Ocean Modelling最新文献

In recent years, Portugal's coastal regions have experienced an increase in the frequency and intensity of severe weather events, including tropical cyclones and extratropical storms. This paper presents an analysis of Hurricane Leslie(2018)'s impact on Portugal, with a specific focus on the complex and often underestimated meteotsunami phenomena accompanying the storm system. Our analysis examines data collected from multiple sources, and employs advanced numerical simulations, integrated within the GeoClaw framework. These simulations encompass both storm surge and meteotsunami effects. One of the findings is the significant role played by meteotsunamis in amplifying coastal sea levels during extreme weather events. The observed sea-level fluctuations closely align with the combined surge-meteotsunami simulations, emphasizing the importance of considering these high-frequency phenomena in coastal hazard assessments.

Due to strong non-linearity, ocean surface gravity waves are difficult to directly and accurately predict, despite their importance for a wide range of coastal, nearshore, and offshore activities. To minimize forecast errors, a hybrid combined improved empirical wavelet transform decomposition (IEWT) and long-short term memory network (LSTM) model has been proposed. Data from National Data Buoy Center buoys deployed in the North Pacific Ocean are taken as an example to verify the models. Wave forecasts using the LSTM, EWT-LSTM, and IWET-LSTM models are compared with the observations at 6, 12, 18, 24 and 48 h forecast windows. Consequently, IEWT-LSTM is superior to EWT-LSTM or LSTM models, especially for larger waves at longer long forecast windows.

Internal solitary waves (ISWs) play a crucial role in the development of various physical and biological processes, and numerous high-precision two-dimensional or three-dimensional numerical models have been developed to simulate the generation and propagation processes of ISWs. However, these numerical models, especially when simulating the interaction between ISWs and ocean circulation, require substantial computational resources. This burden can make it challenging to apply them in real-time or short-term forecasting scenarios. In this study, we propose a new numerical model for ISWs by combining traditional one-dimensional ISW theory with wave refraction theory. The proposed model resolves the issues of ray crossing and divergence, which are commonly encountered in traditional refraction models, by employing equally spaced grids along the wave crest line. As a result, this model is capable of simulating the far-field propagation of ISWs. This model enables rapid prediction of the vertical structure and wave crest morphology of ISWs in specific current fields and at given time frames, and it is utilized to investigate the characteristics and propagation of ISWs generated by the nonlinear steepening of internal tide (IT) in the South China Sea. Comparative analysis with satellite imagery demonstrates the model's accurate representation of ISW processes and phenomena, such as wave crest line discontinuities, diffraction, and wave‒wave interactions when passing through Dongsha Island. Furthermore, propagation time estimates based on this model have errors of ±0.98 h (1σ) over which the ISWs are observed by a mooring system, and the average time difference is 0.81 h

Significant Wave Height (SWH) is crucial in many aspect of ocean engineering. The accurate prediction of SWH has therefore been of immense practical value. Recently, Artificial Intelligence (AI) time series prediction methods have been widely used for single-point short-term SWH time-series forecasting, resulting in many AI-based models claiming to achieve good results. However, the extent to which these complex AI models can outperform traditional methods has largely been overlooked. This study compared five different models - AutoRegressive (AR), eXtreme Gradient Boosting (XGB), Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and WaveNet - for their performance on SWH time series prediction at 16 buoy locations. Surprisingly, the results suggest that the differences of performance among different models are negligible, indicating that all these AI models have only “learned” the linear auto-regression from the data. Additionally, we noticed that many recent studies used signal decomposition method for such time series prediction, and most of them decomposed the test sets, which is WRONG.

In the previous study, the authors have produced an eddy-resolving ocean ensemble analysis product called the local ensemble transform Kalman filter (LETKF)-based ocean research analysis (LORA) over the western North Pacific and Maritime Continent regions using an ocean data assimilation system driven by the Japanese operational atmospheric reanalysis dataset known as the JRA-55. However, the LORA includes warm biases in sea surface temperatures (SSTs) in coastal regions during the boreal winter. In this study, we perform sensitivity experiments with atmospheric forcing using an ocean forcing dataset known as the JRA55-do, which adjusts the JRA-55 to high-quality reference datasets to reduce biases and uncertainties. The results show that the nearshore warm SST biases are significantly improved by the JRA55-do. During the boreal autumn, the improvement comes from mainly two factors: (i) enhancement of surface cooling by latent heat releases caused by removing contamination of weak winds at the land grid cells, and (ii) weakening surface heating by downward shortwave radiation through the adjustment in the JRA55-do.

