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.
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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- Review of bias correction methods for climate model outputs in hydrology A. Menapace et al. 10.1016/j.jhydrol.2025.133213
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- A temporal stochastic bias correction using a machine learning attention model O. Nivron et al. 10.1017/eds.2024.42
- Adjusting spatial dependence of climate model outputs with cycle-consistent adversarial networks B. François et al. 10.1007/s00382-021-05869-8
20 citations as recorded by crossref.
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- Ensembles of climate simulations to anticipate worst case heatwaves during the Paris 2024 Olympics P. Yiou et al. 10.1038/s41612-023-00500-5
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- Future nuclear power outages in a changing climate - A case study on two contrasted French power plants L. Collet et al. 10.1016/j.energy.2025.135207
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- 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. 10.1016/j.jhydrol.2023.129807
- Correcting biases in tropical cyclone intensities in low-resolution datasets using dynamical systems metrics D. Faranda et al. 10.1007/s00382-023-06794-8
- Is time a variable like the others in multivariate statistical downscaling and bias correction? Y. Robin & M. Vrac 10.5194/esd-12-1253-2021
- Assessing multivariate bias corrections of climate simulations on various impact models under climate change D. Allard et al. 10.5194/hess-29-4711-2025
- Combining global climate models using graph cuts S. Thao et al. 10.1007/s00382-022-06213-4
- Contrasting changes in hydrological processes of the Volta River basin under global warming M. Dembélé et al. 10.5194/hess-26-1481-2022
- Impact of bias adjustment strategy on ensemble projections of hydrological extremes P. Astagneau et al. 10.5194/hess-29-5695-2025
- Review of bias correction methods for climate model outputs in hydrology A. Menapace et al. 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. 10.2166/wcc.2023.669
- A temporal stochastic bias correction using a machine learning attention model O. Nivron et al. 10.1017/eds.2024.42
1 citations as recorded by crossref.
Latest update: 28 Oct 2025
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...