Abstract

An accurate spatially continuous air temperature dataset is crucial for multiple applications in environmental and ecological sciences. Existing spatial interpolation methods have relatively low accuracy and the resolution of available long-term gridded products of air temperature for China is coarse. Point observations from meteorological stations can provide long-term air temperature data series but cannot represent spatially continuous information. Here, we devised a method for spatial interpolation of air temperature data from meteorological stations based on powerful machine learning tools. First, to determine the optimal method for interpolation of air temperature data, we employed three machine learning models: random forest, support vector machine, and Gaussian process regression. Comparison of the mean absolute error, root mean square error, coefficient of determination, and residuals revealed that Gaussian process regression had high accuracy and clearly outperformed the other two models regarding interpolation of monthly maximum, minimum, and mean air temperatures. The machine learning methods were compared with three traditional methods used frequently for spatial interpolation: inverse distance weighting, ordinary kriging, and ANUSPLIN. Results showed that the Gaussian process regression model had higher accuracy and greater robustness than the traditional methods regarding interpolation of monthly maximum, minimum, and mean air temperatures in each month. Comparison with the TerraClimate, FLDAS, and ERA5 datasets revealed that the accuracy of the temperature data generated using the Gaussian process regression model was higher. Finally, using the Gaussian process regression method, we produced a long-term (January 1951 to December 2020) gridded monthly air temperature dataset with 1 km resolution and high accuracy for China, which we named GPRChinaTemp1km. The dataset consists of three variables: monthly mean air temperature, monthly maximum air temperature, and monthly minimum air temperature. The obtained GPRChinaTemp1km data were used to analyse the spatiotemporal variations of air temperature using Theil–Sen median trend analysis in combination with the Mann–Kendall test. It was found that the monthly mean and minimum air temperatures across China were characterized by a significant trend of increase in each month, whereas monthly maximum air temperature showed a more spatially heterogeneous pattern with significant increase, non-significant increase, and non-significant decrease. The GPRChinaTemp1km dataset is publicly available at https://doi.org/10.5281/zenodo.5112122 (He et al., 2021a) for monthly maximum air temperature, at https://doi.org/10.5281/zenodo.5111989 (He et al., 2021b) for monthly mean air temperature and at https://doi.org/10.5281/zenodo.5112232 (He et al., 2021c) for monthly minimum air temperature.

Highlights

  • 35 Air temperature is a fundamental variable in various research fields that include the impact of global warming and climate change, ecology, hydrology, agriculture, and human health (Sippel et al, 2020; Abatzoglou et al, 2018; Pathak et al, 2018; Chen et al, 2018)

  • The mean absolute error (MAE) of Gaussian process regression (GPR) and support vector machine (SVM) are close to 1 across the study period, while the MAEs of 235 random forest (RF) are clearly higher than those of both GPR and SVM

  • The root mean square error (RMSE) have the same order as the MAEs, i.e., GPR outperforms both SVM and RF

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Summary

Introduction

35 Air temperature is a fundamental variable in various research fields that include the impact of global warming and climate change, ecology, hydrology, agriculture, and human health (Sippel et al, 2020; Abatzoglou et al, 2018; Pathak et al, 2018; Chen et al, 2018). Long-term records of air temperature data with high spatial resolution are necessary for such research. Using conventional methods for data interpolation in areas with uneven coverage of meteorological stations could diminish the accuracy of the derived data (dos Santos, 2020; Li et al, 2018). 50 use of conventional interpolation methods cannot guarantee the accuracy of the derived spatial datasets of air temperature across China. Various air temperature products are available, e.g., the TerraClimate (Abatzoglou et al, 2018), FLDAS (McNally et al, 2017), and ERA5 (Copernicus Climate Change Service (C3S), 2017) datasets, their spatial resolution is usually coarse (2.5 arc minutes, 0.1 arc degrees, and 0.25 arc degrees, respectively), which restricts their ability to reflect the topographical characteristics and spatial heterogeneity of air temperature across China (Peng et al, 2019; Zhang 55 et al, 2016). Demand remains for a high-resolution long-term spatially continuous dataset of air temperature

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