Articles | Volume 15, issue 10
https://doi.org/10.5194/tc-15-4981-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-15-4981-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
28 Oct 2021
Research article |
| 28 Oct 2021
| 28 Oct 2021
The influence of snow on sea ice as assessed from simulations of CESM2
Marika M. Holland
CORRESPONDING AUTHOR
Climate and Global Dynamics Laboratory, National Center for Atmospheric
Research, Boulder, CO, USA
David Clemens-Sewall
Thayer School of Engineering, Dartmouth College, Hanover, NH, USA
Laura Landrum
Climate and Global Dynamics Laboratory, National Center for Atmospheric
Research, Boulder, CO, USA
Bonnie Light
Polar Science Center, Applied Physics Laboratory, University of Washington, Seattle, WA, USA
Donald Perovich
Thayer School of Engineering, Dartmouth College, Hanover, NH, USA
Chris Polashenski
Thayer School of Engineering, Dartmouth College, Hanover, NH, USA
Madison Smith
Polar Science Center, Applied Physics Laboratory, University of Washington, Seattle, WA, USA
Melinda Webster
Geophysical Institute, University of Alaska Fairbanks, Fairbanks, AK, USA
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Laura L. Landrum, Alice K. DuVivier, Marika M. Holland, Kristen Krumhardt, and Zephyr Sylvester
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Climate evolves in response to changing forcings, as prescribed in simulations. Models and forcings are updated over time to reflect new understanding. This makes it difficult to attribute simulation differences to either model or forcing changes. Here we present new simulations which enable the separation of model structure and forcing influence between two widely used simulation sets. Results indicate a strong influence of aerosol emission uncertainty on historical climate.
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The Earth system changes over a range of time and space scales, and some of these changes are predictable in advance. Short-term weather forecasts are most familiar, but recent work has shown that it is possible to generate useful predictions several seasons or even a decade in advance. This study focuses on predictions over intermediate timescales (up to 24 months in advance) and shows that there is promising potential to forecast a variety of changes in the natural environment.
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High-latitude Arctic wintertime sea ice and snow insulate the relatively warmer ocean from the colder atmosphere. As the climate warms, wintertime Arctic conductive heat fluxes increase even when the sea ice concentrations remain high. Simulations from the Community Earth System Model Large Ensemble (CESM1-LE) show how sea ice and snow thicknesses, as well as the distribution of these thicknesses, significantly impact large-scale calculations of wintertime surface heat budgets in the Arctic.
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The Cryosphere, 16, 419–434, https://doi.org/10.5194/tc-16-419-2022, https://doi.org/10.5194/tc-16-419-2022, 2022
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Climate models represent the atmosphere, ocean, sea ice, and land with equations of varying complexity and are important tools for understanding changes in global climate. Here, we explore how realistic variations in the equations describing how sea ice melt occurs at the edges (called lateral melting) impact ice and climate. We find that these changes impact the progression of the sea-ice–albedo feedback in the Arctic and so make significant changes to the predicted Arctic sea ice.
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This preprint is open for discussion and under review for Ocean Science (OS).
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Distributed optical fibre sensing (DOFS) is a technology that enables continuous, real-time measurements of environmental parameters along a fibre optic cable. Here, we review the recently emerged applications of DOFS in physical oceanography, and offer a perspective on the technology’s potential for future growth in the field.
Robert Massom, Phillip Reid, Stephen Warren, Bonnie Light, Donald Perovich, Luke Bennetts, Petteri Uotila, Siobhan O'Farrell, Michael Meylan, Klaus Meiners, Pat Wongpan, Alexander Fraser, Alessandro Toffoli, Giulio Passerotti, Peter Strutton, Sean Chua, and Melissa Fedrigo
EGUsphere, https://doi.org/10.5194/egusphere-2025-3166, https://doi.org/10.5194/egusphere-2025-3166, 2025
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Ocean waves play a previously-neglected role in the rapid annual melting of Antarctic sea ice by flooding and pulverising floes, removing the snow cover and reducing the albedo by an estimated 0.38–0.54 – to increase solar absorption and enhance the vertical melt rate by up to 5.2 cm/day. Ice algae further decrease the albedo, to increase the melt-rate enhancement to up to 6.1 cm/day. Melting is accelerated by four previously-unconsidered wave-driven positive feedbacks.
Lena G. Buth, Thomas Krumpen, Niklas Neckel, Melinda A. Webster, Gerit Birnbaum, Niels Fuchs, Philipp Heuser, Ole Johannsen, and Christian Haas
EGUsphere, https://doi.org/10.5194/egusphere-2025-1103, https://doi.org/10.5194/egusphere-2025-1103, 2025
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Arctic sea ice is becoming smoother, raising the question of how these changes affect melt pond coverage and thereby surface albedo. Using airborne imagery and laser altimeter data, we investigated how pressure ridges influence melt ponds. The presence of ridges does not directly control pond fraction, but it does influence pond size distribution and pond geometry. Small ponds have a more complex shape on rough ice than on smooth ice, while the opposite is true for large ponds.
