气候变化领域集成服务门户(CCPortal): Statistical downscaling of maximum temperature under CMIP6 global climate models and evaluation of heat wave events using deep learning methods for Indo-Gangetic Plain

CCPortal

DOI	10.1002/joc.8366
	Statistical downscaling of maximum temperature under CMIP6 global climate models and evaluation of heat wave events using deep learning methods for Indo-Gangetic Plain
	Chaturvedi, Manisha; Mall, Rajesh Kumar; Singh, Saumya; Chaubey, Pawan K.; Pandey, Ankur
发表日期	2024
ISSN	0899-8418
EISSN	1097-0088
起始页码	44
结束页码	3
卷号	44 期号:3
英文摘要	The rising global temperature is one of the primary concerns of the world as it impacts the economy, environment and healthcare of any country which are more pronounced a regional level. Assessment of regional impacts of climate change at a local level requires fine resolution of climate data for which a robust and fast downscaling method is needed. In this study, we use three deep learning-based methods, namely long short-term memory network (LSTM), deep neural network (DNN) and recurrent neural network (RNN), to downscale CMIP6 13 GCMS models data (1.25 degrees x 1.25 degrees resolution) global climate model (GCM) maximum temperature (Tmax) at a regional scale of 0.5 degrees x 0.5 degrees spatial resolution for the period 1991-2010 over the Indo-Gangetic Plain (IGP). In addition to the temperature prediction, heat wave events have been also analysed in the study. The study found that LSTM method performs better than DNN and RNN in downscaling of all GCM model datasets when evaluated against observed maximum temperature data from the India Meteorological Department (IMD) in terms of RMSE (0.9-3.5), average of all grid MAE value between (1.2 and 2.68), correlation (0.68-0.9) along with and spatiotemporal variability. LSTM also performed better in heat wave prediction over the region with similar temporal range (12-36 events) and spatial occurrence as compared to the observation (12-28 events). Overall, the study concludes that LSTM performs better than two methods for Indo-Gangetic Plain with best hyper parameter tuning. Hence, we propose to utilize a deep learning framework based on LSTM for downscaling GCM dataset at a finer resolution.
英文关键词	CMIP6; deep learning; downscaling; Indo-Gangetic Plain; temperature
语种	英语
WOS研究方向	Meteorology & Atmospheric Sciences
WOS类目	Meteorology & Atmospheric Sciences
WOS记录号	WOS:001149574300001
来源期刊	INTERNATIONAL JOURNAL OF CLIMATOLOGY
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/302684
作者单位	Banaras Hindu University (BHU); Banaras Hindu University (BHU)
推荐引用方式 GB/T 7714	Chaturvedi, Manisha,Mall, Rajesh Kumar,Singh, Saumya,et al. Statistical downscaling of maximum temperature under CMIP6 global climate models and evaluation of heat wave events using deep learning methods for Indo-Gangetic Plain[J],2024,44(3).
APA	Chaturvedi, Manisha,Mall, Rajesh Kumar,Singh, Saumya,Chaubey, Pawan K.,&Pandey, Ankur.(2024).Statistical downscaling of maximum temperature under CMIP6 global climate models and evaluation of heat wave events using deep learning methods for Indo-Gangetic Plain.INTERNATIONAL JOURNAL OF CLIMATOLOGY,44(3).
MLA	Chaturvedi, Manisha,et al."Statistical downscaling of maximum temperature under CMIP6 global climate models and evaluation of heat wave events using deep learning methods for Indo-Gangetic Plain".INTERNATIONAL JOURNAL OF CLIMATOLOGY 44.3(2024).