The rupture behavior of large oceanic strike-slip earthquakes remains largely unresolved using seismic signals recorded thousands of kilometers away from the source area. Large submarine earthquakes, however, generate hydroacoustic T-waves propagating through the ocean over long distances. Here, we show that these T-waves recorded at regional distances on the Ascension hydrophone array of the International Monitoring System can provide critical information on the earthquake location and rupture behavior. We use recordings from 47 events in oceanic transform faults, ranging in magnitude from 5.6≤Mw ≤7.1, to investigate the rupture processes. We find that most strike-slip earthquakes show unilateral rupture behavior, while a few larger events were more complex. Furthermore, earthquakes in oceanic transforms have longer ruptures than events of the same magnitude in continental faults. We argue that differences in the scaling relation of oceanic and continental strike-slip earthquakes support a lower rigidity in the oceanic lithosphere caused by hydration.

The study uses statistical downscaling methods to generate high-resolution climate projections for Taiwan. Due to the limitations of global climate models (GCMs) in capturing regional climate variations influenced by local topography, downscaling techniques such as Empirical-Statistical Downscaling (ESD) are necessary. Using these methods, we create a dataset to provide detailed climate projections for Taiwan under the Taiwan Climate Change Projection and Adaptation Information Platform (TCCIP). This dataset incorporates historical simulation and future projections under various greenhouse gas emission scenarios from the Coupled Model Intercomparison Project Phase 6 (Eyring et al.). The study details the methodology used in the Taiwan Empirical-Statistical Downscaling (TaiESD) project, including data collection, bias correction, and evaluation processes. Two key elements are utilized: the high-resolution Taiwan Gridded Observation dataset (TGrid) and coarse-resolution simulated datasets from GCMs. The methods of Quantile Mapping (QM) and Quantile Delta Mapping (QDM) were applied to bias-correct temperature and precipitation data. The results indicate that TaiESD effectively captures temperature trends in future climate projections, showing robust consistency with the upstream GCM data. However, precipitation projections exhibit greater variability, reflecting complex local climatic factors. The study highlights the implications for adaptation planning, emphasizing the need for high-resolution data to understand regional climate impacts. Providing a detailed statistical downscaling dataset supports decision-makers in climate adaptation strategies for various sectors, including water resources, disaster management, etc.

The unprecedented expansion of lithium mining in continental brines is sparking concerns that mine-related brine abstraction will stress freshwater resources and harm sensitive wetland ecosystems. These fears stoke preexisting conflicts between indigenous communities, governments, and mining interests. However, these concerns are speculative because until now there has not been a comprehensive evaluation of hydrogeologic responses to brine abstraction. This study characterizes the hydrogeologic relationship between both brine and freshwater abstraction and groundwater discharge to wetlands in continental brine systems utilizing groundwater-flow models. These models demonstrate that density-driven volumetric flux buffers the impacts of brine abstraction on groundwater discharge. Observations of vegetated wetland area near existing lithium brine mines substantiate this conclusion, demonstrating that responsible lithium brine mining must prioritize minimizing freshwater abstraction over brine drawdowns.

Bathymetric data is important for hydrologic and hydraulic applications. It could be used for high accuracy simulations of sediment transport, flooding inundation, watershed conservation, reservoir management, etc. However, accessing riverine bathymetry is time-consuming and expensive, so surveys do not cover many rivers. This study applies a data-driven model, generative adversarial network (GAN), to predict riverbed bathymetry in data-sparse regions. GAN is a popular deep-learning structure commonly applied to generate synthetic data. This study devises a conditional generative adversarial network (CGAN) that can generate channel cross-sections with accessible properties, including channel width, channel depth, thalweg position, curvature of centerline, and bank heights. These crucial properties are normalized to make the model work across river reaches on various spatial scales. The CGAN model is tested by eight input configurations with four training dataset combinations to investigate its advantages and limitations. The results show that the CGAN model has great workability and generality on cross-section generations. Although it merely takes four channel properties as input, it derives normalized root mean square error in elevations around 25%, which is half of the normalized error from the conceptual model in previous literature.

