Ice shelves, the floating extensions of the Antarctic Ice Sheet, provide critical buttressing stresses that resist the seaward flow of ice and help set the position of the grounding line, where the ice goes afloat. As buttressing stresses are diminished by thinning or fracturing and collapse of the ice shelf, glaciers tend to accelerate. Here, we focus on the response of Pine Island Ice Shelf (PIIS) in West Antarctica to multiple calving events and the disintegration of the lateral shear margins. Using observed time-series of the surface velocity fields between 2015 and 2024, we show multiple episodes of acceleration in ice flow and a marked reduction in the buttressing stresses. These observations show that PIIS experienced a significant reduction of its buttressing capacity during the observational record. We then investigate how a model glacier responds to loss in margin buttressing, and are able to broadly reproduce observations. By linking model simulations to observations, we recreate a timeline of buttressing loss on PIIS. These losses likely foreshadow a period of grounding line retreat and acceleration of Pine Island Glacier’s contribution to global mean sea level rise.

The Antarctic Peninsula (AP) is experiencing rapid mass losses due to global warming in recent decades. Understanding high-resolution seasonal ice speed variability is essential for uncovering the spatiotemporal dynamics of glaciers and ice shelves and their response to ongoing climate change. Here, we present Sentinel-1 satellite-based observations of seasonal flow velocity changes in 581 glaciers on the AP during the period 2018-2023. We find that the glacier surface velocities exhibit an acceleration since the 21st century, with this trend intensifying substantially in recent years. Summer velocity acceleration was particularly pronounced in 2018-2023, with an average increase of 6.22 +/- 5.90 % while annual speed increased by > 1.0 %. The summer acceleration of glacier flow was most notable on the Larsen Ice Shelf. Western AP glaciers showed higher velocities and experienced more rapid acceleration than those in the eastern. These changes are primarily driven by warming ocean waters and intensified surface melt. As climate warming persists, the observed acceleration of glacier flow and associated ice shelf mass loss on the AP are projected to persist, depending on the rate and magnitude of future forcing.

A deep-learning model infers large-scale dynamics of Antarctic ice shelves.

Understanding the elastic behavior of the oceanic lithosphere is crucial for interpreting plate dynamics and rheology. While various methods exist to estimate the lithosphere's ability to deform under load, the factors controlling this deformation across different tectonic settings remain poorly quantified.We present a three-stage analysis to systematically evaluate controls on lithospheric flexure across the Western Pacific. First, we calculate a suite of metrics that characterize the elastic deformation properties of the lithosphere using gravity and bathymetry data. Second, we develop a random forest regression framework, a type of machine learning model, to reconstruct these observed deformation properties using a range of geophysical parameters, including gravity, bathymetry, sediment thickness, oceanic crustal age, heat flow, and hotspot proximity. By analyzing the feature importance within this model, we quantify the relative influence of each parameter on lithospheric deformation. Finally, we apply this framework to different tectonic settings (mid-ocean ridges, oceanic plateaus, abyssal plains, and seamount chains) to examine how the controlling factors vary by geological context.This quantitative assessment, leveraging machine learning, advances our understanding of oceanic plate rheology and provides a framework for interpreting lithospheric behavior across different tectonic environments. The results have important implications for understanding plate dynamics and the evolution of the Pacific lithosphere.

