Enabling Sustainable Wind Energy Expansion in Seasonally Stratified Seas (eSWEETS3)

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NERC
NERC
NERC
NERC
NERC

£794,635

April 30, 2024

Feb. 28, 2027

The need for the UK to shift to NetZero was highlighted at COP26 in Glasgow, and there is a clear need for UK energy security. UK policy to achieving these is based on massive expansion of off-shore wind. In 2022 Crown Estate Scotland "ScotWind" auctioned 9,000 km2 of sea space in the northern North Sea, with potential to provide almost 25 GW of offshore wind. Further developments are planned elsewhere, for example, the 300 MW Gwynt Glas Offshore Wind Farm in the Celtic Sea.

These developments mark a shift in off-shore wind generation, away from shallow, well mixed coastal waters to deeper, seasonally stratified shelf seas This shift offers both challenges and opportunities which this proposal will explore.

Large areas of the NW European shelf undergo seasonal thermal stratification. This annual development of a thermocline, separating warm surface water from cold deep water, is fundamental to biological productivity. Spring stratification drives a bloom of growth of the microscopic phytoplankton that are the base of marine food chains. During summer the surface layer is denuded of nutrients and primary production continues in a layer inside the thermocline, where weak turbulent mixing supplies nutrients from the deeper water and mixes oxygen and organic material downward. Tidal flows generate turbulence; the strength of turbulence controls the timing of the spring bloom, mixing at the thermocline, and the timing of remixing of the water in autumn/winter. Determining the interplay between mixing and stratification is fundamental to understanding how shelf sea biological production is supported.

Arrays of large, floating wind turbines are now being deployed over large areas of seasonally-stratifying seas. These structures will inject extra turbulence into the water, as tidal flows move through and past them. This extra turbulence will alter the balance between mixing and stratification: spring stratification and the bloom could occur later, biological growth inside the thermocline could be increased, and more oxygen could be supplied into the deep water. There could be significant benefits of this extra mixing, but we need to understand the whole suite of effects caused by this mixing to aid large-scale roll-out of deep-water renewable energy.

eSWEETS will conduct observations at an existing floating wind farm in the NW North Sea to determine how the extra mixing generated by tides passing through the farm affect the physics, biology and chemistry of the water. We will measure the mixing of nutrients, organic material and oxygen within the farm, and track the down-stream impacts of the mixing as the water moves away from the wind farm and the phytoplankton respond to the new supply of nutrients. We will use autonomous gliders to observe the up-stream and down-stream contrasts in stratification and biology all the way through the stratified part of the year. We will use our observations to formulate the extra mixing in a computer model of the NW European shelf, so that we can then use the model to predict how planned renewable energy developments over the next decades might affect our shelf seas and how those effects might help counter some of the changes we expect in a warming climate.

Stratification is so fundamental to how our seas support biological production that we will develop a new, cost-effective way of monitoring it. We will work with the renewables industry and modellers at the UK Met Office on a technique that allows temperature measurements to be made along the power cables that lie on the seabed between wind farms and the coast. Our vision is that large-scale roll-out of windfarms will lead to the ability to measure stratification across the entire shelf. This monitoring will help the industry (knowledge of operating conditions), government regulators (environment responses to climate change) and to operational scientists at the UK Met Office (constraining models for better predictions).