Articles | Volume 16, issue 2
https://doi.org/10.5194/tc-16-419-2022
© 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/tc-16-419-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Arctic sea ice sensitivity to lateral melting representation in a coupled climate model
Applied Physics Laboratory, University of Washington, Seattle, Washington, USA
Marika Holland
National Center for Atmospheric Research, Boulder, Colorado, USA
Bonnie Light
Applied Physics Laboratory, University of Washington, Seattle, Washington, USA
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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.
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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.
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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.
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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.
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
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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.
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
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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.
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
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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.
Marika M. Holland, David Clemens-Sewall, Laura Landrum, Bonnie Light, Donald Perovich, Chris Polashenski, Madison Smith, and Melinda Webster
The Cryosphere, 15, 4981–4998, https://doi.org/10.5194/tc-15-4981-2021, https://doi.org/10.5194/tc-15-4981-2021, 2021
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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.
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
Short summary
Short summary
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.
Cited articles
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 CESM: Relationship to Climate Sensitivity and
Comparison of CESM1 to CESM2, J. Adv. Model. Earth Sy.,
12, e2020MS002120, https://doi.org/10.1029/2020MS002120, 2020. a
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. a
Boutin, G., Lique, C., Ardhuin, F., Rousset, C., Talandier, C., Accensi, M., and Girard-Ardhuin, F.: Towards a coupled model to investigate wave–sea ice interactions in the Arctic marginal ice zone, The Cryosphere, 14, 709–735, https://doi.org/10.5194/tc-14-709-2020, 2020. a, b, c, d
Boutin, G., Williams, T., Rampal, P., Olason, E., and Lique, C.: Wave–sea-ice interactions in a brittle rheological framework, The Cryosphere, 15, 431–457, https://doi.org/10.5194/tc-15-431-2021, 2021. a, b, c
Computational and Information Systems Laboratory: Cheyenne: HPE/SGI ICE XA System (Climate Simulation Laboratory), National Center for Atmospheric Research, Boulder, CO, https://doi.org/10.5065/D6RX99HX, 2019. a
Curry, J. A., Schramm, J. L., and Ebert, E. E.: Sea ice-albedo climate feedback
mechanism, J. Climate, 8, 240–247, 1995. 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., 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, b, c
DeRepentigny, P., Jahn, A., Holland, M. M., and Smith, A.: Arctic sea ice in
two configurations of the CESM2 during the 20th and 21st centuries, J. Geophys. Res.-Ocean., 125, e2020JC016133, https://doi.org/10.1029/2020JC016133, 2020. a
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, 2019. a
Holland, M. M.: An improved single-column model representation of ocean mixing
associated with summertime leads: Results from a SHEBA case study, J.
Geophys. Res.-Ocean., 108, 3107, https://doi.org/10.1029/2002JC001557, 2003. a
