Observing and characterizing submesoscale dynamics in the Arctic Ocean

• Main Authors: Mary-Louise Timmermans, Andrey Proshutinsky
• Format: Dataset
• Language: unknown
• Published: Arctic Data Center 2015
• Subjects: ANS; Arctic; Arctic Ocean; Sea ice
• Online Access: https://search.dataone.org/view/urn:uuid:67556c02-75c9-43df-9929-8152b5c26b51

Preliminary analysis reveals that the Arctic Ocean mixed layer displays a complicated submesoscale structure with significant horizontal density gradients (fronts) and small eddies a few kilometers, or less, in diameter - features that are not resolved by large-scale numerical models of the Arctic Ocean. Funds are provided to explore the role of submesoscale processes in regulating the climatically-important interactions among the Arctic atmosphere, sea-ice, ocean surface mixed layer and the underlying deeper ocean; a set of essential scientific questions will be addressed: What are the dominant characteristics of submesoscale horizontal density variability in the Arctic Ocean mixed layer? How do submesoscale dynamics in the mixed layer relate to mixed-layer depth and stratification? What mechanisms are responsible for the horizontal density variability in the mixed layer? What vertical motions are associated with submesoscale processes and how do they impact exchanges across the mixed layer? The PIs intend to investigate these aspects of the surface ocean dynamics and thermodynamics by a targeted field experiment accompanied by analysis of recent ocean (temperature, salinity and velocity) and sea-ice measurements, and directed dynamic process studies. Because submesoscale processes occur on spatial scales that are not explicitly resolved by existing models of the Arctic Ocean, their effects must be parameterized in order to accurately predict the dynamics and mixing environment of the ocean. In order to appropriately parameterize these processes, it is first necessary to understand how they are related to the large scale structure and dynamics of the ocean. This work will provide that basic knowledge, thus allowing future improvements to the models.