Open-source software

On this page, you can find open-source software developed by members of the group.

Software packages

ibicus: an open-source Python packages for the comparison and evaluation of bias adjustment methods

Statistical bias adjustment is commonly applied to climate models before using their results in impact studies. However, different methods based on a distributional mapping between observational and model data can change the simulated trends as well as the spatiotemporal and inter-variable consistency of the model, and are prone to misuse if not evaluated thoroughly. To address the practical issues impeding this, we introduce ibicus, an open-source Python package for the implementation of eight different peer-reviewed and widely used bias adjustment methods in a common framework and their comprehensive evaluation. The evaluation framework introduced in ibicus allows the user to analyse changes to the marginal, spatiotemporal and inter-variable structure of user-defined climate indices and distributional properties as well as any alteration of the climate change trend simulated in the model. The package was developed by Fiona Spuler and Jakob Wessel in collaboration with ECMWF.

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. 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. This package is currently in development, led by Richard Alawode.