Articles | Volume 15, issue 2
https://doi.org/10.5194/gmd-15-617-2022
© Author(s) 2022. 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-15-617-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| Highlight paper
| 
26 Jan 2022
Model description paper | Highlight paper |
| 26 Jan 2022
| 26 Jan 2022
The Whole Antarctic Ocean Model (WAOM v1.0): development and evaluation
Institute for Marine and Antarctic Studies, University of Tasmania, Private Bag 129, Hobart, TAS, 7001, Australia
Geography and Spatial Sciences, School of Technology, Environments and Design, University of Tasmania, Hobart, TAS, 7001, Australia
now at: Alfred Wegener Institute, PO Box 120161, 27515 Bremerhaven, Germany
David E. Gwyther
Institute for Marine and Antarctic Studies, University of Tasmania, Private Bag 129, Hobart, TAS, 7001, Australia
Coastal and Regional Oceanography Laboratory, School of Mathematics and Statistics, University of New South Wales, Sydney, NSW, 2052, Australia
Benjamin K. Galton-Fenzi
Australian Antarctic Division, Kingston, TAS, 7050, Australia
Australian Antarctic Program Partnership, Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Australia
The Australian Centre for Excellence in Antarctic Science, University of Tasmania, Hobart, Australia
Kaitlin A. Naughten
British Antarctic Survey, High Cross, Madingley Road, Cambridge, CB3 0ET, United Kingdom
Related authors
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.
Ole Richter, David E. Gwyther, Matt A. King, and Benjamin K. Galton-Fenzi
The Cryosphere, 16, 1409–1429, https://doi.org/10.5194/tc-16-1409-2022, https://doi.org/10.5194/tc-16-1409-2022, 2022
Short summary
Short summary
Tidal currents may play an important role in Antarctic ice sheet retreat by changing the rate at which the ocean melts glaciers. Here, using a computational ocean model, we derive the first estimate of present-day tidal melting that covers all of Antarctica. Our results suggest that large-scale ocean models aiming to accurately predict ice melt rates will need to account for the effects of tides. The inclusion of tide-induced friction at the ice–ocean interface should be prioritized.
Qin Zhou, Chen Zhao, Rupert Gladstone, Tore Hattermann, David Gwyther, and Benjamin Galton-Fenzi
Geosci. Model Dev., 17, 8243–8265, https://doi.org/10.5194/gmd-17-8243-2024, https://doi.org/10.5194/gmd-17-8243-2024, 2024
Short summary
Short summary
We introduce an accelerated forcing approach to address timescale discrepancies between the ice sheets and ocean components in coupled modelling by reducing the ocean simulation duration. The approach is evaluated using idealized coupled models, and its limitations in real-world applications are discussed. Our results suggest it can be a valuable tool for process-oriented coupled ice sheet–ocean modelling and downscaling climate simulations with such models.
Yu Wang, Chen Zhao, Rupert Gladstone, Thomas Zwinger, Benjamin K. Galton-Fenzi, and Poul Christoffersen
The Cryosphere, 18, 5117–5137, https://doi.org/10.5194/tc-18-5117-2024, https://doi.org/10.5194/tc-18-5117-2024, 2024
Short summary
Short summary
Our research delves into the future evolution of Antarctica's Wilkes Subglacial Basin (WSB) and its potential contribution to sea level rise, focusing on how basal melt is implemented at the grounding line in ice flow models. Our findings suggest that these implementation methods can significantly impact the magnitude of future ice loss projections. Under a high-emission scenario, the WSB ice sheet could undergo massive and rapid retreat between 2200 and 2300.
Jan De Rydt, Nicolas C. Jourdain, Yoshihiro Nakayama, Mathias van Caspel, Ralph Timmermann, Pierre Mathiot, Xylar S. Asay-Davis, Hélène Seroussi, Pierre Dutrieux, Ben Galton-Fenzi, David Holland, and Ronja Reese
Geosci. Model Dev., 17, 7105–7139, https://doi.org/10.5194/gmd-17-7105-2024, https://doi.org/10.5194/gmd-17-7105-2024, 2024
Short summary
Short summary
Global climate models do not reliably simulate sea-level change due to ice-sheet–ocean interactions. We propose a community modelling effort to conduct a series of well-defined experiments to compare models with observations and study how models respond to a range of perturbations in climate and ice-sheet geometry. The second Marine Ice Sheet–Ocean Model Intercomparison Project will continue to lay the groundwork for including ice-sheet–ocean interactions in global-scale IPCC-class models.
Sebastian H. R. Rosier, G. Hilmar Gudmundsson, Adrian Jenkins, and Kaitlin A. Naughten
EGUsphere, https://doi.org/10.5194/egusphere-2024-1838, https://doi.org/10.5194/egusphere-2024-1838, 2024
Short summary
Short summary
Glaciers in the Amundsen Sea region of Antarctica have been retreating and losing mass, but their future contribution to global sea level rise remains highly uncertain. We use an ice sheet model and uncertainty quantification methods to evaluate the probable range of mass loss from this region for two future climate scenarios and find that the rate of ice loss until 2100 will likely remain similar to present-day observations, with little sensitivity to climate scenario over this short timeframe.
Jan De Rydt and Kaitlin Naughten
The Cryosphere, 18, 1863–1888, https://doi.org/10.5194/tc-18-1863-2024, https://doi.org/10.5194/tc-18-1863-2024, 2024
Short summary
Short summary
The West Antarctic Ice Sheet is losing ice at an accelerating pace. This is largely due to the presence of warm ocean water around the periphery of the Antarctic continent, which melts the ice. It is generally assumed that the strength of this process is controlled by the temperature of the ocean. However, in this study we show that an equally important role is played by the changing geometry of the ice sheet, which affects the strength of the ocean currents and thereby the melt rates.
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.
Colette Gabrielle Kerry, Moninya Roughan, Shane Keating, David Gwyther, Gary Brassington, Adil Siripatana, and Joao Marcos A. C. Souza
Geosci. Model Dev., 17, 2359–2386, https://doi.org/10.5194/gmd-17-2359-2024, https://doi.org/10.5194/gmd-17-2359-2024, 2024
Short summary
Short summary
Ocean forecasting relies on the combination of numerical models and ocean observations through data assimilation (DA). Here we assess the performance of two DA systems in a dynamic western boundary current, the East Australian Current, across a common modelling and observational framework. We show that the more advanced, time-dependent method outperforms the time-independent method for forecast horizons of 5 d. This advocates the use of advanced methods for highly variable oceanic regions.
Hélène Seroussi, Vincent Verjans, Sophie Nowicki, Antony J. Payne, Heiko Goelzer, William H. Lipscomb, Ayako Abe-Ouchi, Cécile Agosta, Torsten Albrecht, Xylar Asay-Davis, Alice Barthel, Reinhard Calov, Richard Cullather, Christophe Dumas, Benjamin K. Galton-Fenzi, Rupert Gladstone, Nicholas R. Golledge, Jonathan M. Gregory, Ralf Greve, Tore Hattermann, Matthew J. Hoffman, Angelika Humbert, Philippe Huybrechts, Nicolas C. Jourdain, Thomas Kleiner, Eric Larour, Gunter R. Leguy, Daniel P. Lowry, Chistopher M. Little, Mathieu Morlighem, Frank Pattyn, Tyler Pelle, Stephen F. Price, Aurélien Quiquet, Ronja Reese, Nicole-Jeanne Schlegel, Andrew Shepherd, Erika Simon, Robin S. Smith, Fiammetta Straneo, Sainan Sun, Luke D. Trusel, Jonas Van Breedam, Peter Van Katwyk, Roderik S. W. van de Wal, Ricarda Winkelmann, Chen Zhao, Tong Zhang, and Thomas Zwinger
The Cryosphere, 17, 5197–5217, https://doi.org/10.5194/tc-17-5197-2023, https://doi.org/10.5194/tc-17-5197-2023, 2023
Short summary
Short summary
Mass loss from Antarctica is a key contributor to sea level rise over the 21st century, and the associated uncertainty dominates sea level projections. We highlight here the Antarctic glaciers showing the largest changes and quantify the main sources of uncertainty in their future evolution using an ensemble of ice flow models. We show that on top of Pine Island and Thwaites glaciers, Totten and Moscow University glaciers show rapid changes and a strong sensitivity to warmer ocean conditions.
