This session explores the full lifecycle of data and model management in the geophysical setting. This include large scale analytics and the representation, estimation, propagation and use (visualisation, decision making) of uncertainty in environmental systems.

Presentations are encouraged on:
- managing data (and models) in their full lifecycle;
- large scale analytics applied to geophysical data;
- theoretical aspects of uncertainty and its management, including conceptual frameworks and modern developments in managing uncertainty (e.g. emulators / surrogate model approaches);
- informatics approaches to managing and propagating uncertainty, including mechanisms for representing, propagating and visualising uncertainty;
- theoretical and applied work on observation uncertainties, including attempts to quantify, estimate or infer observational uncertainties. Links to data quality concepts and issues of spatial and temporal support (of both observations, reality and models);
- theoretical and applied work on model error (or discrepancy modelling), the link between models and reality, the role of observations (model calibration under uncertainty) and the role of models / approximations in scientific study;
- practical applications within which uncertainty quantification plays a key role, or is a major focus.

The session seeks to bring together practitioners from various disciplines including statistics, mathematics, philosophy, computer science, informatics and environmental science broadly to explore and define the state of the art in managing uncertainty in environmental data and models. The aim is to have a presentation of the key issues in uncertainty management across the environmental sciences, both from a theoretical and an applied perspective.