Course goals
on the basis of realistic scientific data, by the end of the course, student will:
be trained to identify, interpret and reconstruct the role of the ocean in past changes in climate;
be trained to identify, interpret and reconstruct paleoclimate and variations there in;
be trained in general academic skills such as writing reports, presenting scientific concepts.
Content
(Paleo)ocean circulation during different climatic regimes and related proxy variability will be discussed while sequentially introducing different concepts and aspects. Theory and application of marine proxies will be illustrated by relevant case studies. In particular the Glacial world will be contrasted to the (present-day) Interglacial, and compared to high-frequency (e.g. El-Nino) paleoceanographic and proxies variations. Amongst the aspects to be discussed are: Glacial climate and its forcing; sediment dating techniques; paleoproductvity; pCO2 reconstruction; oxygenation; sea surface temperature; deep water circulation; and proxy preservation. Current important scientific questions will be addressed and different view points discussed. The course teaches students hands on scientific research so that they can ‘hit the ground running’ in climate related projects.
Development of Transferable Skills
Ability to work in the team: Presentations, practicals and final research proposal are organized in teams. Students have to distribute tasks, organize the workflow and are responsible for the time planning.
Problem solving: students receive data from previous sea-going expeditions and have to use different approaches to unravel past ocean and climate change.
Verbal communication skills: 50% of the lectures are based on the so-called flip-class room concept in which the students have to transfer expert knowledge to to their peers. This implies that they alos have to set teaching goals, plan a lecture and present the lecture. Subjects are setup in such a way as to stimulate discussion and participate in the discussion.
Analytical / quantitative skills: students have to setup and run simple numerical (inverse) models to to analyse their data. These model runs are subsequently quantitatively compared with real world data.
Technical skills: using the computer programmes Excel for handling large data sets and data transformations. By regularly comparing different analytical approaches students get insight in the prossibilities and limitations of the different techniques.
