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2. Author Correction: A Database of Snow on Sea Ice in the Central Arctic Collected during the MOSAiC expedition

Author

Macfarlane, Amy R., Schneebeli, Martin, Dadic, Ruzica, Tavri, Aikaterini, Immerz, Antonia, Polashenski, Chris, Krampe, Daniela, Clemens-Sewall, David, Wagner, David N., Perovich, Donald K., Henna-Reetta, Hannula, Raphael, Ian, Matero, Ilkka, Regnery, Julia, Smith, Madison M., Nicolaus, Marcel, Jaggi, Matthias, Oggier, Marc, Webster, Melinda A., Lehning, Michael, Kolabutin, Nikolai, Itkin, Polona, Naderpour, Reza, Pirazzini, Roberta, Hämmerle, Stefan, Arndt, Stefanie, and Fons, Steven

Published
2023
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3. A Database of Snow on Sea Ice in the Central Arctic Collected during the MOSAiC expedition

Author

Macfarlane, Amy R., Schneebeli, Martin, Dadic, Ruzica, Tavri, Aikaterini, Immerz, Antonia, Polashenski, Chris, Krampe, Daniela, Clemens-Sewall, David, Wagner, David N., Perovich, Donald K., Henna-Reetta, Hannula, Raphael, Ian, Matero, Ilkka, Regnery, Julia, Smith, Madison M., Nicolaus, Marcel, Jaggi, Matthias, Oggier, Marc, Webster, Melinda A., Lehning, Michael, Kolabutin, Nikolai, Itkin, Polona, Naderpour, Reza, Pirazzini, Roberta, Hämmerle, Stefan, Arndt, Stefanie, and Fons, Steven

Published
2023
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4. Supplementary material to "Arctic Surface Snow Interactions with the Atmosphere: Spatio-Temporal Isotopic Variability During the MOSAiC Expedition"

Author

Mellat, Moein, primary, Macfarlane, Amy R., additional, Brunello, Camilla F., additional, Werner, Martin, additional, Schneebeli, Martin, additional, Dadic, Ruzica, additional, Arndt, Stefanie, additional, Mustonen, Kaisa-Riikka, additional, Welker, Jeffrey M., additional, and Meyer, Hanno, additional

Published
2024
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7. Arctic Surface Snow Interactions with the Atmosphere: Spatio-Temporal Isotopic Variability During the MOSAiC Expedition.

Author

Mellat, Moein, Macfarlane, Amy R., Brunello, Camilla F., Werner, Martin, Schneebeli, Martin, Dadic, Ruzica, Arndt, Stefanie, Mustonen, Kaisa-Riikka, Welker, Jeffrey M., and Meyer, Hanno

Subjects
SURFACE interactions, SEA ice, ATMOSPHERE, ARCTIC climate, ISOTOPIC signatures, ATMOSPHERIC temperature
Abstract

The Arctic Ocean's snow cover is crucial in moderating interactions between sea-ice and the atmosphere, yet fully grasping its isotopic composition and the processes shaping it presents substantial challenges. This study employs a unique dataset from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition to explore the complex interactions between deposition processes and post-depositional changes affecting snow on Arctic sea ice. By examining 911 individual snow isotope measurements collected over a full year, we identify a clear layering within the snowpack: the top layer, with lower δ18O values and higher d-excess values, indicates fresh meteoric snowfall, while the bottom layer, affected by the sea ice beneath, shows higher δ18O values and lower d- d-excess values. By integrating these discrete snow samples with continuous vapour isotope data, our research provides insight into interactions between snow and the atmosphere, as well as the processes that alter isotopic signatures within Arctic snow. We observe a significant difference in δ18O values between snow and vapor during autumn, mainly due to delays in sampling after precipitation events, with d-excess ranges suggesting the impact of Atlantic moisture. Winter months exhibit sharp differences in δ18O and d-excess values, indicating kinetic fractionation amid extreme cold as the RV Polarstern traverses from the Siberian to the Atlantic sector of the Arctic Ocean. Conversely, summer months display a convergence in isotopic signatures, reflecting conditions favouring equilibrium fractionation, highlighted by increased air temperatures and humidity levels. While δ18O in vapour readily responds to changes in air temperature and humidity, surface snow δ18O is influenced more by subsequent processes such as sublimation and wind-driven redistribution. Sublimation, intensified by the snow's prolonged surface residence and facilitated by the porosity of snow, plays a key role in isotopic enrichment. Wind-driven snow redistribution, occurring 67 % of the winter, led to a homogenised and depleted surface snow δ18O signal across the sea ice by spreading lower δ18O meteoric snow. This effect was especially pronounced in ridge snow profiles, where the top layers showed a uniform δ18O signal, in stark contrast to flat ice samples. Furthermore, distinct isotopic patterns were detected along the MOSAiC expedition route from a region close to Samoylov Island to Fram Straight near Ny-Ålesund. Snow samples close to Samoylov Island exhibited notable seasonal δ18O variations, which were indicative of a continental climate. In contrast, samples from Ny-Ålesund displayed more consistent fluctuations, influenced by steady Atlantic moisture. [ABSTRACT FROM AUTHOR]

Published
2024
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9. Snow Loss Into Leads in Arctic Sea Ice: Minimal in Typical Wintertime Conditions, but High During a Warm and Windy Snowfall Event

Author

Clemens‐Sewall, David, primary, Polashenski, Chris, additional, Frey, Markus M., additional, Cox, Christopher J., additional, Granskog, Mats A., additional, Macfarlane, Amy R., additional, Fons, Steven W., additional, Schmale, Julia, additional, Hutchings, Jennifer K., additional, von Albedyll, Luisa, additional, Arndt, Stefanie, additional, Schneebeli, Martin, additional, and Perovich, Don, additional

Published
2023
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11. Sea salt aerosol and ice nucleating particles (INP) in the Central Arctic during winter/spring – a discussion of a source from blowing snow above sea ice

Author

Frey, Markus M., primary, Kirchgäßner, Amélie, additional, van den Heuvel, Floor, additional, Lachlan-Cope, Thomas, additional, Stratmann, Frank, additional, Wex, Heike, additional, Macfarlane, Amy R., additional, Mirrielees, Jessica, additional, Pratt, Kerri, additional, Beck, Ivo, additional, Schmale, Julia, additional, Nishimura, Kouichi, additional, and Brooks, Ian, additional

Published
2023
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12. Thermal Conductivity of Snow on Arctic Sea Ice

Author

Macfarlane, Amy R., primary, Löwe, Henning, additional, Gimenes, Lucille, additional, Wagner, David N., additional, Dadic, Ruzica, additional, Ottersberg, Rafael, additional, Hämmerle, Stefan, additional, and Schneebeli, Martin, additional

Published
2023
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13. Snow Loss Into Leads in Arctic Sea Ice: Minimal in Typical Wintertime Conditions, but High During a Warm and Windy Snowfall Event

