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

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 depe... Full description

Journal Title: Climate Dynamics 2019, Vol.53(5), pp.3603-3623
Main Author: Guo, Qiang
Other Authors: Chen, Jie , Zhang, Xunchang , Shen, Mingxi , Chen, Hua , Guo, Shenglian
Format: Electronic Article Electronic Article
Language: English
Subjects:
ID: ISSN: 0930-7575 ; E-ISSN: 1432-0894 ; DOI: 10.1007/s00382-019-04729-w
Link: http://dx.doi.org/10.1007/s00382-019-04729-w
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recordid: springer_jour10.1007/s00382-019-04729-w
title: A new two-stage multivariate quantile mapping method for bias correcting climate model outputs
format: Article
creator:
  • Guo, Qiang
  • Chen, Jie
  • Zhang, Xunchang
  • Shen, Mingxi
  • Chen, Hua
  • Guo, Shenglian
subjects:
  • Bias correction
  • Inter-variable correlation
  • Statistical downscaling
  • Climate change
  • Global climate model
ispartof: Climate Dynamics, 2019, Vol.53(5), pp.3603-3623
description: 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....
language: eng
source:
identifier: ISSN: 0930-7575 ; E-ISSN: 1432-0894 ; DOI: 10.1007/s00382-019-04729-w
fulltext: fulltext
issn:
  • 1432-0894
  • 14320894
  • 0930-7575
  • 09307575
url: Link


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subjectBias correction ; Inter-variable correlation ; Statistical downscaling ; Climate change ; Global climate model
descriptionBias 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....
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abstractBias 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....
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