Seminar: Reda El Ghawi

The exchange of water and carbon between the land-surface and the atmosphere is regulated by meteorological conditions as well as plant physiological processes. Accurate modeling of the coupled system is not only crucial for understanding local feedback loops, but also for global scale carbon and water cycle interactions. Traditional mechanistic modeling approaches, e.g., the ICON-ESM with the JSBACH4 model, have long been used to study the land-atmosphere coupling. However, their ability to generalize is hampered by relatively rigid functional representations of terrestrial biosphere processes, e.g. semi-empirical parametrizations of stomatal conductance. Here, we develop data-driven, flexible parameterizations controlling the terrestrial processes based on eddy-covariance flux measurements using machine learning (ML).Specifically, we introduce a hybrid modeling approach (integration of data-driven and mechanistic modeling), Hybrid-JSBACH, that aims to replace the empirical parametrizations of the coupled photosynthesis and transpiration modules with pre-trained ML models. We do so by constructing a Python-FORTRAN bridge, allowing us to dynamically call the pre-trained data-driven parameterizations based on JSBACH4 output to replace JSBACH4’s original parameterizations for stomatal (and aerodynamic) conductance, the carboxylation rate of RuBisCO, and the photosynthetic electron transport rate for RuBisCO regeneration, with the Neural Network model. This modeling framework will then serve as the foundation for the coupled simulation of the land-atmosphere interface in ICON-ESM, where the biospheric processes are represented by our hybrid land-surface model.