Preprints
https://doi.org/10.5194/hess-2024-102
https://doi.org/10.5194/hess-2024-102
28 Aug 2024
 | 28 Aug 2024
Status: this preprint is currently under review for the journal HESS.

Assessing multivariate bias corrections of climate simulations on various impact models under climate change

Denis Allard, Mathieu Vrac, Bastien François, and Iñaki García de Cortázar-Atauri

Abstract. Atmospheric variables simulated from climate models often present biases relative to the same variables calculated by reanalysis in the past. In order to use these models to assess the impact of climate change on processes of interest, it is necessary to correct these biases. Currently, the bias correction methods used operationally correct one-dimensional time series and are therefore applied separately, physical variable by physical variable and site by site. Multivariate bias correction methods have been developed to better take into account dependencies between variables and in space. Although the performance of multivariate bias correction methods for adjusting the statistical properties of simulated climate variables has already been evaluated, their effectiveness for different impact models has been little investigated. In this work, we propose a comparison between two multivariate bias correction methods (R2D2 and dOTC) in three different configurations (intervariable, spatial and spatial-intervariable) and a univariate correction (CDF-t) through several highly multivariate impact models (phenological stage, reference evapotranspiration, soil water content, fire weather index) integrating the weather conditions over a whole season. Our results show that CDF-t does a fair job in most situations and that there is no single best MBC method. The performances of multivariate bias correction methods depend both on some characteristics of the studied process and on the configuration of the chosen bias correction method. When all characteristics are important (multivariate, time cumulative and spatial) it is found that dOTC in its spatial-intervariable configuration brings improvements in most cases and no significant improvement in some rare cases. We did not find any multivariate cases where the spatial-intervariable configuration for dOTC performs less well than CDF-t.

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Denis Allard, Mathieu Vrac, Bastien François, and Iñaki García de Cortázar-Atauri

Status: open (extended)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2024-102', Anonymous Referee #1, 01 Oct 2024 reply
  • Introduction:
    • The introduction would benefit from a more detailed and systematic literature review of the development of multivariate bias correction methods. Discussing the advantages and disadvantages of existing methods would provide valuable context and help highlight the contribution of this study.
  • Figure 1:
    • It would be helpful if the subtitles provided more detailed information about each panel. Additionally, labeling the subfigures with identifiers like (a), (b), (c) or numbers like 1, 2, 3 would improve readability and make it easier to reference specific parts of the figure in the text.
  • Line 180:
    • Including a simple formula to show how the Soil Water Content (SWC) is calculated would enhance the reader's understanding of the methodology and the variables involved.
  • Line 187:
    • Please discuss the impact of the simplicity of the model or assumptions mentioned here. Elaborating on how this simplicity might affect the results or interpretations would strengthen the credibility of the study.
  • Line 216:
    • Under climate change, inter-variable correlations might change over time. How does the methodology account for potential changes in inter-variable correlations in future climate scenarios? Addressing this point would clarify the robustness of the bias correction methods when applied under changing climatic conditions.
Citation: https://doi.org/10.5194/hess-2024-102-RC1
Denis Allard, Mathieu Vrac, Bastien François, and Iñaki García de Cortázar-Atauri
Denis Allard, Mathieu Vrac, Bastien François, and Iñaki García de Cortázar-Atauri

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Short summary
Atmospheric variables from climate models often present biases relative to the past. In order to use these models to assess the impact of climate change on processes of interest, it is necessary to correct these biases. We tested several Multivariate Bias Correction Methods (MBCMs) for 5 physical variables that are input variables for 4 process models. We provide recommendations regarding the use of MBCMs when multivariate and time dependent processes are involved.