Master’s/Engineering Internship – Climate change and ocean extremes: what projections for offshore wind and aquaculture? (F/M/X)

Offshore wind farms are designed to operate for several decades but are generally sized based on the ocean conditions observed in the past. However, climate change is changing the ocean: sea level rise, warming of waters and reinforcement of stratification, evolution of circulation and currents (Fox-Kemper et al., 2021). These parameters are involved in the design and operation of wind farms: waves and currents for hydrodynamic loads and scour, temperature and salinity for corrosion, biological colonization of structures or heating of cables. In addition, coastal areas, where other uses such as aquaculture are concentrated, are increasingly exposed to extreme events such as marine heat waves, which correspond to prolonged episodes of abnormally high ocean temperatures (Hobday et al., 2016, Oliver et al., 2018). These extremes can have a strong impact on aquaculture sites and production (Smith et al., 2021).

In order to adapt these coastal socio-economic sectors vulnerable to climate change, it is necessary to anticipate these changes and to characterize not only the average trends, but also the evolution of extreme events at spatial scales adapted to the sites studied. Climate projections, in particular those from the CMIP6 exercise (Coupled Model Intercomparison Project Phase 6, Eyring et al., 2016) used in IPCC reports, are an essential source of information on future climate change. However, their low spatial resolution and simplified representation of some sub-grid ocean processes limit their ability to accurately describe local conditions, especially in coastal areas (Holt et al., 2017). Higher-resolution regional simulations, obtained by dynamic downscaling, allow these climate projections to be refined at a local scale, but they have a high numerical cost and are therefore only available for a limited number of configurations that do not allow for the systematic quantification of all sources of uncertainty. Statistical downscaling, which relies on statistical learning of the relationship between the large and local scales from different simulation datasets, including reanalyses and observations, is a much less expensive alternative (Hobeichi et al., 2023), but whose performance has so far been relatively poorly evaluated for oceanic variables and their extremes. The respective merits of these two approaches to describe oceanic extremes in littoral zones therefore remain to be established.

In this context, this internship will aim to answer two scientific questions:

• How will temperature, salinity, and current conditions evolve on a local scale on the French coast in a context of climate change? With what uncertainties?
• To what extent do statistical methods improve ocean climate projections at the coastal scale, and how do they compare to dynamic approaches to representing future trends and extreme events?

Supported by France Energies Marines, the internship offered is part of two research projects:

• 2C MORE, led by France Energies Marines, which aims to assess the impacts of climate change on offshore wind power;
• ExTempCO, led by the CNRS at the Laboratory of Physical and Space Oceanography as part of the France 2030 Climate – TRACCS programme, is dedicated to the study of marine heatwaves in coastal areas with an application to aquaculture.

The internship will focus on the analysis and improvement of ocean climate projections at the local scale for the adaptation of the coastal socio-economic sectors concerned. The study variables will focus on temperature, currents and salinity. The work will be based on existing Python processing chains, developed within the teams and their partners, which the intern will take over, adapt and complete.