Felicity S. McCormack, Jason L. Roberts, Bernd Kulessa, Alan Aitken, Christine F. Dow, Lawrence Bird, Benjamin K. Galton-Fenzi, Katharina Hochmuth, Richard S. Jones, Andrew N. Mackintosh, and Koi McArthur
The Cryosphere, 17, 4549–4569, https://doi.org/10.5194/tc-17-4549-2023, https://doi.org/10.5194/tc-17-4549-2023, 2023
Short summary
Short summary
Changes in Antarctic surface elevation can cause changes in ice and basal water flow, impacting how much ice enters the ocean. We find that ice and basal water flow could divert from the Totten to the Vanderford Glacier, East Antarctica, under only small changes in the surface elevation, with implications for estimates of ice loss from this region. Further studies are needed to determine when this could occur and if similar diversions could occur elsewhere in Antarctica due to climate change.
Ronja Reese, Julius Garbe, Emily A. Hill, Benoît Urruty, Kaitlin A. Naughten, Olivier Gagliardini, Gaël Durand, Fabien Gillet-Chaulet, G. Hilmar Gudmundsson, David Chandler, Petra M. Langebroek, and Ricarda Winkelmann
The Cryosphere, 17, 3761–3783, https://doi.org/10.5194/tc-17-3761-2023, https://doi.org/10.5194/tc-17-3761-2023, 2023
Short summary
Short summary
We use an ice sheet model to test where current climate conditions in Antarctica might lead. We find that present-day ocean and atmosphere conditions might commit an irreversible collapse of parts of West Antarctica which evolves over centuries to millennia. Importantly, this collapse is not irreversible yet.
David E. Gwyther, Shane R. Keating, Colette Kerry, and Moninya Roughan
Geosci. Model Dev., 16, 157–178, https://doi.org/10.5194/gmd-16-157-2023, https://doi.org/10.5194/gmd-16-157-2023, 2023
Short summary
Short summary
Ocean eddies are important for weather, climate, biology, navigation, and search and rescue. Since eddies change rapidly, models that incorporate or assimilate observations are required to produce accurate eddy timings and locations, yet the model accuracy is rarely assessed below the surface. We use a unique type of ocean model experiment to assess three-dimensional eddy structure in the East Australian Current and explore two pathways in which this subsurface structure is being degraded.
Paul R. Holland, Gemma K. O'Connor, Thomas J. Bracegirdle, Pierre Dutrieux, Kaitlin A. Naughten, Eric J. Steig, David P. Schneider, Adrian Jenkins, and James A. Smith
The Cryosphere, 16, 5085–5105, https://doi.org/10.5194/tc-16-5085-2022, https://doi.org/10.5194/tc-16-5085-2022, 2022
Short summary
Short summary
The Antarctic Ice Sheet is losing ice, causing sea-level rise. However, it is not known whether human-induced climate change has contributed to this ice loss. In this study, we use evidence from climate models and palaeoclimate measurements (e.g. ice cores) to suggest that the ice loss was triggered by natural climate variations but is now sustained by human-forced climate change. This implies that future greenhouse-gas emissions may influence sea-level rise from Antarctica.
David E. Gwyther, Colette Kerry, Moninya Roughan, and Shane R. Keating
Geosci. Model Dev., 15, 6541–6565, https://doi.org/10.5194/gmd-15-6541-2022, https://doi.org/10.5194/gmd-15-6541-2022, 2022
Short summary
Short summary
The ocean current flowing along the southeastern coast of Australia is called the East Australian Current (EAC). Using computer simulations, we tested how surface and subsurface observations might improve models of the EAC. Subsurface observations are particularly important for improving simulations, and if made in the correct location and time, can have impact 600 km upstream. The stability of the current affects model estimates could be capitalized upon in future observing strategies.
Madelaine Rosevear, Benjamin Galton-Fenzi, and Craig Stevens
Ocean Sci., 18, 1109–1130, https://doi.org/10.5194/os-18-1109-2022, https://doi.org/10.5194/os-18-1109-2022, 2022
Short summary
Short summary
Understanding ocean-driven melting of Antarctic ice shelves is critical for predicting future sea level. However, ocean observations from beneath ice shelves are scarce. Here, we present unique ocean and melting data from the Amery Ice Shelf, East Antarctica. We use our observations to evaluate common methods of representing melting in ocean–climate models (melting
parameterisations) and show that these parameterisations overestimate melting when the ocean is warm and/or currents are weak.
Chen Zhao, Rupert Gladstone, Benjamin Keith Galton-Fenzi, David Gwyther, and Tore Hattermann
Geosci. Model Dev., 15, 5421–5439, https://doi.org/10.5194/gmd-15-5421-2022, https://doi.org/10.5194/gmd-15-5421-2022, 2022
Short summary
Short summary
We use a coupled ice–ocean model to explore an oscillation feature found in several contributing models to MISOMIP1. The oscillation is closely related to the discretized grounding line retreat and likely strengthened by the buoyancy–melt feedback and/or melt–geometry feedback near the grounding line, and frequent ice–ocean coupling. Our model choices have a non-trivial impact on mean melt and ocean circulation strength, which might be interesting for the coupled ice–ocean community.
Ole Richter, David E. Gwyther, Matt A. King, and Benjamin K. Galton-Fenzi
The Cryosphere, 16, 1409–1429, https://doi.org/10.5194/tc-16-1409-2022, https://doi.org/10.5194/tc-16-1409-2022, 2022
Short summary
Short summary
Tidal currents may play an important role in Antarctic ice sheet retreat by changing the rate at which the ocean melts glaciers. Here, using a computational ocean model, we derive the first estimate of present-day tidal melting that covers all of Antarctica. Our results suggest that large-scale ocean models aiming to accurately predict ice melt rates will need to account for the effects of tides. The inclusion of tide-induced friction at the ice–ocean interface should be prioritized.
Yu Wang, Chen Zhao, Rupert Gladstone, Ben Galton-Fenzi, and Roland Warner
The Cryosphere, 16, 1221–1245, https://doi.org/10.5194/tc-16-1221-2022, https://doi.org/10.5194/tc-16-1221-2022, 2022
Short summary
Short summary
The thermal structure of the Amery Ice Shelf and its spatial pattern are evaluated and analysed through temperature observations from six boreholes and numerical simulations. The simulations demonstrate significant ice warming downstream along the ice flow and a great variation of the thermal structure across the ice flow. We suggest that the thermal structure of the Amery Ice Shelf is unlikely to be affected by current climate changes on decadal timescales.
Rupert Gladstone, Benjamin Galton-Fenzi, David Gwyther, Qin Zhou, Tore Hattermann, Chen Zhao, Lenneke Jong, Yuwei Xia, Xiaoran Guo, Konstantinos Petrakopoulos, Thomas Zwinger, Daniel Shapero, and John Moore
Geosci. Model Dev., 14, 889–905, https://doi.org/10.5194/gmd-14-889-2021, https://doi.org/10.5194/gmd-14-889-2021, 2021
Short summary
Short summary
Retreat of the Antarctic ice sheet, and hence its contribution to sea level rise, is highly sensitive to melting of its floating ice shelves. This melt is caused by warm ocean currents coming into contact with the ice. Computer models used for future ice sheet projections are not able to realistically evolve these melt rates. We describe a new coupling framework to enable ice sheet and ocean computer models to interact, allowing projection of the evolution of melt and its impact on sea level.
Hélène Seroussi, Sophie Nowicki, Antony J. Payne, Heiko Goelzer, William H. Lipscomb, Ayako Abe-Ouchi, Cécile Agosta, Torsten Albrecht, Xylar Asay-Davis, Alice Barthel, Reinhard Calov, Richard Cullather, Christophe Dumas, Benjamin K. Galton-Fenzi, Rupert Gladstone, Nicholas R. Golledge, Jonathan M. Gregory, Ralf Greve, Tore Hattermann, Matthew J. Hoffman, Angelika Humbert, Philippe Huybrechts, Nicolas C. Jourdain, Thomas Kleiner, Eric Larour, Gunter R. Leguy, Daniel P. Lowry, Chistopher M. Little, Mathieu Morlighem, Frank Pattyn, Tyler Pelle, Stephen F. Price, Aurélien Quiquet, Ronja Reese, Nicole-Jeanne Schlegel, Andrew Shepherd, Erika Simon, Robin S. Smith, Fiammetta Straneo, Sainan Sun, Luke D. Trusel, Jonas Van Breedam, Roderik S. W. van de Wal, Ricarda Winkelmann, Chen Zhao, Tong Zhang, and Thomas Zwinger
The Cryosphere, 14, 3033–3070, https://doi.org/10.5194/tc-14-3033-2020, https://doi.org/10.5194/tc-14-3033-2020, 2020
Short summary
Short summary
The Antarctic ice sheet has been losing mass over at least the past 3 decades in response to changes in atmospheric and oceanic conditions. This study presents an ensemble of model simulations of the Antarctic evolution over the 2015–2100 period based on various ice sheet models, climate forcings and emission scenarios. Results suggest that the West Antarctic ice sheet will continue losing a large amount of ice, while the East Antarctic ice sheet could experience increased snow accumulation.
