## 4 Experiment and Results
### Overall Performance of PDED-ConvLSTM
The improvement as a result of this model over ConvLSTM and Muti-Stacking ConvLSTM in NSE is 1.9% and 0.5%, and in PCC, it is is 1% and 0.9%, respectively. The Climatology model and the PDED-ConvLSTM model exhibit similar correlation performance. This phenomenon occurs because the Climatology model's predictions are based on the historical output data's average. The results show that the predictions of the PDED-ConvLSTM model are more highly correlated with the ground-truth values and are able to capture the trends in the sea ice concentration series. To evaluate the efficiency of the proposed method, we assess the time complexity of PDED-ConvLSTM based on FLOPs (floating-point operations), which is defined as the number of floating point operations per second and is commonly used as a measure of the time complexity of deep learning models. The results of the FLOP comparison between PDED-ConvLSTM and other deep learning models are shown in Table 4. Compared with the ConvLSTM model, although the FLOPs of PDED-ConvLSTM are higher than those  
Figure 5: Trends in RMSE and MAE for different models during melt period. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metrics** & **Model** & **2018** & **2019** & **2020** \\ \hline \multirow{5}{*}{NSE} & Climatology & 0.946 & 0.940 & 0.933 \\  & ConvLSTM & 0.930 & 0.919 & 0.919 \\  & Muti Stacking- & 0.943 & 0.931 & 0.931 \\  & ConvLSTM & & & \\  & PDED- & 0.948 & 0.934 & 0.939 \\  & ConvLSTM & & & \\ \hline \multirow{5}{*}{PCC} & Climatology & 0.973 & 0.970 & 0.967 \\  & ConvLSTM & 0.966 & 0.961 & 0.960 \\ \cline{1-1}  & Muti Stacking- & 0.971 & 0.964 & 0.965 \\ \cline{1-1}  & ConvLSTM & & & \\ \cline{1-1}  & PDED- & 0.975 & 0.968 & 0.971 \\ \cline{1-1}  & Con vLSTM & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: NSE And PCC predicted by different models. of ConvLSTM, its performance is significantly better than the latter. Compared to Multi-Stacking ConvLSTM, PDED-ConvLSTM achieves higher prediction accuracy with lower time complexity, demonstrating its optimization in computational resource utilization. In this study, we further evaluated the performance of our proposed model by analyzing the differences between the predicted values and ground-truth observations from 2018 to 2020 (Predict-Ground Truth). As illustrated in Figures 6-8, red areas indicate an overestimation of sea ice concentration (Predict \(-\) True \(>\) 0), while blue areas indicate an underestimation (Predict \(-\) True \(<\) 0), with deeper colors signifying larger discrepancies. The results reveal that the errors in predictions are primarily concentrated in the areas of first-year ice, with smaller errors in multi-year ice zones. The Climatology model tends to underestimate sea ice concentrations in most cases, whereas deep learning models generally overestimate it. Our proposed PDED-ConvLSTM model demonstrates superior performance in the majority of the study area, with lighter shades and smaller areas of both overestimation and underestimation, indicating the highest accuracy in predictions. The errors were primarily found in the Kara Sea and Barents Sea, likely due to the intensified amplitude of the El Nino-Southern Oscillation (ENSO) during the climate-warming process, which causes significant interannual variability in the SIC of these seas during winter [33]. This leads to larger prediction errors in these marine areas. Additionally, the drift of sea ice through the Beaufort Sea also results in noticeable SIC variations, subsequently increasing the prediction errors in this region [34].