Articles | Volume 16, issue 16
https://doi.org/10.5194/gmd-16-4677-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-4677-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
18 Aug 2023
Model description paper |
| 18 Aug 2023
| 18 Aug 2023
IceTFT v1.0.0: interpretable long-term prediction of Arctic sea ice extent with deep learning
Bin Mu
School of Software Engineering, Tongji University, Shanghai 201804, China
Xiaodan Luo
School of Software Engineering, Tongji University, Shanghai 201804, China
Shijin Yuan
CORRESPONDING AUTHOR
School of Software Engineering, Tongji University, Shanghai 201804, China
Key Laboratory of Research on Marine Hazards Forecasting, National Marine Environmental Forecasting Center, Beijing, China
Related authors
Bin Mu, Yuehan Cui, Shijin Yuan, and Bo Qin
Geosci. Model Dev., 15, 4105–4127, https://doi.org/10.5194/gmd-15-4105-2022, https://doi.org/10.5194/gmd-15-4105-2022, 2022
Short summary
Short summary
An ENSO deep learning forecast model (ENSO-MC) is built to simulate the spatial evolution of sea surface temperature, analyse the precursor and identify the sensitive area. The results reveal the pronounced subsurface features before different types of events and indicate that oceanic thermal anomaly in the central and western Pacific provides a key long-term memory for predictions, demonstrating the potential usage of the ENSO-MC model in simulation, understanding and observations of ENSO.
Bin Mu, Bo Qin, and Shijin Yuan
Geosci. Model Dev., 14, 6977–6999, https://doi.org/10.5194/gmd-14-6977-2021, https://doi.org/10.5194/gmd-14-6977-2021, 2021
Short summary
Short summary
Considering the sophisticated energy exchanges and multivariate coupling in ENSO, we subjectively incorporate the prior physical knowledge into the modeling process and build up an ENSO deep learning forecast model with a multivariate air–sea coupler, named ENSO-ASC, the performance of which outperforms the other state-of-the-art models. The extensive experiments indicate that ENSO-ASC is a powerful tool for both the ENSO prediction and for the analysis of the underlying complex mechanisms.
Runzhuo Fang, Jinfeng Ding, Wenjuan Gao, Xi Liang, Zhuoqi Chen, Chuanfeng Zhao, Haijin Dai, and Lei Liu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-186, https://doi.org/10.5194/essd-2025-186, 2025
Preprint under review for ESSD
Short summary
Short summary
IMPMCT is a dataset containing a 24-year record (2001–2024) of polar storms in the Nordic Seas. These storms, called Polar Mesoscale Cyclones (PMCs), sometimes cause extreme winds and waves, threatening marine operations. IMPMCT combines remote sensing measurements and reanalysis data to construct a comprehensive PMCs archive. It includes 1,184 PMCs tracks, 16,630 cloud patterns, and 4,373 wind records, providing fundamental data for advancing our understanding of their development mechanisms.
Guokun Lyu, Longjiang Mu, Armin Koehl, Ruibo Lei, Xi Liang, and Chuanyu Liu
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-189, https://doi.org/10.5194/gmd-2024-189, 2025
Preprint under review for GMD
Short summary
Short summary
In the sea ice-ocean models, errors in the parameters and missing spatiotemporal variations contribute to the deviations between the simulations and the observations. We extended an adjoint method to optimize spatiotemporally varying parameters together with the atmosphere forcing and the initial conditions using satellite and in-situ observations. Seasonally, this scheme demonstrates a more prominent advantage in mid-autumn and show great potential for accurately reproducing the Arctic changes.