Chad A. Greene, Duncan A. Young, David E. Gwyther, Benjamin K. Galton-Fenzi, and Donald D. Blankenship
The Cryosphere, 12, 2869–2882, https://doi.org/10.5194/tc-12-2869-2018, https://doi.org/10.5194/tc-12-2869-2018, 2018
Short summary
Short summary
We show that Totten Ice Shelf accelerates each spring in response to the breakup of seasonal landfast sea ice at the ice shelf calving front. The previously unreported seasonal flow variability may have aliased measurements in at least one previous study of Totten's response to ocean forcing on interannual timescales. The role of sea ice in buttressing the flow of the ice shelf implies that long-term changes in sea ice cover could have impacts on the mass balance of the East Antarctic Ice Sheet.
Lenneke M. Jong, Rupert M. Gladstone, Benjamin K. Galton-Fenzi, and Matt A. King
The Cryosphere, 12, 2425–2436, https://doi.org/10.5194/tc-12-2425-2018, https://doi.org/10.5194/tc-12-2425-2018, 2018
Short summary
Short summary
We used an ice sheet model to simulate temporary regrounding of a marine ice sheet retreating across a retrograde bedrock slope. We show that a sliding relation incorporating water-filled cavities and the ice overburden pressure at the base allows the temporary regrounding to occur. This suggests that choice of basal sliding relation can be important when modelling grounding line behaviour of regions where potential ice rises and pinning points are present and regrounding could occur.
Sue Cook, Jan Åström, Thomas Zwinger, Benjamin Keith Galton-Fenzi, Jamin Stevens Greenbaum, and Richard Coleman
The Cryosphere, 12, 2401–2411, https://doi.org/10.5194/tc-12-2401-2018, https://doi.org/10.5194/tc-12-2401-2018, 2018
Short summary
Short summary
The growth of fractures on Antarctic ice shelves is important because it controls the amount of ice lost as icebergs. We use a model constructed of multiple interconnected blocks to predict the locations where fractures will form on the Totten Ice Shelf in East Antarctica. The results show that iceberg calving is controlled not only by fractures forming near the front of the ice shelf but also by fractures which formed many kilometres upstream.
Kaitlin A. Naughten, Katrin J. Meissner, Benjamin K. Galton-Fenzi, Matthew H. England, Ralph Timmermann, Hartmut H. Hellmer, Tore Hattermann, and Jens B. Debernard
Geosci. Model Dev., 11, 1257–1292, https://doi.org/10.5194/gmd-11-1257-2018, https://doi.org/10.5194/gmd-11-1257-2018, 2018
Short summary
Short summary
MetROMS and FESOM are two ocean/sea-ice models which resolve Antarctic ice-shelf cavities and consider thermodynamics at the ice-shelf base. We simulate the period 1992–2016 with both models, and with two options for resolution in FESOM, and compare output from the three simulations. Ice-shelf melt rates, sub-ice-shelf circulation, continental shelf water masses, and sea-ice processes are compared and evaluated against available observations.
Felicity S. Graham, Jason L. Roberts, Ben K. Galton-Fenzi, Duncan Young, Donald Blankenship, and Martin J. Siegert
Earth Syst. Sci. Data, 9, 267–279, https://doi.org/10.5194/essd-9-267-2017, https://doi.org/10.5194/essd-9-267-2017, 2017
Short summary
Short summary
Antarctic bed topography datasets are interpolated onto low-resolution grids because our observed topography data are sparsely sampled. This has implications for ice-sheet model simulations, especially in regions prone to instability, such as grounding lines, where detailed knowledge of the topography is required. Here, we constructed a high-resolution synthetic bed elevation dataset using observed covariance properties to assess the dependence of simulated ice-sheet dynamics on grid resolution.
Christopher J. Fogwill, Erik van Sebille, Eva A. Cougnon, Chris S. M. Turney, Steve R. Rintoul, Benjamin K. Galton-Fenzi, Graeme F. Clark, E. M. Marzinelli, Eleanor B. Rainsley, and Lionel Carter
The Cryosphere, 10, 2603–2609, https://doi.org/10.5194/tc-10-2603-2016, https://doi.org/10.5194/tc-10-2603-2016, 2016
Short summary
Short summary
Here we report new data from in situ oceanographic surveys and high-resolution ocean modelling experiments in the Commonwealth Bay region of East Antarctica, where in 2010 there was a major reconfiguration of the regional icescape due to the collision of the 97 km long iceberg B09B with the Mertz Glacier tongue. Here we compare post-calving observations with high-resolution ocean modelling which suggest that this reconfiguration has led to the development of a new polynya off Commonwealth Bay.
D. E. Gwyther, B. K. Galton-Fenzi, J. R. Hunter, and J. L. Roberts
Ocean Sci., 10, 267–279, https://doi.org/10.5194/os-10-267-2014, https://doi.org/10.5194/os-10-267-2014, 2014
Related subject area
Cryosphere
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 global–land snow scheme (GLASS) v1.0 for the GFDL Earth System Model: formulation and evaluation at instrumented sites
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
A new 3D full-Stokes calving algorithm within Elmer/Ice (v9.0)
Clustering simulated snow profiles to form avalanche forecast regions
Simulation of snow albedo and solar irradiance profile with the two-stream radiative transfer in snow (TARTES) v2.0 model
Evaluation of MITgcm-based ocean reanalysis for the Southern Ocean
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)
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
SnowQM 1.0: A fast R Package for bias-correcting spatial fields of snow water equivalent using quantile mapping
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
Snow Multidata Mapping and Modeling (S3M) 5.1: a distributed cryospheric model with dry and wet snow, data assimilation, glacier mass balance, and debris-driven melt
MPAS-Seaice (v1.0.0): sea-ice dynamics on unstructured Voronoi meshes
Explicitly modelling microtopography in permafrost landscapes in a land surface model (JULES vn5.4_microtopography)
Geometric remapping of particle distributions in the Discrete Element Model for Sea Ice (DEMSI v0.0)
Mapping high-resolution basal topography of West Antarctica from radar data using non-stationary multiple-point geostatistics (MPS-BedMappingV1)
NEMO-Bohai 1.0: a high-resolution ocean and sea ice modelling system for the Bohai Sea, China
An improved regional coupled modeling system for Arctic sea ice simulation and prediction: a case study for 2018
WIFF1.0: a hybrid machine-learning-based parameterization of wave-induced sea ice floe fracture
SNICAR-ADv3: a community tool for modeling spectral snow albedo
STEMMUS-UEB v1.0.0: integrated modeling of snowpack and soil water and energy transfer with three complexity levels of soil physical processes
A versatile method for computing optimized snow albedo from spectrally fixed radiative variables: VALHALLA v1.0
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.
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.
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.
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.
Simon Horton, Florian Herla, and Pascal Haegeli
EGUsphere, https://doi.org/10.5194/egusphere-2024-1609, https://doi.org/10.5194/egusphere-2024-1609, 2024
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, location, and time. Tested in the Columbia Mountains during winter 2022–23, it accurately matched real forecast regions and identified major avalanche hazard patterns. This approach simplifies complex model outputs, helping forecasters make informed decisions.
Ghislain Picard and Quentin Libois
EGUsphere, https://doi.org/10.5194/egusphere-2024-1176, https://doi.org/10.5194/egusphere-2024-1176, 2024
Short summary
Short summary
TARTES is a radiative transfer model to compute the reflectivity in the solar domain (albedo), and the profiles of solar light and energy absorption in a multi-layered snowpack whose physical properties are prescribed by the user. It uniquely considers snow grain shape in a flexible way, allowing us to apply the most recent advances showing that snow does not behave as a collection of ice spheres, but instead as a random medium. TARTES is also simple but compares well with other complex models.
Yoshihiro Nakayama, Alena Malyarenko, Hong Zhang, Ou Wang, Matthis Auger, Ian Fenty, Matthew Mazloff, Köhl Armin, and Dimitris Menemenlis
EGUsphere, https://doi.org/10.5194/egusphere-2024-727, https://doi.org/10.5194/egusphere-2024-727, 2024
Short summary
Short summary
Global and basin-scale ocean reanalyses are becoming easily accessible. Yet, such ocean reanalyses are optimized for their entire model domains and their ability to simulate the Southern Ocean requires evaluations. 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.
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.
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.
Adrien Michel, Johannes Aschauer, Tobias Jonas, Stefanie Gubler, Sven Kotlarski, and Christoph Marty
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-298, https://doi.org/10.5194/gmd-2022-298, 2023
Revised manuscript accepted for GMD
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 last 60 years at a resolution of one day and one kilometre. This is the first time that such a dataset has been produced.
