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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- 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
- 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. 10.5194/hess-26-2319-2022
- 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
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- Contrasting changes in hydrological processes of the Volta River basin under global warming M. Dembélé et al. 10.5194/hess-26-1481-2022
- 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: 09 Mar 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...
