Dynamic Macroecology, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Zürcherstrasse 111, 8903, Birmensdorf, Switzerland.
Sci Data. 2020 Jul 23;7(1):248. doi: 10.1038/s41597-020-00587-y.
Predicting future climatic conditions at high spatial resolution is essential for many applications and impact studies in science. Here, we present monthly time series data on precipitation, minimum- and maximum temperature for four downscaled global circulation models. We used model output statistics in combination with mechanistic downscaling (the CHELSA algorithm) to calculate mean monthly maximum and minimum temperatures, as well as monthly precipitation at ~5 km spatial resolution globally for the years 2006-2100. We validated the performance of the downscaling algorithm by comparing model output with the observed climate of the historical period 1950-1969.
预测高空间分辨率的未来气候条件对于科学中的许多应用和影响研究至关重要。在这里,我们提供了四个降尺度全球环流模型的降水、最低和最高温度的月时间序列数据。我们使用模型输出统计数据结合机械降尺度(CHELSA 算法)来计算全球范围内~5km 空间分辨率的月平均最高和最低温度以及月降水量,时间范围为 2006-2100 年。我们通过将模型输出与历史时期 1950-1969 年的观测气候进行比较,来验证降尺度算法的性能。