Articles | Volume 15, issue 2
https://doi.org/10.5194/gmd-15-803-2022
© 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-15-803-2022
© 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
| 
28 Jan 2022
Model description paper |
| 28 Jan 2022
| 28 Jan 2022
WIFF1.0: a hybrid machine-learning-based parameterization of wave-induced sea ice floe fracture
Institute at Brown for Environment and Society, Brown University, Providence, RI, USA
Lettie A. Roach
Department of Atmospheric Sciences, University of Washington, Seattle, WA, USA
NASA Goddard Institute for Space Studies and Center for Climate Systems Research, Columbia University, New York, NY, USA
Related authors
Christopher Horvat, Ellen M. Buckley, and Madelyn Stewart
EGUsphere, https://doi.org/10.5194/egusphere-2024-3864, https://doi.org/10.5194/egusphere-2024-3864, 2025
Short summary
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Since the late 1970s, standard methods for observing sea ice area from satellite contrast its passive microwave emissions to that of the ocean. Since 2018, a new satellite, ICESat-2, may offer a unique and independent way to sample sea ice area at high skill and resolution, using laser altimetry. We develop a new product of sea ice area for the Arctic using ICESat-2 and constrain the biases associated with the use of altimetry instead of passive microwave emissions.
Ellen M. Buckley, Christopher Horvat, and Pittayuth Yoosiri
EGUsphere, https://doi.org/10.5194/egusphere-2024-3861, https://doi.org/10.5194/egusphere-2024-3861, 2024
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Sea ice coverage is a key indicator of changes in polar and global climate. There is a long (40+ year) record of sea ice concentration and area from passive microwave measurements. In this work we show the biases in these data based on high resolution imagery. We also suggest the use of ICESat-2, a high resolution satellite laser, that can supplement the passive microwave estimates.
Momme C. Hell and Christopher Horvat
The Cryosphere, 18, 341–361, https://doi.org/10.5194/tc-18-341-2024, https://doi.org/10.5194/tc-18-341-2024, 2024
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Sea ice is heavily impacted by waves on its margins, and we currently do not have routine observations of waves in sea ice. Here we propose two methods to separate the surface waves from the sea-ice height observations along each ICESat-2 track using machine learning. Both methods together allow us to follow changes in the wave height through the sea ice.
Yanan Wang, Byongjun Hwang, Adam William Bateson, Yevgeny Aksenov, and Christopher Horvat
The Cryosphere, 17, 3575–3591, https://doi.org/10.5194/tc-17-3575-2023, https://doi.org/10.5194/tc-17-3575-2023, 2023
Short summary
Short summary
Sea ice is composed of small, discrete pieces of ice called floes, whose size distribution plays a critical role in the interactions between the sea ice, ocean and atmosphere. This study provides an assessment of sea ice models using new high-resolution floe size distribution observations, revealing considerable differences between them. These findings point not only to the limitations in models but also to the need for more high-resolution observations to validate and calibrate models.
Jill Brouwer, Alexander D. Fraser, Damian J. Murphy, Pat Wongpan, Alberto Alberello, Alison Kohout, Christopher Horvat, Simon Wotherspoon, Robert A. Massom, Jessica Cartwright, and Guy D. Williams
The Cryosphere, 16, 2325–2353, https://doi.org/10.5194/tc-16-2325-2022, https://doi.org/10.5194/tc-16-2325-2022, 2022
Short summary
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The marginal ice zone is the region where ocean waves interact with sea ice. Although this important region influences many sea ice, ocean and biological processes, it has been difficult to accurately measure on a large scale from satellite instruments. We present new techniques for measuring wave attenuation using the NASA ICESat-2 laser altimeter. By measuring how waves attenuate within the sea ice, we show that the marginal ice zone may be far wider than previously realised.