Author

Clemens‐Sewall, David, Polashenski, Chris, Frey, Markus M., Cox, Christopher J., Granskog, Mats A., Macfarlane, Amy R., Fons, Steven W., Schmale, Julia, Hutchings, Jennifer K., von Albedyll, Luisa, Arndt, Stefanie, Schneebeli, Martin, Perovich, Don, Clemens‐Sewall, David, Polashenski, Chris, Frey, Markus M., Cox, Christopher J., Granskog, Mats A., Macfarlane, Amy R., Fons, Steven W., Schmale, Julia, Hutchings, Jennifer K., von Albedyll, Luisa, Arndt, Stefanie, Schneebeli, Martin, and Perovich, Don

Abstract

The amount of snow on Arctic sea ice impacts the ice mass budget. Wind redistribution of snow into open water in leads is hypothesized to cause significant wintertime snow loss. However, there are no direct measurements of snow loss into Arctic leads. We measured the snow lost in four leads in the Central Arctic in winter 2020. We find, contrary to expectations, that under typical winter conditions, minimal snow was lost into leads. However, during a cyclone that delivered warm air temperatures, high winds, and snowfall, 35.0 ± 1.1 cm snow water equivalent (SWE) was lost into a lead (per unit lead area). This corresponded to a removal of 0.7–1.1 cm SWE from the entire surface—∼6%–10% of this site's annual snow precipitation. Warm air temperatures, which increase the length of time that wintertime leads remain unfrozen, may be an underappreciated factor in snow loss into leads.

Published
2023

15. Sea ice concentration satellite retrievals influenced by surface changes due to warm air intrusions: A case study from the MOSAiC expedition

Author

Rückert, Janna E., primary, Rostosky, Philip, additional, Huntemann, Marcus, additional, Clemens-Sewall, David, additional, Ebell, Kerstin, additional, Kaleschke, Lars, additional, Lemmetyinen, Juha, additional, Macfarlane, Amy R., additional, Naderpour, Reza, additional, Stroeve, Julienne, additional, Walbröl, Andreas, additional, and Spreen, Gunnar, additional

Published
2023
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17. Snowfall and snow accumulation during the MOSAiC winter and spring seasons

Author

Wagner, David N., primary, Shupe, Matthew D., additional, Cox, Christopher, additional, Persson, Ola G., additional, Uttal, Taneil, additional, Frey, Markus M., additional, Kirchgaessner, Amélie, additional, Schneebeli, Martin, additional, Jaggi, Matthias, additional, Macfarlane, Amy R., additional, Itkin, Polona, additional, Arndt, Stefanie, additional, Hendricks, Stefan, additional, Krampe, Daniela, additional, Nicolaus, Marcel, additional, Ricker, Robert, additional, Regnery, Julia, additional, Kolabutin, Nikolai, additional, Shimanshuck, Egor, additional, Oggier, Marc, additional, Raphael, Ian, additional, Stroeve, Julienne, additional, and Lehning, Michael, additional

Published
2022
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19. Overview of the MOSAiC expedition: Snow and sea ice

Author

Nicolaus, Marcel, Perovich, Donald K., Spreen, Gunnar, Granskog, Mats A., von Albedyll, Luisa, Angelopoulos, Michael, Anhaus, Philipp, Arndt, Stefanie, Belter, H. Jakob, Bessonov, Vladimir, Birnbaum, Gerit, Brauchle, Jörg, Calmer, Radiance, Cardellach, Estel, Cheng, Bin, Clemens-Sewall, David, Dadic, Ruzica, Damm, Ellen, de Boer, Gijs, Demir, Oguz, Dethloff, Klaus, Divine, Dmitry V., Fong, Allison A., Fons, Steven, Frey, Markus M., Fuchs, Niels, Gabarró, Carolina, Gerland, Sebastian, Goessling, Helge F., Gradinger, Rolf, Haapala, Jari, Haas, Christian, Hamilton, Jonathan, Hannula, Henna-Reetta, Hendricks, Stefan, Herber, Andreas, Heuzé, Céline, Hoppmann, Mario, Høyland, Knut Vilhelm, Huntemann, Marcus, Hutchings, Jennifer K., Hwang, Byongjun, Itkin, Polona, Jacobi, Hans-Werner, Jaggi, Matthias, Jutila, Arttu, Kaleschke, Lars, Katlein, Christian, Kolabutin, Nikolai, Krampe, Daniela, Kristensen, Steen Savstrup, Krumpen, Thomas, Kurtz, Nathan, Lampert, Astrid, Lange, Benjamin Allen, Lei, Ruibo, Light, Bonnie, Linhardt, Felix, Liston, Glen E., Loose, Brice, Macfarlane, Amy R., Mahmud, Mallik, Matero, Ilkka O., Maus, Sönke, Morgenstern, Anne, Naderpour, Reza, Nandan, Vishnu, Niubom, Alexey, Oggier, Marc, Oppelt, Natascha, Pätzold, Falk, Perron, Christophe, Petrovsky, Tomasz, Pirazzini, Roberta, Polashenski, Chris, Rabe, Benjamin, Raphael, Ian A., Regnery, Julia, Rex, Markus, Ricker, Robert, Riemann-Campe, Kathrin, Rinke, Annette, Rohde, Jan, Salganik, Evgenii, Scharien, Randall K., Schiller, Martin, Schneebeli, Martin, Semmling, Maximilian, Shimanchuk, Egor, Shupe, Matthew D., Smith, Madison M., Smolyanitsky, Vasily, Sokolov, Vladimir, Stanton, Tim, Stroeve, Julienne, Thielke, Linda, Timofeeva, Anna, Tonboe, Rasmus Tage, Tavri, Aikaterini, Tsamados, Michel, Wagner, David N., Watkins, Daniel, Webster, Melinda, Wendisch, Manfred, Nicolaus, Marcel, Perovich, Donald K., Spreen, Gunnar, Granskog, Mats A., von Albedyll, Luisa, Angelopoulos, Michael, Anhaus, Philipp, Arndt, Stefanie, Belter, H. Jakob, Bessonov, Vladimir, Birnbaum, Gerit, Brauchle, Jörg, Calmer, Radiance, Cardellach, Estel, Cheng, Bin, Clemens-Sewall, David, Dadic, Ruzica, Damm, Ellen, de Boer, Gijs, Demir, Oguz, Dethloff, Klaus, Divine, Dmitry V., Fong, Allison A., Fons, Steven, Frey, Markus M., Fuchs, Niels, Gabarró, Carolina, Gerland, Sebastian, Goessling, Helge F., Gradinger, Rolf, Haapala, Jari, Haas, Christian, Hamilton, Jonathan, Hannula, Henna-Reetta, Hendricks, Stefan, Herber, Andreas, Heuzé, Céline, Hoppmann, Mario, Høyland, Knut Vilhelm, Huntemann, Marcus, Hutchings, Jennifer K., Hwang, Byongjun, Itkin, Polona, Jacobi, Hans-Werner, Jaggi, Matthias, Jutila, Arttu, Kaleschke, Lars, Katlein, Christian, Kolabutin, Nikolai, Krampe, Daniela, Kristensen, Steen Savstrup, Krumpen, Thomas, Kurtz, Nathan, Lampert, Astrid, Lange, Benjamin Allen, Lei, Ruibo, Light, Bonnie, Linhardt, Felix, Liston, Glen E., Loose, Brice, Macfarlane, Amy R., Mahmud, Mallik, Matero, Ilkka O., Maus, Sönke, Morgenstern, Anne, Naderpour, Reza, Nandan, Vishnu, Niubom, Alexey, Oggier, Marc, Oppelt, Natascha, Pätzold, Falk, Perron, Christophe, Petrovsky, Tomasz, Pirazzini, Roberta, Polashenski, Chris, Rabe, Benjamin, Raphael, Ian A., Regnery, Julia, Rex, Markus, Ricker, Robert, Riemann-Campe, Kathrin, Rinke, Annette, Rohde, Jan, Salganik, Evgenii, Scharien, Randall K., Schiller, Martin, Schneebeli, Martin, Semmling, Maximilian, Shimanchuk, Egor, Shupe, Matthew D., Smith, Madison M., Smolyanitsky, Vasily, Sokolov, Vladimir, Stanton, Tim, Stroeve, Julienne, Thielke, Linda, Timofeeva, Anna, Tonboe, Rasmus Tage, Tavri, Aikaterini, Tsamados, Michel, Wagner, David N., Watkins, Daniel, Webster, Melinda, and Wendisch, Manfred

