Ocean Modelling最新文献

Ocean general circulation models at the eddy-permitting regime are known to under-resolve the mesoscale eddy activity and associated eddy-mean interaction. Under-resolving the mesoscale eddy field has consequences for the resulting mean state, affecting the modelled ocean circulation and biogeochemical responses, and impacting the quality of climate projections. There is an ongoing debate on whether and how a parameterisation should be utilised in the eddy-permitting regime. Focusing on the Gent–McWilliams (GM) based parameterisations, it is known that, on the one hand, not utilising a parameterisation leads to insufficient eddy feedback and results in biases. On the other hand, utilising a parameterisation leads to double-counting of the eddy feedback, and introduces other biases. A recently proposed approach, known as splitting, modifies the way GM-based schemes are applied in eddy-permitting regimes, and has been demonstrated to be effective in an idealised Southern Ocean channel model. In this work, we evaluate whether the splitting approach can lead to improvements in the physical and biogeochemical responses in an idealised double gyre model. Compared with a high resolution mesoscale eddy resolving model truth, the use of the GM-based GEOMETRIC parameterisation together with splitting in the eddy-permitting regime leads to broad improvements in the control pre-industrial scenario and an idealised climate change scenario, over models with and models without the GM-based GEOMETRIC parameterisation active. While there are still some deficiencies, particularly in the subtropical region where the transport is too weak and may need momentum re-injection to reduce the biases, the present work provides further evidence in support of using the splitting procedure together with a GM-based parameterisation in ocean general circulation models at eddy-permitting resolutions.

The ocean is a key player in the Earth's climate, absorbing heat and carbon dioxide from the atmosphere. The ocean's temperature and salinity are influenced by climate change, which can significantly impact marine ecosystems. Reliable data sets of atmospheric and oceanographic parameters are of special interest in coastal productive areas to adequately monitor their variability at regional and local levels. It is therefore essential to continue monitoring and studying the ocean to develop effective mitigation and adaptation strategies. In this context, the main aim of this study is to identify the Earth System Models (ESMs) from the Coupled Model Intercomparison Project 6 (CMIP6) that best capture the variability of water temperature, salinity, and wind speed along the continental Spanish coasts, using them to estimate future impacts in these regions. To achieve this, a multifaceted approach is used, encompassing a historical (2000−2014) assessment comparing ESM outputs to in situ observations from Puertos del Estado (PdE) oceanographic buoys, an examination of the present period (2015−2022) under three IPCC scenarios (SSP1−2.6, SSP2−4.5, and SSP5−8.5), and a projection of future (2023−2100) trends using the same emission scenarios. Results showed that the ESMs from CMIP6 can reproduce the historical patterns of meteo-oceanographic properties, rendering them valuable tools for climate change studies. In the future (2100), considering the most pessimistic scenario (SSP5−8.5), the water temperature may increase by 2.8°C, salinity may decrease by -1.6, and wind speed may decrease by -0.4 m·s⁻¹. These projected changes can significantly impact the Spanish coasts, jeopardizing the growth, reproduction, survival, abundance, and distribution of some marine species.

The ocean surface albedo (OSA) is an important parameter in ocean and climate models for air-sea heat flux calculations. Current OSA schemes are either too simple, making them only suitable for clear sky conditions, or too complex, because they depend on parameters that are not often measured in conventional ocean observations. Using radiation observations at a fixed offshore platform, we propose a simple but effective parameterization scheme of OSA, in which the broadband OSA is an analytical function of both the solar zenith angle and atmospheric transparency. It depends only on the downward shortwave radiation measured at the ocean surface and applies to all sky conditions. During our 15-month radiation observations, the correlation coefficient between the calculated OSA and the observations reached 0.90 for all skies, and the root mean square deviation was 0.0130. Three other OSA observation datasets are also introduced to verify this scheme.

An ensemble of eddy-rich North Atlantic simulations is analyzed, providing estimates of eddy kinetic energy (EKE) wavenumber spectra and spectral budgets below the mixed layer where energy input from surface convection and wind stress are negligible. A wavelet transform technique is used to estimate a spatially localized ‘pseudo-Fourier’ spectrum (Uchidaet al., 2023b), permitting comparisons to be made between spectra at different locations in a highly inhomogeneous and anisotropic environment. The EKE spectra tend to be stable in time but the spectral budgets are highly time dependent. We find evidence of a Gulf Stream imprint on the near Gulf Stream eddy field appearing as enhanced levels of EKE in the (nominally) North–South direction relative to the East–West direction. Surprisingly, this signature of anisotropy holds into the quiescent interior with a tendency of the orientation aligned with maximum EKE being associated with shallower spectral slopes and elevated levels of inverse EKE cascade. Conversely, the angle associated with minimum EKE is aligned with a steeper spectral slope and forward cascade of EKE. Our results also indicate that vertical motion non-negligibly affects the direction of EKE cascade. A summary conclusion is that the spectral characteristics of eddies in the wind-driven gyre below the mixed layer where submesoscale dynamics are expected to be weak tend to diverge from expectations built on inertial-range assumptions, which are stationary in time and horizontally isotropic in space.

The accurate prediction of wave height attenuation due to vegetation is crucial for designing effective and efficient natural and nature-based solutions for flood mitigation, shoreline protection, and coastal ecosystem preservation. Central to these predictions is the estimation of the vegetation drag coefficient (Cd). The present study undertakes a comprehensive evaluation of three distinct methodologies for estimating the drag coefficient: traditional manual calibration, calibration using a novel application of state-of-the-art metaheuristic optimization algorithms, and the integration of an empirical bulk drag coefficient formula (Tanino and Nepf, 2008) into the XBeach non-hydrostatic wave model. These methodologies were tested using a series of existing laboratory experiments involving nearshore vegetation on a sloping beach. A key innovation of the study is the first application of metaheuristic optimization algorithms for calibrating the drag coefficient, which enables efficient automated searches to identify optimal values aligning with measurements. We found that the optimization algorithms rapidly converge to precise drag coefficients, enhancing accuracy and overcoming limitations in manual calibration which can be laborious and inconsistent. While the integrated empirical formula also demonstrates reasonable performance, the optimization approach exemplifies the potential of computational techniques to transform traditional practices of model calibration. Comparing these strategies provides a framework to determine effective methodology based on constraints in determining the vegetation drag coefficient.

Random and time-varying effects are important factors for statistical analysis of wave characteristic variables, including the significant wave height and spectral peak frequency. This paper proposes a statistical analysis method for the accurate statistical analysis of the state of the ocean. Several common distributions are applied as candidates for describing a specific variable, denoted as the marginal distribution. The joint distribution for the wave characteristic variables is constructed using copula functions based on the marginal distributions. The probability and unknown parameters of the marginal distributions are then determined by fully Bayesian inference. The best-fitting marginal distribution is selected based on the posterior probabilities of the candidates. Then, unknown parameters of the candidate copula functions are estimated by maximum likelihood estimation. The best-fitting copula function is selected based on Akaike information criterion, root mean squared error and Nash Sutcliffe efficiency. The proposed method is verified using the National Data Buoy Center dataset for 2019. However, this dataset, collected from a network of almost 100 moored buoys and Coastal-Marine Automated Network (CMAN) stations, contains incomplete data. The results reveal that the best-fitting marginal distribution and copula function may vary with the month. The average and maximum values of the improved RMSE using the proposed method are only 0.0064 and 0.0187, respectively. This indicates the high accuracy of the proposed method for the statistical analysis of wave states even though missing some data.

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.