Articles | Volume 15, issue 10
https://doi.org/10.5194/tc-15-4909-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-15-4909-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Oct 2021
Research article |
| 21 Oct 2021
| 21 Oct 2021
Impacts of snow data and processing methods on the interpretation of long-term changes in Baffin Bay early spring sea ice thickness
Isolde A. Glissenaar
CORRESPONDING AUTHOR
Bristol Glaciology Centre, School of Geographical Sciences, University of Bristol, Bristol, UK
Jack C. Landy
Bristol Glaciology Centre, School of Geographical Sciences, University of Bristol, Bristol, UK
The Earth Observation Laboratory, Department of Physics and Technology, UiT The Arctic University of Norway, Tromsø, Norway
Alek A. Petty
Cryospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
Nathan T. Kurtz
Cryospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Julienne C. Stroeve
Centre for Earth Observation Science, University of Manitoba, Winnipeg, MB, Canada
Department of Earth Sciences, University College London, London, UK
National Snow and Ice Data Center, University of Colorado Boulder, Boulder, CO, USA
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Alistair Duffey, Robbie Mallett, Peter J. Irvine, Michel Tsamados, and Julienne Stroeve
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Geoffrey J. Dawson and Jack C. Landy
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The Cryosphere, 16, 4223–4250, https://doi.org/10.5194/tc-16-4223-2022, https://doi.org/10.5194/tc-16-4223-2022, 2022
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David N. Wagner, Matthew D. Shupe, Christopher Cox, Ola G. Persson, Taneil Uttal, Markus M. Frey, Amélie Kirchgaessner, Martin Schneebeli, Matthias Jaggi, Amy R. Macfarlane, Polona Itkin, Stefanie Arndt, Stefan Hendricks, Daniela Krampe, Marcel Nicolaus, Robert Ricker, Julia Regnery, Nikolai Kolabutin, Egor Shimanshuck, Marc Oggier, Ian Raphael, Julienne Stroeve, and Michael Lehning
The Cryosphere, 16, 2373–2402, https://doi.org/10.5194/tc-16-2373-2022, https://doi.org/10.5194/tc-16-2373-2022, 2022
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Based on measurements of the snow cover over sea ice and atmospheric measurements, we estimate snowfall and snow accumulation for the MOSAiC ice floe, between November 2019 and May 2020. For this period, we estimate 98–114 mm of precipitation. We suggest that about 34 mm of snow water equivalent accumulated until the end of April 2020 and that at least about 50 % of the precipitated snow was eroded or sublimated. Further, we suggest explanations for potential snowfall overestimation.
William Gregory, Julienne Stroeve, and Michel Tsamados
The Cryosphere, 16, 1653–1673, https://doi.org/10.5194/tc-16-1653-2022, https://doi.org/10.5194/tc-16-1653-2022, 2022
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This research was conducted to better understand how coupled climate models simulate one of the large-scale interactions between the atmosphere and Arctic sea ice that we see in observational data, the accurate representation of which is important for producing reliable forecasts of Arctic sea ice on seasonal to inter-annual timescales. With network theory, this work shows that models do not reflect this interaction well on average, which is likely due to regional biases in sea ice thickness.
Marcel Kleinherenbrink, Anton Korosov, Thomas Newman, Andreas Theodosiou, Alexander S. Komarov, Yuanhao Li, Gert Mulder, Pierre Rampal, Julienne Stroeve, and Paco Lopez-Dekker
The Cryosphere, 15, 3101–3118, https://doi.org/10.5194/tc-15-3101-2021, https://doi.org/10.5194/tc-15-3101-2021, 2021
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Harmony is one of the Earth Explorer 10 candidates that has the chance of being selected for launch in 2028. The mission consists of two satellites that fly in formation with Sentinel-1D, which carries a side-looking radar system. By receiving Sentinel-1's signals reflected from the surface, Harmony is able to observe instantaneous elevation and two-dimensional velocity at the surface. As such, Harmony's data allow the retrieval of sea-ice drift and wave spectra in sea-ice-covered regions.
