Articles | Volume 18, issue 7
https://doi.org/10.5194/tc-18-2991-2024
© Author(s) 2024. 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-18-2991-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
02 Jul 2024
Research article |
| 02 Jul 2024
| 02 Jul 2024
Sea ice melt pond bathymetry reconstructed from aerial photographs using photogrammetry: a new method applied to MOSAiC data
Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung, Bremerhaven, Germany
Center for Earth System Sustainability, Institute of Oceanography, Universität Hamburg, Hamburg, Germany
Luisa von Albedyll
Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung, Bremerhaven, Germany
Gerit Birnbaum
Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung, Bremerhaven, Germany
Felix Linhardt
Institute of Geography, Kiel University, Kiel, Germany
Natascha Oppelt
Institute of Geography, Kiel University, Kiel, Germany
Christian Haas
Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung, Bremerhaven, Germany
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Thomas Krumpen, Luisa von Albedyll, Helge F. Goessling, Stefan Hendricks, Bennet Juhls, Gunnar Spreen, Sascha Willmes, H. Jakob Belter, Klaus Dethloff, Christian Haas, Lars Kaleschke, Christian Katlein, Xiangshan Tian-Kunze, Robert Ricker, Philip Rostosky, Janna Rückert, Suman Singha, and Julia Sokolova
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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.
Luisa von Albedyll
Polarforschung, 89, 115–117, https://doi.org/10.5194/polf-89-115-2021, https://doi.org/10.5194/polf-89-115-2021, 2021
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Gemma M. Brett, Gregory H. Leonard, Wolfgang Rack, Christian Haas, Patricia J. Langhorne, and Anne Irvin
The Cryosphere Discuss., https://doi.org/10.5194/tc-2021-61, https://doi.org/10.5194/tc-2021-61, 2021
Manuscript not accepted for further review
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Luisa von Albedyll, Christian Haas, and Wolfgang Dierking
The Cryosphere, 15, 2167–2186, https://doi.org/10.5194/tc-15-2167-2021, https://doi.org/10.5194/tc-15-2167-2021, 2021
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Convergent sea ice motion produces a thick ice cover through ridging. We studied sea ice deformation derived from high-resolution satellite imagery and related it to the corresponding thickness change. We found that deformation explains the observed dynamic thickness change. We show that deformation can be used to model realistic ice thickness distributions. Our results revealed new relationships between thickness redistribution and deformation that could improve sea ice models.
Evelyn Jäkel, Tim Carlsen, André Ehrlich, Manfred Wendisch, Michael Schäfer, Sophie Rosenburg, Konstantina Nakoudi, Marco Zanatta, Gerit Birnbaum, Veit Helm, Andreas Herber, Larysa Istomina, Linlu Mei, and Anika Rohde
The Cryosphere Discuss., https://doi.org/10.5194/tc-2021-14, https://doi.org/10.5194/tc-2021-14, 2021
Preprint withdrawn
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Christian Haas, Patricia J. Langhorne, Wolfgang Rack, Greg H. Leonard, Gemma M. Brett, Daniel Price, Justin F. Beckers, and Alex J. Gough
The Cryosphere, 15, 247–264, https://doi.org/10.5194/tc-15-247-2021, https://doi.org/10.5194/tc-15-247-2021, 2021
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Maria-Elena Vorrath, Juliane Müller, Lorena Rebolledo, Paola Cárdenas, Xiaoxu Shi, Oliver Esper, Thomas Opel, Walter Geibert, Práxedes Muñoz, Christian Haas, Gerhard Kuhn, Carina B. Lange, Gerrit Lohmann, and Gesine Mollenhauer
Clim. Past, 16, 2459–2483, https://doi.org/10.5194/cp-16-2459-2020, https://doi.org/10.5194/cp-16-2459-2020, 2020
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Joshua King, Stephen Howell, Mike Brady, Peter Toose, Chris Derksen, Christian Haas, and Justin Beckers
The Cryosphere, 14, 4323–4339, https://doi.org/10.5194/tc-14-4323-2020, https://doi.org/10.5194/tc-14-4323-2020, 2020
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Physical measurements of snow on sea ice are sparse, making it difficulty to evaluate satellite estimates or model representations. Here, we introduce new measurements of snow properties on sea ice to better understand variability at distances less than 200 m. Our work shows that similarities in the snow structure are found at longer distances on younger ice than older ice.
