Abstract
In recent years, deep learning methods have gradually been applied to prediction tasks related to Arctic sea ice concentration, but relatively little research has been conducted for larger spatial and temporal scales, mainly due to the limited time coverage of observations and reanalysis data. Meanwhile, deep learning predictions of sea ice thickness (SIT) have yet to receive ample attention. In this study, two data-driven deep learning (DL) models are built based on the ConvLSTM and fully convolutional U-net (FC-Unet) algorithms and trained using CMIP6 historical simulations for transfer learning and fine-tuned using reanalysis/observations. These models enable monthly predictions of Arctic SIT without considering the complex physical processes involved. Through comprehensive assessments of prediction skills by season and region, the results suggest that using a broader set of CMIP6 data for transfer learning, as well as incorporating multiple climate variables as predictors, contribute to better prediction results, although both DL models can effectively predict the spatiotemporal features of SIT anomalies. Regarding the predicted SIT anomalies of the FC-Unet model, the spatial correlations with reanalysis reach an average level of 89% over all months, while the temporal anomaly correlation coefficients are close to unity in most cases. The models also demonstrate robust performances in predicting SIT and SIE during extreme events. The effectiveness and reliability of the proposed deep transfer learning models in predicting Arctic SIT can facilitate more accurate pan-Arctic predictions, aiding climate change research and real-time business applications.
Similar content being viewed by others
References
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.
Beadling, R. L., and Coauthors, 2020: Representation of Southern Ocean properties across coupled model intercomparison project generations: CMIP3 to CMIP6. J. Climate, 33, 6555–6581, https://doi.org/10.1175/JCLI-D-19-0970.1.
Bellucci, A., and Coauthors, 2015: Advancements in decadal climate predictability: The role of nonoceanic drivers. Rev. Geophys., 53, 165–202, https://doi.org/10.1002/2014RG000473.
Blanchard-Wrigglesworth, E., M. Bushuk, F. Massonnet, L. C. Hamilton, C. M. Bitz, W. N. Meier, and U. S. Bhatt, 2023: Forecast skill of the Arctic Sea ice outlook 2008–2022. Geophys. Res. Lett., 50, e2022GL102531, https://doi.org/10.1029/2022GL102531.
Bracegirdle, T. J., P. Hyder, and C. R. Holmes, 2018: CMIP5 diversity in southern westerly jet projections related to historical sea ice area: Strong link to strengthening and weak link to shift. J. Climate, 31, 195–211, https://doi.org/10.1175/JCLID-17-0320.1.
Casagrande, F., L. Stachelski, and R. B. de Souza, 2023: Assessment of Antarctic sea ice area and concentration in Coupled Model Intercomparison Project Phase 5 and Phase 6 models. International Journal of Climatology, 43, 1314–1332, https://doi.org/10.1002/joc.7916.
Cavalieri, D. J., and C. L. Parkinson, 2012: Arctic sea ice variability and trends, 1979–2010. The Cryosphere, 6, 881–889, https://doi.org/10.5194/tc-6-881-2012.
Cavalieri, D. J., P. Gloersen, and W. J. Campbell, 1984: Determination of sea ice parameters with the Nimbus 7 SMMR. J. Geophys. Res., 89, 5355–5369, https://doi.org/10.1029/JD089iD04p05355.
Chen, X. Y., X. B. Zhang, J. A. Church, C. S. Watson, M. A. King, D. Monselesan, B. Legresy, and C. Harig, 2017: The increasing rate of global mean sea-level rise during 1993–2014. Nature Climate Change, 7, 492–495, https://doi.org/10.1038/nclimate3325.
Chi, J., and H.-C. Kim, 2017: Prediction of arctic sea ice concentration using a fully data driven deep neural network. Remote Sensing, 9, 1305, https://doi.org/10.3390/rs9121305.
Cohen, J., and Coauthors, 2014: Recent Arctic amplification and extreme mid-latitude weather. Nature Geoscience, 7, 627–637, https://doi.org/10.1038/ngeo2234.
Comiso, J. C., 1986: Characteristics of Arctic winter sea ice from satellite multispectral microwave observations. J. Geophys. Res., 91, 975–994, https://doi.org/10.1029/JC091iC01p00975.
Curry, J. A., J. L. Schramm, and E. E. Ebert, 1995: Sea ice-albedo climate feedback mechanism. Journal of Climate, 8, 240–247, https://doi.org/10.1175/1520-0442(1995)008<0240:SIACFM>2.0.CO;2.
Ding, R. Q., J. P. Li, F. Zheng, J. Feng, and D. Q. Liu, 2016: Estimating the limit of decadal-scale climate predictability using observational data. Climate Dyn., 46, 1563–1580, https://doi.org/10.1007/s00382-015-2662-6.
Dosovitskiy, A., and Coauthors, 2020: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv: 2010. 11929.
Elman, J. L., 1990: Finding structure in time. Cognitive Science, 14, 179–211, https://doi.org/10.1207/s15516709cog1402_1.
Fritzner, S., R. Graversen, and K. H. Christensen, 2020: Assessment of high-resolution dynamical and machine learning models for prediction of sea ice concentration in a regional application. J. Geophys. Res., 125, e2020JC016277, https://doi.org/10.1029/2020JC016277.
