Articles | Volume 17, issue 9
https://doi.org/10.5194/tc-17-3695-2023
© Author(s) 2023. 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-17-3695-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Observing the evolution of summer melt on multiyear sea ice with ICESat-2 and Sentinel-2
Ellen M. Buckley
CORRESPONDING AUTHOR
Center for Fluid Mechanics, Brown University, Providence, RI, USA
Department of Atmospheric and Oceanic Sciences, University of Maryland, College Park, MD, USA
Sinéad L. Farrell
Department of Atmospheric and Oceanic Sciences, University of Maryland, College Park, MD, USA
Department of Geographical Sciences, University of Maryland, College Park, MD, USA
Ute C. Herzfeld
Department of Electrical, Computer and Energy Engineering, University of Colorado Boulder, Boulder, CO, USA
Melinda A. Webster
Geophysical Institute, University of Alaska Fairbanks, Fairbanks, AK, USA
Polar Science Center, University of Washington, Seattle, WA, USA
Thomas Trantow
Department of Electrical, Computer and Energy Engineering, University of Colorado Boulder, Boulder, CO, USA
Oliwia N. Baney
Department of Geographical Sciences, University of Maryland, College Park, MD, USA
Kyle A. Duncan
Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
Huilin Han
Department of Electrical, Computer and Energy Engineering, University of Colorado Boulder, Boulder, CO, USA
Matthew Lawson
Department of Electrical, Computer and Energy Engineering, University of Colorado Boulder, Boulder, CO, 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
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.
Ellen M. Buckley, Leela Cañuelas, Mary-Louise Timmermans, and Monica M. Wilhelmus
The Cryosphere, 18, 5031–5043, https://doi.org/10.5194/tc-18-5031-2024, https://doi.org/10.5194/tc-18-5031-2024, 2024
Short summary
Short summary
Arctic sea ice cover evolves seasonally from large plates separated by long, linear leads in the winter to a mosaic of smaller sea ice floes in the summer. Here, we present a new image segmentation algorithm applied to thousands of images and identify over 9 million individual pieces of ice. We observe the characteristics of the floes and how they evolve throughout the summer as the ice breaks up.
Christopher Horvat, Ellen Buckley, Madelyn Stewart, Poom Yoosiri, and Monica M. Wilhelmus
EGUsphere, https://doi.org/10.5194/egusphere-2023-2312, https://doi.org/10.5194/egusphere-2023-2312, 2023
Preprint withdrawn
Short summary
Short summary
The decline of sea ice area variability is a leading indicator of climate change, and accurate measurement of sea ice area are of high importance. We develop new measurement of sea ice area coverage using the ICESat-2 laser altimeter, typically used to measure the height of the ice surface. The new method performs as well or better than typical passive microwave measurements, especially for sea ice populated with thin fractures in winter.
Kennedy A. Lange, Alice C. Bradley, Kyle Duncan, and Sinéad L. Farrell
The Cryosphere, 19, 2045–2065, https://doi.org/10.5194/tc-19-2045-2025, https://doi.org/10.5194/tc-19-2045-2025, 2025
Short summary
Short summary
Grounded sea ice ridges stabilize nearshore sea ice by anchoring it in the seafloor. In this study, we develop a method to identify grounded ridges in satellite data and measure the height, depth, distance from shore, and width of a thousand ridges across the Alaska Arctic, finding regional differences in these metrics across the coastline. This method lays the groundwork for a better understanding of nearshore ice stability, holding importance for Arctic community food security and safety.
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
Short summary
Short summary
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.
Madison M. Smith, Niels Fuchs, Evgenii Salganik, Donald K. Perovich, Ian Raphael, Mats A. Granskog, Kirstin Schulz, Matthew D. Shupe, and Melinda Webster
The Cryosphere, 19, 619–644, https://doi.org/10.5194/tc-19-619-2025, https://doi.org/10.5194/tc-19-619-2025, 2025
Short summary
Short summary
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.
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.
Ellen M. Buckley, Leela Cañuelas, Mary-Louise Timmermans, and Monica M. Wilhelmus
The Cryosphere, 18, 5031–5043, https://doi.org/10.5194/tc-18-5031-2024, https://doi.org/10.5194/tc-18-5031-2024, 2024
Short summary
Short summary
Arctic sea ice cover evolves seasonally from large plates separated by long, linear leads in the winter to a mosaic of smaller sea ice floes in the summer. Here, we present a new image segmentation algorithm applied to thousands of images and identify over 9 million individual pieces of ice. We observe the characteristics of the floes and how they evolve throughout the summer as the ice breaks up.
Christopher Horvat, Ellen Buckley, Madelyn Stewart, Poom Yoosiri, and Monica M. Wilhelmus
EGUsphere, https://doi.org/10.5194/egusphere-2023-2312, https://doi.org/10.5194/egusphere-2023-2312, 2023
Preprint withdrawn
Short summary
Short summary
The decline of sea ice area variability is a leading indicator of climate change, and accurate measurement of sea ice area are of high importance. We develop new measurement of sea ice area coverage using the ICESat-2 laser altimeter, typically used to measure the height of the ice surface. The new method performs as well or better than typical passive microwave measurements, especially for sea ice populated with thin fractures in winter.
