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Improving CMIP6 Atmospheric River Precipitation Estimation by Cycle-Consistent Generative Adversarial Networks

文献类型：期刊论文

作者Tian, Yuan8; Zhao, Yang6,7; Li, Jianping5,6,7; Xu, Hongxiong4; Zhang, Chi3; Deng, Lin2; Wang, Yinjun4; Peng, Min1
刊名JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES
出版日期2024-07-28
卷号129期号:14页码:23
ISSN号2169-897X
DOI10.1029/2023JD040698
英文摘要Given the important role of Atmospheric River precipitation (ARP) in the global hydrological cycle, accurate representation of ARP is significant. However, general circulation models (GCMs) demonstrate bias in simulating ARP. The target of this study is to quantify the performance of ARP intensity/frequency for CMIP6 simulations, and further to improve ARP estimation of CMIP6 using Cycle-Consistent Generative Adversarial Networks (CycleGAN) with highlighting the more accurate ARP features under the global warming background. The findings of this study are as follows: (a) although ARP intensity/frequency in reserved-optimal CMIP6 overall reproduces the observation, it is still underestimated at the stronger Atmospheric river (AR) scales, particularly for the AR highly active mid-latitude regions. (b) The CycleGAN-based bias correction approach markedly diminishes the bias of the CMIP6 simulations within most of the AR scales among both global and the four AR highly active regions. Moreover, the performance of the ARP in AR highly active regions is significant improvement, which is mainly due to the reduction of the bias at the strongest scale. (c) Relative to reference period (1986-2005), ARP intensity/frequency at the strongest scale increase notably under 3 degrees C warming level, with an average value of 373.3% in intensity and 415.9% in frequency for global and the four key regions before correction, and the value is 451.9% and 492.5% after bias correction. The results illustrate that CycleGAN can effectively improve the ARP simulations of GCMs, and an early warning implies that future strong extreme ARP should potentially surpass the current expected. Despite the important role of Atmospheric River precipitation (ARP) in global hydrological cycle, accurately simulating global ARP remains a great challenge. Here, the content of our study consists of three main parts. Firstly, we quantify the performance of CMIP6 general circulation models (GCMs) on ARP intensity/frequency simulations and find that CMIP6 GCMs underestimates ARP at the Atmospheric river (AR) highly active regions, especially in the strongest AR intensity scale. Secondly, we utilize a deep learning method named CycleGAN to improve global ARP intensity/frequency simulations of CMIP6 GCMs. The results indicate that CycleGAN effectively corrects the ARP intensity/frequency bias within most of the AR intensity scales among both global and the AR highly active regions. Moreover, the performance of ARP in the AR highly active regions is significant improvement, which is mainly due to the reduction of the bias in the strongest scale. Finally, the trained CycleGAN model is used to evaluate ARP under global warming of 1.5, 2, and 3 degrees. The bias correction results show that ARP in the strongest AR intensity scale surpasses the current expected, especially under 3 degrees warming level. CMIP6 models underestimates Atmospheric River precipitation (ARP) at the Atmospheric river (AR) highly active regions, especially in the strongest AR scale The CycleGAN-based deep learning method reduces model bias notably for ARP intensity and frequency, respectively ARP in the strongest AR scale potentially surpasses the current GCM-based projections expected in different degrees of warming after bias correction
WOS关键词BIAS-CORRECTION ; MODEL
资助项目National Key R&D Program of China[2023YFF0805100] ; Laoshan Laboratory[LSKJ202202600] ; Shandong Natural Science Foundation Project[ZR2019ZD12]
WOS研究方向Meteorology & Atmospheric Sciences
语种英语
WOS记录号WOS:001266811200001
出版者AMER GEOPHYSICAL UNION
资助机构National Key R&D Program of China ; Laoshan Laboratory ; Shandong Natural Science Foundation Project
源URL[http://ir.igsnrr.ac.cn/handle/311030/207727]  
专题陆地水循环及地表过程院重点实验室_外文论文
通讯作者Zhao, Yang; Li, Jianping
作者单位1.Shenyang Geotech Invest & Surveying Res Inst Co Lt, Shenyang, Peoples R China
2.China Meteorol Adm, Shanghai Typhoon Inst, Shanghai, Peoples R China
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing, Peoples R China
4.Chinese Acad Meteorol Sci, State Key Lab Severe Weather, Beijing, Peoples R China
5.Laoshan Lab, Qingdao, Peoples R China
6.Ocean Univ China, Coll Ocean & Atmospher Sci, Qingdao, Peoples R China
7.Ocean Univ China, Acad Future Ocean, Frontiers Sci Ctr Deep Ocean Multispheres & Earth, Ctr Ocean Carbon Neutral,Key Lab Phys Oceanog, Qingdao, Peoples R China
8.Beijing Normal Univ, Sch Syst Sci, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Tian, Yuan,Zhao, Yang,Li, Jianping,et al. Improving CMIP6 Atmospheric River Precipitation Estimation by Cycle-Consistent Generative Adversarial Networks[J]. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES,2024,129(14):23.
APA Tian, Yuan.,Zhao, Yang.,Li, Jianping.,Xu, Hongxiong.,Zhang, Chi.,...&Peng, Min.(2024).Improving CMIP6 Atmospheric River Precipitation Estimation by Cycle-Consistent Generative Adversarial Networks.JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES,129(14),23.
MLA Tian, Yuan,et al."Improving CMIP6 Atmospheric River Precipitation Estimation by Cycle-Consistent Generative Adversarial Networks".JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES 129.14(2024):23.

入库方式： OAI收割

来源：地理科学与资源研究所

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