Alek Aaron Petty, Christopher Cardinale, and Madison Smith
EGUsphere, https://doi.org/10.5194/egusphere-2025-766, https://doi.org/10.5194/egusphere-2025-766, 2025
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We put global climate models to the test against NASA’s ICESat-2 satellite to see how well they simulate global sea ice cover. By adding fancy laser data from ICESat-2, we can better assess how well the models are performing compared to the standard assessments of sea ice area. Overall the models do a good job but there’s room for improvement, especially across the Southern Ocean. We should think a bit more about sea ice density if we want more reliable freeboard comparisons.
Ian A. Raphael, Donald K. Perovich, Christopher M. Polashenski, and Robert L. Hawley
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Snow plays competing roles in the sea ice cycle by reflecting sunlight during summer (reducing melt) and insulating the ice from the cold atmosphere during winter (reducing growth). Observing where, when, and how much snow accumulates on sea ice is thus central to understanding the Arctic. Here, we describe a new snow depth observation system that is substantially cheaper and lighter than existing tools, and present a study demonstrating its potential to improve snow measurements on sea ice.
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The fate of freshwater from Arctic sea ice and snowmelt impacts interactions of the atmosphere, sea ice, and ocean. We complete a comprehensive analysis of datasets from a 2020 central Arctic field campaign to understand the drivers of the sea ice freshwater budget and the fate of this water. Over half of the freshwater comes from surface melt, and a majority fraction is incorporated into the ocean. Results suggest that the representation of melt ponds is a key area for future development.
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Antarctic polynyas – areas of open water surrounded by sea ice or sea ice and land – are key players in Antarctic marine ecosystems. Changes in the physical characteristics of polynyas will influence how these ecosystems respond to a changing climate. This work explores how to best compare polynyas identified in satellite data and climate model data to verify that the model captures important features of Antarctic sea ice and marine ecosystems, and we show how polynyas may change.
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Preprint archived
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This study examines how the bulk density of Arctic sea ice varies seasonally, a factor often overlooked in satellite measurements of sea ice thickness. From October to April, we found significant seasonal variations in sea ice bulk density at different spatial scales using direct observations as well as airborne and satellite data. New models were then developed to indirectly predict sea ice bulk density. This advance can improve our ability to monitor changes in Arctic sea ice.
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Preprint archived
Short summary
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This study examines how the density of Arctic sea ice varies seasonally, a factor often overlooked in satellite measurements of sea ice thickness. From October to April, using direct observations and satellite data, we found that sea ice density decreases significantly until mid-January due to increased porosity as the ice ages, and then stabilizes until April. We then developed new models to estimate sea ice density. This advance can improve our ability to monitor changes in Arctic sea ice.
Marika M. Holland, Cecile Hannay, John Fasullo, Alexandra Jahn, Jennifer E. Kay, Michael Mills, Isla R. Simpson, William Wieder, Peter Lawrence, Erik Kluzek, and David Bailey
Geosci. Model Dev., 17, 1585–1602, https://doi.org/10.5194/gmd-17-1585-2024, https://doi.org/10.5194/gmd-17-1585-2024, 2024
Short summary
Short summary
Climate evolves in response to changing forcings, as prescribed in simulations. Models and forcings are updated over time to reflect new understanding. This makes it difficult to attribute simulation differences to either model or forcing changes. Here we present new simulations which enable the separation of model structure and forcing influence between two widely used simulation sets. Results indicate a strong influence of aerosol emission uncertainty on historical climate.
Ellen M. Buckley, Sinéad L. Farrell, Ute C. Herzfeld, Melinda A. Webster, Thomas Trantow, Oliwia N. Baney, Kyle A. Duncan, Huilin Han, and Matthew Lawson
The Cryosphere, 17, 3695–3719, https://doi.org/10.5194/tc-17-3695-2023, https://doi.org/10.5194/tc-17-3695-2023, 2023
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In this study, we use satellite observations to investigate the evolution of melt ponds on the Arctic sea ice surface. We derive melt pond depth from ICESat-2 measurements of the pond surface and bathymetry and melt pond fraction (MPF) from the classification of Sentinel-2 imagery. MPF increases to a peak of 16 % in late June and then decreases, while depth increases steadily. This work demonstrates the ability to track evolving melt conditions in three dimensions throughout the summer.