Seamounts provide a unique record of volcanic processes in the oceans. In the Pacific Ocean, where seamounts are especially abundant, understanding their age and spatial distributions offers valuable insights into tectonic history, melt-extraction processes, and crustal provenance. However, detailed constraints on seamount formation history remain limited by sparse age data and age-dependent preservation, as older seamounts are progressively lost to subduction.To address these challenges, we develop a data-driven approach to estimate seamount ages by analyzing relationships among multiple variables. Our analysis reveals that features such as crustal age, seamount height, and proximity to proposed “hotspots” illuminate the complex interactions between plate tectonics and magmatic processes. Using these relationships, we estimate ages for previously undated seamounts including uncertainty assessments. By adjusting volumetric measurements for ancient crustal area and subduction losses, we identify distinct phases in Pacific volcanism: (1) an Early Cretaceous period dominated by Large Igneous Provinces, (2) a Mid-Late Cretaceous transition marked by increasing non-hotspot seamount volcanism, and (3) a Cenozoic regime characterized by variable spreading rates and evolving ridge-seamount relationships.This reconstruction provides new insights into the relative contributions of clearly plate-related- and other processes to Pacific volcanism through time, suggesting a more complex interplay between lithospheric and sub-lithospheric dynamics than previously recognized. Similar methods could be applied to other oceans, including the Atlantic and Indian Oceans, where they might also be adapted to discriminate crustal types.

Since the 1990s, Pine Island Glacier (PIG) has been a focal point of research due to its vulnerability within the West Antarctic Ice Sheet. Decades of research have interrogated this dynamic glacier system with a focus on its main trunk and the ice shelf section bordering and stabilizing PIG to the south (the 'south shelf'), receiving comparatively less attention. Using satellite-derived observations from 2017 to 2023, we document marked dynamic changes on the south shelf, particularly following PIG's 2018 calving event, which removed >60 km(2) of ice from this section. Measurements of surface deformation, ice velocity and strain rates from synthetic aperture radar and optical imagery show localized acceleration and structural weakening of the south shelf near-coincident with this loss. Our findings, highlighting the role of peripheral ice shelves in glacier-system stability, suggest that PIG's new configuration-characterized by weakening margins and a compromised south shelf-may result in a geometry that grows progressively unstable.

Seamounts are theorized to originate from deep mantle plumes or shallow, plate-related activities. The mantle plume hypothesis suggests that abnormally hot materials rise from the lowermost mantle and produce large volumes of volcanism on the surface. However, the accuracy of morphological analysis and volume estimation is highly influenced by the representation accuracy of irregularly shaped seamounts and the extent to which thick sediment coverage obscures their bases. As a result, the precise contribution of magma from mantle plumes to surface volcanism remains unclear. Our study introduces a novel approach using Gaussian Process Regression to reconstruct the complex topography of seamounts, both above and beneath sedimentary covers. This approach advances previous analyses by (1) taking account of irregular seamount topography and (2) correcting for the varying sediment thicknesses that obscure seamount bases. Our investigation yields two principal findings. 1. Refined Volcanism Distribution Mapping Analysis in the Pacific Ocean indicates that only 18% of total intraplate volcanic activity is attributable to plume-related volcanism. In addition, the volume statistics of plume-related seamounts and those along the Large Low-Shear-Velocity Province margins show no significant distinction from those of other intraplate seamounts. These results suggest that proposed plumes account for only a minority of the volume of intraplate volcanism in the Pacific plate, and that shallow rather than deep processes are dominant. Along the volcanic Kyushu-Palau Ridge, high seamount volumes are observed near lithospheric weak zones, implying that tectonic inheritance significantly influences magma distribution during volcanic arc formation. 2. Comprehensive Morphological Analysis Employing machine learning clustering analysis on high-resolution multibeam bathymetry data, we categorize seamounts in the South China Sea basin into three distinct morphological types: Type I, large seamounts with steep slopes and rounded bases, predominantly located along extinct ridges; Type II, linear seamounts characterized by gentler slopes, situated along ridges; and Type III, smaller, elliptically-based seamounts found along transform faults or off-ridge areas. This morphological classification provides a novel quantitative framework correlating seamount shapes with their tectonic environments during volcanic activity. Overall, this research advances our understanding of seamount genesis, highlighting the importance of shallow tectonic processes in shaping submarine volcanic landscapes.

