Variation of Internal Tides on the Continental Slope of the Southeastern East China Sea

The semidiurnal internal tides (ITs) on the continental slope of the southeastern East China Sea (ECS) exhibited abrupt enhancement in November of 2017. This enhancement resulted from the intensification of the coherent semidiurnal ITs. Coherent and incoherent semidiurnal ITs had a comparative energy contribution in October; however, coherent semidiurnal ITs dominated with a variance contribution of 90% in November. The variance contribution of vertical modes of the semidiurnal ITs varied between October and November, and the mode with most variance contribution changed from the second mode to the first mode. Altimeter data and the observed background currents indicated that the Kuroshio mainstream meandered and abruptly intruded into the ECS in November. The upper layer background currents were significantly related to the kinetic energy of the semidiurnal ITs, and the correlation coefficient between them reached 0.81. The frequent occurrences of the Kuroshio intrusion have suggested that the ITs in the ECS are susceptible to the modulation of the Kuroshio current. Numerical modeling and predication of ITs should consider the meander of the Kuroshio mainstream.

From 1/4° to 1/8°: Influence of Spatial Resolution on Eddy Detection Using Altimeter Data

A substantial portion of ocean eddies, especially small ones, may be missed due to insufficient spatial or temporal sampling by satellite altimetry. In order to illustrate the influence of spatial resolution on eddy detection, this study provides a comparison of eddy identification, tracking, and analysis between two sets of merged altimeter data with spatial resolutions of 1/4° and 1/8°. One main study area (the Mediterranean Sea), and three confirmatory areas (the South-China Sea, the North-West Pacific, and the South-East Pacific) are chosen. The results show that the number and density of eddies captured by the 1/8° data are about twice as much as those captured by the 1/4° data, while the ratios of corresponding eddy parameters, i.e., radius, amplitude, (eddy kinetic energy (EKE)) are about 0.6–0.8 (1.3) for the two datasets (1/8° ÷ 1/4°). Long-term eddy tracking (1993–2018) is conducted in the Mediterranean Sea, indicating that the improvement in spatial resolution will increase the observed values of both the lifetime and the propagation distance of robust eddies. The number of eddies identified using the 1/4° data only accounts for ~30% to 60% of those identified using the 1/8° data. However, for eddies that can be detected using the two datasets, ~5% to 10% present errors (i.e., confusion). In comparison between the four regions, we find that for the enclosed seas with complex conditions, the increase in spatial resolution may lead to more significant improvements in the efficiency and accuracy of eddy detection.

Feature Comparison of Two Mesoscale Eddy Datasets Based on Satellite Altimeter Data

Although a variety of ocean mesoscale eddy datasets are available for researchers to study eddy properties throughout the global ocean, subtle differences in how these datasets are produced often lead to large differences between one another. This study compares the Global Ocean Mesoscale Eddy Atmospheric-Oceanic-Biological interaction Observational Dataset (GOMEAD) with the well-recognized Mesoscale Eddy Trajectory Atlas in four regions with strong eddy activity: the Northwest Pacific Subtropical Front (SF), Kuroshio Extension (KE), South China Sea (SCS), and California Coastal Current (CC), and assesses the relative advantages and disadvantages of each. It was identified that while there is a slight difference in the total number of eddies detected in each dataset, the frequency distribution of eddy radii presents a right-skewed normal distribution, tending towards larger radii eddies, and there are more short- than long-lived eddies. Interestingly, the total number of GOMEAD eddies is 8% smaller than in the META dataset and this is most likely caused by the GOMEAD dataset’s underestimation of total eddy numbers and lifespans due to their presence near islands, and the tendency to eliminate eddies from its database if their radii are too small to be adequately detected. By contrast, the META dataset, due to tracking jumps in detecting eddies, may misidentify two eddies as a single eddy, reducing total number of eddies detected. Additionally, because the META dataset is reliant on satellite observations of sea surface level anomalies (SLAs), when SLAs are weak, the META dataset struggles to detect eddies. The GOMEAD dataset, by contrast, is reliant on applying vector geometry to detect and track eddies, and thus, is largely insulated from this problem. Thus, although both datasets are excellent in detecting and characterizing eddies, users should use the GOMEAD dataset when the region of interest is far from islands or when SLAs are weak but use the META dataset if the region of interest is populated by islands, or if SLAs are intense.

