EGU24-2819, updated on 08 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Hybrid-Modeling of Land-Atmosphere Fluxes Using Integrated Machine Learning in the ICON-ESM Modeling Framework

Reda ElGhawi1,2,3, Christian Reimers1, Reiner Schnur4, Markus Reichstein1, Marco Körner3, Nuno Carvalhais1, and Alexander J. Winkler1
Reda ElGhawi et al.
  • 1Max Planck Institute for Biogeochemistry, Biogeochemical Integration, Jena, Germany
  • 2International Max Planck Research School for Global Biogeochemical Cycles, Jena, Germany
  • 3Department of Aerospace and Geodesy, School of Engineering and Design, Technical University of Munich, Munich, Germany
  • 4Max-Planck-Institute for Meteorology, Hamburg, Germany

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 Earth system model ICON-ESM with the land component JSBACH4, have long been used to study the land-atmosphere coupling. However, these models are hampered by relatively rigid functional representations of terrestrial biospheric processes, e.g., semi-empirical parametrizations for stomatal conductance.

Here, we develop data-driven, flexible parametrizations controlling terrestrial carbon-water coupling based on eddy-covariance flux measurements using machine learning (ML). Specifically, we introduce a hybrid modeling approach (integration of data-driven and mechanistic modeling), that aims to replace specific empirical parametrizations of the coupled photosynthesis (GPP ) and transpiration (Etr ) modules with ML models pre-trained on observations. First, as a proof-of-concept, we train parametrizations based on original JSBACH4 output to showcase that our approach succeeds in reconstructing the original parametrizations, namely latent dynamic features for stomatal (gs) and aerodynamic (ga) conductance, the carboxylation rate of RuBisCO (Vcmax), and the photosynthetic electron transport rate for RuBisCO regeneration (Jmax). Second, we replace JSBACH4’s original parametrizations by dynamically calling the emulator parameterizations trained on the original JSBACH4 output using a Python-FORTRAN bridge. This allows us to assess the impact of data-driven parametrizations on the output in the coupled land-surface model. In the last step, we adopt the approach to infer these parametrizations from FLUXNET observations to construct an observation-informed model of water and carbon fluxes in JSBACH4.

Preliminary results in emulating JSBACH4 parametrizations reveal R2 ranging between 0.91-0.99 and 0.92-0.97 for GPP, Etr, and the sensible heat flux QH  at half-hourly scale for forest and grassland sites, respectively. JSBACH4 with the plugged-in ML-emulator parametrizations provides very similar, but not identical predictions as the original JSBACH4. For example, R2 for Etr (gs) amounts to 0.91 (0.84) and 0.93 (0.86) at grassland and forest sites, respectively. These differences in the transpiration flux between original predictions and JSBACH4 with emulating parametrizations only result in minor changes in the system, e.g., the soil-water budget in the two models is almost the same (R2 of ~0.99). Based on these promising results of our proof-of-concept, we are now preparing the hybrid JSBACH4 model with parametrizations trained on FLUXNET observations.

This modeling framework will then serve as the foundation for coupled land-atmosphere simulations using ICON-ESM, where key biospheric processes are represented by our hybrid observation-informed land-surface model.

How to cite: ElGhawi, R., Reimers, C., Schnur, R., Reichstein, M., Körner, M., Carvalhais, N., and Winkler, A. J.: Hybrid-Modeling of Land-Atmosphere Fluxes Using Integrated Machine Learning in the ICON-ESM Modeling Framework, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2819,, 2024.