Ocean Modelling最新文献

The Hong Kong Observatory has been running an Operational Marine Forecasting System (OMFS) adapted from the Regional Ocean Modelling System (ROMS) coupled with the WaveWatch III and SWAN wave models to provide wave, current and sea temperature forecasts up to 144 h twice a day since December 2021. To facilitate users’ interpretation of model forecasts of significant wave height and current speed in coastal predictions and open seas which are of particular significance in high wind situations, model forecasts were validated against moored buoy observations and wave recorder measurements near the shores of Hong Kong and drifting buoy data over the South China Sea, as well as Mercator Ocean model reanalysis in 2022. The validation results showed that the wave forecasts generally agreed well with the buoy observations with coefficient of determination (R²) of around 0.7 and root-mean-square error (RMSE) of less than 0.2 m up to 72 h ahead. The R² for sea current forecasts ranged between 0.4 and 0.6, and the RMSE was around 8 to 11 cm/s in near shores up to T + 144 forecast hours. Validation against drifting buoy demonstrated that the trend of current forecasts generally agreed well with the measurements. RMSE of surface current forecasts over open seas ranged from 19 cm/s for 24-hour forecast to around 30 cm/s for 144-hour forecast when compared against Mercator Ocean reanalysis. Results from the current downscaling approach could serve as a benchmark reference for HKO to enhance OMFS in the future. In this paper, applications of model forecasts in the provision of marine weather services in Hong Kong are also introduced.

During hurricanes, coupled wave-circulation models are critical tools for public safety. The standard approach is to use a high fidelity circulation model coupled with a wave model that uses the most advanced source terms. As a result, the models can be computationally expensive and so this study investigates the potential consequences of using simplified (reduced order) source terms within the wave model component of the coupled wave-circulation model. The trade-off between run time and accuracy with respect to observations is quantified for a set of two storms that impacted the Gulf of Mexico, Hurricane Ike and Hurricane Ida. Water surface elevations as well as wave statistics (significant wave height, peak period, and mean wave direction) are compared to observations. The usage of the reduced order source terms yielded significant savings in computational cost. Additionally, relatively low amounts of additional error with respect to observations during the simulations with reduced order source terms are observed in our computational experiments. However, large changes in global model outputs of the wave statistics were observed based on the choice of source terms particularly near the track of each hurricane.

Seasonal water circulation and residence times in the large (150 km²) and shallow (1.3 m average dry season depth) Nokoué Lagoon (Benin) are analyzed by means of numerical simulations using the three-dimensional SYMPHONIE model. The average circulation during the four primary hydrological periods throughout the year are studied in detail. Despite the lagoon's shallowness, significant disparities between surface and bottom conditions are observed. During the flood season (September-November), substantial river inflow (∼1200 m³/s) leads to nearly barotropic currents (∼7 cm/s), ‘directly’ linking rivers to the Atlantic Ocean. Rapid flushing results in short water residence times ranging from 3 to 16 days, with freshwater inflow and winds driving lagoon dynamics. During the salinization period (December-January) the circulation transforms into an estuarine pattern, characterized by surface water exiting and oceanic water entering the lagoon at the bottom. Average currents (∼2 cm/s) and recirculation cells are relatively weak, resulting in a prolonged residence time of approximately 4 months. Circulation during this time is dominated by tides, the ocean-lagoon salinity gradient, wind, and river discharge (∼100 m³/s). During low-water months (February to June), minimal river inflow and low lagoon water-levels prevail. Predominant southwest winds generate a small-scale circulation (∼3 cm/s) with a complex pattern of multiple recirculation and retention cells. Residence times vary from 1 to 4 months, declining from February to June. During the lagoon's desalination season (July-August), increasing river inflows again establish a direct river-ocean connection, and average residence times reduce to ∼20 days. Notably, a critical river discharge threshold (∼50-100 m³/s) is identified, beyond which the lagoon empties within days. This study highlights how wind-driven circulation between December and June can trap water along with potential pollutants, while river inflows, tides, and the ocean-lagoon salinity gradient facilitate water discharge. Additionally, it explores the differences between residence and flushing times, as well as some of the limitations identified in the simulations used.

Antarctic sea ice predictions are becoming increasingly important scientifically and operationally due to climate change and increased human activities in the region. Conventional numerical models typically require extensive computational resources and exhibit limited predictive skill on the subseasonal-to-seasonal scale. In this study, a convolutional long short-term memory (ConvLSTM) deep neural network is constructed to predict the 60-day future Antarctic sea ice evolution using only satellite-derived sea ice concentration (SIC) from 1989 to 2016. The network is skillful for approximately one month in predicting the daily spatial distribution of Antarctic SIC between 2018 and 2022, with the best predictive skill found in austral autumn (MAM) and winter (JJA). ConvLSTM also performs well in real-time prediction in February and September when the Antarctic sea ice extent (SIE) reaches the seasonal maximum and minimum, with the monthly mean SIE error mostly below 0.2 million km². The results suggest substantial potential for applying machine learning techniques for skillful Antarctic sea ice prediction.

Oceanic mesoscale eddies play an important role in transports of heat, freshwater, mass in the ocean, therefore understanding three-dimensional structure of oceanic eddies is of significance to climate study and oceanic applications. However, detection of three-dimensional (3D) structures is a big challenge though many algorithms of sea surface 2D eddy detection are developed. In this study, we present a novel approach by using 3D U-Net residual architecture (3D-U-Res-Net) to identify 3D structure of oceanic eddies. The sensitivity tests to input variables are conducted to optimalize the input setting. Trained by 3D eddy data provided by a kinetic eddy detection method, the AI-based method can identify different kinds of eddy vertical structures and moreover can dig out more eddy information in deeper layers. This study has significant implications for the further application of the AI-based algorithm in oceanic study.

