We are experiencing catastrophic global environmental change: global warming, population growth, and the sixth mass extinction. There is a pressing need to understand why our world is changing and predict changes to adapt. Despite progress made through emerging data analytics technologies (e.g., artificial intelligence - AI and machine learning - ML), there remains a need to understand causal effects of interconnected human-natural systems. AI/ML alone cannot capture the feedbacks and processes connecting land, water, atmosphere, and biosphere. For this, we rely on process-based models (e.g., radiative transfer, hydrology, fire behavior) but they often prohibit operational uses. We invite presentations on novel applications of AI/ML and process-based models to provide near-real-time predictive capabilities. We encourage submissions to describe environmental processes and discuss the use of AI/ML for data collection, integration, inference, or computational efficiency. Methods can include: synthetic data, emulation, scale-aware, reduced order, imputation, and data fusion.

• A - Atmospheric Sciences
• EP - Earth and Planetary Surface Processes
• GC - Global Environmental Change
• NH - Natural Hazards