The description of the bed topography under the Greenland and Antarctic Ice Sheets has greatly improved over the past decade through new field campaigns and mapping techniques, leading to BedMachine, a high-resolution gridded bed map widely used by the ice-sheet modelling community. Despite regular updates, BedMachine still suffers from uncertainty in ocean bathymetry and mapping artefacts in the ice-sheet interior. We describe here four recent improvements that address these limitations. In Greenland, we use ICESat-2 surface elevation time series to construct an ensemble of bed elevations that captures finer bed details. In Antarctica, we use Ice-Flow Perturbation Analysis in the interior. This approach provides an estimate of the bed topography using the surface expression of mesoscale bedforms. For periphery ice caps and the Antarctic Peninsula, we use the machine learning-based IceBoost approach, which is capable of inferring fine details based on surface features. Finally, over the continental shelf, we use a new gravity inversion product from the Antarctic Gravity Anomaly Grid, which provides significant refinements to the bathymetry around the entire ice sheet. Overall, these represent major improvements in the description of the bed topography and bathymetry for both ice sheets. This article is part of the Theo Murphy meeting issue 'Next generation ice-sheet bed measurements'.

The landscape shrouded by the Antarctic Ice Sheet provides important insights into its history and influences the ice response to climate forcing. However, knowledge of this critical boundary has depended on interpolation between irregularly distributed geophysical surveys, creating major spatial biases in maps of Antarctica's subglacial landscape. As stress changes associated with ice flow over bedrock obstacles produce ice surface topography, recently acquired, high-resolution satellite maps of the ice surface offer a transformative basis for mapping subglacial landforms. We present a continental-scale elevation map of Antarctica's subglacial topography produced by applying the physics of ice flow to ice surface maps and incorporating geophysical ice thickness observations. Our results enrich understanding of mesoscale (2 to 30 kilometers) subglacial landforms and unmask the spatial distribution of subglacial roughness and geomorphology.

The Greenland Ice Sheet (GrIS) is a major contributor to sea-level rise, yet its long-term response to sustained warmth remains uncertain 1,2,3 . The Holocene represents the most recent interval during which the GrIS experienced centuries of temperatures above preindustrial levels, providing a natural benchmark for assessing its sensitivity to prolonged warming 4,5 . However, reproducing the geologically constrained evolution of the GrIS throughout the Holocene has remained a persistent challenge for ice-sheet models 6,7 . Here we present GrIS simulations forced by paleoclimate reconstructions and evaluated against extensive geological constraints to reconstruct its evolution from the Holocene to 2300 CE. By simultaneously capturing multiple independent records of past ice-sheet change, our ensemble provides a robust foundation for future projections. Under current warming trajectories, the GrIS shrinks below its Holocene minimum extent and volume by 2300 CE, while mass loss rates exceed Holocene maxima and contribute 26–86 cm of sea-level equivalent. Continued warming drives a nonlinear expansion of the ablation zone beyond its Holocene range, whereas substantial emissions reductions limit the sea-level contribution to approximately 10 cm. These results indicate that future warming may push the Greenland Ice Sheet beyond the range of states experienced during the Holocene.

Accurate partitioning of present-day Greenland Ice Sheet (GrIS) mass change is essential for closing the sea-level budget and constraining future projections. Vertical bedrock motion from the Greenland GNSS Network (GNET) has recently been used as a virtual instrument for GrIS mass change, but interpretations diverge. At Jakobshavn Isbræ, GNSS uplift has been linked both to dynamic thinning that leads changes in ice discharge by about 0.87 years, implying predictive power for future ice flux, and to seasonal uplift peaks that precede ice-mass loss by 4.5–9 weeks, interpreted as evidence for substantial transient meltwater storage within the ice sheet. Here we reconcile these seemingly contradictory results by jointly analysing GNET observations, mass-balance products, and a numerical ice-sheet model of Greenland’s major outlet glaciers. We show that there is neither a phase shift between bedrock uplift and ice mass-change signals nor any substantial seasonal missing mass. Instead, we find that the two earlier results stem from an incorrect physical interpretation of the GNSS signal. From our analysis, the variability in bedrock uplift is primarily driven by the advance and retreat of the ice front within roughly 10 kilometres of the glacier termini, a zone that is often poorly captured by input–output methods and coarse-resolution mass-balance products. Our results clarify the physical origin and timing of vertical bedrock shifts in Greenland and provide tighter constraints on the contemporary GrIS mass budget.