Vishnu Nandan, Rosemary Willatt, Robbie Mallett, Julienne Stroeve, Torsten Geldsetzer, Randall Scharien, Rasmus Tonboe, John Yackel, Jack Landy, David Clemens-Sewall, Arttu Jutila, David N. Wagner, Daniela Krampe, Marcus Huntemann, Mallik Mahmud, David Jensen, Thomas Newman, Stefan Hendricks, Gunnar Spreen, Amy Macfarlane, Martin Schneebeli, James Mead, Robert Ricker, Michael Gallagher, Claude Duguay, Ian Raphael, Chris Polashenski, Michel Tsamados, Ilkka Matero, and Mario Hoppmann
The Cryosphere, 17, 2211–2229, https://doi.org/10.5194/tc-17-2211-2023, https://doi.org/10.5194/tc-17-2211-2023, 2023
Short summary
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We show that wind redistributes snow on Arctic sea ice, and Ka- and Ku-band radar measurements detect both newly deposited snow and buried snow layers that can affect the accuracy of snow depth estimates on sea ice. Radar, laser, meteorological, and snow data were collected during the MOSAiC expedition. With frequent occurrence of storms in the Arctic, our results show that
wind-redistributed snow needs to be accounted for to improve snow depth estimates on sea ice from satellite radars.
Ruibo Lei, Mario Hoppmann, Bin Cheng, Marcel Nicolaus, Fanyi Zhang, Benjamin Rabe, Long Lin, Julia Regnery, and Donald K. Perovich
The Cryosphere Discuss., https://doi.org/10.5194/tc-2023-25, https://doi.org/10.5194/tc-2023-25, 2023
Manuscript not accepted for further review
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To characterize the freezing and melting of different types of sea ice, we deployed four IMBs during the MOSAiC second drift. The drifting pattern, together with a large snow accumulation, relatively warm air temperatures, and a rapid increase in oceanic heat close to Fram Strait, determined the seasonal evolution of the ice mass balance. The refreezing of ponded ice and voids within the unconsolidated ridges amplifies the anisotropy of the heat exchange between the ice and the atmosphere/ocean.
Long Lin, Ruibo Lei, Mario Hoppmann, Donald K. Perovich, and Hailun He
The Cryosphere, 16, 4779–4796, https://doi.org/10.5194/tc-16-4779-2022, https://doi.org/10.5194/tc-16-4779-2022, 2022
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Ice mass balance observations indicated that average basal melt onset was comparable in the central Arctic Ocean and approximately 17 d earlier than surface melt in the Beaufort Gyre. The average onset of basal growth lagged behind the surface of the pan-Arctic Ocean for almost 3 months. In the Beaufort Gyre, both drifting-buoy observations and fixed-point observations exhibit a trend towards earlier basal melt onset, which can be ascribed to the earlier warming of the surface ocean.
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.
Stephen G. Yeager, Nan Rosenbloom, Anne A. Glanville, Xian Wu, Isla Simpson, Hui Li, Maria J. Molina, Kristen Krumhardt, Samuel Mogen, Keith Lindsay, Danica Lombardozzi, Will Wieder, Who M. Kim, Jadwiga H. Richter, Matthew Long, Gokhan Danabasoglu, David Bailey, Marika Holland, Nicole Lovenduski, Warren G. Strand, and Teagan King
Geosci. Model Dev., 15, 6451–6493, https://doi.org/10.5194/gmd-15-6451-2022, https://doi.org/10.5194/gmd-15-6451-2022, 2022
Short summary
Short summary
The Earth system changes over a range of time and space scales, and some of these changes are predictable in advance. Short-term weather forecasts are most familiar, but recent work has shown that it is possible to generate useful predictions several seasons or even a decade in advance. This study focuses on predictions over intermediate timescales (up to 24 months in advance) and shows that there is promising potential to forecast a variety of changes in the natural environment.
Laura L. Landrum and Marika M. Holland
The Cryosphere, 16, 1483–1495, https://doi.org/10.5194/tc-16-1483-2022, https://doi.org/10.5194/tc-16-1483-2022, 2022
Short summary
Short summary
High-latitude Arctic wintertime sea ice and snow insulate the relatively warmer ocean from the colder atmosphere. As the climate warms, wintertime Arctic conductive heat fluxes increase even when the sea ice concentrations remain high. Simulations from the Community Earth System Model Large Ensemble (CESM1-LE) show how sea ice and snow thicknesses, as well as the distribution of these thicknesses, significantly impact large-scale calculations of wintertime surface heat budgets in the Arctic.
Madison M. Smith, Marika Holland, and Bonnie Light
The Cryosphere, 16, 419–434, https://doi.org/10.5194/tc-16-419-2022, https://doi.org/10.5194/tc-16-419-2022, 2022
Short summary
Short summary
Climate models represent the atmosphere, ocean, sea ice, and land with equations of varying complexity and are important tools for understanding changes in global climate. Here, we explore how realistic variations in the equations describing how sea ice melt occurs at the edges (called lateral melting) impact ice and climate. We find that these changes impact the progression of the sea-ice–albedo feedback in the Arctic and so make significant changes to the predicted Arctic sea ice.