Eric P. Chassignet, Stephen G. Yeager, Baylor Fox-Kemper, Alexandra Bozec, Frederic Castruccio, Gokhan Danabasoglu, Christopher Horvat, Who M. Kim, Nikolay Koldunov, Yiwen Li, Pengfei Lin, Hailong Liu, Dmitry V. Sein, Dmitry Sidorenko, Qiang Wang, and Xiaobiao Xu
Geosci. Model Dev., 13, 4595–4637, https://doi.org/10.5194/gmd-13-4595-2020, https://doi.org/10.5194/gmd-13-4595-2020, 2020
Short summary
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This paper presents global comparisons of fundamental global climate variables from a suite of four pairs of matched low- and high-resolution ocean and sea ice simulations to assess the robustness of climate-relevant improvements in ocean simulations associated with moving from coarse (∼1°) to eddy-resolving (∼0.1°) horizontal resolutions. Despite significant improvements, greatly enhanced horizontal resolution does not deliver unambiguous bias reduction in all regions for all models.
Christopher Horvat, Lettie A. Roach, Rachel Tilling, Cecilia M. Bitz, Baylor Fox-Kemper, Colin Guider, Kaitlin Hill, Andy Ridout, and Andrew Shepherd
The Cryosphere, 13, 2869–2885, https://doi.org/10.5194/tc-13-2869-2019, https://doi.org/10.5194/tc-13-2869-2019, 2019
Short summary
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Changes in the floe size distribution (FSD) are important for sea ice evolution but to date largely unobserved and unknown. Climate models, forecast centres, ship captains, and logistic specialists cannot currently obtain statistical information about sea ice floe size on demand. We develop a new method to observe the FSD at global scales and high temporal and spatial resolution. With refinement, this method can provide crucial information for polar ship routing and real-time forecasting.
C. Horvat and E. Tziperman
The Cryosphere, 9, 2119–2134, https://doi.org/10.5194/tc-9-2119-2015, https://doi.org/10.5194/tc-9-2119-2015, 2015
Short summary
Short summary
Sea-ice cover is composed of floes of different sizes and thicknesses, whose distribution varies in space and time, and may affect the interaction between sea ice and the ocean and atmosphere, yet is not represented in climate models. We develop and demonstrate a model for the evolution of the joint distribution of floe sizes and thicknesses, subject to melting and freezing, mechanical interactions between floes, and the fracture of floes by waves, forced by atmospheric and ocean forcing fields.
Christopher Horvat, Ellen M. Buckley, and Madelyn Stewart
EGUsphere, https://doi.org/10.5194/egusphere-2024-3864, https://doi.org/10.5194/egusphere-2024-3864, 2025
Short summary
Short summary
Since the late 1970s, standard methods for observing sea ice area from satellite contrast its passive microwave emissions to that of the ocean. Since 2018, a new satellite, ICESat-2, may offer a unique and independent way to sample sea ice area at high skill and resolution, using laser altimetry. We develop a new product of sea ice area for the Arctic using ICESat-2 and constrain the biases associated with the use of altimetry instead of passive microwave emissions.
Ellen M. Buckley, Christopher Horvat, and Pittayuth Yoosiri
EGUsphere, https://doi.org/10.5194/egusphere-2024-3861, https://doi.org/10.5194/egusphere-2024-3861, 2024
Short summary
Short summary
Sea ice coverage is a key indicator of changes in polar and global climate. There is a long (40+ year) record of sea ice concentration and area from passive microwave measurements. In this work we show the biases in these data based on high resolution imagery. We also suggest the use of ICESat-2, a high resolution satellite laser, that can supplement the passive microwave estimates.
Momme C. Hell and Christopher Horvat
The Cryosphere, 18, 341–361, https://doi.org/10.5194/tc-18-341-2024, https://doi.org/10.5194/tc-18-341-2024, 2024
Short summary
Short summary
Sea ice is heavily impacted by waves on its margins, and we currently do not have routine observations of waves in sea ice. Here we propose two methods to separate the surface waves from the sea-ice height observations along each ICESat-2 track using machine learning. Both methods together allow us to follow changes in the wave height through the sea ice.
