Review: „A high-resolution air temperature data set for the Chinese Tienshan Mountains in 1979-2016“ by Gao et al.

2018 ◽  
Author(s):  
Lars Gerlitz
2018 ◽  
Vol 10 (4) ◽  
pp. 2097-2114 ◽  
Author(s):  
Lu Gao ◽  
Jianhui Wei ◽  
Lingxiao Wang ◽  
Matthias Bernhardt ◽  
Karsten Schulz ◽  
...  

Abstract. The Chinese Tian Shan (also known as the Chinese Tianshan Mountains, CTM) have a complex ecological environmental system. They not only have a large number of desert oases but also support many glaciers. The arid climate and the shortage of water resources are the important factors restricting the area's socioeconomic development. This study presents a unique high-resolution (1 km, 6-hourly) air temperature data set for the Chinese Tian Shan (41.1814–45.9945∘ N, 77.3484–96.9989∘ E) from 1979 to 2016 based on a robust elevation correction framework. The data set was validated by 24 meteorological stations at a daily scale. Compared to original ERA-Interim temperature, the Nash–Sutcliffe efficiency coefficient increased from 0.90 to 0.94 for all test sites. Approximately 24 % of the root-mean-square error was reduced from 3.75 to 2.85 ∘C. A skill score based on the probability density function, which was used to validate the reliability of the new data set for capturing the distributions, improved from 0.86 to 0.91 for all test sites. The data set was able to capture the warming trends compared to observations at annual and seasonal scales, except for winter. We concluded that the new high-resolution data set is generally reliable for climate change investigation over the Chinese Tian Shan. However, the new data set is expected to be further validated based on more observations. This data set will be helpful for potential users to improve local climate monitoring, modeling, and environmental studies in the Chinese Tian Shan. The data set presented in this article is published in the Network Common Data Form (NetCDF) at https://doi.org/10.1594/PANGAEA.887700. The data set includes 288 nc files and one user guidance txt file.


2018 ◽  
Author(s):  
Lu Gao ◽  
Jianhui Wei ◽  
Lingxiao Wang ◽  
Matthias Bernhardt ◽  
Karsten Schulz ◽  
...  

Abstract. The Chinese Tianshan Mountains has a complex ecological environment system. It not only has a large number of desert oases, but also gave birth to a large number of glaciers. The arid climate and the shortage of water resources are the important factors to restrict the socio-economic development in this area. This study presents a unique high-resolution (1 km, 6-hourly) air temperature data set for the Chinese Tianshan Mountains (41.1814–45.9945° N, 77.3484–96.9989° E) from 1979 to 2016 based on a robust statistical downscaling framework. The data set was validated by 24 meteorological stations at daily scale. Compared with original ERA-Interim temperature, the Nash-Sutcliffe efficiency coefficient increased from 0.90 to 0.94 over all test sites. Around 24 % of root-mean-square error was reduced from 3.75 to 2.85 °C. A skill score based on the probability density function, which was used to validate the reliability of the new data set for capturing the distributions, enhanced from 0.86 to 0.91 for all test sites. We conclude that the new high-resolution data set is reliable for climate change investigation over the Chinese Tianshan Mountains. This data set would be helpful for the potential users for better local climate monitoring, modelling and environmental studies in the Chinese Tianshan Mountains. The data set presented in this article is published in Network Common Data Form (NetCDF) at doi:10.1594/PANGAEA.887700. The data set includes 288 nc files and one user guidance in txt file.


2016 ◽  
Vol 37 (7) ◽  
pp. 3209-3222 ◽  
Author(s):  
Branislava Jovanovic ◽  
Robert Smalley ◽  
Bertrand Timbal ◽  
Steven Siems

2014 ◽  
Vol 6 (1) ◽  
pp. 61-68 ◽  
Author(s):  
T. J. Osborn ◽  
P. D. Jones

Abstract. The CRUTEM4 (Climatic Research Unit Temperature, version 4) land-surface air temperature data set is one of the most widely used records of the climate system. Here we provide an important additional dissemination route for this data set: online access to monthly, seasonal and annual data values and time series graphs via Google Earth. This is achieved via an interface written in Keyhole Markup Language (KML) and also provides access to the underlying weather station data used to construct the CRUTEM4 data set. A mathematical description of the construction of the CRUTEM4 data set (and its predecessor versions) is also provided, together with an archive of some previous versions and a recommendation for identifying the precise version of the data set used in a particular study. The CRUTEM4 data set used here is available from doi:10.5285/EECBA94F-62F9-4B7C-88D3-482F2C93C468.


2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Andrew Verdin ◽  
Chris Funk ◽  
Pete Peterson ◽  
Martin Landsfeld ◽  
Cascade Tuholske ◽  
...  

Abstract We present a high-resolution daily temperature data set, CHIRTS-daily, which is derived by merging the monthly Climate Hazards center InfraRed Temperature with Stations climate record with daily temperatures from version 5 of the European Centre for Medium-Range Weather Forecasts Re-Analysis. We demonstrate that remotely sensed temperature estimates may more closely represent true conditions than those that rely on interpolation, especially in regions with sparse in situ data. By leveraging remotely sensed infrared temperature observations, CHIRTS-daily provides estimates of 2-meter air temperature for 1983–2016 with a footprint covering 60°S-70°N. We describe this data set and perform a series of validations using station observations from two prominent climate data sources. The validations indicate high levels of accuracy, with CHIRTS-daily correlations with observations ranging from 0.7 to 0.9, and very good representation of heat wave trends.


2019 ◽  
Vol 26 (3) ◽  
pp. 396-408 ◽  
Author(s):  
Feng Chen ◽  
Xuchao Yang ◽  
Chunxiao Ji ◽  
Yuejun Li ◽  
Fangping Deng ◽  
...  

2019 ◽  
Vol 39 (6) ◽  
pp. 3104-3120 ◽  
Author(s):  
Asher Siebert ◽  
Tufa Dinku ◽  
Floribert Vuguziga ◽  
Anthony Twahirwa ◽  
Desire M. Kagabo ◽  
...  

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