Articles | Volume 14, issue 9
https://doi.org/10.5194/gmd-14-5843-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-5843-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
24 Sep 2021
Model description paper |
| 24 Sep 2021
| 24 Sep 2021
fenics_ice 1.0: a framework for quantifying initialization uncertainty for time-dependent ice sheet models
Conrad P. Koziol
School of GeoSciences, Univ. of Edinburgh, City of Edinburgh, United Kingdom
Joe A. Todd
School of GeoSciences, Univ. of Edinburgh, City of Edinburgh, United Kingdom
School of GeoSciences, Univ. of Edinburgh, City of Edinburgh, United Kingdom
James R. Maddison
School of Mathematics and
Maxwell Institute for Mathematical Sciences, Univ. of Edinburgh, City of Edinburgh, United Kingdom
Related authors
Conrad P. Koziol and Neil Arnold
The Cryosphere, 12, 971–991, https://doi.org/10.5194/tc-12-971-2018, https://doi.org/10.5194/tc-12-971-2018, 2018
Short summary
Short summary
We model the summer acceleration of ice velocities at a land-terminating margin of the Greenland Ice Sheet. Model results compare favourably against GPS data, reflecting positively on the model components and the datasets used. When we run the model into the future, we find that summer velocities increase with increasing levels of surface melt but that changes in annual velocities may be limited.
Conrad P. Koziol and Neil Arnold
The Cryosphere, 11, 2783–2797, https://doi.org/10.5194/tc-11-2783-2017, https://doi.org/10.5194/tc-11-2783-2017, 2017
Short summary
Short summary
We develop a new ice sheet model and couple it to an existing subglacial hydrology model. A workflow for initializing the coupled model at the start of summer is proposed and demonstrated on the Russell Glacier area of Western Greenland. This is a first step towards modelling ice velocities during the summer.
Jowan M. Barnes, G. Hilmar Gudmundsson, Daniel N. Goldberg, and Sainan Sun
EGUsphere, https://doi.org/10.5194/egusphere-2025-328, https://doi.org/10.5194/egusphere-2025-328, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
Calving is where ice breaks off the front of glaciers. It has not been included widely in modelling as it is difficult to represent. We use our ice flow model to investigate the effects of calving floating ice shelves in West Antarctica. More calving leads to more ice loss and greater sea level rise, with local differences due to the shape of the bedrock. We find that ocean forcing and calving should be considered equally when trying to improve how models represent the real world.
David T. Bett, Alexander T. Bradley, C. Rosie Williams, Paul R. Holland, Robert J. Arthern, and Daniel N. Goldberg
The Cryosphere, 18, 2653–2675, https://doi.org/10.5194/tc-18-2653-2024, https://doi.org/10.5194/tc-18-2653-2024, 2024
Short summary
Short summary
A new ice–ocean model simulates future ice sheet evolution in the Amundsen Sea sector of Antarctica. Substantial ice retreat is simulated in all scenarios, with some retreat still occurring even with no future ocean melting. The future of small "pinning points" (islands of ice that contact the seabed) is an important control on this retreat. Ocean melting is crucial in causing these features to go afloat, providing the link by which climate change may affect this sector's sea level contribution.
Beatriz Recinos, Daniel Goldberg, James R. Maddison, and Joe Todd
The Cryosphere, 17, 4241–4266, https://doi.org/10.5194/tc-17-4241-2023, https://doi.org/10.5194/tc-17-4241-2023, 2023
Short summary
Short summary
Ice sheet models generate forecasts of ice sheet mass loss, a significant contributor to sea level rise; thus, capturing the complete range of possible projections of mass loss is of critical societal importance. Here we add to data assimilation techniques commonly used in ice sheet modelling (a Bayesian inference approach) and fully characterize calibration uncertainty. We successfully propagate this type of error onto sea level rise projections of three ice streams in West Antarctica.
Helen Ockenden, Robert G. Bingham, Andrew Curtis, and Daniel Goldberg
The Cryosphere, 16, 3867–3887, https://doi.org/10.5194/tc-16-3867-2022, https://doi.org/10.5194/tc-16-3867-2022, 2022
Short summary
Short summary
Hills and valleys hidden under the ice of Thwaites Glacier have an impact on ice flow and future ice loss, but there are not many three-dimensional observations of their location or size. We apply a mathematical theory to new high-resolution observations of the ice surface to predict the bed topography beneath the ice. There is a good correlation with ice-penetrating radar observations. The method may be useful in areas with few direct observations or as a further constraint for other methods.
Alexander Robinson, Daniel Goldberg, and William H. Lipscomb
The Cryosphere, 16, 689–709, https://doi.org/10.5194/tc-16-689-2022, https://doi.org/10.5194/tc-16-689-2022, 2022
Short summary
Short summary
Here we investigate the numerical stability of several commonly used methods in order to determine which of them are capable of resolving the complex physics of the ice flow and are also computationally efficient. We find that the so-called DIVA solver outperforms the others. Its representation of the physics is consistent with more complex methods, while it remains computationally efficient at high resolution.
Jowan M. Barnes, Thiago Dias dos Santos, Daniel Goldberg, G. Hilmar Gudmundsson, Mathieu Morlighem, and Jan De Rydt
The Cryosphere, 15, 1975–2000, https://doi.org/10.5194/tc-15-1975-2021, https://doi.org/10.5194/tc-15-1975-2021, 2021
Short summary
Short summary
Some properties of ice flow models must be initialised using observed data before they can be used to produce reliable predictions of the future. Different models have different ways of doing this, and the process is generally seen as being specific to an individual model. We compare the methods used by three different models and show that they produce similar outputs. We also demonstrate that the outputs from one model can be used in other models without introducing large uncertainties.
Stephen L. Cornford, Helene Seroussi, Xylar S. Asay-Davis, G. Hilmar Gudmundsson, Rob Arthern, Chris Borstad, Julia Christmann, Thiago Dias dos Santos, Johannes Feldmann, Daniel Goldberg, Matthew J. Hoffman, Angelika Humbert, Thomas Kleiner, Gunter Leguy, William H. Lipscomb, Nacho Merino, Gaël Durand, Mathieu Morlighem, David Pollard, Martin Rückamp, C. Rosie Williams, and Hongju Yu
The Cryosphere, 14, 2283–2301, https://doi.org/10.5194/tc-14-2283-2020, https://doi.org/10.5194/tc-14-2283-2020, 2020
Short summary
Short summary
We present the results of the third Marine Ice Sheet Intercomparison Project (MISMIP+). MISMIP+ is one in a series of exercises that test numerical models of ice sheet flow in simple situations. This particular exercise concentrates on the response of ice sheet models to the thinning of their floating ice shelves, which is of interest because numerical models are currently used to model the response to contemporary and near-future thinning in Antarctic ice shelves.
Conrad P. Koziol and Neil Arnold
The Cryosphere, 12, 971–991, https://doi.org/10.5194/tc-12-971-2018, https://doi.org/10.5194/tc-12-971-2018, 2018
Short summary
Short summary
We model the summer acceleration of ice velocities at a land-terminating margin of the Greenland Ice Sheet. Model results compare favourably against GPS data, reflecting positively on the model components and the datasets used. When we run the model into the future, we find that summer velocities increase with increasing levels of surface melt but that changes in annual velocities may be limited.
Conrad P. Koziol and Neil Arnold
The Cryosphere, 11, 2783–2797, https://doi.org/10.5194/tc-11-2783-2017, https://doi.org/10.5194/tc-11-2783-2017, 2017
Short summary
Short summary
We develop a new ice sheet model and couple it to an existing subglacial hydrology model. A workflow for initializing the coupled model at the start of summer is proposed and demonstrated on the Russell Glacier area of Western Greenland. This is a first step towards modelling ice velocities during the summer.
Daniel N. Goldberg, Sri Hari Krishna Narayanan, Laurent Hascoet, and Jean Utke
Geosci. Model Dev., 9, 1891–1904, https://doi.org/10.5194/gmd-9-1891-2016, https://doi.org/10.5194/gmd-9-1891-2016, 2016
Short summary
Short summary
Geophysical adjoint models are powerful tools, allowing sensitivity studies that are not possible otherwise, and enabling optimized fit of models to observing data sets. The complexity involved requires the use of algorithmic differentiation (AD) software, but AD adjoint calculation for ice models can be slow, with prohibitive memory requirements. In this paper, we present a method to improve the performance of ice model adjoint generation, in terms of timing, memory load, and accuracy.
