When: 31 October – 11 November 2022 (plus 28-30 November for remote presentations of final results)
Where: Tjärnö Marine Laboratory, University of Gothenburg, Sweden
Credit: 5 ECTS
Course responsible: Paul Zieger (SU) and Michael Schulz (UiO/MetNo)
Register until September 1st, 2022
*Travel and participation costs will be covered for CHESS members*
The course in 2022 will introduce Earth system analysis as well as data analysis with practical exercises. The course will make use of existing infrastructures, such as climate models (e.g., NorESM, EC-EARTH), model databases (e.g., AeroCom, CMIP6), model data evaluation portals (e.g., AeroCom), and atmospheric and oceanic databases (e.g., EBAS, ORA-IP). Practical work is initiated and accompanied to apply modern visualization, data analysis and statistical tools (e.g., Jupyter notebooks, AeroCom tools). Introductions will be given on the role of aerosols and clouds, observational techniques, Earth system models, climate forcing and climate model evaluation. The course involves a set of relevant lectures and tutorials, with the main emphasis placed on intensive group work and a final report that will be written during and after the course by each student. Before the course, the selected students will be asked to practice the tools to be used on the course (mainly Python and Jupyter notebook) by solving a pre-exercise. The course is primarily aimed at PhD students in atmospheric and biospheric sciences (also advanced MSc students are welcome to apply). During the course the students can either use their own data or utilize provided model data together with long-term aerosol, air, ion, trace gas, meteorological data measured at field stations. Topics for practical work will be suggested depending on student’s background. This year, the topics will relate to the core themes identified in the CRiceS project. Related introductory lectures will be given by researchers from the CRiceS consortia.
For full details and registration, please go to this webpage:
eScience Tools in Climate Science: Linking Observations with Modelling