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.
Francesco Avanzi, Simone Gabellani, Fabio Delogu, Francesco Silvestro, Edoardo Cremonese, Umberto Morra di Cella, Sara Ratto, and Hervé Stevenin
Geosci. Model Dev., 15, 4853–4879, https://doi.org/10.5194/gmd-15-4853-2022, https://doi.org/10.5194/gmd-15-4853-2022, 2022
Short summary
Short summary
Knowing in real time how much snow and glacier ice has accumulated across the landscape has significant implications for water-resource management and flood control. This paper presents a computer model – S3M – allowing scientists and decision makers to predict snow and ice accumulation during winter and the subsequent melt during spring and summer. S3M has been employed for real-world flood forecasting since the early 2000s but is here being made open source for the first time.
Adrian K. Turner, William H. Lipscomb, Elizabeth C. Hunke, Douglas W. Jacobsen, Nicole Jeffery, Darren Engwirda, Todd D. Ringler, and Jonathan D. Wolfe
Geosci. Model Dev., 15, 3721–3751, https://doi.org/10.5194/gmd-15-3721-2022, https://doi.org/10.5194/gmd-15-3721-2022, 2022
Short summary
Short summary
We present the dynamical core of the MPAS-Seaice model, which uses a mesh consisting of a Voronoi tessellation with polygonal cells. Such a mesh allows variable mesh resolution in different parts of the domain and the focusing of computational resources in regions of interest. We describe the velocity solver and tracer transport schemes used and examine errors generated by the model in both idealized and realistic test cases and examine the computational efficiency of the model.
Noah D. Smith, Eleanor J. Burke, Kjetil Schanke Aas, Inge H. J. Althuizen, Julia Boike, Casper Tai Christiansen, Bernd Etzelmüller, Thomas Friborg, Hanna Lee, Heather Rumbold, Rachael H. Turton, Sebastian Westermann, and Sarah E. Chadburn
Geosci. Model Dev., 15, 3603–3639, https://doi.org/10.5194/gmd-15-3603-2022, https://doi.org/10.5194/gmd-15-3603-2022, 2022
Short summary
Short summary
The Arctic has large areas of small mounds that are caused by ice lifting up the soil. Snow blown by wind gathers in hollows next to these mounds, insulating them in winter. The hollows tend to be wetter, and thus the soil absorbs more heat in summer. The warm wet soil in the hollows decomposes, releasing methane. We have made a model of this, and we have tested how it behaves and whether it looks like sites in Scandinavia and Siberia. Sometimes we get more methane than a model without mounds.
Adrian K. Turner, Kara J. Peterson, and Dan Bolintineanu
Geosci. Model Dev., 15, 1953–1970, https://doi.org/10.5194/gmd-15-1953-2022, https://doi.org/10.5194/gmd-15-1953-2022, 2022
Short summary
Short summary
We developed a technique to remap sea ice tracer quantities between circular discrete element distributions. This is needed for a global discrete element method sea ice model being developed jointly by Los Alamos National Laboratory and Sandia National Laboratories that has the potential to better utilize newer supercomputers with graphics processing units and better represent sea ice dynamics. This new remapping technique ameliorates the effect of element distortion created by sea ice ridging.
Zhen Yin, Chen Zuo, Emma J. MacKie, and Jef Caers
Geosci. Model Dev., 15, 1477–1497, https://doi.org/10.5194/gmd-15-1477-2022, https://doi.org/10.5194/gmd-15-1477-2022, 2022
Short summary
Short summary
We provide a multiple-point geostatistics approach to probabilistically learn from training images to fill large-scale irregular geophysical data gaps. With a repository of global topographic training images, our approach models high-resolution basal topography and quantifies the geospatial uncertainty. It generated high-resolution topographic realizations to investigate the impact of basal topographic uncertainty on critical subglacial hydrological flow patterns associated with ice velocity.
Yu Yan, Wei Gu, Andrea M. U. Gierisch, Yingjun Xu, and Petteri Uotila
Geosci. Model Dev., 15, 1269–1288, https://doi.org/10.5194/gmd-15-1269-2022, https://doi.org/10.5194/gmd-15-1269-2022, 2022
Short summary
Short summary
In this study, we developed NEMO-Bohai, an ocean–ice model for the Bohai Sea, China. This study presented the scientific design and technical choices of the parameterizations for the NEMO-Bohai model. The model was calibrated and evaluated with in situ and satellite observations of ocean and sea ice. NEMO-Bohai is intended to be a valuable tool for long-term ocean and ice simulations and climate change studies.
Chao-Yuan Yang, Jiping Liu, and Dake Chen
Geosci. Model Dev., 15, 1155–1176, https://doi.org/10.5194/gmd-15-1155-2022, https://doi.org/10.5194/gmd-15-1155-2022, 2022
Short summary
Short summary
We present an improved coupled modeling system for Arctic sea ice prediction. We perform Arctic sea ice prediction experiments with improved/updated physical parameterizations, which show better skill in predicting sea ice state as well as atmospheric and oceanic state in the Arctic compared with its predecessor. The improved model also shows extended predictive skill of Arctic sea ice after the summer season. This provides an added value of this prediction system for decision-making.
Christopher Horvat and Lettie A. Roach
Geosci. Model Dev., 15, 803–814, https://doi.org/10.5194/gmd-15-803-2022, https://doi.org/10.5194/gmd-15-803-2022, 2022
Short summary
Short summary
Sea ice is a composite of individual pieces, called floes, ranging in horizontal size from meters to kilometers. Variations in sea ice geometry are often forced by ocean waves, a process that is an important target of global climate models as it affects the rate of sea ice melting. Yet directly simulating these interactions is computationally expensive. We present a neural-network-based model of wave–ice fracture that allows models to incorporate their effect without added computational cost.
Mark G. Flanner, Julian B. Arnheim, Joseph M. Cook, Cheng Dang, Cenlin He, Xianglei Huang, Deepak Singh, S. McKenzie Skiles, Chloe A. Whicker, and Charles S. Zender
Geosci. Model Dev., 14, 7673–7704, https://doi.org/10.5194/gmd-14-7673-2021, https://doi.org/10.5194/gmd-14-7673-2021, 2021
Short summary
Short summary
We present the technical formulation and evaluation of a publicly available code and web-based model to simulate the spectral albedo of snow. Our model accounts for numerous features of the snow state and ambient conditions, including the the presence of light-absorbing matter like black and brown carbon, mineral dust, volcanic ash, and snow algae. Carbon dioxide snow, found on Mars, is also represented. The model accurately reproduces spectral measurements of clean and contaminated snow.
Lianyu Yu, Yijian Zeng, and Zhongbo Su
Geosci. Model Dev., 14, 7345–7376, https://doi.org/10.5194/gmd-14-7345-2021, https://doi.org/10.5194/gmd-14-7345-2021, 2021
Short summary
Short summary
We developed an integrated soil–snow–atmosphere model (STEMMUS-UEB) dedicated to the physical description of snow and soil processes with various complexities. With STEMMUS-UEB, we demonstrated that the snowpack affects not only the soil surface moisture conditions (in the liquid and ice phase) and energy-related states (albedo, LE) but also the subsurface soil water and vapor transfer, which contributes to a better understanding of the hydrothermal implications of the snowpack in cold regions.
Florent Veillon, Marie Dumont, Charles Amory, and Mathieu Fructus
Geosci. Model Dev., 14, 7329–7343, https://doi.org/10.5194/gmd-14-7329-2021, https://doi.org/10.5194/gmd-14-7329-2021, 2021
Short summary
Short summary
In climate models, the snow albedo scheme generally calculates only a narrowband or broadband albedo. Therefore, we have developed the VALHALLA method to optimize snow spectral albedo calculations through the determination of spectrally fixed radiative variables. The development of VALHALLA v1.0 with the use of the snow albedo model TARTES and the spectral irradiance model SBDART indicates a considerable reduction in calculation time while maintaining an adequate accuracy of albedo values.
