Andersson, T. R., and Coauthors, 2021: Seasonal arctic sea ice forecasting with probabilistic deep learning. Nature Communications, 12, 5124, https://doi.org/10.1038/s41467-021-25257-4.
Bushuk, M., and Coauthors, 2021: Seasonal prediction and predictability of regional Antarctic sea ice. J. Climate, 34, 6207−6233, https://doi.org/10.1175/JCLI-D-20-0965.1.
Cai, W., and Coauthors, 2023: Southern Ocean warming and its climatic impacts. Science Bulletin, 68, 946−960, https://doi.org/10.1016/j.scib.2023.03.049.
Chen, D. K., and X. J. Yuan, 2004: A Markov model for seasonal forecast of Antarctic sea ice. J. Climate, 17, 3156−3168, https://doi.org/10.1175/1520-0442(2004)017<3156:AMMFSF>2.0.CO;2.
DiGirolamo, N. E., C. L. Parkinson, D. J. Cavalieri, P. Gloersen, and H. J. Zwally, 2022: Sea ice concentrations from nimbus-7 SMMR and DMSP SSM/I-SSMIS passive microwave data. NSIDC-0051, NASA National Snow and Ice Data Center Distributed Active Archive Center, https://doi.org/10.5067/MPYG15WAA4WX.
Fretwell, P. T., A. Boutet, and N. Ratcliffe, 2023: Record low 2022 Antarctic sea ice led to catastrophic breeding failure of emperor penguins. Communications Earth & Environment, 4, 273, https://doi.org/10.1038/s43247-023-00927-x.
Goosse, H., and Coauthors, 2018: Quantifying climate feedbacks in polar regions. Nature Communications, 9, 1919, https://doi.org/10.1038/s41467-018-04173-0.
Hochreiter, S., and J. Schmidhuber, 1997: Long short-term memory. Neural Computation, 9, 1735−1780, https://doi.org/10.1162/neco.1997.9.8.1735.
Holland, M. M., E. Blanchard-Wrigglesworth, J. Kay, and S. Vavrus, 2013: Initial-value predictability of Antarctic sea ice in the community climate system model 3. Geophys. Res. Lett., 40, 2121−2124, https://doi.org/10.1002/grl.50410.
Jung, T., and Coauthors, 2016: Advancing polar prediction capabilities on daily to seasonal time scales. Bull. Amer. Meteor. Soc., 97, 1631−1647, https://doi.org/10.1175/BAMS-D-14-00246.1.
Lecun, Y., L. Bottou, Y. Bengio, and P. Haffner, 1998: Gradient-based learning applied to document recognition. Proc. IEEE, 86, 2278−2324, https://doi.org/10.1109/5.726791.
Libera, S., W. Hobbs, A. Klocker, A. Meyer, and R. Matear, 2022: Ocean-sea ice processes and their role in multi-month predictability of Antarctic sea ice. Geophys. Res. Lett., 49, e2021GL097047, https://doi.org/10.1029/2021GL097047.
Liu, J. P., Z. Zhu, and D. K. Chen, 2023: Lowest Antarctic sea ice record broken for the second year in a row. Ocean-Land-Atmosphere Research, 2, 0007, https://doi.org/10.34133/olar.0007.
Liu, Y., L. Bogaardt, J. Attema, and W. Hazeleger, 2021: Extended-range arctic sea ice forecast with convolutional long short-term memory networks. Mon. Wea. Rev., 149, 1673−1693, https://doi.org/10.1175/MWR-D-20-0113.1.
Massonnet, F., and Coauthors, 2023: SIPN South: Six years of coordinated seasonal Antarctic sea ice predictions. Frontiers in Marine Science, 10, 1148899, https://doi.org/10.3389/fmars.2023.1148899.
Meier, W. N., J. S. Stewart, H. Wilcox, M. A. Hardman, and D. J. Scott., 2021: Near-real-time DMSP SSMIS daily polar gridded sea ice concentrations. NSIDC-0081, NASA National Snow and Ice Data Center Distributed Active Archive Center, https://doi.org/10.5067/YTTHO2FJQ97K.
