干旱区生态环境知识资源中心(Arid): Application of ensemble machine learning model in downscaling and projecting climate variables over different climate regions in Iran

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DOI	10.1007/s11356-021-16964-y
	Application of ensemble machine learning model in downscaling and projecting climate variables over different climate regions in Iran
	Asadollah, Seyed Babak Haji Seyed; Sharafati, Ahmad; Shahid, Shamsuddin
通讯作者	Sharafati, A (corresponding author), Islamic Azad Univ, Dept Civil Engn, Sci & Res Branch, Tehran, Iran.
来源期刊	ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
ISSN	0944-1344
EISSN	1614-7499
出版年	2022
英文摘要	This study evaluates the future climate fluctuations in Iran's eight major climate regions (G1-G8). Synoptic data for the period 1995-2014 was used as the reference for downscaling and estimation of possible alternation of precipitation, maximum and minimum temperature in three future periods, near future (2020-2040), middle future (2040-2060), and far future (2060-2080) for two shared socioeconomic pathways (SSP) scenarios, SSP119 and SSP245. The Gradient Boosting Regression Tree (GBRT) ensemble algorithm has been utilized to implement the downscaling model. Pearson's correlation coefficient (CC) was used to assess the ability of CMIP6 global climate models (GCMs) in replicating observed precipitation and temperature in different climate zones for the based period (1995-2014) to select the most suitable GCM for Iran. The suitability of 21 meteorological variables was evaluated to select the best combination of inputs to develop the GBRT downscaling model. The results revealed GFDL-ESM4 as the most suitable GCM for replicating the synoptic climate of Iran for the base period. Two variables, namely sea surface temperature (ts) and air temperature (tas), are the most suitable variable for developing a downscaling model for precipitation, while ts, tas, and geopotential height (zg) for maximum temperature, and tas, zg, and sea level pressure (psl) for minimum temperature. The GBRT showed significant improvement in downscaling GCM simulation compared to support vector regression, previously found as most suitable for the downscaling climate in Iran. The projected precipitation revealed the highest increase in arid and semi-arid regions (G1) by an average of 144%, while a declination in the margins of the Caspian Sea (G8) by -74%. The projected maximum temperature showed an increase up to +8 degrees C in highland climate regions. The minimum temperature revealed an increase up to +4 degrees C in the Zagros mountains and decreased by -4 degrees C in different climate zones. The results indicate the potential of the GBRT ensemble machine learning model for reliable downscaling of CMIP6 GCMs for better projections of climate.
英文关键词	Global climate model Shared socioeconomic pathways scenarios Gradient Boosting Regression Tree Climate downscaling Climate change projections
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000708374100008
WOS关键词	TEMPORAL-CHANGES ; RAINFALL ; TEMPERATURE ; CMIP5 ; IMPACTS ; SUPPORT ; WEATHER ; EVENTS ; TRENDS
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/363182
作者单位	[Asadollah, Seyed Babak Haji Seyed; Sharafati, Ahmad] Islamic Azad Univ, Dept Civil Engn, Sci & Res Branch, Tehran, Iran; [Shahid, Shamsuddin] Univ Teknol Malaysia, Fac Engn, Sch Civil Engn, Johor Baharu 81310, Malaysia
推荐引用方式 GB/T 7714	Asadollah, Seyed Babak Haji Seyed,Sharafati, Ahmad,Shahid, Shamsuddin. Application of ensemble machine learning model in downscaling and projecting climate variables over different climate regions in Iran[J],2022.
APA	Asadollah, Seyed Babak Haji Seyed,Sharafati, Ahmad,&Shahid, Shamsuddin.(2022).Application of ensemble machine learning model in downscaling and projecting climate variables over different climate regions in Iran.ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH.
MLA	Asadollah, Seyed Babak Haji Seyed,et al."Application of ensemble machine learning model in downscaling and projecting climate variables over different climate regions in Iran".ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH (2022).