Articles | Volume 13, issue 7
https://doi.org/10.5194/tc-13-1843-2019
© Author(s) 2019. 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-13-1843-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Jul 2019
Research article |
| 09 Jul 2019
| 09 Jul 2019
Thermal conductivity of firn at Lomonosovfonna, Svalbard, derived from subsurface temperature measurements
Sergey Marchenko
CORRESPONDING AUTHOR
Department of Earth Sciences, Uppsala University, Uppsala, Sweden
Gong Cheng
Department of Information Technology, Uppsala University, Uppsala, Sweden
Per Lötstedt
Department of Information Technology, Uppsala University, Uppsala, Sweden
Veijo Pohjola
Department of Earth Sciences, Uppsala University, Uppsala, Sweden
Rickard Pettersson
Department of Earth Sciences, Uppsala University, Uppsala, Sweden
Ward van Pelt
Department of Earth Sciences, Uppsala University, Uppsala, Sweden
Carleen Reijmer
Institute for Marine and Atmospheric Research, Utrecht University, Utrecht, the Netherlands
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Baptiste Vandecrux, Ruth Mottram, Peter L. Langen, Robert S. Fausto, Martin Olesen, C. Max Stevens, Vincent Verjans, Amber Leeson, Stefan Ligtenberg, Peter Kuipers Munneke, Sergey Marchenko, Ward van Pelt, Colin R. Meyer, Sebastian B. Simonsen, Achim Heilig, Samira Samimi, Shawn Marshall, Horst Machguth, Michael MacFerrin, Masashi Niwano, Olivia Miller, Clifford I. Voss, and Jason E. Box
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Gong Cheng, Per Lötstedt, and Lina von Sydow
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Gong Cheng and Per Lötstedt
The Cryosphere, 14, 673–691, https://doi.org/10.5194/tc-14-673-2020, https://doi.org/10.5194/tc-14-673-2020, 2020
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We present a time-dependent inverse method for ice sheet modeling. By investigating the sensitivity of the observations of the velocity and the height at the surface to the basal conditions of the ice, we show that if the basal parameters are time dependent, then time cannot be ignored in the inversion. By looking at the numerical features, we conclude that adding the height information of an ice sheet in the velocity inversion procedure could improve the robustness of the inference.
Ward van Pelt, Veijo Pohjola, Rickard Pettersson, Sergey Marchenko, Jack Kohler, Bartłomiej Luks, Jon Ove Hagen, Thomas V. Schuler, Thorben Dunse, Brice Noël, and Carleen Reijmer
The Cryosphere, 13, 2259–2280, https://doi.org/10.5194/tc-13-2259-2019, https://doi.org/10.5194/tc-13-2259-2019, 2019
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Thomas J. Ballinger, Thomas L. Mote, Kyle Mattingly, Angela C. Bliss, Edward Hanna, Dirk van As, Melissa Prieto, Saeideh Gharehchahi, Xavier Fettweis, Brice Noël, Paul C. J. P. Smeets, Carleen H. Reijmer, Mads H. Ribergaard, and John Cappelen
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Constantijn L. Jakobs, Carleen H. Reijmer, Peter Kuipers Munneke, Gert König-Langlo, and Michiel R. van den Broeke
The Cryosphere, 13, 1473–1485, https://doi.org/10.5194/tc-13-1473-2019, https://doi.org/10.5194/tc-13-1473-2019, 2019
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Dorothée Vallot, Sigit Adinugroho, Robin Strand, Penelope How, Rickard Pettersson, Douglas I. Benn, and Nicholas R. J. Hulton
Geosci. Instrum. Method. Data Syst., 8, 113–127, https://doi.org/10.5194/gi-8-113-2019, https://doi.org/10.5194/gi-8-113-2019, 2019
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This paper presents a novel method to quantify the sizes and frequency of calving events from time-lapse camera images. The calving front of a tidewater glacier experiences different episodes of iceberg deliveries that can be captured by a time-lapse camera situated in front of the glacier. An automatic way of detecting calving events is presented here and compared to manually detected events.