Abstract

Year-round observations of the physical snow and ice properties and processes that govern the ice pack evolution and its interaction with the atmosphere and the ocean were conducted during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition of the research vessel Polarstern in the Arctic Ocean from October 2019 to September 2020. This work was embedded into the interdisciplinary design of the 5 MOSAiC teams, studying the atmosphere, the sea ice, the ocean, the ecosystem, and biogeochemical processes. The overall aim of the snow and sea ice observations during MOSAiC was to characterize the physical properties of the snow and ice cover comprehensively in the central Arctic over an entire annual cycle. This objective was achieved by detailed observations of physical properties and of energy and mass balance of snow and ice. By studying snow and sea ice dynamics over nested spatial scales from centimeters to tens of kilometers, the variability across scales can be considered. On-ice observations of in situ and remote sensing properties of the different surface types over all seasons will help to improve numerical process and climate models and to establish and validate novel satellite remote sensing methods; the linkages to accompanying airborne measurements, satellite observations, and results of numerical models are discussed. We found large spatial variabilities of snow metamorphism and thermal regimes impacting sea ice growth. We conclude that the highly variable snow cover needs to be considered in more detail (in observations, remote sensing, and models) to better understand snow-related feedback processes. The ice pack revealed rapid transformations and motions along the drift in all seasons. The number of coupled ice–ocean interface processes observed in detail are expected to guide upcoming research with respect to the changing Arctic sea ice.

Published
2022

20. Snowfall and snow accumulation during the MOSAiC winter and spring seasons

Author

Wagner, David N., Shupe, Matthew D., Cox, Christopher, Persson, Ola G., Uttal, Taneil, Frey, Markus M., Kirchgaessner, Amélie, Schneebeli, Martin, Jaggi, Matthias, Macfarlane, Amy R., Itkin, Polona, Arndt, Stefanie, Hendricks, Stefan, Krampe, Daniela, Nicolaus, Marcel, Ricker, Robert, Regnery, Julia, Kolabutin, Nikolai, Shimanshuck, Egor, Oggier, Marc, Raphael, Ian, Stroeve, Julienne, Lehning, Michael, Wagner, David N., Shupe, Matthew D., Cox, Christopher, Persson, Ola G., Uttal, Taneil, Frey, Markus M., Kirchgaessner, Amélie, Schneebeli, Martin, Jaggi, Matthias, Macfarlane, Amy R., Itkin, Polona, Arndt, Stefanie, Hendricks, Stefan, Krampe, Daniela, Nicolaus, Marcel, Ricker, Robert, Regnery, Julia, Kolabutin, Nikolai, Shimanshuck, Egor, Oggier, Marc, Raphael, Ian, Stroeve, Julienne, and Lehning, Michael

Abstract

Data from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition allowed us to investigate the temporal dynamics of snowfall, snow accumulation and erosion in great detail for almost the whole accumulation season (November 2019 to May 2020). We computed cumulative snow water equivalent (SWE) over the sea ice based on snow depth and density retrievals from a SnowMicroPen and approximately weekly measured snow depths along fixed transect paths. We used the derived SWE from the snow cover to compare with precipitation sensors installed during MOSAiC. The data were also compared with ERA5 reanalysis snowfall rates for the drift track. We found an accumulated snow mass of 38 mm SWE between the end of October 2019 and end of April 2020. The initial SWE over first-year ice relative to second-year ice increased from 50 % to 90 % by end of the investigation period. Further, we found that the Vaisala Present Weather Detector 22, an optical precipitation sensor, and installed on a railing on the top deck of research vessel Polarstern, was least affected by blowing snow and showed good agreements with SWE retrievals along the transect. On the contrary, the OTT Pluvio2 pluviometer and the OTT Parsivel2 laser disdrometer were largely affected by wind and blowing snow, leading to too high measured precipitation rates. These are largely reduced when eliminating drifting snow periods in the comparison. ERA5 reveals good timing of the snowfall events and good agreement with ground measurements with an overestimation tendency. Retrieved snowfall from the ship-based Ka-band ARM zenith radar shows good agreements with SWE of the snow cover and differences comparable to those of ERA5. Based on the results, we suggest the Ka-band radar-derived snowfall as an upper limit and the present weather detector on RV Polarstern as a lower limit of a cumulative snowfall range. Based on these findings, we suggest a cumulative snowfall of 72 to 107 mm and a pre

Published
2022

21. Arctic sea ice albedo: Spectral composition, spatial heterogeneity, and temporal evolution observed during the MOSAiC drift

Author

Light, Bonnie, Smith, Madison M., Perovich, Donald K., Webster, Melinda A., Holland, Marika M., Linhardt, Felix, Raphael, Ian A., Clemens-Sewall, David, Macfarlane, Amy R., Anhaus, Philipp, Bailey, David A., Light, Bonnie, Smith, Madison M., Perovich, Donald K., Webster, Melinda A., Holland, Marika M., Linhardt, Felix, Raphael, Ian A., Clemens-Sewall, David, Macfarlane, Amy R., Anhaus, Philipp, and Bailey, David A.

