## 5 Conclusions  
In this paper, we proposed the PDED-ConvLSTM model, based on ConvLSTM, for the monthly-scale prediction of Arctic sea ice. The PDED-ConvLSTM model comprised the Pyramid Convolution (PC) module and the DED-ConvLSTM module. The DED-ConvLSTM module utilized spatial features learned from the PC module as the input and outputted enhanced spatiotemporal saliency features for the final prediction of the sea ice concentration sequence. This study's findings revealed that the PC module effectively performed multi-scale spatial feature extraction, particularly for SIC data, by expanding its receptive field. In addition, more in-depth and detailed information mining was realized in the DED-ConvLSTM module for long-term historical SIC data, which improved the predictive capability of the model. To assess the model's performance, this study utilized the following performance metrics: RMSE, MAE, PCC, and NSE. The results indicated that the PDED-ConvLSTM model outperformed the other two deep learning models and the traditional climatological model in terms of prediction accuracy. The PDED-ConvLSTM model showed a reduction of 3.6% and 2.9% in the average RMSE and MAE values, respectively, compared to the ConvLSTM model from 2018 to 2020. This suggested a  
Figure 12: RMSE and MAE trends for different input parameters. significant improvement in reducing prediction errors. The numerical improvements showcased the efficiency and reliability of the PDED-ConvLSTM model and its potential application in predicting sea ice concentrations. This study further explored the predictive performance of the model under a five-month lead-time condition. It was found that as the lead time was extended, the difficulty for the model to learn the complex nonlinear relationship between SIC and time correspondingly increased. Specifically, when the lead time was extended from one month to five months, the average RMSE value of the model only decreased by 0.04. This phenomenon was attributed to the model's encoder part, where the processing of the input sequence took into consideration the SIC information from all historical time points. Meanwhile, the decoder effectively utilized the global SIC information provided by the encoder when generating predictions for each respective time point. Consequently, the PDED-ConvLSTM model demonstrated significant advantages in long-term timescale prediction. In the final part of our study, we meticulously examined the impact of three environmental factors, sea water salinity, sea ice thickness, and sea surface temperature on the prediction of SICs. It was particularly noteworthy that sea surface temperature exhibited a significant negative impact on SIC prediction during the melt season. Conversely, sea water salinity and sea ice thickness contributed positively to predictions during the relatively stable period from May to July for SICs. However, these factors shifted to have a negative impact during the months between August and September, when SICs underwent more dramatic changes. This discovery shed light on the complex interplay of different environmental factors in sea ice prediction, especially across varying seasons and stages of sea ice variation. This research study has provided a crucial theoretical foundation for the integration of environmental factors in sea ice concentration prediction models and laid the groundwork for future in-depth studies in this field. The following outlines some prospects for future work. While this study has already encompassed the relationship between sea ice concentration (SIC) and three oceanic factors, future efforts should focus on expanding the variety of oceanic influencing factors and increasing the volume of sample data. This would facilitate a more in-depth understanding of the complex interactions between sea ice concentration and other potential predictive factors. Moreover, determining the optimal lengths for input and output sequences is crucial for enhancing the accuracy and reliability of sea ice concentration predictions.