## 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
In [121], GANs are utilized for SST super-resolution, achieving 2.5 times downscaling for the global ocean and 5 times downscaling for Korean waters, providing more detailed data for oceanographic studies. Increasing ocean temperatures alter atmospheric and oceanic circulations, resulting in more frequent extreme events such as MHWs and the El Nino-Southern Oscillation (ENSO). ENSO is a large-scale oceanic phenomenon characterized by periodic changes in SST across the equatorial Pacific, accompanied by atmospheric shifts (Southern Oscillation). Under normal conditions, east-to-west trade winds push warm surface waters toward the western Pacific, maintaining typical climate patterns. When these trade winds weaken or reverse, SST in the central and eastern Pacific rises abnormally, resulting in heavy rainfall and flooding in South America, alongside droughs and wildfires in Indonesia. Conversely, when the trade winds strengthen significantly, SST in the central and eastern Pacific drops below average, causing opposite climate impacts, including increased rainfall in Asia and Australia and droughs in South America [122]. The complex interactions between oceanic and atmospheric systems make ENSO prediction challenging, particularly as climate change intensifies its frequency and impacts. DL techniques uncover intricate patterns from ocean data, improving the accuracy of ENSO predictions [123; 124; 125]. CNNs are widely used for ENSO prediction due to their ability to capture spatial features from gridded data [126; 127]. [PERSON] et al. [128] employed a CNN to predict ENSO up to 18 months in advance. To address the challenge of limited observational data, the model used transfer learning, first training on historical simulations from CMIP5 and then fine-tuning with reanalysis data from the SODA spanning 1871 to 1973. [PERSON] et al. [129] proposed the Tendency-and-Attention-Informed Deep Residual Network, a residual CNN enhanced with a spatial attention module, to quantify the contributions of various ocean regions. LSTM networks are another popular choice for ENSO forecasting, excelling at capturing temporal dependencies in time-series data. [PERSON] et al. [130] compared LSTM networks with traditional linear regression for ENSO predictions with lead times ranging from 1 to 11 months, and LSTM demonstrated a slight advantage for long-term prediction. ConvLSTM models, which combine CNNs with LSTM, further enhance predictions by leveraging both spatial patterns and time-series dynamics [131; 132]. [PERSON] et al. [133] developed a sequence-to-sequence ConvLSTM model for ENSO prediction, incorporating input features like SST anomalies, sea surface pressure, and ocean currents. The model demonstrated strong predictive performance, achieving correlation coefficients between 0.87 and 0.61 for lead times of 3 to 12 months. Moreover, other architectures, including Bayesian Neural Networks (BNNs) [134], GNNs [135; 136], and Transformers [137; 138], have also been explored for ENSO prediction. For instance, [PERSON] et al. [139] introduced a spatial-temporal Transformer model with attention mechanisms designed to capture the dynamic contributions of different ocean regions over time. Leveraging CMIP5 and CMIP6 datasets, the model demonstrated reliable performance in predicting ENSO events up to 18 months in advance. MHWs are another urgent concern, marked by abnormally high ocean temperatures lasting days to months [140]. These events significantly disrupt marine ecosystems, impacting biodiversity and altering habitat structures [141; 142; 143]. The frequency, intensity, and duration of MHWs have increased due to climate change, making the need for accurate predictions more urgent [144; 145]. SST is crucial for understanding MHWs, as it directly reflects anomalous ocean surface warming. High-resolution global datasets, such as OISST and ERA5, combined with localized in situ observations, form the foundation for accurate analysis.