基于人工神经网络开发中国地区统计降尺度气候预估数据

针对中国地区的气候预估问题,开发一套高时空分辨率(空间分辨率:0.25°;时间分辨率:逐日)的统计降尺度气候变化数据集.降尺度气候预估结果表明:1)在传统降尺度方法的基础上引入人工神经网络算法,开发高时空分辨率的降尺度气候数据,技术上简便可行;2)将这种新方法应用到模式的历史模拟数据上,温度和降水的气候态偏差显著减小,...

Statistical Downscaled Climate Projection Dataset for China Using Artificial Neural Network

A high-resolution climate change dataset for China is developed. The climatic evaluation results of downscaling suggest as follows. 1) It is feasible to establish one statistical downscaling framework by incorpo-rating the advanced deep learning approach, artificial neural network (ANN), with high robustness. 2) The high-resolution climatology generated by this new method match the observations better than GCMs’ raw outputs. Those large bias induced by GCMs in temperature and precipitation can be reduced from 5°C down to 1°C and from 5 mm down to 0.5 mm, respectively. 3) The future climate projected by this new method have as similar long-term trend as the raw GCM’s results with minor differences in amplitude and spatial pattern. It is estimated that a warmer climate in the whole country with temperature increment at 3?4°C and a wetter (drier) climate in North China (South China) can be expected around 2100 A. D. under RCP8.5 scenario. More scientists can be encouraged to use this new ANN-based downscaling method and this released high resolution climate change dataset can be used to a wide range of communities related to global change studies.