Predictions of environmental change are influenced by global carbon, water, and nutrient cycles; climate interactions; and feedbacks to the Earth system. Relevant processes operate at a large range of spatial and temporal scales and vary across terrestrial, coastal, and marine ecosystems. Feedbacks to the Earth system are driven by natural and anthropogenic disturbance agents, acceleration of nutrient and hydrological cycles, eutrophication, acidification, changes in climate and weather extremes, land cover and land use change, and potential climate intervention strategies. At the same time, increased emphasis on environmental sustainability and infrastructure resilience have driven dramatic increases in observational data and large-scale monitoring networks, exploitation of rapidly advancing computational capacity, and advanced artificial intelligence and machine learning methods applied to improving Earth system predictions. This session focuses on characterizing and reducing uncertainties of ecological feedback mechanisms that improve Earth system understanding and predictive ability through the development and application of data-driven approaches, including data mining, machine learning, data assimilation, and hybrid machine learning-/process-based Earth system modeling. Our ability to acquire, process, and store observational data and generate simulation output greatly exceeds our capacity to intelligently assimilate and apply those data to improve our knowledge of the dynamic Earth system. New methods and approaches are required to disentangle complex ecological interactions that have emergent impacts on the larger Earth system.