Brine drainage from sea ice formation plays a critical role in ocean mixing and seasonal variations of halocline in polar oceans. The horizontal scale of brine drainage and its induced convection is much smaller than a climate model grid and a model tends to produce false ocean mixing when brine drainage is averaged over a grid cell. A two-column ocean grid (TCOG) scheme was implemented in the Community Earth System Model (CESM) using coupled sea ice-ocean model setting to explicitly solve the different vertical mixing in the two sub-columns of one model grid with and without brine rejection. The fraction of grid with brine rejection was tested to be equal to the lead fraction or a small constant number in a series of sensitivity model runs forced by the same atmospheric data from 1978 to 2009. The model results were compared to observations from 29 ice tethered profilers (ITP) in the Arctic Ocean Basin from 2004 to 2009. Compared with the control run using a regular ocean grid, the TCOG simulations showed consistent reduction of model errors in salinity and mixed layer depth (MLD). The model using a small constant fraction grid for brine rejection was found to produce the best model comparison with observations, indicating that the horizontal scale of the brine drainage is very small compared to the sea ice cover and even smaller than the lead fraction. Comparable to models using brine rejection parameterization schemes, TCOG achieved more improvements in salinity but similar in MLD.
In response to the Deepwater Horizon (DwH) oil spill event in 2010, the Naval Oceanographic Office deployed a nowcast-forecast system covering the Gulf of Mexico and adjacent Caribbean Sea that was designated Americas Seas, or AMSEAS, which is documented in this manuscript. The DwH disaster provided a challenge to the application of available ocean-forecast capabilities, and also generated a historically large observational dataset. AMSEAS was evaluated by four complementary efforts, each with somewhat different aims and approaches: a university research consortium within an Integrated Ocean Observing System (IOOS) testbed; a petroleum industry consortium, the Gulf of Mexico 3-D Operational Ocean Forecast System Pilot Prediction Project (GOMEX-PPP); a British Petroleum (BP) funded project at the Northern Gulf Institute in response to the oil spill; and the Navy itself. Validation metrics are presented in these different projects for water temperature and salinity profiles, sea surface wind, sea surface temperature, sea surface height, and volume transport, for different forecast time scales. The validation found certain geographic and time biases/errors, and small but systematic improvements relative to earlier regional and global modeling efforts. On the basis of these positive AMSEAS validation studies, an oil spill transport simulation was conducted using archived AMSEAS nowcasts to examine transport into the estuaries east of the Mississippi River. This effort captured the influences of Hurricane Alex and a non-tropical cyclone off the Louisiana coast, both of which pushed oil into the western Mississippi Sound, illustrating the importance of the atmospheric influence on oil spills such as DwH.
On June 4 and 17, 2011, two separate oil spill accidents occurred at platforms B and C of the Penglai 19-3 oilfield located in the Bohai Sea, China. Based on the initial oil spill locations detected from the first available Synthetic Aperture Radar (SAR) image acquired on June 11, 2011, we performed a numerical experiment to simulate the potential oil spill beaching area with the General NOAA Operational Modeling Environment (GNOME) model. The model was driven by ocean surface currents from an operational ocean model (Navy Coastal Ocean Model) and surface winds from operational scatterometer measurements (the Advanced Scatterometer). Under the forcing of wind and ocean currents, some of the oil spills reached land along the coast of Qinhuangdao within 12 days. The results also demonstrate that the ocean currents are likely to carry the remaining oil spills along the Bohai coast towards the northeast. The predicted oil spill beaching area was verified by reported in-situ measurements and former studies based on MODIS observations.
A three-dimensional hydrodynamic model is presented which combines a terrain-following vertical coordinate with a horizontally orthogonal curvilinear coordinate system to fit the complex bottom topography and coastlines near estuaries, continental shelves, and harbors. To solve the governing equations more efficiently, we improve the alternating direction implicit method, which is extensively used in the numerical modeling of horizontal two-dimensional shallow water equations, and extend it to a three-dimensional tidal model with relatively little computational effort. Through several test cases and realistic applications, as presented in the paper, it can be demonstrated that the model is capable of simulating the periodic to-and-fro currents, wind-driven flow, Ekman spirals, and tidal currents in the near-shore region.
