Journal
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES
Volume 126, Issue 2, Pages -Publisher
AMER GEOPHYSICAL UNION
DOI: 10.1029/2020JD033402
Keywords
active layer; climate change; mean annual ground temperature; permafrost; Qinghai-Tibet Plateau
Categories
Funding
- Natural Science Foundations of China [41690142, 41771076, 41961144021, 42071093]
- CAS Light of West China Program
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The study uses statistical and machine learning modeling approaches to accurately simulate the changes in temperature and active layer thickness of permafrost on the Qinghai-Tibet Plateau. The results indicate that the current permafrost area on the plateau is substantial, but it is projected to significantly decrease under various climate change scenarios in the future.
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 degrees 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 x 10(6) km(2) (0.80-1.28 x 10(6) km(2)), and the average MAGT and ALT are -1.35 +/- 0.42 degrees 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.
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