The Impact of Sea-Level Rise on Urban Properties in Tampa Due to Climate Change

Fast urbanization produces a large and growing population in coastal areas. However, the increasing rise in sea levels, one of the most impacts of global warming, makes coastal communities much more vulnerable to flooding than before. While most existing work focuses on understanding the large-scale impacts of sea-level rise, this paper investigates parcel-level property impacts, using a specific coastal city, Tampa, Florida, USA, as an empirical study. This research adopts a spatial-temporal analysis method to identify locations of flooded properties and their costs over a future period. A corrected sea-level rise model based on satellite altimeter data is first used to predict future global mean sea levels. Based on high-resolution LiDAR digital elevation data and property maps, properties to be flooded are identified to evaluate property damage cost. This empirical analysis provides deep understanding of potential flooding risks for individual properties with detailed spatial information, including residential, commercial, industrial, agriculture, and governmental buildings, at a fine spatial scale under three different levels of global warming. The flooded property maps not only help residents to choose location of their properties, but also enable local governments to prevent potential sea-level rising risks for better urban planning. Both spatial and temporal analyses can be easily applied by researchers or governments to other coastal cities for sea-level rise- and climate change-related urban planning and management.

Features of Intraseasonal Variability Observed in the Upper-Layer Current in the Northern South China Sea

This study reveals the features of the strong intraseasonal variability (ISV) of the upper-layer current in the northern South China Sea (NSCS) based on four long-time mooring observations and altimeter data. The ISV of the upper-layer current in the NSCS consists of two dominant periods of 10–65 days and 65–110 days. The ISV with period of 10–65 days is much strong in the Luzon Strait and decays rapidly westward along the slope. The ISV with the period of 65–110 days is relatively strong along the slope with two high cores at 115 and 119°E, whereas it is weak in the Luzon Strait. The 10–65-day ISV can propagate directly from the western Pacific into the NSCS for most of the time. However, due to its long wavelength, the 65–110-day ISV propagates into the NSCS indirectly, possibly similar to the wave diffraction phenomenon. The spatial differences between the two main frequency bands are primarily due to the baroclinic and barotropic instabilities. The spatial distribution of the upper-layer ISV is closely associated with the mesoscale eddy radius of the NSCS. The eddy radius is directly proportional to the strength of 65–110-day ISV, but it is inversely proportional to the strength of 10–65-day ISV.

Quality of the Wind Wave Forecast in the Black Sea Including Storm Wave Analysis

This paper presents the results of wind wave forecasts for the Black Sea. Three different versions utilized were utilized: the WAVEWATCH III model with GFS 0.25 forcing on a regular grid, the WAVEWATCH III model with COSMO-RU07 forcing on a regular grid, and the SWAN model with COSMO-RU07 forcing on an unstructured grid. AltiKa satellite altimeter data were used to assess the quality of wind and wave forecasts for the period from 1 April to 31 December 2017. Wave height and wind speed forecast data were obtained with a lead time of up to 72 h. The presented models provide an adequate forecast in terms of modern wave modeling (a correlation coefficient of 0.8–0.9 and an RMSE of 0.25–0.3 m) when all statistics were analyzed. A clear improvement in the wave forecast quality with the high-resolution wind forecast COSMO-RU07 was not registered. The bias error did not exceed 0.5 m in an SWH range from 0 to 3 m. However, the bias sharply increased to −2 or −3 m for an SWH range of 3–4 m. Wave forecast quality assessments were conducted for several storm cases.

Data assimilation in a reduced gravity ocean model using ERS-l scatterometer and TOPEX altimeter data

This paper discusses import of ERS-1 scatterometer winds and assimilation of sea level variability data derived from TOPEX altimeter on the ocean model using adjoint approach. The model developed for the purpose is linear reduced gravity model for the north-western Indian ocean. Experiments have been done with forcing provided using ERS-l satellite scatterometer and analysed wind forcing provided by Florida State University (FSU). Impact on the model has been studied using the analysed wind stress as well as with ERS-l scatterometer-derived wind stress fields. The cost function has been defined as difference between the model derived sea level and altimeter observations. This misfit between model and observations has been minimised with the model equations as constraints. Assimilation has been done for 30 days using scatterometer wind forcing. It has been observed that assimilated sea level with scatterometer-derived wind forcing gives much better results in comparison to unassimilated sea level.