Adequate Earth system simulations require interactions between the atmosphere, the ocean, and the ice sheets. To this end, numerical solvers that compute the evolution of the different Earth system components are coupled. There are frameworks and libraries for coupling that handle the complex tasks of coordinating solver execution, communicating between processes, and mapping between different meshes. This allows solvers to be developed independently without compromises on numerical methods or technology. Code reuse is improved, both over large, monolithic software systems that reimplement each coupled model as well as over ad-hoc coupling scripts. In this work, we use the preCICE coupling library to couple the Ice-sheet and Sea-level System Model (ISSM) with the subglacial hydrology model CUAS-MPI. An adapter for each model is required to pass meshes and coupled variables between the model and preCICE. We focus mainly on the technical aspects (design, development, and use of the adapters, choice of coupling library, and large-scale performance analysis), using a synthetic setup to verify functionality and correctness. The adapters we developed are generic and reusable for use cases other than ice-hydrology coupling. Computational performance for the coupled system is measured on a high-performance computing cluster. We find that coupling with preCICE has low computational overhead and does not negatively impact scaling. A comparison between unidirectional and bidirectional coupling for the synthetic ice sheet shows that the coupling captures the anticipated feedback mechanisms between the two systems. The coupled simulations are numerically stable, despite the nonlinearities in the physical system. The generic coupling library preCICE is well suited for our use case and has advantages as well as disadvantages over Earth System Model-specific libraries. The new framework and code enable studies of the subglacial hydrological systems of ice sheets, as well as coupling ISSM or CUAS-MPI with other codes, such as in global Earth System Models or process models.

In 2016, we established the first network of GNSS stations on the Northeast Greenland Ice Stream (NEGIS), enabling continuous monitoring of ice flow motion and surface elevation changes. These stations have revealed both short-term variability and longer-term accelerations that propagate far inland from the terminus (Khan 2022; Khan 2024), highlighting the dynamic coupling between the glacier front and the interior of the ice sheet. Building on this effort, in 2024 we deployed four additional GNSS stations on Jakobshavn Isbræ, one of Greenland’s fastest-flowing outlet glaciers. All stations on both Jakobshavn and NEGIS are located along the main glacier trunks, spanning distances of ~20 to ~200 km from the terminus, thereby capturing spatial gradients in flow and deformation.The GNSS sites also enable direct validation of satellite-derived surface elevation products (ICESat-2 and CryoSat-2). Whereas satellite altimetry provides repeat measurements of ice-surface elevation once per month, GNSS observations deliver continuous, hourly records of both vertical and horizontal ice motion. This high temporal resolution allows us to resolve short-lived dynamic events, seasonal signals, and longer-term trends that are not detectable from spaceborne sensors alone. Together, these complementary datasets provide powerful constraints for improving ice-flow models and for assessing the future evolution and stability of the Greenland Ice Sheet.In addition, we apply GNSS interferometric reflectometry (GNSS-IR) to the ice-sheet environment, using reflected GNSS signals to infer changes in ice-surface height and physical properties such as roughness and snow accumulation. This technique adds a new observational dimension to the GNSS network, further enhancing its value for characterizing glacier–atmosphere interactions and surface processes.

Abstract Understanding the coastal zone of the Antarctic Ice Sheet (AIS), where it interacts with the Southern Ocean and warmer air masses, is crucial for predicting Antarctica's influence on the global climate and sea level. This region has multiple tipping mechanisms that could trigger large, rapid, and potentially irreversible changes in the AIS, the Southern Ocean and their global connections in the coming centuries. The AIS remains the largest source of uncertainty in future sea‐level projections. Bed topography beneath the ice shelves and the coastal ice sheet is not yet well documented, and is a major source of this uncertainty. This review assesses current knowledge of the coastal zone and highlights methods to investigate it, including aerogeophysical surveys, ground‐ and ship‐based measurements, satellite observations, and computer modeling. An ensemble analysis of published bed topography data sets identifies significant data gaps and their regional distribution, framed in the context of current ice‐sheet behavior and potential instability. We propose scientific priorities and guidelines for future aerogeophysical surveys, advocating for a comprehensive, coordinated international effort to build a next‐generation data set of Antarctic bed properties. Such an initiative would significantly advance understanding of the role of coastal processes in ice‐sheet dynamics, reducing uncertainties in sea‐level rise projections and improving predictions of future ocean and climate changes.

