Bjerknessenteret for klimaforskning er et samarbeid mellom Universitetet i Bergen, Uni Research, Nansensenteret og Havforskningsinstituttet. 


Place: Pal strat kurssal, 2nd floor Realfagbygget, Allegaten 41


Thursday September 17th we will have a paleoseminar at Geo, where Dirk Sachse, Helmholtz Centre Potsdam, will talk about Spatial patterns of hydrological change during the onset of the Younger Dryas along a W-E gradient over the European continent - insights fromdecadal resolved lacustrine lipid biomarker D/H ratios  

Time:  14:15-15:00. Place: Pal strat kurssal, 2nd floor Realfagbygget, Allegaten 41




Significant gaps exist in our understanding of mechanisms and feedbacks of hydrological changes during abrupt climatic changes, impeding Our ability to robustly predict regional hydrological change under climate change scenarios. Well dated, high-resolution lacustrine sediments and direct paleohydrological proxies, such as leaf wax lipid hydrogen isotope ratios (δD) offer a unique opportunity to decipher paleohydrologcial changes and their mechanisms during past abrupt climate change on unprecedented temporal scales [1].In order to understand spatial differences in the response of the hydrological cycle to abrupt change we have investigated the regionalhydrological expression during the last major abrupt climate change affecting the climate of the Northern hemisphere – the so called Younger Dryas (YD) period (ca. 12.700 to 11.500 ka BP).We investigated the onset of the YD at three sites in western Germany [MFM], eastern Germany [RW] and central Poland [TRZ]) covering a 900km W-E gradient over Europe. All sites are characterized by varved sediments and common tephra layers, allowing the identification of leads and lags in the hydrological response on decadal timescales. By comparing biomarker hydrogen isotope records (δD values) from Three different lakes we find significant differences in the magnitude as well as the duration of lipid biomarker δD changes. Our results showed that changes in biomarker δD occurred before the YD onset at 12.679 years BP in western and eastern Germany, respectively, consistent with the onset of Greenland Stadial 1 in the NGRIP icecore at 12.846 years BP. Interestingly, further east in central Poland biomarker δD values start to decrease only at the YD onset. We infer that hydrological changes at the onset of the YD were strongest and most abrupt in western Europe, where a substantial increase in aridity occurred over just 80 years, resulting in widespread environmental changes. Further east the increase of aridity is more gradual and less pronounced. The different temporal succession and more gradual aridification in eastern Europe could be related to the influence of the Fennoscandian ice sheets and/or the Siberian High on atmospheric circulation in eastern Europe.

[1] Rach O, Brauer A, Wilkes H, Sachse D. Delayed hydrological response to Greenland cooling at the onset of the Younger Dryas in western Europe. Nat Geosci. Nature Publishing Group; 2014;7:109–112.


Time series analysis around the Indo-Australian monsoon

Speaker:  Dr Thomas Stemler, University of Western Australia. Stemler is a guset of Johannes Werner.

Time: September 10th, 12:15-13:00

Place: Pal strat kurssal, 2nd floor Realfagbygget

Speaker:  Dr Thomas Stemler, University of Western Australia. Stemler is a guset of Johannes Werner.


In this talk I will report on the recent advancements of Holocene monsoon dynamics time series analysis and focus on the point of view of a mathematician. Starting from the transient coupling relationships of the Australian monsoon we in particular focus on different methods of time series analysis like climate network reconstruction and I will introduce a new method that is designed to identify significant variations in monsoonal activity, both dry and wet phases, at millennial to multi-centennial time scales from speleothem proxy data.This method, the transformation cost time series method, allows us to analyze irregular sampled data sets, like speleotherm data, without degenerating the quality of the data set.