Don Perovich, Madison Smith, Bonnie Light, and Melinda Webster
The Cryosphere, 15, 4517–4525, https://doi.org/10.5194/tc-15-4517-2021, https://doi.org/10.5194/tc-15-4517-2021, 2021
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During summer, Arctic sea ice melts on its surface and bottom and lateral edges. Some of this fresh meltwater is stored on the ice surface in features called melt ponds. The rest flows into the ocean. The meltwater flowing into the upper ocean affects ice growth and melt, upper ocean properties, and ocean ecosystems. Using field measurements, we found that the summer meltwater was equal to an 80 cm thick layer; 85 % of this meltwater flowed into the ocean and 15 % was stored in melt ponds.
Sean Horvath, Linette Boisvert, Chelsea Parker, Melinda Webster, Patrick Taylor, and Robyn Boeke
The Cryosphere Discuss., https://doi.org/10.5194/tc-2021-297, https://doi.org/10.5194/tc-2021-297, 2021
Preprint withdrawn
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Arctic sea ice has been experiencing a dramatic decline since the late 1970s. A database is presented that combines satellite observations with daily sea ice parcel drift tracks. This dataset consists of daily time series of sea ice parcel locations, sea ice and snow conditions, and atmospheric states. This has multiple applications for the scientific community that can shed light on the atmosphere-snow-sea ice interactions in the changing Arctic environment.
H. Jakob Belter, Thomas Krumpen, Luisa von Albedyll, Tatiana A. Alekseeva, Gerit Birnbaum, Sergei V. Frolov, Stefan Hendricks, Andreas Herber, Igor Polyakov, Ian Raphael, Robert Ricker, Sergei S. Serovetnikov, Melinda Webster, and Christian Haas
The Cryosphere, 15, 2575–2591, https://doi.org/10.5194/tc-15-2575-2021, https://doi.org/10.5194/tc-15-2575-2021, 2021
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Summer sea ice thickness observations based on electromagnetic induction measurements north of Fram Strait show a 20 % reduction in mean and modal ice thickness from 2001–2020. The observed variability is caused by changes in drift speeds and consequential variations in sea ice age and number of freezing-degree days. Increased ocean heat fluxes measured upstream in the source regions of Arctic ice seem to precondition ice thickness, which is potentially still measurable more than a year later.
Nicholas C. Wright, Chris M. Polashenski, Scott T. McMichael, and Ross A. Beyer
The Cryosphere, 14, 3523–3536, https://doi.org/10.5194/tc-14-3523-2020, https://doi.org/10.5194/tc-14-3523-2020, 2020
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This work presents a new dataset of sea ice surface fractions along NASA Operation IceBridge flight tracks created by processing hundreds of thousands of aerial images. These results are then analyzed to investigate the behavior of meltwater on first-year ice in comparison to multiyear ice. We find preliminary evidence that first-year ice frequently has a lower melt pond fraction than adjacent multiyear ice, contrary to established knowledge in the sea ice community.
Cited articles
Andreas, E. L. and Ackley, S. F.: On the differences in ablation seasons of
Arctic and Antarctic sea ice, J. Atmos. Sci., 389, 440–447, https://doi.org/10.1175/1520-0469(1982)039<0440:OTDIAS>2.0.CO;2, 1982.
Bacmeister, J. T., Hannay, C., Medeiros, B., Gettelman, A., Neale, R., Fredriksen, H. B., Lipscomb, W. H., Simpson, I., Bailey, D. A., Holland, M., Lindsay, K., and
Otto-Bliesner, B.: CO2 increase experiments using the Community Earth System
Model (CESM): Relationship to climate sensitivity and comparison of CESM1 to
CESM2, J. Adv. Model. Earth Sy., 125, e2020MS002120, https://doi.org/10.1029/2020MS002120, 2020.
Bailey, D. A., Holland, M. M., DuVivier, A. K., Hunke, E. C., and Turner, A. K:
Impact of a New Sea Ice Thermodynamic Formulation in the CESM2 sea ice
component, J. Adv. Model. Earth Sy., 12, e2020MS002154, https://doi.org/10.1029/2020MS002154, 2020.
Bintanja, R. and Selten, F. M.: Future increases in Arctic precipitation
linked to local evaporation and sea-ice
retreat, Nature, 509, 479–482, 2014.
Bitz, C. M. and Roe, G. H.: A mechanism for the high rate of sea-ice thinning
in the Arctic Ocean, J. Climate, 17, 3622–3631, 2004.
Bitz, C. M., Shell, K. M., Gent, P. R., Bailey, D., Danabasoglu, G., Armour,
K. C., Holland, M. M., and Kiehl, J. T.: Climate sensitivity of the Community
Climate System Model Version 4, J. Climate, 25, 3053–3070,
https://doi.org/10.1175/JCLI-D-11-00290.1, 2012.
Blanchard-Wrigglesworth, E., Armour, K. C., Bitz, C. M., and DeWeaver, E.:
Persistence and inherent predictability of Arctic sea ice in a GCM ensemble
and observations, J. Climate, 24, 231–250, https://doi.org/10.1175/2010JCLI3775.1,
2011.
Blazey, B. A., Holland, M. M., and Hunke, E. C.: Arctic Ocean sea ice snow depth evaluation and bias sensitivity in CCSM, The Cryosphere, 7, 1887–1900, https://doi.org/10.5194/tc-7-1887-2013, 2013.