D. N. Goldberg, P. Heimbach, I. Joughin, and B. Smith
The Cryosphere, 9, 2429–2446, https://doi.org/10.5194/tc-9-2429-2015, https://doi.org/10.5194/tc-9-2429-2015, 2015
Short summary
Short summary
We calibrate a time-dependent ice model through optimal fit to transient observations of surface elevation and velocity, a novel procedure in glaciology and in particular for an ice stream with a dynamic grounding line. We show this procedure gives a level of confidence in model projections that cannot be achieved through more commonly used glaciological data assimilation methods. We show that Smith Glacier is in a state of retreat regardless of climatic forcing for the next several decades.
D. N. Goldberg and P. Heimbach
The Cryosphere, 7, 1659–1678, https://doi.org/10.5194/tc-7-1659-2013, https://doi.org/10.5194/tc-7-1659-2013, 2013
Related subject area
Cryosphere
Towards deep-learning solutions for classification of automated snow height measurements (CleanSnow v1.0.2)
Quantitative sub-ice and marine tracing of Antarctic sediment provenance (TASP v1.0)
Tuning parameters of a sea ice model using machine learning
WRF-Chem simulations of snow nitrate and other physicochemical properties in northern China
Clustering simulated snow profiles to form avalanche forecast regions
SnowQM 1.0: a fast R package for bias-correcting spatial fields of snow water equivalent using quantile mapping
Simulation of snow albedo and solar irradiance profile with the Two-streAm Radiative TransfEr in Snow (TARTES) v2.0 model
Computationally efficient subglacial drainage modelling using Gaussian Process emulators: GlaDS-GP v1.0
Evaluation of MITgcm-based ocean reanalyses for the Southern Ocean
Improvements in the land surface configuration to better simulate seasonal snow cover in the European Alps with the CNRM-AROME (cycle 46) convection-permitting regional climate model
A three-stage model pipeline predicting regional avalanche danger in Switzerland (RAvaFcast v1.0.0): a decision-support tool for operational avalanche forecasting
A Flexible Snow Model (FSM 2.1.0) including a forest canopy
A global–land snow scheme (GLASS) v1.0 for the GFDL Earth System Model: formulation and evaluation at instrumented sites
A gradient-boosted tree framework to model the ice thickness of the World's glaciers (IceBoost v1)
Design and performance of ELSA v2.0: an isochronal model for ice-sheet layer tracing
Southern Ocean Ice Prediction System version 1.0 (SOIPS v1.0): description of the system and evaluation of synoptic-scale sea ice forecasts
Lagrangian tracking of sea ice in Community Ice CodE (CICE; version 5)
openAMUNDSEN v1.0: an open-source snow-hydrological model for mountain regions
OpenFOAM-avalanche 2312: depth-integrated models beyond dense-flow avalanches
Refactoring the elastic–viscous–plastic solver from the sea ice model CICE v6.5.1 for improved performance
CMIP6 models overestimate sea ice melt, growth & conduction relative to ice mass balance buoy estimates
A new 3D full-Stokes calving algorithm within Elmer/Ice (v9.0)
The Utrecht Finite Volume Ice-Sheet Model (UFEMISM version 2.0) – part 1: description and idealised experiments
Coupling framework (1.0) for the Úa (2023b) ice sheet model and the FESOM-1.4 z-coordinate ocean model in an Antarctic domain
A novel numerical implementation for the surface energy budget of melting snowpacks and glaciers
SnowPappus v1.0, a blowing-snow model for large-scale applications of the Crocus snow scheme
A stochastic parameterization of ice sheet surface mass balance for the Stochastic Ice-Sheet and Sea-Level System Model (StISSM v1.0)
Graphics-processing-unit-accelerated ice flow solver for unstructured meshes using the Shallow-Shelf Approximation (FastIceFlo v1.0.1)
A finite-element framework to explore the numerical solution of the coupled problem of heat conduction, water vapor diffusion, and settlement in dry snow (IvoriFEM v0.1.0)
Description and validation of the ice sheet model Nix v1.0
AvaFrame com1DFA (v1.3): a thickness-integrated computational avalanche module – theory, numerics, and testing
Universal differential equations for glacier ice flow modelling
A new model for supraglacial hydrology evolution and drainage for the Greenland Ice Sheet (SHED v1.0)
Modeling sensitivities of thermally and hydraulically driven ice stream surge cycling
A parallel implementation of the confined–unconfined aquifer system model for subglacial hydrology: design, verification, and performance analysis (CUAS-MPI v0.1.0)
Automatic snow type classification of snow micropenetrometer profiles with machine learning algorithms
An empirical model to calculate snow depth from daily snow water equivalent: SWE2HS 1.0
A wind-driven snow redistribution module for Alpine3D v3.3.0: adaptations designed for downscaling ice sheet surface mass balance
The CryoGrid community model (version 1.0) – a multi-physics toolbox for climate-driven simulations in the terrestrial cryosphere
Glacier Energy and Mass Balance (GEMB): a model of firn processes for cryosphere research
Sensitivity of NEMO4.0-SI3 model parameters on sea ice budgets in the Southern Ocean
Introducing CRYOWRF v1.0: multiscale atmospheric flow simulations with advanced snow cover modelling
SUHMO: an adaptive mesh refinement SUbglacial Hydrology MOdel v1.0
Improving snow albedo modeling in the E3SM land model (version 2.0) and assessing its impacts on snow and surface fluxes over the Tibetan Plateau
The Multiple Snow Data Assimilation System (MuSA v1.0)
The Stochastic Ice-Sheet and Sea-Level System Model v1.0 (StISSM v1.0)
Improved representation of the contemporary Greenland ice sheet firn layer by IMAU-FDM v1.2G
Modeling the small-scale deposition of snow onto structured Arctic sea ice during a MOSAiC storm using snowBedFoam 1.0.
Benchmarking the vertically integrated ice-sheet model IMAU-ICE (version 2.0)
SnowClim v1.0: high-resolution snow model and data for the western United States
Jan Svoboda, Marc Ruesch, David Liechti, Corinne Jones, Michele Volpi, Michael Zehnder, and Jürg Schweizer
Geosci. Model Dev., 18, 1829–1849, https://doi.org/10.5194/gmd-18-1829-2025, https://doi.org/10.5194/gmd-18-1829-2025, 2025
Short summary
Short summary
Accurately measuring snow height is key for modeling approaches in climate science, snow hydrology, and avalanche forecasting. Erroneous snow height measurements often occur when snow height is low or changes, for instance during snowfall in summer. We prepare a new benchmark dataset with annotated snow height data and demonstrate how to improve the measurement quality using modern deep-learning approaches. Our approach can be easily implemented in a data pipeline for snow modeling.
James W. Marschalek, Edward Gasson, Tina van de Flierdt, Claus-Dieter Hillenbrand, Martin J. Siegert, and Liam Holder
Geosci. Model Dev., 18, 1673–1708, https://doi.org/10.5194/gmd-18-1673-2025, https://doi.org/10.5194/gmd-18-1673-2025, 2025
Short summary
Short summary
Ice sheet models can help predict how Antarctica's ice sheets respond to environmental change, and such models benefit from comparison to geological data. Here, we use an ice sheet model output and other data to predict the erosion of debris and trace its transport to where it is deposited on the ocean floor. This allows the results of ice sheet modelling to be directly and quantitively compared to real-world data, helping to reduce uncertainty regarding Antarctic sea level contribution.
Anton Korosov, Yue Ying, and Einar Ólason
Geosci. Model Dev., 18, 885–904, https://doi.org/10.5194/gmd-18-885-2025, https://doi.org/10.5194/gmd-18-885-2025, 2025
Short summary
Short summary
We have developed a new method to improve the accuracy of sea ice models, which predict how ice moves and deforms due to wind and ocean currents. Traditional models use parameters that are often poorly defined. The new approach uses machine learning to fine-tune these parameters by comparing simulated ice drift with satellite data. The method identifies optimal settings for the model by analysing patterns in ice deformation. This results in more accurate simulations of sea ice drift forecasting.