Yanan Wang, Byongjun Hwang, Adam William Bateson, Yevgeny Aksenov, and Christopher Horvat
The Cryosphere, 17, 3575–3591, https://doi.org/10.5194/tc-17-3575-2023, https://doi.org/10.5194/tc-17-3575-2023, 2023
Short summary
Short summary
Sea ice is composed of small, discrete pieces of ice called floes, whose size distribution plays a critical role in the interactions between the sea ice, ocean and atmosphere. This study provides an assessment of sea ice models using new high-resolution floe size distribution observations, revealing considerable differences between them. These findings point not only to the limitations in models but also to the need for more high-resolution observations to validate and calibrate models.
Hugues Goosse, Sofia Allende Contador, Cecilia M. Bitz, Edward Blanchard-Wrigglesworth, Clare Eayrs, Thierry Fichefet, Kenza Himmich, Pierre-Vincent Huot, François Klein, Sylvain Marchi, François Massonnet, Bianca Mezzina, Charles Pelletier, Lettie Roach, Martin Vancoppenolle, and Nicole P. M. van Lipzig
The Cryosphere, 17, 407–425, https://doi.org/10.5194/tc-17-407-2023, https://doi.org/10.5194/tc-17-407-2023, 2023
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Using idealized sensitivity experiments with a regional atmosphere–ocean–sea ice model, we show that sea ice advance is constrained by initial conditions in March and the retreat season is influenced by the magnitude of several physical processes, in particular by the ice–albedo feedback and ice transport. Atmospheric feedbacks amplify the response of the winter ice extent to perturbations, while some negative feedbacks related to heat conduction fluxes act on the ice volume.
Jill Brouwer, Alexander D. Fraser, Damian J. Murphy, Pat Wongpan, Alberto Alberello, Alison Kohout, Christopher Horvat, Simon Wotherspoon, Robert A. Massom, Jessica Cartwright, and Guy D. Williams
The Cryosphere, 16, 2325–2353, https://doi.org/10.5194/tc-16-2325-2022, https://doi.org/10.5194/tc-16-2325-2022, 2022
Short summary
Short summary
The marginal ice zone is the region where ocean waves interact with sea ice. Although this important region influences many sea ice, ocean and biological processes, it has been difficult to accurately measure on a large scale from satellite instruments. We present new techniques for measuring wave attenuation using the NASA ICESat-2 laser altimeter. By measuring how waves attenuate within the sea ice, we show that the marginal ice zone may be far wider than previously realised.
Eric P. Chassignet, Stephen G. Yeager, Baylor Fox-Kemper, Alexandra Bozec, Frederic Castruccio, Gokhan Danabasoglu, Christopher Horvat, Who M. Kim, Nikolay Koldunov, Yiwen Li, Pengfei Lin, Hailong Liu, Dmitry V. Sein, Dmitry Sidorenko, Qiang Wang, and Xiaobiao Xu
Geosci. Model Dev., 13, 4595–4637, https://doi.org/10.5194/gmd-13-4595-2020, https://doi.org/10.5194/gmd-13-4595-2020, 2020
Short summary
Short summary
This paper presents global comparisons of fundamental global climate variables from a suite of four pairs of matched low- and high-resolution ocean and sea ice simulations to assess the robustness of climate-relevant improvements in ocean simulations associated with moving from coarse (∼1°) to eddy-resolving (∼0.1°) horizontal resolutions. Despite significant improvements, greatly enhanced horizontal resolution does not deliver unambiguous bias reduction in all regions for all models.
