Articles | Volume 13, issue 11
https://doi.org/10.5194/gmd-13-5367-2020
© Author(s) 2020. 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-13-5367-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
06 Nov 2020
Model description paper |
| 06 Nov 2020
| 06 Nov 2020
R2D2 v2.0: accounting for temporal dependences in multivariate bias correction via analogue rank resampling
Laboratoire des Sciences du Climat et de l'Environnement (LSCE-IPSL), CEA/CNRS/UVSQ, Université Paris-Saclay
Centre d'Etudes de Saclay, Orme des Merisiers, 91191 Gif-sur-Yvette, France
Soulivanh Thao
Laboratoire des Sciences du Climat et de l'Environnement (LSCE-IPSL), CEA/CNRS/UVSQ, Université Paris-Saclay
Centre d'Etudes de Saclay, Orme des Merisiers, 91191 Gif-sur-Yvette, France
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Cited
22 citations as recorded by crossref.
- Development of localized climate projections for Vietnam’s Mekong Delta using univariate and multivariate bias corrections of CMIP6 simulations D. Dong Phuong et al. https://doi.org/10.1007/s00382-026-08347-1
- Time of emergence of compound events: contribution of univariate and dependence properties B. François & M. Vrac https://doi.org/10.5194/nhess-23-21-2023
- Future shifting of annual extreme flows under climate change in the Volta River basin M. Dembélé et al. https://doi.org/10.5194/piahs-385-121-2024
- Effects of Climate Variability on Malaria Transmission in Southern Côte d’Ivoire, West Africa M. Doumbia et al. https://doi.org/10.3390/ijerph20237102
- Uni- and multivariate bias adjustment methods in Nordic catchments: Complexity and performance in a changing climate F. Tootoonchi et al. https://doi.org/10.1016/j.scitotenv.2022.158615
- Should We Use Quantile-Mapping-Based Methods in a Climate Change Context? A “Perfect Model” Experiment M. Vrac et al. https://doi.org/10.3390/cli13070137
- Amplified potential for vegetation stress under climate-change-induced intensifying compound extreme events in the Greater Mediterranean Region P. Olschewski et al. https://doi.org/10.5194/nhess-24-1099-2024
- Ensembles of climate simulations to anticipate worst case heatwaves during the Paris 2024 Olympics P. Yiou et al. https://doi.org/10.1038/s41612-023-00500-5
- Distribution-based pooling for combination and multi-model bias correction of climate simulations M. Vrac et al. https://doi.org/10.5194/esd-15-735-2024
- Future nuclear power outages in a changing climate - A case study on two contrasted French power plants L. Collet et al. https://doi.org/10.1016/j.energy.2025.135207
- Impact of bias nonstationarity on the performance of uni- and multivariate bias-adjusting methods: a case study on data from Uccle, Belgium J. Van de Velde et al. https://doi.org/10.5194/hess-26-2319-2022
- Generative unsupervised downscaling of climate models via domain alignment: Application to wind fields J. Keisler et al. https://doi.org/10.1017/eds.2026.10057
- Uni- and multivariate bias adjustment of climate model simulations in Nordic catchments: Effects on hydrological signatures relevant for water resources management in a changing climate F. Tootoonchi et al. https://doi.org/10.1016/j.jhydrol.2023.129807
- Correcting biases in tropical cyclone intensities in low-resolution datasets using dynamical systems metrics D. Faranda et al. https://doi.org/10.1007/s00382-023-06794-8
- Is time a variable like the others in multivariate statistical downscaling and bias correction? Y. Robin & M. Vrac https://doi.org/10.5194/esd-12-1253-2021
- Assessing multivariate bias corrections of climate simulations on various impact models under climate change D. Allard et al. https://doi.org/10.5194/hess-29-4711-2025
- Combining global climate models using graph cuts S. Thao et al. https://doi.org/10.1007/s00382-022-06213-4
- Contrasting changes in hydrological processes of the Volta River basin under global warming M. Dembélé et al. https://doi.org/10.5194/hess-26-1481-2022
- Impact of bias adjustment strategy on ensemble projections of hydrological extremes P. Astagneau et al. https://doi.org/10.5194/hess-29-5695-2025
- Review of bias correction methods for climate model outputs in hydrology A. Menapace et al. https://doi.org/10.1016/j.jhydrol.2025.133213
- Bias correction of ERA5-Land temperature data using standalone and ensemble machine learning models: a case of northern Italy M. Niazkar et al. https://doi.org/10.2166/wcc.2023.669
- A temporal stochastic bias correction using a machine learning attention model O. Nivron et al. https://doi.org/10.1017/eds.2024.42
22 citations as recorded by crossref.
