## 4 Results and Discussion
### Typhoon-Induced Temperature Anomaly and Model Validation
In the upper ocean layers, particularly between 5 and 50 m, the model shows robust predictive capabilities, as evidenced by the relatively low MAE, which ranges between 0.4 \({}^{\circ}\)C and 0.6 \({}^{\circ}\)C, and the RMSE, which remains close to 0.5 \({}^{\circ}\)C. This level of accuracy is anticipated because shallow waters tend to respond more rapidly to surface wind forcing, making temperature variations more predictable. However, as depth increases--particularly between 100 and 250 m--both the MAE and RMSE show a notable rise. The RMSE peaks at around 2.2 \({}^{\circ}\)C near 125 m, while the MAE reaches approximately 1.5 \({}^{\circ}\)C in this range. These increases in error metrics align with the presence of more complex oceanographic dynamics in intermediate depths, such as the thermocline and the mixed layer. According to [PERSON] et al. (2015), thermocline dynamics play a pivotal role in subsurface thermal structures, where deeper thermoclines can weaken the influence of subsurface variability on sea surface temperature [21]. This can lead to reduced prediction accuracy, as seen in our results, where the presence of strong thermal gradients in the thermocline significantly contributes to higher prediction errors. The variations in thermocline depth and strength, coupled with the dynamic behavior of internal waves and stratification processes, further exacerbate the model's difficulty in capturing temperature changes at these depths. At greater depths, notably at 500 m and beyond, both the MAE and RMSE show a marked decrease, eventually stabilizing. By the time the model reaches 1000 m, the MAE and RMSE have dropped to approximately 0.2 \({}^{\circ}\)C. This reduction in error indicates that deeper ocean temperatures exhibit less variability and are less directly influenced by typhoon-induced disturbances. The stability of temperature variations at these depths results in a higher level of prediction accuracy by the model, reflecting the more consistent and predictable nature of deep ocean thermal structures. To evaluate the sensitivity of the model to variations in key input features, we conducted a detailed sensitivity analysis focusing on uvel, vvel, SST, and SLA, which are crucial for modeling the dynamic interactions between atmospheric forces and oceanic responses, especially under typhoon conditions. Each variable was independently perturbed by 1%, 5%, and 10% to assess its impact on the model's output. As illustrated in Figure 4, even with perturbations as high as 10%, the sensitivity values remained low, with a maximum of 1.230 \(\times\) 10\({}^{-2}\), indicating the robustness of the model despite moderate fluctuations in the input data. Such robustness is vital for accurately reconstructing ocean temperature profiles in highly dynamic and turbulent environments triggered by typhoons. These findings align with the work of [PERSON] et al. (2017), who identified the sensitivity of atmospheric and oceanic models to parameters such as ocean stratification and wind stress coefficients [22]. Their optimization strategy demonstrated that these parameters are essential in influencing model performance in dynamic settings, which supports our results showing that the model maintains strong predictive capacity despite variations in input conditions. Figure 3: Depth-wise evaluation of MAE and RMSE for the FCT model. In order to demonstrate the robustness of the model validation process, five-fold cross-validation was employed, as shown in Figure 5. This approach divides the dataset into five subsets, with each subset sequentially serving as the validation set while the remaining four subsets are used for training. The figure clearly demonstrates both training and validation losses (measured in degrees Celsius) across all folds. The training loss remains consistently low, averaging 0.977 \({}^{\circ}\)C, while the validation loss shows slight variations between folds, peaking at 1.055 \({}^{\circ}\)C in fold 2 and reaching a minimum of 0.906 \({}^{\circ}\)C in fold 3.