Christopher Horvat, Lettie A. Roach, Rachel Tilling, Cecilia M. Bitz, Baylor Fox-Kemper, Colin Guider, Kaitlin Hill, Andy Ridout, and Andrew Shepherd
The Cryosphere, 13, 2869–2885, https://doi.org/10.5194/tc-13-2869-2019, https://doi.org/10.5194/tc-13-2869-2019, 2019
Short summary
Short summary
Changes in the floe size distribution (FSD) are important for sea ice evolution but to date largely unobserved and unknown. Climate models, forecast centres, ship captains, and logistic specialists cannot currently obtain statistical information about sea ice floe size on demand. We develop a new method to observe the FSD at global scales and high temporal and spatial resolution. With refinement, this method can provide crucial information for polar ship routing and real-time forecasting.
Lettie A. Roach, Samuel M. Dean, and James A. Renwick
The Cryosphere, 12, 365–383, https://doi.org/10.5194/tc-12-365-2018, https://doi.org/10.5194/tc-12-365-2018, 2018
Short summary
Short summary
This paper evaluates Antarctic sea ice simulated by global climate models against satellite observations. We find biases in high-concentration and low-concentration sea ice that are consistent across the population of 40 models, in spite of the differences in physics between different models. Targeted model experiments show that biases in low-concentration sea ice can be significantly reduced by enhanced lateral melt, a result that may be valuable for sea ice model development.
C. Horvat and E. Tziperman
The Cryosphere, 9, 2119–2134, https://doi.org/10.5194/tc-9-2119-2015, https://doi.org/10.5194/tc-9-2119-2015, 2015
Short summary
Short summary
Sea-ice cover is composed of floes of different sizes and thicknesses, whose distribution varies in space and time, and may affect the interaction between sea ice and the ocean and atmosphere, yet is not represented in climate models. We develop and demonstrate a model for the evolution of the joint distribution of floe sizes and thicknesses, subject to melting and freezing, mechanical interactions between floes, and the fracture of floes by waves, forced by atmospheric and ocean forcing fields.
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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.
Davor Dundovic, Joseph G. Wallwork, Stephan C. Kramer, Fabien Gillet-Chaulet, Regine Hock, and Matthew D. Piggott
EGUsphere, https://doi.org/10.5194/egusphere-2024-2649, https://doi.org/10.5194/egusphere-2024-2649, 2024
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Accurate numerical studies of glaciers often require high-resolution simulations, which often prove too demanding even for modern computers. In this paper we develop a method that identifies whether different parts of a glacier require high or low resolution based on its physical features, such as its thickness and velocity. We show that by doing so we can achieve a more optimal simulation accuracy for the available computing resources compared to uniform resolution simulations.
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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
Cited articles
Aksenov, Y., Popova, E. E., Yool, A., Nurser, A. J., Williams, T. D., Bertino,
L., and Bergh, J.: On the future navigability of Arctic sea routes:
High-resolution projections of the Arctic Ocean and sea ice, Mar. Policy,
75, 300–317, https://doi.org/10.1016/j.marpol.2015.12.027, 2017. a
Asplin, M. G., Galley, R., Barber, D. G., and Prinsenberg, S.: Fracture of
summer perennial sea ice by ocean swell as a result of Arctic storms,
J. Geophys. Res.-Oceans, 117, 1–12,
https://doi.org/10.1029/2011JC007221, 2012. a
Asplin, M. G., Scharien, R., Else, B., Howell, S., Barber, D. G., Papakyriakou,
T., and Prinsenberg, S.: Implications of fractured Arctic perennial ice
cover on thermodynamic and dynamic sea ice processes, J. Geophys.
Res.-Oceans, 119, 2327–2343, https://doi.org/10.1002/2013JC009557, 2014. a
Bateson, A. W., Feltham, D. L., Schröder, D., Hosekova, L., Ridley, J. K., and Aksenov, Y.: Impact of sea ice floe size distribution on seasonal fragmentation and melt of Arctic sea ice, The Cryosphere, 14, 403–428, https://doi.org/10.5194/tc-14-403-2020, 2020. a, b
Boutin, G., Ardhuin, F., Dumont, D., Sévigny, C., Girard-Ardhuin, F., and
Accensi, M.: Floe size effect on wave-ice interactions: possible effects,
implementation in wave model, and evaluation, J. Geophys.
