skip to main content


Search for: All records

Creators/Authors contains: "Thomson, Jim"

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. Abstract

    High-resolution profiles of vertical velocity obtained from two different surface-following autonomous platforms, Surface Wave Instrument Floats with Tracking (SWIFTs) and a Liquid Robotics SV3 Wave Glider, are used to compute dissipation rate profilesϵ(z) between 0.5 and 5 m depth via the structure function method. The main contribution of this work is to update previous SWIFT methods to account for bias due to surface gravity waves, which are ubiquitous in the near-surface region. We present a technique where the data are prefiltered by removing profiles of wave orbital velocities obtained via empirical orthogonal function (EOF) analysis of the data prior to computing the structure function. Our analysis builds on previous work to remove wave bias in which analytic modifications are made to the structure function model. However, we find the analytic approach less able to resolve the strong vertical gradients inϵ(z) near the surface. The strength of the EOF filtering technique is that it does not require any assumptions about the structure of nonturbulent shear, and does not add any additional degrees of freedom in the least squares fit to the model of the structure function. In comparison to the analytic method,ϵ(z) estimates obtained via empirical filtering have substantially reduced noise and a clearer dependence on near-surface wind speed.

     
    more » « less
  2. This dataset includes vessel-based water-column profile and seabed data collected around Blossom Shoals, a shoal complex offshore of Icy Cape in northwestern Alaska (in the Chukchi Sea). Data were collected from the Research Vessel (R/V) Sikuliaq (offshore) and a companion workboat (inshore). Water-column profile data include salinity, temperature, depth, and turbidity data collected using a RBR Maestro CTD/Tu (conductivity, temperature, depth, turbidity) sensor package. Profile data also include median diameters and volumetric concentrations of suspended particles, where were collected using a Sequoia LISST200X (laser in situ scattering transmissometer). Seabed grab samples were collected from the Sikuliaq using a shipek grab sampler and from the workboat using a hand-operated mini van veen grab sampler. Samplers were bagged and returned chilled to the lab for particle-size analyses in an Escitec Bettersizer S3Plus laser diffraction sensor. Sediments were not treated for organics due to generally low organic contents. Samples contained primarily sand except for a few isolated locations where mud was found. Data were collected in November 2019 during the fall freezeup season when pancake ice were beginning to form. Data were also collected in late September and early October 2020 during a mooring recovery cruise. Single-beam bathymetry data (which were only collected in 2020) were gathered using a commercial fish finder mounted on the workboat and connected to a data logger. 
    more » « less
  3. Abstract

    The attenuation of ocean surface waves during seasonal ice cover is an important control on the evolution of Arctic coastlines. The spatial and temporal variations in this process have been challenging to resolve with conventional sampling using sparse arrays of moorings or buoys. We demonstrate a novel method for persistent observation of wave‐ice interactions using distributed acoustic sensing (DAS) along existing seafloor fiber optic telecommunications cables. DAS measurements span a 36‐km cross‐shore cable on the Beaufort Shelf from Oliktok Point, Alaska. DAS optical sensing of fiber strain‐rate provides a proxy for seafloor pressure, which we calibrate with wave buoy measurements during the ice‐free season (August 2022). We apply this calibration during the ice formation season (November 2021) to obtain unprecedented resolution of variable wave attenuation rates in new, partial ice cover. The location and strength of wave attenuation serve as proxies for ice coverage and thickness, especially during rapidly evolving events.

     
    more » « less
  4. Abstract

    Observations of sea ice and the upper ocean from three moorings in the Beaufort Sea quantify atmosphere–ice–ocean momentum transfer, with a particular focus on the inertial-frequency response. Seasonal variations in the strength of mixed layer (ML) inertial oscillations suggest that sea ice damps momentum transfer from the wind to the ocean, such that the oscillation strength is minimal under sea ice cover. In contrast, the net Ekman transport is unimpacted by the presence of sea ice. The mooring measurements are interpreted with a simplified one-dimensional ice–ocean coupled “slab” model. The model results provide insight into the drivers of the inertial seasonality: namely, that a combination of both sea ice internal stress and ocean ML depth contribute to the seasonal variability of inertial surface currents and inertial sea ice drift, while under-ice roughness does not. Furthermore, the importance of internal stress in damping inertial oscillations is different at each mooring, with a minimal influence at the southernmost mooring (within the seasonal ice zone) and more influence at the northernmost mooring. As the Arctic shifts to a more seasonal sea ice regime, changes in sea ice cover and sea ice internal strength may impact inertial-band ice–ocean coupling and allow for an increase in wind forcing to the ocean.

     
    more » « less
  5. Abstract

    Drifting buoy observations of ocean surface waves in hurricanes are combined with modeled surface wind speeds. The observations include targeted aerial deployments into Hurricane Ian (2022) and opportunistic measurements from the Sofar Ocean Spotter global network in Hurricane Fiona (2022). Analysis focuses on the slope of the waves, as quantified by the spectral mean square slope. At low‐to‐moderate wind speeds (<15 m s−1), slopes increase linearly with wind speed. At higher winds (>15 m s−1), slopes continue to increase, but at a reduced rate. At extreme winds (>30 m s−1), slopes asymptote. The mean square slopes are directly related to the wave spectral shapes, which over the resolved frequency range (0.03–0.5 Hz) are characterized by an equilibrium tail () at moderate winds and a saturation tail () at higher winds. The asymptotic behavior of wave slope as a function of wind speed could contribute to the reduction of surface drag at high wind speeds.

     
    more » « less
  6. null (Ed.)
  7. null (Ed.)
  8. null (Ed.)
  9. Abstract

    In seasonally ice‐free parts of the Arctic Ocean, autumn is characterized by heat loss from the upper ocean to the atmosphere and the onset of freeze up, in which first year sea ice begins to grow in open water areas. The timing of freeze up can be highly spatially variable, complicating efforts to provide accurate sea ice forecasting for marine operations. While melt season anomalies can be used to predict freeze up anomalies in some parts of the Arctic, this one‐dimensional view merits further examination in light of recent work demonstrating the importance of three‐dimensional flows in setting mixed layer properties in marginal ice zones. In this study, we show that horizontal advection of sea ice meltwater hastens freeze up in areas distant from the ice edge. We use nearly 800 temperature and salinity profiles along with satellite imagery collected in the central Beaufort Sea in autumn 2018 to document the roughly 100 km advection of a cold and fresh surface meltwater layer over several weeks. After the meltwater arrived, the mixed layer was cooler and shallower than the mixed layer in adjacent areas unaffected by the meltwater. The cooler and shallower meltwater‐influenced mixed layer promoted earlier ice formation. Within the meltwater‐affected area, advection was nearly as important as heat loss to the atmosphere for seasonally integrated mixed layer heat loss.

     
    more » « less