Gao, Z. H., X. J. Shi, H. Wang, Y. Zhu, Y. B. Wang, M. Li, and D.-Y. Yeung, 2022: Earthformer: Exploring space-time transformers for earth system forecasting. Proc. 36th Int. Conf. on Neural Information Processing Systems, New Orleans, LA, USA, 25 390–25 403.
Ham, Y.-G., J.-H. Kim, and J.-J. Luo, 2019: Deep learning for multi-year ENSO forecasts. Nature, 573, 568–572, https://doi.org/10.1038/s41586-019-1559-7.
He, K. M., X. Y. Zhang, S. Q. Ren, and J. Sun, 2016: Deep residual learning for image recognition. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, IEEE, 770–778, https://doi.org/10.1109/CVPR.2016.90.
Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. Quart. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803.
Hochreiter, S., and J. Schmidhuber, 1997: Long short-term memory. Neural Computation, 9, 1735–1780, https://doi.org/10.1162/neco.l997.9.8.1735.
Hou, Y. R., and Coauthors, 2022: A surface temperature dipole pattern between Eurasia and North America triggered by the Barents-Kara sea-ice retreat in boreal winter. Environmental Research Letters, 17, 114047, https://doi.org/10.1088/1748-9326/ac9ecd.
Huang, J., P. Hitchcock, A. C. Maycock, C. M. McKenna, and W. Tian, 2021: Northern hemisphere cold air outbreaks are more likely to be severe during weak polar vortex conditions. Communications Earth & Environment, 2, 147, https://doi.org/10.1038/s43247-021-00215-6.
Hyder, P., and Coauthors, 2018: Critical Southern Ocean climate model biases traced to atmospheric model cloud errors. Nature Communications, 9, 3625, https://doi.org/10.1038/s41467-018-05634-2.
Johnson, S. J., and Coauthors, 2019: SEAS5: The new ECMWF seasonal forecast system. Geoscientific Model Development, 12, 1087–1117, https://doi.org/10.5194/gmd-12-1087-2019.
Kim, Y. J., H.-C. Kim, D. Han, S. Lee, and J. Im, 2020: 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.
Kirchmeier-Young, M. C., F. W. Zwiers, and N. P. Gillett, 2017: Attribution of extreme events in Arctic sea ice extent. J. Climate, 30, 553–571, https://doi.org/10.1175/JCLI-D-16-0412.1.
Lea, C., M. D. Flynn, R. Vidal, A. Reiter, and G. D. Hager, 2017: Temporal convolutional networks for action segmentation and detection. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, Honolulu, HI, USA, IEEE, 156–165, https://doi.org/10.1109/CVPR.2017.113.
Lin, M., Q. Chen, and S. C. Yan, 2013: Network in network. arXiv preprint arXiv: 1312.4400.
Lindsay, R. W., and J. Zhang, 2006: Assimilation of ice concentration in an ice-ocean model. J. Atmos. Oceanic Technol., 23, 742–749, https://doi.org/10.1175/JTECH1871.1.
Liu, L., 2021: A review of deep learning for cryospheric studies. Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote Sensing, Climate Science, and Geosciences, G. Camps-Vails et al., Eds., John Wiley & Sons Ltd, 258–268, https://doi.org/10.1002/9781119646181.ch17.
Liu, Y. H., and J. R. Key, 2014: Less winter cloud aids summer 2013 Arctic sea ice return from 2012 minimum. Environmental Research Letters, 9, 044002, https://doi.org/10.1088/1748-9326/9/4/044002.
Long, M. Y., L. J. Zhang, S. Y. Hu, and S. M. Qian, 2021: Multi-aspect assessment of CMIP6 models for Arctic sea ice simulation. J. Climate, 34, 1515–1529, https://doi.org/10.1175/JCLI-D-20-0522.1.
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., F. Fetterer, A. K. Windnagel, and J. S. Stewart, 2021: NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration, Version 4 [Data Set]. National Snow and Ice Data Center, Boulder, Colorado, USA, https://doi.org/10.7265/efmz-2t65. Date Accessed 03-08-2024.
Meredith, M., and Coauthors, 2019: Polar regions. Chapter 3, IPCC Special Report on the Ocean and Cryosphere in a Changing Climate, H.-O. Pörtner et al., Eds., Cambridge University Press.
Min, C., X. Y. Zhou, H. Luo, Y. J. Yang, Y. G. Wang, J. L. Zhang, and Q. H. Yang, 2023: Toward quantifying the increasing accessibility of the Arctic Northeast Passage in the past four decades. Adv. Atmos. Sci., 40, 2378–2390, https://doi.org/10.1007/s00376-022-2040-3.
Notz, D., A. Jahn, M. Holland, E. Hunke, F. Massonnet, J. Stroeve, B. Tremblay, and M. Vancoppenolle, 2016: The CMIP6 Sea-Ice Model Intercomparison Project (SIMIP): Understanding sea ice through climate-model simulations. Geoscientific Model Development, 9, 3427–3446, https://doi.org/10.5194/gmd-9-3427-2016.
