# An Interpretable Deep Learning Approach for Detecting Marine Heatwaves Patterns
## 3 Results
### Interpreted Ocean Mechanisms  
By comparing the different clustering results of these 1134 heatwave events, among numerous results, we found representative outputs in these five clustering categories. We found a pattern in the marine heatwave event at the BSG station on 7 August 2020. As shown in Figure 9, in the 180 days preceding the marine heatwave event, the persistency of \(\phi_{i}^{EG}\) for sea surface pressure and wind speed was consistently \(>0\). This suggests that the long-term joint effect of sea surface pressure and wind speed induced the marine heatwave event. Comparing with the clustered results, this marine heatwave event pattern belongs to the first category. The marine heatwave event on 12 August 1989 at the DSN station represents another pattern. As shown in Figure 10, the significant increase in \(\phi_{i}^{EG}\) of sea surface pressure and wind speed occurred just before the marine heatwave event, suggesting that the event was caused by the short-term combined effect of sea surface pressure and wind speed. A comparison with the clustering results reveals that this marine heatwave event pattern belongs to the second category. The marine heatwave event on 3 June 1991 at the NJI station corresponds to a pattern illustrated in Figure 11. During the training period, just before the marine heatwave event was about to occur, a significant increase in the \(\phi_{i}^{EG}\) of sea surface pressure occurred, with some promoting effects seen in wind speed at more distant points in time. This suggests that the marine heatwave event was caused by the short-term anomaly of sea surface pressure and long-term convergence of the sea surface wind field. Comparison with the clustering results shows that this pattern belongs to the third category. Figure 9: The marine heatwave event that occurred at BSG station on 7 August 2020, and its corresponding feature importance. The first and second row plots represent the historical data of different points in the 180 days before the marine heatwave event, with colors indicating the feature importance at different time points. The third row plot represents the predicted and observed sea surface temperature values. The fourth and fifth row plots represent the feature importance of pressure and 10 m wind speed, respectively. A heatwave event at the ZFD station exhibits a pattern, with Figure 12 showing significant feature analysis obtained for the heatwave event that occurred on 11 June 2004. The \(\phi_{i}^{EG}\) of pressure and wind speed remained significant for an extended period before the event. This suggests that atmospheric forces and convergence of the sea surface wind field act in concert, leading to significant changes in wind speed and pressure that trigger the marine heatwave event. The combination of this data with the clustered results shows this marine heatwave event belongs to the fourth category. Figure 13 shows the results of feature importance scores for different features of the heatwave event on 21 April 2019, at the ZLG station. The negligible influence of pressure on the event compared with wind speed is evident, and only the \(\phi_{i}^{EG}\) values of wind speed near the event marked a significant upward trend, while \(\phi_{i}^{EG}\) values of wind speed at other times had hardly any noticeable effect. The pattern analysis indicates that this heatwave event was likely triggered by recent convergence of the sea surface wind field, which can cause convergence and intensification of warm surface water and Ekman downwelling, leading to continued subsurface ocean warming and the subsurface heatwave phenomenon. In this case, while the previous wind speed in the station area might more or less intensify the heatwave event, the recent convergence of the sea surface wind field made an absolute contribution to the event occurrence. Comparison with the clustering results reveals that this marine heatwave event belongs to the fifth category.