Boisvert, L., Webster, M., Petty, A., Markus, T., Bromwich, D.,
and Cullather, R.: Intercomparison of precipitation estimates over the
Arctic Ocean and its peripheral seas from reanalyses, J. Climate, 31, 8441–8462, https://doi.org/10.1175/JCLI-D-18-0125.1, 2018.
Brandt, R. E., Warren, S. G., Worby, A. P., and Grenfell, T. C.: Surface albedo of the Antarctic sea ice zone, J. Climate, 18, 3606–3622, 2005.
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, NCAR Tech. Note TN-4721STR, 100 pp., 2007.
Calonne, N., Flin, F., Morin, S., Lesaffre, B., Rolland du Roscoat, S., and
Geindreau, C.: Numerical and experimental investigations of the effective
thermal conductivity of snow, Geophys. Res. Lett., 38, L23501,
https://doi.org/10.1029/2011GL049234, 2011.
Danabasoglu, G., Lamarque, J.-F., Bachmeister, 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., 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-Kember, B., Kay, J. E., Kinnison, D., Kushner, P. J., 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, 2019 (code available at: https://www.cesm.ucar.edu/models/cesm2/release_download.html, last access: 5 June 2020).
DeRepentigny, P., Jahn, A., Holland, M. M., and Smith, A: Arctic Sea Ice in
two configurations of the Community Earth System Model Version 2 (CESM2)
during the 20th and 21st centuries, J. Geophys. Res.-Oceans, 125, e2020JC016133, https://doi.org/10.1029/2020JC016133, 2020.
DuVivier, A. K., Holland, M. M., Kay, J. E., Tilmes, S., Gettelman, A., and
Bailey, D. A: Arctic and Antarctic sea ice state in the Community Earth
System Model Version 2, J. Geophys. Res.-Oceans, 125, e2019JC015934, https://doi.org/10.1029/2019JC015934,
2020.
Fetterer, F., Knowles, K., Meier, W. N., Savoie, M., and Windnagel, A. K.: Sea Ice Index, Version 3, Boulder, Colorado USA, NSIDC: National Snow and Ice
Data Center [data set], https://doi.org/10.7265/N5K072F8, 2017.
Fichefet, T. and Morales-Maqueda, M. A.: Modelling the influence of snow
accumulation and snow-ice formation on the seasonal cycle of the Antarctic
sea-ice cover, Clim. Dynam., 15, 251–268, https://doi.org/10.1007/s003820050280, 1999.
Gettelman, A. and Morrison, H.: Advanced two-moment bulk microphysics for
global models. Part 1: Off-line tests and comparison with other schemes, J.
Climate, 28, 1268–1287, https://doi.org/10.1175/JCLI-D-14-00102.1, 2015.
Gettelman, A., Hannay, C., Bacmeister, J. T., Neale, R. B., Pendergrass, A. G., Danabasoglu, G., Lamarque, J.-F., Fasullo, J. T., Bailey, D. A., Lawrence, D. M., and Mills, M. J.: High climate sensitivity in the Community Earth
System Model Version 2 (CESM2), Geophys. Res. Lett., 46, 8329–8337, https://doi.org/10.1029/2019GL083978, 2019.
Gordon, A. L.: Seasonality of Southern Ocean sea ice, J. Geophys. Res.,
86, 4193–4197, 1981.
Granskog, M. A., Rosel, A., Dodd, P. A., Divine, D., Gerland, S., Martma, T.,
and Leng, M. J.: Snow contribution to first-year and second-year Arctic sea
ice mass balance north of Svalbard, J. Geophys. Res., 122, 2539–2549,
https://doi.org/10.1002/2016JC012398, 2017.
Hezel, P. J., Zhang, X., Bitz, C. M., Kelly, B. P., and Massonnet, F.:
Projected decline in spring snow depth on Arctic sea ice caused by
progressively later autumn open ocean freeze-up this century, Geophys. Res.
Lett., 39, L17505, https://doi.org/10.1029/2012GL052794, 2012.
Hibler, W. D.: A dynamic thermodynamic sea ice model, J. Phys. Oceanogr.,
9, 817–846, 1979.
Holland, M. M: marikaholland/Snow_On_Ice_TC_2021: Snow on ice data (v1.0), Zenodo [data set], https://doi.org/10.5281/zenodo.5572930, 2021.
Holland, M. M. and Landrum, L.: Factors affecting projected Arctic surface
shortwave heating and albedo change in coupled climate models, Philos. T.
Roy. Soc. A, 373, 20140162, https://doi.org/10.1098/rsta.2014.0162, 2015.
Holland, M. M., Bitz, C. M., Hunke, E. C., Lipscomb, W. H., and Schramm, J. L.:
Influence of the sea ice thickness distribution on polar climate in
CCSM3, J. Climate, 19, 2398–2414, 2006.