This paper proposes a new model to study future coastal maritime climate under climate change context. This new model combines statistical analysis, Monte Carlo simulations and Artificial Neural Networks (ANNs). Statistical analysis and Monte Carlo simulations are used to extrapolate future wave climate under climate change context at a regional level and ANNs are used to propagate these projected sea states obtained in deep water to the coast. The use of ANNs allows for the utilization of large amounts of data at a very low computational cost, and the use of Monte Carlo simulations enables the generation of future climate change projections at a regional level. The combination of the two methodologies results in a very accurate (MSE of 0.02 m and 1 s) and computationally inexpensive hybrid model that allows projections of coastal maritime climate considering climate change. This new methodology has been validated and applied in the Western Mediterranean for the long-term regime and for extreme events, obtaining increases in extreme events up to 1.5 m in wave height and up to 1.8 s in wave period by 2050.

Surface waves are extremely important in a large variety of oceanographic applications and thus, the study of their spatiotemporal characteristics remains crucial. This study analyzes waves in the Caribbean Sea (CS) and western Atlantic Ocean (AO) using a high-resolution (HR) Simulating WAves Nearshore model validated with buoy observations and paired with a HR bathymetric dataset from 2010 – 2019. Island sheltering effects are examined but special attention is given to these effects under Hurricane Dorian in The Bahamas using observations from the China-France Oceanographic Satellite. Results illustrate that wave heights within the CS fluctuated with Caribbean Low-Level Jet activity, but a different wave regime exists within the AO. While wind waves overwhelmingly dominate the wave field and this is true even in the AO, surprisingly, the contribution of swell in the central CS was equal to one site in the AO. Possibly, due to interaction with the shallow Nicaraguan Rise, wave heights were strongly (depth-induced) refracted nearly 45°, a feature unseen in previous research using coarse bathymetric datasets. Island sheltering effects were pervasive and were naturally most pronounced under hurricane conditions. Crucially, New Providence in The Bahamas is vulnerable to hurricane-forced waves funneled through the Grand Bahama and Northeastern Providence Channels.

The areas around estuaries are typically densely populated and economically developed. Therefore, robust flood risk assessment in these areas is critical. One of the key elements of flood risk assessment is the accurate prediction of estuarine water levels. However, the nonlinear interactions between riverine (i.e., upstream river discharge) and marine (i.e., tides) forces complicate the prediction of estuarine water levels. Traditional physics-based and data-driven models have made significant progress in predicting estuarine water levels, but they require upstream river discharge data as inputs. Considering the lack of such data, the development of new approaches is crucial. This study investigated a machine-learning-based light gradient boosting machine (LightGBM) framework for predicting estuarine water levels using historical water levels as the only inputs. Two prediction models based on the LightGBM framework, denoted as LightGBM1 and LightGBM2, are developed. The LightGBM1 model constructs only a single regression model and uses a recursive approach to generate multidimensional outputs. The LightGBM2 model constructs multiple regression models between the same inputs and outputs in each dimension. The LightGBM1 and LightGBM2 models were applied to the Yangtze estuary as a test case. The results demonstrate that both models are effective at predicting short-term (within 48 hours) estuarine water levels, but the statistical performance of LightGBM2 is better overall. For 24-hour prediction, the root-mean-squared errors of the LightGBM1 and LightGBM2 models are in the ranges of 0.14–0.17 m and 0.12–0.15 m, respectively.

Lake Erie has been negatively impacted by multiple stressors, including nutrient enrichment and climate change, that have exacerbated eutrophication and harmful algal blooms. Management of these long-term water quality problems requires numerical models that can be run over years to decades. The three-dimensional hydrodynamics and biogeochemistry models applied to date, however, have not been tested for continuous runs longer than one year and have not been shown to accurately reproduce seasonal variation in phytoplankton species composition (e.g., the development of harmful algal blooms) over decadal timescales. We simulated the three-dimensional nutrient and phytoplankton concentrations in western Lake Erie continuously from 2002 to 2014. Using a single parameter set, we were able to reproduce both seasonal and inter-annual variation in phytoplankton species composition. The model qualitatively reproduced the observed seasonal succession (i.e., variation in phytoplankton species composition), including the spring diatom bloom and late summer cyanobacterial growth. This study demonstrates that three-dimensional models can be applied for multi-year simulations of nutrients and phytoplankton to inform large lake research and management.

This research evaluates the performance of CAS-LICOM3 (Chinese Academy of Science, State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics/Institute of Atmospheric Physics (LASG/IAP) Climate system Ocean Model, version 3) in simulating global coherent mesoscale eddies by comparison to satellite altimeter observations. The simulations of westward and eastward propagating eddies (WPEs and EPEs) and cyclonic and anticyclonic eddies (CEs and AEs) are separately analyzed. The results demonstrate that the simulated spatial-temporal variabilities in global mesoscale eddies agree roughly with the satellite observations. CAS-LICOM3 also reproduces the distinctive features between WPEs and EPEs or between CEs and AEs. However, some systematic biases are found. Globally, CAS-LICOM3 simulates a less frequent and weaker mesoscale eddy field than is observed. WPEs contribute more to these global biases than do EPEs. EPEs are relatively better reproduced than WPEs, exhibiting smaller underestimations and even overestimations in the energetic western boundary current and Antarctic circumpolar current regions. The simulation results for CEs resemble those of AEs, but AEs are comparatively less biased than CEs. These findings provide a basis for improving low-resolution and eddy-resolving ocean general circulation models (OGCMs) and developing submesoscale-resolving OGCMs.