To meet societal needs, modern estuarine science needs to be interdisciplinary and collaborative, combine discovery with hypotheses testing, and be responsive to issues facing both regional and global stakeholders. Such an approach is best conducted with the benefit of data-rich environments, where information from sensors and models is openly accessible within convenient timeframes. Here, we introduce the operational infrastructure of one such data-rich environment, a collaboratory created to support (a) interdisciplinary research in the Columbia River estuary by the multi-institutional team of investigators of the Science and Technology Center for Coastal Margin Observation & Prediction and (b) the integration of scientific knowledge into regional decision making. Core components of the operational infrastructure are an observation network, a modeling system and a cyber-infrastructure, each of which is described. The observation network is anchored on an extensive array of long-term stations, many of them interdisciplinary, and is complemented by on-demand deployment of temporary stations and mobile platforms, often in coordinated field campaigns. The modeling system is based on finite-element unstructured-grid codes and includes operational and process-oriented simulations of circulation, sediments and ecosystem processes. The flow of information is managed through a dedicated cyber-infrastructure, conversant with regional and national observing systems.
Recent modeling studies on the shedding of Loop Current rings suggest that the intensification of the dominant zonal wind field delays the detachment of rings and affects the Loop Current migrations. The atmospheric modulation of the Loop Current migrations is analyzed here using reanalysis winds and altimetry-derived observations. A newly developed methodology is applied to locate the Loop Current front, and a wavelet-based semblance analysis is used to explore correlations with atmospheric forcing. The results show that weakening (intensification) of the zonal wind stress in the eastern Gulf of Mexico is related with the Loop Current excursions to the north (south). Semblance analyses confirm negative correlations between the zonal wind stress and the Loop Current migrations during the past 20 years. The intrusions of the Loop Current might involve an increase of the Yucatan Transport, which would balance the westward Rossby wave speed of a growing loop and delay the ring shedding. The results of this study have consequences for the interpretation of the chaotic processes of ring detachment and Loop Current intrusions, which might be modulated by wind stress.
A deep-ocean mooring system was deployed 100 m away from an active hydrothermal vent over the Southwest Indian Ridge (SWIR), where the water depth is about 2,800 m. One year of data on ocean temperature 50 m away from the ocean floor and on velocities at four levels (44 m, 40 m, 36 m, and 32 m away from the ocean floor) were collected by the mooring system. Multiple-scale variations were extracted from these data: seasonal, tidal, super-tidal, and eddy scales. The semidiurnal tide was the strongest tidal signal among all the tidal constituents in both currents and temperature. With the multiple-scale variation presented in the data, a new method was developed to decompose the data into five parts in terms of temporal scales: time-mean, seasonal, tidal, super-tidal, and eddy. It was shown that both eddy and tidal heat (momentum) fluxes were characterized by variation in the bottom topography: the tidal fluxes of heat and momentum in the along-isobath direction were much stronger than those in the cross-isobath direction. For the heat flux, eddy heat flux was stronger than tidal heat flux in the cross-isobath direction, while eddy heat flux was weaker in the along-isobath direction. For the momentum flux, the eddy momentum flux was weaker than tidal momentum flux in both directions. The eddy momentum fluxes at the four levels had a good relationship with the magnitude of mean currents: it increased with the mean current in an exponential relationship.
Based on oceanic internal wave SAR imaging mechanism and the microwave scattering imaging model for oceanic surface features, we developed a new method to extract internal wave parameters from SAR imagery. Firstly, the initial wind fields are derived from NCEP reanalysis data, the sea water density and oceanic internal wave pycnocline depth are estimated from the Levites data, the surface currents induced by the internal wave are calculated according to the KDV equation. The NRCS profile is then simulated by solving the action balance equation and using the sea surface radar backscatter model. Both the winds and internal wave pycnocline depth are adjusted by using the dichotomy method step by step to make the simulated data approach the SAR image. Then, the wind speed, pycnocline depth, the phase speed, the group velocity and the amplitude of internal wave can be retrieved from SAR imagery when a best fit between simulated signals and the SAR image appears. The method is tested on one scene SAR image near Dongsha Island, in the South China Sea, results show that the simulated oceanic internal wave NRCS profile is in good agreement with that on the SAR image with the correlation coefficient as high as 90%, and the amplitude of oceanic internal wave retrieved from the SAR imagery is comparable with the SODA data. Besides, the phase speeds retrieved from other 16 scene SAR images in the South China Sea are in good agreement with the empirical formula which describes the relations between internal wave phase speed and water depths, both the root mean square and relative error are less than 0.11 m·s−1 and 7%, respectively, indicating that SAR images are useful for internal wave parameters retrieval and the method developed in this paper is convergent and applicable.