Robbie D. C. Mallett, Julienne C. Stroeve, Michel Tsamados, Jack C. Landy, Rosemary Willatt, Vishnu Nandan, and Glen E. Liston
The Cryosphere, 15, 2429–2450, https://doi.org/10.5194/tc-15-2429-2021, https://doi.org/10.5194/tc-15-2429-2021, 2021
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Rasmus T. Tonboe, Vishnu Nandan, John Yackel, Stefan Kern, Leif Toudal Pedersen, and Julienne Stroeve
The Cryosphere, 15, 1811–1822, https://doi.org/10.5194/tc-15-1811-2021, https://doi.org/10.5194/tc-15-1811-2021, 2021
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Lu Zhou, Julienne Stroeve, Shiming Xu, Alek Petty, Rachel Tilling, Mai Winstrup, Philip Rostosky, Isobel R. Lawrence, Glen E. Liston, Andy Ridout, Michel Tsamados, and Vishnu Nandan
The Cryosphere, 15, 345–367, https://doi.org/10.5194/tc-15-345-2021, https://doi.org/10.5194/tc-15-345-2021, 2021
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Snow on sea ice plays an important role in the Arctic climate system. Large spatial and temporal discrepancies among the eight snow depth products are analyzed together with their seasonal variability and long-term trends. These snow products are further compared against various ground-truth observations. More analyses on representation error of sea ice parameters are needed for systematic comparison and fusion of airborne, in situ and remote sensing observations.
Masa Kageyama, Louise C. Sime, Marie Sicard, Maria-Vittoria Guarino, Anne de Vernal, Ruediger Stein, David Schroeder, Irene Malmierca-Vallet, Ayako Abe-Ouchi, Cecilia Bitz, Pascale Braconnot, Esther C. Brady, Jian Cao, Matthew A. Chamberlain, Danny Feltham, Chuncheng Guo, Allegra N. LeGrande, Gerrit Lohmann, Katrin J. Meissner, Laurie Menviel, Polina Morozova, Kerim H. Nisancioglu, Bette L. Otto-Bliesner, Ryouta O'ishi, Silvana Ramos Buarque, David Salas y Melia, Sam Sherriff-Tadano, Julienne Stroeve, Xiaoxu Shi, Bo Sun, Robert A. Tomas, Evgeny Volodin, Nicholas K. H. Yeung, Qiong Zhang, Zhongshi Zhang, Weipeng Zheng, and Tilo Ziehn
Clim. Past, 17, 37–62, https://doi.org/10.5194/cp-17-37-2021, https://doi.org/10.5194/cp-17-37-2021, 2021
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The Last interglacial (ca. 127 000 years ago) is a period with increased summer insolation at high northern latitudes, resulting in a strong reduction in Arctic sea ice. The latest PMIP4-CMIP6 models all simulate this decrease, consistent with reconstructions. However, neither the models nor the reconstructions agree on the possibility of a seasonally ice-free Arctic. Work to clarify the reasons for this model divergence and the conflicting interpretations of the records will thus be needed.
Stephen E. L. Howell, Randall K. Scharien, Jack Landy, and Mike Brady
The Cryosphere, 14, 4675–4686, https://doi.org/10.5194/tc-14-4675-2020, https://doi.org/10.5194/tc-14-4675-2020, 2020
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Melt ponds form on the surface of Arctic sea ice during spring and have been shown to exert a strong influence on summer sea ice area. Here, we use RADARSAT-2 satellite imagery to estimate the predicted peak spring melt pond fraction in the Canadian Arctic Archipelago from 2009–2018. Our results show that RADARSAT-2 estimates of peak melt pond fraction can be used to provide predictive information about summer sea ice area within certain regions of the Canadian Arctic Archipelago.