Xia Wang, Tao Che, Xueyin Ruan, Shanna Yue, Jing Wang, Chun Zhao, and Lei Geng
Geosci. Model Dev., 18, 651–670, https://doi.org/10.5194/gmd-18-651-2025, https://doi.org/10.5194/gmd-18-651-2025, 2025
Short summary
Short summary
We employed the WRF-Chem model to parameterize atmospheric nitrate deposition in snow and evaluate its performance in simulating snow cover, snow depth, and concentrations of dust and nitrate using new observations from northern China. The results generally exhibit reasonable agreement with field observations in northern China, demonstrating the model's capability to simulate snow properties, including concentrations of reservoir species.
Simon Horton, Florian Herla, and Pascal Haegeli
Geosci. Model Dev., 18, 193–209, https://doi.org/10.5194/gmd-18-193-2025, https://doi.org/10.5194/gmd-18-193-2025, 2025
Short summary
Short summary
We present a method for avalanche forecasters to analyze patterns in snowpack model simulations. It uses fuzzy clustering to group small regions into larger forecast areas based on snow characteristics, locations, and temporal history. Tested in the Columbia Mountains in two winter seasons, it closely matched real forecast regions regions and identified major avalanche hazard patterns. This approach simplifies complex model outputs, helping forecasters make informed decisions.
Adrien Michel, Johannes Aschauer, Tobias Jonas, Stefanie Gubler, Sven Kotlarski, and Christoph Marty
Geosci. Model Dev., 17, 8969–8988, https://doi.org/10.5194/gmd-17-8969-2024, https://doi.org/10.5194/gmd-17-8969-2024, 2024
Short summary
Short summary
We present a method to correct snow cover maps (represented in terms of snow water equivalent) to match better-quality maps. The correction can then be extended backwards and forwards in time for periods when better-quality maps are not available. The method is fast and gives good results. It is then applied to obtain a climatology of the snow cover in Switzerland over the past 60 years at a resolution of 1 d and 1 km. This is the first time that such a dataset has been produced.
Ghislain Picard and Quentin Libois
Geosci. Model Dev., 17, 8927–8953, https://doi.org/10.5194/gmd-17-8927-2024, https://doi.org/10.5194/gmd-17-8927-2024, 2024
Short summary
Short summary
The Two-streAm Radiative TransfEr in Snow (TARTES) is a radiative transfer model to compute snow albedo in the solar domain and the profiles of light and energy absorption in a multi-layered snowpack whose physical properties are user defined. It uniquely considers snow grain shape flexibly, based on recent insights showing that snow does not behave as a collection of ice spheres but instead as a random medium. TARTES is user-friendly yet performs comparably to more complex models.
Tim Hill, Derek Bingham, Gwenn E. Flowers, and Matthew J. Hoffman
EGUsphere, https://doi.org/10.22541/essoar.172736254.41350153/v2, https://doi.org/10.22541/essoar.172736254.41350153/v2, 2024
Short summary
Short summary
Subglacial drainage models represent water flow beneath glaciers and ice sheets. Here, we train fast statistical models called Gaussian Process emulators to accelerate subglacial drainage modelling by ~1000 times. We use the fast emulator predictions to show that three of the model parameters are responsible for >90 % of the variance in model outputs. The fast GP emulators will enable future uncertainty quantification and calibration of these models.
Yoshihiro Nakayama, Alena Malyarenko, Hong Zhang, Ou Wang, Matthis Auger, Yafei Nie, Ian Fenty, Matthew Mazloff, Armin Köhl, and Dimitris Menemenlis
Geosci. Model Dev., 17, 8613–8638, https://doi.org/10.5194/gmd-17-8613-2024, https://doi.org/10.5194/gmd-17-8613-2024, 2024
Short summary
Short summary
Global- and basin-scale ocean reanalyses are becoming easily accessible. However, such ocean reanalyses are optimized for their entire model domains and their ability to simulate the Southern Ocean requires evaluation. We conduct intercomparison analyses of Massachusetts Institute of Technology General Circulation Model (MITgcm)-based ocean reanalyses. They generally perform well for the open ocean, but open-ocean temporal variability and Antarctic continental shelves require improvements.
Diego Monteiro, Cécile Caillaud, Matthieu Lafaysse, Adrien Napoly, Mathieu Fructus, Antoinette Alias, and Samuel Morin
Geosci. Model Dev., 17, 7645–7677, https://doi.org/10.5194/gmd-17-7645-2024, https://doi.org/10.5194/gmd-17-7645-2024, 2024
Short summary
Short summary
Modeling snow cover in climate and weather forecasting models is a challenge even for high-resolution models. Recent simulations with CNRM-AROME have shown difficulties when representing snow in the European Alps. Using remote sensing data and in situ observations, we evaluate modifications of the land surface configuration in order to improve it. We propose a new surface configuration, enabling a more realistic simulation of snow cover, relevant for climate and weather forecasting applications.
Alessandro Maissen, Frank Techel, and Michele Volpi
Geosci. Model Dev., 17, 7569–7593, https://doi.org/10.5194/gmd-17-7569-2024, https://doi.org/10.5194/gmd-17-7569-2024, 2024
Short summary
Short summary
By harnessing AI models, this work enables processing large amounts of data, including weather conditions, snowpack characteristics, and historical avalanche data, to predict human-like avalanche forecasts in Switzerland. Our proposed model can significantly assist avalanche forecasters in their decision-making process, thereby facilitating more efficient and accurate predictions crucial for ensuring safety in Switzerland's avalanche-prone regions.
Richard Essery, Giulia Mazzotti, Sarah Barr, Tobias Jonas, Tristan Quaife, and Nick Rutter
EGUsphere, https://doi.org/10.5194/egusphere-2024-2546, https://doi.org/10.5194/egusphere-2024-2546, 2024
Short summary
Short summary
How forests influence accumulation and melt of snow on the ground is of long-standing interest, but uncertainty remains in how best to model forest snow processes. We developed the Flexible Snow Model version 2 to quantify these uncertainties. In a first model demonstration, how unloading of intercepted snow from the forest canopy is represented is responsible for the largest uncertainty. Global mapping of forest distribution is also likely to be a large source of uncertainty in existing models.
Enrico Zorzetto, Sergey Malyshev, Paul Ginoux, and Elena Shevliakova
Geosci. Model Dev., 17, 7219–7244, https://doi.org/10.5194/gmd-17-7219-2024, https://doi.org/10.5194/gmd-17-7219-2024, 2024
Short summary
Short summary
We describe a new snow scheme developed for use in global climate models, which simulates the interactions of snowpack with vegetation, atmosphere, and soil. We test the new snow model over a set of sites where in situ observations are available. We find that when compared to a simpler snow model, this model improves predictions of seasonal snow and of soil temperature under the snowpack, important variables for simulating both the hydrological cycle and the global climate system.
Niccolò Maffezzoli, Eric Rignot, Carlo Barbante, Troels Petersen, and Sebastiano Vascon
EGUsphere, https://doi.org/10.5194/egusphere-2024-2455, https://doi.org/10.5194/egusphere-2024-2455, 2024
Short summary
Short summary
In this work we introduces IceBoost, a machine learning framework to model the ice thickness distribution of all the World's glaciers, with greater accuracy than state-of-the-art methods. The model is trained on 3.7 million measurements globally available and provides skillful estimates across all regions. This advancement will help in better assessing future sea level changes, freshwater resources, with significance for both the scientific community and society at large.
Therese Rieckh, Andreas Born, Alexander Robinson, Robert Law, and Gerrit Gülle
Geosci. Model Dev., 17, 6987–7000, https://doi.org/10.5194/gmd-17-6987-2024, https://doi.org/10.5194/gmd-17-6987-2024, 2024
Short summary
Short summary
We present the open-source model ELSA, which simulates the internal age structure of large ice sheets. It creates layers of snow accumulation at fixed times during the simulation, which are used to model the internal stratification of the ice sheet. Together with reconstructed isochrones from radiostratigraphy data, ELSA can be used to assess ice sheet models and to improve their parameterization. ELSA can be used coupled to an ice sheet model or forced with its output.
