## 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
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Area** & **Reference** & **Application** & **Data** & **Model** & **Results** \\ \hline [PERSON] et al. [118] & SST Prediction & SST, 2 m air temperature (ERA5) & U-Net-LSTM & RMSE increased slightly with lead time: from 0.48 °C (1 month) to 0.63 °C (1 months). \\ \hline [PERSON] et al. [117] & SST Prediction & SST (OISST); sea surface wind (CCMP) and height anomalies (CMEMS) & 3D U-Net & RMSE increased from approximately 0.3 °C to 0.7 °C during the 1-day to 30-day prediction period. \\ \hline [PERSON] et al. [119] & SST Prediction & SST (OISST) & TransDtSt-Part & The RMSE ranged from 0.754 °C to 0.895 °C for the South China Sea and from 0.793 °C to 0.920 °C for the East China Sea over prediction horizons of 30 to 360 days. \\ \hline [PERSON] et al. [121] & Super Resolution & SST (OISST, OSTIA, G1 SST, ERA5, buoys from KMA and NIFS) & GAN & RMSE = 1.60 °C for upscaling 2.5\% on global ocean data. \\ \hline SST, ENSO, and MHWs & ENSO Forecast & SSTA, HCA (CMIP5, SODA, GODAS) & Spatial-Temporal Transformer & Achieved a correlation greater than 0.5 for predictions up to 18 months. \\ \hline [PERSON] et al. [154] & ENSO Forecast & SSTA (ERSST, OISST); HCA (SODA, GODAS) & SERCNN, residual CNN with squeeze-and-excitation attention block & Indian and Atlantic HCA extended ENSO predictability by one season. \\ \hline [PERSON] et al. [149] & MHW Prediction & SST (OISST); SSHA (CMEMS); sea surface wind (CCMP) & 3D U-Net & Achieved RMSE ranging from 0.31 °C (1-day lead) to 0.69 °C (30-day lead) for SST prediction. \\ \hline [PERSON] et al. [152] & MHW Prediction & SSTA (OISST) & U-Net \& ConvLSTM & MHW forecast accuracy decreased with time from 0.32 (1-day), 0.89 (3-day), 0.88 (5-day), to 0.87 (7-day). \\ \hline [PERSON] et al. [155] & Detection & Sentinel-1 SAR image & MSDA-Net, ConvNeXt & MIoU = 93.0\%, Precision = 96.3\%, \\  & & & architecture & Recall = 98.1\%, \\  & & & embedded with an attention mechanism & F1-score = 97.2\%. \\ \cline{2-6} Sea Ice & [PERSON] et al. [156] & Detection & Sentinel-1 SAR image, NSIDC sea ice product & DAU-Net, attention mechanism with U-Net & Accuracy = 94.39\%, IoU = 0.8673, Precision = 0.9355, Recall = 0.9225 \\ \hline [PERSON] et al. [157] & Detection & Sentinel-1 SAR image, MODIS-Terra MSI image & ViSual\_IceD, a U-Net-based model with dual-encoder & Accuracy = 0.942, F1-score = 0.972 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._DL has revolutionized the analysis and prediction of SST and associated extreme events using vast multisource datasets and advanced architectures like CNNs, LSTMs, Transformers, and GNNs. These models provide critical insights into SST dynamics,  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Area** & **Reference** & **Application** & **Data** & **Model** & **Results** \\ \hline \multirow{9}{*}{Sea Ice} & [PERSON] et al. (2015) & Classification & Sentinel-1 SAR image; brightness temperature data (AMSR2); 2 m air temperature, 10 m wind speed, total column & Multitask U-Net & SIC: R2 = 91.7\%; SOD: F1-score = 88.2\%; FLOE: F1-score = 76.4\%. \\  & & & water vapor, total column cloud liquid water (ERA5); sea ice chart (Canadian and Greenland Ice Service) & & \\ \cline{2-5}  & [PERSON] et al. (2019) & Classification & GFGE dataset (Gaofen optical image and Google Earth image); HY dataset (Gaofen optical image from the 2021 Gofen Challenge); SI-STSAR-7 dataset (Sentinel-1 SAR image) & SealceNet, a Global-Local Transformer & GFGE: OA = 91.84\%; F1-score = 84.91\%; HY: OA = 99.22\%, F1-score = 97.27\%; SI-STSAR-7 dataset (Sentinel-1 SAR image) & \multirow{2}{*}{GFGE: OA = 91.84\%, F1-score = 84.91\%; F1-score = 99.22\%, F1-score = 97.27\%; SI-stsar-7: OA = 98.75\%, F1-score = 98.88\%.} \\ \cline{2-5}  & [PERSON] et al. (2016) & SIC Prediction & SIC (NSIDC) & SICFormer, a model based on a 3D-Swin Transformer architecture & MAE = 1.89\%, RMSE = 5.98\%, MAPE = 4.31\% \\ \hline \multirow{9}{*}{ \begin{tabular}{} \end{tabular}