Res.-Oceans, 123, 4779–4805, https://doi.org/10.1029/2017JC013622, 2018. a
Brenowitz, N. D., Beucler, T., Pritchard, M., and Bretherton, C. S.:
Interpreting and stabilizing machine-learning parametrizations of
convection, J. Atmos. Sci., 77, 4357–4375,
https://doi.org/10.1175/JAS-D-20-0082.1, 2020. a
Campin, J.-M., Hill, C., Jones, H., and Marshall, J.: Super-parameterization
in ocean modeling: Application to deep convection, Ocean Model., 36,
90–101, https://doi.org/10.1016/j.ocemod.2010.10.003, 2011. a
Casas‐Prat, M. and Wang, X. L.: Projections of Extreme Ocean Waves in the
Arctic and Potential Implications for Coastal Inundation and Erosion,
J. Geophys. Res.-Oceans, 125, e2019JC015745, https://doi.org/10.1029/2019JC015745,
2020. a
Feltham, D. L., Untersteiner, N., Wettlaufer, J. S., and Worster, M. G.: Sea
ice is a mushy layer, Geophys. Res. Lett., 33, L14501,
https://doi.org/10.1029/2006GL026290, 2006. a
Golden, K. M., Bennetts, L. G., Cherkaev, E., Eisenman, I., Feltham, D.,
Horvat, C., Hunke, E., Jones, C., Perovich, D. K., Ponte-Castaneda, P.,
Strong, C., Sulsky, D., and Wells, A. J.: Modeling Sea Ice, Not.
Am. Math. Soc., 67, 1535–1555, https://doi.org/10.1090/noti2171, 2020. a
Grabowski, W. W.: An Improved Framework for Superparameterization, J. Atmos. Sci., 61, 1940–1952,
https://doi.org/10.1175/1520-0469(2004)061<1940:AIFFS>2.0.CO;2, 2004. a
Herman, A.: Numerical modeling of force and contact networks in fragmented sea
ice, Ann. Glaciol., 54, 114–120, https://doi.org/10.3189/2013AoG62A055, 2013. a
Herman, A., Wenta, M., and Cheng, S.: Sizes and shapes of sea ice floes broken
by waves – a case study from the East Antarctic coast, Front. Earth
Sci., 9, 390, https://doi.org/10.3389/feart.2021.655977, 2021. a
Hibler, W. D.: A Dynamic Thermodynamic Sea Ice Model, J. Phys. Oceangr., 9,
815–846, https://doi.org/10.1175/1520-0485(1979)009<0815:ADTSIM>2.0.CO;2, 1979. a
Horvat, C.: chhorvat/WIFF-Model: Release 1.0 – Including CICE/ICEPACK code (v1.0b), Zenodo [code], https://doi.org/10.5281/zenodo.5793692, 2021. a
Horvat, C. and Roach, L.: Training Data for NN-WIFF (Horvat and Roach, 2021), Zenodo [data set], https://doi.org/10.5281/zenodo.5108636, 2021. a
Horvat, C. and Tziperman, E.: The evolution of scaling laws in the sea ice
floe size distribution, J. Geophys. Res.-Oceans, 122,
7630–7650, https://doi.org/10.1002/2016JC012573, 2017. a, b, c
Horvat, C., Tziperman, E., and Campin, J.-M.: Interaction of sea ice floe
size, ocean eddies, and sea ice melting, Geophys. Res. Lett., 43,
8083–8090, https://doi.org/10.1002/2016GL069742, 2016. a, b
Horvat, C., Roach, L. A., Tilling, R., Bitz, C. M., Fox-Kemper, B., Guider, C., Hill, K., Ridout, A., and Shepherd, A.: Estimating the sea ice floe size distribution using satellite altimetry: theory, climatology, and model comparison, The Cryosphere, 13, 2869–2885, https://doi.org/10.5194/tc-13-2869-2019, 2019. a
