RINGO
Motivation
The RINGO (Readiness of ICOS for necessities of integrated global observations) project, a external page European project, funded by H2020 and in Switzerland also supported by the Federal Office for the Environment FOEN, aims to support external page ICOS RI to become ready for the future in five areas: scientific readiness, geographical readiness, technological readiness, data readiness as well as political and administrative readiness.
Objectives
Thus, RINGO has five objectives, addressing these different components of readiness:
- Scientific readiness: To support the further consolidation of the observational networks and enhance their quality.
- Geographical readiness. To enhance ICOS membership and sustainability by supporting interested countries to join ICOS.
- Technological readiness. To further develop and standardize technologies for greenhouse gas observations.
- Data readiness. To improve data streams.
- Political and administrative readiness. To deepen the global cooperation of observational infrastructures and societal impact.
Tasks
While our group contributes to several tasks, our main work will focus on Tast 3.4 "Making non-CO2 GHG eddy covariance measurements operational". Thus, we will focus on CH4 and N2O exchange which is characterised by episodic fluxes and not yet well understood. The unique nature of CH4 and N2O exchange has implications for the processing, data quality checks and gap filling of their fluxes measured by eddy covariance.
We will use new emerging long-term data series from within and outside ICOS, such as measurements from Chamau, a grassland site within the Swiss FluxNet, and Davos, our forest site within ICOS-CH, to refine the data processing and gap filling strategies. This includes the identification of additional, essential driver variables (including low frequency measurements such as water table depth and management activities, for example) and new methods for quality check and gap filling, different from those that work successfully for CO2.
Publications
Hörtnagl L (2021) DYCO: A Python package to dynamically detect andcompensate for time lags in ecosystem time series. Journal of Open Source Software 6: 2575, doi: external page 10.21105/joss.02575
Graf A, Klosterhalfen A, Arriga N, Bernhofer C, Bogena H, Bornet F, Brüggemann N, Brümmer C, Buchmann N, Chi J, Chipeaux C, Cremonese E, Cuntz M, Dušek J, El-Madany TS, Fares S, Fischer M, Foltynova L, Gielen B, Gottschalk P, Gharun M, Ghiasi S, Grünwald T, Heinemann G, Heinesch B, Heliasz M, Holst J, Hörtnagl L, Ibrom A, Ingwersen J, Jurasinski G, Klatt J, Knohl A, Koebsch F, Konopka J, Korkiakoski M, Kowalska N, Kremer P, Kruijt B, Lafont S, Léonard J, De Ligne A, Longdoz B, Loustau D, Magliulo V, Mammarella I, Manca G, Mauder M, Migliavacca M, Mölder M, Ney P, Nilsson M, Neirynck J, Paul-Limoges E, Peichl M, Pitacco A, Poyda A, Rebmann C, Roland M, Sachs T, Schmidt M, Siebicke L, Schrader F, Šigut L, Tuittila ES, Varlagin A, Vendrame N, Vincke C, Völksch I, Wille C, Weber S, Wizemann HD, Zeeman M, Vereecken H (2020) Altered energy partitioning across terrestrial ecosystems in the European drought year 2018. Philosophical Transactions of the Royal Society B 375: 20190524, doi: external page 10.1098/rstb.2019.0524