Robert Ricker, Steven Fons, Arttu Jutila, Nils Hutter, Kyle Duncan, Sinead L. Farrell, Nathan T. Kurtz, and Renée Mie Fredensborg Hansen
The Cryosphere, 17, 1411–1429, https://doi.org/10.5194/tc-17-1411-2023, https://doi.org/10.5194/tc-17-1411-2023, 2023
Short summary
Short summary
Information on sea ice surface topography is important for studies of sea ice as well as for ship navigation through ice. The ICESat-2 satellite senses the sea ice surface with six laser beams. To examine the accuracy of these measurements, we carried out a temporally coincident helicopter flight along the same ground track as the satellite and measured the sea ice surface topography with a laser scanner. This showed that ICESat-2 can see even bumps of only few meters in the sea ice cover.
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
Short summary
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.
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.
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
Short summary
Short summary
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.
Anja Rösel, Sinead Louise Farrell, Vishnu Nandan, Jaqueline Richter-Menge, Gunnar Spreen, Dmitry V. Divine, Adam Steer, Jean-Charles Gallet, and Sebastian Gerland
The Cryosphere, 15, 2819–2833, https://doi.org/10.5194/tc-15-2819-2021, https://doi.org/10.5194/tc-15-2819-2021, 2021
Short summary
Short summary
Recent observations in the Arctic suggest a significant shift towards a snow–ice regime caused by deep snow on thin sea ice which may result in a flooding of the snowpack. These conditions cause the brine wicking and saturation of the basal snow layers which lead to a subsequent underestimation of snow depth from snow radar mesurements. As a consequence the calculated sea ice thickness will be biased towards higher values.
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
Short summary
Short summary
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.
Renée Mie Fredensborg Hansen, Eero Rinne, Sinéad Louise Farrell, and Henriette Skourup
The Cryosphere, 15, 2511–2529, https://doi.org/10.5194/tc-15-2511-2021, https://doi.org/10.5194/tc-15-2511-2021, 2021
Short summary
Short summary
Ice navigators rely on timely information about ice conditions to ensure safe passage through ice-covered waters, and one parameter, the degree of ice ridging (DIR), is particularly useful. We have investigated the possibility of estimating DIR from the geolocated photons of ICESat-2 (IS2) in the Bay of Bothnia, show that IS2 retrievals from different DIR areas differ significantly, and present some of the first steps in creating sea ice applications beyond e.g. thickness retrieval.
Cited articles
Arias, P., Bellouin, N., Coppola, E., Jones, R., Krinner, G., Marotzke, J.,
Naik, V., Palmer, M., Plattner, G.-K., Rogelj, J., Rojas, M., Sillmann, J.,
Storelvmo, T., Thorne, P., Trewin, B., Achutarao, K., Adhikary, B., Allan,
R., Armour, K., Bala, G., Barimalala, R., Berger, S., Canadell, J. G.,
Cassou, C., Cherchi, A., Collins, W. D., Collins, W. J., Connors, S., Corti,
S., Cruz, F., Dentener, F. J., Dereczynski, C., Luca, A. D., Niang, A. D.,
Doblas-Reyes, P., Dosio, A., Douville, H., Engelbrecht, F., Eyring, V.,
Fischer, E. M., Forster, P., Fox-Kemper, B., Fuglestvedt, J., Fyfe, J.,
Gillett, N., Goldfarb, L., Gorodetskaya, I., Gutierrez, J. M., Hamdi, R.,
Hawkins, E., Hewitt, H., Hope, P., Islam, A. S., Jones, C., Kaufmann, D.,
Kopp, R., Kosaka, Y., Kossin, J., Krakovska, S., Li, J., Lee, J.-Y.,
Masson-Delmotte, V., Mauritsen, T., Maycock, T., Meinshausen, M., ki Min, S.,
Duc, T. N., Otto, F., Pinto, I., Pirani, A., Raghavan, K., Ranasighe, R.,
Ruane, A., Ruiz, L., Sallée, J.-B., Samset, B. H., Sathyendranath, S.,
Monteiro, P. S., Seneviratne, S. I., Sörensson, A. A., Szopa, S.,
Takayabu, I., Treguier, A.-M., van den Hurk, B., Vautard, R., Schuckmann,
K. V., Zaehle, S., Zhang, X., and Zickfeld, K.: Climate Change 2021: The
Physical Science Basis. Contribution of Working Group I to the Sixth
Assessment Report of the Intergovernmental Panel on Climate Change; Technical
Summary, in: The Intergovernmental Panel on Climate Change AR6, edited by
Masson-Delmotte, V., Zhai, P., Pirani, A., Conners, S., Péan, C., Berger,
S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M., Huang, M., Leitzell, K.,
Lonnoy, E., Matthews, J., Maycock, T., Waterfield, T., Yelekçi, O., Yu,
R., and Zhou, B., https://elib.dlr.de/137584/ (last access: February 2022), 2021. a
Armon, M., Dente, E., Shmilovitz, Y., Mushkin, A., Cohen, T. J., Morin, E., and
Enzel, Y.: Determining Bathymetry of Shallow and Ephemeral Desert
Lakes Using Satellite Imagery and Altimetry, Geophys. Res.
