With the ERC funded project STEEPclim we work on better understanding regional differences of climate change and it’s causes. We reconstruct the consequences of past abrupt climate change, in particular changes in aridity and precipitation at the end of the last ice age (15.000 – 10.000 years ago) across the European continent and compare these reconstructions with model results. The general climatic evolution of the recent past, in particular temperature change, is well constrained, for example through the analysis of the Greenland icecores. It is however more difficult to asses the regional effect of past global changes, for example changes in rainfall patterns on the European continent. Future regional hydrological changes are also difficult to predict with state-of-the art climate models, although changes in the water cycle can have a profound effect on ecosystems and human civilization.

Applying novel molecular and isotope geochemical methods on annually laminated lacustrine sediments from 10+ lakes situated across the European continent we reconstruct changes in precipitation patterns and relative humidity in unprecedented detail with a resolution of up to ten years (actually this is weather!) during a time period at the end of the last ice age (the Younger Dryas cold period), characterized by abrupt climate swings. Our scientists extract organic remains of plants and algae, so-called molecular fossils or biomarkers, from these lake sediments to understand past ecosystems. We analyze the content of stable hydrogen and carbon isotopes on these biomarkers (lipids like n-alkanes, fatty acids, sterols) originating from cell walls of algae and leaf waxes of higher terrestrial plants. The distribution of the stable H and C isotopes can be used to reconstruct hydrological changes in and around the lake (aridity, humidity, changes in the source of precipitation) as well as vegetation changes. With the resulting high-resolution dataset covering the whole European continent we develop mechanisms for the observed changes. This understanding of past abrupt (natural) climate change can help to improve climate models to more accurately predict future consequences of (anthropogenic) climate change. 

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