Journal cover Journal topic
Earth System Science Data The data publishing journal
Journal topic

Journal metrics

Journal metrics

  • IF value: 10.951 IF 10.951
  • IF 5-year value: 9.899 IF 5-year
    9.899
  • CiteScore value: 9.74 CiteScore
    9.74
  • SNIP value: 3.111 SNIP 3.111
  • IPP value: 8.99 IPP 8.99
  • SJR value: 5.229 SJR 5.229
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 38 Scimago H
    index 38
  • h5-index value: 33 h5-index 33
Discussion papers
https://doi.org/10.5194/essd-2019-145
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-2019-145
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: data description paper 02 Sep 2019

Submitted as: data description paper | 02 Sep 2019

Review status
This discussion paper is a preprint. A revision of this manuscript was accepted for the journal Earth System Science Data (ESSD) and is expected to appear here in due course.

1-km monthly temperature and precipitation dataset for China from 1901–2017

Shouzhang Peng1, Yongxia Ding2, and Zhi Li3 Shouzhang Peng et al.
  • 1State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling, 712100, China
  • 2School of Geography and Tourism, Shaanxi Normal University, Xi’an, 710169, China
  • 3College of Natural Resources and Environment, Northwest A&F University, Yangling, 712100, China

Abstract. High-spatial-resolution and long-term climate data are highly desirable for understanding climate-related natural processes. China covers a large area with a low density of weather stations in some regions, especially in mountainous regions. This study describes a 0.5' (~ 1 km) dataset of monthly air temperatures at 2 m (minimum, maximum, and mean TMPs) and precipitation (PRE) for China from 1901–2017. The dataset was spatially downscaled from 30' climatic research unit (CRU) time series dataset with the climatology dataset of WorldClim by using Delta spatial downscaling and evaluated using observations during 1951–2016 from 496 weather stations across China. Moreover, the bicubic, bilinear, and nearest-neighbor interpolation methods were compared in the downscaling processes. Among the three interpolation methods, bilinear interpolation exhibited the best performance to generate the downscaled dataset. Compared with the evaluations of the original CRU dataset, the mean absolute error of the new dataset (i.e., 0.5' downscaled dataset with the bilinear interpolation) relatively decreased by 35.4 %–48.7 % for TMPs and 25.7 % for PRE, the root-mean-square error relatively decreased by 32.4 %–44.9 % for TMPs and 25.8 % for PRE, the Nash–Sutcliffe efficiency coefficients relatively increased by 9.6 %–13.8 % for TMPs and 31.6 % for PRE, and the correlation coefficients relatively increased by 0.2 %–0.4 % for TMPs and 5.0 % for PRE. Further, the new dataset could provide detailed climatology data and annual trend of each climatic variable across China, and the results could be well evaluated using observations at the station. Although the evaluation of new dataset was not carried out before 1950 owing to a lack of data availability, the downscaling procedure used data from CRU and WordClim and did not incorporate observations. Thus the quality of the new dataset before 1950 mainly depended on that of the CRU and WordClim datasets. The evaluations showed that the overall quality of the CRU and WordClim datasets was satisfactory, and the downscaling procedure further improved the quality and spatial resolution of the CRU dataset. The new dataset will be useful in investigations related to climate change across China. The dataset presented in this article has been published in Network Common Data Form (NetCDF) at http://doi.org/10.5281/zenodo.3114194 for precipitation (Peng, 2019a) and http://doi.org/10.5281/zenodo.3185722 for air temperatures at 2 m (Peng, 2019b). The dataset includes 156 NetCDF files compressed with zip format and one user guidance text file.

Shouzhang Peng et al.
Interactive discussion
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
Interactive discussion
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
Shouzhang Peng et al.
Data sets

High-spatial-resolution monthly precipitation dataset over China during 1901–2017 S. Peng https://doi.org/10.5281/zenodo.3114194

Model code and software

High-spatial-resolution monthly temperatures dataset over China during 1901–2017 S. Peng https://doi.org/10.5281/zenodo.3185722

Shouzhang Peng et al.
Viewed  
Total article views: 697 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
581 113 3 697 12 3 3
  • HTML: 581
  • PDF: 113
  • XML: 3
  • Total: 697
  • Supplement: 12
  • BibTeX: 3
  • EndNote: 3
Views and downloads (calculated since 02 Sep 2019)
Cumulative views and downloads (calculated since 02 Sep 2019)
Viewed (geographical distribution)  
Total article views: 554 (including HTML, PDF, and XML) Thereof 551 with geography defined and 3 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Cited  
Saved  
No saved metrics found.
Discussed  
No discussed metrics found.
Latest update: 18 Nov 2019
Download
Short summary
This study describes a 1-km monthly minimum, maximum, and mean temperatures and precipitation dataset for the main land area of China during 1901–2017. It is the first dataset developed with such a high spatiotemporal resolution over such a long time period for China. The dataset is well evaluated by the observations using 496 national weather stations, and the evaluation indicated the dataset is sufficiently reliable for use in investigation of climate change across China.
This study describes a 1-km monthly minimum, maximum, and mean temperatures and precipitation...
Citation