Spatiotemporal <scp>freeze–thaw</scp> variations over the <scp>Qinghai‐Tibet</scp> Plateau 1981–2017 from reanalysis

• Published in: International Journal of Climatology
• Main Authors: Qin, Yanhui, Wu, Tonghua, Zhang, Peng, Liu, Wenfeng, Xue, Shanbin, Guo, Zonghe
• Other Authors: National Natural Science Foundation of China
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2020
• Subjects: Atmospheric Science; permafrost
• Online Access: http://dx.doi.org/10.1002/joc.6849; https://onlinelibrary.wiley.com/doi/pdf/10.1002/joc.6849; https://onlinelibrary.wiley.com/doi/full-xml/10.1002/joc.6849; https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/joc.6849

Abstract The thawing and freezing conditions on the Qinghai‐Tibet Plateau (QTP) are considered effective indicators that are widely used in ecology, climate change, cold‐region engineering design, and permafrost mapping. The purpose of this study is to utilize reanalysis to detect spatial variations in freeze–thaw conditions across the QTP from 1981–2017. From a comparison of five recent reanalysis models (MERRA2: National Aeronautics and Space Administration Modern‐Era Reanalysis for Research and Applications, Version 2; ERA: European Center for Medium‐Range Weather Forecasts Interim Reanalysis; GLDAS: Global Land Data Assimilation System NOAH; CFS: National Centers for Environmental Prediction Climate Forecast System Reanalysis, Version 2; and CMFD: China Meteorological Forcing Dataset) against existing sparse observations of 2‐m surface air temperature (SAT), we find that NASA MERRA2 dataset is the most applicable to the QTP. Then, the MERRA2 SAT dataset was selected for this study and was corrected and validated by utilizing calibration models built from the observed data. The results revealed that the correlation coefficients between the corrected MERRA2 SAT and meteorological station observed data increased from 0.52 to 0.93, 0.49 to 0.90, 0.56 to 0.93, and 0.69 to 0.93 in spring, summer, autumn, and winter, respectively. The corrected parameters performed better in the southern and southeastern portions of the QTP. Finally, we utilize the corrected MERRA2 dataset to develop freeze–thaw indices and evaluate statistical trends. We find that relatively high air freezing indices are one of the important factors for the presence of permafrost in the central and northeastern portions of the QTP. Trends in the air thawing and freezing indices in the permafrost and seasonally frozen ground regions indicate that, from 1981 to 2017 in the permafrost regions, the warming was more significant in the summer than in the other seasons. However, the winter warming rates in permafrost and seasonally frozen ground regions ...