StoryPy: A Python-based package to compute climate storylines

Dynamical storylines explore qualitatively different changes in climate driven by forced responses
in large-scale remote drivers, such as Arctic Amplification, tropical amplification, and the stratospheric
polar vortex. This approach helps address uncertainties in regional climate responses by using physical
understanding to link large-scale thermodynamic and dynamic climate responses to regional impacts and
present a small set of projections in a conditional way. By contextualizing events within broader climate
patterns, dynamical storylines aim to deepen understanding of the uncertainties associated with climate
change, particularly in relation to polar, tropical, and global warming.

Our project aims to make this advanced methodology accessible to a broader audience through a
user-friendly Python package and an intuitive interface. Our package, termed StoryPy, provides
a set of functions to analyze multi-model ensembles by focusing on the identification of dynamical
storylines. With customizable options for selecting remote drivers, target seasons, and climate variables
or climatic-impact drivers, the StoryPy provides flexibility and adaptability for various research
and policy applications. In this work we show the usability of the tool by applying it to the case of the
Mediterranean region and analyze regional climate uncertainty associated with drivers including Arctic
Amplification and the Stratospheric polar vortex.

By facilitating the technical complexity of identifying coherent narratives that bridge the gap between
complex climate dynamics and specific, actionable impacts, our hope is that in the long-run this tools
helps to facilitate dialogue among scientists, policymakers, and diverse stakeholder communities.