## 1 Introduction
Due to its intelligent and lightweight characteristics, deep learning methods have been effectively utilized in addressing cryogenic challenges, such as sea ice detection [22], sea ice classification [23], and sea ice prediction. Compared to statistical and numerical models, deep learning models have shown improved accuracy in sea ice forecasting. In 2017, [PERSON] and [PERSON] [24] utilized long short-term memory (LSTM) networks to predict monthly Arctic sea ice concentrations. Their research indicated that this method outperformed autoregressive models in prediction accuracy and was comparable to the performance of the Sea Ice Prediction Network (SIPN). While LSTM networks are effective in capturing temporal features in data, they fall short in capturing spatial information. [PERSON] et al. [25] developed a monthly sea ice concentration (SIC) prediction model using a convolutional neural network (CNN) model. The results indicated that the CNN model outperformed the baseline model, achieving a root mean square error (RMSE) of 0.06. The CNN model was effective in identifying and processing spatial information within the data, but it was less capable of capturing temporal information. [PERSON] et al. [26] compared the performance of CNN and ConvLSTM models in predicting daily-scale Arctic SICs for the year 2018, using two days of data to forecast the following day's sea ice concentration. The average RMSE for the CNN was 8.0%, while the ConvLSTM model showed an improvement, with an average RMSE reduced by 6.9%. The ConvLSTM model integrates the CNN and LSTM networks, capturing both temporal information and global spatial features through convolutional kernels. However, it still faces the temporal dependency issues inherent in recurrent networks, which could lead to the loss of historical information. [PERSON] et al. [27] introduced a U-net-based model, named IceNet, whose experimental results indicated that it surpassed common numerical and statistical models in binary accuracy (BACC) for sea ice extent estimation. While the downsampling operation effectively enlarges the receptive field, it may also lead to a loss in resolution, subsequently affecting the accuracy of pixel-level prediction tasks. [PERSON] et al. (2020) proposed a multi-layer stacking convolutional long short-term memory network (Multi-Stacking-ConvLSTM) for the daily-scale forecasting of Arctic SIC, with a prediction period of seven days. In this setup, the average RMSE for EOF+LSTM was 18.1%, for CNN+LSTM, it was 16.8%, and the Multi-Stacking-ConvLSTM improved sea ice concentration prediction accuracy to 5.3%. [PERSON] et al. (2020) proposed SICNet, a fully data-driven model for day-by-day sea ice prediction on subseasonal to seasonal scales. The experimental results demonstrate that the model achieves an average balanced accuracy (BACC) of over 90.0% during a 20-day prediction period, with an average BACC improvement of 1.8%. [PERSON] et al. (2020) developed the UNet-F/M model, based on the U-Net architecture, specifically for predicting Arctic sea ice melting and freezing periods. The UNet-F/M model demonstrated a significant reduction in mean absolute error (MAE) compared to the PredRNN++ model during a one-month prediction period, with a decrease of 2.0%. The forecasting of sea ice concentrations faces three main challenges. Firstly, traditional numerical and statistical models struggle to handle complex nonlinear relationships and lack computational efficiency. These models often have difficulty adapting to the diversity and complexity of Arctic sea ice concentration variations, resulting in a decreased prediction accuracy and generalization ability in practical applications. Secondly, traditional time-series methods often disregard early information when dealing with long time-series data. Since sea ice concentration forecasting heavily relies on long time-series data, neglecting historical information can directly impact the reliability and accuracy of model predictions.