Lett., 47, e2020GL087367, https://doi.org/10.1029/2020GL087367, 2020. a
Arntsen, A. E., Song, A. J., Perovich, D. K., and Richter-Menge, J. A.:
Observations of the Summer Breakup of an Arctic Sea Ice Cover,
Geophys. Res. Lett., 42, 8057–8063, https://doi.org/10.1002/2015GL065224,
2015. a
Arrigo, K. R., Perovich, D. K., Pickart, R. S., Brown, Z. W., van Dijken,
G. L., Lowry, K. E., Mills, M. M., Palmer, M. A., Balch, W. M., Bahr, F.,
Bates, N. R., Benitez-Nelson, C., Bowler, B., Brownlee, E., Ehn, J. K.,
Frey, K. E., Garley, R., Laney, S. R., Lubelczyk, L., Mathis, J., Matsuoka,
A., Mitchell, B. G., Moore, G. W. K., Ortega-Retuerta, E., Pal, S.,
Polashenski, C. M., Reynolds, R. A., Schieber, B., Sosik, H. M., Stephens,
M., and Swift, J. H.: Massive Phytoplankton Blooms Under Arctic Sea Ice,
Science, 336, 1408–1408, https://doi.org/10.1126/science.1215065, 2012. a
Babbel, B. J., Parrish, C. E., and Magruder, L. A.: ICESat-2 Elevation
Retrievals in Support of Satellite-Derived Bathymetry for Global
Science Applications, Geophys. Res. Lett., 48, e2020GL090629,
https://doi.org/10.1029/2020GL090629, 2021. a
Ballinger, T., Overland, J., Wang, M., Bhatt, U., Hanna, E., Hanssen-Bauer, I.,
Kim, S.-J., Thoman, R., and Walsh, J.: Arctic report card 2020: surface air
temperature, United States, National Oceanic and Atmospheric Administration, Office of Oceanic and Atmospheric Research, Physical Sciences Laboratory (U.S.), Cooperative Institute for Research in the Atmosphere, Fort Collins, Colo., https://doi.org/10.25923/gcw8-2z06, 2020. a
Bourke, R. H. and Garrett, R. P.: Sea ice thickness distribution in the Arctic
Ocean, Cold Reg. Sci. Technol., 13, 259–280, 1987. a
Breivik, L.-A., Eastwood, S., and Lavergne, T.: Use of C-Band Scatterometer
for Sea Ice Edge Identification, IEEE T. Geosci.
Remote, 50, 2669–2677, https://doi.org/10.1109/TGRS.2012.2188898, 2012. a, b
Buckley, E. M.: 2020 Multiyear Ice Region Summer Melt Data, Zenodo [data set], https://doi.org/10.5281/zenodo.7568995, 2023. a
Buckley, E. and Eun, J.: ellenbuckley/MeltEvolution: R1 Melt Evolution Repo (firstrealease), Zenodo [code], https://doi.org/10.5281/zenodo.8280332, 2023. a
Comiso, J. C.: A rapidly declining perennial sea ice cover in the Arctic,
Geophys. Res. Lett., 29, 17–20, https://doi.org/10.1029/2002GL015650, 2002. a
Comiso, J. C., Parkinson, C. L., Gersten, R., and Stock, L.: Accelerated
decline in the Arctic sea ice cover, Geophys. Res. Lett., 35, e2022GL100272, https://doi.org/10.1029/2022GL100272, 2008. a
Curcio, J. A. and Petty, C. C.: The Near Infrared Absorption Spectrum of
Liquid Water, J. Opt. Soc. Am., 41, 302–304, https://doi.org/10.1364/JOSA.41.000302, 1951. a
Curry, J. A., Schramm, J. L., and Ebert, E. E.: Sea Ice-Albedo Climate
Feedback Mechanism, J. Climate, 8, 240–247,
https://doi.org/10.1175/1520-0442(1995)008<0240:SIACFM>2.0.CO;2, 1995. a
Druckenmiller, M. L., Moon, T. A., Thoman, R. L., Ballinger, T. J., Berner,
L. T., Bernhard, G. H., Bhatt, U. S., Bjerke, J. W., Box, J. E., Brown, R.,
Cappelen, J., Christiansen, H. H., Decharme, B., Derksen, C., Divine, D., Drozdov, D. S., Chereque, A. E., Epstein, H. E., Farquharson, L.M., Farrell, S. L., Fausto, R.S., Fettweis, X., Fioletov, V.E., Forbes, B.C., Frost, G. V., Gargulinski, E., Gerland, S., Goetz, S.J., Grabinski, Z., Grooß, J.-U., Haas, C., Hanna, E., Hanssen-Bauer, I., Hendricks, S., Holmes, R. M., Ialongo, I., Isaksen, K., Jain, P., Johnsen, B., Kaleschke, L., Kholodov, A. L., Kim, S.-J., Korsgaard, N. J., Labe, Z., Lakkala, K., Lara, M. J., Loomis, B., Luojus, K., Macander, M.J., Malkova, G.V., Mankoff, K. D., Manney, G.L., McClelland, J. W., Meier, W. N., Mote, T., Mudryk, L., Müller, R., Nyland, K. E., Overland, J. E., Park, T., Pavlova, O., Perovich, D., Petty, A., Phoenix, G. K., Raynolds, M.K., Reijmer, C.H., Richter-Menge, J., Ricker, R., Romanovsky, V. E., Scott, L., Shapiro, H., Shiklomanov, A. I., Shiklomanov, N.I., P. P. Smeets, C. J., Smith, S.L., Soja, A., M. Spencer, R. G., Starkweather, S., Streletskiy, D. A., Suslova, A., Svendby, T., Tank, S. E., Tedesco, M., Tian-Kunze, X., Timmermans, M.-L., Tømmervik, H., Tretiakov, M., Tschudi, M., Vakhutinsky, S., As, D. van, W. van de Wal, R. S., Veraverbeke, S., Walker, D. A., Walsh, J.E., Wang, M., Webster, M., Winton, Ø., Wood, K., York, A., and Ziel, R.: The Arctic, B. Am. Meteorol. Soc., 102,
