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

The water and carbon exchange between the land surface and the atmosphere is regulated by meteorological conditions and plant physiological processes. Traditional mechanistic modeling approaches, for example, the Earth system model ICON‐ESM with the land component JSBACH4 , are hampered by relatively rigid parameterizations for stomatal conductance to model land‐atmosphere coupling. We develop a hybrid modeling approach integrating data‐driven flexible parameterizations based on eddy‐covariance flux measurements (FLUXNET) with mechanistic modeling. We replace specific empirical parametrizations of the coupled photosynthesis (gross primary production [GPP]) and transpiration () modules with feed‐forward neural network models pre‐trained on observations. In a proof‐of‐concept, we demonstrate that our approach reconstructs original JSBACH4 parameterizations for stomatal conductance (), maximum carboxylation rates () and the maximum electron transport rates (), that decisively control GPP and . We then replace JSBACH4 's original parametrizations by calling the emulator parameterizations trained on original JSBACH4 output using a Python‐Fortran bridge. Adapting the approach to observational data, Hybrid‐JSBACH4 infers these parametrizations from eddy‐covariance measurements to construct observation‐informed modeling of water and carbon fluxes in JSBACH4 . The mean hourly residuals of in Hybrid‐JSBACH4 with respect to FLUXNET observations vary between −0.1 and 0.15 kg m −2 hr −1 while the JSBACH4 residuals vary between −0.3 and 0.2 kg m −2 hr −1 for forest and grassland sites. The mean hourly residuals for GPP of Hybrid‐JSBACH4 with respect to observations vary between −0.5 and 0.5 gC m −2 hr −1 , compared to the original JSBACH4 with residuals ranging between −1.0 and 0.5 gC m −2 hr −1 , for forest and grassland sites. Our Hybrid‐JSBACH4 model improves the representation of plant physiological responses, and reduces biases in transpiration and GPP simulations under varying atmospheric dryness and water availability conditions.