A long-term 1&thinsp;km monthly near-surface air temperature dataset over the Tibetan glaciers by fusion of station and satellite observations

Qin, Jun; Pan, Weihao; He, Min; Lu, Ning; Yao, Ling; Jiang, Hou; Zhou, Chenghu

doi:https://doi.org/10.5194/essd-15-331-2023

Articles | Volume 15, issue 1

https://doi.org/10.5194/essd-15-331-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/essd-15-331-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 15, issue 1

Data description paper

|

19 Jan 2023

Data description paper |

| 19 Jan 2023

A long-term 1 km monthly near-surface air temperature dataset over the Tibetan glaciers by fusion of station and satellite observations

Jun Qin, Weihao Pan, Min He, Ning Lu, Ling Yao, Hou Jiang, and Chenghu Zhou

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

Short summary

To enrich a glacial surface air temperature (SAT) product of a 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 a 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.