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A new two-stage multivariate quantile mapping method for bias correcting climate model outputs

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Abstract

Bias correction is an essential technique to correct climate model outputs for local or site-specific climate change impact studies. Most commonly used bias correction methods operate on a single variable, which ignores dependency among multiple variables. The misrepresentation of multivariable dependence may result in biased assessment of climate change impacts. To solve this problem, a new multivariate bias correction method referred to as two-stage quantile mapping (TSQM) is proposed by combining a single-variable bias correction method with a distribution-free shuffle approach. Specifically, a quantile mapping method is used to correct the marginal distribution of single variable and then a distribution-free shuffle approach to introduce proper multivariable correlations. The proposed method is compared with the other four state-of-the-art multivariate bias correction methods for correcting monthly precipitation, and maximum and minimum temperatures simulated by global climate models. The results show that the TSQM method is capable of both bias correcting univariate statistics and inducing proper inter-variable rank correlations. Especially, it outperforms all the other four methods in reproducing inter-variable rank correlations and in simulating mean temperature and potential evaporation for wet and dry months of the validation period. Overall, without complex algorithm and iterations, TSQM is fast, simple and easy to implement, and is proved a competitive bias correction technique to be widely applied in climate change impact studies.

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Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (Grant no. 51779176, 51539009, 51339004) and the Thousand Youth Talents Plan from the Organization Department of CCP Central Committee (Wuhan University, China). The authors would like to thank Dr. Chao Li at the University of Victoria for providing scripts of the JBC method and Dr. Alex Cannon at the Environment and Climate Change Canada (Climate Research Division) for providing scripts of MBCp, MBCr and MBCn methods. The authors would also like to acknowledge the contribution of the Climate Research Unit, the National Meteorological Information Center (China), and the World Climate Research Program Working Group on Coupled Modelling, and to thank the climate modeling groups listed in Table 1 for producing and making their model outputs available.

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Authors and Affiliations

  1. State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, 430072, People’s Republic of China

    Qiang Guo, Jie Chen, Mingxi Shen, Hua Chen & Shenglian Guo

  2. USDA-ARS Grazinglands Research Lab, 7207 West Cheyenne St., El Reno, OK, 73036, USA

    Xunchang Zhang

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  1. Qiang Guo
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Correspondence to Jie Chen.

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Guo, Q., Chen, J., Zhang, X. et al. A new two-stage multivariate quantile mapping method for bias correcting climate model outputs. Clim Dyn 53, 3603–3623 (2019). https://doi.org/10.1007/s00382-019-04729-w

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