Articles | Volume 15, issue 9
https://doi.org/10.5194/gmd-15-3721-2022
© 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-15-3721-2022
© 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
| 
10 May 2022
Model description paper |
| 10 May 2022
| 10 May 2022
MPAS-Seaice (v1.0.0): sea-ice dynamics on unstructured Voronoi meshes
Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA
William H. Lipscomb
Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, CO, USA
Elizabeth C. Hunke
Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA
Douglas W. Jacobsen
Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA
Nicole Jeffery
Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, USA
Darren Engwirda
Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA
Todd D. Ringler
Science Technology Policy Institute, Washington, DC 20006, USA
Jonathan D. Wolfe
Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA
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Preprint withdrawn
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Most modern ocean circulation models only consider the hydrostatic pressure, but for coastal phenomena nonhydrostatic effects become important, creating the need to include the nonhydrostatic pressure. In this work, we present a nonhydrostatic formulation for MPAS-Ocean (MPAS-O NH) and show its correctness on idealized benchmark test cases. MPAS-O NH is the first global nonhydrostatic model at variable resolution and is the first nonhydrostatic ocean model to be fully coupled in a climate model.
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The geothermal heat flux determines how much heat enters from beneath the ice sheet, and thus impacts the temperature and the flow of the ice sheet. In this study we investigate how much geothermal heat flux impacts the initialization of the Greenland ice sheet. We use the Community Ice Sheet Model with two different initialization methods. We find a non-trivial influence of the choice of heat flow boundary conditions on the ice sheet initializations for further designs of ice sheet modeling.
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The Cryosphere, 17, 5197–5217, https://doi.org/10.5194/tc-17-5197-2023, https://doi.org/10.5194/tc-17-5197-2023, 2023
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René R. Wijngaard, Adam R. Herrington, William H. Lipscomb, Gunter R. Leguy, and Soon-Il An
The Cryosphere, 17, 3803–3828, https://doi.org/10.5194/tc-17-3803-2023, https://doi.org/10.5194/tc-17-3803-2023, 2023
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We evaluate the ability of the Community Earth System Model (CESM2) to simulate cryospheric–hydrological variables, such as glacier surface mass balance (SMB), over High Mountain Asia (HMA) by using a global grid (~111 km) with regional refinement (~7 km) over HMA. Evaluations of two different simulations show that climatological biases are reduced, and glacier SMB is improved (but still too negative) by modifying the snow and glacier model and using an updated glacier cover dataset.
Qi Tang, Jean-Christophe Golaz, Luke P. Van Roekel, Mark A. Taylor, Wuyin Lin, Benjamin R. Hillman, Paul A. Ullrich, Andrew M. Bradley, Oksana Guba, Jonathan D. Wolfe, Tian Zhou, Kai Zhang, Xue Zheng, Yunyan Zhang, Meng Zhang, Mingxuan Wu, Hailong Wang, Cheng Tao, Balwinder Singh, Alan M. Rhoades, Yi Qin, Hong-Yi Li, Yan Feng, Yuying Zhang, Chengzhu Zhang, Charles S. Zender, Shaocheng Xie, Erika L. Roesler, Andrew F. Roberts, Azamat Mametjanov, Mathew E. Maltrud, Noel D. Keen, Robert L. Jacob, Christiane Jablonowski, Owen K. Hughes, Ryan M. Forsyth, Alan V. Di Vittorio, Peter M. Caldwell, Gautam Bisht, Renata B. McCoy, L. Ruby Leung, and David C. Bader
Geosci. Model Dev., 16, 3953–3995, https://doi.org/10.5194/gmd-16-3953-2023, https://doi.org/10.5194/gmd-16-3953-2023, 2023
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The Cryosphere, 17, 2681–2700, https://doi.org/10.5194/tc-17-2681-2023, https://doi.org/10.5194/tc-17-2681-2023, 2023
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Constantijn J. Berends, Roderik S. W. van de Wal, Tim van den Akker, and William H. Lipscomb
The Cryosphere, 17, 1585–1600, https://doi.org/10.5194/tc-17-1585-2023, https://doi.org/10.5194/tc-17-1585-2023, 2023
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The rate at which the Antarctic ice sheet will melt because of anthropogenic climate change is uncertain. Part of this uncertainty stems from processes occurring beneath the ice, such as the way the ice slides over the underlying bedrock.