By analyzing velocity changes of the Pine Island Ice Shelf (PIIS) from October 2014 to August 2025 using Sentinel-1A/B imagery, we find that the PIIS flow changed markedly over this period. A distinct slowdown occurred at the central PIIS from 14 March 2022 to 20 January 2023, during which velocities decreased from 13.15 +/- 0.04 to 12.71 +/- 0.17 m day-1 (-0.44 +/- 0.18 m day-1 yr-1). Piglet Glacier, a major tributary of the PIIS, also experienced two deceleration periods between 2023 and 2025. The transitions from acceleration to deceleration and then to near-steady flow coincided with substantial changes in m & eacute;lange configuration. Our analysis clearly demonstrates that the flow of the central PIIS and Piglet Glacier is highly sensitive to mechanical coupling along their shear margins. Specifically, the recent slowdowns in the ice flow are most easily explained by variations in the state and configuration of dense ice m & eacute;lange.

Recent developments in generative modeling have utilized score-based methods coupled with stochastic differential equations to sample from complex probability distributions. However, these and other performant sampling methods generally require gradients of the target probability distribution, which can be unavailable or computationally prohibitive in many scientific and engineering applications. Here, we introduce ensembles within score-based sampling methods to develop gradient-free approximate sampling techniques that leverage the collective dynamics of particle ensembles to compute approximate reverse diffusion drifts. We introduce the underlying methodology, emphasizing its relationship with generative diffusion models and the previously introduced Föllmer sampler. We demonstrate the efficacy of the ensemble strategies through various examples, ranging from low- to medium-dimensionality sampling problems, including multi-modal and highly non-Gaussian probability distributions, and provide comparisons to traditional methods like the No-U-Turn Sampler. Additionally, we showcase these strategies in the context of a high-dimensional Bayesian inversion problem within the geophysical sciences. Our findings highlight the potential of ensemble strategies for modeling complex probability distributions in situations where gradients are unavailable.

Dense, regional-scale, continuously-operating Global Navigation Satellite System (GNSS) networks enable the monitoring of plate motion and regional surface deformation.The spatial extent and density of these networks, as well as the length of observation records, have steadily increased in the past three decades.Software to efficiently analyze the ever-increasing amount of available timeseries should be geographically portable and computationally efficient, allow for automation, use spatial correlation (exploiting the fact that nearby stations experience common signals), and have openly accessible source code as well as documentation.We introduce the DISSTANS Python package, which aims to be generic (therefore portable), parallelizable (fast), and able to exploit the spatial structure of the observation records in a user-assisted, semi-automated framework that includes uncertainty propagation.DISSTANS is open-source, includes an application interface documentation as well as usage tutorials, and is easily extendable.We present two case studies that demonstrate our code, one using a synthetic dataset and one using real GNSS network timeseries.

Floating ice shelves that fringe the coast of Antarctica resist the flow of grounded ice into the ocean. One of the key factors governing the amount of flow resistance an ice shelf provides is the rigidity (related to viscosity) of the ice that constitutes it. Ice rigidity is highly heterogeneous and must be calibrated from spatially continuous surface observations assimilated into an ice-flow model. Realistic uncertainties in calibrated rigidity values are needed to quantify uncertainties in ice sheet and sea-level forecasts. Here, we present a physics-informed machine learning framework for inferring the full probability distribution of rigidity values for a given ice shelf, conditioned on ice surface velocity and thickness fields derived from remote-sensing data. We employ variational inference to jointly train neural networks and a variational Gaussian Process to reconstruct surface observations, rigidity values and uncertainties. Applying the framework to synthetic and large ice shelves in Antarctica demonstrates that rigidity is well-constrained where ice deformation is measurable within the noise level of the observations. Further reduction in uncertainties can be achieved by complementing variational inference with conventional inversion methods. Our results demonstrate a path forward for continuously updated calibrations of ice flow parameters from remote-sensing observations.

