caldera and southwest rift zone. We additionally identify a set of streaks of seismicity in the volcano's northwest flank that are radially oriented about the summit. Using a rate-and-state friction model for earthquake occurrences, we demonstrate that the seismicity rate in this region can be modeled as a function of the stressing history caused by magma accumulation beneath the summit. Finally, we observe a mid-2019 step change in the seismicity rate in the Ka'oiki region that may have altered the stress state of the northeast rift zone in the three years before the eruption. Our observations provide a framework for interpreting future seismic unrest at Mauna Loa.

Mid-latitude auroras are conventionally generated during intense magnetic storms. However, mid-latitude auroras were observed by naked eyes at Beijing China (39°N, 116°E) unusually during a moderate storm event on 1 December 2023 with the minimum Sym-H index only ∼ ${\sim} $ −120 nT. This study combines conjugative in-site and ground-based observations to analyze the auroras and underlying physical processes. Results indicate that both electron and proton auroras appeared at low latitudes. Electron auroras predominantly arise from low-energy electron precipitation, but proton auroras may be explained by energetic tens of keV proton precipitation. Pc1/EMIC waves are observed at low latitudes in the ionosphere, potentially accounting for mid-latitude proton auroras. Downward field-aligned currents (FACs) are also detected at low latitudes, producing significant magnetic perturbations. This study reveals the underlying ionospheric responses to the mid-latitude auroras to understand potential reasons for observing aurora at such mid-latitudes during a moderate storm.

The South China Sea is a typical marginal sea characterized by the presence of numerous seamounts. However, the effect of seamounts on the air-sea CO2 flux has not yet been well studied. In September 2021, the air-sea CO2 flux was measured directly using eddy covariance (EC), and discrete waterside sampling was conducted. The results indicate that the northern South China Sea is a source of atmospheric CO2. Furthermore, EC measurements show that the seamount emits CO2 at an average rate of 0.34 mmol m−2 hr−1, nearly double that of non-seamount areas. We suggest that the upwelling around the seamount transports deep water rich in dissolved inorganic carbon to the upper ocean, increasing the partial pressure of CO2 there. In addition, the increase in nutrients caused by the upwelling would increase the concentration of chlorophyll-a, resulting in a productive area that emits CO2.

In areas of induced seismicity, earthquakes can be triggered by stress changes due to fluid injection and static deformation from fault slip. Here we present a method to distinguish between injection-driven and earthquake-driven triggering of induced seismicity by combining a calibrated, fully coupled, poroelastic stress model of wastewater injection with interpretation of a machine learning algorithm trained on both earthquake catalog and modeled stress features. We investigate seismicity from Paradox Valley, Colorado as an ideal test case: a single, high-pressure injector that has induced thousands of earthquakes since 1991. Using feature importance analysis, we find that injection-driven earthquakes are approximately 22± $\pm $5% of the total catalog but act as background events that can trigger subsequent aftershocks. Injection-driven events also have distinct spatiotemporal clustering properties with a larger b-value, closer proximity to the well, and earlier occurrence in the injection history. Generalization of our technique can help characterize triggering processes in other regions where induced seismicity occurs.

We estimate the global impact of storms on the global structure and dynamics of the night side plasma sheet from observations by the NASA mission Time History of Events and Macroscale Interactions during Substorms (THEMIS). We focus on an intense storm occurring in December 2015 triggered by interplanetary coronal mass ejections (ICMEs). It starts with a storm sudden commencement (SSC) phase (SYM-H ∼ ${\sim} $ +50 nT) followed by a growth phase (SYM-H ∼ ${\sim} $ −188 nT at the minimum) and then a long recovery phase lasting several days. We investigate THEMIS observations when the spacecraft were located in the midnight sector of the plasma sheet at distances typically between 8 and 13 Earth's radii. It is found that the plasma sheet has been globally compressed up to a value of about ∼> ${\sim} > $4 nPa during the SSC and main phases, that is, 8 times larger than its value during the quiet phase before the event. This compression occurs during periods of high dynamic pressure in the ICME (20 nPa) about one order of magnitude larger than its value in the pristine solar wind. We infer a global increase of the lobe magnetic field from 30 to 100 nT, confirmed by THEMIS data just outside the plasma sheet. During the SSC and main phases, the plasma sheet is found thinner by a factor of 2 relative to its thickness at quiet times, while the Tsyganenko T96 magnetic field model shows very stretched magnetic field lines from inner magnetospheric regions toward the night side. During the recovery phase, whereas the interplanetary pressure has dropped off, the plasma sheet tends to gradually recover its quiet phase characteristics (pressure, thickness, magnetic configuration, etc.) during a long recovery phase of several days.