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Mira Berdahl, Gunter Leguy, William H. Lipscomb, Nathan M. Urban, and Matthew J. Hoffman
The Cryosphere, 17, 1513–1543, https://doi.org/10.5194/tc-17-1513-2023, https://doi.org/10.5194/tc-17-1513-2023, 2023
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Contributions to future sea level from the Antarctic Ice Sheet remain poorly constrained. One reason is that ice sheet model initialization methods can have significant impacts on how the ice sheet responds to future forcings. We investigate the impacts of two key parameters used during model initialization. We find that these parameter choices alone can impact multi-century sea level rise by up to 2 m, emphasizing the need to carefully consider these choices for sea level rise predictions.
Nairita Pal, Kristin N. Barton, Mark R. Petersen, Steven R. Brus, Darren Engwirda, Brian K. Arbic, Andrew F. Roberts, Joannes J. Westerink, and Damrongsak Wirasaet
Geosci. Model Dev., 16, 1297–1314, https://doi.org/10.5194/gmd-16-1297-2023, https://doi.org/10.5194/gmd-16-1297-2023, 2023
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Understanding tides is essential to accurately predict ocean currents. Over the next several decades coastal processes such as flooding and erosion will be severely impacted due to climate change. Tides affect currents along the coastal regions the most. In this paper we show the results of implementing tides in a global ocean model known as MPAS–Ocean. We also show how Antarctic ice shelf cavities affect global tides. Our work points towards future research with tide–ice interactions.
Dongyu Feng, Zeli Tan, Darren Engwirda, Chang Liao, Donghui Xu, Gautam Bisht, Tian Zhou, Hong-Yi Li, and L. Ruby Leung
Hydrol. Earth Syst. Sci., 26, 5473–5491, https://doi.org/10.5194/hess-26-5473-2022, https://doi.org/10.5194/hess-26-5473-2022, 2022
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Sea level rise, storm surge and river discharge can cause coastal backwater effects in downstream sections of rivers, creating critical flood risks. This study simulates the backwater effects using a large-scale river model on a coastal-refined computational mesh. By decomposing the backwater drivers, we revealed their relative importance and long-term variations. Our analysis highlights the increasing strength of backwater effects due to sea level rise and more frequent storm surge.
Milena Veneziani, Wieslaw Maslowski, Younjoo J. Lee, Gennaro D'Angelo, Robert Osinski, Mark R. Petersen, Wilbert Weijer, Anthony P. Craig, John D. Wolfe, Darin Comeau, and Adrian K. Turner
Geosci. Model Dev., 15, 3133–3160, https://doi.org/10.5194/gmd-15-3133-2022, https://doi.org/10.5194/gmd-15-3133-2022, 2022
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We present an Earth system model (ESM) simulation, E3SM-Arctic-OSI, with a refined grid to better resolve the Arctic ocean and sea-ice system and low spatial resolution elsewhere. The configuration satisfactorily represents many aspects of the Arctic system and its interactions with the sub-Arctic, while keeping computational costs at a fraction of those necessary for global high-resolution ESMs. E3SM-Arctic can thus be an efficient tool to study Arctic processes on climate-relevant timescales.
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
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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.
Alexander Robinson, Daniel Goldberg, and William H. Lipscomb
The Cryosphere, 16, 689–709, https://doi.org/10.5194/tc-16-689-2022, https://doi.org/10.5194/tc-16-689-2022, 2022
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Here we investigate the numerical stability of several commonly used methods in order to determine which of them are capable of resolving the complex physics of the ice flow and are also computationally efficient. We find that the so-called DIVA solver outperforms the others. Its representation of the physics is consistent with more complex methods, while it remains computationally efficient at high resolution.
David A. Griffin, Mike Herzfeld, Mark Hemer, and Darren Engwirda
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In support of the developing ocean renewable energy sector, and indeed all mariners, we have developed a new tidal model for Australian waters and thoroughly evaluated it using a new compilation of tide gauge and current meter data. We show that while there is certainly room for improvement, the model provides useful predictions of tidal currents for about 80 % (by area) of Australian shelf waters. So we intend to commence publishing tidal current predictions for those regions soon.
Gunter R. Leguy, William H. Lipscomb, and Xylar S. Asay-Davis
The Cryosphere, 15, 3229–3253, https://doi.org/10.5194/tc-15-3229-2021, https://doi.org/10.5194/tc-15-3229-2021, 2021
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We present numerical features of the Community Ice Sheet Model in representing ocean termini glaciers. Using idealized test cases, we show that applying melt in a partly grounded cell is beneficial, in contrast to recent studies. We confirm that parameterizing partly grounded cells yields accurate ice sheet representation at a grid resolution of ~2 km (arguably 4 km), allowing ice sheet simulations at a continental scale. The choice of basal friction law also influences the ice flow.
