EGU24-20863, updated on 11 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Partial land surface emulator forecasts ecosystem states at verified horizons

Marieke Wesselkamp1, Matthew Chantry2, Maria Kalweit3, Ewan Pinnington2, Margarita Choulga2, Joschka Boedecker3, Carsten Dormann1, Florian Pappenberger2, and Gianpaolo Balsamo2
Marieke Wesselkamp et al.
  • 1Freiburg, Environmental sciences and natural ressources, Biometry and environmental systems analysis, Germany (
  • 2Reading, European Center for Medium-range weather forecasts, United Kingdom
  • 3Freiburg, Computer Science, Neurorobotics Laboratory, Germany

While forecasting of climate and earth system processes has long been a task for numerical models, the rapid development of deep learning applications has recently brought forth competitive AI systems for weather prediction. Earth system models (ESMs), even though being an integral part of numerical weather prediction have not yet caught that same attention. ESMs forecast water, carbon and energy fluxes and in the coupling with an atmospheric model, provide boundary and initial conditions. We set up a comparison of different deep learning approaches for improving short-term forecasts of land surface and ecosystem states on a regional scale. Using simulations from the numerical model and combining them with observations, we will partially emulate an existing land surface scheme, conduct a probabilistic forecasts of core ecosystem processes and determine forecast horizons for all variables.

How to cite: Wesselkamp, M., Chantry, M., Kalweit, M., Pinnington, E., Choulga, M., Boedecker, J., Dormann, C., Pappenberger, F., and Balsamo, G.: Partial land surface emulator forecasts ecosystem states at verified horizons, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20863,, 2024.