Abstract Differentiable Earth system models (ESMs) enable powerful applications such as sensitivity analysis, gradient‐based calibration, state estimation, boundary flux inversions, uncertainty quantification, and online machine learning. Reverse‐mode automatic differentiation (AD) efficiently provides gradients for such tasks, yet models have rarely included this capability because of complex, bespoke numerical algorithms. As part of the Differentiable programming in Julia for Earth system modeling (DJ4Earth) initiative, we present improved capabilities of the AD tool Enzyme.jl and the new compiler transpilation tool Reactant.jl, augmented by sophisticated checkpointing algorithms, which, together make general‐purpose AD tractable and efficient for full‐fledged ESM components written in Julia. Operating at the low‐level virtual machine intermediate representation or multi‐level intermediate representation compiler levels, these frameworks support mutable memory, custom kernels, and compiler optimizations before and after differentiation. Julia‐specific challenges related to just‐in‐time compilation and garbage collection are handled efficiently. Reactant further enables automatic performance portability across central processing units, graphics processing units, and tensor processing units, facilitating use of emerging AI‐customized high‐performance computing architectures. We demonstrate these frameworks on four Julia‐based ESM components featuring diverse spatial discretizations and numerical algorithms: the rotating‐sphere shallow water model ShallowWaters.jl, the finite‐volume ocean model Oceananigans.jl, the finite‐element ice sheet model DJUICE.jl, and the spectral atmospheric model SpeedyWeather.jl. Across these ESM components, our tools compute efficient and correct gradients. These results establish a foundation for differentiable, high‐performance and performance‐portable ESMs that can integrate neural networks for unresolved processes, trained online, enabling next‐generation hybrid physics–machine learning ESMs constrained by physical dynamics and observations.

Abstract Mapping subglacial topography along the margin of the Greenland Ice Sheet was revolutionized by reconciling measurements of ice thickness and surface velocity using the principle of mass conservation. Despite evidence that many subglacial valleys resolved by that method extend upstream, it cannot be applied to the ice sheet's slower‐flowing interior, where ice thickness observations are sparse. Here we apply Ice Flow Perturbation Analysis to surface elevation and velocity observations, revealing a vast, island‐wide network of subglacial valleys connected to the peripheral valleys resolved by the mass conservation method. The morphology of this valley network further implicates Greenland's southern and eastern highlands as inception points for the ice sheet and also suggests that this network is modulated by both groundwater flow and Greenland's tectonic inheritance.

Abstract. Ice-marginal lakes are an increasingly common feature of glacierised landscapes, and their sudden drainage beneath glaciers (a jökulhlaup) can threaten downstream communities and infrastructure. Numerous efforts to model jökulhlaups have been made, however, because these models are 1D representations of a single channel connected to a lake, they cannot simulate lateral jökulhlaup propagation through the subglacial system. Here, to simulate jökulhlaups within a 2D subglacial drainage system, we use a fully coupled model of subglacial hydrology and basal sliding with a time-evolving ice-marginal lake located at its boundary. In experiments on a synthetic domain, the model produces stable, recurrent jökulhlaup cycles, and glacier acceleration during flood onset followed by abrupt slowdown at peak flood discharge. Sensitivity testing highlights the efficiency of the subglacial hydrology system as a key control on flood timing, peak discharge, and the basal sliding response. We also explore our model’s ability to represent an observed record of jökulhlaups by applying it to Isunnguata Sermia, West Greenland. The model successfully reproduces variability over a 17-year period, but underpredicts peak flood discharges, likely because its formulation omits ice uplift and lake temperature variability. These results establish the coupling of a lake to 2D subglacial hydrology and ice dynamics as a viable approach for multi-decadal jökulhlaup simulation.