S263–S316, 2021. a, b, c
Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F.,
Hoersch, B., Isola, C., Laberinti, P., Martimort, P., Meygret, A., Spoto, F.,
Sy, O., Marchese, F., and Bargellini, P.: Sentinel-2: ESA's Optical
High-Resolution Mission for GMES Operational Services, Remote Sens. Environ., 120, 25–36, https://doi.org/10.1016/j.rse.2011.11.026, 2012. a, b, c, d
Duncan, K. and Farrell, S. L.: Determining Variability in Arctic Sea Ice
Pressure Ridge Topography With ICESat-2, Geophys. Res. Lett., 49,
e2022GL100272, https://doi.org/10.1029/2022GL100272, 2022. a
Ebert, E. E. and Curry, J. A.: An Intermediate One-Dimensional Thermodynamic
Sea Ice Model for Investigating Ice-Atmosphere Interactions, J.
Geophys. Res.-Oceans, 98, 10085–10109, https://doi.org/10.1029/93JC00656,
1993. a
Eicken, H., Grenfell, T. C., Perovich, D. K., Richter-Menge, J. A., and Frey,
K.: Hydraulic Controls of Summer Arctic Pack Ice Albedo, J.
Geophys. Res.-Oceans, 109, C08007, https://doi.org/10.1029/2003JC001989, 2004. a, b, c
ESA: Copernicus Sentinel-2, MSI Level-1C TOA Reflectance Product, Collection 1. European Space Agency [data set], https://doi.org/10.5270/S2_-742ikth, 2021. a
Fetterer, F., Knowles, K., Meier, W., and Savoie, M., and Windnagel, A.: Sea
Ice Index, Version 3, National Snow & Ice Data Center [data set], https://doi.org/10.7265/N5K072F8, 2017. a, b, c, d
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, b
Flocco, D., Feltham, D. L., Bailey, E., and Schroeder, D.: The Refreezing of
Melt Ponds on Arctic Sea Ice, J. Geophys. Res.-Oceans,
120, 647–659, https://doi.org/10.1002/2014JC010140, 2015. a, b
Fricker, H. A., Arndt, P., Brunt, K. M., Datta, R. T., Fair, Z., Jasinski,
M. F., Kingslake, J., Magruder, L. A., Moussavi, M., and Pope, A.: ICESat-2
Meltwater Depth Estimates: Application to Surface Melt on Amery
Ice Shelf, East Antarctica, Geophys. Res. Lett., 48,
e2020GL090550, https://doi.org/10.1029/2020GL090550, 2021. a, b
Grenfell, T. C. and Perovich, D. K.: Seasonal and Spatial Evolution of Albedo
in a Snow-Ice-Land-Ocean Environment, J. Geophys. Res.-Oceans, 109, C01001, https://doi.org/10.1029/2003JC001866, 2004. a
Herzfeld, U. C., Trantow, T. M., Harding, D., and Dabney, P. W.:
Surface-Height Determination of Crevassed
Glaciers – Mathematical Principles of an Autoadaptive
Density-Dimension Algorithm and Validation Using ICESat-2 Simulator
(SIMPL) Data, IEEE T. Geosci. Remote, 55,
1874–1896, https://doi.org/10.1109/TGRS.2016.2617323, 2017. a, b, c, d
Herzfeld, U., Hayes, A., Palm, S., Hancock, D., Vaughan, M., and Barbieri, K.:
Detection and Height Measurement of Tenuous Clouds and Blowing Snow in
ICESat-2 ATLAS Data, Geophys. Res. Lett., 48, e2021GL093473, https://doi.org/10.1029/2021GL093473,
2021a. a
Herzfeld, U. C., Trantow, T., Lawson, M., Hans, J., and Medley, G.: Surface
heights and crevasse morphologies of surging and fast-moving glaciers from
ICESat-2 laser altimeter data-Application of the density-dimension algorithm
(DDA-ice) and evaluation using airborne altimeter and Planet SkySat data,
Science of Remote Sensing, 3, 100013, https://doi.org/10.1016/j.srs.2020.100013, 2021b. 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, c
Horvat, C., Flocco, D., Rees Jones, D., Roach, L., and Golden, K.: The Effect