Tim Carlsen, Gerit Birnbaum, André Ehrlich, Veit Helm, Evelyn Jäkel, Michael Schäfer, and Manfred Wendisch
The Cryosphere, 14, 3959–3978, https://doi.org/10.5194/tc-14-3959-2020, https://doi.org/10.5194/tc-14-3959-2020, 2020
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The angular reflection of solar radiation by snow surfaces is particularly anisotropic and highly variable. We measured the angular reflection from an aircraft using a digital camera in Antarctica in 2013/14 and studied its variability: the anisotropy increases with a lower Sun but decreases for rougher surfaces and larger snow grains. The applied methodology allows for a direct comparison with satellite observations, which generally underestimated the anisotropy measured within this study.
Marcel König and Natascha Oppelt
The Cryosphere, 14, 2567–2579, https://doi.org/10.5194/tc-14-2567-2020, https://doi.org/10.5194/tc-14-2567-2020, 2020
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We used data that we collected on RV Polarstern cruise PS106 in summer 2017 to develop a model for the derivation of melt pond depth on Arctic sea ice from reflectance measurements. We simulated reflectances of melt ponds of varying color and water depth and used the sun zenith angle and the slope of the log-scaled reflectance at 710 nm to derive pond depth. We validated the model on the in situ melt pond data and found it to derive pond depth very accurately.
H. Jakob Belter, Thomas Krumpen, Stefan Hendricks, Jens Hoelemann, Markus A. Janout, Robert Ricker, and Christian Haas
The Cryosphere, 14, 2189–2203, https://doi.org/10.5194/tc-14-2189-2020, https://doi.org/10.5194/tc-14-2189-2020, 2020
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The validation of satellite sea ice thickness (SIT) climate data records with newly acquired moored sonar SIT data shows that satellite products provide modal rather than mean SIT in the Laptev Sea region. This tendency of satellite-based SIT products to underestimate mean SIT needs to be considered for investigations of sea ice volume transports. Validation of satellite SIT in the first-year-ice-dominated Laptev Sea will support algorithm development for more reliable SIT records in the Arctic.
Thomas Krumpen, Florent Birrien, Frank Kauker, Thomas Rackow, Luisa von Albedyll, Michael Angelopoulos, H. Jakob Belter, Vladimir Bessonov, Ellen Damm, Klaus Dethloff, Jari Haapala, Christian Haas, Carolynn Harris, Stefan Hendricks, Jens Hoelemann, Mario Hoppmann, Lars Kaleschke, Michael Karcher, Nikolai Kolabutin, Ruibo Lei, Josefine Lenz, Anne Morgenstern, Marcel Nicolaus, Uwe Nixdorf, Tomash Petrovsky, Benjamin Rabe, Lasse Rabenstein, Markus Rex, Robert Ricker, Jan Rohde, Egor Shimanchuk, Suman Singha, Vasily Smolyanitsky, Vladimir Sokolov, Tim Stanton, Anna Timofeeva, Michel Tsamados, and Daniel Watkins
The Cryosphere, 14, 2173–2187, https://doi.org/10.5194/tc-14-2173-2020, https://doi.org/10.5194/tc-14-2173-2020, 2020
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In October 2019 the research vessel Polarstern was moored to an ice floe in order to travel with it on the 1-year-long MOSAiC journey through the Arctic. Here we provide historical context of the floe's evolution and initial state for upcoming studies. We show that the ice encountered on site was exceptionally thin and was formed on the shallow Siberian shelf. The analyses presented provide the initial state for the analysis and interpretation of upcoming biogeochemical and ecological studies.