Eef C. H. van Dongen, Nina Kirchner, Martin B. van Gijzen, Roderik S. W. van de Wal, Thomas Zwinger, Gong Cheng, Per Lötstedt, and Lina von Sydow
Geosci. Model Dev., 11, 4563–4576, https://doi.org/10.5194/gmd-11-4563-2018, https://doi.org/10.5194/gmd-11-4563-2018, 2018
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Ice flow forced by gravity is governed by the full Stokes (FS) equations, which are computationally expensive to solve. Therefore, approximations to the FS equations are used, especially when modeling an ice sheet on long time spans. Here, we report a combination of an approximation with the FS equations that allows simulating the dynamics of ice sheets over long time spans without introducing artifacts caused by application of approximations in parts of the domain where they are not valid.
Katrin Lindbäck, Jack Kohler, Rickard Pettersson, Christopher Nuth, Kirsty Langley, Alexandra Messerli, Dorothée Vallot, Kenichi Matsuoka, and Ola Brandt
Earth Syst. Sci. Data, 10, 1769–1781, https://doi.org/10.5194/essd-10-1769-2018, https://doi.org/10.5194/essd-10-1769-2018, 2018
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Jiangjun Ran, Miren Vizcaino, Pavel Ditmar, Michiel R. van den Broeke, Twila Moon, Christian R. Steger, Ellyn M. Enderlin, Bert Wouters, Brice Noël, Catharina H. Reijmer, Roland Klees, Min Zhong, Lin Liu, and Xavier Fettweis
The Cryosphere, 12, 2981–2999, https://doi.org/10.5194/tc-12-2981-2018, https://doi.org/10.5194/tc-12-2981-2018, 2018
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To accurately predict future sea level rise, the mechanisms driving the observed mass loss must be better understood. Here, we combine data from the satellite gravimetry, surface mass balance, and ice discharge to analyze the mass budget of Greenland at various temporal scales. This study, for the first time, suggests the existence of a substantial meltwater storage during summer, with a peak value of 80–120 Gt in July. We highlight its importance for understanding ice sheet mass variability
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.
Solveig H. Winsvold, Andreas Kääb, Christopher Nuth, Liss M. Andreassen, Ward J. J. van Pelt, and Thomas Schellenberger
The Cryosphere, 12, 867–890, https://doi.org/10.5194/tc-12-867-2018, https://doi.org/10.5194/tc-12-867-2018, 2018
Dorothée Vallot, Jan Åström, Thomas Zwinger, Rickard Pettersson, Alistair Everett, Douglas I. Benn, Adrian Luckman, Ward J. J. van Pelt, Faezeh Nick, and Jack Kohler
The Cryosphere, 12, 609–625, https://doi.org/10.5194/tc-12-609-2018, https://doi.org/10.5194/tc-12-609-2018, 2018
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This paper presents a new perspective on the role of ice dynamics and ocean interaction in glacier calving processes applied to Kronebreen, a tidewater glacier in Svalbard. A global modelling approach includes ice flow modelling, undercutting estimation by a combination of glacier energy balance and plume modelling as well as calving by a discrete particle model. We show that modelling undercutting is necessary and calving is influenced by basal friction velocity and geometry.
Penelope How, Douglas I. Benn, Nicholas R. J. Hulton, Bryn Hubbard, Adrian Luckman, Heïdi Sevestre, Ward J. J. van Pelt, Katrin Lindbäck, Jack Kohler, and Wim Boot
The Cryosphere, 11, 2691–2710, https://doi.org/10.5194/tc-11-2691-2017, https://doi.org/10.5194/tc-11-2691-2017, 2017
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This study provides valuable insight into subglacial hydrology and dynamics at tidewater glaciers, which remains a poorly understood area of glaciology. It is a unique study because of the wealth of information provided by simultaneous observations of glacier hydrology at Kronebreen, a tidewater glacier in Svalbard. All these elements build a strong conceptual picture of the glacier's hydrological regime over the 2014 melt season.