Fu Zhao, Xi Liang, Zhongxiang Tian, Ming Li, Na Liu, and Chengyan Liu
Geosci. Model Dev., 17, 6867–6886, https://doi.org/10.5194/gmd-17-6867-2024, https://doi.org/10.5194/gmd-17-6867-2024, 2024
Short summary
Short summary
In this work, we introduce a newly developed Antarctic sea ice forecasting system, namely the Southern Ocean Ice Prediction System (SOIPS). The system is based on a regional sea ice‒ocean‒ice shelf coupled model and can assimilate sea ice concentration observations. By assessing the system's performance in sea ice forecasts, we find that the system can provide reliable Antarctic sea ice forecasts for the next 7 d and has the potential to guide ship navigation in the Antarctic sea ice zone.
Bin Mu, Yuehan Cui, Shijin Yuan, and Bo Qin
Geosci. Model Dev., 15, 4105–4127, https://doi.org/10.5194/gmd-15-4105-2022, https://doi.org/10.5194/gmd-15-4105-2022, 2022
Short summary
Short summary
An ENSO deep learning forecast model (ENSO-MC) is built to simulate the spatial evolution of sea surface temperature, analyse the precursor and identify the sensitive area. The results reveal the pronounced subsurface features before different types of events and indicate that oceanic thermal anomaly in the central and western Pacific provides a key long-term memory for predictions, demonstrating the potential usage of the ENSO-MC model in simulation, understanding and observations of ENSO.
Yu Liang, Haibo Bi, Haijun Huang, Ruibo Lei, Xi Liang, Bin Cheng, and Yunhe Wang
The Cryosphere, 16, 1107–1123, https://doi.org/10.5194/tc-16-1107-2022, https://doi.org/10.5194/tc-16-1107-2022, 2022
Short summary
Short summary
A record minimum July sea ice extent, since 1979, was observed in 2020. Our results reveal that an anomalously high advection of energy and water vapor prevailed during spring (April to June) 2020 over regions with noticeable sea ice retreat. The large-scale atmospheric circulation and cyclones act in concert to trigger the exceptionally warm and moist flow. The convergence of the transport changed the atmospheric characteristics and the surface energy budget, thus causing a severe sea ice melt.
Bin Mu, Bo Qin, and Shijin Yuan
Geosci. Model Dev., 14, 6977–6999, https://doi.org/10.5194/gmd-14-6977-2021, https://doi.org/10.5194/gmd-14-6977-2021, 2021
Short summary
Short summary
Considering the sophisticated energy exchanges and multivariate coupling in ENSO, we subjectively incorporate the prior physical knowledge into the modeling process and build up an ENSO deep learning forecast model with a multivariate air–sea coupler, named ENSO-ASC, the performance of which outperforms the other state-of-the-art models. The extensive experiments indicate that ENSO-ASC is a powerful tool for both the ENSO prediction and for the analysis of the underlying complex mechanisms.
Shihe Ren, Xi Liang, Qizhen Sun, Hao Yu, L. Bruno Tremblay, Bo Lin, Xiaoping Mai, Fu Zhao, Ming Li, Na Liu, Zhikun Chen, and Yunfei Zhang
Geosci. Model Dev., 14, 1101–1124, https://doi.org/10.5194/gmd-14-1101-2021, https://doi.org/10.5194/gmd-14-1101-2021, 2021
Short summary
Short summary
Sea ice plays a crucial role in global energy and water budgets. To get a better simulation of sea ice, we coupled a sea ice model with an atmospheric and ocean model to form a fully coupled system. The sea ice simulation results of this coupled system demonstrated that a two-way coupled model has better performance in terms of sea ice, especially in summer. This indicates that sea-ice–ocean–atmosphere interaction plays a crucial role in controlling Arctic summertime sea ice distribution.