Cited articles
Amblas, D.: Review of dense shelf water observations around Antarctica
(presence or absence), Supplement to: Amblas, D., Dowdeswell, J. A.: Physiographic influences on dense shelf-water cascading down the
Antarctic continental slope, PANGAEA [data set], Earth-Science Reviews, 185, 887–900,
https://doi.org/10.1594/PANGAEA.890758, 2018. a
Asay-Davis, X. S., Jourdain, N. C., and Nakayama, Y.: Developments in
Simulating and Parameterizing Interactions Between the Southern
Ocean and the Antarctic Ice Sheet, Current Climate Change Reports, 3,
316–329, https://doi.org/10.1007/s40641-017-0071-0, 2017. a, b, c
Assmann, K. M., Jenkins, A., Shoosmith, D. R., Walker, D. P., Jacobs, S. S.,
and Nicholls, K. W.: Variability of Circumpolar Deep Water transport
onto the Amundsen Sea Continental shelf through a shelf break trough,
J. Geophys. Res.-Oceans, 118, 6603–6620,
https://doi.org/10.1002/2013JC008871, 2013. a
Beckmann, A., Hellmer, H., and Timmermann, R.: A numerical model of the
Weddell Sea: Large scale circulation and water mass distribution,
J. Geophys. Res.-Lett., 104, 23375–23391, 1999. a
Bett, D. T., Holland, P. R., Garabato, A. C. N., Jenkins, A., Dutrieux, P.,
Kimura, S., and Fleming, A.: The Impact of the Amundsen Sea
Freshwater Balance on Ocean Melting of the West Antarctic Ice
Sheet, J. Geophys. Rese.-Oceans, 125, e2020JC016305,
https://doi.org/10.1029/2020JC016305, 2020. a
Colleoni, F., Santis, L. D., Siddoway, C. S., Bergamasco, A., Golledge, N. R.,
Lohmann, G., Passchier, S., and Siegert, M. J.: Spatio-temporal variability
of processes across Antarctic ice-bed-ocean interfaces, Nat. Commun., 9, 2289, https://doi.org/10.1038/s41467-018-04583-0, 2018. a, b
Cougnon, E. A., Galton‐Fenzi, B. K., Meijers, A. J. S., and Legrésy, B.:
Modeling interannual dense shelf water export in the region of the Mertz
Glacier Tongue (1992–2007), J. Geophys. Res.-Oceans,
118, 5858–5872, https://doi.org/10.1002/2013JC008790, 2013. a, b
Davis, P. E. D., Jenkins, A., Nicholls, K. W., Brennan, P. V., Abrahamsen,
E. P., Heywood, K. J., Dutrieux, P., Cho, K.-H., and Kim, T.-W.: Variability
in Basal Melting Beneath Pine Island Ice Shelf on Weekly to
Monthly Timescales, J. Geophys. Res.-Oceans, 123,
8655–8669, https://doi.org/10.1029/2018JC014464, 2018. a
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi,
S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P.,
Beljaars, A. C. M., Berg, L. v. d., Bidlot, J., Bormann, N., Delsol, C.,
Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B.,
Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M.,
Matricardi, M., McNally, A. P., Monge‐Sanz, B. M., Morcrette, J.-J., Park,
B.-K., Peubey, C., Rosnay, P. d., Tavolato, C., Thépaut, J.-N., and Vitart,
F.: The ERA‐Interim reanalysis: configuration and performance of the
data assimilation system, Q. J. Roy. Meteor. Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011. a
Dinniman, M. S., Klinck, J. M., and Smith, W. O.: Cross-shelf exchange in a
model of the Ross Sea circulation and biogeochemistry, Deep-Sea Res.
Pt. II, 50, 3103–3120,
https://doi.org/10.1016/j.dsr2.2003.07.011, 2003. a
Dupont, T. K. and Alley, R. B.: Assessment of the importance of ice-shelf
buttressing to ice-sheet flow, Geophys. Res. Lett., 32, L04503, https://doi.org/10.1029/2004GL022024, 2005. a
Dutrieux, P., Vaughan, D. G., Corr, H. F. J., Jenkins, A., Holland, P. R., Joughin, I., and Fleming, A. H.: Pine Island glacier ice shelf melt distributed at kilometre scales, The Cryosphere, 7, 1543–1555, https://doi.org/10.5194/tc-7-1543-2013, 2013. a
Egbert, G. D. and Erofeeva, S. Y.: Efficient Inverse Modeling of Barotropic Ocean Tides, J. Atmos. Ocean. Tech., 19, 183–204, https://doi.org/10.1175/1520-0426(2002)019<0183:EIMOBO>2.0.CO;2, 2002. a
Fretwell, P., Pritchard, H. D., Vaughan, D. G., Bamber, J. L., Barrand, N. E., Bell, R., Bianchi, C., Bingham, R. G., Blankenship, D. D., Casassa, G., Catania, G., Callens, D., Conway, H., Cook, A. J., Corr, H. F. J., Damaske, D., Damm, V., Ferraccioli, F., Forsberg, R., Fujita, S., Gim, Y., Gogineni, P., Griggs, J. A., Hindmarsh, R. C. A., Holmlund, P., Holt, J. W., Jacobel, R. W., Jenkins, A., Jokat, W., Jordan, T., King, E. C., Kohler, J., Krabill, W., Riger-Kusk, M., Langley, K. A., Leitchenkov, G., Leuschen, C., Luyendyk, B. P., Matsuoka, K., Mouginot, J., Nitsche, F. O., Nogi, Y., Nost, O. A., Popov, S. V., Rignot, E., Rippin, D. M., Rivera, A., Roberts, J., Ross, N., Siegert, M. J., Smith, A. M., Steinhage, D., Studinger, M., Sun, B., Tinto, B. K., Welch, B. C., Wilson, D., Young, D. A., Xiangbin, C., and Zirizzotti, A.: Bedmap2: improved ice bed, surface and thickness datasets for Antarctica, The Cryosphere, 7, 375–393, https://doi.org/10.5194/tc-7-375-2013, 2013. a, b, c
Greene, C. A., Blankenship, D. D., Gwyther, D. E., Silvano, A., and Wijk,
E. v.: Wind causes Totten Ice Shelf melt and acceleration, Science
Advances, 3, e1701681, https://doi.org/10.1126/sciadv.1701681, 2017. a, b
Griffies, S. M., Danabasoglu, G., Durack, P. J., Adcroft, A. J., Balaji, V., Böning, C. W., Chassignet, E. P., Curchitser, E., Deshayes, J., Drange, H., Fox-Kemper, B., Gleckler, P. J., Gregory, J. M., Haak, H., Hallberg, R. W., Heimbach, P., Hewitt, H. T., Holland, D. M., Ilyina, T., Jungclaus, J. H., Komuro, Y., Krasting, J. P., Large, W. G., Marsland, S. J., Masina, S., McDougall, T. J., Nurser, A. J. G., Orr, J. C., Pirani, A., Qiao, F., Stouffer, R. J., Taylor, K. E., Treguier, A. M., Tsujino, H., Uotila, P., Valdivieso, M., Wang, Q., Winton, M., and Yeager, S. G.: OMIP contribution to CMIP6: experimental and diagnostic protocol for the physical component of the Ocean Model Intercomparison Project, Geosci. Model Dev., 9, 3231–3296, https://doi.org/10.5194/gmd-9-3231-2016, 2016. a
Griffiths, S. D. and Peltier, W. R.: Megatides in the Arctic Ocean under
glacial conditions, Geophys. Res. Lett., 35, e1701681,
https://doi.org/10.1029/2008GL033263, 2008. a
Gudmundsson, G. H.: Ice-shelf buttressing and the stability of marine ice sheets, The Cryosphere, 7, 647–655, https://doi.org/10.5194/tc-7-647-2013, 2013. a
Guo, G., Shi, J., Gao, L., Tamura, T., and Williams, G. D.: Reduced Sea Ice
Production Due to Upwelled Oceanic Heat Flux in Prydz Bay,
East Antarctica, Geophys. Res. Lett., 46, 4782–4789,
https://doi.org/10.1029/2018GL081463, 2019. a
Gwyther, D. E., Cougnon, E. A., Galton-Fenzi, B. K., Roberts, J. L., Hunter,
J. R., and Dinniman, M. S.: Modelling the response of ice shelf basal melting
to different ocean cavity environmental regimes, Ann. Glaciol., 57,
131–141, https://doi.org/10.1017/aog.2016.31, 2016. a, b, c, d
Gwyther, D. E., O’Kane, T. J., Galton-Fenzi, B. K., Monselesan, D. P., and
Greenbaum, J. S.: Intrinsic processes drive variability in basal melting of
the Totten Glacier Ice Shelf, Nat. Commun., 9, 3141,
https://doi.org/10.1038/s41467-018-05618-2, 2018. a, b
Gwyther, D. E., Spain, E. A., King, P., Guihen, D., Williams, G. D., Evans, E.,
Cook, S., Richter, O., Galton‐Fenzi, B. K., and Coleman, R.: Cold Ocean
Cavity and Weak Basal Melting of the Sørsdal Ice Shelf
Revealed by Surveys Using Autonomous Platforms, J.
