数字农科院2.0

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

文献类型： 外文期刊

作者： Fang, Shu;Mao, Kebiao;Xia, Xueqi;Wang, Ping;Shi, Jiancheng;Bateni, Sayed M.;Xu, Tongren;Cao, Mengmeng;Heggy, Essam;Qin, Zhihao

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期刊名称： EARTH SYSTEM SCIENCE DATA

ISSN： 1866-3508

年卷期： 2022 年 14 卷 3 期

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收录情况： SCIE（2022版）

摘要： Near-surface air temperature (T-a) is an important physical parameter that reflects climate change. Many methods are used to obtain the daily maximum (T-max), minimum (T-min), and average (T-avg) temperature, but are affected by multiple factors. To obtain daily T-a data (T-max, T-min, and T-avg) with high spatio-temporal resolution in China, we fully analyzed the advantages and disadvantages of various existing data. Different T-a reconstruction models were constructed for different weather conditions, and the data accuracy was improved by building correction equations for different regions. Finally, a dataset of daily temperature (T-max, T-min, and T-avg) in China from 1979 to 2018 was obtained with a spatial resolution of 0.1 degrees. For T-max, validation using in situ data shows that the root mean square error (RMSE) ranges from 0.86 to 1.78 degrees, the mean absolute error (MAE) varies from 0.63 to 1.40 degrees, and the Pearson coefficient (R-2) ranges from 0.96 to 0.99. For T-min, the RMSE ranges from 0.78 to 2.09 degrees, the MAE varies from 0.58 to 1.61 degrees, and the R-2 ranges from 0.95 to 0.99. For T-avg, the RMSE ranges from 0.35 to 1.00 degrees, the MAE varies from 0.27 to 0.68 degrees, and the R-2 ranges from 0.99 to 1.00. Furthermore, various evaluation indicators were used to analyze the temporal and spatial variation trends of Ta, and the Tavg increase was more than 0.03 degrees C yr(-1), which is consistent with the general global warming trend. In summary, this dataset has high spatial resolution and high accuracy, which compensates for the temperature values (T-max, T-min, and T-avg) previously missing at high spatial resolution and provides key parameters for the study of climate change, especially high-temperature drought and low-temperature chilling damage. The dataset is publicly available at https://doi.org/10.5281/zenodo.5502275 (Fang et al., 2021a).

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