Multi-Temporal Variability Within Antarctic Coastal Polynyas and Their Relationships To Large-Scale Atmospheric Phenomena

• Main Author: Ward, Jason Michael
• Format: Doctoral or Postdoctoral Thesis
• Language: English
• Published: eScholarship, University of California 2018
• Subjects: Geography; Atmospheric sciences; Antarctica; atmosphere; climatology; ocean; polynyas; sea ice; Amundsen Sea; Antarctic; Southern Ocean; The Antarctic; Antarc*; Sea ice
• Online Access: http://www.escholarship.org/uc/item/0tj68195

Open water and thin ice areas, known as coastal polynyas, form along the Antarctic coastline and allow continued interaction between the ocean and atmosphere throughout the sea ice advance season. Coastal polynyas are the most productive locations of sea ice formation, Antarctic bottom water formation, and biological activity in the Southern Ocean. Changes in these elements are greatly controlled by polynya area variability. To carry out an in-depth study of polynya area variability, a 26-year 25-polynya daily area dataset was created and analyzed. The long term trend and the daily, monthly, seasonal, and annual variations are separated to analyze the multi-temporal variability of the polynyas and investigate their individual and regional responses to prominent large-scale atmospheric circulation patterns. Results indicate that most polynya variability occurs at the daily scale, followed by monthly and seasonal variations. Very little variability occurs interannually. Thus, studies done at the annual scale mask most of the polynya activity. Only five of the polynyas have long term trends, which are all non-linear and arise from abrupt changes in the icescape. Three of the significant trends occur within the top four most significant regions of sea ice and bottom water formation. Long term changes in polynya area cause long term changes in the overall productivity of the Southern Ocean. The Southern Annular Mode (SAM), El Nino-Southern Oscillation (ENSO), and the Amundsen Sea Low (ASL) significantly contribute to individual and regional coastal polynya variability. Influence from the SAM and the ASL is primarily driven at the monthly and seasonal scales. Influence from ENSO is driven at the annual scale. Using Pearson correlations, principal component analysis, gaussian mixture models, and hierarchical agglomerative clustering, six regional polynya groups are delineated based on the strength and direction of inter-polynya co-variability. The mean polynya variability within each region is significantly correlated, which is driven at the seasonal scale. While the SAM, ENSO, and ASL are not the primary drivers of regional polynya group delineations, they are significantly influential in the mean variability of each group.