Storyline approach to extreme event characterization

Extreme climate events are invariably highly nonlinear, complex events, resulting from the confluence of multiple causal factors, and often quite singular. In any complex system there is a tension between analysis methods that respect the singularity of the extreme events at the price of statistical repeatability, and those that emphasize statistical repeatability at the price of nonlinearity and complexity; this dichotomy is found across all areas of science. In the climate context, the 'storyline' approach has emerged in recent years as a way of following the first of these two pathways. I will discuss how the storyline approach can be cast within the mathematical framework of causal networks, which provides a way to bridge between the storyline and probabilistic approaches. This also provides a way to interpret data in an appropriately conditional manner, thereby aiding model-measurement comparison.