Articles | Volume 15, issue 6
Research article
24 Jun 2021
Research article |  | 24 Jun 2021

A Bayesian approach towards daily pan-Arctic sea ice freeboard estimates from combined CryoSat-2 and Sentinel-3 satellite observations

William Gregory, Isobel R. Lawrence, and Michel Tsamados

Related authors

Network connectivity between the winter Arctic Oscillation and summer sea ice in CMIP6 models and observations
William Gregory, Julienne Stroeve, and Michel Tsamados
The Cryosphere, 16, 1653–1673,,, 2022
Short summary

Related subject area

Discipline: Sea ice | Subject: Data Assimilation
Local analytical optimal nudging for assimilating AMSR2 sea ice concentration in a high-resolution pan-Arctic coupled ocean (HYCOM 2.2.98) and sea ice (CICE 5.1.2) model
Keguang Wang, Alfatih Ali, and Caixin Wang
The Cryosphere, 17, 4487–4510,,, 2023
Short summary
Towards improving short-term sea ice predictability using deformation observations
Anton Korosov, Pierre Rampal, Yue Ying, Einar Ólason, and Timothy Williams
The Cryosphere, 17, 4223–4240,,, 2023
Short summary
Assimilating CryoSat-2 freeboard to improve Arctic sea ice thickness estimates
Imke Sievers, Till A. S. Rasmussen, and Lars Stenseng
The Cryosphere, 17, 3721–3738,,, 2023
Short summary
Assimilation of satellite swaths versus daily means of sea ice concentration in a regional coupled ocean-sea ice model
Marina Durán Moro, Ann Kristin Sperrevik, Thomas Lavergne, Laurent Bertino, Yvonne Gusdal, Silje Christine Iversen, and Jozef Rusin
The Cryosphere Discuss.,,, 2023
Revised manuscript accepted for TC
Short summary
The effects of assimilating a sub-grid-scale sea ice thickness distribution in a new Arctic sea ice data assimilation system
Nicholas Williams, Nicholas Byrne, Daniel Feltham, Peter Jan Van Leeuwen, Ross Bannister, David Schroeder, Andrew Ridout, and Lars Nerger
The Cryosphere, 17, 2509–2532,,, 2023
Short summary

Cited articles

Aaboe, S., Breivik, L.-A., Sørensen, A., Eastwood, S., and Lavergne, T.: Global sea ice edge and type product user's manual, OSI-403-c & EUMETSAT, 2016. a
Allard, R. A., Farrell, S. L., Hebert, D. A., Johnston, W. F., Li, L., Kurtz, N. T., Phelps, M. W., Posey, P. G., Tilling, R., Ridout, A., and Wallcraft, A. J.: Utilizing CryoSat-2 sea ice thickness to initialize a coupled ice-ocean modeling system, Adv. Space Res., 62, 1265–1280,, 2018. a
Balan-Sarojini, B., Tietsche, S., Mayer, M., Balmaseda, M., Zuo, H., de Rosnay, P., Stockdale, T., and Vitart, F.: Year-round impact of winter sea ice thickness observations on seasonal forecasts, The Cryosphere, 15, 325–344,, 2021. a
Bishop, C. M.: Pattern recognition and machine learning, Springer, ISBN 978-0387-31073-2, chap. 3, 152–165, 2006. a
Blanchard-Wrigglesworth, E. and Bitz, C. M.: Characteristics of Arctic sea-ice thickness variability in GCMs, J. Climate, 27, 8244–8258,, 2014. a
Short summary
Satellite measurements of radar freeboard allow us to compute the thickness of sea ice from space; however attaining measurements across the entire Arctic basin typically takes up to 30 d. Here we present a statistical method which allows us to combine observations from three separate satellites to generate daily estimates of radar freeboard across the Arctic Basin. This helps us understand how sea ice thickness is changing on shorter timescales and what may be causing these changes.