A late Pleistocene dataset of Agulhas Current variability

The interoceanic transfer between the Indian and the Atlantic Oceans known as ‘Agulhas leakage’ is of global significance as it alters the Atlantic Meridional Overturning Circulation (AMOC) on different time scales. Variability in the Agulhas Current regime is key in shaping hydroclimate on the adjacent coastal areas of the African continent today as well as during the past. However, the lack of long, continuous records from the Agulhas Current core region dating beyond the last glacial cycle prevents elucidation of its role in regional and wider global climate changes. This is the first continuous record of hydrographic variability (SST; δ18Osw) from the Agulhas Current core region spanning the past 270,000 years. The data set is analytical sound and provides a solid age model. As such, it can be used by paleoclimate scientists, archaeologists, and climate modelers to evaluate e.g. linkages between the Agulhas Current system and AMOC dynamics, as well as connections between ocean heat transport and Southern African climate change in the past and its impact on human evolution.

Disciplines

Marine geology

Keywords

Agulhas Current, Pleistocene, South African Climate

Location

0N, -40S, 60E, 8W

Data

FileSizeFormatProcessingAccess
Data file in the LiPD data format
21 KoLiPDQuality controlled data
Data file in the excel template that can be converted into a LiPD file using the Python LiPD utilities
238 KoXLS, XLSXQuality controlled data
How to cite
Simon Margit H., Ziegler Martin, Barker Stephen, van der Meer Marcel T. J., Schouten Stefan, Hall Ian R. (2020). A late Pleistocene dataset of Agulhas Current variability. SEANOE. https://doi.org/10.17882/70908
In addition to properly cite this dataset, it would be appreciated that the following work(s) be cited too, when using this dataset in a publication :
Simon Margit H., Ziegler Martin, Barker Stephen, van der Meer Marcel T. J., Schouten Stefan, Hall Ian R. (2020). A late Pleistocene dataset of Agulhas Current variability. Scientific Data, 7 (1), -. https://doi.org/10.1038/s41597-020-00689-7

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