Articles | Volume 13, issue 3
https://doi.org/10.5194/tc-13-1005-2019
© 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-13-1005-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
29 Mar 2019
Research article |
| 29 Mar 2019
| 29 Mar 2019
In situ observed relationships between snow and ice surface skin temperatures and 2 m air temperatures in the Arctic
Pia Nielsen-Englyst
CORRESPONDING AUTHOR
Research & Development, Danish Meteorological Institute (DMI), 2100 Copenhagen Ø, Denmark
DTU Space Institute, Technical University of Denmark (DTU), 2800 Kongens Lyngby, Denmark
Jacob L. Høyer
Research & Development, Danish Meteorological Institute (DMI), 2100 Copenhagen Ø, Denmark
Kristine S. Madsen
Research & Development, Danish Meteorological Institute (DMI), 2100 Copenhagen Ø, Denmark
Rasmus Tonboe
Research & Development, Danish Meteorological Institute (DMI), 2100 Copenhagen Ø, Denmark
Gorm Dybkjær
Research & Development, Danish Meteorological Institute (DMI), 2100 Copenhagen Ø, Denmark
Emy Alerskans
Research & Development, Danish Meteorological Institute (DMI), 2100 Copenhagen Ø, Denmark
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Revised manuscript not accepted
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The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-126, https://doi.org/10.5194/tc-2019-126, 2019
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Thomas Lavergne, Atle Macdonald Sørensen, Stefan Kern, Rasmus Tonboe, Dirk Notz, Signe Aaboe, Louisa Bell, Gorm Dybkjær, Steinar Eastwood, Carolina Gabarro, Georg Heygster, Mari Anne Killie, Matilde Brandt Kreiner, John Lavelle, Roberto Saldo, Stein Sandven, and Leif Toudal Pedersen
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The loss of polar sea ice is an iconic indicator of Earth’s climate change. Many satellite-based algorithms and resulting data exist but they differ widely in specific sea-ice conditions. This spread hinders a robust estimate of the future evolution of sea-ice cover.
In this study, we document three new climate data records of sea-ice concentration generated using satellite data available over the last 40 years. We introduce the novel algorithms, the data records, and their uncertainties.
Rasmus T. Tonboe, Steinar Eastwood, Thomas Lavergne, Atle M. Sørensen, Nicholas Rathmann, Gorm Dybkjær, Leif Toudal Pedersen, Jacob L. Høyer, and Stefan Kern
The Cryosphere, 10, 2275–2290, https://doi.org/10.5194/tc-10-2275-2016, https://doi.org/10.5194/tc-10-2275-2016, 2016
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The EUMETSAT sea ice climate record (ESICR) is based on the Nimbus 7 SMMR (1978–1987), the SSM/I (1987–2009), and the SSMIS (2003–today) microwave radiometer data. It uses a combination of two sea ice concentration algorithms with dynamical tie points, explicit atmospheric correction using numerical weather prediction data for error reduction and it comes with spatially and temporally varying uncertainty estimates describing the residual uncertainties.
Stefan Kern, Anja Rösel, Leif Toudal Pedersen, Natalia Ivanova, Roberto Saldo, and Rasmus Tage Tonboe
The Cryosphere, 10, 2217–2239, https://doi.org/10.5194/tc-10-2217-2016, https://doi.org/10.5194/tc-10-2217-2016, 2016
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Sea ice, frozen seawater floating on polar oceans, is covered by meltwater puddles, so-called melt ponds, during summer. Methods used to compute Arctic sea-ice concentration (SIC) from microwave satellite data are influenced by melt ponds. We apply eight such methods to one microwave dataset and compare SIC with visible data. We conclude all methods fail to distinguish melt ponds from leads between ice floes; SIC biases are negative (positive) for ponded (non-ponded) sea ice and can exceed 20 %.
N. Ivanova, L. T. Pedersen, R. T. Tonboe, S. Kern, G. Heygster, T. Lavergne, A. Sørensen, R. Saldo, G. Dybkjær, L. Brucker, and M. Shokr
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Thirty sea ice algorithms are inter-compared and evaluated systematically over low and high sea ice concentrations, as well as in the presence of thin ice and melt ponds. A hybrid approach is suggested to retrieve sea ice concentration globally for climate monitoring purposes. This approach consists of a combination of two algorithms plus the implementation of a dynamic tie point and atmospheric correction of input brightness temperatures.
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Christian J. Taubenberger, Denis Felikson, and Thomas Neumann
The Cryosphere, 16, 1341–1348, https://doi.org/10.5194/tc-16-1341-2022, https://doi.org/10.5194/tc-16-1341-2022, 2022
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The dynamic mass loss of tidewater glaciers is strongly linked to glacier calving. We study calving mechanisms under a thinning regime, based on 5 years of field and remote-sensing data of Bowdoin Glacier. Our data suggest that Bowdoin Glacier ungrounded recently, and its calving behaviour changed from calving due to surface crevasses to buoyancy-induced calving resulting from basal crevasses. This change may be a precursor to glacier retreat.
Christopher Chambers, Ralf Greve, Bas Altena, and Pierre-Marie Lefeuvre
The Cryosphere, 14, 3747–3759, https://doi.org/10.5194/tc-14-3747-2020, https://doi.org/10.5194/tc-14-3747-2020, 2020
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The topography of the rock below the Greenland ice sheet is not well known. One long valley appears as a line of dips because of incomplete data. So we use ice model simulations that unblock this valley, and these create a watercourse that may represent a form of river over 1000 km long under the ice. When we melt ice at the bottom of the ice sheet only in the deep interior, water can flow down the valley only when the valley is unblocked. It may have developed while an ice sheet was present.
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
The Cryosphere, 13, 2241–2257, https://doi.org/10.5194/tc-13-2241-2019, https://doi.org/10.5194/tc-13-2241-2019, 2019
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Arctic sea ice and the Greenland Ice Sheet (GrIS) are melting later in the year due to a warming climate. Through analyses of weather station, climate model, and reanalysis data, physical links are evaluated between Baffin Bay open water duration and western GrIS melt conditions. We show that sub-Arctic air mass movement across this portion of the GrIS strongly influences late summer and autumn melt, while near-surface, off-ice winds inhibit westerly atmospheric heat transfer from Baffin Bay.
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Short summary
The paper facilitates the construction of a satellite-derived 2 m air temperature (T2m) product for Arctic snow/ice areas. The relationship between skin temperature (Tskin) and T2m is analysed using weather stations. The main factors influencing the relationship are seasonal variations, wind speed and clouds. A clear-sky bias is estimated to assess the effect of cloud-limited satellite observations. The results are valuable when validating satellite Tskin or estimating T2m from satellite Tskin.
The paper facilitates the construction of a satellite-derived 2 m air temperature (T2m) product...