Cited articles
Andersson, T. R., Hosking, J. S., Pérez-Ortiz, M., Paige, B., Elliott, A.,
Russell, C., Law, S., Jones, D. C., Wilkinson, J., Phillips, T., Byrne, J.,
Tietsche, S., Sarojini, B. B., Blanchard-Wrigglesworth, E., Aksenov, Y.,
Downie, R., and Shuckburgh, E.: Seasonal Arctic sea ice forecasting with
probabilistic deep learning, Nat. Commun., 12, 5124, https://doi.org/10.1038/s41467-021-25257-4, 2021. a, b
Bintanja, R. and Selten, F. M.: Future increases in Arctic precipitation linked
to local evaporation and sea-ice retreat, Nature, 509, 479–482, 2014. a
Boisvert, L. N. and Stroeve, J. C.: The Arctic is becoming warmer and wetter as
revealed by the Atmospheric Infrared Sounder, Geophys. Res. Lett.,
42, 4439–4446, 2015. a
Boisvert, L., Wu, D., Vihma, T., and Susskind, J.: Verification of air/surface humidity differences from AIRS and ERA-Interim in support of turbulent flux estimation in the Arctic, J. Geophys. Res.-Atmoss., 120, 945–963,
https://doi.org/10.1002/2014JD021666, 2015. a
Boisvert, L. N., Webster, M. A., Petty, A. A., Markus, T., Bromwich, D. H., and Cullather, R. I.: Intercomparison of precipitation estimatesover the Arctic Ocean and its peripheral seas from reanalyses, J. Climate, 31, 8441–8462, https://doi.org/10.1175/JCLI-D-18-4850125.1, 2018. a
Bushuk, M. and Giannakis, D.: The Seasonality and Interannual
Variability of Arctic Sea Ice Reemergence, J. Climate, 30, 4657–4676, https://doi.org/10.1175/JCLI-D-16-0549.1, 2017. a
Chi, J. and Kim, H. C.: Prediction of Arctic Sea Ice Concentration Using a
Fully Data Driven Deep Neural Network, Remote Sens.-Basel, 9, 1305, https://doi.org/10.3390/rs9121305, 2017. a, b
Chi, J., Bae, J., and Kwon, Y.-J.: Two-Stream Convolutional Long- and
Short-Term Memory Model Using Perceptual Loss for Sequence-to-Sequence Arctic
Sea Ice Prediction, Remote Sens.-Basel, 13, 3413, https://doi.org/10.3390/rs13173413, 2021. a, b
Choi, Y.-S., Ho, C.-H., Park, C.-E., Storelvmo, T., and Tan, I.: Influence of cloud phase composition on climate feedbacks, J. Geophys. Res.-Atmos., 119, 3687–3700, https://doi.org/10.1002/2013JD020582, 2014. a, b
Cohen, J., Screen, J. A., Furtado, J. C., Barlow, M., Whittleston, D., Coumou,
D., Francis, J., Dethloff, K., Entekhabi, D., and Overland, J. A.: Recent
Arctic amplification and extreme mid-latitude weather, Nat. Geosci., 7,
627–637, 2014. a
Fetterer, F., Knowles, K., Meier, W. N., Savoie, M., and Windnagel, A. K.: Sea Ice Index, Version 3, Boulder, Colorado USA. National Snow and Ice Data Center [data set], https://doi.org/10.7265/N5K072F8, 2017. a, b, c
Goosse, H., Kay, J. E., Armour, K. C., Bodas‐Salcedo, A., Chepfer, H.,
Docquier, D., Jonko, A. K., Kushner, P. J., Lecomte, O., Massonnet, F., Park,
H., Pithan, F., Svensson, G., and Vancoppenolle, M.: Quantifying climate
feedbacks in polar regions, Nat. Commun., 9, 1919, https://doi.org/10.1038/s41467-018-04173-0, 2018. a
He-Ping, L. I., You-Ming, X. U., and Rao, S. Q.: Analysis on Influence of Sea
Ice in North Pole Area on Runoff in the Upper Yellow River during Flood Seas
on, Adv. Water Sci., 11, 284–290, 2000. a