Paleointensity observations from meteorites provide insights into planetary formation and evolution. Meteoritic samples are usually dominated by Fe-rich kamacite, that is capable of faithfully recording the ancient dynamo behavior of meteorites’ parent body. To retrieve paleointensity estimates, most experimental protocols assume that samples are dominated by uniformly magnetized particles. However, most magnetic carriers observed in extraterrestrial samples are non-uniformly magnetized. This inconsistency represents a major impediment in reliably reconstructing paleointensities from meteorites. Here we present the State Group Algorithm (SGA); a micromagnetic based model capable of efficiently simulating thermoremance acquisition of magnetic particles with a single-vortex domain state. The results show that iron particles can acquire thermoremance that is linear with the external field up to ∼100 μT. Single-vortex cooling rate effects are generally weaker than those of single-domain particles, providing more accurate paleointensity estimates. A small number of particles exhibit inverse cooling rate effects, leading to underestimates in paleointensity.

Small-scale ocean currents are crucial in regulating Earth’s climate, with a significant impact in the distribution of ocean properties. During the Calibration/Validation phase of the Surface Water and Ocean Topography (SWOT) satellite mission, we performed a high-resolution, multi-platform experiment to evaluate SWOT’s ability to resolve small-scale features, focusing on a ~20-25 km-radius anticyclonic eddy in the Western Mediterranean Sea. Underwater glider data identified biconvex isopycnals, classifying the eddy as an intrathermocline feature with a ~3 cm sea level anomaly. Acoustic Doppler Current Profiler (ADCP) data recorded maximum velocities of ~40 cm/s. SWOT successfully captured the sea level signal and geostrophic currents of the eddy, showing notable improvements over conventional altimetry: 33% in sea level representation compared to glider, 61% in horizontal velocity compared to ADCP, and 44% and 10% in velocity magnitude and direction compared to drifter measurements. This study highlights SWOT’s potential in resolving small-scale ocean dynamics.

Skillful subseasonal-to-seasonal (S2S) forecasts rely heavily on windows of opportunity, often afforded by teleconnected sources of variability throughout the Earth system. Due to its longer decorrelation scale, the stratosphere is highlighted as one such source of variability. Recently, the surface impacts of stratospheric variability on S2S timescales have been viewed not only through the mean flow diagnostics but also through the distinct geometry and behavior of the stratospheric polar vortex in the wintertime (November through March). This analysis will assess the impact of stratospheric polar vortex variability on the troposphere through its interactions with climate modes, primarily the North Atlantic Oscillation (NAO).  In this research, lower stratospheric potential vorticity (PV) that aids in capturing troposphere-stratosphere coupling processes is used to investigate stratospheric sources of S2S prediction skill. The analysis uses ERA-5 reanalysis data to assess stratospheric teleconnections to the NAO index as described by geopotential height (GPH) anomalies and their contributions to S2S predictions of 2-meter wintertime temperature anomalies across the Northern Hemisphere. Using eXplainable Artificial Intelligence (XAI) and Machine Learning, this research builds dynamical-statistical probabilistic forecasts of 2-meter temperature anomaly on the S2S scale over Eurasia through a feed-forward Artificial Neural Network (ANN) framework. The merged ANN model is constructed with data that includes 14-day lead lower stratospheric PV at 100hPa and synoptic scale North Atlantic GPH anomalies at 500hPa. The model output is interpreted through an XAI method called Layerwise Relevance Propagation (LRP), which is used to examine how each input feature influences the 2-meter temperature predictions and highlights their relative importance. Requisite spatial maps of the input features discretely connect physical processes from the stratosphere to the NAO and highlight potential windows of opportunity for confident and correct predictions of  2-meter temperature outcomes on the S2S timescale.

The unique ecosystem of estuaries as well as their social and economic usage such as freshwater abstraction are highly dependent on local salinity. Shifts in salt intrusion can have severe consequences. The salinity dynamics are influenced by several natural factors, especially the river discharge and the tides, but also by human activities such as channel deepening. A thorough understanding of salt transport mechanisms and their response to changing conditions is essential for assessing the effects of both natural variability and human activities on salt intrusion. This study applies a detailed salt transport decomposition method to a high-resolution numerical model of the North German Weser River Estuary. The analysis of the cross-channel integrated transport showed an alternating dominance of two up-estuary salt transport mechanisms: the subtidal shear transport, driven by estuarine circulation and the tidally-correlated depth-averaged transport, such as tidal pumping. A novel decomposition method allowing for two-dimensional maps of salt transport is developed and implemented here to understand the local topographical impacts on the salt transport. Our results highlight that the cross-sectionally integrated transport can underestimate the strength of the resolved transport in specific areas, as opposing flows often occur between the channel and adjacent shoals. Furthermore, we investigate a potential future scenario involving channel deepening, finding that it increases subtidal shear transport, particularly in dredged areas. These findings offer new insights into the spatial complexity of salt dynamics and the impact of anthropogenic changes on estuarine environments.

