Articles | Volume 17, issue 3
https://doi.org/10.5194/gmd-17-1249-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-1249-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
14 Feb 2024
Methods for assessment of models |
| 14 Feb 2024
| 14 Feb 2024
ibicus: a new open-source Python package and comprehensive interface for statistical bias adjustment and evaluation in climate modelling (v1.0.1)
Fiona Raphaela Spuler
Department of Meteorology, University of Reading, Reading, UK
Jakob Benjamin Wessel
CORRESPONDING AUTHOR
Department of Mathematics and Statistics, University of Exeter, Exeter, UK
Edward Comyn-Platt
European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, UK
James Varndell
European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, UK
Chiara Cagnazzo
European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, UK
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Short summary
Before using climate models to study the impacts of climate change, bias adjustment is commonly applied to the models to ensure that they correspond with observations at a local scale. However, this can introduce undesirable distortions into the climate model. In this paper, we present an open-source python package called ibicus to enable the comparison and detailed evaluation of bias adjustment methods, facilitating their transparent and rigorous application.
Before using climate models to study the impacts of climate change, bias adjustment is commonly...