Mira Berdahl, Gunter Leguy, William H. Lipscomb, and Nathan M. Urban
The Cryosphere, 15, 2683–2699, https://doi.org/10.5194/tc-15-2683-2021, https://doi.org/10.5194/tc-15-2683-2021, 2021
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Antarctic ice shelves are vulnerable to warming ocean temperatures and have already begun thinning in response to increased basal melt rates. Sea level is expected to rise due to Antarctic contributions, but uncertainties in rise amount and timing remain largely unquantified. To facilitate uncertainty quantification, we use a high-resolution ice sheet model to build, test, and validate an ice sheet emulator and generate probabilistic sea level rise estimates for 100 and 200 years in the future.
William H. Lipscomb, Gunter R. Leguy, Nicolas C. Jourdain, Xylar Asay-Davis, Hélène Seroussi, and Sophie Nowicki
The Cryosphere, 15, 633–661, https://doi.org/10.5194/tc-15-633-2021, https://doi.org/10.5194/tc-15-633-2021, 2021
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This paper describes Antarctic climate change experiments in which the Community Ice Sheet Model is forced with ocean warming predicted by global climate models. Generally, ice loss begins slowly, accelerates by 2100, and then continues unabated, with widespread retreat of the West Antarctic Ice Sheet. The mass loss by 2500 varies from about 150 to 1300 mm of equivalent sea level rise, based on the predicted ocean warming and assumptions about how this warming drives melting beneath ice shelves.
Heiko Goelzer, Sophie Nowicki, Anthony Payne, Eric Larour, Helene Seroussi, William H. Lipscomb, Jonathan Gregory, Ayako Abe-Ouchi, Andrew Shepherd, Erika Simon, Cécile Agosta, Patrick Alexander, Andy Aschwanden, Alice Barthel, Reinhard Calov, Christopher Chambers, Youngmin Choi, Joshua Cuzzone, Christophe Dumas, Tamsin Edwards, Denis Felikson, Xavier Fettweis, Nicholas R. Golledge, Ralf Greve, Angelika Humbert, Philippe Huybrechts, Sebastien Le clec'h, Victoria Lee, Gunter Leguy, Chris Little, Daniel P. Lowry, Mathieu Morlighem, Isabel Nias, Aurelien Quiquet, Martin Rückamp, Nicole-Jeanne Schlegel, Donald A. Slater, Robin S. Smith, Fiamma Straneo, Lev Tarasov, Roderik van de Wal, and Michiel van den Broeke
The Cryosphere, 14, 3071–3096, https://doi.org/10.5194/tc-14-3071-2020, https://doi.org/10.5194/tc-14-3071-2020, 2020
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In this paper we use a large ensemble of Greenland ice sheet models forced by six different global climate models to project ice sheet changes and sea-level rise contributions over the 21st century.
The results for two different greenhouse gas concentration scenarios indicate that the Greenland ice sheet will continue to lose mass until 2100, with contributions to sea-level rise of 90 ± 50 mm and 32 ± 17 mm for the high (RCP8.5) and low (RCP2.6) scenario, respectively.
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
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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.