Fu Zhao, Xi Liang, Zhongxiang Tian, Ming Li, Na Liu, and Chengyan Liu
Geosci. Model Dev., 17, 6867–6886, https://doi.org/10.5194/gmd-17-6867-2024, https://doi.org/10.5194/gmd-17-6867-2024, 2024
Short summary
Short summary
In this work, we introduce a newly developed Antarctic sea ice forecasting system, namely the Southern Ocean Ice Prediction System (SOIPS). The system is based on a regional sea ice‒ocean‒ice shelf coupled model and can assimilate sea ice concentration observations. By assessing the system's performance in sea ice forecasts, we find that the system can provide reliable Antarctic sea ice forecasts for the next 7 d and has the potential to guide ship navigation in the Antarctic sea ice zone.
Chenhui Ning, Shiming Xu, Yan Zhang, Xuantong Wang, Zhihao Fan, and Jiping Liu
Geosci. Model Dev., 17, 6847–6866, https://doi.org/10.5194/gmd-17-6847-2024, https://doi.org/10.5194/gmd-17-6847-2024, 2024
Short summary
Short summary
Sea ice models are mainly based on non-moving structured grids, which is different from buoy measurements that follow the ice drift. To facilitate Lagrangian analysis, we introduce online tracking of sea ice in Community Ice CodE (CICE). We validate the sea ice tracking with buoys and evaluate the sea ice deformation in high-resolution simulations, which show multi-fractal characteristics. The source code is openly available and can be used in various scientific and operational applications.
Ulrich Strasser, Michael Warscher, Erwin Rottler, and Florian Hanzer
Geosci. Model Dev., 17, 6775–6797, https://doi.org/10.5194/gmd-17-6775-2024, https://doi.org/10.5194/gmd-17-6775-2024, 2024
Short summary
Short summary
openAMUNDSEN is a fully distributed open-source snow-hydrological model for mountain catchments. It includes process representations of an empirical, semi-empirical, and physical nature. It uses temperature, precipitation, humidity, radiation, and wind speed as forcing data and is computationally efficient, of a modular nature, and easily extendible. The Python code is available on GitHub (https://github.com/openamundsen/openamundsen), including documentation (https://doc.openamundsen.org).
Matthias Rauter and Julia Kowalski
Geosci. Model Dev., 17, 6545–6569, https://doi.org/10.5194/gmd-17-6545-2024, https://doi.org/10.5194/gmd-17-6545-2024, 2024
Short summary
Short summary
Snow avalanches can form large powder clouds that substantially exceed the velocity and reach of the dense core. Only a few complex models exist to simulate this phenomenon, and the respective hazard is hard to predict. This work provides a novel flow model that focuses on simple relations while still encapsulating the significant behaviour. The model is applied to reconstruct two catastrophic powder snow avalanche events in Austria.
Till Andreas Soya Rasmussen, Jacob Poulsen, Mads Hvid Ribergaard, Ruchira Sasanka, Anthony P. Craig, Elizabeth C. Hunke, and Stefan Rethmeier
Geosci. Model Dev., 17, 6529–6544, https://doi.org/10.5194/gmd-17-6529-2024, https://doi.org/10.5194/gmd-17-6529-2024, 2024
Short summary
Short summary
Earth system models (ESMs) today strive for better quality based on improved resolutions and improved physics. A limiting factor is the supercomputers at hand and how best to utilize them. This study focuses on the refactorization of one part of a sea ice model (CICE), namely the dynamics. It shows that the performance can be significantly improved, which means that one can either run the same simulations much cheaper or advance the system according to what is needed.
Alex Edward West and Edward William Blockley
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-121, https://doi.org/10.5194/gmd-2024-121, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
This study uses ice mass balance buoys – temperature and height-measuring devices frozen into sea ice – to find how well climate models simulate the melt & growth of, and conduction of heat through, Arctic sea ice. This may help understand why models produce varying amounts of sea ice in the present day. We find models tend to show more melt, growth or conduction for a given ice thickness than the buoys, though the difference is smaller for models with more physically realistic thermodynamics.
Iain Wheel, Douglas I. Benn, Anna J. Crawford, Joe Todd, and Thomas Zwinger
Geosci. Model Dev., 17, 5759–5777, https://doi.org/10.5194/gmd-17-5759-2024, https://doi.org/10.5194/gmd-17-5759-2024, 2024
Short summary
Short summary
Calving, the detachment of large icebergs from glaciers, is one of the largest uncertainties in future sea level rise projections. This process is poorly understood, and there is an absence of detailed models capable of simulating calving. A new 3D calving model has been developed to better understand calving at glaciers where detailed modelling was previously limited. Importantly, the new model is very flexible. By allowing for unrestricted calving geometries, it can be applied at any location.
Constantijn J. Berends, Victor Azizi, Jorge Bernales, and Roderik S. W. van de Wal
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-5, https://doi.org/10.5194/gmd-2024-5, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Ice-sheet models are computer programs that can simulate how the Greenland and Antarctic ice sheets will evolve in the future. The accuracy of these models depends on their resolution: how small the details are that the model can resolve. We have created a model with a variable resolution, which can resolve a lot of detail in areas where lots of changes happen in the ice, and less detail in areas where the ice does not move so much. This makes the model both accurate and fast.
Ole Richter, Ralph Timmermann, G. Hilmar Gudmundsson, and Jan De Rydt
EGUsphere, https://doi.org/10.5194/egusphere-2024-648, https://doi.org/10.5194/egusphere-2024-648, 2024
Short summary
Short summary
The new coupled ice sheet-ocean model addresses challenges related to horizontal resolution through advanced mesh flexibility, enabled by the use of unstructured grids. We describe the new model, verify its functioning in an idealised setting and demonstrate its advantages in a global-ocean/Antarctic ice sheet domain. The results of this study comprise an important step towards improving predictions of the Antarctic contribution to sea level rise over centennial time scales.
Kévin Fourteau, Julien Brondex, Fanny Brun, and Marie Dumont
Geosci. Model Dev., 17, 1903–1929, https://doi.org/10.5194/gmd-17-1903-2024, https://doi.org/10.5194/gmd-17-1903-2024, 2024
Short summary
Short summary
In this paper, we provide a novel numerical implementation for solving the energy exchanges at the surface of snow and ice. By combining the strong points of previous models, our solution leads to more accurate and robust simulations of the energy exchanges, surface temperature, and melt while preserving a reasonable computation time.
Matthieu Baron, Ange Haddjeri, Matthieu Lafaysse, Louis Le Toumelin, Vincent Vionnet, and Mathieu Fructus
Geosci. Model Dev., 17, 1297–1326, https://doi.org/10.5194/gmd-17-1297-2024, https://doi.org/10.5194/gmd-17-1297-2024, 2024
Short summary
Short summary
Increasing the spatial resolution of numerical systems simulating snowpack evolution in mountain areas requires representing small-scale processes such as wind-induced snow transport. We present SnowPappus, a simple scheme coupled with the Crocus snow model to compute blowing-snow fluxes and redistribute snow among grid points at 250 m resolution. In terms of numerical cost, it is suitable for large-scale applications. We present point-scale evaluations of fluxes and snow transport occurrence.
Lizz Ultee, Alexander A. Robel, and Stefano Castruccio
Geosci. Model Dev., 17, 1041–1057, https://doi.org/10.5194/gmd-17-1041-2024, https://doi.org/10.5194/gmd-17-1041-2024, 2024
Short summary
Short summary
The surface mass balance (SMB) of an ice sheet describes the net gain or loss of mass from ice sheets (such as those in Greenland and Antarctica) through interaction with the atmosphere. We developed a statistical method to generate a wide range of SMB fields that reflect the best understanding of SMB processes. Efficiently sampling the variability of SMB will help us understand sources of uncertainty in ice sheet model projections.
Anjali Sandip, Ludovic Räss, and Mathieu Morlighem
Geosci. Model Dev., 17, 899–909, https://doi.org/10.5194/gmd-17-899-2024, https://doi.org/10.5194/gmd-17-899-2024, 2024
Short summary
Short summary
We solve momentum balance for unstructured meshes to predict ice flow for real glaciers using a pseudo-transient method on graphics processing units (GPUs) and compare it to a standard central processing unit (CPU) implementation. We justify the GPU implementation by applying the price-to-performance metric for up to million-grid-point spatial resolutions. This study represents a first step toward leveraging GPU processing power, enabling more accurate polar ice discharge predictions.