Horvat, C., Blanchard‐Wrigglesworth, E., and Petty, A.: Observing waves in
sea ice with ICESat‐2, Geophys. Res. Lett., 47, e2020GL087629,
https://doi.org/10.1029/2020GL087629, 2020. a, b
Hunke, E., Allard, R., Bailey, D. A., Blain, P., Bouchat, A., Craig, T.,
Dupont, F., DuVivier, A., Grumbine, R., Hebert, D. A., Holland, M., Jeffery,
N., Lemieux, J.-F., Rasmussen, T., Ribergaard, M., Roberts, A., Turner, M.,
and Winton, M.: CICE-Consortium/CICE: CICE Version 6.0.2, Zenodo [code],
https://doi.org/10.5281/zenodo.3516944, 2019. a, b, c
JRA-55 and Japan Meteorological Agency: JRA-55: Japanese 55-year Reanalysis,
Daily 3-Hourly and 6-Hourly Data, Research Data Archive at the National
Center for Atmospheric Research, Computational and Information Systems
Laboratory [data set], https://doi.org/10.5065/D6HH6H41, 2013. a
Kohout, A. L., Meylan, M. H., and Plew, D. R.: Wave attenuation in a marginal
ice zone due to the bottom roughness of ice floes, Ann. Glaciol., 52,
118–122, https://doi.org/10.3189/172756411795931525, 2011. a
Langhorne, P. J., Squire, V. A., Fox, C., and Haskell, T. G.: Break-up of sea
ice by ocean waves, Ann. Glaciol., 27, 438–442,
https://doi.org/10.3189/S0260305500017869, 1998. a
Meylan, M. H., Horvat, C., Bitz, C. M., and Bennetts, L. G.: A floe size
dependent scattering model in two- and three-dimensions for wave attenuation
by ice floes, Ocean Model., 161, 101779,
https://doi.org/10.1016/j.ocemod.2021.101779, 2021. a, b
Michel, W. H.: Sea Spectra Simplified, Mar. Technol., 5, 17–30, 1968. a
Pal, A., Mahajan, S., and Norman, M. R.: Using Deep Neural Networks as
Cost‐Effective Surrogate Models for Super‐Parameterized E3SM Radiative
Transfer, Geophys. Res. Lett., 46, 6069–6079,
https://doi.org/10.1029/2018GL081646, 2019. a
Perrie, W. and Hu, Y.: Air–Ice–Ocean Momentum Exchange. Part 1: Energy
Transfer between Waves and Ice Floes, J. Phys. Oceanogr., 26,
1705–1720, https://doi.org/10.1175/1520-0485(1996)026<1705:AMEPTB>2.0.CO;2, 1996. a
Randall, D., Khairoutdinov, M., Arakawa, A., and Grabowski, W.: Breaking the
Cloud Parameterization Deadlock, B. Am. Meteorol.
Soc., 84, 1547–1564, https://doi.org/10.1175/BAMS-84-11-1547, 2003. a
Rasp, S., Pritchard, M. S., and Gentine, P.: Deep learning to represent
subgrid processes in climate models, P. Natl. Acad. Sci. USA, 115, 9684–9689,
https://doi.org/10.1073/pnas.1810286115, 2018. a
Roach, L.: Model output: CICE6 with WIFF, Zenodo [data set], https://doi.org/10.5281/zenodo.5106703, 2021. a
Roach, L. A., Dean, S. M., and Renwick, J. A.: Consistent biases in Antarctic sea ice concentration simulated by climate models, The Cryosphere, 12, 365–383, https://doi.org/10.5194/tc-12-365-2018, 2018a. a
Roach, L. A., Horvat, C., Dean, S. M., and Bitz, C. M.: An emergent sea ice
floe size distribution in a global coupled ocean–sea ice model, J.