of Melt Pond Geometry on the Distribution of Solar Energy Under First-Year
Sea Ice, Geophys. Res. Lett., 47, e2019GL085956, https://doi.org/10.1029/2019GL085956, 2020. 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
Istomina, L., Heygster, G., Huntemann, M., Schwarz, P., Birnbaum, G., Scharien, R., Polashenski, C., Perovich, D., Zege, E., Malinka, A., Prikhach, A., and Katsev, I.: Melt pond fraction and spectral sea ice albedo retrieval from MERIS data – Part 1: Validation against in situ, aerial, and ship cruise data, The Cryosphere, 9, 1551–1566, https://doi.org/10.5194/tc-9-1551-2015, 2015. a
Kwok, R.: Declassified High-Resolution Visible Imagery for Arctic Sea Ice
Investigations: An Overview, Remote Sens. Environ., 142, 44–56,
https://doi.org/10.1016/j.rse.2013.11.015, 2014. a
Kwok, R., Markus, T., Kurtz, N., Petty, A., Neumann, T., Farrell, S.,
Cunningham, G., Hancock, D., Ivanoff, A., and Wimert, J.: Surface height and
sea ice freeboard of the Arctic Ocean from ICESat-2: Characteristics and
early results, J. Geophys. Res.-Oceans, 124, 6942–6959,
2019. a
Kwok, R., Petty, A. A., Bagnardi, M., Kurtz, N. T., Cunningham, G. F., Ivanoff, A., and Kacimi, S.: Refining the sea surface identification approach for determining freeboards in the ICESat-2 sea ice products, The Cryosphere, 15, 821–833, https://doi.org/10.5194/tc-15-821-2021, 2021a. a
Kwok, R., Petty, A. A., Cunningham, G., Markus, T., Hancock, D., Ivanoff, A.,
Wimert, J., Bagnardi, M., Kurtz, N., and and the ICESat-2 Science Team:
ATLAS/ICESat-2 L3A Sea Ice Height, Version 5, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set]
https://doi.org/10.5067/ATLAS/ATL07.005, 2021b. a, b, c, d
Landy, J., Ehn, J., Shields, M., and Barber, D.: Surface and melt pond
evolution on landfast first-year sea ice in the Canadian Arctic Archipelago,
J. Geophys. Res.-Oceans, 119, 3054–3075, 2014. a
Lee, S., Stroeve, J., Tsamados, M., and Khan, A. L.: Machine learning
approaches to retrieve pan-Arctic melt ponds from visible satellite imagery,
Remote Sens. Environ., 247, 111919, https://doi.org/10.1016j.rse.2020.111919, 2020. a
Li, Q., Zhou, C., Zheng, L., Liu, T., and Yang, X.: Monitoring evolution of
melt ponds on first-year and multiyear sea ice in the Canadian Arctic
Archipelago with optical satellite data, Ann. Glaciol., 61, 154–163,
2020. a
Light, B., Grenfell, T. C., and Perovich, D. K.: Transmission and Absorption of
Solar Radiation by Arctic Sea Ice during the Melt Season, J.
Geophys. Res.-Oceans, 113, C03023, https://doi.org/10.1029/2006JC003977, 2008. a, b
Light, B., Smith, M. M., Perovich, D. K., Webster, M. A., Holland, M. M.,
Linhardt, F., Raphael, I. A., Clemens-Sewall, D., Macfarlane, A. R., Anhaus,
P., and Bailey, D.: Arctic sea ice albedo: Spectral composition, spatial
heterogeneity, and temporal evolution observed during the MOSAiC drift, Elem.
Sci. Anth., 10, 000103, https://doi.org/10.1525/elementa.2021.000103, 2022. a
Lu, X., Hu, Y., Yang, Y., Vaughan, M., Palm, S., Trepte, C., Omar, A., Lucker,
P., and Baize, R.: Enabling value added scientific applications of ICESat-2
data with effective removal of afterpulses, Earth and Space Science, 8,
e2021EA001729, https://doi.org/10.1029/2021EA001729, 2021. a
Mäkynen, M., Kern, S., Rösel, A., and Pedersen, L. T.: On the
Estimation of Melt Pond Fraction on the Arctic Sea Ice With ENVISAT
WSM Images, IEEE T. Geosci. Remote, 52,
7366–7379, https://doi.org/10.1109/TGRS.2014.2311476, 2014. a
Markus, T., Cavalieri, D. J., and Ivanoff, A.: The Potential of Using
Landsat 7 ETM+ for the Classification of Sea-Ice Surface Conditions
during Summer, Ann. Glaciol., 34, 415–419,
https://doi.org/10.3189/172756402781817536, 2002. a
Markus, T., Cavalieri, D. J., Tschudi, M. A., and Ivanoff, A.: Comparison of
Aerial Video and Landsat 7 Data over Ponded Sea Ice, Remote Sens.