Julien Brondex, Kévin Fourteau, Marie Dumont, Pascal Hagenmuller, Neige Calonne, François Tuzet, and Henning Löwe
Geosci. Model Dev., 16, 7075–7106, https://doi.org/10.5194/gmd-16-7075-2023, https://doi.org/10.5194/gmd-16-7075-2023, 2023
Short summary
Short summary
Vapor diffusion is one of the main processes governing snowpack evolution, and it must be accounted for in models. Recent attempts to represent vapor diffusion in numerical models have faced several difficulties regarding computational cost and mass and energy conservation. Here, we develop our own finite-element software to explore numerical approaches and enable us to overcome these difficulties. We illustrate the capability of these approaches on established numerical benchmarks.
Daniel Moreno-Parada, Alexander Robinson, Marisa Montoya, and Jorge Alvarez-Solas
EGUsphere, https://doi.org/10.5194/egusphere-2023-2690, https://doi.org/10.5194/egusphere-2023-2690, 2023
Short summary
Short summary
We introduce Nix, an ice-sheet model designed for understanding how large masses of ice behave. Nix as a computer program that simulates the movement and temperature changes in ice sheets. Nix helps us study how ice sheets respond to changes in the atmosphere and ocean. We found that how fast ice melts under the shelves and how heat is exchanged, play a role in determining the future of ice sheets. Nix is a useful tool for learning more about how climate change affects polar ice sheets.
Matthias Tonnel, Anna Wirbel, Felix Oesterle, and Jan-Thomas Fischer
Geosci. Model Dev., 16, 7013–7035, https://doi.org/10.5194/gmd-16-7013-2023, https://doi.org/10.5194/gmd-16-7013-2023, 2023
Short summary
Short summary
Avaframe - the open avalanche framework - provides open-source tools to simulate and investigate snow avalanches. It is utilized for multiple purposes, the two main applications being hazard mapping and scientific research of snow processes. We present the theory, conversion to a computer model, and testing for one of the core modules used for simulations of a particular type of avalanche, the so-called dense-flow avalanches. Tests check and confirm the applicability of the utilized method.
Jordi Bolibar, Facundo Sapienza, Fabien Maussion, Redouane Lguensat, Bert Wouters, and Fernando Pérez
Geosci. Model Dev., 16, 6671–6687, https://doi.org/10.5194/gmd-16-6671-2023, https://doi.org/10.5194/gmd-16-6671-2023, 2023
Short summary
Short summary
We developed a new modelling framework combining numerical methods with machine learning. Using this approach, we focused on understanding how ice moves within glaciers, and we successfully learnt a prescribed law describing ice movement for 17 glaciers worldwide as a proof of concept. Our framework has the potential to discover important laws governing glacier processes, aiding our understanding of glacier physics and their contribution to water resources and sea-level rise.
Prateek Gantayat, Alison F. Banwell, Amber A. Leeson, James M. Lea, Dorthe Petersen, Noel Gourmelen, and Xavier Fettweis
Geosci. Model Dev., 16, 5803–5823, https://doi.org/10.5194/gmd-16-5803-2023, https://doi.org/10.5194/gmd-16-5803-2023, 2023
Short summary
Short summary
We developed a new supraglacial hydrology model for the Greenland Ice Sheet. This model simulates surface meltwater routing, meltwater drainage, supraglacial lake (SGL) overflow, and formation of lake ice. The model was able to reproduce 80 % of observed lake locations and provides a good match between the observed and modelled temporal evolution of SGLs.
Kevin Hank, Lev Tarasov, and Elisa Mantelli
Geosci. Model Dev., 16, 5627–5652, https://doi.org/10.5194/gmd-16-5627-2023, https://doi.org/10.5194/gmd-16-5627-2023, 2023
Short summary
Short summary
Physically meaningful modeling of geophysical system instabilities is numerically challenging, given the potential effects of purely numerical artifacts. Here we explore the sensitivity of ice stream surge activation to numerical and physical model aspects. We find that surge characteristics exhibit a resolution dependency but converge at higher horizontal grid resolutions and are significantly affected by the incorporation of bed thermal and sub-glacial hydrology models.
Yannic Fischler, Thomas Kleiner, Christian Bischof, Jeremie Schmiedel, Roiy Sayag, Raban Emunds, Lennart Frederik Oestreich, and Angelika Humbert
Geosci. Model Dev., 16, 5305–5322, https://doi.org/10.5194/gmd-16-5305-2023, https://doi.org/10.5194/gmd-16-5305-2023, 2023
Short summary
Short summary
Water underneath ice sheets affects the motion of glaciers. This study presents a newly developed code, CUAS-MPI, that simulates subglacial hydrology. It is designed for supercomputers and is hence a parallelized code. We measure the performance of this code for simulations of the entire Greenland Ice Sheet and find that the code works efficiently. Moreover, we validated the code to ensure the correctness of the solution. CUAS-MPI opens new possibilities for simulations of ice sheet hydrology.
Julia Kaltenborn, Amy R. Macfarlane, Viviane Clay, and Martin Schneebeli
Geosci. Model Dev., 16, 4521–4550, https://doi.org/10.5194/gmd-16-4521-2023, https://doi.org/10.5194/gmd-16-4521-2023, 2023
Short summary
Short summary
Snow layer segmentation and snow grain classification are essential diagnostic tasks for cryospheric applications. A SnowMicroPen (SMP) can be used to that end; however, the manual classification of its profiles becomes infeasible for large datasets. Here, we evaluate how well machine learning models automate this task. Of the 14 models trained on the MOSAiC SMP dataset, the long short-term memory model performed the best. The findings presented here facilitate and accelerate SMP data analysis.
Johannes Aschauer, Adrien Michel, Tobias Jonas, and Christoph Marty
Geosci. Model Dev., 16, 4063–4081, https://doi.org/10.5194/gmd-16-4063-2023, https://doi.org/10.5194/gmd-16-4063-2023, 2023
Short summary
Short summary
Snow water equivalent is the mass of water stored in a snowpack. Based on exponential settling functions, the empirical snow density model SWE2HS is presented to convert time series of daily snow water equivalent into snow depth. The model has been calibrated with data from Switzerland and validated with independent data from the European Alps. A reference implementation of SWE2HS is available as a Python package.
Eric Keenan, Nander Wever, Jan T. M. Lenaerts, and Brooke Medley
Geosci. Model Dev., 16, 3203–3219, https://doi.org/10.5194/gmd-16-3203-2023, https://doi.org/10.5194/gmd-16-3203-2023, 2023
Short summary
Short summary
Ice sheets gain mass via snowfall. However, snowfall is redistributed by the wind, resulting in accumulation differences of up to a factor of 5 over distances as short as 5 km. These differences complicate estimates of ice sheet contribution to sea level rise. For this reason, we have developed a new model for estimating wind-driven snow redistribution on ice sheets. We show that, over Pine Island Glacier in West Antarctica, the model improves estimates of snow accumulation variability.
Sebastian Westermann, Thomas Ingeman-Nielsen, Johanna Scheer, Kristoffer Aalstad, Juditha Aga, Nitin Chaudhary, Bernd Etzelmüller, Simon Filhol, Andreas Kääb, Cas Renette, Louise Steffensen Schmidt, Thomas Vikhamar Schuler, Robin B. Zweigel, Léo Martin, Sarah Morard, Matan Ben-Asher, Michael Angelopoulos, Julia Boike, Brian Groenke, Frederieke Miesner, Jan Nitzbon, Paul Overduin, Simone M. Stuenzi, and Moritz Langer
Geosci. Model Dev., 16, 2607–2647, https://doi.org/10.5194/gmd-16-2607-2023, https://doi.org/10.5194/gmd-16-2607-2023, 2023
Short summary
Short summary
The CryoGrid community model is a new tool for simulating ground temperatures and the water and ice balance in cold regions. It is a modular design, which makes it possible to test different schemes to simulate, for example, permafrost ground in an efficient way. The model contains tools to simulate frozen and unfrozen ground, snow, glaciers, and other massive ice bodies, as well as water bodies.