Geophys. Res.-Oceans, 125, e2019JC015882,
https://doi.org/10.1029/2019JC015882, 2020. a
Haid, V. and Timmermann, R.: Simulated heat flux and sea ice production at
coastal polynyas in the southwestern Weddell Sea, J. Geophys.
Res.-Oceans, 118, 2640–2652, https://doi.org/10.1002/jgrc.20133,
2013. a
Hattermann, T.: Antarctic Thermocline Dynamics along a Narrow Shelf
with Easterly Winds, J. Phys. Oceanogr., 48, 2419–2443,
https://doi.org/10.1175/JPO-D-18-0064.1, 2018. a, b
Hattermann, T., Nøst, O. A., Lilly, J. M., and Smedsrud, L. H.: Two years of
oceanic observations below the Fimbul Ice Shelf, Antarctica,
Geophys. Res. Lett., 39, L12605, https://doi.org/10.1029/2012GL051012, 2012. a
Hattermann, T., Smedsrud, L. H., Nøst, O. A., Lilly, J. M., and Galton-Fenzi,
B. K.: Eddy-resolving simulations of the Fimbul Ice Shelf cavity
circulation: Basal melting and exchange with open ocean, Ocean Model.,
82, 28–44, https://doi.org/10.1016/j.ocemod.2014.07.004, 2014. a, b
Hellmer, H. H. and Olbers, D. J.: A two-dimensional model for the thermohaline
circulation under an ice shelf, Antarctic Science, 1, 325–336,
https://doi.org/10.1017/S0954102089000490, 1989. a
Holland, D. M. and Jenkins, A.: Modeling Thermodynamic Ice-Ocean
Interactions at the Base of an Ice Shelf, J. Phys.
Oceanogr., 29, 1787–1800,
https://doi.org/10.1175/1520-0485(1999)029<1787:MTIOIA>2.0.CO;2, 1999. a
Holland, P. R.: The Transient Response of Ice Shelf Melting to
Ocean Change, J. Phys. Oceanogr., 47, 2101–2114,
https://doi.org/10.1175/JPO-D-17-0071.1, 2017. a
Holland, P. R., Feltham, D. L., and Jenkins, A.: Ice Shelf Water plume flow
beneath Filchner-Ronne Ice Shelf, Antarctica, J.
Geophys. Res., 112, C05044, https://doi.org/10.1029/2006JC003915, 2007. a
Holland, P. R., Jenkins, A., and Holland, D. M.: The Response of Ice
Shelf Basal Melting to Variations in Ocean Temperature, J. Clim., 21, 2558–2572, https://doi.org/10.1175/2007JCLI1909.1, 2008. a
Holland, P. R., Corr, H. F. J., Vaughan, D. G., Jenkins, A., and Skvarca, P.:
Marine ice in Larsen Ice Shelf, Geophys. Res. Lett., 36, L11604,
https://doi.org/10.1029/2009GL038162, 2009. a
Horgan, H. J., Walker, R. T., Anandakrishnan, S., and Alley, R. B.: Surface
elevation changes at the front of the Ross Ice Shelf: Implications
for basal melting, J. Geophys. Res.-Oceans, 116, C02005,
https://doi.org/10.1029/2010JC006192, 2011. a
Jacobs, S. S.: Bottom water production and its links with the thermohaline
circulation, Antarctic Science, 16, 427–437,
https://doi.org/10.1017/S095410200400224X, 2004. a
Jacobs, S. S., Helmer, H. H., Doake, C. S. M., Jenkins, A., and Frolich, R. M.:
Melting of ice shelves and the mass balance of Antarctica, J. Glaciol., 38, 375–387, https://doi.org/10.3189/S0022143000002252, 1992. a, b, c
Jacobs, S. S., Jenkins, A., Giulivi, C. F., and Dutrieux, P.: Stronger ocean
circulation and increased melting under Pine Island Glacier ice shelf,
Nat. Geosci., 4, 519–523, https://doi.org/10.1038/ngeo1188, 2011. a
Jacobs, S. S., Giulivi, C. F., Dutrieux, P., Rignot, E., Nitsche, F., and
Mouginot, J.: Getz Ice Shelf melting response to changes in ocean
forcing, J. Geophys. Res.-Oceans, 118, 4152–4168,
https://doi.org/10.1002/jgrc.20298, 2013. a
Jong, L., Gladstone, R., and Galton-Fenzi, B. K.: Coupled ice sheet-ocean
modelling to investigate ocean driven melting of marine ice sheets in
Antarctica, in: 19th EGU General Assembly, EGU2017, Vienna, Austria, 23–28 April 2017, 1573, available at: http://adsabs.harvard.edu/abs/2017EGUGA..19.1573J (last access: 17 January 2022), 2017. a, b
Joughin, I. and Padman, L.: Melting and freezing beneath Filchner-Ronne
Ice Shelf, Antarctica, Geophys. Res. Lett., 30, 1477,
https://doi.org/10.1029/2003GL016941, 2003. a
Kimura, S., Jenkins, A., Regan, H., Holland, P. R., Assmann, K. M., Whitt,
D. B., Wessem, M. V., Berg, W. J. v. d., Reijmer, C. H., and Dutrieux, P.:
Oceanographic Controls on the Variability of Ice-Shelf Basal
Melting and Circulation of Glacial Meltwater in the Amundsen Sea
Embayment, Antarctica, J. Geophys. Res.-Oceans, 122,
10131–10155, https://doi.org/10.1002/2017JC012926, 2017. a
King, M. A. and Padman, L.: Accuracy assessment of ocean tide models around
Antarctica, Geophys. Res. Lett., 32, L23608, https://doi.org/10.1029/2005GL023901, 2005. a, b, c
Kusahara, K. and Hasumi, H.: Modeling Antarctic ice shelf responses to future
climate changes and impacts on the ocean, J. Geophys. Res.-Oceans, 118, 2454–2475, https://doi.org/10.1002/jgrc.20166, 2013. a, b
Kusahara, K., Hasumi, H., and Tamura, T.: Modeling sea ice production and dense
shelf water formation in coastal polynyas around East Antarctica, J. Geophys. Res.-Oceans, 115, C10006, https://doi.org/10.1029/2010JC006133, 2010. a
Large, W. G., McWilliams, J. C., and Doney, S. C.: Oceanic vertical mixing: A
review and a model with a nonlocal boundary layer parameterization, Rev. Geophys., 32, 363–403, https://doi.org/10.1029/94RG01872, 1994. a
Little, C. M., Gnanadesikan, A., and Oppenheimer, M.: How ice shelf morphology
controls basal melting, J. Geophys. Res.-Oceans, 114,
C12007, https://doi.org/10.1029/2008JC005197, 2009. a
Lüpkes, C. and Birnbaum, G.: “Surface Drag in the Arctic Marginal
Sea-ice Zone: A Comparison of Different Parameterisation
Concepts”, Bound.-Lay. Meteorol., 117, 179–211,
https://doi.org/10.1007/s10546-005-1445-8, 2005. a
Mack, S. L., Dinniman, M. S., Klinck, J. M., McGillicuddy, D. J., and Padman,
L.: Modeling Ocean Eddies on Antarctica's Cold Water Continental
Shelves and Their Effects on Ice Shelf Basal Melting, J. Geophys. Res.-Oceans, 124, 5067–5084, https://doi.org/10.1029/2018JC014688,
2019. a
Makinson, K., Holland, P. R., Jenkins, A., Nicholls, K. W., and Holland, D. M.:
Influence of tides on melting and freezing beneath Filchner-Ronne Ice
Shelf, Antarctica, Geophys. Res. Lett., 38, L06601,
https://doi.org/10.1029/2010GL046462, 2011. a, b
Malyarenko, A., Robinson, N. J., Williams, M. J. M., and Langhorne, P. J.: A
Wedge Mechanism for Summer Surface Water Inflow Into the Ross
Ice Shelf Cavity, J. Geophys. Res.-Oceans, 124,
1196–1214, https://doi.org/10.1029/2018JC014594, 2019. a, b
Mathiot, P., Barnier, B., Gallée, H., Molines, J. M., Sommer, J. L., Juza, M.,
and Penduff, T.: Introducing katabatic winds in global ERA40 fields to
simulate their impacts on the Southern Ocean and sea-ice, Ocean
Model., 35, 146–160, https://doi.org/10.1016/j.ocemod.2010.07.001, 2010. a
Mathiot, P., Jenkins, A., Harris, C., and Madec, G.: Explicit representation and parametrised impacts of under ice shelf seas in the Z∗ coordinate ocean model NEMO 3.6, Geosci. Model Dev., 10, 2849–2874, https://doi.org/10.5194/gmd-10-2849-2017, 2017. a, b, c, d
Mazloff, M. R., Heimbach, P., and Wunsch, C.: An Eddy-Permitting Southern