Statistical characteristics of mesoscale eddies in the Eastern China Sea (ECS) are analyzed using altimetry sea surface height anomaly (SSHA) data from 1993 to 2010. A velocity geometry-based automated eddy detection scheme is employed to detect eddies from the SSHA data to generate an eddy data set. About 1,096 eddies (one lifetime of eddies is counted as one eddy) with a lifetime longer than or equal to 4 weeks are identified in this region. The average lifetime and radius of eddies are 7 weeks and 55 km, respectively, and there is no significant difference between cyclonic eddies (CEs) and anticyclonic eddies (AEs) in this respect. Eddies’ lifetimes are generally longer in deep water than in shallow water. Most eddies propagate northeastward along the Kuroshio (advected by the Kuroshio), with more CEs generated on its western side and AEs on its eastern side. The variation of the Kuroshio transport is one of the major mechanisms for eddy genesis, however the generation of AEs on the eastern side of the Kuroshio (to the open ocean) is also subject to other factors, such as the wind stress curl due to the presence of the Ryukyu Islands and the disturbance from the open ocean.
Sea surface temperature (SST) is an important variable for understanding interactions between the ocean and the atmosphere. SST fusion is crucial for acquiring SST products of high spatial resolution and coverage. This study introduces a Bayesian maximum entropy (BME) method for blending daily SSTs from multiple satellite sensors. A new spatiotemporal covariance model of an SST field is built to integrate not only single-day SSTs but also time-adjacent SSTs. In addition, AVHRR 30-year SST climatology data are introduced as soft data at the estimation points to improve the accuracy of blended results within the BME framework. The merged SSTs, with a spatial resolution of 4 km and a temporal resolution of 24 hours, are produced in the Western Pacific Ocean region to demonstrate and evaluate the proposed methodology. Comparisons with in situ drifting buoy observations show that the merged SSTs are accurate and the bias and root-mean-square errors for the comparison are 0.15°C and 0.72°C, respectively.
This study aims to develop new algorithms to retrieve sea surface parameters including concentrations of Chlorophyll a (Chl a) and Suspended Particulate Matter (SPM), and absorbance of Colored Dissolved Organic Matter (aCDOM) by incorporating the contribution of red bands to make them more adaptable to case 2 waters. Optical remote sensing algorithms have demonstrated efficient retrieval of Chl a, SPM, and aCDOM, yet they are not very accurate especially for coastal areas. It has also been found that the default algorithm has overestimated Chl a in the Pearl River Estuary, and shown poor correlation for CDOM absorbance. By incorporating the red band ratios into the algorithm, a correction effect has been shown, which improves the accuracy of quantifying the actual concentration. Modeling and data fitting of the algorithm have been done based on 61 data samples collected in the Pearl River estuary during a cruise from 3 to 11 May 2014. The study also attempts to modify the aerosol correction bands used in SeaDAS to prevent saturation of these bands. The modified algorithms showed an R-Square value of 0.7289 for Chl a fitting, and 0.7338 for CDOM fitting, and corrected overestimation of Chl a concentration in the Pearl River estuary.
This study investigates the effects of light and temperature on the surface water diatoms and chlorophytes, phytoplankton in the Indian Ocean sector of the Southern Ocean (SO) during the austral summer of 1998‒2014. Significant longitudinal variations in hydrographic and biological parameters were observed at the Sub tropical front (STF), Sub Antarctic front (SAF) and Polar front (PF) along 56°E‒58°E. The concentrations of total surface chlorophyll a (Chl a), diatoms, and chlorophytes measured by the National Aeronautics Space Agency (NASA) estimated by the Sea-Viewing Wide Field-of-View Sensors (SeaWiFS), the Moderate Resolution Imaging Spectro Radiometer (MODIS), and the NASA Ocean Biological Model (NOBM) were used in the study. Variations in the concentration of total Chl a was remarkable amongst the fronts during the study period. The contribution of diatoms to the total concentration of surface Chl a increased towards south from the STF to the PF while it decreased in the case of chlorophytes. The maximum photosynthetically active radiation (PAR) was observed at the STF and it progressively decreased to the PF through the SAF. At the PF region the contribution of diatoms to the total Chl a biomass was≥80%. On the other hand, the chlorophytes showed a contrary distribution pattern with≥70% of the total Chl a biomass recorded at the STF which gradually decreased towards the PF, mainly attributed to the temperate adaptation. This clearly reveals that the trend of diatoms increased at the STF and decreased at the SAF and the PF. Further, the trend of chlorophytes was increased at the STF, SAF and PF with a shift in the community in the frontal system of the Indian Ocean sector of the SO.