Environ., 86, 458–469, https://doi.org/10.1016/S0034-4257(03)00124-X, 2003. a
Markus, T., Neumann, T., Martino, A., Abdalati, W., Brunt, K., Csatho, B.,
Farrell, S., Fricker, H., Gardner, A., Harding, D., Jasinski, M., Kwok, R.,
Magruder, L., Lubin, D., Luthcke, S., Morison, J., Nelson, R.,
Neuenschwander, A., Palm, S., Popescu, S., Shum, C., Schutz, B. E., Smith,
B., Yang, Y., and Zwally, J.: The Ice, Cloud, and Land Elevation
Satellite-2 (ICESat-2): Science Requirements, Concept, and
Implementation, Remote Sens. Environ., 190, 260–273,
https://doi.org/10.1016/j.rse.2016.12.029, 2017. a, b
McFeeters, S. K.: The Use of the Normalized Difference Water Index
(NDWI) in the Delineation of Open Water Features, Int. J.
Remote Sens., 17, 1425–1432, https://doi.org/10.1080/01431169608948714, 1996. a, b
Mobley, C. D.: The Optical Properties of Water, in: Handbook of Optics Vol.
I, McGraw-Hill, New York, NY, USA, 43.3–43.56, ISBN 0-07-047740-X, 1995. a
Morassutti, M. P. and Ledrew, E. F.: Albedo and Depth of Melt Ponds on
Sea-Ice, Int. J. Climatol., 16, 817–838,
https://doi.org/10.1002/(SICI)1097-0088(199607)16:7<817::AID-JOC44>3.0.CO;2-5, 1996. a, b, c
Mortin, J., Svensson, G., Graversen, R. G., Kapsch, M.-L., Stroeve, J. C., and
Boisvert, L. N.: Melt Onset over Arctic Sea Ice Controlled by Atmospheric
Moisture Transport, Geophys. Res. Lett., 43, 6636–6642,
https://doi.org/10.1002/2016GL069330, 2016. a
Neumann, T. A., Martino, A. J., Markus, T., Bae, S., Bock, M. R., Brenner,
A. C., Brunt, K. M., Cavanaugh, J., Fernandes, S. T., Hancock, D. W.,
Harbeck, K., Lee, J., Kurtz, N. T., Luers, P. J., Luthcke, S. B., Magruder,
L., Pennington, T. A., Ramos-Izquierdo, L., Rebold, T., Skoog, J., and
Thomas, T. C.: The Ice, Cloud, and Land Elevation Satellite
– 2 Mission: A Global Geolocated Photon Product Derived from
the Advanced Topographic Laser Altimeter System, Remote Sens.
Environ., 233, 111325, https://doi.org/10.1016/j.rse.2019.111325, 2019. a, b, c
Neumann, T. A., Brenner, A., Hancock, D., Robbins, J., Saba, J., Harbeck, K.,
Gibbons, A., Lee, J., Luthcke, S. B., and Rebold, T.: ATLAS/ICESat-2
L2A Global Geolocated Photon Data, Version 5, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set],
https://doi.org/10.5067/ATLAS/ATL03.005, 2021. a, b, c, d
Newton, R., Pfirman, S., Tremblay, L. B., and DeRepentigny, P.: Defining the
“ice shed” of the Arctic Ocean's Last Ice Area and its future evolution,
Earth's Future, 9, e2021EF001988, https://doi.org/10.1029/2021EF001988, 2021. a
Niehaus, H., Spreen, G., Birnbaum, G., Istomina, L., Jäkel, E., Linhardt,
F., Neckel, N., Fuchs, N., Nicolaus, M., Sperzel, T., Tao, R., Webster, M., and Wright N.: Sea Ice Melt
Pond Fraction Derived From Sentinel-2 Data: Along the MOSAiC Drift and
Arctic-Wide, Geophys. Res. Lett., 50, e2022GL102102, https://doi.org/10.1029/2022GL102102, 2023. a, b
OSI-SAF: Global Sea Ice Type (netCDF) – Multimission, EUMETSAT SAF on Ocean and Sea Ice [data set], https://doi.org/10.15770/EUM_SAF_OSI_NRT, 2022. a, b
Palm, S. P., Yang, Y., Herzfeld, U., Hancock, D., Hayes, A., Selmer, P., Hart,
W., and Hlavka, D.: ICESat-2 Atmospheric Channel Description, Data
Processing and First Results, Earth and Space Science, 8,
e2020EA001470, https://doi.org/10.1029/2020EA001470, 2021. a, b
Parkinson, C. L. and Comiso, J. C.: On the 2012 Record Low Arctic Sea Ice
Cover: Combined Impact of Preconditioning and an August Storm,
Geophys. Res. Lett., 40, 1356–1361, https://doi.org/10.1002/grl.50349, 2013. a
Parrish, C. E., Magruder, L. A., Neuenschwander, A. L., Forfinski-Sarkozi,
N., Alonzo, M., and Jasinski, M.: Validation of ICESat-2 ATLAS Bathymetry
and Analysis of ATLAS's Bathymetric Mapping Performance, Remote
Sensing, 11, 1634, https://doi.org/10.3390/rs11141634, 2019. a, b, c
Pedersen, C. A., Roeckner, E., Lüthje, M., and Winther, J.-G.: A new sea
ice albedo scheme including melt ponds for ECHAM5 general circulation model,
