the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 28 Aug 2024
Assessing multivariate bias corrections of climate simulations on various impact models under climate change
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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RC1: 'Comment on hess-2024-102', Anonymous Referee #1, 01 Oct 2024
Reviewer Comments on “Assessing Multivariate Bias Corrections of Climate Simulations on Various Impact Models Under Climate Change”
The manuscript is extensive and well-written, providing a thorough evaluation of two multivariate bias correction (MBC) methods—R2D2 (Vrac, 2018) and dOTC (Robin et al., 2019)—to adjust the inter-variable and spatial dependence structures of five physical variables used as inputs for various process models. For each MBC method, three different configurations were considered (inter-variable, spatial, and spatial-intervariable) to disentangle the relative effects of the various dependence structures. A univariate bias correction method (CDF-t, Michelangeli et al., 2009) was also included to assess the potential added value of MBC methods.
The authors found that CDF-t performs adequately in most situations and that there is no single best MBC method. The performance of multivariate bias correction methods depends on the characteristics of the studied process and 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.
Major Concern:
My main concern is related to the use of quantile mapping as a basis for the MBC methods. It is argued that under future climate scenarios, both extreme dry and wet events are projected to increase, potentially leading to unprecedented extreme conditions. If basic quantile mapping is used as a basis of the bias correction method, there is a risk of losing these extreme events in future projections due to its limitations in extrapolating beyond observed extremes. How do the authors address this issue in their methodology? Have they considered using advanced bias correction techniques that better preserve or model extreme events under climate change?
Additional Comments:
- Abstract:
- The abbreviations R2D2, dOTC, CDF-t, and MBC are used without first providing their full terms. It would enhance clarity to define these abbreviations upon their first mention in the abstract.
- 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 -
RC2: 'Comment on hess-2024-102', Stefano Galmarini, 11 Jun 2025
The paper present a comparative analysis of multivariate bias correction methods, that is performed with great care, excellent methodological set up and wealth of evidence in support of the conclusions. As far as I am concerned the manuscript can proceed toward publication almost as it is, being also extremely well written and clear.
My 'almost' is motivated by two requests of clarification and eventually of modification.
1- the domain area selection: while one can appreciate the selection of highly diverse sub-region of France, one can also wonder why the areas are all comparable in size and what would happen to the analysis should these areas be much larger in size. Was the choice of these sizes motivated by computational constraints or the authors see a problem in the methods performance should the domain be larger? An elaboration on this aspects in the conclusions or the case set up, I assume, is due.
2- From the Conclusions section:
''Galmarini et al. (2024) considered a total of 12 crop models, which are highly multivariate and integrative in time,
but the spatial dimension was not considered at all. They found that R2D2 (I.R2D2 configuration) was among the best
performing method, which is in contradiction with our findings showing that I.R2D2 and SI.R2D2 do not adjust better
than CDF-t for SWC and FWI. The exact reason for this discrepancy should be explored in future work.''While the first part of this conclusion is indisputable, the second part merits a more nuanced language and detailed analysis. In fact in Table 3 of Galmarini et al. (2024) it clearly appears that while multivariate methods outperform univariate ones, CDFt outperforms all its siblings but also scores in the first second and third error rankings frequencies comparable to the other multivariate methods and also R2D2 (ranking 2 and 3). Therefore for the specific comparison with CDFt, Galmarini et al. (2024) conclusion is not that far from those of the current manuscript. Please modify accordingly, if you agree. We agree that CDFt appears to be an outstanding univariate method whose properties should be indeed further investigated.
As for the rest I have nothing else to object and while waiting for the response of the authors I wish to congratulate them on this nice piece of research.
Citation: https://doi.org/10.5194/hess-2024-102-RC2
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