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, 2006a. a
Holland, M. M., Bitz, C. M., and Tremblay, B.: Future abrupt reductions in the
summer Arctic sea ice, Geophys. Res. Lett., 33, L23503, https://doi.org/10.1029/2006GL028024, 2006b. a, b, c, d
Holland, M. M., Clemens-Sewall, D., Landrum, L., Light, B., Perovich, D., Polashenski, C., Smith, M., and Webster, M.: The influence of snow on sea ice as assessed from simulations of CESM2, The Cryosphere, 15, 4981–4998, https://doi.org/10.5194/tc-15-4981-2021, 2021. a
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
Hunke, E. C.: Sea ice volume and age: Sensitivity to physical parameterizations
and thickness resolution in the CICE sea ice model, Ocean Model., 82,
45–59, 2014. a
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-06-012, Tech. rep., Los Alamos National Laboratory,
available at: https://github.com/CICE-Consortium/CICE-svn-trunk/blob/main/cicedoc/cicedoc.pdf (last access: 31 January 2022), 2015. a
Josberger, E. G. and Martin, S.: A laboratory and theoretical study of the
boundary layer adjacent to a vertical melting ice wall in salt water,
J. Fluid Mech., 111, 439–473, 1981. a
Kay, J. E., DeRepentigny, P., Holland, M., Bailey, D., DuVivier, A.,
Blanchard-Wrigglesworth, E., Deser, C., Jahn, A., Singh, H., Smith, M., and
Webster, M.: Less surface sea ice melt in the CESM2 improves Arctic sea ice
simulation with minimal non-polar climate impacts, J. Adv. Model. Earth Sy., in review, https://doi.org/10.1002/essoar.10507477.1, 2022. a, b
Keen, A., Blockley, E., Bailey, D. A., Boldingh Debernard, J., Bushuk, M., Delhaye, S., Docquier, D., Feltham, D., Massonnet, F., O'Farrell, S., Ponsoni, L., Rodriguez, J. M., Schroeder, D., Swart, N., Toyoda, T., Tsujino, H., Vancoppenolle, M., and Wyser, K.: An inter-comparison of the mass budget of the Arctic sea ice in CMIP6 models, The Cryosphere, 15, 951–982, https://doi.org/10.5194/tc-15-951-2021, 2021. a, b, c, d, e, f
Lindsay, R., Zhang, J., Schweiger, A., Steele, M., and Stern, H.: Arctic sea
ice retreat in 2007 follows thinning trend, J. Climate, 22, 165–176,
2009. a
Massonnet, F., Fichefet, T., Goosse, H., Vancoppenolle, M., Mathiot, P., and König Beatty, C.: On the influence of model physics on simulations of Arctic and Antarctic sea ice, The Cryosphere, 5, 687–699, https://doi.org/10.5194/tc-5-687-2011, 2011. a
Massonnet, F., Barthélemy, A., Worou, K., Fichefet, T., Vancoppenolle, M., Rousset, C., and Moreno-Chamarro, E.: On the discretization of the ice thickness distribution in the NEMO3.6-LIM3 global ocean–sea ice model, Geosci. Model Dev., 12, 3745–3758, https://doi.org/10.5194/gmd-12-3745-2019, 2019. a
Maykut, G. A. and Perovich, D. K.: The role of shortwave radiation in the
summer decay of a sea ice cover, J. Geophys. Res.-Ocean., 92, 7032–7044,
https://doi.org/10.1029/JC092iC07p07032, 1987. a, b
Merryfield, W. J., Holland, M. M., and Monahan, A. H.: Multiple equilibria and
abrupt transitions in Arctic summer sea ice extent, Arctic Sea Ice Decline:
Observations, Projections, Mechanisms, and Implications, Geophys. Monogr.
Ser, 180, 151–174, 2008. a
Moreno-Chamarro, E., Ortega, P., and Massonnet, F.: Impact of the ice thickness distribution discretization on the sea ice concentration variability in the NEMO3.6–LIM3 global ocean–sea ice model, Geosci. Model Dev., 13, 4773–4787, https://doi.org/10.5194/gmd-13-4773-2020, 2020. a
