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The optimal multimodel ensemble of bias-corrected CMIP5 climate models over China

The optimal multimodel ensemble of bias-corrected CMIP5 climate models over China
The optimal multimodel ensemble of bias-corrected CMIP5 climate models over China
A multimodel ensemble of general circulation models (GCM) is a popular approach to assess hydrological impacts of climate change at local, regional, and global scales. The traditional multimodel ensemble approach has not considered different uncertainties across GCMs, which can be evaluated from the comparisons of simulations against observations. This study developed a comprehensive index to generate an optimal ensemble for two main climate fields (precipitation and temperature) for the studies of hydrological impacts of climate change over China. The index is established on the skill score of each bias-corrected model and different multimodel combinations using the outputs from phase 5 of the Coupled Model Intercomparison Project (CMIP5). Results show that the optimal ensemble of the nine selected models accurately captures the characteristics of spatial–temporal variabilities of precipitation and temperature over China. We discussed the uncertainty of subset ensembles of ranking models and optimal ensemble based on historical performance. We found that the optimal subset ensemble of nine models has relative smaller uncertainties compared with other subsets. Our proposed framework to postprocess the multimodel ensemble data has a wide range of applications for climate change assessment and impact studies.
1525-755X
845-863
Yang, Xiaoli
340106c3-0997-453e-a12a-7b526f490250
Yu, Xiaohan
58ba5d14-7d18-4f90-a7bf-f196f1c016f8
Wang, Yuqian
9e5f6f6a-1d27-47cc-a06c-239a33120fb7
He, Xiaogang
5fd2fdc9-b14e-4010-8490-c02950d0a62a
Pan, Ming
5f0a6106-cf97-4213-b345-6b220f3d9bc4
Zhang, Mengru
a3ce2653-22d8-47d8-a5a0-b5a58f4eabc9
Liu, Yi
c2a184fe-ad55-4ffb-9b08-df1fcb518e1e
Ren, Liliang
ff52e99b-e5e3-4def-8e13-32384335a2e0
Sheffield, Justin
dd66575b-a4dc-4190-ad95-df2d6aaaaa6b
Yang, Xiaoli
340106c3-0997-453e-a12a-7b526f490250
Yu, Xiaohan
58ba5d14-7d18-4f90-a7bf-f196f1c016f8
Wang, Yuqian
9e5f6f6a-1d27-47cc-a06c-239a33120fb7
He, Xiaogang
5fd2fdc9-b14e-4010-8490-c02950d0a62a
Pan, Ming
5f0a6106-cf97-4213-b345-6b220f3d9bc4
Zhang, Mengru
a3ce2653-22d8-47d8-a5a0-b5a58f4eabc9
Liu, Yi
c2a184fe-ad55-4ffb-9b08-df1fcb518e1e
Ren, Liliang
ff52e99b-e5e3-4def-8e13-32384335a2e0
Sheffield, Justin
dd66575b-a4dc-4190-ad95-df2d6aaaaa6b

Yang, Xiaoli, Yu, Xiaohan, Wang, Yuqian, He, Xiaogang, Pan, Ming, Zhang, Mengru, Liu, Yi, Ren, Liliang and Sheffield, Justin (2020) The optimal multimodel ensemble of bias-corrected CMIP5 climate models over China. Journal of Hydrometeorology, 21 (4), 845-863. (doi:10.1175/JHM-D-19-0141.1).

Record type: Article

Abstract

A multimodel ensemble of general circulation models (GCM) is a popular approach to assess hydrological impacts of climate change at local, regional, and global scales. The traditional multimodel ensemble approach has not considered different uncertainties across GCMs, which can be evaluated from the comparisons of simulations against observations. This study developed a comprehensive index to generate an optimal ensemble for two main climate fields (precipitation and temperature) for the studies of hydrological impacts of climate change over China. The index is established on the skill score of each bias-corrected model and different multimodel combinations using the outputs from phase 5 of the Coupled Model Intercomparison Project (CMIP5). Results show that the optimal ensemble of the nine selected models accurately captures the characteristics of spatial–temporal variabilities of precipitation and temperature over China. We discussed the uncertainty of subset ensembles of ranking models and optimal ensemble based on historical performance. We found that the optimal subset ensemble of nine models has relative smaller uncertainties compared with other subsets. Our proposed framework to postprocess the multimodel ensemble data has a wide range of applications for climate change assessment and impact studies.

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Accepted/In Press date: 24 February 2020
e-pub ahead of print date: 23 April 2020
Published date: April 2020

Identifiers

Local EPrints ID: 441292
URI: http://eprints.soton.ac.uk/id/eprint/441292
ISSN: 1525-755X
PURE UUID: 8f982b99-60e4-4f22-a23c-fffe52b466bf
ORCID for Justin Sheffield: ORCID iD orcid.org/0000-0003-2400-0630

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Date deposited: 09 Jun 2020 16:30
Last modified: 23 Jul 2022 04:55

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Contributors

Author: Xiaoli Yang
Author: Xiaohan Yu
Author: Yuqian Wang
Author: Xiaogang He
Author: Ming Pan
Author: Mengru Zhang
Author: Yi Liu
Author: Liliang Ren

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