Abstract
When meteorological stations are quite unevenly distributed, a simple regional arithmetic mean of observation data may assign an excessive weight to one region with dense stations, which affects the representativeness of the regional mean. In this study, we used the homogenized monthly maximum (Tmax), mean (Tm), and minimum (Tmin) temperature dataset at more than 2400 national surface meteorological stations in China. Based on a thin plate spline (TPS) interpolation method, we selected a three-dimensional (longitude, latitude, and elevation above sea level) interpolation model that is suitable for the temperature curve surface fitting in China, and constructed a gridded temperature dataset with a horizontal resolution of 0.5° during 1961 to 2015. Cross-validation indicates that the annual average of generalized cross-validation (GCV) is relatively small, and the ratio of GCV to the observed temperature is relatively low. Both the root of GCV and the root-mean-square error of temperature are smaller than those of the previous temperature gridded products. A comparison between our dataset and the Climate Research Unit (CRU) TS4.02 dataset indicates that the CRU’s data overestimate the warming trend in China during 1961–1984, whereas underestimate the warming trend during 1985–2015.
Highlights
When researchers study climate changes on a global or regional scale, they often use a regional average to analyze the characteristics of variations (Jones and Briffa 1992)
The ratios of the CHGT the homogenized monthly maximum (Tmax) and Tmin root of GCV (RGCV) and root-mean-square error (RMSE) to the SD values are clearly less than those of the Yan data. These results indicate that the interpolation errors of the CHGT Tmax and Tmin data are lower compared with the Yan results, suggesting a higher quality of our gridded product
In this study, using the homogenized temperature data at more than 2400 surface meteorological stations in China, we constructed a gridded temperature dataset with a horizontal resolution of 0.5° from 1961 to 2015
Summary
When researchers study climate changes on a global or regional scale, they often use a regional average to analyze the characteristics of variations (Jones and Briffa 1992). Robert et al (2005) constructed a gridded dataset of global land surface air temperature (SAT) with a high horizontal resolution using an interpolation method of the thin plate spline (TPS). Nynke et al (2009) constructed a gridded dataset of SAT with a high resolution across Europe using the TPS interpolation method These gridded datasets contain limited observational station data in China. Xu et al (2009) further constructed a gridded dataset of SAT with a horizontal resolution of 0.5° using approximately 700 station data and the TPS method. As temperature is considerably affected by latitude and elevation above sea level, we utilized the threedimensional (longitude, latitude, and elevation) TPS interpolation method to develop a gridded monthly temperature dataset with a horizontal spatial resolution of 0.5° in China during 1961–2015 Using the CHGT gridded temperature dataset, we compared differences in climatic trends between the CHGT and previous gridded datasets
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