Christian R. Steger, Carleen H. Reijmer, and Michiel R. van den Broeke
The Cryosphere, 11, 2507–2526, https://doi.org/10.5194/tc-11-2507-2017, https://doi.org/10.5194/tc-11-2507-2017, 2017
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Mass loss from the Greenland Ice Sheet, which contributes to sea level rise, is currently dominated by surface melt and run-off. The relation between these two variables is rather uncertain due to the firn layer’s potential to buffer melt in solid (refreezing) or liquid (firn aquifer) form. To address this uncertainty, we analyse output of a numerical firn model run over 1960–2014. Results show a spatially variable response of the ice sheet to increasing melt and an upward migration of aquifers.
Johannes Jakob Fürst, Fabien Gillet-Chaulet, Toby J. Benham, Julian A. Dowdeswell, Mariusz Grabiec, Francisco Navarro, Rickard Pettersson, Geir Moholdt, Christopher Nuth, Björn Sass, Kjetil Aas, Xavier Fettweis, Charlotte Lang, Thorsten Seehaus, and Matthias Braun
The Cryosphere, 11, 2003–2032, https://doi.org/10.5194/tc-11-2003-2017, https://doi.org/10.5194/tc-11-2003-2017, 2017
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For the large majority of glaciers and ice caps, there is no information on the thickness of the ice cover. Any attempt to predict glacier demise under climatic warming and to estimate the future contribution to sea-level rise is limited as long as the glacier thickness is not well constrained. Here, we present a two-step mass-conservation approach for mapping ice thickness. Measurements are naturally reproduced. The reliability is readily assessible from a complementary map of error estimates.
Daniel Farinotti, Douglas J. Brinkerhoff, Garry K. C. Clarke, Johannes J. Fürst, Holger Frey, Prateek Gantayat, Fabien Gillet-Chaulet, Claire Girard, Matthias Huss, Paul W. Leclercq, Andreas Linsbauer, Horst Machguth, Carlos Martin, Fabien Maussion, Mathieu Morlighem, Cyrille Mosbeux, Ankur Pandit, Andrea Portmann, Antoine Rabatel, RAAJ Ramsankaran, Thomas J. Reerink, Olivier Sanchez, Peter A. Stentoft, Sangita Singh Kumari, Ward J. J. van Pelt, Brian Anderson, Toby Benham, Daniel Binder, Julian A. Dowdeswell, Andrea Fischer, Kay Helfricht, Stanislav Kutuzov, Ivan Lavrentiev, Robert McNabb, G. Hilmar Gudmundsson, Huilin Li, and Liss M. Andreassen
The Cryosphere, 11, 949–970, https://doi.org/10.5194/tc-11-949-2017, https://doi.org/10.5194/tc-11-949-2017, 2017
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ITMIX – the Ice Thickness Models Intercomparison eXperiment – was the first coordinated performance assessment for models inferring glacier ice thickness from surface characteristics. Considering 17 different models and 21 different test cases, we show that although solutions of individual models can differ considerably, an ensemble average can yield uncertainties in the order of 10 ± 24 % the mean ice thickness. Ways forward for improving such estimates are sketched.
Thomas Schellenberger, Thorben Dunse, Andreas Kääb, Thomas Vikhamar Schuler, Jon Ove Hagen, and Carleen H. Reijmer
The Cryosphere Discuss., https://doi.org/10.5194/tc-2017-5, https://doi.org/10.5194/tc-2017-5, 2017
Preprint withdrawn
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Basin-3, NE-Svalbard, was still surging with 10 m d-1 in July 2016. After a speed peak of 18.8 m d-1 in Dec 2012/Jan 2013, speed-ups are overlying the fast flow every summer. The glacier is massively calving icebergs (5.2 Gt yr-1 ~ 2 L drinking water for every human being daily!) which in the same order of magnitude as all other Svalbard glaciers together.
Since autumn 2015 also Basin-2 is surging with maximum velocities of 8.7 m d-1, an advance of more than 2 km and a mass loss of 0.7 Gt yr-1.
Torbjørn Ims Østby, Thomas Vikhamar Schuler, Jon Ove Hagen, Regine Hock, Jack Kohler, and Carleen H. Reijmer
The Cryosphere, 11, 191–215, https://doi.org/10.5194/tc-11-191-2017, https://doi.org/10.5194/tc-11-191-2017, 2017
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We present modelled climatic mass balance for all glaciers in Svalbard for the period 1957–2014 at 1 km resolution using a coupled surface energy balance and snowpack model, thereby closing temporal and spatial gaps in direct and geodetic mass balance estimates.
