• 2014 Ocean Sciences Meeting (TOS)
  23-28 Feb 2014, Honolulu, HI
• Oceans’13 MTS/IEEE
  23-26 September 2013, San Diego, CA
  Abstract deadline – 3 May
• 19th Conference on Atmospheric and Oceanic Fluid Dynamics
  17–21 June 2013, Newport, Rhode Island
  Abstract deadline – 15 Mar
• Oceans’13 MTS/IEEE
  10-13 June 2013, Bergen, Norway
• EGU General Assembly
  7-12 April 2013, Vienna, Austria
• ASLO Aquatic Sciences Meeting
  17-22 Feb 2013, New Orleans, LA
• AMS Annual Meeting
  6-10 January 2013, Austin, TX
• AGU Fall Meeting
  3-7 December 2012, San Francisco, CA
• Oceans’12 MTS/IEEE
  14-19 Oct 2012, Hampton Roads, VA
• International workshop on Multiscale (Un)-structured mesh numerical ocean Modeling
  17-20 August 2010; Cambridge, MA

In this paper, we quantify the dynamical causes and uncertainties of striking differences in acoustic transmission data collected on the shelf and shelfbreak in the northeastern Taiwan region within the context of the 2008 Quantifying, Predicting, and Exploiting Uncertainty (QPE 2008) pilot experiment. To do so, we employ our coupled oceanographic (4-D) and acoustic (Nx2-D) modeling systems with ocean data assimilation and a best-fit depth-dependent geoacoustic model. Predictions are compared to the measured acoustic data, showing skill. Using an ensemble approach, we study the sensitivity of our results to uncertainties in several factors, including geoacoustic parameters, bottom layer thickness, bathymetry, and ocean conditions. We find that the lack of signal received on the shelfbreak is due to a 20-dB increase in transmission loss (TL) caused by bottom trapping of sound energy during up-slope transmissions over the complex and deeper bathymetry. Sensitivity studies on sediment properties show larger but isotropic TL variations on the shelf and smaller but more anisotropic TL variations over the shelfbreak. Sediment sound-speed uncertainties affect the shape of the probability density functions of the TLs more than uncertainties in sediment densities and attenuations. Diverse thicknesses of sediments lead to only limited effects on the TL. The small bathymetric data uncertainty is modeled and also leads to small TL variations. We discover that the initial transport conditions in the Taiwan Strait can affect acoustic transmissions downstream more than 100 km away, especially above the shelfbreak. Simulations also reveal internal tides and we quantify their spatial and temporal effects on the ocean and acoustic fields. One type of predicted waves are semidiurnal shelfbreak internal tides propagating up-slope with wavelengths around 40-80 km, horizontal phase speeds of 0.5-1 m/s, and vertical peak-to-peak displacements of isotherms of 20-60 m. These waves lead to variations of broadband TL estimates over 5-6-km range that are more isotropic and on bearing average larger (up to 5-8-dB amplitudes) on the shelf than on the complex shelfbreak where the TL varies rapidly with bearing angles.

In this paper, a method for merging partial overlap- ping time series of ocean profiles into a single time series of profiles using empirical orthogonal function (EOF) decomposition with the objective analysis is presented. The method is used to handle internal waves passing two or more mooring locations from multiple directions, a situation where patterns of variability cannot be accounted for with a simple time lag. Data from one mooring are decomposed into linear combination of EOFs. Objective analysis using data from another mooring and these patterns is then used to build the necessary profile for merging the data, which is a linear combination of the EOFs. This method is applied to temperature data collected at a two vertical moorings in the 2006 New Jersey Shelf Shallow Water Experiment (SW06). Resulting profiles specify conditions for 35 days from sea surface to seafloor at a primary site and allow for reliable acoustic propagation modeling, mode decomposition, and beamforming.

Le rechauffement climatique est reel et l’Homme en est le principal responsable

Understanding the complex dynamics of coastal upwelling is essential for coastal ocean dynamics, phytoplankton blooms, and pollution transport. Atmospheric- driven coastal upwelling often occurs when strong alongshore winds and the Coriolis force combine to displace warmer surface waters offshore, leading to upward motions of deeper cooler, nutrient-dense waters to replace these surface waters. Using the models of the MIT Multidisciplinary Simulation, Estimation, and Assimilation System (MSEAS) group, we conduct a large set of simulation sensitivity studies to determine which variables are dominant controls for upwelling events in the Monterey Bay region. Our motivations include determining the dominant atmospheric fluxes and the causes of high-frequency fluctuations found in ocean thermal balances. We focus on the first upwelling event from August 1- 5, 2006 in Monterey Bay that occurred during the Monterey Bay 06 (MB06) at-sea experiment, for which MSEAS data-assimilative baseline simulations already existed.

Using the thermal energy (temperature), salinity and momentum (velocity) conservation equations, full ocean fields in the region as well as both control volume (flux) balances and local differential term-by-term balances for the upwelling event events were computed. The studies of ocean fields concentrate on specific depths: surface-0m, thermocline-30m and undercurrent-150m. Effects of differing atmospheric forcing contributions (wind stress, surface heating/cooling, and evaporation-precipitation) on these full fields and on the volume and term-by-term balances are analyzed. Tidal effects are quantified utilizing pairs of simulations in which tides are either included or not. Effects of data assimilation are also examined.

We find that the wind stress forcing is the most important dynamical parameter in explaining the extent and shape of the upwelling event. This is verified using our large set of sensitivity studies and examining the heat flux balances. The assimilation of data has also an impact because this first upwelling event occurs during the initialization. Tidal forcing and, to a lesser extent, the daily atmospheric and data assimilation cycles explain the higher frequency fluctuations found in the volume averaged time rate of change of thermal energy.