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This dataset is a compilation of published records of 230Thorium - normalised lithogenic and biogenic fluxes from the Southern Ocean, south of 30S. All age models and derived fluxes were taken as published. Lithogenic fluxes are based on 232Th concentrations. Opal and carbonate fluxes are also included where available. In some cases fluxes had to be derived from published data. LGM values for each core represent an average of observations between 28 - 18 ka BP and Holocene values represent an average of observations from 10 - 0 ka BP. These data were collated as part of modelling study of the Southern Ocean during the LGM (Saini et al, Southern Ocean ecosystem response to Last Glacial Maximum boundary conditions, Submitted to Paleoceanography and Paleoclimatology, 2021)
2019-20 Honours project - Environmental Drivers of Antarctic Landfast Sea Ice Formation and Breakout
Antarctic Landfast sea ice (fast ice) is important climatologically, biologically and for logistics for short time-scale anomalies. Until recently, there hasn’t been an accurate, high-resolution fast ice extent dataset which can support an analysis on drivers of fast ice and most studies only investigate fast ice on limited regions of Antarctica in a limited time scale. There is a need to extend the spatial and temporal studying coverage to provide detailed information on the Antarctic coast over a longer period. This is the first detailed analysis to identify and quantify correlation between the environmental anomaly and fast ice anomaly mainly in the east Antarctic coast. By examining regional/local fast ice extent in in east Antarctic coast in the context of the broader and/or remote-teleconnected atmospheric circulation/properties using spatial correlation techniques, a strong correlation between NINO3 region and Lützow-Holm Bay fast ice and similar and significant correlation of regional scale factors from Lützow-Holm Bay to Mawson Coast mainly are found. The results of this thesis suggest that the pack ice, atmospheric factors and oceanic factors are important for interpreting fast ice anomalies. To identify and quantify correlation between the pack ice, temperature at 2m, wind at 10m, snow fall anomaly, sea surface temperature anomaly, ocean heat content anomaly and fast ice anomaly, backward multiple linear regression is conducted to demonstrate some predictive fast ice driver information by quantifying the correlation between different drivers and fast ice anomaly. The multiple linear regression also suggests that oceanic influences including pack ice are generally more important than atmospheric influences. Future experiments could be conducted to interpret fast ice anomalies in the context of the ocean mainly.