Cited articles
Arakawa, A. and Lamb, V. R.: Computational Design of the Basic Dynamical Processes of the UCLA General Circulation Model, Methods in Computational Physics: Advances in Research and Applications, 17, 173–265, https://doi.org/10.1016/B978-0-12-460817-7.50009-4, 1977. a, b
Bailey, T. S., Adams, M. L., Yang, B., and Zika, M. R.: A piecewise linear
finite element discretization of the diffusion equation for arbitrary
polyhedral grids, J. Comput. Phys., 227, 3738–3757,
https://doi.org/10.1016/j.jcp.2007.11.026, 2008. a
Batchelor, G. K.: An introduction to fluid dynamics, 1st edn., Cambridge University
Press, ISBN 978-0-521-66396-0, https://doi.org/10.1017/CBO9780511800955, 1967. a, b, c
Bern, M. and Plassmann, P.: Mesh Generation, in: Handbook of Computational
Geometry, 1st edn., edited by: Sack, J. R. and Urrutia, J., chap. 10, pp. 291–332,
North-Holland, Amsterdam, ISBN 978-0-444-82537-7, https://doi.org/10.1016/B978-0-444-82537-7.X5000-1, 2000. a
Bitz, C. M. and Lipscomb, W. H.: An energy-conserving thermodynamic model of
sea ice, J. Geophys. Res.-Oceans, 104, 15669–15677,
https://doi.org/10.1029/1999JC900100, 1999. a
Briegleb, B. P. and Light, B.: A Delta-Eddington multiple scattering
parameterization for solar radiation in the sea ice component of the
Community Climate System Model, Tech. Rep. NCAR/TN-472+STR, National Center
for Atmospheric Research, Boulder, Colorado USA, https://doi.org/10.5065/D6B27S71, 2007. a
Burrows, S. M., Maltrud, M., Yang, X., Zhu, Q., Jeffery, N., Shi, X., Ricciuto,
D., Wang, S., Bisht, G., Tang, J., Wolfe, J., Harrop, B. E., Singh, B.,
Brent, L., Baldwin, S., Zhou, T., Cameron-Smith, P., Keen, N., Collier, N.,
Xu, M., Hunke, E. C., Elliott, S. M., Turner, A. K., Li, H., Wang, H., Golaz,
J.-C., Bond-Lamberty, B., Hoffman, F. M., Riley, W. J., Thornton, P. E.,
Calvin, K., and Leung, L. R.: The DOE E3SM v1.1 Biogeochemistry
Configuration: Description and Simulated Ecosystem-Climate Responses to
Historical Changes in Forcing, J. Adv. Model. Earth Sy.,
12, e2019MS001766, https://doi.org/10.1029/2019MS001766, 2020. a
Cavalieri, D. J. and Parkinson, C. L.: Arctic sea ice variability and trends, 1979–2010, The Cryosphere, 6, 881–889, https://doi.org/10.5194/tc-6-881-2012, 2012. a
Cavalieri, D. J., Parkinson, C. L., Gloersen, P., and Zwally, H. J.: Sea ice
concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS passive microwave
data, version 1, Tech. Rep., NASA National Snow and Ice Data Center
Distributed Active Archive Center, Boulder, Colorado USA,
https://doi.org/10.5067/8GQ8LZQVL0VL, 1996. a
Chen, C., Gao, G., Qi, J., Proshutinsky, A., Beardsley, R. C., Kowalik, Z.,
Lin, H., and Cowles, G.: A new high-resolution unstructured grid finite
volume Arctic Ocean model (AO-FVCOM): An application for tidal studies,
J. Geophys. Res.-Oceans, 114, C08017, https://doi.org/10.1029/2008JC004941,
2009. a
Collins, W. D., Bitz, C. M., Blackmon, M. L., Bonan, G. B., Bretherton, C. S., Carton, J. A., Chang, P., Doney, S. C., Hack, J. J., Henderson, T. B., Kiehl, J. T., Large, W. G., McKenna, D. S., Santer, B. D., and Smith, R. D.:
The Community Climate System Model Version 3 (CCSM3), J. Climate,
19, 2122–2143, https://doi.org/10.1175/JCLI3761.1, 2006. a
Danabasoglu, G., Lamarque, J.-F., Bacmeister, J., Bailey, D. A., DuVivier,
A. K., Edwards, J., Emmons, L. K., Fasullo, J., Garcia, R., Gettelman, A.,
Hannay, C., Holland, M. M., Large, W. G., Lauritzen, P. H., Lawrence, D. M.,
Lenaerts, J. T. M., Lindsay, K., Lipscomb, W. H., Mills, M. J., Neale, R.,
Oleson, K. W., Otto-Bliesner, B., Phillips, A. S., Sacks, W., Tilmes, S., van
Kampenhout, L., Vertenstein, M., Bertini, A., Dennis, J., Deser, C., Fischer,
C., Fox-Kemper, B., Kay, J. E., Kinnison, D., Kushner, P. J., Larson, V. E.,
Long, M. C., Mickelson, S., Moore, J. K., Nienhouse, E., Polvani, L., Rasch,
P. J., and Strand, W. G.: The Community Earth System Model Version 2 (CESM2),
J. Adv. Model. Earth Sy., 12, e2019MS001916,
https://doi.org/10.1029/2019MS001916, 2020. a
Danilov, S., Wang, Q., Timmermann, R., Iakovlev, N., Sidorenko, D., Kimmritz, M., Jung, T., and Schröter, J.: Finite-Element Sea Ice Model (FESIM), version 2, Geosci. Model Dev., 8, 1747–1761, https://doi.org/10.5194/gmd-8-1747-2015, 2015. a
Dasgupta, G.: Interpolants within convex polygons: Wachpress' shape
functions, J. Aerospac. Eng., 16, 1–8,
https://doi.org/10.1061/(ASCE)0893-1321(2003)16:1(1), 2003. a
Dukowicz, J. K. and Baumgardner, J. R.: Incremental remapping as a
transport/advection algorithm, J. Comput. Phys., 160,
318–335, https://doi.org/10.1006/jcph.2000.6465, 2000. a, b
Dunavant, D. A.: High degree efficient symmetrical Gaussian quadrature rules
for the triangle, Int. J. Numer. Meth.
