A spatiotemporal prediction method for Arctic monthly mean sea ice concentration based on an improved SA-ConvLSTM model

Zhao Xiangyu; Wang Zhiyong; Li Zhenjin; Jing Fang; Cheng Silong

doi:10.12284/hyxb2025095

Volume 47 Issue 10

Oct. 2025

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Zhao Xiangyu，Wang Zhiyong，Li Zhenjin, et al. A spatiotemporal prediction method for Arctic monthly mean sea ice concentration based on an improved SA-ConvLSTM model[J]. Haiyang Xuebao，2025, 47(10)：111–125 doi: 10.12284/hyxb2025095

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A spatiotemporal prediction method for Arctic monthly mean sea ice concentration based on an improved SA-ConvLSTM model

doi: 10.12284/hyxb2025095

1.
College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
2.
College of Resources, Environment and Safety Engineering, Liaoyuan Vocational Technical College, Liaoyuan 136201, China
3.
Moganshan Geospatial Information Laboratory, Deqing 313299, China

Received Date: 2025-05-26
Rev Recd Date: 2025-09-28

Available Online: 2025-10-16

Publish Date: 2025-10-31

Abstract

Abstract

To address the problem of low prediction accuracy for Arctic sea ice concentration during the melting season, this study proposes a method for predicting Arctic sea ice concentration based on an improved SA-ConvLSTM model, enabling two-dimensional spatiotemporal prediction of monthly mean sea ice concentration data for the coming year. The method uses the SA-ConvLSTM model as the core unit, incorporating a Seq2Seq prediction structure and a VGG16-like encoder-decoder architecture to specifically address the uncertainty in selecting the output step length of time series predictions. In addition, a composite loss function is designed to optimize the training process, further enhancing the spatiotemporal prediction accuracy of sea ice concentration distribution. Using the Arctic Ocean as the study area, and based on monthly climate sea ice concentration data jointly released by the National Snow and Ice Data Center (NSIDC) and the National Oceanic and Atmospheric Administration (NOAA), the model predicts the spatiotemporal distribution of Arctic sea ice concentration in 2023 and compares the results with real observations. The results show that, compared with traditional LSTM, ConvLSTM, and the unmodified SA-ConvLSTM models, the improved model achieves significant advantages in all evaluation metrics: root mean square error decreases by 13.18%, 36.10%, and 22.58%; correlation coefficient increases by 1.90%, 5.97%, and 3.31%; structural similarity index improves by 5.38%, 15.00%, and 10.30%; and sea ice area error decreases by 83.46%, 76.53%, and 60.30%, respectively. Furthermore, analysis of predictions for the extreme melting years of 2012 and 2020 further verifies the model’s stability and robustness under abnormal climatic conditions, demonstrating strong adaptability and practical application potential. The proposed spatiotemporal prediction model can more accurately predict the spatial distribution of sea ice during the melting season and effectively capture complex spatiotemporal variations and fine-scale details.
- spatiotemporal prediction,
- sea ice concentration,
- SA-ConvLSTM,
- combined loss function,
- self-attention

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References(24)

References

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