Supporting previous studies, our results indicate increased mass loss over the period.
A detailed analysis of the involved energy fluxes reveals that increased mass loss is caused by atmospheric warming further amplified by feedbacks.
Andreas Bech Mikkelsen, Alun Hubbard, Mike MacFerrin, Jason Eric Box, Sam H. Doyle, Andrew Fitzpatrick, Bent Hasholt, Hannah L. Bailey, Katrin Lindbäck, and Rickard Pettersson
The Cryosphere, 10, 1147–1159, https://doi.org/10.5194/tc-10-1147-2016, https://doi.org/10.5194/tc-10-1147-2016, 2016
Carmen P. Vega, Veijo A. Pohjola, Emilie Beaudon, Björn Claremar, Ward J. J. van Pelt, Rickard Pettersson, Elisabeth Isaksson, Tõnu Martma, Margit Schwikowski, and Carl E. Bøggild
The Cryosphere, 10, 961–976, https://doi.org/10.5194/tc-10-961-2016, https://doi.org/10.5194/tc-10-961-2016, 2016
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To quantify post-depositional relocation of major ions by meltwater in snow and firn at Lomonosovfonna, Svalbard, consecutive ice cores drilled at this site were used to construct a synthetic core. The relocation length of most of the ions was on the order of 1 m between 2007 and 2010. Considering the ionic relocation lengths and annual melt percentages, we estimate that the atmospheric ionic signal remains preserved in recently drilled Lomonosovfonna ice cores at an annual or bi-annual resolution.
J. M. van Wessem, S. R. M. Ligtenberg, C. H. Reijmer, W. J. van de Berg, M. R. van den Broeke, N. E. Barrand, E. R. Thomas, J. Turner, J. Wuite, T. A. Scambos, and E. van Meijgaard
The Cryosphere, 10, 271–285, https://doi.org/10.5194/tc-10-271-2016, https://doi.org/10.5194/tc-10-271-2016, 2016
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This study presents the first high-resolution (5.5 km) modelled estimate of surface mass balance (SMB) over the period 1979–2014 for the Antarctic Peninsula (AP). Precipitation (snowfall and rain) largely determines the SMB, and is exceptionally high over the western mountain slopes, with annual values > 4 m water equivalent. Snowmelt is widespread over the AP, but only runs off into the ocean at some locations: the Larsen B,C, and Wilkins ice shelves, and along the north-western mountains.
T. Schellenberger, T. Dunse, A. Kääb, J. Kohler, and C. H. Reijmer
The Cryosphere, 9, 2339–2355, https://doi.org/10.5194/tc-9-2339-2015, https://doi.org/10.5194/tc-9-2339-2015, 2015
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Kronebreen and Kongsbreen are among the fastest flowing glaciers on Svalbard, and surface speeds reached up to 3.2m d-1 at Kronebreen in summer 2013 and 2.7m d-1 at Kongsbreen in late autumn 2012 as retrieved from SAR satellite data. Both glaciers retreated significantly during the observation period, Kongsbreen up to 1800m or 2.5km2 and Kronebreen up to 850m or 2.8km2. Both glaciers are important contributors to the total dynamic mass loss from the Svalbard archipelago.
Y. Sjöberg, P. Marklund, R. Pettersson, and S. W. Lyon
The Cryosphere, 9, 465–478, https://doi.org/10.5194/tc-9-465-2015, https://doi.org/10.5194/tc-9-465-2015, 2015
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Permafrost peatlands are hydrological and biogeochemical hotspots in discontinuous permafrost areas. We estimate the depths to the permafrost table surface and base across a peatland in northern Sweden using ground penetrating radar and electrical resistivity tomography. Seasonal frost tables, taliks, and the permafrost base could be detected. The results highlight the added value of combining techniques for assessing distributions of permafrost in the rapidly changing sporadic permafrost zone.
