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Thwaites Glacier has experienced accelerating mass loss, with rates increasing over fivefold since the 1990s. We apply transient calibration to two independent ice‐sheet models (STREAMICE and ISSM) using time‐varying velocity and surface elevation data from 2004 to 2017 to project future mass loss through 2067. We test different calibration approaches: constraining to velocities only, surface elevation change only, or both combined. Models calibrated solely to surface elevation change show the best agreement with observed volume‐above‐floatation loss rates and project the largest future mass losses, reaching 180–200 Gt/a by 2067—comparable to current Antarctic‐wide mass balance. These surface‐constrained models produce focused thinning patterns extending ∼100 km inland along Thwaites' deep trough, suggesting potential marine ice sheet instability. In contrast, velocity‐only calibrations show initially high but rapidly stabilizing loss rates. Our results demonstrate that calibration methodology critically influences century‐scale projections, with surface‐elevation‐constrained models providing the most realistic representation of observed dynamical changes.

Projections of sea‐level rise from ice‐sheet shrinkage in a warming world have large uncertainties, linked to limited knowledge of changes at the ocean‐ice sheet interface. This interface most typically is modeled as a grounding line, across which still‐connected ice flows into the ocean to float as an ice shelf, or where icebergs calve from a cliff before the ice begins to float. But, extensive and rapidly increasing evidence shows that this is really a grounding zone, and that processes in this grounding zone omitted from many models could exert major controls on sea‐level rise. Plain Language Summary Marine‐terminating glaciers flow into the ocean across extensive grounding zones. These kilometers‐long and glacier‐wide zones represent the last broad region of glacier contact with the rock and sediments below before the ice enters the ocean as a floating ice shelf or calved icebergs. Loss of this basal drag along with enhanced basal melting caused by tidally driven seawater intrusion leads to faster outflow and rapid thinning of the overlying ice. As a result of the local thinning, grounding zones retreat inland and sea level rises with more loss of previously grounded ice. Most ice‐sheet models used in sea‐level projections do not include grounding‐zone processes, but rather stop their ice‐ocean interactions at a grounding line. They are thereby omitting important dynamic feedbacks and underestimating future sea‐level contributions from the marine‐based sectors of the Greenland and Antarctic ice sheets. Key Points Tidally modulated seawater intrusion leads to loss of ice‐bed contact as well as significant (maximal) basal melting within grounding zones The future dynamics of marine outlet glaciers are ultimately controlled by coupled processes operating within and through grounding zones Despite the importance of grounding zones to ice‐sheet dynamics, most ice‐sheet models used in sea‐level projections do not include them

Pine Island Glacier and Thwaites Glacier in the Amundsen Sea Embayment are among the fastest changing outlet glaciers in West Antarctica with large consequences for global sea level. Yet, assessing how much and how fast both glaciers will weaken if these changes continue remains a major uncertainty as many of the processes that control their ice shelf weakening and grounding line retreat are not well understood. Here, we combine multisource satellite imagery with modeling to uncover the rapid development of damage areas in the shear zones of Pine Island and Thwaites ice shelves. These damage areas consist of highly crevassed areas and open fractures and are first signs that the shear zones of both ice shelves have structurally weakened over the past decade. Idealized model results reveal moreover that the damage initiates a feedback process where initial ice shelf weakening triggers the development of damage in their shear zones, which results in further speedup, shearing, and weakening, hence promoting additional damage development. This damage feedback potentially preconditions these ice shelves for disintegration and enhances grounding line retreat. The results of this study suggest that damage feedback processes are key to future ice shelf stability, grounding line retreat, and sea level contributions from Antarctica. Moreover, they underline the need for incorporating these feedback processes, which are currently not accounted for in most ice sheet models, to improve sea level rise projections.

The basal thermal state of the Antarctic ice sheet strongly influences ice dynamics and mass balance. Although basal thermal states can be simulated using ice-sheet models, significant uncertainties persist, with poorly constrained geothermal heat flux being one important source of uncertainty. The substantial computational cost of ice-sheet models restricts large ensemble simulations essential for uncertainty quantification and sensitivity analyses. To address this limitation, we develop machine-learning emulators for rapidly simulating the basal temperature and melt rate of Totten Glacier. Three distinct machine-learning emulators were trained using outputs of full-Stokes model simulations with different geothermal heat flux fields, achieving accurate replication (R2 up to 0.92). At least five simulations are needed to train a reliable emulator for basal ice temperature. Model interpretability reveals complementary physics learning: Random forest and XGBoost prioritize local dynamic drivers (basal topography, ice velocity), while neural network captures lower-complexity statistical mapping that is more strongly influenced by globally predictive variables (surface temperature, ice thickness). For basal melt rate, surface velocity emerges as the primary driver. These findings highlight substantial potential for integrating machine learning with ice-sheet modeling to efficiently and better predict basal thermal states.