Ocean State Estimate, J. Phys. Oceanogr., 40, 880–899,
https://doi.org/10.1175/2009JPO4236.1, 2010. a, b
McPhee, M. G.: A time‐dependent model for turbulent transfer in a stratified
oceanic boundary layer, J. Geophys. Res.-Oceans, 92,
6977–6986, https://doi.org/10.1029/JC092iC07p06977, 1987. a
Mellor, G. L., Ezer, T., and Oey, L.-Y.: The Pressure Gradient Conundrum
of Sigma Coordinate Ocean Models, J. Atmos. Ocean. Tech., 11, 1126–1134,
https://doi.org/10.1175/1520-0426(1994)011<1126:TPGCOS>2.0.CO;2, 1994. a, b
Mellor, G. L., Oey, L.-Y., and Ezer, T.: Sigma Coordinate Pressure
Gradient Errors and the Seamount Problem, J. Atmos. Ocean. Tech., 15, 1122–1131,
https://doi.org/10.1175/1520-0426(1998)015<1122:SCPGEA>2.0.CO;2, 1998. a
Meneghello, G., Marshall, J., Campin, J.-M., Doddridge, E., and Timmermans,
M.-L.: The Ice-Ocean Governor: Ice-Ocean Stress Feedback
Limits Beaufort Gyre Spin-Up, Geophys. Res. Lett., 45,
11293–11299, https://doi.org/10.1029/2018GL080171, 2018a. a
Meneghello, G., Marshall, J., Timmermans, M.-L., and Scott, J.: Observations of
Seasonal Upwelling and Downwelling in the Beaufort Sea Mediated
by Sea Ice, J. Phys. Oceanogr., 48, 795–805,
https://doi.org/10.1175/JPO-D-17-0188.1, 2018b. a
Menemenlis, D., Campin, J., Heimbach, P., Hill, C., Lee, T., Nguyen, A.,
Schodlok, M., and Zhang, H.: ECCO2: High Resolution Global Ocean
and Sea Ice Data Synthesis, AGU Fall Meeting Abstracts, available at: http://adsabs.harvard.edu/abs/2008AGUFMOS31C1292M (last access: 17 January 2022), 2008. a
Millan, R., Rignot, E., Bernier, V., Morlighem, M., and Dutrieux, P.:
Bathymetry of the Amundsen Sea Embayment sector of West Antarctica
from Operation IceBridge gravity and other data, Geophys. Res. Lett., 44, 1360–1368, https://doi.org/10.1002/2016GL072071, 2017. a
Morrison, A. K., Hogg, A. M., England, M. H., and Spence, P.: Warm
Circumpolar Deep Water transport toward Antarctica driven by local
dense water export in canyons, Science Advances, 6, eaav2516,
https://doi.org/10.1126/sciadv.aav2516, 2020. a
Mouginot, J., Rignot, E., and Scheuchl, B.: MEaSURES Antarctic Boundaries
for IPY 2007–2009 from Satellite Radar, Version 1, Shelves, NSIDC [data set],
https://doi.org/10.5067/SEVV4MR8P1ZN, 2016. a, b, c
Mueller, R. D., Padman, L., Dinniman, M. S., Erofeeva, S. Y., Fricker, H. A.,
and King, M. A.: Impact of tide-topography interactions on basal melting of
Larsen C Ice Shelf, Antarctica, J. Geophys. Res.-Oceans, 117,
C05005, https://doi.org/10.1029/2011JC007263, 2012. a
Mueller, R. D., Hattermann, T., Howard, S. L., and Padman, L.: Tidal influences on a future evolution of the Filchner–Ronne Ice Shelf cavity in the Weddell Sea, Antarctica, The Cryosphere, 12, 453–476, https://doi.org/10.5194/tc-12-453-2018, 2018. a, b
Nakayama, Y., Timmermann, R., Schröder, M., and Hellmer, H. H.: On the
difficulty of modeling Circumpolar Deep Water intrusions onto the
Amundsen Sea continental shelf, Ocean Model., 84, 26–34,
https://doi.org/10.1016/j.ocemod.2014.09.007, 2014. a, b, c, d
Nakayama, Y., Menemenlis, D., Schodlok, M., and Rignot, E.: Amundsen and
Bellingshausen Seas simulation with optimized ocean, sea ice, and
thermodynamic ice shelf model parameters, J. Geophys. Res.-Oceans, 122, 6180–6195, https://doi.org/10.1002/2016JC012538, 2017. a, b, c, d
Nash, R.: International Bathymetric Chart of the Southern Ocean
(IBCSO), Scientific Committee on Antarctic Research (SCAR), available at: https://www.scar.org/science/ibcso/ibcso/ (last access: 17 January 2022), 2019. a
Naughten, K. A., Meissner, K. J., Galton-Fenzi, B. K., England, M. H.,
Timmermann, R., and Hellmer, H. H.: Future Projections of Antarctic Ice
Shelf Melting Based on CMIP5 Scenarios, J. Clim., 31,
5243–5261, https://doi.org/10.1175/JCLI-D-17-0854.1, 2018a. a
Naughten, K. A., Meissner, K. J., Galton-Fenzi, B. K., England, M. H., Timmermann, R., Hellmer, H. H., Hattermann, T., and Debernard, J. B.: Intercomparison of Antarctic ice-shelf, ocean, and sea-ice interactions simulated by MetROMS-iceshelf and FESOM 1.4, Geosci. Model Dev., 11, 1257–1292, https://doi.org/10.5194/gmd-11-1257-2018, 2018b. a, b, c, d, e, f, g, h, i, j, k, l, m, n
Naughten, K. A., Rydt, J. D., Rosier, S. H. R., Holland, P. R., and Ridley,
J. K.: Two-timescale response of a large Antarctic ice shelf to climate
change, Nat. Commun., 12, 1991, https://doi.org/10.1038/s41467-021-22259-0, 2021. a
Nicholls, K. W., Østerhus, S., Makinson, K., Gammelsrød, T., and Fahrbach,
E.: Ice‐ocean processes over the continental shelf of the southern
Weddell Sea, Antarctica: A review, Rev.Geophys., 47,
RG3003, https://doi.org/10.1029/2007RG000250, 2009. a
Nøst, O. A., Biuw, M., Tverberg, V., Lydersen, C., Hattermann, T., Zhou, Q.,
Smedsrud, L. H., and Kovacs, K. M.: Eddy overturning of the Antarctic
Slope Front controls glacial melting in the Eastern Weddell Sea,
J. Geophys. Res.-Oceans, 116, C11014, https://doi.org/10.1029/2011JC006965, 2011. a
Padman, L., Howard, S. L., and Muench, R.: Internal tide generation along the
South Scotia Ridge, Deep-Sea Res. Pt. II, 53, 157–171, https://doi.org/10.1016/j.dsr2.2005.07.011, 2006. a
Padman, L., Siegfried, M. R., and Fricker, H. A.: Ocean Tide Influences on
the Antarctic and Greenland Ice Sheets, Rev. Geophys., 56,
142–184, https://doi.org/10.1002/2016RG000546, 2018. a, b
Parkinson, C. L.: A 40-y record reveals gradual Antarctic sea ice increases
followed by decreases at rates far exceeding the rates seen in the Arctic,
P. Natl. Acad. Sci. USA, 116, 14414–14423, https://doi.org/10.1073/pnas.1906556116, 2019. a
Pawlowicz, R., Beardsley, B., and Lentz, S.: Classical tidal harmonic analysis
including error estimates in MATLAB using T_TIDE, Computers and
Geosciences, 28, 929–937, https://doi.org/10.1016/S0098-3004(02)00013-4, 2002. a
Pritchard, H. D., Ligtenberg, S. R. M., Fricker, H. A., Vaughan, D. G., van den
Broeke, M. R., and Padman, L.: Antarctic ice-sheet loss driven by basal
melting of ice shelves, Nature, 484, 502–505, https://doi.org/10.1038/nature10968,
2012. a, b, c
Purkey, S. G. and Johnson, G. C.: Antarctic Bottom Water Warming and
Freshening: Contributions to Sea Level Rise, Ocean Freshwater
Budgets, and Global Heat Gain, J. Clim., 26, 6105–6122,
https://doi.org/10.1175/JCLI-D-12-00834.1, 2013. a
Richter, O.: kuechenrole/waom: Whole Antarctic Ocean Model, Zenodo [code], https://doi.org/10.5281/zenodo.3738985, 2020a. a, b
Richter, O.: kuechenrole/antarctic_melting: Post- and preprocessing tools for the ROMS Whole Antarctic Ocean Model, Zenodo [data set], https://doi.org/10.5281/zenodo.3738998, 2020b. a, b
Robertson, R.: Modeling internal tides over Fieberling Guyot: resolution,
parameterization, performance, Ocean Dynam., 56, 430–444,
https://doi.org/10.1007/s10236-006-0062-5, 2006. a
