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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Cited
18 citations as recorded by crossref.
- Extreme Precipitation and Flood Estimation Using Large Ensemble Climate Model Outputs: A Review T. Tanaka et al. https://doi.org/10.1061/JHYEFF.HEENG-6665
- Recent south-central Andes water crisis driven by Antarctic amplification is unprecedented over the last eight centuries S. Wang et al. https://doi.org/10.1038/s43247-025-02858-1
- Climate change impact on hydrological droughts: Differences between two ensembles of regional climate projections across Great Britain R. Lane et al. https://doi.org/10.1016/j.ejrh.2026.103417
- Future flood and flood-on-drought peaks and volumes in the Northern Apennines through bias-correction and rainfall-runoff transformation of hourly climate forcings M. Neri et al. https://doi.org/10.1016/j.ejrh.2026.103864
- Assessing financial risk to property portfolios from physical rainfall extremes L. Dawkins et al. https://doi.org/10.5194/nhess-26-4071-2026
- CLIMB: Framework for CLIMate data bias-adjustment and downscaling J. Śledziowski et al. https://doi.org/10.1016/j.softx.2025.102479
- Environmental Justice in the Anthropocene: A Review of Methodological Biases in Impact Assessment Studies E. Johansson et al. https://doi.org/10.51847/1AwvA6oawO
- Climate-driven reduction in biomass production of the Eurasian steppe coincides with nomadic migration during the first millennium CE F. Chen et al. https://doi.org/10.1073/pnas.2513573123
- Near-term climate extremes in Iran based on compound hazards analysis N. Asadi-RahimBeygi et al. https://doi.org/10.1038/s41598-025-29026-x
- Research on Meteorological Drought Risk Prediction in the Daqing River Basin Based on HADGEM3-RA M. Lv & Z. Wang https://doi.org/10.3390/agriculture14101781
- Global Bias-Corrected CORDEX Dataset at Quarter Degree Resolution F. Yakubu et al. https://doi.org/10.1038/s41597-026-08060-y
- Enhancing long-lead rainfall forecasting in data-scarce large watersheds using multi-model fusion A. Tadayon et al. https://doi.org/10.1016/j.ejrh.2025.102936
- Correlated spatiotemporal downscaling of Euro‐CORDEX climatic data for infrastructure resilience assessment A. Chatzidaki et al. https://doi.org/10.1002/joc.8529
- Learning predictable and informative dynamical drivers of extreme precipitation using variational autoencoders F. Spuler et al. https://doi.org/10.5194/wcd-6-995-2025
- Multidisciplinary assessment of the impact of temperature rise and headwind on aircraft take-off performance S. Salles et al. https://doi.org/10.1007/s10584-025-04016-0
- A Bias-Corrected HighResMIP Dataset for Impact Assessment Studies F. Yakubu et al. https://doi.org/10.1038/s41597-026-07709-y
- State of Wildfires 2023–2024 M. Jones et al. https://doi.org/10.5194/essd-16-3601-2024
- Hydrologically relevant bias correction of GPM-IMERG: Performance across quantiles in upland tropical watershed E. Suhartanto et al. https://doi.org/10.1016/j.rineng.2026.110760
18 citations as recorded by crossref.
- Extreme Precipitation and Flood Estimation Using Large Ensemble Climate Model Outputs: A Review T. Tanaka et al. https://doi.org/10.1061/JHYEFF.HEENG-6665
- Recent south-central Andes water crisis driven by Antarctic amplification is unprecedented over the last eight centuries S. Wang et al. https://doi.org/10.1038/s43247-025-02858-1
- Climate change impact on hydrological droughts: Differences between two ensembles of regional climate projections across Great Britain R. Lane et al. https://doi.org/10.1016/j.ejrh.2026.103417
- Future flood and flood-on-drought peaks and volumes in the Northern Apennines through bias-correction and rainfall-runoff transformation of hourly climate forcings M. Neri et al. https://doi.org/10.1016/j.ejrh.2026.103864
- Assessing financial risk to property portfolios from physical rainfall extremes L. Dawkins et al. https://doi.org/10.5194/nhess-26-4071-2026
- CLIMB: Framework for CLIMate data bias-adjustment and downscaling J. Śledziowski et al. https://doi.org/10.1016/j.softx.2025.102479
- Environmental Justice in the Anthropocene: A Review of Methodological Biases in Impact Assessment Studies E. Johansson et al. https://doi.org/10.51847/1AwvA6oawO
- Climate-driven reduction in biomass production of the Eurasian steppe coincides with nomadic migration during the first millennium CE F. Chen et al. https://doi.org/10.1073/pnas.2513573123
- Near-term climate extremes in Iran based on compound hazards analysis N. Asadi-RahimBeygi et al. https://doi.org/10.1038/s41598-025-29026-x
- Research on Meteorological Drought Risk Prediction in the Daqing River Basin Based on HADGEM3-RA M. Lv & Z. Wang https://doi.org/10.3390/agriculture14101781
- Global Bias-Corrected CORDEX Dataset at Quarter Degree Resolution F. Yakubu et al. https://doi.org/10.1038/s41597-026-08060-y
- Enhancing long-lead rainfall forecasting in data-scarce large watersheds using multi-model fusion A. Tadayon et al. https://doi.org/10.1016/j.ejrh.2025.102936
- Correlated spatiotemporal downscaling of Euro‐CORDEX climatic data for infrastructure resilience assessment A. Chatzidaki et al. https://doi.org/10.1002/joc.8529
- Learning predictable and informative dynamical drivers of extreme precipitation using variational autoencoders F. Spuler et al. https://doi.org/10.5194/wcd-6-995-2025
- Multidisciplinary assessment of the impact of temperature rise and headwind on aircraft take-off performance S. Salles et al. https://doi.org/10.1007/s10584-025-04016-0
- A Bias-Corrected HighResMIP Dataset for Impact Assessment Studies F. Yakubu et al. https://doi.org/10.1038/s41597-026-07709-y
- State of Wildfires 2023–2024 M. Jones et al. https://doi.org/10.5194/essd-16-3601-2024
- Hydrologically relevant bias correction of GPM-IMERG: Performance across quantiles in upland tropical watershed E. Suhartanto et al. https://doi.org/10.1016/j.rineng.2026.110760
Saved (final revised paper)
Latest update: 08 Sep 2026
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...