Inaccurate representations of iceberg calving from ice shelves are a large source of uncertainty in mass-loss projections from the Antarctic ice sheet. Here, we address this limitation by implementing and testing a continuum damage-mechanics model in a continental scale ice-sheet model. The damage-mechanics formulation, based on a linear stability analysis and subsequent long-wavelength approximation of crevasses that evolve in a viscous medium, links damage evolution to climate forcing and the large-scale stresses within an ice shelf. We incorporate this model into the BISICLES ice-sheet model and test it by applying it to idealized (1) ice tongues, for which we present analytical solutions and (2) buttressed ice-shelf geometries. Our simulations show that the model reproduces the large disparity in lengths of ice shelves with geometries and melt rates broadly similar to those of four Antarctic ice shelves: Erebus Glacier Tongue (length ~ 13 km), the unembayed portion of Drygalski Ice Tongue (~ 65 km), the Amery Ice Shelf (~ 350 km) and the Ross Ice Shelf (~ 500 km). These results demonstrate that our simple continuum model holds promise for constraining realistic ice-shelf extents in large-scale ice-sheet models in a computationally tractable manner.

Spinning up a highly complex, coupled Earth system model (ESM) is a time consuming and computationally demanding exercise. For models with interactive ice sheet components, this becomes a major challenge, as ice sheets are sensitive to bidirectional feedback processes and equilibrate over glacial timescales of up to many millennia. This work describes and demonstrates a computationally tractable, iterative procedure for spinning up a contemporary, highly complex ESM that includes an interactive ice sheet component. The procedure alternates between a computationally expensive coupled configuration and a computationally cheaper configuration where the atmospheric component is replaced by a data model. By periodically regenerating atmospheric forcing consistent with the coupled system, the data atmosphere remains adequately constrained to ensure that the broader model state evolves realistically. The applicability of the method is demonstrated by spinning up the preindustrial climate in the Community Earth System Model Version 2 (CESM2), coupled to the Community Ice Sheet Model Version 2 (CISM2) over Greenland. The equilibrium climate state is similar to the control climate from a coupled simulation with a prescribed Greenland ice sheet, indicating that the iterative procedure is consistent with a traditional spin‐up approach without interactive ice sheets. These results suggest that the iterative method presented here provides a faster and computationally cheaper method for spinning up a highly complex ESM, with or without interactive ice sheet components. The method described here has been used to develop the climate/ice sheet initial conditions for transient, ice sheet‐enabled simulations with CESM2‐CISM2 in the Coupled Model Intercomparison Project Phase 6 (CMIP6). Plain Language Summary Experiments with Earth system models typically use the preindustrial (1850 CE) climate as a reference point when examining the climate response to a given experiment scenario. A preindustrial simulated climate state is therefore important to develop and represent consistently, which often requires long and computationally expensive spin‐up or equilibration simulations. The latest generation Earth system models include time‐evolving ice sheet components, which complicate the task of generating a self‐consistent simulated preindustrial climate. Additional complexity arises because ice sheets interact with the rest of the climate system through complex processes and feedbacks and respond slowly to climate change over many millennia. This equilibration timescale is computationally intractable using traditional spin‐up/equilibration simulation techniques. To circumvent this challenge, we present a novel method for generating an internally consistent climate state that is suitable for models with interactive ice sheet components. This method uses fewer computational resources than traditional simulation methods, while generating a climate consistent with more expensive methods. We demonstrate the viability of the method by generating the preindustrial control climate in the Community Earth System Model Version 2 (CESM2), which includes an interactive Greenland ice sheet. Key Points We describe a computationally tractable, iterative procedure for spinning up a coupled Earth system‐ice sheet model Equilibrium state from the iterative procedure is similar to a more expensive traditional model spin‐up with prescribed ice sheets The procedure is used for developing initial conditions for transient, fully coupled simulations in the Coupled Model Intercomparison Project phase 6

Subglacial drainage networks regulate the response of ice sheet flow to surface meltwater input to the subglacial environment. Simulating subglacial hydrology evolution is critical to projecting ice sheet sensitivity to climate, and contribution to sea‐level change. However, current numerical subglacial hydrology models are computationally expensive, and, consequently, evolving subglacial hydrology is neglected in large‐scale ice sheet simulations. We present a deep learning emulator of a state‐of‐the‐art subglacial hydrology model, trained at multiple Greenland glaciers. Our emulator performs strongly in both temporal (R2 > 0.99) and spatial (R2 > 0.95) generalization, offers high computational savings, and can be used to force numerical ice sheet models. This will enable century‐ and large‐scale ice sheet model simulations, including interactions between ice flow and increased meltwater input to the subglacial environment. Generally, our work demonstrates that machine learning can further improve ice sheet models, reduce computational bottlenecks, and exploit information from high‐fidelity models and novel observational platforms. Plain Language Summary Meltwater at the surface of ice sheets can drain to the subglacial environment, lubricate the bed, and influence ice sheet flow. Complex numerical subglacial hydrology models represent the subglacial drainage system, but are too computationally expensive to be included in large‐scale and long‐term ice sheet simulations. Consequently, model predictions of future ice sheet contribution to sea‐level rise ignore ice flow modulation by evolving subglacial hydrology. Here, we use deep learning to emulate a state‐of‐the‐art subglacial hydrology model. The emulator can directly force large‐scale ice sheet models to capture ice flow sensitivity to subglacial hydrology. The computational speed and accuracy of our emulator show the potential to use machine learning to efficiently incorporate previously neglected processes into ice sheet models. Key Points We develop a deep learning emulator to simulate evolving subglacial hydrology in response to meltwater input for ice sheet simulations The emulator shows generalization capabilities, large computational savings, and can be used to force numerical ice sheet models We demonstrate that machine learning has substantial potential in improving ice sheet models, through using information‐rich data sets