Geophys. Res.-Oceans, 123, 4322–4337, https://doi.org/10.1029/2017JC013692,
2018b. a, b, c, d
Roach, L. A., Doerr, J., Holmes, C. R., Massonnet, F., Blockley, E. W., Notz,
D., Rackow, T., Raphael, M. N., O'Farrell, S., Bailey, D. A., and Bitz,
C. M.: Antarctic Sea Ice Area in CMIP6, Geophys. Res. Lett., 47, e2019GL086729,
https://doi.org/10.1029/2019gl086729, 2020. a
Roberts, A. F., Hunke, E. C., Allard, R., Bailey, D. A., Craig, A. P., Lemieux,
J.-F., and Turner, M. D.: Quality control for community-based sea-ice model
development, Philos. T. R. Soc. Lond. A, 376, 20170344,
https://doi.org/10.1098/rsta.2017.0344, 2018. a
SIMIP Community: Arctic Sea Ice in CMIP6, Geophys. Res. Lett., 47,
1–26, https://doi.org/10.1029/2019GL086749, 2020. a
Squire, V. A., Dugan, J. P., Wadhams, P., Rottier, P. J., and Liu, a. K.: Of
Ocean Waves and Sea Ice, Annu. Rev. Fluid Mech., 27, 115–168,
https://doi.org/10.1146/annurev.fl.27.010195.000555, 1995. a
Steele, M.: Sea ice melting and floe geometry in a simple ice-ocean model,
J. Geophys. Res.-Oceans, 97, 17729–17738,
https://doi.org/10.1029/92JC01755, 1992. a
Stern, H. L., Schweiger, A. J., Stark, M., Zhang, J., Steele, M., and Hwang,
B.: Seasonal evolution of the sea-ice floe size distribution in the Beaufort
and Chukchi seas, Elementa, 6, 48,
https://doi.org/10.1525/elementa.304, 2018a.
a
Stern, H. L., Schweiger, A. J., Zhang, J., and Steele, M.: On reconciling
disparate studies of the sea-ice floe size distribution, Elementa, 6,
49, https://doi.org/10.1525/elementa.304, 2018b. a
Voermans, J. J., Babanin, A. V., Thomson, J., Smith, M. M., and Shen, H. H.:
Wave Attenuation by Sea Ice Turbulence, Geophys. Res. Lett., 46,
6796–6803, https://doi.org/10.1029/2019GL082945, 2019. a
Voermans, J. J., Rabault, J., Filchuk, K., Ryzhov, I., Heil, P., Marchenko, A.,
Collins III, C. O., Dabboor, M., Sutherland, G., and Babanin, A. V.:
Experimental evidence for a universal threshold characterizing wave-induced
sea ice break-up, The Cryosphere, 14, 4265–4278,
https://doi.org/10.5194/tc-14-4265-2020, 2020. a, b, c
Wadhams, P., Squire, V. a., Goodman, D. J., Cowan, A. M., and Moore, S. C.:
The attenuation rates of ocean waves in the marginal ice zone, J.
Geophys. Res., 93, 6799, https://doi.org/10.1029/JC093iC06p06799, 1988. a
Williams, T. D., Bennetts, L. G., Squire, V. A., Dumont, D., and Bertino, L.:
Wave-ice interactions in the marginal ice zone. Part 2: Numerical
implementation and sensitivity studies along 1D transects of the ocean
surface, Ocean Modelling, 71, 92–101, https://doi.org/10.1016/j.ocemod.2013.05.011,
2013. a
Zhang, J., Schweiger, A., Steele, M., and Stern, H.: Sea ice floe size
distribution in the marginal ice zone: Theory and numerical experiments, J.
Geophys. Res.-Oceans, 120, 3484–3498, https://doi.org/10.1002/2015JC010770, 2015. a, b
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.
Sea ice is a composite of individual pieces, called floes, ranging in horizontal size from...
