Menu


Find the EGU on

Follow us on Twitter Find us on Facebook Find us on Google+ Find us on LinkedIn Find us on YouTube

BG2.5/SSS7.19

Earth observation for monitoring and modeling the global energy, water and carbon cycles over land using model-data integration (co-organized)
Convener: Jean-Christophe Calvet  | Co-Conveners: Gianpaolo Balsamo , Nuno Carvalhais , Roselyne Lacaze , Gregor Schürmann , Thomas Wutzler , Yiqi Luo 
Orals
 / Fri, 02 May, 08:30–12:00  / Room G4
Posters
 / Attendance Fri, 02 May, 13:30–15:00  / Green Posters
An essential component of the adaptation to climate change is the capacity of describing key terrestrial quantities associated with the global energy, water and carbon cycles. Monitoring and modeling the biosphere-atmosphere exchanges of energy, water, carbon and other elements is fundamental in diagnosing and forecasting future Earth system states and dynamics. The underlying ecosystem processes at multiple temporal and spatial scales are still relatively poorly described by Earth system models. Confronting terrestrial biogeochemical models with an ever-increasing amount and diversity of observational data streams is needed.
This objective requires the development of core land data assimilation systems capable of ingesting a wide range of in situ and satellite data sources. Recently, the rapidly growing amount of new satellite data has fostered the development of new land products (vegetation variables, incoming radiation, soil moisture, evaporation) available at a global scale, at spatial resolutions ranging between 1km and 25km.
We welcome studies presenting the most recent advances in (1) the production of land surface biophysical variables from satellite observations, (2) their validation, and (3) their integration into land surface models using data assimilation techniques.
The latter may consider methodological foci or include applications related to the monitoring of terrestrial biosphere fluxes of energy, water, and carbon for timescales going from days to decades. We aim at bringing together contributions that focus on integrating models and multiple sources of observations, ranging from in-situ to satellite measurements.