The speed-up of glaciers following ice shelf collapse can accelerate ice mass loss dramatically. Investigating the deformation of landfast sea ice enables studying its resistive (buttressing) stresses and mechanisms driving ice collapse. Here, we apply offset tracking to Sentinel-1A/B synthetic aperture radar data to obtain a 2014-2022 time-series of horizontal velocity and strain rate fields of landfast ice filling the embayment formerly covered by the Larsen B Ice Shelf, Antarctic Peninsula until 2002. The landfast ice disintegrated in 2022, and we find that it was precipitated by a few large opening rifts. Grounded glaciers did not accelerate instantaneously after the collapse, which implies little buttressing effect from landfast ice, a conclusion also supported by the near-zero correlation between glacier velocity and landfast ice area. Our observations suggest that buttressing stresses are unlikely to be recovered by landfast sea ice over sub-decadal timescales following the collapse of an ice shelf.

The Kyushu-Palau Ridge (KPR) is a remnant arc that initially formed with the subduction of the Pacific plate beneath the West Philippine Sea. The KPR split from the proto-Izu-Bonin-Mariana (IBM) arc with the back-arc spreading at similar to 30 Ma. Even though most of the seamounts have been dated, the factors that drive the volcanic arc evolution along the KPR remain unknown. In this study, we quantify the spatiotemporal distribution of the seamount volume along the KPR, representing a quantitative indicator of erupted volcanism. A spatial filtering method based on the White Top-Hat Transform is used to identify seamounts from the bathymetry. Combining seismic data interpretation and gravity data inversion, we estimate the seamount volume distribution along the KPR. Results show that high concentrations of the seamount volume are in proximity to pre-Oligocene lithospheric weak zones, indicating that tectonic inheritance has facilitated magma migration during the early-stage volcanic arc generation. Subsequently, the younger than 30 Ma age of the majority of the seamounts suggests a late-stage rejuvenated volcanism in the Oligocene. In addition, the spatiotemporal variations of the seamount volume in the northern and central segments are synchronous with the propagated spreading of the Perce Vela and Shikoku basins, further indicating that the rejuvenated volcanism was driven by the propagated back-arc spreading. Rejuvenated volcanism can thus be an important contributive factor to remnant volcanic arc formation.

The Greenland Ice Sheet discharges ice to the ocean through hundreds of outlet glaciers. Recent acceleration of Greenland outlet glaciers has been linked to both oceanic and atmospheric drivers. Here, we leverage temporally dense observations, regional climate model output, and newly developed time series analysis tools to assess the most important forcings causing ice flow variability at one of the largest Greenland outlet glaciers, Helheim Glacier, from 2009 to 2017. We find that ice speed correlates most strongly with catchment-integrated runoff at seasonal to interannual scales, while multi-annual flow variability correlates most strongly with multi-annual terminus variability. The disparate time scales and the influence of subglacial topography on Helheim Glacier's dynamics highlight different regimes that can inform modeling and forecasting of its future. Notably, our results suggest that the recent terminus history observed at Helheim is a response to, rather than the cause of, upstream changes.

Seamounts are submarine volcanoes postulated to be formed either by hot mantle plumes rising from the deep mantle or by shallow, plate-related processes. However, the relative importance of these two mechanisms has not hitherto been quantified. In this study, applying Gaussian Process regression to reconstruct irregular seamount topography above and under the sedimentary layer, we calculate an accurate map of volcanism distribution within the Pacific plate. We find that previous erupted volumes have been underestimated by 75% on average. Our results show that (1) the total erupted volume postulated to be plume-related makes up only 18% of total Pacific intraplate volcanism, and (2) the volume statistics for plume-related seamounts and those along the Large Low-Shear-Velocity Province margins are nearly indistinguishable from the rest of the intraplate seamounts. We conclude that proposed plumes account for only a minority of the volume of intraplate volcanism in the Pacific plate, implying that shallow rather than deep processes are dominant.

