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

• Published in: Geoscientific Model Development 2022-08, Vol.15 (15), p.6059-6083
• Main Authors: Wang, Ping, Mao, Kebiao, Meng, Fei, Qin, Zhihao, Fang, Shu, Bateni, Sayed M
• Format: Article
• Language: English
• Subjects: Accuracy; Air temperature; Analysis; Atmospheric forcing; Calibration; Climate change; Climate models; Climatic analysis; Climatic changes; Climatic conditions; Climatic indexes; Cold days; Datasets; Diurnal variations; El Nino; El Nino phenomena; Environmental protection; Estimation accuracy; Growth models; High temperature; Icing; La Nina; Machine learning; Meteorological observations; Methods; North Atlantic Oscillation; Ocean basins; Ocean warming; Ocean-atmosphere system; Oceans; Precipitation; Regression analysis; Remote sensing; Rivers; Root-mean-square errors; Sea surface; Sea surface temperature; Spatial analysis; Spatial discrimination; Spatial resolution; Summer; Surface temperature; Surface-air temperature relationships; Temperature analysis; Temperature changes; Temporal variations; Topography; Vegetation; Wind
• ISSN: 1991-9603; 1991-959X; 1991-962X; 1991-9603; 1991-962X
• DOI: 10.5194/gmd-15-6059-2022

The daily highest air temperature (Tmax) is a key parameter for global and regional high temperature analysis which is very difficult to obtain 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 with a spatial resolution of 0.1∘ 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 regions. The analysis shows that the mean absolute error (MAE) of the dataset (https://doi.org/10.5281/zenodo.6322881, Wang et al., 2021) after correction with the calibration models is about 1.07 ∘C and the root mean square error (RMSE) is about 1.52 ∘C, which is higher than that before correction to 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 was 0.4∘C per 10 years and 0.39∘C per 10 years, 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 was 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.