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Projection of precipitation extremes for eight global warming targets by 17 CMIP5 models

Authors :
Yong Luo
Jianbin Huang
Zongci Zhao
Ying Xu
Xiaojun Guo
Source :
Natural Hazards. 84(3):2299-2319
Publisher :
Springer Nature

Abstract

Based on the historical and future outputs of 17 coupled model intercomparison project phase 5 (CMIP5) models, simulation of the precipitation extremes in China was evaluated under baseline climate condition compared to a gridded daily observation dataset CN05.1. The variations in precipitation extremes for eight global warming targets were also projected. The 17 individual models and the multi-model ensemble accurately reproduced the spatial distribution of precipitation extremes, although they were limited in their ability to capture the temporal characteristics. A notable dry bias existed in Southeast China, while a wet bias was present in North and Northwest China. The precipitation extremes in China were projected to be more frequent and more intense as global temperature rise reached the 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, and 5.0 °C warming targets. The projected percentage changes in the annual number of days with precipitation >50 mm (R50) and total precipitation during days in which the daily precipitation exceeds the 99th percentile (R99p) are projected to increase by 25.81 and 69.14 % relative to the baseline climate for a 1.5 °C warming target, and by 95.52 and 162.00 % for a 4.0 °C warming target, respectively. As the global mean temperature rise increased from 1.5 to 5 °C, the subregions considerably affected by the East Asian summer monsoon (e.g., Southwest China, South China, and the Yangtze-Huai River Valley) were projected to experience a more dramatic increase in extreme precipitation events, in both number of days and intensity, while North and Northwest China were projected to suffer from relatively slight increases. The model uncertainties in the projected precipitation extremes in China by 17 CMIP5 models increase as global temperature rise increases.

Details

Language :
English
ISSN :
0921030X
Volume :
84
Issue :
3
Database :
OpenAIRE
Journal :
Natural Hazards
Accession number :
edsair.doi.dedup.....4f29f303b4b168b281f6de3ff961fca4
Full Text :
https://doi.org/10.1007/s11069-016-2553-0