During the boreal winter, enhanced cooling by the analysis increments suppresses the growth of the warm SST biases when the JRA55-do is used. However, if the JRA-55 dataset is used, the adaptive observation error inflation (AOEI) scheme acts negatively to keep the nearshore SST biases in winter. Based on the innovation statistics, the AOEI inflates the observation errors when the differences between the squared observation-minus-forecast innovations and the squared forecast ensemble spreads are larger than the prescribed observation error variance, and improves the accuracy in the open ocean, especially around the frontal regions. However, when substantial warm SST biases are formed in the previous season, AOEI's observation error inflation makes the analysis increments smaller and cannot suppress the warm biases.

We also validate the analysis accuracy using various data such as sea surface height and horizontal velocities and find that the JRA55-do has significant advantages. Therefore, continuous maintenance and development of ocean forcing datasets are essential for ocean modeling and data assimilation.

By analyzing an Arctic ice-ocean coupled simulation, we study the linkage between wintertime sea ice drift and atmospheric circulation, and interpret the driving force terms in the sea ice dynamic equation. Sea ice drift anomaly is featured by an anticyclonic (cyclonic) gyre when regulated by negative (positive) phase of Arctic Oscillation with positive (negative) phase of Arctic Dipole, and a quasi-meridional stream from Chukchi-Beaufort (Barents-Kara) Seas to Barents-Kara (Chukchi-Beaufort) Seas when regulated by positive (negative) phase of Arctic Oscillation with positive (negative) phase of Arctic Dipole. Sea ice drift anomaly, when regulated by the mode alone, resembles spatial pattern of leading atmospheric mode. Decomposing sea ice dynamical equation shows that wind-ice stress dominates sea ice drift in areas away from islands and continental coastlines, ocean-ice stress acts as a resistant power to partly cancel the wind-ice stress in these areas, while in the coastal areas such as the thick multiyear ice zone north of the Canadian Arctic Archipelago, the wind-ice and ocean-ice stresses are small, the balance exists between sea surface height potential gradient and internal ice stress divergence. Developing more sophisticated internal ice stress expression in ice model is of great important to correctly project future sea ice change for the ice modeling community.

The present numerical study builds on Ekman (1905)’s work in surface boundary layer and extends the boundary value problem to overcome some of its limitations. Previous studies addressed model’s limitations by assuming that deviations from observations are usually ascribed to different eddy viscosity shapes, but seldom to the presence of baroclinic pressure gradients and shallow seas, which are the mainstays of this work. Improved solutions in the ocean boundary layer are obtained considering both depth-dependent wind-induced eddy viscosity and horizontal density gradients, ranging from well-mixed to highly-stratified conditions in a finite-depth ocean. High-order numerical solutions extend those in previous analytical and numerical works in the literature and widens the parameter space analyzed. Remarkably, the current profiles are obtained without ambiguity as a truly superposition of a geostrophic and a ageostrophic terms. Results indicate that, for a vertically-uniform eddy viscosity without density gradients and in shallow waters, currents are practically aligned with wind. As depth increases, misalignment between currents and wind increases and the complexity of the vertical structure increases. At large depths, Ekman’s values are attained, i.e., deflection angles relative to wind direction, ${\theta }_W$, are ${\theta }_S-{\theta }_W=-45^{\circ }$ at the surface, where the current is maximum, and ${\theta }_T-{\theta }_W=-90°$ for the depth-integrated transport (negative for deflections to the right in the Northern Hemisphere). These features remain regardless of the magnitude of the eddy-viscosity. For non-uniform eddy viscosity, ${\theta }_S-{\theta }_W$ decreases from $-45^{\circ }$ up to $-90^{\circ }$ from low to high stratification level, respectively, whereas ${\theta }_T-{\theta }_W$ is rather insensitive (