Maria-Elena Vorrath, Juliane Müller, Oliver Esper, Gesine Mollenhauer, Christian Haas, Enno Schefuß, and Kirsten Fahl
Biogeosciences, 16, 2961–2981, https://doi.org/10.5194/bg-16-2961-2019, https://doi.org/10.5194/bg-16-2961-2019, 2019
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The study highlights new approaches in the investigation of past sea ice in Antarctica to reconstruct the climate conditions in earth's history and reveal its future development under global warming. We examined the distribution of organic remains from different algae at the Western Antarctic Peninsula and compared it to fossil and satellite records. We evaluated IPSO25 – the sea ice proxy for the Southern Ocean with 25 carbon atoms – as a useful tool for sea ice reconstructions in this region.
Valentin Ludwig, Gunnar Spreen, Christian Haas, Larysa Istomina, Frank Kauker, and Dmitrii Murashkin
The Cryosphere, 13, 2051–2073, https://doi.org/10.5194/tc-13-2051-2019, https://doi.org/10.5194/tc-13-2051-2019, 2019
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Sea-ice concentration, the fraction of an area covered by sea ice, can be observed from satellites with different methods. We combine two methods to obtain a product which is better than either of the input measurements alone. The benefit of our product is demonstrated by observing the formation of an open water area which can now be observed with more detail. Additionally, we find that the open water area formed because the sea ice drifted in the opposite direction and faster than usual.
Stefanie Arndt and Christian Haas
The Cryosphere, 13, 1943–1958, https://doi.org/10.5194/tc-13-1943-2019, https://doi.org/10.5194/tc-13-1943-2019, 2019
Iina Ronkainen, Jonni Lehtiranta, Mikko Lensu, Eero Rinne, Jari Haapala, and Christian Haas
The Cryosphere, 12, 3459–3476, https://doi.org/10.5194/tc-12-3459-2018, https://doi.org/10.5194/tc-12-3459-2018, 2018
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We quantify the sea ice thickness variability in the Bay of Bothnia using various observational data sets. For the first time we use helicopter and shipborne electromagnetic soundings to study changes in drift ice of the Bay of Bothnia. Our results show that the interannual variability of ice thickness is larger in the drift ice zone than in the fast ice zone. Furthermore, the mean thickness of heavily ridged ice near the coast can be several times larger than that of fast ice.
Jan Melchior van Wessem, Willem Jan van de Berg, Brice P. Y. Noël, Erik van Meijgaard, Charles Amory, Gerit Birnbaum, Constantijn L. Jakobs, Konstantin Krüger, Jan T. M. Lenaerts, Stef Lhermitte, Stefan R. M. Ligtenberg, Brooke Medley, Carleen H. Reijmer, Kristof van Tricht, Luke D. Trusel, Lambertus H. van Ulft, Bert Wouters, Jan Wuite, and Michiel R. van den Broeke
The Cryosphere, 12, 1479–1498, https://doi.org/10.5194/tc-12-1479-2018, https://doi.org/10.5194/tc-12-1479-2018, 2018
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We present a detailed evaluation of the latest version of the regional atmospheric climate model RACMO2.3p2 (1979-2016) over the Antarctic ice sheet. The model successfully reproduces the present-day climate and surface mass balance (SMB) when compared with an extensive set of observations and improves on previous estimates of the Antarctic climate and SMB.
This study shows that the latest version of RACMO2 can be used for high-resolution future projections over the AIS.
Paul J. Kushner, Lawrence R. Mudryk, William Merryfield, Jaison T. Ambadan, Aaron Berg, Adéline Bichet, Ross Brown, Chris Derksen, Stephen J. Déry, Arlan Dirkson, Greg Flato, Christopher G. Fletcher, John C. Fyfe, Nathan Gillett, Christian Haas, Stephen Howell, Frédéric Laliberté, Kelly McCusker, Michael Sigmond, Reinel Sospedra-Alfonso, Neil F. Tandon, Chad Thackeray, Bruno Tremblay, and Francis W. Zwiers
The Cryosphere, 12, 1137–1156, https://doi.org/10.5194/tc-12-1137-2018, https://doi.org/10.5194/tc-12-1137-2018, 2018
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Here, the Canadian research network CanSISE uses state-of-the-art observations of snow and sea ice to assess how Canada's climate model and climate prediction systems capture variability in snow, sea ice, and related climate parameters. We find that the system performs well, accounting for observational uncertainty (especially for snow), model uncertainty, and chaotic climate variability. Even for variables like sea ice, where improvement is needed, useful prediction tools can be developed.