Alex S. Gardner, Nicole-Jeanne Schlegel, and Eric Larour
Geosci. Model Dev., 16, 2277–2302, https://doi.org/10.5194/gmd-16-2277-2023, https://doi.org/10.5194/gmd-16-2277-2023, 2023
Short summary
Short summary
This is the first description of the open-source Glacier Energy and Mass Balance (GEMB) model. GEMB models the ice sheet and glacier surface–atmospheric energy and mass exchange, as well as the firn state. The model is evaluated against the current state of the art and in situ observations and is shown to perform well.
Yafei Nie, Chengkun Li, Martin Vancoppenolle, Bin Cheng, Fabio Boeira Dias, Xianqing Lv, and Petteri Uotila
Geosci. Model Dev., 16, 1395–1425, https://doi.org/10.5194/gmd-16-1395-2023, https://doi.org/10.5194/gmd-16-1395-2023, 2023
Short summary
Short summary
State-of-the-art Earth system models simulate the observed sea ice extent relatively well, but this is often due to errors in the dynamic and other processes in the simulated sea ice changes cancelling each other out. We assessed the sensitivity of these processes simulated by the coupled ocean–sea ice model NEMO4.0-SI3 to 18 parameters. The performance of the model in simulating sea ice change processes was ultimately improved by adjusting the three identified key parameters.
Varun Sharma, Franziska Gerber, and Michael Lehning
Geosci. Model Dev., 16, 719–749, https://doi.org/10.5194/gmd-16-719-2023, https://doi.org/10.5194/gmd-16-719-2023, 2023
Short summary
Short summary
Most current generation climate and weather models have a relatively simplistic description of snow and snow–atmosphere interaction. One reason for this is the belief that including an advanced snow model would make the simulations too computationally demanding. In this study, we bring together two state-of-the-art models for atmosphere (WRF) and snow cover (SNOWPACK) and highlight both the feasibility and necessity of such coupled models to explore underexplored phenomena in the cryosphere.
Anne M. Felden, Daniel F. Martin, and Esmond G. Ng
Geosci. Model Dev., 16, 407–425, https://doi.org/10.5194/gmd-16-407-2023, https://doi.org/10.5194/gmd-16-407-2023, 2023
Short summary
Short summary
We present and validate a novel subglacial hydrology model, SUHMO, based on an adaptive mesh refinement framework. We propose the addition of a pseudo-diffusion to recover the wall melting in channels. Computational performance analysis demonstrates the efficiency of adaptive mesh refinement on large-scale hydrologic problems. The adaptive mesh refinement approach will eventually enable better ice bed boundary conditions for ice sheet simulations at a reasonable computational cost.
Dalei Hao, Gautam Bisht, Karl Rittger, Edward Bair, Cenlin He, Huilin Huang, Cheng Dang, Timbo Stillinger, Yu Gu, Hailong Wang, Yun Qian, and L. Ruby Leung
Geosci. Model Dev., 16, 75–94, https://doi.org/10.5194/gmd-16-75-2023, https://doi.org/10.5194/gmd-16-75-2023, 2023
Short summary
Short summary
Snow with the highest albedo of land surface plays a vital role in Earth’s surface energy budget and water cycle. This study accounts for the impacts of snow grain shape and mixing state of light-absorbing particles with snow on snow albedo in the E3SM land model. The findings advance our understanding of the role of snow grain shape and mixing state of LAP–snow in land surface processes and offer guidance for improving snow simulations and radiative forcing estimates in Earth system models.
Esteban Alonso-González, Kristoffer Aalstad, Mohamed Wassim Baba, Jesús Revuelto, Juan Ignacio López-Moreno, Joel Fiddes, Richard Essery, and Simon Gascoin
Geosci. Model Dev., 15, 9127–9155, https://doi.org/10.5194/gmd-15-9127-2022, https://doi.org/10.5194/gmd-15-9127-2022, 2022
Short summary
Short summary
Snow cover plays an important role in many processes, but its monitoring is a challenging task. The alternative is usually to simulate the snowpack, and to improve these simulations one of the most promising options is to fuse simulations with available observations (data assimilation). In this paper we present MuSA, a data assimilation tool which facilitates the implementation of snow monitoring initiatives, allowing the assimilation of a wide variety of remotely sensed snow cover information.
Vincent Verjans, Alexander A. Robel, Helene Seroussi, Lizz Ultee, and Andrew F. Thompson
Geosci. Model Dev., 15, 8269–8293, https://doi.org/10.5194/gmd-15-8269-2022, https://doi.org/10.5194/gmd-15-8269-2022, 2022
Short summary
Short summary
We describe the development of the first large-scale ice sheet model that accounts for stochasticity in a range of processes. Stochasticity allows the impacts of inherently uncertain processes on ice sheets to be represented. This includes climatic uncertainty, as the climate is inherently chaotic. Furthermore, stochastic capabilities also encompass poorly constrained glaciological processes that display strong variability at fine spatiotemporal scales. We present the model and test experiments.
Max Brils, Peter Kuipers Munneke, Willem Jan van de Berg, and Michiel van den Broeke
Geosci. Model Dev., 15, 7121–7138, https://doi.org/10.5194/gmd-15-7121-2022, https://doi.org/10.5194/gmd-15-7121-2022, 2022
Short summary
Short summary
Firn covers the Greenland ice sheet (GrIS) and can temporarily prevent mass loss. Here, we present the latest version of our firn model, IMAU-FDM, with an application to the GrIS. We improved the density of fallen snow, the firn densification rate and the firn's thermal conductivity. This leads to a higher air content and 10 m temperatures. Furthermore we investigate three case studies and find that the updated model shows greater variability and an increased sensitivity in surface elevation.
Océane Hames, Mahdi Jafari, David Nicholas Wagner, Ian Raphael, David Clemens-Sewall, Chris Polashenski, Matthew D. Shupe, Martin Schneebeli, and Michael Lehning
Geosci. Model Dev., 15, 6429–6449, https://doi.org/10.5194/gmd-15-6429-2022, https://doi.org/10.5194/gmd-15-6429-2022, 2022
Short summary
Short summary
This paper presents an Eulerian–Lagrangian snow transport model implemented in the fluid dynamics software OpenFOAM, which we call snowBedFoam 1.0. We apply this model to reproduce snow deposition on a piece of ridged Arctic sea ice, which was produced during the MOSAiC expedition through scan measurements. The model appears to successfully reproduce the enhanced snow accumulation and deposition patterns, although some quantitative uncertainties were shown.
Constantijn J. Berends, Heiko Goelzer, Thomas J. Reerink, Lennert B. Stap, and Roderik S. W. van de Wal
Geosci. Model Dev., 15, 5667–5688, https://doi.org/10.5194/gmd-15-5667-2022, https://doi.org/10.5194/gmd-15-5667-2022, 2022
Short summary
Short summary
The rate at which marine ice sheets such as the West Antarctic ice sheet will retreat in a warming climate and ocean is still uncertain. Numerical ice-sheet models, which solve the physical equations that describe the way glaciers and ice sheets deform and flow, have been substantially improved in recent years. Here we present the results of several years of work on IMAU-ICE, an ice-sheet model of intermediate complexity, which can be used to study ice sheets of both the past and the future.
Abby C. Lute, John Abatzoglou, and Timothy Link
Geosci. Model Dev., 15, 5045–5071, https://doi.org/10.5194/gmd-15-5045-2022, https://doi.org/10.5194/gmd-15-5045-2022, 2022
Short summary
Short summary
We developed a snow model that can be used to quantify snowpack over large areas with a high degree of spatial detail. We ran the model over the western United States, creating a snow and climate dataset for three time periods. Compared to observations of snowpack, the model captured the key aspects of snow across time and space. The model and dataset will be useful in understanding historical and future changes in snowpack, with relevance to water resources, agriculture, and ecosystems.
