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

Download

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

- Printer-friendly version

- 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

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.