Xu Zhongfeng, Han Ying, Tam Chi-Yung, Yang Zong-Liang, Fu Congbin
RCE-TEA, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China.
Earth System Science Programme, The Chinse University of Hong Kong, Hong Kong, China.
Sci Data. 2021 Nov 4;8(1):293. doi: 10.1038/s41597-021-01079-3.
Dynamical downscaling is an important approach to obtaining fine-scale weather and climate information. However, dynamical downscaling simulations are often degraded by biases in the large-scale forcing itself. We constructed a bias-corrected global dataset based on 18 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) dataset. The bias-corrected data have an ERA5-based mean climate and interannual variance, but with a non-linear trend from the ensemble mean of the 18 CMIP6 models. The dataset spans the historical time period 1979-2014 and future scenarios (SSP245 and SSP585) for 2015-2100 with a horizontal grid spacing of (1.25° × 1.25°) at six-hourly intervals. Our evaluation suggests that the bias-corrected data are of better quality than the individual CMIP6 models in terms of the climatological mean, interannual variance and extreme events. This dataset will be useful for dynamical downscaling projections of the Earth's future climate, atmospheric environment, hydrology, agriculture, wind power, etc.
动力降尺度是获取高分辨率天气和气候信息的重要方法。然而，动力降尺度模拟常常因大尺度强迫本身的偏差而退化。我们基于耦合模式比较计划第六阶段（CMIP6）的18个模式和欧洲中期天气预报中心再分析第5版（ERA5）数据集构建了一个偏差校正后的全球数据集。偏差校正后的数据具有基于ERA5的平均气候和年际变化，但具有来自18个CMIP6模式集合平均值的非线性趋势。该数据集涵盖1979 - 2014年的历史时期以及2015 - 2100年的未来情景（SSP245和SSP585），水平网格间距为（1.25°×1.25°），时间间隔为6小时。我们的评估表明，在气候平均、年际变化和极端事件方面，偏差校正后的数据质量优于单个CMIP6模式。该数据集将有助于对地球未来气候、大气环境、水文、农业、风力发电等进行动力降尺度预测。
