The fundamental goals of Science are to formulate generalizations based on observations and testable hypotheses (induction), and to use these generalizations to make statements about particular cases (inference), in support of decision-making.

To do so, the Geosciences nowadays increasingly rely on computer models as a way to store knowledge, as hypothesis testing tools, for inter- and extrapolation and for predictions, all of which are subject to uncertainty.

While this unites the Geosciences, what separates them are the multitude of data, approaches for building and testing of models (structure diagnosis, parameter optimization and evaluation), metrics and scores used, and last but not least ways to estimate and handle uncertainty. This separation obstructs communication across disciplines and with it the building of interdisciplinary models to address interdisciplinary questions.

The goal of this session is therefore to present and discuss possible avenues of progress towards a commonly applicable framework for model building and application along the following questions:

• How to evaluate the appropriateness (generality, parsimony) of models in a generalized way?

• How to evaluate the interplay of data-, model structure- and predictive uncertainty, i.e. the flow of information from data through models to decision-makers?

• How to learn from the encounter of models and data; i.e. how to detect, diagnose and correct model structural errors?

• How to build complex interdisciplinary models that remain falsifiable?

• How to assess limits of predictability of models and the underlying theoretical concepts?

In this session, we solicit contributions addressing i) the general nature, role and limitations of models in the Geosciences, ii) typical disciplinary modelling approaches, their limitations and how to overcome them, iii) interdisciplinary model frameworks, iv) frameworks for generalized model identification, evaluation, and uncertainty/predictability assessment, especially based on Bayesian, information and estimation Theory.