The start of frozen dates over northern permafrost regions with the changing climate

Author:

Li Jialing1,Wu Chaoyang23ORCID,Peñuelas Josep45ORCID,Ran Youhua6,Zhang Yongguang17ORCID

Affiliation:

1. International Institute for Earth System Sciences, Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing University Nanjing China

2. The Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China

3. University of the Chinese Academy of Sciences Beijing China

4. CSIC, Global Ecology Unit CREAF‐CSIC‐UAB, Bellaterra Barcelona Catalonia Spain

5. CREAF, Cerdanyola del Valles Barcelona Catalonia Spain

6. Northwest Institute of Eco‐Environment and Resources Chinese Academy of Sciences Lanzhou China

7. Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, School of Geography and Ocean Science Nanjing University Nanjing China

Abstract

AbstractThe soil freeze–thaw cycle in the permafrost regions has a significant impact on regional surface energy and water balance. Although increasing efforts have been made to understand the responses of spring thawing to climate change, the mechanisms controlling the global interannual variability of the start date of permafrost frozen (SOF) remain unclear. Using long‐term SOF from the combinations of multiple satellite microwave sensors between 1979 and 2020, and analytical techniques, including partial correlation, ridge regression, path analysis, and machine learning, we explored the responses of SOF to multiple climate change factors, including warming (surface and air temperature), start date of permafrost thawing (SOT), soil properties (soil temperature and volume of water), and the snow depth water equivalent (SDWE). Overall, climate warming exhibited the maximum control on SOF, but SOT in spring was also an important driver of SOF variability; among the 65.9% significant SOT and SOF correlations, 79.3% were positive, indicating an overall earlier thawing would contribute to an earlier frozen in winter. The machine learning analysis also suggested that apart from warming, SOT ranked as the second most important determinant of SOF. Therefore, we identified the mechanism responsible for the SOT–SOF relationship using the SEM analysis, which revealed that soil temperature change exhibited the maximum effect on this relationship, irrespective of the permafrost type. Finally, we analyzed the temporal changes in these responses using the moving window approach and found increased effect of soil warming on SOF. In conclusion, these results provide important insights into understanding and predicting SOF variations with future climate change.

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

General Environmental Science,Ecology,Environmental Chemistry,Global and Planetary Change

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