The study of causality is fundamental for understanding complex environmental systems, including attributions of past events and future predictions under changing environmental conditions. Causal approaches span disciplines of Earth sciences and are increasingly practical thanks to rapidly-expanding observational and simulated data and methodology development. We welcome contributions across the geosciences, such as water resources, climate, extreme events, biogeochemistry, and the critical zone, with a focus on causal inference or causal discovery. Examples of applications may include: (i) causal methods developments, applications, or counterfactual approaches that study environmental systems and how they respond to climate change or perturbations, (ii) physically-based or machine learning model studies with a focus on causality, explainability (e.g. explainable AI), functional performance, or process representations, (iii) comparisons of causal inference methods across case studies, time scales, or data sizes, (iv) other studies that “peek into complex system dynamics” in the geosciences.

• B - Biogeosciences
• NG - Nonlinear Geophysics