Articles | Volume 14, issue 7
https://doi.org/10.5194/tc-14-2159-2020
https://doi.org/10.5194/tc-14-2159-2020
Research article
 | 
02 Jul 2020
Research article |  | 02 Jul 2020

Modeling the annual cycle of daily Antarctic sea ice extent

Mark S. Handcock and Marilyn N. Raphael

Related authors

Eighteen-year record of circum-Antarctic landfast-sea-ice distribution allows detailed baseline characterisation and reveals trends and variability
Alexander D. Fraser, Robert A. Massom, Mark S. Handcock, Phillip Reid, Kay I. Ohshima, Marilyn N. Raphael, Jessica Cartwright, Andrew R. Klekociuk, Zhaohui Wang, and Richard Porter-Smith
The Cryosphere, 15, 5061–5077, https://doi.org/10.5194/tc-15-5061-2021,https://doi.org/10.5194/tc-15-5061-2021, 2021
Short summary

Related subject area

Discipline: Sea ice | Subject: Sea Ice
Spring 2021 sea ice transport in the southern Beaufort Sea occurred during coastal-lead opening events
MacKenzie E. Jewell, Jennifer K. Hutchings, and Angela C. Bliss
The Cryosphere, 19, 1413–1430, https://doi.org/10.5194/tc-19-1413-2025,https://doi.org/10.5194/tc-19-1413-2025, 2025
Short summary
National Weather Service Alaska Sea Ice Program: gridded ice concentration maps for the Alaskan Arctic
Astrid Pacini, Michael Steele, and Mary-Beth Schreck
The Cryosphere, 19, 1391–1411, https://doi.org/10.5194/tc-19-1391-2025,https://doi.org/10.5194/tc-19-1391-2025, 2025
Short summary
Improving Seasonal Arctic Sea Ice Predictions with the Combination of Machine Learning and Earth System Model
Zikang He, Yiguo Wang, Julien Brajard, Xidong Wang, and Zheqi Shen
EGUsphere, https://doi.org/10.5194/egusphere-2024-4092,https://doi.org/10.5194/egusphere-2024-4092, 2025
Short summary
Estimation of duration and its changes in Lagrangian observations relying on ice floes in the Arctic Ocean utilizing sea ice motion product
Fanyi Zhang, Ruibo Lei, Meng Qu, Na Li, Ying Chen, and Xiaoping Pang
EGUsphere, https://doi.org/10.5194/egusphere-2024-2723,https://doi.org/10.5194/egusphere-2024-2723, 2024
Short summary
Seasonal evolution of the sea ice floe size distribution in the Beaufort Sea from 2 decades of MODIS data
Ellen M. Buckley, Leela Cañuelas, Mary-Louise Timmermans, and Monica M. Wilhelmus
The Cryosphere, 18, 5031–5043, https://doi.org/10.5194/tc-18-5031-2024,https://doi.org/10.5194/tc-18-5031-2024, 2024
Short summary

Cited articles

Bollerslev, T.: Generalized autoregressive conditional heteroskedasticity, J. Econometrics, 31, 307–327, https://doi.org/10.1016/0304-4076(86)90063-1, 1986. a
Box, G. E. P. and Jenkins, G. M.: Time Series Analysis : Forecasting and Control, Holden-Day, San Francisco, rev. ed. edn., 1976. a
Comiso, J.: Bootstrap sea ice concentrations from NIMBUS-7 SMMR and DMSP SSM/I-SSMIS, Version 3, https://doi.org/10.5067/7Q8HCCWS4I0R, 2017. a
Comiso, J. C. and Steffen, K.: Studies of Antarctic sea ice concentrations from satellite data and their applications, J. Geophys. Res.-Oceans, 106, 31361–31285, 2001. a
Craven, P. and Wahba, G.: Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of Generalized Cross-validation, Numer. Math., 31, 377–403, https://doi.org/10.1007/BF01404567, 1978. a
Download
Short summary
Traditional methods of calculating the annual cycle of sea ice extent disguise the variation of amplitude and timing (phase) of the advance and retreat of the ice. We present a multiscale model that explicitly allows them to vary, resulting in a much improved representation of the cycle. We show that phase is the dominant contributor to the variability in the cycle and that the anomalous decay of Antarctic sea ice in 2016 was due largely to a change of phase.
Share