Palerme, C., and M. Müller, 2021: Calibration of sea ice drift forecasts using random forest algorithms. The Cryosphere, 15, 3989–4004, https://doi.org/10.5194/tc-15-3989-2021.
Pedersen, R. A., and J. H. Christensen, 2019: Attributing Greenland warming patterns to regional Arctic sea ice loss. Geophys. Res. Lett., 46, 10495–10503, https://doi.org/10.1029/2019GL083828.
Previdi, M., K. L. Smith, and L. M. Polvani, 2021: Arctic amplification of climate change: a review of underlying mechanisms. Environmental Research Letters, 16, 093003, https://doi.org/10.1088/1748-9326/aclc29.
Purich, A., W. J. Cai, M. H. England, and T. Cowan, 2016: Evidence for link between modelled trends in Antarctic sea ice and underestimated westerly wind changes. Nature Communications, 7, 10409, https://doi.org/10.1038/ncomms10409.
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, 4304819, https://doi.org/10.1109/TGRS.2022.3177600.
Ronneberger, O., P. Fischer, and T. Brox, 2015: U-Net: Convolutional networks for biomedical image segmentation. Proc. 18th Int. Conf. on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany, Springer, 234–241, https://doi.org/10.1007/978-3-319-24574-4_28.
Saha, S., and Coauthors, 2014: The NCEP climate forecast system version 2. J. Climate, 27, 2185–2208, https://doi.org/10.1175/JCLI-D-12-00823.1.
Serreze, M. C., and R. G. Barry, 2011: Processes and impacts of Arctic amplification: A research synthesis. Global and Planetary Change, 77, 85–96, https://doi.org/10.1016/j.gloplacha.2011.03.004.
Shen, Z. L., A. M. Duan, D. L. Li, and J. X. Li, 2021: Assessment and ranking of climate models in Arctic Sea ice cover simulation: From CMIP5 to CMIP6. J. Climate, 34, 3609–3627, https://doi.org/10.1175/JCLI-D-20-0294.1.
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, MIT Press, 802–810.
Shu, Q., Q. Wang, Z. Y. Song, F. L. Qiao, J. C. Zhao, M. Chu, and X. F. Li, 2020: Assessment of sea ice extent in CMIP6 with comparison to observations and CMIP5. Geophys. Res. Lett., 47, e2020GL087965, https://doi.org/10.1029/2020GL087965.
Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, 2017: Attention is all you need. Proc. 31st Int. Conf. on Neural Information Processing Systems, Long Beach, CA, USA, Curran Associates Inc., 6000–6010.
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.
Woo, S., J. Park, J.-Y. Lee, and I. S. Kweon, 2018: CBAM: Convolutional block attention module. Proc. 15th European Conf. on Computer Vision, Munich, Germany, Springer, 3–19, https://doi.org/10.1007/978-3-030-01234-2_1.
Zhang, J., W. D. Hibler III, M. Steele, and D. A. Rothrock, 1998: Arctic ice–ocean modeling with and without climate restoring. J. Phys. Oceanogr., 28, 191–217, https://doi.org/10.1175/1520-0485(1998)028<0191:AIOMWA>2.0.CO;2.
Zhang, J. L., and D. A. Rothrock, 2003: Modeling global sea ice with a thickness and enthalpy distribution model in generalized curvilinear coordinates. Mon. Wea. Rev., 131, 845–861, https://doi.org/10.1175/1520-0493(2003)131<0845:MGSIWA>2.0.CO;2.
Zhou, T.-J., L.-W. Zou, and X.-L. Chen, 2019: Commentary on the coupled model intercomparison project phase 6 (CMIP6). Climate Change Research, 15, 445–456, https://doi.org/10.12006/j.issn.1673-1719.2019.193.
Zou, Y. F., Y. H. Wang, Y. Z. Zhang, and J.-H. Koo, 2017: Arctic sea ice, Eurasia snow, and extreme winter haze in China. Science Advances, 3, el602751, https://doi.org/10.1126/sciadv.1602751.
Acknowledgements
The authors thank the editor and the two reviewers for their valuable comments, which largely improved the quality of this paper. The authors of this paper were supported by the National Natural Science Foundation of China (Grant Nos. 41976193 and 42176243).
Author information
Authors and Affiliations
Corresponding authors
Additional information
Article Highlights
• Incorporating a broader CMIP6 transfer learning dataset and adding climate variables as predictors contribute to higher skills in two DL models.
• Both DL models effectively predict SIT anomalies across all seasons, while the FC-Unet is superior around the pole and in coastal regions.
• Both DL models accurately reproduce the extreme SIT and SIE anomalies in September 2012 and their rapid recovery during the following year.
This paper is a contribution to the special issue on AI Applications in Atmospheric and Oceanic Science: Pioneering the Future.
Rights and permissions
About this article
Cite this article
Song, C., Zhu, J. & Li, X. Assessments of Data-Driven Deep Learning Models on One-Month Predictions of Pan-Arctic Sea Ice Thickness. Adv. Atmos. Sci. (2024). https://doi.org/10.1007/s00376-023-3259-3
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s00376-023-3259-3