Separating ITCZ- and ENSO-related rainfall changes in the Galápagos over the last 3 kyr using D/H ratios of multiple lipid biomarkers

We present a 3000-yr rainfall reconstruction from the Galápagos Islands that is based on paired biomarker records from the sediment of El Junco Lake. Located in the eastern equatorial Pacific, the climate of the Galápagos Islands is governed by movements of the Intertropical Convergence Zone (ITCZ) and the El Niño-Southern Oscillation (ENSO). We use a novel method for reconstructing past ENSO- and ITCZ-related rainfall changes through analysis of molecular and isotopic biomarker records representing several types of plants and algae that grow under differing climatic conditions. We propose that ?D values of dinosterol, a sterol produced by dinoflagellates, record changes in mean rainfall in El Junco Lake, while dD values of C34 botryococcene, a hydrocarbon unique to the green alga Botryococcus braunii, record changes in rainfall associated with moderate-to-strong El Niño events. We use these proxies to infer changes in mean rainfall and El Niño-related rainfall over the past 3000 yr. During periods in which the inferred change in El Niño-related rainfall opposed the change in mean rainfall, we infer changes in the amount of ITCZ-related rainfall. Simulations with an idealized isotope hydrology model of El Junco Lake help illustrate the interpretation of these proxy reconstructions. Opposing changes in El Niño- and ITCZ-related rainfall appear to account for several of the largest inferred hydrologic changes in El Junco Lake. We propose that these reconstructions can be used to infer changes in frequency and/or intensity of El Niño events and changes in the position of the ITCZ in the eastern equatorial Pacific over the past 3000 yr. Comparison with El Junco Lake sediment grain size records indicates general agreement of inferred rainfall changes over the late Holocene.

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.

Water temperature data from southern elephant seal from Marion Island with links to datasets

As the Antarctic Circumpolar Current crosses the South-West Indian Ocean Ridge, it creates an extensive eddy field characterised by high sea level anomaly variability. We investigated the diving behaviour of female southern elephant seals from Marion Island during their post-moult migrations in relation to this eddy field in order to determine its role in the animals' at-sea dispersal. Most seals dived within the region significantly more often than predicted by chance, and these dives were generally shallower and shorter than dives outside the eddy field. Mixed effects models estimated reductions of 44.33 ± 3.00 m (maximum depth) and 6.37 ± 0.10 min (dive duration) as a result of diving within the region, along with low between-seal variability (maximum depth: 5.5 % and dive duration: 8.4 %). U-shaped dives increased in frequency inside the eddy field, whereas W-shaped dives with multiple vertical movements decreased. Results suggest that Marion Island's adult female elephant seals' dives are characterised by lowered cost-of-transport when they encounter the eddy field during the start and end of their post-moult migrations. This might result from changes in buoyancy associated with varying body condition upon leaving and returning to the island. Our results do not suggest that the eddy field is a vital foraging ground for Marion Island's southern elephant seals. However, because seals preferentially travel through this area and likely forage opportunistically while minimising transport costs, we hypothesise that climate-mediated changes in the nature or position of this region may alter the seals' at-sea dispersal patterns.

PanGet - downloads multiple data sets from PANGAEA

The software PanGet is a special tool for the download of multiple data sets from PANGAEA. It uses the PANGAEA data set ID which is unique and part of the DOI. In a first step a list of ID's of those data sets to be downloaded must be created. There are two choices to define this individual collection of sets. Based on the ID list, the tool will download the data sets. Failed downloads are written to the file *_failed.txt. The functionality of PanGet is also part of the program Pan2Applic (choose File > Download PANGAEA datasets...) and PanTool2 (choose Basic tools > Download PANGAEA datasets...).

Global distributions of diazotrophs abundance and biomass - Depth integrated values computed from a collection of source datasets - Contribution to the MAREDAT World Ocean Atlas of Plankton Functional Types

The MAREDAT atlas covers 11 types of plankton, ranging in size from bacteria to jellyfish. Together, these plankton groups determine the health and productivity of the global ocean and play a vital role in the global carbon cycle. Working within a uniform and consistent spatial and depth grid (map) of the global ocean, the researchers compiled thousands and tens of thousands of data points to identify regions of plankton abundance and scarcity as well as areas of data abundance and scarcity. At many of the grid points, the MAREDAT team accomplished the difficult conversion from abundance (numbers of organisms) to biomass (carbon mass of organisms). The MAREDAT atlas provides an unprecedented global data set for ecological and biochemical analysis and modeling as well as a clear mandate for compiling additional existing data and for focusing future data gathering efforts on key groups in key areas of the ocean. The present data set presents depth integrated values of diazotrophs abundance and biomass, computed from a collection of source data sets.