Primary Convener:
Zhongjing Jiang, University of Illinois Urbana-Champaign

Convener:
Gregory Elsaesser, Columbia University/NASA GISS
Dié WANG, Brookhaven National Laboratory
Duncan Watson-Parris, University of California San Diego

Chair:
Zhongjing Jiang, University of Illinois Urbana-Champaign
Gregory Elsaesser, Columbia University/NASA GISS
Duncan Watson-Parris, University of Oxford

Reducing uncertainty in Earth system models requires integrating uncertainty quantification with optimal observational strategies. However, challenges remain in diagnosing uncertainty sources, calibrating model parameters in the presence of structural errors, and designing observational networks to maximize predictive skill. Advances in AI-based surrogates, large ensembles, Bayesian inference, and Observing System Simulation Experiments (OSSEs) offer new opportunities to enhance model-observation integration and predictions.This session invites contributions that advance uncertainty quantification and observational strategies in Earth system modeling. Topics include, but are not limited to: (1) AI-based surrogates for improved computational efficiency and predictive accuracy; (2) Parameter estimation via large ensembles, such as Perturbed Parameter Ensembles (PPEs); (3) Sensitivity analysis to diagnose dominant uncertainty sources; (4) Bayesian inference and optimization (e.g., MCMC) for model calibration; (5) OSSEs for optimal experimental design and observational network refinement. We welcome submissions from diverse Earth system domains that bridge models and observations to improve predictive capabilities.

Index Terms
1622 Earth system modeling
1626 Global climate models
1627 Coupled models of the climate system
1990 Uncertainty

Suggested Itineraries:
Climate Change and Global Policy
Machine Learning and AI
Open Science and Open Data
Global Impacts‚ Solutions‚ & Policies

Cross-Listed:
A - Atmospheric Sciences
B - Biogeosciences
H - Hydrology
EP - Earth and Planetary Surface Processes

Neighborhoods:
3. Earth Covering