Background mesoscale flows (BMFs) modify the local inertial frequency and affect the energy and vertical propagation speeds of near-inertial waves (NIWs). Despite their significance, the effect of BMFs on the downward energy flux (Fz) of NIWs remains underexplored. This study aimed to estimate the downward energy flux (Fz) of NIWs while accounting for BMF effects using two years of mooring data (November 2017–October 2019) from the Kuroshio Extension. By dividing the data into 11 day segments, the temporal variability of the effective near-inertial frequency and group velocity owing to the BMFs was considered. During winter, when NIWs are active—on a temporal average in anticyclonic flows—Fz increased by 50 %, whereas in cyclonic flows, Fz decreased by 45 % when BMFs were considered. Because cyclonic circulations are twice as frequent, Fz decreased by ~17 %, to 0.37 x 10-3 W m-2. This amount is ~1.8 times greater than that in the northeastern Pacific and accounts for ~28 % of the wind work rate, similar to eddy-resolving high-resolution numerical model results. Thus, the Kuroshio Extension is an important area for downward NIW energy propagation and BMFs should be considered for accurate NIW energetics.

Storm surge events are a key driver of widespread flooding, particularly when combined with astronomical tides superimposed on mean sea level (MSL). Coastal storms exhibit seasonal variability which translates into a seasonal cycle in storm surge activity. It has been demonstrated before that the seasonal cycle shows significant interannual variations including possible long-term trends. Understanding these changes is critical as both changes in the amplitude and the phase of their seasonal cycle may alter the compound flood potential. Changes in the seasonal storm surge cycle and the compounding with the MSL cycle remain largely unknown. A comprehensive analysis of the storm surge seasonal cycle and its links to the MSL seasonal cycle is performed using tide gauge observations from a quasi-global dataset. Harmonic analysis is used to assess the mean and changing storm surge seasonal cycles over time. Extreme value analysis is applied to explore the effect of seasonal changes on storm surge return levels. We also quantify the influence of large-scale climate modes, and we compare how the seasonalities of storm surge and MSL have changed relative to each other. The peak of the storm surge cycle typically occurs during winter for tide gauges outside of tropical cyclone regions, where there is greater variability in the phase of the storm surge cycle. The timing of the peak varied by more than a month at 21% of the tide gauges analyzed. The MSL and storm surge cycles peaked at least once within 30 days at 74% of tide gauges.

Understanding the mechanics and rheological properties of the solid Earth requires integrating observations of earthquake cycle deformation with advanced numerical models of the underlying processes. In this article, we introduce a new numerical modeling tool designed for two-dimensional subduction zone geometries that solves the equations for mechanical equilibrium in the lithosphere-asthenosphere system to predict displacements, strains, and stresses throughout the medium. Using a Boundary Element Method (BEM) framework, we reduce the governing partial differential equations to a set of coupled ordinary differential equations, simulating periodic earthquake cycles in a viscoelastic medium with spatially variable power-law viscous rheologies and rate-dependent fault friction. Our approach overcomes key limitations of existing BEM models by ensuring (1) mechanical consistency across timescales, from individual earthquake cycles to geological periods, and (2) precise stress transfer calculations in a viscoelastic medium. We also demonstrate that for spatially heterogeneous linear viscoelastic materials, the coupled ordinary differential equations can be simplified to an eigenvalue problem, significantly enhancing computational efficiency. These advancements offer a powerful tool for predicting spatiotemporal patterns of surface displacement given complex mantle structures and lay the foundation for high-dimensional inverse problems to infer constitutive properties of the lithosphere-asthenosphere system.