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 black carbon, J. Climate, 25, 1413–1430, https://doi.org/10.1175/JCLI-D-11-00078.1, 2012.
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,
2002.
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.
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 LA-CC-012, available at: https://github.com/CICE-Consortium/CICE-svn-trunk/blob/master/cicedoc/cicedoc.pdf, last access: 18 August 2020, 2015.
Kacimi, S. and Kwok, R.: The Antarctic sea ice cover from ICESat-2 and CryoSat-2: freeboard, snow depth, and ice thickness, The Cryosphere, 14, 4453–4474, https://doi.org/10.5194/tc-14-4453-2020, 2020.
Kattsov, V. M., Walsh, J. E., Chapman, W. L., Govorkova, V. A., Pavlova, T. V., and Zhang, X. D.: Simulation and projection of arctic freshwater budget
components by the IPCC AR4 global climate models, J. Hydrometeorol., 8, 571–589, 2007.
Koenig, L., Martin, S., Studinger, M., and Sonntag, J.: Polar airborne
observations fill gap in satellite data, Eos, 91, 333–334, https://doi.org/10.1029/2010EO380002, 2010.
Kwok, R., Kacimi, S., Webster, M. A., Kurtz, N. T., and Petty, A. A.: Arctic
snow depth and sea ice thickness from ICESat-2 and CryoSat-2 freeboards: a
first examination, J. Geophys. Res.-Oceans, 125, e2019JC016008, https://doi.org/10.1029/2019JC016008, 2020.
Labe, Z., Madnusdottir, G., and Stern, H.: Variability of Arctic sea ice
thickness using PIOMAS and the CESM Large Ensemble, J. Climate, 31,
3233–3247, https://doi.org/10.1175/JCLI-D-17-0436.1, 2018.
Lange, M. A. and Eicken, H.: Textural characteristics of sea ice and the
major mechanisms of ice growth in the Weddell Sea, Ann. Glaciol., 15,
210–215, 1991.
Lawrence, D. M., Fisher, R. A., Koven, C. D., Oleson, K. W., Swenson, S. C.,
Bonan, G., Collier, N., Ghimire, B., van Kampenhout, L., Kennedy, D.,
Kluzek, E., Lawrence, P. J., Li, F., Li, H., Lombardozzi, D., Riley, W. J.,
Sacks, W. J., Shi, M., Vertenstein, M., Wieder, W. R., Xu, C., Ali, A. A.,
Badger, A. M., Bisht, G., van den Broeke, M., Brunke, M. A., Burns, S. P.,
Buzan, J., Clark, M., Craig, A., Dahlin, K., Drewniak, B., Fisher, J. B.,
Flanner, M., Fox, A. M., Gentine, P., Hoffman, F., Keppel-Aleks, G., Knox,
R., Kumar, S., Lenaerts, J., Leung, L. R., Lipscomb, W. H., Lu, Y., Pandey,
A., Pelletier, J. D., Perket, J., Randerson, J. T., Ricciuto, J. M.,
Sanderson, B. M., Slater, A., Subin, Z. M., Tang, J., Thomas, R. Q., Val
Martin, M., and Zeng, X.: The Community Land Model version 5: Description of
new features, benchmarking, and impact of forcing uncertainty, J. Adv. Model. Earth. Sy., 11, 4245–4287, https://doi.org/10.1029/2018MS001583, 2019.
Lecomte, O., Fichefet, T., Vancoppenolle, M., Domine, F., Massonnet, F.,
Mathiot, P., Morin, S., and Barriat, P.: On the formulation of snow thermal
conductivity in large-scale sea ice models, J. Adv. Model. Earth Syst., 5,
542–557, https://doi.org/10.1002/jame.20039, 2013.
Lecomte, O., Fichefet, T., Flocco, D., Schroeder, D., and Vancoppenolle, M.:
Interactions between wind-blown snow redistribution and melt ponds in a
coupled ocean-sea ice model, Ocean Model., 87, 67–80, 2015.
Ledley, T. S.: Snow on sea ice: Competing effects in shaping climate, J.
Geophys. Res., 96, 17195–17208, 1991.
Leonard, K. C. and Maksym, T.: The important of wind-blown snow
redistribution to snow accumulation on Bellingshausen Sea ice, Ann. Glaciol., 52, 271–278, https://doi.org/10.3189/172756411795931651, 2011.
Liston, G. E., Itkin, P., Stroeve, S., Tschudi, M., Stewart, J. S., Pedersen,
S. H., Reinking, A. K., and Elder, K.: A Lagrangian snow-evolution system for
sea-ice applications (SnowModel-LG): Part I–model description, J.
Geophys. Res., 125, 10, https://doi.org/10.1029/2019JC015913, 2020.
Maksym, T. and Jeffries, M. O.: A one-dimensional percolation model of
flooding and snow ice formation with particular reference to sea ice in the
Ross Sea, Antarctica, J. Geophys. Res., 105, 26313–26331, 2000.
