Ocean Dynamics最新文献

The Jakarta Bay Reclamation (JBR) is a long-term protection project to prevent flooding in Jakarta. This study examines the effect of the JBR on water levels using the Regional Ocean Model (ROMS) to measure both the residual water levels (non-astronomic tide) and the total water levels generated by tides and Typhoons Hagibis and Mitag in November 2007. The results show that the tidal range in Jakarta Bay increased after the JBR, reaching 22.4% at Bekasi. The most significant amplitude change is S2 for the principal constituents and MK3 for shallow water constituents. The JBR does not change the direction of the propagation for S2 and MK3 in the Jakarta Bay, but it does change the phase lag. In addition, the JBR affects water elevations caused by tides and typhoons, with increased elevations between 2.69 and 11.53 cm. Although the aims of the land reclamation as a potential engineering solution are to provide for long-term protection against flooding from the sea, during the worst conditions (e.g., spring tides with perigee and remote forcing from typhoons), land reclamation will actually increase total water levels and amplitude of tidal constituents.

Satellite radar altimeters (SA) have been providing ocean wind and wave measurements for over 35 years. These data have been used for modelling data assimilation, improving wind and wave climatology, and determining long-term trends of the oceanic wave parameters. Fixed observational sites (in situ locations), such as buoys, have provided reliable wave observations since the early 1970s. However, their positioning is inhomogeneous, mainly in the Northern Hemisphere, and only provides point measurements. SA significant wave height (SWH) measurements have been proven as accurate as in situ observations, particularly in the open ocean. Progress in coastal altimetry sensors, upgraded data corrections, and new extraction algorithms have recently improved the quality of SA measurements closer to the coast. This study evaluates the performance of 12 SA missions from 1985 to 2020, particularly in nearshore areas. The SA SWH along-track measurements are compared with observations from 402 in situ locations, distributed worldwide within 25 km of the coastline. Results indicate a slight overestimation from the 12 SA missions, mainly for lower sea states (under 2 m high) and closer to the coast (0 to 10 km). The Sentinel-3 mission showed the highest percentages of valid measurements near the coast and presented 72.66% of collocated in situ data. This SA mission has shown the best overall performance closer to the coast, with biases, correlation coefficient, and root-mean-squared error of 0.23 m, 0.85 m, and 0.50 m, respectively. SA undersampling in coastal areas is present and can lead to underestimation during extreme wave events. The cross-validation of the wave data in two regional analyses conducted during periods of severe wave conditions is evaluated for the new altimeters’ generation.

Abstract

Sea surface temperature (SST) is a key indicator of the global climate system and is directly related to marine and coastal ecosystems, weather conditions, and atmospheric events. Marine heat waves (MHWs), characterized by prolonged periods of high SST, affect significantly the oceanic water quality and thus, the local ecosystem, and marine and coastal activities. Given the anticipated increase of MHWs occurrences due to climate change, developing targeted strategies is needed to mitigate their impact. Accurate SST forecasting can significantly contribute to this cause and thus it comprises a crucial, yet challenging, task for the scientific community. Despite the wide variety of existing methods in the literature, the majority of them focus either on providing near-future SST forecasts (a few days until 1 month) or long-term predictions (decades to century) in climate scales based on hypothetical scenarios that need to be proven. In this work, we introduce a robust deep learning-based method for efficient SST forecasting of the interim future (1 year ahead) using high-resolution satellite-derived SST data. Our approach processes daily SST sequences lasting 1 year, along with five other relevant atmospheric variables, to predict the corresponding daily SST timeseries for the subsequent year. The novel method was deployed to accurately forecast SST over the northeastern Mediterranean Seas (Aegean, Ionian, Cretan Seas: AICS). Utilizing the effectiveness of well-established deep learning architectures, our method can provide accurate spatiotemporal predictions for multiple areas at once, without the need to be deployed separately at each sub-region. The modular design of the framework allows customization for different spatial and temporal resolutions according to use case requirements. The proposed model was trained and evaluated using available data from the AICS region over a 15-year time period (2008–2022). The results demonstrate the efficiency of our method in predicting SST variability, even for previously unseen data that are over 2 years in advance, in respect to the training set. The proposed methodology is a valuable tool that also can contribute to MHWs prediction.

Abstract

The modified nonlinear envelope equation of interfacial gravity-capillary waves in a two-layer fluid of infinite depths for broader bandwidth with a uniform velocity of the upper fluid is derived. The derivation is made from Zakharov’s integral equation by relaxing the narrow wave bandwidth restriction to make it more applicable for utilization of a realistic sea wave spectrum. From this equation instability regions are drawn in the perturbed wave number space. The modified equation limits the wave bandwidth of a uniform Stokes wave in an excellent agreement with the accurate numerical results. We have also drawn the growth rate of modulational instability for the case of pure capillary waves.

