1-km monthly temperature and precipitation dataset for China from 1901–2017

Abstract

High-spatial-resolution and long-term climate data are highly desirable for understanding climate-related natural processes. China covers a large area with a low density of weather stations in some regions, especially in mountainous regions. 10This study describes a 0.5' (~1 km) dataset of monthly air temperatures at 2 m (minimum, maximum, and mean TMPs) and precipitation (PRE) for China from 1901-2017. The dataset was spatially downscaled from 30' climatic research unit (CRU) time series dataset with the climatology dataset of WorldClim by using Delta spatial downscaling and evaluated using observations during 1951-2016 from 496 weather stations across China. Moreover, the bicubic, bilinear, and nearest-neighbor interpolation methods were compared in the downscaling processes. Among the three interpolation methods, bilinear 15 interpolation exhibited the best performance to generate the downscaled dataset. Compared with the evaluations of the original CRU dataset, the mean absolute error of the new dataset (i.e., 0.5' downscaled dataset with the bilinear interpolation) relatively decreased by 35.4 %-48.7 % for TMPs and 25.7 % for PRE, the root-mean-square error relatively decreased by 32.4 %-44.9 % for TMPs and 25.8 % for PRE, the Nash-Sutcliffe efficiency coefficients relatively increased by 9.6 %-13.8 % for TMPs and 31.6 % for PRE, and the correlation coefficients relatively increased by 0.2 %-0.4 % for TMPs and 5.0 % for PRE. Further, 20the new dataset could provide detailed climatology data and annual trend of each climatic variable across China, and the results could be well evaluated using observations at the station. Although the evaluation of new dataset was not carried out before 1950 owing to a lack of data availability, the downscaling procedure used data from CRU and WordClim and did not incorporate observations. Thus the quality of the new dataset before 1950 mainly depended on that of the CRU and WordClim datasets. The evaluations showed that the overall quality of the CRU and WordClim datasets was satisfactory, and the 25 downscaling procedure further improved the quality and spatial resolution of the CRU dataset. The new dataset will be useful in investigations related to climate change across China. The dataset presented in this article has been published in Network Common Data Form (NetCDF) at http://doi.org/10.5281/zenodo.3114194 for precipitation (Peng, 2019a) and http://doi.org/10.5281/zenodo.3185722 for air temperatures at 2 m (Peng, 2019b). The dataset includes 156 NetCDF files compressed with zip format and one user guidance text file. 30

The constraints and driving forces of oasis development in arid region: a case study of the Hexi Corridor in northwest China

“…The elevation and slope were calculated from SRTM DEM with a spatial resolution of 90 m downloaded from the USGS website. The raster-based precipitation and temperature data from 1986 to 2015 were collected from the 1 km monthly temperature and precipitation dataset 38 (available at https://doi.org/10.5281/zenodo.3114194 for precipitation and https://doi.org/10.5281/zenodo.3185722 for air temperatures), which were created by spatially downscaling with the resolution of 0.5 arcminutes (~ 1 km). The socio-economic data including population, income, grain yield, and labor for each administration region were obtained from local statistic yearbook 1985–2015 (each yearbook every five years) (Gansu bureau of statistics).…”

The impact of urbanization on body size of Barn Swallows Hirundo rustica gutturalis

“…Finally, given that sampling was conducted across 2 years, year was included as random factor. We considered including climatic variables—average annual temperature and precipitation from 2011 to 2015 as explanatory variables, which were obtained from Loess plateau science data center, National Earth System Science Data Sharing Infrastructure, National Science & Technology Infrastructure of China (http://loess.geodata.cn) (Peng et al., 2019). However, we finally excluded these climatic variables due to their high correlation with latitude and longitude (Table A2).…”

1 km Monthly Precipitation and Temperatures Dataset for China from 1952 to 2019 based on a Brand-New and High-Quality Baseline Climatology Surface

“…Not only can we make full use of the time-series weather stations, but also consider the satellites-driven anomaly as either independent spline variables or linear covariates to further improve the accuracy of the final monthly climate surface. Our results showed that ChinaClim_timeseries was indeed a better climate dataset than Peng's climate surface and CHELSAcruts in China with higher R 2 , and lower RMSE and MAE, especially in high cold https://doi.org/10.5194/essd-2020-361 Previous studies indicated that baseline climatology surface, considering detailed topographic information, the effects of distance to the nearest coast and satellite-driven variables, is physically representative and has a fine-scale distribution of meteorological variables (Marchi et al, 2019;Mosier et al, 2014;Peng et al, 2017;Platts et al, 2015). Thus, a superior baseline climatology surface is helpful to improve the accuracy of the long-term monthly climate surface.…”

“…There was a large amount of evidence to suggested that the CAI method can better generate longterm monthly climate surface (Abatzoglou et al, 2018;Becker et al, 2013;C. Vega et al, 2017;Karger et al, 2017;Mosier et al, 2014;Peng et al, 2019;Willmott and Robeson, 2010). Our results proved Peng's climate surface and CHELSAcruts datasets, relying on coarse CRU anomaly and high-quality baseline climatology surfaces with CAI method, had relatively high accuracy (high R 2 ) with a few weather stations in China at 1km spatial resolution.…”

“…Our results proved Peng's climate surface and CHELSAcruts datasets, relying on coarse CRU anomaly and high-quality baseline climatology surfaces with CAI method, had relatively high accuracy (high R 2 ) with a few weather stations in China at 1km spatial resolution. (Karger et al, 2017;Peng et al, 2019). However, those studies rarely incorporated satellites-driven products into climate interpolation, and the performance of baseline climatology surface covering China (WorldClim2 and CHELSA) using the CAI method was also troubling, especially in high cold Tibetan Plateau.…”