残余物
气候学
均方误差
环境科学
GCM转录因子
集合预报
统计
气候模式
大气环流模式
数学
计量经济学
计算机科学
气候变化
算法
人工智能
地质学
生物
生态学
作者
Yingjie Tao,Tiantian Yang,Mohammad Faridzad,Lin Jiang,Xiaojia He,Xiaoming Zhang
摘要
ABSTRACT The biases in the Global Circulation Models ( GCMs ) are crucial for understanding future climate changes. Currently, most bias correction methodologies suffer from the assumption that model bias is stationary. This paper provides a non‐stationary bias correction model, termed residual‐based bagging tree ( RBT ) model, to reduce simulation biases and to quantify the contributions of single models. Specifically, the proposed model estimates the residuals between individual models and observations, and takes the differences between observations and the ensemble mean into consideration during the model training process. A case study is conducted for 10 major river basins in Mainland China during different seasons. Results show that the proposed model is capable of providing accurate and stable predictions while including the non‐stationarities into the modelling framework. Significant reductions in both bias and root mean squared error are achieved with the proposed RBT model, especially for the central and western parts of China. The proposed RBT model has consistently better performance in reducing biases when compared with the raw ensemble mean, the ensemble mean with simple additive bias correction, and the single best model for different seasons. Furthermore, the contribution of each single GCM in reducing the overall bias is quantified. The single model importance varies between 3.1% and 7.2%. For different future scenarios ( RCP 2.6, RCP 4.5, and RCP 8.5), the results from RBT model suggest temperature increases of 1.44, 2.59, and 4.71 °C by the end of the century, respectively, when compared with the average temperature during 1970–1999.
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