Massom, R. A., Eicken, H., Haas, C., Jeffries, M. O., Drinkwater, M. R., Sturm,
M., Worby, A. P., Wu, X., Lytle, V. I., Ushio, S., Morris, K., Reid, P. A.,
Warren, S. G., and Allison, I.: Snow on Antarctic sea ice, Rev.
Geophys., 39, 413–445, 2001.
Maykut, G. A.: Energy exchange over young sea ice in the central Arctic, J.
Geophys. Res., 83, 3646–3658, 1978.
Maykut, G. A. and Untersteiner, N.: Some results from a time-dependent
thermodynamic model of sea ice, J. Geophys. Res., 76, 1550–1575, 1971.
Meredith, M., Sommerkorn, M., Cassotta, S., Derksen, C., Ekaykin, A., Hollowed, A., Kofinas, G., Mackintosh, A., Melbourne-Thomas, J., Muelbert, M. M. C., Ottersen, G., Pritchard, H., and Schuur, E. A. G.: Polar Regions, in: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate, edited by: Pörtner, H.-O., Roberts, D. C., Masson-Delmotte, V., Zhai, P., Tignor, M., Poloczanska, E., Mintenbeck, K., Alegría, A., Nicolai, M., Okem, A., Petzold, J., Rama, B., and Weyer, N. M., in press, 2021.
Merkouriadi, I., Liston, G. E., Graham, R. M., and Granskog, M. A.: Quantifying
the potential for snow-ice formation in the Arctic Ocean, Geophys. Res.
Lett., 47, e2019GL085020, https://doi.org/10.1029/2019GL085020, 2020.
Mueller, B. L., Gillett, N. P., Monahan, A. H., and Zwiers, F. W.: Attribution
of Arctic sea ice decline from 1953 to 2012 to influences from natural,
greenhouse gas, and anthropogenic aerosol forcing, J. Climate, 31, 7771–7787, https://doi.org/10.1175/JCLI-D-17-0552.1, 2018.
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.
Perket, J., Flanner, M. G., and Kay, J. E.: Diagnosing shortwave cryosphere
radiative effect and its 21st century evolution in CESM, J. Geophys.
Res.-Atmos., 119, 1356–1362, https://doi.org/10.1002/2013JD021139, 2014.
Perovich, D. K., Grenfell, T. C., Light, B., and Hobbs., P. V.: Seasonal
evolution of the albedo of multiyear Arctic sea ice, J. Geophys. Res.,
107, 8044, https://doi.org/10.1029/2000JC000438 2002.
Petty, A. A., Holland, M. M., Bailey, D. A., and Kurtz, N. T.: Warm Arctic,
increased winter sea-ice growth?, Geophys. Res. Lett., 45, 12922–12930,
https://doi.org/10.1029/2018GL079223, 2018.
Powell, D. C., Markus, T., and Stossel, A.: Effects of snow depth forcing on
Southern Ocean sea ice simulations, J. Geophys. Res.-Oceans, 110, C06001,
https://doi.org/10.1029/2003JC002212, 2005.
Raphael, M. N., Handcock, M. S., Holland, M. M., and Landrum L. L.: An
assessment of the temporal variability in the annual cycle of daily
Antarctic sea ice in the NCAR Community Earth System Model, Version 2: A
comparison of the historical runs with observations, J. Geophys. Res.-Oceans, 125, e2020JC016459,https://doi.org/10.1029/2020JC016459, 2020.
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,
2018.
Rothrock, D. A.: The energetics of the plastic deformation of pack ice by
ridging, J. Geophys. Res., 80, 4514–4519, https://doi.org/10.1029/JC080i033p04514, 1975.
Schweiger, A., Lindsay, R., Zhang, J., Steele, M., Stern, H., and Kwok, R.:
Uncertainty in modeled Arctic sea ice volume, J. Geophys. Res., 116, C00D06,
https://doi.org/10.1029/2011JC007084, 2011.
Semtner, A. J.: A model for the thermodynamic growth of sea ice in numerical
investigations of climate, J. Phys. Oceanogr., 6, 379–389, 1976.
Singh H. K. A., Landrum L., Holland M. M., Bailey, D. A., and DuVivier, A. K.:
An overview of Antarctic sea ice in CESM2, Part I: Analysis of the seasonal
cycle in the context of sea ice thermodynamics and coupled
atmosphere-ocean-ice processes, J. Adv. Model. Earth. Sy., 12, e2020MS002143, https://doi.org/10.1029/2020MS002143, 2020.
Steele, M.: Sea ice melting and floe geometry in a simple ice-ocean model,
J. Geophys. Res., 97, 17729–17738, 1992.
Stroeve, J. C., Markus, T., Boisvert, L., Miller, J., and Barrett, A.:
Changes in Arctic melt season and implications for sea ice loss, Geophys.
Res. Lett., 41, 1216–1225, https://doi.org/10.1002/2013GL058951, 2014.
