Multi-model ensemble of CMIP6 projections for future extreme climate stress on wheat in the North China plain

被引:57
|
作者
Bai, Huizi [1 ]
Xiao, Dengpan [1 ,2 ]
Wang, Bin [2 ]
Liu, De Li [2 ,3 ,4 ]
Feng, Puyu [2 ]
Tang, Jianzhao [1 ]
机构
[1] Hebei Acad Sci, Inst Geog Sci, Geog Informat Dev & Applicat Hebei, Engn Technol Res Ctr, Shijiazhuang, Hebei, Peoples R China
[2] Wagga Wagga Agr Inst, NSW Dept Primary Ind, Wagga Wagga, NSW, Australia
[3] Univ New South Wales, Climate Change Res Ctr, Sydney, NSW, Australia
[4] Univ New South Wales, ARC Ctr Excellence Climate Extremes, Sydney, NSW, Australia
基金
中国国家自然科学基金;
关键词
CMIP6; extreme climate; GCM projection; multi-model ensemble; wheat; WINTER-WHEAT; HEAT-STRESS; GRAIN-YIELD; CROP PRODUCTION; IMPACT; TEMPERATURE; DROUGHT; SCENARIO; FROST; RICE;
D O I
10.1002/joc.6674
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
Future extreme climate events will become more intense and frequent with global warming, which is a great threat to wheat productivity in the North China Plain (NCP). Projecting future changes in extreme climate events is an important prerequisite for exploring crop adaptation measures to climate variation. In this study, we calculated 11 extreme climate indices at different wheat growth stages that are sensitive to wheat yield across NCP. The future climate projections were sourced from thirteen Global Climate Models (GCMs) from the Coupled Model Intercomparison Project phase 6 (CMIP6) under two emission scenarios of future societal development pathway (SSP) 245 and SSP585. Daily climate data of thirteen GCMs were generated by using a statistically downscaled method. Two multi-model ensemble methods, that is, arithmetic mean and independence weighted mean (IWM), were used to assess the performance of thirteen GCMs in reproducing historical changes of extreme climate indices. The IWM ensemble results could better reproduce historical changes of extreme climate indices than multi-model arithmetic mean and any individual GCM. We found that the frequency and intensity of heat extremes were projected to increase over the 21st century for both scenarios, but those of cold extremes will decrease. Heat stress intensity (HSI) increases around 1.0 degrees C for SSP245 scenario and 1.6 degrees C for SSP585 scenario by the end of the 21st century. There was no significant change in extreme precipitation indices across the NCP, but the changes of extreme precipitation had strong spatial heterogeneity. Overall, wheat production in the NCP might have a higher frequency of exposure to extreme climate, especially under heat stress. Therefore, adopting effective adaptation measures to mitigate heat stress for wheat is the first priority in the NCP.
引用
收藏
页码:E171 / E186
页数:16
相关论文
共 50 条
  • [1] Multi-model ensemble of CMIP6 projections for future extreme climate changes in wheat production regions of China
    Shi, Zexu
    Xiao, Dengpan
    Bai, Huizi
    Chen, Xinmin
    Lu, Yang
    Ren, Dandan
    Yuan, Jinguo
    Zhang, Man
    [J]. CLIMATE DYNAMICS, 2024, 62 (06) : 5061 - 5081
  • [2] Evaluation of the CMIP6 multi-model ensemble for climate extreme indices
    Kim, Yeon-Hee
    Min, Seung-Ki
    Zhang, Xuebin
    Sillmann, Jana
    Sandstad, Marit
    [J]. WEATHER AND CLIMATE EXTREMES, 2020, 29
  • [3] Projection of future extreme precipitation in Iran based on CMIP6 multi-model ensemble
    Zarrin, Azar
    Dadashi-Roudbari, Abbasali
    [J]. THEORETICAL AND APPLIED CLIMATOLOGY, 2021, 144 (1-2) : 643 - 660
  • [4] Projection of future extreme precipitation in Iran based on CMIP6 multi-model ensemble
    Azar Zarrin
    Abbasali Dadashi-Roudbari
    [J]. Theoretical and Applied Climatology, 2021, 144 : 643 - 660
  • [5] Future projection for climate extremes in the North China plain using multi-model ensemble of CMIP5
    Yanxi Zhao
    Dengpan Xiao
    Huizi Bai
    Jianzhao Tang
    De Li Liu
    Jianmei Luo
    [J]. Meteorology and Atmospheric Physics, 2022, 134
  • [6] Future projection for climate extremes in the North China plain using multi-model ensemble of CMIP5
    Zhao, Yanxi
    Xiao, Dengpan
    Bai, Huizi
    Tang, Jianzhao
    Liu, De Li
    Luo, Jianmei
    [J]. METEOROLOGY AND ATMOSPHERIC PHYSICS, 2022, 134 (05)
  • [7] Assessment and Prediction of Extreme Temperature Indices in the North China Plain by CMIP6 Climate Model
    Wang, Hui
    Wang, Lu
    Yan, Guoying
    Bai, Huizi
    Zhao, Yanxi
    Ju, Minmin
    Xu, Xiaoting
    Yan, Jing
    Xiao, Dengpan
    Chen, Lirong
    [J]. APPLIED SCIENCES-BASEL, 2022, 12 (14):
  • [8] The Characteristics and Evaluation of Future Droughts across China through the CMIP6 Multi-Model Ensemble
    Ma, Zice
    Sun, Peng
    Zhang, Qiang
    Zou, Yifan
    Lv, Yinfeng
    Li, Hu
    Chen, Donghua
    [J]. REMOTE SENSING, 2022, 14 (05)
  • [9] Temperature extremes Projections over Bangladesh from CMIP6 Multi-model Ensemble
    Akter, Mst Yeasmin
    Islam, Abu Reza Md Towfiqul
    Mallick, Javed
    Alam, Md Mahfuz
    Alam, Edris
    Shahid, Shamsuddin
    Biswas, Jatish Chandra
    Alam, G. M. Manirul
    Pal, Subodh Chandra
    Oliver, Md Moinul Hosain
    [J]. THEORETICAL AND APPLIED CLIMATOLOGY, 2024, 155 (09) : 8843 - 8869
  • [10] Global marine heatwave events using the new CMIP6 multi-model ensemble: from shortcomings in present climate to future projections
    Plecha, Sandra M.
    Soares, Pedro M. M.
    [J]. ENVIRONMENTAL RESEARCH LETTERS, 2020, 15 (12):