Preprints
https://doi.org/10.5194/essd-2022-278
https://doi.org/10.5194/essd-2022-278
 
14 Sep 2022
14 Sep 2022
Status: this preprint is currently under review for the journal ESSD.

A 60–year (1961–2020) near-surface air temperature dataset over the glaciers of the Tibetan Plateau

Jun Qin1, Weihao Pan2,3, Min He1,2, Ning Lu1, Ling Yao1, Hou Jiang1, and Chenghu Zhou1 Jun Qin et al.
  • 1State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
  • 2University of Chinese Academy of Sciences, Beijing 100049, China
  • 3Key Laboratory of Tibetan Environmental Changes and Land Surfaces Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China

Abstract. Surface air temperature (SAT) is a key indicator of global warming and plays an important role in glacier melting. On the Tibetan Plateau (TP), there exist a large number of glaciers. However, station SAT observations on these glaciers are extremely scarce, and moreover the available ones are characterized by short time series, which substantively hinder our deep understanding of glacier dynamics due to climate changes on the TP. In this study, an ensemble learning model is constructed and trained to estimate glacial SATs with a spatial resolution of 1 km × 1 km from 2002 to 2020 using monthly MODIS land surface temperature products and many auxiliary variables, such as vegetation index, satellite overpass time and air pressure. The satellite-estimated glacial SATs are validated against SAT observations at glacier validation stations. Then, long-term (1961–2020) glacial SATs on the TP are reconstructed by temporally extending the satellite SAT estimates through Bayesian linear regression. The long-term glacial SAT estimates are validated with root mean squared error, mean bias error, and determination coefficient being 1.61 °C, 0.21 °C, and 0.93, respectively. The comparisons are conducted with other satellite SAT estimates and ERA5-Land reanalysis data over the validation glaciers, showing that the accuracy of our satellite glacial SATs and their temporal extensions are both higher. The preliminary analysis illustrates that the glaciers on the TP as a whole have been undergoing a fast warming but the warming exhibits a great spatial heterogeneity. Our dataset can contribute to the monitoring of glaciers’ warming, analysis of their evolution, etc. on the TP. The dataset is freely available from the National Tibetan Plateau Data Centre at https://doi.org/10.11888/Atmos.tpdc.272550 (Qin, 2022).

Jun Qin et al.

Status: open (until 09 Nov 2022)

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Jun Qin et al.

Data sets

Monthly average air temperature data of glacier surface in the Tibetan Plateau (1961-2020) Jun Qin https://doi.org/10.11888/Atmos.tpdc.272550

Jun Qin et al.

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Short summary
To enrich glacial surface air temperature (SAT) product of long time series, an ensemble learning model is constructed to estimate monthly SATs from satellite land surface temperatures at a spatial resolution of 1 km, and long-term glacial SATs from 1961 to 2020 are reconstructed using Bayesian linear regression. This product reveals the overall warming trend and the spatial heterogeneity of warming on TP glaciers, and helps to monitor glacier warming, analyze glacier evolution, etc.