EVENTS

Understanding the interplay of individual processes within the Earth system is fundamental to predicting change and assessing the impacts of anthropogenic activities. Ecosystem responses to these changes are particularly complex due to the vast heterogeneity of organisms, for which we lack fundamental laws. The rapidly growing volume of observations of ecosystem-atmosphere interactions now paves the way for identifying consistent response pattern, however, many challenges remain. While machine learning (ML) methods have made significant advances, particularly in computer vision and natural language processing, they require adaptation to address the unique needs in Earth system sciences. Especially, the mismatch in spatial scales, ranging from individual organisms to entire landscapes, complicates the integration of diverse observations for Earth system modeling.

This presentation explores the challenges and solutions in integrating mechanistic modeling—specifically land models within Earth system modeling—with observations-informed ML approaches. We focus on three critical processes in the land system with feedbacks to the Earth system: First, we apply ML and causality methods to detect and quantify the effects of rising CO2 on ecosystems, a critical factor influencing the land carbon sink in future climate projections. Second, we explore phenology, the seasonal dynamics of ecosystems, employing various ML techniques to model phenological changes and their potential feedbacks on energy, water, and carbon fluxes to the atmosphere. Third, we examine stomatal conductance, the mechanism by which plants regulate gas exchange with the atmosphere through leaf openings. We present a physics-constrained ML approach to infer this stomatal conductance based on observational data, which is then integrated into Earth system models to simulate feedback loops in the land-atmosphere continuum. Finally, we outline a pathway forward for advancing ML-enhanced Earth system models.