Eng., 21, 1129–1148, https://doi.org/10.1002/nme.1620210612, 1985. a, b, c
E3SM Project, DOE: Energy Exascale Earth System Model v2.0, DOE Code [code], https://doi.org/10.11578/E3SM/dc.20210927.1, 2021. a
Engwirda, D.: JIGSAW-GEO (1.0): locally orthogonal staggered unstructured grid generation for general circulation modelling on the sphere, Geosci. Model Dev., 10, 2117–2140, https://doi.org/10.5194/gmd-10-2117-2017, 2017. a
Flocco, D., Feltham, D. L., and Turner, A. K.: Incorporation of a physically
based melt pond scheme into the sea ice component of a climate model,
J. Geophys. Res.-Oceans, 115, C08012, https://doi.org/10.1029/2009JC005568,
2010. a
Gao, G., Chen, C., Qi, J., and Beardsley, R. C.: An unstructured-grid,
finite-volume sea ice model: Development, validation, and application,
J. Geophys. Res.-Oceans, 116, C00D04, https://doi.org/10.1029/2010JC006688,
2011. a
Golaz, J.-C., Caldwell, P. M., Van Roekel, L. P., Petersen, M. R., Tang, Q.,
Wolfe, J. D., Abeshu, G., Anantharaj, V., Asay-Davis, X. S., Bader, D. C.,
Baldwin, S. A., Bisht, G., Bogenschutz, P. A., Branstetter, M., Brunke,
M. A., Brus, S. R., Burrows, S. M., Cameron-Smith, P. J., Donahue, A. S.,
Deakin, M., Easter, R. C., Evans, K. J., Feng, Y., Flanner, M., Foucar,
J. G., Fyke, J. G., Griffin, B. M., Hannay, C., Harrop, B. E., Hoffman,
M. J., Hunke, E. C., Jacob, R. L., Jacobsen, D. W., Jeffery, N., Jones,
P. W., Keen, N. D., Klein, S. A., Larson, V. E., Leung, L. R., Li, H.-Y.,
Lin, W., Lipscomb, W. H., Ma, P.-L., Mahajan, S., Maltrud, M. E., Mametjanov,
A., McClean, J. L., McCoy, R. B., Neale, R. B., Price, S. F., Qian, Y.,
Rasch, P. J., Reeves Eyre, J. E. J., Riley, W. J., Ringler, T. D., Roberts,
A. F., Roesler, E. L., Salinger, A. G., Shaheen, Z., Shi, X., Singh, B.,
Tang, J., Taylor, M. A., Thornton, P. E., Turner, A. K., Veneziani, M., Wan,
H., Wang, H., Wang, S., Williams, D. N., Wolfram, P. J., Worley, P. H., Xie,
S., Yang, Y., Yoon, J.-H., Zelinka, M. D., Zender, C. S., Zeng, X., Zhang,
C., Zhang, K., Zhang, Y., Zheng, X., Zhou, T., and Zhu, Q.: The DOE E3SM
Coupled Model Version 1: Overview and Evaluation at Standard Resolution,
J. Adv. Model. Earth Sy., 11, 2089–2129,
https://doi.org/10.1029/2018MS001603, 2019. a, b
Griffies, S. M., Biastoch, A., Böning, C., Bryan, F., Danabasoglu, G.,
Chassignet, E. P., England, M. H., Gerdes, R., Haak, H., Hallberg, R. W.,
Hazeleger, W., Jungclaus, J., Large, W. G., Madec, G., Pirani, A., Samuels,
B. L., Scheinert, M., Gupta, A. S., Severijns, C. A., Simmons, H. L.,
Treguier, A. M., Winton, M., Yeager, S., and Yin, J.: Coordinated Ocean-ice
Reference Experiments (COREs), Ocean Model., 26, 1–46,
https://doi.org/10.1016/j.ocemod.2008.08.007, 2009. a, b
Hibler, W. D.: A dynamic thermodynamic sea ice model, J. Phys.