Cited articles
Alexanderian, A., Petra, N., Stadler, G., and Ghattas, O.: A-Optimal Design of
Experiments for Infinite-Dimensional Bayesian Linear Inverse Problems with
Regularized ℓ_0-Sparsification, SIAM J. Sci. Comp.,
36, A2122–A2148, https://doi.org/10.1137/130933381, 2014. a
Alnæs, M., Blechta, J., Hake, J., Johansson, A., Kehlet, B., Logg, A.,
Richardson, C., Ring, J., Rognes, M. E., and Wells, G. N.: The FEniCS
Project Version 1.5, Archive of Numerical Software, 3, 9–23, 2015. a
Alnæs, M. S., Logg, A., Ølgaard, K. B., Rognes, M. E., and Wells, G. N.:
Unified Form Language: A Domain-Specific Language for Weak Formulations of
Partial Differential Equations, ACM T. Math. Softw., 40, 1–37,
https://doi.org/10.1145/2566630, 2014. a
Arthern, R. J., Hindmarsh, R. C. A., and Williams, C. R.: Flow speed within the
Antarctic ice sheet and its controls inferred from satellite observations,
J. Geophys. Res.-Earth, 120, 1171–1188,
https://doi.org/10.1002/2014JF003239, 2015. a
Babaniyi, O., Nicholson, R., Villa, U., and Petra, N.: Inferring the basal sliding coefficient field for the Stokes ice sheet model under rheological uncertainty, The Cryosphere, 15, 1731–1750, https://doi.org/10.5194/tc-15-1731-2021, 2021. a
Bui-Thanh, T., Ghattas, O., Martin, J., and Stadler, G.: A
computational framework for infinite-dimensional Bayesian inverse problems.
Part I: The linearized case, with application to global seismic inversion,
arXiv e-prints, arXiv:1308.1313 2013. a, b, c, d
Chen, P.: Hessian Matrix vs. Gauss-Newton Hessian Matrix, SIAM J. Numer. Anal.,
49, 1417–1435, https://doi.org/10.1137/100799988, 2011. a
Cornford, S. L., Martin, D. F., Graves, D. T., Ranken, D. F., Le Brocq, A. M.,
Gladstone, R. M., Payne, A. J., Ng, E. G., and Lipscomb, W. H.: Adaptive
Mesh, Finite Volume Modeling of Marine Ice Sheets, J. Comput. Phys., 232,
529–549, https://doi.org/10.1016/j.jcp.2012.08.037, 2013. a
Cornford, S. L., Martin, D. F., Payne, A. J., Ng, E. G., Le Brocq, A. M., Gladstone, R. M., Edwards, T. L., Shannon, S. R., Agosta, C., van den Broeke, M. R., Hellmer, H. H., Krinner, G., Ligtenberg, S. R. M., Timmermann, R., and Vaughan, D. G.: Century-scale simulations of the response of the West Antarctic Ice Sheet to a warming climate, The Cryosphere, 9, 1579–1600, https://doi.org/10.5194/tc-9-1579-2015, 2015. a, b, c, d
Cornford, S. L., Seroussi, H., Asay-Davis, X. S., Gudmundsson, G. H., Arthern, R., Borstad, C., Christmann, J., Dias dos Santos, T., Feldmann, J., Goldberg, D., Hoffman, M. J., Humbert, A., Kleiner, T., Leguy, G., Lipscomb, W. H., Merino, N., Durand, G., Morlighem, M., Pollard, D., Rückamp, M., Williams, C. R., and Yu, H.: Results of the third Marine Ice Sheet Model Intercomparison Project (MISMIP+), The Cryosphere, 14, 2283–2301, https://doi.org/10.5194/tc-14-2283-2020, 2020. a
Cuffey, K. and Paterson, W. S. B.: The Physics of Glaciers, Butterworth
Heinemann, Oxford, 4th Edn., 2010. a
Deconto, R. M. and Pollard, D.: Contribution of Antarctica to past and
future sea-level rise, Nature, 531, 591–597, https://doi.org/10.1038/nature17145,
2016. a
Dukowicz, J. K., Price, S. F., and Lipscomp, W. H.: Consistent approximations
and boundary conditions for ice-sheet dynamics from a principle of least
action, J. Glaciol., 56, 480–496, 2010. a
Fürst, J. J., Durand, G., Gillet-Chaulet, F., Merino, N., Tavard, L., Mouginot, J., Gourmelen, N., and Gagliardini, O.: Assimilation of Antarctic velocity observations provides evidence for uncharted pinning points, The Cryosphere, 9, 1427–1443, https://doi.org/10.5194/tc-9-1427-2015, 2015. a
Gagliardini, O., Durand, G., Zwinger, T., Hindmarsh, R. C. A., and Meur, E. L.:
Coupling of ice shelf melting and buttressing is a key process in ice sheet
dynamics, Geophys. Res. Lett., 37, L14501, https://doi.org/10.1029/2010GL043334, 2010. a, b
Gillet-Chaulet, F., Gagliardini, O., Seddik, H., Nodet, M., Durand, G., Ritz, C., Zwinger, T., Greve, R., and Vaughan, D. G.: Greenland ice sheet contribution to sea-level rise from a new-generation ice-sheet model, The Cryosphere, 6, 1561–1576, https://doi.org/10.5194/tc-6-1561-2012, 2012. a
Gladstone, R. M., Lee, V., Rougier, J., Payne, A. J., Hellmer, H.,
Le Brocq, A., Shepherd, A., Edwards, T. L., Gregory, J., and
Cornford, S. L.: Calibrated prediction of Pine Island Glacier retreat
during the 21st and 22nd centuries with a coupled flowline model, Earth
Planet. Sc. Lett., 333, 191–199, https://doi.org/10.1016/j.epsl.2012.04.022,
2012. a
Glen, J. W.: The creep of polycrystalline ice, P. Roy. Soc. Lond. A Mat., 228, 519–538, 1955. a
Goldberg, D. N., Heimbach, P., Joughin, I., and Smith, B.: Committed retreat of Smith, Pope, and Kohler Glaciers over the next 30 years inferred by transient model calibration, The Cryosphere, 9, 2429–2446, https://doi.org/10.5194/tc-9-2429-2015, 2015. a, b
Habermann, M., Truffer, M., and Maxwell, D.: Changing basal conditions during the speed-up of Jakobshavn Isbræ, Greenland, The Cryosphere, 7, 1679–1692, https://doi.org/10.5194/tc-7-1679-2013, 2013. a
Hernandez, V., Roman, J. E., and Vidal, V.: SLEPc: A scalable and flexible
toolkit for the solution of eigenvalue problems, ACM T. Math. Softw.,
31, 351–362, 2005. a
Higham, N. J.: Functions of Matrices: Theory and Computation, Society for
Industrial and Applied Mathematics, Philadelphia, PA, USA, 2008. a
Hindmarsh, R. C. A. and Payne, A. J.: Time-step limits for stable
solutions of the ice-sheet equation, Ann. Glaciol., 23, 74–85,
https://doi.org/10.1017/S0260305500013288, 1996. a
Isaac, T., Petra, N., Stadler, G., and Ghattas, O.: Scalable and efficient
algorithms for the propagation of uncertainty from data through inference to
prediction for large-scale problems, with application to flow of the
Antarctic ice sheet, J. Comput. Phys., 296, 348–368,
https://doi.org/10.1016/j.jcp.2015.04.047, 2015. a, b, c, d, e, f, g, h, i, j, k, l, m, n
Joughin, I., Smith, B., and Holland, D. M.: Sensitivity of 21st Century Sea
Level to Ocean-Induced Thinning of Pine Island Glacier, Antarctica,
Geophys. Res. Lett., 37, L20502, https://doi.org/10.1029/2010GL044819, 2010. a, b
Kalmikov, A. G. and Heimbach, P.: A Hessian-Based Method for Uncertainty
Quantification in Global Ocean State Estimation, SIAM J. Sci.
Comp., 36, S267–S295, https://doi.org/10.1137/130925311, 2014. a, b, c, d
Kaminski, T., Kauker, F., Eicken, H., and Karcher, M.: Exploring the utility of quantitative network design in evaluating Arctic sea ice thickness sampling strategies, The Cryosphere, 9, 1721–1733, https://doi.org/10.5194/tc-9-1721-2015, 2015. a, b
Keuthen, M. and Ulbrich, M.: Moreau–Yosida regularization in shape
optimization with geometric constraints, Comput. Optim.