Despite large uncertainties associated with future mass loss from the Antarctic Ice Sheet, ice-sheet models show that the rate of sea-level rise from Antarctic ice loss in 2025 is strongly predictive of the rate for the next several decades, regardless of emission pathway or model complexity. This finding is robust across all models that were considered in the Intergovernmental Panel on Climate Change Sixth Assessment Report global mean sea-level projections, including the low-likelihood, high-impact scenarios of sea-level rise. Given this strong near-term decadal predictability, ice-sheet models that can accurately reproduce present-day ice-mass loss provide a reliable basis for near-term sea-level planning and adaptation through to mid-century. The predictability breaks down by the end of the twenty-first century as feedbacks, such as those related to marine ice-sheet retreat, begin to emerge, leading to accelerating ice loss. Drawing on these results, we identify key feedback mechanisms that can account for the transition between near-term decadal predictability and the longer-term, feedback-driven evolution, and suggest priorities for ice-sheet model development aimed at resolving long-term sea-level rise uncertainty.

High Mountain Asia (HMA) glaciers are critical for downstream water resources and are an increasing contributor to global sea level rise. Yet, their 21st century evolution remains uncertain because of complex topography, heterogeneous climate forcing, and limited observational constraints. Ongoing glacier mass loss reflects a shift in their buffering capacity, with consequences for the timing and reliability of downstream meltwater supply, as well as for the stability of glacierized landscapes. Quantifying this glacier response requires physically based projections of glacier evolution that adequately capture ice flow and surface processes. Existing regional projections rely on simplified flow-line, shallow-ice flow models approximating ice-dynamic processes. In this study, we simulate the glacier mass change across HMA until the end of 2100 using the Ice-sheet and Sea-level System Model (ISSM). We use a MOno-Layer Higher-Order (MOLHO) ice flow approximation on a non-uniform triangular finite-element mesh at high spatial resolution (30–500 m), locally refined based on present-day observed ice velocities. Basal friction coefficients are inferred through inverse modeling by minimizing the misfit between observed and modeled surface velocities, with independent calibration performed for each HMA subregion using observations from 2022. We use a temperature index method for surface mass balance (SMB) that explicitly accounts for the spatial distribution of supraglacial debris cover. SMB is calibrated using geodetic estimates based on stereo-imagery for the period of 2000 to 2020. We project the glaciers evolution under SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 climate scenarios, using bias corrected climate forcing from five CMIP6 global climate models referenced to ERA5-Land. Our results show that all regions experience mass loss by 2100, but hightlight pronounced spatial heterogeneity in glacier mass change across High Mountain Asia, with strongly varying magnitudes across subregions and climate scenarios. Under the low-emission scenario, projected mass loss remains between 11–40% compared to the glacier mass during 2000, whereas high-emission scenarios lead to substantial ice loss across most regions, ranging between 42–74% in regions such as Nyainqentanglha, Pamir, and eastern Hindu Kush. These results provide improved projections of HMA glacier change and offer valuable insights for assessing future water availability and supporting sustainable water-resource management in High Mountain Asia.

Subglacial topography beneath the Greenland Ice Sheet is a fundamental control on its dynamics and response to changes in the climate system. Yet, it remains challenging to measure directly, and existing representations of the subglacial topography rely on a limited number of observations. Although the use of mass conservation and the development of BedMachine Greenland substantially improved the representation of the bed topography, this approach is limited to fast-flowing sectors and is less effective in regions with complex, alpine topography. As an alternative to traditional numerical methods, recent work has explored using Physics Informed Neural Networks (PINNs), constrained by only one physical law, to solve forward and inverse problems in ice sheet modeling. Building on this work, we assess three PINN frameworks constrained by distinct conservation laws, showing that PINNs informed with a single conservation law are not sufficient for regions with sparse measurements and complex topographies. To that end, we introduce a novel approach that involves coupling two conservation laws within a PINN framework to infer the subglacial topography and test this approach for three regions with distinct environments in Greenland. This PINN is trained with both the conservation of mass and an approximation of the conservation of momentum (the Shelfy-Stream Approximation), which allows us to simultaneously infer the ice thickness and basal shear stress using observations of ice velocities, surface elevation, surface mass balance, and ice thinning rates in a mixed inversion problem. We compare the predicted ice thickness to ground-truth ice-penetrating radar measurements of ice thickness, showing that the PINN informed with two conservation laws is capable of inferring ice thickness in sparsely surveyed regions. Furthermore, comparisons of predicted bed topographies with BedMachine Greenland show that this approach is capable of discovering new bed features in slower-moving regions and in regions of complex topography, highlighting its potential for better constraining the bed topography of the Greenland Ice Sheet.