T. Dunse, T. Schellenberger, J. O. Hagen, A. Kääb, T. V. Schuler, and C. H. Reijmer
The Cryosphere, 9, 197–215, https://doi.org/10.5194/tc-9-197-2015, https://doi.org/10.5194/tc-9-197-2015, 2015
M. Schäfer, F. Gillet-Chaulet, R. Gladstone, R. Pettersson, V. A. Pohjola, T. Strozzi, and T. Zwinger
The Cryosphere, 8, 1951–1973, https://doi.org/10.5194/tc-8-1951-2014, https://doi.org/10.5194/tc-8-1951-2014, 2014
K. Lindbäck, R. Pettersson, S. H. Doyle, C. Helanow, P. Jansson, S. S. Kristensen, L. Stenseng, R. Forsberg, and A. L. Hubbard
Earth Syst. Sci. Data, 6, 331–338, https://doi.org/10.5194/essd-6-331-2014, https://doi.org/10.5194/essd-6-331-2014, 2014
H. Fréville, E. Brun, G. Picard, N. Tatarinova, L. Arnaud, C. Lanconelli, C. Reijmer, and M. van den Broeke
The Cryosphere, 8, 1361–1373, https://doi.org/10.5194/tc-8-1361-2014, https://doi.org/10.5194/tc-8-1361-2014, 2014
J. M. van Wessem, C. H. Reijmer, J. T. M. Lenaerts, W. J. van de Berg, M. R. van den Broeke, and E. van Meijgaard
The Cryosphere, 8, 125–135, https://doi.org/10.5194/tc-8-125-2014, https://doi.org/10.5194/tc-8-125-2014, 2014
J. Ahlkrona, N. Kirchner, and P. Lötstedt
Geosci. Model Dev., 6, 2135–2152, https://doi.org/10.5194/gmd-6-2135-2013, https://doi.org/10.5194/gmd-6-2135-2013, 2013
C. Nuth, J. Kohler, M. König, A. von Deschwanden, J. O. Hagen, A. Kääb, G. Moholdt, and R. Pettersson
The Cryosphere, 7, 1603–1621, https://doi.org/10.5194/tc-7-1603-2013, https://doi.org/10.5194/tc-7-1603-2013, 2013
A. P. Ahlstrøm, S. B. Andersen, M. L. Andersen, H. Machguth, F. M. Nick, I. Joughin, C. H. Reijmer, R. S. W. van de Wal, J. P. Merryman Boncori, J. E. Box, M. Citterio, D. van As, R. S. Fausto, and A. Hubbard
Earth Syst. Sci. Data, 5, 277–287, https://doi.org/10.5194/essd-5-277-2013, https://doi.org/10.5194/essd-5-277-2013, 2013
W. J. J. van Pelt, J. Oerlemans, C. H. Reijmer, R. Pettersson, V. A. Pohjola, E. Isaksson, and D. Divine
The Cryosphere, 7, 987–1006, https://doi.org/10.5194/tc-7-987-2013, https://doi.org/10.5194/tc-7-987-2013, 2013
S. H. Doyle, A. L. Hubbard, C. F. Dow, G. A. Jones, A. Fitzpatrick, A. Gusmeroli, B. Kulessa, K. Lindback, R. Pettersson, and J. E. Box
The Cryosphere, 7, 129–140, https://doi.org/10.5194/tc-7-129-2013, https://doi.org/10.5194/tc-7-129-2013, 2013
M. A. G. den Ouden, C. H. Reijmer, V. Pohjola, R. S. W. van de Wal, J. Oerlemans, and W. Boot
The Cryosphere, 4, 593–604, https://doi.org/10.5194/tc-4-593-2010, https://doi.org/10.5194/tc-4-593-2010, 2010
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Short summary
Thermal conductivity (k) of firn at Lomonosovfonna, Svalbard, is estimated using measured temperature evolution and density. The optimized k values (0.2–1.6 W (m K)−1) increase downwards and over time and are most sensitive to systematic errors in measured temperature values and their depths, particularly in the lower part of the profile. Compared to the density-based parameterizations, derived k values are consistently larger, suggesting a faster conductive heat exchange in firn.
Thermal conductivity (k) of firn at Lomonosovfonna, Svalbard, is estimated using measured...