Roquet, F., Williams, G., Hindell, M. A., Harcourt, R., McMahon, C., Guinet,
C., Charrassin, J.-B., Reverdin, G., Boehme, L., Lovell, P., and Fedak, M.: A
Southern Indian Ocean database of hydrographic profiles obtained with
instrumented elephant seals, Sci. Data, 1, 140028,
https://doi.org/10.1038/sdata.2014.28, 2014. a
Schaffer, J., Timmermann, R., Arndt, J. E., Kristensen, S. S., Mayer, C., Morlighem, M., and Steinhage, D.: A global, high-resolution data set of ice sheet topography, cavity geometry, and ocean bathymetry, Earth Syst. Sci. Data, 8, 543–557, https://doi.org/10.5194/essd-8-543-2016, 2016. a
Schmidtko, S., Johnson, G. C., and Lyman, J. M.: MIMOC: A global monthly
isopycnal upper-ocean climatology with mixed layers, J. Geophys. Res.-Oceans, 118, 1658–1672, https://doi.org/10.1002/jgrc.20122,
2013. a
Schnaase, F. and Timmermann, R.: Representation of shallow grounding zones in
an ice shelf-ocean model with terrain-following coordinates, Ocean Model.,
144, 101487, https://doi.org/10.1016/j.ocemod.2019.101487, 2019. a, b
Schodlok, M. P., Menemenlis, D., and Rignot, E.: Ice shelf basal melt rates
around Antarctica from simulations and observations, J. Geophys. Res.-Oceans, 121, 1085–1109, https://doi.org/10.1002/2015JC011117, 2016. a, b, c
Shchepetkin, A. F. and McWilliams, J. C.: A method for computing horizontal
pressure-gradient force in an oceanic model with a nonaligned vertical
coordinate, J. Geophys. Res.-Oceans, 108, 3090,
https://doi.org/10.1029/2001JC001047, 2003. a
Shchepetkin, A. F. and McWilliams, J. C.: The regional oceanic modeling system
(ROMS): a split-explicit, free-surface, topography-following-coordinate
oceanic model, Ocean Modelling, 9, 347–404,
https://doi.org/10.1016/j.ocemod.2004.08.002, 2005. a
Shean, D. E., Joughin, I. R., Dutrieux, P., Smith, B. E., and Berthier, E.: Ice shelf basal melt rates from a high-resolution digital elevation model (DEM) record for Pine Island Glacier, Antarctica, The Cryosphere, 13, 2633–2656, https://doi.org/10.5194/tc-13-2633-2019, 2019. a
Sherwood, C. R., Aretxabaleta, A. L., Harris, C. K., Rinehimer, J. P., Verney, R., and Ferré, B.: Cohesive and mixed sediment in the Regional Ocean Modeling System (ROMS v3.6) implemented in the Coupled Ocean–Atmosphere–Wave–Sediment Transport Modeling System (COAWST r1234), Geosci. Model Dev., 11, 1849–1871, https://doi.org/10.5194/gmd-11-1849-2018, 2018. a
Sikirić, M. D., Janeković, I., and Kuzmić, M.: A new approach to bathymetry
smoothing in sigma-coordinate ocean models, Ocean Model., 29, 128–136,
https://doi.org/10.1016/j.ocemod.2009.03.009, 2009. a, b, c
Silvano, A., Rintoul, S. R., Peña‐Molino, B., and Williams, G. D.:
Distribution of water masses and meltwater on the continental shelf near the
Totten and Moscow University ice shelves, J. Geophys. Res.-Oceans, 122, 2050–2068, https://doi.org/10.1002/2016JC012115, 2017. a, b
Silvano, A., Rintoul, S. R., Peña-Molino, B., Hobbs, W. R., van Wijk, E., Aoki,
S., Tamura, T., and Williams, G. D.: Freshening by glacial meltwater enhances
melting of ice shelves and reduces formation of Antarctic Bottom Water,
Sci. Adv., 4, eaap9467, https://doi.org/10.1126/sciadv.aap9467, 2018. a, b
Stern, A. A., Dinniman, M. S., Zagorodnov, V., Tyler, S. W., and Holland,
D. M.: Intrusion of warm surface water beneath the McMurdo Ice Shelf,
Antarctica, J. Geophys. Res.-Oceans, 118, 7036–7048,
https://doi.org/10.1002/2013JC008842, 2013. a, b
Stewart, A. L. and Thompson, A. F.: Eddy‐mediated transport of warm
Circumpolar Deep Water across the Antarctic Shelf Break,
Geophys. Res. Lett., 42, 432–440, https://doi.org/10.1002/2014GL062281, 2015. a, b, c
Stewart, A. L., Klocker, A., and Menemenlis, D.: Circum-Antarctic Shoreward
Heat Transport Derived From an Eddy- and Tide-Resolving
Simulation, Geophys. Res. Lett., 45, 834–845,
https://doi.org/10.1002/2017GL075677, 2018. a, b, c
Stewart, C. L., Christoffersen, P., Nicholls, K. W., Williams, M. J. M., and
Dowdeswell, J. A.: Basal melting of Ross Ice Shelf from solar heat
absorption in an ice-front polynya, Nat. Geosci., 12, 435–440,
https://doi.org/10.1038/s41561-019-0356-0, 2019. a, b
Tamura, T., Ohshima, K. I., Nihashi, S., and Hasumi, H.: Estimation of
Surface Heat/Salt Fluxes Associated with Sea Ice
Growth/Melt in the Southern Ocean, Scientific Online Letters on the Atmosphere (SOLA), 7, 17–20,
https://doi.org/10.2151/sola.2011-005, 2011. a, b
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An Overview of CMIP5 and
the Experiment Design, B. Am. Meteorol. Soc.,
93, 485–498, https://doi.org/10.1175/BAMS-D-11-00094.1, 2011. a
Thoma, M., Jenkins, A., Holland, D. M., and Jacobs, S. S.: Modelling
Circumpolar Deep Water intrusions on the Amundsen Sea continental
shelf, Antarctica, Geophys. Res. Lett., 35, L18602,
https://doi.org/10.1029/2008GL034939, 2008. a, b
Timmermann, R. and Goeller, S.: Response to Filchner–Ronne Ice Shelf cavity warming in a coupled ocean–ice sheet model – Part 1: The ocean perspective, Ocean Sci., 13, 765–776, https://doi.org/10.5194/os-13-765-2017, 2017. a
Timmermann, R. and Hellmer, H.: Southern Ocean warming and increased ice
shelf basal melting in the twenty-first and twenty-second centuries based on
coupled ice-ocean finite-element modelling, Ocean Dynam., 63, 1011–1026,
https://doi.org/10.1007/s10236-013-0642-0, 2013.
a
Timmermann, R., Beckmann, A., and Hellmer, H. H.: Simulations of ice-ocean
dynamics in the Weddell Sea 1. Model configuration and validation,
J. Geophys. Res.-Oceans, 107, 3024, https://doi.org/10.1029/2000JC000741, 2002. a
Williams, G. D., Herraiz-Borreguero, L., Roquet, F., Tamura, T., Ohshima,
K. I., Fukamachi, Y., Fraser, A. D., Gao, L., Chen, H., McMahon, C. R.,
Harcourt, R., and Hindell, M.: The suppression of Antarctic bottom water
formation by melting ice shelves in Prydz Bay, Nat. Commun., 7,
12577, https://doi.org/10.1038/ncomms12577, 2016. a
Wåhlin, A. K., Steiger, N., Darelius, E., Assmann, K. M., Glessmer, M. S., Ha,
H. K., Herraiz-Borreguero, L., Heuzé, C., Jenkins, A., Kim, T. W., Mazur,
A. K., Sommeria, J., and Viboud, S.: Ice front blocking of ocean heat
transport to an Antarctic ice shelf, Nature, 578, 568–571,
https://doi.org/10.1038/s41586-020-2014-5, 2020. a
Short summary
Here we present an improved model of the Antarctic continental shelf ocean and demonstrate that it is capable of reproducing present-day conditions. The improvements are fundamental and regard the inclusion of tides and ocean eddies. We conclude that the model is well suited to gain new insights into processes that are important for Antarctic ice sheet retreat and global ocean changes. Hence, the model will ultimately help to improve projections of sea level rise and climate change.
Here we present an improved model of the Antarctic continental shelf ocean and demonstrate that...