Tim Carlsen, Gerit Birnbaum, André Ehrlich, Johannes Freitag, Georg Heygster, Larysa Istomina, Sepp Kipfstuhl, Anaïs Orsi, Michael Schäfer, and Manfred Wendisch
The Cryosphere, 11, 2727–2741, https://doi.org/10.5194/tc-11-2727-2017, https://doi.org/10.5194/tc-11-2727-2017, 2017
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The optical size of snow grains (ropt) affects the reflectivity of snow surfaces and thus the local surface energy budget in particular in polar regions. The temporal evolution of ropt retrieved from ground-based, airborne, and spaceborne remote sensing could reproduce optical in situ measurements for a 2-month period in central Antarctica (2013/14). The presented validation study provided a unique testbed for retrievals of ropt under Antarctic conditions where in situ data are scarce.
Robert Ricker, Stefan Hendricks, Lars Kaleschke, Xiangshan Tian-Kunze, Jennifer King, and Christian Haas
The Cryosphere, 11, 1607–1623, https://doi.org/10.5194/tc-11-1607-2017, https://doi.org/10.5194/tc-11-1607-2017, 2017
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We developed the first merging of CryoSat-2 and SMOS sea-ice thickness retrievals. ESA’s Earth Explorer SMOS satellite can detect thin sea ice, whereas its companion CryoSat-2, designed to observe thicker perennial sea ice, lacks sensitivity. Using these satellite missions together completes the picture of the changing Arctic sea ice and provides a more accurate and comprehensive view on the actual state of Arctic sea-ice thickness.
François Ritter, Hans Christian Steen-Larsen, Martin Werner, Valérie Masson-Delmotte, Anais Orsi, Melanie Behrens, Gerit Birnbaum, Johannes Freitag, Camille Risi, and Sepp Kipfstuhl
The Cryosphere, 10, 1647–1663, https://doi.org/10.5194/tc-10-1647-2016, https://doi.org/10.5194/tc-10-1647-2016, 2016
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We present successful continuous measurements of water vapor isotopes performed in Antarctica in January 2013. The interest is to understand the impact of the water vapor isotopic composition on the near-surface snow isotopes. Our study reveals a diurnal cycle in the snow isotopic composition in phase with the vapor. This finding suggests fractionation during the sublimation of the ice, which has an important consequence on the interpretation of water isotope variations in ice cores.
T. Krumpen, R. Gerdes, C. Haas, S. Hendricks, A. Herber, V. Selyuzhenok, L. Smedsrud, and G. Spreen
The Cryosphere, 10, 523–534, https://doi.org/10.5194/tc-10-523-2016, https://doi.org/10.5194/tc-10-523-2016, 2016
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We present an extensive data set of ground-based and airborne electromagnetic ice thickness measurements covering Fram Strait in summer between 2001 and 2012. An investigation of back trajectories of surveyed sea ice using satellite-based sea ice motion data allows us to examine the connection between thickness variability, ice age and source area. In addition, we determine across and along strait gradients in ice thickness and associated volume fluxes.
