干旱区生态环境知识资源中心(Arid): An improved deep learning procedure for statistical downscaling of climate data

Arid

DOI	10.1016/j.heliyon.2023.e18200
	An improved deep learning procedure for statistical downscaling of climate data
	Kheir, Ahmed M. S.; Elnashar, Abdelrazek; Mosad, Alaa; Govind, Ajit
通讯作者	Kheir, AMS
来源期刊	HELIYON
EISSN	2405-8440
出版年	2023
卷号	9 期号:7
英文摘要	Recent climate change (CC) scenarios from the Coupled Model Intercomparison Project Phase 6 (CMIP6) have just been released in coarse resolution. Deep learning (DL) based on statistical downscaling has recently been used, but more research is needed, particularly in arid regions, because little is known about their suitability for extrapolating future CC scenarios. Here we analyzed this issue by downscaling maximum, and minimum temperature over the Egyptian domain based on one General Circulation Model (GCM) as CanESM5 and two shared socioeconomic pathways (SSPs) as SSP4.5 and SSP8.5 from CMIP6 using Convolutional Neural Network (CNN) herein after called CNNSD. The downscaled maximum and minimum temperatures based CNNSD was able to reproduce the observed climate over historical and future periods at a finer resolution (0.1 degrees), reducing the biases exhibited by the original scenario. To the best of our knowledge, this is the first time CNN has been used to downscale CMIP6 scenarios, particularly in arid regions. The downscaled analysis showed that maximum and minimum temperatures are expected to rise by 4.8 degrees C and 4.0 degrees C, respectively, in the future (2015-2100), compared to the historical period, under the moderate scenario (SSP4.5). Meanwhile, under the Fossil-fueled Development scenario (SSP8.5), these values will rise by 6.3 degrees C and 4.2 degrees C, respectively as analyzed by the CNNSD. The developed approach could be used not only in Egypt but also in other developing countries, which are especially vulnerable to climate change and has a scarcity of related research. The established downscaled approach's supply can be used to provide climate services, as a driver for impact studies and adaptation decisions, and as information for policy development. More research is needed, however, to include multi-GCMs to quantify the uncertainties between GCMs and SSPs, improving the outputs for use in climate change impacts and adaptations for food and nutrition security.
英文关键词	Climate change scenarios GCM SSP Convolutional neural network Bias adjustment Standardization
类型	Article
语种	英语
开放获取类型	gold, Green Published
收录类别	SCI-E
WOS记录号	WOS:001049343200001
WOS关键词	MODEL ; PRECIPITATION ; TEMPERATURE ; IMPACT ; SIMULATIONS ; PROJECTIONS ; ENSEMBLE ; BASIN ; CMIP5
WOS类目	Multidisciplinary Sciences
WOS研究方向	Science & Technology - Other Topics
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/396816
推荐引用方式 GB/T 7714	Kheir, Ahmed M. S.,Elnashar, Abdelrazek,Mosad, Alaa,et al. An improved deep learning procedure for statistical downscaling of climate data[J],2023,9(7).
APA	Kheir, Ahmed M. S.,Elnashar, Abdelrazek,Mosad, Alaa,&Govind, Ajit.(2023).An improved deep learning procedure for statistical downscaling of climate data.HELIYON,9(7).
MLA	Kheir, Ahmed M. S.,et al."An improved deep learning procedure for statistical downscaling of climate data".HELIYON 9.7(2023).