Earth and Space Science Open Archive This preprint has been submitted to and is under consideration at Geophysical Research Letters. ESSOAr is a venue for early communication or feedback before peer review. Data may be preliminary.Learn more about preprints preprintOpen AccessYou are viewing the latest version by default [v1]Statistical inference of the ice velocity response to meltwater runoff, terminus position, and bed topography at Helheim Glacier, GreenlandAuthorsLizzUlteeiDDenisFeliksoniDBrentMinchewiDLeigh AStearnsiDBryanRieliDSee all authors Lizz UlteeiDCorresponding Author• Submitting AuthorMassachusetts Institute of TechnologyiDhttps://orcid.org/0000-0002-8780-3089view email addressThe email was not providedcopy email addressDenis FeliksoniDNASA Goddard Space Flight CenteriDhttps://orcid.org/0000-0002-3785-5112view email addressThe email was not providedcopy email addressBrent MinchewiDMassachusetts Institute of TechnologyiDhttps://orcid.org/0000-0002-5991-3926view email addressThe email was not providedcopy email addressLeigh A StearnsiDUniversity of KansasiDhttps://orcid.org/0000-0001-7358-7015view email addressThe email was not providedcopy email addressBryan RieliDMassachusetts Institute of TechnologyiDhttps://orcid.org/0000-0003-1940-3910view email addressThe email was not providedcopy email address

Reliable projections of sea‐level rise depend on accurate representations of how fast‐flowing glaciers slip along their beds. The mechanics of slip are often parameterized as a constitutive relation (or “sliding law”) whose proper form remains uncertain. Here, we present a novel deep learning‐based framework for learning the time evolution of drag at glacier beds from time‐dependent ice velocity and elevation observations. We use a feedforward neural network, informed by the governing equations of ice flow, to infer spatially and temporally varying basal drag and associated uncertainties from data. We test the framework on 1D and 2D ice flow simulation outputs and demonstrate the recovery of the underlying basal mechanics under various levels of observational and modeling uncertainties. We apply this framework to time‐dependent velocity data for Rutford Ice Stream, Antarctica, and present evidence that ocean‐tide‐driven changes in subglacial water pressure drive changes in ice flow over the tidal cycle.

The manuscript "Proper orthogonal decomposition of ice velocity identifies drivers of flow variability at Sermeq Kujalleq (Jakobshavn Isbræ)" explores the application of POD to ice velocity time series in order to efficiently decompose time series into orthogonal spatial and temporal modes. This decomposition could potentially isolate velocity signals originating from distinct forcing mechanisms, such as changes in terminus position or enhanced basal sliding due to subglacial hydrology. These signals may or may not be readily apparent from the raw velocity time series, so POD could potentially reveal key signals to enhance our understanding of ice dynamics.

The recent influx of remote sensing data provides new opportunities for quantifying spatiotemporal variations in glacier surface velocity and elevation fields. Here, we introduce a flexible time series reconstruction and decomposition technique for forming continuous, time-dependent surface velocity and elevation fields from discontinuous data and partitioning these time series into short- and long-term variations. The time series reconstruction consists of a sparsity-regularized least-squares regression for modeling time series as a linear combination of generic basis functions of multiple temporal scales, allowing us to capture complex variations in the data using simple functions. We apply this method to the multitemporal evolution of Sermeq Kujalleq (Jakobshavn Isbræ), Greenland. Using 555 ice velocity maps generated by the Greenland Ice Mapping Project and covering the period 2009–2019, we show that the amplification in seasonal velocity variations in 2012–2016 was coincident with a longer-term speedup initiating in 2012. Similarly, the reduction in post-2017 seasonal velocity variations was coincident with a longer-term slowdown initiating around 2017. To understand how these perturbations propagate through the glacier, we introduce an approach for quantifying the spatially varying and frequency-dependent phase velocities and attenuation length scales of the resulting traveling waves. We hypothesize that these traveling waves are predominantly kinematic waves based on their long periods, coincident changes in surface velocity and elevation, and connection with variations in the terminus position. This ability to quantify wave propagation enables an entirely new framework for studying glacier dynamics using remote sensing data.