Journal cover Journal topic
The Cryosphere An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 4.790 IF 4.790
  • IF 5-year value: 5.921 IF 5-year
    5.921
  • CiteScore value: 5.27 CiteScore
    5.27
  • SNIP value: 1.551 SNIP 1.551
  • IPP value: 5.08 IPP 5.08
  • SJR value: 3.016 SJR 3.016
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 63 Scimago H
    index 63
  • h5-index value: 51 h5-index 51
TC | Volume 13, issue 1
The Cryosphere, 13, 49–78, 2019
https://doi.org/10.5194/tc-13-49-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
The Cryosphere, 13, 49–78, 2019
https://doi.org/10.5194/tc-13-49-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 09 Jan 2019

Research article | 09 Jan 2019

Version 2 of the EUMETSAT OSI SAF and ESA CCI sea-ice concentration climate data records

Thomas Lavergne et al.
Related authors  
Satellite Passive Microwave Sea-Ice Concentration Data Set Intercomparison: Closed Ice and Ship-Based Observations
Stefan Kern, Thomas Lavergne, Dirk Notz, Leif Toudal Pedersen, Rasmus Tage Tonboe, Roberto Saldo, and Atle MacDonald Soerensen
The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-120,https://doi.org/10.5194/tc-2019-120, 2019
Revised manuscript accepted for TC
Short summary
A new tracking algorithm for sea ice age distribution estimation
Anton Andreevich Korosov, Pierre Rampal, Leif Toudal Pedersen, Roberto Saldo, Yufang Ye, Georg Heygster, Thomas Lavergne, Signe Aaboe, and Fanny Girard-Ardhuin
The Cryosphere, 12, 2073–2085, https://doi.org/10.5194/tc-12-2073-2018,https://doi.org/10.5194/tc-12-2073-2018, 2018
Short summary
Related subject area  
Discipline: Sea ice | Subject: Remote Sensing
Estimating the sea ice floe size distribution using satellite altimetry: theory, climatology, and model comparison
Christopher Horvat, Lettie A. Roach, Rachel Tilling, Cecilia M. Bitz, Baylor Fox-Kemper, Colin Guider, Kaitlin Hill, Andy Ridout, and Andrew Shepherd
The Cryosphere, 13, 2869–2885, https://doi.org/10.5194/tc-13-2869-2019,https://doi.org/10.5194/tc-13-2869-2019, 2019
Short summary
The 2018 North Greenland polynya observed by a newly introduced merged optical and passive microwave sea-ice concentration dataset
Valentin Ludwig, Gunnar Spreen, Christian Haas, Larysa Istomina, Frank Kauker, and Dmitrii Murashkin
The Cryosphere, 13, 2051–2073, https://doi.org/10.5194/tc-13-2051-2019,https://doi.org/10.5194/tc-13-2051-2019, 2019
Short summary
Satellite Passive Microwave Sea-Ice Concentration Data Set Intercomparison: Closed Ice and Ship-Based Observations
Stefan Kern, Thomas Lavergne, Dirk Notz, Leif Toudal Pedersen, Rasmus Tage Tonboe, Roberto Saldo, and Atle MacDonald Soerensen
The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-120,https://doi.org/10.5194/tc-2019-120, 2019
Revised manuscript accepted for TC
Short summary
Estimation of turbulent heat flux over leads using satellite thermal images
Meng Qu, Xiaoping Pang, Xi Zhao, Jinlun Zhang, Qing Ji, and Pei Fan
The Cryosphere, 13, 1565–1582, https://doi.org/10.5194/tc-13-1565-2019,https://doi.org/10.5194/tc-13-1565-2019, 2019
Short summary
Snow-driven uncertainty in CryoSat-2-derived Antarctic sea ice thickness – insights from McMurdo Sound
Daniel Price, Iman Soltanzadeh, Wolfgang Rack, and Ethan Dale
The Cryosphere, 13, 1409–1422, https://doi.org/10.5194/tc-13-1409-2019,https://doi.org/10.5194/tc-13-1409-2019, 2019
Short summary
Cited articles  
Andersen, S., Tonboe, R., Kern, S., and Schyberg, H.: Improved retrieval of sea ice total concentration from spaceborne passive microwave observations using Numerical Weather Prediction model fields: An intercomparison of nine algorithms, Remote Sens. Environ., 104, 374–392, 2006. 
Andersen, S., Toudal Pedersen, L., Heygster, G., Tonboe, R., and Kaleschke, L.: Intercomparison of passive microwave sea ice concentration retrievals over the high concentration Arctic sea ice, J. Geophys. Res., 112, C08004, https://doi.org/10.1029/2006JC003543, 2007. 
Ashcroft, P. and Wentz, F. J.: AMSR-E/Aqua L2A Global Swath Spatially-Resampled Brightness Temperatures, Version 3 [2002–2010], NASA National Snow and Ice Data Center Distributed Active Archive Center, Boulder, Colorado, USA, https://doi.org/10.5067/AMSR-E/AE_L2A.003, 2013. 
Bellprat, O., Massonnet, F., Siegert, S., Prodhomme, C., Macias-Gómez, D., Guemas, V., and Doblas-Reyes, F.: Uncertainty propagation in observational references to climate model scales, Remote Sens. Environ., 203, 101–108, https://doi.org/10.1016/j.rse.2017.06.034, 2017. 
Brodzik, M. J., Billingsley, B., Haran, T., Raup, B., and Savoie, M. H.: EASE-Grid 2.0: Incremental but Significant Improvements for Earth-Gridded Data Sets, ISPRS Int. Geo.-Inf., 1, 32–45, https://doi.org/10.3390/ijgi1010032, 2012. 
Publications Copernicus
Download
Short summary
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.
The loss of polar sea ice is an iconic indicator of Earth’s climate change. Many...
Citation