- Development of localized climate projections for Vietnam’s Mekong Delta using univariate and multivariate bias corrections of CMIP6 simulations D. Dong Phuong et al. https://doi.org/10.1007/s00382-026-08347-1
- Time of emergence of compound events: contribution of univariate and dependence properties B. François & M. Vrac https://doi.org/10.5194/nhess-23-21-2023
- Future shifting of annual extreme flows under climate change in the Volta River basin M. Dembélé et al. https://doi.org/10.5194/piahs-385-121-2024
- Effects of Climate Variability on Malaria Transmission in Southern Côte d’Ivoire, West Africa M. Doumbia et al. https://doi.org/10.3390/ijerph20237102
- Uni- and multivariate bias adjustment methods in Nordic catchments: Complexity and performance in a changing climate F. Tootoonchi et al. https://doi.org/10.1016/j.scitotenv.2022.158615
- Should We Use Quantile-Mapping-Based Methods in a Climate Change Context? A “Perfect Model” Experiment M. Vrac et al. https://doi.org/10.3390/cli13070137
- Amplified potential for vegetation stress under climate-change-induced intensifying compound extreme events in the Greater Mediterranean Region P. Olschewski et al. https://doi.org/10.5194/nhess-24-1099-2024
- Ensembles of climate simulations to anticipate worst case heatwaves during the Paris 2024 Olympics P. Yiou et al. https://doi.org/10.1038/s41612-023-00500-5
- Distribution-based pooling for combination and multi-model bias correction of climate simulations M. Vrac et al. https://doi.org/10.5194/esd-15-735-2024
- Future nuclear power outages in a changing climate - A case study on two contrasted French power plants L. Collet et al. https://doi.org/10.1016/j.energy.2025.135207
- Impact of bias nonstationarity on the performance of uni- and multivariate bias-adjusting methods: a case study on data from Uccle, Belgium J. Van de Velde et al. https://doi.org/10.5194/hess-26-2319-2022
- Generative unsupervised downscaling of climate models via domain alignment: Application to wind fields J. Keisler et al. https://doi.org/10.1017/eds.2026.10057
- Uni- and multivariate bias adjustment of climate model simulations in Nordic catchments: Effects on hydrological signatures relevant for water resources management in a changing climate F. Tootoonchi et al. https://doi.org/10.1016/j.jhydrol.2023.129807
- Correcting biases in tropical cyclone intensities in low-resolution datasets using dynamical systems metrics D. Faranda et al. https://doi.org/10.1007/s00382-023-06794-8
- Is time a variable like the others in multivariate statistical downscaling and bias correction? Y. Robin & M. Vrac https://doi.org/10.5194/esd-12-1253-2021
- Assessing multivariate bias corrections of climate simulations on various impact models under climate change D. Allard et al. https://doi.org/10.5194/hess-29-4711-2025
- Combining global climate models using graph cuts S. Thao et al. https://doi.org/10.1007/s00382-022-06213-4
- Contrasting changes in hydrological processes of the Volta River basin under global warming M. Dembélé et al. https://doi.org/10.5194/hess-26-1481-2022
- Impact of bias adjustment strategy on ensemble projections of hydrological extremes P. Astagneau et al. https://doi.org/10.5194/hess-29-5695-2025
- Review of bias correction methods for climate model outputs in hydrology A. Menapace et al. https://doi.org/10.1016/j.jhydrol.2025.133213
- Bias correction of ERA5-Land temperature data using standalone and ensemble machine learning models: a case of northern Italy M. Niazkar et al. https://doi.org/10.2166/wcc.2023.669
- A temporal stochastic bias correction using a machine learning attention model O. Nivron et al. https://doi.org/10.1017/eds.2024.42
Saved (final revised paper)
Latest update: 14 Sep 2026
Short summary
We propose a multivariate bias correction (MBC) method to adjust the spatial and/or inter-variable properties of climate simulations, while also accounting for their temporal dependences (e.g., autocorrelations).
It consists on a method reordering the ranks of the time series according to their multivariate distance to a reference time series.
Results show that temporal correlations are improved while spatial and inter-variable correlations are still satisfactorily corrected.
We propose a multivariate bias correction (MBC) method to adjust the spatial and/or...