Holton, J. R. and Hakim, G. J.: An Introduction to Dynamic Meteorology, vol. Academic Press, 88, https://doi.org/10.1016/C2009-0-63394-8, 2013. a
Huang, B., Liu, C., Banzon, V., Freeman, E., Graham, G., Hankins, B., Smith, T., and Zhang, H.-M.: Improvements of the daily optimum interpolation sea surface temperature (DOISST) version 2.1, J. Climate, 34, 2923–2939, https://doi.org/10.1175/JCLI-D-20-0166.1, 2021. a, b
Huang, T., Lühr, H., Wang, H., and Xiong, C.: The relationship of high-latitude thermospheric wind with ionospheric horizontal current,500 as observed by CHAMP satellite, J. Geophys. Res.-Space, 122, 12–378, https://doi.org/10.1002/2017JA024614, 2017. a
Huang, X., Chen, X., and Yue, Q.: Band-by-band contributions to the longwave cloud radiative feedbacks, Geophys. Res. Lett., 46, 6998–7006, https://doi.org/10.1029/2019GL083466, 2019. a
Huang, Y., Kleindessner, M., Munishkin, A., Varshney, D., Guo, P., and Wang,
J.: Benchmarking of Data-Driven Causality Discovery Approaches in the
Interactions of Arctic Sea Ice and Atmosphere, Front. Big Data, 4, 642,
https://doi.org/10.3389/fdata.2021.642182, 2021. a, b, c
Japan Meteorological Agency: JRA-55: Japanese 55-year Reanalysis,
Monthly Means and Variances, Computational and Information Systems Laboratory [data set],
https://doi.org/10.5065/D60G3H5B, 2013. a, b
Johannessen, O. M., Bobylev, L. P., Shalina, E. V., and Sandven, S.: Sea ice in
the Arctic: past, present and future, Springer, https://doi.org/10.1007/978-3-030-21301-5, 2020. a
Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L.,
Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Chelliah, M.,
Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K. C., Ropelewski, C., Wang,
J., Leetmaa, A., Reynolds, R., Jenne, R., and Joseph, D.: The NCEP/NCAR
40-Year Reanalysis Project, B. Am. Meteorol. Soc.,
77, 437–472,
https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2, 1996. a, b
Kapsch, M.-L., Graversen, R. G., Tjernström, M., and Bintanja, R.: The Effect
of Downwelling Longwave and Shortwave Radiation on Arctic Summer Sea Ice,
J. Climate, 29, 1143–1159, https://doi.org/10.1175/JCLI-D-15-0238.1, 2016. a, b, c, d
Kapsch, M.-L., Skific, N., Graversen, R. G., Tjernström, M., and Francis, J. A.: Summers with low Arctic sea ice linked to persistence of spring atmospheric circulation patterns, Clim. Dynam., 52, 2497–2512, https://doi.org/10.1007/s00382-018-4279-z, 2019. a
Kay, J. E. and Wood, R.: Timescale analysis of aerosol sensitivity during homogeneous freezing and implications for upper tropospheric water vapor budgets, Geophys. Res. Lett., 35, L10809, https://doi.org/10.1029/2007GL032628, 2008. a
Kim, Y. J., Kim, H.-C., Han, D., Lee, S., and Im, J.: Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks, The Cryosphere, 14, 1083–1104, https://doi.org/10.5194/tc-14-1083-2020, 2020. a, b, c
Kwok, R. and Untersteiner, N.: The thinning of Arctic sea ice, Phys. Today, 64,
36–41, 2011. a
Liang, X., Losch, M., Nerger, L., Mu, L., Yang, Q., and Liu, C.: Using Sea
Surface Temperature Observations to Constrain Upper Ocean Properties in an
Arctic Sea Ice‐Ocean Data Assimilation System, J. Geophys.