In this paper, a fusion model based on convolution and self-attention with multi-subimage input model (CNN-SA-MS) is proposed to estimate significant wave height (SWH) from shipborne X-band radar images. The model takes multiple radar subimages as input simultaneously, which not only improves the accuracy of SWH inversion by including more information, but also avoids the restriction of selecting a single subimage in the upwind direction and dependence on external devices for wind data provision. Based on the characteristics of radar images and computational efficiency considerations, this paper selects three radar subimages as the input for the model. The comparison data from buoys and ECMWF are used for training and testing. After averaging the results of 64 radar images, the root mean square error (RMSE) and correlation coefficient (CC) of the CNN-SA-MS model are 0.197 m and 0.903, respectively. The results show that the CNN-SA-MS model improves the accuracy and stability of SWH estimation compared to single-subimage CNN regression model. For the two time periods with significant discrepancies between radar data and ECMWF predictions, we introduce satellite altimeter information as a source of reference for evaluation. The resulting analysis indicates that the significant wave height estimates generated by CNN-SA-MS model are more reliable.

Ocean waves are generally a mix of wind sea and swell. Given the significant disparities in their impact on engineering, the separation of wind sea and swell is of great significance for marine research and engineering applications. This work focuses on studying the methods for separating wind sea and swell from directional wave spectra in finite-depth waters (i.e., the south coast of Sri Lanka). The error caused by deep-water dispersion relationship in the identification of wind sea using wave age (WA) criterion in finite-depth waters is revealed. The magnitude of error increases with decreasing water depth and higher wind speeds. Subsequently, the impact of WA thresholds on the partitioned results of wind sea and swell is examined, followed by a summary on the procedure determining an appropriate WA threshold. Finally, effort is devoted to studying the overshoot phenomenon (OP) criterion, which does not rely on wind data. Overall, the OP criterion performs consistently with the WA criterion. However, the generation and dissipation of OP require some time. Therefore, the OP criterion exhibits a lag in capturing the growing wind sea as well as the transition of the wind sea to a young swell. Misclassification of wind sea by the OP criterion further contaminates the bulk parameters of swell. Moreover, when the wind direction changes slowly, the delays of OP-based wind sea become negligible, leading to improved identification of wind sea and swell.

Abstract

This study examines the seasonal and intraseasonal modulation of near-inertial wind power associated with fluctuations in unidirectional wind speed in the Bay of Bengal (BoB). For that purpose, we use concurrent measurements of high-resolution in situ near-surface current and wind speed from six moorings in the BoB. It is found that the annual mean of near-inertial wind power in the BoB shows roughly similar magnitude (0.25–0.35 mW m⁻²) at all the mooring locations. However, in response to the seasonal evolution of monsoonal wind forcing, near-inertial wind power shows significant annual variability, with a maximum during summer (~ 0.4–0.5 mW m⁻²) and fall (~ 0.3–0.4 mW m⁻²) and a minimum during winter (~ 0.1 mW m⁻²) and spring (~ 0.2 mW m⁻²). In addition, it is also found that modulation of near-inertial wind power due to summer monsoon intraseasonal oscillation (MISO), such as its magnitude, reaches as large as ~ 1 mW m⁻² at the mooring in the northern BoB during phases 3–4 of MISO. Using a high vertical resolution of current profile data, the near-inertial kinetic energy (NIKE) budget in the mixed layer in the northern BoB shows good temporal correspondence with the magnitude of the rate of change of NIKE and near-inertial wind power, with a maximum magnitude of the rate of change of NIKE lags the wind power by 24 hr. The NIKE budget also indicates that a significant portion of near-inertial wind power dissipates in the mixed layer and rarely energises the depth regime underneath the mixed layer.

The primary aim of this research is to investigate how the presence of toxicity, stemming from phytoplankton, impacts fishing activities, catch levels, and financial returns. It is hypothesized that this toxicity arises when zooplankton accumulates harmful substances while consuming phytoplankton. To achieve this objective, we analyze a model resembling a prey-predator relationship involving phytoplankton. We examine the stable conditions in our model by utilizing eigenvalue analysis and calculate the optimal fishing effort that maximizes profitability for fishermen, employing the concept of generalized Nash equilibrium. Additionally, we explore the most effective harvesting strategy by applying Pontryagin’s maximum principle. In our numerical simulations, we identify the key variables that influence all economic aspects of the model, including fishing effort, catch levels, and benefits. Furthermore, we compare our results with findings from previous research.

Decadal variability in the ocean is an important indicator of climate system shifts and has considerable influences on marine ecosystems. We investigate the responses of decadal variability over the global ocean regions using nine CMIP6 models (BCC-CSM2-MR, CESM2-WACCM, CMCC-ESM2, EC-Earth3-Veg-LR, FGOAL-f3-L, INM-CM5-0, MIROC6, MPI-ESM1-2-LR, and NorESM2-MM). Our results show that climate models can capture the Pacific Decadal Oscillation, Tropical Pacific Decadal Variability, South Pacific Decadal Oscillation, and Atlantic Multidecadal Variability under present-day conditions. The ocean decadal variabilities are becoming weaker and their periods are decreasing, especially under the strong global warming scenario. However, there is a discrepancy between the Tropical Pacific Decadal Variability and the other three modes of climate variability. This might be caused by the nearly unchanged atmospheric forcing in the equatorial region, which is decreasing in the higher latitude regions.