L. Istomina, G. Heygster, M. Huntemann, P. Schwarz, G. Birnbaum, R. Scharien, C. Polashenski, D. Perovich, E. Zege, A. Malinka, A. Prikhach, and I. Katsev
The Cryosphere, 9, 1551–1566, https://doi.org/10.5194/tc-9-1551-2015, https://doi.org/10.5194/tc-9-1551-2015, 2015
A. Tetzlaff, C. Lüpkes, G. Birnbaum, J. Hartmann, T. Nygård, and T. Vihma
The Cryosphere, 8, 1757–1762, https://doi.org/10.5194/tc-8-1757-2014, https://doi.org/10.5194/tc-8-1757-2014, 2014
T. Vihma, R. Pirazzini, I. Fer, I. A. Renfrew, J. Sedlar, M. Tjernström, C. Lüpkes, T. Nygård, D. Notz, J. Weiss, D. Marsan, B. Cheng, G. Birnbaum, S. Gerland, D. Chechin, and J. C. Gascard
Atmos. Chem. Phys., 14, 9403–9450, https://doi.org/10.5194/acp-14-9403-2014, https://doi.org/10.5194/acp-14-9403-2014, 2014
D. Price, W. Rack, P. J. Langhorne, C. Haas, G. Leonard, and K. Barnsdale
The Cryosphere, 8, 1031–1039, https://doi.org/10.5194/tc-8-1031-2014, https://doi.org/10.5194/tc-8-1031-2014, 2014
S. Willmes, M. Nicolaus, and C. Haas
The Cryosphere, 8, 891–904, https://doi.org/10.5194/tc-8-891-2014, https://doi.org/10.5194/tc-8-891-2014, 2014
L. Rabenstein, T. Krumpen, S. Hendricks, C. Koeberle, C. Haas, and J. A. Hoelemann
The Cryosphere, 7, 947–959, https://doi.org/10.5194/tc-7-947-2013, https://doi.org/10.5194/tc-7-947-2013, 2013
Related subject area
Discipline: Sea ice | Subject: Field Studies
Formation and fate of freshwater on an ice floe in the Central Arctic
Observations and modeling of areal surface albedo and surface types in the Arctic
Thickness of multi-year sea ice on the northern Canadian polar shelf: a second look after 40 years
Madison M. Smith, Niels Fuchs, Evgenii Salganik, Donald K. Perovich, Ian Raphael, Mats A. Granskog, Kirstin Schulz, Matthew D. Shupe, and Melinda Webster
EGUsphere, https://doi.org/10.5194/egusphere-2024-1977, https://doi.org/10.5194/egusphere-2024-1977, 2024
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The fate of freshwater from Arctic sea ice and snow melt impacts interactions of the atmosphere, sea ice, and ocean. We complete a comprehensive analysis of datasets from a Central Arctic field campaign in 2020 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.
Evelyn Jäkel, Sebastian Becker, Tim R. Sperzel, Hannah Niehaus, Gunnar Spreen, Ran Tao, Marcel Nicolaus, Wolfgang Dorn, Annette Rinke, Jörg Brauchle, and Manfred Wendisch
The Cryosphere, 18, 1185–1205, https://doi.org/10.5194/tc-18-1185-2024, https://doi.org/10.5194/tc-18-1185-2024, 2024
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The results of the surface albedo scheme of a coupled regional climate model were evaluated against airborne and ground-based measurements conducted in the European Arctic in different seasons between 2017 and 2022. We found a seasonally dependent bias between measured and modeled surface albedo for cloudless and cloudy situations. The strongest effects of the albedo model bias on the net irradiance were most apparent in the presence of optically thin clouds.
Humfrey Melling
The Cryosphere, 16, 3181–3197, https://doi.org/10.5194/tc-16-3181-2022, https://doi.org/10.5194/tc-16-3181-2022, 2022
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The Canadian polar shelf has the world’s thickest old sea ice. Its islands impede ice drift to warmer seas. The first year of data from up-looking sonar viewing this shelf’s ice reveal that thick (> 3 m) old ice remains plentiful here, in contrast to its growing scarcity elsewhere. Arctic circulation continues to pack ice against the islands and during storms to create by ridging the very thick ice found here. This study reveals the importance of ridging to the mass balance of Arctic sea ice.