Julienne Stroeve, Vishnu Nandan, Rosemary Willatt, Rasmus Tonboe, Stefan Hendricks, Robert Ricker, James Mead, Robbie Mallett, Marcus Huntemann, Polona Itkin, Martin Schneebeli, Daniela Krampe, Gunnar Spreen, Jeremy Wilkinson, Ilkka Matero, Mario Hoppmann, and Michel Tsamados
The Cryosphere, 14, 4405–4426, https://doi.org/10.5194/tc-14-4405-2020, https://doi.org/10.5194/tc-14-4405-2020, 2020
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This study provides a first look at the data collected by a new dual-frequency Ka- and Ku-band in situ radar over winter sea ice in the Arctic Ocean. The instrument shows potential for using both bands to retrieve snow depth over sea ice, as well as sensitivity of the measurements to changing snow and atmospheric conditions.
Michael Studinger, Brooke C. Medley, Kelly M. Brunt, Kimberly A. Casey, Nathan T. Kurtz, Serdar S. Manizade, Thomas A. Neumann, and Thomas B. Overly
The Cryosphere, 14, 3287–3308, https://doi.org/10.5194/tc-14-3287-2020, https://doi.org/10.5194/tc-14-3287-2020, 2020
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We use repeat airborne geophysical data consisting of laser altimetry, snow, and Ku-band radar and optical imagery to analyze the spatial and temporal variability in surface roughness, slope, wind deposition, and snow accumulation at 88° S. We find small–scale variability in snow accumulation based on the snow radar subsurface layering, indicating areas of strong wind redistribution are prevalent at 88° S. There is no slope–independent relationship between surface roughness and accumulation.
Cited articles
Andersen, S., Breivik, L. A., Eastwood, S., Godøy, Ø., Lavergne, T., Lind, M., Procires, M., Schyberg, H., and Tonboe, R.:
Ocean and Sea Ice SAF,
Sea Ice Product Manual, version 3.8, oSI SAF document no. SAF/OSI/met.no/TEC/MA/125,
EUMETSAT, Darmstadt, Germany, 2012. a
Armstrong, R. and Brodzik, M.:
DMSP SSM/I-SSMIS Pathfinder Daily EASE-Grid Brightness Temperatures, Version 2, NASA National Snow and Ice Data Center Distributed Active Archive Center
[data set],
https://doi.org/10.5067/3EX2U1DV3434, 1994. a
Bi, H., Zhang, Z., Wang, Y., Xu, X., Liang, Y., Huang, J., Liu, Y., and Fu, M.: Baffin Bay sea ice inflow and outflow: 1978–1979 to 2016–2017, The Cryosphere, 13, 1025–1042, https://doi.org/10.5194/tc-13-1025-2019, 2019. a
Blanchard-Wrigglesworth, E., Webster, M. A., Farrell, S. L., and Bitz, C. M.:
Reconstruction of snow on Arctic sea ice,
J. Geophys. Res.-Oceans,
123, 3588–3602, https://doi.org/10.1002/2017JC013364, 2018. a
Connor, L. N., Laxon, S. W., Ridout, A. L., Krabill, W. B., and McAdoo, D. C.:
Comparison of Envisat radar and airborne laser altimeter measurements over Arctic sea ice,
Remote Sens. Environ.,
113, 563–570, https://doi.org/10.1016/j.rse.2008.10.015, 2009. a
Cuny, J., Rhines, P. B., and Kwok, R.:
Davis Strait volume, freshwater and heat fluxes,
Oceanographic Research Papers,
52, 519–542, https://doi.org/10.1016/j.dsr.2004.10.006, 2005. a
Curry, B., Lee, C. M., and Petrie, B.:
Volume, freshwater, and heat fluxes through Davis Strait, 2004-05,
J. Phys. Oceanogr.,
41, 429–436, https://doi.org/10.1175/2010JPO4536.1, 2011. a
Curry, B., Lee, C. M., Petrie, B., Moritz, R. E., and Kwok, R.:
Multiyear volume, liquid freshwater, and sea ice transport through Davis Strait, 2004-10,
J. Phys. Oceanogr.,
44, 1244–1266, https://doi.org/10.1175/JPO-D-13-0177.1, 2014. a
Day, J., Hawkins, E., and Tietsche, S.:
Will Arctic sea ice thickness initialization improve seasonal forecast skill,
Geophys. Res. Lett.,
41, 7566–7575, https://doi.org/10.1002/2014GL061694, 2014. a
Canadian Ice Service, Environment and Climate Change Canada: Canadian Ice Service Arctic Weekly Regional Ice Data, available at: https://iceweb1.cis.ec.gc.ca/Archive/page1.xhtml?lang=en
last access: April 2021. a
Fenty, I. and Heimbach, P.:
Coupled sea ice-ocean-state estimation in the Labrador Sea and Baffin Bay,
J. Phys. Oceanogr.,
43, 884–904, https://doi.org/10.1175/JPO-D-12-065.1, 2013. a
GEBCO Compilation Group:
GEBCO 2019 Grid,
https://doi.org/10.5285/836f016a-33be-6ddc-e053-6c86abc0788e, 2019. a
Geiger, C., Müller, H. R., Samluk, J. P., Bernstein, E. R., and Richter-Menge, J.:
Impact of spatial aliasing on sea-ice thickness measurements,
Ann. Glaciol.,
56, 353–362, https://doi.org/10.3189/2015AoG69A644, 2015. a
Giles, K. A., Laxon, S. W., Wingham, D. J., Wallis, D. W., Krabill, W. B., Leuschen, C. J., McAdoo, D., Manizade, S. S., and Raney, R. K.:
Combined airborne laser and radar altimeter measurements over the Fram Strait in May 2002,
Remote Sens. Environ.,
111, 182–194, https://doi.org/10.1016/j.rse.2007.02.037, 2007. a
Glissenaar, I.:
Sea ice thickness and snow depth in Baffin Bay (March 2003–2020), data.bris Research Data Repository [data set], https://doi.org/10.5523/bris.2peywz756l8182cpwlanm4ratz, 2021. a
Hendricks, S., Paul, S., and Rinne, E.:
ESA Sea Ice Climate Change Initiative (Sea_Ice_cci):
Northern hemisphere sea ice thickness from CryoSat-2 on the satellite swath (L2P), v2.0, CEDA Archive,
Centre for Environmental Data Analysis [data set], https://doi.org/10.5285/5b6033bfb7f241e89132a83fdc3d5364, 2018a. a, b
Hendricks, S., Paul, S., and Rinne, E.:
ESA Sea Ice Climate Change Initiative (Sea_Ice_cci):
Northern hemisphere sea ice thickness from Envisat on the satellite swath (L2P), v2.0, CEDA Archive,
Centre for Environmental Data Analysis [data set],
https://doi.org/10.5285/54e2ee0803764b4e84c906da3f16d81b,
2018b. a, b, c
Holland, M. M., Bitz, C. M., Eby, M., and Weaver, A. J.:
The role of ice–ocean interactions in the variability of the North Atlantic Thermohaline Circulation,
J. Climate,
14, 656–675, https://doi.org/10.1175/1520-0442(2001)014<0656:TROIOI>2.0.CO;2, 2001. a
Jeffries, M., Overland, J., and Perovich, D.:
The Arctic shifts to a new normal,
Phys. Today,
66, 35–40, https://doi.org/10.1063/PT.3.2147, 2013. a
Kern, S., Skourop, H., and Khvorostovsky, K.:
Sea Ice Climate Change Initiative:
Phase 2 – D4.1 Product Validation & Intercomparison Report (PVIR-SIT), Tech. rep.,
ESA, 2018. a
Kurtz, N., Markus, T., Cavalieri, D., Sparling, L. C., Krabill, W. B., Gasiewski, A. J., and Sonntag, J. G.:
Estimation of sea ice thickness distributions through the combination of snow depth and satellite laser altimetry data,
J. Geophys. Res.-Oceans,
114, C10007, https://doi.org/10.1029/2009JC005292, 2009. a, b, c, d
Kurtz, N. T. and Farrell, S. L.:
Large-scale surveys of snow depth on Arctic sea ice from Operation IceBridge,
Geophys. Res. Lett.,
