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ARS Home » Plains Area » El Reno, Oklahoma » Grazinglands Research Laboratory » Agroclimate and Natural Resources Research » Research » Publications at this Location » Publication #351537

Research Project: Uncertainty of Future Water Availability Due to Climate Change and Impacts on the Long Term Sustainability and Resilience of Agricultural Lands in the Southern Great Plains

Location: Agroclimate and Natural Resources Research

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

Author
item Guo, Qiang - Wuhan University
item Chen, Jie - Wuhan University
item Zhang, Xunchang
item Shen, Mingxi - Wuhan University
item Chen, Hua - Wuhan University
item Guo, Shenglian - Wuhan University

Submitted to: Climate Dynamics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/12/2019
Publication Date: 3/22/2019
Citation: Guo, Q., Chen, J., Zhang, X.J., Shen, M., Chen, H., Guo, S. 2019. A new two-stage multivariate quantile mapping method for bias correcting climate model outputs. Climate Dynamics. https://doi.org/10.1007/s00382-019-04729-w.
DOI: https://doi.org/10.1007/s00382-019-04729-w

Interpretive Summary: 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 climate change impacts. To solve this problem, this study proposes a new multivariate bias correction method (referred to as TSQM) by combining a single-variable bias correction method with a distribution-free shuffle approach. Specifically, a quantile mapping method is first used to correct the distributions of single variables and a distribution-free shuffle approach is then used to introduce the inherent multivariable correlations among the variables. For demonstration, the proposed method is compared with the other three state-of-the-art multivariate bias correction methods for correcting monthly precipitation, and maximum and minimum temperatures simulated by climate models. The results showed that the TSQM method is capable of bias correcting both univariate statistics and inducing proper inter-variable rank correlations. Especially, it outperforms all other three methods in reproducing inter-variable rank correlations. 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. This work will be useful to climatologists and hydrologists who are interested in assessing the climate change impacts on natural resources.

Technical 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 climate change impacts. To solve this problem, this study proposes a new multivariate bias correction method (referred to as TSQM) by combining a single-variable bias correction method to a distribution-free shuffle approach. Specifically, a quantile mapping method is first used to correct the distributions of single variables and a distribution-free shuffle approach is then used to introduce the inherent multivariable correlations among the variables. For demonstration, the proposed method is compared with the other three state-of-the-art multivariate bias correction methods for correcting monthly precipitation, and maximum and minimum temperatures simulated by climate models. The results showed that the TSQM method is capable of bias correcting both univariate statistics and inducing proper inter-variable rank correlations. Especially, it outperforms all other three methods in reproducing inter-variable rank correlations. 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.