Objectives
Ocean currents are critically important to our global climate system, but ocean dynamics are not well understood or characterized, leaving a significant gap in current climate modeling. To address this, DYNACLIM is combining satellite observations of salinity, sea surface temperature, and sea surface height with ocean dynamics models. Researchers will apply their new approach to the Arctic Ocean - a key region in our understanding of the climate crisis as more remote sensing data is available. Improving our understanding in this area will contribute to better European policies to reduce the climatic impact of maritime transport, and add urgency to climate warnings around melting ice in the Arctic.
By transporting heat and energy, ocean currents play a major role in shaping the climate of Earth’s many regions. However, the characterization of ocean dynamics remains one of the key problems in oceanography. This project addresses the reconstruction of ocean currents from satellite observations. Using a semi-analytical quasi-geostrophic model, this proposal aims to exploit the available spatiotemporal sampling capacity of remotely sensed variables such as salinity, sea surface temperature, and sea surface high to reconstruct the ocean's three-dimensional dynamics. This approach will be applied to the Arctic Ocean; a region of relevant importance in order to better understand the consequences of climate change and where enhanced satellite salinity products are recently being produced. Reinforcing knowledge on the Arctic dynamics represents a community priority as they represent key areas that regulate the climate system, that are suffering the fastest rates of change due to global warming. Moreover, improving the circulation understanding in this area would also contribute to European politics to reduce the climatic impact of maritime transport contamination.
Conclusions
The results show that the SQG approach makes it possible to reconstruct three-dimensional dynamics in the Arctic Ocean by using only surface information, such as sea surface height or surface velocities, and information on the stratification of the water column; however, the approach is not able to reconstruct three-dimensional dynamics using only sea surface buoyancy. Accurate reconstructions (correlation coefficients greater than 0.8) are found up to 400 m in the Nordic Seas, up to 200 m in the Barents Seas, and up to 100 m in the Beaufort Sea, taking the geostrophic velocity from the reanalysis system as a reference. Areas that are more stratified, such as the East Siberian Sea, hinder the reconstruction of the upper ocean dynamics from surface information, showing correlation coefficients greater than 0.6 only up to 100 m. Because there is less stratification in the water column during the winter and spring months, better 3D reconstructions are obtained during these seasons.