Cited articles
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Fuchs, N.: PASTA-ice sea ice image classification: calibration files and training data, Zenodo [data set], https://doi.org/10.5281/zenodo.7513632, 2023b. a
Fuchs, N. and Birnbaum, G.: Aerial images of sea ice surfaces recorded for the project TEMPO joining PS106 campaigns PASCAL and SiPCA north of Svalbard in May/June/July 2017, Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Pangaea [data set], https://doi.org/10.1594/PANGAEA.927787, 2021. a
Fuchs, N. and Birnbaum, G.: Melt pond bathymetry of the MOSAiC floe derived by photogrammetry – spatially fully resolved pond depth maps of an Arctic sea ice floe, Pangaea [data set], https://doi.org/10.1594/PANGAEA.964520, 2024. a
Fuchs, N., König, M., and Birnbaum, G.: Estimating melt pond bathymetry from aerial images using photogrammetry, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10214, https://doi.org/10.5194/egusphere-egu21-10214, 2021. a
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Herzfeld, U. C., Trantow, T. M., Han, H., Buckley, E., Farrell, S. L., and Lawson, M.: Automated Detection and Depth Determination of Melt Ponds on Sea Ice in ICESat-2 ATLAS Data – The Density-Dimension Algorithm for Bifurcating Sea-Ice Reflectors (DDA-Bifurcate-Seaice), IEEE T. Geosci. Remote Sens., 61, 1–22, https://doi.org/10.1109/TGRS.2023.3268073, 2023. a, b
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
Huang, W., Lu, P., Lei, R., Xie, H., and Li, Z.: Melt pond distribution and geometry in high Arctic sea ice derived from aerial investigations, Ann. Glaciol., 57, 105–118, https://doi.org/10.1017/aog.2016.30, 2016. a
Hutter, N., Hendricks, S., Jutila, A., Ricker, R., von Albedyll, L., Birnbaum, G., and Haas, C.: Digital elevation models of the sea-ice surface from airborne laser scanning during MOSAiC, Sci. Data, 10, 729, https://doi.org/10.1038/s41597-023-02565-6, 2023. a
Itkin, P., Hendricks, S., Webster, M., von Albedyll, L., Arndt, S., Divine, D., Jaggi, M., Oggier, M., Raphael, I., Ricker, R., Rohde, J., Schneebeli, M., and Liston, G. E.: Sea ice and snow characteristics from year-long transects at the MOSAiC Central Observatory, Elementa: Science of the Anthropocene, 11, 00048, https://doi.org/10.1525/elementa.2022.00048, 2023. a
Jordahl, K., den Bossche, J. V., Fleischmann, M., Wasserman, J., McBride, J., Gerard, J., Tratner, J., Perry, M. T., Badaracco, A. G., Farmer, C., Hjelle, G. A., Snow, A. D., Cochran, M., Gillies, S., Culbertson, L., Bartos, M., Eubank, N., Maxalbert, Bilogur, A., Rey, S., Ren, C., Arribas-Bel, D., Wasser, L., Wolf, L. J., Journois, M., Wilson, J., Greenhall, A., Holdgraf, C., Filipe, and Leblanc, F.: geopandas/geopandas: v0.8.1, Zenodo [code], https://doi.org/10.5281/zenodo.3946761, 2020. a
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König, M. and Oppelt, N.: A linear model to derive melt pond depth on Arctic sea ice from hyperspectral data, The Cryosphere, 14, 2567–2579, https://doi.org/10.5194/tc-14-2567-2020, 2020. a
Lee, S., Stroeve, J., Webster, M., Fuchs, N., and Perovich, D. K.: Inter-comparison of melt pond products from optical satellite imagery, Remote Sens. Environ., 301, 113920, https://doi.org/10.1016/j.rse.2023.113920, 2024. a
Lüthje, M., Feltham, D. L., Taylor, P. D., and Worster, M. G.: Modeling the summertime evolution of sea-ice melt ponds, J. Geophys. Res.-Oceans, 111, 1–17, https://doi.org/10.1029/2004JC002818, 2006. a
