Wave forecasts, though integral to ocean engineering activities, are often conducted using computationally expensive and time-consuming numerical models with accuracies that are blunted by numerical-model-inherent limitations. Additionally, artificial neural networks, though significantly computationally cheaper, faster, and effective, also experience difficulties with nonlinearities in the wave generation and evolution processes. To solve both problems, this study employs and couples empirical mode decomposition (EMD) and a long short-term memory (LSTM) network in a joint model for significant wave height forecasting, a method widely used in wind speed forecasting, but not yet for wave heights. Following a comparative analysis, the results demonstrate that EMD-LSTM significantly outperforms LSTM at every forecast horizon (3, 6, 12, 24, 48, and 72 h), considerably improving forecasting accuracy, especially for forecasts exceeding 24 h. Additionally, EMD-LSTM responds faster than LSTM to large waves. An error analysis comparing LSTM and EMD-LSTM demonstrates that LSTM errors are more systematic. This study also identifies that LSTM is not able to adequately predict high-frequency significant wave height intrinsic mode functions, which leaves room for further improvements.

A (an) cyclonic (anticyclonic) eddy is usually associated with a cold (warm) core caused by the eddy-induced divergence (convergence) motion. However, there are also some cyclonic (anticyclonic) eddies with warm (cold) cores in the North Pacific, named cyclonic warm-core eddies (CWEs) and anticyclonic cold-core eddies (ACEs) in this study, respectively. Their spatio-temporal characteristics and regional dependence are analyzed using the multi-satellite merged remote sensing datasets. The CWEs are mainly concentrated in the northwestern and southeastern North Pacific. However, besides these two areas, the ACEs are also concentrated in the northeastern Pacific. The annual mean number decreases year by year for both CWEs and ACEs, and the decreasing rate of the CWEs is about two times as large as that of the ACEs. Moreover, the CWEs and ACEs also exhibit a significant seasonal variation, which are intense in summer and weak in winter. Based on the statistics of dynamic characteristics in seven subregions, the Kuroshio Extension region could be considered as the most active area for the CWEs and ACEs. Two possible mechanisms for CW-ACEs generation are discussed by analyzing two cases.

Marine heatwaves (MHWs) are extreme events characterized by abnormally high sea surface temperatures, and they have significant impacts on marine ecosystems and human society. The rapid and accurate forecasting of MHWs is crucial for preventing and responding to the impacts they can lead to. However, the research on relevant forecasting methods is limited, and a dedicated forecasting system specifically tailored for the South China Sea (SCS) region has yet to be reported. This study proposes a novel forecasting system utilizing U-Net and ConvLSTM models to predict MHWs in the SCS. Specifically, the U-Net model is used to forecast the intensity of MHWs, while the ConvLSTM model is employed to predict the probability of their occurrence. The indication of an MHW relies on both the intensity forecasted by the U-Net model exceeding threshold T and the occurrence probability predicted by the ConvLSTM model surpassing threshold P. Incorporating sensitivity analysis, optimal thresholds for T are determined as 0.9 °C, 0.8 °C, 1.0 °C, and 1.0 °C for 1-, 3-, 5-, and 7-day forecast lead times, respectively. Similarly, optimal thresholds for P are identified as 0.29, 0.30, 0.20, and 0.28. Employing these thresholds yields the highest forecast accuracy rates of 0.92, 0.89, 0.88, and 0.87 for the corresponding forecast lead times. This innovative approach gives better predictions of MHWs in the SCS, providing invaluable reference information for marine management authorities to make well-informed decisions and issue timely MHW warnings.

Mesoscale eddies are important to ocean circulation due to their roles in the transport of mass, energy, and heat. This study employs a combination of data sources to initiate a statistical analysis of eddy spatiotemporal characteristics in the Bay of Bengal (BOB) to elucidate the sea surface and vertical structures of the eddies and their impacts on sea surface chlorophyll (Chl) distributions. The results suggest that 1237 cyclonic eddies (CEs) and 1121 anticyclonic eddies (AEs) were detected in 26 years. The number of two eddy polarities was almost the same, and most of them spread to the west or southwest direction. The vertical change of temperature (T) and salinity (S) caused by the eddies is studied and the anomalous eddies, i.e., a CE (AE) eddy with warm (cold) water at the center, are mainly distributed on the northeast side of the Island of Sri Lanka. Furthermore, CEs are found to increase Chl concentration in the surrounding sea by approximately 11.15%, while AEs decrease concentrations also by approximately 11.25%. Changes in Chl concentrations occur most rapidly during the mature and intensification eddy phases. Observations also indicate that the strong local current and wind fields are the primary mechanisms in eddy generation.

Using satellite altimetry sea surface height anomalies (SSHA) and Argo profiles, we investigated eddy’s statistical characteristics, 3-D structures, eddy-induced physical parameter changes, and heat/freshwater transports in the South China Sea (SCS). In total, 31,744 cyclonic eddies (CEs, snapshot) and 29,324 anticyclonic eddies (AEs) were detected in the SCS between 1 January 2005 and 31 December 2016. The composite analysis has uncovered that changes in physical parameters modulated by eddies are mainly confined to the upper 400 m. The maximum change of temperature (T), salinity (S) and potential density (σ_θ) within the composite CE reaches −1.5 °C at about 70 m, 0.1 psu at about 50 m, and 0.5 kg m⁻³ at about 60 m, respectively. In contrast, the maximum change of T, S and σ_θ in the composite AE reaches 1.6 °C (about 110 m), −0.1 psu (about 70 m), and −0.5 kg m⁻³ (about 90 m), respectively. The maximum swirl velocity within the composite CE and AE reaches 0.3 m s⁻¹. The zonal freshwater transport induced by CEs and AEs is (373.6 ± 9.7)×10³ m³ s⁻¹ and (384.2 ± 10.8)×10³ m³ s⁻¹, respectively, contributing up to (8.5 ± 0.2)% and (8.7 ± 0.2)% of the annual mean transport through the Luzon Strait.