Res.-Oceans, 124, 4727–4743,
https://doi.org/10.1029/2019JC015073, 2019. a
Liang, X., Li, X., Bi, H., Losch, M., Gao, Y., Zhao, F., Tian, Z., and Liu, C.:
A Comparison of Factors That Led to the Extreme Sea Ice Minima in the
Twenty-First Century in the Arctic Ocean, J. Climate, 35, 1249–1265, https://doi.org/10.1175/JCLI-D-21-0199.1, 2022. a
Liou, K.-N.: An introduction to atmospheric radiation, 2nd Edn., vol. 84, Elsevier, ISBN: 9780124514515, 2002. a
Liu, J., Song, M., Horton, R. M., and Hu, Y.: Reducing spread in climate model
projections of a September ice-free Arctic, P. Natl. Acad. Sci. USA, 110, 12571–12576,
2013. a
Liu, X. Y. and Liu, H. L.: Investigation of influence of atmospheric
variability on sea ice variation trend in recent years in the Arctic with
numerical sea ice-ocean coupled model, Chinese J. Geophys., 55,
2867–2875, 2012. a
Luo, B., Luo, D., Wu, L., Zhong, L., and Simmonds, I.: Atmospheric
circulation patterns which promote winter Arctic sea ice decline,
Environ. Res. Lett., 12, 054017, https://doi.org/10.1088/1748-9326/69d0,
2017. a
Luo, X.: The code source of IceTFT v1.0.0, Zenodo [code], https://doi.org/10.5281/zenodo.7409157, 2022. a
Mu, B., Li, J., Yuan, S., Luo, X., and Dai, G.: NAO Index Prediction using LSTM
and ConvLSTM Networks Coupled with Discrete Wavelet Transform, in: 2019
International Joint Conference on Neural Networks (IJCNN), 1–8,
https://doi.org/10.1109/IJCNN.2019.8851968, 2019. a
Mu, B., Qin, B., Yuan, S., and Qin, X.: A Climate Downscaling Deep Learning
Model considering the Multiscale Spatial Correlations and Chaos of
Meteorological Events, Math. Probl. Eng., 2020, 1–17,
https://doi.org/10.1155/2020/7897824, 2020. a
Mu, B., Qin, B., and Yuan, S.: ENSO-ASC 1.0.0: ENSO deep learning forecast model with a multivariate air–sea coupler, Geosci. Model Dev., 14, 6977–6999, https://doi.org/10.5194/gmd-14-6977-2021, 2021. a
Mu, B., Cui, Y., Yuan, S., and Qin, B.: Simulation, precursor analysis and targeted observation sensitive area identification for two types of ENSO using ENSO-MC v1.0, Geosci. Model Dev., 15, 4105–4127, https://doi.org/10.5194/gmd-15-4105-2022, 2022. a
National Oceanic and Atmospheric Administration Physical Sciences Laboratory, Boulder Climate and Weather Information: Boulder-Monthly-Means-Snowfall: 1.0.0 (snowfall), Zenodo [data set], https://doi.org/10.5281/zenodo.7533097, 2023. a
Overland, J. E. and Wang, M.: Large-scale atmospheric circulation changes are associated with the recent loss of Arctic sea ice, Tellus A, 62, 1–9, https://doi.org/10.1111/j.1600-0870.2009.00421.x, 2010. a
Overland, J. E. and Wang, M.: When will the summer Arctic be nearly sea ice
free?, Geophys. Res. Lett., 40, 2097–2101, 2013. a
Parkinson, C. L., Cavalieri, D. J., Gloersen, P., Zwally, H. J., and Comiso,
J. C.: Arctic sea ice extents, areas, and trends, 1978–1996, J.
Geophys. Res.-Oceans, 104, 20837–20856, 1999. a
Perovich, D., Grenfell, T., Light, B., and Hobbs, P.: Seasonal evolution of the albedo of multiyear Arctic sea
ice, J. Geophys. Res., 107, 8044, https://doi.org/10.1029/2000JC000438, 2002. a
Perovich, D. K., Light, B., Eicken, H., Jones, K. F., Runciman, K., and Nghiem,
S. V.: Increasing solar heating of the Arctic Ocean and adjacent seas,
1979–2005: Attribution and role in the ice‐albedo feedback, Geophys.
