## 4 Experiment and Results
### Overall Performance of PDED-ConvLSTM  
The spatial distribution of the predicted values and ground-truth during the melt season (May to September) from 2018 to 2020 is shown in Figure 4. The PDED-ConvLSTM model proposed in this paper accurately captures the sea ice melting process. The spatial distribution of the predicted sea ice concentration closely resembles the ground truth, with a clear delineation of the ice edges. The PDED-ConvLSTM model particularly excels from May to July when the sea ice concentration (SIC) is higher, while its performance slightly decreases in August and September when the SIC is lower. Specifically, the model tends to underestimate sea ice concentration (SIC) on the Pacific side of the Arctic Ocean, especially in the Beaufort Sea, Chukchi Sea, and East Siberian Sea. Overall, the PDED-ConvLSTM model proposed in our study demonstrates commendable performance both in months with stable SIC variations and those with high variability. We explored the advantages of the PDED-ConvLSTM model over other deep learning models based on evaluation metrics. The selected models for comparison include ConvLSTM, Climatology, and Multi-Stacking ConvLSTM. The Climatology model represents the average SIC at the same time each year over the preceding 10 years of the respective target prediction time. It is a forecasting method based on the average of historical data, commonly used in climate change and meteorological studies as a baseline model. As depicted in Figure 5, the trends in RMSE and MAE predicted by our proposed method in comparison to other methods are presented. The results demonstrate that the three deep learning models exhibit similar trends in RMSE/MAE. The PDED-ConvLSTM \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Encoder** & **Pyramid** & \multirow{2}{*}{**PC**} & \multirow{2}{*}{**RMSE**} & \multirow{2}{*}{**MAE**} & \multirow{2}{*}{**PCC**} & \multirow{2}{*}{**NSE**} \\  & **Decoder** & **Dilated** & & & & & \\  & **Structure** & **Convolution** & & & & & \\ \hline NO\_PC & ✔ & ✔ & & 0.089 & 0.035 & 0.958 & 0.927 \\ NO\_ED & & ✔ & ✔ & 0.087 & 0.035 & 0.961 & 0.928 \\ NO\_PD & ✔ & & ✔ & 0.092 & 0.041 & 0.942 & 0.911 \\ PDED- & ✔ & ✔ & ✔ & 0.073 & 0.022 & 0.971 & 0.940 \\ ConvLSTM & & ✔ & ✔ & 0.073 & 0.022 & 0.971 & 0.940 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Ablation experiment. model shows lower RMSE/MAE values than the other models, indicating its superior precision. The performance of the Climatology model is comparable to that of the Multi-Stacking ConvLSTM, with the ConvLSTM model showing the least favorable results. The Climatology model, which predicts concentrations based on average values, exhibits a different trend compared to the deep learning models. Compared to Climatology, ConvLSTM, and Muti-Stacking ConvLSTM, the PDED-ConvLSTM model reduces the average RMSE and MAE from 2018 to 2020 by 3.5%, 3.6%, and 2.1%, and by 2.7%, 2.9%, and 1.8%, respectively. The results show significant advantages of the PDED-ConvLSTM model over existing models. The ConvLSTM model can capture both temporal and spatial information. The Muti-Stacking ConvLSTM model, although improving the performance of the model through multiple stacked network layers, still struggles with long time series. The Climatology model bases its predictions on historical averages, limiting its ability to deal with dynamic trends with a certain degree of flexibility and accuracy. Figure 4: Spatial distribution of predicted and ground truth values for PDED-ConvLSTM. (**a**) Ground truth for 2018. (**b**) Prediction for 2018. (**c**) Ground truth for 2019. (**d**) Prediction for 2019. (**e**) Ground truth for 2020. (**f**) Prediction for 2020. The results presented in Table 3 indicate that our proposed PDED-ConvLSTM model demonstrates superior performance, achieving the lowest PCC and NSE values compared to other deep learning models. The average NSE and PCC values of the PDED-ConvLSTM model are 0.941 and 0.971, respectively.