Nicolaus, M., Perovich, D., Spreen, G., Granskog, M., Albedyll, L., Angelopoulos, M., Anhaus, P., Arndt, S., Belter, H., Bessonov, V., Birnbaum, G., Brauchle, J., Calmer, R., Cardellach, E., Cheng, B., Clemens-Sewall, D., Dadic, R., Damm, E., Boer, G., Demir, O., Dethloff, K., Divine, D., Fong, A., Fons, S., Frey, M., Fuchs, N., Gabarró, C., Gerland, S., Goessling, H., Gradinger, R., Haapala, J., Haas, C., Hamilton, J., Hannula, H.-R., Hendricks, S., Herber, A., Heuzé, C., Hoppmann, M., Høyland, K., Huntemann, M., Hutchings, J., Hwang, B., Itkin, P., Jacobi, H.-W., Jaggi, M., Jutila, A., Kaleschke, L., Katlein, C., Kolabutin, N., Krampe, D., Kristensen, S., Krumpen, T., Kurtz, N., Lampert, A., Lange, B., Lei, R., Light, B., Linhardt, F., Liston, G., Loose, B., Macfarlane, A., Mahmud, M., Matero, I., Maus, S., Morgenstern, A., Naderpour, R., Nandan, V., Niubom, A., Oggier, M., Oppelt, N., Pätzold, F., Perron, C., Petrovsky, T., Pirazzini, R., Polashenski, C., Rabe, B., Raphael, I., Regnery, J., Rex, M., Ricker, R., Riemann-Campe, K., Rinke, A., Rohde, J., Salganik, E., Scharien, R., Schiller, M., Schneebeli, M., Semmling, M., Shimanchuk, E., Shupe, M., Smith, M., Smolyanitsky, V., Sokolov, V., Stanton, T., Stroeve, J., Thielke, L., Timofeeva, A., Tonboe, R., Tavri, A., Tsamados, M., Wagner, D., Watkins, D., Webster, M., and Wendisch, M.: Overview of the MOSAiC expedition – Snow and Sea Ice,
Elementa Science of the Anthropocene, https://doi.org/10.1525/elementa.2021.000046, 2021. a
Notz, D. and Bitz, C. M.: Sea ice in Earth system models, Sea ice, 3, 304–325,
2017. a
Perovich, D. K., Grenfell, T. C., Richter-Menge, J. A., Light, B., Tucker,
W. B., and Eicken, H.: Thin and thinner: Sea ice mass balance measurements
during SHEBA, J. Geophys. Res.-Ocean., 108, https://doi.org/10.1029/2001JC001079, 2003. a
Petty, A. A., Holland, P. R., and Feltham, D. L.: Sea ice and the ocean mixed layer over the Antarctic shelf seas, The Cryosphere, 8, 761–783, https://doi.org/10.5194/tc-8-761-2014, 2014. a
Richter-Menge, J. A., Perovich, D. K., and Pegau, W. S.: Summer ice dynamics
during SHEBA and its effect on the ocean heat content, Ann. Glaciol.,
33, 201–206, 2001. a
Rigor, I. G., Wallace, J. M., and Colony, R. L.: Response of sea ice to the
Arctic Oscillation, J. Climate, 15, 2648–2663, 2002. a
Rothrock, D. A. and Thorndike, A. S.: Measuring the sea ice floe size
distribution, J. Geophys. Res.-Ocean., 89, 6477–6486,
https://doi.org/10.1029/JC089iC04p06477, 1984. a
Skyllingstad, E. D., Paulson, C. A., and Pegau, W. S.: Simulation of turbulent
exchange processes in summertime leads, J. Geophys. Res.-Ocean., 110, C05021, https://doi.org/10.1029/2004JC002502, 2005.
a, b
Smith, M. M.: Lateral_melting_TC_2022: Data for sea ice sensitivity to lateral melting, CESM2, in: The Cryosphere (Vol. 16, pp. 1–16), Zenodo [data set], https://doi.org/10.5281/zenodo.5941594, 2022. a
Stroeve, J., Holland, M. M., Meier, W., Scambos, T., and Serreze, M.: Arctic
sea ice decline: Faster than forecast, Geophys. Res. Lett., 34, https://doi.org/10.1029/2007GL029703,
2007. a
Thorndike, A. S., Rothrock, D., Maykut, G., and Colony, R.: The thickness
distribution of sea ice, J. Geophys. Res., 80, 4501–4513,
1975. a
Tsamados, M., Feltham, D., Petty, A., Schroeder, D., and Flocco, D.: Processes
controlling surface, bottom and lateral melt of Arctic sea ice in a state of
the art sea ice model, Philos. T. Roy. Soc. A, 373, 20140167, https://doi.org/10.1098/rsta.2014.0167, 2015. 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.-Ocean., 120, 1253–1275, 2015. a
Ungermann, M., Tremblay, L. B., Martin, T., and Losch, M.: Impact of the ice
strength formulation on the performance of a sea ice thickness distribution
model in the A rctic, J. Geophys. Res.-Ocean., 122,
2090–2107, 2017. 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.-Ocean., 120, 3484–3498, https://doi.org/10.1002/2015JC010770, 2015. a, b, c
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.
Climate models represent the atmosphere, ocean, sea ice, and land with equations of varying...