38, L20505, https://doi.org/10.1029/2011GL049216, 2011. a
Kurtz, N., Studinger, M., Harbeck, J., Onana, V.-D.-P., and Yi, D.:
IceBridge L4 Sea Ice Freeboard, Snow Depth, and Thickness, Version 1,
NASA National Snow and Ice Data Center Distributed Active Archive Center
[data set],
https://doi.org/10.5067/G519SHCKWQV6, 2015. a
Kwok, R.:
Variability of Nares Strait ice flux,
Geophys. Res. Lett.,
32, L24502, https://doi.org/10.1029/2005GL024768, 2005. a
Kwok, R.:
Arctic sea ice thickness, volume, and multiyear ice coverage: Losses and coupled variability (1958–2018),
Environ. Res. Lett.,
13, 105005, https://doi.org/10.1088/1748-9326/aae3ec, 2018. a, b, c
Kwok, R. and Cunningham, G. F.:
Variability of Arctic sea ice thickness and volume from CryoSat-2,
Philos. T. R. Soc. A,
373, 2045, https://doi.org/10.1098/rsta.2014.0157, 2015. a
Kwok, R., Cunningham, G. F., Zwally, H. J., and Yi, D.:
ICESat over Arctic sea ice: Interpretation of altimetric and reflectivity profiles,
J. Geophys. Res.,
111, C06006, https://doi.org/10.1029/2005JC003175, 2006. a
Kwok, R., Cunningham, G. F., Zwally, H. J., and Yi, D.:
Ice, Cloud, and land Elevation Satellite (ICESat) over Arctic sea ice: Retrieval of freeboard,
J. Geophys. Res.,
112, C12013, https://doi.org/10.1029/2006JC003978, 2007. a
Kwok, R., Petty, A. A., Cunningham, G., Markus, T., Hancock, D., Ivanoff, A., Wimert, J., Bagnardi, M., Kurtz, N., and the ICESat-2 Science Team: ATLAS/ICESat-2 L3A Sea Ice Freeboard, Version 4, March 2019 & March 2020, Boulder, Colorado USA, NSIDC: National Snow and Ice Data Center [data set], https://doi.org/10.5067/ATLAS/ATL10.004, 2021. a, b
Landy, J. C., Petty, A. A., Tsamados, M., and Stroeve, J. C.:
Sea ice roughness overlooked as a key source of uncertainty in CryoSat-2 ice freeboard retrievals,
J. Geophys. Res.-Oceans,
125, 1–40, https://doi.org/10.1029/2019jc015820, 2020. a, b
Laxon, S. W., Giles, K. A., Ridout, A. L., Wingham, D. J., Willatt, R., Cullen, R., Kwok, R., Schweiger, A., Zhang, J., Haas, C., Hendricks, S., Krishfield, R., Kurtz, N., Farrell, S., and Davidson, M.:
CryoSat-2 estimates of Arctic sea ice thickness and volume,
Geophys. Res. Lett.,
40, 732–737, https://doi.org/10.1002/grl.50193, 2013. a, b
Lee, C. M., Petrie, B., Gobat, J. I., Soukhovtsev, V., Abriel, J., van Thiel, K., and Scotney, M.:
An Observational Array for High-Resolution year-round measurements of volume, freshwater and ice flux variability in Davis Strait: Cruise report for R/V Knorr 179-05, 22 September–4 October 2004, Tech. Rep. APL-UW TR 0408,
Applied Physics Laboratory, University of Washington,
available at: https://iop.apl.washington.edu/assets/pdfs/APL_UW_TR_0408.pdf (last access: October 2019), 2004. a
Liston, G., Itkin, P., Stroeve, J., Tschudi, M., Stewart, J. S., Pedersan, S. H., Reinking, A. K., and Elder, K.:
A Langrangian snow-evolution system for sea-ice applications (SnowModel-LG): Part I – Model Description,
J. Geophys. Res.-Oceans,
125, e2019JC015913, https://doi.org/10.1029/2019JC015913, 2020. a, b, c, d
Mallett, R. D. C., Stroeve, J. C., Tsamados, M., Landy, J. C., Willatt, R., Nandan, V., and Liston, G. E.: Faster decline and higher variability in the sea ice thickness of the marginal Arctic seas when accounting for dynamic snow cover, The Cryosphere, 15, 2429–2450, https://doi.org/10.5194/tc-15-2429-2021, 2021. a, b