Published
2022

22. Overview of the MOSAiC expedition: Snow and sea ice

Author

German Research Foundation, National Science Foundation (US), European Commission, Agencia Estatal de Investigación (España), Department of Energy (US), National Aeronautics and Space Administration (US), European Space Agency, Canadian Space Agency, Research Council of Norway, Natural Environment Research Council (UK), Swedish Research Council, Swedish Polar Research Secretariat, Swiss Polar Institute, Dr. Werner-Petersen Foundation, European Organisation for the Exploitation of Meteorological Satellites, Nicolaus, Marcel, Perovich, Donald K., Spreen, Gunnar, Granskog, Mats A., von Albedyll, Luisa, Angelopoulos, Michael, Anhaus, Philipp, Arndt, Stefanie, Belter, H. Jakob, Bessonov, Vladimir, Birnbaum, Gerit, Wagner, David N., Watkins, Daniel, Webster, Melinda, Wendisch, Manfred, Brauchle, Jörg, Calmer, Radiance, Cardellach, Estel, Cheng, Bin, Clemens-Sewall, David, Dadic, Ruzica, Damm, Ellen, Boer, Gijs de, Demir, Oguz, Dethloff, Klaus, Divine, Dmitry V., Fong, Allison A., Fons, Steven, Frey, Markus M., Fuchs, Niel, Gabarró, Carolina, Gerland, Sebastian, Goessling, Helge F., Gradinger, Rolf, Haapala, Jari, Haas, Christian, Hamilton, Jonathan, Hannula, Henna-Reetta, Hendricks, Stefan, Herber, Adreas, Heuzé, Céline, Hoppmann, Mario, Høyland, Knut Vilhelm, Huntemann, Marcus, Hutchings, Jennifer K., Hwang, Byongjun, Itkin, Polona, Jacobi, Hans-Werner, Jaggi, Matthias, Jutila, Arttu, Kaleschke, Lars, Katlein, Christian, Kolabutin, Nikolai, Krampe, Daniela, Kristensen, Steen Savstrup, Krumpen, Thomas, Kurtz, Nathan, Lampert, Astrid, Lange, Benjamin Allen, Lei, Ruibo, Light, Bonnie, Linhardt, Felix, Liston, Glen E., Loose, Brice, Macfarlane, Amy R., Mahmud, Mallik S., Matero, Ilkka O., Maus, Sönke, Morgenstern, Anne, Naderpour, Reza, Nandan, Vishnu, Niubom, Alexey, Oggier, Marc, Oppelt, Natascha, Pätzold, Falk, Perron, Christophe, Petrovsky, Tomasz, Pirazzini, Roberta, Polashenski, Chris, Rabe, Benjamin, Raphael, Ian A., Regnery, Julia, Rex, Markus, Ricker, Robert, Riemann-Campe, K., Rinke, Annette, Rohde, Jan, Salganik, Evgenii, Scharien, Randy, Schiller, Martin, Schneebeli, Martin, Semmling, Maximilian, Shimanchuk, Egor, Shupe, Matthew D., Smith, Madison, Smolyanitsky, Vasily, Sokolov, Vladimir, Stanton, Tim, Stroeve, Julienne, Thielke, Linda, Timofeeva, Anna, Tonboe, Rasmus, Tavrii, Aikaterini, Tsamados, Michel, German Research Foundation, National Science Foundation (US), European Commission, Agencia Estatal de Investigación (España), Department of Energy (US), National Aeronautics and Space Administration (US), European Space Agency, Canadian Space Agency, Research Council of Norway, Natural Environment Research Council (UK), Swedish Research Council, Swedish Polar Research Secretariat, Swiss Polar Institute, Dr. Werner-Petersen Foundation, European Organisation for the Exploitation of Meteorological Satellites, Nicolaus, Marcel, Perovich, Donald K., Spreen, Gunnar, Granskog, Mats A., von Albedyll, Luisa, Angelopoulos, Michael, Anhaus, Philipp, Arndt, Stefanie, Belter, H. Jakob, Bessonov, Vladimir, Birnbaum, Gerit, Wagner, David N., Watkins, Daniel, Webster, Melinda, Wendisch, Manfred, Brauchle, Jörg, Calmer, Radiance, Cardellach, Estel, Cheng, Bin, Clemens-Sewall, David, Dadic, Ruzica, Damm, Ellen, Boer, Gijs de, Demir, Oguz, Dethloff, Klaus, Divine, Dmitry V., Fong, Allison A., Fons, Steven, Frey, Markus M., Fuchs, Niel, Gabarró, Carolina, Gerland, Sebastian, Goessling, Helge F., Gradinger, Rolf, Haapala, Jari, Haas, Christian, Hamilton, Jonathan, Hannula, Henna-Reetta, Hendricks, Stefan, Herber, Adreas, Heuzé, Céline, Hoppmann, Mario, Høyland, Knut Vilhelm, Huntemann, Marcus, Hutchings, Jennifer K., Hwang, Byongjun, Itkin, Polona, Jacobi, Hans-Werner, Jaggi, Matthias, Jutila, Arttu, Kaleschke, Lars, Katlein, Christian, Kolabutin, Nikolai, Krampe, Daniela, Kristensen, Steen Savstrup, Krumpen, Thomas, Kurtz, Nathan, Lampert, Astrid, Lange, Benjamin Allen, Lei, Ruibo, Light, Bonnie, Linhardt, Felix, Liston, Glen E., Loose, Brice, Macfarlane, Amy R., Mahmud, Mallik S., Matero, Ilkka O., Maus, Sönke, Morgenstern, Anne, Naderpour, Reza, Nandan, Vishnu, Niubom, Alexey, Oggier, Marc, Oppelt, Natascha, Pätzold, Falk, Perron, Christophe, Petrovsky, Tomasz, Pirazzini, Roberta, Polashenski, Chris, Rabe, Benjamin, Raphael, Ian A., Regnery, Julia, Rex, Markus, Ricker, Robert, Riemann-Campe, K., Rinke, Annette, Rohde, Jan, Salganik, Evgenii, Scharien, Randy, Schiller, Martin, Schneebeli, Martin, Semmling, Maximilian, Shimanchuk, Egor, Shupe, Matthew D., Smith, Madison, Smolyanitsky, Vasily, Sokolov, Vladimir, Stanton, Tim, Stroeve, Julienne, Thielke, Linda, Timofeeva, Anna, Tonboe, Rasmus, Tavrii, Aikaterini, and Tsamados, Michel

Abstract

Year-round observations of the physical snow and ice properties and processes that govern the ice pack evolution and its interaction with the atmosphere and the ocean were conducted during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition of the research vessel Polarstern in the Arctic Ocean from October 2019 to September 2020. This work was embedded into the interdisciplinary design of the 5 MOSAiC teams, studying the atmosphere, the sea ice, the ocean, the ecosystem, and biogeochemical processes. The overall aim of the snow and sea ice observations during MOSAiC was to characterize the physical properties of the snow and ice cover comprehensively in the central Arctic over an entire annual cycle. This objective was achieved by detailed observations of physical properties and of energy and mass balance of snow and ice. By studying snow and sea ice dynamics over nested spatial scales from centimeters to tens of kilometers, the variability across scales can be considered. On-ice observations of in situ and remote sensing properties of the different surface types over all seasons will help to improve numerical process and climate models and to establish and validate novel satellite remote sensing methods; the linkages to accompanying airborne measurements, satellite observations, and results of numerical models are discussed. We found large spatial variabilities of snow metamorphism and thermal regimes impacting sea ice growth. We conclude that the highly variable snow cover needs to be considered in more detail (in observations, remote sensing, and models) to better understand snow-related feedback processes. The ice pack revealed rapid transformations and motions along the drift in all seasons. The number of coupled ice–ocean interface processes observed in detail are expected to guide upcoming research with respect to the changing Arctic sea ice

