Climate modelling workshop

With the arrival of one of our new post-docs, Ozan Mert Göktürk, regional climate modelling work at SapienCE has commenced in 2019. To talk about the way forward and to enhance collaboration between climate reconstruction and modelling specialists, SapienCE scientists gathered in Bergen for a one-day workshop at the end of August. The focus was on the period around 70 000 years ago, as this is a crucial era for the evolution of modern human behaviour.

In the age of technology, we have both traditional and advanced Earth observation systems, which generate a wealth of precise information. For instance, satellitebased data sets of sea surface temperature, land use, vegetation cover and coastline position can easily be obtained and used by modelers of present day climate. Since these ‘parameters’ have significant influence on local and regional climate, any information regarding them will help to improve modelling results substantially. However when it comes to paleoclimate research, we have none of this direct environmental information at our disposal. Modelling of ancient climates is highly reliant on environmental proxies derived from natural archives e.g. inferring ancient vegetation cover from pollen preserved in marine sediments.

During the workshop, we reviewed the environmental and climatic proxy data that are already available or being produced by SapienCE researchers for the period of interest. These include the relative abundance of shellfish species at Blombos Cave as a proxy of the coastline distance (Karen van Niekerk), micromammal remains from multiple sites giving clues about changing vegetation cover over time (Turid Hillestad Nel), and sea surface temperatures along the South African coast reconstructed from marine cores (Margit Simon).

The take-home message from this busy day was that we need to ensure that we use all of the available data. Abundant information can be collected from natural archives, and alongside other proxy data produced by fellow scientists in the field, these will help constrain past environmental conditions in southern Africa and enhance the reliability of our climate modelling results.