Markus, T. and Cavalieri, D. J.:
Snow depth distribution over sea ice in the Southern Ocean from satellite passive microwave data, edited by: Jeffries, M. O.,
https://doi.org/10.1029/AR074p0019, 1998. a, b, c
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
Min, C., Yang, Q., Mu, L., Kauker, F., and Ricker, R.: Ensemble-based estimation of sea-ice volume variations in the Baffin Bay, The Cryosphere, 15, 169–181, https://doi.org/10.5194/tc-15-169-2021, 2021. a, b
Moritz, R.:
Davis Strait Freshwater Flux Array, available at: http://psc.apl.uw.edu/sea_ice_cdr/Sources/Davis_Strait.html, last access:
October 2019. a
Obbard, M. E., Derocher, A. E., Lunn, N.J., Peacock, E., Stirling, I., and Thiemann, G. W.: Research on Polar Bears in Canada, 2005–2009, in: Polar Bears: Proceedings of the 15th Working Meeting of the IUCN/SSC Polar Bear Specialist Group, 29 June–3 July 2009, Copenhagen, Denmark, edited by: Obbard, M. E., Thiemann, G. W., Peacock, E., and DeBruyn, T. D., 115–132, 2010. a
Paul, S., Hendricks, S., Ricker, R., Kern, S., and Rinne, E.: Empirical parametrization of Envisat freeboard retrieval of Arctic and Antarctic sea ice based on CryoSat-2: progress in the ESA Climate Change Initiative, The Cryosphere, 12, 2437–2460, https://doi.org/10.5194/tc-12-2437-2018, 2018. a
Petty, A. A., Webster, M., Boisvert, L., and Markus, T.: The NASA Eulerian Snow on Sea Ice Model (NESOSIM) v1.0: initial model development and analysis, Geosci. Model Dev., 11, 4577–4602, https://doi.org/10.5194/gmd-11-4577-2018, 2018. a, b
Rango, A., Chang, A. T. C., and Foster, J. L.:
Utilization of spaceborne microwave radiometers for monitoring snowpack properties,
Nord. Hydrol.,
10, 25–40, https://doi.org/10.2166/nh.1979.0003, 1979. a
Ricker, R., Hendricks, S., Kaleschke, L., Tian-Kunze, X., King, J., and Haas, C.: A weekly Arctic sea-ice thickness data record from merged CryoSat-2 and SMOS satellite data, The Cryosphere, 11, 1607–1623, https://doi.org/10.5194/tc-11-1607-2017, 2017. a, b
Ricker, R., Hendricks, S., Tian-Kunze, X., and Kaleschke, L.:
CryoSat-SMOS Merged Sea Ice Thickness, available at: https://spaces.awi.de/pages/viewpage.action?pageId=291898639, last access: July 2021b. a
Rostosky, P., Spreen, G., Farrell, S. L., Frost, T., Heygster, G., and Melsheimer, C.:
Snow depth retrieval on Arctic sea ice from passive microwave radiometers – Improvements and extensions to multiyear ice using lower frequencies,
J. Geophys. Res.-Oceans,
123, 7120–7138, https://doi.org/10.1029/2018JC014028, 2018. a
Serreze, M. and Stroeve, J.:
Arctic sea ice trends, variability and implications for seasonal ice forecasting,
Philos. T. R. Soc. A,
373, 2045, https://doi.org/10.1098/rsta.2014.0159, 2015. a
Stroeve, J. and Notz, D.:
Changing state of Arctic sea ice across all seasons,
Environ. Res. Lett.,
13, 103001, https://doi.org/10.1088/1748-9326/aade56, 2018. a, b
Stroeve, J., Liston, G. E., Buzzard, S., Zhou, L., Mallett, R., Barrett, A., Tschudi, M., Tsamados, M., Itkin, P., and Scott Stewart, J.:
A Lagrangian Snow-Evolution System for Sea Ice Applications (SnowModel-LG): Part II – Analyses,
J. Geophys. Res.-Oceans,
125, e2019JC015900, https://doi.org/10.1029/2019jc015900, 2020. a