Oceanogr., 9, 815–846,
https://doi.org/10.1175/1520-0485(1979)009<0815:ADTSIM>2.0.CO;2, 1979. a, b
Hoffman, M. J., Perego, M., Price, S. F., Lipscomb, W. H., Zhang, T., Jacobsen, D., Tezaur, I., Salinger, A. G., Tuminaro, R., and Bertagna, L.: MPAS-Albany Land Ice (MALI): a variable-resolution ice sheet model for Earth system modeling using Voronoi grids, Geosci. Model Dev., 11, 3747–3780, https://doi.org/10.5194/gmd-11-3747-2018, 2018. a
Holland, M. M., Bailey, D. A., Briegleb, B. P., Light, B., and Hunke, E.:
Improved sea ice shortwave radiation physics in CCSM4: The impact of melt
ponds and aerosols on arctic sea ice, J. Climate, 25, 1413–1430,
https://doi.org/10.1175/JCLI-D-11-00078.1, 2012. a, b
Hunke, E., Allard, R., Bailey, D., Blain, P., Craig, T., Damsgaard, A., Dupont,
F., DuVivier, A., Grumbine, R., Holland, M., Jeffery, N., Lemieux, J.-F.,
Roberts, A., Turner, M., and Winton, M.: CICE Consortium/Icepack version
1.1.0 (Version Icepack1.1.0), Zenodo [data set], https://doi.org/10.5281/zenodo.1891650, 2018. a
Hunke, E. C.: Viscous-plastic sea ice dynamics with the EVP model:
Linearization issues, J. Comput. Phys., 170, 18–38,
https://doi.org/10.1006/jcph.2001.6710, 2001. a, b, c
Hunke, E. C. and Dukowicz, J. K.: An elastic-viscous-plastic model for sea ice
dynamics, J. Phys. Oceanogr., 27, 1849–1867,
https://doi.org/10.1175/1520-0485(1997)027<1849:AEVPMF>2.0.CO;2, 1997. a, b, c, d
Hunke, E. C. and Dukowicz, J. K.: The elastic-viscous-plastic sea ice dynamics
model in general orthogonal curvilinear coordinates on a sphere –
Incorporation of metric terms, Mon. Weather Rev., 130, 1848–1865,
https://doi.org/10.1175/1520-0493(2002)130<1848:TEVPSI>2.0.CO;2, 2002. a, b, c, d, e, f, g
Hunke, E. C. and Holland, M. M.: Global atmospheric forcing data for Arctic
ice-ocean modeling, J. Geophys. Res.-Oceans, 112, C04S14,
https://doi.org/10.1029/2006JC003640, 2007. a
Hunke, E. C., Hebert, D. A., and Lecomte, O.: Level-ice melt ponds in the Los
Alamos sea ice model, CICE, Ocean Model., 71, 26–42,
https://doi.org/10.1016/j.ocemod.2012.11.008, 2013. a, b, c
Hunke, E. C., Lipscomb, W. H., Turner, A. K., Jeffery, N., and Elliott, S.:
CICE: the Los Alamos sea ice model documentation and software user's manual
version 5.1, Tech. Rep., Los Alamos National Laboratory, LA-CC-06-012, https://github.com/CICE-Consortium/CICE-svn-trunk/blob/main/cicedoc/cicedoc.pdf (last access: 4 April 2022), 2015. a, b, c, d, e, f
Hutchings, J. K., Jasak, H., and Laxon, S. W.: A strength implicit correction
scheme for the viscous-plastic sea ice model, Ocean Model., 7, 111–133,
https://doi.org/10.1016/S1463-5003(03)00040-4, 2004. a
Hutter, N. and Losch, M.: Feature-based comparison of sea ice deformation in lead-permitting sea ice simulations, The Cryosphere, 14, 93–113, https://doi.org/10.5194/tc-14-93-2020, 2020. a
Ingram, W. J., Wilson, C. A., and Mitchell, J. F. B.: Modeling climate change:
An assessment of sea ice and surface albedo feedbacks, J.
