Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 5.240 IF 5.240
  • IF 5-year value: 5.768 IF 5-year
    5.768
  • CiteScore value: 8.9 CiteScore
    8.9
  • SNIP value: 1.713 SNIP 1.713
  • IPP value: 5.53 IPP 5.53
  • SJR value: 3.18 SJR 3.18
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 71 Scimago H
    index 71
  • h5-index value: 51 h5-index 51
Preprints
https://doi.org/10.5194/gmd-2020-132
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2020-132
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: model description paper 30 Jun 2020

Submitted as: model description paper | 30 Jun 2020

Review status
This preprint is currently under review for the journal GMD.

R2D2: Accounting for temporal dependences in multivariate bias correction via analogue ranks resampling

Mathieu Vrac and Soulivanh Thao Mathieu Vrac and 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

Abstract. Over the last few years, multivariate bias correction methods have been developed to adjust spatial and/or inter-variable dependence properties of climate simulations. Most of them do not correct – and sometimes even degrade – the associated temporal features. Here, we propose a multivariate method to adjust the spatial and/or inter-variable properties while also accounting for the temporal dependence, such as autocorrelations. Our method consists in an extension of a previously developed approach that relies on an analogue-based method applied to the ranks of the time series to be corrected, rather than applied to their ``raw’’ values. Several configurations are tested and compared on daily temperature and precipitation simulations over Europe from one Earth System Model. Those differ by the conditioning information used to compute the analogues, and can include multiple variables at each given time, a univariate variable lagged over several time steps, or both – multiple variables lagged over time steps. Compared to the initial approach, results of the multivariate corrections show that, while the spatial and inter-variable correlations are still satisfactorily corrected even when increasing the dimension of the conditioning, the temporal autocorrelations are improved with some of the tested configurations of this extension. A major result is also that the choice of the information to condition the analogues is key since it partially drives the capability of the proposed method to reconstruct proper multivariate dependencies.

Mathieu Vrac and Soulivanh Thao

Interactive discussion

Status: open (until 25 Aug 2020)
Status: open (until 25 Aug 2020)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
[Subscribe to comment alert] Printer-friendly Version - Printer-friendly version Supplement - Supplement

Mathieu Vrac and Soulivanh Thao

Mathieu Vrac and Soulivanh Thao

Viewed

Total article views: 39 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
23 11 5 39 2 3 5
  • HTML: 23
  • PDF: 11
  • XML: 5
  • Total: 39
  • Supplement: 2
  • BibTeX: 3
  • EndNote: 5
Views and downloads (calculated since 30 Jun 2020)
Cumulative views and downloads (calculated since 30 Jun 2020)

Viewed (geographical distribution)

Total article views: 20 (including HTML, PDF, and XML) Thereof 20 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved

No saved metrics found.

Discussed

No discussed metrics found.
Latest update: 10 Jul 2020
Publications Copernicus
Download
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...
Citation