Abstract
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.
Highlights
Near-surface air temperature is the primary indicator of climate change and significantly impacts local as well as global water, energy, and matter cycles (e.g., Bolstad et al, 1998; Gao et al, 2012, 2014a; Prince et al, 1998)
Previous studies have shown that the elevation difference between the ERA-Interim grid and the individual site is a key factor for errors in high mountains such as the European Alps and on the Tibetan Plateau (Gao et al, 2012, 2014a, 2017)
Gao et al (2012, 2017) claimed that the elevation correction method based on ERA-Interim internal, vertical lapse rates outperformed several conventional methods such as the use of fixed monthly lapse rates and observed lapse rates from meteorological stations in the European Alps and on the Tibetan Plateau
Summary
Near-surface air temperature is the primary indicator of climate change and significantly impacts local as well as global water, energy, and matter cycles (e.g., Bolstad et al, 1998; Gao et al, 2012, 2014a; Prince et al, 1998). Because of the heterogeneity over the land surface, many hydrological and climatic impact models use applications of high resolution, which tend to run on a scale of 0.1–1 km (Bernhardt and Schulz, 2010; Gao et al, 2012; Maraun et al, 2010) To this end, downscaling and correcting reanalysis data are necessary (Gao et al, 2012, 2014b, 2016). This method has been successfully applied in cases in the European Alps and on the Tibetan Plateau (Gao et al, 2012, 2017) This approach has the potential to be used to correct ERA-Interim temperature data for any other high mountainous areas.
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