View in EDS

Simulation of the Present and Future Projection of Permafrost on the Qinghai‐Tibet Plateau with Statistical and Machine Learning Models.

Saved in:
Bibliographic Details
Title: Simulation of the Present and Future Projection of Permafrost on the Qinghai‐Tibet Plateau with Statistical and Machine Learning Models.
Authors: Ni, Jie1,2, Wu, Tonghua1,3 thuawu@lzb.ac.cn, Zhu, Xiaofan1, Hu, Guojie1, Zou, Defu1, Wu, Xiaodong1, Li, Ren1, Xie, Changwei1, Qiao, Yongping1, Pang, Qiangqiang1, Hao, Junming1,2,4, Yang, Cheng1,2
Source: Journal of Geophysical Research. Atmospheres. 1/27/2021, Vol. 126 Issue 2, p1-20. 20p.
Subject Terms: *Permafrost, *Earth temperature, *Climate change, Machine learning
Geographic Terms: Tibetan Plateau
Abstract: 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. Key Points: The combined statistical method with machine learning is efficient to obtain the thermal regime of permafrost on the Qinghai‐Tibet Plateau (QTP)The present permafrost area on the QTP is ∼1.04 × 106 km2, and the average mean annual ground temperature and active layer thickness are −1.35 ± 0.42°C and 2.3 ± 0.60 m, respectivelyThe future changes of permafrost are projected to be pronounced due to climate change, but region‐specific [ABSTRACT FROM AUTHOR]
Copyright of Journal of Geophysical Research. Atmospheres is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: GreenFILE
Description
Abstract: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. Key Points: The combined statistical method with machine learning is efficient to obtain the thermal regime of permafrost on the Qinghai‐Tibet Plateau (QTP)The present permafrost area on the QTP is ∼1.04 × 106 km2, and the average mean annual ground temperature and active layer thickness are −1.35 ± 0.42°C and 2.3 ± 0.60 m, respectivelyThe future changes of permafrost are projected to be pronounced due to climate change, but region‐specific [ABSTRACT FROM AUTHOR]
ISSN:2169897X
DOI:10.1029/2020JD033402