Zircon U--Pb geochronology is a vital tool for dating geological events, yet interpreting discordant zircon ages---often the result of Pb loss due to fluid-rock interactions---remains challenging. Although frequently disregarded, discordant data can yield valuable geological insights. Here, we introduce a Python application that models the timing of Pb loss in zircon, enabling efficient analysis of discordant U--Pb data. Our application uses the concordant-discordant comparison test in Tera-Wasserburg concordia space to classify zircon analyses based on a customisable discordance threshold. Featuring a user-friendly interface, the application models possible Pb loss events and calculates optimal Pb loss ages by minimising the dissimilarity between concordant and reconstructed age distributions. When applied to the Mount Isa Inlier in northeastern Australia, the tool identifies a major Pb loss event around 481 Ma, corresponding to Palaeozoic tectonic activity along Gondwana's active margin, including the Delamerian Orogeny. Older Pb loss ages cluster along major NE-SW fault systems, suggesting that long-lived, reactivated structures facilitated fluid migration and subsequent Pb mobilisation in zircon. Sensitivity analyses confirm the consistency of Pb loss age estimates across various model parameters. This study provides new insights into fluid-rock interactions in the Mount Isa Inlier and offers a methodological framework for exploring Pb loss in zircon U--Pb datasets. The application is broadly applicable for future research in regions with complex tectonic histories.

The impact of aviation on climate change due to CO2 emissions is well established and is associated with much less uncertainty than the non CO2 effects. Among the non CO2 effects contrails formation and their evolution into cirrus-type clouds remain a subject of considerable uncertainty. A frequently overlooked source of uncertainty arises from the sensitivity of climate models to adjustable parameters used to represent the effect of subgrid-scale processes. The limited number of state-of-the-art climate models with an explicit contrail representation makes it challenging to evaluate model sensitivity of contrail radiative forcing to parameters and backgroud atmospheric conditions. To better characterize the contrail radiative forcing and its evolution it is therefore necessary to develop their representation within a large range of existing climate models. Here we develop and evaluate a new parameterization of contrail cirrus for the ARPEGE-Climat atmospheric model. The representation of the ice-supersaturated regions, where contrails persist, agrees well with in-situ and satellite observations, as well as the simulated contrail microphysical properties. With this parameterization, and using the ERA5 reanalysis to nudge the ARPEGE-Climat model, we estimate that for the air traffic of the year 2019 the global mean annual contrail coverage is 1.27%. In addition, the global mean annual instantaneous radiative forcing is estimated to be 66.2 mW/m2, although this result is sensitive to the choice of some key parameters of the contrail-cirrus parameterization. These findings are consistent with similar published results obtained using the same flight inventory but based on different methods of representing contrail effects.

The Indian Summer Monsoon Rainfall (ISMR) contributes to 80% of India’s annual precipitation, affecting 1.4 billion people. The performance of CMIP6 models in simulating ISMR is still inadequate. Traditional dynamic or statistical downscaling techniques have only modestly improved ISMR simulations. While deep learning models like YNet have shown promise in global downscaling efforts, we found their application to ISMR has been less effective, particularly for extreme rainfall and intraseasonal variability. We developed two advanced super-resolution deep learning models to overcome these limitations: YNet_D and YNet_DE. YNet_D enhances the downscaling accuracy by integrating atmospheric variables, informed by monsoon dynamics, with coarse precipitation data as predictors. YNet_DE prioritizes extreme rainfall using a weighted loss function, effectively addressing the challenge of simulating extremes. Both models outperform YNet, with YNet_D and YNet_DE achieving root mean squared errors (RMSE) of 12.25 mm and 11.97 mm, respectively, compared to YNet’s 12.41 mm. In terms of extremes, YNet_D and YNet_DE reduced the bias in the 95th percentile (R95p) from 78.27 mm with YNet to 56.52 mm and 40.22 mm, respectively. Additionally, both models showed significant improvements in simulating ISMR’s intraseasonal and interannual variations and demonstrated strong transferability to General Circulation Models (GCMs), with YNet_DE notably lowering the multi-model mean bias for R95p from 114.3 mm in the original GCMs and 130.0 mm in YNet simulations to 64.4 mm. Unlike conventional statistical methods, these models retain the critical physical dynamics of ISMR, offering a robust solution that preserves the system’s variability and complexity.