Abstract 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.

Ice dynamics plays a primary role in rapid sea level rise, and our approach to ice dynamic modeling therefore determines our ability to assess future ice mass changes and resulting global implications. Over the last several years, the coupling of subglacial hydrology and ice dynamics models has allowed an enhanced analysis of the impact of basal boundary conditions and drainage networks on ice flux. Here, we discuss the progress to date of hydrology-ice dynamics coupling within the Ice-sheet and Sea-level System Model (ISSM), the impacts from coupling on hydrology and ice dynamic development, and the challenges that remain to better represent the ice-bed system in both catchment and continent-scale simulations. We also examine the role of projected surface melt in Antarctica and how that may affect subglacial hydrology development, basal sliding, and ocean melt under floating ice shelves. We outline the next steps for the field of hydrology-ice dynamics coupling and how this can benefit the wider glaciological community.

The retreat of Greenlandic glaciers through calving has major implications for the ice sheet's mass balance and future sea-level rise contributions. Despite its importance, the implementation of calving in ice sheet models remains contested, with several calving laws suggested to parametrise this process. While the performance of some of these calving laws has been tested for Antarctic ice shelves and Greenland's grounded outlet glaciers, it is unclear which calving law would best capture the observed behaviour of Greenland's ice shelves. Petermann, Ryder, and Nioghalvfjerdsbr ae (79N) glaciers terminate as Greenland's three largest ice shelves, accounting for 90 % of the remaining floating ice and buttressing similar to 15 % of the ice sheet's mass. Here we build on other systematic calving studies by comparing five calving laws at Greenland's three largest ice shelves using the Ice-sheet and Sea-level System Model (ISSM). We begin by constraining the performance of each law against observed terminus fluctuations between 2008 and 2024, and continue with projections to 2300 under various climate forcings. When evaluated against observed terminus changes, we recommend the use of a von Mises or Crevasse Depth calving law owing to their consistent performance and similar tuning parameters across the three ice shelves. However, in our extended projection runs, we find that calving parametrisations have little influence on grounding line discharge rates, which are instead driven by the choice of climate forcings. Large ice shelf calving or collapse events are scarce, and only in these rare cases do we find any pronounced increase in ice discharge. Our results indicate either continued buttressing potential from Greenland's ice shelves into the coming centuries or fundamental flaws in the current set of calving laws when calibrated to contemporary ice-shelf behaviour.

Abstract We present a global dataset of glacier ice thickness modeled with IceBoost v2.0, a gradient-boosted decision tree scheme trained on 7 million ice thickness measurements and informed by physical and geometrical predictors. We model the distributed ice thickness for every glacier in the two latest Randolph Glacier Inventory releases (RGI v6.0 and v7.0), totaling 215,547 and 274,531 glacier outlines, respectively, plus 955 ice masses contiguous with the Greenland Ice Sheet. IceBoost v2.0 represents the third existing RGI v6.0 global ice volume estimate, and the first for RGI v7.0. On RGI v6.0 we find a global glacier volume of (150 ± 38) × 10 3 km 3 , consistent with the two previous estimates of (141 ± 40) × 10 3 km 3 and (158 ± 41) × 10 3 km 3 . The corresponding sea-level equivalent (SLE), 323 ± 91 mm, is likewise consistent with the two earlier values of 311 ± 100 mm and 324 ± 84 mm. On RGI v7.0 we find a global glacier volume of (149 ± 38) × 10 3 km 3 and SLE of 323 ± 91 mm. Reconstructed ice thickness distributions can vary substantially across models for individual glaciers, ice caps, and even large glacier complexes. Compared to measurements, IceBoost v2.0 root mean square error is 20–45% lower than that of other models in the high Arctic, and comparable elsewhere. We examine major glaciated regions and compare results with the other models. Confidence in our estimates is highest at high latitudes, where abundant training data adequately sample the feature space. Over steep and mountainous terrain, small glaciers, and lower-latitude regions with limited training data, confidence is lower. IceBoost v2.0 is applicable to ice sheet margins. On the Geikie Plateau (East Greenland), we find nearly twice as much ice as previously reported, highlighting the potential for improved constraints on bed topography in this region. No physical laws are explicitly imposed during training, so sufficient and high-quality training data are crucial. The quality of the generated maps depends on the accuracy of the training data, the Digital Elevation Model, ice velocity fields, and glacier geometries, including nunataks. Using the Jensen Gap, we probe the model’s curvature with respect to input errors and find it is strongly concave over low-slope, thick-ice regions, implying a potential downward bias in predicted thickness under input uncertainty. The released dataset can be used to model future glacier evolution and sea-level rise, inform the design of glaciological surveys and field campaigns, as well as guide policies on freshwater management.

