Virtual training in greenhouse gas measurement techniques in collaboration with South Africa

Altogether 25 participants from eight different countries took part in the virtual ‘Eddy Covariance Flux Measurements Intensive Course’ 24.-26.11.2021. The course was led by Christian Brümmer together with Gregor Feig of South African Environmental Observation Network (SAEON) / Expanded Freshwater and Terrestrial Environmental Observation Network (EFTEON). The entire AK Micrometeorology Working group, along with partners and colleagues from the CSIR (Council for Scientific and Industrial Research), Rhodes University, Stellenbosch University and the German Weather Service (DWD) contributed to the programme as lecturers.

Following first day’s lectures on the theoretical background of the EC flux measurements technique and instrumentation, the focus was largely on practical hands-on sessions. The participants were given real datasets and led step-by-step through the processing, filtering, screening, gap filling, handling and plotting of EC data. On the final course day, young researchers from South Africa and Germany presented their ongoing EC projects, and their results as well as practical field-work challenges were discussed. An online course platform was established to facilitate discussion between participants and direct support from course tutors. At a final wrap-up session in December, each student presents their own analyses of either carbon, water or energy fluxes.

This was a repetition of the course that first took place in 2019 at one of the ‘Ecosystem Management Support for Climate Change in Southern Africa’ (EMSAfrica) project field sites in Vuwani, University of Venda, South Africa. The site hosts one of the three EC towers established by the AK Micrometeorology group in collaboration with South African partners as part of the predecessor project ARS AfricaE. At the end of the EMSAfrica project in 2022, all of the towers will be transferred to the ownership of the South African partners to strengthen the regional long-term observation network.