Evolution of Oceanic Near-Surface Stratification in Response to an Autumn Storm

Natasha S. Lucas, Alan L. M. Grant, Tom P. Rippeth, Jeff A. Polton, Matthew R. Palmer, Liam Brannigan, Stephen E. Belcher
  • Journal of Physical Oceanography, November 2019, American Meteorological Society
  • DOI: 10.1175/jpo-d-19-0007.1

Insight into the Surface Layer of the Ocean During a Storm

Photo by Jeremy Bishop on Unsplash

Photo by Jeremy Bishop on Unsplash

What is it about?

Why are we interested in the surface of the ocean? The surface of the ocean is the interface between the ocean interior and atmosphere. Heat, gases and momentum are exchanged at this boundary, this has implications on weather and climate. . How did we measure it? We went out to the North Atlantic, off the continental shelf, in September 2012 for a month. We deployed an Ocean Microstructure Glider (yup, known as OMG!!). This instrument is pretty new and pretty special! (Image Available in 'Resources') . Why is it special? The OMG measures energy dissipated, temperature and salinity, it glides autonomously under the water. The most common instrument to measure energy dissipated previous to this needed to be deployed from a ship on a cable, thus only in calm seas. . We use the energy dissipated as a proxy for mixing. Mixing moves water packets of varying temperature and nutrients vertically in the water column, which ultimately affects the exchange between the atmosphere. . The OMG glided from surface to 100m depth on its little mission for ~10 days. In that time we went from calm weather to a hefty storm, (so hefty we had to turn the ship 'hove to' and wait it out for 2 days!!), but our little OMG glided on. This meant we obtained unprecedented measurements of energy dissipated in the water column during a big storm. . These measurements are accompanied by many others, but notably by a wave buoy, giving us the wave-field which was vital for the understanding of the wave energy input; the wind and wave work together and cause underwater streamwise vortices that drive momentum, known as Langmuir turbulence and often seen on lakes and oceans in the form of long parallel lines of 'froth' or other flotsam. . We then went about the process of building what's known as a parameterisation* to fit a model to the observed results. Key Image Explained (Image Available in 'Resources'): The key image in the paper is figure 2, attached in colour here. To explain it... . The vertical red lines are day and night, the vertical green; the boundary of the storm. Panel a) shows the wind and wave. You can see it low for the first 5 days and then building during the storm. Panel b) shows the air pressure and buoyancy flux (explained shortly) Panel c) and d) are the 'money shots'. c) is the temperature of the water by depth, d) is the energy dissipated by depth. . Let's break this down.. Before the storm you can see the very surface water being heated in the daytime, (panel c), this results in a 'stable' system, where the hot water sits at the surface and there is little energy dissipated, (panel d). At night however the heat is lost from the ocean surface, (shown with the buoyancy flux in panel b), which results in cold water sitting on warmer water, that cooler water sinks and results in energy dissipation (by convection). There is a clear cycle of this over the 5 days (mostly). . During the storm you can see clearly the massively increased energy in the system from the wind and wave input at the surface! Also, the large gradient temperature band* at about 40m broadens. This is highlighted with the white lines in panel c which are isotherms; think contour lines on a map. From the ocean surface down to the bottom of this region, (the dark blue line in panel d), is where we fitted the parameterizations. This can now be extended and fitted into climate models for future predictions.

Why is it important?

We can't make accurate predictions into the future for weather and climate, there is too much going on, not to mention chaos, but the more we test and refine equations that fit what we observe, the better these predictions will be. We’ve used observational measurements to help define a parameterisation* that models the response of the ocean surface during a storm. This can now be extended and fitted into climate models for future predictions. *basically a bit of math that will fit the results we observe here to other storms, allowing models of weather and climate to work better

Perspectives

Natasha Lucas

Paper can be provided in colour (much more definition on images) if requested from n.lucas@bangor.ac.uk

Read Publication

http://dx.doi.org/10.1175/jpo-d-19-0007.1

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