Res. Lett., 34, L19505, https://doi.org/10.1029/2007GL031480, 2007. a
Pruppacher, H. R. and Klett, J. D.: Microphysics of Clouds and Precipitation, 18, 381–382, https://doi.org/10.1080/02786829808965531, 1978. a
Polyakova, E. I., Journel, A. G., Polyakov, I. V., and Bhatt, U. S.: Changing
relationship between the North Atlantic Oscillation and key North Atlantic
climate parameters, Geophys. Res. Lett., 33, 1–4, https://doi.org/10.1029/2005GL024573, 2006. a
Ramsayer, K.: 2020 Arctic Sea Ice Minimum at Second Lowest on Record, NASA
Global Climate Change, Vital Signs of the Planet, https://www.nasa.gov/feature/goddard/2020/2020-arctic-sea-ice-minimum-at-second-lowest-on-record (last access: 22 September 2020), 2020. a
Ren, Y., Li, X., and Zhang, W.: A data-driven deep learning model for weekly
sea ice concentration prediction of the Pan-Arctic during the melting season,
IEEE T. Geosci. Remote, 60, 4304819, https://doi.org/10.1109/TGRS.2022.3177600, 2022. a
Reynolds, R. W., Smith, T. M., Liu, C., Chelton, D. B., Casey, K. S., and Schlax, M. G.: Daily High-Resolution-Blended Analyses for Sea Surface Temperature, J. Climate, 20, 5473–5496, https://doi.org/10.1175/JCLI-D-14-00293.1, 2007. a, b
Rinke, A., Knudsen, E. M., Mewes, D., Dorn, W., Handorf, D., Dethloff, K., and Moore, J.: Arctic summer sea ice melt and related atmospheric conditions in coupled regional climate model simulations and observations, J. Geophys. Res.-Atmo., 124, 6027–6039, https://doi.org/10.1029/2018JD030207, 2019. a
Screen, J. A. and Simmonds, I.: The central role of diminishing sea ice in
recent Arctic temperature amplification, Nature, 464, 1334–1337, 2010. a
Screen, J. A. and Simmonds, I.: Declining summer snowfall in the Arctic:
causes, impacts and feedbacks, Clim. Dynam., 38, 2243–2256, 2012. a
Sea Ice Outlook: 2019 June Report, https://www.arcus.org/sipn/sea-ice-outlook/2019/june, last access: 21 June 2019a. a
Sea Ice Outlook: 2019 July Report, https://www.arcus.org/sipn/sea-ice-outlook/2019/july, last access: 24 July 2019b. a
Sea Ice Outlook: 2019 August Report, https://www.arcus.org/sipn/sea-ice-outlook/2019/august, last access: 30 August 2019c. a
Sea Ice Outlook: 2020 June Report, https://www.arcus.org/sipn/sea-ice-outlook/2020/june, last access: 26 June 2020a. a
Sea Ice Outlook: 2020 July Report, https://www.arcus.org/sipn/sea-ice-outlook/2020/july, last access: 27 July 2020b. a
Sea Ice Outlook: 2020 August Report, https://www.arcus.org/sipn/sea-ice-outlook/2020/august, last access: 31 August 2020c. a
Sea Ice Outlook: 2021 June Report, https://www.arcus.org/sipn/sea-ice-outlook/2021/june, last access: 26 June 2021a. a
Sea Ice Outlook: 2021 July Report, https://www.arcus.org/sipn/sea-ice-outlook/2021/july, last access: 27 July 2021b. a
Sea Ice Outlook: 2021 August Report, https://www.arcus.org/sipn/sea-ice-outlook/2021/august, last access: 31 August 2021c. a
Sea Ice Outlook: 2021 September Report, https://www.arcus.org/sipn/sea-ice-outlook/2021/september, last accessed: 21 September 2021d. a
Sea Ice Outlook: 2022 June Report, https://www.arcus.org/sipn/sea-ice-outlook/2022/june, last access: 27 June 2022a. a
Sea Ice Outlook: 2022 July Report, https://www.arcus.org/sipn/sea-ice-outlook/2022/july, last access: 26 July 2022b. a
