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The Optimal Multimodel Ensemble of Bias-Corrected CMIP5 Climate Models over China.

Authors :
Yang, Xiaoli
Yu, Xiaohan
Wang, Yuqian
He, Xiaogang
Pan, Ming
Zhang, Mengru
Liu, Yi
Ren, Liliang
Sheffield, Justin
Source :
Journal of Hydrometeorology; Apr2020, Vol. 21 Issue 4, p845-863, 19p
Publication Year :
2020

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
1525755X
Volume :
21
Issue :
4
Database :
Complementary Index
Journal :
Journal of Hydrometeorology
Publication Type :
Academic Journal
Accession number :
142870880
Full Text :
https://doi.org/10.1175/JHM-D-19-0141.1