Published
2022

23. Overview of the MOSAiC expedition

Author

Nicolaus, Marcel, Perovich, Donald K., Spreen, Gunnar, Granskog, Mats A., von Albedyll, Luisa, Angelopoulos, Michael, Anhaus, Philipp, Arndt, Stefanie, Belter, H. Jakob, Bessonov, Vladimir, Birnbaum, Gerit, Brauchle, Jörg, Calmer, Radiance, Cardellach, Estel, Cheng, Bin, Clemens-Sewall, David, Dadic, Ruzica, Damm, Ellen, de Boer, Gijs, Demir, Oguz, Dethloff, Klaus, Divine, Dmitry V., Fong, Allison A., Fons, Steven, Frey, Markus M., Fuchs, Niels, Gabarró, Carolina, Gerland, Sebastian, Goessling, Helge F., Gradinger, Rolf, Haapala, Jari, Haas, Christian, Hamilton, Jonathan, Hannula, Henna-Reetta, Hendricks, Stefan, Herber, Andreas, Heuzé, Céline, Hoppmann, Mario, Høyland, Knut Vilhelm, Huntemann, Marcus, Hutchings, Jennifer K., Hwang, Byongjun, Itkin, Polona, Jacobi, Hans-Werner, Jaggi, Matthias, Jutila, Arttu, Kaleschke, Lars, Katlein, Christian, Kolabutin, Nikolai, Krampe, Daniela, Kristensen, Steen Savstrup, Krumpen, Thomas, Kurtz, Nathan, Lampert, Astrid, Lange, Benjamin Allen, Lei, Ruibo, Light, Bonnie, Linhardt, Felix, Liston, Glen E., Loose, Brice, Macfarlane, Amy R., Mahmud, Mallik, Matero, Ilkka O., Maus, Sönke, Morgenstern, Anne, Naderpour, Reza, Nandan, Vishnu, Niubom, Alexey, Oggier, Marc, Oppelt, Natascha, Pätzold, Falk, Perron, Christophe, Petrovsky, Tomasz, Pirazzini, Roberta, Polashenski, Chris, Rabe, Benjamin, Raphael, Ian A., Regnery, Julia, Rex, Markus, Ricker, Robert, Riemann-Campe, Kathrin, Rinke, Annette, Rohde, Jan, Salganik, Evgenii, Scharien, Randall K., Schiller, Martin, Schneebeli, Martin, Semmling, Maximilian, Shimanchuk, Egor, Shupe, Matthew D., Smith, Madison M., Smolyanitsky, Vasily, Sokolov, Vladimir, Stanton, Tim, Stroeve, Julienne, Thielke, Linda, Timofeeva, Anna, Tonboe, Rasmus Tage, Tavri, Aikaterini, Tsamados, Michel, Wagner, David N., Watkins, Daniel, Webster, Melinda, Wendisch, Manfred, Nicolaus, Marcel, Perovich, Donald K., Spreen, Gunnar, Granskog, Mats A., von Albedyll, Luisa, Angelopoulos, Michael, Anhaus, Philipp, Arndt, Stefanie, Belter, H. Jakob, Bessonov, Vladimir, Birnbaum, Gerit, Brauchle, Jörg, Calmer, Radiance, Cardellach, Estel, Cheng, Bin, Clemens-Sewall, David, Dadic, Ruzica, Damm, Ellen, de Boer, Gijs, Demir, Oguz, Dethloff, Klaus, Divine, Dmitry V., Fong, Allison A., Fons, Steven, Frey, Markus M., Fuchs, Niels, Gabarró, Carolina, Gerland, Sebastian, Goessling, Helge F., Gradinger, Rolf, Haapala, Jari, Haas, Christian, Hamilton, Jonathan, Hannula, Henna-Reetta, Hendricks, Stefan, Herber, Andreas, Heuzé, Céline, Hoppmann, Mario, Høyland, Knut Vilhelm, Huntemann, Marcus, Hutchings, Jennifer K., Hwang, Byongjun, Itkin, Polona, Jacobi, Hans-Werner, Jaggi, Matthias, Jutila, Arttu, Kaleschke, Lars, Katlein, Christian, Kolabutin, Nikolai, Krampe, Daniela, Kristensen, Steen Savstrup, Krumpen, Thomas, Kurtz, Nathan, Lampert, Astrid, Lange, Benjamin Allen, Lei, Ruibo, Light, Bonnie, Linhardt, Felix, Liston, Glen E., Loose, Brice, Macfarlane, Amy R., Mahmud, Mallik, Matero, Ilkka O., Maus, Sönke, Morgenstern, Anne, Naderpour, Reza, Nandan, Vishnu, Niubom, Alexey, Oggier, Marc, Oppelt, Natascha, Pätzold, Falk, Perron, Christophe, Petrovsky, Tomasz, Pirazzini, Roberta, Polashenski, Chris, Rabe, Benjamin, Raphael, Ian A., Regnery, Julia, Rex, Markus, Ricker, Robert, Riemann-Campe, Kathrin, Rinke, Annette, Rohde, Jan, Salganik, Evgenii, Scharien, Randall K., Schiller, Martin, Schneebeli, Martin, Semmling, Maximilian, Shimanchuk, Egor, Shupe, Matthew D., Smith, Madison M., Smolyanitsky, Vasily, Sokolov, Vladimir, Stanton, Tim, Stroeve, Julienne, Thielke, Linda, Timofeeva, Anna, Tonboe, Rasmus Tage, Tavri, Aikaterini, Tsamados, Michel, Wagner, David N., Watkins, Daniel, Webster, Melinda, and Wendisch, Manfred

Abstract

Year-round observations of the physical snow and ice properties and processes that govern the ice pack evolution and its interaction with the atmosphere and the ocean were conducted during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition of the research vessel Polarstern in the Arctic Ocean from October 2019 to September 2020. This work was embedded into the interdisciplinary design of the 5 MOSAiC teams, studying the atmosphere, the sea ice, the ocean, the ecosystem, and biogeochemical processes. The overall aim of the snow and sea ice observations during MOSAiC was to characterize the physical properties of the snow and ice cover comprehensively in the central Arctic over an entire annual cycle. This objective was achieved by detailed observations of physical properties and of energy and mass balance of snow and ice. By studying snow and sea ice dynamics over nested spatial scales from centimeters to tens of kilometers, the variability across scales can be considered. On-ice observations of in situ and remote sensing properties of the different surface types over all seasons will help to improve numerical process and climate models and to establish and validate novel satellite remote sensing methods; the linkages to accompanying airborne measurements, satellite observations, and results of numerical models are discussed. We found large spatial variabilities of snow metamorphism and thermal regimes impacting sea ice growth. We conclude that the highly variable snow cover needs to be considered in more detail (in observations, remote sensing, and models) to better understand snow-related feedback processes. The ice pack revealed rapid transformations and motions along the drift in all seasons. The number of coupled ice–ocean interface processes observed in detail are expected to guide upcoming research with respect to the changing Arctic sea ice.