Sea Ice Outlook: 2022 August Report, https://www.arcus.org/sipn/sea-ice-outlook/2022/august, last access: 25 August 2022c. a
Sea Ice Outlook: 2022 September Report, https://www.arcus.org/sipn/sea-ice-outlook/2022/september, last accessed: 22 September 2022d. a
Stroeve, J., Hamilton, L. C., Bitz, C. M., and Blanchard-Wrigglesworth, E.:
Predicting September sea ice: Ensemble skill of the SEARCH Sea Ice Outlook
2008–2013, Geophys. Res. Lett., 41, 2411–2418, 2014. a
Stroeve, J. C., Kattsov, V., Barrett, A., Serreze, M., Pavlova, T., Holland,
M., and Meier, W. N.: Trends in Arctic sea ice extent from CMIP5, CMIP3 and
observations, Geophys. Res. Lett., 39, L16502, https://doi.org/10.1029/2012GL052676, 2012. a
Sturm, M., Holmgren, J., and Perovich, D. K.: Winter snow cover on the sea ice of the Arctic Ocean at the
Surface Heat Budget of the Arctic Ocean (SHEBA): Temporal evolution and
spatial variability, J. Geophys. Res., 107, 8047, https://doi.org/10.1029/2000JC000400, 2002. a
Tong, J., Chen, M., Qiu, Y., Yanping, L. I., Cao, J., Sciences, O. E.,
University, X., and of Marine Environmental Science, S. K. L.: Contrasting
patterns of river runoff and sea-ice melted water in the Canada Basin, Acta
Oceanol. Sin., 33, 46–52, https://doi.org/10.1007/s13131-014-0488-4, 2014. a
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N.,
Kaiser, Ł., and Polosukhin, I.: Attention is all you need, ArXiv [preprint], https://doi.org/10.48550/arXiv.1706.03762, 2017. a
Voosen, P.: New feedbacks speed up the demise of Arctic sea ice, Science, 369,
1043–1044,
https://doi.org/10.1126/science.369.6507.1043, 2020. a
Wallace, J. M. and Hobbs, P. V.: Atmospheric Science: An Introductory Survey, 2nd Edn., Academic Press, https://doi.org/10.1016/C2009-0-00034-8, 2006. a
Watanabe, E., Wang, J., Sumi, A., and Hasumi, H.: Arctic dipole anomaly and its contribution to sea ice export from the Arctic Ocean in the 20th century, Geophys. Res. Lett., 33, 160–176, https://doi.org/10.1029/2006GL028112, 2006. a
Weatherly, J. W. and Walsh, J. E.: The effects of precipitation and river
runoff in a coupled ice-ocean model of the Arctic, Clim. Dynam., 12,
785–798, 1996. a
Wei, K., Liu, J., Bao, Q., He, B., Ma, J., Li, M., Song, M., and Zhu, Z.:
Subseasonal to seasonal Arctic sea-ice prediction: A grand challenge of
climate science, Atmos. Ocean. Sci. Lett., 14, 100052, https://doi.org/10.1016/J.AOSL.2021.100052, 2021.
a
Zheng, F., Sun, Y., Yang, Q., and Longjiang, M. U.: Evaluation of Arctic
Sea-ice Cover and Thickness Simulated by MITgcm, Adv. Atmos.
Sci., 38, 29–48, https://doi.org/10.1007/s00376-020-9223-6, 2021. a
Short summary
To improve the long-term forecast skill for sea ice extent (SIE), we introduce IceTFT, which directly predicts 12 months of averaged Arctic SIE. The results show that IceTFT has higher forecasting skill. We conducted a sensitivity analysis of the variables in the IceTFT model. These sensitivities can help researchers study the mechanisms of sea ice development, and they also provide useful references for the selection of variables in data assimilation or the input of deep learning models.
To improve the long-term forecast skill for sea ice extent (SIE), we introduce IceTFT, which...