Urban flood monitoring is crucial for understanding flood processes and implementing management strategies. However, current monitoring systems cannot comprehensively capture urban flooding dynamics. Here we explore the use of cutting-edge Large Multimodal Models (LMMs) to estimate floodwater depth from ground-level images, as alternative observational approaches. Evaluated on two urban flood image datasets, LMMs generate estimations exhibiting acceptable concordance to ground truth, with GPT-4 achieving the highest accuracy of 0.65 and a Spearman correlation coefficient of 0.88. Furthermore, a combined effect of image complexity and textual prompt is found to influence LMMs’ performance. Our study systematically demonstrates, for the first time, that LMMs can be effective tools for imaging-based urban flood monitoring, enlarging the data for flood forecasting and model calibration.

Atmospheric reaeration is a key source of dissolved oxygen in rivers, plays a critical role for assessing aquatic ecosystem health and understanding the mechanisms that support ecological functions. However, traditional water quality models often fail to capture the temporal and spatial dynamics of the reaeration coefficient (k2), thereby hindering accurate predictions of dissolved oxygen concentration distributions. This study employed a recirculating water tank to conduct experiments under various wave and flow conditions, quantitatively analyzing the impact of hydrodynamic factors-such as water depth, flow velocity, and wave action-on oxygen transfer at the air-water interface. The results indicated that as water depth increases and flow velocity decreases, efficiency of oxygen transfer declines. This trend occurs because increased depths and reduced velocities diminish interface disturbance, thereby weakening gas-liquid exchange. Conversely, wave action significantly enhances oxygen transfer, particularly under stronger wave conditions. By integrating gas transfer theory with experimental data, we developed a mapping relationship between k2 and hydrodynamic variables. This framework enabled the development of a multi-parameter dynamic simulation model that integrates hydrodynamics with dissolved oxygen transfer. The model is based on real-time data of hydrodynamic parameters and realizes the dynamic update of the whole domain complex k2, which can accurately simulate the spatial and temporal distribution and transfer of dissolved oxygen under complex hydrodynamic conditions. Driven by dynamic data, the model greatly improves the applicability and accuracy of the traditional mean generalization model, and can be used as an effective tool for predicting dissolved oxygen levels in complex aquatic environments.

We have detected and characterized traveling ionospheric disturbances (TIDs) in the mid-latitude ionosphere over the European region using Kharkiv incoherent scatter (IS) radar data acquired near solstices and equinoxes during solar cycle 24 and under magnetically quiet conditions. We analyzed the diurnal, seasonal and solar activity dependence of both large-scale (LS) and medium-scale (MS) TIDs. We studied 140 TID events and estimated their energetic (relative amplitudes), temporal (dominant periods, frequency of occurrence), and spatial (heights of maximum relative amplitudes, vertical and horizontal phase velocities and wavelengths) characteristics. We suggest that the most probable generation mechanisms of magnetically quiet LS and MS TIDs are solar terminators passages, enhanced gravity wave activity and dissipation, severe tropospheric convection, coupling processes between Es layer and F region, polarization electric fields, and Perkins’s instability. We have shown a positive correlation between the heights of the maximum relative TID amplitudes and the solar activity for LSTIDs. The TID characteristics obtained during extremely quiet conditions provide a useful context for analysing the possible TID response to localized energy releases including those of anthropogenic origin, and enable improvements in global and regional ionospheric models by clarifying the contribution of wave processes to the overall energy budget of the atmosphere and ionosphere.

The inductive component of the magnetospheric electric field, which is associated with the temporal change of magnetic field, provides an additional means of local plasma energization and transport in addition to the electrostatic counterpart. This study examines the detailed response of the inner magnetosphere to inductive electric fields and the associated electric-driven convection corresponding to different solar wind conditions. A novel modeling capability is employed to self-consistently simulate the electromagnetic and plasma environment of the entire magnetospheric cavity. The explicit separation of the electric field by source (inductive vs. electrostatic) and subsequent implementation of inductive effects in the ring current model allow us to investigate, for the first time, the effect of the inductive electric field on the kinetics and evolution of the ring current system. The simulation results presented in this study demonstrate that the inductive component of the electric field is capable of providing an additional source for long-lasting plasma drifts, which in turn significantly alter the trajectories of both thermal and energetic particles. Such changes in the plasma drift, which arise due to the inductive electric fields, further reshape the storm-time ring current morphology and alter the degree of the ring current asymmetry, as well as the timing and the peak of the ion pressure. The total ion energy is increasing at a faster rate than the supply of energetic ions to the ring current, suggesting that the inductive electric field provides effective and accumulative local energization for the trapped ring current population without confining additional particles.