Middle Eocene to late Oligocene calcareous nannofossils of ODP Hole 120-748B

A major deterioration in global climate occurred through the Eocene-Oligocene time interval, characterized by long-term cooling in both terrestrial and marine environments. During this long-term cooling trend, however, recent studies have documented several short-lived warming and cooling phases. In order to further investigate high-latitude climate during these events, we developed a high-resolution calcareous nannofossil record from ODP Site 748 Hole B for the interval spanning the late middle Eocene to the late Oligocene (~42 to 26 Ma). The primary goals of this study were to construct a detailed biostratigraphic record and to use nannofossil assemblage variations to interpret short-term changes in surface-water temperature and nutrient conditions. The principal nannofossil assemblage variations are identified using a temperate-warm-water taxa index (Twwt), from which three warming and five cooling events are identified within the middle Eocene to the earliest Oligocene interval. Among these climatic trends, the cooling event at ~39 Ma (Cooling Event B) is recorded here for the first time. Variations in fine-fraction d18O values at Site 748 are associated with changes in the Twwt index, supporting the idea that significant short-term variability in surface-water conditions occurred in the Kerguelen Plateau area during the middle and late Eocene. Furthermore, ODP Site 748 calcareous nannofossil paleoecology confirms the utility of these microfossils for biostratigraphic, paleoclimatic, and paleoceanographic reconstructions at Southern Ocean sites during the Paleogene.

Reference list of 120 datasets from time series station Payerne used for exploratory search

The analysis of time-dependent data is an important problem in many application domains, and interactive visualization of time-series data can help in understanding patterns in large time series data. Many effective approaches already exist for visual analysis of univariate time series supporting tasks such as assessment of data quality, detection of outliers, or identification of periodically or frequently occurring patterns. However, much fewer approaches exist which support multivariate time series. The existence of multiple values per time stamp makes the analysis task per se harder, and existing visualization techniques often do not scale well. We introduce an approach for visual analysis of large multivariate time-dependent data, based on the idea of projecting multivariate measurements to a 2D display, visualizing the time dimension by trajectories. We use visual data aggregation metaphors based on grouping of similar data elements to scale with multivariate time series. Aggregation procedures can either be based on statistical properties of the data or on data clustering routines. Appropriately defined user controls allow to navigate and explore the data and interactively steer the parameters of the data aggregation to enhance data analysis. We present an implementation of our approach and apply it on a comprehensive data set from the field of earth bservation, demonstrating the applicability and usefulness of our approach.