Parkinson, C. L., 2019: A 40-y record reveals gradual Antarctic sea ice increases followed by decreases at rates far exceeding the rates seen in the Arctic. Proceedings of the National Academy of Sciences of the United States of America, 116 , 14 414−14 423, https://doi.org/10.1073/pnas.1906556116.
Payne, R., J. Martin, A. Monahan, and M. Sigmond, 2023: Seasonal predictions of regional and Pan-Antarctic sea ice with a dynamical forecast system. Atmos. -Ocean, 61, 273−292, https://doi.org/10.1080/07055900.2023.2252387.
Purich, A., and E. W. Doddridge, 2023: Record low Antarctic sea ice coverage indicates a new sea ice state. Communications Earth & Environment, 4, 314, https://doi.org/10.1038/s43247-023-00961-9.
Ren, Y. B., and X. F. Li, 2023: Predicting the daily sea ice concentration on a subseasonal scale of the pan-arctic during the melting season by a deep learning model. IEEE Trans. Geosci. Remote Sens., 61, 1−15, https://doi.org/10.1109/TGRS.2023.3279089.
Ren, Y. B., X. F. Li, and W. H. Zhang, 2022: A data-driven deep learning model for weekly sea ice concentration prediction of the pan-arctic during the melting season. IEEE Trans. Geosci. Remote Sens., 60, 1−19, https://doi.org/10.1109/TGRS.2022.3177600.
Schmidhuber, J., 2015: Deep learning in neural networks: An overview. Neural Networks, 61, 85−117, https://doi.org/10.1016/j.neunet.2014.09.003.
Shi, X. J., Z. R. Chen, H. Wang, D.-Y. Yeung, W.-K. Wong, and W.-C. Woo, 2015: Convolutional LSTM network: A machine learning approach for precipitation nowcasting. Proc. 28th Int. Conf. on Neural Information Processing Systems, Montreal, Canada, ACM, 802−810.
Shokr, M., and Y. F. Ye, 2023: Why does arctic sea ice respond more evidently than Antarctic sea ice to climate change. Ocean-Land-Atmosphere Research, 2, 0006, https://doi.org/10.34133/olar.0006.
Thepaut, J.-N., D. Dee, R. Engelen, and B. Pinty, 2018: The Copernicus Programme and its climate change service. IGARSS 2018 - 2018 IEEE Int. Geoscience Remote Sens. Symp., Valencia, Spain, IEEE, 1591−1593, https://doi.org/10.1109/IGARSS.2018.8518067.
Wang, J. F., H. Luo, Q. H. Yang, J. P. Liu, L. J. Yu, Q. Shi, and B. Han, 2022: An unprecedented record low Antarctic sea-ice extent during austral summer 2022. Adv. Atmos. Sci., 39, 1591−1597, https://doi.org/10.1007/s00376-022-2087-1.
Wang, Y. H., X. J. Yuan, Y. B. Ren, M. Bushuk, Q. Shu, C. H. Li, and X. F. Li, 2023: Subseasonal prediction of regional Antarctic sea ice by a deep learning model. Geophys. Res. Lett., 50, e2023GL104347, https://doi.org/10.1029/2023GL104347.
Xiong, T. S., J. X. He, H. Wang, X. W. Tang, Z. Shi, and Q. Y. Zeng, 2021: Contextual Sa-attention convolutional LSTM for precipitation Nowcasting: A spatiotemporal sequence forecasting view. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14 , 12 479−12 491, https://doi.org/10.1109/JSTARS.2021.3128522.
Zampieri, L., H. F. Goessling, and T. Jung, 2019: Predictability of Antarctic sea ice edge on subseasonal time scales. Geophys. Res. Lett., 46, 9719−9727, https://doi.org/10.1029/2019GL084096.