Geophys. Res.-Atmos., 94, 8609–8622,
https://doi.org/10.1029/JD094iD06p08609, 1989. a
Jeffery, N. and Hunke, E. C.: Modeling the winter-spring transition of
first-year ice in the western Weddell Sea, J. Geophys. Res.-Oceans, 119, 5891–5920, https://doi.org/10.1002/2013JC009634, 2014. a
Jeffery, N., Elliott, S., Hunke, E. C., Lipscomb, W. H., and Turner, A.:
Biogeochemistry of CICE: The Los Alamos sea ice model documentation and
software user's manual, zbgc_colpkg modifications to version 5.0., Tech.
Rep., LA-UR-16-27780, Los Alamos National Laboratory, Los Alamos, NM, USA, https://doi.org/10.2172/1329842,
2016. a
Jeffery, N., Maltrud, M. E., Hunke, E. C., Wang, S., Wolfe, J., Turner, A. K.,
Burrows, S. M., Shi, X., Lipscomb, W. H., Maslowski, W., and Calvin, K. V.:
Investigating controls on sea ice algal production using E3SMv1.1-BGC, Ann. Glaciol., 61, 51–72, https://doi.org/10.1017/aog.2020.7, 2020. a
Karypis, G. and Kumar, V.: A Fast and High Quality Multilevel Scheme for
Partitioning Irregular Graphs, SIAM J. Sci. Comput., 20, 359–392,
https://doi.org/10.1137/S1064827595287997, 1999. a
Killworth, P. D.: Deep convection in the World Ocean, Rev. Geophys.,
21, 1–26, https://doi.org/10.1029/RG021i001p00001, 1983. a
Koldunov, N. V., Aizinger, V., Rakowsky, N., Scholz, P., Sidorenko, D., Danilov, S., and Jung, T.: Scalability and some optimization of the Finite-volumE Sea ice–Ocean Model, Version 2.0 (FESOM2), Geosci. Model Dev., 12, 3991–4012, https://doi.org/10.5194/gmd-12-3991-2019, 2019. a
Large, W. G. and Yeager, S. G.: The global climatology of an interannually
varying air–sea flux data set, Clim. Dynam., 33, 341–364,
https://doi.org/10.1007/s00382-008-0441-3, 2009. a
Lietaer, O., Fichefet, T., and Legat, V.: The effects of resolving the
Canadian Arctic Archipelago in a finite element sea ice model, Ocean
Model., 24, 140–152, https://doi.org/10.1016/j.ocemod.2008.06.002, 2008. a
Lipscomb, W. H.: Remapping the thickness distribution in sea ice models,
J. Geophys. Res.-Oceans, 106, 13989–14000,
https://doi.org/10.1029/2000JC000518, 2001. a
Lipscomb, W. H. and Hunke, E. C.: Modeling sea ice transport using incremental
remapping, Mon. Weather Rev., 132, 1341–1354,
https://doi.org/10.1175/1520-0493(2004)132<1341:MSITUI>2.0.CO;2, 2004. a, b, c, d
Lipscomb, W. H., Hunke, E. C., Maslowski, W., and Jakacki, J.: Ridging,
strength, and stability in high-resolution sea ice models, J.
Geophys. Res.-Oceans, 112, C03S91, https://doi.org/10.1029/2005JC003355,
2007. a
Malvern, L. E.: Introduction to the Mechanics of Continuous Medium,
Prentice-Hall, Inc., Englewood Cliffs, ISBN 978-0134876030, 1969. a
Mehlmann, C. and Korn, P.: Sea-ice dynamics on triangular grids, J.
Comput. Phys., 428, 110086,
https://doi.org/10.1016/j.jcp.2020.110086, 2021. a
Murray, R. J.: Explicit generation of orthogonal grids for ocean models,
J. Comput. Phys., 126, 251–273,
https://doi.org/10.1006/jcph.1996.0136, 1996. a
NOAA: Version 2 forcing for Coordinated Ocean-ice Reference Experiments (CORE), NOAA [data set], https://data1.gfdl.noaa.gov/nomads/forms/core/COREv2.html, last access: 28 November 2021. a
Parkinson, C. L. and Cavalieri, D. J.: Antarctic sea ice variability and trends, 1979–2010, The Cryosphere, 6, 871–880, https://doi.org/10.5194/tc-6-871-2012, 2012. a
