气候变化领域集成服务门户(CCPortal): Permafrost Degradation Risk Evaluation in the Qinghai-Tibet Plateau Under Climate Change Based on Machine Learning Models

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DOI	10.1029/2023JD039611
	Permafrost Degradation Risk Evaluation in the Qinghai-Tibet Plateau Under Climate Change Based on Machine Learning Models
	Zhang, Mingyi; Li, Renwei; Pei, Wansheng; Zhou, Yanqiao; Li, Guanji; Yang, Sheng
发表日期	2024
ISSN	2169-897X
EISSN	2169-8996
卷号	129 期号:2
英文摘要	Permafrost in the Qinghai-Tibet Plateau (QTP) is sensitive to climate warming, but the associated degradation risk still lacks accurate evaluation. To address this issue, machine learning (ML) models are established to simulate the mean annual ground temperature (MAGT) and active layer thickness (ALT), and climate data from shared socioeconomic pathways (SSPs) are prepared for evaluation in the future period. Based on the projections, permafrost is expected to remain relatively stable under the SSP1-2.6 scenario, and large-scale permafrost degradation will occur after the 2050s, resulting in area losses of 30.15% (SSP2-4.5), 58.96% (SSP3-7.0), and 65.97% (SSP5-8.5) in the 2090s relative to the modeling period (2006-2018). The average permafrost MAGT (ALT) is predicted to increase by 0.50 degrees C (59 cm), 0.67 degrees C (89 cm), and 0.79 degrees C (97 cm) in the 2090s with respect to the modeling period under the SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios, respectively. Permafrost in the Qilian Mountains and Three Rivers Source region are fragile and vulnerable to degradation. In the future period, permafrost on the sunny slopes is more prone to degradation and the sunny-shade slope effect of permafrost distribution will be further enhanced under climate warming. The lower limit of permafrost distribution is expected to rise by about 100 m in the 2050s under the SSP2-4.5 scenario. These findings can provide valuable insights about future permafrost changes in the QTP. In the past decades, the Qinghai-Tibet Plateau (QTP) warmed at more than twice the global average, and permafrost degradation within this process has become widely acknowledged. To project the possible changes, a combination of climate data from global climate model, machine learning model, and permafrost field observation data were used, based on a comprehensive review of previous studies. The findings indicate that permafrost in the QTP is not expected to undergo significant degradation under the SSP1-2.6 scenario. However, noticeable permafrost degradation is projected to occur after the 2050s under the SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios, particularly in the Qilian Mountains and Three Rivers Source region. It is predicted that permafrost on sunny slopes is more susceptible to degradation under climate warming, and the permafrost area difference between the sunny and shade slopes will be further expanded. The mean annual air temperature of the QTP will rise by about 1.5 degrees C in the 2050s under the SSP2-4.5 scenario relative to the average between 2006 and 2018, which may lead to a 100 m rise on the low limit of permafrost distribution. Permafrost area of the Qinghai-Tibet Plateau (QTP) is expected to lose by 30.15% (SSP2-4.5) to 65.97% (SSP5-8.5) in the 2090sPermafrost in the Qilian Mountains and Three Rivers Source region are fragile and vulnerable to degradationThe lower limit of permafrost distribution in the QTP is forecasted to rise by about 100 m in the 2050s under the SSP2-4.5 scenario
关键词	climate warming permafrost degradation mean annual ground temperature active layer thickness Qinghai-Tibet Plateau
英文关键词	THERMAL STATE; ACTIVE LAYER; AREA
WOS研究方向	Meteorology & Atmospheric Sciences
WOS记录号	WOS:001146857200001
来源期刊	JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/282791
作者单位	Chinese Academy of Sciences; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS
推荐引用方式 GB/T 7714	Zhang, Mingyi,Li, Renwei,Pei, Wansheng,et al. Permafrost Degradation Risk Evaluation in the Qinghai-Tibet Plateau Under Climate Change Based on Machine Learning Models[J],2024,129(2).
APA	Zhang, Mingyi,Li, Renwei,Pei, Wansheng,Zhou, Yanqiao,Li, Guanji,&Yang, Sheng.(2024).Permafrost Degradation Risk Evaluation in the Qinghai-Tibet Plateau Under Climate Change Based on Machine Learning Models.JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES,129(2).
MLA	Zhang, Mingyi,et al."Permafrost Degradation Risk Evaluation in the Qinghai-Tibet Plateau Under Climate Change Based on Machine Learning Models".JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES 129.2(2024).