Published
2022

24. Thermal Conductivity of Snow on Arctic Sea Ice.

Author

Macfarlane, Amy R., Löwe, Henning, Gimenes, Lucille, Wagner, David N., Dadic, Ruzica, Ottersberg, Rafael, Hämmerle, Stefan, and Schneebeli, Martin

Subjects
THERMAL conductivity, SEA ice, THERMAL properties, SNOW cover, SNOW accumulation, GLACIAL Epoch, THERMAL resistance
Abstract

Snow significantly impacts the seasonal growth of Arctic sea ice due to its thermally insulating properties. Various measurements and parametrizations of thermal properties exist, but an assessment of the entire seasonal evolution of thermal conductivity and snow resistance is hitherto lacking. Using the comprehensive snow data set from the MOSAiC expedition, we have evaluated for the first time the seasonal evolution of the snow's thermal conductivity and thermal resistance on different ice ages (leads, first and second-year ice) and topographic features (ridges). Combining different measurement parametrizations and assessing the robustness against spatial variability, we infer and quantify a hitherto undocumented feature in the seasonal dynamics of snow on sea ice. We observe an increase in thermal conductivity up to March and a decrease thereafter, both on first-year and second-year ice before the melt period started. Since a similar non-monotonic behaviour is extracted for the snow depth, the thermal resistance of snow on level sea ice remains approximately constant with a value of 515 ± 404 m2 K W−1 on first-year ice and 660 ± 475m2 K W−1 on second-year ice. We found approximately three times higher thermal resistance on ridges (1411 ± 910 m2 K W−1). Our findings are that the micropenetrometer-derived thermal conductivities give accurate values, and confirm that spatial variability of the snow cover is vertically and horizontally large. The implications of our findings for Arctic sea ice are discussed. [ABSTRACT FROM AUTHOR]

Published
2023
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25. Arctic sea ice albedo: Spectral composition, spatial heterogeneity, and temporal evolution observed during the MOSAiC drift

Author

Light, Bonnie, primary, Smith, Madison M., additional, Perovich, Donald K., additional, Webster, Melinda A., additional, Holland, Marika M., additional, Linhardt, Felix, additional, Raphael, Ian A., additional, Clemens-Sewall, David, additional, Macfarlane, Amy R., additional, Anhaus, Philipp, additional, and Bailey, David A., additional

Published
2022
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26. Overview of the MOSAiC expedition: Snow and sea ice

Author

Nicolaus, Marcel, primary, Perovich, Donald K., additional, Spreen, Gunnar, additional, Granskog, Mats A., additional, von Albedyll, Luisa, additional, Angelopoulos, Michael, additional, Anhaus, Philipp, additional, Arndt, Stefanie, additional, Belter, H. Jakob, additional, Bessonov, Vladimir, additional, Birnbaum, Gerit, additional, Brauchle, Jörg, additional, Calmer, Radiance, additional, Cardellach, Estel, additional, Cheng, Bin, additional, Clemens-Sewall, David, additional, Dadic, Ruzica, additional, Damm, Ellen, additional, de Boer, Gijs, additional, Demir, Oguz, additional, Dethloff, Klaus, additional, Divine, Dmitry V., additional, Fong, Allison A., additional, Fons, Steven, additional, Frey, Markus M., additional, Fuchs, Niels, additional, Gabarró, Carolina, additional, Gerland, Sebastian, additional, Goessling, Helge F., additional, Gradinger, Rolf, additional, Haapala, Jari, additional, Haas, Christian, additional, Hamilton, Jonathan, additional, Hannula, Henna-Reetta, additional, Hendricks, Stefan, additional, Herber, Andreas, additional, Heuzé, Céline, additional, Hoppmann, Mario, additional, Høyland, Knut Vilhelm, additional, Huntemann, Marcus, additional, Hutchings, Jennifer K., additional, Hwang, Byongjun, additional, Itkin, Polona, additional, Jacobi, Hans-Werner, additional, Jaggi, Matthias, additional, Jutila, Arttu, additional, Kaleschke, Lars, additional, Katlein, Christian, additional, Kolabutin, Nikolai, additional, Krampe, Daniela, additional, Kristensen, Steen Savstrup, additional, Krumpen, Thomas, additional, Kurtz, Nathan, additional, Lampert, Astrid, additional, Lange, Benjamin Allen, additional, Lei, Ruibo, additional, Light, Bonnie, additional, Linhardt, Felix, additional, Liston, Glen E., additional, Loose, Brice, additional, Macfarlane, Amy R., additional, Mahmud, Mallik, additional, Matero, Ilkka O., additional, Maus, Sönke, additional, Morgenstern, Anne, additional, Naderpour, Reza, additional, Nandan, Vishnu, additional, Niubom, Alexey, additional, Oggier, Marc, additional, Oppelt, Natascha, additional, Pätzold, Falk, additional, Perron, Christophe, additional, Petrovsky, Tomasz, additional, Pirazzini, Roberta, additional, Polashenski, Chris, additional, Rabe, Benjamin, additional, Raphael, Ian A., additional, Regnery, Julia, additional, Rex, Markus, additional, Ricker, Robert, additional, Riemann-Campe, Kathrin, additional, Rinke, Annette, additional, Rohde, Jan, additional, Salganik, Evgenii, additional, Scharien, Randall K., additional, Schiller, Martin, additional, Schneebeli, Martin, additional, Semmling, Maximilian, additional, Shimanchuk, Egor, additional, Shupe, Matthew D., additional, Smith, Madison M., additional, Smolyanitsky, Vasily, additional, Sokolov, Vladimir, additional, Stanton, Tim, additional, Stroeve, Julienne, additional, Thielke, Linda, additional, Timofeeva, Anna, additional, Tonboe, Rasmus Tage, additional, Tavri, Aikaterini, additional, Tsamados, Michel, additional, Wagner, David N., additional, Watkins, Daniel, additional, Webster, Melinda, additional, and Wendisch, Manfred, additional

Published
2022
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27. Automatic classification and segmentation of Snow Micro Penetrometer profiles with machine learning algorithms.