J. Geophys. Res.-Atmos., 114, D08101, https://doi.org/10.1029/2008JD010440, 2009. a
Perovich, D. K. and Polashenski, C.: Albedo Evolution of Seasonal Arctic
Sea Ice, Geophys. Res. Lett., 39, L08501, https://doi.org/10.1029/2012GL051432, 2012. a, b, c, d
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.-Oceans, 107, 8044, https://doi.org/10.1029/2000JC000438,
2002a. a
Perovich, D., Meier, W., Tschudi, M., Hendricks, S., Petty, A. A., Divine, D.,
Farrell, S., Gerland, S., Haas, C., Kaleschke, L., Pavlova, O., Ricker, R.,
Tian-Kunze, X., Wood, K., and Webster, M.: Arctic Report Card 2020:
Sea Ice, United States. National Oceanic and Atmospheric Administration. Office of Oceanic and Atmospheric Research, Physical Sciences Laboratory (U.S.), Cooperative Institute for Research in the Atmosphere (Fort Collins, Colo.), https://doi.org/10.25923/N170-9H57, 2020. a, b
Perovich, D., Smith, M., Light, B., and Webster, M.: Meltwater sources and sinks for multiyear Arctic sea ice in summer, The Cryosphere, 15, 4517–4525, https://doi.org/10.5194/tc-15-4517-2021, 2021. a, b
Petrich, C., Eicken, H., Polashenski, C. M., Sturm, M., Harbeck, J. P.,
Perovich, D. K., and Finnegan, D. C.: Snow Dunes: A Controlling Factor of
Melt Pond Distribution on Arctic Sea Ice, J. Geophys.
Res.-Oceans, 117, C09029, https://doi.org/10.1029/2012JC008192, 2012. a
Rösel, A. and Kaleschke, L.: Exceptional Melt Pond Occurrence in the Years
2007 and 2011 on the Arctic Sea Ice Revealed from MODIS Satellite
Data, J. Geophys. Res.-Oceans, 117, C05018,
https://doi.org/10.1029/2011JC007869, 2012. a
Rösel, A., Kaleschke, L., and Birnbaum, G.: Melt ponds on Arctic sea ice determined from MODIS satellite data using an artificial neural network, The Cryosphere, 6, 431–446, https://doi.org/10.5194/tc-6-431-2012, 2012. a
Scharien, R. K., Segal, R., Nasonova, S., Nandan, V., Howell, S. E., and Haas,
C.: Winter Sentinel-1 backscatter as a predictor of spring Arctic sea ice
melt pond fraction, Geophys. Res. Lett., 44, 12–262, 2017. a
Scott, F. and Feltham, D. L.: A Model of the Three-Dimensional Evolution of
Arctic Melt Ponds on First-Year and Multiyear Sea Ice, J.
Geophys. Res.-Oceans, 115, C12064, https://doi.org/10.1029/2010JC006156, 2010. a, b, c, d
Shu, Q., Wang, Q., Song, Z., Qiao, F., Zhao, J., Chu, M., and Li, X.:
Assessment of Sea Ice Extent in CMIP6 With Comparison to
Observations and CMIP5, Geophys. Res. Lett., 47,
e2020GL087965, https://doi.org/10.1029/2020GL087965, 2020. a
Shupe, M. D., Rex, M., Dethloff, K., Damm, E., Fong, A. A., Gradinger, R.,
Heuze, C., Loose, B., Makarov, A., Maslowski, W., Nicolaus, M., Perovich, D.,
Rabe, B., Rinke, A., Sokolov, V., and Sommerfeld, A.: The MOSAiC
Expedition: A Year Drifting with the Arctic Sea Ice, Arctic Report
Card, United States, National Oceanic and Atmospheric Administration, Office of Oceanic and Atmospheric Research, Physical Sciences Laboratory (U.S.), Cooperative Institute for Research in the Atmosphere (Fort Collins, Colo.), https://doi.org/10.25923/9g3v-xh92, 2020. a
Sivaraj, K., Solander, K., Abolt, C., Hunke, E., and Whelsky, A.:
Characterization of Arctic Sea Ice Melt Pond Dynamics with Remote
Sensing, in: American Geophysical Union Fall Meeting, C22A-46, 12–16 December 2022, Chicago, IL, USA, https://ui.adsabs.harvard.edu/abs/2022AGUFM.C22A..46S/abstract (last access: February 2022), 2022. a
Smith, B., Fricker, H. A., Holschuh, N., Gardner, A. S., Adusumilli, S., Brunt,
K. M., Csatho, B., Harbeck, K., Huth, A., Neumann, T., Nilsson, J., and
Siegfried, M. R.: Land Ice Height-Retrieval Algorithm for NASA's
ICESat-2 Photon-Counting Laser Altimeter, Remote Sens. Environ.,
233, 111352, https://doi.org/10.1016/j.rse.2019.111352, 2019. a
Stammerjohn, S., Massom, R., Rind, D., and Martinson, D.: Regions of Rapid Sea
Ice Change: An Inter-Hemispheric Seasonal Comparison, Geophys.