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Miao, X., Xie, H., Ackley, S. F., Perovich, D. K., and Ke, C.: Object-based detection of Arctic sea ice and melt ponds using high spatial resolution aerial photographs, Cold Reg. Sci. Technol., 119, 211–222, https://doi.org/10.1016/j.coldregions.2015.06.014, 2015. a, b, c
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Neckel, N., Fuchs, N., Birnbaum, G., Hutter, N., Jutila, A., Buth, L., von Albedyll, L., Ricker, R., and Haas, C.: “Helicopter-borne RGB orthomosaics and photogrammetric Digital Elevation Models from the MOSAiC Expedition”, Pangaea [data set], https://doi.org/10.1594/PANGAEA.949433, 2022. a, b, c
Neckel, N., Fuchs, N., Birnbaum, G., Hutter, N., Jutila, A., Buth, L., Albedyll, L. V., Ricker, R., and Haas, C.: Helicopter-borne RGB orthomosaics and photogrammetric digital elevation models from the MOSAiC Expedition, Sci. Data, 10, 426, https://doi.org/10.1038/s41597-023-02318-5, 2023. a, b, c, d, e
Nicolaus, M., Perovich, D. K., Spreen, G., Granskog, M. A., von Albedyll, L., Angelopoulos, M., Anhaus, P., Arndt, S., Jakob Belter, H., Bessonov, V., Birnbaum, G., Brauchle, J., Calmer, R., Cardellach, E., Cheng, B., Clemens-Sewall, D., Dadic, R., Damm, E., de Boer, G., Demir, O., Dethloff, K., Divine, D. V., Fong, A. A., Fons, S., Frey, M. M., Fuchs, N., Gabarró, C., Gerland, S., Goessling, H. F., Gradinger, R., Haapala, J., Haas, C., Hamilton, J., Hannula, H. R., Hendricks, S., Herber, A., Heuzé, C., Hoppmann, M., Høyland, K. V., Huntemann, M., Hutchings, J. K., Hwang, B., Itkin, P., Jacobi, H. W., Jaggi, M., Jutila, A., Kaleschke, L., Katlein, C., Kolabutin, N., Krampe, D., Kristensen, S. S., Krumpen, T., Kurtz, N., Lampert, A., Lange, B. A., Lei, R., Light, B., Linhardt, F., Liston, G. E., Loose, B., Macfarlane, A. R., Mahmud, M., Matero, I. O., 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. A., Regnery, J., Rex, M., Ricker, R., Riemann-Campe, K., Rinke, A., Rohde, J., Salganik, E., Scharien, R. K., Schiller, M., Schneebeli, M., Semmling, M., Shimanshuck, E., Shupe, M. D., Smith, M. M., Smolyanitsky, V., Sokolov, V., Stanton, T. P., Stroeve, J., Thielke, L., Timofeeva, A., Tonboe, R. T., Tavri, A., Tsamados, M., Wagner, D. N., Watkins, D., Webster, M., and Wendisch, M.: Overview of the MOSAiC expedition: Snow and sea ice, Elementa: Science of the Anthropocene, 10, 000046, https://doi.org/10.1525/elementa.2021.000046, 2022. a
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Oppelt, N. and Linhardt, F.: Water colour spectro-radiometry platform raw data (cau_boeoetle_1) from pack ice during legs 4 and 5 of the MOSAiC expedition with POLARSTERN to the Arctic Ocean, Pangaea [data set], https://doi.org/10.1594/PANGAEA.955777, 2023. a
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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, c
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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. Geophy. Res.-Oceans, 108, 26–1, https://doi.org/10.1029/2001jc001079, 2003. a, b
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Short summary
Melt ponds are key components of the Arctic sea ice system, yet methods to derive comprehensive pond depth data are missing. We present a shallow-water bathymetry retrieval to derive this elementary pond property at high spatial resolution from aerial images. The retrieval method is presented in a user-friendly way to facilitate replication. Furthermore, we provide pond properties on the MOSAiC expedition floe, giving insights into the three-dimensional pond evolution before and after drainage.
Melt ponds are key components of the Arctic sea ice system, yet methods to derive comprehensive...
