气候变化领域集成服务门户(CCPortal): Simulation of the Present and Future Projection of Permafrost on the Qinghai-Tibet Plateau with Statistical and Machine Learning Models

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DOI	10.1029/2020JD033402
	Simulation of the Present and Future Projection of Permafrost on the Qinghai-Tibet Plateau with Statistical and Machine Learning Models
	Ni J.; Wu T.; Zhu X.; Hu G.; Zou D.; Wu X.; Li R.; Xie C.; Qiao Y.; Pang Q.; Hao J.; Yang C.
发表日期	2021
ISSN	2169897X
卷号	126 期号:2
英文摘要	The comprehensive understanding of the occurred changes of permafrost, including the changes of mean annual ground temperature (MAGT) and active layer thickness (ALT), on the Qinghai-Tibet Plateau (QTP) is critical to project permafrost changes due to climate change. Here, we use statistical and machine learning (ML) modeling approaches to simulate the present and future changes of MAGT and ALT in the permafrost regions of the QTP. The results show that the combination of statistical and ML method is reliable to simulate the MAGT and ALT, with the root-mean-square error of 0.53°C and 0.69 m for the MAGT and ALT, respectively. The results show that the present (2000–2015) permafrost area on the QTP is 1.04 × 106 km2 (0.80–1.28 × 106 km2), and the average MAGT and ALT are −1.35 ± 0.42°C and 2.3 ± 0.60 m, respectively. According to the classification system of permafrost stability, 37.3% of the QTP permafrost is suffering from the risk of disappearance. In the future (2061–2080), the near-surface permafrost area will shrink significantly under different Representative Concentration Pathway scenarios (RCPs). It is predicted that the permafrost area will be reduced to 42% of the present area under RCP8.5. Overall, the future changes of MAGT and ALT are pronounced and region-specific. As a result, the combined statistical method with ML requires less parameters and input variables for simulation permafrost thermal regimes and could present an efficient way to figure out the response of permafrost to climatic changes on the QTP. © 2020. American Geophysical Union. All Rights Reserved.
英文关键词	active layer; climate change; mean annual ground temperature; permafrost; Qinghai-Tibet Plateau
语种	英语
来源期刊	Journal of Geophysical Research: Atmospheres
来源机构	中国科学院西北生态环境资源研究院
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/185543
作者单位	Cryosphere Research Station on the Qinghai-Tibet Plateau, State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China; Southern Marine Science and Engineering Guangdong Laboratory, Guangzhou, China; School of Civil Engineering, Lanzhou University of Technology, Lanzhou, China
推荐引用方式 GB/T 7714	Ni J.,Wu T.,Zhu X.,et al. Simulation of the Present and Future Projection of Permafrost on the Qinghai-Tibet Plateau with Statistical and Machine Learning Models[J]. 中国科学院西北生态环境资源研究院,2021,126(2).
APA	Ni J..,Wu T..,Zhu X..,Hu G..,Zou D..,...&Yang C..(2021).Simulation of the Present and Future Projection of Permafrost on the Qinghai-Tibet Plateau with Statistical and Machine Learning Models.Journal of Geophysical Research: Atmospheres,126(2).
MLA	Ni J.,et al."Simulation of the Present and Future Projection of Permafrost on the Qinghai-Tibet Plateau with Statistical and Machine Learning Models".Journal of Geophysical Research: Atmospheres 126.2(2021).