Stroeve, J. C., Schroder, D., Tsamados, M., and Feltham, D.: Warm winter, thin ice?, The Cryosphere, 12, 1791–1809, https://doi.org/10.5194/tc-12-1791-2018, 2018.
Sturm, M. and Massom, R. A.: Snow in the sea ice system: Friend or foe?, 3rd edition, Sea Ice, Wiley and Blackwell, Oxford, United Kingdom, 65–109, 2017.
Sturm, M., Holmgren, J., König, M., and Morris, K.: The thermal
conductivity of seasonal snow, J. Glaciol., 43, 26–41, https://doi.org/10.3189/S0022143000002781, 1997.
Sturm, M., Perovich, D. K., and Holmgren, J.: Thermal conductivity and heat
transfer through the snow on the ice of the Beaufort Sea, J. Geophys. Res.,
107, 8043, 10.1029/2000JC000409, 2002a.
Sturm, M., Holmgren, J., and Perovich, D. K.: Winter snow cover on the sea
ice of the Arctic Ocean and the Surface Heat Budget of the Arctic Ocean
(SHEBA): Temporal evolution and spatial variability, J. Geophys. Res., 107,
8047, https://doi.org/10.1029/2000JC000400, 2002b.
Thorndike, A. S., Rothrock, D. S., Maykut, G. A., and Colony, R.: Thickness
distribution of sea ice, J. Geophys. Res., 80, 4501–4513, 1975.
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.
Vihma, T., Screen, J., Tjernstrom, M., Newton, B., Zhang, X., Popova, V.,
Deser, C., Holland, M., and Prowse, T.: The atmospheric role in the Arctic
water cycle: processes, past and future change, and their impacts, J.
Geophys. Res.-Biogeosci., 121, 586–620, https://doi.org/10.1002/2015JG003132, 2015.
Warren, S. G., Rigor, I. G., Untersteiner, N., Radionov, V. F., Bryazgin, N. N.,
Aleksandrov, Y. I., and Colony, R.: Snow depth on Arctic sea ice, J. Climate,
12, 1814–1829, 1999.
Webster, M. A., Rigor, I. G., Nghiem, S. V., Kurtz, N. T., Farrell, S. L., Perovich, D. K., and Sturm, M.: Interdecadal changes in snow depth on Arctic sea ice,
J. Geophys. Res.-Oceans, 119, 5395–5406, 2014.
Webster, M., Gerland, S., Holland, M., Hunke, E., Kwok, R., Lecomte, O.,
Massom, R., Perovich, D., and Sturm, M.: Snow in the changing sea ice
systems, Nature Climate Change, 8, 946–953, https://doi.org/10.1038/s41558-018-0286-7, 2018.
Webster M. A., DuVivier A. K., Holland M. M., and Bailey D. A.: Snow on
Arctic sea ice in a warming climate as simulated in CESM2, J. Adv. Model. Earth Sy., 125, e2020JC016308, https://doi.org/10.1029/2020JC016308,
2021.
Wilchinsky, A. V., Heorton, H. D. B. S., Feltham, D. L., and Holland, P. R.: Study
of the impact of ice formation in leads upon the sea ice pack mass balance
using a new frazil and grease ice parameterization, J. Phys. Oceanogr., 45, 2025–2047, https://doi.org/10.1175/JPO-D-14-0184.1, 2015.
Worby, A. P., Massom, R. A., Allison, I., Lytle, V. I., and Heil, P.: East
Antarctic sea ice: A review of its structure, properties and drift, in: Antarctic sea ice: physical processes, interactions and
variability, edited by:
Jeffries, M. O., Washington, DC, American Geophysical Union, 41–67, 1998.
Worby, A. P., Geiger, C. A., Paget, M. J., Van Woert, M. L., Ackley, S. F., and
DeLiberty, T. L.: Thickness distribution of Antarctic sea ice, J. Geophys.
Res.-Oceans, 113, C05S92, https://doi.org/10.1029/2007JC004254, 2008.
Wu, X., Budd, W. F., Lytle, V. I., and Massom, R. A.: The effect of snow on
Antarctic sea ice simulations in a coupled atmosphere-sea ice mode, Clim.
Dyn., 15, 127–143, 1999.
Zhang, R., Wang, H., Fu, Q., Rasch, P. J., and Wang, X.: Unraveling driving
forces explaining significant reduction in satellite-inferred Arctic surface
albedo since the 1980s, P. Natl. Acad. Sci. USA, 116, 23947–23953,
https://doi.org/10.1073/pnas.1915258116, 2019.
Short summary
As the most reflective and most insulative natural material, snow has important climate effects. For snow on sea ice, its high reflectivity reduces ice melt. However, its high insulating capacity limits ice growth. These counteracting effects make its net influence on sea ice uncertain. We find that with increasing snow, sea ice in both hemispheres is thicker and more extensive. However, the drivers of this response are different in the two hemispheres due to different climate conditions.
As the most reflective and most insulative natural material, snow has important climate effects....