Res. Lett., 39, L06501, https://doi.org/10.1029/2012GL050874, 2012. a, b
Stroeve, J., 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, 2014. a
Taylor, P. D. and Feltham, D. L.: A Model of Melt Pond Evolution on Sea Ice,
J. Geophys. Res.-Oceans, 109, C12007, https://doi.org/10.1029/2004JC002361,
2004. a
Thomas, N., Pertiwi, A. P., Traganos, D., Lagomasino, D., Poursanidis, D.,
Moreno, S., and Fatoyinbo, L.: Space-Borne Cloud-Native Satellite-Derived
Bathymetry (SDB) Models Using ICESat-2 And Sentinel-2, Geophys.
Res. Lett., 48, e2020GL092170, https://doi.org/10.1029/2020GL092170, 2021. a
Tilling, R., Kurtz, N. T., Bagnardi, M., Petty, A. A., and Kwok, R.: Detection
of Melt Ponds on Arctic Summer Sea Ice From ICESat-2, Geophys.
Res. Lett., 47, e2020GL090644, https://doi.org/10.1029/2020GL090644, 2020. a, b, c, d
Valgur, M., Jonas, Kersten, Delucchi, L., Baier, G., Malte, unnic, Staniewicz, S., Leonard Kioi kinyanjui, Bahr, V., Salembier, P., martinber, Keller, G., dwlsalmeida, Castro, C., and Raspopov, A.: sentinelsat/sentinelsat: v0.13 (v0.13), Zenodo [code], https://doi.org/10.5281/zenodo.2629555, 2019.
a
Vermote, E. and Wolfe, R.: MODIS/Terra Surface Reflectance Daily L2G Global 250m SIN Grid V061, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MOD09GQ.061, 2021. a
Wang, M. and Overland, J. E.: A sea ice free summer Arctic within 30 years?,
Geophys. Res. Lett., 36, L07502, https://doi.org/10.1029/2009GL037820, 2009. a
Wang, M. and Overland, J. E.: A Sea Ice Free Summer Arctic within 30 Years:
An Update from CMIP5 Models, Geophys. Res. Lett., 39, L18501,
https://doi.org/10.1029/2012GL052868, 2012. a
Webster, M. A., Rigor, I. G., Perovich, D. K., Richter-Menge, J. A.,
Polashenski, C. M., and Light, B.: Seasonal Evolution of Melt Ponds on
Arctic Sea Ice, J. Geophys. Res.-Oceans, 120, 5968–5982,
https://doi.org/10.1002/2015JC011030, 2015. a
Webster, M., Rigor, I., and Wright, N.: Observing Arctic Sea Ice,
Oceanography, 35, 29–37, https://doi.org/10.5670/oceanog.2022.115, 2022a. a
Webster, M. A., Holland, M., Wright, N. C., Hendricks, S., Hutter, N., Itkin,
P., Light, B., Linhardt, F., Perovich, D. K., Raphael, I. A., Smith, M. M.,
von Albedyll, L., and Zhang, J.: Spatiotemporal Evolution of Melt Ponds on
Arctic Sea Ice: MOSAiC Observations and Model Results, Elementa:
Science of the Anthropocene, 10, 000072, https://doi.org/10.1525/elementa.2021.000072,
2022b. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p
Wright, N. C. and Polashenski, C. M.: How machine learning and high-resolution
imagery can improve melt pond retrieval from MODIS over current spectral
unmixing techniques, J. Geophys. Res.-Oceans, 125,
e2019JC015569, https://doi.org/10.1029/2019JC015569, 2020. a
Wright, N. C., Polashenski, C. M., McMichael, S. T., and Beyer, R. A.: Observations of sea ice melt from Operation IceBridge imagery, The Cryosphere, 14, 3523–3536, https://doi.org/10.5194/tc-14-3523-2020, 2020. a
Yackel, J., Barber, D., and Hanesiak, J.: Melt ponds on sea ice in the Canadian
Archipelago: 1. Variability in morphological and radiative properties,
J. Geophys. Res.-Oceans, 105, 22049–22060, 2000. a
Zhang, J., Schweiger, A., Webster, M., Light, B., Steele, M., Ashjian, C.,
Campbell, R., and Spitz, Y.: Melt Pond Conditions on Declining Arctic
Sea Ice Over 1979–2016: Model Development, Validation,
and Results, J. Geophys. Res.-Oceans, 123, 7983–8003,
https://doi.org/10.1029/2018JC014298, 2018. a
Short summary
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.
In this study, we use satellite observations to investigate the evolution of melt ponds on the...