The shallow shelf approximation, a balance of membrane stresses for ice flow, is an effective “sliding law” for ice sheet modeling. Our use of it as a sliding law becomes a standard model for ice stream flow when the sliding velocity is large (100 m a−1 and faster). Following Schoof (2006a), we describe the basal resistance as plastic till for which the yield stress is given by a Mohr‐Coulomb formula. Pore water pressure is related to basal melt rate. The velocity field used in the mass continuity and conservation of energy equations is an average of velocities from the shallow shelf approximation and the nonsliding shallow ice approximation. Using this scheme, our model has realistic, time‐dependent ice streams which exhibit the range of surface velocities seen in actual ice streams. We demonstrate the model at high spatial resolution (5 km grid) over multiple millenia using its implementation in the Parallel Ice Sheet Model. Numerical experiments show that the entire scheme is stable with respect to many parameter changes. Some experiments reveal significant ice stream variability in a hypothetical steady climate, with characteristic cycles on the order of 1000 years. We believe this is the first practical whole ice sheet model with a unified treatment of vertical shear stresses and membrane stresses. It is capable of high‐resolution, thermomechanically coupled, multimillenia simulations of ice sheets containing ice streams.

Ice flow models of the Antarctic ice sheet are commonly used to simulate its future evolution in response to different climate scenarios and assess the mass loss that would contribute to future sea level rise. However, there is currently no consensus on estimates of the future mass balance of the ice sheet, primarily because of differences in the representation of physical processes, forcings employed and initial states of ice sheet models. This study presents results from ice flow model simulations from 13 international groups focusing on the evolution of the Antarctic ice sheet during the period 2015–2100 as part of the Ice Sheet Model Intercomparison for CMIP6 (ISMIP6). They are forced with outputs from a subset of models from the Coupled Model Intercomparison Project Phase 5 (CMIP5), representative of the spread in climate model results. Simulations of the Antarctic ice sheet contribution to sea level rise in response to increased warming during this period varies between -7:8 and 30.0 cm of sea level equivalent (SLE) under Representative Concentration Pathway (RCP) 8.5 scenario forcing. These numbers are relative to a control experiment with constant climate conditions and should therefore be added to the mass loss contribution under climate conditions similar to present-day conditions over the same period. The simulated evolution of the West Antarctic ice sheet varies widely among models, with an overall mass loss, up to 18.0 cm SLE, in response to changes in oceanic conditions. East Antarctica ass change varies between -6.1 and 8.3 cm SLE in the simulations, with a significant increase in surface mass balance outweighing the increased ice discharge under most RCP 8.5 scenario forcings. The inclusion of ice shelf collapse, here assumed to be caused by large amounts of liquid water ponding at the surface of ice shelves, yields an additional simulated mass loss of 28mm compared to simulations without ice shelf collapse. The largest sources of uncertainty come from the climate forcing, the ocean-induced melt rates, the calibration of these melt rates based on oceanic conditions taken outside of ice shelf cavities and the ice sheet dynamic response to these oceanic changes. Results under RCP 2.6 scenario based on two CMIP5 climate models show an additional mass loss of 0 and 3 cm of SLE on average compared to simulations done under present-day conditions for the two CMIP5 forcings used and display limited mass gain in East Antarctica.

The basal thermal state of the Antarctic ice sheet (AIS)—whether the base is frozen or thawed—fundamentally underpins its flow and is an important factor in understanding its large-scale response to external forcings. Here, we present a first synthesis of the AIS basal thermal state combining two indirect and independent methods: (1) a compilation of nine three-dimensional thermomechanical simulations that calculate AIS basal temperature as part of the Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6) and (2) an estimate of the basal slip ratio, defined as the ratio of observed surface speed to deformational speed. This synthesis is evaluated against direct observations from deep boreholes and predicted flowpaths for water originating from subglacial lakes detected by altimetry and radar sounding. The synthesis predicts a thawed bed across most of West Antarctica and localized regions in East Antarctica. Most of the Antarctic Peninsula, the Transantarctic Mountains and several regions of East Antarctica are likely frozen at the bed. Overall, our synthesis suggests 46% of the AIS bed is likely thawed, 18% likely frozen and the remaining 36% is uncertain. Additional observations, particularly at the continental scale, are required to improve our understanding of Antarctica’s basal thermal state.