Quantifying CMIP6 model uncertainties in extreme precipitation projections

被引:73
作者
John, Amal [1 ,3 ,4 ]
Douville, Herve [1 ]
Ribes, Aurelien [1 ]
Yiou, Pascal [2 ,3 ]
机构
[1] Ctr Natl Rech Meteorol, Meteo France, CNRS, Toulouse, France
[2] IPSL, UMR CEA CNRS UVSQ 8212, Lab Sci Climat & Environm, F-91191 Gif Sur Yvette, France
[3] Univ Toulouse, Toulouse, France
[4] CNRM Meteo Franc, CNRM, GMGEC, CLIMSTAT,DESR, 42 Ave Gaspard Coriolis, F-31100 Toulouse, France
关键词
Climate change; Precipitation; Uncertainty; Extremes; CLIMATE EXTREMES; FUTURE CHANGES; TRENDS; TEMPERATURE; AMPLIFICATION; ENSEMBLE;
D O I
10.1016/j.wace.2022.100435
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
Projected changes in precipitation extremes and their uncertainties are evaluated using an ensemble of global climate models from phase 6 of the Coupled Model Intercomparison Project (CMIP). They are scaled by corresponding changes either in global mean surface temperature (delta GSAT) or in local surface temperature (delta T) and are expressed in terms of 20-yr return values (RV20) of annual maximum one-day precipitation. Our main objective is to quantify the model response uncertainty and to highlight the regions where changes may not be consistent with the widely used assumption of a Clausius-Clapeyron (CC) rate of asymptotic to 7%/K. When using a single realization for each model, as in the latest report from the Intergovernmental Panel on Climate Change (IPCC), the assessed inter-model spread includes both model uncertainty and internal variability, which can be however assessed separately using a large ensemble. Despite the overestimated inter-model spread, our results show a robust enhancement of extreme precipitation with more than 90% of models simulating an increase of RV20. Moreover, this increase is consistent with the CC rate of asymptotic to 7%/K over about 83% of the global land domain when scaled by (delta GSAT). Our results also advocate for producing multiple single model initial condition ensembles in the next CMIP projections, to better filter internal variability out in estimating the response of extreme events.
引用
收藏
页数:11
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