Please note that this session was withdrawn and is no longer available in the respective programme. This withdrawal might have been the result of a merge with another session.

ITS1.18/CL0.1.8 | Causal Inference for Earth System Sciences: Methods, Challenges, and Applications
Causal Inference for Earth System Sciences: Methods, Challenges, and Applications
Convener: Fernando Iglesias-SuarezECSECS | Co-conveners: Gustau Camps-Valls, Marlene KretschmerECSECS, Evgenia GalytskaECSECS, Rebecca HermanECSECS

Learning causal relationships from Earth system data is of paramount importance for understanding its complex dynamics, predicting future changes, and informing effective mitigation and adaptation strategies. Causal inference provides a powerful framework for unraveling cause-effect relationships of different processes within Earth system sciences. This session welcomes contributions that highlight innovative approaches, methodologies, and case studies employing causal inference techniques across Earth sciences.

The session aims to foster interdisciplinary discussions, encourage collaborations, and promote the development of robust causal analysis frameworks tailored to the unique characteristics of the Earth system. We welcome presentations from researchers across different disciplines, highlighting theoretical advancements and practical applications of causal inference to improve our understanding of Earth system processes.

The topics of interest for this session include, but are not limited to:
- Causal discovery methods: algorithms and methodologies for uncovering causal networks among Earth system processes;
- Causal effect estimation: statistical techniques to estimate causal effects of interventions or natural forcings in the Earth system;
- Applications of causal inference: case studies investigating causal pathways and mechanisms driving natural and anthropogenic perturbations such as climate change, land-ocean interactions, extreme events, etc;
- Causal modeling and network analysis: development and application of causal models, network analysis, and graphical models to capture the intricate interconnections and feedbacks within dynamical systems;
- Causal model evaluation: application of causal dependencies to assess climate models performance;
- Challenges and limitations: associated with the application of causal inference, including issues related to violations of assumptions, or uncertainty quantification.