Author

Kaltenborn, Julia, Macfarlane, Amy R., Clay, Viviane, and Schneebeli, Martin

Subjects
*AUTOMATIC classification, *PENETROMETERS, *SNOW accumulation, *SEA ice, *ARTIFICIAL neural networks, *MACHINE learning
Abstract

Snow-layer segmentation and classification is an essential diagnostic task for a wide variety of cryospheric applications. The SnowMicroPen (SMP) measures the snowpack's penetration force at submillimetre resolution against the snow depth. The resulting depth-force profile can be parameterized for density and specific surface area. However, no information on traditional snow types is currently extracted automatically. The labeling of snow types is a time-intensive task that requires practice and becomes infeasible for large datasets. Previous work showed that automated segmentation and classification is in theory possible, but can either not be applied to data straight from the field or needs additional time-costly information, such as from classified snow pits. To address this gap, we evaluate how well machine learning models can automatically segment and classify SMP profiles. We trained fourteen different models, among them semi-supervised models and artificial neural networks (ANNs), on the MOSAiC SMP dataset, a large collection of snow profiles on Arctic sea ice. We found that SMP profiles can be successfully segmented and classified into snow classes, based solely on the SMP's signal. The model comparison provided in this study enables practitioners to choose a model that is suitable for their task and dataset. The findings presented will facilitate and accelerate snow type identification through SMP profiles. Overall, snowdragon creates a link between traditional snow classification and high-resolution force-depth profiles. With such a tool, traditional snow profile observations can be compared to SMP profiles. [ABSTRACT FROM AUTHOR]

Published
2022
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28. Snow microstructure on sea ice: Importance for remote sensing applications

Author

Macfarlane, Amy R., Arndt, Stefanie, Dadic, Ruzica, Gabarró, Carolina, Light, Bonnie, Mahmud, Mallik S., Naderpour, Reza, Scharien, Randy, Smith, Madison, Spreen, Gunnar, Stroeve, Julienne, Tavrii, Aikaterini, Wagner, David N., Schneebeli, Martin, Macfarlane, Amy R., Arndt, Stefanie, Dadic, Ruzica, Gabarró, Carolina, Light, Bonnie, Mahmud, Mallik S., Naderpour, Reza, Scharien, Randy, Smith, Madison, Spreen, Gunnar, Stroeve, Julienne, Tavrii, Aikaterini, Wagner, David N., and Schneebeli, Martin

Abstract

Snow plays a key role in interpreting satellite remote sensing data from both active and passive sensors in the high Arctic and therefore impacts retrieved sea ice variables from these systems ( e.g., sea ice extent, thickness and age). Because there is high spatial and temporal variability in snow properties, this porous layer adds uncertainty to the interpretation of signals from spaceborne optical sensors, microwave radiometers, and radars (scatterometers, SAR, altimeters). We therefore need to improve our understanding of physical snow properties, including the snow specific surface area, snow wetness and the stratigraphy of the snowpack on different ages of sea ice in the high Arctic. The MOSAiC expedition provided a unique opportunity to deploy equivalent remote sensing sensors in-situ on the sea ice similar to those mounted on satellite platforms. To aid in the interpretation of the in situ remote sensing data collected, we used a micro computed tomography (micro-CT) device. This instrument was installed on board the Polarstern and was used to evaluate geometric and physical snow properties of in-situ snow samples. This allowed us to relate the snow samples directly to the data from the remote sensing instruments, with the goal of improving interpretation of satellite retrievals. Our data covers the full annual evolution of the snow cover properties on multiple ice types and ice topographies including level first-year (FYI), level multi-year ice (MYI) and ridges. First analysis of the data reveals possible uncertainties in the retrieved remote sensing data products related to previously unknown seasonal processes in the snowpack. For example, the refrozen porous summer ice surface, known as surface scattering layer, caused the formation of a hard layer at the multiyear ice/snow interface in the winter months, leading to significant differences in the snow stratigraphy and remote sensing signals from first-year ice, which has not experienced summer melt, and m

Published
2021

29. Snowfall and snow accumulation processes during the MOSAiC winter and spring season

Author

Wagner, David N., primary, Shupe, Matthew D., additional, Persson, Ola G., additional, Uttal, Taneil, additional, Frey, Markus M., additional, Kirchgaessner, Amélie, additional, Schneebeli, Martin, additional, Jaggi, Matthias, additional, Macfarlane, Amy R., additional, Itkin, Polona, additional, Arndt, Stefanie, additional, Hendricks, Stefan, additional, Krampe, Daniela, additional, Ricker, Robert, additional, Regnery, Julia, additional, Kolabutin, Nikolai, additional, Shimanshuck, Egor, additional, Oggier, Marc, additional, Raphael, Ian, additional, and Lehning, Michael, additional

Published
2021
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30. Snowfall and snow accumulation processes during MOSAiC

Author

Wagner, David N., primary, Shupe, Matthew D., additional, Persson, Ola G., additional, Uttal, Taneil, additional, Frey, Markus, additional, Kirchgaessner, Amélie, additional, Schneebeli, Martin, additional, Jaggi, Matthias, additional, Macfarlane, Amy R., additional, Itkin, Polona, additional, Arndt, Stefanie, additional, Hendricks, Stefan, additional, Krampe, Daniela, additional, Regnery, Julia, additional, Ricker, Robert, additional, Kolabutin, Nikolai, additional, Shimanchuck, Egor, additional, Oggier, Marc, additional, Raphael, Ian, additional, and Lehning, Michael, additional

Published
2021
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33. Snow microstructure on sea ice: Importance for remote sensing applications

Author

Macfarlane, Amy R., primary, Arndt, Stefanie, additional, Dadic, Ruzica, additional, Gabarró, Carolina, additional, Light, Bonnie, additional, Mahmud, Mallik, additional, Naderpour, Reza, additional, Scharien, Randall, additional, Smith, Madison, additional, Spreen, Gunnar, additional, Stroeve, Julienne, additional, Tavri, Aikaterini, additional, Wagner, David N., additional, and Schneebeli, Martin, additional

Published
2021
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34. Snow on sea ice

Author

Mallett, Robbie D.C., Nandan, Vishnu, Macfarlane, Amy R., Campbell, Karley, and Stroeve, Julienne C.

Published
2013
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35. Snowfall and snow accumulation during the MOSAiC winter and spring seasons

Author

Wagner, David N., Shupe, Matthew D., Cox, Christopher, Persson, Ola G., Uttal, Taneil, Frey, Markus M., Kirchgaessner, Amélie, Schneebeli, Martin, Jaggi, Matthias, MacFarlane, Amy R., Itkin, Polona, Arndt, Stefanie, Hendricks, Stefan, Krampe, Daniela, Nicolaus, Marcel, Ricker, Robert, Regnery, Julia, Kolabutin, Nikolai, Shimanshuck, Egor, Oggier, Marc, Raphael, Ian, Stroeve, Julienne, and Lehning, Michael

Subjects
sea-ice, thermodynamics, model, variability, surface heat-budget, microstructure, blowing snow, precipitation, cover, redistribution
Abstract

Data from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition allowed us to investigate the temporal dynamics of snowfall, snow accumulation and erosion in great detail for almost the whole accumulation season (November 2019 to May 2020). We computed cumulative snow water equivalent (SWE) over the sea ice based on snow depth and density retrievals from a SnowMicroPen and approximately weekly measured snow depths along fixed transect paths. We used the derived SWE from the snow cover to compare with precipitation sensors installed during MOSAiC. The data were also compared with ERA5 reanalysis snowfall rates for the drift track. We found an accumulated snow mass of 38 mm SWE between the end of October 2019 and end of April 2020. The initial SWE over first-year ice relative to second-year ice increased from 50 % to 90 % by end of the investigation period. Further, we found that the Vaisala Present Weather Detector 22, an optical precipitation sensor, and installed on a railing on the top deck of research vessel Polarstern, was least affected by blowing snow and showed good agreements with SWE retrievals along the transect. On the contrary, the OTT Pluvio2 pluviometer and the OTT Parsivel2 laser disdrometer were largely affected by wind and blowing snow, leading to too high measured precipitation rates. These are largely reduced when eliminating drifting snow periods in the comparison. ERA5 reveals good timing of the snowfall events and good agreement with ground measurements with an overestimation tendency. Retrieved snowfall from the ship-based Ka-band ARM zenith radar shows good agreements with SWE of the snow cover and differences comparable to those of ERA5. Based on the results, we suggest the Ka-band radar-derived snowfall as an upper limit and the present weather detector on RV Polarstern as a lower limit of a cumulative snowfall range. Based on these findings, we suggest a cumulative snowfall of 72 to 107 mm and a precipitation mass loss of the snow cover due to erosion and sublimation as between 47 % and 68 %, for the time period between 31 October 2019 and 26 April 2020. Extending this period beyond available snow cover measurements, we suggest a cumulative snowfall of 98–114 mm.

