## 4 Impact of Climate Change on Oceans: Leveraging DL for Monitoring and Analysis
### Understanding Impacts on Physical Properties via DL
#### 4.1.2 DL for Predicting Ocean Warming Trends and Associated Extreme Weather Events  
As global temperatures rise, oceans absorb excess heat from the atmosphere, leading to an increase in SST, known as ocean warming [111]. This process has significant consequences, including threatening marine biodiversity, rising sea levels, and intensifying extreme weather events. Analyzing historical SST patterns and forecasting future trends require robust analytical methods, with DL proving to be a highly effective approach. DL models, such as CNNs and RNNs, offer significant advantages in capturing spatial and temporal features, making them powerful tools for SST prediction [112]. [PERSON] et al. [113] proposed a GRU-CNN hybrid model to integrate temporal and spatial information for accurate SST prediction. Using OISST data specific to the East China Sea and the Yellow Sea, the model demonstrated superior performance, achieving high accuracy across forecasting horizons ranging from 3 days to 1 month, highlighting its robustness and strong predictive capabilities. [PERSON] et al. [114] introduced attention mechanisms in GRU models to adaptively assign weights for key time steps, further improving SST prediction accuracy. In addition to GRU, models combining LSTM with CNNs, known as ConvLSTM architectures, are also widely used for SST prediction due to their ability to effectively capture both temporal dependencies and spatial features. Both [PERSON] et al. [115] and [PERSON] et al. [116] utilized ConvLSTM for SST forecasting, with [PERSON] et al. further enhancing the model by incorporating dilation convolutional kernels. This modification enabled the model to capture varying spatial scales, significantly improving its ability to learn multiscale spatial features. Beyond CNNs and RNNs, other architectures like U-Net and Transformers have also been explored for SST prediction, offering exceptional capabilities in capturing spatial and temporal features. U-Net, a specialized model with an encoder-decoder structure, is particularly effective for spatial feature extraction in SST forecasting. [PERSON] et al. [117] introduced a 3D U-Net model that integrates both spatial and temporal information, outperforming ConvLSTM models with smaller prediction errors across all forecasting periods. Similarly, [PERSON] et al. [118] combined U-Net and LSTM architectures to predict global SSTs up to 24 months in advance. Using ERA5 monthly mean SST data and 2 m air temperature data from 1950 to 2021, their model demonstrated high accuracy, particularly in the equatorial and subtropical Pacific regions. Transformers, on the other hand, are effective for time-series modeling and well suited for long-term SST prediction. [PERSON] et al. [119] introduced a Transformer-based model specifically designed for fine-grained daily SST forecasting. The model incorporated temporal embedding, attention distillation, and partial stacked connections to enhance performance. Using OISST data from September 1981 to 2023, the model achieved accurate forecasts for up to 360 days across five regions in the China Sea, outperforming existing methods and highlighting the significant potential of Transformers for reliable, long-term SST prediction. In addition to SST prediction, deep neural networks have shown significant advantages in SST super-resolution tasks, which aim to enhance the spatial resolution of SST data [120]. Traditional interpolation methods often struggle to capture the complex spatial patterns present in SST fields. In contrast, DL models, such as CNNs and GANs, excel at learning intricate spatial relationships from satellite measurements, making them effective for this purpose. [PERSON] et al. [70] addressed the limitations of moderate spatial resolution in SST measurements from infrared sensors, which are typically greater than 1 km. Leveraging high-resolution information from optical bands, they developed a CNN-based super-resolution neural network to enhance the resolution of Sentinel-3 SLSTR thermal images by a factor of 5.