Parkinson, C. L., Cavalieri, D. J., Gloersen, P., Zwally, H. J., and Comiso,
J. C.: Arctic sea ice extents, areas, and trends, 1978–1996, J.
Geophys. Res.-Oceans, 104, 20837–20856,
https://doi.org/10.1029/1999JC900082, 1999. a
Reddy, J. N.: An introduction to the finite element method, 2nd edn., McGraw-Hill, New York, ISBN: 9780070513556, 1993. a
Ringler, T., Thuburn, J., Klemp, J., and Skamarock, W.: A unified approach to
energy conservation and potential vorticity dynamics for
arbitrarily-structured C-grids, J. Comput. Phys., 229,
3065–3090, https://doi.org/10.1016/j.jcp.2009.12.007, 2010. a, b
Ringler, T., Petersen, M., Higdon, R. L., Jacobsen, D., Jones, P. W., and
Maltrud, M.: A multi-resolution approach to global ocean modeling, Ocean
Model., 69, 211–232, https://doi.org/10.1016/j.ocemod.2013.04.010, 2013. a, b
Rosati, A. and Miyakoda, K.: A general circulation model for upper ocean
simulation, J. Phys. Oceanogr., 18, 1601–1626,
https://doi.org/10.1175/1520-0485(1988)018<1601:AGCMFU>2.0.CO;2, 1988. a
Röske, F.: An atlas of surface fluxes based on the ECMWF reanalysis. A
climatological data set to force global ocean general circulation models,
Tech. Rep., 323, Max-Planck-Inst. Für Meteorol., Hamburg, Germany, http://hdl.handle.net/21.11116/0000-0003-2E85-4 (last access: 4 April 2022), 2001. a
Schweiger, A., Lindsay, R., Zhang, J., Steele, M., Stern, H., and Kwok, R.:
Uncertainty in modeled Arctic sea ice volume, J. Geophys.
Res.-Oceans, 116, C00D06, https://doi.org/10.1029/2011JC007084, 2011. a
Skamarock, W. C., Klemp, J. B., Duda, M. G., Fowler, L. D., Park, S.-H., and
Ringler, T. D.: A multiscale nonhydrostatic atmospheric model using
centroidal Voronoi tesselations and C-grid staggering, Mon. Weather
Rev., 140, 3090–3105, https://doi.org/10.1175/MWR-D-11-00215.1, 2012. a
Smith, R., Kortas, S., and Meltz, B.: Curvilinear coordinates for global ocean
models, Tech. Rep. LA-UR-95-1146, Los Alamos National Laboratory, https://lanl-primo.hosted.exlibrisgroup.com/permalink/f/1hmhmmc/01LANL_ALMA5199299490003761 (last access: 4 April 2022), 1995.
a
Turner, A.: MPAS-Seaice v1.0.0 data set, Zenodo [data set], https://doi.org/10.5281/zenodo.6230907, 2022. a
Turner, A. K. and Hunke, E. C.: Impacts of a mushy-layer thermodynamic
approach in global sea-ice simulations using the CICE sea-ice model, J. Geophys. Res.-Oceans, 120, 1253–1275, https://doi.org/10.1002/2014JC010358,
2015. a
Turner, A. K., Hunke, E. C., and Bitz, C. M.: Two modes of sea-ice gravity
drainage: A parameterization for large-scale modeling, J.
Geophys. Res.-Oceans, 118, 2279–2294, https://doi.org/10.1002/jgrc.20171,
2013. a
van Leer, B.: Towards the ultimate conservative difference scheme. V. A
second-order sequel to Godunov's method, J. Comput. Phys.,
32, 101–136, https://doi.org/10.1016/0021-9991(79)90145-1, 1979. a
Wang, Q., Danilov, S., Sidorenko, D., Timmermann, R., Wekerle, C., Wang, X., Jung, T., and Schröter, J.: The Finite Element Sea Ice-Ocean Model (FESOM) v.1.4: formulation of an ocean general circulation model, Geosci. Model Dev., 7, 663–693, https://doi.org/10.5194/gmd-7-663-2014, 2014. a
Yi, D. and Zwally, H. J.: Arctic sea ice freeboard and thickness, version 1.,
Tech. Rep., NSIDC: National Snow and Ice Data Center, Boulder, Colorado USA [data set],
https://doi.org/10.5067/SXJVJ3A2XIZT, 2009. a
Zwally, H. J., Comiso, J. C., Parkinson, C. L., Cavalieri, D. J., and Gloersen, P.: Variability of Antarctic sea ice 1979–1998, J. Geophys. Res., 107, 3041, https://doi.org/10.1029/2000JC000733, 2002. a
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.
We present the dynamical core of the MPAS-Seaice model, which uses a mesh consisting of a...