36. Thermal Conductivity of Snow on Arctic Sea Ice

Author

Macfarlane, Amy R., Löwe, Henning, Gimenes, Lucille, Wagner, David N., Dadiz, Ruzica, Hämmerle, Stefan, Schneebeli, Martin, Dadic, Ruzica, and Ottersberg, Rafael

Subjects
Thermal conductivity, Snow, sea ice, MOSAiC
Abstract

Snow significantly impacts the seasonal growth of Arctic sea ice due to its thermally insulating properties. Various measurements and parametrizations of thermal properties exist, but an assessment of the entire seasonal evolution of thermal conductivity and snow resistance is hitherto lacking. Using the comprehensive snow data set from the MOSAiC expedition, we have evaluated for the first time the seasonal evolution of the snow's thermal conductivity and thermal resistance on different ice ages (leads, first and second-year ice) and topographic features (ridges). Combining different measurement parametrizations and assessing the robustness against spatial variability, we infer and quantify a hitherto undocumented feature in the seasonal dynamics of snow on sea ice. We observe an increase in thermal conductivity up to March and a decrease thereafter, both on first-year and second-year ice before the melt period started. Since a similar non-monotonic behaviour is extracted for the snow depth, the thermal resistance of snow on level sea ice remains approximately constant with a value of 515 ± 404 m2 K W−1 on first-year ice and 660 ± 475m2 K W−1 on second-year ice. We found approximately three times higher thermal resistance on ridges (1411 ± 910 m2 K W−1). Our findings are that the micropenetrometer-derived thermal conductivities give accurate values, and confirm that spatial variability of the snow cover is vertically and horizontally large. The implications of our findings for Arctic sea ice are discussed.

37. Overview of the MOSAiC expedition: Snow and sea ice

Author

Nicolaus, Marcel, Perovich, Donald K., Spreen, Gunnar, Granskog, Mats A., von Albedyll, Luisa, Angelopoulos, Michael, Anhaus, Philipp, Arndt, Stefanie, Belter, H. Jakob, Bessonov, Vladimir, Birnbaum, Gerit, Brauchle, Joerg, Calmer, Radiance, Cardellach, Estel, Cheng, Bin, Clemens-Sewall, David, Dadic, Ruzica, Damm, Ellen, de Boer, Gijs, Demir, Oguz, Dethloff, Klaus, Divine, Dmitry, V, Fong, Allison A., Fons, Steven, Frey, Markus M., Fuchs, Niels, Gabarro, Carolina, Gerland, Sebastian, Goessling, Helge F., Gradinger, Rolf, Haapala, Jari, Haas, Christian, Hamilton, Jonathan, Hannula, Henna-Reetta, Hendricks, Stefan, Herber, Andreas, Heuze, Celine, Hoppmann, Mario, Hoyland, Knut Vilhelm, Huntemann, Marcus, Hutchings, Jennifer K., Hwang, Byongjun, Itkin, Polona, Jacobi, Hans-Werner, Jaggi, Matthias, Jutila, Arttu, Kaleschke, Lars, Katlein, Christian, Kolabutin, Nikolai, Krampe, Daniela, Kristensen, Steen Savstrup, Krumpen, Thomas, Kurtz, Nathan, Lampert, Astrid, Lange, Benjamin Allen, Lei, Ruibo, Light, Bonnie, Linhardt, Felix, Liston, Glen E., Loose, Brice, Macfarlane, Amy R., Mahmud, Mallik, Matero, Ilkka O., Morgenstern, Anne, Naderpour, Reza, Nandan, Vishnu, Niubom, Alexey, Oggier, Marc, Oppelt, Natascha, Perron, Christophe, Petrovsky, Tomasz, Pirazzini, Roberta, Polashenski, Chris, Rabe, Benjamin, Raphael, Ian A., Regnery, Julia, Rex, Markus, Ricker, Robert, Riemann-Campe, Kathrin, Rinke, Annette, Rohde, Jan, Salganik, Evgenii, Scharien, Randall K., Schiller, Martin, Schneebeli, Martin, Semmling, Maximilian, Shimanchuk, Egor, Shupe, Matthew D., Smith, Madison M., Smolyanitsky, Vasily, Sokolov, Vladimir, Stanton, Tim, Stroeve, Julienne, Thielke, Linda, Timofeeva, Anna, Tonboe, Rasmus Tage, Tavri, Aikaterini, Tsamados, Michel, Wagner, David N., Watkins, Daniel, Webster, Melinda, and Wendisch, Manfred

Subjects
atmosphere-ice-ocean interaction, depth, deformation, arctic drift study, temperature, snow and sea ice, thickness, thermodynamics, frequency, interdisciplinary research, impact, pack ice, mass-balance, coupled climate system, radar
Abstract

Year-round observations of the physical snow and ice properties and processes that govern the ice pack evolution and its interaction with the atmosphere and the ocean were conducted during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition of the research vessel Polarstern in the Arctic Ocean from October 2019 to September 2020. This work was embedded into the interdisciplinary design of the 5 MOSAiC teams, studying the atmosphere, the sea ice, the ocean, the ecosystem, and biogeochemical processes. The overall aim of the snow and sea ice observations during MOSAiC was to characterize the physical properties of the snow and ice cover comprehensively in the central Arctic over an entire annual cycle. This objective was achieved by detailed observations of physical properties and of energy and mass balance of snow and ice. By studying snow and sea ice dynamics over nested spatial scales from centimeters to tens of kilometers, the variability across scales can be considered. On-ice observations of in situ and remote sensing properties of the different surface types over all seasons will help to improve numerical process and climate models and to establish and validate novel satellite remote sensing methods; the linkages to accompanying airborne measurements, satellite observations, and results of numerical models are discussed. We found large spatial variabilities of snow metamorphism and thermal regimes impacting sea ice growth. We conclude that the highly variable snow cover needs to be considered in more detail (in observations, remote sensing, and models) to better understand snow-related feedback processes. The ice pack revealed rapid transformations and motions along the drift in all seasons. The number of coupled ice-ocean interface processes observed in detail are expected to guide upcoming research with respect to the changing Arctic sea ice.

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