Using Machine Learning to Classify Oceanographic Structures in the Amundsen Sea, Antarctica.

Abstract:

The remote and often ice-covered Amundsen Sea Embayment in Antarctica is important for transporting relatively warm modified Circumpolar Deep Water (mCDW) to the Western Antarctic Ice Sheet, potentially accelerating its thinning and contribution to sea level rise. To investigate potential pathways and variability of mCDW, 3809 CTD profiles are classified using a machine learning approach (Profile Classification Model). Five vertical regimes are identified, and areas of larger variability highlighted. Three spatial regimes are captured: Off-Shelf, Eastern and Central Troughs. The on-shelf profiles further show a separation between cold and warm modes. In each trough, a warm and a cold mode can be observed, with the warm mode having warmer temperatures and fresher salinities close to the surface and colder temperatures and lower salinities at depth. The warm modes of the two on-shelf regimes are mainly observed in summer and autumn, at the end of the maximum positive heat fluxes, while the cold modes are mainly observed from autumn to spring. The variability is higher north of Burke Island and at the southern end of the Eastern Trough, which reflects the convergence of different mCDW pathways between the Eastern and the Central Trough. Finally, a clear but variable clockwise circulation is identified in Pine Island Bay.