NASA's ICESat-2 mission was launched in 2018, carrying a photon-counting laser altimeter, with a primary objective of measuring height changes across Earth's surface. ICESat-2 has provided measurements of ice surface height between 88º N and S, repeated four times per year, with high vertical accuracy and along-track spatial resolution. Its accuracy and coverage has enabled near-complete recovery of height changes across the ice sheets, capturing subtle changes in the interior, and rapid changes along the dynamic margins with steep slopes and the floating peripheral ice shelves. The ICESat-2 Science Team has developed a suite of algorithms that produce along-track and gridded land ice height products at various levels of processing, all freely available at the National Snow and Ice Data Center. Here, we describe three higher-level land-ice data products derived from ATL06 and their underlying algorithms: along-track height change (ATL11), digital elevation model (ATL14) and gridded surface height change (ATL15). We demonstrate the suitability of each data product for studying different ice sheet regions. We then show height changes for Greenland and Antarctica from ATL15 during the first 6 years of the ICESat-2 mission (October 2018-December 2024), illustrating how ICESat-2 measurements can distinguish the multi-year trends from seasonal fluctuations.

Uncertainty in the future contribution of the ice sheets to sea level rise associated with different climate forcings has been well studied in the most recent Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6). Similarly, the uncertainty due to differences in basal sliding laws or calving laws has also been thoroughly investigated. However, the uncertainty due to the bed topography has not yet been rigorously quantified. The majority of the models within the ISMIP6 ensemble use a single bed topography map, BedMachine Greenland, thereby hindering our ability to better understand how uncertainties in the bed topography affect the overall uncertainty in future projections of sea level rise. To address this, we follow the methodology from Castleman et al. (2022) and create an ensemble of 32 bed topography maps with realistic bed roughness by perturbing the BedMachine bed topography using discrete wavelet decomposition techniques. We update the initial bed topography in ice sheet models from Choi et al., (2021), which provide Greenland-wide, high-resolution, data constrained projections that include calving dynamics, and run projections out to 2300. Though we expect northwest and central west Greenland glaciers to contribute more to sea level rise than other glaciers, we find that models initialized with BedMachine bed topographies tend to overestimate mass loss in these regions. We also find that the addition of bed roughness reduces the future contribution of the ice sheet to sea level rise over the 21st century, but to a lesser extent than the deep, wide subglacial basins of Antarctica. Lastly, we also determine that the uncertainty in the future contribution of the Greenland Ice Sheet due to different climate forcings and the uncertainty due to the bed topography are comparable at the end of this century, however the uncertainty due to climate forcings dominates in the long run.

Abstract. Ice calving plays a significant role in ice sheet mass loss. Predictions of future ice sheet mass balance require accurate representations of the calving process in numerical ice models to determine rates of future global mean sea level rise. Whilst calving has recently begun to have been implemented in numerical models there has not been a systematic investigation into how this implementation compares between different ice flow models. The Calving Model Intercomparison Project (CalvingMIP) has been established to address this question by providing a framework of experiments to investigate the accuracy of simulated calving rates and how simulated properties at the calving front evolve over time. Our initial focus has been on how calving is implemented in models, therefore focusing on calving algorithms, rather than on how much ice should be calved at a particular time (calving law). Our results, from thirteen different numerical modelling groups, show that the majority of calving algorithms implemented are able to accurately implement a given calving rate with an ice front that evolves smoothly throughout the calving process. These results show that we can have confidence in the models capacity to accurately implement calving laws in the future.