Preprints
https://doi.org/10.5194/essd-2021-309
https://doi.org/10.5194/essd-2021-309

  20 Oct 2021

20 Oct 2021

Review status: this preprint is currently under review for the journal ESSD.

A dataset of daily near-surface air temperature in China from 1979 to 2018

Shu Fang1,2,, Kebiao Mao1,3,, Xueqi Xia2, Ping Wang1,4, Jiancheng Shi5, Sayed M. Bateni6, Tongren Xu7, Mengmeng Cao1, and Essam Heggy8,9 Shu Fang et al.
  • 1Institute of agricultural resources and regional planning, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
  • 2School of Earth Sciences and Resources, China University of Geosciences, Beijing, 100083, China
  • 3School of Physics and Electronic-Engineering, Ningxia University, Yinchuan 750021, China
  • 4School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan, 250100, China
  • 5National Space Science Center, Chinese Academy of Sciences, Beijing, 100190, China
  • 6Department of Civil and Environmental Engineering and Water Resources Research Center, University of Hawaii at Manoa, Honolulu, HI 96822, USA
  • 7State Key Laboratory of Earth Surface Processes and Resource Ecology, School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
  • 8Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089, USA
  • 9Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
  • These authors contributed equally to this work.

Abstract. Ta (Near-surface air temperature) is an important physical parameter that reflects climate change. Although there are currently many methods to obtain the daily maximum (Tmax), minimum (Tmin), and average (Tavg) temperature (meteorological stations, remote sensing, and reanalysis data), these methods are affected by many factors. In order to obtain daily Ta data (Tmax, Tmin, and Tavg) with high spatial and temporal resolution in China, we fully analyzed the advantages and disadvantages of various existing data (reanalysis, remote sensing, and in situ data). Different Ta reconstruction models are constructed for different weather conditions, and we further improve data accuracy through building correction equations for different regions. Finally, a dataset of daily temperature (Tmax, Tmin, and Tavg) in China from 1979 to 2018 was obtained with a spatial resolution of 0.1°. For Tmax, validation using in situ data shows that the root mean square error (RMSE) ranges from 0.86 °C to 1.78 °C, the mean absolute error (MAE) varies from 0.63 °C to 1.40 °C, and the Pearson coefficient (R2) ranges from 0.96 to 0.99. For Tmin, RMSE ranges from 0.78 °C to 2.09 °C, the MAE varies from 0.58 °C to 1.61 °C, and the R2 ranges from 0.95 to 0.99. For Tavg, RMSE ranges from 0.35 °C to 1.00 °C, the MAE varies from 0.27 °C to 0.68 °C, and the R2 ranges from 0.99 to 1.00. Furthermore, a variety of evaluation indicators were used to analyze the temporal and spatial variation trends of Ta, and the Tavg increase was more than 0.0 °C/a, which is consistent with the general global warming trend. In conclusion, this dataset had a high spatial resolution and reliable accuracy, which makes up for the previous missing temperature value (Tmax, Tmin, and Tavg) at high spatial resolution. This dataset also provides key parameters for the study of climate change, especially high-temperature drought and low-temperature chilling damage, which is publicly available with the following DOI: https://doi.org/10.5281/zenodo.5502275 (Fang et al., 2021a).

Shu Fang et al.

Status: open (until 10 Jan 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on essd-2021-309', kebiao mao, 28 Oct 2021 reply
  • CC2: 'Comment on essd-2021-309', Shengli Wu, 30 Oct 2021 reply
    • AC1: 'Reply on CC2', Shu Fang, 08 Nov 2021 reply
  • AC2: 'Comment on essd-2021-309', Shu Fang, 14 Nov 2021 reply
  • RC1: 'Comment on essd-2021-309', Anonymous Referee #1, 23 Nov 2021 reply

Shu Fang et al.

Data sets

A Daily near-surface Air Temperature Dataset for China from 1979 – 2018 Shu Fang; Kebiao Mao; Xueqi Xia; Ping Wang; Jiancheng Shi; Sayed M. Bateni; Tongren Xu; Mengmeng Cao; Essam Heggy https://doi.org/10.5281/zenodo.5502275

Model code and software

A Daily near-surface Air Temperature Dataset for China from 1979 – 2018 Shu Fang; Kebiao Mao; Xueqi Xia; Ping Wang; Jiancheng Shi; Sayed M. Bateni; Tongren Xu; Mengmeng Cao; Essam Heggy https://doi.org/10.5281/zenodo.5513811

Shu Fang et al.

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Short summary
Air temperature is an important parameter reflecting climate change, and the current method of obtaining daily temperature is affected by many factors. In this study, we constructed a temperature model based on weather conditions and established a correction equation. The dataset of daily air temperature (Tmax, Tmin, and Tavg) in China from 1979 to 2018 was obtained with a spatial resolution of 0.1°. Accuracy verification shows that the data set has reliable accuracy and high spatial resolution.