A high-resolution air temperature data set for the Chinese Tian Shan in 1979–2016

Gao, Lu; Wei, Jianhui; Wang, Lingxiao; Bernhardt, Matthias; Schulz, Karsten; Chen, Xingwei

doi:https://doi.org/10.5194/essd-10-2097-2018

Articles | Volume 10, issue 4

https://doi.org/10.5194/essd-10-2097-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/essd-10-2097-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 10, issue 4

Review article

|

27 Nov 2018

Review article |

| 27 Nov 2018

A high-resolution air temperature data set for the Chinese Tian Shan in 1979–2016

Lu Gao, Jianhui Wei, Lingxiao Wang, Matthias Bernhardt, Karsten Schulz, and Xingwei Chen

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Final revised paper (published on 27 Nov 2018)
Supplement to the final revised paper
Preprint (discussion started on 28 Jun 2018)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Review: „A high-resolution air temperature data set for the Chinese Tienshan Mountains in 1979-2016“ by Gao et al.', Lars Gerlitz, 09 Jul 2018
- AC1: 'Response to Reviewer 1', Lu Gao, 16 Sep 2018
RC2: 'suggestion', Anonymous Referee #2, 20 Jul 2018
- AC2: 'Response to Reviewer 2', Lu Gao, 16 Sep 2018
RC3: 'Review for “A high-resolution air temperature data set for the Chinese Tianshan Mountains in 1979-2016”', Anonymous Referee #3, 26 Jul 2018
- AC3: 'Response to Reviewer 3', Lu Gao, 16 Sep 2018
SC1: 'Some comments and suggestions', Li Ma, 17 Aug 2018
- AC4: 'Response to the short comments', Lu Gao, 16 Sep 2018

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Short summary

High-resolution temperature data sets are important for the Chinese Tian Shan, which has a complex ecological environment system. This study presents a unique high-resolution (1 km, 6-hourly) air temperature data set for this area from 1979 to 2016 based on a robust statistical downscaling framework. The strongest advantage of this method is its independence of local meteorological stations due to a model internal, vertical lapse rate scheme. This method was validated for other mountains.