globalchange  > 气候减缓与适应
DOI: 10.1002/joc.5188
论文题名:
Non-stationary bias correction of monthly CMIP5 temperature projections over China using a residual-based bagging tree model
作者: Tao Y.; Yang T.; Faridzad M.; Jiang L.; He X.; Zhang X.
刊名: International Journal of Climatology
ISSN: 8998418
出版年: 2018
卷: 38, 期:1
起始页码: 467
结束页码: 482
语种: 英语
英文关键词: bagging trees ; China ; ensemble ; GCMs ; temperature
Scopus关键词: Climate change ; Forestry ; Mean square error ; Temperature ; Bagging trees ; China ; Ensemble ; GCMs ; Global circulation model ; Root mean squared errors ; Temperature increase ; Temperature projection ; Climate models ; air temperature ; climate prediction ; CMIP ; correction ; ensemble forecasting ; general circulation model ; river basin ; China
英文摘要: 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. © 2017 Royal Meteorological Society
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/117154
Appears in Collections:气候减缓与适应

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作者单位: Department of Civil and Environmental Engineering, University of California, Irvine, CA, United States; State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and Earth System Science, Beijing Normal University, China; China's Agenda21, The Administrative Center, Beijing, China; State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China, Institute of Water Resources and Hydropower Research, Beijing, China

Recommended Citation:
Tao Y.,Yang T.,Faridzad M.,et al. Non-stationary bias correction of monthly CMIP5 temperature projections over China using a residual-based bagging tree model[J]. International Journal of Climatology,2018-01-01,38(1)
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