Preprints
https://doi.org/10.5194/gmd-2021-435
https://doi.org/10.5194/gmd-2021-435
Submitted as: development and technical paper
17 Feb 2022
Submitted as: development and technical paper | 17 Feb 2022
Status: this preprint is currently under review for the journal GMD.

A daily highest air temperature estimation method and spatial-temporal changes analysis of high temperature in China from 1979 to 2018

Ping Wang1,2,, Kebiao Mao1,3,, Fei Meng2, Zhihao Qin3, Shu Fang4, Sayed M. Bateni5, and Mansour Almazroui6,7 Ping Wang et al.
  • 1School of Physics and Electronic-Engineering, Ningxia University, Yinchuan 750021, China
  • 2School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250100, China
  • 3Institute of agricultural resources and regional planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
  • 4School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China
  • 5Department of Civil and Environmental Engineering and Water Resources Research Center, University of Hawaii at Manoa, Honolulu, HI 96822, USA
  • 6Centre of Excellence for Climate Change Research/Department of Meteorology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
  • 7Climatic Research Unit, School of Environmental Sciences, University of East Anglia, Norwich, UK
  • These authors contributed equally to this work.

Abstract. The daily highest air temperature (Tmax) is a key parameter for global and regional high temperature analysis, which is very difficult to be obtained in areas where there are no meteorological observation stations. This study proposes an estimation framework for obtaining high-precision Tmax. Firstly, we build a near surface air temperature diurnal variation model to estimate Tmax for China from 1979 to 2018 based on multi-source data. Then in order to further improve the estimation accuracy, we divided China into six regions according to climate conditions and topography, and established calibration models for different region. The analysis shows that the mean absolute error (MAE) of the dataset (https://doi.org/10.5281/zenodo.5602897) is about 1.07 °C and RMSE is 1.52 °C, which improves the accuracy of the traditional method by nearly 1 °C. The spatial-temporal variations analysis of Tmax in China indicated that the annual and seasonal mean Tmax in most areas of China showed an increasing trend. In summer and autumn, the Tmax in northeast China increased the fastest among the six regions, which were 0.4 °C/10a and 0.39 °C/10a, respectively. The number of summer days and warm days showed an increasing trend in all regions, while the number of icing days and cold days showed a decreasing trend. The abnormal temperature changes mainly occurred in El Niño years or La Niña years. We found that the influence of the Indian Ocean Basin Warming (IOBW) on air temperature in China were generally greater than those of the North Atlantic Oscillation and the NINO3.4 area sea surface temperature after making analysis of ocean climate modal indices with air temperature. In general, this Tmax dataset and analysis are of great significance to the study of climate change in China, especially for environmental protection.

Ping Wang et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CEC1: 'Comment on gmd-2021-435', Juan Antonio Añel, 01 Mar 2022
    • AC1: 'Reply on CEC1', kebiao mao, 03 Mar 2022
  • RC1: 'Comment on gmd-2021-435', Anonymous Referee #1, 02 Apr 2022
    • AC2: 'Reply on RC1', kebiao mao, 13 Apr 2022
  • RC2: 'Comment on gmd-2021-435', Anonymous Referee #2, 18 May 2022

Ping Wang et al.

Data sets

A Daily Maximum Air Temperature dataset in China from 1979 to 2018 Ping Wang; Kebiao Mao; Fei Meng; Zhihao Qin; Shu Fang; Sayed M. Bateni; Mansour Almazroui https://doi.org/10.5281/zenodo.5602897

Ping Wang et al.

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Short summary
In order to obtain the key parameters of high temperature spatial-temporal variation analysis, this study proposed a daily highest air temperature (Tmax) estimation frame to build a Tmax dataset in China from 1979 to 2018. We found that the annual and seasonal mean Tmax in most areas of China showed an increasing trend.The abnormal temperature changes mainly occurred in El Niño years or La Niña years. IOBW had a stronger influence on China's warming events than other factors.