Appl., 62, 181–216, 2015. a
Khodabakhshi, P., Willcox, K. E., and Gunzburger, M.: A multifidelity method
for a nonlocal diffusion model, Appl. Math. Lett., 121, 107361, https://doi.org/10.1016/j.aml.2021.107361,
2021. a
Larour, E., Rignot, E., Joughin, I., and Aubry, D.: Rheology of the Ronne Ice
Shelf, Antarctica, inferred from satellite radar interferometry data using
an inverse control method, Geophys. Res. Lett., 32, L05503,
https://doi.org/10.1029/2004GL021693, 2005. a
Larour, E., Utke, J., Csatho, B., Schenk, A., Seroussi, H., Morlighem, M., Rignot, E., Schlegel, N., and Khazendar, A.: Inferred basal friction and surface mass balance of the Northeast Greenland Ice Stream using data assimilation of ICESat (Ice Cloud and land Elevation Satellite) surface altimetry and ISSM (Ice Sheet System Model), The Cryosphere, 8, 2335–2351, https://doi.org/10.5194/tc-8-2335-2014, 2014. a
Logg, A., Mardal, K.-A., and Wells, G.: Automated Solution of Differential
Equations by the Finite Element Method: The FEniCS Book, Springer Publishing
Company, Incorporated, 2012. a
Loose, N., Heimbach, P., Pillar, H., and Nisancioglu, K.: Quantifying
Dynamical Proxy Potential through Oceanic Teleconnections in the
North Atlantic, Earth and Space Science Open Archive,
Earth and Space Science Open
Archive, https://doi.org/10.1002/essoar.10502065.1, 2020. a
MacAyeal, D. R.: Large-scale ice flow over a viscous basal sediment: Theory and
application to Ice Stream B, Antarctica, J. Geophys.
Res.-Sol. Ea., 94, 4071–4087, 1989. a
MacAyeal, D. R.: The basal stress distribution of Ice Stream E, Antarctica,
inferred by control methods, J. Geophys. Res., 97, 595–603,
1992. a
Maddison, J. R., Goldberg, D. N., and Goddard, B. D.: Automated Calculation of
Higher Order Partial Differential Equation Constrained Derivative
Information, SIAM J. Sci. Comp., 41, C417–C445,
https://doi.org/10.1137/18M1209465, 2019. a, b, c
Martin, J., Wilcox, L. C., Burstedde, C., and Ghattas, O.: A Stochastic Newton
MCMC Method for Large-Scale Statistical Inverse Problems with Application to
Seismic Inversion, SIAM J. Sci. Comp., 34, A1460–A1487,
https://doi.org/10.1137/110845598, 2012. a, b
Morales, J. L. and Nocedal, J.: Remark on “Algorithm 778: L-BFGS-B: Fortran
Subroutines for Large-Scale Bound Constrained Optimization”, ACM T.
Math. Softw., 38, 1–4, https://doi.org/10.1145/2049662.2049669, 2011. a
Morlighem, M., Rignot, E., Seroussi, G., Larour, E., Ben Dhia, H., and Aubry,
D.: Spatial patterns of basal drag inferred using control methods from a
full-Stokes and simpler models for Pine Island Glacier, West Antarctica,
Geophys. Res. Lett., 37, L14502, https://doi.org/10.1029/2010GL043853, 2010. a, b, c
Nias, I. J., Cornford, S. L., and Paybe, A. J.: Contrasting the modelled
sensitivity of the Amundsen Sea Embayment ice streams, J. Glaciol.,
62, 552–562, https://doi.org/10.1017/jog.2016.40, 2016. a
Pattyn, F., Perichon, L., Aschwanden, A., Breuer, B., de Smedt, B., Gagliardini, O., Gudmundsson, G. H., Hindmarsh, R. C. A., Hubbard, A., Johnson, J. V., Kleiner, T., Konovalov, Y., Martin, C., Payne, A. J., Pollard, D., Price, S., Rückamp, M., Saito, F., Souček, O., Sugiyama, S., and Zwinger, T.: Benchmark experiments for higher-order and full-Stokes ice sheet models (ISMIP–HOM), The Cryosphere, 2, 95–108, https://doi.org/10.5194/tc-2-95-2008, 2008. a, b
Petra, N., Martin, J., Stadler, G., and Ghattas, O.: A Computational Framework
for Infinite-Dimensional Bayesian Inverse Problems, Part II: Stochastic
Newton MCMC with Application to Ice Sheet Flow Inverse Problems, SIAM J. Sci. Comp., 36, A1525–A1555, https://doi.org/10.1137/130934805, 2014. a, b, c
Raymond, M. J. and Gudmundsson, G. H.: Estimating basal properties of ice streams from surface measurements: a non-linear Bayesian inverse approach applied to synthetic data, The Cryosphere, 3, 265–278, https://doi.org/10.5194/tc-3-265-2009, 2009. a
Rignot, E., Mouginot, J., and Scheuchl, B.: MEaSUREs InSAR-Based Antarctica
Ice Velocity Map, Version 2, Boulder, Colorado USA, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set],
https://doi.org/10.5067/D7GK8F5J8M8R,
2017. a
Ritz, C., Edwards, T. L., Durand, G., Payne, A. J., Peyaud, V., and
Hindmarsh, R. C. A.: Potential sea-level rise from Antarctic ice-sheet
instability constrained by observations, Nature, 528, 115–118,
https://doi.org/10.1038/nature16147, 2015. a
Robel, A. A., Seroussi, H., and Roe, G. H.: Marine ice sheet instability
amplifies and skews uncertainty in projections of future sea-level rise,
P. Natl. Acad. Sci. USA, 116, 14887–14892,
https://doi.org/10.1073/pnas.1904822116, 2019. a
Rommelaere, V.: Large-scale rheology of the Ross Ice Shelf, Antarctica,
computed by a control method, J. Glaciol., 24, 694–712, 1997. a
Schoof, C.: A variational approach to ice stream flow, J. Fluid Mech., 556,
227–251, 2006. a
Sergienko, O. V., MacAyeal, D. R., and Thom, J. E.: Reconstruction of snow/firn
thermal diffusivities from observed temperature variation: Application to
iceberg C16, Ross Sea, Antarctica, 2004-07, Ann. Glaciol., 49, 91–95,
2008. a
Shapero, D. R., Badgeley, J. A., Hoffman, A. O., and Joughin, I. R.: icepack: a new glacier flow modeling package in Python, version 1.0, Geosci. Model Dev., 14, 4593–4616, https://doi.org/10.5194/gmd-14-4593-2021, 2021. a
Thacker, W. C.: The role of the Hessian matrix in fitting models to
measurements, J. Geophys. Res., 94, 6177–6196,
https://doi.org/10.1029/JC094iC05p06177, 1989. a
Tierney, L.: Markov Chains for Exploring Posterior Distributions, Ann.
Stat., 22, 1701–1728, https://doi.org/10.1214/aos/1176325750, 1994. a
Todd, J. A., Koziol, C. P., Goldberg, D. N., and Maddison, J. R.: EdiGlacUQ/fenics_ice: fenics_ice (v1.0.1), Zenodo [code], https://doi.org/10.5281/zenodo.5153231, 2021. a
Vieli, A. and Payne, A. J.: Application of control methods for modelling the
flow of Pine Island Glacier, West Antarctica, Ann. Glaciol., 36,
197–204, 2003. a
Villa, U., Petra, N., and Ghattas, O.: hIPPYlib: an Extensible Software
Framework for Large-scale Deterministic and Bayesian Inverse Problems,
J. Open Source Softw., 3, p. 940, https://doi.org/10.21105/joss.00940, 2018. a
Waddington, E., Neumann, T., Koutnik, M., Marshall, H., and Morse, D.:
Inference of accumulation-rate patterns from deep layers in glaciers and ice
sheets, J. Glaciol., 53, 694–712, 2007. a
Zhu, C., Byrd, R. H., Lu, P., and Nocedal, J.: Algorithm 778: L-BFGS-B: Fortran
Subroutines for Large-Scale Bound-Constrained Optimization, ACM T. Math.
Softw., 23, 550–560, https://doi.org/10.1145/279232.279236, 1997. a
Short summary
Sea level change due to the loss of ice sheets presents great risk for coastal communities. Models are used to forecast ice loss, but their evolution depends strongly on properties which are hidden from observation and must be inferred from satellite observations. Common methods for doing so do not allow for quantification of the uncertainty inherent or how it will affect forecasts. We provide a framework for quantifying how this
initialization uncertaintyaffects ice loss forecasts.
Sea level change due to the loss of ice sheets presents great risk for coastal communities....