Tang, C. L., Ross, C. K., Yao, T., Petrie, B., DeTracey, B. M., and Dunlap, E.:
The circulation, water masses and sea-ice of Baffin Bay,
Prog. Oceanogr.,
63, 183–228, https://doi.org/10.1016/j.pocean.2004.09.005, 2004. a
Tilling, R., Ridout, A., and Shepherd, A.:
Assessing the Impact of Lead and Floe Sampling on Arctic Sea Ice Thickness Estimates from Envisat and CryoSat-2,
J. Geophys. Res.-Oceans,
124, 7473–7485, https://doi.org/10.1029/2019JC015232, 2019. a
Tilling, R. L., Ridout, A., and Shepherd, A.:
Estimating Arctic sea ice thickness and volume using CryoSat-2 radar altimeter data,
Adv. Space Res.,
62, 1203–1225, https://doi.org/10.1016/j.asr.2017.10.051, 2018. a
Ulaby, F., Moore, R., and Fung, A.:
Microwave remote sensing: active and passive, Volume 3 – From Theory to Applications, Artech House,
Norwood, Massachusetts, USA, equation number: E.80, 1986. a
Warren, S. G., Rigor, I. G., Untersteiner, N., Radinov, V. F., Bryazgin, N. N., Aleksandrov, Y. I., and Colony, R.:
Snow depth on Arctic sea ice,
J. Climate,
12, 1814–1829, https://doi.org/10.1175/1520-0442(1999)012<1814:SDOASI>2.0.CO;2, 1999. a, b, c, d
Webster, M. A., Rignor, I. G., Nghiem, S. V., Kurtz, N. T., Farrell, S. L., Perovich, D. K., and Sturm, M.:
Interdecadal changes in snow depth on Arctic sea ice,
J. Geophys. Res.-Oceans,
119, 5395–5406, https://doi.org/10.1002/2014JC009985, 2014. a
Wingham, D. J., Francis, C. R., Baker, S., Bouzinac, C., Brockley, D., Cullen, R., de Chateau-Thierry, P., Laxon, S. W., Mallow, U., Mavrocordatos, C., Phalippou, L., Ratier, G., Rey, L., Rostan, F., Viau, P., and Wallis, D. W.:
CryoSat: A mission to determine the fluctuations in Earth's land and marine ice fields,
Adv. Space Res.,
37, 841–871, https://doi.org/10.1016/j.asr.2005.07.027, 2006. a, b
Zhou, L., Stroeve, J., Xu, S., Petty, A., Tilling, R., Winstrup, M., Rostosky, P., Lawrence, I. R., Liston, G. E., Ridout, A., Tsamados, M., and Nandan, V.: Inter-comparison of snow depth over Arctic sea ice from reanalysis reconstructions and satellite retrieval, The Cryosphere, 15, 345–367, https://doi.org/10.5194/tc-15-345-2021, 2021. a
Zwally, H. J., Schutz, R., Hancock, D., and Dimarzio, J.:
GLAS/ICESat L2 Sea Ice Altimetry Data (HDF5), Version 34, NASA National Snow and Ice Data Center Distributed Active Archive Center, Boulder, Colorado, USA, NSIDC,
https://doi.org/10.5067/ICESAT/GLAS/DATA210,
2014. a, b
Zygmuntowska, M., Rampal, P., Ivanova, N., and Smedsrud, L. H.: Uncertainties in Arctic sea ice thickness and volume: new estimates and implications for trends, The Cryosphere, 8, 705–720, https://doi.org/10.5194/tc-8-705-2014, 2014. a
Short summary
Scientists can estimate sea ice thickness using satellites that measure surface height. To determine the sea ice thickness, we also need to know the snow depth and density. This paper shows that the chosen snow depth product has a considerable impact on the findings of sea ice thickness state and trends in Baffin Bay, showing mean thinning with some snow depth products and mean thickening with others. This shows that it is important to better understand and monitor snow depth on sea ice.
Scientists can estimate sea ice thickness using satellites that measure surface height. To...
